diff --git a/.github/workflows/test-spack.yml b/.github/workflows/test-spack.yml
index 23439c7bc4e541e343d5eb40889461d8153c8958..b78a3475805b2caac1d67a67ac78e31291eaa1b8 100644
--- a/.github/workflows/test-spack.yml
+++ b/.github/workflows/test-spack.yml
@@ -35,6 +35,7 @@ jobs:
         uses: actions/checkout@v3
         with:
           path: arbor
+          submodules: recursive
 
       - name: clone spack develop
         if: ${{ matrix.spack-version == 'develop' }}
diff --git a/.gitmodules b/.gitmodules
index cd515e55423d824434cdb01dc86558a8a87ecd7d..5ab557ea13aa0aeae42b58cc6df63ed3e6e1b460 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -26,3 +26,6 @@
 	path = ext/pugixml
 	url = https://github.com/zeux/pugixml.git
 	branch = master
+[submodule "ext/units"]
+	path = ext/units
+	url = https://github.com/LLNL/units.git
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 3a0e9e85d956037bcf69ea3115712e6b807f56d1..a765d7b2f2da00565f5f5b329f5b0f433c43ebd3 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -259,6 +259,9 @@ install(FILES mechanisms/BuildModules.cmake DESTINATION ${ARB_INSTALL_DATADIR})
 cmake_dependent_option(ARB_USE_BUNDLED_FMT "Use bundled FMT lib." ON "ARB_USE_BUNDLED_LIBS" OFF)
 cmake_dependent_option(ARB_USE_BUNDLED_PUGIXML "Use bundled XML lib." ON "ARB_USE_BUNDLED_LIBS" OFF)
 cmake_dependent_option(ARB_USE_BUNDLED_GTEST "Use bundled GoogleTest." ON "ARB_USE_BUNDLED_LIBS" OFF)
+# TODO When we get a units spack package...
+#cmake_dependent_option(ARB_USE_BUNDLED_UNITS "Use bundled LLNL units." ON "ARB_USE_BUNDLED_LIBS" OFF)
+set(ARB_USE_BUNDLED_UNITS ON CACHE STRING "Use bundled LLNL units.")
 
 cmake_dependent_option(ARB_USE_BUNDLED_JSON "Use bundled Niels Lohmann's json library." ON "ARB_USE_BUNDLED_LIBS" OFF)
 if(NOT ARB_USE_BUNDLED_JSON)
@@ -290,9 +293,20 @@ else()
     endif()
 endif()
 
+add_library(ext-units INTERFACE)
+if(ARB_USE_BUNDLED_UNITS)
+    target_link_libraries(ext-units INTERFACE units::units)
+else()
+    message(FATAL, "TODO: At the time of Arbor 0.10.0 there is no Spack package")
+endif()
+
+
 add_subdirectory(ext)
 install(TARGETS ext-hwloc EXPORT arbor-targets)
 install(TARGETS ext-random123 EXPORT arbor-targets)
+target_link_libraries(arbor-public-deps INTERFACE ext-units)
+install(TARGETS ext-units EXPORT arbor-targets)
+install(TARGETS units compile_flags_target EXPORT arbor-targets)
 
 # Keep track of packages we need to add to the generated CMake config
 # file for arbor.
diff --git a/arbor/backends/gpu/threshold_watcher.hpp b/arbor/backends/gpu/threshold_watcher.hpp
index ccf15066fbaca55eaaf1f0e5c29f4d44b74368f0..d3d7e5824f57db5e9a498f046b7965ab793f9b96 100644
--- a/arbor/backends/gpu/threshold_watcher.hpp
+++ b/arbor/backends/gpu/threshold_watcher.hpp
@@ -72,7 +72,7 @@ public:
         v_prev_(num_cv),
         // TODO: allocates enough space for 10 spikes per watch.
         // A more robust approach might be needed to avoid overflows.
-        stack_(10*size(), context.gpu)
+        stack_(100*size(), context.gpu)
     {
         crossings_.reserve(stack_.capacity());
         // reset() needs to be called before this is ready for use
diff --git a/arbor/cable_cell.cpp b/arbor/cable_cell.cpp
index 210fdd3cea655f3ee3e70f12d2ce1578660d6e62..21dd4e5392e64a48aea91253bea935cbe4beed26 100644
--- a/arbor/cable_cell.cpp
+++ b/arbor/cable_cell.cpp
@@ -2,7 +2,6 @@
 #include <sstream>
 #include <unordered_map>
 #include <variant>
-#include <vector>
 
 #include <arbor/cable_cell.hpp>
 #include <arbor/morph/label_dict.hpp>
@@ -10,9 +9,6 @@
 #include <arbor/morph/mprovider.hpp>
 #include <arbor/util/pp_util.hpp>
 
-#include "util/piecewise.hpp"
-#include "util/rangeutil.hpp"
-#include "util/span.hpp"
 #include "util/strprintf.hpp"
 
 namespace arb {
@@ -37,7 +33,7 @@ std::string show(const paintable& item) {
             else if constexpr (std::is_same_v<axial_resistivity, T>) {
                 os << "axial-resistivity";
             }
-            else if constexpr (std::is_same_v<temperature_K, T>) {
+            else if constexpr (std::is_same_v<temperature, T>) {
                 os << "temperature-kelvin";
             }
             else if constexpr (std::is_same_v<membrane_capacitance, T>) {
diff --git a/arbor/cable_cell_param.cpp b/arbor/cable_cell_param.cpp
index e3b425de7a45c956117d31184fedbe583975cada..a5c4e20792c16c0c467aba3c35b4c7abd22dcc7c 100644
--- a/arbor/cable_cell_param.cpp
+++ b/arbor/cable_cell_param.cpp
@@ -75,30 +75,30 @@ cable_cell_parameter_set neuron_parameter_defaults = {
 std::vector<defaultable> cable_cell_parameter_set::serialize() const {
     std::vector<defaultable> D;
     if (init_membrane_potential) {
-        D.push_back(arb::init_membrane_potential{*this->init_membrane_potential});
+        D.push_back(arb::init_membrane_potential{*this->init_membrane_potential*units::mV});
     }
     if (temperature_K) {
-        D.push_back(arb::temperature_K{*this->temperature_K});
+        D.push_back(arb::temperature{*this->temperature_K*units::Kelvin});
     }
     if (axial_resistivity) {
-        D.push_back(arb::axial_resistivity{*this->axial_resistivity});
+        D.push_back(arb::axial_resistivity{*this->axial_resistivity*units::Ohm*units::cm});
     }
     if (membrane_capacitance) {
-        D.push_back(arb::membrane_capacitance{*this->membrane_capacitance});
+        D.push_back(arb::membrane_capacitance{*this->membrane_capacitance*units::F/units::m2});
     }
 
     for (const auto& [name, data]: ion_data) {
         if (data.init_int_concentration) {
-            D.push_back(init_int_concentration{name, *data.init_int_concentration});
+            D.push_back(init_int_concentration{name, *data.init_int_concentration*units::mM});
         }
         if (data.init_ext_concentration) {
-            D.push_back(init_ext_concentration{name, *data.init_ext_concentration});
+            D.push_back(init_ext_concentration{name, *data.init_ext_concentration*units::mM});
         }
         if (data.init_reversal_potential) {
-            D.push_back(init_reversal_potential{name, *data.init_reversal_potential});
+            D.push_back(init_reversal_potential{name, *data.init_reversal_potential*units::mV});
         }
         if (data.diffusivity) {
-            D.push_back(ion_diffusivity{name, *data.diffusivity});
+            D.push_back(ion_diffusivity{name, *data.diffusivity*units::m2/units::s});
         }
     }
 
@@ -133,32 +133,32 @@ decor& decor::set_default(defaultable what) {
             [this] (auto&& p) {
                 using T = std::decay_t<decltype(p)>;
                 if constexpr (std::is_same_v<init_membrane_potential, T>) {
-                    if (p.value.type() != iexpr_type::scalar) throw cable_cell_error{"Default values cannot have a scale."};
-                    defaults_.init_membrane_potential = *p.value.get_scalar();
+                    if (p.scale.type() != iexpr_type::scalar) throw cable_cell_error{"Default values cannot have a scale."};
+                    defaults_.init_membrane_potential = *p.scale.get_scalar()*p.value;
                 }
                 else if constexpr (std::is_same_v<axial_resistivity, T>) {
-                    if (p.value.type() != iexpr_type::scalar) throw cable_cell_error{"Default values cannot have a scale."};
-                    defaults_.axial_resistivity = *p.value.get_scalar();
+                    if (p.scale.type() != iexpr_type::scalar) throw cable_cell_error{"Default values cannot have a scale."};
+                    defaults_.axial_resistivity = *p.scale.get_scalar()*p.value;
                 }
-                else if constexpr (std::is_same_v<temperature_K, T>) {
-                    if (p.value.type() != iexpr_type::scalar) throw cable_cell_error{"Default values cannot have a scale."};
-                    defaults_.temperature_K = *p.value.get_scalar();
+                else if constexpr (std::is_same_v<temperature, T>) {
+                    if (p.scale.type() != iexpr_type::scalar) throw cable_cell_error{"Default values cannot have a scale."};
+                    defaults_.temperature_K = *p.scale.get_scalar()*p.value;
                 }
                 else if constexpr (std::is_same_v<membrane_capacitance, T>) {
-                    if (p.value.type() != iexpr_type::scalar) throw cable_cell_error{"Default values cannot have a scale."};
-                    defaults_.membrane_capacitance = *p.value.get_scalar();
+                    if (p.scale.type() != iexpr_type::scalar) throw cable_cell_error{"Default values cannot have a scale."};
+                    defaults_.membrane_capacitance = *p.scale.get_scalar()*p.value;
                 }
                 else if constexpr (std::is_same_v<init_int_concentration, T>) {
-                    if (p.value.type() != iexpr_type::scalar) throw cable_cell_error{"Default values cannot have a scale."};
-                    defaults_.ion_data[p.ion].init_int_concentration = *p.value.get_scalar();
+                    if (p.scale.type() != iexpr_type::scalar) throw cable_cell_error{"Default values cannot have a scale."};
+                    defaults_.ion_data[p.ion].init_int_concentration = *p.scale.get_scalar()*p.value;
                 }
                 else if constexpr (std::is_same_v<init_ext_concentration, T>) {
-                    if (p.value.type() != iexpr_type::scalar) throw cable_cell_error{"Default values cannot have a scale."};
-                    defaults_.ion_data[p.ion].init_ext_concentration = *p.value.get_scalar();
+                    if (p.scale.type() != iexpr_type::scalar) throw cable_cell_error{"Default values cannot have a scale."};
+                    defaults_.ion_data[p.ion].init_ext_concentration = *p.scale.get_scalar()*p.value;
                 }
                 else if constexpr (std::is_same_v<init_reversal_potential, T>) {
-                    if (p.value.type() != iexpr_type::scalar) throw cable_cell_error{"Default values cannot have a scale."};
-                    defaults_.ion_data[p.ion].init_reversal_potential = *p.value.get_scalar();
+                    if (p.scale.type() != iexpr_type::scalar) throw cable_cell_error{"Default values cannot have a scale."};
+                    defaults_.ion_data[p.ion].init_reversal_potential = *p.scale.get_scalar()*p.value;
                 }
                 else if constexpr (std::is_same_v<ion_reversal_potential_method, T>) {
                     defaults_.reversal_potential_method[p.ion] = p.method;
@@ -167,8 +167,9 @@ decor& decor::set_default(defaultable what) {
                     defaults_.discretization = std::forward<cv_policy>(p);
                 }
                 else if constexpr (std::is_same_v<ion_diffusivity, T>) {
-                    if (p.value.type() != iexpr_type::scalar) throw cable_cell_error{"Default values cannot have a scale."};
-                    defaults_.ion_data[p.ion].diffusivity = p.value.get_scalar();
+                    if (p.scale.type() != iexpr_type::scalar) throw cable_cell_error{"Default values cannot have a scale."};
+                    auto s = p.scale.get_scalar();
+                    defaults_.ion_data[p.ion].diffusivity = s ? std::optional{*s*p.value} : s;
                 }
             },
             what);
diff --git a/arbor/fvm_layout.cpp b/arbor/fvm_layout.cpp
index 5baa95c036f59e7a182a9eb189b71603839c9ac3..2e667031f34761bcf079e7ffe9409151ef14b0be 100644
--- a/arbor/fvm_layout.cpp
+++ b/arbor/fvm_layout.cpp
@@ -1,7 +1,6 @@
 #include <algorithm>
 #include <optional>
 #include <set>
-#include <stdexcept>
 #include <unordered_set>
 #include <unordered_map>
 #include <vector>
@@ -20,17 +19,11 @@
 #include "fvm_layout.hpp"
 #include "threading/threading.hpp"
 #include "util/maputil.hpp"
-#include "util/meta.hpp"
-#include "util/partition.hpp"
 #include "util/piecewise.hpp"
 #include "util/pw_over_cable.hpp"
 #include "util/rangeutil.hpp"
-#include "util/transform.hpp"
-#include "util/unique.hpp"
 #include "util/strprintf.hpp"
 
-#include <iostream>
-
 namespace arb {
 
 using util::assign;
@@ -275,16 +268,16 @@ fvm_cv_discretize(const cable_cell& cell, const cable_cell_parameter_set& global
 
     double dflt_resistivity = *(dflt.axial_resistivity | global_dflt.axial_resistivity);
     double dflt_capacitance = *(dflt.membrane_capacitance | global_dflt.membrane_capacitance);
-    double dflt_potential =   *(dflt.init_membrane_potential | global_dflt.init_membrane_potential);
+    double dflt_potential   = *(dflt.init_membrane_potential | global_dflt.init_membrane_potential);
     double dflt_temperature = *(dflt.temperature_K | global_dflt.temperature_K);
 
-    const auto& assignments = cell.region_assignments();
-    const auto& resistivity = assignments.get<axial_resistivity>();
-    const auto& capacitance = assignments.get<membrane_capacitance>();
-    const auto& potential   = assignments.get<init_membrane_potential>();
-    const auto& temperature = assignments.get<temperature_K>();
-    const auto& diffusivity = assignments.get<ion_diffusivity>();
-    const auto& provider    = cell.provider();
+    const auto& assignments   = cell.region_assignments();
+    const auto& resistivity   = assignments.get<axial_resistivity>();
+    const auto& capacitance   = assignments.get<membrane_capacitance>();
+    const auto& potential     = assignments.get<init_membrane_potential>();
+    const auto& temperature_K = assignments.get<temperature>();
+    const auto& diffusivity   = assignments.get<ion_diffusivity>();
+    const auto& provider      = cell.provider();
 
     struct inv_diff {
         iexpr value;
@@ -310,8 +303,9 @@ fvm_cv_discretize(const cable_cell& cell, const cable_cell_parameter_set& global
         auto diffusive = std::any_of(data.begin(),
                                      data.end(),
                                      [](const auto& kv) {
-                                         const auto& v = kv.second.value.get_scalar();
-                                         return !v || *v != 0.0 || *v == *v;
+                                         const auto& [k, v] = kv;
+                                         auto s = v.scale.get_scalar();
+                                         return !s || *s*v.value != 0.0;
                                      });
         if (diffusive) {
             // Provide a (non-sensical) default.
@@ -337,7 +331,7 @@ fvm_cv_discretize(const cable_cell& cell, const cable_cell_parameter_set& global
         for (msize_t i = 0; i<n_branch; ++i) {
             auto cable = mcable{i, 0., 1.};
             auto scale_param = [&, ion=ion](const auto&,
-                                   const inv_diff& par) {
+                                   const inv_diff& par) -> double {
                 auto ie = thingify(par.value, provider);
                 auto sc = ie->eval(provider, cable);
                 if (def <= 0.0 || std::isnan(def)) {
@@ -360,9 +354,9 @@ fvm_cv_discretize(const cable_cell& cell, const cable_cell_parameter_set& global
     for (msize_t i = 0; i<n_branch; ++i) {
         auto cable = mcable{i, 0., 1.};
         auto scale_param = [&](const auto&,
-                               const axial_resistivity& par) {
-            auto ie = thingify(par.value, provider);
-            auto sc = ie->eval(provider, cable);
+                               const axial_resistivity& par) -> double {
+            auto ie = thingify(par.scale, provider);
+            auto sc = par.value*ie->eval(provider, cable);
             return sc;
         };
         ax_res_0.emplace_back(pw_over_cable(resistivity, cable, dflt_resistivity, scale_param));
@@ -419,15 +413,15 @@ fvm_cv_discretize(const cable_cell& cell, const cable_cell_parameter_set& global
         double cv_length = 0;
 
         for (mcable cable: cv_cables) {
-            auto scale_param = [&](const auto&, const auto& par) {
-                auto ie = thingify(par.value, provider);
-                auto sc = ie->eval(provider, cable);
+            auto scale_param = [&](const auto&, const auto& par) -> double {
+                auto ie = thingify(par.scale, provider);
+                auto sc = par.value*ie->eval(provider, cable);
                 return sc;
             };
 
-            auto pw_capacitance = pw_over_cable(capacitance, cable, dflt_capacitance, scale_param);
-            auto pw_potential   = pw_over_cable(potential,   cable, dflt_potential,   scale_param);
-            auto pw_temperature = pw_over_cable(temperature, cable, dflt_temperature, scale_param);
+            auto pw_capacitance = pw_over_cable(capacitance,   cable, dflt_capacitance, scale_param);
+            auto pw_potential   = pw_over_cable(potential,     cable, dflt_potential,   scale_param);
+            auto pw_temperature = pw_over_cable(temperature_K, cable, dflt_temperature, scale_param);
 
             D.cv_area[i]                 += embedding.integrate_area(cable);
             D.cv_capacitance[i]          += embedding.integrate_area(cable.branch, pw_capacitance);
@@ -544,7 +538,7 @@ bool cables_intersect_location(Seq&& cables, const mlocation& x) {
     auto eqr = std::equal_range(begin(cables), end(cables), x.branch, cmp_branch{});
 
     return util::any_of(util::make_range(eqr),
-        [&x](const mcable& c) { return c.prox_pos<=x.pos && x.pos<=c.dist_pos; });
+                        [&x](const mcable& c) { return c.prox_pos<=x.pos && x.pos<=c.dist_pos; });
 }
 
 voltage_reference_pair fvm_voltage_reference_points(const morphology& morph, const cv_geometry& geom, arb_size_type cell_idx, const mlocation& site) {
@@ -1312,9 +1306,9 @@ make_ion_config(fvm_ion_map build_data,
 
             for (const mcable& cable: data.D.geometry.cables(cv)) {
                 auto scale_param = [&](const auto&,
-                                   const auto& par) {
-                    auto ie = thingify(par.value, provider);
-                    auto sc = ie->eval(provider, cable);
+                                   const auto& par) -> double {
+                    auto ie = thingify(par.scale, provider);
+                    auto sc = par.value*ie->eval(provider, cable);
                     return sc;
                 };
 
diff --git a/arbor/include/arbor/cable_cell.hpp b/arbor/include/arbor/cable_cell.hpp
index e119508a49a440ae6a3e3fb53c2e8df960b49f4f..82958231603f96be7baca08775af7d1e206702fa 100644
--- a/arbor/include/arbor/cable_cell.hpp
+++ b/arbor/include/arbor/cable_cell.hpp
@@ -4,7 +4,6 @@
 #include <string>
 #include <unordered_map>
 #include <utility>
-#include <variant>
 #include <vector>
 
 #include <arbor/export.hpp>
@@ -239,7 +238,7 @@ using location_assignment =
 
 using cable_cell_region_map = static_typed_map<region_assignment,
     density, voltage_process, init_membrane_potential, axial_resistivity,
-    temperature_K, membrane_capacitance, init_int_concentration,
+    temperature, membrane_capacitance, init_int_concentration,
     ion_diffusivity, init_ext_concentration, init_reversal_potential>;
 
 using cable_cell_location_map = static_typed_map<location_assignment,
diff --git a/arbor/include/arbor/cable_cell_param.hpp b/arbor/include/arbor/cable_cell_param.hpp
index 59bb5bb0148843354c2678bee743be8cfd2e417a..d68c0c73e21e59b774ef8443f5477ef508a9150b 100644
--- a/arbor/include/arbor/cable_cell_param.hpp
+++ b/arbor/include/arbor/cable_cell_param.hpp
@@ -13,14 +13,17 @@
 #include <arbor/mechcat.hpp>
 #include <arbor/morph/locset.hpp>
 #include <arbor/morph/primitives.hpp>
+#include <arbor/units.hpp>
 
 namespace arb {
 
+namespace U = arb::units;
+
 // Specialized arbor exception for errors in cell building.
 
 struct ARB_SYMBOL_VISIBLE cable_cell_error: arbor_exception {
     cable_cell_error(const std::string& what):
-        arbor_exception("cable_cell: "+what) {}
+        arbor_exception("cable_cell: " + what) {}
 };
 
 // Ion inital concentration and reversal potential
@@ -30,28 +33,43 @@ struct ARB_SYMBOL_VISIBLE cable_cell_error: arbor_exception {
 // separately (see below).
 
 struct cable_cell_ion_data {
-    std::optional<double> init_int_concentration;
-    std::optional<double> init_ext_concentration;
-    std::optional<double> init_reversal_potential;
-    std::optional<double> diffusivity;
+    std::optional<double> init_int_concentration;  // mM
+    std::optional<double> init_ext_concentration;  // mM
+    std::optional<double> init_reversal_potential; // mV
+    std::optional<double> diffusivity;             // m²/s
 };
 
-// Clamp current is described by a sine wave with amplitude governed by a
-// piecewise linear envelope. A frequency of zero indicates that the current is
-// simply that given by the envelope.
-//
-// The envelope is given by a series of envelope_point values:
-// * The time points must be monotonically increasing.
-// * Onset and initial amplitude is given by the first point.
-// * The amplitude for time after the last time point is that of the last
-//   amplitude point; an explicit zero amplitude point must be provided if the
-//   envelope is intended to have finite support.
-//
-// Periodic envelopes are not supported, but may well be a feature worth
-// considering in the future.
-
+/**
+ * Current clamp; described by a sine wave with amplitude governed by a
+ * piecewise linear envelope. A frequency of zero indicates that the current is
+ * simply that given by the envelope.
+ *
+ * The envelope is given by a series of envelope_point values:
+ * * The time points must be monotonically increasing.
+ * * Onset and initial amplitude is given by the first point.
+ * * The amplitude for time after the last time point is that of the last
+ *   amplitude point; an explicit zero amplitude point must be provided if the
+ *   envelope is intended to have finite support.
+ *
+ * Periodic envelopes are not supported, but may well be a feature worth
+ * considering in the future.
+ */
 struct ARB_SYMBOL_VISIBLE i_clamp {
     struct envelope_point {
+        /**
+         * Current at point in time
+         *
+         * @param t, must be convertible to time
+         * @param amplitude must be convertible to current
+         */
+        envelope_point(const U::quantity& time,
+                       const U::quantity& current):
+            t(time.value_as(U::ms)),
+            amplitude(current.value_as(U::nA)) {
+
+            if (std::isnan(t)) throw std::domain_error{"Time must be finite and convertible to ms."};
+            if (std::isnan(amplitude)) throw std::domain_error{"Amplitude must be finite and convertible to nA."};
+    }
         double t;         // [ms]
         double amplitude; // [nA]
     };
@@ -64,69 +82,147 @@ struct ARB_SYMBOL_VISIBLE i_clamp {
     // a trivial stimulus, providing no current at all.
     i_clamp() = default;
 
-    // The simple constructor describes a constant amplitude stimulus starting from t=0.
-    explicit i_clamp(double amplitude, double frequency = 0, double phase = 0):
-        envelope({{0., amplitude}}),
-        frequency(frequency),
-        phase(phase)
+    /**
+     *  Constant amplitude stimulus starting at t = 0.
+     *
+     * @param amplitude must be convertible to current
+     * @param frequency, must be convertible to frequency; gives a sine current if not zero
+     * @param frequency, must be convertible to radians, phase shift of sine.
+     */
+
+    explicit i_clamp(const U::quantity& amplitude,
+                     const U::quantity& frequency = 0*U::kHz,
+                     const U::quantity& phase = 0*U::rad):
+        i_clamp{{{0.0*U::ms, amplitude}}, frequency, phase}
     {}
 
     // Describe a stimulus by envelope and frequency.
-    explicit i_clamp(std::vector<envelope_point> envelope, double frequency = 0, double phase = 0):
+    explicit i_clamp(std::vector<envelope_point> envelope,
+                     const U::quantity& f = 0*U::kHz,
+                     const U::quantity& phi = 0*U::rad):
         envelope(std::move(envelope)),
-        frequency(frequency),
-        phase(phase)
-    {}
+        frequency(f.value_as(U::kHz)),
+        phase(phi.value_as(U::rad))
+    {
+        if (std::isnan(frequency)) throw std::domain_error{"Frequency must be finite and convertible to kHz."};
+        if (std::isnan(phase)) throw std::domain_error{"Phase must be finite and convertible to rad."};
+    }
 
     // A 'box' stimulus with fixed onset time, duration, and constant amplitude.
-    static i_clamp box(double onset, double duration, double amplitude, double frequency = 0, double phase = 0) {
-        return i_clamp({{onset, amplitude}, {onset+duration, amplitude}, {onset+duration, 0.}}, frequency, phase);
+    static i_clamp box(const U::quantity& onset,
+                       const U::quantity& duration,
+                       const U::quantity& amplitude,
+                       const U::quantity& frequency =  0*U::kHz,
+                       const U::quantity& phase = 0*U::rad) {
+        return i_clamp({{onset, amplitude}, {onset+duration, amplitude}, {onset+duration, 0.*U::nA}},
+                       frequency,
+                       phase);
     }
-
 };
 
 // Threshold detector description.
 struct ARB_SYMBOL_VISIBLE threshold_detector {
-    double threshold;
+    threshold_detector(const U::quantity& m): threshold(m.value_as(U::mV)) {
+        if (std::isnan(threshold)) throw std::domain_error{"Threshold must be finite and in [mV]."};
+    }
+    static threshold_detector from_raw_millivolts(double v) { return {v*U::mV}; }
+    double threshold; // [mV]
 };
 
 // Setter types for painting physical and ion parameters or setting
 // cell-wide default:
 
 struct ARB_SYMBOL_VISIBLE init_membrane_potential {
-    iexpr value = NAN;         // [mV]
+    double value = NAN;      // [mV]
+    iexpr scale = 1;         // [1]
+
+    init_membrane_potential() = default;
+    init_membrane_potential(const U::quantity& m, iexpr scale=1):
+      value(m.value_as(U::mV)), scale{scale} {
+        if (std::isnan(value)) throw std::domain_error{"Value must be finite and in [mV]."};
+    }
 };
 
-struct ARB_SYMBOL_VISIBLE temperature_K {
-    iexpr value = NAN;         // [K]
+
+struct ARB_SYMBOL_VISIBLE temperature {
+    double value = NAN;      // [K]
+    iexpr scale = 1;         // [1]
+
+    temperature() = default;
+    temperature(const U::quantity& m, iexpr scale=1):
+      value(m.value_as(U::Kelvin)), scale{scale} {
+        if (std::isnan(value)) throw std::domain_error{"Value must be finite and in [K]."};
+    }
 };
 
 struct ARB_SYMBOL_VISIBLE axial_resistivity {
-    iexpr value = NAN;         // [Ω·cm]
+    double value = NAN;      // [Ω·cm]
+    iexpr scale = 1;         // [1]
+
+    axial_resistivity() = default;
+    axial_resistivity(const U::quantity& m, iexpr scale=1):
+      value(m.value_as(U::cm*U::Ohm)), scale{scale} {
+        if (std::isnan(value)) throw std::domain_error{"Value must be finite and in [Ω·cm]."};
+    }
 };
 
 struct ARB_SYMBOL_VISIBLE membrane_capacitance {
-    iexpr value = NAN;         // [F/m²]
+    double value = NAN;      // [F/m²]
+    iexpr scale = 1;         // [1]
+
+    membrane_capacitance() = default;
+    membrane_capacitance(const U::quantity& m, iexpr scale=1):
+      value(m.value_as(U::F/U::m2)), scale{scale} {
+        if (std::isnan(value)) throw std::domain_error{"Value must be finite and in [F/m²]."};
+    }
 };
 
 struct ARB_SYMBOL_VISIBLE init_int_concentration {
     std::string ion = "";
-    iexpr value = NAN;         // [mM]
+    double value = NAN;      // [mM]
+    iexpr scale = 1;         // [1]
+
+    init_int_concentration() = default;
+    init_int_concentration(const std::string& ion, const U::quantity& m, iexpr scale=1):
+      ion{ion}, value(m.value_as(U::mM)), scale{scale} {
+        if (std::isnan(value)) throw std::domain_error{"Value must be finite and in [mM]."};
+    }
 };
 
 struct ARB_SYMBOL_VISIBLE ion_diffusivity {
     std::string ion = "";
-    iexpr value = NAN;         // [m^2/s]
+    double value = NAN;      // [m²/s]
+    iexpr scale = 1;         // [1]
+
+    ion_diffusivity() = default;
+    ion_diffusivity(const std::string& ion, const U::quantity& m, iexpr scale=1):
+      ion{ion}, value(m.value_as(U::m2/U::s)), scale{scale} {
+        if (std::isnan(value)) throw std::domain_error{"Value must be finite and in [m²/s]."};
+    }
 };
 
 struct ARB_SYMBOL_VISIBLE init_ext_concentration {
     std::string ion = "";
-    iexpr value = NAN;         // [mM]
+    double value = NAN;      // [mM]
+    iexpr scale = 1;         // [1]
+
+    init_ext_concentration() = default;
+    init_ext_concentration(const std::string& ion, const U::quantity& m, iexpr scale=1):
+      ion{ion}, value(m.value_as(U::mM)), scale{scale} {
+        if (std::isnan(value)) throw std::domain_error{"Value must be finite and in [mM]."};
+    }
 };
 
 struct ARB_SYMBOL_VISIBLE init_reversal_potential {
     std::string ion = "";
-    iexpr value = NAN;         // [mV]
+    double value = NAN;      // [mV]
+    iexpr scale = 1;         // [1]
+
+    init_reversal_potential() = default;
+    init_reversal_potential(const std::string& ion, const U::quantity& m, iexpr scale=1):
+      ion{ion}, value(m.value_as(U::mV)), scale{scale} {
+        if (std::isnan(value)) throw std::domain_error{"Value must be finite and in [mV]."};
+    }
 };
 
 // Mechanism description, viz. mechanism name and
@@ -150,8 +246,12 @@ struct ARB_SYMBOL_VISIBLE mechanism_desc {
     };
 
     // implicit
-    mechanism_desc(std::string name): name_(std::move(name)) {}
-    mechanism_desc(const char* name): name_(name) {}
+    mechanism_desc(std::string name): name_(std::move(name)) {
+        if (name_.empty()) throw cable_cell_error("mechanism_desc: null name");
+    }
+    mechanism_desc(const char* name): name_(name) {
+        if (name_.empty()) throw cable_cell_error("mechanism_desc: null name");
+    }
 
     mechanism_desc() = default;
     mechanism_desc(const mechanism_desc&) = default;
@@ -255,7 +355,7 @@ struct ARB_SYMBOL_VISIBLE scaled_mechanism {
 using paintable =
     std::variant<init_membrane_potential,
                  axial_resistivity,
-                 temperature_K,
+                 temperature,
                  membrane_capacitance,
                  ion_diffusivity,
                  init_int_concentration,
@@ -274,7 +374,7 @@ using placeable =
 using defaultable =
     std::variant<init_membrane_potential,
                  axial_resistivity,
-                 temperature_K,
+                 temperature,
                  membrane_capacitance,
                  ion_diffusivity,
                  init_int_concentration,
@@ -351,18 +451,32 @@ struct ARB_SYMBOL_VISIBLE cable_cell_global_properties {
     cable_cell_parameter_set default_parameters;
 
     // Convenience methods for adding a new ion together with default ion values.
-    void add_ion(const std::string& ion_name, int charge, double init_iconc, double init_econc, double init_revpot, double diffusivity=0.0) {
+    void add_ion(const std::string& ion_name,
+                 int charge,
+                 const U::quantity& init_iconc,
+                 const U::quantity& init_econc,
+                 const U::quantity& init_revpot,
+                 const U::quantity& diffusivity=0.0*U::m2/U::s) {
         ion_species[ion_name] = charge;
 
         auto &ion_data = default_parameters.ion_data[ion_name];
-        ion_data.init_int_concentration  = init_iconc;
-        ion_data.init_ext_concentration  = init_econc;
-        ion_data.init_reversal_potential = init_revpot;
-        ion_data.diffusivity             = diffusivity;
+        ion_data.init_int_concentration = init_iconc.value_as(U::mM);
+        if (std::isnan(*ion_data.init_int_concentration)) throw std::domain_error("init_int_concentration must be finite and convertible to mM");
+        ion_data.init_ext_concentration = init_econc.value_as(U::mM);
+        if (std::isnan(*ion_data.init_ext_concentration)) throw std::domain_error("init_ext_concentration must be finite and convertible to mM");
+        ion_data.init_reversal_potential = init_revpot.value_as(U::mV);
+        if (std::isnan(*ion_data.init_reversal_potential)) throw std::domain_error("init_reversal_potential must be finite and convertible to mV");
+        ion_data.diffusivity = diffusivity.value_as(U::m2/U::s);
+        if (std::isnan(*ion_data.diffusivity) || *ion_data.diffusivity < 0) throw std::domain_error("diffusivity must be positive, finite, and convertible to m2/s");
     }
 
-    void add_ion(const std::string& ion_name, int charge, double init_iconc, double init_econc, mechanism_desc revpot_mechanism, double diffusivity=0.0) {
-        add_ion(ion_name, charge, init_iconc, init_econc, 0, diffusivity);
+    void add_ion(const std::string& ion_name,
+                 int charge,
+                 const U::quantity& init_iconc,
+                 const U::quantity& init_econc,
+                 mechanism_desc revpot_mechanism,
+                 const U::quantity& diffusivity=0.0*U::m2/U::s) {
+        add_ion(ion_name, charge, init_iconc, init_econc, 0*U::mV, diffusivity);
         default_parameters.reversal_potential_method[ion_name] = std::move(revpot_mechanism);
     }
 };
diff --git a/arbor/include/arbor/event_generator.hpp b/arbor/include/arbor/event_generator.hpp
index c378ef7d0382e2fc907e51f423f1a85e1325d85d..cab7e5fe2bf5c5e3ef7b33e4cd3f7a1c7c8c4784 100644
--- a/arbor/include/arbor/event_generator.hpp
+++ b/arbor/include/arbor/event_generator.hpp
@@ -1,10 +1,5 @@
 #pragma once
 
-#include <algorithm>
-#include <cstdint>
-#include <memory>
-#include <random>
-#include <type_traits>
 #include <optional>
 
 #include <arbor/assert.hpp>
@@ -101,34 +96,29 @@ private:
 inline
 event_generator empty_generator(
     cell_local_label_type target,
-    float weight)
-{
+    float weight) {
     return event_generator(std::move(target), weight, schedule());
 }
 
 
 // Generate events at integer multiples of dt that lie between tstart and tstop.
 
-inline event_generator regular_generator(
-    cell_local_label_type target,
-    float weight,
-    time_type tstart,
-    time_type dt,
-    time_type tstop=terminal_time)
-{
+inline event_generator regular_generator(cell_local_label_type target,
+                                         float weight,
+                                         const units::quantity& tstart,
+                                         const units::quantity& dt,
+                                         const units::quantity& tstop=terminal_time*units::ms) {
     return event_generator(std::move(target), weight, regular_schedule(tstart, dt, tstop));
 }
 
-template <typename RNG>
-inline event_generator poisson_generator(
-    cell_local_label_type target,
-    float weight,
-    time_type tstart,
-    time_type rate_kHz,
-    const RNG& rng,
-    time_type tstop=terminal_time)
-{
-    return event_generator(std::move(target), weight, poisson_schedule(tstart, rate_kHz, rng, tstop));
+inline event_generator poisson_generator(cell_local_label_type target,
+                                         float weight,
+                                         const units::quantity& tstart,
+                                         const units::quantity& rate_kHz,
+                                         seed_type seed = default_seed,
+                                         const units::quantity& tstop=terminal_time*units::ms) {
+    // TODO(TH) handle seed
+    return event_generator(std::move(target), weight, poisson_schedule(tstart, rate_kHz, seed, tstop));
 }
 
 
@@ -137,10 +127,16 @@ inline event_generator poisson_generator(
 template<typename S> inline
 event_generator explicit_generator(cell_local_label_type target,
                                    float weight,
-                                   const S& s)
-{
+                                   const S& s) {
     return event_generator(std::move(target), weight, explicit_schedule(s));
 }
 
+template<typename S> inline
+event_generator explicit_generator_from_milliseconds(cell_local_label_type target,
+                                                     float weight,
+                                                     const S& s) {
+    return event_generator(std::move(target), weight, explicit_schedule_from_milliseconds(s));
+}
+
 } // namespace arb
 
diff --git a/arbor/include/arbor/iexpr.hpp b/arbor/include/arbor/iexpr.hpp
index 15d8452795945fae709e25cfc8d17dbb32455c11..c0eea2de90f80d661ed5dba461b166431ba53338 100644
--- a/arbor/include/arbor/iexpr.hpp
+++ b/arbor/include/arbor/iexpr.hpp
@@ -74,14 +74,14 @@ struct ARB_SYMBOL_VISIBLE iexpr {
     static iexpr distal_distance(region reg);
 
     static iexpr interpolation(double prox_value,
-        locset prox_list,
-        double dist_value,
-        locset dist_list);
+                               locset prox_list,
+                               double dist_value,
+                               locset dist_list);
 
     static iexpr interpolation(double prox_value,
-        region prox_list,
-        double dist_value,
-        region dist_list);
+                               region prox_list,
+                               double dist_value,
+                               region dist_list);
 
     static iexpr radius(double scale);
 
diff --git a/arbor/include/arbor/lif_cell.hpp b/arbor/include/arbor/lif_cell.hpp
index fedee7a2561f90d337913017c1feb2b1efc15b0a..e9d5c2fdf5962ff391dd15d1460dc5c4cc43a022 100644
--- a/arbor/include/arbor/lif_cell.hpp
+++ b/arbor/include/arbor/lif_cell.hpp
@@ -3,27 +3,30 @@
 #include <arbor/serdes.hpp>
 #include <arbor/common_types.hpp>
 #include <arbor/export.hpp>
+#include <arbor/units.hpp>
 
 namespace arb {
 
+namespace U = arb::units;
+using namespace U::literals;
+
 // Model parameters of leaky integrate and fire neuron model.
 struct ARB_SYMBOL_VISIBLE lif_cell {
     cell_tag_type source; // Label of source.
     cell_tag_type target; // Label of target.
 
     // Neuronal parameters.
-    double tau_m = 10;    // Membrane potential decaying constant [ms].
-    double V_th = 10;     // Firing threshold [mV].
-    double C_m = 20;      // Membrane capacitance [pF].
-    double E_L = 0;       // Resting potential [mV].
-    double E_R = E_L;     // Reset potential [mV].
-    double V_m = E_L;     // Initial value of the Membrane potential [mV].
-    double t_ref = 2;     // Refractory period [ms].
+    U::quantity tau_m = 10_ms;    // Membrane potential decaying constant [ms].
+    U::quantity V_th  = 10_mV;    // Firing threshold [mV].
+    U::quantity C_m   = 20_pF;    // Membrane capacitance [pF].
+    U::quantity E_L   =  0_mV;    // Resting potential [mV].
+    U::quantity E_R   =  0_mV;    // Reset potential [mV].
+    U::quantity V_m   =  0_mV;    // Initial value of the Membrane potential [mV].
+    U::quantity t_ref =  2_ms;    // Refractory period [ms].
 
     lif_cell() = default;
     lif_cell(cell_tag_type source, cell_tag_type target): source(std::move(source)), target(std::move(target)) {}
 
-    ARB_SERDES_ENABLE(lif_cell, source, target, tau_m, V_th, C_m, E_L, E_R, V_m, t_ref);
 };
 
 // LIF probe metadata, to be passed to sampler callbacks. Intentionally left blank.
diff --git a/arbor/include/arbor/morph/isometry.hpp b/arbor/include/arbor/morph/isometry.hpp
index cb06ee28dd58145a51fbc3fba2f8c4fa122da05f..3c10bd1c51f3791f84932e84670bc0c995baa3ba 100644
--- a/arbor/include/arbor/morph/isometry.hpp
+++ b/arbor/include/arbor/morph/isometry.hpp
@@ -7,6 +7,7 @@ namespace arb {
 // Represent a 3-d isometry as a rotation (via quaterion q_)
 // and a subsequent translation (tx_, ty_, tz_).
 struct ARB_ARBOR_API isometry {
+    using quaternion = arb::math::quaternion;
 
     isometry() = default;
 
@@ -28,7 +29,6 @@ struct ARB_ARBOR_API isometry {
     }
 
 private:
-    using quaternion = arb::math::quaternion;
     quaternion q_{1, 0, 0, 0};
     double tx_ = 0, ty_ = 0, tz_ = 0;
 
diff --git a/arbor/include/arbor/recipe.hpp b/arbor/include/arbor/recipe.hpp
index 797490f320e421651b76f658bfb24848656765ae..dbcba5fcde4d0b11881827405f418b5593aa3904 100644
--- a/arbor/include/arbor/recipe.hpp
+++ b/arbor/include/arbor/recipe.hpp
@@ -11,6 +11,8 @@
 
 namespace arb {
 
+namespace U = arb::units;
+
 struct probe_info {
     cell_tag_type tag;
 
@@ -40,7 +42,6 @@ struct probe_info {
 // are notionally described in terms of external cell identifiers instead
 // of internal gids, but we are not making the distinction between the
 // two in the current code. These two types could well be merged.
-
 template<typename L>
 struct cell_connection_base {
     // Connection end-points are represented by pairs
@@ -48,11 +49,14 @@ struct cell_connection_base {
     L source;
     cell_local_label_type target;
 
-    float weight;
-    float delay;
+    float weight; // [()]
+    float delay;  // [ms]
 
-    cell_connection_base(L src, cell_local_label_type dst, float w, float d):
-        source(std::move(src)), target(std::move(dst)), weight(w), delay(d) {}
+    cell_connection_base(L src, cell_local_label_type dst, float w, const U::quantity& d):
+        source(std::move(src)), target(std::move(dst)), weight(w), delay(d.value_as(U::ms)) {
+        if (std::isnan(weight)) throw std::out_of_range("Connection weight must be finite.");
+        if (std::isnan(delay) || delay < 0)  throw std::out_of_range("Connection delay must be non-negative and infinite in units of [ms].");
+    }
 };
 
 using cell_connection     = cell_connection_base<cell_global_label_type>;
@@ -61,10 +65,12 @@ using ext_cell_connection = cell_connection_base<cell_remote_label_type>;
 struct gap_junction_connection {
     cell_global_label_type peer;
     cell_local_label_type local;
-    double weight; //unit-less
+    double weight; // [()]
 
     gap_junction_connection(cell_global_label_type peer, cell_local_label_type local, double g):
-        peer(std::move(peer)), local(std::move(local)), weight(g) {}
+        peer(std::move(peer)), local(std::move(local)), weight(g) {
+        if (std::isnan(weight)) throw std::out_of_range("Gap junction weight must be finite.");
+    }
 };
 
 struct ARB_ARBOR_API has_gap_junctions {
diff --git a/arbor/include/arbor/schedule.hpp b/arbor/include/arbor/schedule.hpp
index da3921bc361511c4ff985b00e6f0f86eb5fcd112..8856cceac254feb9af5838d6df898179bf8e3b77 100644
--- a/arbor/include/arbor/schedule.hpp
+++ b/arbor/include/arbor/schedule.hpp
@@ -1,190 +1,39 @@
 #pragma once
 
 #include <algorithm>
-#include <iterator>
 #include <memory>
-#include <random>
 #include <type_traits>
 #include <utility>
 #include <vector>
+#include <random>
 
 #include <arbor/assert.hpp>
 #include <arbor/common_types.hpp>
 #include <arbor/util/extra_traits.hpp>
 #include <arbor/export.hpp>
 #include <arbor/serdes.hpp>
+#include <arbor/units.hpp>
 
 // Time schedules for probe–sampler associations.
 namespace arb {
 
+using engine_type = std::mt19937_64;
+using seed_type = std::remove_cv_t<decltype(engine_type::default_seed)>;
+
+constexpr static auto default_seed = engine_type::default_seed;
+
 using time_event_span = std::pair<const time_type*, const time_type*>;
 
 inline time_event_span as_time_event_span(const std::vector<time_type>& v) {
-    return {v.data(), v.data()+v.size()};
+    return {v.data(), v.data() + v.size()};
 }
 
-// Common schedules
-
-// Default schedule is empty.
-
-struct ARB_ARBOR_API empty_schedule {
-    void reset() {}
-    time_event_span events(time_type t0, time_type t1) {
-        static time_type no_time;
-        return {&no_time, &no_time};
-    }
-};
-
-// Schedule at k·dt for integral k≥0 within the interval [t0, t1).
-struct ARB_ARBOR_API regular_schedule_impl {
-    explicit regular_schedule_impl(time_type t0, time_type dt, time_type t1):
-        t0_(t0), t1_(t1), dt_(dt), oodt_(1./dt)
-    {
-        if (t0_<0) t0_ = 0;
-    };
-
-    void reset() {}
-    time_event_span events(time_type t0, time_type t1);
-
-    ARB_SERDES_ENABLE(regular_schedule_impl, t0_, t1_, dt_, oodt_);
-
-private:
-    time_type t0_, t1_, dt_;
-    time_type oodt_;
-
-    std::vector<time_type> times_;
-};
-
-// Schedule at times given explicitly via a provided sorted sequence.
-struct ARB_ARBOR_API explicit_schedule_impl {
-    explicit_schedule_impl(const explicit_schedule_impl&) = default;
-    explicit_schedule_impl(explicit_schedule_impl&&) = default;
-
-    template <typename Seq>
-    explicit explicit_schedule_impl(const Seq& seq):
-        start_index_(0)
-    {
-        using std::begin;
-        using std::end;
-
-        times_.assign(begin(seq), end(seq));
-        arb_assert(std::is_sorted(times_.begin(), times_.end()));
-    }
-
-    void reset() {
-        start_index_ = 0;
-    }
-
-    time_event_span events(time_type t0, time_type t1);
-
-    ARB_SERDES_ENABLE(explicit_schedule_impl, start_index_, times_);
-
-private:
-    std::ptrdiff_t start_index_;
-    std::vector<time_type> times_;
-};
-
-// Schedule at Poisson point process with rate 1/mean_dt,
-// restricted to non-negative times.
-template <typename RandomNumberEngine>
-class poisson_schedule_impl {
-public:
-    poisson_schedule_impl(time_type tstart, time_type rate_kHz, const RandomNumberEngine& rng, time_type tstop):
-        tstart_(tstart), exp_(rate_kHz), rng_(rng), reset_state_(rng), next_(tstart), tstop_(tstop)
-    {
-        arb_assert(tstart_>=0);
-        arb_assert(tstart_ <= tstop_);
-        step();
-    }
-
-    void reset() {
-        rng_ = reset_state_;
-        next_ = tstart_;
-        step();
-    }
-
-    time_event_span events(time_type t0, time_type t1) {
-        // if we start after the maximal allowed time, we have nothing to do
-        if (t0 >= tstop_) return {};
-
-        // restrict by maximal allowed time
-        t1 = std::min(t1, tstop_);
-
-        times_.clear();
-
-        while (next_<t0) { step(); }
-
-        while (next_<t1) {
-            times_.push_back(next_);
-            step();
-        }
-
-        return as_time_event_span(times_);
-    }
-
-    void step() { next_ += exp_(rng_); }
-
-    time_type tstart_;
-    std::exponential_distribution<time_type> exp_;
-    RandomNumberEngine rng_;
-    RandomNumberEngine reset_state_;
-    time_type next_;
-    std::vector<time_type> times_;
-    time_type tstop_;
-};
-
 // Type erased wrapper
 // A schedule describes a sequence of time values used for sampling. Schedules
 // are queried monotonically in time: if two method calls `events(t0, t1)`
 // and `events(t2, t3)` are made without an intervening call to `reset()`,
 // then 0 ≤ _t0_ ≤ _t1_ ≤ _t2_ ≤ _t3_.
-
-template<typename K>
-void serialize(arb::serializer& s, const K& k, const arb::explicit_schedule_impl&);
-template<typename K>
-void deserialize(arb::serializer& s, const K& k, arb::explicit_schedule_impl&);
-
-template<typename K>
-void serialize(arb::serializer& s, const K& k, const arb::regular_schedule_impl&);
-template<typename K>
-void deserialize(arb::serializer& s, const K& k, arb::regular_schedule_impl&);
-
-template <typename K>
-ARB_ARBOR_API void serialize(::arb::serializer& ser,
-                  const K& k,
-                  const arb::empty_schedule& t) {
-    ser.begin_write_map(::arb::to_serdes_key(k));
-    ser.end_write_map();
-}
-
-template <typename K>
-ARB_ARBOR_API void deserialize(::arb::serializer& ser,
-                        const K& k,
-                        arb::empty_schedule& t) {
-    ser.begin_read_map(::arb::to_serdes_key(k));
-    ser.end_read_map();
-}
-
-// These are custom to get the reset in.
-template<typename K, typename R>
-void serialize(::arb::serializer& ser, const K& k, const ::arb::poisson_schedule_impl<R>& t) {
-    ser.begin_write_map(arb::to_serdes_key(k));
-    ARB_SERDES_WRITE(tstart_);
-    ARB_SERDES_WRITE(tstop_);
-    ser.end_write_map();
-}
-
-template<typename K, typename R>
-void deserialize(::arb::serializer& ser, const K& k, ::arb::poisson_schedule_impl<R>& t) {
-    ser.begin_read_map(arb::to_serdes_key(k));
-    ARB_SERDES_READ(tstart_);
-    ARB_SERDES_READ(tstop_);
-    ser.end_read_map();
-    t.reset();
-}
-
-class schedule {
-public:
+struct ARB_ARBOR_API schedule {
     schedule();
 
     template <typename Impl, typename = std::enable_if_t<!std::is_same_v<util::remove_cvref_t<Impl>, schedule>>>
@@ -206,12 +55,13 @@ public:
         return *this;
     }
 
-    time_event_span events(time_type t0, time_type t1) {
-        return impl_->events(t0, t1);
-    }
+    time_event_span events(time_type t0, time_type t1) { return impl_->events(t0, t1); }
 
     void reset() { impl_->reset(); }
 
+    // Discard the next n events. Used in tests.
+    auto discard(std::size_t n) { return impl_->discard(n); }
+
     template<typename K>
     friend ARB_ARBOR_API void serialize(serializer& s, const K& k, const schedule& v) { v.impl_->t_serialize(s, to_serdes_key(k)); }
     template<typename K>
@@ -219,10 +69,10 @@ public:
 )); }
 
 private:
-
     struct interface {
         virtual time_event_span events(time_type t0, time_type t1) = 0;
         virtual void reset() = 0;
+        virtual void discard(std::size_t n) = 0;
         virtual std::unique_ptr<interface> clone() = 0;
         virtual ~interface() {}
         virtual void t_serialize(serializer&, const std::string&k) const = 0;
@@ -237,46 +87,37 @@ private:
     struct wrap: interface {
         explicit wrap(const Impl& impl): wrapped(impl) {}
         explicit wrap(Impl&& impl): wrapped(std::move(impl)) {}
-        virtual time_event_span events(time_type t0, time_type t1) override { return wrapped.events(t0, t1); }
-        virtual void reset() override { wrapped.reset(); }
-        virtual iface_ptr clone() override { return std::make_unique<wrap<Impl>>(wrapped); }
-        virtual void t_serialize(serializer& s, const std::string& k) const override { serialize(s, k, wrapped); }
-        virtual void t_deserialize(serializer& s, const std::string& k) override { deserialize(s, k, wrapped); }
+        time_event_span events(time_type t0, time_type t1) override { return wrapped.events(t0, t1); }
+        void reset() override { wrapped.reset(); }
+        void discard(std::size_t n) override { return wrapped.discard(n); }
+        iface_ptr clone() override { return std::make_unique<wrap<Impl>>(wrapped); }
+        void t_serialize(serializer& s, const std::string& k) const override { wrapped.t_serialize(s, k); }
+        void t_deserialize(serializer& s, const std::string& k) override { wrapped.t_deserialize(s, k); }
 
         Impl wrapped;
     };
 };
 
 // Constructors
-inline schedule::schedule(): schedule(empty_schedule{}) {}
 
-inline schedule regular_schedule(time_type t0,
-                                 time_type dt,
-                                 time_type t1 = std::numeric_limits<time_type>::max()) {
-    return schedule(regular_schedule_impl(t0, dt, t1));
-}
+/// Regular schedule with start `t0`, interval `dt`, and optional end `t1`.
+schedule ARB_ARBOR_API regular_schedule(const units::quantity& t0,
+                                        const units::quantity& dt,
+                                        const units::quantity& t1 = std::numeric_limits<time_type>::max()*units::ms);
 
-inline schedule regular_schedule(time_type dt) {
-    return regular_schedule(0, dt);
-}
+/// Regular schedule with interval `dt`.
+schedule ARB_ARBOR_API regular_schedule(const units::quantity& dt);
 
-template <typename Seq>
-inline schedule explicit_schedule(const Seq& seq) {
-    return schedule(explicit_schedule_impl(seq));
-}
+schedule ARB_ARBOR_API explicit_schedule(const std::vector<units::quantity>& seq);
+schedule ARB_ARBOR_API explicit_schedule_from_milliseconds(const std::vector<time_type>& seq);
 
-inline schedule explicit_schedule(const std::initializer_list<time_type>& seq) {
-    return schedule(explicit_schedule_impl(seq));
-}
+schedule ARB_ARBOR_API poisson_schedule(const units::quantity& tstart,
+                                        const units::quantity& rate,
+                                        seed_type seed = default_seed,
+                                        const units::quantity& tstop=terminal_time*units::ms);
 
-template <typename RandomNumberEngine>
-inline schedule poisson_schedule(time_type rate_kHz, const RandomNumberEngine& rng, time_type tstop=terminal_time) {
-    return schedule(poisson_schedule_impl<RandomNumberEngine>(0., rate_kHz, rng, tstop));
-}
-
-template <typename RandomNumberEngine>
-inline schedule poisson_schedule(time_type tstart, time_type rate_kHz, const RandomNumberEngine& rng, time_type tstop=terminal_time) {
-    return schedule(poisson_schedule_impl<RandomNumberEngine>(tstart, rate_kHz, rng, tstop));
-}
+schedule ARB_ARBOR_API poisson_schedule(const units::quantity& rate,
+                                        seed_type seed = default_seed,
+                                        const units::quantity& tstop=terminal_time*units::ms);
 
 } // namespace arb
diff --git a/arbor/include/arbor/simulation.hpp b/arbor/include/arbor/simulation.hpp
index c6cf845d9c5ca0ced75d30bde2edd191da2fb428..b5de7b4a5cd3f416ddceddd541fa8f2904f3d280 100644
--- a/arbor/include/arbor/simulation.hpp
+++ b/arbor/include/arbor/simulation.hpp
@@ -1,8 +1,6 @@
 #pragma once
 
-#include <array>
 #include <memory>
-#include <unordered_map>
 #include <vector>
 #include <functional>
 
@@ -50,7 +48,7 @@ public:
 
     void reset();
 
-    time_type run(time_type tfinal, time_type dt);
+    time_type run(const units::quantity& tfinal, const units::quantity& dt);
 
     // Minimum delay in network Ï„
     time_type min_delay();
diff --git a/arbor/include/arbor/spike_event.hpp b/arbor/include/arbor/spike_event.hpp
index d3ffdb5024443798f7d2cfbd230905a580a94846..d44cd7cd449b7b113a5c6e6e503758b51466190e 100644
--- a/arbor/include/arbor/spike_event.hpp
+++ b/arbor/include/arbor/spike_event.hpp
@@ -3,7 +3,6 @@
 #include <arbor/arb_types.hpp>
 
 #include <iosfwd>
-#include <tuple>
 #include <vector>
 
 #include <arbor/export.hpp>
diff --git a/arbor/include/arbor/units.hpp b/arbor/include/arbor/units.hpp
new file mode 100644
index 0000000000000000000000000000000000000000..c361209a561ebbd6dccde84a5c98a4fc80ff73d9
--- /dev/null
+++ b/arbor/include/arbor/units.hpp
@@ -0,0 +1,189 @@
+#pragma once
+
+#include <units/units.hpp>
+
+namespace arb::units {
+
+using quantity = ::units::precise_measurement;
+
+// Allow unary minus on quantities. Seemingly doesn't catch literals such as -10_mV
+inline quantity operator-(const quantity& q) { return (-1*q); }
+
+using unit = ::units::precise_unit;
+using ::units::to_string;
+using ::units::unit_cast_from_string;
+
+
+using ::units::precise::pico;
+using ::units::precise::nano;
+using ::units::precise::micro;
+using ::units::precise::milli;
+using ::units::precise::centi;
+using ::units::precise::deci;
+
+using ::units::precise::kilo;
+using ::units::precise::mega;
+using ::units::precise::giga;
+
+using ::units::precise::deg;
+using ::units::precise::rad;
+
+constexpr inline auto Celsius = ::units::precise::degC;
+constexpr inline auto Kelvin  = ::units::precise::Kelvin;
+
+using ::units::precise::s;
+using ::units::precise::ms;
+constexpr inline auto us = micro * s;
+using ::units::precise::ns;
+
+using ::units::precise::m;
+constexpr inline auto cm = centi * m;
+constexpr inline auto mm = milli * m;
+constexpr inline auto um = micro * m;
+constexpr inline auto nm = nano  * m;
+
+constexpr inline auto  Ohm = ::units::precise::ohm;
+constexpr inline auto kOhm = kilo * Ohm;
+constexpr inline auto MOhm = mega * Ohm;
+
+// Siemens
+constexpr inline auto S  = Ohm.pow(-1);
+constexpr inline auto mS = milli * S;
+constexpr inline auto uS = micro * S;
+
+constexpr inline auto  A = ::units::precise::Ampere;
+constexpr inline auto mA = milli * A;
+constexpr inline auto uA = micro * A;
+constexpr inline auto nA = nano  * A;
+constexpr inline auto pA = pico  * A;
+
+constexpr inline auto V = ::units::precise::volt;
+constexpr inline auto mV = milli * V;
+
+constexpr inline auto  Hz = ::units::precise::second.pow(-1);
+constexpr inline auto kHz = kilo * Hz;
+
+constexpr inline auto F  = ::units::precise::farad;
+constexpr inline auto mF = milli * F;
+constexpr inline auto uF = micro * F;
+constexpr inline auto nF = nano  * F;
+constexpr inline auto pF = pico  * F;
+
+constexpr inline auto m2 = m*m;
+constexpr inline auto cm2 = cm*cm;
+constexpr inline auto mm2 = mm*mm;
+constexpr inline auto um2 = um*um;
+constexpr inline auto nm2 = nm*nm;
+
+constexpr inline auto nil = ::units::precise::one;
+
+// Coulomb
+constexpr inline auto C = ::units::precise::coulomb;
+
+// mol and molarity
+using ::units::precise::mol;
+constexpr inline auto M = mol / m.pow(3);
+constexpr inline auto mM = milli * M;
+
+using ::units::is_valid;
+
+namespace literals {
+constexpr inline quantity operator ""_s(long double v) { return v*s; }
+constexpr inline quantity operator ""_ms(long double v) { return v*ms; }
+constexpr inline quantity operator ""_us(long double v) { return v*us; }
+constexpr inline quantity operator ""_ns(long double v) { return v*ns; }
+
+constexpr inline quantity operator ""_m(long double v) { return v*m; }
+constexpr inline quantity operator ""_cm(long double v) { return v*cm; }
+constexpr inline quantity operator ""_mm(long double v) { return v*mm; }
+constexpr inline quantity operator ""_um(long double v) { return v*um; }
+constexpr inline quantity operator ""_nm(long double v) { return v*nm; }
+
+constexpr inline quantity operator ""_m2(long double v) { return v*m2; }
+constexpr inline quantity operator ""_cm2(long double v) { return v*cm2; }
+constexpr inline quantity operator ""_mm2(long double v) { return v*mm2; }
+constexpr inline quantity operator ""_um2(long double v) { return v*um2; }
+constexpr inline quantity operator ""_nm2(long double v) { return v*nm2; }
+
+constexpr inline quantity operator ""_Ohm(long double v) { return v*Ohm; }
+constexpr inline quantity operator ""_kOhm(long double v) { return v*kOhm; }
+constexpr inline quantity operator ""_MOhm(long double v) { return v*MOhm; }
+
+constexpr inline quantity operator ""_S(long double v) { return v*S; }
+constexpr inline quantity operator ""_mS(long double v) { return v*mS; }
+constexpr inline quantity operator ""_uS(long double v) { return v*uS; }
+
+constexpr inline quantity operator ""_A(long double v) { return v*A; }
+constexpr inline quantity operator ""_mA(long double v) { return v*mA; }
+constexpr inline quantity operator ""_uA(long double v) { return v*uA; }
+constexpr inline quantity operator ""_nA(long double v) { return v*nA; }
+constexpr inline quantity operator ""_pA(long double v) { return v*pA; }
+
+constexpr inline quantity operator ""_V(long double v) { return v*V; }
+constexpr inline quantity operator ""_mV(long double v) { return v*mV; }
+
+constexpr inline quantity operator ""_Hz(long double v) { return v*Hz; }
+constexpr inline quantity operator ""_kHz(long double v) { return v*kHz; }
+
+constexpr inline quantity operator ""_F(long double v)  { return v*F; }
+constexpr inline quantity operator ""_mF(long double v) { return v*mF; }
+constexpr inline quantity operator ""_uF(long double v) { return v*uF; }
+constexpr inline quantity operator ""_nF(long double v) { return v*nF; }
+constexpr inline quantity operator ""_pF(long double v) { return v*pF; }
+
+constexpr inline quantity operator ""_mol(long double v) { return v*mol; }
+constexpr inline quantity operator ""_M(long double v) { return v*M; }
+constexpr inline quantity operator ""_mM(long double v) { return v*mM; }
+
+constexpr inline quantity operator ""_C(long double v) { return v*C; }
+
+constexpr inline quantity operator ""_s(unsigned long long v) { return v*s; }
+constexpr inline quantity operator ""_ms(unsigned long long v) { return v*ms; }
+constexpr inline quantity operator ""_us(unsigned long long v) { return v*us; }
+constexpr inline quantity operator ""_ns(unsigned long long v) { return v*ns; }
+
+constexpr inline quantity operator ""_m(unsigned long long v) { return v*m; }
+constexpr inline quantity operator ""_cm(unsigned long long v) { return v*cm; }
+constexpr inline quantity operator ""_mm(unsigned long long v) { return v*mm; }
+constexpr inline quantity operator ""_um(unsigned long long v) { return v*um; }
+constexpr inline quantity operator ""_nm(unsigned long long v) { return v*nm; }
+
+constexpr inline quantity operator ""_m2(unsigned long long v) { return v*m2; }
+constexpr inline quantity operator ""_cm2(unsigned long long v) { return v*cm2; }
+constexpr inline quantity operator ""_mm2(unsigned long long v) { return v*mm2; }
+constexpr inline quantity operator ""_um2(unsigned long long v) { return v*um2; }
+constexpr inline quantity operator ""_nm2(unsigned long long v) { return v*nm2; }
+
+constexpr inline quantity operator ""_Ohm(unsigned long long v) { return v*Ohm; }
+constexpr inline quantity operator ""_kOhm(unsigned long long v) { return v*kOhm; }
+constexpr inline quantity operator ""_MOhm(unsigned long long v) { return v*MOhm; }
+
+constexpr inline quantity operator ""_S(unsigned long long v) { return v*S; }
+constexpr inline quantity operator ""_mS(unsigned long long v) { return v*mS; }
+constexpr inline quantity operator ""_uS(unsigned long long v) { return v*uS; }
+
+constexpr inline quantity operator ""_A(unsigned long long v) { return v*A; }
+constexpr inline quantity operator ""_mA(unsigned long long v) { return v*mA; }
+constexpr inline quantity operator ""_uA(unsigned long long v) { return v*uA; }
+constexpr inline quantity operator ""_nA(unsigned long long v) { return v*nA; }
+constexpr inline quantity operator ""_pA(unsigned long long v) { return v*pA; }
+
+constexpr inline quantity operator ""_V(unsigned long long v) { return v*V; }
+constexpr inline quantity operator ""_mV(unsigned long long v) { return v*mV; }
+
+constexpr inline quantity operator ""_Hz(unsigned long long v) { return v*Hz; }
+constexpr inline quantity operator ""_kHz(unsigned long long v) { return v*kHz; }
+
+constexpr inline quantity operator ""_F(unsigned long long v)  { return v*F; }
+constexpr inline quantity operator ""_mF(unsigned long long v) { return v*mF; }
+constexpr inline quantity operator ""_uF(unsigned long long v) { return v*uF; }
+constexpr inline quantity operator ""_nF(unsigned long long v) { return v*nF; }
+constexpr inline quantity operator ""_pF(unsigned long long v) { return v*pF; }
+
+constexpr inline quantity operator ""_mol(unsigned long long v) { return v*mol; }
+constexpr inline quantity operator ""_M(unsigned long long v) { return v*M; }
+constexpr inline quantity operator ""_mM(unsigned long long v) { return v*mM; }
+
+constexpr inline quantity operator ""_C(unsigned long long v) { return v*C; }
+} // literals
+} // units
diff --git a/arbor/lif_cell_group.cpp b/arbor/lif_cell_group.cpp
index 6698d0edefa9ea3584b2bbc0a93c5a3c0f64fed4..5af84c88748e3bd2d406c0ac397b31a9e3c8a202 100644
--- a/arbor/lif_cell_group.cpp
+++ b/arbor/lif_cell_group.cpp
@@ -18,7 +18,7 @@ lif_cell_group::lif_cell_group(const std::vector<cell_gid_type>& gids,
     for (auto gid: gids_) {
         const auto& cell = util::any_cast<lif_cell>(rec.get_cell_description(gid));
         // set up cell state
-        cells_.push_back(cell);
+        cells_.emplace_back(cell);
         last_time_updated_.push_back(0.0);
         last_time_sampled_.push_back(-1.0);
         // tell our caller about this cell's connections
@@ -95,7 +95,7 @@ void lif_cell_group::reset() {
 
 // produce voltage V_m at t1, given cell state at t0 and no spikes in [t0, t1)
 static double
-lif_decay(const lif_cell& cell, double t0, double t1) {
+lif_decay(const lif_lowered_cell& cell, double t0, double t1) {
     return (cell.V_m - cell.E_L)*exp((t0 - t1)/cell.tau_m) + cell.E_L;
 }
 
diff --git a/arbor/lif_cell_group.hpp b/arbor/lif_cell_group.hpp
index 9572d06bd8ea7f5d7b15754e7b98b7d15b509393..ff922334adc7b6683c7c043e4b2845155d3a1549 100644
--- a/arbor/lif_cell_group.hpp
+++ b/arbor/lif_cell_group.hpp
@@ -16,6 +16,46 @@
 
 namespace arb {
 
+// Model parameters of leaky integrate and fire neuron model.
+struct ARB_SYMBOL_VISIBLE lif_lowered_cell {
+    cell_tag_type source; // Label of source.
+    cell_tag_type target; // Label of target.
+
+    // Neuronal parameters.
+    double tau_m = 10;    // Membrane potential decaying constant [ms].
+    double V_th = 10;     // Firing threshold [mV].
+    double C_m = 20;      // Membrane capacitance [pF].
+    double E_L = 0;       // Resting potential [mV].
+    double E_R = E_L;     // Reset potential [mV].
+    double V_m = E_L;     // Initial value of the Membrane potential [mV].
+    double t_ref = 2;     // Refractory period [ms].
+
+    lif_lowered_cell() = default;
+    lif_lowered_cell(const lif_cell& lif) {
+        source = lif.source;
+        target = lif.target;
+
+        tau_m = lif.tau_m.value_as(U::ms);
+        V_th = lif.V_th.value_as(U::mV);
+        C_m = lif.C_m.value_as(U::pF);
+        E_L = lif.E_L.value_as(U::mV);
+        E_R = lif.E_R.value_as(U::mV);
+        V_m = lif.V_m.value_as(U::mV);
+        t_ref = lif.t_ref.value_as(U::ms);
+
+        if (std::isnan(V_th)) throw std::out_of_range("V_th must be finite and in [mV]");
+        if (std::isnan(tau_m) || tau_m < 0) throw std::out_of_range("tau_m must be positive, finite, and in [ms]");
+        if (std::isnan(C_m) || C_m < 0) throw std::out_of_range("C_m must be positive, finite, and in [pF]");
+        if (std::isnan(E_L)) throw std::out_of_range("E_L must be finite and in [mV]");
+        if (std::isnan(E_R)) throw std::out_of_range("E_R must be finite and in [mV]");
+        if (std::isnan(V_m)) throw std::out_of_range("V_m must be finite and in [mV]");
+        if (std::isnan(t_ref) || t_ref < 0) throw std::out_of_range("t_ref must be positive, finite, and in [ms]");
+    }
+
+    ARB_SERDES_ENABLE(lif_lowered_cell, source, target, tau_m, V_th, C_m, E_L, E_R, V_m, t_ref);
+};
+
+
 struct ARB_ARBOR_API lif_cell_group: public cell_group {
     lif_cell_group() = default;
 
@@ -59,7 +99,7 @@ private:
     std::vector<cell_gid_type> gids_;
 
     // Cells that belong to this group.
-    std::vector<lif_cell> cells_;
+    std::vector<lif_lowered_cell> cells_;
 
     // Spikes that are generated (not necessarily sorted).
     std::vector<spike> spikes_;
diff --git a/arbor/schedule.cpp b/arbor/schedule.cpp
index 2a0d6befd6028c2f9c3c92def54b914f19f21a2d..b31545d1d60a623ed86cbca5e39f67ae6e3e86b5 100644
--- a/arbor/schedule.cpp
+++ b/arbor/schedule.cpp
@@ -1,53 +1,269 @@
 #include <algorithm>
-#include <iterator>
-#include <numeric>
-#include <utility>
 #include <vector>
 
 #include <arbor/common_types.hpp>
 #include <arbor/schedule.hpp>
 
-// Implementations for specific schedules.
-
 namespace arb {
 
-// Regular schedule implementation.
+// Schedule at Poisson point process with rate 1/mean_dt,
+// restricted to non-negative times.
+struct poisson_schedule_impl {
+    poisson_schedule_impl(time_type tstart, time_type rate_kHz, seed_type seed, time_type tstop):
+        tstart_(tstart), rate_(rate_kHz), exp_(rate_kHz), rng_(seed), seed_(seed), next_(tstart), tstop_(tstop) {
+        if (!std::isfinite(tstart_))  throw std::domain_error("Poisson schedule: start must be finite and in [ms]");
+        if (!std::isfinite(tstop_))   throw std::domain_error("Poisson schedule: stop must be finite and in [ms]");
+        if (!std::isfinite(rate_kHz)) throw std::domain_error("Poisson schedule: rate must be finite and in [kHz]");
+        if (!std::isfinite(tstart_) || tstart_ < 0) throw std::domain_error("Poisson schedule: start must be >= 0 and finite.");
+        if (!std::isfinite(tstop_)  || tstop_ < tstart_) throw std::domain_error("Poisson schedule: stop must be >= start and finite.");
+        step();
+    }
+
+    void reset() {
+        rng_ = engine_type{seed_};
+        if (discard_ > 0) rng_.discard(discard_);
+        exp_ = std::exponential_distribution<time_type>{rate_};
+        next_ = tstart_;
+        step();
+    }
+
+    void discard(std::size_t n) { discard_ = n; reset(); }
 
-time_event_span regular_schedule_impl::events(time_type t0, time_type t1) {
-    times_.clear();
+    time_event_span events(time_type t0, time_type t1) {
+        // if we start after the maximal allowed time, we have nothing to do
+        if (t0 >= tstop_) return {};
 
-    t0 = std::max(t0, t0_);
-    t1 = std::min(t1, t1_);
+        // restrict by maximal allowed time
+        t1 = std::min(t1, tstop_);
 
-    if (t1>t0) {
-        times_.reserve(1+std::size_t((t1-t0)*oodt_));
+        times_.clear();
 
-        long long n = t0*oodt_;
-        time_type t = n*dt_;
+        while (next_ < t0) { step(); }
 
-        while (t<t0) {
-            t = (++n)*dt_;
+        while (next_ < t1) {
+            times_.push_back(next_);
+            step();
         }
 
-        while (t<t1) {
-            times_.push_back(t);
-            t = (++n)*dt_;
+        return as_time_event_span(times_);
+    }
+
+    void step() { next_ += exp_(rng_); }
+
+    template<typename K>
+    void t_serialize(::arb::serializer& ser, const K& k) const {
+        const auto& t = *this;
+        ser.begin_write_map(arb::to_serdes_key(k));
+        ARB_SERDES_WRITE(tstart_);
+        ARB_SERDES_WRITE(tstop_);
+        ser.end_write_map();
+    }
+
+    template<typename K>
+    void t_deserialize(::arb::serializer& ser, const K& k) {
+        auto& t = *this;
+        ser.begin_read_map(arb::to_serdes_key(k));
+        ARB_SERDES_READ(tstart_);
+        ARB_SERDES_READ(tstop_);
+        ser.end_read_map();
+        t.reset();
+    }
+
+    time_type tstart_;
+    time_type rate_;
+    std::exponential_distribution<time_type> exp_;
+    engine_type rng_;
+    seed_type seed_;
+    time_type next_;
+    std::vector<time_type> times_;
+    time_type tstop_;
+    std::size_t discard_ = 0;
+};
+
+schedule poisson_schedule(const units::quantity& tstart,
+                          const units::quantity& rate,
+                          seed_type seed,
+                          const units::quantity& tstop) {
+    return schedule(poisson_schedule_impl(tstart.value_as(units::ms),
+                                          rate.value_as(units::kHz),
+                                          seed,
+                                          tstop.value_as(units::ms)));
+}
+
+schedule poisson_schedule(const units::quantity& rate,
+                          seed_type seed,
+                          const units::quantity& tstop) {
+    return poisson_schedule(0.*units::ms, rate, seed, tstop);
+}
+
+
+struct empty_schedule_impl {
+    void reset() {}
+    time_event_span events(time_type t0, time_type t1) {
+        static time_type no_time;
+        return {&no_time, &no_time};
+    }
+
+
+    void t_serialize(::arb::serializer& ser,
+                  const std::string& k) const {
+        ser.begin_write_map(::arb::to_serdes_key(k));
+        ser.end_write_map();
+    }
+
+    void t_deserialize(::arb::serializer& ser,
+                     const std::string& k) {
+        ser.begin_read_map(::arb::to_serdes_key(k));
+        ser.end_read_map();
+    }
+
+    void discard(std::size_t) {}
+};
+
+schedule::schedule(): schedule(empty_schedule_impl{}) {}
+
+// Schedule at k·dt for integral k≥0 within the interval [t0, t1).
+struct ARB_ARBOR_API regular_schedule_impl {
+    explicit regular_schedule_impl(time_type t0, time_type dt, time_type t1):
+        t0_(t0), t1_(t1), dt_(dt), oodt_(1./dt) {
+        if (!std::isfinite(t0_)) throw std::domain_error("Regular schedule: start must be finite and in [ms]");
+        if (!std::isfinite(t1_)) throw std::domain_error("Regular schedule: stop must be finite and in [ms]");
+        if (!std::isfinite(dt_)) throw std::domain_error("Regular schedule: step must be finite and in [ms]");
+        if (dt_ <= 0)  throw std::domain_error("regular schedule: dt must be > 0 and finite.");
+        if (t0_ < 0)   throw std::domain_error("regular schedule: start must be >= 0 and finite.");
+        if (t1_ < t0_) throw std::domain_error("regular schedule: stop must be >= start and finite.");
+    };
+
+    void reset() {}
+
+    ARB_SERDES_ENABLE(regular_schedule_impl, t0_, t1_, dt_, oodt_);
+
+    template<typename K>
+    void t_serialize(::arb::serializer& ser, const K& k) const {
+        const auto& t = *this;
+        ser.begin_write_map(arb::to_serdes_key(k));
+        ARB_SERDES_WRITE(t0_);
+        ARB_SERDES_WRITE(t1_);
+        ARB_SERDES_WRITE(dt_);
+        ser.end_write_map();
+    }
+
+    template<typename K>
+    void t_deserialize(::arb::serializer& ser, const K& k) {
+        auto& t = *this;
+        ser.begin_read_map(arb::to_serdes_key(k));
+        ARB_SERDES_READ(t0_);
+        ARB_SERDES_READ(t1_);
+        ARB_SERDES_READ(dt_);
+        oodt_ = 1.0/dt_;
+        ser.end_read_map();
+    }
+
+    time_event_span events(time_type t0, time_type t1) {
+        times_.clear();
+
+        t0 = std::max(t0, t0_);
+        t1 = std::min(t1, t1_);
+
+        if (t1>t0) {
+            times_.reserve(1+std::size_t((t1-t0)*oodt_));
+
+            long long n = t0*oodt_;
+            time_type t = n*dt_;
+
+            while (t<t0) {
+                t = (++n)*dt_;
+            }
+
+            while (t<t1) {
+                times_.push_back(t);
+                t = (++n)*dt_;
+            }
         }
+
+        return as_time_event_span(times_);
     }
 
-    return as_time_event_span(times_);
+    void discard(std::size_t) {}
+
+    time_type t0_, t1_, dt_;
+    time_type oodt_;
+
+    std::vector<time_type> times_;
+};
+
+schedule regular_schedule(const units::quantity& t0,
+                          const units::quantity& dt,
+                          const units::quantity& t1) {
+    return schedule(regular_schedule_impl(t0.value_as(units::ms),
+                                          dt.value_as(units::ms),
+                                          t1.value_as(units::ms)));
 }
 
-// Explicit schedule implementation.
+schedule regular_schedule(const units::quantity& dt) { return regular_schedule(0*units::ms, dt); }
 
-time_event_span explicit_schedule_impl::events(time_type t0, time_type t1) {
-    time_event_span view = as_time_event_span(times_);
+// Schedule at times given explicitly via a provided sorted sequence.
+struct explicit_schedule_impl {
+    explicit_schedule_impl(const explicit_schedule_impl&) = default;
+    explicit_schedule_impl(explicit_schedule_impl&&) = default;
 
-    const time_type* lb = std::lower_bound(view.first+start_index_, view.second, t0);
-    const time_type* ub = std::lower_bound(lb, view.second, t1);
+    explicit explicit_schedule_impl(std::vector<time_type> seq):
+        start_index_(0), times_(std::move(seq)) {
+        time_type last = -1;
+        for (auto t: times_) {
+            if (!std::isfinite(t)) throw std::domain_error("explicit schedule: times must be finite and in [ms]");
+            if (t < 0)             throw std::domain_error("explicit schedule: times must be >= 0 and finite.");
+            if (t < last)          throw std::domain_error("explicit schedule: times must be sorted.");
+            last = t;
+        }
+    }
 
-    start_index_ = ub-view.first;
-    return {lb, ub};
+    void reset() { start_index_ = 0; }
+
+    time_event_span events(time_type t0, time_type t1) {
+        time_event_span view = as_time_event_span(times_);
+
+        const time_type* lb = std::lower_bound(view.first+start_index_, view.second, t0);
+        const time_type* ub = std::lower_bound(lb, view.second, t1);
+
+        start_index_ = ub-view.first;
+        return {lb, ub};
+    }
+
+    template<typename K>
+    void t_serialize(::arb::serializer& ser, const K& k) const {
+        const auto& t = *this;
+        ser.begin_write_map(arb::to_serdes_key(k));
+        ARB_SERDES_WRITE(start_index_);
+        ARB_SERDES_WRITE(times_);
+        ser.end_write_map();
+    }
+
+    template<typename K>
+    void t_deserialize(::arb::serializer& ser, const K& k) {
+        auto& t = *this;
+        ser.begin_read_map(arb::to_serdes_key(k));
+        ARB_SERDES_READ(start_index_);
+        ARB_SERDES_READ(times_);
+        ser.end_read_map();
+    }
+
+    void discard(std::size_t) {}
+
+    std::ptrdiff_t start_index_;
+    std::vector<time_type> times_;
+};
+
+schedule explicit_schedule(const std::vector<units::quantity>& seq) {
+    std::vector<time_type> res;
+    res.reserve(seq.size());
+    for (const auto& t: seq) res.push_back(t.value_as(units::ms));
+    return schedule(explicit_schedule_impl(std::move(res)));
 }
 
+schedule explicit_schedule_from_milliseconds(const std::vector<time_type>& seq) {
+    return schedule(explicit_schedule_impl(seq));
+}
+
+
 } // namespace arb
diff --git a/arbor/simulation.cpp b/arbor/simulation.cpp
index dac25d6fef281e67b90fd5ca5a496a466d9539a0..95963e85fd13f6067d271003d16d1dc9322fc079 100644
--- a/arbor/simulation.cpp
+++ b/arbor/simulation.cpp
@@ -1,5 +1,4 @@
 #include <memory>
-#include <set>
 #include <vector>
 
 #include <arbor/export.hpp>
@@ -15,13 +14,10 @@
 #include "cell_group.hpp"
 #include "cell_group_factory.hpp"
 #include "communication/communicator.hpp"
-#include "execution_context.hpp"
 #include "merge_events.hpp"
 #include "thread_private_spike_store.hpp"
 #include "threading/threading.hpp"
-#include "util/filter.hpp"
 #include "util/maputil.hpp"
-#include "util/partition.hpp"
 #include "util/span.hpp"
 #include "profile/profiler_macro.hpp"
 
@@ -398,6 +394,9 @@ time_type simulation_state::run(time_type tfinal, time_type dt) {
     // Requires state at end of run(), with epoch_.id==k:
     //     * U(k) and D(k) have completed.
 
+    if (!std::isfinite(tfinal) || tfinal < 0) throw std::domain_error("simulation: tfinal must be finite, positive, and in [ms]");
+    if (!std::isfinite(dt) || tfinal < 0) throw std::domain_error("simulation: dt must be finite, positive, and in [ms]");
+
     if (tfinal<=epoch_.t1) return epoch_.t1;
 
     // Compute following epoch, with max time tfinal.
@@ -596,11 +595,12 @@ void simulation::reset() {
 
 void simulation::update(const connectivity& rec) { impl_->update(rec); }
 
-time_type simulation::run(time_type tfinal, time_type dt) {
-    if (dt <= 0.0) {
-        throw domain_error("Finite time-step must be supplied.");
-    }
-    return impl_->run(tfinal, dt);
+time_type simulation::run(const units::quantity& tfinal, const units::quantity& dt) {
+    auto dt_ms = dt.value_as(units::ms);
+    if (dt_ms <= 0.0 || std::isnan(dt_ms)) throw domain_error("Finite time-step must be supplied.");
+    auto tfinal_ms = tfinal.value_as(units::ms);
+    if (tfinal_ms <= 0.0 || std::isnan(tfinal_ms)) throw domain_error("Finite time-step must be supplied.");
+    return impl_->run(tfinal_ms, dt_ms);
 }
 
 sampler_association_handle simulation::add_sampler(
diff --git a/arborio/cableio.cpp b/arborio/cableio.cpp
index b0383b74a922e58eacff3e3b424e40c8ac6d9f3a..4ca05cc64a9c0362486aa49fe449172acd15b6cc 100644
--- a/arborio/cableio.cpp
+++ b/arborio/cableio.cpp
@@ -19,7 +19,7 @@ namespace arborio {
 
 using namespace arb;
 
-ARB_ARBORIO_API std::string acc_version() {return "0.1-dev";}
+ARB_ARBORIO_API std::string acc_version() {return "0.9-dev";}
 
 cableio_parse_error::cableio_parse_error(const std::string& msg, const arb::src_location& loc):
     arb::arbor_exception(msg+" at :"+
@@ -44,28 +44,31 @@ s_expr mksexp(const iexpr& j) {
     return parse_s_expr(s.str());
 }
 s_expr mksexp(const init_membrane_potential& p) {
-    return slist("membrane-potential"_symbol, mksexp(p.value));
+    return slist("membrane-potential"_symbol, p.value, mksexp(p.scale));
+}
+s_expr mksexp(const arb::units::quantity& p) {
+    return slist("quantity"_symbol, p.value(), s_expr(arb::units::to_string(p.units())));
 }
 s_expr mksexp(const axial_resistivity& r) {
-    return slist("axial-resistivity"_symbol, mksexp(r.value));
+    return slist("axial-resistivity"_symbol, r.value, mksexp(r.scale));
 }
-s_expr mksexp(const temperature_K& t) {
-    return slist("temperature-kelvin"_symbol, mksexp(t.value));
+s_expr mksexp(const temperature& t) {
+    return slist("temperature-kelvin"_symbol, t.value, mksexp(t.scale));
 }
 s_expr mksexp(const membrane_capacitance& c) {
-    return slist("membrane-capacitance"_symbol, mksexp(c.value));
+    return slist("membrane-capacitance"_symbol, c.value, mksexp(c.scale));
 }
 s_expr mksexp(const init_int_concentration& c) {
-    return slist("ion-internal-concentration"_symbol, s_expr(c.ion), mksexp(c.value));
+    return slist("ion-internal-concentration"_symbol, s_expr(c.ion), c.value, mksexp(c.scale));
 }
 s_expr mksexp(const init_ext_concentration& c) {
-    return slist("ion-external-concentration"_symbol, s_expr(c.ion), mksexp(c.value));
+    return slist("ion-external-concentration"_symbol, s_expr(c.ion), c.value, mksexp(c.scale));
 }
 s_expr mksexp(const init_reversal_potential& c) {
-    return slist("ion-reversal-potential"_symbol, s_expr(c.ion), mksexp(c.value));
+    return slist("ion-reversal-potential"_symbol, s_expr(c.ion), c.value, mksexp(c.scale));
 }
 s_expr mksexp(const ion_diffusivity& c) {
-    return slist("ion-diffusivity"_symbol, s_expr(c.ion), mksexp(c.value));
+    return slist("ion-diffusivity"_symbol, s_expr(c.ion), c.value, mksexp(c.scale));
 }
 s_expr mksexp(const i_clamp& c) {
     std::vector<s_expr> evlps;
@@ -224,15 +227,13 @@ using branch_tuple   = std::tuple<int,int,std::vector<arb::msegment>>;
 using version_tuple  = std::tuple<std::string>;
 
 // Define makers for defaultables, paintables, placeables
-#define ARBIO_DEFINE_ONE_ARG(name) arb::name make_##name(double val) { return arb::name{val};}
+#define ARBIO_DEFINE_ONE_ARG(name) arb::name make_##name(double val) { return arb::name(val);}
 #define ARBIO_DEFINE_ION_ARG(name) arb::name make_##name(const std::string& ion, double val) { return arb::name{ion, val};}
-#define ARBIO_DEFINE_IEXPR_ION_ARG(name) arb::name make_##name(const std::string& ion, iexpr val) { return arb::name{ion, val};}
-#define ARBIO_DEFINE_IEXPR_ARG(name) arb::name make_##name(iexpr val) { return arb::name{val}; }
+#define ARBIO_DEFINE_IEXPR_ION_ARG(name) arb::name make_##name(const std::string& ion, double base, iexpr scale) { arb::name res; res.ion = ion; res.value = base; res.scale = scale; return res; }
+#define ARBIO_DEFINE_IEXPR_ARG(name) arb::name make_##name(double base, iexpr scale) { arb::name res; res.value = base; res.scale = scale; return res; }
 
-ARB_PP_FOREACH(ARBIO_DEFINE_ONE_ARG, threshold_detector)
-ARB_PP_FOREACH(ARBIO_DEFINE_IEXPR_ARG, init_membrane_potential, temperature_K, axial_resistivity, membrane_capacitance)
-ARB_PP_FOREACH(ARBIO_DEFINE_ION_ARG, ion_diffusivity)
-ARB_PP_FOREACH(ARBIO_DEFINE_IEXPR_ION_ARG, init_int_concentration, init_ext_concentration, init_reversal_potential)
+ARB_PP_FOREACH(ARBIO_DEFINE_IEXPR_ARG, init_membrane_potential, temperature, axial_resistivity, membrane_capacitance)
+ARB_PP_FOREACH(ARBIO_DEFINE_IEXPR_ION_ARG, ion_diffusivity, init_int_concentration, init_ext_concentration, init_reversal_potential)
 
 #undef ARBIO_DEFINE_ONE_ARG
 #undef ARBIO_DEFINE_ION_ARG
@@ -244,18 +245,22 @@ std::vector<arb::i_clamp::envelope_point> make_envelope(const std::vector<std::v
     std::transform(vec.begin(), vec.end(), std::back_inserter(envlp),
         [](const auto& x) {
             auto t = std::get<envelope_tuple>(x);
-            return arb::i_clamp::envelope_point{std::get<0>(t), std::get<1>(t)};
+            return arb::i_clamp::envelope_point{std::get<0>(t)*arb::units::ms, std::get<1>(t)*arb::units::nA};
         });
     return envlp;
 }
 arb::i_clamp make_i_clamp(const std::vector<arb::i_clamp::envelope_point>& envlp, double freq, double phase) {
-    return arb::i_clamp(envlp, freq, phase);
+    return arb::i_clamp(envlp, freq*arb::units::kHz, phase*arb::units::rad);
 }
 pulse_tuple make_envelope_pulse(double delay, double duration, double amplitude) {
     return pulse_tuple{delay, duration, amplitude};
 }
 arb::i_clamp make_i_clamp_pulse(const pulse_tuple& p, double freq, double phase) {
-    return arb::i_clamp::box(std::get<0>(p), std::get<1>(p), std::get<2>(p), freq, phase);
+    return arb::i_clamp::box(std::get<0>(p)*arb::units::ms,
+                             std::get<1>(p)*arb::units::ms,
+                             std::get<2>(p)*arb::units::nA,
+                             freq*arb::units::kHz,
+                             phase*arb::units::rad);
 }
 arb::cv_policy make_cv_policy(const cv_policy& p) {
     return p;
@@ -652,26 +657,21 @@ parse_hopefully<std::any> eval(const s_expr& e, const eval_map& map, const eval_
     return util::unexpected(cableio_parse_error("Expression is not integer, real expression of the form (op <args>) nor tuple of the form (e0 e1 ... en)", location(e)));
 }
 
-#define ARBIO_ADD_EVAL(name, fun, ty) { name, make_call<ty>(fun, "'" name "' with 1 argument (val:" #ty ")") }
-#define ARBIO_ADD_ION_EVAL(name, fun, ty) { name, make_call<std::string, ty>(fun, "'" name "' with 2 argument (ion:string, val:" #ty ")") }
+arb::units::quantity make_quantity(double v, std::string u) { return v*arb::units::unit_cast_from_string(u); }
+
+#define ARBIO_ADD_EVAL(name, fun) { name, make_call<double, iexpr>(fun, "'" name "' with 2 arguments: val:real and ex:iexpr") }
+#define ARBIO_ADD_ION_EVAL(name, fun) { name, make_call<std::string, double, iexpr>(fun, "'" name "' with 3 arguments: ion:string, val:real, and ex:iexpr") }
 
 eval_map named_evals{
-    ARBIO_ADD_EVAL("membrane-potential", make_init_membrane_potential, double),
-    ARBIO_ADD_EVAL("membrane-potential", make_init_membrane_potential, iexpr),
-    ARBIO_ADD_EVAL("temperature-kelvin", make_temperature_K, double),
-    ARBIO_ADD_EVAL("temperature-kelvin", make_temperature_K, iexpr),
-    ARBIO_ADD_EVAL("axial-resistivity", make_axial_resistivity, double),
-    ARBIO_ADD_EVAL("axial-resistivity", make_axial_resistivity, iexpr),
-    ARBIO_ADD_EVAL("membrane-capacitance", make_membrane_capacitance, double),
-    ARBIO_ADD_EVAL("membrane-capacitance", make_membrane_capacitance, iexpr),
-    ARBIO_ADD_ION_EVAL("ion-internal-concentration", make_init_int_concentration, double),
-    ARBIO_ADD_ION_EVAL("ion-internal-concentration", make_init_int_concentration, iexpr),
-    ARBIO_ADD_ION_EVAL("ion-external-concentration", make_init_ext_concentration, double),
-    ARBIO_ADD_ION_EVAL("ion-external-concentration", make_init_ext_concentration, iexpr),
-    ARBIO_ADD_ION_EVAL("ion-reversal-potential", make_init_reversal_potential, double),
-    ARBIO_ADD_ION_EVAL("ion-reversal-potential", make_init_reversal_potential, iexpr),
-    ARBIO_ADD_ION_EVAL("ion-diffusivity", make_ion_diffusivity, double),
-    // Intentionally left out! ARBIO_ADD_ION_EVAL("ion-diffusivity", make_ion_diffusivity, iexpr),
+    ARBIO_ADD_EVAL("membrane-potential", make_init_membrane_potential),
+    ARBIO_ADD_EVAL("temperature-kelvin", make_temperature),
+    ARBIO_ADD_EVAL("axial-resistivity", make_axial_resistivity),
+    ARBIO_ADD_EVAL("membrane-capacitance", make_membrane_capacitance),
+    ARBIO_ADD_ION_EVAL("ion-internal-concentration", make_init_int_concentration),
+    ARBIO_ADD_ION_EVAL("ion-external-concentration", make_init_ext_concentration),
+    ARBIO_ADD_ION_EVAL("ion-reversal-potential", make_init_reversal_potential),
+    ARBIO_ADD_ION_EVAL("ion-diffusivity", make_ion_diffusivity),
+    {"quantity", make_call<double, std::string>(make_quantity, "'quantity' with a value:real and a unit:string")},
     {"envelope", make_arg_vec_call<envelope_tuple>(make_envelope,
         "'envelope' with one or more pairs of start time and amplitude (start:real amplitude:real)")},
     {"envelope-pulse", make_call<double, double, double>(make_envelope_pulse,
@@ -680,7 +680,7 @@ eval_map named_evals{
         "'current-clamp' with 3 arguments (env:envelope freq:real phase:real)")},
     {"current-clamp", make_call<pulse_tuple, double, double>(make_i_clamp_pulse,
         "'current-clamp' with 3 arguments (env:envelope_pulse freq:real phase:real)")},
-    {"threshold-detector", make_call<double>(make_threshold_detector,
+    {"threshold-detector", make_call<double>(arb::threshold_detector::from_raw_millivolts,
         "'threshold-detector' with 1 argument (threshold:real)")},
     {"mechanism", make_mech_call("'mechanism' with a name argument, and 0 or more parameter settings"
         "(name:string (param:string val:real))")},
@@ -700,7 +700,7 @@ eval_map named_evals{
     {"place", make_call<locset, synapse, std::string>(make_place, "'place' with 3 arguments (ls:locset mech:synapse name:string)")},
 
     {"paint", make_call<region, init_membrane_potential>(make_paint, "'paint' with 2 arguments (reg:region v:membrane-potential)")},
-    {"paint", make_call<region, temperature_K>(make_paint, "'paint' with 2 arguments (reg:region v:temperature-kelvin)")},
+    {"paint", make_call<region, temperature>(make_paint, "'paint' with 2 arguments (reg:region v:temperature-kelvin)")},
     {"paint", make_call<region, membrane_capacitance>(make_paint, "'paint' with 2 arguments (reg:region v:membrane-capacitance)")},
     {"paint", make_call<region, axial_resistivity>(make_paint, "'paint' with 2 arguments (reg:region v:axial-resistivity)")},
     {"paint", make_call<region, init_int_concentration>(make_paint, "'paint' with 2 arguments (reg:region v:ion-internal-concentration)")},
@@ -711,7 +711,7 @@ eval_map named_evals{
     {"paint", make_call<region, scaled_mechanism<density>>(make_paint, "'paint' with 2 arguments (reg:region v:scaled-mechanism)")},
 
     {"default", make_call<init_membrane_potential>(make_default, "'default' with 1 argument (v:membrane-potential)")},
-    {"default", make_call<temperature_K>(make_default, "'default' with 1 argument (v:temperature-kelvin)")},
+    {"default", make_call<temperature>(make_default, "'default' with 1 argument (v:temperature-kelvin)")},
     {"default", make_call<membrane_capacitance>(make_default, "'default' with 1 argument (v:membrane-capacitance)")},
     {"default", make_call<axial_resistivity>(make_default, "'default' with 1 argument (v:axial-resistivity)")},
     {"default", make_call<init_int_concentration>(make_default, "'default' with 1 argument (v:ion-internal-concentration)")},
diff --git a/arborio/parse_helpers.hpp b/arborio/parse_helpers.hpp
index 5b0c37f9a7506fb46a42d52d1e578b81f42e7482..e1bc02f4cf93366e0eede306ab7d55c19aadfe50 100644
--- a/arborio/parse_helpers.hpp
+++ b/arborio/parse_helpers.hpp
@@ -3,7 +3,6 @@
 #include <any>
 #include <string>
 #include <sstream>
-#include <iostream>
 #include <typeinfo>
 
 #include <arbor/assert.hpp>
@@ -12,6 +11,8 @@
 #include <arbor/morph/locset.hpp>
 #include <arbor/morph/region.hpp>
 #include <arbor/iexpr.hpp>
+#include <arbor/s_expr.hpp>
+
 
 namespace arborio {
 using namespace arb;
diff --git a/doc/concepts/decor.rst b/doc/concepts/decor.rst
index 89e5ee7d4a60d0226f8e4885f1222a8c7e8d1254..ea96543e749861663d7f4d1329051876e86e6c65 100644
--- a/doc/concepts/decor.rst
+++ b/doc/concepts/decor.rst
@@ -204,7 +204,8 @@ resistivity, and membrane capacitance, as well as all ion parameters
 
 
     # initial value for the membrane potential as inhomogeneous expression.
-    decor.paint('(all)', Vm='(mul 42 (diameter))')
+    # we give a pair of a base value and a scaling iexpr
+    decor.paint('(all)', Vm=(23, '(mul 42 (diameter))'))
 
 .. _cablecell-ions:
 
diff --git a/doc/concepts/units.rst b/doc/concepts/units.rst
new file mode 100644
index 0000000000000000000000000000000000000000..d52ed30ef28759d1389611c31d5f94b6b05f633c
--- /dev/null
+++ b/doc/concepts/units.rst
@@ -0,0 +1,192 @@
+.. _units:
+
+Units in Arbor
+==============
+
+.. note::
+
+   This is a work in progress. The near goal term is to make this coverage
+   complete, but expect some exceptions. Notably, the interfaces of individual
+   mechanism are not yet integrated, since NMODL files -- despite explicitly
+   specifying units -- do not make good use of the feature.
+
+A large part of the interface of Arbor -- both in C++ and Python -- is covered
+by units of measurement. This gives the API a way to communicate the intended
+units of parameters and return values and users can largely use their preferred
+system of units, as automatic conversion is provided. For performance reasons,
+this extends to the outermost layer only, after which Arbor uses its own
+internal system of measurement.
+
+We leverage the `units library <https://github.com/llnl/units>`_ published by LLNL.
+
+Arbor is focussed on SI units, and we provide the following presets for both the
+C++ and Python modules.
+
+.. table:: Provided dimensionalities and units
+   :widths: auto
+
+   =============   =======
+   Dimension       Unit
+   =============   =======
+   Temperature     Kelvin
+                   Celsius
+   Length          m
+                   cm
+                   mm
+                   um
+                   nm
+   Time            s
+                   ms
+                   us
+                   ns
+   Resistance      Ohm
+                   kOhm
+                   MOhm
+   Conductivity    S
+                   mS
+                   uS
+   Current         A
+                   mA
+                   uA
+                   nA
+                   pA
+   Potential       V
+                   mV
+   Frequency       Hz
+                   kHz
+   Capacity        F
+                   mF
+                   uF
+                   nF
+                   pF
+   Area            m2
+                   cm2
+                   mm2
+                   um2
+                   nm2
+   Charge          C
+   Mol             mol
+   Molarity        M = mol/l
+                   mM
+   Angle           rad
+                   deg
+   =============   =======
+
+Further units may be derived from existing ones by mean of multiplication and
+division with the obvious semantics, the existing metric prefixes, or by extending
+the catalogue of units via the underlying units library.
+
+.. table:: Provided metric prefixes
+   :widths: auto
+
+   =============   =======   =============   =======
+   Prefix          Scale     Prefix          Scale
+   =============   =======   =============   =======
+    pico            1e-12     kilo            1e3
+    nano            1e-9      mega            1e6
+    micro           1e-6      giga            1e9
+    milli           1e-3
+    centi           1e-2
+   =============   =======   =============   =======
+
+Parameters are passed into Arbor via a ``quantity``, which comprise a value and
+a unit. We construct a quantity by multiplication of a scalar value by a unit.
+Multiplication of two quantities will result in the pointwise product of the
+values and units; like one would expect.
+
+.. code-block:: python
+
+    # two kilometers, dimension is length
+    l = 2 * km
+
+    # three kilometers, but with the scaling factored out
+    s = 3 * kilo * m
+
+    # multiplication of two lengths gives an area
+    a = l * s
+    # is now 6 square kilometers
+
+Units and quantities work intuitively and largely the same across C++ and
+Python, but we provide some details below.
+
+C++
+---
+
+Units are defined in the ``units`` namespace, and exist at runtime, since we
+need to cater to dynamical language bindings. In the ``units::literals``
+namespace, we find user defined literals for all units above, e.g. ``10_mV``.
+Integral powers of units are constructed using the ``.pow(int)`` member, e.g.
+``m2 = m.pow(2)``. Units and quantities can be converted to and from strings
+using the ``std::string to_string(const T&)`` and ``T from_string_cast(const std::string&)``
+functions. Conversion between different units is done like this
+
+.. code-block:: c++
+
+    namespace U = arb::units;
+
+    // membrane capacitance in SI
+    auto c_m = 42*U::F/U::m.pow(2) // same as 42*U::F*U::m.pow(-2)
+    // convert to different unit and extract value
+    auto c_m_ = c_m.value_as(U::uF*U::cm.pow(-2))
+    // invalid conversions result in NaN values, so check
+    if (std::isnan(c_m_)) throw std::domain_error("Invalid value");
+
+however, Arbor does this whenever values pass its interface.
+
+.. cpp::namespace:: arb::units
+
+.. cpp:class:: unit
+
+    Describes a unit of measurement.
+
+    .. method:: pow(int)
+
+        Raise unit to integral power.
+
+.. cpp:class:: quantity
+
+    A tuple of a value and a unit of measurement.
+
+    .. method:: value_as(unit)
+
+        Convert to another unit and return converted value, possibly NaN, if
+        malformed.
+
+
+Python
+------
+
+Units are defined in the ``units`` sub-module. Integral powers of units are
+constructed using the ``**`` operator, e.g. ``m2 = m ** 2``. Units and
+quantities can be converted to a string using the ``str()`` function.
+Conversion between different units is done like this
+
+.. code-block:: python
+
+    from arbor import units as U
+    from math import isnan
+
+    # membrane capacitance in SI
+    c_m = 42*U.F/U.m**2
+    # convert to different unit and extract value
+    c_m_ = c_m.value_as(U.uF*U.cm**-2)
+    # invalid conversions result in NaN values, so check
+    if isnan(c_m_):
+        raise ValueError("Invalid value")
+
+however, Arbor does this whenever values pass its interface.
+
+.. currentmodule:: arbor.units
+
+.. class:: unit
+
+    Describes a unit of measurement.
+
+.. class:: quantity
+
+    A tuple of a value and a unit of measurement.
+
+    .. method:: value_as
+
+        Convert to another unit and return converted value, possibly NaN, if
+        malformed.
diff --git a/doc/cpp/cable_cell.rst b/doc/cpp/cable_cell.rst
index 9013db7dcd8cf556d39911d0344463f5180c2ae7..1b564c3374a6a2c3f18bbb32aee7a4a126b37395 100644
--- a/doc/cpp/cable_cell.rst
+++ b/doc/cpp/cable_cell.rst
@@ -169,19 +169,19 @@ value should be taken from the cell or global parameter set.
    Internal and external concentrations are given in millimolars, i.e. mol/m³.
    Reversal potential is given in millivolts.
 
-   .. cpp:member:: util::optional<double> init_membrane_potential
+   .. cpp:member:: util::optional<units::quantity> init_membrane_potential
 
    Initial membrane potential in millivolts.
 
-   .. cpp:member:: util::optional<double> temperature_K
+   .. cpp:member:: util::optional<units::quantity> temperature
 
    Local temperature in Kelvin.
 
-   .. cpp:member:: util::optional<double> axial_resistivity
+   .. cpp:member:: util::optional<units::quantity> axial_resistivity
 
    Local resistivity of the intracellular medium, in ohm-centimetres.
 
-   .. cpp:member:: util::optional<double> membrane_capacitance
+   .. cpp:member:: util::optional<units::quantity> membrane_capacitance
 
    Local areal capacitance of the cell membrane, in Farads per square metre.
 
diff --git a/doc/cpp/interconnectivity.rst b/doc/cpp/interconnectivity.rst
index 9f572b1f90e833f2f045f7ac06aeb540d6f9d200..9bd2bc49a97d8dc6d56eb8b08a02aedeac2edab9 100644
--- a/doc/cpp/interconnectivity.rst
+++ b/doc/cpp/interconnectivity.rst
@@ -34,9 +34,9 @@ Interconnectivity
         the `expsyn` synapse interprets it as a conductance
         with units μS (micro-Siemens).
 
-    .. cpp:member:: float delay
+    .. cpp:member:: units::quantity delay
 
-        Delay of the connection (milliseconds).
+        Delay of the connection (milliseconds), must be positive and finite.
 
 .. cpp:class:: ext_cell_connection
 
diff --git a/doc/cpp/lif_cell.rst b/doc/cpp/lif_cell.rst
index 60a72881a4a63e19ecb28974f4499372b37bc2be..dec8f34e4f9667de27c1b2d8247fff4ff34356f8 100644
--- a/doc/cpp/lif_cell.rst
+++ b/doc/cpp/lif_cell.rst
@@ -25,30 +25,30 @@ LIF cells
         The label of the single built-in target on the cell. Used for forming connections to the cell in the
         :cpp:class:`recipe` by creating a :cpp:class:`connection`.
 
-    .. cpp:member:: double tau_m
+    .. cpp:member:: const arb::units::quantity& tau_m
 
-        Membrane potential decaying constant [ms].
+        Membrane potential decaying constant [ms]. Must be finite and positive.
 
-    .. cpp:member:: double V_th
+    .. cpp:member:: const arb::units::quantity& V_th
 
-        Firing threshold [mV].
+        Firing threshold [mV], must be finite.
 
-    .. cpp:member:: double C_m
+    .. cpp:member:: const arb::units::quantity& C_m
 
-        Membrane capacitance [pF].
+        Membrane capacitance [pF], must be finite and positive.
 
-    .. cpp:member:: double E_L
+    .. cpp:member:: const arb::units::quantity& E_L
 
-        Resting potential [mV].
+        Resting potential [mV], must be finite.
 
-    .. cpp:member:: double E_R
+    .. cpp:member:: const arb::units::quantity& E_R
 
-        Reset potential [mV].
+        Reset potential [mV], must be finite.
 
-    .. cpp:member:: double V_m
+    .. cpp:member:: const arb::units::quantity& V_m
 
-        Initial value of the Membrane potential [mV].
+        Initial value of the Membrane potential [mV], must be finite.
 
-    .. cpp:member:: double t_ref
+    .. cpp:member:: const arb::units::quantity& t_ref
 
-        Refractory period [ms].
+        Refractory period [ms]. Must be finite and positive.
diff --git a/doc/fileformat/cable_cell.rst b/doc/fileformat/cable_cell.rst
index 7038520287c5a2492db2587dbb8497303582a565..be35921ba7738facd3bcc374efe32eefc0054d0b 100644
--- a/doc/fileformat/cable_cell.rst
+++ b/doc/fileformat/cable_cell.rst
@@ -396,7 +396,7 @@ Parsable arbor-components and meta-data
 The formats described above can be used to generate a :ref:`label dictionary <labels>`,
 :ref:`decoration <cablecell-decoration>`, :ref:`morphology <morph>`, or :ref:`cable cell <cablecell>`
 object. These are denoted as arbor-components. Arbor-components need to be accompanied by *meta-data*
-specifying the version of the format being used. The only version currently supported is ``0.1-dev``.
+specifying the version of the format being used. The only version currently supported is ``0.9-dev``.
 
 .. label:: (version val:string)
 
@@ -418,7 +418,7 @@ Label-dict
 .. code:: lisp
 
    (arbor-component
-     (meta-data (version "0.1-dev"))
+     (meta-data (version "0.9-dev"))
      (label-dict
        (region-def "my_soma" (tag 1))
        (locset-def "root" (root))))
@@ -429,7 +429,7 @@ Decoration
 .. code:: lisp
 
    (arbor-component
-     (meta-data (version "0.1-dev"))
+     (meta-data (version "0.9-dev"))
      (decor
        (default (membrane-potential -55.000000))
        (place (locset "root") (synapse (mechanism "expsyn")) "root_synapse")
@@ -441,7 +441,7 @@ Morphology
 .. code:: lisp
 
    (arbor-component
-     (meta-data (version "0.1-dev"))
+     (meta-data (version "0.9-dev"))
      (morphology
         (branch 0 -1
           (segment 0 (point 0 0 0 2) (point 4 0 0 2) 1)
@@ -454,7 +454,7 @@ Cable-cell
 .. code:: lisp
 
    (arbor-component
-     (meta-data (version "0.1-dev"))
+     (meta-data (version "0.9-dev"))
      (cable-cell
        (label-dict
          (region-def "my_soma" (tag 1))
diff --git a/doc/index.rst b/doc/index.rst
index 66401c9bc7ad90af40d91be47243ee8fdbefd19d..a7d623b98c6a177536257ad24537563c35d5b68a 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -150,6 +150,7 @@ A full list of our software attributions can be found `here <https://github.com/
    concepts/index
    concepts/recipe
    concepts/cell
+   concepts/units
    concepts/interconnectivity
    concepts/hardware
    concepts/domdec
diff --git a/doc/python/interconnectivity.rst b/doc/python/interconnectivity.rst
index e5c40905b5e33f78dc81da5f7633c032c182eea2..2063f412efa150b56e8b85805d5179e06c341265 100644
--- a/doc/python/interconnectivity.rst
+++ b/doc/python/interconnectivity.rst
@@ -38,7 +38,7 @@ Interconnectivity
 
     .. attribute:: delay
 
-        The delay time of the connection [ms]. Must be positive.
+        The delay time of the connection [ms]. Must be positive and finite.
 
     .. note::
 
@@ -66,7 +66,7 @@ Interconnectivity
                src  = (2, "detector") # gid and locset label of the source
                dest = "syn" # gid of the destination is determined by the argument to `connections_on`.
                w    = 0.01  # weight of the connection. Correspondes to 0.01 μS on expsyn mechanisms
-               d    = 10 # delay in ms
+               d    = 10 * arbor.units.ms # delay
                return [arbor.connection(src, dest, w, d)]
 
 .. class:: gap_junction_connection
diff --git a/doc/python/lif_cell.rst b/doc/python/lif_cell.rst
index e9450ef75fec9906759b969bc634196f63b2140a..4fae8c3271e0b1eb566808c39cb17568137ef682 100644
--- a/doc/python/lif_cell.rst
+++ b/doc/python/lif_cell.rst
@@ -10,20 +10,24 @@ LIF cells
     A benchmarking cell (leaky integrate-and-fire), used by Arbor developers to test communication performance,
     with neuronal parameters:
 
-    .. function:: lif_cell(source, target)
+    .. function:: lif_cell(source, target, **, tau_m, V_th, C_m, E_L, E_R, V_m, t_ref)
 
-        Constructor: assigns the label ``source`` to the single built-in source on the cell; and assigns the
-        label ``target`` to the single built-in target on the cell.
+        Constructor: assigns the label ``source`` to the single built-in source
+        on the cell; and assigns the label ``target`` to the single built-in
+        target on the cell. Optional parameters may be given and set the
+        correspoding physical parameters of the cell
 
     .. attribute:: source
 
-        The label of the single built-in source on the cell. Used for forming connections from the cell in the
-        :py:class:`arbor.recipe` by creating a :py:class:`arbor.connection`.
+        The label of the single built-in source on the cell. Used for forming
+        connections from the cell in the :py:class:`arbor.recipe` by creating a
+        :py:class:`arbor.connection`.
 
     .. attribute:: target
 
-        The label of the single built-in target on the cell. Used for forming connections to the cell in the
-        :py:class:`arbor.recipe` by creating a :py:class:`arbor.connection`.
+        The label of the single built-in target on the cell. Used for forming
+        connections to the cell in the :py:class:`arbor.recipe` by creating a
+        :py:class:`arbor.connection`.
 
     .. attribute:: tau_m
 
diff --git a/doc/python/recipe.rst b/doc/python/recipe.rst
index 6e23106dcd7e11f093620ef7d458b220d03f7575..6acc390867e69323a6940f1f80faef325fa28edf 100644
--- a/doc/python/recipe.rst
+++ b/doc/python/recipe.rst
@@ -79,7 +79,7 @@ Recipe
         ``connection.dest`` on the post-synaptic target ``gid``, and a valid
         source label ``connection.source.lid`` on the pre-synaptic source
         ``connection.source.gid``. See :class:`connection`. Note that in
-        contrast to :class:`cell_connection`, this uses two numeric identifiers
+        contrast to :class:`connection`, this uses two numeric identifiers
         for the source instead.
 
         By default returns an empty list.
diff --git a/doc/tutorial/calcium_stdp_curve.rst b/doc/tutorial/calcium_stdp_curve.rst
index d14d8bb07071c0ce83377d79516c1609a222cf1f..95bc2b221a232d84bd0354024e97c8a83dfb0983 100644
--- a/doc/tutorial/calcium_stdp_curve.rst
+++ b/doc/tutorial/calcium_stdp_curve.rst
@@ -80,98 +80,102 @@ details about the numerical solver can be found in the :ref:`developers guide <s
 The Model
 ---------
 
-In this tutorial, the neuron model itself is simple with only
-passive (leaky) membrane dynamics, and it receives regular synaptic current
-input in one arbitrary chosen control volume (CV) to trigger regular spikes.
+In this tutorial, the neuron model itself is simple with only passive (leaky)
+membrane dynamics, and it receives regular synaptic current input in one
+arbitrary chosen control volume (CV) to trigger regular spikes.
 
 First we import some required modules:
 
 .. literalinclude:: ../../python/example/calcium_stdp.py
    :language: python
-   :lines: 13-18
+   :lines: 13-15
 
 Next we set the simulation parameters in order to reproduce the plasticity curve:
 
 .. literalinclude:: ../../python/example/calcium_stdp.py
    :language: python
-   :lines: 20-41
-
-The time lag resolution, together with the maximum time lag, determine the number of cases we want
-to simulate. For each such case, however, we need to run many simulations in order to get a
-statistically meaningful result. The number of simulations per case is given by the ensemble size
-and the initial conditions. In our case, we have two inital states, :math:`\rho(0)=0` and
-:math:`\rho(0)=1`, and for each initial state we want to run :math:`100` simulations. We note, that
-the stochastic synapse mechanism does not alter the state of the cell, but couples one-way only by
-reacting to spikes. Therefore, we are allowed to simply place :math:`100` synapses per initial state
-onto the cell without worrying about interference. Moreover, this has the benefit of exposing
+   :lines: 20-40
+
+The time lag resolution, together with the maximum time lag, determine the
+number of cases we want to simulate. For each such case, however, we need to run
+many simulations in order to get a statistically meaningful result. The number
+of simulations per case is given by the ensemble size and the initial
+conditions. In our case, we have two inital states, :math:`\rho(0)=0` and
+:math:`\rho(0)=1`, and for each initial state we want to run :math:`100`
+simulations. We note, that the stochastic synapse mechanism does not alter the
+state of the cell, but couples one-way only by reacting to spikes. Therefore, we
+are allowed to simply place :math:`100` synapses per initial state onto the cell
+without worrying about interference. Moreover, this has the benefit of exposing
 parallelism that Arbor can take advantage of.
 
-Thus, we create a simple cell with a midpoint at which we place our mechanisms:
+Thus, we create a simple cell and mark the midpoint at which we place our mechanisms:
 
 .. literalinclude:: ../../python/example/calcium_stdp.py
    :language: python
-   :lines: 44-67
+   :lines: 43-67
 
-Since our stochastic mechanism `calcium_based_synapse` is not within Arbor's default set of
-mechanism, we need to extend the mechanism catalogue within the cable cell properties:
+Since our stochastic mechanism `calcium_based_synapse` is not within Arbor's
+default set of mechanism, we need to extend the mechanism catalogue within the
+cable cell properties:
 
 .. literalinclude:: ../../python/example/calcium_stdp.py
    :language: python
-   :lines: 70-74
+   :lines: 69-71
 
 Our cell and cell properties can then later be used to create a simple recipe:
 
 .. literalinclude:: ../../python/example/calcium_stdp.py
    :language: python
-   :lines: 77-103
+   :lines: 74-98
 
-Note, that the recipe takes a cell, cell properties and a list of event generators as constructor
-arguments and returns them with its corresponding methods. Furthermore, the recipe also returns a
-list of probes which contains only one item: A query for our mechanism's state variable
-:math:`\rho`. Since we placed a number of these mechanisms on our cell, we will receive a vector of
-values when probing.
+Note, that the recipe takes a cell and a list of time offsets as constructor
+arguments, which are used to create the cells of the ensemble. Furthermore, the
+recipe also returns a list of probes which contains only one item: A query for
+our mechanism's state variable :math:`\rho`. Since we placed a number of these
+mechanisms on our cell, we will receive a vector of values when probing.
 
 Next we set up the simulation logic:
 
 .. literalinclude:: ../../python/example/calcium_stdp.py
    :language: python
-   :lines: 106-134
+   :lines: 101-125
 
-The pre- and postsynaptic events are generated at explicit schedules, where the presynaptic event
-is shifted in time by :math:`D -\text{time lag}` with respect to the presynaptic event, which in
-turn is generated regularly with the frequency :math:`f`. The postsynaptic events are driven by the
-deterministic synapse with weight `1.0`, while the presynaptic events are generated at the
-stochastic calcium synapses. The postsynaptic weight can be set arbitrarily as long as it is large
-enough to trigger the spikes.
+The pre- and postsynaptic events are generated as regular schedules, where the
+presynaptic event is shifted in time by :math:`D -\text{time lag}` with respect
+to the presynaptic event, which in turn is generated regularly with the
+frequency :math:`f`. The postsynaptic events are driven by the deterministic
+synapse with weight `1.0`, while the presynaptic events are generated at the
+stochastic calcium synapses. The postsynaptic weight can be set arbitrarily as
+long as it is large enough to trigger the spikes.
 
 Thus, we have all ingredients to create the recipe
 
 .. literalinclude:: ../../python/example/calcium_stdp.py
    :language: python
-   :lines: 135-136
+   :lines: 127-128
 
 Now, we need to initialize the simulation, register a probe and run the simulation:
 
 .. literalinclude:: ../../python/example/calcium_stdp.py
    :language: python
-   :lines: 138-153
+   :lines: 133-140
 
-Since we are interested in the long-term average value, we only query the probe at the end of the
-simulation.
+Since we are interested in the long-term average value, we only query the probe
+at the end of the simulation.
 
-After the simulation is finished, we calculate the change in synaptic strength by evaluating the
-transition probabilies from initial DOWN state to final UP state and vice versa.
+After the simulation is finished, we calculate the change in synaptic strength
+by evaluating the transition probabilies from initial DOWN state to final UP
+state and vice versa.
 
 .. literalinclude:: ../../python/example/calcium_stdp.py
    :language: python
-   :lines: 155-173
+   :lines: 142-160
 
-Since we need to run our simulation for each time lag case anew, we spawn a bunch of threads to
-carry out the work in parallel:
+We process all configured timelags
 
 .. literalinclude:: ../../python/example/calcium_stdp.py
    :language: python
-   :lines: 176-177
+   :lines: 163
 
 The collected results can then be plotted:
 
diff --git a/doc/tutorial/network_ring.rst b/doc/tutorial/network_ring.rst
index dce2e62fc7d5352dff89334f0eddcda2aa7cf04c..cbe44933c851a406abe13a850c5d40aa6d116c34 100644
--- a/doc/tutorial/network_ring.rst
+++ b/doc/tutorial/network_ring.rst
@@ -27,7 +27,7 @@ We construct the following :term:`morphology` and label the soma and dendrite:
 
 .. literalinclude:: ../../python/example/network_ring.py
    :language: python
-   :lines: 20-46
+   :lines: 12-42
 
 In step **(2)** we create a :term:`label` for both the root and the site of the synapse.
 These locations will form the endpoints of the connections between the cells.
@@ -40,7 +40,7 @@ These locations will form the endpoints of the connections between the cells.
 
 .. literalinclude:: ../../python/example/network_ring.py
    :language: python
-   :lines: 48-58
+   :lines: 44-54
 
 After we've created a basic :py:class:`arbor.decor`, step **(3)** places a synapse with an exponential decay (``'expsyn'``) on the ``'synapse_site'``.
 The synapse is given the label ``'syn'``, which is later used to form :py:class:`arbor.connection` objects terminating *at* the cell.
@@ -63,67 +63,94 @@ Step **(4)** places a threshold detector at the ``'root'``. The detector is give
 
 .. literalinclude:: ../../python/example/network_ring.py
    :language: python
-   :lines: 60-71
+   :lines: 56-65
 
 The recipe
 **********
 
-To create a model with multiple connected cells, we need to use a :py:class:`recipe <arbor.recipe>`.
-The recipe is where the different cells and the :ref:`connections <interconnectivity>` between them are defined.
-
-Step **(5)** shows a class definition for a recipe with multiple cells. Instantiating the class requires the desired
-number of cells as input. Compared to the :ref:`simple cell recipe <tutorialsinglecellrecipe>`, the main differences
-are connecting the cells **(8)**, returning a configurable number of cells **(6)** and returning a new cell per ``gid`` **(7)**.
-
-Step **(8)** creates an :py:class:`arbor.connection` between consecutive cells. If a cell has gid ``gid``, the
-previous cell has a gid ``(gid-1)%self.ncells``. The connection has a weight of 0.01 (inducing a conductance of 0.01 μS
-in the target mechanism ``expsyn``) and a delay of 5 ms.
-The first two arguments to :py:class:`arbor.connection` are the **source** and **target** of the connection.
-
-The **source** is a :py:class:`arbor.cell_global_label` object containing a cell index ``gid``, the source label
-corresponding to a valid detector label on the cell and an optional selection policy (for choosing a single detector
-out of potentially many detectors grouped under the same label - remember, in this case the number of detectors labeled
-'detector' is 1).
-The :py:class:`arbor.cell_global_label` can be initialized with a ``(gid, label)`` tuple, in which case the selection
-policy is the default :py:attr:`arbor.selection_policy.univalent`; or a ``(gid, (label, policy))`` tuple.
-
-The **target** is a :py:class:`arbor.cell_local_label` object containing a cell index ``gid``, the target label
-corresponding to a valid synapse label on the cell and an optional selection policy (for choosing a single synapse
-out of potentially many synapses grouped under the same label - remember, in this case the number of synapses labeled
-'syn' is 1).
-The :py:class:`arbor.cell_local_label` can be initialized with a ``label`` string, in which case the selection
-policy is the default :py:attr:`arbor.selection_policy.univalent`; or a ``(label, policy)`` tuple. The ``gid``
-of the target cell doesn't need to be explicitly added to the connection, it is the argument to the
-:py:func:`arbor.recipe.connections_on` method.
-
-Step **(9)** attaches an :py:class:`arbor.event_generator` on the 0th target (synapse) on the 0th cell; this means it
-is connected to the ``"synapse_site"`` on cell 0. This initiates the signal cascade through the network. The
-:class:`arbor.explicit_schedule` in instantiated with a list of times in milliseconds, so here a single event at the 1
-ms mark is emitted. Note that this synapse is connected twice, once to the event generator, and once to another cell.
+.. literalinclude:: ../../python/example/network_ring.py
+   :language: python
+   :lines: 70-112
+
+To create a model with multiple connected cells, we need to use a
+:py:class:`recipe <arbor.recipe>`. The recipe is where the different cells and
+the :ref:`connections <interconnectivity>` between them are defined.
+
+Step **(5)** shows a class definition for a recipe with multiple cells.
+Instantiating the class requires the desired number of cells as input. Compared
+to the :ref:`simple cell recipe <tutorialsinglecellrecipe>`, the main
+differences are connecting the cells **(8)**, returning a configurable number of
+cells **(6)** and returning a new cell per ``gid`` **(7)**.
+
+Step **(8)** creates an :py:class:`arbor.connection` between consecutive cells.
+If a cell has gid ``gid``, the previous cell has a gid ``(gid-1)%self.ncells``.
+The connection has a weight of 0.01 (inducing a conductance of 0.01 μS in the
+target mechanism ``expsyn``) and a delay of 5 ms. The first two arguments to
+:py:class:`arbor.connection` are the **source** and **target** of the
+connection.
+
+The **source** is a :py:class:`arbor.cell_global_label` object containing a cell
+index ``gid``, the source label corresponding to a valid detector label on the
+cell and an optional selection policy (for choosing a single detector out of
+potentially many detectors grouped under the same label - remember, in this case
+the number of detectors labeled 'detector' is 1). The
+:py:class:`arbor.cell_global_label` can be initialized with a ``(gid, label)``
+tuple, in which case the selection policy is the default
+:py:attr:`arbor.selection_policy.univalent`; or a ``(gid, (label, policy))``
+tuple.
+
+The **target** is a :py:class:`arbor.cell_local_label` object containing a cell
+index ``gid``, the target label corresponding to a valid synapse label on the
+cell and an optional selection policy (for choosing a single synapse out of
+potentially many synapses grouped under the same label - remember, in this case
+the number of synapses labeled 'syn' is 1). The
+:py:class:`arbor.cell_local_label` can be initialized with a ``label`` string,
+in which case the selection policy is the default
+:py:attr:`arbor.selection_policy.univalent`; or a ``(label, policy)`` tuple. The
+``gid`` of the target cell doesn't need to be explicitly added to the
+connection, it is the argument to the :py:func:`arbor.recipe.connections_on`
+method.
+
+Step **(9)** attaches an :py:class:`arbor.event_generator` on the 0th target
+(synapse) on the 0th cell; this means it is connected to the ``"synapse_site"``
+on cell 0. This initiates the signal cascade through the network. The
+:class:`arbor.explicit_schedule` in instantiated with a list of times in
+milliseconds, so here a single event at the 1 ms mark is emitted. Note that this
+synapse is connected twice, once to the event generator, and once to another
+cell.
 
 Step **(10)** places a :term:`probe` at the ``"root"`` of each cell.
 
-Step **(11)** instantiates the recipe with 4 cells.
-
 .. literalinclude:: ../../python/example/network_ring.py
    :language: python
-   :lines: 74-122
+   :lines: 115-117
+
+Step **(11)** instantiates the recipe with 4 cells.
 
 The execution
 *************
 
-To create a simulation, we need at minimum to supply the recipe, and in addition can supply a :class:`arbor.context`
-and :py:class:`arbor.domain_decomposition`. The first lets Arbor know what hardware it should use, the second how to
-destribute the work over that hardware. By default, contexts are configured to use 1 thread and domain decompositons to
-divide work equally over all threads.
+To create a simulation, we need at minimum to supply the recipe, and in addition
+can supply a :class:`arbor.context` and :py:class:`arbor.domain_decomposition`.
+The first lets Arbor know what hardware it should use, the second how to
+destribute the work over that hardware. By default, contexts are configured to
+use 1 thread and domain decompositons to divide work equally over all threads.
 
-Step **(12)** creates a simulation object from the recipe. Optionally, the :py:class:`~arbor.simulation` constructor takes two more
-parameters: a :class:`arbor.context` and a :class:`arbor.domain_decomposition`. In :ref:`a followup of this tutorial <tutorialgpu>` that will be demonstrated.
-For now, it is enough to know that for simulations that don't require customized execution those arguments can be left out. Without
-further arguments Arbor will use all locally available threads.
+.. literalinclude:: ../../python/example/network_ring.py
+   :language: python
+   :lines: 119-137
+
+Step **(12)** creates a simulation object from the recipe. Optionally, the
+:py:class:`~arbor.simulation` constructor takes two more parameters: a
+:class:`arbor.context` and a :class:`arbor.domain_decomposition`. In :ref:`a
+followup of this tutorial <tutorialgpu>` that will be demonstrated. For now, it
+is enough to know that for simulations that don't require customized execution
+those arguments can be left out. Without further arguments Arbor will use all
+locally available threads.
 
-Step **(13)** sets all spike generators to record using the :py:class:`arbor.spike_recording.all` policy.
-This means the timestamps of the generated events will be kept in memory. Be default, these are discarded.
+Step **(13)** sets all spike generators to record using the
+:py:class:`arbor.spike_recording.all` policy. This means the timestamps of the
+generated events will be kept in memory. Be default, these are discarded.
 
 In addition to having the timestamps of spikes, we want to extract the voltage as a function of time.
 
@@ -138,10 +165,6 @@ these handles for later use.
 
 Step **(15)** executes the simulation for a duration of 100 ms.
 
-.. literalinclude:: ../../python/example/network_ring.py
-   :language: python
-   :lines: 124-140
-
 The results
 ***********
 
@@ -149,7 +172,7 @@ Step **(16)** prints the timestamps of the spikes:
 
 .. literalinclude:: ../../python/example/network_ring.py
    :language: python
-   :lines: 142-145
+   :lines: 139-142
 
 Step **(17)** generates a plot of the sampling data.
 :py:func:`arbor.simulation.samples` takes a ``handle`` of the probe we wish to examine. It returns a list
@@ -161,7 +184,7 @@ It could have described a :term:`locset`.)
 
 .. literalinclude:: ../../python/example/network_ring.py
    :language: python
-   :lines: 147-
+   :lines: 144-
 
 Since we have created ``ncells`` cells, we have ``ncells`` traces. We should be seeing phase shifted traces, as the action potential propagated through the network.
 
diff --git a/doc/tutorial/network_ring_gpu.rst b/doc/tutorial/network_ring_gpu.rst
index 5be7ba570b490d0b2c634bc72e52572abbf30024..6f0c0b2f533b1a5d2da771a254e592af14abddf9 100644
--- a/doc/tutorial/network_ring_gpu.rst
+++ b/doc/tutorial/network_ring_gpu.rst
@@ -3,7 +3,10 @@
 GPU and profiling
 =================
 
-In this example, the ring network created in an :ref:`earlier tutorial <tutorialnetworkring>` will be used to run the model with a GPU. In addition, it is shown how to profile the performance difference. Only the differences with that tutorial will be described.
+In this example, the ring network created in an :ref:`earlier tutorial
+<tutorialnetworkring>` will be used to run the model with a GPU. In addition, it
+is shown how to profile the performance difference. Only the differences with
+that tutorial will be described.
 
 .. Note::
 
@@ -17,19 +20,25 @@ In this example, the ring network created in an :ref:`earlier tutorial <tutorial
 The hardware context
 ********************
 
-An :ref:`execution context <modelcontext>` describes the hardware resources on which the simulation will run.
-It contains the thread pool used to parallelise work on the local CPU, and optionally describes GPU resources
-and the MPI communicator for distributed simulations. In some other examples, the :class:`arbor.single_cell_model`
-object created the execution context :class:`arbor.context` behind the scenes. The details of the execution
-context can be customized by the user. We may specify the number of threads in the thread pool; determine the
-id of the GPU to be used; or create our own MPI communicator.
-
-Step **(11)** creates a hardware context where we set the :py:attr:`~arbor.proc_allocation.gpu_id`. This requires 
-that you have built Arbor manually, with GPU support (see :ref:`here <in_python_adv>` how to do that). On a regular 
-consumer device with a single GPU, the index you should pass is ``0``. Change the value to run the example with and 
-without GPU. The number of threads :class:`~arbor.context.threads` are (when no MPI is used) set to
-:py:func:`arbor.env.thread_concurrency`. This value corresponds to the number of locally available threads as best as
-can be established by Arbor at the start of the program.
+An :ref:`execution context <modelcontext>` describes the hardware resources on
+which the simulation will run. It contains the thread pool used to parallelise
+work on the local CPU, and optionally describes GPU resources and the MPI
+communicator for distributed simulations. In some other examples, the
+:class:`arbor.single_cell_model` object created the execution context
+:class:`arbor.context` behind the scenes. The details of the execution context
+can be customized by the user. We may specify the number of threads in the
+thread pool; determine the id of the GPU to be used; or create our own MPI
+communicator.
+
+Step **(11)** creates a hardware context where we set the
+:py:attr:`~arbor.proc_allocation.gpu_id`. This requires that you have built
+Arbor manually, with GPU support (see :ref:`here <in_python_adv>` how to do
+that). On a regular consumer device with a single GPU, the index you should pass
+is ``0``. Change the value to run the example with and without GPU. The number
+of threads :class:`~arbor.context.threads` are (when no MPI is used) set to
+:py:func:`arbor.env.thread_concurrency`. This value corresponds to the number of
+locally available threads as best as can be established by Arbor at the start of
+the program.
 
 .. note::
    
@@ -42,12 +51,17 @@ can be established by Arbor at the start of the program.
 Profiling
 *********
 
-Arbor comes with a :class:`arbor.meter_manager` to help you profile your simulations. In this case, you can run the 
-example with ``gpu_id=None`` and ``gpu_id=0`` and observe the difference with the :class:`~arbor.meter_manager`.
+Arbor comes with a :class:`arbor.meter_manager` to help you profile your
+simulations. In this case, you can run the example with ``gpu_id=None`` and
+``gpu_id=0`` and observe the difference with the :class:`~arbor.meter_manager`.
+If you are interested in more detailled report, Arbor also offers a region based
+profiler which is aimed at developers and must be enabled at build time.
 
-Step **(12)** sets up the meter manager and starts it using the (only) context. This way, only Arbor related execution is measured, not Python code.
+Step **(12)** sets up the meter manager and starts it using the (only) context.
+This way, only Arbor related execution is measured, not Python code.
 
-Step **(13)** instantiates the recipe and sets the first checkpoint on the meter manager. We now have the time it took to construct the recipe.
+Step **(13)** instantiates the recipe and sets the first checkpoint on the meter
+manager. We now have the time it took to construct the recipe.
 
 .. literalinclude:: ../../python/example/network_ring_gpu.py
    :language: python
@@ -56,32 +70,41 @@ Step **(13)** instantiates the recipe and sets the first checkpoint on the meter
 The domain decomposition
 ************************
 
-The domain decomposition describes the distribution of the cells over the available computational resources.
-The :class:`arbor.single_cell_model` also handled that without our knowledge in the previous examples.
-Now, we have to define it ourselves.
-
-The :class:`arbor.domain_decomposition` class can be manually created by the user, by deciding which cells
-go on which ranks. Or we can use a load balancer that can partition the cells across ranks according to
-some rules. Arbor provides :class:`arbor.partition_load_balance`, which, using the recipe and execution
-context, creates the :class:`arbor.domain_decomposition` object for us.
-
-A way to customize :class:`arbor.partition_load_balance` is by providing a :class:`arbor.partition_hint`. They let
-you configure how cells are distributed over the resources in the :class:`~arbor.context`, but without requiring you
-to know the precise configuration of a :class:`~arbor.context` up front. Whether you run your simulation on your 
-laptop CPU, desktop GPU, CPU cluster of GPU cluster, using :class:`partition hints<arbor.partition_hint>` you can
-just say: use GPUs, if available. You only have to change the :class:`~arbor.context` to actually define which 
-hardware Arbor will execute on.
-
-Step **(14)** creates a :class:`arbor.partition_hint`, and tells it to put 1000 cells in a groups allocated to GPUs, 
-and to prefer the utilisation of the GPU if present. In fact, the default distribution strategy of 
-:class:`arbor.partition_load_balance` already spreads out cells as evenly as possible over CPUs, and groups
-(up to 1000) on GPUs, so strictly speaking it was not necessary to give that part of the hint.
-Lastly, a dictionary is created with which hints are assigned to a particular :class:`arbor.cell_kind`.
-Different kinds may favor different execution, hence the option.
-In this simulation, there are only :class:`arbor.cell_kind.cable`, so we assign the hint to that kind.
-
-Step **(15)** creates a :class:`arbor.partition_load_balance` with the recipe, context and hints created above.
-Another checkpoint will help us understand how long creating the load balancer took.
+The domain decomposition describes the distribution of the cells over the
+available computational resources. The :class:`arbor.single_cell_model` also
+handled that without our knowledge in the previous examples. Now, we have to
+define it ourselves.
+
+The :class:`arbor.domain_decomposition` class can be manually created by the
+user, by deciding which cells go on which ranks. Or we can use a load balancer
+that can partition the cells across ranks according to some rules. Arbor
+provides :class:`arbor.partition_load_balance`, which, using the recipe and
+execution context, creates the :class:`arbor.domain_decomposition` object for
+us.
+
+A way to customize :class:`arbor.partition_load_balance` is by providing a
+:class:`arbor.partition_hint`. They let you configure how cells are distributed
+over the resources in the :class:`~arbor.context`, but without requiring you to
+know the precise configuration of a :class:`~arbor.context` up front. Whether
+you run your simulation on your laptop CPU, desktop GPU, CPU cluster of GPU
+cluster, using :class:`partition hints<arbor.partition_hint>` you can just say:
+use GPUs, if available. You only have to change the :class:`~arbor.context` to
+actually define which hardware Arbor will execute on.
+
+Step **(14)** creates a :class:`arbor.partition_hint`, and tells it to put 1000
+cells in a groups allocated to GPUs, and to prefer the utilisation of the GPU if
+present. In fact, the default distribution strategy of
+:class:`arbor.partition_load_balance` already spreads out cells as evenly as
+possible over CPUs, and groups (up to 1000) on GPUs, so strictly speaking it was
+not necessary to give that part of the hint. Lastly, a dictionary is created
+with which hints are assigned to a particular :class:`arbor.cell_kind`.
+Different kinds may favor different execution, hence the option. In this
+simulation, there are only :class:`arbor.cell_kind.cable`, so we assign the hint
+to that kind.
+
+Step **(15)** creates a :class:`arbor.partition_load_balance` with the recipe,
+context and hints created above. Another checkpoint will help us understand how
+long creating the load balancer took.
 
 .. literalinclude:: ../../python/example/network_ring_gpu.py
    :language: python
@@ -91,38 +114,43 @@ Another checkpoint will help us understand how long creating the load balancer t
 The simulation
 **************
 
-Step **(16)** creates a :class:`arbor.simulation`, sets the spike recorders to record, creates a :term:`handle`
-to their eventual results and makes another checkpoint.
+Step **(16)** creates a :class:`arbor.simulation`, sets the spike recorders to
+record, creates a :term:`handle` to their eventual results and makes another
+checkpoint.
 
 .. literalinclude:: ../../python/example/network_ring_gpu.py
    :language: python
-   :lines: 150-154
+   :lines: 150-156
 
 The execution
 *************
 
-Step **(17)** runs the simulation. Since we have more cells this time, which are connected in series,
-it will take some time for the action potential to propagate. In the :ref:`ring network <tutorialnetworkring>`
-we could see it takes about 5 ms for the signal to propagate through one cell, so let's set the runtime to
-``5*ncells``. Then, another checkpoint, so that we'll know how long the simulation took.
+Step **(17)** runs the simulation. Since we have more cells this time, which are
+connected in series, it will take some time for the action potential to
+propagate. In the :ref:`ring network <tutorialnetworkring>` we could see it
+takes about 5 ms for the signal to propagate through one cell, so let's set the
+runtime to ``5*ncells``. Then, another checkpoint, so that we'll know how long
+the simulation took.
 
 .. literalinclude:: ../../python/example/network_ring_gpu.py
    :language: python
-   :lines: 156-159
+   :lines: 158-161
 
 The results
 ***********
 
-The scientific results should be similar, other than number of cells, to those in :ref:`ring network <tutorialnetworkring>`,
-so we'll not discuss them here. Let's turn our attention to the :class:`~arbor.meter_manager`.
+The scientific results should be similar, other than number of cells, to those
+in :ref:`ring network <tutorialnetworkring>`, so we'll not discuss them here.
+Let's turn our attention to the :class:`~arbor.meter_manager`.
 
 .. literalinclude:: ../../python/example/network_ring_gpu.py
    :language: python
-   :lines: 161-163
+   :lines: 163-165
 
-Step **(18)** shows how :class:`arbor.meter_report` can be used to read out the :class:`~arbor.meter_manager`.
-It generates a table with the time between checkpoints. As an example, the following table is the result of a run
-on a 2019 laptop CPU:
+Step **(18)** shows how :class:`arbor.meter_report` can be used to read out the
+:class:`~arbor.meter_manager`. It generates a table with the time between
+checkpoints. As an example, the following table is the result of a run on a 2019
+laptop CPU:
 
 ::
 
diff --git a/doc/tutorial/network_two_cells_gap_junctions.rst b/doc/tutorial/network_two_cells_gap_junctions.rst
index 8e1293ec8054f200d9cbec7bcf0a2f5cd4f4c066..241bfb37a6fd01b65c4ab12050083320fa0e4583 100644
--- a/doc/tutorial/network_two_cells_gap_junctions.rst
+++ b/doc/tutorial/network_two_cells_gap_junctions.rst
@@ -3,9 +3,11 @@
 Two cells connected via a gap junction
 ======================================
 
-In this example, we will set up two cells connected via a gap junction.
-The cells have different leak potentials.
-We will investigate how the equilibrium potentials of the two cells change because of the gap junction connection.
+In this example, we will set up two cells connected via a gap junction. Each of
+the cells has a passive leak current as its only dynamics. This plus the gap
+junction will produce an equilibrium potential different from both the resting
+potentials. We will investigate how the equilibrium potentials of the two cells
+change due of the gap junction connection.
 
 .. figure:: network_two_cells_gap_junctions_circuit.svg
     :width: 400
@@ -22,6 +24,9 @@ We will investigate how the equilibrium potentials of the two cells change becau
    3. Running the simulation and extracting the results.
    4. Adding a gap junction connection.
 
+We assume prior exposure to the concepts of cable cells, recipes, and simple
+networks.
+
 Walk-through
 ************
 
@@ -29,54 +34,73 @@ We set up a recipe for the simulation of two cells
 
 .. literalinclude:: ../../python/example/network_two_cells_gap_junctions.py
    :language: python
-   :lines: 13-45
+   :lines: 13-37
 
 in which we store the relevant parameters for the two cells, all of which are
-shared except the equilibrium potentials in ``Vms``. Next, we define callbacks
-to define our cell population:
+shared except the equilibrium potentials in ``Vms``. These are used to build
+the network.
+
+Let's quickly check some standard callbacks:
 
-- ``num_cells`` returns the number of cells in the network, ie 2
+- ``num_cells`` returns the number of cells in the network, fixed as 2
 - ``cell_kind`` specifies that we handle ``cable_cell`` exclusively.
-- ``global_properties`` returns a list of standard parameters based on the defaults of the NEURON simulator.
-- ``cell_description`` member function constructs the morphology and sets the properties of the cells as well as the gap junction mechanisms and the discretization policy. It returns the finished ``cable_cell``, matching the ``cell_kind`` callback.
+- ``global_properties`` returns a list of standard parameters based on the
+  defaults of the NEURON simulator.
+- ``probes`` record the membrane potential at the cell mid.
 
-.. literalinclude:: ../../python/example/network_two_cells_gap_junctions.py
-   :language: python
-   :lines: 49-93
+The two remaining methods are:
+
+``cell_description``
+--------------------
 
-We build the network conections, here a single, bidirectional gap junction
+We construct a basic, single segment morphology from the ``length`` and
+``radius`` parameters. The decor sets the basic parameters and adds the passive
+leak current ``pas`` with the given resting value ``Vms[gid]`` and conductivity
+``g``.
 
 .. literalinclude:: ../../python/example/network_two_cells_gap_junctions.py
    :language: python
-   :lines: 97-105
+   :lines: 58
 
-And, finally, we return a set of probes which are passed in during construction
+The only new item is the placement of the gap junction endpoint at ``midpoint``
+with the basic, builtin ``gj`` dynamics type (other dynamics may be defined and
+used).
 
-.. literalinclude:: ../../python/example/network_two_cells_gap_junctions.py
-   :language: python
-   :lines: 108-109
 
-We parse the command line arguments which are used to set parameters in the recipe
+``gap_junctions_on``
+-------------------
+
+Similar to ``connections_on``, this method returns a list of gap junction
+connections and these are defined in the same manner.
 
 .. literalinclude:: ../../python/example/network_two_cells_gap_junctions.py
    :language: python
-   :lines: 113-147
+   :lines: 69-70
+
+By ``(gid + 1) % 2`` we define two connections, one per cell, between the cells.
+This is due to the uni-directional definition of gap junctions in Arbor.
 
-Next, we define a list of probes and construct the recipe and simulation
+Running the simulation
+**********************
+
+To allow runtime configuration, we define a parser for command line arguments
+which are used to set parameters in the recipe
 
 .. literalinclude:: ../../python/example/network_two_cells_gap_junctions.py
    :language: python
-   :lines: 149-154
+   :lines: 76-98
+
 
-Having set up the simulation, we setting probe sampling on a regular grid with
-width equal to the timestep :math:`dt`. Now, we can run it and access the sampling values
+We then set up the simulation and configure sampling width equal to the timestep
+:math:`dt`. Now, we can run the network.
 
 .. literalinclude:: ../../python/example/network_two_cells_gap_junctions.py
    :language: python
-   :lines: 156-178
+   :lines: 100-110
 
 All that is left to do is to put this into a plot. The output plot below shows
-how the potential of the two cells approaches their equilibrium potentials
+how the potential of the two cells approaches their equilibrium potentials, which
+can be computed from the given parameters.
 
 .. math::
 
@@ -90,7 +114,7 @@ how the potential of the two cells approaches their equilibrium potentials
 
 
 The full code
-*************
+**************
 
 You can find the full code of the example at ``python/examples/network_two_cells_gap_junctions.py``
 
diff --git a/doc/tutorial/probe_lfpykit.rst b/doc/tutorial/probe_lfpykit.rst
index 5d25710010e859cbcc91e796f5a603c7e4b475f7..b1126b2b2d33dd64c969876ce92600fae7f54a87 100644
--- a/doc/tutorial/probe_lfpykit.rst
+++ b/doc/tutorial/probe_lfpykit.rst
@@ -5,11 +5,12 @@ Extracellular signals (LFPykit)
 
 This example takes elements from other tutorials to create a geometrically
 detailed single cell model from an SWC morphology file, and adds predictions of
-extracellular potentials using the `LFPykit <https://lfpykit.readthedocs.io/en/latest>`_ Python library.
-LFPykit provides a few different classes facilitating
-calculations of extracellular potentials and related electroencephalography (EEG)
-and magnetoencephalography (MEG) signals from geometrically detailed neuron models under various assumptions.
-These are signals that mainly stem from transmembrane currents.
+extracellular potentials using the `LFPykit
+<https://lfpykit.readthedocs.io/en/latest>`_ Python library. LFPykit provides a
+few different classes facilitating calculations of extracellular potentials and
+related electroencephalography (EEG) and magnetoencephalography (MEG) signals
+from geometrically detailed neuron models under various assumptions. These are
+signals that mainly stem from transmembrane currents.
 
 .. Note::
 
@@ -25,16 +26,15 @@ These are signals that mainly stem from transmembrane currents.
 The line source approximation
 -----------------------------
 
-First, let's describe how one can compute extracellular potentials from transmembrane currents of a number of segments,
-assuming that each segment can be treated as an equivalent line current source using a formalism invented
-by Gary R. Holt and Christof Koch [1]_.
-The implementation used in this tutorial rely on :class:`lfpykit.LineSourcePotential`.
-This class conveniently defines a 2D linear response matrix
-:math:`\mathbf{M}` between transmembrane current array
-:math:`\mathbf{I}` (nA) of a neuron model's
-geometry (:class:`lfpykit.CellGeometry`) and the
-corresponding extracellular electric potential in different extracellular locations
-:math:`\mathbf{V}_{e}` (mV) so
+First, let's describe how one can compute extracellular potentials from
+transmembrane currents of a number of segments, assuming that each segment can
+be treated as an equivalent line current source using a formalism invented by
+Gary R. Holt and Christof Koch [1]_. The implementation used in this tutorial
+rely on :class:`lfpykit.LineSourcePotential`. This class conveniently defines a
+2D linear response matrix :math:`\mathbf{M}` between transmembrane current array
+:math:`\mathbf{I}` (nA) of a neuron model's geometry
+(:class:`lfpykit.CellGeometry`) and the corresponding extracellular electric
+potential in different extracellular locations :math:`\mathbf{V}_{e}` (mV) so
 
 .. math:: \mathbf{V}_{e} = \mathbf{M} \mathbf{I}
 
@@ -60,16 +60,18 @@ electrode point contact to the axis of the line segment is denoted
 segment is denoted :math:`h_{ji}` and longitudinal distance from the other
 end of the segment is denoted :math:`l_{ji}= L_i + h_{ji}`.
 
-.. Note::
-
-    **Assumptions:**
+.. admonition:: Assumptions
 
-    1. The extracellular conductivity :math:`\sigma` is infinite, homogeneous, frequency independent (linear) and isotropic
-    2. Each segment is treated as a straight line source with homogeneous current density between its start and end point coordinate.
-       Although Arbor allows segments to be defined as conical frusta with varying radius, we shall assume that any variation in
-       radius is small relative to overall segment length.
+    1. The extracellular conductivity :math:`\sigma` is infinite, homogeneous,
+       frequency independent (linear) and isotropic
+    2. Each segment is treated as a straight line source with homogeneous
+       current density between its start and end point coordinate. Although
+       Arbor allows segments to be defined as conical frusta with varying
+       radius, we shall assume that any variation in radius is small relative to
+       overall segment length.
     3. Each extracellular measurement site is treated as a point
-    4. The minimum distance to a line source is set equal to the average segment radius to avoid singularities.
+    4. The minimum distance to a line source is set equal to the average segment
+       radius to avoid singularities.
 
 
 .. _tutorial_lfpykit-model:
@@ -85,13 +87,14 @@ First we import some required modules:
 
 .. literalinclude:: ../../python/example/probe_lfpykit.py
    :language: python
-   :lines: 14-17
+   :lines: 15-18
 
-We define a basic :class:`Recipe <arbor.recipe>` class, holding a cell and three probes (voltage, stimulus current and total current):
+We define a basic :class:`Recipe <arbor.recipe>` class, holding a cell and three
+probes (voltage, stimulus current and total current):
 
 .. literalinclude:: ../../python/example/probe_lfpykit.py
    :language: python
-   :lines: 22-54
+   :lines: 24-51
 
 Then, load a morphology on ``SWC`` file format (interpreted according to :ref:`Arbor's specifications <morph-formats>`).
 Similar to the tutorial :ref:`"A simple single cell model" <tutorialsinglecellswc>`,
@@ -103,89 +106,81 @@ we use the morphology file in ``python/example/single_cell_detailed.swc``:
 
   A graphic depiction of ``single_cell_detailed.swc``.
 
-Pass the filename as an argument to the simulation script:
-
-.. literalinclude:: ../../python/example/probe_lfpykit.py
-   :language: python
-   :lines: 57-66
-
-As a target for a current stimuli we define an :class:`arbor.location` :
+Read the morphology from a file or use the default
 
 .. literalinclude:: ../../python/example/probe_lfpykit.py
    :language: python
-   :lines: 68-69
+   :lines: 54-65
 
-Next, we let a basic function define our cell model, taking the morphology and clamp location as
-input.
-The function defines various attributes (:class:`~arbor.label_dict`, :class:`~arbor.decor`) for
-the cell model,
-sets sinusoid current clamp as stimuli using :class:`~arbor.iclamp`
-defines discretization policy (:class:`~arbor.cv_policy_fixed_per_branch`)
-and returns the corresponding :class:`~arbor.place_pwlin` and :class:`~arbor.cable_cell` objects for use later:
+As a target for a current stimulus we define
 
 .. literalinclude:: ../../python/example/probe_lfpykit.py
    :language: python
-   :lines: 72-114
+   :lines: 67-68
 
-Store the function output:
+Next, we define our cell model, based on the morphology and clamp location. We
+set various attributes (:class:`~arbor.label_dict`, :class:`~arbor.decor`) for
+the cell model, attaches a sinusoid current using :class:`~arbor.iclamp`, and
+sets discretization policy ats :class:`~arbor.cv_policy_fixed_per_branch`.
 
 .. literalinclude:: ../../python/example/probe_lfpykit.py
    :language: python
-   :lines: 117-118
+   :lines: 70-94
 
-Next, we instantiate :class:`Recipe`, and execute the model for a few hundred ms,
-sampling the different signals every 1 ms:
+Next, we instantiate :class:`Recipe`, configure the sampling, and execute the
+model for a few hundred ms:
 
 .. literalinclude:: ../../python/example/probe_lfpykit.py
    :language: python
-   :lines: 120-133
+   :lines: 99-112
 
-Extract recorded membrane voltages, electrode and transmembrane currents.
-Note that membrane voltages at branch points and intersections between CVs are dropped as
-we only illustrate membrane voltages of segments with finite lengths.
+Extract recorded membrane voltages, electrode and transmembrane currents. Note
+that membrane voltages at branch points and intersections between CVs are
+dropped as we only illustrate membrane voltages of segments with finite lengths.
 
 .. literalinclude:: ../../python/example/probe_lfpykit.py
    :language: python
-   :lines: 135-150
+   :lines: 114-133
 
-Finally we sum the stimulation and transmembrane currents, allowing the stimuli to mimic a synapse
-current embedded in the membrane itself rather than an intracellular electrode current:
+Finally we sum the stimulation and transmembrane currents, allowing the stimuli
+to mimic a synapse current embedded in the membrane itself rather than an
+intracellular electrode current:
 
 .. literalinclude:: ../../python/example/probe_lfpykit.py
    :language: python
-   :lines: 152-158
+   :lines: 135-137
 
 .. _tutorial_lfpykit-lfpykit:
 
 Compute extracellular potentials
 --------------------------------
 
-Here we utilize the LFPykit library to map
-transmembrane currents recorded during the
-simulation to extracellular potentials in vicinity to the cell.
-We shall account for every segment in each CV using the so-called line-source approximation described :ref:`above <tutorial_lfpykit-linesource>`.
+Here we utilize the LFPykit library to map trans-membrane currents recorded
+during the simulation to extracellular potentials in vicinity to the cell. We
+shall account for every segment in each CV using the so-called line-source
+approximation described :ref:`above <tutorial_lfpykit-linesource>`.
 
-First we define a couple of inherited classes to interface LFPykit
-(as this library is not solely written for Arbor).
-Starting with a class inherited from :class:`lfpykit.CellGeometry`:
+First, we define a couple of inherited classes to interface with LFPykit, as
+this library is not solely written for Arbor. Starting with a class inherited
+from :class:`lfpykit.CellGeometry`:
 
 .. literalinclude:: ../../python/example/probe_lfpykit.py
    :language: python
-   :lines: 168-200
+   :lines: 145-180
 
 Then, a class inherited from :class:`lfpykit.LineSourcePotential`.
 Other use cases may inherit from any other parent class defined in :mod:`lfpykit.models` in a similar manner:
 
 .. literalinclude:: ../../python/example/probe_lfpykit.py
    :language: python
-   :lines: 202-251
+   :lines: 183-231
 
-With these two classes one may then compute extracellular potentials from transmembrane
-currents in space with a few lines of code:
+With these two classes one may then compute extracellular potentials from
+transmembrane currents in space with a few lines of code:
 
 .. literalinclude:: ../../python/example/probe_lfpykit.py
    :language: python
-   :lines: 254-276
+   :lines: 234-256
 
 .. _tutorial_lfpykit-illustration:
 
diff --git a/doc/tutorial/single_cell_allen.rst b/doc/tutorial/single_cell_allen.rst
index b1fae81fafdd41a7a34b94dee08f1cc787a4b94a..652e9193123fe987a51fcf43c573189e4cf38be8 100644
--- a/doc/tutorial/single_cell_allen.rst
+++ b/doc/tutorial/single_cell_allen.rst
@@ -37,33 +37,47 @@ In the "Biophysical - all active" model (zip) file you'll find:
 
 We will replicate the "Sweep 35" experiment, which applies a current of 150 nA for a duration of 1 s.
 
-The morphology
---------------
+Morphology and Labels
+---------------------
 
 .. literalinclude:: ../../python/example/single_cell_allen.py
    :language: python
    :dedent:
-   :lines: 74-80
+   :lines: 80-81
 
-Step **(1)** loads the ``swc`` file using :func:`arbor.load_swc_neuron`. Since the ``swc`` specification is informal, a few different interpretations exist, and we use the appropriate one. The interpretations are described :ref:`here <formatswc-arbor>`.
+Step **(1)** loads the ``swc`` file using :func:`arbor.load_swc_neuron`. Since
+the ``swc`` specification is informal, a few different interpretations exist,
+and we use the appropriate one. The interpretations are described :ref:`here
+<formatswc-arbor>`.
 
-Step **(2)** sets the labels to the defaults of the ``swc``
-`specification <http://www.neuronland.org/NLMorphologyConverter/MorphologyFormats/SWC/Spec.html>`_,
-plus a label for the midpoint of the soma. (You can verify in the ``swc`` file, the first branch is the soma.)
+.. literalinclude:: ../../python/example/single_cell_allen.py
+   :language: python
+   :dedent:
+   :lines: 83-86
+
+Step **(2)** sets the labels to the defaults of the ``swc`` `specification
+<http://www.neuronland.org/NLMorphologyConverter/MorphologyFormats/SWC/Spec.html>`_,
+plus a label for the midpoint of the soma. (You can verify in the ``swc`` file,
+the first branch is the soma.)
 
 The parameter fit
 -----------------
 
-The most complicated part is transferring the values for the appropriate parameters in parameter fit file to an
-:class:`arbor.decor`. The file file is a ``json`` file, which is fortunate; Python comes with a ``json`` package
-in its standard library. The `passive` and `conditions` block contains cell-wide defaults, while the `genome`
-section contains the parameters for all the mechanism properties. In certain cases, parameters names include the
-mechanism name, so some processing needs to take place.
-
-Step **(3)** shows the precise steps needed to load the fit parameter file into a list of global properties,
-region specific properties, reversal potentials, and mechanism parameters. This is not a generic function that will successfully load any Allen model, but it can be used as a starting point. The function composes 4 components out of the ``json`` file:
-
-1. global electro-physiological parameters,
+The most complicated part is transferring the values for the appropriate
+parameters in parameter fit file to an :class:`arbor.decor`. The file file is a
+``json`` file, which is fortunate; Python comes with a ``json`` package in its
+standard library. The `passive` and `conditions` block contains cell-wide
+defaults, while the `genome` section contains the parameters for all the
+mechanism properties. In certain cases, parameters names include the mechanism
+name, so some processing needs to take place.
+
+Step **(3)** shows the precise steps needed to load the fit parameter file into
+a list of global properties, region specific properties, reversal potentials,
+and mechanism parameters. This is not a generic function that will successfully
+load any Allen model, but it can be used as a starting point. The function
+composes 4 components out of the ``json`` file:
+
+1. global electro-physiological parameters
 2. a set of electro-physiological parameters per region,
 3. a set of reversal potentials per ion species and region,
 4. a set of mechanisms with parameters per region.
@@ -71,34 +85,44 @@ region specific properties, reversal potentials, and mechanism parameters. This
 .. literalinclude:: ../../python/example/single_cell_allen.py
    :language: python
    :dedent:
-   :lines: 12-72,82,83
+   :lines: 19-76
 
 The decor
 ---------
 
-With the ingredients for the :class:`arbor.decor` extracted, we continue with the function that will return the cable cell from the model as an :class:`arbor.cable_cell`.
+With the ingredients for the :class:`arbor.decor` extracted, we continue with
+the function that will return the cable cell from the model as an
+:class:`arbor.cable_cell`.
 
 .. literalinclude:: ../../python/example/single_cell_allen.py
    :language: python
    :dedent:
-   :lines: 85-117
+   :lines: 79-138
 
 Step **(4)** creates an empty :class:`arbor.decor`.
 
-Step **(5)** assigns global (cell-wide) properties using :func:`arbor.decor.set_property`. In addition, initial
-internal and external calcium concentrations are set, and configured to be determined by the Nernst equation.
+Step **(5)** assigns global (cell-wide) properties using
+:func:`arbor.decor.set_property`. In addition, initial internal and external
+calcium concentrations are set, and configured to be determined by the Nernst
+equation.
 
 .. note::
-    Setting the calcium reversal potential to be determined by the Nernst equation has to be done manually, in order to mirror
-    `an implicit Neuron behavior <https://neuron.yale.edu/neuron/static/new_doc/modelspec/programmatic/ions.html>`_,
-    for which the fit parameters were obtained. This behavior can be stated as the following rule:
 
-    If the internal or external concentration of an ion is written, and its reversal potential is read but not
-    written, then the Nernst equation is used continuously during the simulation to update the reversal potential of
-    the ion according to the Nernst equation
+   Setting the calcium reversal potential to be determined by the Nernst
+    equation has to be done manually, in order to mirror `an implicit Neuron
+    behavior
+    <https://neuron.yale.edu/neuron/static/new_doc/modelspec/programmatic/ions.html>`_,
+    for which the fit parameters were obtained. This behavior can be stated as
+    the following rule:
 
-Step **(6)** overrides the global properties for all *regions* for which the fit parameters file specifies adapted
-values. Regional properties are :func:`painted <arbor.decor.paint>`, and are painted over (e.g. replacing) the defaults.
+    If the internal or external concentration of an ion is written, and its
+    reversal potential is read but not written, then the Nernst equation is used
+    continuously during the simulation to update the reversal potential of the
+    ion according to the Nernst equation
+
+Step **(6)** overrides the global properties for all *regions* for which the fit
+parameters file specifies adapted values. Regional properties are :func:`painted
+<arbor.decor.paint>`, and are painted over (e.g. replacing) the defaults.
 
 Step **(7)** sets the regional reversal potentials.
 
@@ -106,10 +130,12 @@ Step **(8)** assigns the regional mechanisms.
 
 Now that the electro-physiology is all set up, let's move on to the experimental setup.
 
-Step **(9)** configures the :class:`stimulus <arbor.iclamp>` of 150 nA for a duration of 1 s, starting after 200 ms
-of the start of the simulation. We'll also install a :class:`arbor.threshold_detector` that triggers at -40 mV. (The
-location is usually the soma, as is confirmed by coordinates found in the experimental dataset at
-``488683423.nwb/general/intracellular_ephys/Electrode 1/location``)
+Step **(9)** configures the :class:`stimulus <arbor.iclamp>` of 150 nA for a
+duration of 1 s, starting after 200 ms of the start of the simulation. We'll
+also install a :class:`arbor.threshold_detector` that triggers at -40 mV. (The
+location is usually the soma, as is confirmed by coordinates found in the
+experimental dataset at ``488683423.nwb/general/intracellular_ephys/Electrode
+1/location``)
 
 Step **(10)** specifies a maximum :term:`control volume` length of 20 μm.
 
@@ -121,45 +147,56 @@ The model
 .. literalinclude:: ../../python/example/single_cell_allen.py
    :language: python
    :dedent:
-   :lines: 120-131
+   :lines: 141-152
 
-Step **(12)** instantiates the :class:`arbor.cable_cell` and an :class:`arbor.single_cell_model`.
+Step **(12)** instantiates the :class:`arbor.cable_cell` and an
+:class:`arbor.single_cell_model`.
 
-Step **(13)** shows how to install a probe to the ``"midpoint"``, with a sampling frequency of 200 kHz.
+Step **(13)** shows how to install a probe to the ``"midpoint"``, with a
+sampling frequency of 200 kHz.
 
-Step **(14)** installs the :class:`arbor.allen_catalogue`, thereby making its mechanisms available to the definitions added to the decor.
+Step **(14)** installs the :class:`arbor.allen_catalogue`, thereby making its
+mechanisms available to the definitions added to the decor.
 
 Step **(15)** starts the simulation for a duration of 1.4 s and a timestep of 5 ms.
 
 The result
 ----------
 
-Let's look at the result! In step **(16)** we first load the reference generated with Neuron and the AllenSDK.
-Then, we extract Arbor's output, accessible after the simulation ran at
-:class:`arbor.single_cell_model.traces`. Then, we plot them, together with the :class:`arbor.single_cell_model.spikes` in step **(17)**.
+Let's look at the result! In step **(16)** we first load the reference generated
+with Neuron and the AllenSDK. Then, we extract Arbor's output, accessible after
+the simulation ran at :class:`arbor.single_cell_model.traces`. Then, we plot
+them, together with the :class:`arbor.single_cell_model.spikes` in step
+**(17)**.
 
 .. literalinclude:: ../../python/example/single_cell_allen.py
    :language: python
    :dedent:
-   :lines: 133-
+   :lines: 154-
 
 .. figure:: single_cell_allen_result.svg
     :width: 400
     :align: center
 
-    Plot of experiment 35 of the Allen model, compared to the reference generated by the AllenSDK. In green: the threshold detector output; in shaded grey: the stimulus.
+    Plot of experiment 35 of the Allen model, compared to the reference
+    generated by the AllenSDK. In green: the threshold detector output; in
+    shaded grey: the stimulus.
 
 .. note::
 
-  The careful observer notices that this trace does not match the experimental data shown on the Allen website
-  (or in the ``488683423.nwb`` file). Sweep 35 clearly has 5 spikes, not 4. That is because in the Allen SDK,
-  the axon in the ``swc`` file is replaced with a stub, see
-  `this paper <https://www.biorxiv.org/content/10.1101/2020.04.09.030239v1.full>`_ and `this AllenSDK Issue <https://github.com/AllenInstitute/AllenSDK/issues/1683>`_.
-  However, that adapted morphology is not exportable back to a modified ``swc`` file. When we tried to mimic
-  the procedure, we did not obtain the experimental trace.
-
-  Therefore, we used the unmodified morphology in Arbor *and* the Neuron reference (by commenting out the
-  changes the Allen SDK makes to the morphology) in order to make a 1:1 comparison possible.
+  The careful observer notices that this trace does not match the experimental
+  data shown on the Allen website (or in the ``488683423.nwb`` file). Sweep 35
+  clearly has 5 spikes, not 4. That is because in the Allen SDK, the axon in the
+  ``swc`` file is replaced with a stub, see `this paper
+  <https://www.biorxiv.org/content/10.1101/2020.04.09.030239v1.full>`_ and `this
+  AllenSDK Issue <https://github.com/AllenInstitute/AllenSDK/issues/1683>`_.
+  However, that adapted morphology is not exportable back to a modified ``swc``
+  file. When we tried to mimic the procedure, we did not obtain the experimental
+  trace.
+
+  Therefore, we used the unmodified morphology in Arbor *and* the Neuron
+  reference (by commenting out the changes the Allen SDK makes to the
+  morphology) in order to make a 1:1 comparison possible.
 
 The full code
 -------------
diff --git a/doc/tutorial/single_cell_detailed.rst b/doc/tutorial/single_cell_detailed.rst
index d7cc8f0868ba9b23fd43d3922131fd5a077104ce..c5e638ba3604216021df488bbe662f9c7d20716d 100644
--- a/doc/tutorial/single_cell_detailed.rst
+++ b/doc/tutorial/single_cell_detailed.rst
@@ -41,39 +41,54 @@ We begin by constructing the following morphology:
    :width: 600
    :align: center
 
-This can be done by manually building a segment tree:
+This can be done by manually building a segment tree. The important bit here is
+that ``append`` will take an id to attach to and return the newly added id. This
+is exceptionally handy when building a tree structure, as we can elect to
+remember or overwrite the last id. Alternatively, you could use numeric ids --
+they are just sequentially numbered by insertion order --, but we find that this
+becomes tedious quickly. The image above shows the numeric ids for the specific
+insertion order below, but different orders will produce the same morphology.
 
 .. code-block:: python
 
-    import arbor
-    from arbor import mpoint
-    from arbor import mnpos
-
-    # Define the morphology by manually building a segment tree
-
+    # Construct an empty segment tree.
     tree = arbor.segment_tree()
 
-    # Start with segment 0: a cylindrical soma with tag 1
-    tree.append(mnpos, mpoint(0.0, 0.0, 0.0, 2.0), mpoint( 40.0, 0.0, 0.0, 2.0), tag=1)
-    # Construct the first section of the dendritic tree with tag 3,
-    # comprised of segments 1 and 2, attached to soma segment 0.
-    tree.append(0,     mpoint(40.0, 0.0, 0.0, 0.8), mpoint( 80.0,  0.0, 0.0, 0.8), tag=3)
-    tree.append(1,     mpoint(80.0, 0.0, 0.0, 0.8), mpoint(120.0, -5.0, 0.0, 0.8), tag=3)
-    # Construct the rest of the dendritic tree: segments 3, 4 and 5.
-    tree.append(2,     mpoint(120.0, -5.0, 0.0, 0.8), mpoint(200.0,  40.0, 0.0, 0.4), tag=3)
-    tree.append(3,     mpoint(200.0, 40.0, 0.0, 0.4), mpoint(260.0,  60.0, 0.0, 0.2), tag=3)
-    tree.append(2,     mpoint(120.0, -5.0, 0.0, 0.5), mpoint(190.0, -30.0, 0.0, 0.5), tag=3)
-    # Construct a special region of the tree made of segments 6, 7, and 8
-    # differentiated from the rest of the tree using tag 4.
-    tree.append(5,     mpoint(190.0, -30.0, 0.0, 0.5), mpoint(240.0, -70.0, 0.0, 0.2), tag=4)
-    tree.append(5,     mpoint(190.0, -30.0, 0.0, 0.5), mpoint(230.0, -10.0, 0.0, 0.2), tag=4)
-    tree.append(7,     mpoint(230.0, -10.0, 0.0, 0.2), mpoint(360.0, -20.0, 0.0, 0.2), tag=4)
-    # Construct segments 9 and 10 that make up the axon with tag 2.
-    # Segment 9 is at the root, where its proximal end will be connected to the
-    # proximal end of the soma segment.
-    tree.append(mnpos, mpoint( 0.0, 0.0, 0.0, 2.0), mpoint(  -70.0, 0.0, 0.0, 0.4), tag=2)
-    tree.append(9,     mpoint(-70.0, 0.0, 0.0, 0.4), mpoint(-100.0, 0.0, 0.0, 0.4), tag=2)
-
+    # The root of the tree has no parent
+    root = arbor.mnpos
+
+    # The root segment: a cylindrical soma with tag 1
+    # NOTE: append returns the added segment's id, which we can use to
+    #       attach the next segments.
+    soma = tree.append(root, (0.0, 0.0, 0.0, 2.0), (40.0, 0.0, 0.0, 2.0), tag=1)
+
+    # Attach the first section of the dendritic tree with tag 3 to the soma
+    # up to the first fork
+    dend = tree.append(soma, (40.0, 0.0, 0.0, 0.8), ( 80.0,  0.0, 0.0, 0.8), tag=3)
+    dend = tree.append(dend, (80.0, 0.0, 0.0, 0.8), (120.0, -5.0, 0.0, 0.8), tag=3)
+
+    # Construct upper part of the first fork
+    # NOTE: We do not overwrite the parent here, as we need to attach the
+    #       lower fork later. Instead we use new names for this branch.
+    dend_u = tree.append(dend, (120.0, -5.0, 0.0, 0.8), (200.0,  40.0, 0.0, 0.4), tag=3)
+    dend_u = tree.append(dend_u, (200.0, 40.0, 0.0, 0.4), (260.0,  60.0, 0.0, 0.2), tag=3)
+
+    # Construct lower part of the first fork
+    dend_l = tree.append(dend, (120.0, -5.0, 0.0, 0.5), (190.0, -30.0, 0.0, 0.5), tag=3)
+
+    # Attach another fork to the last segment, ``p``.
+    # Upper part
+    dend_lu = tree.append(dend_l, (190.0, -30.0, 0.0, 0.5), (240.0, -70.0, 0.0, 0.2), tag=4)
+    # Lower part
+    dend_ll = tree.append(dend_l, (190.0, -30.0, 0.0, 0.5), (230.0, -10.0, 0.0, 0.2), tag=4)
+    dend_ll = tree.append(dend_ll, (230.0, -10.0, 0.0, 0.2), (360.0, -20.0, 0.0, 0.2), tag=4)
+
+    # Construct the axon with tag 2, attaching to the root ``mnpos``, where its
+    # proximal end will be connected to the proximal end of the soma segment implicitly.
+    axon = tree.append(root, (0.0, 0.0, 0.0, 2.0), (-70.0, 0.0, 0.0, 0.4), tag=2)
+    axon = tree.append(axon, (-70.0, 0.0, 0.0, 0.4), (-100.0, 0.0, 0.0, 0.4), tag=2)
+
+    # Turn segment tree into a morphology.
     morph = arbor.morphology(tree);
 
 The same morphology can be represented using an SWC file (interpreted according
@@ -97,31 +112,39 @@ The morphology can then be loaded from ``single_cell_detailed.swc`` in the follo
 
 .. literalinclude:: ../../python/example/single_cell_detailed.py
    :language: python
-   :lines: 10-20
+   :lines: 11-22
+
+We allow passing of any valid SWC file so you can easily experiment with your
+own morphologies but employ some Python magic to grab the default file which
+lives in the same directory as the example. In general, using a data file for
+constructing morphologies should be preferred.
+
 
 The label dictionary
 ^^^^^^^^^^^^^^^^^^^^
 
-Next, we can define **region** and **location** expressions and give them labels.
-The regions and locations are defined using an Arbor-specific DSL, and the labels
-can be stored in a :class:`arbor.label_dict`.
+Next, we can define **region** and **location** expressions and give them
+labels. The regions and locations are defined using an Arbor-specific DSL, and
+the labels can be stored in a :class:`arbor.label_dict`.
 
 .. Note::
 
-   The expressions in the label dictionary don't actually refer to any concrete regions
-   or locations of the morphology at this point. They are merely descriptions that can be
-   applied to any morphology, and depending on its geometry, they will generate different
-   regions and locations. However, we will show some figures illustrating the effect of
-   applying these expressions to the above morphology, in order to better visualize the
-   final cell.
+   The expressions in the label dictionary don't actually refer to any concrete
+   regions or locations of the morphology at this point. They are merely queries
+   that can be applied to any morphology, and depending on its geometry, they
+   will generate different regions and locations. However, we will show some
+   figures illustrating the effect of applying these expressions to the above
+   morphology, in order to better visualize the final cell.
 
-   More information on region and location expressions is available :ref:`here <labels>`.
+   More information on region and location expressions is available :ref:`here
+   <labels>`.
 
 The SWC file format allows association of ``tags`` with parts of the morphology
 and reserves tag values 1-4 for commonly used sections (see `here
 <http://www.neuronland.org/NLMorphologyConverter/MorphologyFormats/SWC/Spec.html>`__
-for the SWC file format). In Arbor, these tags can be added to a :class:`arbor.label_dict` using
-the :meth:`~arbor.label_dict.add_swc_tags` method, which will define
+for the SWC file format). In Arbor, these tags can be added to a
+:class:`arbor.label_dict` using the :meth:`~arbor.label_dict.add_swc_tags`
+method, which will define
 
 .. list-table:: Default SWC Tags
    :widths: 25 25 50
@@ -162,9 +185,9 @@ Both ways will generate the following regions when applied to the previously def
 
   From left to right: regions "soma", "axon", "dend" and "last"
 
-We can also define a region that represents the whole cell; and to make things a bit more interesting,
-a region that includes the parts of the morphology that have a radius greater than 1.5 μm. This is done
-in the following way:
+We can also define a region that represents the whole cell; and to make things a
+bit more interesting, a region that includes the parts of the morphology that
+have a radius greater than 1.5 μm. This is done in the following way:
 
 .. literalinclude:: ../../python/example/single_cell_detailed.py
    :language: python
@@ -227,6 +250,23 @@ This will generate the following 2 locsets when applied to the previously define
 
   Left: locset "custom_terminal"; right: locset "axon_terminal"
 
+.. note::
+
+   We show the use of the label dictionary here, but everywhere a label is
+   valid, you can use the DSL expression directly, so
+
+   .. code-block:: python
+
+       decor.paint('(all)', arbor.density("pas"))
+
+   is perfectly acceptable, as is
+
+   .. code-block:: python
+
+       all = '(all)'
+       decor.paint(all, arbor.density("pas"))
+
+
 The decorations
 ^^^^^^^^^^^^^^^
 
@@ -243,79 +283,87 @@ region or location expressions.
 
   More information on decors can be found :ref:`here <cablecell-decoration>`.
 
-The decor object can have default values for properties, which can then be overridden on specific
-regions. It is in general better to explicitly set all the default properties of your cell,
-to avoid the confusion to having simulator-specific default values. This will therefore be our first
-step:
+The decor object can have default values for properties, which can then be
+overridden on specific regions. It is in general better to explicitly set all
+the default properties of your cell, to avoid the confusion to having
+simulator-specific default values. This will therefore be our first step:
 
 .. literalinclude:: ../../python/example/single_cell_detailed.py
    :language: python
-   :lines: 48-52
-
-We have set the default initial membrane voltage to -55 mV; the default initial
-temperature to 300 K; the default axial resistivity to 35.4 Ω·cm; and the default membrane
-capacitance to 0.01 F/m².
-
-We also set the initial properties of the *na* and *k* ions because they will be utilized
-by the density mechanisms that we will be adding shortly.
-For both ions we set the default initial concentration and external concentration measures in mM;
-and we set the default initial reversal potential in mV. For the *na* ion, we additionally indicate
-the the progression on the reversal potential during the simulation will be dictated by the
-`Nernst equation <https://en.wikipedia.org/wiki/Nernst_equation>`_.
-
-It happens, however, that we want the temperature of the "custom" region defined in the label
-dictionary earlier to be colder, and the initial voltage of the "soma" region to be higher.
-We can override the default properties by *painting* new values on the relevant regions using
-:meth:`arbor.decor.paint`.
+   :lines: 48-62
+
+We have set the default initial membrane voltage mV; the default initial
+temperature; the default axial resistivity to; and the default membrane
+capacitance to. Also, the initial properties of the *na* and *k* ions have been
+changed. They will be utilized by the density mechanisms that we will be adding
+shortly. For both ions we set the default initial concentration and external
+concentration measures; and we set the default initial reversal potential. For
+the *na* ion, we additionally indicate the the progression on the reversal
+potential during the simulation will be dictated by the `Nernst equation
+<https://en.wikipedia.org/wiki/Nernst_equation>`_. In the case that defaults are
+not set at the cell level, there is also a global default which we will define a
+bit later.
+
+It happens, however, that we want the temperature of the "custom" region defined
+in the label dictionary earlier to be colder, and the initial voltage of the
+"soma" region to be higher. We can override the default properties by *painting*
+new values on the relevant regions using :meth:`arbor.decor.paint`.
 
 .. literalinclude:: ../../python/example/single_cell_detailed.py
   :language: python
-  :lines: 53-55
+  :lines: 63-65
 
-With the default and initial values taken care of, we now add some density mechanisms. Let's *paint*
-a *pas* density mechanism everywhere on the cell using the previously defined "all" region; an *hh*
-density mechanism on the "custom" region; and an *Ih* density mechanism on the "dend" region. The *Ih*
-mechanism has a custom 'gbar' parameter.
+With the default and initial values taken care of, we now add some density
+mechanisms. Let's *paint* a *pas* density mechanism everywhere on the cell using
+the previously defined "all" region; an *hh* density mechanism on the "custom"
+region; and an *Ih* density mechanism on the "dend" region. The *Ih* mechanism
+has a custom 'gbar' parameter.
 
 .. literalinclude:: ../../python/example/single_cell_detailed.py
    :language: python
-   :lines: 8,56-59
+   :lines: 66-69
 
-The decor object is also used to *place* stimuli and threshold detectors on the cell using :meth:`arbor.decor.place`.
-We place 3 current clamps of 2 nA on the "root" locset defined earlier, starting at time = 10, 30, 50 ms and
-lasting 1ms each. As well as threshold detectors on the "axon_terminal" locset for voltages above -10 mV.
-Every placement gets a label. The labels of detectors and synapses are used to form connection from and to them
-in the recipe.
+The decor object is also used to *place* stimuli and threshold detectors on the
+cell using :meth:`arbor.decor.place`. We place 3 current clamps of 2 nA on the
+"root" locset defined earlier, starting at time = 10, 30, 50 ms and lasting 1ms
+each. As well as threshold detectors on the "axon_terminal" locset for voltages
+above -10 mV. Every placement gets a label. The labels of detectors and synapses
+are used to form connection from and to them in the recipe.
 
 .. literalinclude:: ../../python/example/single_cell_detailed.py
    :language: python
-   :lines: 60-64
+   :lines: 70-74
 
 .. Note::
 
-   The number of individual locations in the ``'axon_terminal'`` locset depends on the underlying morphology and the
-   number of axon branches in the morphology. The number of detectors that get added on the cell is equal to the number
-   of locations in the locset, and the label ``'detector'`` refers to all of them. If we want to refer to a single
-   detector from the group (to form a network connection for example), we need a :py:class:`arbor.selection_policy`.
-
-Finally, there's one last property that impacts the behavior of a model: the discretisation.
-Cells in Arbor are simulated as discrete components called control volumes (CV). The size of
-a CV has an impact on the accuracy of the results of the simulation. Usually, smaller CVs
-are more accurate because they simulate the continuous nature of a neuron more closely.
-
-The user controls the discretisation using a :class:`arbor.cv_policy`. There are a few different policies to
-choose from, and they can be composed with one another. In this example, we would like the "soma" region
-to be a single CV, and the rest of the morphology to be comprised of CVs with a maximum length of 1 μm:
+   The number of individual locations in the ``'axon_terminal'`` locset depends
+   on the underlying morphology and the number of axon branches in the
+   morphology. The number of detectors that get added on the cell is equal to
+   the number of locations in the locset, and the label ``'detector'`` refers to
+   all of them collectively. If we want to refer to a single detector from the
+   group (to form a network connection for example), we need a
+   :py:class:`arbor.selection_policy`.
+
+Finally, there's one last property that impacts the behavior of a model: the
+discretisation. Cells in Arbor are simulated as discrete components called
+control volumes (CV). The size of a CV has an impact on the accuracy of the
+results of the simulation. Usually, smaller CVs are more accurate because they
+simulate the continuous nature of a neuron more closely.
+
+The user controls the discretisation using a :class:`arbor.cv_policy`. There are
+a few different policies to choose from, and they can be composed with one
+another. In this example, we would like the "soma" region to be a single CV, and
+the rest of the morphology to be comprised of CVs with a maximum length of 1 μm:
 
 .. literalinclude:: ../../python/example/single_cell_detailed.py
    :language: python
-   :lines: 65-66
+   :lines: 75-76
 
 Finally, we create the cell.
 
 .. literalinclude:: ../../python/example/single_cell_detailed.py
    :language: python
-   :lines: 68-70
+   :lines: 79-80
 
 The model
 *********
@@ -324,75 +372,82 @@ Having created the cell, we construct an :class:`arbor.single_cell_model`.
 
 .. literalinclude:: ../../python/example/single_cell_detailed.py
    :language: python
-   :lines: 72-74
+   :lines: 82-83
 
 The global properties
 ^^^^^^^^^^^^^^^^^^^^^
 
 The global properties of a single cell model include:
 
-1. The **mechanism catalogue**: A mechanism catalogue is a collection of density and point
-   mechanisms. Arbor has 3 built-in mechanism catalogues: ``default``, ``allen`` and ``bbp``. The mechanism
-   catalogue in the global properties of the model must include the catalogues of all the
-   mechanisms painted on the cell decor. The default is to use the ``default_catalogue``.
+1. The **mechanism catalogue**: A mechanism catalogue is a collection of density
+   and point mechanisms. Arbor has 3 built-in mechanism catalogues: ``default``,
+   ``allen`` and ``bbp``. The mechanism catalogue in the global properties of
+   the model must include the catalogues of all the mechanisms painted on the
+   cell decor. The default is to use the ``default_catalogue``.
 
-2. The default **parameters**: The initial membrane voltage; the initial temperature; the
-   axial resistivity; the membrane capacitance; the ion parameters; and the discretisation
-   policy.
+2. The default **parameters**: The initial membrane voltage; the initial
+   temperature; the axial resistivity; the membrane capacitance; the ion
+   parameters; and the discretisation policy.
 
 .. Note::
 
-   You may notice that the same parameters can be set both at the cell level and at
-   the model level. This is intentional. The model parameters apply to all the cells in a model,
-   whereas the cell parameters apply only to that specific cell.
-
-   The idea is that the user is able to define a set of global properties for all cells in a model
-   which can then be overridden for individual cells, and overridden yet again on certain
-   regions of the cells.
-
-   You may now be wondering why this is needed for the `single cell model` where there is only one
-   cell by design. You can use this feature to ease moving from simulating a set of single cell models
-   to simulating a network of these cells.
-   For example, a user may choose to individually test several single cell models before simulating
-   their interactions. By using the same global properties for each *model*, and customizing the *cell*
-   global properties, it becomes possible to use the cell descriptions of each cell, unchanged, in a
+   You may notice that the same parameters can be set both at the cell level and
+   at the model level. This is intentional. The model parameters apply to all
+   the cells in a model, whereas the cell parameters apply only to that specific
+   cell.
+
+   The idea is that the user is able to define a set of global properties for
+   all cells in a model which can then be overridden for individual cells, and
+   overridden yet again on certain regions of the cells.
+
+   You may now be wondering why this is needed for the `single cell model` where
+   there is only one cell by design. You can use this feature to ease moving
+   from simulating a set of single cell models to simulating a network of these
+   cells. For example, a user may choose to individually test several single
+   cell models before simulating their interactions. By using the same global
+   properties for each *model*, and customizing the *cell* global properties, it
+   becomes possible to use the cell descriptions of each cell, unchanged, in a
    larger network model.
 
-Earlier in the example we mentioned that it is better to explicitly set all the default properties
-of your cell, while that is true, it is better yet to set the default properties of the entire
-model:
+Earlier in the example we mentioned that it is better to explicitly set all the
+default properties of your cell, while that is true, it is better yet to set the
+default properties of the entire model:
 
 .. _tutorialsinglecellswc-gprop:
 
 .. literalinclude:: ../../python/example/single_cell_detailed.py
    :language: python
-   :lines: 76-80
+   :lines: 85-94
 
-We set the same properties as we did earlier when we were creating the *decor* of the cell, except
-for the initial membrane voltage, which is -65 mV as opposed to -55 mV.
+We set the same properties as we did earlier when we were creating the *decor*
+of the cell, except for the initial membrane voltage, which is -65 mV as opposed
+to -55 mV.
 
-During the decoration step, we also made use of 3 mechanisms: *pas*, *hh* and *Ih*. As it happens,
-the *pas* and *hh* mechanisms are in the default Arbor catalogue, whereas the *Ih* mechanism is in
-the "allen" catalogue. We can extend the default catalogue as follow:
+During the decoration step, we also made use of 3 mechanisms: *pas*, *hh* and
+*Ih*. As it happens, the *pas* and *hh* mechanisms are in the default Arbor
+catalogue, whereas the *Ih* mechanism is in the "allen" catalogue. We can extend
+the default catalogue as follow:
 
 .. literalinclude:: ../../python/example/single_cell_detailed.py
    :language: python
-   :lines: 82-86
+   :lines: 96-100
 
 Now all three mechanisms in the *decor* object have been made available to the model.
 
 The probes
 ^^^^^^^^^^
 
-The model is almost ready for simulation. Except that the only output we would be able to
-measure at this point is the spikes from the threshold detectors placed in the decor.
+The model itself is ready for simulation. Except that the only output we would
+be able to measure at this point is the spikes from the threshold detectors
+placed in the decor.
 
-The :class:`arbor.single_cell_model` can also measure the voltage on specific locations of the cell.
-We can indicate the location we would like to probe using labels from the :class:`label_dict`:
+The :class:`arbor.single_cell_model` can also measure the voltage on specific
+locations of the cell. We can indicate the location we would like to probe using
+labels from the :class:`label_dict`:
 
 .. literalinclude:: ../../python/example/single_cell_detailed.py
    :language: python
-   :lines: 88-92
+   :lines: 102-104
 
 The simulation
 ^^^^^^^^^^^^^^
@@ -401,31 +456,35 @@ The cell and model descriptions are now complete and we can run the simulation:
 
 .. literalinclude:: ../../python/example/single_cell_detailed.py
    :language: python
-   :lines: 94-102
+   :lines: 106-107
 
 The results
 ^^^^^^^^^^^
 
-Finally we move on to the data collection segment of the example. We have added a threshold detector
-on the "axon_terminal" locset. The :class:`arbor.single_cell_model` automatically registers all
-spikes on the cell from all threshold detectors on the cell and saves the times at which they occurred.
+Finally we move on to the data collection segment of the example. We have added
+a threshold detector on the "axon_terminal" locset. The
+:class:`arbor.single_cell_model` automatically registers all spikes on the cell
+from all threshold detectors on the cell and saves the times at which they
+occurred.
 
 .. literalinclude:: ../../python/example/single_cell_detailed.py
    :language: python
-   :lines: 98-102
+   :lines: 109-112
 
-A more interesting result of the simulation is perhaps the output of the voltage probe previously
-placed on the "custom_terminal" locset. The model saves the output of the probes as [time, value]
-pairs which can then be plotted. We use `pandas` and `seaborn` for the plotting, but the user can
-choose the any other library:
+A more interesting result of the simulation is perhaps the output of the voltage
+probe previously placed on the "custom_terminal" locset. The model saves the
+output of the probes as [time, value] pairs which can then be plotted. We use
+`pandas` and `seaborn` for the plotting, but the user can choose the any other
+library:
 
 .. literalinclude:: ../../python/example/single_cell_detailed.py
    :language: python
-   :lines: 5,6,104-
+   :lines: 114-138
 
-The following plot is generated. The orange line is slightly delayed from the blue line, which is
-what we'd expect because branch 4 is longer than branch 3 of the morphology. We also see 3 spikes,
-corresponding to each of the current clamps placed on the cell.
+The following plot is generated. The orange line is slightly delayed from the
+blue line, which is what we'd expect because branch 4 is longer than branch 3 of
+the morphology. We also see 3 spikes, corresponding to each of the current
+clamps placed on the cell.
 
 .. figure:: single_cell_detailed_result.svg
     :width: 400
diff --git a/doc/tutorial/single_cell_detailed_recipe.rst b/doc/tutorial/single_cell_detailed_recipe.rst
index 959d06241c4df26c02942f220ddb4344e0048579..a7bf6b21570dab17ea30c9326351713bc8b804c9 100644
--- a/doc/tutorial/single_cell_detailed_recipe.rst
+++ b/doc/tutorial/single_cell_detailed_recipe.rst
@@ -20,75 +20,82 @@ This time, we'll learn a bit more about setting up advanced features using a :cl
 The cell
 ********
 
-We can copy the cell description code or reuse ``single_cell_detailed.swc`` from the
-:ref:`original example <tutorialsinglecellswc-cell>` where it is explained in detail.
+We reuse the cell construction code from :ref:`original example
+<tutorialsinglecellswc-cell>` where it is explained in detail.
+Constructing cells outside the recipe is not required, but may be
+convenient and potentially faster if many copies of the same cell
+are required.
 
 The recipe
 **********
 
-The :class:`arbor.single_cell_model` of the original example created a :class:`arbor.recipe` under
-the hood, and abstracted away the details so we were unaware of its existence. In this example, we will
-examine the recipe in detail: how to create one, and why it is needed.
+The :class:`arbor.single_cell_model` used in the original example created a
+:class:`arbor.recipe` under the hood, and abstracted away the details so we were
+unaware of its existence. In this example, we will examine the recipe in detail:
+how to create one and why it is needed.
 
 .. literalinclude:: ../../python/example/single_cell_detailed_recipe.py
    :language: python
-   :lines: 74-109
+   :lines: 78-126
 
 Let's go through the recipe point by point.
 
-Step **(5)** creates a ``single_recipe`` class that inherits from :class:`arbor.recipe`.
-:meth:`arbor.recipe.num_cells`, :meth:`arbor.recipe.cell_kind` and :meth:`arbor.recipe.cell_description`
-always have to be implemented by the user. We'll also implement :meth:`arbor.recipe.global_properties` to be able
-to decorate :class:`arbor.cell_kind.cable` cells with mechanisms and :meth:`arbor.recipe.probes` to be able to
-insert the probe.
-
-Step **(5.1)** defines the class constructor. As per :class:`arbor.recipe` instructions, we call
-``arbor.recipe.__init__(self)`` to ensure correct initialization of memory in the C++ class.
-
-We then create the ``self.the_props`` variable. This will hold the global properties of the model, which apply
-to all the cells in the network. We initialize it with :class:`arbor.cable_global_properties`, which comes with the 
-``default`` mechanism catalogue built-in. We set all the properties of the system similar to what we did in
-the :ref:`original example <tutorialsinglecellswc-gprop>`. One last important step is to extend ``self.the_props``
-to include the Allen catalogue, because it holds the *Ih* mechanism. The *hh* and *pas* mechanisms came with the
-default catalogue.
-
-Step **(5.2)** overrides the :meth:`~arbor.recipe.num_cells` method. It takes no arguments. We simply return 1,
-as we are only simulating one cell in this example.
-
-Step **(5.3)** overrides the :meth:`~arbor.recipe.cell_kind` method. It takes one argument: ``gid``.
-Given the gid, this method returns the kind of the cell. Our defined cell is a
-:class:`arbor.cell_kind.cable`, so we simply return that.
-
-Step **(5.4)** overrides the :meth:`~arbor.recipe.cell_description` method. It takes one argument: ``gid``.
-Given the gid, this method returns the cell description which is the cell object passed to the constructor
-of the recipe. We return ``cell``, the cell created just above.
-
-Step **(5.5)** overrides the :meth:`~arbor.recipe.probes` method. It takes one argument: ``gid``.
-Given the gid, this method returns all the probes on the cell. The probes can be of many different kinds
-measuring different quantities on different locations of the cell. Like in the original example, we will
-create the voltage probe at the ``"custom_terminal"`` locset. This probe was registered directly using the 
-:class:`arbor.single_cell_model` object. Now it has to be explicitly created and registered in the recipe.
-
-Step **(5.6)** overrides the :meth:`~arbor.recipe.global_properties` method. It takes one argument: ``kind``.
-This method returns the default global properties of the model which apply to all cells in the network of
-that kind. We only use ``cable`` cells in this example (but there are more) and thus always return a
-``cable_cell_properties`` object. We return ``self.the_props`` which we defined in step **(1)**.
-
-.. Note::
-
-   You may wonder why the method :meth:`arbor.recipe.cell_kind` is required, since it can be inferred by examining the cell description.
-   The recipe was designed to allow building simulations efficiently in a distributed system with minimum
-   communication. Some parts of the model initialization require only the cell kind,
-   not the full cell description which can be quite expensive to build. Providing these
-   descriptions separately saves time and resources for the user.
-
-   More information on the recipe can be found :ref:`here <modelrecipe>`.
-
-Now we can instantiate a ``single_recipe`` object.
+Step **(5.1)** defines the class constructor. As per :class:`arbor.recipe`
+instructions, we call ``arbor.recipe.__init__(self)`` as the very first thing.
+This is to ensure correct initialization.
+
+We then create the ``self.the_props`` variable. This will hold the global
+properties of the model, which apply to all the cells in the network. We
+initialize it with :class:`arbor.cable_global_properties`, which comes with the
+``default`` mechanism catalogue built-in. We set all the properties of the
+system similar to what we did in the :ref:`original example
+<tutorialsinglecellswc-gprop>`. One last important step is to extend
+``self.the_props`` to include the Allen catalogue, because it holds the *Ih*
+mechanism. The *hh* and *pas* mechanisms came with the default catalogue.
+
+Step **(5.2)** overrides the :meth:`~arbor.recipe.num_cells` method. It takes no
+arguments and returns the count of cells in the simulation. The global id (gid)
+of cells runs between 0 and the value returned from here and is used to query
+recipes for cell descriptions. Technically, this method doesn't need overriding,
+but the default is zero, resulting in an empty simulation.
+
+Step **(5.3)** overrides the :meth:`~arbor.recipe.cell_kind` method. It takes
+one argument: ``gid``. Given the gid, this method returns the kind of the cell.
+Our defined cell is a :class:`arbor.cell_kind.cable`, so we simply return that.
+Arbor uses the kind to determine what description is expected from ``cell_description``;
+if the two do not match, an error will occur.
+
+Step **(5.4)** overrides the :meth:`~arbor.recipe.cell_description` method. It
+takes one argument: ``gid``. Given the gid, this method returns the cell
+description which is the cell object passed to the constructor of the recipe. We
+return ``cell``, the cell created just above.
+
+.. note::
+
+   While splitting the kind and description into two methods may seem redundant,
+   it allows Arbor to optimize the simulation layout before constructing any
+   cells.
+
+Step **(5.5)** overrides the :meth:`~arbor.recipe.probes` method. It takes one
+argument: ``gid``. Given the gid, this method returns all the probes on the
+cell. The probes can be of many different kinds measuring different quantities
+on different locations of the cell. Like in the original example, we will create
+the voltage probe at the ``"custom_terminal"`` locset. This probe was registered
+directly using the :class:`arbor.single_cell_model` object. Now it has to be
+explicitly created and registered in the recipe.
+
+Step **(5.6)** overrides the :meth:`~arbor.recipe.global_properties` method. It
+takes one argument: ``kind``. This method returns the default global properties
+of the model which apply to all cells in the network of that kind. We only use
+``cable`` cells in this example (but there are more) and thus always return a
+``cable_cell_properties`` object. We return ``self.the_props`` which we defined
+in step **(1)**.
 
 .. literalinclude:: ../../python/example/single_cell_detailed_recipe.py
    :language: python
-   :lines: 112-113
+   :lines: 129-130
+
+Now we can instantiate a ``single_recipe`` object.
 
 The simulation
 **************
@@ -100,55 +107,60 @@ This was handled by the :class:`arbor.single_cell_model` object in the original
 
 .. literalinclude:: ../../python/example/single_cell_detailed_recipe.py
    :language: python
-   :lines: 115-122
+   :lines: 135-138
 
-We would like to get a list of the spikes on the cell during the runtime of the simulation, and we would like
-to plot the voltage registered by the probe on the "custom_terminal" locset.
+We would like to get a list of the spikes on the cell during the runtime of the
+simulation, and we would like to plot the voltage registered by the probe on the
+"custom_terminal" locset. Without the call to ``sample``, the probe will be
+present, but no data will recorded. This can help to toggle different probes and
+possible save some memory on the unsampled ones. Sampling requires a unqiue
+identifier for the probe we want to attach to, which consists of the gid of the
+cell and the label we gave to the ``place`` method when setting the probe. Each
+sampler is identified by an opaque handle that can be used to retrieve the
+recorded data.
 
-The lines handling probe sampling warrant a second look. First, we declared ``probeset_id`` to be a
-:class:`arbor.cell_member`, with :class:`arbor.cell_member.gid` = 0 and :class:`arbor.cell_member.index` = 0.
-This variable serves as a global identifier of a probe on a cell, namely the first declared probe on the
-cell with ``gid = 0``, which is id of the only probe we created on the only cell in the model.
-
-Next, we instructed the simulation to sample ``probeset_id`` at a frequency of 50 kHz. That function returns a
-:term:`handle` which we will use to :ref:`extract the results <pycablecell-probesample>` of the sampling after running the simulation.
-
-We can now run the simulation we just instantiated for a duration of 100 ms with a time step of 0.025 ms.
+We can now run the simulation we just instantiated for a duration of 100 ms with
+a time step of 0.025 ms.
 
 .. literalinclude:: ../../python/example/single_cell_detailed_recipe.py
    :language: python
-   :lines: 124-125
+   :lines: 140-141
 
 The results
 ***********
 
-The last step is result collection. We instructed the simulation to record the spikes on the cell, and
-to sample the probe.
+The last step is result collection. We instructed the simulation to record the
+spikes on the cell, and to sample the probe.
 
 We can print the times of the spikes:
 
 .. literalinclude:: ../../python/example/single_cell_detailed_recipe.py
    :language: python
-   :lines: 127-131
+   :lines: 143-147
 
 The probe results, again, warrant some more explanation:
 
 .. literalinclude:: ../../python/example/single_cell_detailed_recipe.py
    :language: python
-   :lines: 133-137
-
-``sim.samples()`` takes a ``handle`` of the probe we wish to examine. It returns a list
-of ``(data, meta)`` terms: ``data`` being the time and value series of the probed quantity; and
-``meta`` being the location of the probe. The size of the returned list depends on the number of
-discrete locations pointed to by the handle. We placed the probe on the "custom_terminal" locset which is
-represented by 2 locations on the morphology. We therefore expect the length of ``sim.samples(handle)``
-to be 2.
-
-We plot the results using pandas and seaborn as we did in the original example, and expect the same results:
+   :lines: 159
+
+``sim.samples`` takes a ``handle`` that is associated to the probe we wish to
+examine. These opaque objects are returned from the calls to ``sim.sample``.
+Each call returns a list of ``(data, meta)`` objects. Here, ``meta`` describes
+the location set of the probe, which can be a single place or a list of places.
+The other item -- ``data`` -- is a numpy array comprising one column for the
+time and one for each eantry in the location list. The size of the returned list
+depends on the number of discrete locations pointed to by the handle. We placed
+the probe on the "custom_terminal" locset which is represented by 2 locations on
+the morphology. We therefore expect the length of ``sim.samples(handle)`` to
+be 2.
+
+We plot the results using pandas and seaborn as we did in the original example,
+and expect the same results:
 
 .. literalinclude:: ../../python/example/single_cell_detailed_recipe.py
    :language: python
-   :lines: 139-
+   :lines: 149-
 
 The following plot is generated. Identical to the plot of the original example.
 
@@ -160,4 +172,5 @@ The following plot is generated. Identical to the plot of the original example.
 The full code
 *************
 
-You can find the full code of the example at ``python/examples/single_cell_detailed_recipe.py``.
+You can find the full code of the example at
+``python/examples/single_cell_detailed_recipe.py``.
diff --git a/doc/tutorial/single_cell_model.rst b/doc/tutorial/single_cell_model.rst
index 49b096345c6bb0b6b34143cab4a62a228c503efb..769ea8380e1bff806472b5fd07fd71815fea3468 100644
--- a/doc/tutorial/single_cell_model.rst
+++ b/doc/tutorial/single_cell_model.rst
@@ -12,7 +12,8 @@ introduce Arbor's cell modelling concepts and approach.
 
    **Concepts covered in this example:**
 
-   1. Intro to building a morphology from a :class:`arbor.segment_tree`.
+   0. The Arbor library and units.
+   1. Intro to morphologies.
    2. Intro to region and locset expressions.
    3. Intro to decors and cell decorations.
    4. Building a :class:`arbor.cable_cell` object.
@@ -21,56 +22,96 @@ introduce Arbor's cell modelling concepts and approach.
 
 .. _tutorialsinglecell-cell:
 
+Setup and introduction to units
+-------------------------------
+
+We begin by importing the Arbor library and its unit support.
+
+.. literalinclude:: ../../python/example/single_cell_model.py
+   :language: python
+   :lines: 4-5
+
+As we will refer to both quite often, we assign aliases ``A`` and ``U``, to
+minimize typing. Over the course of this introduction, you will notice that most
+of Arbor's user interface is making use of units. This requires a bit of typing,
+but makes the physical quantities obvious and allows for easy conversion of
+models. You can use any sensible unit for a given dimension and Arbor will
+convert as needed, e.g. you can write ``5 * U.mm`` instead of ``5000 * U.um``.
+Handing a mismatching dimension to a method will cause a runtime error, so in
+the example above, ``5 * U.mV`` will be rejected.
+
 The cell
 --------
 
-The most trivial representation of a cell in Arbor is to model the entire cell as a
-single cylinder. The following example shows the steps required to construct a model of a
-cylindrical cell with a length of 6 μm and a radius of 3 μm; Hodgkin–Huxley dynamics
-and a current clamp stimulus, then run the model for 30 ms.
+The most trivial representation of a cell in Arbor is to model the entire cell
+as a single cylinder. The following example shows the steps required to
+construct a model of a cylindrical cell with a length of 6 μm and a radius of 3
+μm; Hodgkin–Huxley dynamics and a current clamp stimulus, then run the model for
+30 ms.
 
-The first step is to construct the cell. In Arbor, the abstract representation used to
-define a cell with branching cable morphology is a ``cable_cell``, which holds a
-description of the cell's morphology, named regions and locations on the morphology, and
-descriptions of ion channels, synapses, threshold detectors and electrical properties.
+The first step is to construct the cell. In Arbor, the abstract representation
+used to define a cell with branching cable morphology is a ``cable_cell``, which
+holds a description of the cell's morphology, named regions and locations on the
+morphology, and descriptions of ion channels, synapses, threshold detectors and
+electrical properties. We will go over these one by one.
 
-Our *single-segment HH cell* has a simple morphology and dynamics, constructed as follows:
+Our *cell* has a simple morphology comprising a single segment, which is why we
+use an explicit construction. Normally, one would read the morphology from file
+and Arbor handles most standard formats natively.
 
 .. literalinclude:: ../../python/example/single_cell_model.py
    :language: python
-   :lines: 4,6-23
-
-Step **(1)** constructs a :class:`arbor.segment_tree` (see also :ref:`segment tree<morph-segment_tree>`).
-The segment tree is the representation used to construct the morphology of a cell. A segment is
-a tapered cone with a tag; the tag can be used to classify the type of the segment (for example
-soma, dendrite etc). To create a segment tree representing our single-cylinder cell, we need to add
-one segment to our ``tree`` object. We use the :meth:`arbor.segment_tree.append` method, which takes
-4 arguments: the parent segment which does not exist for the first segment, so we use :class:`arbor.mnpos`;
-the proximal :class:`arbor.mpoint` (location and radius) of the segment; the distal :class:`arbor.mpoint`
-of the segment; and the tag.
-
-Step **(2)** creates a dictionary of labels (:class:`arbor.label_dict<arbor.label_dict>`). Labels give
-names to :term:`regions<region>` and :term:`location<locset>` described using a DSL
-based on s-expressions. Labels from the dictionary can then be used to facilitate adding synapses,
-dynamics, stimuli and probes to the cell. We add two labels:
-
-* ``soma`` defines a *region* with ``(tag  1)``. Note that this corresponds to the
-  ``tag`` parameter that was used to define the single segment in step (1).
-* ``midpoint`` defines a *location* at ``(location 0 0.5)``, which is the mid point ``0.5``
-  of branch ``0``, which corresponds to the midpoint of the soma on the morphology defined in step (1).
-
-Step **(3)** constructs a :class:`arbor.decor` that describes the distribution and placement
-of dynamics and properties on a cell.  The cell's default properties can be modified, and we can use
-:meth:`arbor.decor.paint` and :meth:`arbor.decor.place` to further customise it in the
-following way:
-
-* :meth:`arbor.decor.set_property` is used to set some default properties on the entire cell.
-  In the above example we set the initial membrane potential to -40 mV.
-* :meth:`arbor.decor.paint` is used to set properties or add dynamics to a region of the cell.
-  We call this method 'painting' to convey that we are working on sections of a cell, as opposed to
-  precise locations: for example, we might want to *paint* a density ion channel on all dendrites,
-  and then *place* a synapse at the tip of the axon. In the above example we paint
-  HH dynamics on the region we previously named ``"soma"`` in our label dictionary.
+   :lines: 9-11
+
+This constructs a :class:`arbor.segment_tree` (see also :ref:`segment
+tree<morph-segment_tree>`) containing a single segment. You can skip the rest of
+this paragraph on first reading, it explains the details of constructing a
+morphology from scratch. The segment tree is the representation used to
+construct the morphology of a cell. A segment is a tapered cone with a tag; the
+tag can be used to classify the type of the segment (for example soma, dendrite
+etc). To create a segment tree representing our single-cylinder cell, we need to
+add one segment to our ``tree`` object. We use the
+:meth:`arbor.segment_tree.append` method, which takes 4 arguments: the parent
+segment which does not exist for the first segment, so we use
+:class:`arbor.mnpos`; the proximal :class:`arbor.mpoint` (location and radius)
+of the segment; the distal :class:`arbor.mpoint` of the segment; and the tag.
+
+.. literalinclude:: ../../python/example/single_cell_model.py
+   :language: python
+   :lines: 13-14
+
+Next, we create a dictionary of labels
+(:class:`arbor.label_dict<arbor.label_dict>`) to assign properties to. This is a
+handy tool to connect part of your morphology to semantically meaningful names.
+Labels give names to :term:`regions<region>` and :term:`location<locset>`
+described using a DSL based on s-expressions. Labels from the dictionary can
+then be used to facilitate adding synapses, dynamics, stimuli and probes to the
+cell. We add two labels:
+
+* ``soma`` defines a *region* with ``(tag 1)``. Note that this corresponds to
+  the ``tag`` parameter that was used to define the single segment in step (1).
+* ``midpoint`` defines a *location* at ``(location 0 0.5)``, which is the mid
+  point ``0.5`` of branch ``0``, which corresponds to the midpoint of the soma
+  on the morphology defined in step (1).
+
+.. literalinclude:: ../../python/example/single_cell_model.py
+   :language: python
+   :lines: 16-23
+
+The final piece constructs a :class:`arbor.decor` describing the distribution
+and placement of dynamics and properties on a cell. The cell's default
+properties can be modified, and we can use :meth:`arbor.decor.paint` and
+:meth:`arbor.decor.place` to further customise it in the following way:
+
+* :meth:`arbor.decor.set_property` is used to set some default properties on the
+  entire cell. In the above example we set the initial membrane potential
+  to -40 mV.
+* :meth:`arbor.decor.paint` is used to set properties or add dynamics to a
+  region of the cell. We call this method 'painting' to convey that we are
+  working on sections of a cell, as opposed to precise locations: for example,
+  we might want to *paint* a density ion channel on all dendrites, and then
+  *place* a synapse at the tip of the axon. In the above example we paint HH
+  dynamics on the region we previously named ``"soma"`` in our label dictionary.
 * :meth:`arbor.decor.place` is used to add objects on a precise
   :class:`arbor.location` on a cell. Examples of objects that are *placed* are synapses,
   threshold detectors, current stimuli, and probes. In the above example we place a current stimulus
@@ -78,15 +119,23 @@ following way:
   on the location we previously labelled ``"midpoint"``. We also place a :class:`arbor.threshold_detector`
   with a threshold of -10 mV on the same location.
 
-Step **(4)** constructs the :class:`arbor.cable_cell` from the segment tree and dictionary of labelled regions and locations.
+.. literalinclude:: ../../python/example/single_cell_model.py
+   :language: python
+   :lines: 25-26
+
+The three ingredients -- morphology, labels, and decor -- are joined into a cable cell.
 
 The single cell model
 ---------------------
 
-Once the cell description has been built, the next step is to build and run the simulation.
-Arbor provides an interface for constructing single cell models with the
-:class:`arbor.single_cell_model` helper that creates a model from a cell description, with
-an interface for recording outputs and running the simulation.
+Once the cell description has been built, the next step is to build and run the
+simulation. Arbor provides an interface for constructing single cell models with
+the :class:`arbor.single_cell_model` helper that creates a model from a cell
+description, with an interface for recording outputs and running the simulation.
+
+.. literalinclude:: ../../python/example/single_cell_model.py
+   :language: python
+   :lines: 28-29
 
 The single cell model has 4 main functions:
 
@@ -95,38 +144,44 @@ The single cell model has 4 main functions:
 3. It **runs** the simulation.
 4. It collects **spikes** from threshold detectors and voltage **traces** from registered probes.
 
-Right now, we'll only set a probe and run the simulation.
+Right now, we'll only set a probe. The model is complete without, but to see
+the results, we need to extract some data.
 
 .. literalinclude:: ../../python/example/single_cell_model.py
    :language: python
-   :lines: 25-32
+   :lines: 31-32
 
-Step **(5)** instantiates the :class:`arbor.single_cell_model`
-with our single-compartment cell.
+Note, that the probe is given a location from the label dictionary ``midpoint``,
+the value to record ``voltage``, the sampling frequency, and finally a tag by
+which we can reference it later, here ``Um``.
 
-Step **(6)** adds a :meth:`arbor.single_cell_model.probe`
-used to record variables from the model. Three pieces of information are
-provided: the type of quantity we want probed (voltage), the location where we want to
-probe ('"midpoint"'), and the frequency at which we want to sample (10 kHz).
+Now, we can start the actual simulation:
 
-Step **(7)** runs the actual simulation for a duration of 30 ms.
+.. literalinclude:: ../../python/example/single_cell_model.py
+   :language: python
+   :lines: 34-35
 
 The results
 -----------
 
-Our cell and model have been defined and we have run our simulation. Now we can look at what
-the threshold detector and a voltage probes from our model have produced.
+Our cell and model have been defined and we have run our simulation. Now we can
+look at the resulting spikes and membrane potential.
+
+.. literalinclude:: ../../python/example/single_cell_model.py
+   :language: python
+   :lines: 37-40
+
+To print the spike times, we use :meth:`arbor.single_cell_model.spikes`. A
+single spike should be generated at around the same time the stimulus we
+provided in step (3) gets activated (10ms).
+
+And, finally, we plot the membrane potential
 
 .. literalinclude:: ../../python/example/single_cell_model.py
    :language: python
-   :lines: 34-51
+   :lines: 42-48
 
-Step **(8)** accesses :meth:`arbor.single_cell_model.spikes`
-to print the spike times. A single spike should be generated at around the same time the stimulus
-we provided in step (3) gets activated (10ms).
 
-Step **(9)** plots the measured potentials during the runtime of the simulation. The sampled quantities
-can be accessed through :meth:`arbor.single_cell_model.traces`.
 We should be seeing something like this:
 
 .. figure:: single_cell_model_result.svg
@@ -138,4 +193,6 @@ We should be seeing something like this:
 The full code
 -------------
 
-You can find the source code for this example in full at ``python/examples/single_cell_model.py``.
+You can find the source code for this example in full at
+``python/examples/single_cell_model.py`` which comes in at roughly 10 lines of
+Python to define and simulate a cell from scratch.
diff --git a/doc/tutorial/single_cell_recipe.rst b/doc/tutorial/single_cell_recipe.rst
index 40d3233578e6bb5da4c6a07bfeadb58443608de4..d5046d850a3582a382a0ec13f20d46d6dbce1122 100644
--- a/doc/tutorial/single_cell_recipe.rst
+++ b/doc/tutorial/single_cell_recipe.rst
@@ -6,10 +6,10 @@ A simple single cell recipe
 This example builds the same single cell model as :ref:`tutorialsinglecell`,
 except using a :class:`arbor.recipe` and :class:`arbor.simulation` instead of a :class:`arbor.single_cell_model`.
 
-Recipes are an important concept in Arbor. They represent the most versatile tool
-for building a complex network of cells. We will go though this example of a model
-of a single cell, before using the recipe to represent more complex networks in
-subsequent examples.
+Recipes are an important concept in Arbor. They represent the most versatile
+tool for building a complex network of cells. We will go though this example of
+a model of a single cell, before using the recipe to represent more complex
+networks in subsequent examples.
 
 .. Note::
 
@@ -32,18 +32,20 @@ where construction of the cell is explained in detail.
 The recipe
 ----------
 
-In the :ref:`original example <tutorialsinglecell-cell>`, the :class:`arbor.single_cell_model` creates
-a :class:`arbor.recipe` under the hood, and abstracts away a few details that you may want control over
-in more complex simulations. Let's go into those abstractions and create an analogous :class:`arbor.recipe`
-manually.
+In the :ref:`original example <tutorialsinglecell-cell>`, the
+:class:`arbor.single_cell_model` creates a :class:`arbor.recipe` under the hood,
+and abstracts away a few details that you may want control over in more complex
+simulations. Let's go into those abstractions and create an analogous
+:class:`arbor.recipe` manually.
 
-Creating a recipe starts with creating a class that inherits from :class:`arbor.recipe`. There are a number of
-methods that *must* be overridden, and a number than *can optionally* be overridden, as explained in the
-:class:`arbor.recipe` documentation. Beyond this, it is up to you, the user, to structure your code as you
-find convenient.
+Creating a recipe starts with creating a class that inherits from
+:class:`arbor.recipe`. There are a number of methods that *must* be overridden,
+and a number than *can optionally* be overridden, as explained in the
+:class:`arbor.recipe` documentation. Beyond this, it is up to you, the user, to
+structure your code as you find convenient.
 
-One of the methods that must be overridden is :meth:`arbor.recipe.num_cells`. It returns `0` by default and 
-models without cells are quite boring!
+One of the methods that must be overridden is :meth:`arbor.recipe.num_cells`. It
+returns `0` by default and models without cells are quite boring!
 
 .. literalinclude:: ../../python/example/single_cell_recipe.py
    :language: python
@@ -51,78 +53,87 @@ models without cells are quite boring!
 
 Step **(4)** describes the recipe that will reflect our single cell model.
 
-Step **(4.1)** defines the class constructor. It can take any shape you need, but it
-is important to call base class' constructor. If the overridden methods of the class
-need to return an object, it may be a good idea to have the returned object be a
-member of the class. With this constructor, we could easily change the cell and probes
-of the model, should we want to do so. Here we initialize the cell properties to match
-Neuron's defaults using Arbor's built-in :meth:`arbor.neuron_cable_properties` and
-extend with Arbor's own :meth:`arbor.default_catalogue`.
+Step **(4.1)** defines the class constructor. It can take any shape you need,
+but it is important to call base class' constructor as the first action. If the
+overridden methods of the class need to return an object, it may be a good idea
+to have the returned object be a member of the class. With this constructor, we
+could easily change the cell and probes of the model, should we want to do so.
+Here we initialize the cell properties to match Neuron's defaults using Arbor's
+built-in :meth:`arbor.neuron_cable_properties` and extend with Arbor's own
+:meth:`arbor.default_catalogue`.
 
-Step **(4.2)** defines that this model has one cell.
+Step **(4.2)** states that this model has one cell.
 
-Step **(4.3)** returns :class:`arbor.cell_kind.cable`, the :class:`arbor.cell_kind`
-associated with the cable cell defined above. If you mix multiple cell kinds and
-descriptions in one recipe, make sure a particular ``gid`` returns matching cell kinds
-and descriptions.
+Step **(4.3)** returns :class:`arbor.cell_kind.cable`, the
+:class:`arbor.cell_kind` associated with the cable cell defined above. If you
+mix multiple cell kinds and descriptions in one recipe, make sure a particular
+``gid`` returns matching cell kinds and descriptions.
 
-Step **(4.4)** returns the cell description defined earlier. If we
-were modelling multiple cells of different kinds, we would need to make sure that the
+Step **(4.4)** returns the cell description defined earlier. If we were
+modelling multiple cells of different kinds, we would need to make sure that the
 cell returned by :meth:`arbor.recipe.cell_description` has the same cell kind as
 returned by :meth:`arbor.recipe.cell_kind` for every :gen:`gid`.
 
-Step **(4.5)** returns the same probe as in the ``single_cell_model``: a single voltage probe located at "midpoint".
+Step **(4.5)** returns the same probe as in the ``single_cell_model``: a single
+voltage probe located at "midpoint".
 
-Step **(4.6)** returns the properties that will be applied to all cells of that kind in the model.
+Step **(4.6)** returns the properties that will be applied to all cells of that
+kind in the model.
 
-More methods can be overridden if your model requires that, see :class:`arbor.recipe` for options.
+More methods may be overridden if your model requires that, see
+:class:`arbor.recipe` for options.
 
 Now we instantiate the recipe
 
 .. literalinclude:: ../../python/example/single_cell_recipe.py
    :language: python
-   :lines: 64-67
+   :lines: 60-61
 
 The simulation
 --------------
 
-:class:`arbor.single_cell_model` does not only take care of the recipe, it also takes
-care of defining how the simulation will be run. When you create and use your own
-recipe, you can to do this manually, in the form of defining a execution context
-and a domain decomposition. Fortunately, the default constructors of
-:class:`arbor.context` and :class:`arbor.partition_load_balance` are sufficient for
-this model, and is what :class:`arbor.single_cell_model` does under the hood!
-In addition, if all you need is the default context and domain decomposition, they can be
-left out and the :class:`arbor.simulation` object can be contructed from just the recipe.
+:class:`arbor.single_cell_model` does not only take care of the recipe, it also
+takes care of defining how the simulation will be run. When you create and use
+your own recipe, you can to do this manually, in the form of defining a
+execution context and a domain decomposition. Fortunately, the default
+constructors of :class:`arbor.context` and :class:`arbor.partition_load_balance`
+are sufficient for this model, and is what :class:`arbor.single_cell_model` does
+under the hood! In addition, if all you need is the default context and domain
+decomposition, they can be left out and the :class:`arbor.simulation` object can
+be contructed from just the recipe.
 
 The details of manual hardware configuration will be left for another tutorial.
 
 .. literalinclude:: ../../python/example/single_cell_recipe.py
    :language: python
-   :lines: 68-79
+   :lines: 63-73
 
 Step **(6)** instantiates the simulation.
 
 Step **(7)** sets up the probe added in step 5. In the
-:class:`arbor.single_cell_model` version of this example, the probe frequency and
-simulation duration are the same. Note that the frequency is set with a :class:`arbor.regular_schedule`,
-which takes a time and not a frequency. Also note that spike recording must be
-switched on. For :ref:`extraction of the probe traces <pycablecell-probesample>` later on, we store a :term:`handle`.
+:class:`arbor.single_cell_model` version of this example, the probe frequency
+and simulation duration are the same. Note that the frequency is set with a
+:class:`arbor.regular_schedule`, which takes a time and not a frequency. Also
+note that spike recording must be switched on. For :ref:`extraction of the probe
+traces <pycablecell-probesample>` later on, we store a :term:`handle`. Then, we
+start the simulation.
 
 The results
-----------------------------------------------------
+-----------
 
 Apart from creating :class:`arbor.recipe` ourselves, we have changed nothing
-about this simulation compared to :ref:`the original tutorial <tutorialsinglecell>`.
-If we create the same analysis of the results we therefore expect the same results.
+about this simulation compared to :ref:`the original tutorial
+<tutorialsinglecell>`. If we create the same analysis of the results we
+therefore expect the same results.
 
 .. literalinclude:: ../../python/example/single_cell_recipe.py
    :language: python
-   :lines: 80-
+   :lines: 75-
 
 Step **(8)** plots the measured potentials during the runtime of the simulation.
-Retrieving the sampled quantities is a little different, these have to be accessed
-through the simulation object: :meth:`arbor.simulation.spikes` and :meth:`arbor.simulation.samples`.
+Retrieving the sampled quantities is a little different, these have to be
+accessed through the simulation object: :meth:`arbor.simulation.spikes` and
+:meth:`arbor.simulation.samples`.
 
 We should be seeing something like this:
 
diff --git a/example/bench/bench.cpp b/example/bench/bench.cpp
index 1057ff74f90053e22901c9e50044a74b014db927..0b4367238d775d289c0368295f32c6c7bde344cb 100644
--- a/example/bench/bench.cpp
+++ b/example/bench/bench.cpp
@@ -30,6 +30,8 @@
 #include <arborenv/with_mpi.hpp>
 #endif
 
+namespace U = arb::units;
+
 struct bench_params {
     struct cell_params {
         double spike_freq_hz = 10;   // Frequency in hz that cell will generate (poisson) spikes.
@@ -85,14 +87,12 @@ public:
     }
 
     arb::util::unique_any get_cell_description(arb::cell_gid_type gid) const override {
-        std::mt19937_64 rng(gid);
-
         // The time_sequence of the cell produces the series of time points at
         // which it will spike. We use a poisson_schedule with a random sequence
         // seeded with the gid. In this way, a cell's random stream depends only
         // on its gid, and will hence give reproducable results when run with
         // different MPI ranks and threads.
-        auto sched = arb::poisson_schedule(1e-3*params_.cell.spike_freq_hz, rng);
+        auto sched = arb::poisson_schedule(params_.cell.spike_freq_hz*arb::units::Hz, gid);
 
         return arb::benchmark_cell("src", "tgt", sched, params_.cell.realtime_ratio);
     }
@@ -120,7 +120,7 @@ public:
             // Draw random source and adjust to avoid self-connections if neccesary.
             arb::cell_gid_type src = dist(rng);
             if (src>=gid) ++src;
-            arb::cell_connection con({src, "src"}, {"tgt"}, 1.f, params_.network.min_delay);
+            arb::cell_connection con({src, "src"}, {"tgt"}, 1.f, params_.network.min_delay*U::ms);
             cons.push_back(con);
         }
 
@@ -169,7 +169,7 @@ int main(int argc, char** argv) {
         meters.checkpoint("model-build", context);
 
         // Run the simulation for 100 ms, with time steps of 0.01 ms.
-        sim.run(params.duration, 0.01);
+        sim.run(params.duration*arb::units::ms, 0.01*arb::units::ms);
         meters.checkpoint("model-run", context);
 
         // write meters
diff --git a/example/brunel/brunel.cpp b/example/brunel/brunel.cpp
index b68e8e3322bd702f3305190a97792a6d22c411d5..bfcfca9d0bb6d3c95c6ff6da00c2d5e592bac251 100644
--- a/example/brunel/brunel.cpp
+++ b/example/brunel/brunel.cpp
@@ -3,7 +3,6 @@
 #include <fstream>
 #include <iomanip>
 #include <iostream>
-#include <memory>
 #include <optional>
 #include <set>
 #include <vector>
@@ -129,20 +128,17 @@ public:
 
     util::unique_any get_cell_description(cell_gid_type gid) const override {
         auto cell = lif_cell("src", "tgt");
-        cell.tau_m = 10;
-        cell.V_th = 10;
-        cell.C_m = 20;
-        cell.E_L = 0;
-        cell.V_m = 0;
-        cell.t_ref = 2;
+        cell.tau_m = 10*U::ms;
+        cell.V_th = 10*U::mV;
+        cell.C_m = 20*U::pF;
+        cell.E_L = 0*U::mV;
+        cell.V_m = 0*U::mV;
+        cell.t_ref = 2*U::ms;
         return cell;
     }
 
     std::vector<event_generator> event_generators(cell_gid_type gid) const override {
-        std::mt19937_64 G;
-        G.seed(gid + seed_);
-        time_type t0 = 0;
-        return {poisson_generator({"tgt"}, weight_ext_, t0, lambda_, G)};
+        return {poisson_generator({"tgt"}, weight_ext_, 0*arb::units::ms, lambda_*arb::units::kHz, gid + seed_)};
     }
 
 private:
@@ -263,7 +259,7 @@ int main(int argc, char** argv) {
         meters.checkpoint("model-init", context);
 
         // Run simulation.
-        sim.run(options.tfinal, options.dt);
+        sim.run(options.tfinal*arb::units::ms, options.dt*arb::units::ms);
 
         meters.checkpoint("model-simulate", context);
 
@@ -324,7 +320,7 @@ void add_subset(cell_gid_type gid,
     while(m) {
         cell_gid_type val = dis(gen);
         if (!seen.count(val)) {
-            conns.push_back({{val, src}, {tgt}, weight, delay});
+            conns.push_back({{val, src}, {tgt}, weight, delay*U::ms});
             seen.insert(val);
             m--;
         }
diff --git a/example/busyring/ring.cpp b/example/busyring/ring.cpp
index a955a0a34a37e66f8ccbe2a800153154279e957f..1d53fceb387e01e5937eae1f80fb6b5bd942c940 100644
--- a/example/busyring/ring.cpp
+++ b/example/busyring/ring.cpp
@@ -1,11 +1,8 @@
 #include <fstream>
 #include <iomanip>
 #include <iostream>
-#include <sstream>
 #include <algorithm>
 #include <array>
-#include <cstring>
-#include <functional>
 
 #include <nlohmann/json.hpp>
 
@@ -41,6 +38,7 @@ using arb::time_type;
 using arb::cable_probe_membrane_voltage;
 
 using namespace arborio::literals;
+namespace U = arb::units;
 
 // Writes voltage trace as a json file.
 void write_trace_json(std::string fname, const arb::trace_data<double>& trace);
@@ -90,7 +88,7 @@ public:
         const auto group_start = s*group;
         const auto group_end = std::min(group_start+s, num_cells_);
         cell_gid_type src = gid==group_start? group_end-1: gid-1;
-        cons.push_back(arb::cell_connection({src, "d"}, {"p"}, event_weight_, min_delay_));
+        cons.push_back(arb::cell_connection({src, "d"}, {"p"}, event_weight_, min_delay_*U::ms));
 
         // Used to pick source cell for a connection.
         std::uniform_int_distribution<cell_gid_type> dist(0, num_cells_-2);
@@ -104,7 +102,7 @@ public:
             if (src==gid) ++src;
             const float delay = min_delay_+delay_dist(src_gen);
             cons.push_back(
-                arb::cell_connection({src, "d"}, {"p"}, 0.f, delay));
+                arb::cell_connection({src, "d"}, {"p"}, 0.f, delay*U::ms));
         }
         return cons;
     }
@@ -113,7 +111,7 @@ public:
     // This generates a single event that will kick start the spiking on the sub-ring.
     std::vector<arb::event_generator> event_generators(cell_gid_type gid) const override {
         if (gid%params_.ring_size == 0) {
-            return {arb::explicit_generator({"p"}, event_weight_, std::vector<float>{1.0f})};
+            return {arb::explicit_generator_from_milliseconds({"p"}, event_weight_, std::vector{1.0})};
         } else {
             return {};
         }
@@ -231,7 +229,7 @@ int main(int argc, char** argv) {
             // the cell_member type points to (cell 0, probe 0)
             auto probe_id = arb::cell_address_type{0, "Um"};
             // The schedule for sampling is 10 samples every 1 ms.
-            auto sched = arb::regular_schedule(0.1);
+            auto sched = arb::regular_schedule(0.1*U::ms);
             // Now attach the sampler at probe_id, with sampling schedule sched, writing to voltage
             sim.add_sampler(arb::one_probe(probe_id), sched, arb::make_simple_sampler(voltage));
         }
@@ -250,7 +248,7 @@ int main(int argc, char** argv) {
         // Run the simulation.
         if (root) sim.set_epoch_callback(arb::epoch_progress_bar());
         if (root) std::cout << "running simulation\n" << std::endl;
-        sim.run(params.duration, params.dt);
+        sim.run(params.duration*U::ms, params.dt*U::ms);
 
         meters.checkpoint("model-run", context);
 
@@ -394,14 +392,14 @@ arb::cable_cell complex_cell(arb::cell_gid_type gid, const cell_parameters& para
 
     arb::decor decor;
 
-    decor.paint(rall, arb::init_reversal_potential{"k",  -107.0});
-    decor.paint(rall, arb::init_reversal_potential{"na", 53.0});
+    decor.paint(rall, arb::init_reversal_potential{"k",  -107.0*U::mV});
+    decor.paint(rall, arb::init_reversal_potential{"na",   53.0*U::mV});
 
-    decor.paint(soma, arb::axial_resistivity{133.577});
-    decor.paint(soma, arb::membrane_capacitance{4.21567e-2});
+    decor.paint(soma, arb::axial_resistivity{133.577*U::Ohm*U::cm});
+    decor.paint(soma, arb::membrane_capacitance{4.21567e-2*U::F/U::m2});
 
-    decor.paint(dend, arb::axial_resistivity{68.355});
-    decor.paint(dend, arb::membrane_capacitance{2.11248e-2});
+    decor.paint(dend, arb::axial_resistivity{68.355*U::Ohm*U::cm});
+    decor.paint(dend, arb::membrane_capacitance{2.11248e-2*U::F/U::m2});
 
     decor.paint(soma, arb::density("pas/e=-76.4024", {{"g", 0.000119174}}));
     decor.paint(soma, arb::density("NaV",            {{"gbar", 0.0499779}}));
@@ -423,7 +421,7 @@ arb::cable_cell complex_cell(arb::cell_gid_type gid, const cell_parameters& para
         decor.place(syns, arb::synapse("expsyn"), "s");
     }
 
-    decor.place(cntr, arb::threshold_detector{-20.0}, "d");
+    decor.place(cntr, arb::threshold_detector{-20.0*U::mV}, "d");
 
     decor.set_default(arb::cv_policy_every_segment());
 
@@ -443,10 +441,10 @@ arb::cable_cell branch_cell(arb::cell_gid_type gid, const cell_parameters& param
 
     decor.paint(soma, arb::density{"hh"});
     decor.paint(dnds, arb::density{"pas"});
-    decor.set_default(arb::axial_resistivity{100}); // [Ω·cm]
+    decor.set_default(arb::axial_resistivity{100*U::Ohm*U::cm}); // [Ω·cm]
 
     // Add spike threshold detector at the soma.
-    decor.place(arb::mlocation{0,0}, arb::threshold_detector{10}, "d");
+    decor.place(arb::mlocation{0,0}, arb::threshold_detector{10*U::mV}, "d");
 
     // Add a synapse to proximal end of first dendrite.
     decor.place(arb::mlocation{1, 0}, arb::synapse{"expsyn"}, "p");
diff --git a/example/diffusion/diffusion.cpp b/example/diffusion/diffusion.cpp
index ba2405b074ace26ce07b9c997e03e34c7e20aa33..0d2952baca03eb0685496e79a2a10aaff0c3cb80 100644
--- a/example/diffusion/diffusion.cpp
+++ b/example/diffusion/diffusion.cpp
@@ -21,18 +21,20 @@
 using namespace arborio::literals;
 using namespace arb;
 
+namespace U = arb::units;
+
 struct linear: public recipe {
      linear(double ext, double dx, double Xi, double beta): l{ext}, d{dx}, i{Xi}, b{beta} {
         gprop.default_parameters = neuron_parameter_defaults;
         gprop.default_parameters.discretization = cv_policy_max_extent{d};
-        gprop.add_ion("bla", 1, 23, 42, 0, b);
+        gprop.add_ion("bla", 1, 23*U::mM, 42*U::mM, 0*U::mV, b*U::m2/U::s);
     }
 
     cell_size_type num_cells()                                   const override { return 1; }
     cell_kind get_cell_kind(cell_gid_type)                       const override { return cell_kind::cable; }
     std::any get_global_properties(cell_kind)                    const override { return gprop; }
     std::vector<probe_info> get_probes(cell_gid_type)            const override { return {{cable_probe_ion_diff_concentration_cell{"na"}, "nad"}}; }
-    std::vector<event_generator> event_generators(cell_gid_type) const override { return {explicit_generator({"Zap"}, 0.005, std::vector<float>{0.f})}; }
+    std::vector<event_generator> event_generators(cell_gid_type) const override { return {explicit_generator_from_milliseconds({"Zap"}, 0.005, std::vector{0.})}; }
     util::unique_any get_cell_description(cell_gid_type)         const override {
         // Stick morphology
         // -----|-----
@@ -41,9 +43,9 @@ struct linear: public recipe {
         tree.append(0, { -l, 0, 0, 3}, {l, 0, 0, 3}, 2);
         // Setup
         decor decor;
-        decor.set_default(init_int_concentration{"na", i});
-        decor.set_default(ion_diffusivity{"na", b});
-        decor.paint("(tag 1)"_reg, ion_diffusivity{"na", b});
+        decor.set_default(init_int_concentration{"na", i*U::mM});
+        decor.set_default(ion_diffusivity{"na", b*U::mM});
+        decor.paint("(tag 1)"_reg, ion_diffusivity{"na", b*U::mM});
         decor.place("(location 0 0.5)"_ls, synapse("inject/x=bla", {{"alpha", 200.0*l}}), "Zap");
         decor.paint("(all)"_reg, density("decay/x=bla"));
         return cable_cell({tree}, decor);
@@ -123,7 +125,7 @@ int main(int argc, char** argv) {
     auto C = make_context({1, O.gpu});
     auto R = linear{O.L, O.dx, O.Xi, O.dX};
     simulation S(R, C, partition_load_balance(R, C));
-    S.add_sampler(all_probes, regular_schedule(O.ds), sampler);
-    S.run(O.T, O.dt);
+    S.add_sampler(all_probes, regular_schedule(O.ds*U::ms), sampler);
+    S.run(O.T*U::ms, O.dt*U::ms);
     out.close();
 }
diff --git a/example/drybench/drybench.cpp b/example/drybench/drybench.cpp
index c5300d1aaab5131f4b4aab3d668c9ca0e6369e73..15e439584a7de5cf68038a56ff5e81657ee1bcec 100644
--- a/example/drybench/drybench.cpp
+++ b/example/drybench/drybench.cpp
@@ -1,10 +1,4 @@
-/*
- * A miniapp that demonstrates how to use dry_run mode
- *
- */
-
 #include <fstream>
-#include <iomanip>
 #include <iostream>
 
 #include <nlohmann/json.hpp>
@@ -30,6 +24,8 @@
 #include <sup/json_meter.hpp>
 #include <sup/json_params.hpp>
 
+namespace U = arb::units;
+
 struct bench_params {
     struct cell_params {
         double spike_freq_hz = 20;   // Frequency in hz that cell will generate (poisson) spikes.
@@ -79,7 +75,6 @@ std::ostream& operator<<(std::ostream& o, const bench_params& p);
 using arb::cell_gid_type;
 using arb::cell_lid_type;
 using arb::cell_size_type;
-using arb::cell_member_type;
 using arb::cell_kind;
 using arb::time_type;
 
@@ -100,8 +95,7 @@ public:
     }
 
     arb::util::unique_any get_cell_description(cell_gid_type gid) const override {
-        using RNG = std::mt19937_64;
-        auto gen = arb::poisson_schedule(params_.cell.spike_freq_hz/1000, RNG(gid));
+        auto gen = arb::poisson_schedule(params_.cell.spike_freq_hz*arb::units::Hz, gid);
         return arb::benchmark_cell("src", "tgt", std::move(gen), params_.cell.realtime_ratio);
     }
 
@@ -120,7 +114,7 @@ public:
         for (unsigned i=0; i<params_.network.fan_in; ++i) {
             auto src = source_distribution(src_gen);
             if (src>=gid) ++src;
-            conns.push_back(arb::cell_connection({src, "src"}, {"tgt"}, 1.f, params_.network.min_delay));
+            conns.push_back(arb::cell_connection({src, "src"}, {"tgt"}, 1.f, params_.network.min_delay*U::ms));
         }
 
         return conns;
@@ -162,7 +156,7 @@ int main(int argc, char** argv) {
         meters.checkpoint("model-init", ctx);
 
         // Run the simulation for 100 ms, with time steps of 0.025 ms.
-        sim.run(params.duration, 0.025);
+        sim.run(params.duration*arb::units::ms, 0.025*arb::units::ms);
 
         meters.checkpoint("model-run", ctx);
 
diff --git a/example/dryrun/branch_cell.hpp b/example/dryrun/branch_cell.hpp
index 6b930d5d1750a59baf91e2b852aed121b1e1b536..665e4f53ed57a4afb0a17d1f31a3202095a415b3 100644
--- a/example/dryrun/branch_cell.hpp
+++ b/example/dryrun/branch_cell.hpp
@@ -17,6 +17,8 @@
 
 using namespace arborio::literals;
 
+namespace U = arb::units;
+
 // Parameters used to generate the random cell morphologies.
 struct cell_parameters {
     cell_parameters() = default;
@@ -35,7 +37,7 @@ struct cell_parameters {
     unsigned synapses = 1;
 };
 
-cell_parameters parse_cell_parameters(nlohmann::json& json) {
+inline cell_parameters parse_cell_parameters(nlohmann::json& json) {
     cell_parameters params;
     sup::param_from_json(params.max_depth, "depth", json);
     sup::param_from_json(params.branch_probs, "branch-probs", json);
@@ -55,7 +57,7 @@ double interp(const std::array<T,2>& r, unsigned i, unsigned n) {
     return r[0] + p*(r1-r0);
 }
 
-arb::cable_cell branch_cell(arb::cell_gid_type gid, const cell_parameters& params) {
+inline arb::cable_cell branch_cell(arb::cell_gid_type gid, const cell_parameters& params) {
     arb::segment_tree tree;
 
     // Add soma.
@@ -110,12 +112,14 @@ arb::cable_cell branch_cell(arb::cell_gid_type gid, const cell_parameters& param
     labels.set("dend", tagged(dtag));
 
     auto decor = arb::decor()
-        .set_default(arb::axial_resistivity{100})                             // [Ω·cm]
-        .paint("soma"_lab, arb::density("hh"))                                // Add HH dynamics to soma.
-        .paint("dend"_lab, arb::density("pas"))                               // Leaky current everywhere else.
-        .place(arb::mlocation{0,0}, arb::threshold_detector{10}, "detector")  // Add spike threshold detector at the soma.
-        .place(arb::mlocation{0, 0.5}, arb::synapse("expsyn"), "synapse")     // Add a synapse to the mid point of the first dendrite.
-        .set_default(arb::cv_policy_every_segment());                         // Make a CV between every sample in the sample tree.
+        .set_default(arb::axial_resistivity{100*U::Ohm*U::cm})             // [Ω·cm]
+        .paint("soma"_lab, arb::density("hh"))                             // Add HH dynamics to soma.
+        .paint("dend"_lab, arb::density("pas"))                            // Leaky current everywhere else.
+        .place(arb::mlocation{0,0},
+               arb::threshold_detector{10*U::mV},
+               "detector")                                                 // Add spike threshold detector at the soma.
+        .place(arb::mlocation{0, 0.5}, arb::synapse("expsyn"), "synapse")  // Add a synapse to the mid point of the first dendrite.
+        .set_default(arb::cv_policy_every_segment());                      // Make a CV between every sample in the sample tree.
 
     // Add additional synapses that will not be connected to anything.
     for (unsigned i=1u; i<params.synapses; ++i) {
diff --git a/example/dryrun/dryrun.cpp b/example/dryrun/dryrun.cpp
index 666bc5aae999c349c23f27ec08d6d98c5fca2d09..b49b651f0727540dce76fb79abd27444cd767177 100644
--- a/example/dryrun/dryrun.cpp
+++ b/example/dryrun/dryrun.cpp
@@ -1,8 +1,3 @@
-/*
- * A miniapp that demonstrates how to use dry_run mode
- *
- */
-
 #include <any>
 #include <cassert>
 #include <fstream>
@@ -56,7 +51,6 @@ run_params read_options(int argc, char** argv);
 using arb::cell_gid_type;
 using arb::cell_lid_type;
 using arb::cell_size_type;
-using arb::cell_member_type;
 using arb::cell_kind;
 using arb::time_type;
 
@@ -103,7 +97,7 @@ public:
         auto src = source_distribution(src_gen);
         if (src>=gid) ++src;
 
-        return {arb::cell_connection({src, "detector"}, {"synapse"}, event_weight_, min_delay_)};
+        return {arb::cell_connection({src, "detector"}, {"synapse"}, event_weight_, min_delay_*U::ms)};
     }
 
     // Return an event generator on every 20th gid. This function needs to generate events
@@ -112,7 +106,7 @@ public:
     std::vector<arb::event_generator> event_generators(cell_gid_type gid) const override {
         std::vector<arb::event_generator> gens;
         if (gid%20 == 0) {
-            gens.push_back(arb::explicit_generator({"synapse"}, event_weight_, std::vector<float>{1.0f}));
+            gens.push_back(arb::explicit_generator_from_milliseconds({"synapse"}, event_weight_, std::vector{1.0}));
         }
         return gens;
     }
@@ -179,8 +173,8 @@ int main(int argc, char** argv) {
 
         // The id of the only probe on the cell: the cell_member type points to (cell 0, probe 0)
         auto probeset_id = arb::cell_address_type{0, "Um"};
-        // The schedule for sampling is 10 samples every 1 ms.
-        auto sched = arb::regular_schedule(1);
+        // The schedule for sampling every 1 ms.
+        auto sched = arb::regular_schedule(1*arb::units::ms);
         // This is where the voltage samples will be stored as (time, value) pairs
         arb::trace_vector<double> voltage;
         // Now attach the sampler at probeset_id, with sampling schedule sched, writing to voltage
@@ -198,7 +192,7 @@ int main(int argc, char** argv) {
         meters.checkpoint("model-init", ctx);
 
         // Run the simulation for 100 ms, with time steps of 0.025 ms.
-        sim.run(params.duration, 0.025);
+        sim.run(params.duration*arb::units::ms, 0.025*arb::units::ms);
 
         meters.checkpoint("model-run", ctx);
 
diff --git a/example/gap_junctions/gap_junctions.cpp b/example/gap_junctions/gap_junctions.cpp
index 75e8f12dbf1e2c2936c414ed5ec8efbb87565443..bf20ad6facd0d9a833353ea434dcd7e4117bc4fe 100644
--- a/example/gap_junctions/gap_junctions.cpp
+++ b/example/gap_junctions/gap_junctions.cpp
@@ -34,6 +34,8 @@
 
 using namespace arborio::literals;
 
+namespace U = arb::units;
+
 #ifdef ARB_MPI_ENABLED
 #include <mpi.h>
 #include <arborenv/with_mpi.hpp>
@@ -84,7 +86,7 @@ public:
         if(gid % params_.n_cells_per_cable || (int)gid - 1 < 0) {
             return{};
         }
-        return {arb::cell_connection({gid - 1, "detector"}, {"syn"}, params_.event_weight, params_.event_min_delay)};
+        return {arb::cell_connection({gid - 1, "detector"}, {"syn"}, params_.event_weight, params_.event_min_delay*U::ms)};
     }
 
     std::vector<arb::probe_info> get_probes(cell_gid_type gid) const override {
@@ -175,7 +177,7 @@ int main(int argc, char** argv) {
 
         // Set up the probe that will measure voltage in the cell.
 
-        auto sched = arb::regular_schedule(0.025);
+        auto sched = arb::regular_schedule(0.025*U::ms);
         // This is where the voltage samples will be stored as (time, value) pairs
         std::vector<arb::trace_vector<double>> voltage_traces(decomp.num_local_cells());
 
@@ -200,7 +202,7 @@ int main(int argc, char** argv) {
 
         std::cout << "running simulation" << std::endl;
         // Run the simulation for 100 ms, with time steps of 0.025 ms.
-        sim.run(params.sim_duration, 0.025);
+        sim.run(params.sim_duration*U::ms, 0.025*U::ms);
 
         meters.checkpoint("model-run", context);
 
@@ -275,15 +277,15 @@ arb::cable_cell gj_cell(cell_gid_type gid, unsigned ncell, double stim_duration)
     tree.append(0, {0,0,2*soma_rad, dend_rad}, {0,0,2*soma_rad+300, dend_rad}, 3);  // dendrite
 
     auto decor = arb::decor{}
-        .set_default(arb::axial_resistivity{100})       // [Ω·cm]
-        .set_default(arb::membrane_capacitance{0.018})  // [F/m²]
+        .set_default(arb::axial_resistivity{100*U::Ohm*U::cm})     // [Ω·cm]
+        .set_default(arb::membrane_capacitance{0.018*U::F/U::m2})  // [F/m²]
         // Paint density channels on all parts of the cell
         .paint("(all)"_reg, arb::density{"nax", {{"gbar", 0.04}, {"sh", 10}}})
         .paint("(all)"_reg, arb::density{"kdrmt", {{"gbar", 0.0001}}})
         .paint("(all)"_reg, arb::density{"kamt", {{"gbar", 0.004}}})
         .paint("(all)"_reg, arb::density{"pas/e=-65", {{"g", 1.0/12000.0}}})
         // Add a spike detector to the soma.
-        .place(arb::mlocation{0,0}, arb::threshold_detector{10}, "detector")
+        .place(arb::mlocation{0,0}, arb::threshold_detector{10*U::mV}, "detector")
         // Add two gap junction sites.
         .place(arb::mlocation{0, 1}, arb::junction{"gj"}, "local_1")
         .place(arb::mlocation{0, 0}, arb::junction{"gj"}, "local_0")
@@ -292,7 +294,7 @@ arb::cable_cell gj_cell(cell_gid_type gid, unsigned ncell, double stim_duration)
 
     // Attach a stimulus to the first cell of the first group
     if (!gid) {
-        auto stim = arb::i_clamp::box(0, stim_duration, 0.4);
+        auto stim = arb::i_clamp::box(0*U::ms, stim_duration*U::ms, 0.4*U::nA);
         decor.place(arb::mlocation{0, 0.5}, stim, "stim");
     }
 
diff --git a/example/generators/generators.cpp b/example/generators/generators.cpp
index 08adb2cf86f7929ddc29d4960cc4c6fcfabf39c1..858fe05e6bfb4c801dac3d17e8782d75136b342a 100644
--- a/example/generators/generators.cpp
+++ b/example/generators/generators.cpp
@@ -10,7 +10,6 @@
 #include <cassert>
 #include <fstream>
 #include <iomanip>
-#include <iostream>
 
 #include <nlohmann/json.hpp>
 
@@ -99,15 +98,14 @@ public:
 
         // Add excitatory generator
         gens.push_back(
-            arb::poisson_generator({"syn"},               // Target synapse index on cell `gid`
-                                   w_e,                   // Weight of events to deliver
-                                   t0,                    // Events start being delivered from this time
-                                   lambda_e,              // Expected frequency (kHz)
-                                   RNG(29562872)));       // Random number generator to use
+            arb::poisson_generator({"syn"},                  // Target synapse index on cell `gid`
+                                   w_e,                      // Weight of events to deliver
+                                   t0*arb::units::ms,        // Events start being delivered from this time
+                                   lambda_e*arb::units::kHz, // Expected frequency (kHz)
+                                   29562872));               // Random number generator to use
 
         // Add inhibitory generator
-        gens.emplace_back(
-            arb::poisson_generator({"syn"}, w_i, t0, lambda_i,  RNG(86543891)));
+        gens.emplace_back(arb::poisson_generator({"syn"}, w_i, t0*arb::units::ms, lambda_i*arb::units::kHz, 86543891));
 
         return gens;
     }
@@ -138,14 +136,14 @@ int main() {
     // The id of the only probe on the cell: the cell_member type points to (cell 0, probe 0)
     auto probeset_id = arb::cell_address_type{0, "Um"};
     // The schedule for sampling is 10 samples every 1 ms.
-    auto sched = arb::regular_schedule(0.1);
+    auto sched = arb::regular_schedule(0.1*arb::units::ms);
     // This is where the voltage samples will be stored as (time, value) pairs
     arb::trace_vector<double> voltage;
     // Now attach the sampler at probeset_id, with sampling schedule sched, writing to voltage
     sim.add_sampler(arb::one_probe(probeset_id), sched, arb::make_simple_sampler(voltage));
 
     // Run the simulation for 100 ms, with time steps of 0.01 ms.
-    sim.run(100, 0.01);
+    sim.run(100*arb::units::ms, 0.01*arb::units::ms);
 
     // Write the samples to a json file.
     write_trace_json(voltage.at(0));
diff --git a/example/lfp/lfp.cpp b/example/lfp/lfp.cpp
index 041b1862935e9eb46eddb1f4c33a1f749ad76405..b02a5a97cf10d1b837986596be2b38ce8ec1573a 100644
--- a/example/lfp/lfp.cpp
+++ b/example/lfp/lfp.cpp
@@ -19,12 +19,13 @@
 
 using std::any;
 using arb::util::any_cast;
-using arb::util::any_ptr;
 using arb::util::unique_any;
 using arb::cell_gid_type;
 
 using namespace arborio::literals;
 
+namespace U = arb::units;
+
 // Recipe represents one cable cell with one synapse, together with probes for total trans-membrane current, membrane voltage,
 // ionic current density, and synaptic conductance. A sequence of spikes are presented to the one synapse on the cell.
 
@@ -83,8 +84,8 @@ private:
         synapse_location_ = "(on-components 0.5 (tag 1))"_ls;
         auto dec = decor()
             // Use NEURON defaults for reversal potentials, ion concentrations etc., but override ra, cm.
-            .set_default(axial_resistivity{100})     // [Ω·cm]
-            .set_default(membrane_capacitance{0.01}) // [F/m²]
+            .set_default(axial_resistivity{100*U::Ohm*U::cm})     // [Ω·cm]
+            .set_default(membrane_capacitance{0.01*U::F/U::m2}) // [F/m²]
             // Twenty CVs per branch on the dendrites (tag 4).
             .set_default(cv_policy_fixed_per_branch(20, arb::reg::tagged(4)))
             // Add pas and hh mechanisms:
@@ -185,7 +186,7 @@ int main(int argc, char** argv) {
     const double dt = 0.1;        // [ms]
 
     // Weight 0.005 μS, onset at t = 0 ms, mean frequency 0.1 kHz.
-    auto events = arb::poisson_generator({"syn"}, .005, 0., 0.1, std::minstd_rand{});
+    auto events = arb::poisson_generator({"syn"}, .005, 0.*U::ms, 100*U::Hz);
     lfp_demo_recipe recipe(events);
     arb::simulation sim(recipe);
 
@@ -203,7 +204,7 @@ int main(int argc, char** argv) {
 
     lfp_sampler lfp(placed_cell, current_cables, electrodes, 3.0);
 
-    auto sample_schedule = arb::regular_schedule(sample_dt);
+    auto sample_schedule = arb::regular_schedule(sample_dt*U::ms);
     sim.add_sampler(arb::one_probe({0, "Itotal"}), sample_schedule, lfp.callback());
 
     arb::trace_vector<double, arb::mlocation> membrane_voltage;
@@ -215,7 +216,7 @@ int main(int argc, char** argv) {
     arb::trace_vector<double> synapse_g;
     sim.add_sampler(arb::one_probe({0, "expsyn-g"}), sample_schedule, make_simple_sampler(synapse_g));
 
-    sim.run(t_stop, dt);
+    sim.run(t_stop*U::ms, dt*U::ms);
 
     // Output results in JSON format suitable for plotting by plot-lfp.py script.
 
diff --git a/example/ornstein_uhlenbeck/ou.cpp b/example/ornstein_uhlenbeck/ou.cpp
index d3669e62bcdcf2c7fc7ce098d01deea505ac2aef..369a54856f60aa806090f6e1ad54fce89bfedeb0 100644
--- a/example/ornstein_uhlenbeck/ou.cpp
+++ b/example/ornstein_uhlenbeck/ou.cpp
@@ -120,10 +120,10 @@ int main(int argc, char** argv) {
     // setup sampler and add it to the simulation with regular schedule
     std::vector<arb_value_type> data;
     sampler s{data, ncvs, nsteps};
-    sim.add_sampler(arb::all_probes, arb::regular_schedule(dt), s);
+    sim.add_sampler(arb::all_probes, arb::regular_schedule(dt*arb::units::ms), s);
 
     // run the simulation
-    sim.run(nsteps*dt, dt);
+    sim.run(nsteps*dt*arb::units::ms, dt*arb::units::ms);
 
     // evaluate the mean for each time step across the ensembe of realizations
     // (each control volume is a independent realization of the Ornstein-Uhlenbeck process)
diff --git a/example/plasticity/branch_cell.hpp b/example/plasticity/branch_cell.hpp
index 527f26012c4a47dac2b285d25c66347f18054e55..23567cd93a96e00558a5530c8e9999f148c7d17c 100644
--- a/example/plasticity/branch_cell.hpp
+++ b/example/plasticity/branch_cell.hpp
@@ -118,7 +118,7 @@ arb::cable_cell branch_cell(arb::cell_gid_type gid, const cell_parameters& param
     decor.set_default(arb::axial_resistivity{100}); // [Ω·cm]
 
     // Add spike threshold detector at the soma.
-    decor.place(arb::mlocation{0,0}, arb::threshold_detector{10}, "detector");
+    decor.place(arb::mlocation{0,0}, arb::threshold_detector{10*arb::units::mV}, "detector");
 
     // Add a synapse to the mid point of the first dendrite.
     decor.place(arb::mlocation{0, 0.5}, arb::synapse("expsyn"), "primary_syn");
@@ -132,7 +132,5 @@ arb::cable_cell branch_cell(arb::cell_gid_type gid, const cell_parameters& param
     // Make a CV between every sample in the sample tree.
     decor.set_default(arb::cv_policy_every_segment());
 
-    arb::cable_cell cell(arb::morphology(tree), labels, decor);
-
-    return cell;
+    return {arb::morphology(tree), labels, decor};
 }
diff --git a/example/plasticity/plasticity.cpp b/example/plasticity/plasticity.cpp
index ad07a91bc0e01de8c2570f0fa3489947f197d7d5..90299d8a853ab02e4b38f17fd63a4173098f4d0c 100644
--- a/example/plasticity/plasticity.cpp
+++ b/example/plasticity/plasticity.cpp
@@ -13,6 +13,8 @@
 
 using namespace arborio::literals;
 
+namespace U = arb::units;
+
 // Fan out network with n members
 // - one spike source at gid 0
 // - n-1 passive, soma-only cable cells
@@ -55,17 +57,18 @@ struct recipe: public arb::recipe {
         return {};
     }
     // Connect cell `to` to the spike source
-    void add_connection(arb::cell_gid_type to) { assert(to > 0); connected[to] = {arb::cell_connection({0, src}, {syn}, weight, delay)}; }
+    void add_connection(arb::cell_gid_type to) { assert(to > 0); connected[to] = {
+        arb::cell_connection({0, src}, {syn}, weight, delay*U::ms)}; }
     // Return the cell at gid
     arb::util::unique_any get_cell_description(arb::cell_gid_type gid) const override {
         // source at gid 0
-        if (gid == 0) return arb::spike_source_cell{src, arb::regular_schedule(f_spike)};
+        if (gid == 0) return arb::spike_source_cell{src, arb::regular_schedule(f_spike*arb::units::ms)};
         // all others are receiving cable cells; single CV w/ HH
         arb::segment_tree tree; tree.append(arb::mnpos, {-r_soma, 0, 0, r_soma}, {r_soma, 0, 0, r_soma}, 1);
         auto decor = arb::decor{}
             .paint(all, arb::density("hh", {{"gl", 5}}))
             .place(center, arb::synapse("expsyn"), syn)
-            .place(center, arb::threshold_detector{-10.0}, det)
+            .place(center, arb::threshold_detector{-10.0*arb::units::mV}, det)
             .set_default(arb::cv_policy_every_segment());
         return arb::cable_cell({tree}, decor);
     }
@@ -110,11 +113,11 @@ int main(int argc, char** argv) {
     rec.add_connection(1);
     auto ctx = arb::make_context(arb::proc_allocation{8, -1});
     auto sim = arb::simulation(rec, ctx);
-    sim.add_sampler(arb::all_probes, arb::regular_schedule(dt), sampler);
+    sim.add_sampler(arb::all_probes, arb::regular_schedule(dt*arb::units::ms), sampler);
     sim.set_global_spike_callback(spike_cb);
     print_header(0, 1);
-    sim.run(1.0, dt);
+    sim.run(1.0*arb::units::ms, dt*arb::units::ms);
     rec.add_connection(2);
     print_header(1, 2);
-    sim.run(2.0, dt);
+    sim.run(2.0*arb::units::ms, dt*arb::units::ms);
 }
diff --git a/example/probe-demo/probe-demo.cpp b/example/probe-demo/probe-demo.cpp
index 19982c66c5b31674430a731c2b4539a9ebe9f3e7..ed58297ae71d987007ca144471358b21583ff928 100644
--- a/example/probe-demo/probe-demo.cpp
+++ b/example/probe-demo/probe-demo.cpp
@@ -2,7 +2,6 @@
 #include <functional>
 #include <iomanip>
 #include <iostream>
-#include <stdexcept>
 #include <string>
 #include <tuple>
 #include <utility>
@@ -119,15 +118,15 @@ struct cable_recipe: public arb::recipe {
 
         auto decor = arb::decor{}
             .paint(arb::reg::all(), arb::density("hh"))                         // HH mechanism over whole cell.
-            .place(arb::mlocation{0, 0.}, arb::i_clamp{1.}, "iclamp")           // Inject a 1 nA current indefinitely.
+            .place(arb::mlocation{0, 0.}, arb::i_clamp{1.*arb::units::nA}, "iclamp")           // Inject a 1 nA current indefinitely.
             .place(arb::mlocation{0, 0.}, arb::synapse("expsyn"), "synapse1")   // a synapse
             .place(arb::mlocation{0, 0.5}, arb::synapse("expsyn"), "synapse2"); // another synapse
         return arb::cable_cell(tree, decor);
     }
 
     virtual std::vector<arb::event_generator> event_generators(arb::cell_gid_type) const override {
-        return {arb::poisson_generator({"synapse1"}, .005, 0., 0.1, std::minstd_rand{}),
-                arb::poisson_generator({"synapse2"}, .1, 0., 0.1, std::minstd_rand{})};
+        return {arb::poisson_generator({"synapse1"}, .005, 0.*arb::units::ms, 0.1*arb::units::kHz),
+                arb::poisson_generator({"synapse2"}, .1,   0.*arb::units::ms, 0.1*arb::units::kHz)};
     }
 
 };
@@ -144,13 +143,13 @@ int main(int argc, char** argv) {
         arb::simulation sim(R);
 
         sim.add_sampler(arb::all_probes,
-                arb::regular_schedule(opt.sample_dt),
+                arb::regular_schedule(opt.sample_dt*arb::units::ms),
                 opt.scalar_probe? scalar_sampler: vector_sampler);
 
         // CSV header for sample output:
         std::cout << "t, " << (opt.scalar_probe? "x, ": "x0, x1, ") << opt.value_name << '\n';
 
-        sim.run(opt.sim_end, opt.sim_dt);
+        sim.run(opt.sim_end*arb::units::ms, opt.sim_dt*arb::units::ms);
     }
     catch (to::option_error& e) {
         to::usage_error(argv[0], "[OPTIONS]... PROBE\nTry '--help' for more information.", e.what());
diff --git a/example/remote/remote.cpp b/example/remote/remote.cpp
index bc75669b723ec0ac63554e3c73642c3281ecb629..9bbdf7c99e36b8690f1061b5118c0fe34f20c868 100644
--- a/example/remote/remote.cpp
+++ b/example/remote/remote.cpp
@@ -1,11 +1,14 @@
 #include <iomanip>
 #include <iostream>
-#include <cassert>
 
 #include <arbor/context.hpp>
 #include <arbor/lif_cell.hpp>
 #include <arbor/simulation.hpp>
 #include <arbor/version.hpp>
+#include <arbor/recipe.hpp>
+#include <arbor/units.hpp>
+
+namespace U = arb::units;
 
 #include <mpi.h>
 #include <arbor/communication/remote.hpp>
@@ -19,20 +22,20 @@ struct remote_recipe: public arb::recipe {
         arb::cell_size_type num_cells() const override { return size; }
         arb::util::unique_any get_cell_description(arb::cell_gid_type) const override {
             auto lif = arb::lif_cell("src", "tgt");
-            lif.tau_m =   2.0;
-            lif.V_th  = -10.0;
-            lif.C_m   =  20.0;
-            lif.E_L   = -23.0;
-            lif.V_m   = -23.0;
-            lif.E_R   = -23.0;
-            lif.t_ref =   0.2;
+            lif.tau_m =   2.0*U::ms;
+            lif.V_th  = -10.0*U::mV;
+            lif.C_m   =  20.0*U::pF;
+            lif.E_L   = -23.0*U::mV;
+            lif.V_m   = -23.0*U::mV;
+            lif.E_R   = -23.0*U::mV;
+            lif.t_ref =   0.2*U::ms;
             return lif;
         }
         std::vector<arb::ext_cell_connection> external_connections_on(arb::cell_gid_type) const override {
             std::vector<arb::ext_cell_connection> res;
             // Invent some fictious cells outside of Arbor's realm
             for (arb::cell_gid_type gid = 0; gid < 10; gid++) {
-                res.emplace_back(arb::cell_remote_label_type{gid}, arb::cell_local_label_type{"tgt"}, weight, delay);
+                res.emplace_back(arb::cell_remote_label_type{gid}, arb::cell_local_label_type{"tgt"}, weight, delay*U::ms);
             }
             return res;
         }
@@ -75,9 +78,9 @@ int main() {
         std::cerr << "[ARB]" << mpi.rank << " " << mpi.local_rank << '\n';
         std::cerr << "[ARB] Got min delay=" << mid << '\n';
         sim.add_sampler(arb::all_probes,
-                        arb::regular_schedule(0.05),
+                        arb::regular_schedule(0.05*arb::units::ms),
                         sampler);
-        sim.run(T, dt);
+        sim.run(T*arb::units::ms, dt*arb::units::ms);
         std::cout << std::fixed << std::setprecision(4);
         std::cerr << "[ARB] Trace\n";
         for (const auto& [t, v]: trace) std::cout << " " << t << " " << v << '\n';
diff --git a/example/ring/branch_cell.hpp b/example/ring/branch_cell.hpp
index 61740a99290f08445db1cf86caedc233dfdb2d7c..92aa9f61196b7cf63b96dd400704c6a527d312f7 100644
--- a/example/ring/branch_cell.hpp
+++ b/example/ring/branch_cell.hpp
@@ -17,6 +17,8 @@
 
 using namespace arborio::literals;
 
+namespace U = arb::units;
+
 // Parameters used to generate the random cell morphologies.
 struct cell_parameters {
     cell_parameters() = default;
@@ -35,7 +37,7 @@ struct cell_parameters {
     unsigned synapses = 1;
 };
 
-cell_parameters parse_cell_parameters(nlohmann::json& json) {
+inline cell_parameters parse_cell_parameters(nlohmann::json& json) {
     cell_parameters params;
     sup::param_from_json(params.max_depth, "depth", json);
     sup::param_from_json(params.branch_probs, "branch-probs", json);
@@ -55,7 +57,7 @@ double interp(const std::array<T,2>& r, unsigned i, unsigned n) {
     return r[0] + p*(r1-r0);
 }
 
-arb::cable_cell branch_cell(arb::cell_gid_type gid, const cell_parameters& params) {
+inline arb::cable_cell branch_cell(arb::cell_gid_type gid, const cell_parameters& params) {
     arb::segment_tree tree;
 
     // Add soma.
@@ -113,8 +115,8 @@ arb::cable_cell branch_cell(arb::cell_gid_type gid, const cell_parameters& param
     auto decor = arb::decor{}
         .paint("soma"_lab, arb::density("hh"))
         .paint("dend"_lab, arb::density("pas"))
-        .set_default(arb::axial_resistivity{100}) // [Ω·cm]
-        .place(arb::mlocation{0,0}, arb::threshold_detector{10}, "detector")   // Add spike threshold detector at the soma.
+        .set_default(arb::axial_resistivity{100*U::Ohm*U::cm}) // [Ω·cm]
+        .place(arb::mlocation{0,0}, arb::threshold_detector{10*U::mV}, "detector")   // Add spike threshold detector at the soma.
         .place(arb::mlocation{0, 0.5}, arb::synapse("expsyn"), "primary_syn"); // Add a synapse to the mid point of the first dendrite.
     // Add additional synapses that will not be connected to anything.
     if (params.synapses > 1) {
diff --git a/example/ring/ring.cpp b/example/ring/ring.cpp
index 22d201f955da4a6f39b1a1e7c79bb1ae449d593e..db488dfcd4b6bc7585948a4f8dda613b3c71883e 100644
--- a/example/ring/ring.cpp
+++ b/example/ring/ring.cpp
@@ -1,10 +1,4 @@
-/*
- * A miniapp that demonstrates how to make a ring model
- *
- */
-
 #include <any>
-#include <cassert>
 #include <fstream>
 #include <iomanip>
 #include <iostream>
@@ -90,7 +84,7 @@ public:
     std::vector<arb::cell_connection> connections_on(cell_gid_type gid) const override {
         std::vector<arb::cell_connection> cons;
         cell_gid_type src = gid? gid-1: num_cells_-1;
-        cons.push_back(arb::cell_connection({src, "detector"}, {"primary_syn"}, event_weight_, min_delay_));
+        cons.push_back(arb::cell_connection({src, "detector"}, {"primary_syn"}, event_weight_, min_delay_*U::ms));
         return cons;
     }
 
@@ -99,7 +93,7 @@ public:
     std::vector<arb::event_generator> event_generators(cell_gid_type gid) const override {
         std::vector<arb::event_generator> gens;
         if (!gid) {
-            gens.push_back(arb::explicit_generator({"primary_syn"}, event_weight_, std::vector<float>{1.0f}));
+            gens.push_back(arb::explicit_generator_from_milliseconds({"primary_syn"}, event_weight_, std::vector{1.0}));
         }
         return gens;
     }
@@ -168,8 +162,8 @@ int main(int argc, char** argv) {
 
         // The id of the only probe on the cell: the cell_member type points to (cell 0, probe 0)
         auto probeset_id = arb::cell_address_type{0, "Um"};
-        // The schedule for sampling is 10 samples every 1 ms.
-        auto sched = arb::regular_schedule(1);
+        // The schedule for sampling every 1 ms.
+        auto sched = arb::regular_schedule(1*arb::units::ms);
         // This is where the voltage samples will be stored as (time, value) pairs
         arb::trace_vector<double> voltage;
         // Now attach the sampler at probeset_id, with sampling schedule sched, writing to voltage
@@ -191,7 +185,7 @@ int main(int argc, char** argv) {
         }
         std::cout << "running simulation\n" << std::endl;
         // Run the simulation for 100 ms, with time steps of 0.025 ms.
-        sim.run(params.duration, 0.025);
+        sim.run(params.duration*arb::units::ms, 0.025*arb::units::ms);
 
         meters.checkpoint("model-run", context);
 
diff --git a/example/single/single.cpp b/example/single/single.cpp
index fe07488916c6dfa19ec04c0a2b26ae2f7fa3cdb7..d5590a5520f1cece44a2e1da81254f6bd348a6a0 100644
--- a/example/single/single.cpp
+++ b/example/single/single.cpp
@@ -1,6 +1,5 @@
 #include <any>
 #include <fstream>
-#include <iomanip>
 #include <iostream>
 #include <stdexcept>
 #include <string>
@@ -87,18 +86,18 @@ int main(int argc, char** argv) {
         arb::simulation sim(R);
 
         // Attach a sampler to the probe described in the recipe, sampling every 0.1 ms.
-
         arb::trace_vector<double> traces;
-        sim.add_sampler(arb::all_probes, arb::regular_schedule(0.1), arb::make_simple_sampler(traces));
-
-        // Trigger the single synapse (target is gid 0, index 0) at t = 1 ms with
-        // the given weight.
+        sim.add_sampler(arb::all_probes,
+                        arb::regular_schedule(0.1*arb::units::ms),
+                        arb::make_simple_sampler(traces));
 
+        // Trigger the single synapse (target is gid 0, index 0) at t = 1 ms
+        // with the given weight.
         arb::spike_event spike = {0, 1., opt.syn_weight};
         arb::cell_spike_events cell_spikes = {0, {spike}};
         sim.inject_events({cell_spikes});
 
-        sim.run(opt.t_end, opt.dt);
+        sim.run(opt.t_end*arb::units::ms, opt.dt*arb::units::ms);
 
         for (auto entry: traces.at(0)) {
             std::cout << entry.t << ", " << entry.v << "\n";
diff --git a/example/v_clamp/v-clamp.cpp b/example/v_clamp/v-clamp.cpp
index 15eeb65e8f82d9518b7755400f62ae45e3545103..dbbc15170cf37fd4154db5c698513c2882d14a13 100644
--- a/example/v_clamp/v-clamp.cpp
+++ b/example/v_clamp/v-clamp.cpp
@@ -1,6 +1,5 @@
 #include <any>
 #include <fstream>
-#include <iomanip>
 #include <iostream>
 #include <stdexcept>
 #include <string>
@@ -92,7 +91,7 @@ int main(int argc, char** argv) {
         // Attach a sampler to the probe described in the recipe, sampling every 0.1 ms.
 
         arb::trace_vector<double> traces;
-        sim.add_sampler(arb::all_probes, arb::regular_schedule(0.1), arb::make_simple_sampler(traces));
+        sim.add_sampler(arb::all_probes, arb::regular_schedule(0.1*arb::units::ms), arb::make_simple_sampler(traces));
 
         // Trigger the single synapse (target is gid 0, index 0) at t = 1 ms with
         // the given weight.
@@ -101,7 +100,7 @@ int main(int argc, char** argv) {
         arb::cell_spike_events cell_spikes = {0, {spike}};
         sim.inject_events({cell_spikes});
 
-        sim.run(opt.t_end, opt.dt);
+        sim.run(opt.t_end*arb::units::ms, opt.dt*arb::units::ms);
 
         for (auto entry: traces.at(0)) {
             std::cout << entry.t << ", " << entry.v << "\n";
diff --git a/ext/CMakeLists.txt b/ext/CMakeLists.txt
index 23b9c6587963264f77fd84c179828469eff3a638..009a5bc22377f370f99248ed411ac2c6fe4f96b6 100644
--- a/ext/CMakeLists.txt
+++ b/ext/CMakeLists.txt
@@ -118,3 +118,19 @@ else()
         target_link_libraries(ext-gtest INTERFACE GTest::gtest GTest::gtest_main)
     endif()
 endif()
+
+if (ARB_USE_BUNDLED_UNITS)
+    set(UNITS_ENABLE_TESTS OFF CACHE INTERNAL "")
+    set(UNITS_BUILD_STATIC_LIBRARY ON CACHE INTERNAL "")
+    set(UNITS_BUILD_SHARED_LIBRARY OFF CACHE INTERNAL "")
+    set(UNITS_BUILD_CONVERTER_APP OFF CACHE INTERNAL "")
+    set(UNITS_BUILD_WEBSERVER OFF CACHE INTERNAL "")
+    set(UNITS_INSTALL ON CACHE INTERNAL "")
+    # set(UNITS_NAMESPACE "llnl::units" CACHE INTERNAL "")
+
+    add_subdirectory("${PROJECT_SOURCE_DIR}/ext/units" "${PROJECT_BINARY_DIR}/ext/units")
+
+    mark_as_advanced(UNITS_BUILD_OBJECT_LIBRARY)
+    mark_as_advanced(UNITS_HEADER_ONLY)
+    mark_as_advanced(UNITS_NAMESPACE)
+endif()
diff --git a/ext/sup/include/sup/export.hpp b/ext/sup/include/sup/export.hpp
new file mode 100644
index 0000000000000000000000000000000000000000..a446e3daee98febf6ce277d581996d336c45bd1f
--- /dev/null
+++ b/ext/sup/include/sup/export.hpp
@@ -0,0 +1,41 @@
+#pragma once
+
+//#ifndef ARB_EXPORT_DEBUG
+//#   define ARB_EXPORT_DEBUG
+//#endif
+
+#include <arbor/util/visibility.hpp>
+
+/* library build type (ARB_SUP_STATIC_LIBRARY/ARB_SUP_SHARED_LIBRARY) */
+#define ARB_SUP_STATIC_LIBRARY
+
+#ifndef ARB_SUP_EXPORTS
+#   if defined(arbor_sup_EXPORTS)
+        /* we are building arbor-sup dynamically */
+#       ifdef ARB_EXPORT_DEBUG
+#           pragma message "we are building arbor-sup dynamically"
+#       endif
+#       define ARB_SUP_API ARB_SYMBOL_EXPORT
+#   elif defined(arbor_sup_EXPORTS_STATIC)
+        /* we are building arbor-sup statically */
+#       ifdef ARB_EXPORT_DEBUG
+#           pragma message "we are building arbor-sup statically"
+#       endif
+#       define ARB_SUP_API
+#   else
+        /* we are using the library arbor-sup */
+#       if defined(ARB_SUP_SHARED_LIBRARY)
+            /* we are importing arbor-sup dynamically */
+#           ifdef ARB_EXPORT_DEBUG
+#              pragma message "we are importing arbor-sup dynamically"
+#           endif
+#           define ARB_SUP_API ARB_SYMBOL_IMPORT
+#       else
+            /* we are importing arbor-sup statically */
+#           ifdef ARB_EXPORT_DEBUG
+#               pragma message "we are importing arbor-sup statically"
+#           endif
+#           define ARB_SUP_API
+#       endif
+#   endif
+#endif
diff --git a/ext/units b/ext/units
new file mode 160000
index 0000000000000000000000000000000000000000..e7aff9f8e4cc1ce19b1ea7e7095036e64123601f
--- /dev/null
+++ b/ext/units
@@ -0,0 +1 @@
+Subproject commit e7aff9f8e4cc1ce19b1ea7e7095036e64123601f
diff --git a/mechanisms/stochastic/calcium_based_synapse.mod b/mechanisms/stochastic/calcium_based_synapse.mod
index 4d45a19e1c58668c0deece261723c9ce2a1a05e3..d21656a8b406cab5b3f2646744d18963391af7bb 100644
--- a/mechanisms/stochastic/calcium_based_synapse.mod
+++ b/mechanisms/stochastic/calcium_based_synapse.mod
@@ -58,19 +58,9 @@ PARAMETER {
     tau_Ca = 20 (ms)
 }
 
-ASSIGNED {
-    one_over_tau
-    one_over_tau_Ca
-    sigma_over_sqrt_tau
-}
-
 INITIAL {
     c = 0
     rho = rho_0
-
-    one_over_tau = 1/tau
-    one_over_tau_Ca = 1/tau_Ca
-    sigma_over_sqrt_tau = sigma/(tau^0.5)
 }
 
 BREAKPOINT {
@@ -82,12 +72,16 @@ WHITE_NOISE {
 }
 
 DERIVATIVE state {
-    LOCAL hsp
-    LOCAL hsd
+    LOCAL hsp, hsd, d_rho, d_w
+
     hsp = step_right(c - theta_p)
     hsd = step_right(c - theta_d)
-    rho' = (-rho*(1-rho)*(rho_star-rho) + gamma_p*(1-rho)*hsp - gamma_d*rho*hsd)*one_over_tau + (hsp + hsd)^0.5*sigma_over_sqrt_tau*W
-    c' = -c*one_over_tau_Ca
+
+    d_rho = (rho*(rho - 1)*(rho_star - rho) + gamma_p*(1 - rho)*hsp - gamma_d*rho*hsd)/tau
+    d_w = sigma*((hsp + hsd)/tau)^0.5
+
+    rho' = d_rho + d_w*W
+    c' = -c/tau_Ca
 }
 
 NET_RECEIVE(weight) {
diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index de26283cd2d09ff40105e2056ae0b20b8f181a39..21b0bdc16fef4dd0cef28dd89b7100eb470ef7d1 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -36,11 +36,13 @@ set(pyarb_source
     mpi.cpp
     profiler.cpp
     pyarb.cpp
+    label_dict.cpp
     recipe.cpp
     schedule.cpp
     simulation.cpp
     single_cell_model.cpp
     env.cpp
+    units.cpp
 )
 
 set_property(SOURCE config.cpp PROPERTY COMPILE_DEFINITIONS ARB_BINARY="${CMAKE_INSTALL_BINDIR}"         APPEND)
@@ -117,4 +119,5 @@ endif()
 
 install(TARGETS pyarb DESTINATION ${_python_module_install_path})
 install(FILES ${CMAKE_CURRENT_SOURCE_DIR}/__init__.py DESTINATION ${_python_module_install_path})
+install(DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/stubs/arbor/ DESTINATION ${_python_module_install_path})
 install(FILES ${PROJECT_SOURCE_DIR}/VERSION ${PROJECT_SOURCE_DIR}/README.md ${PROJECT_SOURCE_DIR}/LICENSE DESTINATION ${_python_module_install_path})
diff --git a/python/cable_cell_io.cpp b/python/cable_cell_io.cpp
index 61d8d9f0ed9466edb1c34064bba1116f529f9168..e3cff7aa8004e8c80d4b164cf33ab6bd67cfc702 100644
--- a/python/cable_cell_io.cpp
+++ b/python/cable_cell_io.cpp
@@ -3,8 +3,6 @@
 #include <pybind11/iostream.h>
 
 #include <fstream>
-#include <iostream>
-#include <iomanip>
 
 #include <arbor/cable_cell.hpp>
 #include <arbor/util/any_visitor.hpp>
@@ -13,8 +11,7 @@
 
 #include "error.hpp"
 #include "util.hpp"
-#include "strprintf.hpp"
-#include "proxy.hpp"
+#include "label_dict.hpp"
 
 namespace pyarb {
 
@@ -53,6 +50,9 @@ void write_component(const arborio::cable_cell_component& component, py::object
 }
 
 void register_cable_loader(pybind11::module& m) {
+    pybind11::class_<arborio::meta_data> component_meta_data(m, "component_meta_data");
+    pybind11::class_<arborio::cable_cell_component> cable_component(m, "cable_component");
+
     m.def("load_component",
           &load_component,
           pybind11::arg("filename_or_descriptor"),
@@ -75,9 +75,7 @@ void register_cable_loader(pybind11::module& m) {
           "Write decor to file.");
 
     m.def("write_component",
-          [](const arb::label_dict& d, py::object fn) {
-            return write_component<arb::label_dict>(d, fn);
-          },
+          [](const label_dict_proxy& d, py::object fn) { return write_component<arb::label_dict>(d.dict, fn); },
           pybind11::arg("object"),
           pybind11::arg("filename_or_descriptor"),
           "Write label_dict to file.");
@@ -99,12 +97,10 @@ void register_cable_loader(pybind11::module& m) {
           "Write cable_cell to file.");
 
     // arborio::meta_data
-    pybind11::class_<arborio::meta_data> component_meta_data(m, "component_meta_data");
     component_meta_data
         .def_readwrite("version", &arborio::meta_data::version, "cable-cell component version.");
 
     // arborio::cable_cell_component
-    pybind11::class_<arborio::cable_cell_component> cable_component(m, "cable_component");
     cable_component
         .def_readwrite("meta_data", &arborio::cable_cell_component::meta, "cable-cell component meta-data.")
         .def_property_readonly(
diff --git a/python/cells.cpp b/python/cells.cpp
index c1c446b53cddd9c5351e4ee3e96697645dde1e89..93415fb6a8ebf1863458ccb0bc55c91426afe3e0 100644
--- a/python/cells.cpp
+++ b/python/cells.cpp
@@ -1,15 +1,12 @@
 #include <algorithm>
-#include <any>
-#include <cstddef>
 #include <optional>
 #include <string>
-#include <unordered_map>
 #include <utility>
-#include <vector>
 
 #include <pybind11/operators.h>
-#include <pybind11/pybind11.h>
 #include <pybind11/stl.h>
+#include <pybind11/cast.h>
+#include <pybind11/pytypes.h>
 
 #include <arborio/cv_policy_parse.hpp>
 #include <arborio/label_parse.hpp>
@@ -18,6 +15,7 @@
 #include <arbor/cable_cell.hpp>
 #include <arbor/lif_cell.hpp>
 #include <arbor/cv_policy.hpp>
+#include <arbor/cable_cell_param.hpp>
 #include <arbor/morph/cv_data.hpp>
 #include <arbor/morph/label_dict.hpp>
 #include <arbor/morph/locset.hpp>
@@ -27,21 +25,21 @@
 #include <arbor/spike_source_cell.hpp>
 #include <arbor/util/any_cast.hpp>
 #include <arbor/util/unique_any.hpp>
+#include <arbor/cv_policy.hpp>
 
-#include "arbor/cable_cell_param.hpp"
-#include "arbor/cv_policy.hpp"
 #include "conversion.hpp"
 #include "error.hpp"
-#include "proxy.hpp"
-#include "pybind11/cast.h"
-#include "pybind11/stl.h"
-#include "pybind11/pytypes.h"
+#include "label_dict.hpp"
 #include "schedule.hpp"
 #include "strprintf.hpp"
 #include "util.hpp"
 
 namespace pyarb {
 
+namespace U = arb::units;
+
+namespace py = pybind11;
+
 template <typename T>
 std::string to_string(const T& t) {
     std::stringstream ss;
@@ -78,12 +76,21 @@ std::string to_string(const arb::cable_cell_global_properties& props) {
     }
     s += "}\n";
     s += util::pprintf("  parameters: {Vm: {}, cm: {}, rL: {}, tempK: {}}\n",
-            D.init_membrane_potential, D.membrane_capacitance,
-            D.axial_resistivity, D.temperature_K);
+                       D.init_membrane_potential, D.membrane_capacitance,
+                       D.axial_resistivity, D.temperature_K);
     s += "}";
     return s;
 }
 
+struct ion_settings {
+    int charge = 0;
+    std::optional<double> internal_concentration;
+    std::optional<double> external_concentration;
+    std::optional<double> diffusivity;
+    std::optional<double> reversal_potential;
+    std::string reversal_potential_method = "const";
+};
+
 //
 // cv_policy helpers
 //
@@ -110,7 +117,7 @@ arb::cv_policy make_cv_policy_max_extent(double cv_length, const std::string& re
 
 // Helper for finding a mechanism description in a Python object.
 // Allows rev_pot_method to be specified with string or mechanism_desc
-std::optional<arb::mechanism_desc> maybe_method(pybind11::object method) {
+std::optional<arb::mechanism_desc> maybe_method(py::object method) {
     if (!method.is_none()) {
         if (auto m=try_cast<std::string>(method)) {
             return *m;
@@ -132,7 +139,8 @@ std::optional<arb::mechanism_desc> maybe_method(pybind11::object method) {
 std::string lif_str(const arb::lif_cell& c){
     return util::pprintf(
         "<arbor.lif_cell: tau_m {}, V_th {}, C_m {}, E_L {}, V_m {}, t_ref {}>",
-        c.tau_m, c.V_th, c.C_m, c.E_L, c.V_m, c.t_ref);
+        U::to_string(c.tau_m), U::to_string(c.V_th), U::to_string(c.C_m),
+        U::to_string(c.E_L), U::to_string(c.V_m), U::to_string(c.t_ref));
 }
 
 
@@ -146,29 +154,77 @@ std::string scaled_density_desc_str(const arb::scaled_mechanism<arb::density>& p
             mechanism_desc_str(p.t_mech.mech), util::dictionary_csv(p.scale_expr));
 }
 
-void register_cells(pybind11::module& m) {
-    using namespace pybind11::literals;
-    using std::optional;
+// Argument to construct a paintable object.
+using Q = U::quantity;
+using QnS = std::tuple<U::quantity, std::string>;
+using paintable_arg = std::variant<Q, QnS>;
 
-    // arb::spike_source_cell
+std::tuple<Q, arb::iexpr> value_and_scale(const paintable_arg& arg) {
+    if (std::holds_alternative<Q>(arg)) {
+        return {std::get<Q>(arg), 1};
+    }
+    else {
+        const auto& [val, scale] = std::get<QnS>(arg);
+        return {val, arborio::parse_iexpr_expression(scale).unwrap()};
+    }
+}
 
-    pybind11::class_<arb::spike_source_cell> spike_source_cell(m, "spike_source_cell",
+void register_cells(py::module& m) {
+    using namespace py::literals;
+    using std::optional;
+
+    py::class_<arb::spike_source_cell> spike_source_cell(m, "spike_source_cell",
         "A spike source cell, that generates a user-defined sequence of spikes that act as inputs for other cells in the network.");
+    py::class_<arb::cell_cv_data> cell_cv_data(m, "cell_cv_data",
+            "Provides information on the CVs representing the discretization of a cable-cell.");
+    py::class_<arb::benchmark_cell> benchmark_cell(m, "benchmark_cell",
+                                                   "A benchmarking cell, used by Arbor developers to test communication performance.\n"
+                                                   "A benchmark cell generates spikes at a user-defined sequence of time points, and\n"
+                                                   "the time taken to integrate a cell can be tuned by setting the realtime_ratio,\n"
+                                                   "for example if realtime_ratio=2, a cell will take 2 seconds of CPU time to\n"
+                                                   "simulate 1 second.\n");
+    py::class_<arb::lif_cell> lif_cell(m, "lif_cell", "A leaky integrate-and-fire cell.");
+    py::class_<arb::cv_policy> cv_policy(m, "cv_policy", "Describes the rules used to discretize (compartmentalise) a cable cell morphology.");
+    py::class_<ion_settings> py_ion_data(m, "ion_settings");
+    py::class_<arb::cable_cell_global_properties> gprop(m, "cable_global_properties");
+    py::class_<arb::decor> decor(m, "decor",
+                                 "Description of the decorations to be applied to a cable cell, that is the painted,\n"
+                                 "placed and defaulted properties, mecahanisms, ion species etc.");
+    py::class_<arb::cable_cell> cable_cell(m, "cable_cell",
+                                           "Represents morphologically-detailed cell models, with morphology represented as a\n"
+                                           "tree of one-dimensional cable segments.");
+    py::class_<arb::init_membrane_potential> membrane_potential(m, "membrane_potential", "Setting the initial membrane voltage.");
+    py::class_<arb::ion_reversal_potential_method> revpot_method(m, "reversal_potential_method", "Describes the mechanism used to compute eX for ion X.");
+    py::class_<arb::membrane_capacitance> membrane_capacitance(m, "membrane_capacitance", "Setting the membrane capacitance.");
+    py::class_<arb::temperature> temperature_K(m, "temperature", "Setting the temperature.");
+    py::class_<arb::axial_resistivity> axial_resistivity(m, "axial_resistivity", "Setting the axial resistivity.");
+    py::class_<arb::init_reversal_potential> reversal_potential(m, "reversal_potential", "Setting the initial reversal potential.");
+    py::class_<arb::init_int_concentration> int_concentration(m, "int_concentration", "Setting the initial internal ion concentration.");
+    py::class_<arb::init_ext_concentration> ext_concentration(m, "ext_concentration", "Setting the initial external ion concentration.");
+    py::class_<arb::ion_diffusivity> ion_diffusivity(m, "ion_diffusivity", "Setting the ion diffusivity.");
+    py::class_<arb::density> density(m, "density", "For painting a density mechanism on a region.");
+    py::class_<arb::voltage_process> voltage_process(m, "voltage_process", "For painting a voltage_process mechanism on a region.");
+    py::class_<arb::scaled_mechanism<arb::density>> scaled_mechanism(m, "scaled_mechanism", "For painting a scaled density mechanism on a region.");
+    py::class_<arb::cable_cell_ion_data> ion_data(m, "ion_data");
+    py::class_<arb::threshold_detector> detector(m, "threshold_detector", "A spike detector, generates a spike when voltage crosses a threshold. Can be used as source endpoint for an arbor.connection.");
+    py::class_<arb::synapse> synapse(m, "synapse", "For placing a synaptic mechanism on a locset.");
+    py::class_<arb::junction> junction(m, "junction", "For placing a gap-junction mechanism on a locset.");
+    py::class_<arb::i_clamp> i_clamp(m, "iclamp", "A current clamp for injecting a DC or fixed frequency current governed by a piecewise linear envelope.");
 
     spike_source_cell
-        .def(pybind11::init<>(
+        .def(py::init<>(
             [](arb::cell_tag_type source_label, const regular_schedule_shim& sched){
                 return arb::spike_source_cell{std::move(source_label), sched.schedule()};}),
             "source_label"_a, "schedule"_a,
             "Construct a spike source cell with a single source labeled 'source_label'.\n"
             "The cell generates spikes on 'source_label' at regular intervals.")
-        .def(pybind11::init<>(
+        .def(py::init<>(
             [](arb::cell_tag_type source_label, const explicit_schedule_shim& sched){
                 return arb::spike_source_cell{std::move(source_label), sched.schedule()};}),
             "source_label"_a, "schedule"_a,
             "Construct a spike source cell with a single source labeled 'source_label'.\n"
             "The cell generates spikes on 'source_label' at a sequence of user-defined times.")
-        .def(pybind11::init<>(
+        .def(py::init<>(
             [](arb::cell_tag_type source_label, const poisson_schedule_shim& sched){
                 return arb::spike_source_cell{std::move(source_label), sched.schedule()};}),
             "source_label"_a, "schedule"_a,
@@ -177,29 +233,20 @@ void register_cells(pybind11::module& m) {
         .def("__repr__", [](const arb::spike_source_cell&){return "<arbor.spike_source_cell>";})
         .def("__str__",  [](const arb::spike_source_cell&){return "<arbor.spike_source_cell>";});
 
-    // arb::benchmark_cell
-
-    pybind11::class_<arb::benchmark_cell> benchmark_cell(m, "benchmark_cell",
-        "A benchmarking cell, used by Arbor developers to test communication performance.\n"
-        "A benchmark cell generates spikes at a user-defined sequence of time points, and\n"
-        "the time taken to integrate a cell can be tuned by setting the realtime_ratio,\n"
-        "for example if realtime_ratio=2, a cell will take 2 seconds of CPU time to\n"
-        "simulate 1 second.\n");
-
     benchmark_cell
-        .def(pybind11::init<>(
+        .def(py::init<>(
             [](arb::cell_tag_type source_label, arb::cell_tag_type target_label, const regular_schedule_shim& sched, double ratio){
                 return arb::benchmark_cell{std::move(source_label), std::move(target_label), sched.schedule(), ratio};}),
             "source_label"_a, "target_label"_a,"schedule"_a, "realtime_ratio"_a=1.0,
             "Construct a benchmark cell that generates spikes on 'source_label' at regular intervals.\n"
             "The cell has one source labeled 'source_label', and one target labeled 'target_label'.")
-        .def(pybind11::init<>(
+        .def(py::init<>(
             [](arb::cell_tag_type source_label, arb::cell_tag_type target_label, const explicit_schedule_shim& sched, double ratio){
                 return arb::benchmark_cell{std::move(source_label), std::move(target_label),sched.schedule(), ratio};}),
             "source_label"_a, "target_label"_a, "schedule"_a, "realtime_ratio"_a=1.0,
             "Construct a benchmark cell that generates spikes on 'source_label' at a sequence of user-defined times.\n"
             "The cell has one source labeled 'source_label', and one target labeled 'target_label'.")
-        .def(pybind11::init<>(
+        .def(py::init<>(
             [](arb::cell_tag_type source_label, arb::cell_tag_type target_label, const poisson_schedule_shim& sched, double ratio){
                 return arb::benchmark_cell{std::move(source_label), std::move(target_label), sched.schedule(), ratio};}),
             "source_label"_a, "target_label"_a, "schedule"_a, "realtime_ratio"_a=1.0,
@@ -208,17 +255,35 @@ void register_cells(pybind11::module& m) {
         .def("__repr__", [](const arb::benchmark_cell&){return "<arbor.benchmark_cell>";})
         .def("__str__",  [](const arb::benchmark_cell&){return "<arbor.benchmark_cell>";});
 
-    // arb::lif_cell
-
-    pybind11::class_<arb::lif_cell> lif_cell(m, "lif_cell",
-        "A leaky integrate-and-fire cell.");
-
     lif_cell
-        .def(pybind11::init<>(
-            [](arb::cell_tag_type source_label, arb::cell_tag_type target_label){
-                return arb::lif_cell(std::move(source_label), std::move(target_label));}),
+        .def(py::init<>(
+            [](arb::cell_tag_type source_label,
+               arb::cell_tag_type target_label,
+               std::optional<U::quantity> tau_m,
+               std::optional<U::quantity> V_th,
+               std::optional<U::quantity> C_m,
+               std::optional<U::quantity> E_L,
+               std::optional<U::quantity> V_m,
+               std::optional<U::quantity> t_ref) {
+                auto cell = arb::lif_cell{std::move(source_label), std::move(target_label)};
+                if (tau_m) cell.tau_m = *tau_m;
+                if (V_th) cell.V_th = *V_th;
+                if (C_m) cell.C_m = *C_m;
+                if (E_L) cell.E_L = *E_L;
+                if (V_m) cell.V_m = *V_m;
+                if (t_ref) cell.t_ref = *t_ref;
+                return cell;
+            }),
             "source_label"_a, "target_label"_a,
-            "Construct a lif cell with one source labeled 'source_label', and one target labeled 'target_label'.")
+             py::kw_only(), "tau_m"_a=py::none(), "V_th"_a=py::none(), "C_m"_a=py::none(), "E_L"_a=py::none(), "V_m"_a=py::none(), "t_ref"_a=py::none(),
+             "Construct a lif cell with one source labeled 'source_label', and one target labeled 'target_label'."
+             "Can optionally take physical parameters:\n"
+             " * tau_m: Membrane potential decaying constant [ms].\n"
+             " * V_th: Firing threshold [mV].\n"
+             " * C_m: Membrane capacitance [pF].\n"
+             " * E_L: Resting potential [mV].\n"
+             " * V_m: Initial value of the Membrane potential [mV].\n"
+             " * t_ref: Refractory period [ms].")
         .def_readwrite("tau_m", &arb::lif_cell::tau_m,
             "Membrane potential decaying constant [ms].")
         .def_readwrite("V_th", &arb::lif_cell::V_th,
@@ -240,96 +305,14 @@ void register_cells(pybind11::module& m) {
         .def("__repr__", &lif_str)
         .def("__str__",  &lif_str);
 
-    // arb::label_dict
-
-    pybind11::class_<label_dict_proxy> label_dict(m, "label_dict",
-        "A dictionary of labelled region and locset definitions, with a\n"
-        "unique label assigned to each definition.");
-    label_dict
-        .def(pybind11::init<>(),
-             "Create an empty label dictionary.")
-        .def(pybind11::init<const std::unordered_map<std::string, std::string>&>(),
-            "Initialize a label dictionary from a dictionary with string labels as keys,"
-            " and corresponding definitions as strings.")
-        .def(pybind11::init<const label_dict_proxy&>(),
-            "Initialize a label dictionary from another one")
-        .def(pybind11::init([](pybind11::iterator& it) {
-                label_dict_proxy ld;
-                for (; it != pybind11::iterator::sentinel(); ++it) {
-                    const auto tuple = it->cast<pybind11::sequence>();
-                    const auto key   = tuple[0].cast<std::string>();
-                    const auto value = tuple[1].cast<std::string>();
-                    ld.set(key, value);
-                }
-                return ld;
-            }),
-            "Initialize a label dictionary from an iterable of key, definition pairs")
-        .def("add_swc_tags",
-             [](label_dict_proxy& l) { return l.add_swc_tags(); },
-             "Add standard SWC tagged regions.\n"
-             " - soma: (tag 1)\n"
-             " - axon: (tag 2)\n"
-             " - dend: (tag 3)\n"
-             " - apic: (tag 4)")
-        .def("__setitem__",
-            [](label_dict_proxy& l, const char* name, const char* desc) {
-                l.set(name, desc);})
-        .def("__getitem__",
-            [](label_dict_proxy& l, const char* name) {
-                if (auto v = l.getitem(name)) return v.value();
-                throw pybind11::key_error(name);
-            })
-        .def("__len__", &label_dict_proxy::size)
-        .def("__iter__",
-            [](const label_dict_proxy &ld) {
-                return pybind11::make_key_iterator(ld.cache.begin(), ld.cache.end());},
-            pybind11::keep_alive<0, 1>())
-        .def("__contains__",
-             [](const label_dict_proxy &ld, const char* name) {
-                 return ld.contains(name);})
-        .def("keys",
-            [](const label_dict_proxy &ld) {
-                return pybind11::make_key_iterator(ld.cache.begin(), ld.cache.end());},
-            pybind11::keep_alive<0, 1>())
-        .def("items",
-             [](const label_dict_proxy &ld) {
-                 return pybind11::make_iterator(ld.cache.begin(), ld.cache.end());},
-             pybind11::keep_alive<0, 1>())
-        .def("values",
-             [](const label_dict_proxy &ld) {
-                 return pybind11::make_value_iterator(ld.cache.begin(), ld.cache.end());
-             },
-             pybind11::keep_alive<0, 1>())
-        .def("append", [](label_dict_proxy& l, const label_dict_proxy& other, const char* prefix) {
-                l.import(other, prefix);
-            },
-            "other"_a, "The label_dict to be imported"
-            "prefix"_a="", "optional prefix appended to the region and locset labels",
-            "Import the entries of a another label dictionary with an optional prefix.")
-        .def("update", [](label_dict_proxy& l, const label_dict_proxy& other) {
-                l.import(other);
-            },
-            "other"_a, "The label_dict to be imported"
-            "Import the entries of a another label dictionary.")
-        .def_readonly("regions", &label_dict_proxy::regions,
-             "The region definitions.")
-        .def_readonly("locsets", &label_dict_proxy::locsets,
-             "The locset definitions.")
-        .def("__repr__", [](const label_dict_proxy& d){return d.to_string();})
-        .def("__str__",  [](const label_dict_proxy& d){return d.to_string();});
-
-    // arb::cv_policy wrappers
-
-    pybind11::class_<arb::cv_policy> cv_policy(m, "cv_policy",
-            "Describes the rules used to discretize (compartmentalise) a cable cell morphology.");
     cv_policy
-        .def(pybind11::init([](const std::string& expression) { return arborio::parse_cv_policy_expression(expression).unwrap(); }),
+        .def(py::init([](const std::string& expression) { return arborio::parse_cv_policy_expression(expression).unwrap(); }),
             "expression"_a, "A valid CV policy expression")
         .def_property_readonly("domain",
                                [](const arb::cv_policy& p) {return util::pprintf("{}", p.domain());},
                                "The domain on which the policy is applied.")
-        .def(pybind11::self + pybind11::self)
-        .def(pybind11::self | pybind11::self)
+        .def(py::self + py::self)
+        .def(py::self | py::self)
         .def("__repr__", [](const arb::cv_policy& p) {
             std::stringstream ss;
             ss << p;
@@ -370,27 +353,25 @@ void register_cells(pybind11::module& m) {
           "Policy to use the same number of CVs for each branch.");
 
     // arb::cell_cv_data
-    pybind11::class_<arb::cell_cv_data> cell_cv_data(m, "cell_cv_data",
-            "Provides information on the CVs representing the discretization of a cable-cell.");
     cell_cv_data
             .def_property_readonly("num_cv", [](const arb::cell_cv_data& data){return data.size();},
                  "Return the number of CVs in the cell.")
             .def("cables",
                  [](const arb::cell_cv_data& d, unsigned index) {
-                    if (index >= d.size()) throw pybind11::index_error("index out of range");
+                    if (index >= d.size()) throw py::index_error("index out of range");
                     return d.cables(index);
                  },
                  "index"_a, "Return a list of cables representing the CV at the given index.")
             .def("children",
                  [](const arb::cell_cv_data& d, unsigned index) {
-                     if (index >= d.size()) throw pybind11::index_error("index out of range");
+                     if (index >= d.size()) throw py::index_error("index out of range");
                      return d.children(index);
                  },
                  "index"_a,
                  "Return a list of indices of the CVs representing the children of the CV at the given index.")
             .def("parent",
                  [](const arb::cell_cv_data& d, unsigned index) {
-                     if (index >= d.size()) throw pybind11::index_error("index out of range");
+                     if (index >= d.size()) throw py::index_error("index out of range");
                      return d.parent(index);
                  },
                  "index"_a,
@@ -414,9 +395,9 @@ void register_cells(pybind11::module& m) {
                                                    "Only 'area' and 'length' are supported)", integrate_along));
 
               auto object_vec = arb::intersect_region(arborio::parse_region_expression(reg).unwrap(), cvs, integrate_area);
-              auto tuple_vec = std::vector<pybind11::tuple>(object_vec.size());
+              auto tuple_vec = std::vector<py::tuple>(object_vec.size());
               std::transform(object_vec.begin(), object_vec.end(), tuple_vec.begin(),
-                             [](const auto& t)  { return pybind11::make_tuple(t.idx, t.proportion); });
+                             [](const auto& t)  { return py::make_tuple(t.idx, t.proportion); });
               return tuple_vec;
           },
           "reg"_a,  "A region on a cell",
@@ -427,79 +408,74 @@ void register_cells(pybind11::module& m) {
           "`proportion` is the proportion of the CV (itegrated by area or length) included in the region."
     );
 
-    pybind11::class_<arb::init_membrane_potential> membrane_potential(m, "membrane_potential", "Setting the initial membrane voltage.");
     membrane_potential
-        .def(pybind11::init([](double v) -> arb::init_membrane_potential { return {v}; }))
-        .def("__repr__", [](const arb::init_membrane_potential& d){
-            return "Vm=" + to_string(d.value);});
+        .def(py::init([](const U::quantity& v) -> arb::init_membrane_potential { return {v }; }))
+        .def("__repr__",
+             [](const arb::init_membrane_potential& d){
+                 return "Vm=" + to_string(d.value) + " scale=" + to_string(d.scale);});
+
+    revpot_method
+        .def(py::init([](const std::string& ion,
+                         const arb::mechanism_desc& d) -> arb::ion_reversal_potential_method {
+            return {ion, d};
+        }))
+        .def("__repr__", [](const arb::ion_reversal_potential_method& d) {
+            return "ion" + d.ion + " method=" + d.method.name();});
 
-    pybind11::class_<arb::membrane_capacitance> membrane_capacitance(m, "membrane_capacitance", "Setting the membrane capacitance.");
     membrane_capacitance
-        .def(pybind11::init([](double v) -> arb::membrane_capacitance { return {v}; }))
+        .def(py::init([](const U::quantity& v) -> arb::membrane_capacitance { return {v}; }))
         .def("__repr__", [](const arb::membrane_capacitance& d){return "Cm=" + to_string(d.value);});
 
-    pybind11::class_<arb::temperature_K> temperature_K(m, "temperature_K", "Setting the temperature.");
     temperature_K
-        .def(pybind11::init([](double v) -> arb::temperature_K { return {v}; }))
-        .def("__repr__", [](const arb::temperature_K& d){return "T=" + to_string(d.value);});
+        .def(py::init([](const U::quantity& v) -> arb::temperature { return {v}; }))
+        .def("__repr__", [](const arb::temperature& d){return "T=" + to_string(d.value);});
 
-    pybind11::class_<arb::axial_resistivity> axial_resistivity(m, "axial_resistivity", "Setting the axial resistivity.");
     axial_resistivity
-        .def(pybind11::init([](double v) -> arb::axial_resistivity { return {v}; }))
+        .def(py::init([](const U::quantity& v) -> arb::axial_resistivity { return {v}; }))
         .def("__repr__", [](const arb::axial_resistivity& d){return "Ra" + to_string(d.value);});
 
-    pybind11::class_<arb::init_reversal_potential> reversal_potential(m, "reversal_potential", "Setting the initial reversal potential.");
     reversal_potential
-        .def(pybind11::init([](const std::string& i, double v) -> arb::init_reversal_potential { return {i, v}; }))
+        .def(py::init([](const std::string& i, const U::quantity& v) -> arb::init_reversal_potential { return {i, v}; }))
         .def("__repr__", [](const arb::init_reversal_potential& d){return "e" + d.ion + "=" + to_string(d.value);});
 
-    pybind11::class_<arb::init_int_concentration> int_concentration(m, "int_concentration", "Setting the initial internal ion concentration.");
     int_concentration
-        .def(pybind11::init([](const std::string& i, double v) -> arb::init_int_concentration { return {i, v}; }))
+        .def(py::init([](const std::string& i, const U::quantity& v) -> arb::init_int_concentration { return {i, v}; }))
         .def("__repr__", [](const arb::init_int_concentration& d){return d.ion + "i" + "=" + to_string(d.value);});
 
-    pybind11::class_<arb::init_ext_concentration> ext_concentration(m, "ext_concentration", "Setting the initial external ion concentration.");
     ext_concentration
-        .def(pybind11::init([](const std::string& i, double v) -> arb::init_ext_concentration { return {i, v}; }))
+        .def(py::init([](const std::string& i, const U::quantity& v) -> arb::init_ext_concentration { return {i, v}; }))
         .def("__repr__", [](const arb::init_ext_concentration& d){return d.ion + "o" + "=" + to_string(d.value);});
 
-    pybind11::class_<arb::ion_diffusivity> ion_diffusivity(m, "ion_diffusivity", "Setting the ion diffusivity.");
     ion_diffusivity
-        .def(pybind11::init([](const std::string& i, double v) -> arb::ion_diffusivity { return {i, v}; }))
+        .def(py::init([](const std::string& i, const U::quantity& v) -> arb::ion_diffusivity { return {i, v}; }))
         .def("__repr__", [](const arb::ion_diffusivity& d){return "D" + d.ion + "=" + to_string(d.value);});
 
-    pybind11::class_<arb::density> density(m, "density", "For painting a density mechanism on a region.");
     density
-        .def(pybind11::init([](const std::string& name) {return arb::density(name);}))
-        .def(pybind11::init([](arb::mechanism_desc mech) {return arb::density(mech);}))
-        .def(pybind11::init([](const std::string& name, const std::unordered_map<std::string, double>& params) {return arb::density(name, params);}))
-        .def(pybind11::init([](arb::mechanism_desc mech, const std::unordered_map<std::string, double>& params) {return arb::density(mech, params);}))
-        .def(pybind11::init([](const std::string& name, pybind11::kwargs parms) {return arb::density(name, util::dict_to_map<double>(parms));}))
-        .def(pybind11::init([](arb::mechanism_desc mech, pybind11::kwargs params) {return arb::density(mech, util::dict_to_map<double>(params));}))
+        .def(py::init([](const std::string& name) {return arb::density(name);}))
+        .def(py::init([](arb::mechanism_desc mech) {return arb::density(mech);}))
+        .def(py::init([](const std::string& name, const std::unordered_map<std::string, double>& params) {return arb::density(name, params);}))
+        .def(py::init([](arb::mechanism_desc mech, const std::unordered_map<std::string, double>& params) {return arb::density(mech, params);}))
+        .def(py::init([](const std::string& name, py::kwargs parms) {return arb::density(name, util::dict_to_map<double>(parms));}))
+        .def(py::init([](arb::mechanism_desc mech, py::kwargs params) {return arb::density(mech, util::dict_to_map<double>(params));}))
         .def_readonly("mech", &arb::density::mech, "The underlying mechanism.")
         .def("__repr__", [](const arb::density& d){return "<arbor.density " + mechanism_desc_str(d.mech) + ">";})
         .def("__str__", [](const arb::density& d){return "<arbor.density " + mechanism_desc_str(d.mech) + ">";});
 
-    pybind11::class_<arb::voltage_process> voltage_process(m, "voltage_process", "For painting a voltage_process mechanism on a region.");
     voltage_process
-        .def(pybind11::init([](const std::string& name) {return arb::voltage_process(name);}))
-        .def(pybind11::init([](arb::mechanism_desc mech) {return arb::voltage_process(mech);}))
-        .def(pybind11::init([](const std::string& name, const std::unordered_map<std::string, double>& params) {return arb::voltage_process(name, params);}))
-        .def(pybind11::init([](arb::mechanism_desc mech, const std::unordered_map<std::string, double>& params) {return arb::voltage_process(mech, params);}))
-        .def(pybind11::init([](arb::mechanism_desc mech, pybind11::kwargs params) {return arb::voltage_process(mech, util::dict_to_map<double>(params));}))
-        .def(pybind11::init([](const std::string& name, pybind11::kwargs parms) {return arb::voltage_process(name, util::dict_to_map<double>(parms));}))
+        .def(py::init([](const std::string& name) {return arb::voltage_process(name);}))
+        .def(py::init([](arb::mechanism_desc mech) {return arb::voltage_process(mech);}))
+        .def(py::init([](const std::string& name, const std::unordered_map<std::string, double>& params) {return arb::voltage_process(name, params);}))
+        .def(py::init([](arb::mechanism_desc mech, const std::unordered_map<std::string, double>& params) {return arb::voltage_process(mech, params);}))
+        .def(py::init([](arb::mechanism_desc mech, py::kwargs params) {return arb::voltage_process(mech, util::dict_to_map<double>(params));}))
+        .def(py::init([](const std::string& name, py::kwargs parms) {return arb::voltage_process(name, util::dict_to_map<double>(parms));}))
         .def_readonly("mech", &arb::voltage_process::mech, "The underlying mechanism.")
         .def("__repr__", [](const arb::voltage_process& d){return "<arbor.voltage_process " + mechanism_desc_str(d.mech) + ">";})
         .def("__str__", [](const arb::voltage_process& d){return "<arbor.voltage_process " + mechanism_desc_str(d.mech) + ">";});
 
-    // arb::scaled_mechanism<arb::density>
-
-    pybind11::class_<arb::scaled_mechanism<arb::density>> scaled_mechanism(
-        m, "scaled_mechanism", "For painting a scaled density mechanism on a region.");
     scaled_mechanism
-        .def(pybind11::init(
+        .def(py::init(
             [](arb::density dens) { return arb::scaled_mechanism<arb::density>(std::move(dens)); }))
-        .def(pybind11::init(
+        .def(py::init(
             [](arb::density dens, const std::unordered_map<std::string, std::string>& scales) {
                 auto s = arb::scaled_mechanism<arb::density>(std::move(dens));
                 for (const auto& [k, v]: scales) {
@@ -507,8 +483,8 @@ void register_cells(pybind11::module& m) {
                 }
                 return s;
             }))
-        .def(pybind11::init(
-            [](arb::density dens, pybind11::kwargs scales) {
+        .def(py::init(
+            [](arb::density dens, py::kwargs scales) {
                 auto s = arb::scaled_mechanism<arb::density>(std::move(dens));
                 for (const auto& [k, v]: util::dict_to_map<std::string>(scales)) {
                     s.scale(k, arborio::parse_iexpr_expression(v).unwrap());
@@ -521,8 +497,8 @@ void register_cells(pybind11::module& m) {
                 s.scale(std::move(name), arborio::parse_iexpr_expression(ex).unwrap());
                 return s;
             },
-            pybind11::arg("name"),
-            pybind11::arg("ex"),
+            py::arg("name"),
+            py::arg("ex"),
             "Add a scaling expression to a parameter.")
         .def("__repr__",
             [](const arb::scaled_mechanism<arb::density>& d) {
@@ -532,60 +508,60 @@ void register_cells(pybind11::module& m) {
             return "<arbor.scaled_mechanism<density> " + scaled_density_desc_str(d) + ">";
         });
 
-    // arb::synapse
-
-    pybind11::class_<arb::synapse> synapse(m, "synapse", "For placing a synaptic mechanism on a locset.");
     synapse
-        .def(pybind11::init([](const std::string& name) {return arb::synapse(name);}))
-        .def(pybind11::init([](arb::mechanism_desc mech) {return arb::synapse(mech);}))
-        .def(pybind11::init([](const std::string& name, const std::unordered_map<std::string, double>& params) {return arb::synapse(name, params);}))
-        .def(pybind11::init([](arb::mechanism_desc mech, const std::unordered_map<std::string, double>& params) {return arb::synapse(mech, params);}))
-        .def(pybind11::init([](const std::string& name, pybind11::kwargs parms) {return arb::synapse(name, util::dict_to_map<double>(parms));}))
-        .def(pybind11::init([](arb::mechanism_desc mech, pybind11::kwargs params) {return arb::synapse(mech, util::dict_to_map<double>(params));}))
+        .def(py::init([](const std::string& name) {return arb::synapse(name);}))
+        .def(py::init([](arb::mechanism_desc mech) {return arb::synapse(mech);}))
+        .def(py::init([](const std::string& name, const std::unordered_map<std::string, double>& params) {return arb::synapse(name, params);}))
+        .def(py::init([](arb::mechanism_desc mech, const std::unordered_map<std::string, double>& params) {return arb::synapse(mech, params);}))
+        .def(py::init([](const std::string& name, py::kwargs parms) {return arb::synapse(name, util::dict_to_map<double>(parms));}))
+        .def(py::init([](arb::mechanism_desc mech, py::kwargs params) {return arb::synapse(mech, util::dict_to_map<double>(params));}))
         .def_readonly("mech", &arb::synapse::mech, "The underlying mechanism.")
         .def("__repr__", [](const arb::synapse& s){return "<arbor.synapse " + mechanism_desc_str(s.mech) + ">";})
         .def("__str__", [](const arb::synapse& s){return "<arbor.synapse " + mechanism_desc_str(s.mech) + ">";});
 
-    // arb::junction
-
-    pybind11::class_<arb::junction> junction(m, "junction", "For placing a gap-junction mechanism on a locset.");
     junction
-        .def(pybind11::init([](const std::string& name) {return arb::junction(name);}))
-        .def(pybind11::init([](arb::mechanism_desc mech) {return arb::junction(mech);}))
-        .def(pybind11::init([](const std::string& name, const std::unordered_map<std::string, double>& params) {return arb::junction(name, params);}))
-        .def(pybind11::init([](const std::string& name, pybind11::kwargs parms) {return arb::junction(name, util::dict_to_map<double>(parms));}))
-        .def(pybind11::init([](arb::mechanism_desc mech, const std::unordered_map<std::string, double>& params) {return arb::junction(mech, params);}))
-        .def(pybind11::init([](arb::mechanism_desc mech, pybind11::kwargs params) {return arb::junction(mech, util::dict_to_map<double>(params));}))
+        .def(py::init([](const std::string& name) {return arb::junction(name);}))
+        .def(py::init([](arb::mechanism_desc mech) {return arb::junction(mech);}))
+        .def(py::init([](const std::string& name, const std::unordered_map<std::string, double>& params) {return arb::junction(name, params);}))
+        .def(py::init([](const std::string& name, py::kwargs parms) {return arb::junction(name, util::dict_to_map<double>(parms));}))
+        .def(py::init([](arb::mechanism_desc mech, const std::unordered_map<std::string, double>& params) {return arb::junction(mech, params);}))
+        .def(py::init([](arb::mechanism_desc mech, py::kwargs params) {return arb::junction(mech, util::dict_to_map<double>(params));}))
         .def_readonly("mech", &arb::junction::mech, "The underlying mechanism.")
         .def("__repr__", [](const arb::junction& j){return "<arbor.junction " + mechanism_desc_str(j.mech) + ">";})
         .def("__str__", [](const arb::junction& j){return "<arbor.junction " + mechanism_desc_str(j.mech) + ">";});
 
-    // arb::i_clamp
-
-    pybind11::class_<arb::i_clamp> i_clamp(m, "iclamp",
-        "A current clamp for injecting a DC or fixed frequency current governed by a piecewise linear envelope.");
     i_clamp
-        .def(pybind11::init(
-                [](double ts, double dur, double cur, double frequency, double phase) {
+        .def(py::init(
+                [](const U::quantity& ts,
+                   const U::quantity& dur,
+                   const U::quantity& cur,
+                   const U::quantity& frequency,
+                   const U::quantity& phase) {
                     return arb::i_clamp::box(ts, dur, cur, frequency, phase);
-                }), "tstart"_a, "duration"_a, "current"_a, pybind11::kw_only(), "frequency"_a=0, "phase"_a=0,
-                "Construct finite duration current clamp, constant amplitude")
-        .def(pybind11::init(
-                [](double cur, double frequency, double phase) {
+                }),
+             "tstart"_a, "duration"_a, "current"_a,
+             py::kw_only(), py::arg_v("frequency", 0*U::kHz, "0.0*arbor.units.kHz"), py::arg_v("phase", 0*U::rad, "0.0*arbor.units.rad"),
+             "Construct finite duration current clamp, constant amplitude")
+        .def(py::init(
+                [](const U::quantity& cur,
+                   const U::quantity& frequency,
+                   const U::quantity& phase) {
                     return arb::i_clamp{cur, frequency, phase};
-                }), "current"_a, pybind11::kw_only(), "frequency"_a=0, "phase"_a=0,
-                "Construct constant amplitude current clamp")
-        .def(pybind11::init(
-                [](std::vector<std::pair<double, double>> envl, double frequency, double phase) {
-                    arb::i_clamp clamp;
-                    for (const auto& p: envl) {
-                        clamp.envelope.push_back({p.first, p.second});
-                    }
-                    clamp.frequency = frequency;
-                    clamp.phase = phase;
-                    return clamp;
-                }), "envelope"_a, pybind11::kw_only(), "frequency"_a=0, "phase"_a=0,
-                "Construct current clamp according to (time, amplitude) linear envelope")
+                }),
+             "current"_a,
+             py::kw_only(), py::arg_v("frequency", 0*U::kHz, "0.0*arbor.units.kHz"), py::arg_v("phase", 0*U::rad, "0.0*arbor.units.rad"),
+             "Construct constant amplitude current clamp")
+        .def(py::init(
+                [](std::vector<std::pair<const U::quantity&, const U::quantity&>> envl,
+                   const U::quantity& frequency,
+                   const U::quantity& phase) {
+                    std::vector<arb::i_clamp::envelope_point> env;
+                    for (const auto& [t, a]: envl) env.push_back({t, a});
+                    return arb::i_clamp{env, frequency, phase};
+                }),
+             "envelope"_a,
+             py::kw_only(), py::arg_v("frequency", 0*U::kHz, "0.0*arbor.units.kHz"), py::arg_v("phase", 0*U::rad, "0.0*arbor.units.rad"),
+             "Construct current clamp according to (time, amplitude) linear envelope")
         .def_property_readonly("envelope",
                 [](const arb::i_clamp& obj) {
                     std::vector<std::pair<double, double>> envl;
@@ -598,16 +574,13 @@ void register_cells(pybind11::module& m) {
         .def_readonly("frequency", &arb::i_clamp::frequency, "Oscillation frequency (kHz), zero implies DC stimulus.")
         .def_readonly("phase", &arb::i_clamp::phase, "Oscillation initial phase (rad)")
         .def("__repr__", [](const arb::i_clamp& c) {
-            return util::pprintf("<arbor.iclamp: frequency {} Hz>", c.frequency);})
+            return util::pprintf("<arbor.iclamp: frequency {} kHz>", c.frequency);})
         .def("__str__", [](const arb::i_clamp& c) {
-            return util::pprintf("<arbor.iclamp: frequency {} Hz>", c.frequency);});
+            return util::pprintf("<arbor.iclamp: frequency {} kHz>", c.frequency);});
 
-    // arb::threshold_detector
-    pybind11::class_<arb::threshold_detector> detector(m, "threshold_detector",
-            "A spike detector, generates a spike when voltage crosses a threshold. Can be used as source endpoint for an arbor.connection.");
     detector
-        .def(pybind11::init(
-            [](double thresh) { return arb::threshold_detector{thresh}; }),
+        .def(py::init(
+            [](const U::quantity& thresh) { return arb::threshold_detector{thresh}; }),
             "threshold"_a, "Voltage threshold of spike detector [mV]")
         .def_readonly("threshold", &arb::threshold_detector::threshold, "Voltage threshold of spike detector [mV]")
         .def("__repr__", [](const arb::threshold_detector& d){
@@ -615,24 +588,12 @@ void register_cells(pybind11::module& m) {
         .def("__str__", [](const arb::threshold_detector& d){
             return util::pprintf("(threshold_detector {})", d.threshold);});
 
-    // arb::cable_cell_global_properties
-    pybind11::class_<arb::cable_cell_ion_data> ion_data(m, "ion_data");
     ion_data
         .def_readonly("internal_concentration", &arb::cable_cell_ion_data::init_int_concentration,  "Internal concentration.")
         .def_readonly("external_concentration", &arb::cable_cell_ion_data::init_ext_concentration,  "External concentration.")
         .def_readonly("diffusivity",            &arb::cable_cell_ion_data::diffusivity,             "Diffusivity.")
         .def_readonly("reversal_concentration", &arb::cable_cell_ion_data::init_reversal_potential, "Reversal potential.");
 
-    struct ion_settings {
-        int charge = 0;
-        std::optional<double> internal_concentration;
-        std::optional<double> external_concentration;
-        std::optional<double> diffusivity;
-        std::optional<double> reversal_potential;
-        std::string reversal_potential_method = "const";
-    };
-
-    pybind11::class_<ion_settings> py_ion_data(m, "ion_settings");
     ion_data
         .def_property_readonly("charge",                    [](const ion_settings& s) { return s.charge; },                    "Valence.")
         .def_property_readonly("internal_concentration",    [](const ion_settings& s) { return s.internal_concentration; },    "Internal concentration.")
@@ -641,46 +602,42 @@ void register_cells(pybind11::module& m) {
         .def_property_readonly("reversal_potential",        [](const ion_settings& s) { return s.reversal_potential; },        "Reversal potential.")
         .def_property_readonly("reversal_potential_method", [](const ion_settings& s) { return s.reversal_potential_method; }, "Reversal potential method.");
 
-    pybind11::class_<arb::cable_cell_global_properties> gprop(m, "cable_global_properties");
     gprop
-        .def(pybind11::init<>())
-        .def(pybind11::init<const arb::cable_cell_global_properties&>())
+        .def(py::init<>())
+        .def(py::init<const arb::cable_cell_global_properties&>())
         .def("check", [](const arb::cable_cell_global_properties& props) {
                 arb::check_global_properties(props);},
                 "Test whether all default parameters and ion species properties have been set.")
         .def_readwrite("coalesce_synapses",  &arb::cable_cell_global_properties::coalesce_synapses,
                 "Flag for enabling/disabling linear syanpse coalescing.")
-        // set cable properties
-        .def_property("membrane_potential",
-                      [](const arb::cable_cell_global_properties& props) { return props.default_parameters.init_membrane_potential; },
-                      [](arb::cable_cell_global_properties& props, double u) { props.default_parameters.init_membrane_potential = u; })
         .def_property("membrane_voltage_limit",
                       [](const arb::cable_cell_global_properties& props) { return props.membrane_voltage_limit_mV; },
                       [](arb::cable_cell_global_properties& props, std::optional<double> u) { props.membrane_voltage_limit_mV = u; })
-        .def_property("membrane_capacitance",
-                      [](const arb::cable_cell_global_properties& props) { return props.default_parameters.membrane_capacitance; },
-                      [](arb::cable_cell_global_properties& props, double u) { props.default_parameters.membrane_capacitance = u; })
-        .def_property("temperature",
-                      [](const arb::cable_cell_global_properties& props) { return props.default_parameters.temperature_K; },
-                      [](arb::cable_cell_global_properties& props, double u) { props.default_parameters.temperature_K = u; })
-        .def_property("axial_resistivity",
-                      [](const arb::cable_cell_global_properties& props) { return props.default_parameters.axial_resistivity; },
-                      [](arb::cable_cell_global_properties& props, double u) { props.default_parameters.axial_resistivity = u; })
+        // set cable properties
+        .def_property_readonly("membrane_potential",
+                               [](const arb::cable_cell_global_properties& props) { return props.default_parameters.init_membrane_potential; })
+        .def_property_readonly("membrane_capacitance",
+                      [](const arb::cable_cell_global_properties& props) { return props.default_parameters.membrane_capacitance; })
+        .def_property_readonly("temperature",
+                      [](const arb::cable_cell_global_properties& props) { return props.default_parameters.temperature_K; })
+        .def_property_readonly("axial_resistivity",
+                               [](const arb::cable_cell_global_properties& props) { return props.default_parameters.axial_resistivity; })
         .def("set_property",
             [](arb::cable_cell_global_properties& props,
-               optional<double> Vm, optional<double> cm,
-               optional<double> rL, optional<double> tempK)
-            {
-                if (Vm) props.default_parameters.init_membrane_potential = Vm;
-                if (cm) props.default_parameters.membrane_capacitance=cm;
-                if (rL) props.default_parameters.axial_resistivity=rL;
-                if (tempK) props.default_parameters.temperature_K=tempK;
+               optional<U::quantity> Vm, optional<U::quantity> cm,
+               optional<U::quantity> rL, optional<U::quantity> tempK) {
+                if (Vm) props.default_parameters.init_membrane_potential=Vm.value().value_as(U::mV);
+                if (cm) props.default_parameters.membrane_capacitance=cm.value().value_as(U::F/U::m2);
+                if (rL) props.default_parameters.axial_resistivity=rL.value().value_as(U::Ohm*U::cm);
+                if (tempK) props.default_parameters.temperature_K=tempK.value().value_as(U::Kelvin);
             },
-            pybind11::arg_v("Vm",    pybind11::none(), "initial membrane voltage [mV]."),
-            pybind11::arg_v("cm",    pybind11::none(), "membrane capacitance [F/m²]."),
-            pybind11::arg_v("rL",    pybind11::none(), "axial resistivity [Ω·cm]."),
-            pybind11::arg_v("tempK", pybind11::none(), "temperature [Kelvin]."),
-            "Set global default values for cable and cell properties.")
+             "Vm"_a=py::none(), "cm"_a=py::none(), "rL"_a=py::none(), "tempK"_a=py::none(),
+             "Set global default values for cable and cell properties.\n"
+             " * Vm:    initial membrane voltage [mV].\n"
+             " * cm:    membrane capacitance [F/m²].\n"
+             " * rL:    axial resistivity [Ω·cm].\n"
+             " * tempK: temperature [Kelvin].\n"
+             "These values can be overridden on specific regions using the paint interface.")
         // add/modify ion species
         .def("unset_ion",
              [](arb::cable_cell_global_properties& props, const char* ion) {
@@ -690,41 +647,40 @@ void register_cells(pybind11::module& m) {
              },
              "Remove ion species from properties.")
         .def("set_ion",
-            [](arb::cable_cell_global_properties& props, const char* ion,
-               optional<double> valence, optional<double> int_con,
-               optional<double> ext_con, optional<double> rev_pot,
-               pybind11::object method, optional<double> diff)
-            {
-                if (!props.ion_species.count(ion) && !valence) {
-                    throw std::runtime_error(util::pprintf("New ion species: '{}', missing valence", ion));
-                }
-                if (valence) props.ion_species[ion] = *valence;
-
-                auto& data = props.default_parameters.ion_data[ion];
-                if (int_con) data.init_int_concentration  = *int_con;
-                if (ext_con) data.init_ext_concentration  = *ext_con;
-                if (rev_pot) data.init_reversal_potential = *rev_pot;
-                if (diff)    data.diffusivity             = *diff;
-
-                if (auto m = maybe_method(method)) {
-                    props.default_parameters.reversal_potential_method[ion] = *m;
-                }
-            },
-            pybind11::arg("ion"),
-            pybind11::arg_v("valence", pybind11::none(), "valence of the ion species."),
-            pybind11::arg_v("int_con", pybind11::none(), "initial internal concentration [mM]."),
-            pybind11::arg_v("ext_con", pybind11::none(), "initial external concentration [mM]."),
-            pybind11::arg_v("rev_pot", pybind11::none(), "reversal potential [mV]."),
-            pybind11::arg_v("method",  pybind11::none(), "method for calculating reversal potential."),
-            pybind11::arg_v("diff",    pybind11::none(), "diffusivity [m^2/s]."),
-            "Set the global default properties of ion species named 'ion'.\n"
-            "There are 3 ion species predefined in arbor: 'ca', 'na' and 'k'.\n"
-            "If 'ion' in not one of these ions it will be added to the list, making it\n"
-            "available to mechanisms. The user has to provide the valence of a previously\n"
-            "undefined ion the first time this function is called with it as an argument.\n"
-            "Species concentrations and reversal potential can be overridden on\n"
-            "specific regions using the paint interface, while the method for calculating\n"
-            "reversal potential is global for all compartments in the cell, and can't be\n"
+             [](arb::cable_cell_global_properties& props, const char* ion,
+                optional<int> valence, optional<U::quantity> int_con,
+                optional<U::quantity> ext_con, optional<U::quantity> rev_pot,
+                py::object method, optional<U::quantity> diff) {
+                 if (!props.ion_species.count(ion) && !valence) {
+                     throw std::runtime_error(util::pprintf("New ion species: '{}', missing valence", ion));
+                 }
+                 if (valence) props.ion_species[ion] = *valence;
+
+                 auto& data = props.default_parameters.ion_data[ion];
+                 if (int_con) data.init_int_concentration  = int_con.value().value_as(U::mM);
+                 if (ext_con) data.init_ext_concentration  = ext_con.value().value_as(U::mM);
+                 if (rev_pot) data.init_reversal_potential = rev_pot.value().value_as(U::mV);
+                 if (diff)    data.diffusivity             = diff.value().value_as(U::m2/U::s);
+
+                 if (auto m = maybe_method(method)) {
+                     props.default_parameters.reversal_potential_method[ion] = *m;
+                 }
+             },
+             "ion"_a, "valence"_a=py::none(), "int_con"_a=py::none(), "ext_con"_a=py::none(), "rev_pot"_a=py::none(), "method"_a =py::none(), "diff"_a=py::none(),
+             "Set the global default properties of ion species named 'ion'.\n"
+             " * valence: valence of the ion species [e].\n"
+             " * int_con: initial internal concentration [mM].\n"
+             " * ext_con: initial external concentration [mM].\n"
+             " * rev_pot: reversal potential [mV].\n"
+             " * method:  mechanism for calculating reversal potential.\n"
+             " * diff:   diffusivity [m^2/s].\n"
+             "There are 3 ion species predefined in arbor: 'ca', 'na' and 'k'.\n"
+             "If 'ion' in not one of these ions it will be added to the list, making it\n"
+             "available to mechanisms. The user has to provide the valence of a previously\n"
+             "undefined ion the first time this function is called with it as an argument.\n"
+             "Species concentrations and reversal potential can be overridden on\n"
+             "specific regions using the paint interface, while the method for calculating\n"
+             "reversal potential is global for all compartments in the cell, and can't be\n"
              "overriden locally.")
         .def_property_readonly("ion_data",
                       [](const arb::cable_cell_global_properties& props) { return props.default_parameters.ion_data; })
@@ -766,56 +722,55 @@ void register_cells(pybind11::module& m) {
     },
     "default NEURON cable_global_properties");
 
-    // arb::decor
-
-    pybind11::class_<arb::decor> decor(m, "decor",
-            "Description of the decorations to be applied to a cable cell, that is the painted,\n"
-            "placed and defaulted properties, mecahanisms, ion species etc.");
     decor
-        .def(pybind11::init<>())
-        .def(pybind11::init<const arb::decor&>())
+        .def(py::init<>())
+        .def(py::init<const arb::decor&>())
         // Set cell-wide default values for properties
         .def("set_property",
-            [](arb::decor& d,
-               optional<double> Vm, optional<double> cm,
-               optional<double> rL, optional<double> tempK)
-            {
-                if (Vm) d.set_default(arb::init_membrane_potential{*Vm});
-                if (cm) d.set_default(arb::membrane_capacitance{*cm});
-                if (rL) d.set_default(arb::axial_resistivity{*rL});
-                if (tempK) d.set_default(arb::temperature_K{*tempK});
-                return d;
-            },
-            pybind11::arg_v("Vm",    pybind11::none(), "initial membrane voltage [mV]."),
-            pybind11::arg_v("cm",    pybind11::none(), "membrane capacitance [F/m²]."),
-            pybind11::arg_v("rL",    pybind11::none(), "axial resistivity [Ω·cm]."),
-            pybind11::arg_v("tempK", pybind11::none(), "temperature [Kelvin]."),
-            "Set default values for cable and cell properties. These values can be overridden on specific regions using the paint interface.")
+             [](arb::decor& d,
+                optional<U::quantity> Vm, optional<U::quantity> cm,
+                optional<U::quantity> rL, optional<U::quantity> tempK) {
+                 if (Vm) d.set_default(arb::init_membrane_potential{*Vm});
+                 if (cm) d.set_default(arb::membrane_capacitance{*cm});
+                 if (rL) d.set_default(arb::axial_resistivity{*rL});
+                 if (tempK) d.set_default(arb::temperature{*tempK});
+                 return d;
+             },
+             "Vm"_a=py::none(), "cm"_a=py::none(), "rL"_a=py::none(), "tempK"_a=py::none(),
+             "Set default values for cable and cell properties:\n"
+             " * Vm:    initial membrane voltage [mV].\n"
+             " * cm:    membrane capacitance [F/m²].\n"
+             " * rL:    axial resistivity [Ω·cm].\n"
+             " * tempK: temperature [Kelvin].\n"
+             "These values can be overridden on specific regions using the paint interface.")
         // modify parameters for an ion species.
         .def("set_ion",
-            [](arb::decor& d, const char* ion,
-               optional<double> int_con, optional<double> ext_con,
-               optional<double> rev_pot, pybind11::object method,
-               optional<double> diff)
-            {
-                if (int_con) d.set_default(arb::init_int_concentration{ion, *int_con});
-                if (ext_con) d.set_default(arb::init_ext_concentration{ion, *ext_con});
-                if (rev_pot) d.set_default(arb::init_reversal_potential{ion, *rev_pot});
-                if (diff)    d.set_default(arb::ion_diffusivity{ion, *diff});
-                if (auto m = maybe_method(method)) d.set_default(arb::ion_reversal_potential_method{ion, *m});
-                return d;
-            },
-            pybind11::arg("ion"),
-            pybind11::arg_v("int_con", pybind11::none(), "initial internal concentration [mM]."),
-            pybind11::arg_v("ext_con", pybind11::none(), "initial external concentration [mM]."),
-            pybind11::arg_v("rev_pot", pybind11::none(), "reversal potential [mV]."),
-            pybind11::arg_v("method",  pybind11::none(), "method for calculating reversal potential."),
-            pybind11::arg_v("diff",    pybind11::none(), "diffusivity [m^2/s]."),
-            "Set the properties of ion species named 'ion' that will be applied\n"
-            "by default everywhere on the cell. Species concentrations and reversal\n"
-            "potential can be overridden on specific regions using the paint interface, \n"
-            "while the method for calculating reversal potential is global for all\n"
-            "compartments in the cell, and can't be overriden locally.")
+             [](arb::decor& d, const char* ion,
+                optional<U::quantity> int_con, optional<U::quantity> ext_con,
+                optional<U::quantity> rev_pot, py::object method,
+                optional<U::quantity> diff) {
+                 if (int_con) d.set_default(arb::init_int_concentration{ion, *int_con});
+                 if (ext_con) d.set_default(arb::init_ext_concentration{ion, *ext_con});
+                 if (rev_pot) d.set_default(arb::init_reversal_potential{ion, *rev_pot});
+                 if (diff)    d.set_default(arb::ion_diffusivity{ion, *diff});
+                 if (auto m = maybe_method(method)) d.set_default(arb::ion_reversal_potential_method{ion, *m});
+                 return d;
+             },
+             "ion"_a, "int_con"_a=py::none(), "ext_con"_a=py::none(), "rev_pot"_a=py::none(), "method"_a =py::none(), "diff"_a=py::none(),
+             "Set the cell-level properties of ion species named 'ion'.\n"
+             " * int_con: initial internal concentration [mM].\n"
+             " * ext_con: initial external concentration [mM].\n"
+             " * rev_pot: reversal potential [mV].\n"
+             " * method:  mechanism for calculating reversal potential.\n"
+             " * diff:    diffusivity [m^2/s].\n"
+             "There are 3 ion species predefined in arbor: 'ca', 'na' and 'k'.\n"
+             "If 'ion' in not one of these ions it will be added to the list, making it\n"
+             "available to mechanisms. The user has to provide the valence of a previously\n"
+             "undefined ion the first time this function is called with it as an argument.\n"
+             "Species concentrations and reversal potential can be overridden on\n"
+             "specific regions using the paint interface, while the method for calculating\n"
+             "reversal potential is global for all compartments in the cell, and can't be\n"
+             "overriden locally.")
         .def("paintings",
             [](arb::decor& dec) {
                 std::vector<std::tuple<std::string, arb::paintable>> result;
@@ -862,77 +817,67 @@ void register_cells(pybind11::module& m) {
         .def("paint",
             [](arb::decor& dec,
                const char* region,
-               optional<std::variant<double, std::string>> Vm,
-               optional<std::variant<double, std::string>> cm,
-               optional<std::variant<double, std::string>> rL,
-               optional<std::variant<double, std::string>> tempK) {
-                auto r = arborio::parse_region_expression(region).unwrap();
+               optional<paintable_arg> Vm, optional<paintable_arg> cm,
+               optional<paintable_arg> rL, optional<paintable_arg> tempK) {
+                auto reg = arborio::parse_region_expression(region).unwrap();
                 if (Vm) {
-                    if (std::holds_alternative<double>(*Vm)) {
-                        dec.paint(r, arb::init_membrane_potential{std::get<double>(*Vm)});
-                    }
-                    else {
-                        const auto& s = std::get<std::string>(*Vm);
-                        auto ie = arborio::parse_iexpr_expression(s).unwrap();
-                        dec.paint(r, arb::init_membrane_potential{ie});
-                    }
+                    const auto& [v, s] = value_and_scale(*Vm);
+                    dec.paint(reg, arb::init_membrane_potential{v, s});
                 }
                 if (cm) {
-                    if (std::holds_alternative<double>(*cm)) {
-                        dec.paint(r, arb::membrane_capacitance{std::get<double>(*cm)});
-                    }
-                    else {
-                        const auto& s = std::get<std::string>(*cm);
-                        auto ie = arborio::parse_iexpr_expression(s).unwrap();
-                        dec.paint(r, arb::membrane_capacitance{ie});
-                    }
+                    const auto& [v, s] = value_and_scale(*cm);
+                    dec.paint(reg, arb::membrane_capacitance{v, s});
                 }
                 if (rL) {
-                    if (std::holds_alternative<double>(*rL)) {
-                        dec.paint(r, arb::axial_resistivity{std::get<double>(*rL)});
-                    }
-                    else {
-                        const auto& s = std::get<std::string>(*rL);
-                        auto ie = arborio::parse_iexpr_expression(s).unwrap();
-                        dec.paint(r, arb::axial_resistivity{ie});
-                    }
+                    const auto& [v, s] = value_and_scale(*rL);
+                    dec.paint(reg, arb::axial_resistivity{v, s});
                 }
                 if (tempK) {
-                    if (std::holds_alternative<double>(*tempK)) {
-                        dec.paint(r, arb::temperature_K{std::get<double>(*tempK)});
-                    }
-                    else {
-                        const auto& s = std::get<std::string>(*tempK);
-                        auto ie = arborio::parse_iexpr_expression(s).unwrap();
-                        dec.paint(r, arb::temperature_K{ie});
-                    }
+                    const auto& [v, s] = value_and_scale(*tempK);
+                    dec.paint(reg, arb::temperature{v, s});
                 }
                 return dec;
             },
-            pybind11::arg("region"),
-            pybind11::arg_v("Vm",    pybind11::none(), "initial membrane voltage [mV]."),
-            pybind11::arg_v("cm",    pybind11::none(), "membrane capacitance [F/m²]."),
-            pybind11::arg_v("rL",    pybind11::none(), "axial resistivity [Ω·cm]."),
-            pybind11::arg_v("tempK", pybind11::none(), "temperature [Kelvin]."),
-            "Set cable properties on a region.")
+            "region"_a, "Vm"_a=py::none(), "cm"_a=py::none(), "rL"_a=py::none(), "tempK"_a=py::none(),
+            "Set cable properties on a region.\n"
+             "Set global default values for cable and cell properties.\n"
+             " * Vm:    initial membrane voltage [mV].\n"
+             " * cm:    membrane capacitance [F/m²].\n"
+             " * rL:    axial resistivity [Ω·cm].\n"
+             " * tempK: temperature [Kelvin].\n"
+             "Each value can be given as a plain quantity or a tuple of (quantity, 'scale') where scale is an iexpr.")
         // Paint ion species initial conditions on a region.
         .def("paint",
             [](arb::decor& dec, const char* region, const char* name,
-               optional<double> int_con, optional<double> ext_con,
-               optional<double> rev_pot, optional<double> diff) {
+               optional<paintable_arg> int_con, optional<paintable_arg> ext_con,
+               optional<paintable_arg> rev_pot, optional<paintable_arg> diff) {
                 auto r = arborio::parse_region_expression(region).unwrap();
-                if (int_con) dec.paint(r, arb::init_int_concentration{name, *int_con});
-                if (ext_con) dec.paint(r, arb::init_ext_concentration{name, *ext_con});
-                if (rev_pot) dec.paint(r, arb::init_reversal_potential{name, *rev_pot});
-                if (diff)    dec.paint(r, arb::ion_diffusivity{name, *diff});
+                if (int_con) {
+                    const auto& [v, s] = value_and_scale(*int_con);
+                    dec.paint(r, arb::init_int_concentration{name, v, s});
+                }
+                if (ext_con) {
+                    const auto& [v, s] = value_and_scale(*ext_con);
+                    dec.paint(r, arb::init_ext_concentration{name, v, s});
+                }
+                if (rev_pot) {
+                    const auto& [v, s] = value_and_scale(*rev_pot);
+                    dec.paint(r, arb::init_reversal_potential{name, v, s});
+                }
+                if (diff) {
+                    const auto& [v, s] = value_and_scale(*diff);
+                    dec.paint(r, arb::ion_diffusivity{name, v, s});
+                }
                 return dec;
             },
-            "region"_a, pybind11::kw_only(), "ion_name"_a,
-            pybind11::arg_v("int_con", pybind11::none(), "Initial internal concentration [mM]"),
-            pybind11::arg_v("ext_con", pybind11::none(), "Initial external concentration [mM]"),
-            pybind11::arg_v("rev_pot", pybind11::none(), "Initial reversal potential [mV]"),
-            pybind11::arg_v("diff",    pybind11::none(), "Diffusivity [m^2/s]"),
-            "Set ion species properties conditions on a region.")
+            "region"_a, py::kw_only(), "ion"_a, "int_con"_a=py::none(), "ext_con"_a=py::none(), "rev_pot"_a=py::none(), "diff"_a=py::none(),
+            "Set ion species properties conditions on a region.\n"
+             " * int_con: initial internal concentration [mM].\n"
+             " * ext_con: initial external concentration [mM].\n"
+             " * rev_pot: reversal potential [mV].\n"
+             " * method:  mechanism for calculating reversal potential.\n"
+             " * diff:   diffusivity [m^2/s].\n"
+             "Each value can be given as a plain quantity or a tuple of (quantity, 'scale') where scale is an iexpr.\n")
         // Place synapses
         .def("place",
             [](arb::decor& dec, const char* locset, const arb::synapse& mechanism, const char* label_name) {
@@ -967,39 +912,30 @@ void register_cells(pybind11::module& m) {
             "The group of spike detectors has the label 'label', used for forming connections between cells.")
         .def("discretization",
             [](arb::decor& dec, const arb::cv_policy& p) { return dec.set_default(p); },
-            pybind11::arg("policy"),
+            py::arg("policy"),
              "A cv_policy used to discretise the cell into compartments for simulation")
         .def("discretization",
             [](arb::decor& dec, const std::string& p) {
                 return dec.set_default(arborio::parse_cv_policy_expression(p).unwrap());
             },
-            pybind11::arg("policy"),
+            py::arg("policy"),
             "An s-expression string representing a cv_policy used to discretise the "
             "cell into compartments for simulation");
 
-    // arb::cable_cell
-
-    pybind11::class_<arb::cable_cell> cable_cell(m, "cable_cell",
-        "Represents morphologically-detailed cell models, with morphology represented as a\n"
-        "tree of one-dimensional cable segments.");
     cable_cell
-        .def(pybind11::init(
+        .def(py::init(
             [](const arb::morphology& m, const arb::decor& d, const std::optional<label_dict_proxy>& l) {
                 if (l) return arb::cable_cell(m, d, l->dict);
                 return arb::cable_cell(m, d);
             }),
-            "morphology"_a,
-             "decor"_a,
-             pybind11::arg_v("labels", pybind11::none(), "Labels"),
+            "morphology"_a, "decor"_a, "labels"_a=py::none(),
             "Construct with a morphology, decor, and label dictionary.")
-        .def(pybind11::init(
+        .def(py::init(
             [](const arb::segment_tree& t, const arb::decor& d, const std::optional<label_dict_proxy>& l) {
                 if (l) return arb::cable_cell({t}, d, l->dict);
                 return arb::cable_cell({t}, d);
             }),
-            "segment_tree"_a,
-             "decor"_a,
-             pybind11::arg_v("labels", pybind11::none(), "Labels"),
+            "segment_tree"_a, "decor"_a, "labels"_a=py::none(),
             "Construct with a morphology derived from a segment tree, decor, and label dictionary.")
         .def_property_readonly("num_branches",
             [](const arb::cable_cell& c) {return c.morphology().num_branches();},
diff --git a/python/example/brunel.py b/python/example/brunel.py
index 00beea6bfa11b86c9719129658237ec80b0edef5..f163b9ab4325e6b6c5f1792c87f26ac6122a3982 100755
--- a/python/example/brunel.py
+++ b/python/example/brunel.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 
-import arbor
+import arbor as A
+from arbor import units as U
 import argparse
 import numpy as np
 from numpy.random import RandomState
@@ -32,7 +33,7 @@ def sample_subset(gen, gid, start, end, m):
     return idx[:m]
 
 
-class brunel_recipe(arbor.recipe):
+class brunel_recipe(A.recipe):
     def __init__(
         self,
         nexc,
@@ -45,7 +46,7 @@ class brunel_recipe(arbor.recipe):
         poiss_lambda,
         seed=42,
     ):
-        arbor.recipe.__init__(self)
+        A.recipe.__init__(self)
 
         # Make sure that in_degree_prop in the interval (0, 1]
         if not 0.0 < in_degree_prop <= 1.0:
@@ -56,7 +57,7 @@ class brunel_recipe(arbor.recipe):
 
         self.ncells_exc_ = nexc
         self.ncells_inh_ = ninh
-        self.delay_ = delay
+        self.delay_ = delay * U.ms
         self.seed_ = seed
 
         # Set up the parameters.
@@ -67,25 +68,25 @@ class brunel_recipe(arbor.recipe):
         self.in_degree_inh_ = round(in_degree_prop * ninh)
         # each cell receives next incoming Poisson sources with mean rate poiss_lambda, which is equivalent
         # to a single Poisson source with mean rate next*poiss_lambda
-        self.lambda_ = next * poiss_lambda
+        self.lambda_ = next * poiss_lambda * U.kHz
 
     def num_cells(self):
         return self.ncells_exc_ + self.ncells_inh_
 
     def cell_kind(self, gid):
-        return arbor.cell_kind.lif
+        return A.cell_kind.lif
 
     def connections_on(self, gid):
         gen = RandomState(gid + self.seed_)
         connections = []
         # Add incoming excitatory connections.
         connections = [
-            arbor.connection((i, "src"), "tgt", self.weight_exc_, self.delay_)
+            A.connection((i, "src"), "tgt", self.weight_exc_, self.delay_)
             for i in sample_subset(gen, gid, 0, self.ncells_exc_, self.in_degree_exc_)
         ]
         # Add incoming inhibitory connections.
         connections += [
-            arbor.connection((i, "src"), "tgt", self.weight_inh_, self.delay_)
+            A.connection((i, "src"), "tgt", self.weight_inh_, self.delay_)
             for i in sample_subset(
                 gen,
                 gid,
@@ -98,19 +99,25 @@ class brunel_recipe(arbor.recipe):
         return connections
 
     def cell_description(self, gid):
-        cell = arbor.lif_cell("src", "tgt")
-        cell.tau_m = 10
-        cell.V_th = 10
-        cell.C_m = 20
-        cell.E_L = 0
-        cell.V_m = 0
-        cell.t_ref = 2
-        return cell
+        return A.lif_cell(
+            "src",
+            "tgt",
+            tau_m=10 * U.ms,
+            V_th=10 * U.mV,
+            V_m=0 * U.mV,
+            E_L=0 * U.mV,
+            C_m=20 * U.pF,
+            t_ref=2 * U.ms,
+        )
 
     def event_generators(self, gid):
-        t0 = 0
-        sched = arbor.poisson_schedule(t0, self.lambda_, gid + self.seed_)
-        return [arbor.event_generator("tgt", self.weight_ext_, sched)]
+        return [
+            A.event_generator(
+                "tgt",
+                self.weight_ext_,
+                A.poisson_schedule(freq=self.lambda_, seed=gid + self.seed_),
+            )
+        ]
 
 
 if __name__ == "__main__":
@@ -228,12 +235,12 @@ if __name__ == "__main__":
         for k, v in vars(opt).items():
             print(f"{k} = {v}")
 
-    context = arbor.context()
-    if arbor.config()["profiling"]:
-        arbor.profiler_initialize(context)
+    context = A.context()
+    if A.config()["profiling"]:
+        A.profiler_initialize(context)
     print(context)
 
-    meters = arbor.meter_manager()
+    meters = A.meter_manager()
     meters.start(context)
 
     recipe = brunel_recipe(
@@ -250,27 +257,27 @@ if __name__ == "__main__":
 
     meters.checkpoint("recipe-create", context)
 
-    hint = arbor.partition_hint()
+    hint = A.partition_hint()
     hint.cpu_group_size = 5000
-    hints = {arbor.cell_kind.lif: hint}
-    decomp = arbor.partition_load_balance(recipe, context, hints)
+    hints = {A.cell_kind.lif: hint}
+    decomp = A.partition_load_balance(recipe, context, hints)
     print(decomp)
 
     meters.checkpoint("load-balance", context)
 
-    sim = arbor.simulation(recipe, context, decomp)
-    sim.record(arbor.spike_recording.all)
+    sim = A.simulation(recipe, context, decomp)
+    sim.record(A.spike_recording.all)
 
     meters.checkpoint("simulation-init", context)
 
-    sim.run(opt.tfinal, opt.dt)
+    sim.run(opt.tfinal * U.ms, opt.dt * U.ms)
 
     meters.checkpoint("simulation-run", context)
 
     # Print profiling information
-    print(arbor.meter_report(meters, context))
-    if arbor.config()["profiling"]:
-        print(arbor.profiler_summary())
+    print(A.meter_report(meters, context))
+    if A.config()["profiling"]:
+        print(A.profiler_summary())
 
     # Print spike times
     print(f"{len(sim.spikes())} spikes generated.")
diff --git a/python/example/calcium_stdp.py b/python/example/calcium_stdp.py
index 4d3d576ea4170ffe5832f971f5f73606ab0f0153..b642beea8cbde1c2febcd71d8e4ad5a0e312fbf1 100644
--- a/python/example/calcium_stdp.py
+++ b/python/example/calcium_stdp.py
@@ -10,19 +10,17 @@
 # The synapse dynamics is affected by additive white noise. The results reproduce the spike
 # timing-dependent plasticity curve for the DP case described in Table S1 (supplemental material).
 
-import arbor
-import random
-import multiprocessing
-import numpy  # You may have to pip install these.
-import pandas  # You may have to pip install these.
-import seaborn  # You may have to pip install these.
+import arbor as A
+from arbor import units as U
+import numpy as np  # You may have to pip install these.
+import pandas as pd  # You may have to pip install these.
+import seaborn as sns  # You may have to pip install these.
 
 # (1) Set simulation paramters
-
-# Spike response delay (ms)
-D = 13.7
-# Spike frequency in Hertz
-f = 1.0
+# Spike response delay
+D = 13.7 * U.ms
+# Spike frequency
+f = 1000 * U.Hz
 # Number of spike pairs
 num_spikes = 30
 # time lag resolution
@@ -32,155 +30,113 @@ stdp_max_dt = 100.0
 # Ensemble size per initial value
 ensemble_per_rho_0 = 100
 # Simulation time step
-dt = 0.1
-# List of initial values for 2 states
+dt = 0.1 * U.ms
+# List of initial values for 2 states; we need a synapse for each sample path
 rho_0 = [0] * ensemble_per_rho_0 + [1] * ensemble_per_rho_0
-# We need a synapse for each sample path
-num_synapses = len(rho_0)
 # Time lags between spike pairs (post-pre: < 0, pre-post: > 0)
-stdp_dt = numpy.arange(-stdp_max_dt, stdp_max_dt + stdp_dt_step, stdp_dt_step)
-
+stdp_dt = np.arange(-stdp_max_dt, stdp_max_dt + stdp_dt_step, stdp_dt_step) * U.ms
+# Time between stimuli
+T = 1000.0 / f
+# Simulation duration
+tfinal = num_spikes * T
 
 # (2) Make the cell
-
 # Create a morphology with a single (cylindrical) segment of length=diameter=6 μm
-tree = arbor.segment_tree()
-tree.append(arbor.mnpos, arbor.mpoint(-3, 0, 0, 3), arbor.mpoint(3, 0, 0, 3), tag=1)
+tree = A.segment_tree()
+tree.append(A.mnpos, (-3, 0, 0, 3), (3, 0, 0, 3), tag=1)
 
 # Define the soma and its midpoint
-labels = arbor.label_dict({"soma": "(tag 1)", "midpoint": "(location 0 0.5)"})
+labels = A.label_dict({"soma": "(tag 1)", "midpoint": "(location 0 0.5)"})
 
 # Create and set up a decor object
 decor = (
-    arbor.decor()
-    .set_property(Vm=-40)
-    .paint('"soma"', arbor.density("pas"))
-    .place('"midpoint"', arbor.synapse("expsyn"), "driving_synapse")
-    .place('"midpoint"', arbor.threshold_detector(-10), "detector")
+    A.decor()
+    .set_property(Vm=-40 * U.mV)
+    .paint('"soma"', A.density("pas"))
+    .place('"midpoint"', A.synapse("expsyn"), "driving_synapse")
+    .place('"midpoint"', A.threshold_detector(-10 * U.mV), "detector")
 )
-for i in range(num_synapses):
-    mech = arbor.mechanism("calcium_based_synapse")
-    mech.set("rho_0", rho_0[i])
-    decor.place('"midpoint"', arbor.synapse(mech), f"calcium_synapse_{i}")
+for ix, rho in enumerate(rho_0):
+    decor.place(
+        '"midpoint"',
+        A.synapse("calcium_based_synapse", rho_0=rho),
+        f"calcium_synapse_{ix}",
+    )
 
 # Create cell
-cell = arbor.cable_cell(tree, decor, labels)
-
-
-# (3) Create extended catalogue including stochastic mechanisms
-
-cable_properties = arbor.neuron_cable_properties()
-cable_properties.catalogue = arbor.default_catalogue()
-cable_properties.catalogue.extend(arbor.stochastic_catalogue(), "")
-
+cell = A.cable_cell(tree, decor, labels)
 
-# (4) Recipe
 
-
-class stdp_recipe(arbor.recipe):
-    def __init__(self, cell, props, gens):
-        arbor.recipe.__init__(self)
+# (3) Recipe
+class stdp_recipe(A.recipe):
+    def __init__(self, cell, time_lags):
+        A.recipe.__init__(self)
         self.the_cell = cell
-        self.the_props = props
-        self.the_gens = gens
+        # create extended catalogue including stochastic mechanisms
+        self.the_props = A.neuron_cable_properties()
+        self.the_props.catalogue.extend(A.stochastic_catalogue(), "")
+        self.time_lags = time_lags
+        self.num = len(time_lags)
 
     def num_cells(self):
-        return 1
+        return self.num
 
-    def cell_kind(self, gid):
-        return arbor.cell_kind.cable
+    def cell_kind(self, _):
+        return A.cell_kind.cable
 
-    def cell_description(self, gid):
+    def cell_description(self, _):
         return self.the_cell
 
-    def global_properties(self, kind):
+    def global_properties(self, _):
         return self.the_props
 
-    def probes(self, gid):
-        return [
-            arbor.cable_probe_point_state_cell("calcium_based_synapse", "rho", "rho")
-        ]
+    def probes(self, _):
+        return [A.cable_probe_point_state_cell("calcium_based_synapse", "rho", "rho")]
 
     def event_generators(self, gid):
-        return self.the_gens
-
-
-# (5) run simulation for a given time lag
-
-
-def run(time_lag):
-    # Time between stimuli
-    T = 1000.0 / f
-
-    # Simulation duration
-    t1 = num_spikes * T
+        # Time difference between post and pre spike including delay
+        d = D - self.time_lags[gid]
+        # Stimulus and sample times
+        t0_post = max(-d.value, 0) * U.ms
+        t0_pre = max(d.value, 0) * U.ms
+        sched_post = A.regular_schedule(t0_post, T, tfinal)
+        sched_pre = A.regular_schedule(t0_pre, T, tfinal)
 
-    # Time difference between post and pre spike including delay
-    d = -time_lag + D
+        # Create strong enough driving stimulus
+        generators = [A.event_generator("driving_synapse", 1.0, sched_post)]
 
-    # Stimulus and sample times
-    t0_post = 0.0 if d >= 0 else -d
-    t0_pre = d if d >= 0 else 0.0
-    stimulus_times_post = numpy.arange(t0_post, t1, T)
-    stimulus_times_pre = numpy.arange(t0_pre, t1, T)
-    sched_post = arbor.explicit_schedule(stimulus_times_post)
-    sched_pre = arbor.explicit_schedule(stimulus_times_pre)
+        # Stimulus for calcium synapses
+        for ix, _ in enumerate(rho_0):
+            # Zero weight -> just modify synaptic weight via stdp
+            generators.append(
+                A.event_generator(f"calcium_synapse_{ix}", 0.0, sched_pre)
+            )
+        return generators
 
-    # Create strong enough driving stimulus
-    generators = [arbor.event_generator("driving_synapse", 1.0, sched_post)]
 
-    # Stimulus for calcium synapses
-    for i in range(num_synapses):
-        # Zero weight -> just modify synaptic weight via stdp
-        generators.append(arbor.event_generator(f"calcium_synapse_{i}", 0.0, sched_pre))
+# (4) run simulation for all lags
+# Create recipe
+rec = stdp_recipe(cell, stdp_dt)
 
-    # Create recipe
-    recipe = stdp_recipe(cell, cable_properties, generators)
+# Create simulation
+print(A.config())
+sim = A.simulation(rec, seed=42)
 
-    # Select one thread and no GPU
-    alloc = arbor.proc_allocation(threads=1, gpu_id=None)
-    context = arbor.context(alloc, mpi=None)
-    domains = arbor.partition_load_balance(recipe, context)
+# Register probe to read out stdp curve
+handles = [
+    sim.sample((gid, "rho"), A.explicit_schedule([tfinal - dt]))
+    for gid in range(len(stdp_dt))
+]
 
-    # Get random seed
-    random_seed = random.getrandbits(64)
+sim.record(A.spike_recording.all)
 
-    # Create simulation
-    sim = arbor.simulation(recipe, context, domains, random_seed)
+# Run simulation
+sim.run(tfinal, dt)
 
-    # Register prope to read out stdp curve
-    handle = sim.sample((0, "rho"), arbor.explicit_schedule([t1 - dt]))
 
-    # Run simulation
-    sim.run(t1, dt)
-
-    # Process sampled data
-    data, meta = sim.samples(handle)[0]
-    data_down = data[-1, 1 : ensemble_per_rho_0 + 1]
-    data_up = data[-1, ensemble_per_rho_0 + 1 :]
-    # Initial fraction of synapses in DOWN state
-    beta = 0.5
-    # Synaptic strength ratio UP to DOWN (w1/w0)
-    b = 5
-    # Transition indicator form DOWN to UP
-    P_UA = (data_down > 0.5).astype(float)
-    # Transition indicator from UP to DOWN
-    P_DA = (data_up < 0.5).astype(float)
-    # Return change in synaptic strength
-    ds_A = (
-        (1 - P_UA) * beta
-        + P_DA * (1 - beta)
-        + b * (P_UA * beta + (1 - P_DA) * (1 - beta))
-    ) / (beta + (1 - beta) * b)
-    return pandas.DataFrame({"ds": ds_A, "ms": time_lag, "type": "Arbor"})
-
-
-with multiprocessing.Pool() as p:
-    results = p.map(run, stdp_dt)
-
-results = map(run, stdp_dt)
-
-ref = numpy.array(
+# (5) Process sampled data
+# Add reference
+ref = np.array(
     [
         [-100, 0.9793814432989691],
         [-95, 0.981715028725338],
@@ -225,11 +181,30 @@ ref = numpy.array(
         [100, 0.9918730512544945],
     ]
 )
-df_ref = pandas.DataFrame({"ds": ref[:, 1], "ms": ref[:, 0], "type": "Reference"})
 
-df = pandas.concat(results, ignore_index=True)
-df = pandas.concat([df, df_ref], ignore_index=True)
-plt = seaborn.relplot(kind="line", data=df, x="ms", y="ds", hue="type")
+results = [pd.DataFrame({"ds": ref[:, 1], "ms": ref[:, 0], "type": "Reference"})]
+for handle, time_lag in zip(handles, stdp_dt):
+    data, _ = sim.samples(handle)[0]
+    data_down = data[-1, 1 : ensemble_per_rho_0 + 1]
+    data_up = data[-1, ensemble_per_rho_0 + 1 :]
+    # Initial fraction of synapses in DOWN state
+    beta = 0.5
+    # Synaptic strength ratio UP to DOWN (w1/w0)
+    b = 5
+    # Transition indicator form DOWN to UP
+    P_UA = (data_down > 0.5).astype(float)
+    # Transition indicator from UP to DOWN
+    P_DA = (data_up < 0.5).astype(float)
+    # Return change in synaptic strength
+    ds_A = (
+        (1 - P_UA) * beta
+        + P_DA * (1 - beta)
+        + b * (P_UA * beta + (1 - P_DA) * (1 - beta))
+    ) / (beta + (1 - beta) * b)
+    results.append(pd.DataFrame({"ds": ds_A, "ms": time_lag.value, "type": "Arbor"}))
+
+df = pd.concat(results, ignore_index=True)
+plt = sns.relplot(kind="line", data=df, x="ms", y="ds", hue="type")
 plt.set_xlabels("lag time difference (ms)")
 plt.set_ylabels("change in synaptic strenght (after/before)")
 plt._legend.set_title("")
diff --git a/python/example/diffusion.py b/python/example/diffusion.py
index cc51201a6ccc53db5459f13e6adef73db3f5d6fc..8dda5e78f7fe3d4bbbf2188f95aa3bd5de2161d3 100644
--- a/python/example/diffusion.py
+++ b/python/example/diffusion.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 
 import arbor as A
+from arbor import units as U
 import seaborn as sns
 import matplotlib.pyplot as plt
 
@@ -15,35 +16,43 @@ class recipe(A.recipe):
     def num_cells(self):
         return 1
 
-    def cell_kind(self, gid):
+    def cell_kind(self, _):
         return A.cell_kind.cable
 
-    def cell_description(self, gid):
+    def cell_description(self, _):
         return self.the_cell
 
-    def probes(self, gid):
+    def probes(self, _):
         return self.the_probes
 
-    def global_properties(self, kind):
+    def global_properties(self, _):
         return self.the_props
 
-    def event_generators(self, gid):
-        return [A.event_generator("Zap", 0.005, A.explicit_schedule([0.0]))]
+    def event_generators(self, _):
+        return [A.event_generator("Zap", 0.005, A.explicit_schedule([0.0 * U.ms]))]
 
 
 tree = A.segment_tree()
-s = tree.append(A.mnpos, A.mpoint(-3, 0, 0, 3), A.mpoint(3, 0, 0, 3), tag=1)
-_ = tree.append(s, A.mpoint(3, 0, 0, 1), A.mpoint(33, 0, 0, 1), tag=3)
-
-dec = A.decor()
-dec.set_ion("na", int_con=0.0, diff=0.005)
-dec.place("(location 0 0.5)", A.synapse("inject/x=na", {"alpha": 200.0}), "Zap")
-dec.paint("(all)", A.density("decay/x=na"))
-dec.discretization(A.cv_policy("(max-extent 5)"))
-
-# Set up ion diffusion
-dec.set_ion("na", int_con=1.0, ext_con=140, rev_pot=50, diff=0.005)
-dec.paint("(tag 1)", ion_name="na", int_con=100.0, diff=0.01)
+s = A.mnpos
+s = tree.append(s, (-3, 0, 0, 3), (3, 0, 0, 3), tag=1)
+_ = tree.append(s, (3, 0, 0, 1), (33, 0, 0, 1), tag=3)
+
+dec = (
+    A.decor()
+    .set_ion("na", int_con=0.0 * U.mM, diff=0.005 * U.m2 / U.s)
+    .place("(location 0 0.5)", A.synapse("inject/x=na", {"alpha": 200.0}), "Zap")
+    .paint("(all)", A.density("decay/x=na"))
+    .discretization(A.cv_policy("(max-extent 5)"))
+    # Set up ion diffusion
+    .set_ion(
+        "na",
+        int_con=1.0 * U.mM,
+        ext_con=140 * U.mM,
+        rev_pot=50 * U.mV,
+        diff=0.005 * U.m2 / U.s,
+    )
+    .paint("(tag 1)", ion="na", int_con=100.0 * U.mM, diff=0.01 * U.m2 / U.s)
+)
 
 prb = [
     A.cable_probe_ion_diff_concentration_cell("na", "nad"),
@@ -51,9 +60,9 @@ prb = [
 cel = A.cable_cell(tree, dec)
 rec = recipe(cel, prb)
 sim = A.simulation(rec)
-hdl = (sim.sample((0, "nad"), A.regular_schedule(0.1)),)
+hdl = (sim.sample((0, "nad"), A.regular_schedule(0.1 * U.ms)),)
 
-sim.run(tfinal=0.5)
+sim.run(tfinal=0.5 * U.ms)
 
 sns.set_theme()
 fg, ax = plt.subplots()
diff --git a/python/example/dynamic-catalogue.py b/python/example/dynamic-catalogue.py
index 6ed1a83f71fd1f4dd613252439cccbe20106127d..da14b77fca8c2178695092f29dd04d847abc7f12 100644
--- a/python/example/dynamic-catalogue.py
+++ b/python/example/dynamic-catalogue.py
@@ -2,20 +2,21 @@
 
 from pathlib import Path
 
-import arbor as arb
+import arbor as A
+from arbor import units as U
 
 cat = Path("cat-catalogue.so").resolve()
 
 
-class recipe(arb.recipe):
+class recipe(A.recipe):
     def __init__(self):
-        arb.recipe.__init__(self)
-        self.tree = arb.segment_tree()
-        self.tree.append(arb.mnpos, (0, 0, 0, 10), (1, 0, 0, 10), 1)
-        self.props = arb.neuron_cable_properties()
-        self.props.catalogue = arb.load_catalogue(cat)
-        d = arb.decor().paint("(all)", "dummy").set_property(Vm=0.0)
-        self.cell = arb.cable_cell(self.tree, d)
+        A.recipe.__init__(self)
+        self.tree = A.segment_tree()
+        self.tree.append(A.mnpos, (0, 0, 0, 10), (1, 0, 0, 10), 1)
+        self.props = A.neuron_cable_properties()
+        self.props.catalogue = A.load_catalogue(cat)
+        d = A.decor().paint("(all)", "dummy").set_property(Vm=0.0 * U.mV)
+        self.cell = A.cable_cell(self.tree, d)
 
     def global_properties(self, _):
         return self.props
@@ -24,7 +25,7 @@ class recipe(arb.recipe):
         return 1
 
     def cell_kind(self, gid):
-        return arb.cell_kind.cable
+        return A.cell_kind.cable
 
     def cell_description(self, gid):
         return self.cell
@@ -40,5 +41,5 @@ where <arbor> is the location of the arbor source tree."""
     exit(1)
 
 rcp = recipe()
-sim = arb.simulation(rcp)
-sim.run(tfinal=30)
+sim = A.simulation(rcp)
+sim.run(tfinal=30 * U.ms)
diff --git a/python/example/gap_junctions.py b/python/example/gap_junctions.py
index 9dd5ac27fac856d6b1aab291e72560afd5110b14..67c028a661e8a3af3cf8cbc473463276eea93423 100644
--- a/python/example/gap_junctions.py
+++ b/python/example/gap_junctions.py
@@ -1,8 +1,9 @@
 #!/usr/bin/env python3
 
-import arbor
-import pandas
-import seaborn
+import arbor as A
+from arbor import units as U
+import pandas as pd
+import seaborn as sns
 import matplotlib.pyplot as plt
 
 # Construct chains of cells linked with gap junctions,
@@ -20,18 +21,16 @@ import matplotlib.pyplot as plt
 
 def make_cable_cell(gid):
     # Build a segment tree
-    tree = arbor.segment_tree()
+    tree = A.segment_tree()
 
     # Soma with radius 5 μm and length 2 * radius = 10 μm, (tag = 1)
-    s = tree.append(
-        arbor.mnpos, arbor.mpoint(-10, 0, 0, 5), arbor.mpoint(0, 0, 0, 5), tag=1
-    )
+    s = tree.append(A.mnpos, A.mpoint(-10, 0, 0, 5), A.mpoint(0, 0, 0, 5), tag=1)
 
     # Single dendrite with radius 2 μm and length 40 μm, (tag = 2)
-    tree.append(s, arbor.mpoint(0, 0, 0, 2), arbor.mpoint(40, 0, 0, 2), tag=2)
+    tree.append(s, A.mpoint(0, 0, 0, 2), A.mpoint(40, 0, 0, 2), tag=2)
 
     # Label dictionary for cell components
-    labels = arbor.label_dict(
+    labels = A.label_dict(
         {
             # Mark location for synapse site at midpoint of dendrite (branch 0  soma + dendrite)
             "synapse_site": "(location 0 0.6)",
@@ -44,26 +43,26 @@ def make_cable_cell(gid):
 
     # Paint dynamics onto the cell, hh on soma and passive properties on dendrite
     decor = (
-        arbor.decor()
-        .paint('"soma"', arbor.density("hh"))
-        .paint('"dend"', arbor.density("pas"))
+        A.decor()
+        .paint('"soma"', A.density("hh"))
+        .paint('"dend"', A.density("pas"))
         # Attach one synapse and gap junction each on their labeled sites
-        .place('"synapse_site"', arbor.synapse("expsyn"), "syn")
-        .place('"gj_site"', arbor.junction("gj"), "gj")
+        .place('"synapse_site"', A.synapse("expsyn"), "syn")
+        .place('"gj_site"', A.junction("gj"), "gj")
         # Attach detector to cell root
-        .place('"root"', arbor.threshold_detector(-10), "detector")
+        .place('"root"', A.threshold_detector(-10 * U.mV), "detector")
     )
 
-    return arbor.cable_cell(tree, decor, labels)
+    return A.cable_cell(tree, decor, labels)
 
 
 # Create a recipe that generates connected chains of cells
-class chain_recipe(arbor.recipe):
+class chain_recipe(A.recipe):
     def __init__(self, ncells_per_chain, nchains):
-        arbor.recipe.__init__(self)
+        A.recipe.__init__(self)
         self.nchains = nchains
         self.ncells_per_chain = ncells_per_chain
-        self.props = arbor.neuron_cable_properties()
+        self.props = A.neuron_cable_properties()
 
     def num_cells(self):
         return self.ncells_per_chain * self.nchains
@@ -72,7 +71,7 @@ class chain_recipe(arbor.recipe):
         return make_cable_cell(gid)
 
     def cell_kind(self, gid):
-        return arbor.cell_kind.cable
+        return A.cell_kind.cable
 
     # Create synapse connection between last cell of one chain and first cell of following chain
     def connections_on(self, gid):
@@ -80,9 +79,7 @@ class chain_recipe(arbor.recipe):
             return []
         else:
             src = gid - 1
-            w = 0.05
-            d = 10
-            return [arbor.connection((src, "detector"), "syn", w, d)]
+            return [A.connection((src, "detector"), "syn", 0.05, 10 * U.ms)]
 
     # Create gap junction connections between a cell within a chain and its neighbor(s)
     def gap_junctions_on(self, gid):
@@ -95,23 +92,23 @@ class chain_recipe(arbor.recipe):
         prev_cell = gid - 1
 
         if next_cell < chain_end:
-            conns.append(arbor.gap_junction_connection((gid + 1, "gj"), "gj", 0.015))
+            conns.append(A.gap_junction_connection((gid + 1, "gj"), "gj", 0.015))
         if prev_cell >= chain_begin:
-            conns.append(arbor.gap_junction_connection((gid - 1, "gj"), "gj", 0.015))
+            conns.append(A.gap_junction_connection((gid - 1, "gj"), "gj", 0.015))
 
         return conns
 
     # Event generator at first cell
     def event_generators(self, gid):
         if gid == 0:
-            sched = arbor.explicit_schedule([1])
+            sched = A.explicit_schedule([1 * U.ms])
             weight = 0.1
-            return [arbor.event_generator("syn", weight, sched)]
+            return [A.event_generator("syn", weight, sched)]
         return []
 
     # Place a probe at the root of each cell
     def probes(self, gid):
-        return [arbor.cable_probe_membrane_voltage('"root"', "Um")]
+        return [A.cable_probe_membrane_voltage('"root"', "Um")]
 
     def global_properties(self, kind):
         return self.props
@@ -130,18 +127,18 @@ ncells = nchains * ncells_per_chain
 recipe = chain_recipe(ncells_per_chain, nchains)
 
 # Create a default simulation
-sim = arbor.simulation(recipe)
+sim = A.simulation(recipe)
 
 # Set spike generators to record
-sim.record(arbor.spike_recording.all)
+sim.record(A.spike_recording.all)
 
 # Sampler
 handles = [
-    sim.sample((gid, "Um"), arbor.regular_schedule(0.1)) for gid in range(ncells)
+    sim.sample((gid, "Um"), A.regular_schedule(0.1 * U.ms)) for gid in range(ncells)
 ]
 
-# Run simulation for 100 ms
-sim.run(100)
+# Run simulation
+sim.run(100 * U.ms)
 print("Simulation finished")
 
 # Print spike times
@@ -155,11 +152,11 @@ df_list = []
 for gid in range(ncells):
     samples, meta = sim.samples(handles[gid])[0]
     df_list.append(
-        pandas.DataFrame(
+        pd.DataFrame(
             {"t/ms": samples[:, 0], "U/mV": samples[:, 1], "Cell": f"cell {gid}"}
         )
     )
 
-df = pandas.concat(df_list, ignore_index=True)
-seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV", hue="Cell", errorbar=None)
+df = pd.concat(df_list, ignore_index=True)
+sns.relplot(data=df, kind="line", x="t/ms", y="U/mV", hue="Cell", errorbar=None)
 plt.show()
diff --git a/python/example/network_ring.py b/python/example/network_ring.py
index 73f7a4ce6aff4975777b3c3566393c48faf9c6d9..7dc460d4945c67ca2cfad787603605374a5cc455 100755
--- a/python/example/network_ring.py
+++ b/python/example/network_ring.py
@@ -1,54 +1,48 @@
 #!/usr/bin/env python3
 # This script is included in documentation. Adapt line numbers if touched.
 
-import arbor
+import arbor as A
+from arbor import units as U
 import pandas  # You may have to pip install these
 import seaborn  # You may have to pip install these
 from math import sqrt
 
-print(arbor.__path__)
-
-# Construct a cell with the following morphology.
-# The soma (at the root of the tree) is marked 's', and
-# the end of each branch i is marked 'bi'.
-#
-#         b1
-#        /
-# s----b0
-#        \
-#         b2
-
 
 def make_cable_cell(gid):
     # (1) Build a segment tree
-    tree = arbor.segment_tree()
-
+    # The dendrite (dend) attaches to the soma and has two simple segments
+    # attached.
+    #
+    #             left
+    #            /
+    # soma - dend
+    #            \
+    #             right
+    tree = A.segment_tree()
+    root = A.mnpos
     # Soma (tag=1) with radius 6 μm, modelled as cylinder of length 2*radius
-    s = tree.append(
-        arbor.mnpos, arbor.mpoint(-12, 0, 0, 6), arbor.mpoint(0, 0, 0, 6), tag=1
-    )
-
-    # (b0) Single dendrite (tag=3) of length 50 μm and radius 2 μm attached to soma.
-    b0 = tree.append(s, arbor.mpoint(0, 0, 0, 2), arbor.mpoint(50, 0, 0, 2), tag=3)
-
+    soma = tree.append(root, (-12, 0, 0, 6), (0, 0, 0, 6), tag=1)
+    # Single dendrite (tag=3) of length 50 μm and radius 2 μm attached to soma.
+    dend = tree.append(soma, (0, 0, 0, 2), (50, 0, 0, 2), tag=3)
     # Attach two dendrites (tag=3) of length 50 μm to the end of the first dendrite.
-    # (b1) Radius tapers from 2 to 0.5 μm over the length of the dendrite.
-    tree.append(
-        b0,
-        arbor.mpoint(50, 0, 0, 2),
-        arbor.mpoint(50 + 50 / sqrt(2), 50 / sqrt(2), 0, 0.5),
+    # Radius tapers from 2 to 0.5 μm over the length of the dendrite.
+    l = 50 / sqrt(2)
+    _ = tree.append(
+        dend,
+        (50, 0, 0, 2),
+        (50 + l, l, 0, 0.5),
         tag=3,
     )
-    # (b2) Constant radius of 1 μm over the length of the dendrite.
-    tree.append(
-        b0,
-        arbor.mpoint(50, 0, 0, 1),
-        arbor.mpoint(50 + 50 / sqrt(2), -50 / sqrt(2), 0, 1),
+    # Constant radius of 1 μm over the length of the dendrite.
+    _ = tree.append(
+        dend,
+        (50, 0, 0, 1),
+        (50 + l, -l, 0, 1),
         tag=3,
     )
 
     # Associate labels to tags
-    labels = arbor.label_dict(
+    labels = A.label_dict(
         {
             "soma": "(tag 1)",
             "dend": "(tag 3)",
@@ -61,29 +55,27 @@ def make_cable_cell(gid):
 
     # (3) Create a decor and a cable_cell
     decor = (
-        arbor.decor()
+        A.decor()
         # Put hh dynamics on soma, and passive properties on the dendrites.
-        .paint('"soma"', arbor.density("hh")).paint('"dend"', arbor.density("pas"))
+        .paint('"soma"', A.density("hh")).paint('"dend"', A.density("pas"))
         # (4) Attach a single synapse.
-        .place('"synapse_site"', arbor.synapse("expsyn"), "syn")
+        .place('"synapse_site"', A.synapse("expsyn"), "syn")
         # Attach a detector with threshold of -10 mV.
-        .place('"root"', arbor.threshold_detector(-10), "detector")
+        .place('"root"', A.threshold_detector(-10 * U.mV), "detector")
     )
 
-    return arbor.cable_cell(tree, decor, labels)
+    return A.cable_cell(tree, decor, labels)
 
 
 # (5) Create a recipe that generates a network of connected cells.
-class ring_recipe(arbor.recipe):
+class ring_recipe(A.recipe):
     def __init__(self, ncells):
-        # The base C++ class constructor must be called first, to ensure that
-        # all memory in the C++ class is initialized correctly.
-        arbor.recipe.__init__(self)
+        # Base class constructor must be called first for proper initialization.
+        A.recipe.__init__(self)
         self.ncells = ncells
-        self.props = arbor.neuron_cable_properties()
+        self.props = A.neuron_cable_properties()
 
-    # (6) The num_cells method that returns the total number of cells in the model
-    # must be implemented.
+    # (6) Returns the total number of cells in the model; must be implemented.
     def num_cells(self):
         return self.ncells
 
@@ -91,31 +83,32 @@ class ring_recipe(arbor.recipe):
     def cell_description(self, gid):
         return make_cable_cell(gid)
 
-    # The kind method returns the type of cell with gid.
-    # Note: this must agree with the type returned by cell_description.
-    def cell_kind(self, gid):
-        return arbor.cell_kind.cable
+    # Return the type of cell; must be implemented and match cell_description.
+    def cell_kind(self, _):
+        return A.cell_kind.cable
 
-    # (8) Make a ring network. For each gid, provide a list of incoming connections.
+    # (8) For each gid, provide a list of incoming connections.
     def connections_on(self, gid):
+        # This defines the ring by connecting from the last gid. The first src
+        # comes from the _last_ gid, closing the ring.
         src = (gid - 1) % self.ncells
         w = 0.01  # 0.01 μS on expsyn
-        d = 5  # ms delay
-        return [arbor.connection((src, "detector"), "syn", w, d)]
+        d = 5 * U.ms
+        return [A.connection((src, "detector"), "syn", w, d)]
 
     # (9) Attach a generator to the first cell in the ring.
     def event_generators(self, gid):
         if gid == 0:
-            sched = arbor.explicit_schedule([1])  # one event at 1 ms
+            sched = A.explicit_schedule([1 * U.ms])  # one event at 1 ms
             weight = 0.1  # 0.1 μS on expsyn
-            return [arbor.event_generator("syn", weight, sched)]
+            return [A.event_generator("syn", weight, sched)]
         return []
 
     # (10) Place a probe at the root of each cell.
     def probes(self, gid):
-        return [arbor.cable_probe_membrane_voltage('"root"', "Um")]
+        return [A.cable_probe_membrane_voltage('"root"', "Um")]
 
-    def global_properties(self, kind):
+    def global_properties(self, _):
         return self.props
 
 
@@ -129,18 +122,18 @@ recipe = ring_recipe(ncells)
 # - Use GPU if present
 # - No MPI
 # Other constructors of simulation can be used to change all of these.
-sim = arbor.simulation(recipe)
+sim = A.simulation(recipe)
 
 # (13) Set spike generators to record
-sim.record(arbor.spike_recording.all)
+sim.record(A.spike_recording.all)
 
 # (14) Attach a sampler to the voltage probe on cell 0. Sample rate of 10 sample every ms.
 handles = [
-    sim.sample((gid, "Um"), arbor.regular_schedule(0.1)) for gid in range(ncells)
+    sim.sample((gid, "Um"), A.regular_schedule(0.1 * U.ms)) for gid in range(ncells)
 ]
 
 # (15) Run simulation for 100 ms
-sim.run(100)
+sim.run(100 * U.ms)
 print("Simulation finished")
 
 # (16) Print spike times
@@ -150,16 +143,15 @@ for sp in sim.spikes():
 
 # (17) Plot the recorded voltages over time.
 print("Plotting results ...")
-df_list = []
+dfs = []
 for gid in range(ncells):
     samples, meta = sim.samples(handles[gid])[0]
-    df_list.append(
+    dfs.append(
         pandas.DataFrame(
             {"t/ms": samples[:, 0], "U/mV": samples[:, 1], "Cell": f"cell {gid}"}
         )
     )
-
-df = pandas.concat(df_list, ignore_index=True)
+df = pandas.concat(dfs, ignore_index=True)
 seaborn.relplot(
     data=df, kind="line", x="t/ms", y="U/mV", hue="Cell", errorbar=None
 ).savefig("network_ring_result.svg")
diff --git a/python/example/network_ring_gpu.py b/python/example/network_ring_gpu.py
index e54e099992539dd2aeb98f5a3042c3672025b34a..fb46bdb565ca144bbdfefcba324dd3afca0f4c14 100644
--- a/python/example/network_ring_gpu.py
+++ b/python/example/network_ring_gpu.py
@@ -1,9 +1,10 @@
 #!/usr/bin/env python3
 # This script is included in documentation. Adapt line numbers if touched.
 
-import arbor
-import pandas  # You may have to pip install these
-import seaborn  # You may have to pip install these
+import arbor as A
+from arbor import units as U
+import pandas as pd  # You may have to pip install these
+import seaborn as sns  # You may have to pip install these
 from math import sqrt
 
 # Construct a cell with the following morphology.
@@ -17,37 +18,36 @@ from math import sqrt
 #         b2
 
 
-def make_cable_cell(gid):
+def make_cable_cell(_):
     # (1) Build a segment tree
-    tree = arbor.segment_tree()
+    tree = A.segment_tree()
 
     # Soma (tag=1) with radius 6 μm, modelled as cylinder of length 2*radius
-    s = tree.append(
-        arbor.mnpos, arbor.mpoint(-12, 0, 0, 6), arbor.mpoint(0, 0, 0, 6), tag=1
-    )
+    s = A.mnpos
+    s = tree.append(s, (-12, 0, 0, 6), (0, 0, 0, 6), tag=1)
 
     # Single dendrite (tag=3) of length 50 μm and radius 2 μm attached to soma.
-    b0 = tree.append(s, arbor.mpoint(0, 0, 0, 2), arbor.mpoint(50, 0, 0, 2), tag=3)
+    b0 = tree.append(s, (0, 0, 0, 2), (50, 0, 0, 2), tag=3)
 
     # Attach two dendrites (tag=3) of length 50 μm to the end of the first dendrite.
     # As there's no further use for them, we discard the returned handles.
     # (b1) Radius tapers from 2 to 0.5 μm over the length of the dendrite.
     _ = tree.append(
         b0,
-        arbor.mpoint(50, 0, 0, 2),
-        arbor.mpoint(50 + 50 / sqrt(2), 50 / sqrt(2), 0, 0.5),
+        (50, 0, 0, 2),
+        (50 + 50 / sqrt(2), 50 / sqrt(2), 0, 0.5),
         tag=3,
     )
     # (b2) Constant radius of 1 μm over the length of the dendrite.
     _ = tree.append(
         b0,
-        arbor.mpoint(50, 0, 0, 1),
-        arbor.mpoint(50 + 50 / sqrt(2), -50 / sqrt(2), 0, 1),
+        (50, 0, 0, 1),
+        (50 + 50 / sqrt(2), -50 / sqrt(2), 0, 1),
         tag=3,
     )
 
     # Associate labels to tags
-    labels = arbor.label_dict()
+    labels = A.label_dict()
     labels["soma"] = "(tag 1)"
     labels["dend"] = "(tag 3)"
 
@@ -57,31 +57,31 @@ def make_cable_cell(gid):
     labels["root"] = "(root)"
 
     # (3) Create a decor and a cable_cell
-    decor = arbor.decor()
+    decor = A.decor()
 
     # Put hh dynamics on soma, and passive properties on the dendrites.
-    decor.paint('"soma"', arbor.density("hh"))
-    decor.paint('"dend"', arbor.density("pas"))
+    decor.paint('"soma"', A.density("hh"))
+    decor.paint('"dend"', A.density("pas"))
 
     # (4) Attach a single synapse.
-    decor.place('"synapse_site"', arbor.synapse("expsyn"), "syn")
+    decor.place('"synapse_site"', A.synapse("expsyn"), "syn")
 
     # Attach a detector with threshold of -10 mV.
-    decor.place('"root"', arbor.threshold_detector(-10), "detector")
+    decor.place('"root"', A.threshold_detector(-10 * U.mV), "detector")
 
-    cell = arbor.cable_cell(tree, decor, labels)
+    cell = A.cable_cell(tree, decor, labels)
 
     return cell
 
 
 # (5) Create a recipe that generates a network of connected cells.
-class ring_recipe(arbor.recipe):
+class ring_recipe(A.recipe):
     def __init__(self, ncells):
         # The base C++ class constructor must be called first, to ensure that
         # all memory in the C++ class is initialized correctly.
-        arbor.recipe.__init__(self)
+        A.recipe.__init__(self)
         self.ncells = ncells
-        self.props = arbor.neuron_cable_properties()
+        self.props = A.neuron_cable_properties()
 
     # (6) The num_cells method that returns the total number of cells in the model
     # must be implemented.
@@ -94,40 +94,40 @@ class ring_recipe(arbor.recipe):
 
     # The kind method returns the type of cell with gid.
     # Note: this must agree with the type returned by cell_description.
-    def cell_kind(self, gid):
-        return arbor.cell_kind.cable
+    def cell_kind(self, _):
+        return A.cell_kind.cable
 
     # (8) Make a ring network. For each gid, provide a list of incoming connections.
     def connections_on(self, gid):
         src = (gid - 1) % self.ncells
         w = 0.01  # 0.01 μS on expsyn
         d = 5  # ms delay
-        return [arbor.connection((src, "detector"), "syn", w, d)]
+        return [A.connection((src, "detector"), "syn", w, d * U.ms)]
 
     # (9) Attach a generator to the first cell in the ring.
     def event_generators(self, gid):
         if gid == 0:
-            sched = arbor.explicit_schedule([1])  # one event at 1 ms
+            sched = A.explicit_schedule([1 * U.ms])  # one event at 1 ms
             weight = 0.1  # 0.1 μS on expsyn
-            return [arbor.event_generator("syn", weight, sched)]
+            return [A.event_generator("syn", weight, sched)]
         return []
 
     # (10) Place a probe at the root of each cell.
-    def probes(self, gid):
-        return [arbor.cable_probe_membrane_voltage('"root"', "Um")]
+    def probes(self, _):
+        return [A.cable_probe_membrane_voltage('"root"', "Um")]
 
-    def global_properties(self, kind):
+    def global_properties(self, _):
         return self.props
 
 
 # (11) Set up the hardware context
 # gpu_id set to None will not use a GPU.
 # gpu_id=0 instructs Arbor to the first GPU present in your system
-context = arbor.context(gpu_id=None)
+context = A.context(gpu_id=None)
 print(context)
 
 # (12) Set up and start the meter manager
-meters = arbor.meter_manager()
+meters = A.meter_manager()
 meters.start(context)
 
 # (13) Instantiate recipe
@@ -136,36 +136,38 @@ recipe = ring_recipe(ncells)
 meters.checkpoint("recipe-create", context)
 
 # (14) Define a hint at to the execution.
-hint = arbor.partition_hint()
+hint = A.partition_hint()
 hint.prefer_gpu = True
 hint.gpu_group_size = 1000
 print(hint)
-hints = {arbor.cell_kind.cable: hint}
+hints = {A.cell_kind.cable: hint}
 
 # (15) Domain decomp
-decomp = arbor.partition_load_balance(recipe, context, hints)
+decomp = A.partition_load_balance(recipe, context, hints)
 print(decomp)
 meters.checkpoint("load-balance", context)
 
 # (16) Simulation init and set spike generators to record
-sim = arbor.simulation(recipe, context, decomp)
-sim.record(arbor.spike_recording.all)
-handles = [sim.sample((gid, "Um"), arbor.regular_schedule(1)) for gid in range(ncells)]
+sim = A.simulation(recipe, context, decomp)
+sim.record(A.spike_recording.all)
+handles = [
+    sim.sample((gid, "Um"), A.regular_schedule(1 * U.ms)) for gid in range(ncells)
+]
 meters.checkpoint("simulation-init", context)
 
 # (17) Run simulation
-sim.run(ncells * 5)
+sim.run(ncells * 5 * U.ms)
 print("Simulation finished")
 meters.checkpoint("simulation-run", context)
 
 # (18) Results
 # Print profiling information
-print(f"{arbor.meter_report(meters, context)}")
+print(f"{A.meter_report(meters, context)}")
 
 # Print spike times
 print("spikes:")
-for sp in sim.spikes():
-    print(" ", sp)
+for (gid, lid), t in sim.spikes():
+    print(f" * t={t:.3f}ms gid={gid} lid={lid}")
 
 # Plot the recorded voltages over time.
 print("Plotting results ...")
@@ -173,12 +175,12 @@ df_list = []
 for gid in range(ncells):
     samples, meta = sim.samples(handles[gid])[0]
     df_list.append(
-        pandas.DataFrame(
+        pd.DataFrame(
             {"t/ms": samples[:, 0], "U/mV": samples[:, 1], "Cell": f"cell {gid}"}
         )
     )
 
-df = pandas.concat(df_list, ignore_index=True)
-seaborn.relplot(
+df = pd.concat(df_list, ignore_index=True)
+sns.relplot(
     data=df, kind="line", x="t/ms", y="U/mV", hue="Cell", errorbar=None
 ).savefig("network_ring_gpu_result.svg")
diff --git a/python/example/network_ring_mpi.py b/python/example/network_ring_mpi.py
index 73621685ed8d4db6a63981af5746ca14b6ccbf38..98dfb73b1e5db565f45c2851bf31ce63fd98b54f 100644
--- a/python/example/network_ring_mpi.py
+++ b/python/example/network_ring_mpi.py
@@ -1,8 +1,9 @@
 #!/usr/bin/env python3
 # This script is included in documentation. Adapt line numbers if touched.
 
-import arbor
-import pandas
+import arbor as A
+from arbor import units as U
+import pandas as pd
 from math import sqrt
 
 # Run with srun -n NJOBS python network_ring_mpi.py
@@ -20,35 +21,34 @@ from math import sqrt
 
 def make_cable_cell(gid):
     # (1) Build a segment tree
-    tree = arbor.segment_tree()
+    tree = A.segment_tree()
 
     # Soma (tag=1) with radius 6 μm, modelled as cylinder of length 2*radius
-    s = tree.append(
-        arbor.mnpos, arbor.mpoint(-12, 0, 0, 6), arbor.mpoint(0, 0, 0, 6), tag=1
-    )
+    s = A.mnpos
+    s = tree.append(s, (-12, 0, 0, 6), (0, 0, 0, 6), tag=1)
 
     # Single dendrite (tag=3) of length 50 μm and radius 2 μm attached to soma.
-    b0 = tree.append(s, arbor.mpoint(0, 0, 0, 2), arbor.mpoint(50, 0, 0, 2), tag=3)
+    b0 = tree.append(s, (0, 0, 0, 2), (50, 0, 0, 2), tag=3)
 
     # Attach two dendrites (tag=3) of length 50 μm to the end of the first dendrite.
     # As there's no further use for them, we discard the returned handles.
     # (b1) Radius tapers from 2 to 0.5 μm over the length of the dendrite.
     _ = tree.append(
         b0,
-        arbor.mpoint(50, 0, 0, 2),
-        arbor.mpoint(50 + 50 / sqrt(2), 50 / sqrt(2), 0, 0.5),
+        (50, 0, 0, 2),
+        (50 + 50 / sqrt(2), 50 / sqrt(2), 0, 0.5),
         tag=3,
     )
     # (b2) Constant radius of 1 μm over the length of the dendrite.
     _ = tree.append(
         b0,
-        arbor.mpoint(50, 0, 0, 1),
-        arbor.mpoint(50 + 50 / sqrt(2), -50 / sqrt(2), 0, 1),
+        (50, 0, 0, 1),
+        (50 + 50 / sqrt(2), -50 / sqrt(2), 0, 1),
         tag=3,
     )
 
     # Associate labels to tags
-    labels = arbor.label_dict(
+    labels = A.label_dict(
         {
             "soma": "(tag 1)",
             "dend": "(tag 3)",
@@ -61,26 +61,26 @@ def make_cable_cell(gid):
 
     # (3) Create a decor and a cable_cell
     decor = (
-        arbor.decor()
+        A.decor()
         # Put hh dynamics on soma, and passive properties on the dendrites.
-        .paint('"soma"', arbor.density("hh")).paint('"dend"', arbor.density("pas"))
+        .paint('"soma"', A.density("hh")).paint('"dend"', A.density("pas"))
         # (4) Attach a single synapse.
-        .place('"synapse_site"', arbor.synapse("expsyn"), "syn")
+        .place('"synapse_site"', A.synapse("expsyn"), "syn")
         # Attach a detector with threshold of -10 mV.
-        .place('"root"', arbor.threshold_detector(-10), "detector")
+        .place('"root"', A.threshold_detector(-10), "detector")
     )
 
-    return arbor.cable_cell(tree, decor, labels)
+    return A.cable_cell(tree, decor, labels)
 
 
 # (5) Create a recipe that generates a network of connected cells.
-class ring_recipe(arbor.recipe):
+class ring_recipe(A.recipe):
     def __init__(self, ncells):
         # The base C++ class constructor must be called first, to ensure that
         # all memory in the C++ class is initialized correctly.
-        arbor.recipe.__init__(self)
+        A.recipe.__init__(self)
         self.ncells = ncells
-        self.props = arbor.neuron_cable_properties()
+        self.props = A.neuron_cable_properties()
 
     # (6) The num_cells method that returns the total number of cells in the model
     # must be implemented.
@@ -94,26 +94,26 @@ class ring_recipe(arbor.recipe):
     # The kind method returns the type of cell with gid.
     # Note: this must agree with the type returned by cell_description.
     def cell_kind(self, gid):
-        return arbor.cell_kind.cable
+        return A.cell_kind.cable
 
     # (8) Make a ring network. For each gid, provide a list of incoming connections.
     def connections_on(self, gid):
         src = (gid - 1) % self.ncells
         w = 0.01  # 0.01 μS on expsyn
-        d = 5  # ms delay
-        return [arbor.connection((src, "detector"), "syn", w, d)]
+        d = 5 * U.ms  # delay
+        return [A.connection((src, "detector"), "syn", w, d)]
 
     # (9) Attach a generator to the first cell in the ring.
     def event_generators(self, gid):
         if gid == 0:
-            sched = arbor.explicit_schedule([1])  # one event at 1 ms
+            sched = A.explicit_schedule([1 * U.ms])  # one event at 1 ms
             weight = 0.1  # 0.1 μS on expsyn
-            return [arbor.event_generator("syn", weight, sched)]
+            return [A.event_generator("syn", weight, sched)]
         return []
 
     # (10) Place a probe at the root of each cell.
     def probes(self, gid):
-        return [arbor.cable_probe_membrane_voltage('"root"')]
+        return [A.cable_probe_membrane_voltage('"root"')]
 
     def global_properties(self, kind):
         return self.props
@@ -124,24 +124,24 @@ ncells = 500
 recipe = ring_recipe(ncells)
 
 # (12) Create an MPI communicator, and use it to create a hardware context
-arbor.mpi_init()
-comm = arbor.mpi_comm()
+A.mpi_init()
+comm = A.mpi_comm()
 print(comm)
-context = arbor.context(mpi=comm)
+context = A.context(mpi=comm)
 print(context)
 
 # (13) Create a default domain decomposition and simulation
-sim = arbor.simulation(recipe, context)
+sim = A.simulation(recipe, context)
 
 # (14) Set spike generators to record
-sim.record(arbor.spike_recording.all)
+sim.record(A.spike_recording.all)
 
 # (15) Attach a sampler to the voltage probe on cell 0. Sample rate of 1 sample every ms.
 # Sampling period increased w.r.t network_ring.py to reduce amount of data
-handles = [sim.sample((gid, 0), arbor.regular_schedule(1)) for gid in range(ncells)]
+handles = [sim.sample((gid, 0), A.regular_schedule(1 * U.ms)) for gid in range(ncells)]
 
 # (16) Run simulation
-sim.run(ncells * 5)
+sim.run(ncells * 5 * U.ms)
 print("Simulation finished")
 
 # (17) Store the recorded voltages
@@ -151,11 +151,11 @@ for gid in range(ncells):
     if len(sim.samples(handles[gid])):
         samples, meta = sim.samples(handles[gid])[0]
         df_list.append(
-            pandas.DataFrame(
+            pd.DataFrame(
                 {"t/ms": samples[:, 0], "U/mV": samples[:, 1], "Cell": f"cell {gid}"}
             )
         )
 
 if len(df_list):
-    df = pandas.concat(df_list, ignore_index=True)
+    df = pd.concat(df_list, ignore_index=True)
     df.to_csv(f"result_mpi_{context.rank}.csv", float_format="%g")
diff --git a/python/example/network_two_cells_gap_junctions.py b/python/example/network_two_cells_gap_junctions.py
index 5a2db8574a2ea1838fcdb70d673233a900ece4cf..c1298230721f514f4bbd83489ef05945b04f7bee 100755
--- a/python/example/network_two_cells_gap_junctions.py
+++ b/python/example/network_two_cells_gap_junctions.py
@@ -1,216 +1,148 @@
 #!/usr/bin/env python3
 
-from builtins import enumerate
-import arbor
-import argparse
+import arbor as A
+from arbor import units as U
+from argparse import ArgumentParser
 import numpy as np
-
-import pandas  # You may have to pip install these.
-import seaborn  # You may have to pip install these.
+import pandas as pd  # You may have to pip install these.
+import seaborn as sns  # You may have to pip install these.
 import matplotlib.pyplot as plt
 
 
-class TwoCellsWithGapJunction(arbor.recipe):
-    def __init__(
-        self, probes, Vms, length, radius, cm, rL, g, gj_g, cv_policy_max_extent
-    ):
+class TwoCellsWithGapJunction(A.recipe):
+    def __init__(self, Vms, length, radius, cm, rL, g, gj_g, max_extent):
         """
-        probes -- list of probes
-
         Vms -- membrane leak potentials of the two cells
         length -- length of cable in μm
         radius -- radius of cable in μm
-        cm -- membrane capacitance in F/m^2
+        cm -- membrane capacitance in F/m²
         rL -- axial resistivity in Ω·cm
-        g -- membrane conductivity in S/cm^2
+        g -- membrane conductivity in S/cm²
         gj_g -- gap junction conductivity in μS
-
-        cv_policy_max_extent -- maximum extent of control volume in μm
+        max_extent -- maximum extent of control volume in μm
         """
 
-        # The base C++ class constructor must be called first, to ensure that
-        # all memory in the C++ class is initialized correctly.
-        arbor.recipe.__init__(self)
-
-        self.the_probes = probes
+        # Call base constructor first to ensure proper initialization
+        A.recipe.__init__(self)
 
-        self.Vms = Vms
-        self.length = length
-        self.radius = radius
-        self.cm = cm
-        self.rL = rL
-        self.g = g
-        self.gj_g = gj_g
-
-        self.cv_policy_max_extent = cv_policy_max_extent
-
-        self.the_props = arbor.neuron_cable_properties()
+        self.Vms = [Vm * U.mV for Vm in Vms]
+        self.length = length * U.um
+        self.radius = radius * U.um
+        self.area = self.length * 2 * np.pi * self.radius
+        self.cm = cm * U.F / U.m2
+        self.rL = rL * U.Ohm * U.cm
+        self.g = g * U.S / U.cm2
+        self.gj_g = gj_g * U.uS
+        self.max_extent = max_extent
+        self.the_props = A.neuron_cable_properties()
 
     def num_cells(self):
         return 2
 
-    def cell_kind(self, gid):
-        assert gid in [0, 1]
-        return arbor.cell_kind.cable
+    def cell_kind(self, _):
+        return A.cell_kind.cable
 
-    def global_properties(self, kind):
-        assert kind == arbor.cell_kind.cable
+    def global_properties(self, _):
         return self.the_props
 
     def cell_description(self, gid):
-        """A high level description of the cell with global identifier gid.
+        tree = A.segment_tree()
+        r, l = self.radius.value, self.length.value
+        tree.append(A.mnpos, (0, 0, 0, r), (l, 0, 0, r), tag=1)
 
-        For example the morphology, synapses and ion channels required
-        to build a multi-compartment neuron.
-        """
-        assert gid in [0, 1]
-
-        tree = arbor.segment_tree()
-
-        tree.append(
-            arbor.mnpos,
-            arbor.mpoint(0, 0, 0, self.radius),
-            arbor.mpoint(self.length, 0, 0, self.radius),
-            tag=1,
-        )
-
-        labels = arbor.label_dict({"cell": "(tag 1)", "gj_site": "(location 0 0.5)"})
+        labels = A.label_dict({"midpoint": "(location 0 0.5)"})
 
         decor = (
-            arbor.decor()
-            .set_property(Vm=self.Vms[gid])
-            .set_property(cm=self.cm)
-            .set_property(rL=self.rL)
-            # add a gap junction mechanism at the "gj_site" location and label that specific mechanism on that location "gj_label"
-            .place('"gj_site"', arbor.junction("gj", g=self.gj_g), "gj_label")
-            .paint('"cell"', arbor.density(f"pas/e={self.Vms[gid]}", g=self.g))
+            A.decor()
+            .set_property(Vm=self.Vms[gid], cm=self.cm, rL=self.rL)
+            .place('"midpoint"', A.junction("gj", g=self.gj_g.value), "gj")
+            .paint("(all)", A.density(f"pas/e={self.Vms[gid].value}", g=self.g.value))
         )
 
-        if self.cv_policy_max_extent is not None:
-            policy = arbor.cv_policy_max_extent(self.cv_policy_max_extent)
-            decor.discretization(policy)
+        if self.max_extent is not None:
+            decor.discretization(A.cv_policy_max_extent(self.max_extent))
         else:
-            decor.discretization(arbor.cv_policy_single())
+            decor.discretization(A.cv_policy_single())
 
-        return arbor.cable_cell(tree, decor, labels)
+        return A.cable_cell(tree, decor, labels)
 
     def gap_junctions_on(self, gid):
-        # create a bidirectional gap junction from cell 0 at label "gj_label" to cell 1 at label "gj_label" and back.
-        if gid == 0:
-            tgt = 1
-        elif gid == 1:
-            tgt = 0
-        else:
-            raise RuntimeError("Invalid GID for example.")
-        return [arbor.gap_junction_connection((tgt, "gj_label"), "gj_label", 1)]
-
-    def probes(self, gid):
-        assert gid in [0, 1]
-        return self.the_probes
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Two cells connected via a gap junction"
+        return [A.gap_junction_connection(((gid + 1) % 2, "gj"), "gj", 1)]
+
+    def probes(self, _):
+        return [A.cable_probe_membrane_voltage('"midpoint"', "Um")]
+
+
+# parse the command line arguments
+parser = ArgumentParser(description="Two cells connected via a gap junction")
+
+parser.add_argument(
+    "--Vms",
+    help="membrane leak potentials [mV]",
+    type=float,
+    default=[-100, -60],
+    nargs=2,
+)
+parser.add_argument("--length", help="cell length [μm]", type=float, default=100)
+parser.add_argument("--radius", help="cell radius [μm]", type=float, default=3)
+parser.add_argument(
+    "--cm", help="membrane capacitance [F/m²]", type=float, default=0.005
+)
+parser.add_argument("--rL", help="axial resistivity [Ω·cm]", type=float, default=90)
+parser.add_argument("--g", help="leak conductivity [S/cm²]", type=float, default=0.001)
+parser.add_argument(
+    "--gj_g", help="gap junction conductivity [μS]", type=float, default=0.01
+)
+parser.add_argument("--max-extent", help="discretization length [μm]", type=float)
+
+args = parser.parse_args()
+
+# set up membrane voltage probes at the position of the gap junction
+rec = TwoCellsWithGapJunction(**vars(args))
+
+# configure the simulation and handles for the probes
+sim = A.simulation(rec)
+
+T = 5 * U.ms
+dt = 0.01 * U.ms
+
+# generate handles for all probes and gids.
+handles = [sim.sample((gid, "Um"), A.regular_schedule(dt)) for gid in [0, 1]]
+
+# run the simulation
+sim.run(tfinal=T, dt=dt)
+
+# retrieve the sampled membrane voltages
+print("Plotting results ...")
+df_list = []
+for gid, handle in enumerate(handles):
+    data, meta = sim.samples(handle)[0]
+    df_list.append(
+        pd.DataFrame({"t/ms": data[:, 0], "U/mV": data[:, 1], "Cell": f"{gid}"})
     )
+df = pd.concat(df_list, ignore_index=True)
 
-    parser.add_argument(
-        "--Vms",
-        help="membrane leak potentials in mV",
-        type=float,
-        default=[-100, -60],
-        nargs=2,
-    )
-    parser.add_argument("--length", help="cell length in μm", type=float, default=100)
-    parser.add_argument("--radius", help="cell radius in μm", type=float, default=3)
-    parser.add_argument(
-        "--cm", help="membrane capacitance in F/m^2", type=float, default=0.005
-    )
-    parser.add_argument(
-        "--rL", help="axial resistivity in Ω·cm", type=float, default=90
-    )
-    parser.add_argument(
-        "--g", help="membrane conductivity in S/cm^2", type=float, default=0.001
-    )
+# plot the membrane potentials of the two cells as function of time
+fg, ax = plt.subplots()
+sns.lineplot(ax=ax, data=df, x="t/ms", y="U/mV", hue="Cell", errorbar=None)
 
-    parser.add_argument(
-        "--gj_g", help="gap junction conductivity in μS", type=float, default=0.01
-    )
+# use total and gap junction conductance to compute weight
+w = (rec.gj_g + rec.area * rec.g) / (2 * rec.gj_g + rec.area * rec.g)
 
-    parser.add_argument(
-        "--cv_policy_max_extent",
-        help="maximum extent of control volume in μm",
-        type=float,
-    )
 
-    # parse the command line arguments
-    args = parser.parse_args()
-
-    # set up membrane voltage probes at the position of the gap junction
-    probes = [arbor.cable_probe_membrane_voltage('"gj_site"', "Um")]
-    recipe = TwoCellsWithGapJunction(probes, **vars(args))
-
-    # configure the simulation and handles for the probes
-    sim = arbor.simulation(recipe)
-
-    T = 5
-    dt = 0.01
-    handles = [
-        sim.sample((gid, "Um"), arbor.regular_schedule(dt))
-        for i, _ in enumerate(probes)
-        for gid in range(recipe.num_cells())
-    ]
-
-    # run the simulation for 5 ms
-    sim.run(tfinal=T, dt=dt)
-
-    # retrieve the sampled membrane voltages and convert to a pandas DataFrame
-    print("Plotting results ...")
-    df_list = []
-    for probe, handle in enumerate(handles):
-        samples, meta = sim.samples(handle)[0]
-        df_list.append(
-            pandas.DataFrame(
-                {"t/ms": samples[:, 0], "U/mV": samples[:, 1], "Cell": f"{probe}"}
-            )
-        )
-    df = pandas.concat(df_list, ignore_index=True)
-
-    fig, ax = plt.subplots()
-
-    # plot the membrane potentials of the two cells as function of time
-    seaborn.lineplot(ax=ax, data=df, x="t/ms", y="U/mV", hue="Cell", errorbar=None)
-
-    # area of cells
-    area = args.length * 1e-6 * 2 * np.pi * args.radius * 1e-6
-
-    # total and gap junction conductance in base units
-    cell_g = area * args.g / 1e-4
-    si_gj_g = args.gj_g * 1e-6
-
-    # weight
-    w = (si_gj_g + cell_g) / (2 * si_gj_g + cell_g)
-
-    # indicate the expected equilibrium potentials
-    for i, j in [[0, 1], [1, 0]]:
-        weighted_potential = args.Vms[i] + w * (args.Vms[j] - args.Vms[i])
-        ax.axhline(weighted_potential, linestyle="dashed", color="black", alpha=0.5)
-        ax.text(
-            2,
-            weighted_potential,
-            f"$\\tilde U_{j} = U_{j} + w\\cdot(U_{j} - U_{i})$",
-            va="center",
-            ha="center",
-            backgroundcolor="w",
-        )
-        ax.text(
-            2, args.Vms[j], f"$U_{j}$", va="center", ha="center", backgroundcolor="w"
-        )
+# indicate the expected equilibrium potentials
+def note(ax, x, y, txt):
+    ax.text(x, y, txt, va="center", ha="center", backgroundcolor="w")
+
 
-    ax.set_xlim(0, T)
+for i, j in [[0, 1], [1, 0]]:
+    Vj, Vi = args.Vms[j], args.Vms[i]
+    Vw = Vi + w.value * (Vj - Vi)
+    ax.axhline(Vi, linestyle="dashed", color="black", alpha=0.5)
+    ax.axhline(Vw, linestyle="dashed", color="black", alpha=0.5)
+    note(ax, 2, Vw, rf"$\tilde U_{j} = U_{j} + w\cdot(U_{j} - U_{i})$")
+    note(ax, 2, Vj, rf"$U_{j}$")
 
-    # plot the initial/nominal resting potentials
-    for gid, Vm in enumerate(args.Vms):
-        ax.axhline(Vm, linestyle="dashed", color="black", alpha=0.5)
+ax.set_xlim(0, T.value)
 
-    fig.savefig("two_cell_gap_junctions_result.svg")
+fg.savefig("two_cell_gap_junctions_result.svg")
diff --git a/python/example/ou_lif/ou_lif.py b/python/example/ou_lif/ou_lif.py
index dde3240480753a21be2031080406f6829dcba972..fe992a14c7ee59ac05060b6e641e602855dba076 100644
--- a/python/example/ou_lif/ou_lif.py
+++ b/python/example/ou_lif/ou_lif.py
@@ -4,7 +4,8 @@
 
 import random
 import subprocess
-import arbor as arb
+import arbor as A
+from arbor import units as U
 import numpy as np
 import matplotlib.pyplot as plt
 
@@ -14,8 +15,8 @@ def make_catalogue():
     out = subprocess.getoutput("arbor-build-catalogue ou_lif . --cpu True")
     print(out)
     # load the new catalogue and extend it with builtin stochastic catalogue
-    cat = arb.load_catalogue("./ou_lif-catalogue.so")
-    cat.extend(arb.stochastic_catalogue(), "")
+    cat = A.load_catalogue("./ou_lif-catalogue.so")
+    cat.extend(A.stochastic_catalogue(), "")
     return cat
 
 
@@ -23,16 +24,16 @@ def make_cell():
     # cell morphology
     # ===============
 
-    tree = arb.segment_tree()
+    tree = A.segment_tree()
     radius = 1e-10  # radius of cylinder (in µm)
     height = 2 * radius  # height of cylinder (in µm)
     tree.append(
-        arb.mnpos,
-        arb.mpoint(-height / 2, 0, 0, radius),
-        arb.mpoint(height / 2, 0, 0, radius),
+        A.mnpos,
+        A.mpoint(-height / 2, 0, 0, radius),
+        A.mpoint(height / 2, 0, 0, radius),
         tag=1,
     )
-    labels = arb.label_dict({"center": "(location 0 0.5)"})
+    labels = A.label_dict({"center": "(location 0 0.5)"})
 
     # LIF density mechanism
     # =====================
@@ -50,7 +51,7 @@ def make_cell():
     # reversal potential in mV
     V_rev = -65.0
     # spiking threshold in mV
-    V_th = -55.0
+    V_th = -55.0 * U.mV
     # initial synaptic weight in nC
     h_0 = 4.20075
     # leak resistance in MOhm
@@ -58,7 +59,7 @@ def make_cell():
     # membrane time constant in ms
     tau_mem = 2.0
 
-    lif = arb.mechanism("lif")
+    lif = A.mechanism("lif")
     lif.set("R_leak", R_leak)
     lif.set("R_reset", 1e-10)
     # set to initial value to zero (background input is applied via stochastic ou_bg_mech)
@@ -66,7 +67,7 @@ def make_cell():
     lif.set("i_factor", i_factor)
     lif.set("V_rev", V_rev)
     lif.set("V_reset", -70.0)
-    lif.set("V_th", V_th)
+    lif.set("V_th", V_th.value_as(U.mV))
     # refractory time in ms
     lif.set("t_ref", tau_mem)
 
@@ -80,7 +81,7 @@ def make_cell():
     # volatility in nA
     sigma_bg = 0.5
     # instantiate mechanism
-    ou_bg = arb.mechanism("ou_input")
+    ou_bg = A.mechanism("ou_input")
     ou_bg.set("mu", mu_bg)
     ou_bg.set("sigma", sigma_bg)
     ou_bg.set("tau", tau_syn)
@@ -99,7 +100,7 @@ def make_cell():
     # volatility in nA
     sigma_stim = np.sqrt((1000.0 * N * f) / (2 * tau_syn)) * w_out
     # instantiate mechanism
-    ou_stim = arb.mechanism("ou_input")
+    ou_stim = A.mechanism("ou_input")
     ou_stim.set("mu", mu_stim)
     ou_stim.set("sigma", sigma_stim)
     ou_stim.set("tau", tau_syn)
@@ -107,26 +108,26 @@ def make_cell():
     # paint and place mechanisms
     # ==========================
 
-    decor = arb.decor()
-    decor.set_property(Vm=V_rev, cm=c_mem)
-    decor.paint("(all)", arb.density(lif))
-    decor.place('"center"', arb.synapse(ou_stim), "ou_stim")
-    decor.place('"center"', arb.synapse(ou_bg), "ou_bg")
-    decor.place('"center"', arb.threshold_detector(V_th), "spike_detector")
+    decor = A.decor()
+    decor.set_property(Vm=V_rev * U.mV, cm=c_mem * U.Ohm * U.cm)
+    decor.paint("(all)", A.density(lif))
+    decor.place('"center"', A.synapse(ou_stim), "ou_stim")
+    decor.place('"center"', A.synapse(ou_bg), "ou_bg")
+    decor.place('"center"', A.threshold_detector(V_th), "spike_detector")
 
-    return arb.cable_cell(tree, decor, labels)
+    return A.cable_cell(tree, decor, labels)
 
 
-class ou_recipe(arb.recipe):
+class ou_recipe(A.recipe):
     def __init__(self, cell, cat):
-        arb.recipe.__init__(self)
+        A.recipe.__init__(self)
 
         # simulation runtime parameters in ms
-        self.runtime = 20000
-        self.dt = 0.2
+        self.runtime = 20 * U.s
+        self.dt = 0.2 * U.ms
 
         # initialize catalogue and cell properties
-        self.the_props = arb.neuron_cable_properties()
+        self.the_props = A.neuron_cable_properties()
         self.the_props.catalogue = cat
         self.the_cell = cell
 
@@ -141,7 +142,7 @@ class ou_recipe(arb.recipe):
         }
 
     def cell_kind(self, gid):
-        return arb.cell_kind.cable
+        return A.cell_kind.cable
 
     def cell_description(self, gid):
         return self.the_cell
@@ -155,9 +156,9 @@ class ou_recipe(arb.recipe):
     def probes(self, gid):
         # probe membrane potential, total current, and external input currents
         return [
-            arb.cable_probe_membrane_voltage('"center"'),
-            arb.cable_probe_total_ion_current_cell(),
-            arb.cable_probe_point_state_cell("ou_input", "I_ou"),
+            A.cable_probe_membrane_voltage('"center"', "Um"),
+            A.cable_probe_total_ion_current_cell("Itot"),
+            A.cable_probe_point_state_cell("ou_input", "I_ou", "Iou"),
         ]
 
     def event_generators(self, gid):
@@ -170,81 +171,83 @@ class ou_recipe(arb.recipe):
         gens.extend(self.get_generators(self.bg_prot))
         return gens
 
-    # Returns arb.event_generator instances that describe the specifics of a given
+    # Returns A.event_generator instances that describe the specifics of a given
     # input/stimulation protocol for a mechanism implementing an Ornstein-Uhlenbeck process.
     # Here, if the value of the 'weight' parameter is 1, stimulation is switched on,
     # whereas if it is -1, stimulation is switched off.
     def get_generators(self, protocol):
         prot_name = protocol["scheme"]  # name of the protocol (defining its structure)
-        start_time = protocol["time_start"]  # time at which the stimulus starts in s
+        start_time = (
+            protocol["time_start"] * U.ms
+        )  # time at which the stimulus starts in ms
         label = protocol["label"]  # target synapse (mechanism label)
 
         if prot_name == "ONEPULSE":
             # create regular schedules to implement a stimulation pulse that lasts for 0.1 s
-            stim_on = arb.event_generator(
+            stim_on = A.event_generator(
                 label,
                 1,
-                arb.regular_schedule(start_time, self.dt, start_time + self.dt),
+                A.regular_schedule(start_time, self.dt, start_time + self.dt),
             )
-            stim_off = arb.event_generator(
+            stim_off = A.event_generator(
                 label,
                 -1,
-                arb.regular_schedule(
-                    start_time + 100, self.dt, start_time + 100 + self.dt
+                A.regular_schedule(
+                    start_time + 0.1 * U.s, self.dt, start_time + 0.1 * U.s + self.dt
                 ),
             )
             return [stim_on, stim_off]
 
         elif prot_name == "TRIPLET":
             # create regular schedules to implement pulses that last for 0.1 s each
-            stim1_on = arb.event_generator(
+            stim1_on = A.event_generator(
                 label,
                 1,
-                arb.regular_schedule(start_time, self.dt, start_time + self.dt),
+                A.regular_schedule(start_time, self.dt, start_time + self.dt),
             )
-            stim1_off = arb.event_generator(
+            stim1_off = A.event_generator(
                 label,
                 -1,
-                arb.regular_schedule(
-                    start_time + 100, self.dt, start_time + 100 + self.dt
+                A.regular_schedule(
+                    start_time + 0.1 * U.s, self.dt, start_time + 0.1 * U.s + self.dt
                 ),
             )
-            stim2_on = arb.event_generator(
+            stim2_on = A.event_generator(
                 label,
                 1,
-                arb.regular_schedule(
-                    start_time + 500, self.dt, start_time + 500 + self.dt
+                A.regular_schedule(
+                    start_time + 0.5 * U.s, self.dt, start_time + 0.5 * U.s + self.dt
                 ),
             )
-            stim2_off = arb.event_generator(
+            stim2_off = A.event_generator(
                 label,
                 -1,
-                arb.regular_schedule(
-                    start_time + 600, self.dt, start_time + 600 + self.dt
+                A.regular_schedule(
+                    start_time + 0.6 * U.s, self.dt, start_time + 0.6 * U.s + self.dt
                 ),
             )
-            stim3_on = arb.event_generator(
+            stim3_on = A.event_generator(
                 label,
                 1,
-                arb.regular_schedule(
-                    start_time + 1000, self.dt, start_time + 1000 + self.dt
+                A.regular_schedule(
+                    start_time + 1 * U.s, self.dt, start_time + 1 * U.s + self.dt
                 ),
             )
-            stim3_off = arb.event_generator(
+            stim3_off = A.event_generator(
                 label,
                 -1,
-                arb.regular_schedule(
-                    start_time + 1100, self.dt, start_time + 1100 + self.dt
+                A.regular_schedule(
+                    start_time + 1.1 * U.s, self.dt, start_time + 1.1 * U.s + self.dt
                 ),
             )
             return [stim1_on, stim1_off, stim2_on, stim2_off, stim3_on, stim3_off]
 
         elif prot_name == "FULL":
             # create a regular schedule that lasts for the full runtime
-            stim_on = arb.event_generator(
+            stim_on = A.event_generator(
                 label,
                 1,
-                arb.regular_schedule(start_time, self.dt, start_time + self.dt),
+                A.regular_schedule(start_time, self.dt, start_time + self.dt),
             )
             return [stim_on]
 
@@ -266,23 +269,23 @@ if __name__ == "__main__":
     print("random_seed = " + str(random_seed))
 
     # select one thread and no GPU
-    alloc = arb.proc_allocation(threads=1, gpu_id=None)
-    context = arb.context(alloc, mpi=None)
-    domains = arb.partition_load_balance(recipe, context)
+    alloc = A.proc_allocation(threads=1, gpu_id=None)
+    context = A.context(alloc, mpi=None)
+    domains = A.partition_load_balance(recipe, context)
 
     # create simulation
-    sim = arb.simulation(recipe, context, domains, seed=random_seed)
+    sim = A.simulation(recipe, context, domains, seed=random_seed)
 
     # create schedule for recording
-    reg_sched = arb.regular_schedule(0, recipe.dt, recipe.runtime)
+    reg_sched = A.regular_schedule(0 * U.ms, recipe.dt, recipe.runtime)
 
     # set handles to probe membrane potential and currents
     gid = 0
-    handle_mem = sim.sample((gid, 0), reg_sched)  # membrane potential
-    handle_tot_curr = sim.sample((gid, 1), reg_sched)  # total current
-    handle_curr = sim.sample((gid, 2), reg_sched)  # input current
+    handle_mem = sim.sample((gid, "Um"), reg_sched)  # membrane potential
+    handle_tot_curr = sim.sample((gid, "Itot"), reg_sched)  # total current
+    handle_curr = sim.sample((gid, "Iou"), reg_sched)  # input current
 
-    sim.record(arb.spike_recording.all)
+    sim.record(A.spike_recording.all)
     sim.run(tfinal=recipe.runtime, dt=recipe.dt)
 
     # get traces and spikes from simulator
diff --git a/python/example/ou_lif/traces.svg b/python/example/ou_lif/traces.svg
index 13a3c49c53c81a7fb3f56af40aad109ed9fe0548..b9a3a5c87667e465992117273f45d9870648a1fc 100644
--- a/python/example/ou_lif/traces.svg
+++ b/python/example/ou_lif/traces.svg
@@ -1,16 +1,16 @@
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+<svg xmlns:xlink="http://www.w3.org/1999/xlink" width="712.25125pt" height="711.754375pt" viewBox="0 0 712.25125 711.754375" xmlns="http://www.w3.org/2000/svg" version="1.1">
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    <cc:Work>
     <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
-    <dc:date>2022-11-09T15:50:52.860599</dc:date>
+    <dc:date>2023-11-30T19:09:25.458406</dc:date>
     <dc:format>image/svg+xml</dc:format>
     <dc:creator>
      <cc:Agent>
-      <dc:title>Matplotlib v3.6.1, https://matplotlib.org/</dc:title>
+      <dc:title>Matplotlib v3.7.1, https://matplotlib.org/</dc:title>
      </cc:Agent>
     </dc:creator>
    </cc:Work>
@@ -21,18 +21,18 @@
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   <g id="axes_1">
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diff --git a/python/example/plasticity.py b/python/example/plasticity.py
index ab57d099a935b349d86a05a741b4502e27da8126..f88ed93128cb299269229729bf738dae9d413dc9 100644
--- a/python/example/plasticity.py
+++ b/python/example/plasticity.py
@@ -1,4 +1,5 @@
 import arbor as A
+from arbor import units as U
 
 
 class recipe(A.recipe):
@@ -10,7 +11,7 @@ class recipe(A.recipe):
         self.cells = n
         # Uniform weights and delays for the connectivity.
         self.weight = 0.75
-        self.delay = 0.1
+        self.delay = 0.1 * U.ms
         # Track the connections from the source cell to a given gid.
         self.connected = set()
 
@@ -34,7 +35,7 @@ class recipe(A.recipe):
     def cell_description(self, gid):
         # Cell 0 is reserved for the spike source, spiking every 0.0125ms.
         if gid == 0:
-            return A.spike_source_cell("source", A.regular_schedule(0.0125))
+            return A.spike_source_cell("source", A.regular_schedule(12.5 * U.us))
         # All cells >= 1 are cable cells w/ a simple, soma-only morphology
         # comprising two segments of radius r=3um and length l=3um *each*.
         #
@@ -54,7 +55,7 @@ class recipe(A.recipe):
         #   - synapse to receive incoming spikes from the source cell.
         decor.place("(location 0 0.5)", A.synapse("expsyn"), "synapse")
         #   - detector for reporting spikes on the cable cells.
-        decor.place("(location 0 0.5)", A.threshold_detector(-10.0), "detector")
+        decor.place("(location 0 0.5)", A.threshold_detector(-10.0 * U.mV), "detector")
         # return the cable cell description
         return A.cable_cell(tree, decor)
 
@@ -92,7 +93,7 @@ rec.add_connection_to_spike_source(1)
 sim = A.simulation(rec, ctx)
 sim.record(A.spike_recording.all)
 # then run the simulation for a bit
-sim.run(0.25, 0.025)
+sim.run(0.25 * U.ms, 0.025 * U.ms)
 # update the simulation to
 #
 #    spike_source <gid=0> ----> cable_cell <gid=1>
@@ -101,7 +102,7 @@ sim.run(0.25, 0.025)
 rec.add_connection_to_spike_source(2)
 sim.update(rec)
 # and run the simulation for another bit.
-sim.run(0.5, 0.025)
+sim.run(0.5 * U.ms, 0.025 * U.ms)
 # when finished, print spike times and locations.
 source_spikes = 0
 print("Spikes:")
diff --git a/python/example/probe_lfpykit.py b/python/example/probe_lfpykit.py
index a382b9ef74d11234581e87fcc077c803b5413133..d83b2732d2e2d48161ef7c6bf8e664328ea43f68 100644
--- a/python/example/probe_lfpykit.py
+++ b/python/example/probe_lfpykit.py
@@ -13,13 +13,15 @@
 # import modules
 import sys
 import numpy as np
-import arbor
+import arbor as A
+from arbor import units as U
 import lfpykit
 import matplotlib.pyplot as plt
 from matplotlib.collections import PolyCollection
+from pathlib import Path
 
 
-class Recipe(arbor.recipe):
+class Recipe(A.recipe):
     def __init__(self, cell):
         super().__init__()
 
@@ -29,108 +31,88 @@ class Recipe(arbor.recipe):
         self.iprobeset_id = (0, 1)
         self.cprobeset_id = (0, 2)
 
-        self.the_props = arbor.neuron_cable_properties()
-
     def num_cells(self):
         return 1
 
-    def num_sources(self, gid):
-        return 0
-
-    def cell_kind(self, gid):
-        return arbor.cell_kind.cable
+    def cell_kind(self, _):
+        return A.cell_kind.cable
 
-    def cell_description(self, gid):
+    def cell_description(self, _):
         return self.the_cell
 
-    def global_properties(self, kind):
-        return self.the_props
+    def global_properties(self, _):
+        return A.neuron_cable_properties()
 
-    def probes(self, gid):
+    def probes(self, _):
         return [
-            arbor.cable_probe_membrane_voltage_cell("Um-all"),
-            arbor.cable_probe_total_current_cell("Itotal-all"),
-            arbor.cable_probe_stimulus_current_cell("Istim-all"),
+            A.cable_probe_membrane_voltage_cell("Um-all"),
+            A.cable_probe_total_current_cell("Itotal-all"),
+            A.cable_probe_stimulus_current_cell("Istim-all"),
         ]
 
 
 # Read the SWC filename from input
-# Example from docs: single_cell_detailed.swc
-if len(sys.argv) < 2:
-    print("No SWC file passed to the program")
-    sys.exit(0)
-
-filename = sys.argv[1]
-
-# define morphology (needed for ``arbor.place_pwlin`` and ``arbor.cable_cell`` below)
-morphology = arbor.load_swc_arbor(filename)
+if len(sys.argv) == 1:
+    print("No SWC file passed to the program, using default.")
+    filename = Path(__file__).parent / "single_cell_detailed.swc"
+elif len(sys.argv) == 2:
+    filename = Path(sys.argv[1])
+else:
+    print("Usage: single_cell_detailed.py [SWC file name]")
+    sys.exit(1)
+
+# define morphology (needed for ``A.place_pwlin`` and ``A.cable_cell`` below)
+morphology = A.load_swc_arbor(filename)
 
 # define a location on morphology for current clamp
-clamp_location = arbor.location(4, 1 / 6)
-
-
-def make_cable_cell(morphology, clamp_location):
-    # number of CVs per branch
-    cvs_per_branch = 3
-
-    # Label dictionary
-    labels = arbor.label_dict()
+clamp_location = A.location(4, 1 / 6)
+
+# define a sinusoid input current
+iclamp = A.iclamp(
+    5 * U.ms,  # stimulation onset
+    1e8 * U.ms,  # stimulation duration
+    -0.001 * U.nA,  # stimulation amplitude
+    frequency=100 * U.Hz,  # stimulation frequency
+    phase=0 * U.rad,  # stimulation phase
+)
 
-    # decor
-    decor = (
-        arbor.decor()
-        # set initial voltage, temperature, axial resistivity, membrane capacitance
-        .set_property(
-            Vm=-65,  # Initial membrane voltage (mV)
-            tempK=300,  # Temperature (Kelvin)
-            rL=10000,  # Axial resistivity (Ω cm)
-            cm=0.01,  # Membrane capacitance (F/m**2)
-        )
-        # set passive mechanism all over
-        # passive mech w. leak reversal potential (mV)
-        .paint("(all)", arbor.density("pas/e=-65", g=0.0001))
+decor = (
+    A.decor()
+    # set initial voltage, temperature, axial resistivity, membrane capacitance
+    .set_property(
+        Vm=-65 * U.mV,  # Initial membrane voltage (mV)
+        tempK=300 * U.Kelvin,  # Temperature (Kelvin)
+        rL=10 * U.kOhm * U.cm,  # Axial resistivity (Ω cm)
+        cm=0.01 * U.F / U.m2,  # Membrane capacitance (F/m**2)
     )
+    # set passive mech w. leak reversal potential (mV)
+    .paint("(all)", A.density("pas/e=-65", g=0.0001))
+    # attach the stimulus
+    .place(str(clamp_location), iclamp, "iclamp")
+    # use a fixed 3 CVs per branch
+    .discretization(A.cv_policy_fixed_per_branch(3))
+)
 
-    # set number of CVs per branch
-    policy = arbor.cv_policy_fixed_per_branch(cvs_per_branch)
-    decor.discretization(policy)
-
-    # place sinusoid input current
-    iclamp = arbor.iclamp(
-        5,  # stimulation onset (ms)
-        1e8,  # stimulation duration (ms)
-        -0.001,  # stimulation amplitude (nA)
-        frequency=0.1,  # stimulation frequency (kHz)
-        phase=0,
-    )  # stimulation phase)
-    decor.place(str(clamp_location), iclamp, '"iclamp"')
-
-    # create ``arbor.place_pwlin`` object
-    p = arbor.place_pwlin(morphology)
-
-    # create cell and set properties
-    cell = arbor.cable_cell(morphology, decor, labels)
-
-    return p, cell
-
-
-# get place_pwlin and cable_cell objects
-p, cell = make_cable_cell(morphology, clamp_location)
+# place_pwlin can be queried with region/locset expressions to obtain
+# geometrical objects, like points and segments, essentially recovering
+# geometry from morphology.
+ppwl = A.place_pwlin(morphology)
+cell = A.cable_cell(morphology, decor)
 
 # instantiate recipe with cell
-recipe = Recipe(cell)
+rec = Recipe(cell)
 
 # instantiate simulation
-sim = arbor.simulation(recipe)
+sim = A.simulation(rec)
 
 # set up sampling on probes with sampling every 1 ms
-schedule = arbor.regular_schedule(1.0)
+schedule = A.regular_schedule(1.0 * U.ms)
 v_handle = sim.sample(0, "Um-all", schedule)
 i_handle = sim.sample(0, "Itotal-all", schedule)
 c_handle = sim.sample(0, "Istim-all", schedule)
 
 # run simulation for 500 ms of simulated activity and collect results.
-sim.run(tfinal=500)
+sim.run(tfinal=500 * U.ms)
 
 # extract time, V_m, I_m and I_c for each CV
 V_m_samples, V_m_meta = sim.samples(v_handle)[0]
@@ -144,8 +126,8 @@ inds = np.array([m.dist != m.prox for m in V_m_meta])
 V_m_samples = V_m_samples[:, np.r_[True, inds]]
 V_m_meta = np.array(V_m_meta)[inds].tolist()
 
-# assert that the remaining cables comprising the metadata for each probe
-# are identical, as well as the reported sample times.
+# assert that the remaining cables comprising the metadata for each probe are
+# identical, as well as the reported sample times.
 assert V_m_meta == I_m_meta
 assert (V_m_samples[:, 0] == I_m_samples[:, 0]).all()
 
@@ -169,16 +151,16 @@ I_m = I_c_samples[:, 1:] + I_m_samples[:, 1:]  # (nA)
 class ArborCellGeometry(lfpykit.CellGeometry):
     """
     Class inherited from  ``lfpykit.CellGeometry`` for easier forward-model
-    predictions in Arbor that keeps track of arbor.segment information
+    predictions in Arbor that keeps track of A.segment information
     for each CV.
 
     Parameters
     ----------
-    p: ``arbor.place_pwlin`` object
-        3-d locations and cables in a morphology (cf. ``arbor.place_pwlin``)
+    p: ``A.place_pwlin`` object
+        3-d locations and cables in a morphology (cf. ``A.place_pwlin``)
     cables: ``list``
-        ``list`` of corresponding ``arbor.cable`` objects where transmembrane
-        currents are recorded (cf. ``arbor.cable_probe_total_current_cell``)
+        ``list`` of corresponding ``A.cable`` objects where transmembrane
+        currents are recorded (cf. ``A.cable_probe_total_current_cell``)
 
     See also
     --------
@@ -190,7 +172,7 @@ class ArborCellGeometry(lfpykit.CellGeometry):
         CV_ind = np.array([], dtype=int)  # tracks which CV owns segment
         for i, m in enumerate(cables):
             segs = p.segments([m])
-            for j, seg in enumerate(segs):
+            for seg in segs:
                 x = np.row_stack([x, [seg.prox.x, seg.dist.x]])
                 y = np.row_stack([y, [seg.prox.y, seg.dist.y]])
                 z = np.row_stack([z, [seg.prox.z, seg.dist.z]])
@@ -253,7 +235,7 @@ class ArborLineSourcePotential(lfpykit.LineSourcePotential):
 
 
 # create ``ArborCellGeometry`` instance
-cell_geometry = ArborCellGeometry(p, I_m_meta)
+cell_geometry = ArborCellGeometry(ppwl, I_m_meta)
 
 # define locations where extracellular potential is predicted in vicinity
 # of cell.
@@ -421,7 +403,7 @@ cb2.set_label(r"$V_m$ (mV)")
 ax.add_collection(get_segment_outlines(cell_geometry))
 
 # add marker denoting clamp location
-point = p.at(clamp_location)
+point = ppwl.at(clamp_location)
 ax.plot(point.x, point.y, "ko", ms=10, label="stimulus")
 
 ax.legend()
diff --git a/python/example/single_cell_allen.py b/python/example/single_cell_allen.py
index df96149279b2c71271364b361d4d81579e663ab0..6daaf09f9131a20df9d098431b72914172fabd04 100644
--- a/python/example/single_cell_allen.py
+++ b/python/example/single_cell_allen.py
@@ -2,14 +2,19 @@
 
 from collections import defaultdict
 from dataclasses import dataclass
+from typing import Optional
 import json
-import arbor
-import seaborn
-import pandas
+import arbor as A
+from arbor import units as U
+import seaborn as sns
+import pandas as pd
 import matplotlib.pyplot as plt
+from pathlib import Path
 
+here = Path(__file__).parent
 
-# (3) A function that parses the Allen parameter fit file into components for an arbor.decor
+
+# (3) A function that parses the Allen parameter fit file into components for an A.decor
 # NB. Needs to be adjusted when using a different model
 def load_allen_fit(fit):
     with open(fit) as fd:
@@ -18,10 +23,10 @@ def load_allen_fit(fit):
     # cable parameters convenience class
     @dataclass
     class parameters:
-        cm: float = None
-        tempK: float = None
-        Vm: float = None
-        rL: float = None
+        cm: Optional[float] = None
+        tempK: Optional[float] = None
+        Vm: Optional[float] = None
+        rL: Optional[float] = None
 
     param = defaultdict(parameters)
     mechs = defaultdict(dict)
@@ -71,30 +76,44 @@ def load_allen_fit(fit):
     return default, regs, ions, mechs, fit["fitting"][0]["junction_potential"]
 
 
-def make_cell(swc, fit):
+def make_cell(base, swc, fit):
     # (1) Load the swc file passed into this function
-    morphology = arbor.load_swc_neuron(swc)
+    morphology = A.load_swc_neuron(base / swc)
+
     # (2) Label the region tags found in the swc with the names used in the parameter fit file.
-    # In addition, label the midpoint of the somarbor.
-    labels = arbor.label_dict().add_swc_tags()
+    # In addition, label the midpoint of the soma.
+    labels = A.label_dict().add_swc_tags()
     labels["midpoint"] = "(location 0 0.5)"
 
-    # (3) A function that parses the Allen parameter fit file into components for an arbor.decor
-    dflt, regions, ions, mechanisms, offset = load_allen_fit(fit)
+    # (3) A function that parses the Allen parameter fit file into components
+    dflt, regions, ions, mechanisms, offset = load_allen_fit(base / fit)
 
     # (4) Instantiate an empty decor.
-    decor = arbor.decor()
+    decor = A.decor()
+
     # (5) assign global electro-physiology parameters
-    decor.set_property(tempK=dflt.tempK, Vm=dflt.Vm, cm=dflt.cm, rL=dflt.rL)
+    decor.set_property(
+        tempK=dflt.tempK * U.Kelvin,
+        Vm=dflt.Vm * U.mV,
+        cm=dflt.cm * U.F / U.m2,
+        rL=dflt.rL * U.Ohm * U.cm,
+    )
+
     # (6) override regional electro-physiology parameters
     for region, vs in regions:
-        decor.paint(f'"{region}"', tempK=vs.tempK, Vm=vs.Vm, cm=vs.cm, rL=vs.rL)
+        decor.paint(
+            f'"{region}"',
+            tempK=vs.tempK * U.Kelvin,
+            Vm=vs.Vm * U.Vm,
+            cm=vs.cm * U.F / U.m2,
+            rL=vs.rL * U.Ohm * U.cm,
+        )
+
     # (7) set reversal potentials
     for region, ion, e in ions:
-        decor.paint(f'"{region}"', ion_name=ion, rev_pot=e)
-    decor.set_ion(
-        "ca", int_con=5e-5, ext_con=2.0, method=arbor.mechanism("nernst/x=ca")
-    )
+        decor.paint(f'"{region}"', ion=ion, rev_pot=e)
+    decor.set_ion("ca", int_con=5e-5 * U.mM, ext_con=2.0 * U.mM, method="nernst/x=ca")
+
     # (8) assign ion dynamics
     for region, mech, values in mechanisms:
         nm = mech
@@ -106,58 +125,61 @@ def make_cell(swc, fit):
                 sp = ","
             else:
                 vs[k] = v
-        decor.paint(f'"{region}"', arbor.density(arbor.mechanism(nm, vs)))
+        decor.paint(f'"{region}"', A.density(A.mechanism(nm, vs)))
+
     # (9) attach stimulus and detector
-    decor.place('"midpoint"', arbor.iclamp(200, 1000, 0.15), "ic")
-    decor.place('"midpoint"', arbor.threshold_detector(-40), "sd")
+    decor.place('"midpoint"', A.iclamp(0.2 * U.s, 1 * U.s, 150 * U.pA), "ic")
+    decor.place('"midpoint"', A.threshold_detector(-40 * U.mV), "sd")
+
     # (10) discretisation strategy: max compartment length
-    decor.discretization(arbor.cv_policy_max_extent(20))
+    decor.discretization(A.cv_policy_max_extent(20))
 
     # (11) Create cell
-    return arbor.cable_cell(morphology, decor, labels), offset
+    return A.cable_cell(morphology, decor, labels), offset
 
 
 # (12) Create cell, model
-cell, offset = make_cell("single_cell_allen.swc", "single_cell_allen_fit.json")
-model = arbor.single_cell_model(cell)
+cell, offset = make_cell(here, "single_cell_allen.swc", "single_cell_allen_fit.json")
+model = A.single_cell_model(cell)
 
 # (13) Set the probe
-model.probe("voltage", '"midpoint"', frequency=200)
+model.probe("voltage", '"midpoint"', "Um", frequency=1 / (5 * U.us))
 
 # (14) Install the Allen mechanism catalogue.
-model.properties.catalogue.extend(arbor.allen_catalogue(), "")
+model.properties.catalogue.extend(A.allen_catalogue(), "")
 
 # (15) Run simulation
-model.run(tfinal=1400, dt=0.005)
+model.run(tfinal=1.4 * U.s, dt=5 * U.us)
 
 # (16) Load and scale reference
 reference = (
-    1000.0 * pandas.read_csv("single_cell_allen_neuron_ref.csv")["U/mV"].values[:-1]
-    + offset
+    1e3 * pd.read_csv(here / "single_cell_allen_neuron_ref.csv")["U/mV"] + offset
 )
 
 # (17) Plot
-df_list = []
-df_list.append(
-    pandas.DataFrame(
-        {
-            "t/ms": model.traces[0].time,
-            "U/mV": model.traces[0].value,
-            "Simulator": "Arbor",
-        }
-    )
-)
-df_list.append(
-    pandas.DataFrame(
-        {"t/ms": model.traces[0].time, "U/mV": reference, "Simulator": "Neuron"}
-    )
-)
-df = pandas.concat(df_list, ignore_index=True)
-seaborn.relplot(
-    data=df, kind="line", x="t/ms", y="U/mV", hue="Simulator", errorbar=None
+df = pd.concat(
+    [
+        pd.DataFrame(
+            {
+                "Simulator": "Arbor",
+                "t/ms": model.traces[0].time,
+                "U/mV": model.traces[0].value,
+            }
+        ),
+        pd.DataFrame(
+            {
+                "Simulator": "Neuron",
+                "t/ms": model.traces[0].time,
+                "U/mV": reference.values,
+            }
+        ),
+    ],
+    ignore_index=True,
 )
+
+sns.relplot(data=df, kind="line", x="t/ms", y="U/mV", hue="Simulator", errorbar=None)
 plt.scatter(
-    model.spikes, [-40] * len(model.spikes), color=seaborn.color_palette()[2], zorder=20
+    model.spikes, [-40] * len(model.spikes), color=sns.color_palette()[2], zorder=20
 )
 plt.bar(
     200,
@@ -168,4 +190,4 @@ plt.bar(
     label="Stimulus",
     color="0.9",
 )
-plt.savefig("single_cell_allen_result.svg")
+plt.savefig("single_cell_allen_result.pdf")
diff --git a/python/example/single_cell_bluepyopt/l5pc/C060114A7_axon_replacement.acc b/python/example/single_cell_bluepyopt/l5pc/C060114A7_axon_replacement.acc
index 9318d73175ed3d4efe0c821b0971a892c4d1afda..ad5cfa7de8fd373c2cb8c88fb961a99203ac2330 100644
--- a/python/example/single_cell_bluepyopt/l5pc/C060114A7_axon_replacement.acc
+++ b/python/example/single_cell_bluepyopt/l5pc/C060114A7_axon_replacement.acc
@@ -1,6 +1,6 @@
 (arbor-component 
   (meta-data 
-    (version "0.1-dev"))
+    (version "0.9-dev"))
   (morphology 
     (branch 0 -1 
       (segment 0 
@@ -18,4 +18,4 @@
       (segment 3 
         (point 262.494171 -39.637466 -3.380000 0.460000)
         (point 262.242889 -54.635361 -3.380000 0.460000)
-        2))))
\ No newline at end of file
+        2))))
diff --git a/python/example/single_cell_bluepyopt/l5pc/C060114A7_modified.acc b/python/example/single_cell_bluepyopt/l5pc/C060114A7_modified.acc
index 0f0910d58e54870073c75ee48e2ce3310a5e3a5b..317282f7d5f4237269bbb3dc3278b931ed2c760c 100644
--- a/python/example/single_cell_bluepyopt/l5pc/C060114A7_modified.acc
+++ b/python/example/single_cell_bluepyopt/l5pc/C060114A7_modified.acc
@@ -1,6 +1,6 @@
 (arbor-component 
   (meta-data 
-    (version "0.1-dev"))
+    (version "0.9-dev"))
   (morphology 
     (branch 0 -1 
       (segment 0 
@@ -21699,4 +21699,4 @@
       (segment 5374 
         (point 262.494171 -39.637466 -3.380000 0.460000)
         (point 262.242889 -54.635361 -3.380000 0.460000)
-        2))))
\ No newline at end of file
+        2))))
diff --git a/python/example/single_cell_bluepyopt/l5pc/l5pc_decor.acc b/python/example/single_cell_bluepyopt/l5pc/l5pc_decor.acc
index 89c7735b8f22ab5ea080d6f51f34752967a65b92..29831b6715db70518dbc6b62f03089141234b781 100644
--- a/python/example/single_cell_bluepyopt/l5pc/l5pc_decor.acc
+++ b/python/example/single_cell_bluepyopt/l5pc/l5pc_decor.acc
@@ -1,20 +1,20 @@
 (arbor-component
-  (meta-data (version "0.1-dev"))
+  (meta-data (version "0.9-dev"))
   (decor
-    (default (membrane-potential -65))
-    (default (temperature-kelvin 307.14999999999998))
-    (default (membrane-capacitance 0.01))
-    (default (axial-resistivity 100))
+    (default (membrane-potential -65 (scalar 1.0)))
+    (default (temperature-kelvin 307.14999999999998 (scalar 1.0)))
+    (default (membrane-capacitance 0.01 (scalar 1.0)))
+    (default (axial-resistivity 100 (scalar 1.0)))
     (paint (region "all") (density (mechanism "default::pas/e=-75" ("g" 3.0000000000000001e-05))))
-    (paint (region "apic") (ion-reversal-potential "na" 50))
-    (paint (region "apic") (ion-reversal-potential "k" -85))
-    (paint (region "apic") (membrane-capacitance 0.02))
+    (paint (region "apic") (ion-reversal-potential "na" 50 (scalar 1.0)))
+    (paint (region "apic") (ion-reversal-potential "k" -85 (scalar 1.0)))
+    (paint (region "apic") (membrane-capacitance 0.02 (scalar 1.0)))
     (paint (region "apic") (density (mechanism "BBP::NaTs2_t" ("gNaTs2_tbar" 0.026145000000000002))))
     (paint (region "apic") (density (mechanism "BBP::SKv3_1" ("gSKv3_1bar" 0.0042259999999999997))))
     (paint (region "apic") (density (mechanism "BBP::Im" ("gImbar" 0.00014300000000000001))))
     (paint (region "apic") (scaled-mechanism (density (mechanism "BBP::Ih" ("gIhbar" 8.0000000000000007e-05))) ("gIhbar" (add (scalar -0.86960000000000004) (mul (scalar 2.0870000000000002) (exp (mul (distance (region "soma")) (scalar 0.0030999999999999999) ) ) ) ))))
-    (paint (region "axon") (ion-reversal-potential "na" 50))
-    (paint (region "axon") (ion-reversal-potential "k" -85))
+    (paint (region "axon") (ion-reversal-potential "na" 50 (scalar 1.0)))
+    (paint (region "axon") (ion-reversal-potential "k" -85 (scalar 1.0)))
     (paint (region "axon") (density (mechanism "BBP::NaTa_t" ("gNaTa_tbar" 3.1379679999999999))))
     (paint (region "axon") (density (mechanism "BBP::Nap_Et2" ("gNap_Et2bar" 0.0068269999999999997))))
     (paint (region "axon") (density (mechanism "BBP::K_Pst" ("gK_Pstbar" 0.97353800000000001))))
@@ -24,14 +24,14 @@
     (paint (region "axon") (density (mechanism "BBP::Ca_HVA" ("gCa_HVAbar" 0.00098999999999999999))))
     (paint (region "axon") (density (mechanism "BBP::Ca_LVAst" ("gCa_LVAstbar" 0.0087519999999999994))))
     (paint (region "axon") (density (mechanism "BBP::CaDynamics_E2" ("gamma" 0.0029099999999999998) ("decay" 287.19873100000001))))
-    (paint (region "dend") (membrane-capacitance 0.02))
+    (paint (region "dend") (membrane-capacitance 0.02 (scalar 1.0)))
     (paint (region "dend") (density (mechanism "BBP::Ih" ("gIhbar" 8.0000000000000007e-05))))
-    (paint (region "soma") (ion-reversal-potential "na" 50))
-    (paint (region "soma") (ion-reversal-potential "k" -85))
+    (paint (region "soma") (ion-reversal-potential "na" 50 (scalar 1.0)))
+    (paint (region "soma") (ion-reversal-potential "k" -85 (scalar 1.0)))
     (paint (region "soma") (density (mechanism "BBP::NaTs2_t" ("gNaTs2_tbar" 0.98395500000000002))))
     (paint (region "soma") (density (mechanism "BBP::SKv3_1" ("gSKv3_1bar" 0.30347200000000002))))
     (paint (region "soma") (density (mechanism "BBP::SK_E2" ("gSK_E2bar" 0.0084069999999999995))))
     (paint (region "soma") (density (mechanism "BBP::Ca_HVA" ("gCa_HVAbar" 0.00099400000000000009))))
     (paint (region "soma") (density (mechanism "BBP::Ca_LVAst" ("gCa_LVAstbar" 0.00033300000000000002))))
     (paint (region "soma") (density (mechanism "BBP::CaDynamics_E2" ("gamma" 0.00060899999999999995) ("decay" 210.48528400000001))))
-    (paint (region "soma") (density (mechanism "BBP::Ih" ("gIhbar" 8.0000000000000007e-05))))))
\ No newline at end of file
+    (paint (region "soma") (density (mechanism "BBP::Ih" ("gIhbar" 8.0000000000000007e-05))))))
diff --git a/python/example/single_cell_bluepyopt/l5pc/l5pc_label_dict.acc b/python/example/single_cell_bluepyopt/l5pc/l5pc_label_dict.acc
index 08c4efd5da155296ad37754a23f78492469ceb3a..c15ec60578c47a1e7291b70cf936dc9163d22c02 100644
--- a/python/example/single_cell_bluepyopt/l5pc/l5pc_label_dict.acc
+++ b/python/example/single_cell_bluepyopt/l5pc/l5pc_label_dict.acc
@@ -1,9 +1,9 @@
 (arbor-component
-  (meta-data (version "0.1-dev"))
+  (meta-data (version "0.9-dev"))
   (label-dict 
     (region-def "all" (all)) 
     (region-def "apic" (tag 4)) 
     (region-def "axon" (tag 2)) 
     (region-def "dend" (tag 3)) 
     (region-def "soma" (tag 1)) 
-    (region-def "myelin" (tag 5))))
\ No newline at end of file
+    (region-def "myelin" (tag 5))))
diff --git a/python/example/single_cell_bluepyopt/simplecell/simple_cell_decor.acc b/python/example/single_cell_bluepyopt/simplecell/simple_cell_decor.acc
index e5af159ce8a8ea84d4ef33ec0196b7ea9b476b04..c46b2578efde4be85407816ac6b307ceee78fa11 100644
--- a/python/example/single_cell_bluepyopt/simplecell/simple_cell_decor.acc
+++ b/python/example/single_cell_bluepyopt/simplecell/simple_cell_decor.acc
@@ -1,5 +1,5 @@
 (arbor-component
-  (meta-data (version "0.1-dev"))
+  (meta-data (version "0.9-dev"))
   (decor
-    (paint (region "soma") (membrane-capacitance 0.01))
-    (paint (region "soma") (density (mechanism "default::hh" ("gnabar" 0.10299326453483033) ("gkbar" 0.027124836082684685))))))
\ No newline at end of file
+    (paint (region "soma") (membrane-capacitance 0.01 (scalar 1)))
+    (paint (region "soma") (density (mechanism "default::hh" ("gnabar" 0.10299326453483033) ("gkbar" 0.027124836082684685))))))
diff --git a/python/example/single_cell_bluepyopt/simplecell/simple_cell_label_dict.acc b/python/example/single_cell_bluepyopt/simplecell/simple_cell_label_dict.acc
index 08c4efd5da155296ad37754a23f78492469ceb3a..c15ec60578c47a1e7291b70cf936dc9163d22c02 100644
--- a/python/example/single_cell_bluepyopt/simplecell/simple_cell_label_dict.acc
+++ b/python/example/single_cell_bluepyopt/simplecell/simple_cell_label_dict.acc
@@ -1,9 +1,9 @@
 (arbor-component
-  (meta-data (version "0.1-dev"))
+  (meta-data (version "0.9-dev"))
   (label-dict 
     (region-def "all" (all)) 
     (region-def "apic" (tag 4)) 
     (region-def "axon" (tag 2)) 
     (region-def "dend" (tag 3)) 
     (region-def "soma" (tag 1)) 
-    (region-def "myelin" (tag 5))))
\ No newline at end of file
+    (region-def "myelin" (tag 5))))
diff --git a/python/example/single_cell_cable.py b/python/example/single_cell_cable.py
index dc3f525e1d5ba69904c3fbde7103e443dc45fc37..fc6cceb156f58282735b6b89ca344b990b2ebc2f 100755
--- a/python/example/single_cell_cable.py
+++ b/python/example/single_cell_cable.py
@@ -1,14 +1,15 @@
 #!/usr/bin/env python3
 
-import arbor
+import arbor as A
+from arbor import units as U
 import argparse
 import numpy as np
 
-import pandas
-import seaborn  # You may have to pip install these.
+import pandas as pd
+import seaborn as sns  # You may have to pip install these.
 
 
-class Cable(arbor.recipe):
+class Cable(A.recipe):
     def __init__(
         self,
         probes,
@@ -40,7 +41,7 @@ class Cable(arbor.recipe):
         cv_policy_max_extent -- maximum extent of control volume in μm
         """
 
-        arbor.recipe.__init__(self)
+        A.recipe.__init__(self)
 
         self.the_probes = probes
 
@@ -51,25 +52,19 @@ class Cable(arbor.recipe):
         self.rL = rL
         self.g = g
 
-        self.stimulus_start = stimulus_start
-        self.stimulus_duration = stimulus_duration
-        self.stimulus_amplitude = stimulus_amplitude
+        self.stimulus_start = stimulus_start * U.ms
+        self.stimulus_duration = stimulus_duration * U.ms
+        self.stimulus_amplitude = stimulus_amplitude * U.nA
 
         self.cv_policy_max_extent = cv_policy_max_extent
 
-        self.the_props = arbor.neuron_cable_properties()
+        self.the_props = A.neuron_cable_properties()
 
     def num_cells(self):
         return 1
 
-    def num_sources(self, _):
-        return 0
-
     def cell_kind(self, _):
-        return arbor.cell_kind.cable
-
-    def probes(self, _):
-        return self.the_probes
+        return A.cell_kind.cable
 
     def global_properties(self, _):
         return self.the_props
@@ -81,34 +76,39 @@ class Cable(arbor.recipe):
         to build a multi-compartment neuron.
         """
 
-        tree = arbor.segment_tree()
+        tree = A.segment_tree()
 
         tree.append(
-            arbor.mnpos,
-            arbor.mpoint(0, 0, 0, self.radius),
-            arbor.mpoint(self.length, 0, 0, self.radius),
+            A.mnpos,
+            (0, 0, 0, self.radius),
+            (self.length, 0, 0, self.radius),
             tag=1,
         )
 
-        labels = arbor.label_dict({"cable": "(tag 1)", "start": "(location 0 0)"})
+        labels = A.label_dict({"cable": "(tag 1)", "start": "(location 0 0)"})
 
         decor = (
-            arbor.decor()
-            .set_property(Vm=self.Vm, cm=self.cm, rL=self.rL)
-            .paint('"cable"', arbor.density(f"pas/e={self.Vm}", g=self.g))
+            A.decor()
+            .set_property(
+                Vm=self.Vm * U.mV, cm=self.cm * U.F / U.m2, rL=self.rL * U.Ohm * U.cm
+            )
+            .paint('"cable"', A.density(f"pas/e={self.Vm}", g=self.g))
             .place(
                 '"start"',
-                arbor.iclamp(
+                A.iclamp(
                     self.stimulus_start, self.stimulus_duration, self.stimulus_amplitude
                 ),
                 "iclamp",
             )
         )
 
-        policy = arbor.cv_policy_max_extent(self.cv_policy_max_extent)
+        policy = A.cv_policy_max_extent(self.cv_policy_max_extent)
         decor.discretization(policy)
 
-        return arbor.cable_cell(tree, decor, labels)
+        return A.cable_cell(tree, decor, labels)
+
+    def probes(self, _):
+        return self.the_probes
 
 
 def get_rm(g):
@@ -196,34 +196,32 @@ if __name__ == "__main__":
     probe_locations = [
         (f"(location 0 {r})", f"Um-(0, {r})") for r in np.linspace(0, 1, 11)
     ]
-    probes = [
-        arbor.cable_probe_membrane_voltage(loc, tag) for loc, tag in probe_locations
-    ]
+    probes = [A.cable_probe_membrane_voltage(loc, tag) for loc, tag in probe_locations]
     recipe = Cable(probes, **vars(args))
 
     # configure the simulation and handles for the probes
-    sim = arbor.simulation(recipe)
-    dt = 0.001
+    sim = A.simulation(recipe)
+    dt = 1 * U.us
     handles = [
-        sim.sample((0, tag), arbor.regular_schedule(dt)) for _, tag in probe_locations
+        sim.sample((0, tag), A.regular_schedule(dt)) for _, tag in probe_locations
     ]
 
     # run the simulation for 30 ms
-    sim.run(tfinal=30, dt=dt)
+    sim.run(tfinal=30 * U.ms, dt=dt)
 
-    # retrieve the sampled membrane voltages and convert to a pandas DataFrame
+    # retrieve the sampled membrane voltages and convert to a pd DataFrame
     print("Plotting results ...")
     df_list = []
     for probe in range(len(handles)):
         samples, meta = sim.samples(handles[probe])[0]
         df_list.append(
-            pandas.DataFrame(
+            pd.DataFrame(
                 {"t/ms": samples[:, 0], "U/mV": samples[:, 1], "Probe": f"{probe}"}
             )
         )
 
-    df = pandas.concat(df_list, ignore_index=True)
-    seaborn.relplot(
+    df = pd.concat(df_list, ignore_index=True)
+    sns.relplot(
         data=df, kind="line", x="t/ms", y="U/mV", hue="Probe", errorbar=None
     ).set(xlim=(9, 14)).savefig("single_cell_cable_result.svg")
 
diff --git a/python/example/single_cell_detailed.py b/python/example/single_cell_detailed.py
index 1c3d7312bca825c01747f8f192a4f3d83738cdc9..b58e2f924ac8e2f7fda811af511267e8b4fd3e6f 100755
--- a/python/example/single_cell_detailed.py
+++ b/python/example/single_cell_detailed.py
@@ -1,28 +1,28 @@
 #!/usr/bin/env python3
 # This script is included in documentation. Adapt line numbers if touched.
 
-import arbor
-import pandas
-import seaborn
+import arbor as A
+from arbor import units as U
+import pandas as pd
+import seaborn as sns
 import sys
-from arbor import density
+from pathlib import Path
 
 # (1) Read the morphology from an SWC file.
+if len(sys.argv) == 1:
+    print("No SWC file passed to the program, using default.")
+    filename = Path(__file__).parent / "single_cell_detailed.swc"
+elif len(sys.argv) == 2:
+    filename = Path(sys.argv[1])
+else:
+    print("Usage: single_cell_detailed.py [SWC file name]")
+    sys.exit(1)
 
-# Read the SWC filename from input
-# Example from docs: single_cell_detailed.swc
 
-if len(sys.argv) < 2:
-    print("No SWC file passed to the program")
-    sys.exit(0)
-
-filename = sys.argv[1]
-morph = arbor.load_swc_arbor(filename)
+morph = A.load_swc_arbor(filename)
 
 # (2) Create and populate the label dictionary.
-
-
-labels = arbor.label_dict(
+labels = A.label_dict(
     {
         # Regions:
         # Add a label for a region that includes the whole morphology
@@ -43,70 +43,78 @@ labels = arbor.label_dict(
 ).add_swc_tags()
 
 # (3) Create and populate the decor.
-# NB. This can be written more compactly using method chaining
-
-decor = arbor.decor()
-# Set the default properties of the cell (this overrides the model defaults).
-decor.set_property(Vm=-55)
-decor.set_ion("na", int_con=10, ext_con=140, rev_pot=50, method="nernst/na")
-decor.set_ion("k", int_con=54.4, ext_con=2.5, rev_pot=-77)
-# Override the cell defaults.
-decor.paint('"custom"', tempK=270)
-decor.paint('"soma"', Vm=-50)
-# Paint density mechanisms.
-decor.paint('"all"', density("pas"))
-decor.paint('"custom"', density("hh"))
-decor.paint('"dend"', density("Ih", gbar=0.001))
-# Place stimuli and detectors.
-decor.place('"root"', arbor.iclamp(10, 1, current=2), "iclamp0")
-decor.place('"root"', arbor.iclamp(30, 1, current=2), "iclamp1")
-decor.place('"root"', arbor.iclamp(50, 1, current=2), "iclamp2")
-decor.place('"axon_terminal"', arbor.threshold_detector(-10), "detector")
-# Set discretisation: Soma as one CV, 1um everywhere else
-decor.discretization('(replace (single (region "soma")) (max-extent 1.0))')
+decor = (
+    A.decor()
+    # Set the default properties of the cell (this overrides the model defaults).
+    .set_property(
+        Vm=-55 * U.mV,
+        tempK=300 * U.Kelvin,
+        rL=35.4 * U.Ohm * U.cm,
+        cm=0.01 * U.F / U.m2,
+    )
+    .set_ion(
+        "na",
+        int_con=10 * U.mM,
+        ext_con=140 * U.mM,
+        rev_pot=50 * U.mV,
+        method="nernst/na",
+    )
+    .set_ion("k", int_con=54.4 * U.mM, ext_con=2.5 * U.mM, rev_pot=-77 * U.mV)
+    # Override the cell defaults.
+    .paint('"custom"', tempK=270 * U.Kelvin)
+    .paint('"soma"', Vm=-50 * U.mV)
+    # Paint density mechanisms.
+    .paint('"all"', A.density("pas"))
+    .paint('"custom"', A.density("hh"))
+    .paint('"dend"', A.density("Ih", gbar=0.001))
+    # Place stimuli and detectors.
+    .place('"root"', A.iclamp(10 * U.ms, 1 * U.ms, current=2 * U.nA), "iclamp0")
+    .place('"root"', A.iclamp(30 * U.ms, 1 * U.ms, current=2 * U.nA), "iclamp1")
+    .place('"root"', A.iclamp(50 * U.ms, 1 * U.ms, current=2 * U.nA), "iclamp2")
+    .place('"axon_terminal"', A.threshold_detector(-10 * U.mV), "detector")
+    # Set discretisation: Soma as one CV, 1um everywhere else
+    .discretization('(replace (single (region "soma")) (max-extent 1.0))')
+)
 
 # (4) Create the cell.
-
-cell = arbor.cable_cell(morph, decor, labels)
+cell = A.cable_cell(morph, decor, labels)
 
 # (5) Construct the model
-
-model = arbor.single_cell_model(cell)
+model = A.single_cell_model(cell)
 
 # (6) Set the model default properties
-
-model.properties.set_property(Vm=-65, tempK=300, rL=35.4, cm=0.01)
-model.properties.set_ion("na", int_con=10, ext_con=140, rev_pot=50, method="nernst/na")
-model.properties.set_ion("k", int_con=54.4, ext_con=2.5, rev_pot=-77)
-
-# Extend the default catalogue with the Allen catalogue.
-# The function takes a second string parameter that can prefix
-# the name of the mechanisms to avoid collisions between catalogues
-# in this case we have no collisions so we use an empty prefix string.
-model.properties.catalogue.extend(arbor.allen_catalogue(), "")
+model.properties.set_property(
+    Vm=-65 * U.mV, tempK=300 * U.Kelvin, rL=35.4 * U.Ohm * U.cm, cm=0.01 * U.F / U.m2
+)
+model.properties.set_ion(
+    "na", int_con=10 * U.mM, ext_con=140 * U.mM, rev_pot=50 * U.mV, method="nernst/na"
+)
+model.properties.set_ion(
+    "k", int_con=54.4 * U.mM, ext_con=2.5 * U.mM, rev_pot=-77 * U.mV
+)
+
+# Extend the default catalogue with the Allen catalogue. The function takes a
+# second string parameter that can prefix the name of the mechanisms to avoid
+# collisions between catalogues in this case we have no collisions so we use an
+# empty prefix string.
+model.properties.catalogue.extend(A.allen_catalogue(), "")
 
 # (7) Add probes.
-
-# Add voltage probes on the "custom_terminal" locset
-# which sample the voltage at 50 kHz
-model.probe("voltage", where='"custom_terminal"', tag="Um", frequency=50)
+# Add a voltage probe on "custom_terminal"
+model.probe("voltage", where='"custom_terminal"', tag="Um", frequency=50 * U.kHz)
 
 # (8) Run the simulation for 100 ms, with a dt of 0.025 ms
-
-model.run(tfinal=100, dt=0.025)
+model.run(tfinal=100 * U.ms, dt=25 * U.us)
 
 # (9) Print the spikes.
-
 print(len(model.spikes), "spikes recorded:")
 for s in model.spikes:
-    print(s)
+    print(f" * t={s:.3f} ms")
 
 # (10) Plot the voltages
-
-df_list = []
-for t in model.traces:
-    df_list.append(
-        pandas.DataFrame(
+df = pd.concat(
+    [
+        pd.DataFrame(
             {
                 "t/ms": t.time,
                 "U/mV": t.value,
@@ -114,9 +122,12 @@ for t in model.traces:
                 "Variable": t.variable,
             }
         )
-    )
-df = pandas.concat(df_list, ignore_index=True)
-seaborn.relplot(
+        for t in model.traces
+    ],
+    ignore_index=True,
+)
+
+sns.relplot(
     data=df,
     kind="line",
     x="t/ms",
diff --git a/python/example/single_cell_detailed_recipe.py b/python/example/single_cell_detailed_recipe.py
index c68d192dbfe1f479886b50f593cc4c555aa9c819..5d28457e98283c5530521e6a634662a29ac0aee3 100644
--- a/python/example/single_cell_detailed_recipe.py
+++ b/python/example/single_cell_detailed_recipe.py
@@ -1,27 +1,27 @@
 #!/usr/bin/env python3
 # This script is included in documentation. Adapt line numbers if touched.
 
-import arbor
-import pandas
-import seaborn
+import arbor as A
+from arbor import units as U
+import pandas as pd
+import seaborn as sns
 import sys
-from arbor import density
+from pathlib import Path
 
 # (1) Read the morphology from an SWC file.
+if len(sys.argv) == 1:
+    print("No SWC file passed to the program, using default.")
+    filename = Path(__file__).parent / "single_cell_detailed.swc"
+elif len(sys.argv) == 2:
+    filename = Path(sys.argv[1])
+else:
+    print("Usage: single_cell_detailed.py [SWC file name]")
+    sys.exit(1)
 
-# Read the SWC filename from input
-# Example from docs: single_cell_detailed.swc
-
-if len(sys.argv) < 2:
-    print("No SWC file passed to the program")
-    sys.exit(0)
-
-filename = sys.argv[1]
-morph = arbor.load_swc_arbor(filename)
+morph = A.load_swc_arbor(filename)
 
 # (2) Create and populate the label dictionary.
-
-labels = arbor.label_dict(
+labels = A.label_dict(
     {
         # Regions:
         # Add a label for a region that includes the whole morphology
@@ -42,51 +42,68 @@ labels = arbor.label_dict(
 ).add_swc_tags()  # Add SWC pre-defined regions
 
 # (3) Create and populate the decor.
-
 decor = (
-    arbor.decor()
+    A.decor()
     # Set the default properties of the cell (this overrides the model defaults).
-    .set_property(Vm=-55)
-    .set_ion("na", int_con=10, ext_con=140, rev_pot=50, method="nernst/na")
-    .set_ion("k", int_con=54.4, ext_con=2.5, rev_pot=-77)
+    .set_property(Vm=-55 * U.mV)
+    .set_ion(
+        "na",
+        int_con=10 * U.mM,
+        ext_con=140 * U.mM,
+        rev_pot=50 * U.mV,
+        method="nernst/na",
+    )
+    .set_ion("k", int_con=54.4 * U.mM, ext_con=2.5 * U.mM, rev_pot=-77 * U.mV)
     # Override the cell defaults.
-    .paint('"custom"', tempK=270)
-    .paint('"soma"', Vm=-50)
+    .paint('"custom"', tempK=270 * U.Kelvin)
+    .paint('"soma"', Vm=-50 * U.mV)
     # Paint density mechanisms.
-    .paint('"all"', density("pas"))
-    .paint('"custom"', density("hh"))
-    .paint('"dend"', density("Ih", gbar=0.001))
+    .paint('"all"', A.density("pas"))
+    .paint('"custom"', A.density("hh"))
+    .paint('"dend"', A.density("Ih", gbar=0.001))
     # Place stimuli and detectors.
-    .place('"root"', arbor.iclamp(10, 1, current=2), "iclamp0")
-    .place('"root"', arbor.iclamp(30, 1, current=2), "iclamp1")
-    .place('"root"', arbor.iclamp(50, 1, current=2), "iclamp2")
-    .place('"axon_terminal"', arbor.threshold_detector(-10), "detector")
+    .place('"root"', A.iclamp(10 * U.ms, 1 * U.ms, current=2 * U.nA), "iclamp0")
+    .place('"root"', A.iclamp(30 * U.ms, 1 * U.ms, current=2 * U.nA), "iclamp1")
+    .place('"root"', A.iclamp(50 * U.ms, 1 * U.ms, current=2 * U.nA), "iclamp2")
+    .place('"axon_terminal"', A.threshold_detector(-10 * U.mV), "detector")
     # Set discretisation: Soma as one CV, 1um everywhere else
     .discretization('(replace (single (region "soma")) (max-extent 1.0))')
 )
 
 
 # (4) Create the cell.
-
-cell = arbor.cable_cell(morph, decor, labels)
+cell = A.cable_cell(morph, decor, labels)
 
 
-# (5) Create a class that inherits from arbor.recipe
-class single_recipe(arbor.recipe):
+# (5) Create a class that inherits from A.recipe
+class single_recipe(A.recipe):
     # (5.1) Define the class constructor
     def __init__(self):
         # The base C++ class constructor must be called first, to ensure that
         # all memory in the C++ class is initialized correctly.
-        arbor.recipe.__init__(self)
-
-        self.the_props = arbor.cable_global_properties()
-        self.the_props.set_property(Vm=-65, tempK=300, rL=35.4, cm=0.01)
+        A.recipe.__init__(self)
+
+        self.the_props = A.cable_global_properties()
+        self.the_props.set_property(
+            Vm=-65 * U.mV,
+            tempK=300 * U.Kelvin,
+            rL=35.4 * U.Ohm * U.cm,
+            cm=0.01 * U.F / U.m2,
+        )
         self.the_props.set_ion(
-            ion="na", int_con=10, ext_con=140, rev_pot=50, method="nernst/na"
+            ion="na",
+            int_con=10 * U.mM,
+            ext_con=140 * U.mM,
+            rev_pot=50 * U.mV,
+            method="nernst/na",
         )
-        self.the_props.set_ion(ion="k", int_con=54.4, ext_con=2.5, rev_pot=-77)
-        self.the_props.set_ion(ion="ca", int_con=5e-5, ext_con=2, rev_pot=132.5)
-        self.the_props.catalogue.extend(arbor.allen_catalogue(), "")
+        self.the_props.set_ion(
+            ion="k", int_con=54.4 * U.mM, ext_con=2.5 * U.mM, rev_pot=-77 * U.mV
+        )
+        self.the_props.set_ion(
+            ion="ca", int_con=5e-5 * U.mM, ext_con=2 * U.mM, rev_pot=132.5 * U.mV
+        )
+        self.the_props.catalogue.extend(A.allen_catalogue(), "")
 
     # (5.2) Override the num_cells method
     def num_cells(self):
@@ -94,7 +111,7 @@ class single_recipe(arbor.recipe):
 
     # (5.3) Override the cell_kind method
     def cell_kind(self, _):
-        return arbor.cell_kind.cable
+        return A.cell_kind.cable
 
     # (5.4) Override the cell_description method
     def cell_description(self, _):
@@ -102,10 +119,10 @@ class single_recipe(arbor.recipe):
 
     # (5.5) Override the probes method
     def probes(self, _):
-        return [arbor.cable_probe_membrane_voltage('"custom_terminal"', "Um")]
+        return [A.cable_probe_membrane_voltage('"custom_terminal"', "Um")]
 
     # (5.6) Override the global_properties method
-    def global_properties(self, gid):
+    def global_properties(self, _):
         return self.the_props
 
 
@@ -113,42 +130,39 @@ class single_recipe(arbor.recipe):
 recipe = single_recipe()
 
 # (6) Create a simulation
-sim = arbor.simulation(recipe)
+sim = A.simulation(recipe)
 
 # Instruct the simulation to record the spikes and sample the probe
-sim.record(arbor.spike_recording.all)
+sim.record(A.spike_recording.all)
 
-handle = sim.sample((0, "Um"), arbor.regular_schedule(0.02))
+handle = sim.sample((0, "Um"), A.regular_schedule(0.02 * U.ms))
 
 # (7) Run the simulation
-sim.run(tfinal=100, dt=0.025)
+sim.run(tfinal=100 * U.ms, dt=0.025 * U.ms)
 
-# (8) Print or display the results
+# (8) Print spikes
 spikes = sim.spikes()
 print(len(spikes), "spikes recorded:")
-for s in spikes:
-    print(s)
-
-data = []
-meta = []
-for d, m in sim.samples(handle):
-    data.append(d)
-    meta.append(m)
-
-df_list = []
-for i in range(len(data)):
-    df_list.append(
-        pandas.DataFrame(
+for (gid, lid), t in spikes:
+    print(f" * t={t:.3f}ms gid={gid} lid={lid}")
+
+# (8) Plot the membrane potential
+df = pd.concat(
+    [
+        pd.DataFrame(
             {
-                "t/ms": data[i][:, 0],
-                "U/mV": data[i][:, 1],
-                "Location": str(meta[i]),
+                "t/ms": data[:, 0],
+                "U/mV": data[:, 1],
+                "Location": str(meta),
                 "Variable": "voltage",
             }
         )
-    )
-df = pandas.concat(df_list, ignore_index=True)
-seaborn.relplot(
+        for data, meta in sim.samples(handle)
+    ],
+    ignore_index=True,
+)
+
+sns.relplot(
     data=df,
     kind="line",
     x="t/ms",
diff --git a/python/example/single_cell_model.py b/python/example/single_cell_model.py
index c744738d73923a5c53af1bbb44af7509af23fca8..9903494cee6749255b24289bfe98d364b6ec1919 100755
--- a/python/example/single_cell_model.py
+++ b/python/example/single_cell_model.py
@@ -1,52 +1,49 @@
 #!/usr/bin/env python3
 # This script is included in documentation. Adapt line numbers if touched.
 
-import arbor
-import pandas  # You may have to pip install these.
-import seaborn  # You may have to pip install these.
+import arbor as A
+from arbor import units as U
+import pandas as pd  # You may have to pip install these.
+import seaborn as sns  # You may have to pip install these.
 
 # (1) Create a morphology with a single (cylindrical) segment of length=diameter=6 μm
-tree = arbor.segment_tree()
-tree.append(arbor.mnpos, arbor.mpoint(-3, 0, 0, 3), arbor.mpoint(3, 0, 0, 3), tag=1)
+tree = A.segment_tree()
+tree.append(A.mnpos, A.mpoint(-3, 0, 0, 3), A.mpoint(3, 0, 0, 3), tag=1)
 
 # (2) Define the soma and its midpoint
-labels = arbor.label_dict({"soma": "(tag 1)", "midpoint": "(location 0 0.5)"})
+labels = A.label_dict({"soma": "(tag 1)", "midpoint": "(location 0 0.5)"})
 
 # (3) Create and set up a decor object
-
 decor = (
-    arbor.decor()
-    .set_property(Vm=-40)
-    .paint('"soma"', arbor.density("hh"))
-    .place('"midpoint"', arbor.iclamp(10, 2, 0.8), "iclamp")
-    .place('"midpoint"', arbor.threshold_detector(-10), "detector")
+    A.decor()
+    .set_property(Vm=-40 * U.mV)
+    .paint('"soma"', A.density("hh"))
+    .place('"midpoint"', A.iclamp(10 * U.ms, 2 * U.ms, 0.8 * U.nA), "iclamp")
+    .place('"midpoint"', A.threshold_detector(-10 * U.mV), "detector")
 )
 
 # (4) Create cell and the single cell model based on it
-cell = arbor.cable_cell(tree, decor, labels)
+cell = A.cable_cell(tree, decor, labels)
 
 # (5) Make single cell model.
-m = arbor.single_cell_model(cell)
+m = A.single_cell_model(cell)
 
 # (6) Attach voltage probe sampling at 10 kHz (every 0.1 ms).
-m.probe("voltage", '"midpoint"', tag="Um", frequency=10)
+m.probe("voltage", '"midpoint"', tag="Um", frequency=10 * U.kHz)
 
 # (7) Run simulation for 30 ms of simulated activity.
-m.run(tfinal=30)
+m.run(tfinal=30 * U.ms)
 
 # (8) Print spike times.
-if len(m.spikes) > 0:
-    print("{} spikes:".format(len(m.spikes)))
-    for s in m.spikes:
-        print("{:3.3f}".format(s))
-else:
-    print("no spikes")
+print("{} spikes:".format(len(m.spikes)))
+for s in m.spikes:
+    print(f" * {s:3.3f} ms")
 
 # (9) Plot the recorded voltages over time.
 print("Plotting results ...")
-seaborn.set_theme()  # Apply some styling to the plot
-df = pandas.DataFrame({"t/ms": m.traces[0].time, "U/mV": m.traces[0].value})
-seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV", errorbar=None).savefig(
+sns.set_theme()  # Apply some styling to the plot
+df = pd.DataFrame({"t/ms": m.traces[0].time, "U/mV": m.traces[0].value})
+sns.relplot(data=df, kind="line", x="t/ms", y="U/mV", errorbar=None).savefig(
     "single_cell_model_result.svg"
 )
 
diff --git a/python/example/single_cell_nml.py b/python/example/single_cell_nml.py
index adbdb268a61ae1d9b7ba1f9922428a23338181e5..c6ea8169390b54ef6e61b1d495bc50cbd8ce7ce1 100755
--- a/python/example/single_cell_nml.py
+++ b/python/example/single_cell_nml.py
@@ -1,8 +1,8 @@
 #!/usr/bin/env python3
-import arbor
-from arbor import mechanism as mech
-import pandas
-import seaborn
+import arbor as A
+from arbor import units as U
+import pandas as pd
+import seaborn as sns
 import sys
 
 # Load a cell morphology from an nml file.
@@ -14,7 +14,7 @@ if len(sys.argv) < 2:
 filename = sys.argv[1]
 
 # Read the NeuroML morphology from the file.
-morpho_nml = arbor.neuroml(filename)
+morpho_nml = A.neuroml(filename)
 
 # Read the morphology data associated with morphology "m1".
 morpho_data = morpho_nml.morphology("m1")
@@ -28,7 +28,7 @@ morpho_named = morpho_data.named_segments()
 morpho_groups = morpho_data.groups()
 
 # Create new label dict with some locsets.
-labels = arbor.label_dict(
+labels = A.label_dict(
     {
         "stim_site": "(location 1 0.5)",  # site for the stimulus, in the middle of branch 1.
         "axon_end": '(restrict-to (terminal) (region "axon"))',  # end of the axon.
@@ -45,52 +45,51 @@ print("Label dictionary regions: ", labels.regions, "\n")
 print("Label dictionary locsets: ", labels.locsets, "\n")
 
 decor = (
-    arbor.decor()
+    A.decor()
     # Set initial membrane potential to -55 mV
-    .set_property(Vm=-55)
+    .set_property(Vm=-55 * U.mV)
     # Use Nernst to calculate reversal potential for calcium.
-    .set_ion("ca", method=mech("nernst/x=ca"))
+    .set_ion("ca", method="nernst/x=ca")
     # hh mechanism on the soma and axon.
-    .paint('"soma"', arbor.density("hh"))
-    .paint('"axon"', arbor.density("hh"))
+    .paint('"soma"', A.density("hh"))
+    .paint('"axon"', A.density("hh"))
     # pas mechanism the dendrites.
-    .paint('"dend"', arbor.density("pas"))
+    .paint('"dend"', A.density("pas"))
     # Increase resistivity on dendrites.
-    .paint('"dend"', rL=500)
+    .paint('"dend"', rL=500 * U.Ohm * U.cm)
     # Attach stimuli that inject 4 nA current for 1 ms, starting at 3 and 8 ms.
-    .place('"root"', arbor.iclamp(10, 1, current=5), "iclamp0")
-    .place('"stim_site"', arbor.iclamp(3, 1, current=0.5), "iclamp1")
-    .place('"stim_site"', arbor.iclamp(10, 1, current=0.5), "iclamp2")
-    .place('"stim_site"', arbor.iclamp(8, 1, current=4), "iclamp3")
+    .place('"root"', A.iclamp(10 * U.ms, 1 * U.ms, current=5 * U.nA), "iclamp0")
+    .place('"stim_site"', A.iclamp(3 * U.ms, 1 * U.ms, current=0.5 * U.nA), "iclamp1")
+    .place('"stim_site"', A.iclamp(10 * U.ms, 1 * U.ms, current=0.5 * U.nA), "iclamp2")
+    .place('"stim_site"', A.iclamp(8 * U.ms, 1 * U.ms, current=4 * U.nA), "iclamp3")
     # Detect spikes at the soma with a voltage threshold of -10 mV.
-    .place('"axon_end"', arbor.threshold_detector(-10), "detector")
+    .place('"axon_end"', A.threshold_detector(-10 * U.mV), "detector")
     # Set discretisation: Soma as one CV, 1um everywhere else
     .discretization('(replace (single (region "soma")) (max-extent 1.0))')
 )
 
 # Combine morphology with region and locset definitions to make a cable cell.
-cell = arbor.cable_cell(morpho, decor, labels)
+cell = A.cable_cell(morpho, decor, labels)
 
 print(cell.locations('"axon_end"'))
 
 # Make single cell model.
-m = arbor.single_cell_model(cell)
+m = A.single_cell_model(cell)
 
 # Attach voltage probes that sample at 50 kHz.
-m.probe("voltage", where='"root"', tag="Um-root", frequency=50)
-m.probe("voltage", where='"stim_site"', tag="Um-stim", frequency=50)
-m.probe("voltage", where='"axon_end"', tag="Um-axon", frequency=50)
+m.probe("voltage", where='"root"', tag="Um-root", frequency=50 * U.kHz)
+m.probe("voltage", where='"stim_site"', tag="Um-stim", frequency=50 * U.kHz)
+m.probe("voltage", where='"axon_end"', tag="Um-axon", frequency=50 * U.kHz)
 
 # Simulate the cell for 15 ms.
-tfinal = 15
-m.run(tfinal)
+m.run(15 * U.ms)
 print("Simulation done.")
 
 # Print spike times.
 if len(m.spikes) > 0:
     print("{} spikes:".format(len(m.spikes)))
     for s in m.spikes:
-        print("  {:7.4f}".format(s))
+        print(f"  {s:7.4f} ms")
 else:
     print("no spikes")
 
@@ -99,7 +98,7 @@ print("Plotting results ...")
 df_list = []
 for t in m.traces:
     df_list.append(
-        pandas.DataFrame(
+        pd.DataFrame(
             {
                 "t/ms": t.time,
                 "U/mV": t.value,
@@ -109,9 +108,9 @@ for t in m.traces:
         )
     )
 
-df = pandas.concat(df_list, ignore_index=True)
+df = pd.concat(df_list, ignore_index=True)
 
-seaborn.relplot(
+sns.relplot(
     data=df,
     kind="line",
     x="t/ms",
diff --git a/python/example/single_cell_recipe.py b/python/example/single_cell_recipe.py
index 9ebc1c01e5db9ddbd8bfca4bd9c11fd3c31137c2..dba7f3a29b7106c4e11ac78b42ea6b145019d89e 100644
--- a/python/example/single_cell_recipe.py
+++ b/python/example/single_cell_recipe.py
@@ -1,44 +1,40 @@
 #!/usr/bin/env python3
 # This script is included in documentation. Adapt line numbers if touched.
 
-import arbor
-import pandas  # You may have to pip install these.
-import seaborn  # You may have to pip install these.
+import arbor as A
+from arbor import units as U
+import pandas as pd  # You may have to pip install these.
+import seaborn as sns  # You may have to pip install these.
 
 # The corresponding generic recipe version of `single_cell_model.py`.
 
 # (1) Create a morphology with a single (cylindrical) segment of length=diameter=6 μm
-
-tree = arbor.segment_tree()
-tree.append(arbor.mnpos, arbor.mpoint(-3, 0, 0, 3), arbor.mpoint(3, 0, 0, 3), tag=1)
+tree = A.segment_tree()
+tree.append(A.mnpos, A.mpoint(-3, 0, 0, 3), A.mpoint(3, 0, 0, 3), tag=1)
 
 # (2) Define the soma and its midpoint
-
-labels = arbor.label_dict({"soma": "(tag 1)", "midpoint": "(location 0 0.5)"})
+labels = A.label_dict({"soma": "(tag 1)", "midpoint": "(location 0 0.5)"})
 
 # (3) Create cell and set properties
-
 decor = (
-    arbor.decor()
-    .set_property(Vm=-40)
-    .paint('"soma"', arbor.density("hh"))
-    .place('"midpoint"', arbor.iclamp(10, 2, 0.8), "iclamp")
-    .place('"midpoint"', arbor.threshold_detector(-10), "detector")
+    A.decor()
+    .set_property(Vm=-40 * U.mV)
+    .paint('"soma"', A.density("hh"))
+    .place('"midpoint"', A.iclamp(10 * U.ms, 2 * U.ms, 0.8 * U.nA), "iclamp")
+    .place('"midpoint"', A.threshold_detector(-10 * U.mV), "detector")
 )
 
-cell = arbor.cable_cell(tree, decor, labels)
+cell = A.cable_cell(tree, decor, labels)
+
 
 # (4) Define a recipe for a single cell and set of probes upon it.
 # This constitutes the corresponding generic recipe version of
 # `single_cell_model.py`.
-
-
-class single_recipe(arbor.recipe):
-    # (4.1) The base class constructor must be called first, to ensure that
-    # all memory in the wrapped C++ class is initialized correctly.
+class single_recipe(A.recipe):
+    # (4.1) Base constructor must be called, to ensure correct initialization.
     def __init__(self):
-        arbor.recipe.__init__(self)
-        self.the_props = arbor.neuron_cable_properties()
+        A.recipe.__init__(self)
+        self.the_props = A.neuron_cable_properties()
 
     # (4.2) Override the num_cells method
     def num_cells(self):
@@ -46,7 +42,7 @@ class single_recipe(arbor.recipe):
 
     # (4.3) Override the cell_kind method
     def cell_kind(self, _):
-        return arbor.cell_kind.cable
+        return A.cell_kind.cable
 
     # (4.4) Override the cell_description method
     def cell_description(self, gid):
@@ -54,7 +50,7 @@ class single_recipe(arbor.recipe):
 
     # (4.5) Override the probes method with a voltage probe located on "midpoint"
     def probes(self, _):
-        return [arbor.cable_probe_membrane_voltage('"midpoint"', "Um")]
+        return [A.cable_probe_membrane_voltage('"midpoint"', "Um")]
 
     # (4.6) Override the global_properties method
     def global_properties(self, kind):
@@ -62,37 +58,31 @@ class single_recipe(arbor.recipe):
 
 
 # (5) Instantiate recipe.
-
 recipe = single_recipe()
 
-# (6) Create simulation. When their defaults are sufficient, context and domain decomposition don't
-# have to be manually specified and the simulation can be created with just the recipe as argument.
-
-sim = arbor.simulation(recipe)
-
-# (7) Create and run simulation and set up 10 kHz (every 0.1 ms) sampling on the probe.
-# The probe is located on cell 0, and is the 0th probe on that cell, thus has probeset_id (0, 0).
+# (6) Create simulation. When their defaults are sufficient, context and domain
+# decomposition don't have to be manually specified and the simulation can be
+# created with just the recipe as argument.
+sim = A.simulation(recipe)
 
-sim.record(arbor.spike_recording.all)
-handle = sim.sample((0, "Um"), arbor.regular_schedule(0.1))
-sim.run(tfinal=30)
+# (7) Create and run simulation and set up 10 kHz (every 0.1 ms) sampling on the
+# probe. The probe is located on cell 0, and is the 0th probe on that cell, thus
+# has probeset_id (0, 0).
+sim.record(A.spike_recording.all)
+handle = sim.sample((0, "Um"), A.regular_schedule(0.1 * U.ms))
+sim.run(tfinal=30 * U.ms)
 
 # (8) Collect results.
-
 spikes = sim.spikes()
 data, meta = sim.samples(handle)[0]
 
-if len(spikes) > 0:
-    print("{} spikes:".format(len(spikes)))
-    for t in spikes["time"]:
-        print("{:3.3f}".format(t))
-else:
-    print("no spikes")
+print("{} spikes:".format(len(spikes)))
+for t in spikes["time"]:
+    print(f" * {t:3.3f} ms")
 
 print("Plotting results ...")
-
-df = pandas.DataFrame({"t/ms": data[:, 0], "U/mV": data[:, 1]})
-seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV", errorbar=None).savefig(
+df = pd.DataFrame({"t/ms": data[:, 0], "U/mV": data[:, 1]})
+sns.relplot(data=df, kind="line", x="t/ms", y="U/mV", errorbar=None).savefig(
     "single_cell_recipe_result.svg"
 )
 
diff --git a/python/example/single_cell_stdp.py b/python/example/single_cell_stdp.py
index 9e840127174a78019ab89bffab7beba4e874480f..d460fbd7d6bab8a02b6dbf9dddb395c520e6064c 100755
--- a/python/example/single_cell_stdp.py
+++ b/python/example/single_cell_stdp.py
@@ -1,71 +1,68 @@
 #!/usr/bin/env python3
 
-import arbor
+import arbor as A
+from arbor import units as U
 import numpy as np
 import pandas as pd
 import seaborn as sns  # You may have to pip install these.
 
 
-class single_recipe(arbor.recipe):
+class single_recipe(A.recipe):
     def __init__(self, dT, n_pairs):
-        arbor.recipe.__init__(self)
+        A.recipe.__init__(self)
         self.dT = dT
         self.n_pairs = n_pairs
 
-        self.the_props = arbor.neuron_cable_properties()
+        self.the_props = A.neuron_cable_properties()
 
     def num_cells(self):
         return 1
 
     def cell_kind(self, gid):
-        return arbor.cell_kind.cable
+        return A.cell_kind.cable
 
     def cell_description(self, gid):
-        tree = arbor.segment_tree()
-        tree.append(
-            arbor.mnpos, arbor.mpoint(-3, 0, 0, 3), arbor.mpoint(3, 0, 0, 3), tag=1
-        )
+        tree = A.segment_tree()
+        tree.append(A.mnpos, (-3, 0, 0, 3), (3, 0, 0, 3), tag=1)
 
-        labels = arbor.label_dict({"soma": "(tag 1)", "center": "(location 0 0.5)"})
+        labels = A.label_dict({"soma": "(tag 1)", "center": "(location 0 0.5)"})
 
         decor = (
-            arbor.decor()
-            .set_property(Vm=-40)
-            .paint("(all)", arbor.density("hh"))
-            .place('"center"', arbor.threshold_detector(-10), "detector")
-            .place('"center"', arbor.synapse("expsyn"), "synapse")
+            A.decor()
+            .set_property(Vm=-40 * U.mV)
+            .paint("(all)", A.density("hh"))
+            .place('"center"', A.threshold_detector(-10 * U.mV), "detector")
+            .place('"center"', A.synapse("expsyn"), "synapse")
             .place(
                 '"center"',
-                arbor.synapse("expsyn_stdp", max_weight=1.0),
+                A.synapse("expsyn_stdp", max_weight=1.0),
                 "stpd_synapse",
             )
         )
 
-        return arbor.cable_cell(tree, decor, labels)
+        return A.cable_cell(tree, decor, labels)
 
     def event_generators(self, gid):
         """two stimuli: one that makes the cell spike, the other to monitor STDP"""
 
-        stimulus_times = np.linspace(50, 500, self.n_pairs)
+        stimulus_times = np.linspace(50, 500, self.n_pairs) * U.ms
 
         # strong enough stimulus
-        spike = arbor.event_generator(
-            "synapse", 1.0, arbor.explicit_schedule(stimulus_times)
-        )
+        spike = A.event_generator("synapse", 1.0, A.explicit_schedule(stimulus_times))
 
         # zero weight -> just modify synaptic weight via stdp
-        stdp = arbor.event_generator(
-            "stpd_synapse", 0.0, arbor.explicit_schedule(stimulus_times - self.dT)
+        stdp = A.event_generator(
+            "stpd_synapse", 0.0, A.explicit_schedule(stimulus_times - self.dT)
         )
 
         return [spike, stdp]
 
     def probes(self, gid):
         def mk(s, t):
-            return arbor.cable_probe_point_state(1, "expsyn_stdp", state=s, tag=t)
+            return A.cable_probe_point_state(1, "expsyn_stdp", state=s, tag=t)
 
         return [
-            arbor.cable_probe_membrane_voltage('"center"', "Um"),
+            A.cable_probe_membrane_voltage('"center"', "Um"),
             mk("g", "state-g"),
             mk("apost", "state-apost"),
             mk("apre", "state-apre"),
@@ -79,11 +76,11 @@ class single_recipe(arbor.recipe):
 def run(dT, n_pairs=1, do_plots=False):
     recipe = single_recipe(dT, n_pairs)
 
-    sim = arbor.simulation(recipe)
+    sim = A.simulation(recipe)
 
-    sim.record(arbor.spike_recording.all)
+    sim.record(A.spike_recording.all)
 
-    reg_sched = arbor.regular_schedule(0.1)
+    reg_sched = A.regular_schedule(0.1 * U.ms)
     handles = {
         "U": sim.sample((0, "Um"), reg_sched),
         "g": sim.sample((0, "state-g"), reg_sched),
@@ -92,7 +89,7 @@ def run(dT, n_pairs=1, do_plots=False):
         "weight_plastic": sim.sample((0, "state-weight"), reg_sched),
     }
 
-    sim.run(tfinal=600)
+    sim.run(tfinal=0.6 * U.s)
 
     if do_plots:
         print("Plotting detailed results ...")
@@ -107,7 +104,7 @@ def run(dT, n_pairs=1, do_plots=False):
     return weight_plastic[:, 1][-1]
 
 
-data = np.array([(dT, run(dT)) for dT in np.arange(-20, 20, 0.5)])
+data = np.array([(dT, run(dT * U.ms)) for dT in np.arange(-20, 20, 0.5)])
 df = pd.DataFrame({"t/ms": data[:, 0], "dw": data[:, 1]})
 print("Plotting results ...")
 sns.relplot(data=df, x="t/ms", y="dw", kind="line", errorbar=None).savefig(
diff --git a/python/example/single_cell_swc.py b/python/example/single_cell_swc.py
index d5cbf46d69fc58e3842cccf5fee114ba22efa1e4..097c11b60be5f61fc95913571c0f013e4e5c2f66 100755
--- a/python/example/single_cell_swc.py
+++ b/python/example/single_cell_swc.py
@@ -10,9 +10,10 @@
 #     preserve surface area and correct starting locations of cables
 #     attached to the soma.
 
-import arbor
-import pandas
-import seaborn
+import arbor as A
+from arbor import units as U
+import pandas as pd
+import seaborn as sns
 import sys
 
 # Load a cell morphology from an swc file.
@@ -22,10 +23,10 @@ if len(sys.argv) < 2:
     sys.exit(0)
 
 filename = sys.argv[1]
-morpho = arbor.load_swc_arbor(filename)
+morpho = A.load_swc_arbor(filename)
 
 # Define the regions and locsets in the model.
-labels = arbor.label_dict(
+labels = A.label_dict(
     {
         "root": "(root)",  # the start of the soma in this morphology is at the root of the cell.
         "stim_site": "(location 0 0.5)",  # site for the stimulus, in the middle of branch 0.
@@ -34,46 +35,43 @@ labels = arbor.label_dict(
 ).add_swc_tags()  # Finally, add the SWC default labels.
 
 decor = (
-    arbor.decor()
+    A.decor()
     # Set initial membrane potential to -55 mV
-    .set_property(Vm=-55)
+    .set_property(Vm=-55 * U.mV)
     # Use Nernst to calculate reversal potential for calcium.
-    .set_ion("ca", method=arbor.mechanism("nernst/x=ca"))
+    .set_ion("ca", method=A.mechanism("nernst/x=ca"))
     # hh mechanism on the soma and axon.
-    .paint('"soma"', arbor.density("hh"))
-    .paint('"axon"', arbor.density("hh"))
+    .paint('"soma"', A.density("hh"))
+    .paint('"axon"', A.density("hh"))
     # pas mechanism the dendrites.
-    .paint('"dend"', arbor.density("pas"))
+    .paint('"dend"', A.density("pas"))
     # Increase resistivity on dendrites.
-    .paint('"dend"', rL=500)
+    .paint('"dend"', rL=500 * U.Ohm * U.cm)
     # Attach stimuli that inject 4 nA current for 1 ms, starting at 3 and 8 ms.
-    .place('"root"', arbor.iclamp(10, 1, current=5), "iclamp0")
-    .place('"stim_site"', arbor.iclamp(3, 1, current=0.5), "iclamp1")
-    .place('"stim_site"', arbor.iclamp(10, 1, current=0.5), "iclamp2")
-    .place('"stim_site"', arbor.iclamp(8, 1, current=4), "iclamp3")
+    .place('"root"', A.iclamp(10 * U.ms, 1 * U.ms, current=5 * U.nA), "iclamp0")
+    .place('"stim_site"', A.iclamp(3 * U.ms, 1 * U.ms, current=0.5 * U.nA), "iclamp1")
+    .place('"stim_site"', A.iclamp(10 * U.ms, 1 * U.ms, current=0.5 * U.nA), "iclamp2")
+    .place('"stim_site"', A.iclamp(8 * U.ms, 1 * U.ms, current=4 * U.nA), "iclamp3")
     # Detect spikes at the soma with a voltage threshold of -10 mV.
-    .place('"axon_end"', arbor.threshold_detector(-10), "detector")
+    .place('"axon_end"', A.threshold_detector(-10 * U.mV), "detector")
     # Create the policy used to discretise the cell into CVs.
     # Use a single CV for the soma, and CVs of maximum length 1 μm elsewhere.
     .discretization('(replace (single (region "soma")) (max-extent 1.0))')
 )
 
 # Combine morphology with region and locset definitions to make a cable cell.
-cell = arbor.cable_cell(morpho, decor, labels)
-
-print(cell.locations('"axon_end"'))
+cell = A.cable_cell(morpho, decor, labels)
 
 # Make single cell model.
-m = arbor.single_cell_model(cell)
+m = A.single_cell_model(cell)
 
 # Attach voltage probes that sample at 50 kHz.
-m.probe("voltage", tag="Um-root", where='"root"', frequency=50)
-m.probe("voltage", tag="Um-stim", where='"stim_site"', frequency=50)
-m.probe("voltage", tag="Um-axon", where='"axon_end"', frequency=50)
+m.probe("voltage", tag="Um-root", where='"root"', frequency=50 * U.kHz)
+m.probe("voltage", tag="Um-stim", where='"stim_site"', frequency=50 * U.kHz)
+m.probe("voltage", tag="Um-axon", where='"axon_end"', frequency=50 * U.kHz)
 
 # Simulate the cell for 15 ms.
-tfinal = 15
-m.run(tfinal)
+m.run(15 * U.ms)
 print("Simulation done.")
 
 # Print spike times.
@@ -89,7 +87,7 @@ print("Plotting results ...")
 df_list = []
 for t in m.traces:
     df_list.append(
-        pandas.DataFrame(
+        pd.DataFrame(
             {
                 "t/ms": t.time,
                 "U/mV": t.value,
@@ -99,9 +97,9 @@ for t in m.traces:
         )
     )
 
-df = pandas.concat(df_list, ignore_index=True)
+df = pd.concat(df_list, ignore_index=True)
 
-seaborn.relplot(
+sns.relplot(
     data=df,
     kind="line",
     x="t/ms",
diff --git a/python/example/v-clamp.py b/python/example/v-clamp.py
index 819c0925c736b22260f6df5d90fa284775e8621d..3b17c05df929544fb94707489395d11abdd00332 100755
--- a/python/example/v-clamp.py
+++ b/python/example/v-clamp.py
@@ -1,38 +1,38 @@
 #!/usr/bin/env python3
 
-import arbor
-import pandas  # You may have to pip install these.
-import seaborn  # You may have to pip install these.
+import arbor as A
+from arbor import units as U
+import pandas as pd  # You may have to pip install these.
+import seaborn as sns  # You may have to pip install these.
 
 # (1) Create a morphology with a single (cylindrical) segment of length=diameter=6 μm
-tree = arbor.segment_tree()
-tree.append(arbor.mnpos, arbor.mpoint(-3, 0, 0, 3), arbor.mpoint(3, 0, 0, 3), tag=1)
+tree = A.segment_tree()
+tree.append(A.mnpos, A.mpoint(-3, 0, 0, 3), A.mpoint(3, 0, 0, 3), tag=1)
 
 # (2) Define the soma and its midpoint
-labels = arbor.label_dict({"soma": "(tag 1)", "midpoint": "(location 0 0.5)"})
+labels = A.label_dict({"soma": "(tag 1)", "midpoint": "(location 0 0.5)"})
 
 # (3) Create and set up a decor object
-
 decor = (
-    arbor.decor()
-    .set_property(Vm=-40)
-    .paint('"soma"', arbor.density("hh"))
-    .paint('"soma"', arbor.voltage_process("v_clamp/v0=-42"))
-    .place('"midpoint"', arbor.iclamp(10, 2, 0.8), "iclamp")
-    .place('"midpoint"', arbor.threshold_detector(-10), "detector")
+    A.decor()
+    .set_property(Vm=-40 * U.mV)
+    .paint('"soma"', A.density("hh"))
+    .paint('"soma"', A.voltage_process("v_clamp/v0=-42"))
+    .place('"midpoint"', A.iclamp(10 * U.ms, 2 * U.ms, 0.8 * U.nA), "iclamp")
+    .place('"midpoint"', A.threshold_detector(-10 * U.mV), "detector")
 )
 
 # (4) Create cell and the single cell model based on it
-cell = arbor.cable_cell(tree, decor, labels)
+cell = A.cable_cell(tree, decor, labels)
 
 # (5) Make single cell model.
-m = arbor.single_cell_model(cell)
+m = A.single_cell_model(cell)
 
 # (6) Attach voltage probe sampling at 10 kHz (every 0.1 ms).
-m.probe("voltage", '"midpoint"', "Um", frequency=10)
+m.probe("voltage", '"midpoint"', "Um", frequency=10 * U.kHz)
 
 # (7) Run simulation for 30 ms of simulated activity.
-m.run(tfinal=30)
+m.run(tfinal=30 * U.ms)
 
 # (8) Print spike times.
 if len(m.spikes) > 0:
@@ -44,8 +44,8 @@ else:
 
 # (9) Plot the recorded voltages over time.
 print("Plotting results ...")
-df = pandas.DataFrame({"t/ms": m.traces[0].time, "U/mV": m.traces[0].value})
-seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV", errorbar=None).savefig(
+df = pd.DataFrame({"t/ms": m.traces[0].time, "U/mV": m.traces[0].value})
+sns.relplot(data=df, kind="line", x="t/ms", y="U/mV", errorbar=None).savefig(
     "v-clamp.svg"
 )
 
diff --git a/python/identifiers.cpp b/python/identifiers.cpp
index 5a9ff9a1ebb20c6cd2beac6d69b17095a6347d50..e58a8a035de0ce86c80c12eb34431326d83cc787 100644
--- a/python/identifiers.cpp
+++ b/python/identifiers.cpp
@@ -1,6 +1,8 @@
 #include <pybind11/pybind11.h>
 
 #include <arbor/common_types.hpp>
+#include <arbor/recipe.hpp>
+#include <arbor/spike.hpp>
 
 #include "strprintf.hpp"
 
@@ -21,6 +23,11 @@ void register_identifiers(py::module& m) {
         .value("univalent", arb::lid_selection_policy::assert_univalent,
                "Assert that there is only one possible location associated with a labeled item on the cell. The model throws an exception if the assertion fails.");
 
+    py::class_<arb::cell_address_type> cell_address(m, "cell_address");
+    cell_address
+        .def_readwrite("gid", &arb::cell_address_type::gid)
+        .def_readwrite("tag", &arb::cell_address_type::tag);
+
     py::class_<arb::cell_local_label_type> cell_local_label_type(m, "cell_local_label",
         "For local identification of an item.\n\n"
         "cell_local_label identifies:\n"
@@ -146,6 +153,21 @@ void register_identifiers(py::module& m) {
             "Use GPU backend.")
         .value("multicore", arb::backend_kind::multicore,
             "Use multicore backend.");
+
+    // Probes
+    py::class_<arb::probe_info> probe(m, "probe");
+    probe
+        .def("__repr__", [](const arb::probe_info& p){return util::pprintf("<arbor.probe: tag {}>", p.tag);})
+        .def("__str__",  [](const arb::probe_info& p){return util::pprintf("<arbor.probe: tag {}>", p.tag);});
+
+    py::class_<arb::spike> spike(m, "spike");
+    spike
+        .def(py::init([](const arb::cell_member_type& m, arb::time_type t) -> arb::spike { return {m, t}; }))
+        .def_readwrite("source", &arb::spike::source, "The global identifier of the cell.")
+        .def_readwrite("time", &arb::spike::time, "The time of spike.")
+        .def("__repr__", [](const arb::spike& s){return util::pprintf("<arbor.spike: {}>", s);})
+        .def("__str__",  [](const arb::spike& s){return util::pprintf("<arbor.spike: {}>", s);});
+
 }
 
 } // namespace pyarb
diff --git a/python/label_dict.cpp b/python/label_dict.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..ff71d1481d84382750a00c64ac65b6ae4b75b731
--- /dev/null
+++ b/python/label_dict.cpp
@@ -0,0 +1,88 @@
+#include "label_dict.hpp"
+
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+
+namespace pyarb {
+
+namespace py = pybind11;
+using namespace py::literals;
+void register_label_dict(py::module& m) {
+
+    py::class_<label_dict_proxy> label_dict(m, "label_dict",
+        "A dictionary of labelled region and locset definitions, with a\n"
+        "unique label assigned to each definition.");
+    label_dict
+        .def(py::init<>(),
+             "Create an empty label dictionary.")
+        .def(py::init<const std::unordered_map<std::string, std::string>&>(),
+            "Initialize a label dictionary from a dictionary with string labels as keys,"
+            " and corresponding definitions as strings.")
+        .def(py::init<const label_dict_proxy&>(),
+            "Initialize a label dictionary from another one")
+        .def(py::init([](py::iterator& it) {
+                label_dict_proxy ld;
+                for (; it != py::iterator::sentinel(); ++it) {
+                    const auto tuple = it->cast<py::sequence>();
+                    const auto key   = tuple[0].cast<std::string>();
+                    const auto value = tuple[1].cast<std::string>();
+                    ld.set(key, value);
+                }
+                return ld;
+            }),
+            "Initialize a label dictionary from an iterable of key, definition pairs")
+        .def("add_swc_tags",
+             [](label_dict_proxy& l) { return l.add_swc_tags(); },
+             "Add standard SWC tagged regions.\n"
+             " - soma: (tag 1)\n"
+             " - axon: (tag 2)\n"
+             " - dend: (tag 3)\n"
+             " - apic: (tag 4)")
+        .def("__setitem__",
+            [](label_dict_proxy& l, const char* name, const char* desc) {
+                l.set(name, desc);})
+        .def("__getitem__",
+            [](label_dict_proxy& l, const char* name) {
+                if (auto v = l.getitem(name)) return v.value();
+                throw py::key_error(name);
+            })
+        .def("__len__", &label_dict_proxy::size)
+        .def("__iter__",
+            [](const label_dict_proxy &ld) {
+                return py::make_key_iterator(ld.cache.begin(), ld.cache.end());},
+            py::keep_alive<0, 1>())
+        .def("__contains__",
+             [](const label_dict_proxy &ld, const char* name) {
+                 return ld.contains(name);})
+        .def("keys",
+            [](const label_dict_proxy &ld) {
+                return py::make_key_iterator(ld.cache.begin(), ld.cache.end());},
+            py::keep_alive<0, 1>())
+        .def("items",
+             [](const label_dict_proxy &ld) {
+                 return py::make_iterator(ld.cache.begin(), ld.cache.end());},
+             py::keep_alive<0, 1>())
+        .def("values",
+             [](const label_dict_proxy &ld) {
+                 return py::make_value_iterator(ld.cache.begin(), ld.cache.end());
+             },
+             py::keep_alive<0, 1>())
+        .def("append", [](label_dict_proxy& l, const label_dict_proxy& other, const char* prefix) {
+                l.import(other, prefix);
+            },
+            "other"_a, "The label_dict to be imported"
+            "prefix"_a="", "optional prefix appended to the region and locset labels",
+            "Import the entries of a another label dictionary with an optional prefix.")
+        .def("update", [](label_dict_proxy& l, const label_dict_proxy& other) {
+                l.import(other);
+            },
+            "other"_a, "The label_dict to be imported"
+            "Import the entries of a another label dictionary.")
+        .def_readonly("regions", &label_dict_proxy::regions,
+             "The region definitions.")
+        .def_readonly("locsets", &label_dict_proxy::locsets,
+             "The locset definitions.")
+        .def("__repr__", [](const label_dict_proxy& d){return d.to_string();})
+        .def("__str__",  [](const label_dict_proxy& d){return d.to_string();});
+}
+}
diff --git a/python/proxy.hpp b/python/label_dict.hpp
similarity index 98%
rename from python/proxy.hpp
rename to python/label_dict.hpp
index 31333e132e6451985113ad2422efee285a97b159..9d62fd1455bee646b3d0dc75327ba56c23cbac26 100644
--- a/python/proxy.hpp
+++ b/python/label_dict.hpp
@@ -1,7 +1,7 @@
 #pragma once
 
-#include <any>
 #include <string>
+#include <unordered_map>
 
 #include <arborio/label_parse.hpp>
 
@@ -27,9 +27,7 @@ struct label_dict_proxy {
     label_dict_proxy() = default;
 
     label_dict_proxy(const str_map& in) {
-        for (auto& i: in) {
-            set(i.first, i.second);
-        }
+        for (auto& [k, v]: in) set(k, v);
     }
 
     label_dict_proxy& add_swc_tags() {
diff --git a/python/mechanism.cpp b/python/mechanism.cpp
index 5092b17e83f7f48cdc4368bd8e1da9e0b0f87fba..31600b5bdb9cc7b6657098be36693542c3cb0939 100644
--- a/python/mechanism.cpp
+++ b/python/mechanism.cpp
@@ -1,5 +1,4 @@
 #include <optional>
-#include <stdexcept>
 
 #include <pybind11/pybind11.h>
 #include "pybind11/pytypes.h"
@@ -12,7 +11,6 @@
 #include "arbor/mechinfo.hpp"
 
 #include "util.hpp"
-#include "conversion.hpp"
 #include "strprintf.hpp"
 
 namespace pyarb {
diff --git a/python/morphology.cpp b/python/morphology.cpp
index 3c0e3579a934e6017909a3e40f2898473f66c739..dacf19ede0fe8f121d404c3c5fc342825e0eb6d1 100644
--- a/python/morphology.cpp
+++ b/python/morphology.cpp
@@ -1,4 +1,3 @@
-#include <fstream>
 #include <tuple>
 #include <variant>
 
@@ -22,7 +21,7 @@
 
 #include "util.hpp"
 #include "error.hpp"
-#include "proxy.hpp"
+#include "label_dict.hpp"
 #include "strprintf.hpp"
 
 namespace py = pybind11;
@@ -42,15 +41,23 @@ void check_trailing(std::istream& in, std::string fname) {
 void register_morphology(py::module& m) {
     using namespace py::literals;
 
-    //
-    //  primitives: points, segments, locations, cables... etc.
-    //
-
     m.attr("mnpos") = arb::mnpos;
 
+    py::class_<arb::morphology> morph(m, "morphology", "A cell morphology.");
+    py::class_<arb::mlocation> location(m, "location", "A location on a cable cell.");
+    py::class_<arb::mextent> extent(m, "extent", "A potentially empty region on a morphology.");
+    py::class_<arb::mpoint> mpoint(m, "mpoint");
+    py::class_<arb::mcable> cable(m, "cable");
+    py::class_<arb::isometry> isometry(m, "isometry");
+    py::class_<arb::place_pwlin> place(m, "place_pwlin");
+    py::class_<arb::segment_tree> segment_tree(m, "segment_tree");
+    py::class_<arborio::asc_morphology> asc_morphology(m, "asc_morphology", "The morphology and label dictionary meta-data loaded from a Neurolucida ASCII (.asc) file.");
+    py::class_<arborio::nml_morphology_data> nml_morph_data(m, "neuroml_morph_data");
+    py::class_<arborio::neuroml> neuroml(m, "neuroml");
+    py::class_<arb::mprovider> prov(m, "morphology_provider");
+    py::class_<arb::msegment> msegment(m, "msegment");
+
     // arb::mlocation
-    py::class_<arb::mlocation> location(m, "location",
-        "A location on a cable cell.");
     location
         .def(py::init(
             [](arb::msize_t branch, double pos) {
@@ -73,7 +80,6 @@ void register_morphology(py::module& m) {
             [](arb::mlocation l) { return util::pprintf("(location {} {})", l.branch, l.pos); });
 
     // arb::mpoint
-    py::class_<arb::mpoint> mpoint(m, "mpoint");
     mpoint
         .def(py::init<double, double, double, double>(),
              "x"_a, "y"_a, "z"_a, "radius"_a,
@@ -98,14 +104,12 @@ void register_morphology(py::module& m) {
     py::implicitly_convertible<py::tuple, arb::mpoint>();
 
     // arb::msegment
-    py::class_<arb::msegment> msegment(m, "msegment");
     msegment
         .def_readonly("prox", &arb::msegment::prox, "the location and radius of the proximal end.")
         .def_readonly("dist", &arb::msegment::dist, "the location and radius of the distal end.")
         .def_readonly("tag", &arb::msegment::tag, "tag meta-data.");
 
     // arb::mcable
-    py::class_<arb::mcable> cable(m, "cable");
     cable
         .def(py::init(
             [](arb::msize_t bid, double prox, double dist) {
@@ -127,7 +131,6 @@ void register_morphology(py::module& m) {
         .def("__repr__", [](const arb::mcable& c) { return util::pprintf("{}", c); });
 
     // arb::isometry
-    py::class_<arb::isometry> isometry(m, "isometry");
     isometry
         .def(py::init<>(), "Construct a trivial isometry.")
         .def("__call__", [](arb::isometry& iso, arb::mpoint& p) {
@@ -178,10 +181,9 @@ void register_morphology(py::module& m) {
             "Construct a rotation isometry of angle theta about the given axis in the direction described by a tuple.");
 
     // arb::place_pwlin
-    py::class_<arb::place_pwlin> place(m, "place_pwlin");
     place
         .def(py::init<const arb::morphology&, const arb::isometry&>(),
-            "morphology"_a, "isometry"_a=arb::isometry{},
+            "morphology"_a, py::arg_v("isometry", arb::isometry(), "id"),
             "Construct a piecewise-linear placement object from the given morphology and optional isometry.")
         .def("at", &arb::place_pwlin::at, "location"_a,
             "Return an interpolated mpoint corresponding to the location argument.")
@@ -210,7 +212,6 @@ void register_morphology(py::module& m) {
             "Returns the location and its distance from the point.");
 
     // arb::place_pwlin
-    py::class_<arb::mprovider> prov(m, "morphology_provider");
     prov
         .def(py::init<const arb::morphology&>(),
             "morphology"_a,
@@ -228,14 +229,7 @@ void register_morphology(py::module& m) {
              },
              "Turn a region into an extent.");
 
-
-
-    //
-    // Higher-level data structures (segment_tree, morphology)
-    //
-
     // arb::segment_tree
-    py::class_<arb::segment_tree> segment_tree(m, "segment_tree");
     segment_tree
         // constructors
         .def(py::init<>())
@@ -290,6 +284,37 @@ void register_morphology(py::module& m) {
         .def("__str__", [](const arb::segment_tree& s) {
                 return util::pprintf("<arbor.segment_tree:\n{}>", s);});
 
+    // arb::morphology
+    morph
+        // constructors
+        .def(py::init(
+                [](arb::segment_tree t){
+                    return arb::morphology(std::move(t));
+                }))
+        // morphology's interface is read-only by design, so most of it can
+        // be implemented as read-only properties.
+        .def_property_readonly("empty",
+                [](const arb::morphology& m){return m.empty();},
+                "Whether the morphology is empty.")
+        .def_property_readonly("num_branches",
+                [](const arb::morphology& m){return m.num_branches();},
+                "The number of branches in the morphology.")
+        .def("branch_parent", &arb::morphology::branch_parent,
+                "i"_a, "The parent branch of branch i.")
+        .def("branch_children", &arb::morphology::branch_children,
+                "i"_a, "The child branches of branch i.")
+        .def("branch_segments",
+                [](const arb::morphology& m, arb::msize_t i) {
+                    return m.branch_segments(i);
+                },
+                "i"_a, "A list of the segments in branch i, ordered from proximal to distal ends of the branch.")
+        .def("to_segment_tree", &arb::morphology::to_segment_tree,
+                "Convert this morphology to a segment_tree.")
+        .def("__str__",
+                [](const arb::morphology& m) {
+                    return util::pprintf("<arbor.morphology:\n{}>", m);
+                });
+
     using morph_or_tree = std::variant<arb::segment_tree, arb::morphology>;
 
     // Function that creates a morphology/segment_tree from an swc file.
@@ -309,9 +334,8 @@ void register_morphology(py::module& m) {
                 throw pyarb_error(util::pprintf("Arbor SWC: parse error: {}", e.what()));
             }
         },
-        "filename_or_stream"_a,
-        pybind11::arg_v("raw", false, "Return a segment tree instead of a fully formed morphology"),
-        "Generate a morphology/segment_tree from an SWC file following the rules prescribed by Arbor.\n"
+        "filename_or_stream"_a, "raw"_a=false,
+        "Generate a morphology/segment_tree (raw=False/True) from an SWC file following the rules prescribed by Arbor.\n"
         "Specifically:\n"
         " * Single-segment somas are disallowed.\n"
         " * There are no special rules related to somata. They can be one or multiple branches\n"
@@ -333,50 +357,13 @@ void register_morphology(py::module& m) {
                 throw pyarb_error(util::pprintf("NEURON SWC: parse error: {}", e.what()));
             }
         },
-        "filename_or_stream"_a,
-        pybind11::arg_v("raw", false, "Return a segment tree instead of a fully formed morphology"),
-        "Generate a morphology from an SWC file following the rules prescribed by NEURON.\n"
+        "filename_or_stream"_a, "raw"_a=false,
+        "Generate a morphology/segment_tree (raw=False/True) from an SWC file following the rules prescribed by NEURON.\n"
         "See the documentation https://docs.arbor-sim.org/en/latest/fileformat/swc.html\n"
         "for a detailed description of the interpretation.");
 
-
-    // arb::morphology
-
-    py::class_<arb::morphology> morph(m, "morphology");
-    morph
-        // constructors
-        .def(py::init(
-                [](arb::segment_tree t){
-                    return arb::morphology(std::move(t));
-                }))
-        // morphology's interface is read-only by design, so most of it can
-        // be implemented as read-only properties.
-        .def_property_readonly("empty",
-                [](const arb::morphology& m){return m.empty();},
-                "Whether the morphology is empty.")
-        .def_property_readonly("num_branches",
-                [](const arb::morphology& m){return m.num_branches();},
-                "The number of branches in the morphology.")
-        .def("branch_parent", &arb::morphology::branch_parent,
-                "i"_a, "The parent branch of branch i.")
-        .def("branch_children", &arb::morphology::branch_children,
-                "i"_a, "The child branches of branch i.")
-        .def("branch_segments",
-                [](const arb::morphology& m, arb::msize_t i) {
-                    return m.branch_segments(i);
-                },
-                "i"_a, "A list of the segments in branch i, ordered from proximal to distal ends of the branch.")
-        .def("to_segment_tree", &arb::morphology::to_segment_tree,
-                "Convert this morphology to a segment_tree.")
-        .def("__str__",
-                [](const arb::morphology& m) {
-                    return util::pprintf("<arbor.morphology:\n{}>", m);
-                });
-
     // Neurolucida ASCII, or .asc, file format support.
 
-    py::class_<arborio::asc_morphology> asc_morphology(m, "asc_morphology",
-            "The morphology and label dictionary meta-data loaded from a Neurolucida ASCII (.asc) file.");
     asc_morphology
         .def_readonly("morphology",
                 &arborio::asc_morphology::morphology,
@@ -404,12 +391,10 @@ void register_morphology(py::module& m) {
                 throw pyarb_error(util::pprintf("error loading neurolucida asc file: {}", e.what()));
             }
         },
-        "filename_or_stream"_a,
-        pybind11::arg_v("raw", false, "Return a segment tree instead of a fully formed morphology"),
-        "Load a morphology or segment_tree and meta data from a Neurolucida ASCII .asc file.");
+        "filename_or_stream"_a, "raw"_a=false,
+        "Load a morphology or segment_tree (raw=True) and meta data from a Neurolucida ASCII .asc file.");
 
     // arborio::morphology_data
-    py::class_<arborio::nml_morphology_data> nml_morph_data(m, "neuroml_morph_data");
     nml_morph_data
         .def_readonly("cell_id",
             &arborio::nml_morphology_data::cell_id,
@@ -434,7 +419,6 @@ void register_morphology(py::module& m) {
             "Map from segmentGroup ids to their corresponding segment ids.");
 
     // arborio::neuroml
-    py::class_<arborio::neuroml> neuroml(m, "neuroml");
     neuroml
         // constructors
         .def(py::init(
diff --git a/python/probes.cpp b/python/probes.cpp
index 161c438be3bc924423a59336e7dc2e652ebfc5de..ad734fff894f3cf1a0bde76325fb09f448f36b85 100644
--- a/python/probes.cpp
+++ b/python/probes.cpp
@@ -153,11 +153,8 @@ void register_probe_meta_maps(pyarb_global_ptr g) {
         });
 }
 
-
-
 // Wrapper functions around cable_cell probe types that return arb::probe_info values:
 // (Probe tag value is implicitly left at zero.)
-
 arb::probe_info cable_probe_membrane_voltage(const char* where, const std::string& tag) {
     return {arb::cable_probe_membrane_voltage{arborio::parse_locset_expression(where).unwrap()}, tag};
 }
@@ -242,7 +239,6 @@ arb::probe_info lif_probe_voltage(const std::string& tag) {
     return {arb::lif_probe_voltage{}, tag};
 }
 
-
 // Add wrappers to module, recorder factories to global data.
 
 void register_cable_probes(pybind11::module& m, pyarb_global_ptr global_ptr) {
@@ -251,11 +247,13 @@ void register_cable_probes(pybind11::module& m, pyarb_global_ptr global_ptr) {
 
     // Probe metadata wrappers:
 
-    py::class_<arb::lif_probe_metadata> lif_probe_metadata(m, "lif_probe_metadata",
-        "Probe metadata associated with a LIF cell probe.");
+    py::class_<arb::lif_probe_metadata> lif_probe_metadata(m,
+                                                           "lif_probe_metadata",
+                                                           "Probe metadata associated with a LIF cell probe.");
 
-    py::class_<arb::cable_probe_point_info> cable_probe_point_info(m, "cable_probe_point_info",
-        "Probe metadata associated with a cable cell probe for point process state.");
+    py::class_<arb::cable_probe_point_info> cable_probe_point_info(m,
+                                                                   "cable_probe_point_info",
+                                                                   "Probe metadata associated with a cable cell probe for point process state.");
 
     cable_probe_point_info
         .def_readwrite("target", &arb::cable_probe_point_info::target,
@@ -281,11 +279,11 @@ void register_cable_probes(pybind11::module& m, pyarb_global_ptr global_ptr) {
           "Probe specification for cable cell membrane voltage associated with each cable in each CV.",
           "tag"_a);
     m.def("cable_probe_axial_current", &cable_probe_axial_current,
-        "Probe specification for cable cell axial current at points in a location set.",
-        "where"_a, "tag"_a);
+          "Probe specification for cable cell axial current at points in a location set.",
+          "where"_a, "tag"_a);
     m.def("cable_probe_total_ion_current_density", &cable_probe_total_ion_current_density,
-        "Probe specification for cable cell total transmembrane current density excluding capacitive currents at points in a location set.",
-        "where"_a, "tag"_a);
+          "Probe specification for cable cell total transmembrane current density excluding capacitive currents at points in a location set.",
+          "where"_a, "tag"_a);
     m.def("cable_probe_total_ion_current_cell",
           &cable_probe_total_ion_current_cell,
           "Probe specification for cable cell total transmembrane current excluding capacitive currents for each cable in each CV.",
diff --git a/python/profiler.cpp b/python/profiler.cpp
index b3fe1011bee6becfa014d1bc5fb0a2de898b6752..c23f7a80c3b520dc312ae0b305c1883301d42aad 100644
--- a/python/profiler.cpp
+++ b/python/profiler.cpp
@@ -1,5 +1,3 @@
-#include <sstream>
-
 #include <pybind11/pybind11.h>
 #include <pybind11/stl.h>
 
@@ -32,9 +30,9 @@ void register_profiler(pybind11::module& m) {
                 manager.checkpoint(name, ctx.context);
             },
             "name"_a, "context"_a,
-            "Create a new checkpoint. Records the time since the last checkpoint\
-             (or the call to start if no previous checkpoints exist),\
-             and restarts the timer for the next checkpoint.")
+            "Create a new checkpoint. Records the time since the last checkpoint"
+            "(or the call to start if no previous checkpoints exist),"
+            "and restarts the timer for the next checkpoint.")
         .def_property_readonly("checkpoint_names", &arb::profile::meter_manager::checkpoint_names,
             "A list of all metering checkpoint names.")
         .def_property_readonly("times", &arb::profile::meter_manager::times,
@@ -65,8 +63,8 @@ void register_profiler(pybind11::module& m) {
               arb::profile::print_profiler_summary(stream, limit);
               return stream.str();
           },
-          pybind11::arg_v("limit", 0.0, "Print contributions above <limit> percent. Defaults to showing all.")
-    );
+          "limit"_a=0.0,
+          "Show summary of the profile; printing contributions above `limit` percent. Defaults to showing all.");
 #endif
 }
 
diff --git a/python/pyarb.cpp b/python/pyarb.cpp
index e15194f29a732d553562d77512cffd15f352f7b9..a835da796369123fc1cac842a5c0dbd36c5c5497 100644
--- a/python/pyarb.cpp
+++ b/python/pyarb.cpp
@@ -30,6 +30,8 @@ void register_schedules(pybind11::module& m);
 void register_simulation(pybind11::module& m, pyarb_global_ptr);
 void register_arborenv(pybind11::module& m);
 void register_single_cell(pybind11::module& m);
+void register_units(pybind11::module& m);
+void register_label_dict(pybind11::module& m);
 
 #ifdef ARB_MPI_ENABLED
 void register_mpi(pybind11::module& m);
@@ -47,22 +49,26 @@ PYBIND11_MODULE(_arbor, m) {
     m.doc() = "arbor: multicompartment neural network models.";
     m.attr("__version__") = ARB_VERSION;
 
-    pyarb::register_cable_loader(m);
+    // NOTE: This is precisely ordered so that we do not leak C++ types!
+    pyarb::register_units(m);
+    pyarb::register_identifiers(m);
+    pyarb::register_label_dict(m);
+    pyarb::register_schedules(m);
+    pyarb::register_event_generators(m);
+    pyarb::register_morphology(m);
     pyarb::register_cable_probes(m, global_ptr);
+    pyarb::register_mechanisms(m);
     pyarb::register_cells(m);
+
+    pyarb::register_cable_loader(m);
     pyarb::register_config(m);
     pyarb::register_contexts(m);
+    pyarb::register_recipe(m);
     pyarb::register_domain_decomposition(m);
-    pyarb::register_event_generators(m);
-    pyarb::register_identifiers(m);
-    pyarb::register_mechanisms(m);
-    pyarb::register_morphology(m);
     pyarb::register_profiler(m);
-    pyarb::register_recipe(m);
-    pyarb::register_schedules(m);
     pyarb::register_simulation(m, global_ptr);
-    pyarb::register_single_cell(m);
     pyarb::register_arborenv(m);
+    pyarb::register_single_cell(m);
 
     // This is the fallback. All specific translators take precedence by being
     // registered *later*.
@@ -87,8 +93,8 @@ PYBIND11_MODULE(_arbor, m) {
     });
 
     // Translate Arbor errors -> Python exceptions.
-    pybind11::register_exception<arb::file_not_found_error>(m, "FileNotFoundError", PyExc_FileNotFoundError);
-    pybind11::register_exception<arb::zero_thread_requested_error>(m, "ValueError", PyExc_ValueError);
+    pybind11::register_exception<arb::file_not_found_error>(m, "ArbFileNotFoundError", PyExc_FileNotFoundError);
+    pybind11::register_exception<arb::zero_thread_requested_error>(m, "ArbValueError", PyExc_ValueError);
 
 
     #ifdef ARB_MPI_ENABLED
diff --git a/python/recipe.cpp b/python/recipe.cpp
index bd1e01cff2d5367c7106207355c5f736b512cb46..667be8dec32a5635bb8d17ccf55560c1443845e5 100644
--- a/python/recipe.cpp
+++ b/python/recipe.cpp
@@ -21,6 +21,8 @@
 
 namespace pyarb {
 
+namespace U = arb::units;
+
 // Convert a cell description inside a Python object to a cell description in a
 // unique_any, as required by the recipe interface.
 // This helper is only to be called while holding the GIL. We require this guard
@@ -137,7 +139,7 @@ void register_recipe(pybind11::module& m) {
         "Describes a connection between two cells:\n"
         "  Defined by source and destination end points (that is pre-synaptic and post-synaptic respectively), a connection weight and a delay time.");
     cell_connection
-        .def(pybind11::init<arb::cell_global_label_type, arb::cell_local_label_type, float, float>(),
+        .def(pybind11::init<arb::cell_global_label_type, arb::cell_local_label_type, float, const U::quantity&>(),
             "source"_a, "dest"_a, "weight"_a, "delay"_a,
             "Construct a connection with arguments:\n"
             "  source:      The source end point of the connection.\n"
@@ -213,10 +215,5 @@ void register_recipe(pybind11::module& m) {
         .def("__str__",  [](const ::pyarb::recipe&){return "<arbor.recipe>";})
         .def("__repr__", [](const ::pyarb::recipe&){return "<arbor.recipe>";});
 
-    // Probes
-    pybind11::class_<arb::probe_info> probe(m, "probe");
-    probe
-        .def("__repr__", [](const arb::probe_info& p){return util::pprintf("<arbor.probe: tag {}>", p.tag);})
-        .def("__str__",  [](const arb::probe_info& p){return util::pprintf("<arbor.probe: tag {}>", p.tag);});
 }
 } // namespace pyarb
diff --git a/python/schedule.cpp b/python/schedule.cpp
index 70950a4a60ae5a50102fbcacfb4aee10aa2519af..e1061d3631184ca109512da8dcb1269c4a09372c 100644
--- a/python/schedule.cpp
+++ b/python/schedule.cpp
@@ -13,57 +13,63 @@ namespace py = pybind11;
 namespace pyarb {
 
 std::ostream& operator<<(std::ostream& o, const regular_schedule_shim& x) {
-    return o << "<arbor.regular_schedule: tstart "
-             << util::to_string(x.tstart) << " ms, dt "
-             << x.dt << " ms, tstop "
-             << util::to_string(x.tstop) << " ms>";
+    if (x.tstop.has_value()) {
+        return o << "<arbor.regular_schedule: tstart=" << arb::units::to_string(x.tstart)
+                 << ", dt=" << arb::units::to_string(x.dt)
+                 << ", tstop " << arb::units::to_string(x.tstop.value()) << ">";
+    }
+    else {
+        return o << "<arbor.regular_schedule: tstart=" << arb::units::to_string(x.tstart)
+                 << ", dt=" << arb::units::to_string(x.dt) << ">";
+    }
 }
 
 std::ostream& operator<<(std::ostream& o, const explicit_schedule_shim& e) {
-    o << "<arbor.explicit_schedule: times [";
-    return o << util::csv(e.times) << "] ms>";
-};
+    return o << "<arbor.explicit_schedule: times [" << util::csv(e.times) << "] ms>";
+}
 
 std::ostream& operator<<(std::ostream& o, const poisson_schedule_shim& p) {
-    return o << "<arbor.poisson_schedule: tstart " << p.tstart << " ms"
-             << ", tstop " << util::to_string(p.tstop) << " ms"
-             << ", freq " << p.freq << " kHz"
+    return o << "<arbor.poisson_schedule: tstart " << arb::units::to_string(p.tstart)
+             << ", tstop " << arb::units::to_string(p.tstop)
+             << ", freq " << arb::units::to_string(p.freq)
              << ", seed " << p.seed << ">";
-};
+}
 
 static std::vector<arb::time_type> as_vector(std::pair<const arb::time_type*, const arb::time_type*> ts) {
     return std::vector<arb::time_type>(ts.first, ts.second);
 }
 
-//
-// regular_schedule shim
-//
-
-regular_schedule_shim::regular_schedule_shim(arb::time_type t0, arb::time_type delta_t, py::object t1) {
+regular_schedule_shim::regular_schedule_shim(const arb::units::quantity& t0,
+                                             const arb::units::quantity& delta_t,
+                                             std::optional<arb::units::quantity> t1) {
     set_tstart(t0);
     set_dt(delta_t);
     set_tstop(t1);
 }
 
-regular_schedule_shim::regular_schedule_shim(arb::time_type delta_t) {
-    set_tstart(0.);
+regular_schedule_shim::regular_schedule_shim(const arb::units::quantity& delta_t) {
+    set_tstart(0.*arb::units::ms);
     set_dt(delta_t);
 }
 
-void regular_schedule_shim::set_tstart(arb::time_type t) {
-    pyarb::assert_throw(is_nonneg()(t), "tstart must be a non-negative number");
+void regular_schedule_shim::set_tstart(const arb::units::quantity& t) {
+    pyarb::assert_throw(is_nonneg()(t.value()), "tstart must be a non-negative number");
+    pyarb::assert_throw(arb::units::is_valid(t.convert_to(arb::units::ms)), "must be convertible to time");
     tstart = t;
-};
+}
 
-void regular_schedule_shim::set_tstop(py::object t) {
-    tstop = py2optional<time_type>(
-            t, "tstop must be a non-negative number, or None", is_nonneg());
-};
+void regular_schedule_shim::set_tstop(std::optional<arb::units::quantity> t) {
+    if (t.has_value()) {
+        pyarb::assert_throw(arb::units::is_valid(t.value().convert_to(arb::units::ms)), "must be convertible to time");
+    }
+    tstop = t;
+}
 
-void regular_schedule_shim::set_dt(arb::time_type delta_t) {
-    pyarb::assert_throw(is_positive()(delta_t), "dt must be a positive number");
-    dt = delta_t;
-};
+void regular_schedule_shim::set_dt(const arb::units::quantity& t) {
+    pyarb::assert_throw(is_positive()(t.value()), "dt must be a positive number");
+    pyarb::assert_throw(arb::units::is_valid(t.convert_to(arb::units::ms)), "must be convertible to time");
+    dt = t;
+}
 
 regular_schedule_shim::time_type regular_schedule_shim::get_tstart() const {
     return tstart;
@@ -78,10 +84,9 @@ regular_schedule_shim::opt_time_type regular_schedule_shim::get_tstop() const {
 }
 
 arb::schedule regular_schedule_shim::schedule() const {
-    return arb::regular_schedule(
-            tstart,
-            dt,
-            tstop.value_or(arb::terminal_time));
+    return arb::regular_schedule(tstart,
+                                 dt,
+                                 tstop.value_or(arb::terminal_time*arb::units::ms));
 }
 
 std::vector<arb::time_type> regular_schedule_shim::events(arb::time_type t0, arb::time_type t1) {
@@ -93,17 +98,15 @@ std::vector<arb::time_type> regular_schedule_shim::events(arb::time_type t0, arb
     return as_vector(sched.events(t0, t1));
 }
 
-//
-// explicit_schedule shim
-//
-
-//struct explicit_schedule_shim {
-explicit_schedule_shim::explicit_schedule_shim(std::vector<arb::time_type> t) {
-    set_times(t);
+explicit_schedule_shim::explicit_schedule_shim(const std::vector<arb::units::quantity>& seq) {
+    std::vector<arb::time_type> ts;
+    ts.reserve(seq.size());
+    for (const auto t: seq) ts.push_back(t.value_as(arb::units::ms));
+    set_times_ms(std::move(ts));
 }
 
 // getter and setter (in order to assert when being set)
-void explicit_schedule_shim::set_times(std::vector<arb::time_type> t) {
+void explicit_schedule_shim::set_times_ms(std::vector<arb::time_type> t) {
     times = std::move(t);
 
     // Sort the times in ascending order if necessary
@@ -118,12 +121,12 @@ void explicit_schedule_shim::set_times(std::vector<arb::time_type> t) {
     }
 };
 
-std::vector<arb::time_type> explicit_schedule_shim::get_times() const {
+std::vector<arb::time_type> explicit_schedule_shim::get_times_ms() const {
     return times;
 }
 
 arb::schedule explicit_schedule_shim::schedule() const {
-    return arb::explicit_schedule(times);
+    return arb::explicit_schedule_from_milliseconds(times);
 }
 
 std::vector<arb::time_type> explicit_schedule_shim::events(arb::time_type t0, arb::time_type t1) {
@@ -135,71 +138,50 @@ std::vector<arb::time_type> explicit_schedule_shim::events(arb::time_type t0, ar
     return as_vector(sched.events(t0, t1));
 }
 
-//
-// poisson_schedule shim
-//
-
-poisson_schedule_shim::poisson_schedule_shim(
-        arb::time_type ts,
-        arb::time_type f,
-        rng_type::result_type s,
-        py::object tstop)
-{
+poisson_schedule_shim::poisson_schedule_shim(const arb::units::quantity& ts,
+                                             const arb::units::quantity& f,
+                                             arb::seed_type s,
+                                             const arb::units::quantity& tstop) {
     set_tstart(ts);
     set_freq(f);
     seed = s;
     set_tstop(tstop);
 }
 
-poisson_schedule_shim::poisson_schedule_shim(arb::time_type f) {
-    set_tstart(0.);
-    set_freq(f);
-    seed = 0;
-}
-
-void poisson_schedule_shim::set_tstart(arb::time_type t) {
-    pyarb::assert_throw(is_nonneg()(t), "tstart must be a non-negative number");
+void poisson_schedule_shim::set_tstart(const arb::units::quantity& t) {
+    pyarb::assert_throw(is_nonneg()(t.value()), "tstart must be a non-negative number");
     tstart = t;
 };
 
-void poisson_schedule_shim::set_freq(arb::time_type f) {
-    pyarb::assert_throw(is_nonneg()(f), "frequency must be a non-negative number");
+void poisson_schedule_shim::set_freq(const arb::units::quantity& f) {
+    pyarb::assert_throw(is_nonneg()(f.value()), "frequency must be a non-negative number");
     freq = f;
 };
 
-void poisson_schedule_shim::set_tstop(py::object t) {
-    tstop = py2optional<arb::time_type>(
-            t, "tstop must be a non-negative number, or None", is_nonneg());
+void poisson_schedule_shim::set_tstop(const arb::units::quantity& t) {
+    pyarb::assert_throw(is_nonneg()(t.value()), "frequency must be a non-negative number");
+    tstop = t;
 };
 
-arb::time_type poisson_schedule_shim::get_tstart() const {
-    return tstart;
-}
-
-arb::time_type poisson_schedule_shim::get_freq() const {
-    return freq;
-}
-
-poisson_schedule_shim::opt_time_type poisson_schedule_shim::get_tstop() const {
-    return tstop;
-}
-
 arb::schedule poisson_schedule_shim::schedule() const {
-    return arb::poisson_schedule(tstart, freq, rng_type(seed), tstop.value_or(arb::terminal_time));
+    return arb::poisson_schedule(tstart, freq, seed, tstop);
 }
 
-std::vector<arb::time_type> poisson_schedule_shim::events(arb::time_type t0, arb::time_type t1) {
-    pyarb::assert_throw(is_nonneg()(t0), "t0 must be a non-negative number");
-    pyarb::assert_throw(is_nonneg()(t1), "t1 must be a non-negative number");
+std::vector<arb::time_type> poisson_schedule_shim::events(const arb::units::quantity& t0,
+                                                          const arb::units::quantity& t1) {
+    auto beg = t0.value_as(arb::units::ms);
+    auto end = t1.value_as(arb::units::ms);
+    pyarb::assert_throw(is_nonneg()(beg), "t0 must be a non-negative number");
+    pyarb::assert_throw(is_nonneg()(end), "t1 must be a non-negative number");
 
     arb::schedule sched = poisson_schedule_shim::schedule();
 
-    return as_vector(sched.events(t0, t1));
+    return as_vector(sched.events(beg, end));
 }
 
 void register_schedules(py::module& m) {
     using namespace py::literals;
-    using time_type = arb::time_type;
+    using time_type = arb::units::quantity;
 
     py::class_<schedule_shim_base> schedule_base(m, "schedule_base", "Schedule abstract base class.");
 
@@ -208,13 +190,13 @@ void register_schedules(py::module& m) {
         "Describes a regular schedule with multiples of dt within the interval [tstart, tstop).");
 
     regular_schedule
-        .def(py::init<time_type, time_type, py::object>(),
+        .def(py::init<const time_type&, const time_type&, std::optional<time_type>>(),
             "tstart"_a, "dt"_a, "tstop"_a = py::none(),
             "Construct a regular schedule with arguments:\n"
             "  tstart: The delivery time of the first event in the sequence [ms].\n"
             "  dt:     The interval between time points [ms].\n"
             "  tstop:  No events delivered after this time [ms], None by default.")
-        .def(py::init<time_type>(),
+        .def(py::init<const time_type&>(),
             "dt"_a,
             "Construct a regular schedule, starting from t = 0 and never terminating, with arguments:\n"
             "  dt:     The interval between time points [ms].\n")
@@ -240,10 +222,10 @@ void register_schedules(py::module& m) {
             "times"_a,
             "Construct an explicit schedule with argument:\n"
             "  times: A list of times [ms], [] by default.")
-        .def_property("times", &explicit_schedule_shim::get_times, &explicit_schedule_shim::set_times,
+        .def_property("times_ms", &explicit_schedule_shim::get_times_ms, &explicit_schedule_shim::set_times_ms,
             "A list of times [ms].")
         .def("events", &explicit_schedule_shim::events,
-            "A view of monotonically increasing time values in the half-open interval [t0, t1).")
+            "A view of monotonically increasing time values in the half-open interval [t0, t1) in [ms].")
         .def("__str__",  util::to_string<explicit_schedule_shim>)
         .def("__repr__", util::to_string<explicit_schedule_shim>);
 
@@ -252,17 +234,19 @@ void register_schedules(py::module& m) {
         "Describes a schedule according to a Poisson process within the interval [tstart, tstop).");
 
     poisson_schedule
-        .def(py::init<time_type, time_type, std::mt19937_64::result_type, py::object>(),
-             "tstart"_a = 0., "freq"_a, "seed"_a = 0, "tstop"_a = py::none(),
-            "Construct a Poisson schedule with arguments:\n"
-            "  tstart: The delivery time of the first event in the sequence [ms], 0 by default.\n"
-            "  freq:   The expected frequency [kHz].\n"
-            "  seed:   The seed for the random number generator, 0 by default.\n"
-            "  tstop:  No events delivered after this time [ms], None by default.")
-        .def(py::init<time_type>(),
-            "freq"_a,
-            "Construct a Poisson schedule, starting from t = 0, default seed, with:\n"
-            "  freq:   The expected frequency [kHz], 10 by default.\n")
+        .def(py::init<>(
+                 [](const time_type& f,
+                    const time_type& t0,
+                    arb::seed_type s,
+                    std::optional<time_type> t1) -> poisson_schedule_shim {
+                     return poisson_schedule_shim{t0, f, s, t1.value_or(arb::terminal_time*arb::units::ms)};
+                 }),
+             "freq"_a, py::kw_only(), py::arg_v("tstart", 0.*arb::units::ms, "0.0*arbor.units.ms"), "seed"_a = 0, "tstop"_a=py::none(),
+             "Construct a Poisson schedule with arguments:\n"
+             "  tstart: The delivery time of the first event in the sequence [ms], 0 by default.\n"
+             "  freq:   The expected frequency [kHz].\n"
+             "  seed:   The seed for the random number generator, 0 by default.\n"
+             "  tstop:  No events delivered after this time [ms], None by default.")
         .def_property("tstart", &poisson_schedule_shim::get_tstart, &poisson_schedule_shim::set_tstart,
             "The delivery time of the first event in the sequence [ms].")
         .def_property("freq", &poisson_schedule_shim::get_freq, &poisson_schedule_shim::set_freq,
diff --git a/python/schedule.hpp b/python/schedule.hpp
index 619f77236adbecb3090dad8da873b9aa9dcb60d0..e321cb4d234f59952dd89e51fa1fe68226d505ab 100644
--- a/python/schedule.hpp
+++ b/python/schedule.hpp
@@ -1,7 +1,6 @@
 #pragma once
 
 #include <optional>
-#include <random>
 #include <vector>
 
 #include <pybind11/pybind11.h>
@@ -16,8 +15,6 @@ namespace pyarb {
 // to an arb::schedule object.
 struct schedule_shim_base {
     schedule_shim_base() = default;
-    schedule_shim_base(const schedule_shim_base&) = delete;
-    schedule_shim_base& operator=(schedule_shim_base&) = delete;
     virtual ~schedule_shim_base() {}
 
     virtual arb::schedule schedule() const = 0;
@@ -28,20 +25,23 @@ struct schedule_shim_base {
 // a regular_schedule in python are manipulating this type. This is converted to
 // an arb::regular_schedule when a C++ recipe is created from a Python recipe.
 struct regular_schedule_shim: schedule_shim_base {
-    using time_type = arb::time_type;
+    using time_type = arb::units::quantity;
     using opt_time_type = std::optional<time_type>;
 
-    time_type tstart = {};
-    time_type dt = 0;
-    opt_time_type tstop = {};
+    time_type tstart;
+    time_type dt = 0*arb::units::ms;
+    opt_time_type tstop;
 
-    regular_schedule_shim(time_type t0, time_type delta_t, pybind11::object t1);
-    explicit regular_schedule_shim(time_type delta_t);
+    regular_schedule_shim(const time_type& t0,
+                          const time_type& delta_t,
+                          opt_time_type t1);
+
+    explicit regular_schedule_shim(const time_type& delta_t);
 
     // getter and setter (in order to assert when being set)
-    void set_tstart(time_type t);
-    void set_dt(time_type delta_t);
-    void set_tstop(pybind11::object t);
+    void set_tstart(const time_type& t);
+    void set_dt(const time_type& delta_t);
+    void set_tstop(opt_time_type t);
 
     time_type get_tstart() const;
     time_type get_dt() const;
@@ -60,11 +60,11 @@ struct explicit_schedule_shim: schedule_shim_base {
     std::vector<arb::time_type> times;
 
     explicit_schedule_shim() = default;
-    explicit_schedule_shim(std::vector<arb::time_type> t);
+    explicit_schedule_shim(const std::vector<arb::units::quantity>& t);
 
     // getter and setter (in order to assert when being set)
-    void set_times(std::vector<arb::time_type> t);
-    std::vector<arb::time_type> get_times() const;
+    void set_times_ms(std::vector<arb::time_type> t);
+    std::vector<arb::time_type> get_times_ms() const;
 
     arb::schedule schedule() const override;
 
@@ -76,28 +76,35 @@ struct explicit_schedule_shim: schedule_shim_base {
 // Python are manipulating this type. This is converted to an
 // arb::poisson_schedule when a C++ recipe is created from a Python recipe.
 struct poisson_schedule_shim: schedule_shim_base {
-    using rng_type = std::mt19937_64;
-    using opt_time_type = std::optional<arb::time_type>;
+    arb::units::quantity tstart; // ms
+    arb::units::quantity freq;   // kHz
+    arb::units::quantity tstop; // ms
+    arb::seed_type seed = arb::default_seed;
 
-    arb::time_type tstart; // ms
-    arb::time_type freq; // kHz
-    opt_time_type  tstop; // ms
-    rng_type::result_type seed;
+    poisson_schedule_shim(const poisson_schedule_shim&) = default;
+    poisson_schedule_shim(poisson_schedule_shim&&) = default;
 
-    poisson_schedule_shim(arb::time_type ts, arb::time_type f, rng_type::result_type s, pybind11::object tstop);
-    poisson_schedule_shim(arb::time_type f);
+    poisson_schedule_shim() = default;
+    ~poisson_schedule_shim() = default;
 
-    void set_tstart(arb::time_type t);
-    void set_freq(arb::time_type f);
-    void set_tstop(pybind11::object t);
+    poisson_schedule_shim(const arb::units::quantity& ts,
+                          const arb::units::quantity& f,
+                          arb::seed_type s,
+                          const arb::units::quantity& tstop);
 
-    arb::time_type get_tstart() const;
-    arb::time_type get_freq() const;
-    opt_time_type get_tstop() const;
+    void set_tstart(const arb::units::quantity& t);
+    void set_freq(const arb::units::quantity& f);
+    void set_tstop(const arb::units::quantity& f);
 
-    arb::schedule schedule() const override;
+    const auto& get_tstop() const { return tstop; }
+    const auto& get_tstart() const { return tstart; }
+    const auto& get_freq() const { return freq; }
+
+    // TODO(TH) this should be symmetrical...
+    std::vector<arb::time_type> events(const arb::units::quantity& t0, const arb::units::quantity& t1);
+
+    arb::schedule schedule() const;
 
-    std::vector<arb::time_type> events(arb::time_type t0, arb::time_type t1);
 };
 
 }
diff --git a/python/simulation.cpp b/python/simulation.cpp
index 71abd3b0be47abe6a983f9dd2d924f88d48452a0..d7ea8a9ad2d3588d7ddaf45a08c312d9c7a1deb2 100644
--- a/python/simulation.cpp
+++ b/python/simulation.cpp
@@ -1,6 +1,8 @@
 #include <memory>
 #include <pybind11/numpy.h>
 #include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <pybind11/functional.h>
 
 #include <arbor/common_types.hpp>
 #include <arbor/sampling.hpp>
@@ -116,7 +118,7 @@ public:
         }
     }
 
-    arb::time_type run(arb::time_type tfinal, arb::time_type dt) {
+    arb::time_type run(const arb::units::quantity& tfinal, const arb::units::quantity& dt) {
         return sim_->run(tfinal, dt);
     }
 
@@ -201,8 +203,8 @@ public:
     }
 };
 
-void register_simulation(pybind11::module& m, pyarb_global_ptr global_ptr) {
-    using namespace pybind11::literals;
+void register_simulation(py::module& m, pyarb_global_ptr global_ptr) {
+    using namespace py::literals;
 
     py::enum_<spike_recording>(m, "spike_recording")
        .value("off", spike_recording::off)
@@ -216,57 +218,58 @@ void register_simulation(pybind11::module& m, pyarb_global_ptr global_ptr) {
     simulation
         // A custom constructor that wraps a python recipe with arb::recipe_shim
         // before forwarding it to the arb::recipe constructor.
-        .def(pybind11::init(
+        .def(py::init(
                  [global_ptr](std::shared_ptr<recipe>& rec,
-                              const std::shared_ptr<context_shim>& ctx_,
-                              const std::optional<arb::domain_decomposition>& decomp,
+                              std::optional<std::shared_ptr<context_shim>> ctx_,
+                              std::optional<arb::domain_decomposition> decomp,
                               std::uint64_t seed) {
-                try {
-                    auto ctx = ctx_ ? ctx_ : std::make_shared<context_shim>(make_context_shim());
-                    auto dec = decomp.value_or(arb::partition_load_balance(recipe_shim(rec), ctx->context));
-                    return new simulation_shim(rec, *ctx, dec, seed, global_ptr);
-                }
-                catch (...) {
-                    py_reset_and_throw();
-                    throw;
-                }
-            }),
-            // Release the python gil, so that callbacks into the python recipe don't deadlock.
-            pybind11::call_guard<pybind11::gil_scoped_release>(),
-            "Initialize the model described by a recipe, with cells and network distributed\n"
-            "according to the domain decomposition and computational resources described by a context.",
+                     try {
+                         auto ctx = ctx_.value_or(std::make_shared<context_shim>(make_context_shim()));
+                         auto dec = decomp.value_or(arb::partition_load_balance(recipe_shim(rec), ctx->context));
+                         return new simulation_shim(rec, *ctx, dec, seed, global_ptr);
+                     }
+                     catch (...) {
+                         py_reset_and_throw();
+                         throw;
+                     }
+                 }),
+             // Release the python gil, so that callbacks into the python recipe don't deadlock.
+             py::call_guard<py::gil_scoped_release>(),
              "recipe"_a,
-             pybind11::arg_v("context", pybind11::none(), "Execution context"),
-             pybind11::arg_v("domains", pybind11::none(), "Domain decomposition"),
-             pybind11::arg_v("seed", 0u, "Random number generator seed"))
+             "context"_a=py::none(),
+             "domains"_a=py::none(),
+             "seed"_a=0u,
+             "Initialize the model described by a recipe, with cells and network distributed\n"
+             "according to the domain decomposition and computational resources described by a\n"
+             "context. Initialize PRNG using seed")
         .def("set_remote_spike_filter",
-                      &simulation_shim::set_remote_spike_filter,
-                      "pred"_a,
-                      "Add a callback to filter spikes going out over external connections. `pred` is"
-                      "a callable on the `spike` type. **Caution**: This will be extremely slow; use C++ "
-                      "if you want to make use of this.")
+             &simulation_shim::set_remote_spike_filter,
+             "pred"_a,
+             "Add a callback to filter spikes going out over external connections. `pred` is"
+             "a callable on the `spike` type. **Caution**: This will be extremely slow; use C++ "
+             "if you want to make use of this.")
         .def("update", &simulation_shim::update,
-             pybind11::call_guard<pybind11::gil_scoped_release>(),
+             py::call_guard<py::gil_scoped_release>(),
              "Rebuild the connection table from recipe::connections_on and the event"
              "generators based on recipe::event_generators.",
              "recipe"_a)
         .def("deserialize", &simulation_shim::deserialize,
-             pybind11::call_guard<pybind11::gil_scoped_release>(),
+             py::call_guard<py::gil_scoped_release>(),
              "Deserialize the simulation object from a JSON string."
              "json"_a)
         .def("serialize", &simulation_shim::serialize,
-             pybind11::call_guard<pybind11::gil_scoped_release>(),
+             py::call_guard<py::gil_scoped_release>(),
              "Serialize the simulation object to a JSON string.")
         .def("reset", &simulation_shim::reset,
-            pybind11::call_guard<pybind11::gil_scoped_release>(),
+            py::call_guard<py::gil_scoped_release>(),
             "Reset the state of the simulation to its initial state.")
         .def("clear_samplers", &simulation_shim::clear_samplers,
-             pybind11::call_guard<pybind11::gil_scoped_release>(),
+             py::call_guard<py::gil_scoped_release>(),
              "Clearing spike and sample information. restoring memory")
         .def("run", &simulation_shim::run,
-            pybind11::call_guard<pybind11::gil_scoped_release>(),
+            py::call_guard<py::gil_scoped_release>(),
             "Run the simulation from current simulation time to tfinal [ms], with maximum time step size dt [ms].",
-            "tfinal"_a, "dt"_a=0.025)
+            "tfinal"_a, py::arg_v("dt", 0.025*arb::units::ms, "0.025*arbor.units.ms"))
         .def("record", &simulation_shim::record,
             "Disable or enable local or global spike recording.")
         .def("spikes", &simulation_shim::spikes,
diff --git a/python/single_cell_model.cpp b/python/single_cell_model.cpp
index 020a637373dcd98305a14cf37e374d6148b8b6c0..6bd0f7b0011048ded935dad0d6101fd6f97b68e3 100644
--- a/python/single_cell_model.cpp
+++ b/python/single_cell_model.cpp
@@ -18,7 +18,7 @@
 #include "event_generator.hpp"
 #include "error.hpp"
 #include "strprintf.hpp"
-#include "proxy.hpp"
+#include "label_dict.hpp"
 
 using arb::util::any_cast;
 
@@ -34,7 +34,7 @@ namespace pyarb {
 // Stores the location and sampling frequency for a probe in a single cell model.
 struct probe_site {
     arb::locset locset;     // Location of sample on morphology.
-    double frequency;       // Sampling frequency [kHz].
+    arb::units::quantity frequency;       // Sampling frequency [kHz].
     arb::cell_tag_type tag; // Tag = unique name
 };
 
@@ -161,12 +161,15 @@ public:
     //      m.probe('voltage', '(location 2 0.5)')
     //      m.probe('voltage', 'term')
 
-    void probe(const std::string& what, const arb::locset& where, const arb::cell_tag_type& tag, double frequency) {
+    void probe(const std::string& what,
+               const arb::locset& where,
+               const arb::cell_tag_type& tag,
+               const arb::units::quantity& frequency) {
         if (what != "voltage") {
             throw pyarb_error(
                 util::pprintf("{} does not name a valid variable to trace (currently only 'voltage' is supported)", what));
         }
-        if (frequency<=0) {
+        if (frequency.value() <= 0) {
             throw pyarb_error(
                 util::pprintf("sampling frequency is not greater than zero", what));
         }
@@ -177,7 +180,7 @@ public:
         event_generators_.push_back(event_generator);
     }
 
-    void run(double tfinal, double dt) {
+    void run(const arb::units::quantity& tfinal, const arb::units::quantity& dt) {
         single_cell_recipe rec(cell_, probes_, gprop, event_generators_);
 
         auto domdec = arb::partition_load_balance(rec, ctx_);
@@ -238,24 +241,24 @@ void register_single_cell(pybind11::module& m) {
                                const label_dict_proxy& l) -> single_cell_model {
             return single_cell_model(arb::cable_cell({m}, d, l.dict));
         }),
-             "tree"_a, "decor"_a, "labels"_a=arb::decor{},
+             "tree"_a, "decor"_a, "labels"_a=label_dict_proxy{},
              "Build single cell model from cable cell components")
         .def(pybind11::init([](const arb::morphology& m,
                                const arb::decor& d,
                                const label_dict_proxy& l) -> single_cell_model {
             return single_cell_model(arb::cable_cell(m, d, l.dict));
         }),
-             "morph"_a, "decor"_a, "labels"_a=arb::decor{},
+             "morph"_a, "decor"_a, "labels"_a=label_dict_proxy{},
              "Build single cell model from cable cell components")
         .def(pybind11::init<arb::cable_cell>(),
             "cell"_a, "Initialise a single cell model for a cable cell.")
         .def("run",
              &single_cell_model::run,
              "tfinal"_a,
-             "dt"_a = 0.025,
+             pybind11::arg_v("dt", 0.025*arb::units::ms, "0.025*arbor.units.ms"),
              "Run model from t=0 to t=tfinal ms.")
         .def("probe",
-             [](single_cell_model& m, const char* what, const char* where, const char* tag, double frequency) {
+             [](single_cell_model& m, const char* what, const char* where, const char* tag, const arb::units::quantity& frequency) {
                  m.probe(what, arborio::parse_locset_expression(where).unwrap(), tag, frequency);},
              "what"_a, "where"_a, "tag"_a, "frequency"_a,
              "Sample a variable on the cell.\n"
@@ -264,7 +267,7 @@ void register_single_cell(pybind11::module& m) {
              " tag:       Unique name for this probe.\n"
              " frequency: The target frequency at which to sample [kHz].")
         .def("probe",
-             [](single_cell_model& m, const char* what, const arb::mlocation& where, const char* tag, double frequency) {
+             [](single_cell_model& m, const char* what, const arb::mlocation& where, const char* tag, const arb::units::quantity& frequency) {
                  m.probe(what, where, tag, frequency);},
              "what"_a, "where"_a, "tag"_a, "frequency"_a,
              "Sample a variable on the cell.\n"
diff --git a/python/stubs/arbor/__init__.pyi b/python/stubs/arbor/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..12a28c97542f832931e69a9e5ca229d25441ef21
--- /dev/null
+++ b/python/stubs/arbor/__init__.pyi
@@ -0,0 +1,287 @@
+from __future__ import annotations
+from arbor._arbor import ArbFileNotFoundError
+from arbor._arbor import ArbValueError
+from arbor._arbor import MechCatItemIterator
+from arbor._arbor import MechCatKeyIterator
+from arbor._arbor import MechCatValueIterator
+from arbor._arbor import allen_catalogue
+from arbor._arbor import asc_morphology
+from arbor._arbor import axial_resistivity
+from arbor._arbor import backend
+from arbor._arbor import bbp_catalogue
+from arbor._arbor import benchmark_cell
+from arbor._arbor import cable
+from arbor._arbor import cable_cell
+from arbor._arbor import cable_component
+from arbor._arbor import cable_global_properties
+from arbor._arbor import cable_probe_axial_current
+from arbor._arbor import cable_probe_density_state
+from arbor._arbor import cable_probe_density_state_cell
+from arbor._arbor import cable_probe_ion_current_cell
+from arbor._arbor import cable_probe_ion_current_density
+from arbor._arbor import cable_probe_ion_diff_concentration
+from arbor._arbor import cable_probe_ion_diff_concentration_cell
+from arbor._arbor import cable_probe_ion_ext_concentration
+from arbor._arbor import cable_probe_ion_ext_concentration_cell
+from arbor._arbor import cable_probe_ion_int_concentration
+from arbor._arbor import cable_probe_ion_int_concentration_cell
+from arbor._arbor import cable_probe_membrane_voltage
+from arbor._arbor import cable_probe_membrane_voltage_cell
+from arbor._arbor import cable_probe_point_info
+from arbor._arbor import cable_probe_point_state
+from arbor._arbor import cable_probe_point_state_cell
+from arbor._arbor import cable_probe_stimulus_current_cell
+from arbor._arbor import cable_probe_total_current_cell
+from arbor._arbor import cable_probe_total_ion_current_cell
+from arbor._arbor import cable_probe_total_ion_current_density
+from arbor._arbor import catalogue
+from arbor._arbor import cell_address
+from arbor._arbor import cell_cv_data
+from arbor._arbor import cell_global_label
+from arbor._arbor import cell_kind
+from arbor._arbor import cell_local_label
+from arbor._arbor import cell_member
+from arbor._arbor import component_meta_data
+from arbor._arbor import config
+from arbor._arbor import connection
+from arbor._arbor import context
+from arbor._arbor import cv_data
+from arbor._arbor import cv_policy
+from arbor._arbor import cv_policy_every_segment
+from arbor._arbor import cv_policy_explicit
+from arbor._arbor import cv_policy_fixed_per_branch
+from arbor._arbor import cv_policy_max_extent
+from arbor._arbor import cv_policy_single
+from arbor._arbor import decor
+from arbor._arbor import default_catalogue
+from arbor._arbor import density
+from arbor._arbor import domain_decomposition
+from arbor._arbor import env
+from arbor._arbor import event_generator
+from arbor._arbor import explicit_schedule
+from arbor._arbor import ext_concentration
+from arbor._arbor import extent
+from arbor._arbor import gap_junction_connection
+from arbor._arbor import group_description
+from arbor._arbor import iclamp
+from arbor._arbor import int_concentration
+from arbor._arbor import intersect_region
+from arbor._arbor import ion_data
+from arbor._arbor import ion_dependency
+from arbor._arbor import ion_diffusivity
+from arbor._arbor import ion_settings
+from arbor._arbor import isometry
+from arbor._arbor import junction
+from arbor._arbor import label_dict
+from arbor._arbor import lif_cell
+from arbor._arbor import lif_probe_metadata
+from arbor._arbor import lif_probe_voltage
+from arbor._arbor import load_asc
+from arbor._arbor import load_catalogue
+from arbor._arbor import load_component
+from arbor._arbor import load_swc_arbor
+from arbor._arbor import load_swc_neuron
+from arbor._arbor import location
+from arbor._arbor import mechanism
+from arbor._arbor import mechanism_field
+from arbor._arbor import mechanism_info
+from arbor._arbor import membrane_capacitance
+from arbor._arbor import membrane_potential
+from arbor._arbor import meter_manager
+from arbor._arbor import meter_report
+from arbor._arbor import morphology
+from arbor._arbor import morphology_provider
+from arbor._arbor import mpoint
+from arbor._arbor import msegment
+from arbor._arbor import neuroml
+from arbor._arbor import neuroml_morph_data
+from arbor._arbor import neuron_cable_properties
+from arbor._arbor import partition_by_group
+from arbor._arbor import partition_hint
+from arbor._arbor import partition_load_balance
+from arbor._arbor import place_pwlin
+from arbor._arbor import poisson_schedule
+from arbor._arbor import print_config
+from arbor._arbor import probe
+from arbor._arbor import proc_allocation
+from arbor._arbor import recipe
+from arbor._arbor import regular_schedule
+from arbor._arbor import reversal_potential
+from arbor._arbor import reversal_potential_method
+from arbor._arbor import scaled_mechanism
+from arbor._arbor import schedule_base
+from arbor._arbor import segment_tree
+from arbor._arbor import selection_policy
+from arbor._arbor import simulation
+from arbor._arbor import single_cell_model
+from arbor._arbor import spike
+from arbor._arbor import spike_recording
+from arbor._arbor import spike_source_cell
+from arbor._arbor import stochastic_catalogue
+from arbor._arbor import synapse
+from arbor._arbor import temperature
+from arbor._arbor import threshold_detector
+from arbor._arbor import trace
+from arbor._arbor import units
+from arbor._arbor import voltage_process
+from arbor._arbor import write_component
+from . import _arbor
+
+__all__ = [
+    "ArbFileNotFoundError",
+    "ArbValueError",
+    "MechCatItemIterator",
+    "MechCatKeyIterator",
+    "MechCatValueIterator",
+    "allen_catalogue",
+    "asc_morphology",
+    "axial_resistivity",
+    "backend",
+    "bbp_catalogue",
+    "benchmark_cell",
+    "build_catalogue",
+    "cable",
+    "cable_cell",
+    "cable_component",
+    "cable_global_properties",
+    "cable_probe_axial_current",
+    "cable_probe_density_state",
+    "cable_probe_density_state_cell",
+    "cable_probe_ion_current_cell",
+    "cable_probe_ion_current_density",
+    "cable_probe_ion_diff_concentration",
+    "cable_probe_ion_diff_concentration_cell",
+    "cable_probe_ion_ext_concentration",
+    "cable_probe_ion_ext_concentration_cell",
+    "cable_probe_ion_int_concentration",
+    "cable_probe_ion_int_concentration_cell",
+    "cable_probe_membrane_voltage",
+    "cable_probe_membrane_voltage_cell",
+    "cable_probe_point_info",
+    "cable_probe_point_state",
+    "cable_probe_point_state_cell",
+    "cable_probe_stimulus_current_cell",
+    "cable_probe_total_current_cell",
+    "cable_probe_total_ion_current_cell",
+    "cable_probe_total_ion_current_density",
+    "catalogue",
+    "cell_address",
+    "cell_cv_data",
+    "cell_global_label",
+    "cell_kind",
+    "cell_local_label",
+    "cell_member",
+    "component_meta_data",
+    "config",
+    "connection",
+    "context",
+    "cv_data",
+    "cv_policy",
+    "cv_policy_every_segment",
+    "cv_policy_explicit",
+    "cv_policy_fixed_per_branch",
+    "cv_policy_max_extent",
+    "cv_policy_single",
+    "decor",
+    "default_catalogue",
+    "density",
+    "domain_decomposition",
+    "env",
+    "event_generator",
+    "explicit_schedule",
+    "ext_concentration",
+    "extent",
+    "gap_junction_connection",
+    "group_description",
+    "iclamp",
+    "int_concentration",
+    "intersect_region",
+    "ion_data",
+    "ion_dependency",
+    "ion_diffusivity",
+    "ion_settings",
+    "isometry",
+    "junction",
+    "label_dict",
+    "lif_cell",
+    "lif_probe_metadata",
+    "lif_probe_voltage",
+    "load_asc",
+    "load_catalogue",
+    "load_component",
+    "load_swc_arbor",
+    "load_swc_neuron",
+    "location",
+    "mechanism",
+    "mechanism_field",
+    "mechanism_info",
+    "membrane_capacitance",
+    "membrane_potential",
+    "meter_manager",
+    "meter_report",
+    "mnpos",
+    "modcc",
+    "morphology",
+    "morphology_provider",
+    "mpoint",
+    "msegment",
+    "neuroml",
+    "neuroml_morph_data",
+    "neuron_cable_properties",
+    "partition_by_group",
+    "partition_hint",
+    "partition_load_balance",
+    "place_pwlin",
+    "poisson_schedule",
+    "print_config",
+    "probe",
+    "proc_allocation",
+    "recipe",
+    "regular_schedule",
+    "reversal_potential",
+    "reversal_potential_method",
+    "scaled_mechanism",
+    "schedule_base",
+    "segment_tree",
+    "selection_policy",
+    "simulation",
+    "single_cell_model",
+    "spike",
+    "spike_recording",
+    "spike_source_cell",
+    "stochastic_catalogue",
+    "synapse",
+    "temperature",
+    "threshold_detector",
+    "trace",
+    "units",
+    "voltage_process",
+    "write_component",
+]
+
+def build_catalogue(): ...
+def modcc(): ...
+
+__config__: dict = {
+    "mpi": False,
+    "mpi4py": False,
+    "gpu": None,
+    "vectorize": True,
+    "profiling": False,
+    "neuroml": True,
+    "bundled": True,
+    "version": "0.9.1-dev",
+    "source": "2023-12-08T14:40:50+01:00 327c56d229571dac097e7400a9b5e04fc8d7a514 modified",
+    "build_config": "DEBUG",
+    "arch": "native",
+    "prefix": "/usr/local",
+    "python_lib_path": "/opt/homebrew/lib/python3.11/site-packages",
+    "binary_path": "bin",
+    "lib_path": "lib",
+    "data_path": "share",
+    "CXX": "/opt/homebrew/bin/clang++",
+    "pybind-version": "2.11.1",
+    "timestamp": "Jan  2 2024 09:57:33",
+}
+__version__: str = "0.9.1-dev"
+mnpos: int = 4294967295
diff --git a/python/stubs/arbor/_arbor/__init__.pyi b/python/stubs/arbor/_arbor/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..aabd4f3de210a0e84da571d95e2cccc55ffb9198
--- /dev/null
+++ b/python/stubs/arbor/_arbor/__init__.pyi
@@ -0,0 +1,2991 @@
+"""
+arbor: multicompartment neural network models.
+"""
+from __future__ import annotations
+import typing
+from . import env
+from . import units
+
+__all__ = [
+    "ArbFileNotFoundError",
+    "ArbValueError",
+    "MechCatItemIterator",
+    "MechCatKeyIterator",
+    "MechCatValueIterator",
+    "allen_catalogue",
+    "asc_morphology",
+    "axial_resistivity",
+    "backend",
+    "bbp_catalogue",
+    "benchmark_cell",
+    "cable",
+    "cable_cell",
+    "cable_component",
+    "cable_global_properties",
+    "cable_probe_axial_current",
+    "cable_probe_density_state",
+    "cable_probe_density_state_cell",
+    "cable_probe_ion_current_cell",
+    "cable_probe_ion_current_density",
+    "cable_probe_ion_diff_concentration",
+    "cable_probe_ion_diff_concentration_cell",
+    "cable_probe_ion_ext_concentration",
+    "cable_probe_ion_ext_concentration_cell",
+    "cable_probe_ion_int_concentration",
+    "cable_probe_ion_int_concentration_cell",
+    "cable_probe_membrane_voltage",
+    "cable_probe_membrane_voltage_cell",
+    "cable_probe_point_info",
+    "cable_probe_point_state",
+    "cable_probe_point_state_cell",
+    "cable_probe_stimulus_current_cell",
+    "cable_probe_total_current_cell",
+    "cable_probe_total_ion_current_cell",
+    "cable_probe_total_ion_current_density",
+    "catalogue",
+    "cell_address",
+    "cell_cv_data",
+    "cell_global_label",
+    "cell_kind",
+    "cell_local_label",
+    "cell_member",
+    "component_meta_data",
+    "config",
+    "connection",
+    "context",
+    "cv_data",
+    "cv_policy",
+    "cv_policy_every_segment",
+    "cv_policy_explicit",
+    "cv_policy_fixed_per_branch",
+    "cv_policy_max_extent",
+    "cv_policy_single",
+    "decor",
+    "default_catalogue",
+    "density",
+    "domain_decomposition",
+    "env",
+    "event_generator",
+    "explicit_schedule",
+    "ext_concentration",
+    "extent",
+    "gap_junction_connection",
+    "group_description",
+    "iclamp",
+    "int_concentration",
+    "intersect_region",
+    "ion_data",
+    "ion_dependency",
+    "ion_diffusivity",
+    "ion_settings",
+    "isometry",
+    "junction",
+    "label_dict",
+    "lif_cell",
+    "lif_probe_metadata",
+    "lif_probe_voltage",
+    "load_asc",
+    "load_catalogue",
+    "load_component",
+    "load_swc_arbor",
+    "load_swc_neuron",
+    "location",
+    "mechanism",
+    "mechanism_field",
+    "mechanism_info",
+    "membrane_capacitance",
+    "membrane_potential",
+    "meter_manager",
+    "meter_report",
+    "mnpos",
+    "morphology",
+    "morphology_provider",
+    "mpoint",
+    "msegment",
+    "neuroml",
+    "neuroml_morph_data",
+    "neuron_cable_properties",
+    "partition_by_group",
+    "partition_hint",
+    "partition_load_balance",
+    "place_pwlin",
+    "poisson_schedule",
+    "print_config",
+    "probe",
+    "proc_allocation",
+    "recipe",
+    "regular_schedule",
+    "reversal_potential",
+    "reversal_potential_method",
+    "scaled_mechanism",
+    "schedule_base",
+    "segment_tree",
+    "selection_policy",
+    "simulation",
+    "single_cell_model",
+    "spike",
+    "spike_recording",
+    "spike_source_cell",
+    "stochastic_catalogue",
+    "synapse",
+    "temperature",
+    "threshold_detector",
+    "trace",
+    "units",
+    "voltage_process",
+    "write_component",
+]
+
+class ArbFileNotFoundError(FileNotFoundError):
+    pass
+
+class ArbValueError(ValueError):
+    pass
+
+class MechCatItemIterator:
+    def __iter__(self) -> MechCatItemIterator: ...
+    def __next__(self) -> tuple[str, mechanism_info]: ...
+
+class MechCatKeyIterator:
+    def __iter__(self) -> MechCatKeyIterator: ...
+    def __next__(self) -> str: ...
+
+class MechCatValueIterator:
+    def __iter__(self) -> MechCatValueIterator: ...
+    def __next__(self) -> mechanism_info: ...
+
+class asc_morphology:
+    """
+    The morphology and label dictionary meta-data loaded from a Neurolucida ASCII (.asc) file.
+    """
+
+    @property
+    def labels(self) -> label_dict:
+        """
+        The four canonical regions are labeled 'soma', 'axon', 'dend' and 'apic'.
+        """
+    @property
+    def morphology(self) -> morphology:
+        """
+        The cable cell morphology.
+        """
+    @property
+    def segment_tree(self) -> segment_tree:
+        """
+        The raw segment tree.
+        """
+
+class axial_resistivity:
+    """
+    Setting the axial resistivity.
+    """
+
+    def __init__(self, arg0: units.quantity) -> None: ...
+    def __repr__(self) -> str: ...
+
+class backend:
+    """
+    Enumeration used to indicate which hardware backend to execute a cell group on.
+
+    Members:
+
+      gpu : Use GPU backend.
+
+      multicore : Use multicore backend.
+    """
+
+    __members__: typing.ClassVar[
+        dict[str, backend]
+    ]  # value = {'gpu': <backend.gpu: 1>, 'multicore': <backend.multicore: 0>}
+    gpu: typing.ClassVar[backend]  # value = <backend.gpu: 1>
+    multicore: typing.ClassVar[backend]  # value = <backend.multicore: 0>
+    def __eq__(self, other: typing.Any) -> bool: ...
+    def __getstate__(self) -> int: ...
+    def __hash__(self) -> int: ...
+    def __index__(self) -> int: ...
+    def __init__(self, value: int) -> None: ...
+    def __int__(self) -> int: ...
+    def __ne__(self, other: typing.Any) -> bool: ...
+    def __repr__(self) -> str: ...
+    def __setstate__(self, state: int) -> None: ...
+    def __str__(self) -> str: ...
+    @property
+    def name(self) -> str: ...
+    @property
+    def value(self) -> int: ...
+
+class benchmark_cell:
+    """
+    A benchmarking cell, used by Arbor developers to test communication performance.
+    A benchmark cell generates spikes at a user-defined sequence of time points, and
+    the time taken to integrate a cell can be tuned by setting the realtime_ratio,
+    for example if realtime_ratio=2, a cell will take 2 seconds of CPU time to
+    simulate 1 second.
+    """
+
+    @typing.overload
+    def __init__(
+        self,
+        source_label: str,
+        target_label: str,
+        schedule: regular_schedule,
+        realtime_ratio: float = 1.0,
+    ) -> None:
+        """
+        Construct a benchmark cell that generates spikes on 'source_label' at regular intervals.
+        The cell has one source labeled 'source_label', and one target labeled 'target_label'.
+        """
+    @typing.overload
+    def __init__(
+        self,
+        source_label: str,
+        target_label: str,
+        schedule: explicit_schedule,
+        realtime_ratio: float = 1.0,
+    ) -> None:
+        """
+        Construct a benchmark cell that generates spikes on 'source_label' at a sequence of user-defined times.
+        The cell has one source labeled 'source_label', and one target labeled 'target_label'.
+        """
+    @typing.overload
+    def __init__(
+        self,
+        source_label: str,
+        target_label: str,
+        schedule: poisson_schedule,
+        realtime_ratio: float = 1.0,
+    ) -> None:
+        """
+        Construct a benchmark cell that generates spikeson 'source_label' at times defined by a Poisson sequence.
+        The cell has one source labeled 'source_label', and one target labeled 'target_label'.
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+
+class cable:
+    __hash__: typing.ClassVar[None] = None
+    def __eq__(self, arg0: cable) -> bool: ...
+    def __init__(self, branch: int, prox: float, dist: float) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def branch(self) -> int:
+        """
+        The id of the branch on which the cable lies.
+        """
+    @property
+    def dist(self) -> float:
+        """
+        The relative position of the distal end of the cable on its branch ∈ [0,1].
+        """
+    @property
+    def prox(self) -> float:
+        """
+        The relative position of the proximal end of the cable on its branch ∈ [0,1].
+        """
+
+class cable_cell:
+    """
+    Represents morphologically-detailed cell models, with morphology represented as a
+    tree of one-dimensional cable segments.
+    """
+
+    @typing.overload
+    def __init__(
+        self, morphology: morphology, decor: decor, labels: label_dict | None = None
+    ) -> None:
+        """
+        Construct with a morphology, decor, and label dictionary.
+        """
+    @typing.overload
+    def __init__(
+        self, segment_tree: segment_tree, decor: decor, labels: label_dict | None = None
+    ) -> None:
+        """
+        Construct with a morphology derived from a segment tree, decor, and label dictionary.
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def cables(self, label: str) -> list[cable]:
+        """
+        The cable segments of the cell morphology for a region label.
+        """
+    def locations(self, label: str) -> list[location]:
+        """
+        The locations of the cell morphology for a locset label.
+        """
+    @property
+    def num_branches(self) -> int:
+        """
+        The number of unbranched cable sections in the morphology.
+        """
+
+class cable_component:
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def component(self) -> morphology | label_dict | decor | cable_cell:
+        """
+        cable-cell component.
+        """
+    @property
+    def meta_data(self) -> component_meta_data:
+        """
+        cable-cell component meta-data.
+        """
+    @meta_data.setter
+    def meta_data(self, arg0: component_meta_data) -> None: ...
+
+class cable_global_properties:
+    membrane_voltage_limit: float | None
+    @typing.overload
+    def __init__(self) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: cable_global_properties) -> None: ...
+    def __str__(self) -> str: ...
+    def check(self) -> None:
+        """
+        Test whether all default parameters and ion species properties have been set.
+        """
+    def set_ion(
+        self,
+        ion: str,
+        valence: int | None = None,
+        int_con: units.quantity | None = None,
+        ext_con: units.quantity | None = None,
+        rev_pot: units.quantity | None = None,
+        method: typing.Any = None,
+        diff: units.quantity | None = None,
+    ) -> None:
+        """
+        Set the global default properties of ion species named 'ion'.
+         * valence: valence of the ion species [e].
+         * int_con: initial internal concentration [mM].
+         * ext_con: initial external concentration [mM].
+         * rev_pot: reversal potential [mV].
+         * method:  mechanism for calculating reversal potential.
+         * diff:   diffusivity [m^2/s].
+        There are 3 ion species predefined in arbor: 'ca', 'na' and 'k'.
+        If 'ion' in not one of these ions it will be added to the list, making it
+        available to mechanisms. The user has to provide the valence of a previously
+        undefined ion the first time this function is called with it as an argument.
+        Species concentrations and reversal potential can be overridden on
+        specific regions using the paint interface, while the method for calculating
+        reversal potential is global for all compartments in the cell, and can't be
+        overriden locally.
+        """
+    def set_property(
+        self,
+        Vm: units.quantity | None = None,
+        cm: units.quantity | None = None,
+        rL: units.quantity | None = None,
+        tempK: units.quantity | None = None,
+    ) -> None:
+        """
+        Set global default values for cable and cell properties.
+         * Vm:    initial membrane voltage [mV].
+         * cm:    membrane capacitance [F/m²].
+         * rL:    axial resistivity [Ω·cm].
+         * tempK: temperature [Kelvin].
+        These values can be overridden on specific regions using the paint interface.
+        """
+    def unset_ion(self, arg0: str) -> None:
+        """
+        Remove ion species from properties.
+        """
+    @property
+    def axial_resistivity(self) -> float | None: ...
+    @axial_resistivity.setter
+    def axial_resistivity(self, arg1: float) -> None: ...
+    @property
+    def catalogue(self) -> catalogue:
+        """
+        The mechanism catalogue.
+        """
+    @catalogue.setter
+    def catalogue(self, arg0: catalogue) -> None: ...
+    @property
+    def coalesce_synapses(self) -> bool:
+        """
+        Flag for enabling/disabling linear syanpse coalescing.
+        """
+    @coalesce_synapses.setter
+    def coalesce_synapses(self, arg0: bool) -> None: ...
+    @property
+    def ion_data(self) -> dict[str, ion_data]: ...
+    @property
+    def ion_reversal_potential(self) -> dict[str, mechanism]: ...
+    @property
+    def ion_valence(self) -> dict[str, int]: ...
+    @property
+    def ions(self) -> dict[str, ion_settings]:
+        """
+        Return a view of all ion settings.
+        """
+    @property
+    def membrane_capacitance(self) -> float | None: ...
+    @membrane_capacitance.setter
+    def membrane_capacitance(self, arg1: float) -> None: ...
+    @property
+    def membrane_potential(self) -> float | None: ...
+    @membrane_potential.setter
+    def membrane_potential(self, arg1: float) -> None: ...
+    @property
+    def temperature(self) -> float | None: ...
+    @temperature.setter
+    def temperature(self, arg1: float) -> None: ...
+
+class cable_probe_point_info:
+    """
+    Probe metadata associated with a cable cell probe for point process state.
+    """
+
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def location(self) -> location:
+        """
+        Location of point process instance on cell.
+        """
+    @location.setter
+    def location(self, arg0: location) -> None: ...
+    @property
+    def multiplicity(self) -> int:
+        """
+        Number of coalesced point processes (linear synapses) associated with this instance.
+        """
+    @multiplicity.setter
+    def multiplicity(self, arg0: int) -> None: ...
+    @property
+    def target(self) -> int:
+        """
+        The target index of the point process instance on the cell.
+        """
+    @target.setter
+    def target(self, arg0: int) -> None: ...
+
+class catalogue:
+    def __contains__(self, name: str) -> bool:
+        """
+        Is 'name' in the catalogue?
+        """
+    def __getitem__(self, arg0: str) -> mechanism_info: ...
+    @typing.overload
+    def __init__(self) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: catalogue) -> None: ...
+    def __iter__(self) -> MechCatKeyIterator:
+        """
+        Return an iterator over all mechanism names in this catalogues.
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def derive(
+        self,
+        name: str,
+        parent: str,
+        globals: dict[str, float] = {},
+        ions: dict[str, str] = {},
+    ) -> None: ...
+    def extend(self, other: catalogue, prefix: str) -> None:
+        """
+        Import another catalogue, possibly with a prefix. Will overwrite in case of name collisions.
+        """
+    def is_derived(self, name: str) -> bool:
+        """
+        Is 'name' a derived mechanism or can it be implicitly derived?
+        """
+    def items(self) -> MechCatItemIterator:
+        """
+        Return an iterator over all (name, mechanism) tuples  in this catalogues.
+        """
+    def keys(self) -> MechCatKeyIterator:
+        """
+        Return an iterator over all mechanism names in this catalogues.
+        """
+    def values(self) -> MechCatValueIterator:
+        """
+        Return an iterator over all mechanism info values in this catalogues.
+        """
+
+class cell_address:
+    gid: int
+    tag: str
+
+class cell_cv_data:
+    """
+    Provides information on the CVs representing the discretization of a cable-cell.
+    """
+
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def cables(self, index: int) -> list[cable]:
+        """
+        Return a list of cables representing the CV at the given index.
+        """
+    def children(self, index: int) -> list[int]:
+        """
+        Return a list of indices of the CVs representing the children of the CV at the given index.
+        """
+    def parent(self, index: int) -> int:
+        """
+        Return the index of the CV representing the parent of the CV at the given index.
+        """
+    @property
+    def num_cv(self) -> int:
+        """
+        Return the number of CVs in the cell.
+        """
+
+class cell_global_label:
+    """
+    For global identification of an item.
+
+    cell_global_label members:
+    (1) a unique cell identified by its gid.
+    (2) a cell_local_label, referring to a labeled group of items on the cell and a policy for selecting a single item out of the group.
+    """
+
+    @typing.overload
+    def __init__(self, gid: int, label: str) -> None:
+        """
+        Construct a cell_global_label identifier from a gid and a label argument identifying an item on the cell.
+        The default round_robin policy is used for selecting one of possibly multiple items on the cell associated with the label.
+        """
+    @typing.overload
+    def __init__(self, gid: int, label: cell_local_label) -> None:
+        """
+        Construct a cell_global_label identifier with arguments:
+          gid:   The global identifier of the cell.
+          label: The cell_local_label representing the label and selection policy of an item on the cell.
+        """
+    @typing.overload
+    def __init__(self, arg0: tuple) -> None:
+        """
+        Construct a cell_global_label identifier with tuple argument (gid, label):
+          gid:   The global identifier of the cell.
+          label: The cell_local_label representing the label and selection policy of an item on the cell.
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def gid(self) -> int:
+        """
+        The global identifier of the cell.
+        """
+    @gid.setter
+    def gid(self, arg0: int) -> None: ...
+    @property
+    def label(self) -> cell_local_label:
+        """
+        The cell_local_label representing the label and selection policy of an item on the cell.
+        """
+    @label.setter
+    def label(self, arg0: cell_local_label) -> None: ...
+
+class cell_kind:
+    """
+    Enumeration used to identify the cell kind, used by the model to group equal kinds in the same cell group.
+
+    Members:
+
+      benchmark : Proxy cell used for benchmarking.
+
+      cable : A cell with morphology described by branching 1D cable segments.
+
+      lif : Leaky-integrate and fire neuron.
+
+      spike_source : Proxy cell that generates spikes from a spike sequence provided by the user.
+    """
+
+    __members__: typing.ClassVar[
+        dict[str, cell_kind]
+    ]  # value = {'benchmark': <cell_kind.benchmark: 3>, 'cable': <cell_kind.cable: 0>, 'lif': <cell_kind.lif: 1>, 'spike_source': <cell_kind.spike_source: 2>}
+    benchmark: typing.ClassVar[cell_kind]  # value = <cell_kind.benchmark: 3>
+    cable: typing.ClassVar[cell_kind]  # value = <cell_kind.cable: 0>
+    lif: typing.ClassVar[cell_kind]  # value = <cell_kind.lif: 1>
+    spike_source: typing.ClassVar[cell_kind]  # value = <cell_kind.spike_source: 2>
+    def __eq__(self, other: typing.Any) -> bool: ...
+    def __getstate__(self) -> int: ...
+    def __hash__(self) -> int: ...
+    def __index__(self) -> int: ...
+    def __init__(self, value: int) -> None: ...
+    def __int__(self) -> int: ...
+    def __ne__(self, other: typing.Any) -> bool: ...
+    def __repr__(self) -> str: ...
+    def __setstate__(self, state: int) -> None: ...
+    def __str__(self) -> str: ...
+    @property
+    def name(self) -> str: ...
+    @property
+    def value(self) -> int: ...
+
+class cell_local_label:
+    """
+    For local identification of an item.
+
+    cell_local_label identifies:
+    (1) a labeled group of one or more items on one or more locations on the cell.
+    (2) a policy for selecting one of the items.
+    """
+
+    @typing.overload
+    def __init__(self, label: str) -> None:
+        """
+        Construct a cell_local_label identifier from a label argument identifying a group of one or more items on a cell.
+        The default round_robin policy is used for selecting one of possibly multiple items associated with the label.
+        """
+    @typing.overload
+    def __init__(self, label: str, policy: selection_policy) -> None:
+        """
+        Construct a cell_local_label identifier with arguments:
+          label:  The identifier of a group of one or more items on a cell.
+          policy: The policy for selecting one of possibly multiple items associated with the label.
+        """
+    @typing.overload
+    def __init__(self, arg0: tuple) -> None:
+        """
+        Construct a cell_local_label identifier with tuple argument (label, policy):
+          label:  The identifier of a group of one or more items on a cell.
+          policy: The policy for selecting one of possibly multiple items associated with the label.
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def label(self) -> str:
+        """
+        The identifier of a a group of one or more items on a cell.
+        """
+    @label.setter
+    def label(self, arg0: str) -> None: ...
+    @property
+    def policy(self) -> selection_policy:
+        """
+        The policy for selecting one of possibly multiple items associated with the label.
+        """
+    @policy.setter
+    def policy(self, arg0: selection_policy) -> None: ...
+
+class cell_member:
+    """
+    For global identification of a cell-local item.
+
+    Items of cell_member must:
+      (1) be associated with a unique cell, identified by the member gid;
+      (2) identify an item within a cell-local collection by the member index.
+    """
+
+    @typing.overload
+    def __init__(self, gid: int, index: int) -> None:
+        """
+        Construct a cell member identifier with arguments:
+          gid:     The global identifier of the cell.
+          index:   The cell-local index of the item.
+        """
+    @typing.overload
+    def __init__(self, arg0: tuple) -> None:
+        """
+        Construct a cell member identifier with tuple argument (gid, index):
+          gid:     The global identifier of the cell.
+          index:   The cell-local index of the item.
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def gid(self) -> int:
+        """
+        The global identifier of the cell.
+        """
+    @gid.setter
+    def gid(self, arg0: int) -> None: ...
+    @property
+    def index(self) -> int:
+        """
+        Cell-local index of the item.
+        """
+    @index.setter
+    def index(self, arg0: int) -> None: ...
+
+class component_meta_data:
+    @property
+    def version(self) -> str:
+        """
+        cable-cell component version.
+        """
+    @version.setter
+    def version(self, arg0: str) -> None: ...
+
+class connection:
+    """
+    Describes a connection between two cells:
+      Defined by source and destination end points (that is pre-synaptic and post-synaptic respectively), a connection weight and a delay time.
+    """
+
+    def __init__(
+        self,
+        source: cell_global_label,
+        dest: cell_local_label,
+        weight: float,
+        delay: units.quantity,
+    ) -> None:
+        """
+        Construct a connection with arguments:
+          source:      The source end point of the connection.
+          dest:        The destination end point of the connection.
+          weight:      The weight delivered to the target synapse (unit defined by the type of synapse target).
+          delay:       The delay of the connection [ms].
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def delay(self) -> float:
+        """
+        The delay time of the connection [ms].
+        """
+    @delay.setter
+    def delay(self, arg0: float) -> None: ...
+    @property
+    def dest(self) -> cell_local_label:
+        """
+        The destination label of the connection.
+        """
+    @dest.setter
+    def dest(self, arg0: cell_local_label) -> None: ...
+    @property
+    def source(self) -> cell_global_label:
+        """
+        The source gid and label of the connection.
+        """
+    @source.setter
+    def source(self, arg0: cell_global_label) -> None: ...
+    @property
+    def weight(self) -> float:
+        """
+        The weight of the connection.
+        """
+    @weight.setter
+    def weight(self, arg0: float) -> None: ...
+
+class context:
+    """
+    An opaque handle for the hardware resources used in a simulation.
+    """
+
+    @typing.overload
+    def __init__(self) -> None:
+        """
+        Construct a local context with proc_allocation = env.default_allocation().
+        """
+    @typing.overload
+    def __init__(
+        self,
+        *,
+        threads: int = 1,
+        gpu_id: typing.Any = None,
+        mpi: typing.Any = None,
+        inter: typing.Any = None,
+        bind_procs: bool = False,
+        bind_threads: bool = False,
+    ) -> None:
+        """
+        Construct a context with arguments:
+          threads: The number of threads available locally for execution. Must be set to 1 at minimum. 1 by default.
+          gpu_id:  The identifier of the GPU to use, None by default. Only available if arbor.__config__['gpu']!="none".
+          mpi:     The MPI communicator, None by default. Only available if arbor.__config__['mpi']==True.
+          inter:   An MPI intercommunicator used to connect to external simulations, None by default. Only available if arbor.__config__['mpi']==True.
+          bind_procs:   Create process binding mask.
+          bind_threads: Create thread binding mask.
+        """
+    @typing.overload
+    def __init__(
+        self,
+        alloc: proc_allocation,
+        *,
+        mpi: typing.Any = None,
+        inter: typing.Any = None,
+    ) -> None:
+        """
+        Construct a context with arguments:
+          alloc:   The computational resources to be used for the simulation.
+          mpi:     The MPI communicator, None by default. Only available if arbor.__config__['mpi']==True.
+          inter:   An MPI intercommunicator used to connect to external simulations, None by default. Only available if arbor.__config__['mpi']==True.
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def has_gpu(self) -> bool:
+        """
+        Whether the context has a GPU.
+        """
+    @property
+    def has_mpi(self) -> bool:
+        """
+        Whether the context uses MPI for distributed communication.
+        """
+    @property
+    def rank(self) -> int:
+        """
+        The numeric id of the local domain (equivalent to MPI rank).
+        """
+    @property
+    def ranks(self) -> int:
+        """
+        The number of distributed domains (equivalent to the number of MPI ranks).
+        """
+    @property
+    def threads(self) -> int:
+        """
+        The number of threads in the context's thread pool.
+        """
+
+class cv_policy:
+    """
+    Describes the rules used to discretize (compartmentalise) a cable cell morphology.
+    """
+
+    def __add__(self, arg0: cv_policy) -> cv_policy: ...
+    def __init__(self, expression: str) -> None:
+        """
+        A valid CV policy expression
+        """
+    def __or__(self, arg0: cv_policy) -> cv_policy: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def domain(self) -> str:
+        """
+        The domain on which the policy is applied.
+        """
+
+class decor:
+    """
+    Description of the decorations to be applied to a cable cell, that is the painted,
+    placed and defaulted properties, mecahanisms, ion species etc.
+    """
+
+    @typing.overload
+    def __init__(self) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: decor) -> None: ...
+    def defaults(
+        self,
+    ) -> list[
+        membrane_potential
+        | axial_resistivity
+        | temperature
+        | membrane_capacitance
+        | ion_diffusivity
+        | int_concentration
+        | ext_concentration
+        | reversal_potential
+        | reversal_potential_method
+        | cv_policy
+    ]:
+        """
+        Return a view of all defaults.
+        """
+    @typing.overload
+    def discretization(self, policy: cv_policy) -> decor:
+        """
+        A cv_policy used to discretise the cell into compartments for simulation
+        """
+    @typing.overload
+    def discretization(self, policy: str) -> decor:
+        """
+        An s-expression string representing a cv_policy used to discretise the cell into compartments for simulation
+        """
+    @typing.overload
+    def paint(self, region: str, mechanism: density) -> decor:
+        """
+        Associate a density mechanism with a region.
+        """
+    @typing.overload
+    def paint(self, region: str, mechanism: voltage_process) -> decor:
+        """
+        Associate a voltage process mechanism with a region.
+        """
+    @typing.overload
+    def paint(self, region: str, mechanism: scaled_mechanism) -> None:
+        """
+        Associate a scaled density mechanism with a region.
+        """
+    @typing.overload
+    def paint(
+        self,
+        region: str,
+        Vm: units.quantity | str | None = None,
+        cm: units.quantity | str | None = None,
+        rL: units.quantity | str | None = None,
+        tempK: units.quantity | str | None = None,
+    ) -> decor:
+        """
+        Set cable properties on a region.
+        Set global default values for cable and cell properties.
+         * Vm:    initial membrane voltage [mV].
+         * cm:    membrane capacitance [F/m²].
+         * rL:    axial resistivity [Ω·cm].
+         * tempK: temperature [Kelvin].
+        """
+    @typing.overload
+    def paint(
+        self,
+        region: str,
+        *,
+        ion: str,
+        int_con: units.quantity | None = None,
+        ext_con: units.quantity | None = None,
+        rev_pot: units.quantity | None = None,
+        diff: units.quantity | None = None,
+    ) -> decor:
+        """
+        Set ion species properties conditions on a region.
+         * int_con: initial internal concentration [mM].
+         * ext_con: initial external concentration [mM].
+         * rev_pot: reversal potential [mV].
+         * method:  mechanism for calculating reversal potential.
+         * diff:   diffusivity [m^2/s].
+        """
+    def paintings(
+        self,
+    ) -> list[
+        tuple[
+            str,
+            membrane_potential
+            | axial_resistivity
+            | temperature
+            | membrane_capacitance
+            | ion_diffusivity
+            | int_concentration
+            | ext_concentration
+            | reversal_potential
+            | density
+            | voltage_process
+            | scaled_mechanism,
+        ]
+    ]:
+        """
+        Return a view of all painted items.
+        """
+    @typing.overload
+    def place(self, locations: str, synapse: synapse, label: str) -> decor:
+        """
+        Place one instance of 'synapse' on each location in 'locations'.The group of synapses has the label 'label', used for forming connections between cells.
+        """
+    @typing.overload
+    def place(self, locations: str, junction: junction, label: str) -> decor:
+        """
+        Place one instance of 'junction' on each location in 'locations'.The group of junctions has the label 'label', used for forming gap-junction connections between cells.
+        """
+    @typing.overload
+    def place(self, locations: str, iclamp: iclamp, label: str) -> decor:
+        """
+        Add a current stimulus at each location in locations.The group of current stimuli has the label 'label'.
+        """
+    @typing.overload
+    def place(self, locations: str, detector: threshold_detector, label: str) -> decor:
+        """
+        Add a voltage spike detector at each location in locations.The group of spike detectors has the label 'label', used for forming connections between cells.
+        """
+    def placements(
+        self,
+    ) -> list[tuple[str, iclamp | threshold_detector | synapse | junction, str]]:
+        """
+        Return a view of all placed items.
+        """
+    def set_ion(
+        self,
+        ion: str,
+        int_con: units.quantity | None = None,
+        ext_con: units.quantity | None = None,
+        rev_pot: units.quantity | None = None,
+        method: typing.Any = None,
+        diff: units.quantity | None = None,
+    ) -> decor:
+        """
+        Set the cell-level properties of ion species named 'ion'.
+         * int_con: initial internal concentration [mM].
+         * ext_con: initial external concentration [mM].
+         * rev_pot: reversal potential [mV].
+         * method:  mechanism for calculating reversal potential.
+         * diff:    diffusivity [m^2/s].
+        There are 3 ion species predefined in arbor: 'ca', 'na' and 'k'.
+        If 'ion' in not one of these ions it will be added to the list, making it
+        available to mechanisms. The user has to provide the valence of a previously
+        undefined ion the first time this function is called with it as an argument.
+        Species concentrations and reversal potential can be overridden on
+        specific regions using the paint interface, while the method for calculating
+        reversal potential is global for all compartments in the cell, and can't be
+        overriden locally.
+        """
+    def set_property(
+        self,
+        Vm: units.quantity | None = None,
+        cm: units.quantity | None = None,
+        rL: units.quantity | None = None,
+        tempK: units.quantity | None = None,
+    ) -> decor:
+        """
+        Set default values for cable and cell properties:
+         * Vm:    initial membrane voltage [mV].
+         * cm:    membrane capacitance [F/m²].
+         * rL:    axial resistivity [Ω·cm].
+         * tempK: temperature [Kelvin].
+        These values can be overridden on specific regions using the paint interface.
+        """
+
+class density:
+    """
+    For painting a density mechanism on a region.
+    """
+
+    @typing.overload
+    def __init__(self, arg0: str) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: mechanism) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: str, arg1: dict[str, float]) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: mechanism, arg1: dict[str, float]) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: str, **kwargs) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: mechanism, **kwargs) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def mech(self) -> mechanism:
+        """
+        The underlying mechanism.
+        """
+
+class domain_decomposition:
+    """
+    The domain decomposition is responsible for describing the distribution of cells across cell groups and domains.
+    """
+
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def gid_domain(self, gid: int) -> int:
+        """
+        Query the domain id that a cell assigned to (using global identifier gid).
+        """
+    @property
+    def domain_id(self) -> int:
+        """
+        The index of the local domain.
+        Always 0 for non-distributed models, and corresponds to the MPI rank for distributed runs.
+        """
+    @property
+    def groups(self) -> list[group_description]:
+        """
+        Descriptions of the cell groups on the local domain.
+        """
+    @property
+    def num_domains(self) -> int:
+        """
+        Number of domains that the model is distributed over.
+        """
+    @property
+    def num_global_cells(self) -> int:
+        """
+        Total number of cells in the global model (sum of num_local_cells over all domains).
+        """
+    @property
+    def num_groups(self) -> int:
+        """
+        Total number of cell groups in the local domain.
+        """
+    @property
+    def num_local_cells(self) -> int:
+        """
+        Total number of cells in the local domain.
+        """
+
+class event_generator:
+    def __init__(
+        self, target: cell_local_label, weight: float, sched: schedule_base
+    ) -> None:
+        """
+        Construct an event generator with arguments:
+          target: The target synapse label and selection policy.
+          weight: The weight of events to deliver.
+          sched:  A schedule of the events.
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def target(self) -> cell_local_label:
+        """
+        The target synapse (gid, local_id).
+        """
+    @target.setter
+    def target(self, arg0: cell_local_label) -> None: ...
+    @property
+    def weight(self) -> float:
+        """
+        The weight of events to deliver.
+        """
+    @weight.setter
+    def weight(self, arg0: float) -> None: ...
+
+class explicit_schedule(schedule_base):
+    """
+    Describes an explicit schedule at a predetermined (sorted) sequence of times.
+    """
+
+    @typing.overload
+    def __init__(self) -> None:
+        """
+        Construct an empty explicit schedule.
+        """
+    @typing.overload
+    def __init__(self, times: list[units.quantity]) -> None:
+        """
+        Construct an explicit schedule with argument:
+          times: A list of times [ms], [] by default.
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def events(self, arg0: float, arg1: float) -> list[float]:
+        """
+        A view of monotonically increasing time values in the half-open interval [t0, t1) in [ms].
+        """
+    @property
+    def times_ms(self) -> list[float]:
+        """
+        A list of times [ms].
+        """
+    @times_ms.setter
+    def times_ms(self, arg1: list[float]) -> None: ...
+
+class ext_concentration:
+    """
+    Setting the initial external ion concentration.
+    """
+
+    def __init__(self, arg0: str, arg1: units.quantity) -> None: ...
+    def __repr__(self) -> str: ...
+
+class extent:
+    """
+    A potentially empty region on a morphology.
+    """
+
+class gap_junction_connection:
+    """
+    Describes a gap junction between two gap junction sites.
+    """
+
+    def __init__(
+        self, peer: cell_global_label, local: cell_local_label, weight: float
+    ) -> None:
+        """
+        Construct a gap junction connection with arguments:
+          peer:  remote half of the gap junction connection.
+          local: local half of the gap junction connection.
+          weight:  Gap junction connection weight [unit-less].
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def local(self) -> cell_local_label:
+        """
+        Local label of the gap junction connection.
+        """
+    @local.setter
+    def local(self, arg0: cell_local_label) -> None: ...
+    @property
+    def peer(self) -> cell_global_label:
+        """
+        Remote gid and label of the gap junction connection.
+        """
+    @peer.setter
+    def peer(self, arg0: cell_global_label) -> None: ...
+    @property
+    def weight(self) -> float:
+        """
+        Gap junction connection weight [unit-less].
+        """
+    @weight.setter
+    def weight(self, arg0: float) -> None: ...
+
+class group_description:
+    """
+    The indexes of a set of cells of the same kind that are grouped together in a cell group.
+    """
+
+    def __init__(self, kind: cell_kind, gids: list[int], backend: backend) -> None:
+        """
+        Construct a group description with cell kind, list of gids, and backend kind.
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def backend(self) -> backend:
+        """
+        The hardware backend on which the cell group will run.
+        """
+    @property
+    def gids(self) -> list[int]:
+        """
+        The list of gids of the cells in the group.
+        """
+    @property
+    def kind(self) -> cell_kind:
+        """
+        The type of cell in the cell group.
+        """
+
+class iclamp:
+    """
+    A current clamp for injecting a DC or fixed frequency current governed by a piecewise linear envelope.
+    """
+
+    @typing.overload
+    def __init__(
+        self,
+        tstart: units.quantity,
+        duration: units.quantity,
+        current: units.quantity,
+        *,
+        frequency: units.quantity = ...,
+        phase: units.quantity = ...,
+    ) -> None:
+        """
+        Construct finite duration current clamp, constant amplitude
+        """
+    @typing.overload
+    def __init__(
+        self,
+        current: units.quantity,
+        *,
+        frequency: units.quantity = ...,
+        phase: units.quantity = ...,
+    ) -> None:
+        """
+        Construct constant amplitude current clamp
+        """
+    @typing.overload
+    def __init__(
+        self,
+        envelope: list[tuple[units.quantity, units.quantity]],
+        *,
+        frequency: units.quantity = ...,
+        phase: units.quantity = ...,
+    ) -> None:
+        """
+        Construct current clamp according to (time, amplitude) linear envelope
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def envelope(self) -> list[tuple[float, float]]:
+        """
+        List of (time [ms], amplitude [nA]) points comprising the piecewise linear envelope
+        """
+    @property
+    def frequency(self) -> float:
+        """
+        Oscillation frequency (kHz), zero implies DC stimulus.
+        """
+    @property
+    def phase(self) -> float:
+        """
+        Oscillation initial phase (rad)
+        """
+
+class int_concentration:
+    """
+    Setting the initial internal ion concentration.
+    """
+
+    def __init__(self, arg0: str, arg1: units.quantity) -> None: ...
+    def __repr__(self) -> str: ...
+
+class ion_data:
+    @property
+    def charge(self) -> int:
+        """
+        Valence.
+        """
+    @property
+    def diffusivity(self) -> float | None:
+        """
+        Diffusivity.
+        """
+    @property
+    def external_concentration(self) -> float | None:
+        """
+        External concentration.
+        """
+    @property
+    def internal_concentration(self) -> float | None:
+        """
+        Internal concentration.
+        """
+    @property
+    def reversal_concentration(self) -> float | None:
+        """
+        Reversal potential.
+        """
+    @property
+    def reversal_potential(self) -> float | None:
+        """
+        Reversal potential.
+        """
+    @property
+    def reversal_potential_method(self) -> str:
+        """
+        Reversal potential method.
+        """
+
+class ion_dependency:
+    """
+    Information about a mechanism's dependence on an ion species.
+    """
+
+    def __init__(self, arg0: ion_dependency) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def read_rev_pot(self) -> bool: ...
+    @property
+    def write_ext_con(self) -> bool: ...
+    @property
+    def write_int_con(self) -> bool: ...
+    @property
+    def write_rev_pot(self) -> bool: ...
+
+class ion_diffusivity:
+    """
+    Setting the ion diffusivity.
+    """
+
+    def __init__(self, arg0: str, arg1: units.quantity) -> None: ...
+    def __repr__(self) -> str: ...
+
+class ion_settings:
+    pass
+
+class isometry:
+    @staticmethod
+    @typing.overload
+    def rotate(theta: float, x: float, y: float, z: float) -> isometry:
+        """
+        Construct a rotation isometry of angle theta about the axis in direction (x, y, z).
+        """
+    @staticmethod
+    @typing.overload
+    def rotate(theta: float, axis: tuple) -> isometry:
+        """
+        Construct a rotation isometry of angle theta about the given axis in the direction described by a tuple.
+        """
+    @staticmethod
+    @typing.overload
+    def translate(x: float, y: float, z: float) -> isometry:
+        """
+        Construct a translation isometry from displacements x, y, and z.
+        """
+    @staticmethod
+    @typing.overload
+    def translate(arg0: tuple) -> isometry:
+        """
+        Construct a translation isometry from the first three components of a tuple.
+        """
+    @staticmethod
+    @typing.overload
+    def translate(arg0: mpoint) -> isometry:
+        """
+        Construct a translation isometry from the x, y, and z components of an mpoint.
+        """
+    @typing.overload
+    def __call__(self, arg0: mpoint) -> mpoint:
+        """
+        Apply isometry to mpoint argument.
+        """
+    @typing.overload
+    def __call__(self, arg0: tuple) -> tuple:
+        """
+        Apply isometry to first three components of tuple argument.
+        """
+    def __init__(self) -> None:
+        """
+        Construct a trivial isometry.
+        """
+    def __mul__(self, arg0: isometry) -> isometry: ...
+
+class junction:
+    """
+    For placing a gap-junction mechanism on a locset.
+    """
+
+    @typing.overload
+    def __init__(self, arg0: str) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: mechanism) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: str, arg1: dict[str, float]) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: str, **kwargs) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: mechanism, arg1: dict[str, float]) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: mechanism, **kwargs) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def mech(self) -> mechanism:
+        """
+        The underlying mechanism.
+        """
+
+class label_dict:
+    """
+    A dictionary of labelled region and locset definitions, with a
+    unique label assigned to each definition.
+    """
+
+    @staticmethod
+    def append(*args, **kwargs) -> None:
+        """
+        Import the entries of a another label dictionary with an optional prefix.
+        """
+    def __contains__(self, arg0: str) -> bool: ...
+    def __getitem__(self, arg0: str) -> str: ...
+    @typing.overload
+    def __init__(self) -> None:
+        """
+        Create an empty label dictionary.
+        """
+    @typing.overload
+    def __init__(self, arg0: dict[str, str]) -> None:
+        """
+        Initialize a label dictionary from a dictionary with string labels as keys, and corresponding definitions as strings.
+        """
+    @typing.overload
+    def __init__(self, arg0: label_dict) -> None:
+        """
+        Initialize a label dictionary from another one
+        """
+    @typing.overload
+    def __init__(self, arg0: typing.Iterator) -> None:
+        """
+        Initialize a label dictionary from an iterable of key, definition pairs
+        """
+    def __iter__(self) -> typing.Iterator: ...
+    def __len__(self) -> int: ...
+    def __repr__(self) -> str: ...
+    def __setitem__(self, arg0: str, arg1: str) -> None: ...
+    def __str__(self) -> str: ...
+    def add_swc_tags(self) -> label_dict:
+        """
+        Add standard SWC tagged regions.
+         - soma: (tag 1)
+         - axon: (tag 2)
+         - dend: (tag 3)
+         - apic: (tag 4)
+        """
+    def items(self) -> typing.Iterator: ...
+    def keys(self) -> typing.Iterator: ...
+    def update(self, other: label_dict) -> None:
+        """
+        The label_dict to be importedImport the entries of a another label dictionary.
+        """
+    def values(self) -> typing.Iterator: ...
+    @property
+    def locsets(self) -> list[str]:
+        """
+        The locset definitions.
+        """
+    @property
+    def regions(self) -> list[str]:
+        """
+        The region definitions.
+        """
+
+class lif_cell:
+    """
+    A leaky integrate-and-fire cell.
+    """
+
+    def __init__(
+        self,
+        source_label: str,
+        target_label: str,
+        *,
+        tau_m: units.quantity | None = None,
+        V_th: units.quantity | None = None,
+        C_m: units.quantity | None = None,
+        E_L: units.quantity | None = None,
+        V_m: units.quantity | None = None,
+        t_ref: units.quantity | None = None,
+    ) -> None:
+        """
+        Construct a lif cell with one source labeled 'source_label', and one target labeled 'target_label'.Can optionally take physical parameters:
+         * tau_m: Membrane potential decaying constant [ms].
+         * V_th: Firing threshold [mV].
+         * C_m: Membrane capacitance [pF].
+         * E_L: Resting potential [mV].
+         * V_m: Initial value of the Membrane potential [mV].
+         * t_ref: Refractory period [ms].
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def C_m(self) -> units.quantity:
+        """
+        Membrane capacitance [pF].
+        """
+    @C_m.setter
+    def C_m(self, arg0: units.quantity) -> None: ...
+    @property
+    def E_L(self) -> units.quantity:
+        """
+        Resting potential [mV].
+        """
+    @E_L.setter
+    def E_L(self, arg0: units.quantity) -> None: ...
+    @property
+    def E_R(self) -> units.quantity:
+        """
+        Reset potential [mV].
+        """
+    @E_R.setter
+    def E_R(self, arg0: units.quantity) -> None: ...
+    @property
+    def V_m(self) -> units.quantity:
+        """
+        Initial value of the Membrane potential [mV].
+        """
+    @V_m.setter
+    def V_m(self, arg0: units.quantity) -> None: ...
+    @property
+    def V_th(self) -> units.quantity:
+        """
+        Firing threshold [mV].
+        """
+    @V_th.setter
+    def V_th(self, arg0: units.quantity) -> None: ...
+    @property
+    def source(self) -> str:
+        """
+        Label of the single build-in source on the cell.
+        """
+    @source.setter
+    def source(self, arg0: str) -> None: ...
+    @property
+    def t_ref(self) -> units.quantity:
+        """
+        Refractory period [ms].
+        """
+    @t_ref.setter
+    def t_ref(self, arg0: units.quantity) -> None: ...
+    @property
+    def target(self) -> str:
+        """
+        Label of the single build-in target on the cell.
+        """
+    @target.setter
+    def target(self, arg0: str) -> None: ...
+    @property
+    def tau_m(self) -> units.quantity:
+        """
+        Membrane potential decaying constant [ms].
+        """
+    @tau_m.setter
+    def tau_m(self, arg0: units.quantity) -> None: ...
+
+class lif_probe_metadata:
+    """
+    Probe metadata associated with a LIF cell probe.
+    """
+
+class location:
+    """
+    A location on a cable cell.
+    """
+
+    __hash__: typing.ClassVar[None] = None
+    def __eq__(self, arg0: location) -> bool: ...
+    def __init__(self, branch: int, pos: float) -> None:
+        """
+        Construct a location specification holding:
+          branch:   The id of the branch.
+          pos:      The relative position (from 0., proximal, to 1., distal) on the branch.
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def branch(self) -> int:
+        """
+        The id of the branch.
+        """
+    @property
+    def pos(self) -> float:
+        """
+        The relative position on the branch (∈ [0.,1.], where 0. means proximal and 1. distal).
+        """
+
+class mechanism:
+    @typing.overload
+    def __init__(self, name: str) -> None:
+        """
+        The name of the mechanism
+        """
+    @typing.overload
+    def __init__(self, name: str, params: dict[str, float]) -> None:
+        """
+        Example usage setting parameters:
+          m = arbor.mechanism('expsyn', {'tau': 1.4})
+        will create parameters for the 'expsyn' mechanism, with the provided value
+        for 'tau' overrides the default. If a parameter is not set, the default
+        (as defined in NMODL) is used.
+
+        Example overriding a global parameter:
+          m = arbor.mechanism('nernst/R=8.3145,F=96485')
+        """
+    @typing.overload
+    def __init__(self, name: str, **kwargs) -> None:
+        """
+        Example usage setting parameters:
+          m = arbor.mechanism('expsyn', tau=1.4})
+        will create parameters for the 'expsyn' mechanism, with the provided value
+        for 'tau' overrides the default. If a parameter is not set, the default
+        (as defined in NMODL) is used.
+
+        Example overriding a global parameter:
+          m = arbor.mechanism('nernst/R=8.3145,F=96485')
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def set(self, name: str, value: float) -> None:
+        """
+        Set parameter value.
+        """
+    @property
+    def name(self) -> str:
+        """
+        The name of the mechanism.
+        """
+    @property
+    def values(self) -> dict[str, float]:
+        """
+        A dictionary of parameter values with parameter name as key.
+        """
+
+class mechanism_field:
+    """
+    Basic information about a mechanism field.
+    """
+
+    def __init__(self, arg0: mechanism_field) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def default(self) -> float: ...
+    @property
+    def max(self) -> float: ...
+    @property
+    def min(self) -> float: ...
+    @property
+    def units(self) -> str: ...
+
+class mechanism_info:
+    """
+    Meta data about a mechanism's fields and ion dependendencies.
+    """
+
+    def __init__(self, arg0: mechanism_info) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def globals(self) -> dict[str, mechanism_field]:
+        """
+        Global fields have one value common to an instance of a mechanism, are constant in time and set at instantiation.
+        """
+    @property
+    def ions(self) -> dict[str, ion_dependency]:
+        """
+        Ion dependencies.
+        """
+    @property
+    def kind(self) -> str:
+        """
+        String representation of the kind of the mechanism.
+        """
+    @property
+    def linear(self) -> bool:
+        """
+        True if a synapse mechanism has linear current contributions so that multiple instances on the same compartment can be coalesced.
+        """
+    @property
+    def parameters(self) -> dict[str, mechanism_field]:
+        """
+        Parameter fields may vary across the extent of a mechanism, but are constant in time and set at instantiation.
+        """
+    @property
+    def post_events(self) -> bool:
+        """
+        True if a synapse mechanism has a `POST_EVENT` procedure defined.
+        """
+    @property
+    def state(self) -> dict[str, mechanism_field]:
+        """
+        State fields vary in time and across the extent of a mechanism, and potentially can be sampled at run-time.
+        """
+
+class membrane_capacitance:
+    """
+    Setting the membrane capacitance.
+    """
+
+    def __init__(self, arg0: units.quantity) -> None: ...
+    def __repr__(self) -> str: ...
+
+class membrane_potential:
+    """
+    Setting the initial membrane voltage.
+    """
+
+    def __init__(self, arg0: units.quantity, arg1: str | None) -> None: ...
+    def __repr__(self) -> str: ...
+
+class meter_manager:
+    """
+    Manage metering by setting checkpoints and starting the timing region.
+    """
+
+    def __init__(self) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def checkpoint(self, name: str, context: context) -> None:
+        """
+        Create a new checkpoint. Records the time since the last checkpoint(or the call to start if no previous checkpoints exist),and restarts the timer for the next checkpoint.
+        """
+    def start(self, context: context) -> None:
+        """
+        Start the metering. Records a time stamp,             that marks the start of the first checkpoint timing region.
+        """
+    @property
+    def checkpoint_names(self) -> list[str]:
+        """
+        A list of all metering checkpoint names.
+        """
+    @property
+    def times(self) -> list[float]:
+        """
+        A list of all metering times.
+        """
+
+class meter_report:
+    """
+    Summarises the performance meter results, used to print a report to screen or file.
+    If a distributed context is used, the report will contain a summary of results from all MPI ranks.
+    """
+
+    def __init__(self, manager: meter_manager, context: context) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+
+class morphology:
+    """
+    A cell morphology.
+    """
+
+    def __init__(self, arg0: segment_tree) -> None: ...
+    def __str__(self) -> str: ...
+    def branch_children(self, i: int) -> list[int]:
+        """
+        The child branches of branch i.
+        """
+    def branch_parent(self, i: int) -> int:
+        """
+        The parent branch of branch i.
+        """
+    def branch_segments(self, i: int) -> list[msegment]:
+        """
+        A list of the segments in branch i, ordered from proximal to distal ends of the branch.
+        """
+    def to_segment_tree(self) -> segment_tree:
+        """
+        Convert this morphology to a segment_tree.
+        """
+    @property
+    def empty(self) -> bool:
+        """
+        Whether the morphology is empty.
+        """
+    @property
+    def num_branches(self) -> int:
+        """
+        The number of branches in the morphology.
+        """
+
+class morphology_provider:
+    def __init__(self, morphology: morphology) -> None:
+        """
+        Construct a morphology provider.
+        """
+    def reify_locset(self, arg0: str) -> list[location]:
+        """
+        Turn a locset into a list of locations.
+        """
+    def reify_region(self, arg0: str) -> extent:
+        """
+        Turn a region into an extent.
+        """
+
+class mpoint:
+    __hash__: typing.ClassVar[None] = None
+    def __eq__(self, arg0: mpoint) -> bool: ...
+    @typing.overload
+    def __init__(self, x: float, y: float, z: float, radius: float) -> None:
+        """
+        Create an mpoint object from parameters x, y, z, and radius, specified in µm.
+        """
+    @typing.overload
+    def __init__(self, arg0: tuple) -> None:
+        """
+        Create an mpoint object from a tuple (x, y, z, radius), specified in µm.
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def radius(self) -> float:
+        """
+        Radius of cable at sample location centred at coordinates [μm].
+        """
+    @property
+    def x(self) -> float:
+        """
+        X coordinate [μm].
+        """
+    @property
+    def y(self) -> float:
+        """
+        Y coordinate [μm].
+        """
+    @property
+    def z(self) -> float:
+        """
+        Z coordinate [μm].
+        """
+
+class msegment:
+    @property
+    def dist(self) -> mpoint:
+        """
+        the location and radius of the distal end.
+        """
+    @property
+    def prox(self) -> mpoint:
+        """
+        the location and radius of the proximal end.
+        """
+    @property
+    def tag(self) -> int:
+        """
+        tag meta-data.
+        """
+
+class neuroml:
+    def __init__(self, arg0: typing.Any) -> None:
+        """
+        Construct NML morphology from filename or stream.
+        """
+    def cell_ids(self) -> list[str]:
+        """
+        Query top-level cells.
+        """
+    def cell_morphology(
+        self, cell_id: str, allow_spherical_root: bool = False
+    ) -> neuroml_morph_data | None:
+        """
+        Retrieve nml_morph_data associated with cell_id.
+        """
+    def morphology(
+        self, morph_id: str, allow_spherical_root: bool = False
+    ) -> neuroml_morph_data | None:
+        """
+        Retrieve top-level nml_morph_data associated with morph_id.
+        """
+    def morphology_ids(self) -> list[str]:
+        """
+        Query top-level standalone morphologies.
+        """
+
+class neuroml_morph_data:
+    def groups(self) -> label_dict:
+        """
+        Label dictionary containing one region expression for each segmentGroup id.
+        """
+    def named_segments(self) -> label_dict:
+        """
+        Label dictionary containing one region expression for each name applied to one or more segments.
+        """
+    def segments(self) -> label_dict:
+        """
+        Label dictionary containing one region expression for each segment id.
+        """
+    @property
+    def cell_id(self) -> str | None:
+        """
+        Cell id, or empty if morphology was taken from a top-level <morphology> element.
+        """
+    @property
+    def group_segments(self) -> dict[str, list[int]]:
+        """
+        Map from segmentGroup ids to their corresponding segment ids.
+        """
+    @property
+    def id(self) -> str:
+        """
+        Morphology id.
+        """
+    @property
+    def morphology(self) -> morphology:
+        """
+        Morphology constructed from a signle NeuroML <morphology> element.
+        """
+
+class partition_hint:
+    """
+    Provide a hint on how the cell groups should be partitioned.
+    """
+
+    max_size: typing.ClassVar[int] = 18446744073709551615
+    def __init__(
+        self,
+        cpu_group_size: int = 1,
+        gpu_group_size: int = 18446744073709551615,
+        prefer_gpu: bool = True,
+    ) -> None:
+        """
+        Construct a partition hint with arguments:
+          cpu_group_size: The size of cell group assigned to CPU, each cell in its own group by default.
+                          Must be positive, else set to default value.
+          gpu_group_size: The size of cell group assigned to GPU, all cells in one group by default.
+                          Must be positive, else set to default value.
+          prefer_gpu:     Whether GPU is preferred, True by default.
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def cpu_group_size(self) -> int:
+        """
+        The size of cell group assigned to CPU.
+        """
+    @cpu_group_size.setter
+    def cpu_group_size(self, arg0: int) -> None: ...
+    @property
+    def gpu_group_size(self) -> int:
+        """
+        The size of cell group assigned to GPU.
+        """
+    @gpu_group_size.setter
+    def gpu_group_size(self, arg0: int) -> None: ...
+    @property
+    def prefer_gpu(self) -> bool:
+        """
+        Whether GPU usage is preferred.
+        """
+    @prefer_gpu.setter
+    def prefer_gpu(self, arg0: bool) -> None: ...
+
+class place_pwlin:
+    def __init__(self, morphology: morphology, isometry: isometry = ...) -> None:
+        """
+        Construct a piecewise-linear placement object from the given morphology and optional isometry.
+        """
+    def all_at(self, location: location) -> list[mpoint]:
+        """
+        Return list of all possible interpolated mpoints corresponding to the location argument.
+        """
+    def all_segments(self, arg0: list[cable]) -> list[msegment]:
+        """
+        Return maximal list of non-overlapping full or partial msegments whose union is coterminous with the extent of the given list of cables.
+        """
+    def at(self, location: location) -> mpoint:
+        """
+        Return an interpolated mpoint corresponding to the location argument.
+        """
+    def closest(self, arg0: float, arg1: float, arg2: float) -> tuple:
+        """
+        Find the location on the morphology that is closest to a 3d point. Returns the location and its distance from the point.
+        """
+    def segments(self, arg0: list[cable]) -> list[msegment]:
+        """
+        Return minimal list of full or partial msegments whose union is coterminous with the extent of the given list of cables.
+        """
+
+class poisson_schedule(schedule_base):
+    """
+    Describes a schedule according to a Poisson process within the interval [tstart, tstop).
+    """
+
+    def __init__(
+        self,
+        freq: units.quantity,
+        *,
+        tstart: units.quantity = ...,
+        seed: int = 0,
+        tstop: units.quantity | None = None,
+    ) -> None:
+        """
+        Construct a Poisson schedule with arguments:
+          tstart: The delivery time of the first event in the sequence [ms], 0 by default.
+          freq:   The expected frequency [kHz].
+          seed:   The seed for the random number generator, 0 by default.
+          tstop:  No events delivered after this time [ms], None by default.
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def events(self, arg0: units.quantity, arg1: units.quantity) -> list[float]:
+        """
+        A view of monotonically increasing time values in the half-open interval [t0, t1).
+        """
+    @property
+    def freq(self) -> units.quantity:
+        """
+        The expected frequency [kHz].
+        """
+    @freq.setter
+    def freq(self, arg1: units.quantity) -> None: ...
+    @property
+    def seed(self) -> int:
+        """
+        The seed for the random number generator.
+        """
+    @seed.setter
+    def seed(self, arg0: int) -> None: ...
+    @property
+    def tstart(self) -> units.quantity:
+        """
+        The delivery time of the first event in the sequence [ms].
+        """
+    @tstart.setter
+    def tstart(self, arg1: units.quantity) -> None: ...
+    @property
+    def tstop(self) -> units.quantity:
+        """
+        No events delivered after this time [ms].
+        """
+    @tstop.setter
+    def tstop(self, arg1: units.quantity) -> None: ...
+
+class probe:
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+
+class proc_allocation:
+    """
+    Enumerates the computational resources on a node to be used for simulation.
+    """
+
+    def __init__(
+        self,
+        *,
+        threads: int = 1,
+        gpu_id: typing.Any = None,
+        bind_procs: bool = False,
+        bind_threads: bool = False,
+    ) -> None:
+        """
+        Construct an allocation with arguments:
+          threads:      The number of threads available locally for execution. Must be set to 1 at minimum. 1 by default.
+          gpu_id:       The identifier of the GPU to use, None by default.
+          bind_procs:   Create process binding mask.
+          bind_threads: Create thread binding mask.
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def bind_procs(self) -> bool:
+        """
+        Try to bind MPI procs?
+        """
+    @bind_procs.setter
+    def bind_procs(self, arg1: bool) -> None: ...
+    @property
+    def bind_threads(self) -> bool:
+        """
+        Try to bind threads?
+        """
+    @bind_threads.setter
+    def bind_threads(self, arg1: bool) -> None: ...
+    @property
+    def gpu_id(self) -> int | None:
+        """
+        The identifier of the GPU to use.
+        Corresponds to the integer parameter used to identify GPUs in CUDA API calls.
+        """
+    @gpu_id.setter
+    def gpu_id(self, arg1: typing.Any) -> None: ...
+    @property
+    def has_gpu(self) -> bool:
+        """
+        Whether a GPU is being used (True/False).
+        """
+    @property
+    def threads(self) -> int:
+        """
+        The number of threads available locally for execution.
+        """
+    @threads.setter
+    def threads(self, arg1: int) -> None: ...
+
+class recipe:
+    """
+    A description of a model, describing the cells and the network via a cell-centric interface.
+    """
+
+    def __init__(self) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def cell_description(self, gid: int) -> typing.Any:
+        """
+        High level description of the cell with global identifier gid.
+        """
+    def cell_kind(self, gid: int) -> cell_kind:
+        """
+        The kind of cell with global identifier gid.
+        """
+    def connections_on(self, gid: int) -> list[connection]:
+        """
+        A list of all the incoming connections to gid, [] by default.
+        """
+    def event_generators(self, gid: int) -> list[typing.Any]:
+        """
+        A list of all the event generators that are attached to gid, [] by default.
+        """
+    def external_connections_on(self, gid: int) -> list[connection]:
+        """
+        A list of all the incoming connections from _remote_ locations to gid, [] by default.
+        """
+    def gap_junctions_on(self, gid: int) -> list[gap_junction_connection]:
+        """
+        A list of the gap junctions connected to gid, [] by default.
+        """
+    def global_properties(self, kind: cell_kind) -> typing.Any:
+        """
+        The default properties applied to all cells of type 'kind' in the model.
+        """
+    def num_cells(self) -> int:
+        """
+        The number of cells in the model.
+        """
+    def probes(self, gid: int) -> list[probe]:
+        """
+        The probes to allow monitoring.
+        """
+
+class regular_schedule(schedule_base):
+    """
+    Describes a regular schedule with multiples of dt within the interval [tstart, tstop).
+    """
+
+    @typing.overload
+    def __init__(
+        self,
+        tstart: units.quantity,
+        dt: units.quantity,
+        tstop: units.quantity | None = None,
+    ) -> None:
+        """
+        Construct a regular schedule with arguments:
+          tstart: The delivery time of the first event in the sequence [ms].
+          dt:     The interval between time points [ms].
+          tstop:  No events delivered after this time [ms], None by default.
+        """
+    @typing.overload
+    def __init__(self, dt: units.quantity) -> None:
+        """
+        Construct a regular schedule, starting from t = 0 and never terminating, with arguments:
+          dt:     The interval between time points [ms].
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def events(self, arg0: float, arg1: float) -> list[float]:
+        """
+        A view of monotonically increasing time values in the half-open interval [t0, t1).
+        """
+    @property
+    def dt(self) -> units.quantity:
+        """
+        The interval between time points [ms].
+        """
+    @dt.setter
+    def dt(self, arg1: units.quantity) -> None: ...
+    @property
+    def tstart(self) -> units.quantity:
+        """
+        The delivery time of the first event in the sequence [ms].
+        """
+    @tstart.setter
+    def tstart(self, arg1: units.quantity) -> None: ...
+    @property
+    def tstop(self) -> units.quantity | None:
+        """
+        No events delivered after this time [ms].
+        """
+    @tstop.setter
+    def tstop(self, arg1: units.quantity | None) -> None: ...
+
+class reversal_potential:
+    """
+    Setting the initial reversal potential.
+    """
+
+    def __init__(self, arg0: str, arg1: units.quantity) -> None: ...
+    def __repr__(self) -> str: ...
+
+class reversal_potential_method:
+    """
+    Describes the mechanism used to compute eX for ion X.
+    """
+
+    def __init__(self, arg0: str, arg1: mechanism) -> None: ...
+    def __repr__(self) -> str: ...
+
+class scaled_mechanism:
+    """
+    For painting a scaled density mechanism on a region.
+    """
+
+    @typing.overload
+    def __init__(self, arg0: density) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: density, arg1: dict[str, str]) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: density, **kwargs) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def scale(self, name: str, ex: str) -> scaled_mechanism:
+        """
+        Add a scaling expression to a parameter.
+        """
+
+class schedule_base:
+    """
+    Schedule abstract base class.
+    """
+
+class segment_tree:
+    def __init__(self) -> None: ...
+    def __str__(self) -> str: ...
+    @typing.overload
+    def append(self, parent: int, prox: mpoint, dist: mpoint, tag: int) -> int:
+        """
+        Append a segment to the tree.
+        """
+    @typing.overload
+    def append(self, parent: int, dist: mpoint, tag: int) -> int:
+        """
+        Append a segment to the tree.
+        """
+    @typing.overload
+    def append(
+        self, parent: int, x: float, y: float, z: float, radius: float, tag: int
+    ) -> int:
+        """
+        Append a segment to the tree, using the distal location of the parent segment as the proximal end.
+        """
+    def apply_isometry(self, arg0: isometry) -> segment_tree:
+        """
+        Apply an isometry to all segments in the tree.
+        """
+    def equivalent(self, arg0: segment_tree) -> bool:
+        """
+        Two trees are equivalent, but not neccessarily identical, ie they have the same segments and structure.
+        """
+    def is_fork(self, i: int) -> bool:
+        """
+        True if segment has more than one child.
+        """
+    def is_root(self, i: int) -> bool:
+        """
+        True if segment has no parent.
+        """
+    def is_terminal(self, i: int) -> bool:
+        """
+        True if segment has no children.
+        """
+    def join_at(self, arg0: int, arg1: segment_tree) -> segment_tree:
+        """
+        Join two subtrees at a given id, such that said id becomes the parent of the inserted sub-tree.
+        """
+    def reserve(self, arg0: int) -> None: ...
+    def split_at(self, arg0: int) -> tuple[segment_tree, segment_tree]:
+        """
+        Split into a pair of trees at the given id, such that one tree is the subtree rooted at id and the other is the original tree without said subtree.
+        """
+    def tag_roots(self, arg0: int) -> list[int]:
+        """
+        Get roots of tag region of this segment tree.
+        """
+    @property
+    def empty(self) -> bool:
+        """
+        Indicates whether the tree is empty (i.e. whether it has size 0)
+        """
+    @property
+    def parents(self) -> list[int]:
+        """
+        A list with the parent index of each segment.
+        """
+    @property
+    def segments(self) -> list[msegment]:
+        """
+        A list of the segments.
+        """
+    @property
+    def size(self) -> int:
+        """
+        The number of segments in the tree.
+        """
+
+class selection_policy:
+    """
+    Enumeration used to identify a selection policy, used by the model for selecting one of possibly multiple locations on the cell associated with a labeled item.
+
+    Members:
+
+      round_robin : Iterate round-robin over all possible locations.
+
+      round_robin_halt : Halts at the current location until the round_robin policy is called (again).
+
+      univalent : Assert that there is only one possible location associated with a labeled item on the cell. The model throws an exception if the assertion fails.
+    """
+
+    __members__: typing.ClassVar[
+        dict[str, selection_policy]
+    ]  # value = {'round_robin': <selection_policy.round_robin: 0>, 'round_robin_halt': <selection_policy.round_robin_halt: 1>, 'univalent': <selection_policy.univalent: 2>}
+    round_robin: typing.ClassVar[
+        selection_policy
+    ]  # value = <selection_policy.round_robin: 0>
+    round_robin_halt: typing.ClassVar[
+        selection_policy
+    ]  # value = <selection_policy.round_robin_halt: 1>
+    univalent: typing.ClassVar[
+        selection_policy
+    ]  # value = <selection_policy.univalent: 2>
+    def __eq__(self, other: typing.Any) -> bool: ...
+    def __getstate__(self) -> int: ...
+    def __hash__(self) -> int: ...
+    def __index__(self) -> int: ...
+    def __init__(self, value: int) -> None: ...
+    def __int__(self) -> int: ...
+    def __ne__(self, other: typing.Any) -> bool: ...
+    def __repr__(self) -> str: ...
+    def __setstate__(self, state: int) -> None: ...
+    def __str__(self) -> str: ...
+    @property
+    def name(self) -> str: ...
+    @property
+    def value(self) -> int: ...
+
+class simulation:
+    """
+    The executable form of a model.
+    A simulation is constructed from a recipe, and then used to update and monitor model state.
+    """
+
+    @staticmethod
+    def deserialize(*args, **kwargs) -> None: ...
+    def __init__(
+        self,
+        recipe: recipe,
+        context: context | None = None,
+        domains: domain_decomposition | None = None,
+        seed: int = 0,
+    ) -> None:
+        """
+        Initialize the model described by a recipe, with cells and network distributed
+        according to the domain decomposition and computational resources described by a
+        context. Initialize PRNG using seed
+        """
+    def clear_samplers(self) -> None:
+        """
+        Clearing spike and sample information. restoring memory
+        """
+    @typing.overload
+    def probe_metadata(self, probeset_id: cell_address) -> list:
+        """
+        Retrieve metadata associated with given probe id.
+        """
+    @typing.overload
+    def probe_metadata(self, addr: tuple[int, str]) -> list:
+        """
+        Retrieve metadata associated with given probe id.
+        """
+    @typing.overload
+    def probe_metadata(self, gid: int, tag: str) -> list:
+        """
+        Retrieve metadata associated with given probe id.
+        """
+    def progress_banner(self) -> None:
+        """
+        Show a text progress bar during simulation.
+        """
+    def record(self, arg0: spike_recording) -> None:
+        """
+        Disable or enable local or global spike recording.
+        """
+    def remove_all_samplers(self, arg0: int) -> None:
+        """
+        Remove all sampling on the simulatr.
+        """
+    def remove_sampler(self, handle: int) -> None:
+        """
+        Remove sampling associated with the given handle.
+        """
+    def reset(self) -> None:
+        """
+        Reset the state of the simulation to its initial state.
+        """
+    def run(self, tfinal: units.quantity, dt: units.quantity = ...) -> float:
+        """
+        Run the simulation from current simulation time to tfinal [ms], with maximum time step size dt [ms].
+        """
+    @typing.overload
+    def sample(self, probeset_id: cell_address, schedule: schedule_base) -> int:
+        """
+        Record data from probes with given probeset_id according to supplied schedule.
+        Returns handle for retrieving data or removing the sampling.
+        """
+    @typing.overload
+    def sample(self, gid: int, tag: str, schedule: schedule_base) -> int:
+        """
+        Record data from probes with given probeset_id=(gid, tag) according to supplied schedule.
+        Returns handle for retrieving data or removing the sampling.
+        """
+    @typing.overload
+    def sample(self, probeset_id: tuple[int, str], schedule: schedule_base) -> int:
+        """
+        Record data from probes with given probeset_id=(gid, tag) according to supplied schedule.
+        Returns handle for retrieving data or removing the sampling.
+        """
+    def samples(self, handle: int) -> list:
+        """
+        Retrieve sample data as a list, one element per probe associated with the query.
+        """
+    def serialize(self) -> str:
+        """
+        Serialize the simulation object to a JSON string.
+        """
+    def set_remote_spike_filter(self, pred: typing.Callable[[spike], bool]) -> None:
+        """
+        Add a callback to filter spikes going out over external connections. `pred` isa callable on the `spike` type. **Caution**: This will be extremely slow; use C++ if you want to make use of this.
+        """
+    def spikes(self) -> typing.Any:
+        """
+        Retrieve recorded spikes as numpy array.
+        """
+    def update(self, recipe: recipe) -> None:
+        """
+        Rebuild the connection table from recipe::connections_on and the eventgenerators based on recipe::event_generators.
+        """
+
+class single_cell_model:
+    """
+    Wrapper for simplified description, and execution, of single cell models.
+    """
+
+    @typing.overload
+    def __init__(
+        self, tree: segment_tree, decor: decor, labels: label_dict = ...
+    ) -> None:
+        """
+        Build single cell model from cable cell components
+        """
+    @typing.overload
+    def __init__(
+        self, morph: morphology, decor: decor, labels: label_dict = ...
+    ) -> None:
+        """
+        Build single cell model from cable cell components
+        """
+    @typing.overload
+    def __init__(self, cell: cable_cell) -> None:
+        """
+        Initialise a single cell model for a cable cell.
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    def event_generator(self, event_generator: event_generator) -> None:
+        """
+        Register an event generator.
+         event_generator: An Arbor event generator.
+        """
+    @typing.overload
+    def probe(self, what: str, where: str, tag: str, frequency: units.quantity) -> None:
+        """
+        Sample a variable on the cell.
+         what:      Name of the variable to record (currently only 'voltage').
+         where:     Location on cell morphology at which to sample the variable.
+         tag:       Unique name for this probe.
+         frequency: The target frequency at which to sample [kHz].
+        """
+    @typing.overload
+    def probe(
+        self, what: str, where: location, tag: str, frequency: units.quantity
+    ) -> None:
+        """
+        Sample a variable on the cell.
+         what:      Name of the variable to record (currently only 'voltage').
+         where:     Location on cell morphology at which to sample the variable.
+         tag:       Unique name for this probe.
+         frequency: The target frequency at which to sample [kHz].
+        """
+    def run(self, tfinal: units.quantity, dt: units.quantity = ...) -> None:
+        """
+        Run model from t=0 to t=tfinal ms.
+        """
+    @property
+    def cable_cell(self) -> cable_cell:
+        """
+        The cable cell held by this model.
+        """
+    @property
+    def properties(self) -> cable_global_properties:
+        """
+        Global properties.
+        """
+    @properties.setter
+    def properties(self, arg0: cable_global_properties) -> None: ...
+    @property
+    def spikes(self) -> list[float]:
+        """
+        Holds spike times [ms] after a call to run().
+        """
+    @property
+    def traces(self) -> list[trace]:
+        """
+        Holds sample traces after a call to run().
+        """
+
+class spike:
+    def __init__(self, arg0: cell_member, arg1: float) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def source(self) -> cell_member:
+        """
+        The global identifier of the cell.
+        """
+    @source.setter
+    def source(self, arg0: cell_member) -> None: ...
+    @property
+    def time(self) -> float:
+        """
+        The time of spike.
+        """
+    @time.setter
+    def time(self, arg0: float) -> None: ...
+
+class spike_recording:
+    """
+    Members:
+
+      off
+
+      local
+
+      all
+    """
+
+    __members__: typing.ClassVar[
+        dict[str, spike_recording]
+    ]  # value = {'off': <spike_recording.off: 0>, 'local': <spike_recording.local: 1>, 'all': <spike_recording.all: 2>}
+    all: typing.ClassVar[spike_recording]  # value = <spike_recording.all: 2>
+    local: typing.ClassVar[spike_recording]  # value = <spike_recording.local: 1>
+    off: typing.ClassVar[spike_recording]  # value = <spike_recording.off: 0>
+    def __eq__(self, other: typing.Any) -> bool: ...
+    def __getstate__(self) -> int: ...
+    def __hash__(self) -> int: ...
+    def __index__(self) -> int: ...
+    def __init__(self, value: int) -> None: ...
+    def __int__(self) -> int: ...
+    def __ne__(self, other: typing.Any) -> bool: ...
+    def __repr__(self) -> str: ...
+    def __setstate__(self, state: int) -> None: ...
+    def __str__(self) -> str: ...
+    @property
+    def name(self) -> str: ...
+    @property
+    def value(self) -> int: ...
+
+class spike_source_cell:
+    """
+    A spike source cell, that generates a user-defined sequence of spikes that act as inputs for other cells in the network.
+    """
+
+    @typing.overload
+    def __init__(self, source_label: str, schedule: regular_schedule) -> None:
+        """
+        Construct a spike source cell with a single source labeled 'source_label'.
+        The cell generates spikes on 'source_label' at regular intervals.
+        """
+    @typing.overload
+    def __init__(self, source_label: str, schedule: explicit_schedule) -> None:
+        """
+        Construct a spike source cell with a single source labeled 'source_label'.
+        The cell generates spikes on 'source_label' at a sequence of user-defined times.
+        """
+    @typing.overload
+    def __init__(self, source_label: str, schedule: poisson_schedule) -> None:
+        """
+        Construct a spike source cell with a single source labeled 'source_label'.
+        The cell generates spikes on 'source_label' at times defined by a Poisson sequence.
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+
+class synapse:
+    """
+    For placing a synaptic mechanism on a locset.
+    """
+
+    @typing.overload
+    def __init__(self, arg0: str) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: mechanism) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: str, arg1: dict[str, float]) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: mechanism, arg1: dict[str, float]) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: str, **kwargs) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: mechanism, **kwargs) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def mech(self) -> mechanism:
+        """
+        The underlying mechanism.
+        """
+
+class temperature:
+    """
+    Setting the temperature.
+    """
+
+    def __init__(self, arg0: units.quantity) -> None: ...
+    def __repr__(self) -> str: ...
+
+class threshold_detector:
+    """
+    A spike detector, generates a spike when voltage crosses a threshold. Can be used as source endpoint for an arbor.connection.
+    """
+
+    def __init__(self, threshold: units.quantity) -> None:
+        """
+        Voltage threshold of spike detector [mV]
+        """
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def threshold(self) -> float:
+        """
+        Voltage threshold of spike detector [mV]
+        """
+
+class trace:
+    """
+    Values and meta-data for a sample-trace on a single cell model.
+    """
+
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def location(self) -> location:
+        """
+        Location on cell morphology.
+        """
+    @property
+    def time(self) -> list[float]:
+        """
+        Time stamps of samples [ms].
+        """
+    @property
+    def value(self) -> list[float]:
+        """
+        Sample values.
+        """
+    @property
+    def variable(self) -> str:
+        """
+        Name of the variable being recorded.
+        """
+
+class voltage_process:
+    """
+    For painting a voltage_process mechanism on a region.
+    """
+
+    @typing.overload
+    def __init__(self, arg0: str) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: mechanism) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: str, arg1: dict[str, float]) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: mechanism, arg1: dict[str, float]) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: mechanism, **kwargs) -> None: ...
+    @typing.overload
+    def __init__(self, arg0: str, **kwargs) -> None: ...
+    def __repr__(self) -> str: ...
+    def __str__(self) -> str: ...
+    @property
+    def mech(self) -> mechanism:
+        """
+        The underlying mechanism.
+        """
+
+def allen_catalogue() -> catalogue: ...
+def bbp_catalogue() -> catalogue: ...
+def cable_probe_axial_current(where: str, tag: str) -> probe:
+    """
+    Probe specification for cable cell axial current at points in a location set.
+    """
+
+def cable_probe_density_state(
+    where: str, mechanism: str, state: str, tag: str
+) -> probe:
+    """
+    Probe specification for a cable cell density mechanism state variable at points in a location set.
+    """
+
+def cable_probe_density_state_cell(mechanism: str, state: str, tag: str) -> probe:
+    """
+    Probe specification for a cable cell density mechanism state variable on each cable in each CV where defined.
+    """
+
+def cable_probe_ion_current_cell(ion: str, tag: str) -> probe:
+    """
+    Probe specification for cable cell ionic current across each cable in each CV.
+    """
+
+def cable_probe_ion_current_density(where: str, ion: str, tag: str) -> probe:
+    """
+    Probe specification for cable cell ionic current density at points in a location set.
+    """
+
+def cable_probe_ion_diff_concentration(where: str, ion: str, tag: str) -> probe:
+    """
+    Probe specification for cable cell diffusive ionic concentration at points in a location set.
+    """
+
+def cable_probe_ion_diff_concentration_cell(ion: str, tag: str) -> probe:
+    """
+    Probe specification for cable cell diffusive ionic concentration for each cable in each CV.
+    """
+
+def cable_probe_ion_ext_concentration(where: str, ion: str, tag: str) -> probe:
+    """
+    Probe specification for cable cell external ionic concentration at points in a location set.
+    """
+
+def cable_probe_ion_ext_concentration_cell(ion: str, tag: str) -> probe:
+    """
+    Probe specification for cable cell external ionic concentration for each cable in each CV.
+    """
+
+def cable_probe_ion_int_concentration(where: str, ion: str, tag: str) -> probe:
+    """
+    Probe specification for cable cell internal ionic concentration at points in a location set.
+    """
+
+def cable_probe_ion_int_concentration_cell(ion: str, tag: str) -> probe:
+    """
+    Probe specification for cable cell internal ionic concentration for each cable in each CV.
+    """
+
+def cable_probe_membrane_voltage(where: str, tag: str) -> probe:
+    """
+    Probe specification for cable cell membrane voltage interpolated at points in a location set.
+    """
+
+def cable_probe_membrane_voltage_cell(tag: str) -> probe:
+    """
+    Probe specification for cable cell membrane voltage associated with each cable in each CV.
+    """
+
+def cable_probe_point_state(target: int, mechanism: str, state: str, tag: str) -> probe:
+    """
+    Probe specification for a cable cell point mechanism state variable value at a given target index.
+    """
+
+def cable_probe_point_state_cell(mechanism: str, state: str, tag: str) -> probe:
+    """
+    Probe specification for a cable cell point mechanism state variable value at every corresponding target.
+    """
+
+def cable_probe_stimulus_current_cell(tag: str) -> probe:
+    """
+    Probe specification for cable cell stimulus current across each cable in each CV.
+    """
+
+def cable_probe_total_current_cell(tag: str) -> probe:
+    """
+    Probe specification for cable cell total transmembrane current for each cable in each CV.
+    """
+
+def cable_probe_total_ion_current_cell(tag: str) -> probe:
+    """
+    Probe specification for cable cell total transmembrane current excluding capacitive currents for each cable in each CV.
+    """
+
+def cable_probe_total_ion_current_density(where: str, tag: str) -> probe:
+    """
+    Probe specification for cable cell total transmembrane current density excluding capacitive currents at points in a location set.
+    """
+
+def config() -> dict:
+    """
+    Get Arbor's configuration.
+    """
+
+def cv_data(cell: cable_cell) -> cell_cv_data | None:
+    """
+    Returns a cell_cv_data object representing the CVs comprising the cable-cell according to the discretization policy provided in the decor of the cell. Returns None if no CV-policy was provided in the decor.
+    """
+
+def cv_policy_every_segment(domain: str = "(all)") -> cv_policy:
+    """
+    Policy to create one compartment per component of a region.
+    """
+
+def cv_policy_explicit(locset: str, domain: str = "(all)") -> cv_policy:
+    """
+    Policy to create compartments at explicit locations.
+    """
+
+def cv_policy_fixed_per_branch(n: int, domain: str = "(all)") -> cv_policy:
+    """
+    Policy to use the same number of CVs for each branch.
+    """
+
+def cv_policy_max_extent(length: float, domain: str = "(all)") -> cv_policy:
+    """
+    Policy to use as many CVs as required to ensure that no CV has a length longer than a given value.
+    """
+
+def cv_policy_single(domain: str = "(all)") -> cv_policy:
+    """
+    Policy to create one compartment per component of a region.
+    """
+
+def default_catalogue() -> catalogue: ...
+def intersect_region(reg: str, data: cell_cv_data, integrate_along: str) -> list[tuple]:
+    """
+    Returns a list of [index, proportion] tuples identifying the CVs present in the region.
+    `index` is the index of the CV in the cell_cv_data object provided as an argument.
+    `proportion` is the proportion of the CV (itegrated by area or length) included in the region.
+    """
+
+def lif_probe_voltage(tag: str) -> probe:
+    """
+    Probe specification for LIF cell membrane voltage.
+    """
+
+def load_asc(
+    filename_or_stream: typing.Any, raw: bool = False
+) -> segment_tree | asc_morphology:
+    """
+    Load a morphology or segment_tree (raw=True) and meta data from a Neurolucida ASCII .asc file.
+    """
+
+def load_catalogue(arg0: typing.Any) -> catalogue: ...
+def load_component(filename_or_descriptor: typing.Any) -> cable_component:
+    """
+    Load arbor-component (decor, morphology, label_dict, cable_cell) from file.
+    """
+
+def load_swc_arbor(
+    filename_or_stream: typing.Any, raw: bool = False
+) -> segment_tree | morphology:
+    """
+    Generate a morphology/segment_tree (raw=False/True) from an SWC file following the rules prescribed by Arbor.
+    Specifically:
+     * Single-segment somas are disallowed.
+     * There are no special rules related to somata. They can be one or multiple branches
+       and other segments can connect anywhere along them.
+     * A segment is always created between a sample and its parent, meaning there
+       are no gaps in the resulting morphology.
+    """
+
+def load_swc_neuron(
+    filename_or_stream: typing.Any, raw: bool = False
+) -> segment_tree | morphology:
+    """
+    Generate a morphology/segment_tree (raw=False/True) from an SWC file following the rules prescribed by NEURON.
+    See the documentation https://docs.arbor-sim.org/en/latest/fileformat/swc.html
+    for a detailed description of the interpretation.
+    """
+
+def neuron_cable_properties() -> cable_global_properties:
+    """
+    default NEURON cable_global_properties
+    """
+
+def partition_by_group(
+    recipe: recipe, context: context, groups: list[group_description]
+) -> domain_decomposition:
+    """
+    Construct a domain_decomposition that assigned the groups of cell provided as argument
+    to the local hardware resources described by context on the calling rank.
+    The cell_groups are guaranteed to be present on the calling rank.
+    """
+
+def partition_load_balance(
+    recipe: recipe, context: context, hints: dict[cell_kind, partition_hint] = {}
+) -> domain_decomposition:
+    """
+    Construct a domain_decomposition that distributes the cells in the model described by recipe
+    over the distributed and local hardware resources described by context.
+    Optionally, provide a dictionary of partition hints for certain cell kinds, by default empty.
+    """
+
+def print_config() -> None:
+    """
+    Print Arbor's configuration.
+    """
+
+def stochastic_catalogue() -> catalogue: ...
+@typing.overload
+def write_component(
+    object: cable_component, filename_or_descriptor: typing.Any
+) -> None:
+    """
+    Write cable_component to file.
+    """
+
+@typing.overload
+def write_component(object: decor, filename_or_descriptor: typing.Any) -> None:
+    """
+    Write decor to file.
+    """
+
+@typing.overload
+def write_component(object: label_dict, filename_or_descriptor: typing.Any) -> None:
+    """
+    Write label_dict to file.
+    """
+
+@typing.overload
+def write_component(object: morphology, filename_or_descriptor: typing.Any) -> None:
+    """
+    Write morphology to file.
+    """
+
+@typing.overload
+def write_component(object: cable_cell, filename_or_descriptor: typing.Any) -> None:
+    """
+    Write cable_cell to file.
+    """
+
+__version__: str = "0.9.1-dev"
+mnpos: int = 4294967295
diff --git a/python/stubs/arbor/_arbor/env.pyi b/python/stubs/arbor/_arbor/env.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..103e23303334f797a3b6c05e67c61aa2e7938cac
--- /dev/null
+++ b/python/stubs/arbor/_arbor/env.pyi
@@ -0,0 +1,46 @@
+"""
+Wrappers for arborenv.
+"""
+from __future__ import annotations
+import arbor._arbor
+import typing
+
+__all__ = [
+    "default_allocation",
+    "default_concurrency",
+    "default_gpu",
+    "find_private_gpu",
+    "get_env_num_threads",
+    "thread_concurrency",
+]
+
+def default_allocation() -> arbor._arbor.proc_allocation:
+    """
+    Attempts to detect the number of locally available CPU cores. Returns 1 if unable to detect the number of cores. Use with caution in combination with MPI.
+    """
+
+def default_concurrency() -> arbor._arbor.proc_allocation:
+    """
+    Returns number of threads to use from get_env_num_threads(), or else from thread_concurrency() if get_env_num_threads() returns zero.
+    """
+
+def default_gpu() -> int | None:
+    """
+    Determine GPU id to use from the ARBENV_GPU_ID environment variable, or from the first available GPU id of those detected.
+    """
+
+def find_private_gpu(arg0: typing.Any) -> None:
+    """
+    Identify a private GPU id per node, only available if built with GPU and MPI.
+      mpi:     The MPI communicator.
+    """
+
+def get_env_num_threads() -> int:
+    """
+    Retrieve user-specified number of threads to use from the environment variable ARBENV_NUM_THREADS.
+    """
+
+def thread_concurrency() -> int:
+    """
+    Attempts to detect the number of locally available CPU cores. Returns 1 if unable to detect the number of cores. Use with caution in combination with MPI.
+    """
diff --git a/python/stubs/arbor/_arbor/py.typed b/python/stubs/arbor/_arbor/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/python/stubs/arbor/_arbor/units.pyi b/python/stubs/arbor/_arbor/units.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..f5a79e4944891e1c6c1ee5fa9c591f76b36481c1
--- /dev/null
+++ b/python/stubs/arbor/_arbor/units.pyi
@@ -0,0 +1,185 @@
+"""
+Units and quantities for driving the user interface.
+"""
+from __future__ import annotations
+import typing
+
+__all__ = [
+    "A",
+    "C",
+    "Celsius",
+    "F",
+    "Hz",
+    "Kelvin",
+    "M",
+    "MOhm",
+    "Ohm",
+    "S",
+    "V",
+    "cm",
+    "cm2",
+    "deg",
+    "giga",
+    "kHz",
+    "kOhm",
+    "kilo",
+    "m",
+    "m2",
+    "mA",
+    "mM",
+    "mS",
+    "mV",
+    "mega",
+    "micro",
+    "milli",
+    "mm",
+    "mm2",
+    "mol",
+    "ms",
+    "nA",
+    "nF",
+    "nano",
+    "nil",
+    "nm",
+    "nm2",
+    "ns",
+    "pA",
+    "pF",
+    "pico",
+    "quantity",
+    "rad",
+    "s",
+    "uA",
+    "uF",
+    "uS",
+    "um",
+    "um2",
+    "unit",
+    "us",
+]
+
+class quantity:
+    """
+    A quantity, comprising a magnitude and a unit.
+    """
+
+    __hash__: typing.ClassVar[None] = None
+    def __add__(self, arg0: quantity) -> quantity: ...
+    def __eq__(self, arg0: quantity) -> bool: ...
+    @typing.overload
+    def __mul__(self, arg0: quantity) -> quantity: ...
+    @typing.overload
+    def __mul__(self, arg0: float) -> quantity: ...
+    @typing.overload
+    def __mul__(self, arg0: unit) -> quantity: ...
+    def __ne__(self, arg0: quantity) -> bool: ...
+    def __pow__(self: unit, arg0: int) -> unit: ...
+    def __repr__(self) -> str:
+        """
+        Convert quantity to string.
+        """
+    def __rmul__(self, arg0: float) -> quantity: ...
+    def __rtruediv__(self, arg0: float) -> quantity: ...
+    def __str__(self) -> str:
+        """
+        Convert quantity to string.
+        """
+    def __sub__(self, arg0: quantity) -> quantity: ...
+    @typing.overload
+    def __truediv__(self, arg0: quantity) -> quantity: ...
+    @typing.overload
+    def __truediv__(self, arg0: float) -> quantity: ...
+    @typing.overload
+    def __truediv__(self, arg0: unit) -> quantity: ...
+    def value_as(self, unit: unit) -> float:
+        """
+        Convert quantity to given unit and return magnitude.
+        """
+    @property
+    def units(self) -> unit:
+        """
+        Return units.
+        """
+    @property
+    def value(self) -> float:
+        """
+        Return magnitude.
+        """
+
+class unit:
+    """
+    A unit.
+    """
+
+    __hash__: typing.ClassVar[None] = None
+    def __eq__(self, arg0: unit) -> bool: ...
+    @typing.overload
+    def __mul__(self, arg0: unit) -> unit: ...
+    @typing.overload
+    def __mul__(self, arg0: float) -> quantity: ...
+    def __ne__(self, arg0: unit) -> bool: ...
+    def __pow__(self, arg0: int) -> unit: ...
+    def __repr__(self) -> str:
+        """
+        Convert unit to string.
+        """
+    def __rmul__(self, arg0: float) -> quantity: ...
+    def __rtruediv__(self, arg0: float) -> quantity: ...
+    def __str__(self) -> str:
+        """
+        Convert unit to string.
+        """
+    @typing.overload
+    def __truediv__(self, arg0: unit) -> unit: ...
+    @typing.overload
+    def __truediv__(self, arg0: float) -> quantity: ...
+
+A: unit  # value = A
+C: unit  # value = C
+Celsius: unit  # value = °C
+F: unit  # value = F
+Hz: unit  # value = Hz
+Kelvin: unit  # value = K
+M: unit  # value = mol/m^3
+MOhm: unit  # value = 1/uS
+Ohm: unit  # value = 1/S
+S: unit  # value = S
+V: unit  # value = V
+cm: unit  # value = cm
+cm2: unit  # value = cm^2
+deg: unit  # value = deg
+giga: unit  # value = 1000000000
+kHz: unit  # value = kHz
+kOhm: unit  # value = 1/mS
+kilo: unit  # value = 1000
+m: unit  # value = m
+m2: unit  # value = m^2
+mA: unit  # value = mA
+mM: unit  # value = umol/L
+mS: unit  # value = mS
+mV: unit  # value = mV
+mega: unit  # value = 1000000
+micro: unit  # value = 9.99999997475242708e-07
+milli: unit  # value = 0.00100000004749745131
+mm: unit  # value = mm
+mm2: unit  # value = mm^2
+mol: unit  # value = mol
+ms: unit  # value = ms
+nA: unit  # value = nA
+nF: unit  # value = nF
+nano: unit  # value = 9.99999971718068537e-10
+nil: unit  # value =
+nm: unit  # value = nm
+nm2: unit  # value = nm^2
+ns: unit  # value = ns
+pA: unit  # value = pA
+pF: unit  # value = pF
+pico: unit  # value = 9.999999960041972e-13
+rad: unit  # value = rad
+s: unit  # value = s
+uA: unit  # value = uA
+uF: unit  # value = uF
+uS: unit  # value = uS
+um: unit  # value = um
+um2: unit  # value = um^2
+us: unit  # value = us
diff --git a/python/stubs/arbor/py.typed b/python/stubs/arbor/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/python/stubs/py.typed b/python/stubs/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/python/test/fixtures.py b/python/test/fixtures.py
index 11b901175076864c5d2e8233c2779e1328d9c20d..1071653855f5b86939495b94a7120388d3984525 100644
--- a/python/test/fixtures.py
+++ b/python/test/fixtures.py
@@ -1,4 +1,5 @@
-import arbor
+import arbor as A
+from arbor import units as U
 import functools
 from functools import lru_cache as cache
 from pathlib import Path
@@ -6,8 +7,8 @@ import subprocess
 import atexit
 import inspect
 
-_mpi_enabled = arbor.__config__["mpi"]
-_mpi4py_enabled = arbor.__config__["mpi4py"]
+_mpi_enabled = A.__config__["mpi"]
+_mpi4py_enabled = A.__config__["mpi4py"]
 
 # The API of `functools`'s caches went through a bunch of breaking changes from
 # 3.6 to 3.9. Patch them up in a local `cache` function.
@@ -78,13 +79,13 @@ def _finalize_mpi():
 
         MPI.Finalize()
     else:
-        arbor.mpi_finalize()
+        A.mpi_finalize()
 
 
 @_fixture
 def context():
     """
-    Fixture that produces an MPI sensitive `arbor.context`
+    Fixture that produces an MPI sensitive `A.context`
     """
     if _mpi_enabled:
         if _mpi4py_enabled:
@@ -94,13 +95,13 @@ def context():
                 print("Context fixture initializing mpi4py", flush=True)
                 MPI.Initialize()
                 atexit.register(_finalize_mpi)
-            return arbor.context(arbor.proc_allocation(), mpi=MPI.COMM_WORLD)
-        elif not arbor.mpi_is_initialized():
+            return A.context(A.proc_allocation(), mpi=MPI.COMM_WORLD)
+        elif not A.mpi_is_initialized():
             print("Context fixture initializing mpi", flush=True)
-            arbor.mpi_init()
+            A.mpi_init()
             atexit.register(_finalize_mpi)
-        return arbor.context(arbor.proc_allocation(), mpi=arbor.mpi_comm())
-    return arbor.context(arbor.proc_allocation())
+        return A.context(A.proc_allocation(), mpi=A.mpi_comm())
+    return A.context(A.proc_allocation())
 
 
 class _BuildCatError(Exception):
@@ -165,16 +166,16 @@ def _build_cat(name, path, context):
 @repo_path()
 def dummy_catalogue(repo_path):
     """
-    Fixture that returns a dummy `arbor.catalogue`
+    Fixture that returns a dummy `A.catalogue`
     which contains the `dummy` mech.
     """
     path = repo_path / "test" / "unit" / "dummy"
     cat_path = _build_cat("dummy", path)
-    return arbor.load_catalogue(str(cat_path))
+    return A.load_catalogue(str(cat_path))
 
 
 @_fixture
-class empty_recipe(arbor.recipe):
+class empty_recipe(A.recipe):
     """
     Blank recipe fixture.
     """
@@ -183,14 +184,14 @@ class empty_recipe(arbor.recipe):
 
 
 @_fixture
-class art_spiker_recipe(arbor.recipe):
+class art_spiker_recipe(A.recipe):
     """
     Recipe fixture with 3 artificial spiking cells and one cable cell.
     """
 
     def __init__(self):
         super().__init__()
-        self.the_props = arbor.neuron_cable_properties()
+        self.the_props = A.neuron_cable_properties()
         self.trains = [[0.8, 2, 2.1, 3], [0.4, 2, 2.2, 3.1, 4.5], [0.2, 2, 2.8, 3]]
 
     def num_cells(self):
@@ -198,9 +199,9 @@ class art_spiker_recipe(arbor.recipe):
 
     def cell_kind(self, gid):
         if gid < 3:
-            return arbor.cell_kind.spike_source
+            return A.cell_kind.spike_source
         else:
-            return arbor.cell_kind.cable
+            return A.cell_kind.cable
 
     def connections_on(self, gid):
         return []
@@ -215,41 +216,44 @@ class art_spiker_recipe(arbor.recipe):
         if gid < 3:
             return []
         else:
-            return [arbor.cable_probe_membrane_voltage('"midpoint"', "Um")]
+            return [A.cable_probe_membrane_voltage('"midpoint"', "Um")]
 
     def _cable_cell_elements(self):
         # (1) Create a morphology with a single (cylindrical) segment of length=diameter
         #  = # 6 μm
-        tree = arbor.segment_tree()
+        tree = A.segment_tree()
         tree.append(
-            arbor.mnpos,
-            arbor.mpoint(-3, 0, 0, 3),
-            arbor.mpoint(3, 0, 0, 3),
+            A.mnpos,
+            (-3, 0, 0, 3),
+            (3, 0, 0, 3),
             tag=1,
         )
 
         # (2) Define the soma and its midpoint
-        labels = arbor.label_dict({"soma": "(tag 1)", "midpoint": "(location 0 0.5)"})
+        labels = A.label_dict({"soma": "(tag 1)", "midpoint": "(location 0 0.5)"})
 
         # (3) Create cell and set properties
-        decor = arbor.decor()
-        decor.set_property(Vm=-40)
-        decor.paint('"soma"', arbor.density("hh"))
-        decor.place('"midpoint"', arbor.iclamp(10, 2, 0.8), "iclamp")
-        decor.place('"midpoint"', arbor.threshold_detector(-10), "detector")
+        decor = (
+            A.decor()
+            .set_property(Vm=-40 * U.mV)
+            .paint('"soma"', A.density("hh"))
+            .place('"midpoint"', A.iclamp(10 * U.ms, 2 * U.ms, 0.8 * U.nA), "iclamp")
+            .place('"midpoint"', A.threshold_detector(-10 * U.mV), "detector")
+        )
 
         # return tuple of tree, labels, and decor for creating a cable cell (can still
-        # be modified before calling arbor.cable_cell())
+        # be modified before calling A.cable_cell())
         return tree, labels, decor
 
     def cell_description(self, gid):
         if gid < 3:
-            return arbor.spike_source_cell(
-                "src", arbor.explicit_schedule(self.trains[gid])
-            )
+            return A.spike_source_cell("src", self.schedule(gid))
         else:
             tree, labels, decor = self._cable_cell_elements()
-            return arbor.cable_cell(tree, decor, labels)
+            return A.cable_cell(tree, decor, labels)
+
+    def schedule(self, gid):
+        return A.explicit_schedule([t * U.ms for t in self.trains[gid]])
 
 
 @_fixture
@@ -274,5 +278,5 @@ def sum_weight_hh_spike_2():
 @context()
 @art_spiker_recipe()
 def art_spiking_sim(context, art_spiker_recipe):
-    dd = arbor.partition_load_balance(art_spiker_recipe, context)
-    return arbor.simulation(art_spiker_recipe, context, dd)
+    dd = A.partition_load_balance(art_spiker_recipe, context)
+    return A.simulation(art_spiker_recipe, context, dd)
diff --git a/python/test/unit/test_catalogues.py b/python/test/unit/test_catalogues.py
index fef3965e9c3d9d97333d8aeda820997b70cd29bb..a8437cab4dc4af343969e7deae6c51734f3f1df4 100644
--- a/python/test/unit/test_catalogues.py
+++ b/python/test/unit/test_catalogues.py
@@ -1,29 +1,30 @@
 from .. import fixtures
 import unittest
-import arbor as arb
+import arbor as A
+from arbor import units as U
 
 """
 tests for (dynamically loaded) catalogues
 """
 
 
-class recipe(arb.recipe):
+class recipe(A.recipe):
     def __init__(self):
-        arb.recipe.__init__(self)
-        self.tree = arb.segment_tree()
-        self.tree.append(arb.mnpos, (0, 0, 0, 10), (1, 0, 0, 10), 1)
-        self.props = arb.neuron_cable_properties()
+        A.recipe.__init__(self)
+        self.tree = A.segment_tree()
+        self.tree.append(A.mnpos, (0, 0, 0, 10), (1, 0, 0, 10), 1)
+        self.props = A.neuron_cable_properties()
         try:
-            self.props.catalogue = arb.load_catalogue("dummy-catalogue.so")
+            self.props.catalogue = A.load_catalogue("dummy-catalogue.so")
         except Exception:
             print("Catalogue not found. Are you running from build directory?")
             raise
-        self.props.catalogue = arb.default_catalogue()
+        self.props.catalogue = A.default_catalogue()
 
-        d = arb.decor()
-        d.paint("(all)", arb.density("pas"))
-        d.set_property(Vm=0.0)
-        self.cell = arb.cable_cell(self.tree, d)
+        d = A.decor()
+        d.paint("(all)", A.density("pas"))
+        d.set_property(Vm=0.0 * U.mV)
+        self.cell = A.cable_cell(self.tree, d)
 
     def global_properties(self, _):
         return self.props
@@ -32,7 +33,7 @@ class recipe(arb.recipe):
         return 1
 
     def cell_kind(self, gid):
-        return arb.cell_kind.cable
+        return A.cell_kind.cable
 
     def cell_description(self, gid):
         return self.cell
@@ -41,7 +42,7 @@ class recipe(arb.recipe):
 class TestCatalogues(unittest.TestCase):
     def test_nonexistent(self):
         with self.assertRaises(FileNotFoundError):
-            arb.load_catalogue("_NO_EXIST_.so")
+            A.load_catalogue("_NO_EXIST_.so")
 
     @fixtures.dummy_catalogue()
     def test_shared_catalogue(self, dummy_catalogue):
@@ -58,10 +59,10 @@ class TestCatalogues(unittest.TestCase):
 
     def test_simulation(self):
         rcp = recipe()
-        ctx = arb.context()
-        dom = arb.partition_load_balance(rcp, ctx)
-        sim = arb.simulation(rcp, ctx, dom)
-        sim.run(tfinal=30)
+        ctx = A.context()
+        dom = A.partition_load_balance(rcp, ctx)
+        sim = A.simulation(rcp, ctx, dom)
+        sim.run(tfinal=30 * U.ms)
 
     def test_empty(self):
         def len(cat):
@@ -70,9 +71,9 @@ class TestCatalogues(unittest.TestCase):
         def hash_(cat):
             return hash(" ".join(sorted(cat)))
 
-        cat = arb.catalogue()
-        ref = arb.default_catalogue()
-        other = arb.default_catalogue()
+        cat = A.catalogue()
+        ref = A.default_catalogue()
+        other = A.default_catalogue()
         # Test empty constructor
         self.assertEqual(0, len(cat), "Expected no mechanisms in `arbor.catalogue()`.")
         # Test empty extend
@@ -98,7 +99,7 @@ class TestCatalogues(unittest.TestCase):
             hash_(cat),
             "Extending empty with cat should turn empty into cat.",
         )
-        cat = arb.catalogue()
+        cat = A.catalogue()
         cat.extend(other, "prefix/")
         self.assertNotEqual(
             hash_(other),
diff --git a/python/test/unit/test_clear_samplers.py b/python/test/unit/test_clear_samplers.py
index fcfb6d7d95668b55c3b40f0600628103884e46e3..bc7b3e2540e8287af7d44444cdb1af2730ea18f4 100644
--- a/python/test/unit/test_clear_samplers.py
+++ b/python/test/unit/test_clear_samplers.py
@@ -5,6 +5,7 @@
 import unittest
 import arbor as A
 import numpy as np
+from arbor import units as U
 
 from .. import fixtures
 from .. import cases
@@ -21,11 +22,11 @@ class TestClearSamplers(unittest.TestCase):
     def test_spike_clearing(self, art_spiking_sim):
         sim = art_spiking_sim
         sim.record(A.spike_recording.all)
-        handle = sim.sample((3, "Um"), A.regular_schedule(0.1))
+        handle = sim.sample((3, "Um"), A.regular_schedule(0.1 * U.ms))
 
         # baseline to test against Run in exactly the same stepping to make sure there are no rounding differences
-        sim.run(3, 0.01)
-        sim.run(5, 0.01)
+        sim.run(3 * U.ms, 0.01 * U.ms)
+        sim.run(5 * U.ms, 0.01 * U.ms)
         spikes = sim.spikes()
         times = spikes["time"].tolist()
         gids = spikes["source"]["gid"].tolist()
@@ -34,7 +35,7 @@ class TestClearSamplers(unittest.TestCase):
         sim.reset()
 
         # simulated with clearing the memory inbetween the steppings
-        sim.run(3, 0.01)
+        sim.run(3 * U.ms, 0.01 * U.ms)
         spikes = sim.spikes()
         times_t = spikes["time"].tolist()
         gids_t = spikes["source"]["gid"].tolist()
@@ -51,7 +52,7 @@ class TestClearSamplers(unittest.TestCase):
         self.assertEqual(0, data_test.size)
 
         # run the next part of the simulation
-        sim.run(5, 0.01)
+        sim.run(5 * U.ms, 0.01 * U.ms)
         spikes = sim.spikes()
         times_t.extend(spikes["time"].tolist())
         gids_t.extend(spikes["source"]["gid"].tolist())
diff --git a/python/test/unit/test_decor.py b/python/test/unit/test_decor.py
index 59dcf0d90081f590903f69fb12fc9e5757ecd223..18f71f57cc029904a8c60470e9fc8ba99e875010 100644
--- a/python/test/unit/test_decor.py
+++ b/python/test/unit/test_decor.py
@@ -2,6 +2,7 @@
 
 import unittest
 import arbor as A
+from arbor import units as U
 
 """
 Tests for decor and decoration wrappers.
@@ -12,29 +13,31 @@ TODO: Coverage for more than just iclamp.
 class TestDecorClasses(unittest.TestCase):
     def test_iclamp(self):
         # Constant amplitude iclamp:
-        clamp = A.iclamp(10)
+        clamp = A.iclamp(10 * U.nA)
         self.assertEqual(0, clamp.frequency)
         self.assertEqual([(0, 10)], clamp.envelope)
 
-        clamp = A.iclamp(10, frequency=20)
+        clamp = A.iclamp(current=10 * U.nA, frequency=20 * U.kHz)
         self.assertEqual(20, clamp.frequency)
         self.assertEqual([(0, 10)], clamp.envelope)
 
         # Square pulse:
-        clamp = A.iclamp(100, 20, 3)
+        clamp = A.iclamp(100 * U.ms, 20 * U.ms, 3 * U.nA)
         self.assertEqual(0, clamp.frequency)
         self.assertEqual([(100, 3), (120, 3), (120, 0)], clamp.envelope)
 
-        clamp = A.iclamp(100, 20, 3, frequency=7)
+        clamp = A.iclamp(100 * U.ms, 20 * U.ms, 3 * U.nA, frequency=7 * U.kHz)
         self.assertEqual(7, clamp.frequency)
         self.assertEqual([(100, 3), (120, 3), (120, 0)], clamp.envelope)
 
         # Explicit envelope:
         envelope = [(1, 10), (3, 30), (5, 50), (7, 0)]
-        clamp = A.iclamp(envelope)
+        clamp = A.iclamp([(t * U.ms, i * U.nA) for t, i in envelope])
         self.assertEqual(0, clamp.frequency)
         self.assertEqual(envelope, clamp.envelope)
 
-        clamp = A.iclamp(envelope, frequency=7)
+        clamp = A.iclamp(
+            [(t * U.ms, i * U.nA) for t, i in envelope], frequency=7 * U.kHz
+        )
         self.assertEqual(7, clamp.frequency)
         self.assertEqual(envelope, clamp.envelope)
diff --git a/python/test/unit/test_event_generators.py b/python/test/unit/test_event_generators.py
index b43387029ceed3777b9e9ebd5c5b060751984f5b..d3e352c52490d665f644d90b6ca9f4e361489c32 100644
--- a/python/test/unit/test_event_generators.py
+++ b/python/test/unit/test_event_generators.py
@@ -5,6 +5,7 @@
 import unittest
 
 import arbor as arb
+from arbor import units as U
 
 """
 all tests for event generators (regular, explicit, poisson)
@@ -14,7 +15,7 @@ all tests for event generators (regular, explicit, poisson)
 class TestEventGenerator(unittest.TestCase):
     def test_event_generator_regular_schedule(self):
         cm = arb.cell_local_label("tgt0")
-        rs = arb.regular_schedule(2.0, 1.0, 100.0)
+        rs = arb.regular_schedule(2.0 * U.ms, 1.0 * U.ms, 100.0 * U.ms)
         rg = arb.event_generator(cm, 3.14, rs)
         self.assertEqual(rg.target.label, "tgt0")
         self.assertEqual(rg.target.policy, arb.selection_policy.univalent)
@@ -22,14 +23,14 @@ class TestEventGenerator(unittest.TestCase):
 
     def test_event_generator_explicit_schedule(self):
         cm = arb.cell_local_label("tgt1", arb.selection_policy.round_robin)
-        es = arb.explicit_schedule([0, 1, 2, 3, 4.4])
+        es = arb.explicit_schedule([0 * U.ms, 1 * U.ms, 2 * U.ms, 3 * U.ms, 4.4 * U.ms])
         eg = arb.event_generator(cm, -0.01, es)
         self.assertEqual(eg.target.label, "tgt1")
         self.assertEqual(eg.target.policy, arb.selection_policy.round_robin)
         self.assertAlmostEqual(eg.weight, -0.01)
 
     def test_event_generator_poisson_schedule(self):
-        ps = arb.poisson_schedule(0.0, 10.0, 0)
+        ps = arb.poisson_schedule(freq=10.0 * U.kHz, seed=0)
         pg = arb.event_generator("tgt2", 42.0, ps)
         self.assertEqual(pg.target.label, "tgt2")
         self.assertEqual(pg.target.policy, arb.selection_policy.univalent)
diff --git a/python/test/unit/test_io.py b/python/test/unit/test_io.py
index 7c2cdee510ac7360585fb1ab8043b66d1e985b86..dec6be09b5edc8398168b0ccceaf5217221d246b 100644
--- a/python/test/unit/test_io.py
+++ b/python/test/unit/test_io.py
@@ -10,7 +10,7 @@ from functools import partial
 
 acc = """(arbor-component
   (meta-data
-    (version "0.1-dev"))
+    (version "0.9-dev"))
   (cable-cell
     (morphology
       (branch 0 -1
@@ -25,25 +25,25 @@ acc = """(arbor-component
         (location 0 0.5)))
     (decor
       (default
-        (membrane-potential -40.000000))
+        (membrane-potential -40.000000 (scalar 1)))
       (default
-        (ion-internal-concentration "ca" 0.000050))
+        (ion-internal-concentration "ca" 0.000050 (scalar 1)))
       (default
-        (ion-external-concentration "ca" 2.000000))
+        (ion-external-concentration "ca" 2.000000 (scalar 1)))
       (default
-        (ion-reversal-potential "ca" 132.457934))
+        (ion-reversal-potential "ca" 132.457934 (scalar 1)))
       (default
-        (ion-internal-concentration "k" 54.400000))
+        (ion-internal-concentration "k" 54.400000 (scalar 1)))
       (default
-        (ion-external-concentration "k" 2.500000))
+        (ion-external-concentration "k" 2.500000 (scalar 1)))
       (default
-        (ion-reversal-potential "k" -77.000000))
+        (ion-reversal-potential "k" -77.000000 (scalar 1)))
       (default
-        (ion-internal-concentration "na" 10.000000))
+        (ion-internal-concentration "na" 10.000000 (scalar 1)))
       (default
-        (ion-external-concentration "na" 140.000000))
+        (ion-external-concentration "na" 140.000000 (scalar 1)))
       (default
-        (ion-reversal-potential "na" 50.000000))
+        (ion-reversal-potential "na" 50.000000 (scalar 1)))
       (paint
         (tag 1)
         (density
diff --git a/python/test/unit/test_multiple_connections.py b/python/test/unit/test_multiple_connections.py
index 7f8af4fcc4fb14307eaa6b110b4edb7496d78e52..b15a5d92368e9b5ff43c012e66da937885affa25 100644
--- a/python/test/unit/test_multiple_connections.py
+++ b/python/test/unit/test_multiple_connections.py
@@ -6,7 +6,8 @@ import unittest
 import types
 import numpy as np
 
-import arbor as arb
+import arbor as A
+from arbor import units as U
 from .. import fixtures
 
 """
@@ -38,7 +39,7 @@ class TestMultipleConnections(unittest.TestCase):
     # Method creating a new mechanism for a synapse with STDP
     def create_syn_mechanism(self, scale_contrib=1):
         # create new synapse mechanism
-        syn_mechanism = arb.mechanism("expsyn_stdp")
+        syn_mechanism = A.mechanism("expsyn_stdp")
 
         # set pre- and postsynaptic contributions for STDP
         syn_mechanism.set("Apre", 0.01 * scale_contrib)
@@ -89,9 +90,7 @@ class TestMultipleConnections(unittest.TestCase):
         def cell_description(self, gid):
             # spike source neuron
             if gid < 3:
-                return arb.spike_source_cell(
-                    "spike_source", arb.explicit_schedule(self.trains[gid])
-                )
+                return A.spike_source_cell("spike_source", self.schedule(gid))
 
             # spike-receiving cable neuron
             elif gid == 3:
@@ -101,16 +100,16 @@ class TestMultipleConnections(unittest.TestCase):
 
                 decor.place(
                     '"midpoint"',
-                    arb.synapse(create_syn_mechanism(scale_stdp)),
+                    A.synapse(create_syn_mechanism(scale_stdp)),
                     "postsyn_target",
                 )  # place synapse for input from one presynaptic neuron at the center of the soma
                 decor.place(
                     '"midpoint"',
-                    arb.synapse(create_syn_mechanism(scale_stdp)),
+                    A.synapse(create_syn_mechanism(scale_stdp)),
                     "postsyn_target",
                 )  # place synapse for input from another presynaptic neuron at the center of the soma
                 # (using the same label as above!)
-                return arb.cable_cell(tree, decor, labels)
+                return A.cable_cell(tree, decor, labels)
 
         art_spiker_recipe.cell_description = types.MethodType(
             cell_description, art_spiker_recipe
@@ -140,15 +139,15 @@ class TestMultipleConnections(unittest.TestCase):
         self.assertAlmostEqual(connections_from_recipe[3].delay, 1.4)
 
         # construct domain_decomposition and simulation object
-        sim = arb.simulation(art_spiker_recipe, context)
-        sim.record(arb.spike_recording.all)
+        sim = A.simulation(art_spiker_recipe, context)
+        sim.record(A.spike_recording.all)
 
         # create schedule and handle to record the membrane potential of neuron 3
-        reg_sched = arb.regular_schedule(0, self.dt, self.runtime)
+        reg_sched = A.regular_schedule(0 * U.ms, self.dt * U.ms, self.runtime * U.ms)
         handle_mem = sim.sample((3, "Um"), reg_sched)
 
         # run the simulation
-        sim.run(self.runtime, self.dt)
+        sim.run(self.runtime * U.ms, self.dt * U.ms)
 
         return sim, handle_mem
 
@@ -175,29 +174,29 @@ class TestMultipleConnections(unittest.TestCase):
 
             # incoming to neuron 3
             elif gid == 3:
-                source_label_0 = arb.cell_global_label(
+                source_label_0 = A.cell_global_label(
                     0, "spike_source"
                 )  # referring to the "spike_source" label of neuron 0
-                source_label_1 = arb.cell_global_label(
+                source_label_1 = A.cell_global_label(
                     1, "spike_source"
                 )  # referring to the "spike_source" label of neuron 1
 
-                target_label_rr = arb.cell_local_label(
-                    "postsyn_target", arb.selection_policy.round_robin
+                target_label_rr = A.cell_local_label(
+                    "postsyn_target", A.selection_policy.round_robin
                 )  # referring to the current item in the "postsyn_target" label group of neuron 3, moving to the next item afterwards
 
-                conn_0_3_n1 = arb.connection(
-                    source_label_0, target_label_rr, weight, 0.2
+                conn_0_3_n1 = A.connection(
+                    source_label_0, target_label_rr, weight, 0.2 * U.ms
                 )  # first connection from neuron 0 to 3
-                conn_0_3_n2 = arb.connection(
-                    source_label_0, target_label_rr, weight, 0.2
+                conn_0_3_n2 = A.connection(
+                    source_label_0, target_label_rr, weight, 0.2 * U.ms
                 )  # second connection from neuron 0 to 3
                 # NOTE: this is not connecting to the same target label item as 'conn_0_3_n1' because 'round_robin' has been used before!
-                conn_1_3_n1 = arb.connection(
-                    source_label_1, target_label_rr, weight2, 1.4
+                conn_1_3_n1 = A.connection(
+                    source_label_1, target_label_rr, weight2, 1.4 * U.ms
                 )  # first connection from neuron 1 to 3
-                conn_1_3_n2 = arb.connection(
-                    source_label_1, target_label_rr, weight2, 1.4
+                conn_1_3_n2 = A.connection(
+                    source_label_1, target_label_rr, weight2, 1.4 * U.ms
                 )  # second connection from neuron 1 to 3
                 # NOTE: this is not connecting to the same target label item as 'conn_1_3_n1' because 'round_robin' has been used before!
 
@@ -237,31 +236,31 @@ class TestMultipleConnections(unittest.TestCase):
 
             # incoming to neuron 3
             elif gid == 3:
-                source_label_0 = arb.cell_global_label(
+                source_label_0 = A.cell_global_label(
                     0, "spike_source"
                 )  # referring to the "spike_source" label of neuron 0
-                source_label_1 = arb.cell_global_label(
+                source_label_1 = A.cell_global_label(
                     1, "spike_source"
                 )  # referring to the "spike_source" label of neuron 1
 
-                target_label_rr_halt = arb.cell_local_label(
-                    "postsyn_target", arb.selection_policy.round_robin_halt
+                target_label_rr_halt = A.cell_local_label(
+                    "postsyn_target", A.selection_policy.round_robin_halt
                 )  # referring to the current item in the "postsyn_target" label group of neuron 3
-                target_label_rr = arb.cell_local_label(
-                    "postsyn_target", arb.selection_policy.round_robin
+                target_label_rr = A.cell_local_label(
+                    "postsyn_target", A.selection_policy.round_robin
                 )  # referring to the current item in the "postsyn_target" label group of neuron 3, moving to the next item afterwards
 
-                conn_0_3_n1 = arb.connection(
-                    source_label_0, target_label_rr_halt, weight, 0.2
+                conn_0_3_n1 = A.connection(
+                    source_label_0, target_label_rr_halt, weight, 0.2 * U.ms
                 )  # first connection from neuron 0 to 3
-                conn_0_3_n2 = arb.connection(
-                    source_label_0, target_label_rr, weight, 0.2
+                conn_0_3_n2 = A.connection(
+                    source_label_0, target_label_rr, weight, 0.2 * U.ms
                 )  # second connection from neuron 0 to 3
-                conn_1_3_n1 = arb.connection(
-                    source_label_1, target_label_rr_halt, weight2, 1.4
+                conn_1_3_n1 = A.connection(
+                    source_label_1, target_label_rr_halt, weight2, 1.4 * U.ms
                 )  # first connection from neuron 1 to 3
-                conn_1_3_n2 = arb.connection(
-                    source_label_1, target_label_rr, weight2, 1.4
+                conn_1_3_n2 = A.connection(
+                    source_label_1, target_label_rr, weight2, 1.4 * U.ms
                 )  # second connection from neuron 1 to 3
 
                 return [conn_0_3_n1, conn_0_3_n2, conn_1_3_n1, conn_1_3_n2]
@@ -298,25 +297,25 @@ class TestMultipleConnections(unittest.TestCase):
 
             # incoming to neuron 3
             elif gid == 3:
-                source_label_0 = arb.cell_global_label(
+                source_label_0 = A.cell_global_label(
                     0, "spike_source"
                 )  # referring to the "spike_source" label of neuron 0
-                source_label_1 = arb.cell_global_label(
+                source_label_1 = A.cell_global_label(
                     1, "spike_source"
                 )  # referring to the "spike_source" label of neuron 1
 
-                target_label_uni_n1 = arb.cell_local_label(
-                    "postsyn_target_1", arb.selection_policy.univalent
+                target_label_uni_n1 = A.cell_local_label(
+                    "postsyn_target_1", A.selection_policy.univalent
                 )  # referring to an only item in the "postsyn_target_1" label group of neuron 3
-                target_label_uni_n2 = arb.cell_local_label(
-                    "postsyn_target_2", arb.selection_policy.univalent
+                target_label_uni_n2 = A.cell_local_label(
+                    "postsyn_target_2", A.selection_policy.univalent
                 )  # referring to an only item in the "postsyn_target_2" label group of neuron 3
 
-                conn_0_3 = arb.connection(
-                    source_label_0, target_label_uni_n1, weight, 0.2
+                conn_0_3 = A.connection(
+                    source_label_0, target_label_uni_n1, weight, 0.2 * U.ms
                 )  # connection from neuron 0 to 3
-                conn_1_3 = arb.connection(
-                    source_label_1, target_label_uni_n2, weight2, 1.4
+                conn_1_3 = A.connection(
+                    source_label_1, target_label_uni_n2, weight2, 1.4 * U.ms
                 )  # connection from neuron 1 to 3
 
                 return [conn_0_3, conn_1_3]
@@ -331,9 +330,7 @@ class TestMultipleConnections(unittest.TestCase):
         def cell_description(self, gid):
             # spike source neuron
             if gid < 3:
-                return arb.spike_source_cell(
-                    "spike_source", arb.explicit_schedule(self.trains[gid])
-                )
+                return A.spike_source_cell("spike_source", self.schedule(gid))
 
             # spike-receiving cable neuron
             elif gid == 3:
@@ -341,17 +338,17 @@ class TestMultipleConnections(unittest.TestCase):
 
                 decor.place(
                     '"midpoint"',
-                    arb.synapse(create_syn_mechanism()),
+                    A.synapse(create_syn_mechanism()),
                     "postsyn_target_1",
                 )  # place synapse for input from one presynaptic neuron at the center of the soma
                 decor.place(
                     '"midpoint"',
-                    arb.synapse(create_syn_mechanism()),
+                    A.synapse(create_syn_mechanism()),
                     "postsyn_target_2",
                 )  # place synapse for input from another presynaptic neuron at the center of the soma
                 # (using another label as above!)
 
-                return arb.cable_cell(tree, decor, labels)
+                return A.cable_cell(tree, decor, labels)
 
         art_spiker_recipe.cell_description = types.MethodType(
             cell_description, art_spiker_recipe
@@ -371,15 +368,15 @@ class TestMultipleConnections(unittest.TestCase):
         self.assertAlmostEqual(connections_from_recipe[1].delay, 1.4)
 
         # construct simulation object
-        sim = arb.simulation(art_spiker_recipe, context)
-        sim.record(arb.spike_recording.all)
+        sim = A.simulation(art_spiker_recipe, context)
+        sim.record(A.spike_recording.all)
 
         # create schedule and handle to record the membrane potential of neuron 3
-        reg_sched = arb.regular_schedule(0, self.dt, self.runtime)
+        reg_sched = A.regular_schedule(0 * U.ms, self.dt * U.ms, self.runtime * U.ms)
         handle_mem = sim.sample((3, "Um"), reg_sched)
 
         # run the simulation
-        sim.run(self.runtime, self.dt)
+        sim.run(self.runtime * U.ms, self.dt * U.ms)
 
         # evaluate the outcome
         self.evaluate_outcome(sim, handle_mem)
diff --git a/python/test/unit/test_probes.py b/python/test/unit/test_probes.py
index 417ea1d51d01953e295e6f1d506836f22faf5b0b..f72bfb4e3ee801f55bae2bfb613b54e112e0fbeb 100644
--- a/python/test/unit/test_probes.py
+++ b/python/test/unit/test_probes.py
@@ -2,6 +2,7 @@
 
 import unittest
 import arbor as A
+from arbor import units as U
 import numpy as np
 
 """
@@ -22,7 +23,7 @@ class cc_recipe(A.recipe):
 
         dec.place("(location 0 0.08)", A.synapse("expsyn"), "syn0")
         dec.place("(location 0 0.09)", A.synapse("exp2syn"), "syn1")
-        dec.place("(location 0 0.1)", A.iclamp(20.0), "iclamp")
+        dec.place("(location 0 0.1)", A.iclamp(20.0 * U.nA), "iclamp")
         dec.paint("(all)", A.density("hh"))
 
         self.cell = A.cable_cell(st, dec)
@@ -184,9 +185,9 @@ class lif_recipe(A.recipe):
 
     def cell_description(self, gid):
         cell = A.lif_cell("src", "tgt")
-        cell.E_L = -42
-        cell.V_m = -23
-        cell.t_ref = 0.2
+        cell.E_L = -42 * U.mV
+        cell.V_m = -23 * U.mV
+        cell.t_ref = 0.2 * U.ms
         return cell
 
 
@@ -202,8 +203,8 @@ class TestLifProbes(unittest.TestCase):
     def test_probe_result(self):
         rec = lif_recipe()
         sim = A.simulation(rec)
-        hdl = sim.sample(0, "Um", A.regular_schedule(0.1))
-        sim.run(1.0, 0.05)
+        hdl = sim.sample(0, "Um", A.regular_schedule(0.1 * U.ms))
+        sim.run(1.0 * U.ms, 0.05 * U.ms)
         smp = sim.samples(hdl)
         exp = np.array(
             [
diff --git a/python/test/unit/test_profiling.py b/python/test/unit/test_profiling.py
index 9249573c87c3b947f6d2f690a9aa4ef98bb69fff..80a0f8d688de2e0298b740af086724a14007b556 100644
--- a/python/test/unit/test_profiling.py
+++ b/python/test/unit/test_profiling.py
@@ -4,7 +4,8 @@
 
 import unittest
 
-import arbor as arb
+import arbor as A
+from arbor import units as U
 import functools
 
 """
@@ -30,17 +31,17 @@ def lazy_skipIf(condition, reason):
     return inner_decorator
 
 
-class a_recipe(arb.recipe):
+class a_recipe(A.recipe):
     def __init__(self):
-        arb.recipe.__init__(self)
-        self.props = arb.neuron_cable_properties()
+        A.recipe.__init__(self)
+        self.props = A.neuron_cable_properties()
         self.trains = [[0.8, 2, 2.1, 3], [0.4, 2, 2.2, 3.1, 4.5], [0.2, 2, 2.8, 3]]
 
     def num_cells(self):
         return 3
 
     def cell_kind(self, gid):
-        return arb.cell_kind.spike_source
+        return A.cell_kind.spike_source
 
     def connections_on(self, gid):
         return []
@@ -55,44 +56,45 @@ class a_recipe(arb.recipe):
         return []
 
     def cell_description(self, gid):
-        return arb.spike_source_cell("src", arb.explicit_schedule(self.trains[gid]))
+        sched = A.explicit_schedule([t * U.ms for t in self.trains[gid]])
+        return A.spike_source_cell("src", sched)
 
 
 def skipWithoutSupport():
-    return not bool(arb.config().get("profiling", False))
+    return not bool(A.config().get("profiling", False))
 
 
 class TestProfiling(unittest.TestCase):
     def test_support(self):
-        self.assertTrue("profiling" in arb.config(), "profiling key not in config")
-        profiling_support = arb.config()["profiling"]
+        self.assertTrue("profiling" in A.config(), "profiling key not in config")
+        profiling_support = A.config()["profiling"]
         self.assertEqual(bool, type(profiling_support), "profiling flag should be bool")
         if profiling_support:
             self.assertTrue(
-                hasattr(arb, "profiler_initialize"),
+                hasattr(A, "profiler_initialize"),
                 "missing profiling interface with profiling support",
             )
             self.assertTrue(
-                hasattr(arb, "profiler_summary"),
+                hasattr(A, "profiler_summary"),
                 "missing profiling interface with profiling support",
             )
         else:
             self.assertFalse(
-                hasattr(arb, "profiler_initialize"),
+                hasattr(A, "profiler_initialize"),
                 "profiling interface without profiling support",
             )
             self.assertFalse(
-                hasattr(arb, "profiler_summary"),
+                hasattr(A, "profiler_summary"),
                 "profiling interface without profiling support",
             )
 
     @lazy_skipIf(skipWithoutSupport, "run test only with profiling support")
     def test_summary(self):
-        context = arb.context()
-        arb.profiler_initialize(context)
+        context = A.context()
+        A.profiler_initialize(context)
         recipe = a_recipe()
-        dd = arb.partition_load_balance(recipe, context)
-        arb.simulation(recipe, context, dd).run(1)
-        summary = arb.profiler_summary()
+        dd = A.partition_load_balance(recipe, context)
+        A.simulation(recipe, context, dd).run(1 * U.ms)
+        summary = A.profiler_summary()
         self.assertEqual(str, type(summary), "profiler summary must be str")
         self.assertTrue(summary, "empty summary")
diff --git a/python/test/unit/test_schedules.py b/python/test/unit/test_schedules.py
index 0a7c747ec111c21b86d8d46c4f1dfdfc60941959..d23d81e2a080233dff3febaf88a042d35dc941d4 100644
--- a/python/test/unit/test_schedules.py
+++ b/python/test/unit/test_schedules.py
@@ -4,7 +4,8 @@
 
 import unittest
 
-import arbor as arb
+import arbor as A
+from arbor import units as U
 
 """
 all tests for schedules (regular, explicit, poisson)
@@ -13,28 +14,28 @@ all tests for schedules (regular, explicit, poisson)
 
 class TestRegularSchedule(unittest.TestCase):
     def test_none_ctor_regular_schedule(self):
-        rs = arb.regular_schedule(tstart=0, dt=0.1, tstop=None)
-        self.assertEqual(rs.dt, 0.1)
+        rs = A.regular_schedule(tstart=0 * U.ms, dt=0.1 * U.ms, tstop=None)
+        self.assertEqual(rs.dt, 0.1 * U.ms)
 
     def test_tstart_dt_tstop_ctor_regular_schedule(self):
-        rs = arb.regular_schedule(10.0, 1.0, 20.0)
-        self.assertEqual(rs.tstart, 10.0)
-        self.assertEqual(rs.dt, 1.0)
-        self.assertEqual(rs.tstop, 20.0)
+        rs = A.regular_schedule(10.0 * U.ms, 1.0 * U.ms, 20.0 * U.ms)
+        self.assertEqual(rs.tstart, 10.0 * U.ms)
+        self.assertEqual(rs.dt, 1.0 * U.ms)
+        self.assertEqual(rs.tstop, 20.0 * U.ms)
 
     def test_set_tstart_dt_tstop_regular_schedule(self):
-        rs = arb.regular_schedule(0.1)
-        self.assertAlmostEqual(rs.dt, 0.1, places=1)
-        rs.tstart = 17.0
-        rs.dt = 0.5
-        rs.tstop = 42.0
-        self.assertEqual(rs.tstart, 17.0)
-        self.assertAlmostEqual(rs.dt, 0.5, places=1)
-        self.assertEqual(rs.tstop, 42.0)
+        rs = A.regular_schedule(0.1 * U.ms)
+        self.assertAlmostEqual(rs.dt.value_as(U.ms), 0.1, places=1)
+        rs.tstart = 17.0 * U.ms
+        rs.dt = 0.5 * U.ms
+        rs.tstop = 42.0 * U.ms
+        self.assertEqual(rs.tstart, 17.0 * U.ms)
+        self.assertAlmostEqual(rs.dt.value_as(U.ms), 0.5, places=1)
+        self.assertEqual(rs.tstop, 42.0 * U.ms)
 
     def test_events_regular_schedule(self):
         expected = [0, 0.25, 0.5, 0.75, 1.0]
-        rs = arb.regular_schedule(tstart=0.0, dt=0.25, tstop=1.25)
+        rs = A.regular_schedule(tstart=0.0 * U.ms, dt=0.25 * U.ms, tstop=1.25 * U.ms)
         self.assertEqual(expected, rs.events(0.0, 1.25))
         self.assertEqual(expected, rs.events(0.0, 5.0))
         self.assertEqual([], rs.events(5.0, 10.0))
@@ -43,41 +44,34 @@ class TestRegularSchedule(unittest.TestCase):
         with self.assertRaisesRegex(
             RuntimeError, "tstart must be a non-negative number"
         ):
-            arb.regular_schedule(tstart=-1.0, dt=0.1)
+            A.regular_schedule(tstart=-1.0 * U.ms, dt=0.1 * U.ms)
         with self.assertRaisesRegex(RuntimeError, "dt must be a positive number"):
-            arb.regular_schedule(dt=-0.1)
+            A.regular_schedule(dt=-0.1 * U.ms)
         with self.assertRaisesRegex(RuntimeError, "dt must be a positive number"):
-            arb.regular_schedule(dt=0)
+            A.regular_schedule(dt=0 * U.ms)
         with self.assertRaises(TypeError):
-            arb.regular_schedule(dt=None)
+            A.regular_schedule(dt=None)
         with self.assertRaises(TypeError):
-            arb.regular_schedule(dt="dt")
-        with self.assertRaisesRegex(
-            RuntimeError, "tstop must be a non-negative number, or None"
-        ):
-            arb.regular_schedule(tstart=0, dt=0.1, tstop="tstop")
+            A.regular_schedule(dt="dt")
+        with self.assertRaises(TypeError):
+            A.regular_schedule(tstart=0 * U.ms, dt=0.1 * U.ms, tstop="tstop")
         with self.assertRaisesRegex(RuntimeError, "t0 must be a non-negative number"):
-            rs = arb.regular_schedule(0.0, 1.0, 10.0)
+            rs = A.regular_schedule(0.0 * U.ms, 1.0 * U.ms, 10.0 * U.ms)
             rs.events(-1, 0)
         with self.assertRaisesRegex(RuntimeError, "t1 must be a non-negative number"):
-            rs = arb.regular_schedule(0.0, 1.0, 10.0)
+            rs = A.regular_schedule(0.0 * U.ms, 1.0 * U.ms, 10.0 * U.ms)
             rs.events(0, -10)
 
 
 class TestExplicitSchedule(unittest.TestCase):
     def test_times_contor_explicit_schedule(self):
-        es = arb.explicit_schedule([1, 2, 3, 4.5])
-        self.assertEqual(es.times, [1, 2, 3, 4.5])
-
-    def test_set_times_explicit_schedule(self):
-        es = arb.explicit_schedule()
-        es.times = [42, 43, 44, 55.5, 100]
-        self.assertEqual(es.times, [42, 43, 44, 55.5, 100])
+        es = A.explicit_schedule([t * U.ms for t in range(1, 6)])
+        self.assertEqual(es.events(0, 1000000), [1, 2, 3, 4, 5])
 
     def test_events_explicit_schedule(self):
         times = [0.1, 0.3, 1.0, 2.2, 1.25, 1.7]
         expected = [0.1, 0.3, 1.0]
-        es = arb.explicit_schedule(times)
+        es = A.explicit_schedule([t * U.ms for t in times])
         for i in range(len(expected)):
             self.assertAlmostEqual(expected[i], es.events(0.0, 1.25)[i], places=2)
         expected = [0.3, 1.0, 1.25, 1.7]
@@ -85,47 +79,47 @@ class TestExplicitSchedule(unittest.TestCase):
             self.assertAlmostEqual(expected[i], es.events(0.3, 1.71)[i], places=2)
 
     def test_exceptions_explicit_schedule(self):
-        with self.assertRaisesRegex(
-            RuntimeError, "explicit time schedule cannot contain negative values"
-        ):
-            arb.explicit_schedule([-1])
+        with self.assertRaises(RuntimeError):
+            A.explicit_schedule([-1 * U.ms])
         with self.assertRaises(TypeError):
-            arb.explicit_schedule(["times"])
+            A.explicit_schedule(["times"])
         with self.assertRaises(TypeError):
-            arb.explicit_schedule([None])
+            A.explicit_schedule([None])
         with self.assertRaises(TypeError):
-            arb.explicit_schedule([[1, 2, 3]])
+            A.explicit_schedule([[1, 2, 3]])
         with self.assertRaisesRegex(RuntimeError, "t1 must be a non-negative number"):
-            rs = arb.regular_schedule(0.1)
+            rs = A.regular_schedule(0.1 * U.ms)
             rs.events(1.0, -1.0)
 
 
 class TestPoissonSchedule(unittest.TestCase):
     def test_freq_poisson_schedule(self):
-        ps = arb.poisson_schedule(42.0)
-        self.assertEqual(ps.freq, 42.0)
+        ps = A.poisson_schedule(42.0 * U.kHz)
+        self.assertEqual(ps.freq, 42.0 * U.kHz)
 
     def test_freq_tstart_contor_poisson_schedule(self):
-        ps = arb.poisson_schedule(freq=5.0, tstart=4.3)
-        self.assertEqual(ps.freq, 5.0)
-        self.assertEqual(ps.tstart, 4.3)
+        ps = A.poisson_schedule(freq=5.0 * U.kHz, tstart=4.3 * U.ms)
+        self.assertEqual(ps.freq, 5.0 * U.kHz)
+        self.assertEqual(ps.tstart, 4.3 * U.ms)
 
     def test_freq_seed_contor_poisson_schedule(self):
-        ps = arb.poisson_schedule(freq=5.0, seed=42)
-        self.assertEqual(ps.freq, 5.0)
+        ps = A.poisson_schedule(freq=5.0 * U.kHz, seed=42)
+        self.assertEqual(ps.freq, 5.0 * U.kHz)
         self.assertEqual(ps.seed, 42)
 
     def test_tstart_freq_seed_contor_poisson_schedule(self):
-        ps = arb.poisson_schedule(10.0, 100.0, 1000)
-        self.assertEqual(ps.tstart, 10.0)
-        self.assertEqual(ps.freq, 100.0)
+        ps = A.poisson_schedule(tstart=10.0 * U.ms, freq=100.0 * U.kHz, seed=1000)
+        self.assertEqual(ps.tstart, 10.0 * U.ms)
+        self.assertEqual(ps.freq, 100.0 * U.kHz)
         self.assertEqual(ps.seed, 1000)
 
     def test_events_poisson_schedule(self):
         expected = [17.4107, 502.074, 506.111, 597.116]
-        ps = arb.poisson_schedule(0.0, 0.01, 0)
+        ps = A.poisson_schedule(tstart=0.0 * U.ms, freq=0.01 * U.kHz, seed=0)
         for i in range(len(expected)):
-            self.assertAlmostEqual(expected[i], ps.events(0.0, 600.0)[i], places=3)
+            self.assertAlmostEqual(
+                expected[i], ps.events(0.0 * U.ms, 600.0 * U.ms)[i], places=3
+            )
         expected = [
             5030.22,
             5045.75,
@@ -140,50 +134,52 @@ class TestPoissonSchedule(unittest.TestCase):
             5808.33,
         ]
         for i in range(len(expected)):
-            self.assertAlmostEqual(expected[i], ps.events(5000.0, 6000.0)[i], places=2)
+            self.assertAlmostEqual(
+                expected[i], ps.events(5000.0 * U.ms, 6000.0 * U.ms)[i], places=2
+            )
 
     def test_exceptions_poisson_schedule(self):
         with self.assertRaises(TypeError):
-            arb.poisson_schedule()
+            A.poisson_schedule()
         with self.assertRaises(TypeError):
-            arb.poisson_schedule(tstart=10.0)
+            A.poisson_schedule(tstart=10.0 * U.ms)
         with self.assertRaises(TypeError):
-            arb.poisson_schedule(seed=1432)
+            A.poisson_schedule(seed=1432)
         with self.assertRaisesRegex(
             RuntimeError, "tstart must be a non-negative number"
         ):
-            arb.poisson_schedule(freq=34.0, tstart=-10.0)
+            A.poisson_schedule(freq=34.0 * U.kHz, tstart=-10.0 * U.ms)
         with self.assertRaises(TypeError):
-            arb.poisson_schedule(freq=34.0, tstart=None)
+            A.poisson_schedule(freq=34.0 * U.kHz, tstart=None)
         with self.assertRaises(TypeError):
-            arb.poisson_schedule(freq=34.0, tstart="tstart")
+            A.poisson_schedule(freq=34.0, tstart="tstart")
         with self.assertRaisesRegex(
             RuntimeError, "frequency must be a non-negative number"
         ):
-            arb.poisson_schedule(freq=-100.0)
+            A.poisson_schedule(freq=-100.0 * U.kHz)
         with self.assertRaises(TypeError):
-            arb.poisson_schedule(freq="freq")
+            A.poisson_schedule(freq="freq")
         with self.assertRaises(TypeError):
-            arb.poisson_schedule(freq=34.0, seed=-1)
+            A.poisson_schedule(freq=34.0 * U.kHz, seed=-1)
         with self.assertRaises(TypeError):
-            arb.poisson_schedule(freq=34.0, seed=10.0)
+            A.poisson_schedule(freq=34.0 * U.kHz, seed=10.0)
         with self.assertRaises(TypeError):
-            arb.poisson_schedule(freq=34.0, seed="seed")
+            A.poisson_schedule(freq=34.0 * U.kHz, seed="seed")
         with self.assertRaises(TypeError):
-            arb.poisson_schedule(freq=34.0, seed=None)
+            A.poisson_schedule(freq=34.0 * U.kHz, seed=None)
         with self.assertRaisesRegex(RuntimeError, "t0 must be a non-negative number"):
-            ps = arb.poisson_schedule(0, 0.01)
-            ps.events(-1.0, 1.0)
+            ps = A.poisson_schedule(tstart=0 * U.ms, freq=0.01 * U.kHz)
+            ps.events(-1.0 * U.ms, 1.0 * U.ms)
         with self.assertRaisesRegex(RuntimeError, "t1 must be a non-negative number"):
-            ps = arb.poisson_schedule(0, 0.01)
-            ps.events(1.0, -1.0)
-        with self.assertRaisesRegex(
-            RuntimeError, "tstop must be a non-negative number, or None"
-        ):
-            arb.poisson_schedule(0, 0.1, tstop="tstop")
-            ps.events(1.0, -1.0)
+            ps = A.poisson_schedule(tstart=0 * U.ms, freq=0.01 * U.kHz)
+            ps.events(1.0 * U.ms, -1.0 * U.ms)
+        with self.assertRaises(TypeError):
+            ps = A.poisson_schedule(tstart=0 * U.ms, freq=0.1 * U.kHz, tstop="tstop")
+            ps.events(1.0 * U.ms, -1.0 * U.ms)
 
     def test_tstop_poisson_schedule(self):
         tstop = 50
-        events = arb.poisson_schedule(0.0, 1, 0, tstop).events(0, 100)
+        events = A.poisson_schedule(
+            tstart=0.0 * U.ms, freq=1 * U.kHz, seed=0, tstop=tstop * U.ms
+        ).events(0 * U.ms, 100 * U.ms)
         self.assertTrue(max(events) < tstop)
diff --git a/python/test/unit/test_spikes.py b/python/test/unit/test_spikes.py
index cd1e6b81e5e00066e38117b6bd0deed4249836db..146a3a0acdd56045e0d4b1318ffae9a7c142a1ef 100644
--- a/python/test/unit/test_spikes.py
+++ b/python/test/unit/test_spikes.py
@@ -4,6 +4,7 @@
 
 import unittest
 import arbor as A
+from arbor import units as U
 from .. import fixtures
 
 """
@@ -18,11 +19,11 @@ class TestSpikes(unittest.TestCase):
         sim = art_spiking_sim
         sim.record(A.spike_recording.all)
         # run simulation in 5 steps, forcing 5 epochs
-        sim.run(1, 0.01)
-        sim.run(2, 0.01)
-        sim.run(3, 0.01)
-        sim.run(4, 0.01)
-        sim.run(5, 0.01)
+        sim.run(1 * U.ms, 0.01 * U.ms)
+        sim.run(2 * U.ms, 0.01 * U.ms)
+        sim.run(3 * U.ms, 0.01 * U.ms)
+        sim.run(4 * U.ms, 0.01 * U.ms)
+        sim.run(5 * U.ms, 0.01 * U.ms)
 
         spikes = sim.spikes()
         times = spikes["time"].tolist()
diff --git a/python/units.cpp b/python/units.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..53df38aa418449f3723f49c65e674cd554d1a977
--- /dev/null
+++ b/python/units.cpp
@@ -0,0 +1,129 @@
+#include <pybind11/pybind11.h>
+#include <pybind11/operators.h>
+
+#include <arbor/units.hpp>
+
+namespace pyarb {
+
+namespace py = pybind11;
+
+void register_units(py::module& m) {
+    using namespace py::literals;
+
+    auto u = m.def_submodule("units", "Units and quantities for driving the user interface.");
+
+    py::class_<arb::units::unit> unit(u, "unit", "A unit.");
+    py::class_<arb::units::quantity> quantity(u, "quantity", "A quantity, comprising a magnitude and a unit.");
+
+    unit
+        .def(py::self * py::self)
+        .def(py::self == py::self)
+        .def(py::self != py::self)
+        .def(py::self / py::self)
+        .def(py::self * double())
+        .def(py::self / double())
+        .def(double() * py::self)
+        .def(double() / py::self)
+        .def("__pow__", [](const arb::units::unit &b, int e) { return b.pow(e); }, py::is_operator())
+        .def("__str__",
+             [](const arb::units::unit& u) { return arb::units::to_string(u); },
+             "Convert unit to string.")
+        .def("__repr__",
+             [](const arb::units::unit& u) { return arb::units::to_string(u); },
+             "Convert unit to string.");
+
+    quantity
+        .def(py::self * py::self)
+        .def(py::self / py::self)
+        .def(py::self == py::self)
+        .def(py::self != py::self)
+        .def(py::self + py::self)
+        .def(py::self - py::self)
+        .def(py::self * double())
+        .def(py::self / double())
+        .def(double() * py::self)
+        .def(double() / py::self)
+        .def(py::self * arb::units::unit())
+        .def(py::self / arb::units::unit())
+        .def("__pow__", [](const arb::units::unit &b, int e) { return b.pow(e); }, py::is_operator())
+        .def("value_as",
+             [](const arb::units::quantity& q, const arb::units::unit& u) { return q.value_as(u); },
+             "unit"_a,
+             "Convert quantity to given unit and return magnitude.")
+        .def_property_readonly("value",
+                               [](const arb::units::quantity& q) { return q.value(); },
+                               "Return magnitude.")
+        .def_property_readonly("units",
+                               [](const arb::units::quantity& q) { return q.units(); },
+                               "Return units.")
+        .def("__str__",
+             [](const arb::units::quantity& q) { return arb::units::to_string(q); },
+             "Convert quantity to string.")
+        .def("__repr__",
+             [](const arb::units::quantity& q) { return arb::units::to_string(q); },
+             "Convert quantity to string.");
+
+    u.attr("m")   = py::cast(arb::units::m);
+    u.attr("cm")  = py::cast(arb::units::cm);
+    u.attr("mm")  = py::cast(arb::units::mm);
+    u.attr("um")  = py::cast(arb::units::um);
+    u.attr("nm")  = py::cast(arb::units::nm);
+
+    u.attr("m2")   = py::cast(arb::units::m2);
+    u.attr("cm2")  = py::cast(arb::units::cm2);
+    u.attr("mm2")  = py::cast(arb::units::mm2);
+    u.attr("um2")  = py::cast(arb::units::um2);
+    u.attr("nm2")  = py::cast(arb::units::nm2);
+
+    u.attr("s")   = py::cast(arb::units::s);
+    u.attr("ms")  = py::cast(arb::units::ms);
+    u.attr("us")  = py::cast(arb::units::us);
+    u.attr("ns")  = py::cast(arb::units::ns);
+    u.attr("Hz")  = py::cast(arb::units::Hz);
+    u.attr("kHz") = py::cast(arb::units::kHz);
+
+    u.attr("Ohm")  = py::cast(arb::units::Ohm);
+    u.attr("kOhm") = py::cast(arb::units::kOhm);
+    u.attr("MOhm") = py::cast(arb::units::MOhm);
+
+    u.attr("S")  = py::cast(arb::units::S);
+    u.attr("mS")  = py::cast(arb::units::mS);
+    u.attr("uS")  = py::cast(arb::units::uS);
+
+    u.attr("F")  = py::cast(arb::units::F);
+    u.attr("uF") = py::cast(arb::units::uF);
+    u.attr("nF") = py::cast(arb::units::nF);
+    u.attr("pF") = py::cast(arb::units::pF);
+
+    u.attr("A")  = py::cast(arb::units::A);
+    u.attr("mA") = py::cast(arb::units::mA);
+    u.attr("uA") = py::cast(arb::units::uA);
+    u.attr("nA") = py::cast(arb::units::nA);
+    u.attr("pA") = py::cast(arb::units::pA);
+
+    u.attr("V")  = py::cast(arb::units::V);
+    u.attr("mV") = py::cast(arb::units::mV);
+
+    u.attr("C")  = py::cast(arb::units::C);
+
+    u.attr("rad") = py::cast(arb::units::rad);
+    u.attr("deg") = py::cast(arb::units::deg);
+
+    u.attr("Kelvin")  = py::cast(arb::units::Kelvin);
+    u.attr("Celsius") = py::cast(arb::units::Celsius);
+
+    u.attr("mol") = py::cast(arb::units::mol);
+    u.attr("M")   = py::cast(arb::units::M);
+    u.attr("mM")  = py::cast(arb::units::mM);
+
+    u.attr("pico")  = py::cast(arb::units::pico);
+    u.attr("nano")  = py::cast(arb::units::nano);
+    u.attr("micro") = py::cast(arb::units::micro);
+    u.attr("milli") = py::cast(arb::units::milli);
+    u.attr("kilo")  = py::cast(arb::units::kilo);
+    u.attr("mega")  = py::cast(arb::units::mega);
+    u.attr("giga")  = py::cast(arb::units::giga);
+
+    u.attr("nil") = py::cast(arb::units::nil);
+}
+} // pyarb
diff --git a/python/util.hpp b/python/util.hpp
index 846b62cc18246a6b0c0ad8aa81ab7b43fcba8113..b111d4eafc888ec7ab676e0168fc4e1fc5d093c1 100644
--- a/python/util.hpp
+++ b/python/util.hpp
@@ -3,10 +3,11 @@
 #include <fstream>
 #include <string>
 
+#include <arbor/arbexcept.hpp>
+
 #include <pybind11/pybind11.h>
 
 #include "strprintf.hpp"
-#include "error.hpp"
 
 namespace pyarb {
 namespace util {
diff --git a/spack/package.py b/spack/package.py
index 50c1c5ea2b5043b9a72a359a119fa62bfe786acf..460dc48f75ce25168f9c1a80e5bda9d208fd8216 100644
--- a/spack/package.py
+++ b/spack/package.py
@@ -14,13 +14,15 @@ class Arbor(CMakePackage, CudaPackage):
     git = "https://github.com/arbor-sim/arbor.git"
     url = "https://github.com/arbor-sim/arbor/releases/download/v0.8.1/arbor-v0.9.0-full.tar.gz"
     maintainers = ("thorstenhater", "brenthuisman", "haampie")
+    submodules = True
 
-    version("master", branch="master")
-    version("develop")
+    version("master", branch="master", submodules=True)
+    version("develop", branch="master", submodules=True)
     version(
         "0.9.0",
         sha256="5f9740955c821aca81e23298c17ad64f33f635756ad9b4a0c1444710f564306a",
         url="https://github.com/arbor-sim/arbor/releases/download/v0.9.0/arbor-v0.9.0-full.tar.gz",
+        submodules=True,
     )
     version(
         "0.8.1",
@@ -132,6 +134,8 @@ class Arbor(CMakePackage, CudaPackage):
             self.spec.compiler.name, self.spec.compiler.version
         )
         args.append("-DARB_CXX_FLAGS_TARGET=" + opt_flags)
+        # Needed, spack has no units package
+        args.append("-DARB_USE_BUNDLED_UNITS=ON")
 
         return args
 
diff --git a/test/common_cells.cpp b/test/common_cells.cpp
index 5d1f3aa2bc7dddbc2d4b1e20d98f9bdc159988a6..903e4e0a4315aab94c852d58a99e071518545ab3 100644
--- a/test/common_cells.cpp
+++ b/test/common_cells.cpp
@@ -1,5 +1,4 @@
 #include <arborio/label_parse.hpp>
-#include "arbor/morph/morphology.hpp"
 #include "common_cells.hpp"
 
 namespace arb {
@@ -181,7 +180,9 @@ cable_cell_description make_cell_soma_only(bool with_stim) {
     auto c = builder.make_cell();
     c.decorations.paint("soma"_lab, density("hh"));
     if (with_stim) {
-        c.decorations.place(builder.location({0,0.5}), i_clamp{10., 100., 0.1}, "cc");
+        c.decorations.place(builder.location({0,0.5}),
+                            i_clamp::box(10.*arb::units::ms, 100.*arb::units::ms, 0.1*arb::units::nA),
+                            "cc");
     }
 
     return {c.morph, c.labels, c.decorations};
@@ -216,7 +217,9 @@ cable_cell_description make_cell_ball_and_stick(bool with_stim) {
     c.decorations.paint("soma"_lab, density("hh"));
     c.decorations.paint("dend"_lab, density("pas"));
     if (with_stim) {
-        c.decorations.place(builder.location({1,1}), i_clamp{5, 80, 0.3}, "cc");
+        c.decorations.place(builder.location({1,1}),
+                            i_clamp::box(5*arb::units::ms, 80*arb::units::ms, 0.3*arb::units::nA),
+                            "cc");
     }
 
     return {c.morph, c.labels, c.decorations};
@@ -254,8 +257,12 @@ cable_cell_description make_cell_ball_and_3stick(bool with_stim) {
     c.decorations.paint("soma"_lab, density("hh"));
     c.decorations.paint("dend"_lab, density("pas"));
     if (with_stim) {
-        c.decorations.place(builder.location({2,1}), i_clamp{5.,  80., 0.45}, "cc0");
-        c.decorations.place(builder.location({3,1}), i_clamp{40., 10.,-0.2}, "cc1");
+        c.decorations.place(builder.location({2,1}),
+                            i_clamp::box(5.*arb::units::ms,  80.*arb::units::ms, 0.45*arb::units::nA),
+                            "cc0");
+        c.decorations.place(builder.location({3,1}),
+                            i_clamp::box(40.*arb::units::ms, 10.*arb::units::ms,-0.2*arb::units::nA),
+                            "cc1");
     }
 
     return {c.morph, c.labels, c.decorations};
diff --git a/test/common_cells.hpp b/test/common_cells.hpp
index 9b22af70e7c5cb75409d81993aa9dea6dd3d8181..6e9cda85a7b0ba03208f387ca220148ca730abd7 100644
--- a/test/common_cells.hpp
+++ b/test/common_cells.hpp
@@ -1,5 +1,3 @@
-#include <cmath>
-
 #include <arbor/cable_cell.hpp>
 #include <arbor/mechinfo.hpp>
 #include <arbor/morph/label_dict.hpp>
diff --git a/test/simple_recipes.hpp b/test/simple_recipes.hpp
index 504d54d9dc03bda4b5106fa25d1fe425f7d131f4..52b6638f4179320a9525a5fb78e962e53e854f2e 100644
--- a/test/simple_recipes.hpp
+++ b/test/simple_recipes.hpp
@@ -10,13 +10,14 @@
 #include <arbor/cable_cell.hpp>
 #include <arbor/cable_cell_param.hpp>
 #include <arbor/recipe.hpp>
+#include <arbor/units.hpp>
 #include <arbor/util/unique_any.hpp>
 
 
-#include "util/rangeutil.hpp"
-
 namespace arb {
 
+namespace U = units;
+
 // Common functionality: maintain an unordered map of probe data
 // per gid, built with `add_probe()`.
 
@@ -48,7 +49,7 @@ public:
     }
 
     void add_ion(const std::string& ion_name, int charge, double init_iconc, double init_econc, double init_revpot) {
-        cell_gprop_.add_ion(ion_name, charge, init_iconc, init_econc, init_revpot);
+        cell_gprop_.add_ion(ion_name, charge, init_iconc*U::mM, init_econc*U::mM, init_revpot*U::mV);
     }
 
     void nernst_ion(const std::string& ion_name) {
diff --git a/test/ubench/merge.cpp b/test/ubench/merge.cpp
index 1097fa734189192c749c1e610eed129ea95be9af..c179ff2f89951cbed0becef4d43dc201dd4aa70e 100644
--- a/test/ubench/merge.cpp
+++ b/test/ubench/merge.cpp
@@ -1,5 +1,3 @@
-#include <random>
-#include <unordered_map>
 #include <vector>
 
 #include <benchmark/benchmark.h>
@@ -10,20 +8,18 @@
 #include <arbor/event_generator.hpp>
 #include <arbor/schedule.hpp>
 
-constexpr auto T = 1000.0; // ms
-
-using rndgen = std::mt19937_64;
+constexpr auto T = 1000.0*arb::units::ms; // ms
 
 struct payload {
     payload(std::size_t ncells, std::size_t ev_per_cell) {
         auto dt = T/ev_per_cell;
         for(auto cell = 0ull; cell < ncells; ++cell) {
-            auto gen = arb::poisson_schedule(1/dt, rndgen{cell});
-            auto times = gen.events(0, T);
+            auto gen = arb::poisson_schedule(1/dt, cell);
+            auto times = gen.events(0, T.value_as(arb::units::ms));
             evts.emplace_back();
             auto& evt = evts.back();
             for (auto t: arb::util::make_range(times)) {
-                evt.emplace_back(arb::spike_event{42, t, 0.23});
+                evt.emplace_back(42, t, 0.23);
                 ++size;
             }
             span.emplace_back(arb::util::make_range(evt.data(), evt.data() + evt.size()));
diff --git a/test/unit-distributed/test_communicator.cpp b/test/unit-distributed/test_communicator.cpp
index 9f06a3dda997fe885b5eceefa59a3c8a1d933074..0100a30e0bb06ec5049a99d11154085cfe5b1938 100644
--- a/test/unit-distributed/test_communicator.cpp
+++ b/test/unit-distributed/test_communicator.cpp
@@ -1,7 +1,6 @@
 #include <gtest/gtest.h>
 #include "test.hpp"
 
-#include <tuple>
 #include <vector>
 
 #include <arbor/domain_decomposition.hpp>
@@ -11,7 +10,6 @@
 #include <arbor/spike_event.hpp>
 
 #include "communication/communicator.hpp"
-#include "execution_context.hpp"
 #include "fvm_lowered_cell.hpp"
 #include "lif_cell_group.hpp"
 #include "cable_cell_group.hpp"
@@ -24,6 +22,7 @@
 #endif
 
 using namespace arb;
+namespace U = arb::units;
 
 TEST(communicator, policy_basics) {
 
@@ -204,7 +203,7 @@ namespace {
                 tree.append(arb::mnpos, {0, 0, 0.0, 1.0}, {0, 0, 200, 1.0}, 1);
                 arb::decor decor;
                 decor.set_default(arb::cv_policy_fixed_per_branch(10));
-                decor.place(arb::mlocation{0, 0.5}, arb::threshold_detector{10}, "src");
+                decor.place(arb::mlocation{0, 0.5}, arb::threshold_detector{10*arb::units::mV}, "src");
                 decor.place(arb::mlocation{0, 0.5}, arb::synapse("expsyn"), "tgt");
                 return arb::cable_cell(arb::morphology(tree), decor);
             }
@@ -221,10 +220,9 @@ namespace {
             // delay is 1
             cell_global_label_type src = {gid==0? size_-1: gid-1, "src"};
             cell_local_label_type dst = {"tgt"};
-            return {cell_connection(
-                        src, dst,   // end points
-                        float(gid), // weight
-                        1.0f)};     // delay
+            return {cell_connection(src, dst,    // end points
+                                    float(gid),  // weight
+                                    1.0*U::ms)}; // delay
         }
 
         std::any get_global_properties(arb::cell_kind kind) const override {
@@ -277,7 +275,7 @@ namespace {
             tree.append(arb::mnpos, {0, 0, 0.0, 1.0}, {0, 0, 200, 1.0}, 1);
             arb::decor decor;
             decor.set_default(arb::cv_policy_fixed_per_branch(10));
-            decor.place(arb::mlocation{0, 0.5}, arb::threshold_detector{10}, "src");
+            decor.place(arb::mlocation{0, 0.5}, arb::threshold_detector{10*arb::units::mV}, "src");
             decor.place(arb::ls::uniform(arb::reg::all(), 0, size_, gid), arb::synapse("expsyn"), "tgt");
             return arb::cable_cell(arb::morphology(tree), decor);
         }
@@ -289,12 +287,10 @@ namespace {
             std::vector<cell_connection> cons;
             cons.reserve(size_);
             for (auto sid: util::make_span(0, size_)) {
-                cell_connection con(
-                        {sid, {"src", arb::lid_selection_policy::round_robin}}, // source
-                        {"tgt", arb::lid_selection_policy::round_robin},        // destination
-                        float(gid+sid), // weight
-                        1.0f);          // delay
-                cons.push_back(con);
+                cons.emplace_back(cell_connection{{sid, {"src", arb::lid_selection_policy::round_robin}}, // source
+                                                  {"tgt", arb::lid_selection_policy::round_robin},        // destination
+                                                  float(gid+sid),                                         // weight
+                                                  1.0f*U::ms});                                           // delay
             }
             return cons;
         }
@@ -354,8 +350,8 @@ namespace {
                 decor.place(arb::ls::uniform(arb::reg::all(), 2, 2, gid), arb::synapse("expsyn"), "synapses_1");
             }
             else {
-                decor.place(arb::ls::uniform(arb::reg::all(), 0, 2, gid), arb::threshold_detector{10}, "detectors_0");
-                decor.place(arb::ls::uniform(arb::reg::all(), 3, 3, gid), arb::threshold_detector{10}, "detectors_1");
+                decor.place(arb::ls::uniform(arb::reg::all(), 0, 2, gid), arb::threshold_detector{10*arb::units::mV}, "detectors_0");
+                decor.place(arb::ls::uniform(arb::reg::all(), 3, 3, gid), arb::threshold_detector{10*arb::units::mV}, "detectors_1");
             }
             return arb::cable_cell(arb::morphology(tree), decor);
         }
@@ -402,20 +398,17 @@ namespace {
             if (gid%3 != 1) {
                 for (auto sid: util::make_span(0, ncells_)) {
                     if (sid%3 == 1) {
-                        cons.push_back({{sid, "detectors_0", pol::round_robin}, {"synapses_0", pol::round_robin}, 1.0, 1.0});
-                        cons.push_back({{sid, "detectors_0", pol::round_robin}, {"synapses_0", pol::round_robin}, 1.0, 1.0});
-                        cons.push_back({{sid, "detectors_0", pol::round_robin}, {"synapses_0", pol::round_robin}, 1.0, 1.0});
-                        cons.push_back({{sid, "detectors_0", pol::round_robin}, {"synapses_0", pol::round_robin}, 1.0, 1.0});
-                        cons.push_back({{sid, "detectors_0", pol::round_robin}, {"synapses_0", pol::round_robin}, 1.0, 1.0});
-                        cons.push_back({{sid, "detectors_0", pol::round_robin}, {"synapses_0", pol::round_robin}, 1.0, 1.0});
-                        cons.push_back({{sid, "detectors_0", pol::round_robin}, {"synapses_0", pol::round_robin}, 1.0, 1.0});
-
-                        cons.push_back({{sid, "detectors_0", pol::round_robin}, {"synapses_1", pol::assert_univalent}, 1.0, 1.0});
-
-                        cons.push_back({{sid, "detectors_1", pol::round_robin}, {"synapses_0", pol::round_robin}, 1.0, 1.0});
-                        cons.push_back({{sid, "detectors_1", pol::round_robin}, {"synapses_0", pol::round_robin}, 1.0, 1.0});
-
-                        cons.push_back({{sid, "detectors_1", pol::assert_univalent}, {"synapses_1", pol::round_robin}, 1.0, 1.0});
+                        cons.push_back({{sid, "detectors_0", pol::round_robin}, {"synapses_0", pol::round_robin},      1.0, 1.0*U::ms});
+                        cons.push_back({{sid, "detectors_0", pol::round_robin}, {"synapses_0", pol::round_robin},      1.0, 1.0*U::ms});
+                        cons.push_back({{sid, "detectors_0", pol::round_robin}, {"synapses_0", pol::round_robin},      1.0, 1.0*U::ms});
+                        cons.push_back({{sid, "detectors_0", pol::round_robin}, {"synapses_0", pol::round_robin},      1.0, 1.0*U::ms});
+                        cons.push_back({{sid, "detectors_0", pol::round_robin}, {"synapses_0", pol::round_robin},      1.0, 1.0*U::ms});
+                        cons.push_back({{sid, "detectors_0", pol::round_robin}, {"synapses_0", pol::round_robin},      1.0, 1.0*U::ms});
+                        cons.push_back({{sid, "detectors_0", pol::round_robin}, {"synapses_0", pol::round_robin},      1.0, 1.0*U::ms});
+                        cons.push_back({{sid, "detectors_0", pol::round_robin}, {"synapses_1", pol::assert_univalent}, 1.0, 1.0*U::ms});
+                        cons.push_back({{sid, "detectors_1", pol::round_robin}, {"synapses_0", pol::round_robin},      1.0, 1.0*U::ms});
+                        cons.push_back({{sid, "detectors_1", pol::round_robin}, {"synapses_0", pol::round_robin},      1.0, 1.0*U::ms});
+                        cons.push_back({{sid, "detectors_1", pol::assert_univalent}, {"synapses_1", pol::round_robin}, 1.0, 1.0*U::ms});
                     }
                 }
             }
diff --git a/test/unit-distributed/test_domain_decomposition.cpp b/test/unit-distributed/test_domain_decomposition.cpp
index 40e76c6dc803fca2cc38642b01bf40f8c833051f..4c4ccea430a8aa4b2fb1a0a44138d6a0f2cd0c6c 100644
--- a/test/unit-distributed/test_domain_decomposition.cpp
+++ b/test/unit-distributed/test_domain_decomposition.cpp
@@ -1,10 +1,5 @@
 #include <gtest/gtest.h>
 
-#include <cstdio>
-#include <fstream>
-#include <iostream>
-#include <stdexcept>
-#include <string>
 #include <vector>
 
 #include <arbor/context.hpp>
@@ -16,9 +11,7 @@
 #include <arborenv/default_env.hpp>
 
 #include "util/span.hpp"
-
 #include "../simple_recipes.hpp"
-#include "test.hpp"
 
 #ifdef TEST_MPI
 #include <mpi.h>
diff --git a/test/unit/test_cable_cell.cpp b/test/unit/test_cable_cell.cpp
index 8b0a4d460dff135dd2d46dc03b801c78150b2cf0..7e8930fee0a8addbd04c6a91021d2cae520fc308 100644
--- a/test/unit/test_cable_cell.cpp
+++ b/test/unit/test_cable_cell.cpp
@@ -4,6 +4,7 @@
 
 #include <arbor/util/hash_def.hpp>
 #include <arbor/cable_cell.hpp>
+#include <arbor/units.hpp>
 #include <arbor/cable_cell_param.hpp>
 
 #include <arborio/label_parse.hpp>
@@ -35,9 +36,9 @@ TEST(cable_cell, lid_ranges) {
     // Note: there are 2 terminal points.
     decorations.place("term"_lab, synapse("expsyn"), "t0");
     decorations.place("term"_lab, synapse("expsyn"), "t1");
-    decorations.place("term"_lab, threshold_detector{-10}, "s0");
+    decorations.place("term"_lab, threshold_detector{-10*arb::units::mV}, "s0");
     decorations.place(empty_sites, synapse("expsyn"), "t2");
-    decorations.place("term"_lab, threshold_detector{-20}, "s1");
+    decorations.place("term"_lab, threshold_detector{-20*arb::units::mV}, "s1");
     decorations.place(three_sites, synapse("expsyn"), "t3");
     decorations.place("term"_lab, synapse("exp2syn"), "t3");
 
diff --git a/test/unit/test_cable_cell_group.cpp b/test/unit/test_cable_cell_group.cpp
index f143df0a25942155bce49e92789598706a8cf788..cbb38714549640d2e33ebc52fd798e59fb85b9ab 100644
--- a/test/unit/test_cable_cell_group.cpp
+++ b/test/unit/test_cable_cell_group.cpp
@@ -7,7 +7,6 @@
 #include "epoch.hpp"
 #include "fvm_lowered_cell.hpp"
 #include "cable_cell_group.hpp"
-#include "util/rangeutil.hpp"
 
 #include "common.hpp"
 #include "../common_cells.hpp"
@@ -29,8 +28,8 @@ namespace {
         auto d = builder.make_cell();
         d.decorations.paint("soma"_lab, density("hh"));
         d.decorations.paint("dend"_lab, density("pas"));
-        d.decorations.place(builder.location({1,1}), i_clamp::box(5, 80, 0.3), "clamp0");
-        d.decorations.place(builder.location({0, 0}), threshold_detector{0}, "detector0");
+        d.decorations.place(builder.location({1,1}), i_clamp::box(5*arb::units::ms, 80*arb::units::ms, 0.3*arb::units::nA), "clamp0");
+        d.decorations.place(builder.location({0, 0}), threshold_detector{0*arb::units::mV}, "detector0");
         return d;
     }
 }
@@ -72,7 +71,7 @@ TEST(cable_cell_group, sources) {
     for (int i=0; i<20; ++i) {
         auto desc = make_cell();
         if (i==0 || i==3 || i==17) {
-            desc.decorations.place(mlocation{0, 0.3}, threshold_detector{2.3}, "detector1");
+            desc.decorations.place(mlocation{0, 0.3}, threshold_detector{2.3*arb::units::mV}, "detector1");
         }
         cells.emplace_back(desc);
 
diff --git a/test/unit/test_cable_cell_group_gpu.cpp b/test/unit/test_cable_cell_group_gpu.cpp
index 3f5fbfdaad2890100e4aeb171962e11c7a9fdcd6..d2dcff02fc25d7ee9a52703d76ec51941fa5f85e 100644
--- a/test/unit/test_cable_cell_group_gpu.cpp
+++ b/test/unit/test_cable_cell_group_gpu.cpp
@@ -15,6 +15,8 @@
 using namespace arb;
 using namespace arborio::literals;
 
+namespace U = arb::units;
+
 namespace {
     cable_cell_description make_cell() {
         soma_cell_builder builder(12.6157/2.0);
@@ -22,8 +24,8 @@ namespace {
         auto d = builder.make_cell();
         d.decorations.paint("soma"_lab, density("hh"));
         d.decorations.paint("dend"_lab, density("pas"));
-        d.decorations.place(builder.location({1,1}), i_clamp::box(5, 80, 0.3), "clamp0");
-        d.decorations.place(builder.location({0, 0}), threshold_detector{0}, "detector0");
+        d.decorations.place(builder.location({1,1}), i_clamp::box(5*U::ms, 80*U::ms, 0.3*U::nA), "clamp0");
+        d.decorations.place(builder.location({0, 0}), threshold_detector{0*U::mV}, "detector0");
         return d;
     }
 }
diff --git a/test/unit/test_cv_layout.cpp b/test/unit/test_cv_layout.cpp
index 8852f61abaefb6ad824806980d848437a200f292..67512a8868391e5afd6f4ad40e1e86ab6e1c6f1e 100644
--- a/test/unit/test_cv_layout.cpp
+++ b/test/unit/test_cv_layout.cpp
@@ -6,15 +6,16 @@
 #include <arbor/morph/morphology.hpp>
 #include <arbor/morph/locset.hpp>
 
+#include <gtest/gtest.h>
+
 #include "fvm_layout.hpp"
 #include "util/span.hpp"
 
-#include "common.hpp"
 #include "common_morphologies.hpp"
 #include "../common_cells.hpp"
 
 using namespace arb;
-using util::make_span;
+namespace U = arb::units;
 
 TEST(cv_layout, empty) {
     using namespace common_morphology;
@@ -92,9 +93,9 @@ TEST(cv_layout, cable) {
     params.init_membrane_potential = 0;
 
     decor decs;
-    decs.paint(reg::cable(0, 0.0, 0.2), init_membrane_potential{10});
-    decs.paint(reg::cable(0, 0.2, 0.7), init_membrane_potential{20});
-    decs.paint(reg::cable(0, 0.7, 1.0), init_membrane_potential{30});
+    decs.paint(reg::cable(0, 0.0, 0.2), init_membrane_potential{10*U::mV});
+    decs.paint(reg::cable(0, 0.2, 0.7), init_membrane_potential{20*U::mV});
+    decs.paint(reg::cable(0, 0.7, 1.0), init_membrane_potential{30*U::mV});
     cable_cell c(morph, decs);
 
     params.discretization = cv_policy_explicit(ls::nil());
diff --git a/test/unit/test_diffusion.cpp b/test/unit/test_diffusion.cpp
index 348b135ca890fa4971ebc6688d17958f2f9fd927..54529cd5a7edcf0b68181fcb980b0b76e2282f04 100644
--- a/test/unit/test_diffusion.cpp
+++ b/test/unit/test_diffusion.cpp
@@ -1,5 +1,4 @@
 #include <cmath>
-#include <numeric>
 #include <string>
 #include <vector>
 
@@ -26,6 +25,7 @@ using namespace std::string_literals;
 using namespace arborio::literals;
 
 using namespace arb;
+namespace U = arb::units;
 
 constexpr double epsilon  = 1e-6;
 #ifdef ARB_GPU_ENABLED
@@ -55,7 +55,7 @@ struct linear: public recipe {
     std::vector<arb::event_generator> event_generators(arb::cell_gid_type gid) const override {
         std::vector<arb::event_generator> result;
         for (const auto& [t, w]: inject_at) {
-            result.push_back(arb::explicit_generator({"Zap"}, w, std::vector<float>{t}));
+            result.push_back(arb::explicit_generator_from_milliseconds({"Zap"}, w, std::vector<arb::time_type>{t}));
         }
         return result;
     }
@@ -72,13 +72,13 @@ struct linear: public recipe {
     linear& add_inject() { decor.place("(location 0 0.5)"_ls, arb::synapse("inject/x=na", {{"alpha", 200.0*cv_length}}), "Zap"); return *this; }
     linear& add_event(double t, float w) { inject_at.push_back({t, w}); return *this; }
     linear& set_diffusivity(double d, std::optional<region> rg = {}) {
-        if (rg) decor.paint(*rg, ion_diffusivity{"na", d});
-        else    decor.set_default(ion_diffusivity{"na", d});
+        if (rg) decor.paint(*rg, ion_diffusivity{"na", d*U::m2/U::s});
+        else    decor.set_default(ion_diffusivity{"na", d*U::m2/U::s});
         return *this;
     }
     linear& set_concentration(double d, std::optional<region> rg = {}) {
-        if (rg) decor.paint(*rg, init_int_concentration{"na", d});
-        else    decor.set_default(init_int_concentration{"na", d});
+        if (rg) decor.paint(*rg, init_int_concentration{"na", d*U::mM});
+        else    decor.set_default(init_int_concentration{"na", d*U::mM});
         return *this;
     }
 };
@@ -117,8 +117,8 @@ testing::AssertionResult run(const linear& rec, const result_t exp) {
     };
     auto ctx = make_context({arbenv::default_concurrency(), with_gpu});
     auto sim = simulation{rec, ctx, partition_load_balance(rec, ctx)};
-    sim.add_sampler(arb::all_probes, arb::regular_schedule(0.1), sampler);
-    sim.run(0.11, 0.01);
+    sim.add_sampler(arb::all_probes, arb::regular_schedule(0.1*arb::units::ms), sampler);
+    sim.run(0.11*arb::units::ms, 0.01*arb::units::ms);
     return all_near(sample_values, exp, epsilon);
 }
 
@@ -315,37 +315,37 @@ TEST(diffusion, setting_diffusivity) {
     // BAD: Trying to use a diffusive ion, but b=0.
     {
         R r;
-        r.gprop.add_ion("bla", 1, 23, 42, 0, 0);
-        EXPECT_THROW(simulation(r).run(1, 1), illegal_diffusive_mechanism);
+        r.gprop.add_ion("bla", 1, 23*U::mM, 42*U::mM, 0*U::mV, 0*U::m2/U::s);
+        EXPECT_THROW(simulation(r).run(1*arb::units::ms, 1*arb::units::ms), illegal_diffusive_mechanism);
     }
     // BAD: Trying to use a partially diffusive ion
     {
         R r;
-        r.gprop.add_ion("bla", 1, 23, 42, 0, 0);
-        r.dec.paint("(tag 1)"_reg, ion_diffusivity{"bla", 13});
-        EXPECT_THROW(simulation(r).run(1, 1), cable_cell_error);
+        r.gprop.add_ion("bla", 1, 23*U::mM, 42*U::mM, 0*U::mV, 0*U::m2/U::s);
+        r.dec.paint("(tag 1)"_reg, ion_diffusivity{"bla", 13*U::m2/U::s});
+        EXPECT_THROW(simulation(r).run(1*arb::units::ms, 1*arb::units::ms), cable_cell_error);
     }
     // OK: Using the global default
     {
         R r;
-        r.gprop.add_ion("bla", 1, 23, 42, 0, 8);
-        r.dec.paint("(tag 1)"_reg, ion_diffusivity{"bla", 13});
-        EXPECT_NO_THROW(simulation(r).run(1, 1));
+        r.gprop.add_ion("bla", 1, 23*U::mM, 42*U::mM, 0*U::mV, 8*U::m2/U::s);
+        r.dec.paint("(tag 1)"_reg, ion_diffusivity{"bla", 13*U::m2/U::s});
+        EXPECT_NO_THROW(simulation(r).run(1*arb::units::ms, 1*arb::units::ms));
     }
     // OK: Using the cell default
     {
         R r;
-        r.gprop.add_ion("bla", 1, 23, 42, 0, 0);
-        r.dec.set_default(ion_diffusivity{"bla", 8});
-        r.dec.paint("(tag 1)"_reg, ion_diffusivity{"bla", 13});
-        EXPECT_NO_THROW(simulation(r).run(1, 1));
+        r.gprop.add_ion("bla", 1, 23*U::mM, 42*U::mM, 0*U::mV, 0*U::m2/U::s);
+        r.dec.set_default(ion_diffusivity{"bla", 8*U::m2/U::s});
+        r.dec.paint("(tag 1)"_reg, ion_diffusivity{"bla", 13*U::m2/U::s});
+        EXPECT_NO_THROW(simulation(r).run(1*arb::units::ms, 1*arb::units::ms));
     }
     // BAD: Using an unknown species
     {
         R r;
-        r.dec.set_default(ion_diffusivity{"bla", 8});
-        r.dec.paint("(tag 1)"_reg, ion_diffusivity{"bla", 13});
-        EXPECT_THROW(simulation(r).run(1, 1), cable_cell_error);
+        r.dec.set_default(ion_diffusivity{"bla", 8*U::m2/U::s});
+        r.dec.paint("(tag 1)"_reg, ion_diffusivity{"bla", 13*U::m2/U::s});
+        EXPECT_THROW(simulation(r).run(1*arb::units::ms, 1*arb::units::ms), cable_cell_error);
     }
 
 }
diff --git a/test/unit/test_domain_decomposition.cpp b/test/unit/test_domain_decomposition.cpp
index ab8a7c3e4aa5a187379f1d41ad44464c77f09c2e..10e0b754c6b0d4b9ec199d2008826b7d56383c10 100644
--- a/test/unit/test_domain_decomposition.cpp
+++ b/test/unit/test_domain_decomposition.cpp
@@ -1,7 +1,5 @@
 #include <gtest/gtest.h>
 
-#include <stdexcept>
-
 #include <arbor/context.hpp>
 #include <arbor/domdecexcept.hpp>
 #include <arbor/domain_decomposition.hpp>
diff --git a/test/unit/test_event_delivery.cpp b/test/unit/test_event_delivery.cpp
index 89718a1958154271158b2d26224ecb2645f30710..8800224e249e6669f65d0ca9c28b01e65547ec6e 100644
--- a/test/unit/test_event_delivery.cpp
+++ b/test/unit/test_event_delivery.cpp
@@ -37,7 +37,7 @@ struct test_recipe: public n_cable_cell_recipe {
 
         decor decorations;
         decorations.place(mlocation{0, 0.5}, synapse("expsyn"), "synapse");
-        decorations.place(mlocation{0, 0.5}, threshold_detector{-64}, "detector");
+        decorations.place(mlocation{0, 0.5}, threshold_detector{-64*arb::units::mV}, "detector");
         decorations.place(mlocation{0, 0.5}, junction("gj"), "gapjunction");
         cable_cell c(st, decorations, labels);
 
@@ -74,7 +74,7 @@ std::vector<cell_gid_type> run_test_sim(const recipe& R, const group_gids_type&
     }
 
     sim.inject_events(cell_events);
-    sim.run((n+1)*ev_delta_t, 0.01);
+    sim.run((n+1)*ev_delta_t*arb::units::ms, 0.01*arb::units::ms);
 
     std::vector<cell_gid_type> spike_gids;
     util::sort_by(spikes, [](auto s) { return s.time; });
diff --git a/test/unit/test_event_generators.cpp b/test/unit/test_event_generators.cpp
index 024fbc1a0818603a5a042f311b52e56207ab0c5c..54a8dbae6f05222ea1114aac7bcfacad742c76f6 100644
--- a/test/unit/test_event_generators.cpp
+++ b/test/unit/test_event_generators.cpp
@@ -5,10 +5,6 @@
 #include <arbor/event_generator.hpp>
 #include <arbor/spike_event.hpp>
 
-#include "util/rangeutil.hpp"
-
-#include "common.hpp"
-
 using namespace arb;
 
 namespace{
@@ -18,7 +14,7 @@ namespace{
 }
 
 TEST(event_generators, assign_and_copy) {
-    event_generator gen = regular_generator({"l2"}, 5., 0.5, 0.75);
+    event_generator gen = regular_generator({"l2"}, 5., 0.5*arb::units::ms, 0.75*arb::units::ms);
     gen.resolve_label([](const cell_local_label_type&) {return 2;});
     spike_event expected{2, 0.75, 5.};
 
@@ -57,7 +53,7 @@ TEST(event_generators, regular) {
     cell_lid_type lid = 3;
     float weight = 3.14;
 
-    event_generator gen = regular_generator(label, weight, t0, dt);
+    event_generator gen = regular_generator(label, weight, t0*arb::units::ms, dt*arb::units::ms);
     gen.resolve_label([lid](const cell_local_label_type&) {return lid;});
 
     // Helper for building a set of expected events.
@@ -93,7 +89,7 @@ TEST(event_generators, seq) {
         expected.push_back({0, time, weight});
     }
 
-    event_generator gen = explicit_generator(l0, weight, times);
+    event_generator gen = explicit_generator_from_milliseconds(l0, weight, times);
     gen.resolve_label([](const cell_local_label_type&) {return 0;});
 
     EXPECT_EQ(expected, as_vector(gen.events(0, 100.))); gen.reset();
@@ -126,8 +122,6 @@ TEST(event_generators, seq) {
 }
 
 TEST(event_generators, poisson) {
-    std::mt19937_64 G;
-
     time_type t0 = 0;
     time_type t1 = 10;
     time_type lambda = 10; // expect 10 events per ms
@@ -135,7 +129,7 @@ TEST(event_generators, poisson) {
     cell_lid_type lid = 2;
     float weight = 42;
 
-    event_generator gen = poisson_generator(label, weight, t0, lambda, G);
+    event_generator gen = poisson_generator(label, weight, t0*arb::units::ms, lambda*arb::units::kHz);
     gen.resolve_label([lid](const cell_local_label_type&) {return lid;});
 
     pse_vector int1 = as_vector(gen.events(0, t1));
diff --git a/test/unit/test_fvm_layout.cpp b/test/unit/test_fvm_layout.cpp
index 5569171d7d4c434cbd3dbc37b81a0e335bad7437..956bc965d6299709ad51cf65abbb42eeabe9542d 100644
--- a/test/unit/test_fvm_layout.cpp
+++ b/test/unit/test_fvm_layout.cpp
@@ -21,7 +21,6 @@
 #include "util/maputil.hpp"
 #include "util/rangeutil.hpp"
 #include "util/span.hpp"
-#include "io/sepval.hpp"
 
 #include "common.hpp"
 #include "common_morphologies.hpp"
@@ -43,6 +42,8 @@ using fvm_cell = arb::fvm_lowered_cell_impl<backend>;
 // instantiate template class
 template class arb::fvm_lowered_cell_impl<arb::multicore::backend>;
 
+namespace U = arb::units;
+
 namespace {
     struct system {
         std::vector<soma_cell_builder> builders;
@@ -71,10 +72,12 @@ namespace {
             auto description = builder.make_cell();
             description.decorations.paint("soma"_lab, density("hh"));
             description.decorations.paint("dend"_lab, density("pas"));
-            description.decorations.place(builder.location({1,1}), i_clamp{5, 80, 0.3}, "clamp");
-
+            description.decorations.place(builder.location({1,1}),
+                                          i_clamp{5*arb::units::nA, 80*arb::units::kHz, 0.3*arb::units::rad},
+                                          "clamp");
             s.builders.push_back(std::move(builder));
             descriptions.push_back(description);
+
         }
 
         // Cell 1: ball and 3-stick, but with uneven dendrite
@@ -119,14 +122,18 @@ namespace {
             auto c1 = reg::cable(b1-1, b.location({b1, 0}).pos, 1);
             auto c2 = reg::cable(b2-1, b.location({b2, 0}).pos, 1);
             auto c3 = reg::cable(b3-1, b.location({b3, 0}).pos, 1);
-            desc.decorations.paint(c1, membrane_capacitance{0.017});
-            desc.decorations.paint(c2, membrane_capacitance{0.013});
-            desc.decorations.paint(c3, membrane_capacitance{0.018});
+            desc.decorations.paint(c1, membrane_capacitance{0.017*U::F*U::m.pow(-2)});
+            desc.decorations.paint(c2, membrane_capacitance{0.013*U::F*U::m.pow(-2)});
+            desc.decorations.paint(c3, membrane_capacitance{0.018*U::F*U::m.pow(-2)});
 
-            desc.decorations.place(b.location({2,1}), i_clamp{5.,  80., 0.45}, "clamo0");
-            desc.decorations.place(b.location({3,1}), i_clamp{40., 10.,-0.2}, "clamp1");
+            desc.decorations.place(b.location({2,1}),
+                                   i_clamp::box( 5.*arb::units::ms, 80.*arb::units::ms,  0.45*arb::units::nA),
+                                   "clamp0");
+            desc.decorations.place(b.location({3,1}),
+                                   i_clamp::box(40.*arb::units::ms, 10.*arb::units::ms, -0.2*arb::units::nA),
+                                   "clamp1");
 
-            desc.decorations.set_default(axial_resistivity{90});
+            desc.decorations.set_default(axial_resistivity{90*U::Ohm*U::cm});
 
             s.builders.push_back(std::move(b));
             descriptions.push_back(desc);
@@ -1619,9 +1626,9 @@ TEST(fvm_layout, revpot) {
     gprop.catalogue = make_unit_test_catalogue();
 
     gprop.ion_species = {{"a", 1}, {"b", 2}, {"c", 3}};
-    gprop.add_ion("a", 1, 10., 0, 0);
-    gprop.add_ion("b", 2, 30., 0, 0);
-    gprop.add_ion("c", 3, 50., 0, 0);
+    gprop.add_ion("a", 1, 10.*U::mM, 0*U::mM, 0*U::mV);
+    gprop.add_ion("b", 2, 30.*U::mM, 0*U::mM, 0*U::mV);
+    gprop.add_ion("c", 3, 50.*U::mM, 0*U::mM, 0*U::mV);
 
     gprop.default_parameters.reversal_potential_method["a"] = "write_eX/a";
     mechanism_desc write_eb_ec = "write_multiple_eX/x=b,y=c";
@@ -1955,7 +1962,7 @@ TEST(fvm_layout, inhomogeneous_parameters) {
     // capacitance scales with CV area
     {
         auto decor = arb::decor{}
-            .set_default(membrane_capacitance{23.0});
+            .set_default(membrane_capacitance{23.0*U::F*U::m.pow(-2)});
         auto D = fvm_cv_discretize({morph, decor}, param);
         for (unsigned ix = 0; ix < D.size(); ++ix) {
             EXPECT_NEAR(D.cv_area[ix]*23.0, D.cv_capacitance[ix], 1e-6);
@@ -1966,8 +1973,8 @@ TEST(fvm_layout, inhomogeneous_parameters) {
     // NOTE the diameter is evaluated at a _different_ spot (CV center!)
     {
         auto decor = arb::decor{}
-            .set_default(membrane_capacitance{23.0})
-            .paint(reg::tagged(1), membrane_capacitance{23.0*iexpr::diameter()});
+            .set_default(membrane_capacitance{23.0*U::F*U::m.pow(-2)})
+            .paint(reg::tagged(1), membrane_capacitance{23.0*U::F*U::m.pow(-2), iexpr::diameter()});
         auto D = fvm_cv_discretize({morph, decor}, param);
         EXPECT_EQ(D.size(), 30ul);
         for (unsigned ix = 0; ix < D.size(); ++ix) {
@@ -1981,7 +1988,7 @@ TEST(fvm_layout, inhomogeneous_parameters) {
     // Defaults do not have a scale
     {
         auto decor = arb::decor{};
-        EXPECT_THROW(decor.set_default(membrane_capacitance{23.0*iexpr::diameter()}), arb::cable_cell_error);
+        EXPECT_THROW(decor.set_default(membrane_capacitance{23.0*U::F*U::m.pow(-2), iexpr::diameter()}), arb::cable_cell_error);
     }
 }
 
diff --git a/test/unit/test_fvm_lowered.cpp b/test/unit/test_fvm_lowered.cpp
index 28cc19562ef0dda0e3156f90ebf6731689aaee24..ef83d361fa949252aa0528f1e53256ca6cbe7e07 100644
--- a/test/unit/test_fvm_lowered.cpp
+++ b/test/unit/test_fvm_lowered.cpp
@@ -33,6 +33,7 @@
 #include "../common_cells.hpp"
 #include "../simple_recipes.hpp"
 
+
 using namespace std::string_literals;
 using namespace arborio::literals;
 
@@ -229,7 +230,7 @@ TEST(fvm_lowered, target_handles) {
     descriptions[1].decorations.place(mlocation{2, 0.2}, synapse("exp2syn"), "syn2");
     descriptions[1].decorations.place(mlocation{2, 0.8}, synapse("expsyn"), "syn3");
 
-    descriptions[1].decorations.place(mlocation{0, 0}, threshold_detector{3.3}, "detector");
+    descriptions[1].decorations.place(mlocation{0, 0}, threshold_detector{3.3*arb::units::mV}, "detector");
 
     cable_cell cells[] = {descriptions[0], descriptions[1]};
 
@@ -285,9 +286,9 @@ TEST(fvm_lowered, stimulus) {
     auto desc = make_cell_ball_and_stick(false);
 
     // At end of stick
-    desc.decorations.place(mlocation{0,1},   i_clamp::box(5., 80., 0.3), "clamp0");
+    desc.decorations.place(mlocation{0,1},   i_clamp::box(5.*arb::units::ms, 80.*arb::units::ms, 0.3*arb::units::nA), "clamp0");
     // On the soma CV, which is over the approximate interval: (cable 0 0 0.1)
-    desc.decorations.place(mlocation{0,0.05}, i_clamp::box(1., 2.,  0.1), "clamp1");
+    desc.decorations.place(mlocation{0,0.05}, i_clamp::box(1.*arb::units::ms, 2.*arb::units::ms, 0.1*arb::units::nA), "clamp1");
 
     std::vector<cable_cell> cells{desc};
 
@@ -356,7 +357,8 @@ TEST(fvm_lowered, ac_stimulus) {
     const double max_time = 8; // (ms)
 
     // Envelope is linear ramp from 0 to max_time.
-    dec.place(mlocation{0, 0}, i_clamp({{0, 0}, {max_time, max_amplitude}, {max_time, 0}}, freq, phase), "clamp");
+    dec.place(mlocation{0, 0},
+              i_clamp({{0*arb::units::ms, 0*arb::units::nA}, {max_time*arb::units::ms, max_amplitude*arb::units::nA}, {max_time*arb::units::ms, 0*arb::units::nA}}, freq*arb::units::kHz, phase*arb::units::rad), "clamp");
     std::vector<cable_cell> cells = {cable_cell(tree, dec)};
 
     cable_cell_global_properties gprop;
@@ -470,12 +472,12 @@ TEST(fvm_lowered, derived_mechs) {
                 }
             };
 
-        float times[] = {10.f, 20.f};
+        std::vector<double> times{10.f, 20.f};
 
         auto decomp = partition_load_balance(rec, context);
         simulation sim(rec, context, decomp);
-        sim.add_sampler(all_probes, explicit_schedule(times), sampler);
-        sim.run(30.0, 1.f/1024);
+        sim.add_sampler(all_probes, explicit_schedule_from_milliseconds(times), sampler);
+        sim.run(30.0*arb::units::ms, 1.f/1024*arb::units::ms);
 
         ASSERT_EQ(2u, samples[0].size());
         ASSERT_EQ(2u, samples[1].size());
@@ -505,7 +507,7 @@ TEST(fvm_lowered, null_region) {
 
     auto decomp = partition_load_balance(rec, context);
     simulation sim(rec, context, decomp);
-    EXPECT_NO_THROW(sim.run(30.0, 1.f/1024));
+    EXPECT_NO_THROW(sim.run(30.0*arb::units::ms, 1.f/1024*arb::units::ms));
 }
 
 
@@ -826,7 +828,7 @@ TEST(fvm_lowered, post_events_shared_state) {
             auto ndetectors = detectors_per_cell_[gid];
             auto offset = 1.0 / ndetectors;
             for (unsigned i = 0; i < ndetectors; ++i) {
-                decor.place(arb::mlocation{0, offset * i}, arb::threshold_detector{10}, "detector"+std::to_string(i));
+                decor.place(arb::mlocation{0, offset * i}, arb::threshold_detector{10*arb::units::mV}, "detector"+std::to_string(i));
             }
             decor.place(arb::mlocation{0, 0.5}, synapse_, "syanpse");
 
@@ -920,15 +922,15 @@ TEST(fvm_lowered, label_data) {
                 decor.set_default(arb::cv_policy_fixed_per_branch(10));
                 decor.place(uniform(all(), 0, 3, 42), arb::synapse("expsyn"), "4_synapses");
                 decor.place(uniform(all(), 4, 4, 42), arb::synapse("expsyn"), "1_synapse");
-                decor.place(uniform(all(), 5, 5, 42), arb::threshold_detector{10}, "1_detector");
+                decor.place(uniform(all(), 5, 5, 42), arb::threshold_detector{10*arb::units::mV}, "1_detector");
 
                 cells_.push_back(arb::cable_cell(arb::morphology(tree), decor));
             }
             {
                 arb::decor decor;
                 decor.set_default(arb::cv_policy_fixed_per_branch(10));
-                decor.place(uniform(all(), 0, 2, 24), arb::threshold_detector{10}, "3_detectors");
-                decor.place(uniform(all(), 3, 4, 24), arb::threshold_detector{10}, "2_detectors");
+                decor.place(uniform(all(), 0, 2, 24), arb::threshold_detector{10*arb::units::mV}, "3_detectors");
+                decor.place(uniform(all(), 3, 4, 24), arb::threshold_detector{10*arb::units::mV}, "2_detectors");
                 decor.place(uniform(all(), 5, 6, 24), arb::junction("gj"), "2_gap_junctions");
                 decor.place(uniform(all(), 7, 7, 24), arb::junction("gj"), "1_gap_junction");
 
diff --git a/test/unit/test_lif_cell_group.cpp b/test/unit/test_lif_cell_group.cpp
index 7a03cd8022084fa12f354c3d0c3cc4a7cec3f736..8e13b222faa0b5b1e9bd153cd00c943110b9b124 100644
--- a/test/unit/test_lif_cell_group.cpp
+++ b/test/unit/test_lif_cell_group.cpp
@@ -12,9 +12,11 @@
 #include <arbor/simulation.hpp>
 #include <arbor/spike_source_cell.hpp>
 
-#include "lif_cell_group.hpp"
-
 using namespace arb;
+
+namespace U = arb::units;
+using namespace U::literals;
+
 // Simple ring network of LIF neurons.
 // with one regularly spiking cell (fake cell) connected to the first cell in the ring.
 class ring_recipe: public arb::recipe {
@@ -44,14 +46,14 @@ public:
         std::vector<cell_connection> connections;
         // gid-1 >= 0 since gid != 0
         auto src_gid = (gid - 1) % n_lif_cells_;
-        cell_connection conn({src_gid, "src"}, {"tgt"}, weight_, delay_);
+        cell_connection conn({src_gid, "src"}, {"tgt"}, weight_, delay_*U::ms);
         connections.push_back(conn);
 
         // If first LIF cell, then add
         // the connection from the last LIF cell as well
         if (gid == 1) {
             auto src_gid = n_lif_cells_;
-            cell_connection conn({src_gid, "src"}, {"tgt"}, weight_, delay_);
+            cell_connection conn({src_gid, "src"}, {"tgt"}, weight_, delay_*U::ms);
             connections.push_back(conn);
         }
 
@@ -62,7 +64,7 @@ public:
         // regularly spiking cell.
         if (gid == 0) {
             // Produces just a single spike at time 0ms.
-            return spike_source_cell("src", explicit_schedule({0.f}));
+            return spike_source_cell("src", explicit_schedule_from_milliseconds({0.}));
         }
         // LIF cell.
         auto cell = lif_cell("src", "tgt");
@@ -90,14 +92,8 @@ public:
     }
 
     std::vector<cell_connection> connections_on(cell_gid_type gid) const override {
-        if (gid == 0) {
-            return {};
-        }
-        std::vector<cell_connection> connections;
-        cell_connection conn({gid-1, "src"}, {"tgt"}, weight_, delay_);
-        connections.push_back(conn);
-
-        return connections;
+        if (gid == 0) return {};
+        return {{{gid-1, "src"}, {"tgt"}, weight_, delay_*U::ms}};
     }
 
     util::unique_any get_cell_description(cell_gid_type gid) const override {
@@ -127,17 +123,17 @@ public:
         for (size_t ix = 0; ix < n_conn_; ++ix) res.emplace_back(cell_global_label_type{0, "src"},
                                                                  cell_local_label_type{"tgt"},
                                                                  0.0,
-                                                                 0.005);
+                                                                 5*U::us);
         return res;
     }
     util::unique_any get_cell_description(cell_gid_type gid) const override {
         auto cell = lif_cell("src", "tgt");
         if (gid == 0) {
-            cell.E_R = -23;
-            cell.V_m = -18;
-            cell.E_L = -13;
-            cell.t_ref = 0.8;
-            cell.tau_m = 5;
+            cell.E_R = -23.0*U::mV;
+            cell.V_m = -18.0*U::mV;
+            cell.E_L = -13.0*U::mV;
+            cell.t_ref = 0.8*U::ms;
+            cell.tau_m = 5*U::ms;
         }
         return cell;
     }
@@ -149,7 +145,7 @@ public:
         }
     }
     std::vector<event_generator> event_generators(cell_gid_type) const override {
-        return {regular_generator({"tgt"}, 200.0, 2.0, 1.0, 6.0)};
+        return {regular_generator({"tgt"}, 200.0, 2.0*U::ms, 1.0*U::ms, 6.0*U::ms)};
     }
 
     size_t n_conn_ = 0;
@@ -195,10 +191,7 @@ TEST(lif_cell_group, spikes) {
     events.push_back({0, {{0, 50, 1000}}});
 
     sim.inject_events(events);
-
-    time_type tfinal = 100;
-    time_type dt = 0.01;
-    sim.run(tfinal, dt);
+    sim.run(100*U::ms, 0.01*U::ms);
 
     // we expect 4 spikes: 2 by both neurons
     EXPECT_EQ(4u, sim.num_spikes());
@@ -211,9 +204,6 @@ TEST(lif_cell_group, ring)
     double weight = 1000;
     double delay = 1;
 
-    // Total simulation time.
-    time_type simulation_time = 100;
-
     auto recipe = ring_recipe(num_lif_cells, weight, delay);
     // Creates a simulation with a ring recipe of lif neurons
     simulation sim(recipe);
@@ -227,7 +217,7 @@ TEST(lif_cell_group, ring)
     );
 
     // Runs the simulation for simulation_time with given timestep
-    sim.run(simulation_time, 0.01);
+    sim.run(100*U::ms, 0.01*U::ms);
     // The total number of cells in all the cell groups.
     // There is one additional fake cell (regularly spiking cell).
     EXPECT_EQ(num_lif_cells + 1u, recipe.num_cells());
@@ -274,7 +264,7 @@ TEST(lif_cell_group, probe) {
     auto rec = probe_recipe{};
     auto sim = simulation(rec);
 
-    sim.add_sampler(all_probes, regular_schedule(0.025), fun);
+    sim.add_sampler(all_probes, regular_schedule(0.025*U::ms), fun);
 
     std::vector<double> spikes;
 
@@ -282,7 +272,7 @@ TEST(lif_cell_group, probe) {
         [&spikes](const std::vector<spike>& spk) { for (const auto& s: spk) spikes.push_back(s.time); }
     );
 
-    sim.run(10, 0.005);
+    sim.run(10*U::ms, 0.005*U::ms);
     std::vector<Um_type> exp = {{ 0, -18 },
                                 { 0.025, -17.9750624 },
                                 { 0.05, -17.9502492 },
@@ -710,7 +700,7 @@ TEST(lif_cell_group, probe_with_connections) {
     auto rec = probe_recipe{5};
     auto sim = simulation(rec);
 
-    sim.add_sampler(all_probes, regular_schedule(0.025), fun);
+    sim.add_sampler(all_probes, regular_schedule(0.025*U::ms), fun);
 
     std::vector<double> spikes;
 
@@ -718,7 +708,7 @@ TEST(lif_cell_group, probe_with_connections) {
         [&spikes](const std::vector<spike>& spk) { for (const auto& s: spk) spikes.push_back(s.time); }
     );
 
-    sim.run(10, 0.005);
+    sim.run(10*U::ms, 0.005*U::ms);
     std::vector<Um_type> exp = {{ 0, -18 },
                                 { 0.025, -17.9750624 },
                                 { 0.05, -17.9502492 },
diff --git a/test/unit/test_merge_events.cpp b/test/unit/test_merge_events.cpp
index 7b17c4094bfbab4de6f65356bbc52f36722b9ab8..98060b610e761bba9eff9c94e14fa92cb2eabc06 100644
--- a/test/unit/test_merge_events.cpp
+++ b/test/unit/test_merge_events.cpp
@@ -28,13 +28,15 @@ static void merge_events(
     const pse_vector& old_events,
     pse_vector& pending,
     std::vector<event_generator>& generators,
-    pse_vector& new_events)
-{
+    pse_vector& new_events) {
     util::sort(pending);
-    merge_cell_events(t_from, t_to, util::range_pointer_view(old_events), util::range_pointer_view(pending), generators, new_events);
+    merge_cell_events(t_from, t_to,
+                      util::range_pointer_view(old_events),
+                      util::range_pointer_view(pending),
+                      generators,
+                      new_events);
 }
 
-
 std::vector<event_generator> empty_gens;
 
 // Test the trivial case of merging empty sets
@@ -157,7 +159,7 @@ TEST(merge_events, X)
         {0, 26, 4},
     };
 
-    auto gen = regular_generator({"l0"}, 42.f, t0, 5);
+    auto gen = regular_generator({"l0"}, 42.f, t0*arb::units::ms, 5*arb::units::ms);
     gen.resolve_label([](const cell_local_label_type&) {return 2;});
     std::vector<event_generator> generators = {gen};
 
@@ -203,8 +205,8 @@ TEST(merge_events, tourney_seq)
     util::sort(expected);
 
     auto
-        g1 = explicit_generator(l0, w1, times),
-        g2 = explicit_generator(l0, w2, times);
+        g1 = explicit_generator_from_milliseconds(l0, w1, times),
+        g2 = explicit_generator_from_milliseconds(l0, w2, times);
     g1.resolve_label([](const cell_local_label_type&) {return 0;});
     g2.resolve_label([](const cell_local_label_type&) {return 0;});
 
@@ -223,9 +225,7 @@ TEST(merge_events, tourney_seq)
 }
 
 // Test the tournament tree on a large set of Poisson generators.
-TEST(merge_events, tourney_poisson)
-{
-    using rndgen = std::mt19937_64;
+TEST(merge_events, tourney_poisson) {
     // Number of poisson generators.
     // Not a power of 2, so that there will be "null" leaf nodes in the
     // tournament tree.
@@ -241,8 +241,8 @@ TEST(merge_events, tourney_poisson)
         float weight = i;
         // the first and last generators have the same seed to test that sorting
         // of events with the same time but different weights works properly.
-        rndgen G(i%(ngen-1));
-        auto gen = poisson_generator(label, weight, t0, lambda, G);
+        auto G = i%(ngen-1);
+        auto gen = poisson_generator(label, weight, t0*arb::units::ms, lambda*arb::units::kHz, G);
         gen.resolve_label([lid](const cell_local_label_type&) {return lid;});
         generators.push_back(std::move(gen));
     }
diff --git a/test/unit/test_probe.cpp b/test/unit/test_probe.cpp
index fc87b1cd4300bed134297ee32e7306043a514343..6b82baecc7ce8732aaad6c7611b4cd2163e43388 100644
--- a/test/unit/test_probe.cpp
+++ b/test/unit/test_probe.cpp
@@ -1,7 +1,6 @@
 #include <gtest/gtest.h>
 
 #include <cmath>
-#include <map>
 #include <vector>
 
 #include <arbor/cable_cell.hpp>
@@ -40,6 +39,7 @@
 
 using namespace arb;
 using util::any_cast;
+namespace U = arb::units;
 
 using multicore_fvm_cell = fvm_lowered_cell_impl<multicore::backend>;
 using multicore_shared_state = multicore::backend::shared_state;
@@ -110,7 +110,7 @@ void run_v_i_probe_test(context ctx) {
 
     bs.decorations.set_default(cv_policy_fixed_per_branch(1));
 
-    auto stim = i_clamp::box(0, 100, 0.3);
+    auto stim = i_clamp::box(0.*U::ms, 100*U::ms, 0.3*U::nA);
     bs.decorations.place(mlocation{1, 1}, stim, "clamp");
 
     cable1d_recipe rec((cable_cell(bs)));
@@ -778,15 +778,15 @@ void run_axial_and_ion_current_sampled_probe_test(context ctx) {
     cv_policy policy = cv_policy_fixed_per_branch(n_cv);
     d.set_default(policy);
 
-    d.place(mlocation{0, 0}, i_clamp(0.3), "clamp");
+    d.place(mlocation{0, 0}, i_clamp(0.3*U::nA), "clamp");
 
     // The time constant will be membrane capacitance / membrane conductance.
     // For τ = 0.1 ms, set conductance to 0.01 S/cm² and membrance capacitance
     // to 0.01 F/m².
 
     d.paint(reg::all(), density("ca_linear", {{"g", 0.01}})); // [S/cm²]
-    d.set_default(membrane_capacitance{0.01}); // [F/m²]
-    const double tau = 0.1; // [ms]
+    d.set_default(membrane_capacitance{0.01*U::F/U::m2}); // [F/m²]
+    auto tau = 0.1*U::ms;
 
     cable1d_recipe rec(cable_cell(m, d));
     rec.catalogue() = cat;
@@ -826,7 +826,7 @@ void run_axial_and_ion_current_sampled_probe_test(context ctx) {
     std::vector<double> i_axial(n_axial_probe);
     std::vector<double> i_memb(n_cv), i_stim(n_cv);
 
-    sim.add_sampler(all_probes, explicit_schedule({20*tau}),
+    sim.add_sampler(all_probes, explicit_schedule(std::vector{20*tau}),
         [&](probe_metadata pm,
             std::size_t n_sample,
             const sample_record* samples) {
@@ -869,13 +869,13 @@ void run_axial_and_ion_current_sampled_probe_test(context ctx) {
             }
         });
 
-    const double dt = 0.025; // [ms]
-    sim.run(20*tau+dt, dt);
+    auto dt = 0.025*U::ms; // [ms]
+    sim.run(20*tau + dt, dt);
 
     ASSERT_EQ(n_cv, i_memb.size());
     ASSERT_EQ(n_cv, i_stim.size());
 
-    for (unsigned i = 0; i<n_cv; ++i) {
+    for (unsigned i = 0; i < n_cv; ++i) {
         // Axial currents are in the distal (increasing CV index) direction,
         // while membrane currents are from intra- to extra-cellular medium.
         //
@@ -897,11 +897,11 @@ void run_axial_and_ion_current_sampled_probe_test(context ctx) {
 
 template <typename SampleData, typename SampleMeta = void>
 auto run_simple_samplers(const arb::context& ctx,
-                         double t_end,
+                         U::quantity t_end,
                          const std::vector<cable_cell>& cells,
                          const cell_address_type& probe,
                          const std::vector<std::any>& probe_addrs,
-                         const std::vector<double>& when) {
+                         const std::vector<U::quantity>& when) {
     cable1d_recipe rec(cells, false);
     rec.catalogue() = make_unit_test_catalogue(global_default_catalogue());
     unsigned n_probe = probe_addrs.size();
@@ -917,17 +917,17 @@ auto run_simple_samplers(const arb::context& ctx,
                         make_simple_sampler(traces[i]));
     }
 
-    sim.run(t_end, 0.025);
+    sim.run(t_end, 0.025*U::ms);
     return traces;
 }
 
 template <typename SampleData, typename SampleMeta = void>
 auto run_simple_sampler(const arb::context& ctx,
-                        double t_end,
+                        U::quantity t_end,
                         const std::vector<cable_cell>& cells,
                         const cell_address_type& probe,
                         const std::any& probe_addr,
-                        const std::vector<double>& when) {
+                        const std::vector<U::quantity>& when) {
     return run_simple_samplers<SampleData, SampleMeta>(ctx, t_end, cells, probe, {probe_addr}, when).at(0);
 }
 
@@ -945,10 +945,11 @@ void run_multi_probe_test(context ctx) {
     d.paint(reg::branch(2), density("param_as_state", {{"p", 20.}}));
     d.paint(reg::branch(5), density("param_as_state", {{"p", 50.}}));
 
-    auto tracev = run_simple_sampler<double, mlocation>(ctx, 0.1,
+    auto tracev = run_simple_sampler<double, mlocation>(ctx, 0.1*U::ms,
                                                         {cable_cell{m, d}},
                                                         {0, "probe"},
-                                                        cable_probe_density_state{ls::terminal(), "param_as_state", "s"}, {0.});
+                                                        cable_probe_density_state{ls::terminal(), "param_as_state", "s"},
+                                                        {0.0*U::ms});
 
     // Expect to have received a sample on each of the terminals of branches 1, 2, and 5.
     ASSERT_EQ(3u, tracev.size());
@@ -984,14 +985,14 @@ void run_v_sampled_probe_test(context ctx) {
     // samples at the same point on each cell will give the same value at
     // 0.3 ms, but different at 0.6 ms.
 
-    d0.place(mlocation{1, 1}, i_clamp::box(0, 0.5, 1.), "clamp0");
-    d1.place(mlocation{1, 1}, i_clamp::box(0, 1.0, 1.), "clamp1");
+    d0.place(mlocation{1, 1}, i_clamp::box(0*U::ms, 0.5*U::ms, 1.*U::nA), "clamp0");
+    d1.place(mlocation{1, 1}, i_clamp::box(0*U::ms, 1.0*U::ms, 1.*U::nA), "clamp1");
     mlocation probe_loc{1, 0.2};
 
     std::vector<cable_cell> cells = {{bs.morph, d0, bs.labels}, {bs.morph, d1, bs.labels}};
 
-    const double t_end = 1.; // [ms]
-    std::vector<double> when = {0.3, 0.6}; // Sample at 0.3 and 0.6 ms.
+    const auto t_end = 1.*U::ms; // [ms]
+    std::vector when = {0.3*U::ms, 0.6*U::ms}; // Sample at 0.3 and 0.6 ms.
 
     auto trace0 = run_simple_sampler<double, mlocation>(ctx, t_end, cells, {0, "Um-loc"},
                                                         cable_probe_membrane_voltage{probe_loc},
@@ -1038,14 +1039,14 @@ void run_total_current_probe_test(context ctx) {
     // For τ = 0.1 ms, set conductance to 0.01 S/cm² and membrance capacitance
     // to 0.01 F/m².
 
-    const double tau = 0.1;     // [ms]
-    d0.place(mlocation{0, 0}, i_clamp(0.3), "clamp0");
+    auto tau = 0.1*U::ms;     // [ms]
+    d0.place(mlocation{0, 0}, i_clamp(0.3*U::nA), "clamp0");
 
     d0.paint(reg::all(), density("ca_linear", {{"g", 0.01}})); // [S/cm²]
-    d0.set_default(membrane_capacitance{0.01}); // [F/m²]
+    d0.set_default(membrane_capacitance{0.01*U::F/U::m2}); // [F/m²]
     // Tweak membrane capacitance on cells[1] so as to change dynamics a bit.
     auto d1 = d0;
-    d1.set_default(membrane_capacitance{0.009}); // [F/m²]
+    d1.set_default(membrane_capacitance{0.009*U::F/U::m2}); // [F/m²]
 
     // We'll run each set of tests twice: once with a trivial (zero-volume) CV
     // at the fork points, and once with a non-trivial CV centred on the fork
@@ -1069,19 +1070,22 @@ void run_total_current_probe_test(context ctx) {
         for (unsigned i = 0; i<2; ++i) {
             SCOPED_TRACE(i);
 
-            const double t_end = 21*tau; // [ms]
+            auto t_end = 21*tau; // [ms]
 
             traces[i] = run_simple_sampler<std::vector<double>, mcable_list>(ctx, t_end, cells,
                                                                              {i, "Itotal"},
-                                                                             cable_probe_total_current_cell{}, {tau, 20*tau}).at(0);
+                                                                             cable_probe_total_current_cell{},
+                                                                             {tau, 20*tau}).at(0);
 
             ion_traces[i] = run_simple_sampler<std::vector<double>, mcable_list>(ctx, t_end, cells,
                                                                                  {i, "Iion"},
-                                                                                 cable_probe_total_ion_current_cell{}, {tau, 20*tau}).at(0);
+                                                                                 cable_probe_total_ion_current_cell{},
+                                                                                 {tau, 20*tau}).at(0);
 
             stim_traces[i] = run_simple_sampler<std::vector<double>, mcable_list>(ctx, t_end, cells,
                                                                                   {i, "Istim"},
-                                                                                  cable_probe_stimulus_current_cell{}, {tau, 20*tau}).at(0);
+                                                                                  cable_probe_stimulus_current_cell{},
+                                                                                  {tau, 20*tau}).at(0);
 
             ASSERT_EQ(2u, traces[i].size());
             ASSERT_EQ(2u, ion_traces[i].size());
@@ -1157,19 +1161,21 @@ void run_stimulus_probe_test(context ctx) {
     // Model two simple stick cable cells, 3 CVs each, and stimuli on cell 0, cv 1
     // and cell 1, cv 2. Run both cells in the same cell group.
 
-    const double stim_until = 1.; // [ms]
+    auto stim_from = 0.*U::ms;
+    auto stim_until = 1.*U::ms;
+
     auto m = make_stick_morphology();
     cv_policy policy = cv_policy_fixed_per_branch(3);
 
     decor d0, d1;
     d0.set_default(policy);
-    d0.place(mlocation{0, 0.5}, i_clamp::box(0., stim_until, 10.), "clamp0");
-    d0.place(mlocation{0, 0.5}, i_clamp::box(0., stim_until, 20.), "clamp1");
+    d0.place(mlocation{0, 0.5}, i_clamp::box(stim_from, stim_until, 10.*U::nA), "clamp0");
+    d0.place(mlocation{0, 0.5}, i_clamp::box(stim_from, stim_until, 20.*U::nA), "clamp1");
     double expected_stim0 = 30;
 
     d1.set_default(policy);
-    d1.place(mlocation{0, 1}, i_clamp::box(0., stim_until, 30.), "clamp0");
-    d1.place(mlocation{0, 1}, i_clamp::box(0., stim_until, -10.), "clamp1");
+    d1.place(mlocation{0, 1}, i_clamp::box(stim_from, stim_until,  30.*U::nA), "clamp0");
+    d1.place(mlocation{0, 1}, i_clamp::box(stim_from, stim_until, -10.*U::nA), "clamp1");
     double expected_stim1 = 20;
 
     std::vector<cable_cell> cells = {{m, d0}, {m, d1}};
@@ -1181,7 +1187,7 @@ void run_stimulus_probe_test(context ctx) {
     for (unsigned i: {0u, 1u}) {
         traces[i] = run_simple_sampler<std::vector<double>, mcable_list>(ctx, 2.5*stim_until, cells, {i, "Istim"},
                                                                          cable_probe_stimulus_current_cell{},
-                                                                         {stim_until/2, 2*stim_until}).at(0);
+                                                                         {0.5*stim_until, 2*stim_until}).at(0);
 
         ASSERT_EQ(3u, traces[i].meta.size());
         for ([[maybe_unused]] unsigned cv: {0u, 1u, 2u}) {
diff --git a/test/unit/test_recipe.cpp b/test/unit/test_recipe.cpp
index 27b04cb7498d6d8fc712ac6e0308a3c8b386bb84..c388f357b7740fd31817c14692d1266723bbad83 100644
--- a/test/unit/test_recipe.cpp
+++ b/test/unit/test_recipe.cpp
@@ -17,83 +17,73 @@
 #include "../common_cells.hpp"
 
 using namespace arb;
-using arb::util::make_span;
+namespace U = arb::units;
 
 namespace {
-    class custom_recipe: public recipe {
-    public:
-        custom_recipe(std::vector<cable_cell> cells,
-                      std::vector<std::vector<cell_connection>> conns,
-                      std::vector<std::vector<gap_junction_connection>> gjs,
-                      std::vector<std::vector<arb::event_generator>> gens):
-            num_cells_(cells.size()),
-            connections_(conns),
-            gap_junctions_(gjs),
-            event_generators_(gens),
-            cells_(cells) {}
-
-        cell_size_type num_cells() const override {
-            return num_cells_;
-        }
-        arb::util::unique_any get_cell_description(cell_gid_type gid) const override {
-            return cells_.at(gid);
-        }
-        cell_kind get_cell_kind(cell_gid_type gid) const override {
-            return cell_kind::cable;
-        }
-        std::vector<gap_junction_connection> gap_junctions_on(cell_gid_type gid) const override {
-            return gap_junctions_.at(gid);
-        }
-        std::vector<cell_connection> connections_on(cell_gid_type gid) const override {
-            return connections_.at(gid);
-        }
-        std::vector<arb::event_generator> event_generators(cell_gid_type gid) const override {
-            return event_generators_.at(gid);
-        }
-        std::any get_global_properties(cell_kind) const override {
-            arb::cable_cell_global_properties a;
-            a.default_parameters = arb::neuron_parameter_defaults;
-            return a;
-        }
-
-    private:
-        cell_size_type num_cells_;
-        std::vector<std::vector<cell_connection>> connections_;
-        std::vector<std::vector<gap_junction_connection>> gap_junctions_;
-        std::vector<std::vector<arb::event_generator>> event_generators_;
-        std::vector<cable_cell> cells_;
-    };
-
-    cable_cell custom_cell(cell_size_type num_detectors, cell_size_type num_synapses, cell_size_type num_gj) {
-        arb::segment_tree tree;
-        tree.append(arb::mnpos, {0,0,0,10}, {0,0,20,10}, 1); // soma
-        tree.append(0, {0,0, 20, 2}, {0,0, 320, 2}, 3);  // dendrite
-
-        arb::cable_cell cell(tree, {});
-
-        arb::decor decorations;
-
-        // Add a num_detectors detectors to the cell.
-        for (auto i: util::make_span(num_detectors)) {
-            decorations.place(arb::mlocation{0,(double)i/num_detectors}, arb::threshold_detector{10}, "detector"+std::to_string(i));
-        }
-
-        // Add a num_synapses synapses to the cell.
-        for (auto i: util::make_span(num_synapses)) {
-            decorations.place(arb::mlocation{0,(double)i/num_synapses}, arb::synapse("expsyn"), "synapse"+std::to_string(i));
-        }
-
-        // Add a num_gj gap_junctions to the cell.
-        for (auto i: util::make_span(num_gj)) {
-            decorations.place(arb::mlocation{0,(double)i/num_gj}, arb::junction("gj"), "gapjunction"+std::to_string(i));
-        }
-
-        return arb::cable_cell(tree, decorations);
+struct custom_recipe: public recipe {
+    custom_recipe(std::vector<cable_cell> cells,
+                  std::vector<std::vector<cell_connection>> conns,
+                  std::vector<std::vector<gap_junction_connection>> gjs,
+                  std::vector<std::vector<arb::event_generator>> gens):
+        num_cells_(cells.size()),
+        connections_(std::move(conns)),
+        gap_junctions_(std::move(gjs)),
+        event_generators_(std::move(gens)),
+        cells_(std::move(cells)) {
+        gprop.default_parameters = arb::neuron_parameter_defaults;
     }
+
+    cell_size_type num_cells() const override { return num_cells_; }
+    arb::util::unique_any get_cell_description(cell_gid_type gid) const override { return cells_.at(gid); }
+    cell_kind get_cell_kind(cell_gid_type gid) const override { return cell_kind::cable; }
+    std::vector<gap_junction_connection> gap_junctions_on(cell_gid_type gid) const override { return gap_junctions_.at(gid); }
+    std::vector<cell_connection> connections_on(cell_gid_type gid) const override { return connections_.at(gid); }
+    std::vector<arb::event_generator> event_generators(cell_gid_type gid) const override { return event_generators_.at(gid); }
+    std::any get_global_properties(cell_kind) const override { return gprop; }
+
+private:
+    cell_size_type num_cells_;
+    std::vector<std::vector<cell_connection>> connections_;
+    std::vector<std::vector<gap_junction_connection>> gap_junctions_;
+    std::vector<std::vector<arb::event_generator>> event_generators_;
+    std::vector<cable_cell> cells_;
+    arb::cable_cell_global_properties gprop;
+};
+
+cable_cell custom_cell(cell_size_type num_detectors, cell_size_type num_synapses, cell_size_type num_gj) {
+    arb::segment_tree tree;
+    tree.append(arb::mnpos, {0,0,0,10}, {0,0,20,10}, 1); // soma
+    tree.append(0, {0,0, 20, 2}, {0,0, 320, 2}, 3);  // dendrite
+
+    arb::cable_cell cell(tree, {});
+
+    arb::decor decorations;
+
+    // Add a num_detectors detectors to the cell.
+    for (auto i: util::make_span(num_detectors)) {
+        decorations.place(arb::mlocation{0,(double)i/num_detectors},
+                          arb::threshold_detector{10*arb::units::mV}, "detector"+std::to_string(i));
+    }
+
+    // Add a num_synapses synapses to the cell.
+    for (auto i: util::make_span(num_synapses)) {
+        decorations.place(arb::mlocation{0,(double)i/num_synapses},
+                          arb::synapse("expsyn"),
+                          "synapse"+std::to_string(i));
+    }
+
+    // Add a num_gj gap_junctions to the cell.
+    for (auto i: util::make_span(num_gj)) {
+        decorations.place(arb::mlocation{0,(double)i/num_gj},
+                          arb::junction("gj"),
+                          "gapjunction"+std::to_string(i));
+    }
+
+    return arb::cable_cell(tree, decorations);
 }
+} // namespace
 
-TEST(recipe, gap_junctions)
-{
+TEST(recipe, gap_junctions) {
     auto context = make_context({arbenv::default_concurrency(), -1});
 
     auto cell_0 = custom_cell(0, 0, 3);
@@ -139,13 +129,13 @@ TEST(recipe, connections)
     auto cell_1 = custom_cell(2, 1, 0);
     std::vector<arb::cell_connection> conns_0, conns_1;
     {
-        conns_0 = {{{1, "detector0"}, {"synapse0"}, 0.1, 0.1},
-                   {{1, "detector1"}, {"synapse0"}, 0.1, 0.1},
-                   {{1, "detector0"}, {"synapse1"}, 0.2, 0.4}};
+        conns_0 = {{{1, "detector0"}, {"synapse0"}, 0.1, 0.1*U::ms},
+                   {{1, "detector1"}, {"synapse0"}, 0.1, 0.1*U::ms},
+                   {{1, "detector0"}, {"synapse1"}, 0.2, 0.4*U::ms}};
 
-        conns_1 = {{{0, "detector0"}, {"synapse0"}, 0.1, 0.2},
-                   {{0, "detector0"}, {"synapse0"}, 0.3, 0.1},
-                   {{0, "detector0"}, {"synapse0"}, 0.1, 0.8}};
+        conns_1 = {{{0, "detector0"}, {"synapse0"}, 0.1, 0.2*U::ms},
+                   {{0, "detector0"}, {"synapse0"}, 0.3, 0.1*U::ms},
+                   {{0, "detector0"}, {"synapse0"}, 0.1, 0.8*U::ms}};
 
         auto recipe_0 = custom_recipe({cell_0, cell_1}, {conns_0, conns_1}, {{}, {}},  {{}, {}});
         auto decomp_0 = partition_load_balance(recipe_0, context);
@@ -153,13 +143,13 @@ TEST(recipe, connections)
         EXPECT_NO_THROW(simulation(recipe_0, context, decomp_0));
     }
     {
-        conns_0 = {{{1, "detector0"}, {"synapse0"}, 0.1, 0.1},
-                   {{2, "detector1"}, {"synapse0"}, 0.1, 0.1},
-                   {{1, "detector0"}, {"synapse1"}, 0.2, 0.4}};
+        conns_0 = {{{1, "detector0"}, {"synapse0"}, 0.1, 0.1*U::ms},
+                   {{2, "detector1"}, {"synapse0"}, 0.1, 0.1*U::ms},
+                   {{1, "detector0"}, {"synapse1"}, 0.2, 0.4*U::ms}};
 
-        conns_1 = {{{0, "detector0"}, {"synapse0"}, 0.1, 0.2},
-                   {{0, "detector0"}, {"synapse0"}, 0.3, 0.1},
-                   {{0, "detector0"}, {"synapse0"}, 0.1, 0.8}};
+        conns_1 = {{{0, "detector0"}, {"synapse0"}, 0.1, 0.2*U::ms},
+                   {{0, "detector0"}, {"synapse0"}, 0.3, 0.1*U::ms},
+                   {{0, "detector0"}, {"synapse0"}, 0.1, 0.8*U::ms}};
 
         auto recipe_1 = custom_recipe({cell_0, cell_1}, {conns_0, conns_1}, {{}, {}},  {{}, {}});
         auto decomp_1 = partition_load_balance(recipe_1, context);
@@ -167,13 +157,13 @@ TEST(recipe, connections)
         EXPECT_THROW(simulation(recipe_1, context, decomp_1), arb::bad_connection_source_gid);
     }
     {
-        conns_0 = {{{1, "detector0"}, {"synapse0"}, 0.1, 0.1},
-                   {{1, "detector1"}, {"synapse0"}, 0.1, 0.1},
-                   {{1, "detector3"}, {"synapse1"}, 0.2, 0.4}};
+        conns_0 = {{{1, "detector0"}, {"synapse0"}, 0.1, 0.1*U::ms},
+                   {{1, "detector1"}, {"synapse0"}, 0.1, 0.1*U::ms},
+                   {{1, "detector3"}, {"synapse1"}, 0.2, 0.4*U::ms}};
 
-        conns_1 = {{{0, "detector0"}, {"synapse0"}, 0.1, 0.2},
-                   {{0, "detector0"}, {"synapse0"}, 0.3, 0.1},
-                   {{0, "detector0"}, {"synapse0"}, 0.1, 0.8}};
+        conns_1 = {{{0, "detector0"}, {"synapse0"}, 0.1, 0.2*U::ms},
+                   {{0, "detector0"}, {"synapse0"}, 0.3, 0.1*U::ms},
+                   {{0, "detector0"}, {"synapse0"}, 0.1, 0.8*U::ms}};
 
         auto recipe_2 = custom_recipe({cell_0, cell_1}, {conns_0, conns_1}, {{}, {}},  {{}, {}});
         auto decomp_2 = partition_load_balance(recipe_2, context);
@@ -181,13 +171,13 @@ TEST(recipe, connections)
         EXPECT_THROW(simulation(recipe_2, context, decomp_2), arb::bad_connection_label);
     }
     {
-        conns_0 = {{{1, "detector0"}, {"synapse0"}, 0.1, 0.1},
-                   {{1, "detector1"}, {"synapse0"}, 0.1, 0.1},
-                   {{1, "detector0"}, {"synapse1"}, 0.2, 0.4}};
+        conns_0 = {{{1, "detector0"}, {"synapse0"}, 0.1, 0.1*U::ms},
+                   {{1, "detector1"}, {"synapse0"}, 0.1, 0.1*U::ms},
+                   {{1, "detector0"}, {"synapse1"}, 0.2, 0.4*U::ms}};
 
-        conns_1 = {{{0, "detector0"}, {"synapse0"}, 0.1, 0.2},
-                   {{0, "detector0"}, {"synapse9"}, 0.3, 0.1},
-                   {{0, "detector0"}, {"synapse0"}, 0.1, 0.8}};
+        conns_1 = {{{0, "detector0"}, {"synapse0"}, 0.1, 0.2*U::ms},
+                   {{0, "detector0"}, {"synapse9"}, 0.3, 0.1*U::ms},
+                   {{0, "detector0"}, {"synapse0"}, 0.1, 0.8*U::ms}};
 
         auto recipe_4 = custom_recipe({cell_0, cell_1}, {conns_0, conns_1}, {{}, {}},  {{}, {}});
         auto decomp_4 = partition_load_balance(recipe_4, context);
@@ -203,18 +193,18 @@ TEST(recipe, event_generators) {
     auto cell_1 = custom_cell(2, 1, 0);
     {
         std::vector<arb::event_generator>
-            gens_0 = {arb::explicit_generator({"synapse0"}, 0.1, std::vector<arb::time_type>{1.0}),
-                      arb::explicit_generator({"synapse1"}, 0.1, std::vector<arb::time_type>{2.0})},
-            gens_1 = {arb::explicit_generator({"synapse0"}, 0.1, std::vector<arb::time_type>{1.0})};
+            gens_0 = {arb::explicit_generator_from_milliseconds({"synapse0"}, 0.1, std::vector{1.0}),
+                      arb::explicit_generator_from_milliseconds({"synapse1"}, 0.1, std::vector{2.0})},
+            gens_1 = {arb::explicit_generator_from_milliseconds({"synapse0"}, 0.1, std::vector{1.0})};
 
         auto recipe_0 = custom_recipe({cell_0, cell_1}, {{}, {}}, {{}, {}},  {gens_0, gens_1});
         auto decomp_0 = partition_load_balance(recipe_0, context);
 
-        EXPECT_NO_THROW(simulation(recipe_0, context, decomp_0).run(1, 0.1));
+        EXPECT_NO_THROW(simulation(recipe_0, context, decomp_0).run(1*arb::units::ms, 0.1*arb::units::ms));
     }
     {
         std::vector<arb::event_generator>
-            gens_0 = {arb::regular_generator({"totally-not-a-synapse-42"}, 0.1, 0, 0.001)},
+            gens_0 = {arb::regular_generator({"totally-not-a-synapse-42"}, 0.1, 0*arb::units::ms, 0.001*arb::units::ms)},
             gens_1 = {};
 
         auto recipe_0 = custom_recipe({cell_0, cell_1}, {{}, {}}, {{}, {}},  {gens_0, gens_1});
diff --git a/test/unit/test_s_expr.cpp b/test/unit/test_s_expr.cpp
index 5a7b8c876c38932bb66d52bfd147f597950b9360..17e9dc3df20be1bc072c2699c1cfe9bb57a42379 100644
--- a/test/unit/test_s_expr.cpp
+++ b/test/unit/test_s_expr.cpp
@@ -558,28 +558,28 @@ std::ostream& operator<<(std::ostream& o, const threshold_detector& p) {
     return o << "(threshold-detector " << p.threshold << ')';
 }
 std::ostream& operator<<(std::ostream& o, const init_membrane_potential& p) {
-    return o << "(membrane-potential " << p.value << ')';
+    return o << "(membrane-potential " << p.value << " " << p.scale << ')';
 }
-std::ostream& operator<<(std::ostream& o, const temperature_K& p) {
-    return o << "(temperature-kelvin " << p.value << ')';
+std::ostream& operator<<(std::ostream& o, const temperature& p) {
+    return o << "(temperature-kelvin " << p.value << " " << p.scale << ')';
 }
 std::ostream& operator<<(std::ostream& o, const axial_resistivity& p) {
-    return o << "(axial-resistivity " << p.value << ')';
+    return o << "(axial-resistivity " << p.value << " " << p.scale << ')';
 }
 std::ostream& operator<<(std::ostream& o, const membrane_capacitance& p) {
-    return o << "(membrane-capacitance " << p.value << ')';
+    return o << "(membrane-capacitance " << p.value << " " << p.scale << ')';
 }
 std::ostream& operator<<(std::ostream& o, const init_int_concentration& p) {
-    return o << "(ion-internal-concentration \"" << p.ion << "\" " << p.value << ')';
+    return o << "(ion-internal-concentration \"" << p.ion << "\" " << p.value << " " << p.scale << ')';
 }
 std::ostream& operator<<(std::ostream& o, const ion_diffusivity& p) {
-    return o << "(ion-diffusivity \"" << p.ion << "\" " << p.value << ')';
+    return o << "(ion-diffusivity \"" << p.ion << "\" " << p.value << " " << p.scale << ')';
 }
 std::ostream& operator<<(std::ostream& o, const init_ext_concentration& p) {
-    return o << "(ion-external-concentration \"" << p.ion << "\" " << p.value << ')';
+    return o << "(ion-external-concentration \"" << p.ion << "\" " << p.value << " " << p.scale << ')';
 }
 std::ostream& operator<<(std::ostream& o, const init_reversal_potential& p) {
-    return o << "(ion-reversal-potential \"" << p.ion << "\" " << p.value << ')';
+    return o << "(ion-reversal-potential \"" << p.ion << "\" " << p.value << " " << p.scale << ')';
 }
 std::ostream& operator<<(std::ostream& o, const mechanism_desc& m) {
     o << "(mechanism \"" << m.name() << "\"";
@@ -710,35 +710,15 @@ std::string round_trip_component(std::istream& stream) {
     }
 }
 
-TEST(decor_literals, double_to_iexpr_promotion) {
-    using namespace cable_s_expr;
-    std::vector<std::pair<std::string, std::string>> literals = {
-        {"(membrane-potential -65.1)", "(membrane-potential (scalar -65.1))"},
-        {"(temperature-kelvin 301)", "(temperature-kelvin (scalar 301))"},
-        {"(axial-resistivity 102)", "(axial-resistivity (scalar 102))"},
-    };
-
-    for (const auto& [in, out]: literals) {
-        std::string res;
-        if (auto x = arborio::parse_expression(in)) {
-            std::visit([&](auto&& p){res = to_string(p);}, *(eval_cast_variant<defaultable>(*x)));
-        }
-        else {
-            res = x.error().what();
-        }
-        EXPECT_EQ(res, out);
-    }
-}
-
 TEST(decor_literals, round_tripping) {
     auto paint_default_literals = {
-        "(membrane-potential (scalar -65.1))",
-        "(temperature-kelvin (scalar 301))",
-        "(axial-resistivity (scalar 102))",
-        "(membrane-capacitance (scalar 0.01))",
-        "(ion-internal-concentration \"ca\" (scalar 75.1))",
-        "(ion-external-concentration \"h\" (scalar -50.1))",
-        "(ion-reversal-potential \"na\" (scalar 30))"};
+        "(membrane-potential 2 (scalar -65.1))",
+        "(temperature-kelvin 3 (scalar 301))",
+        "(axial-resistivity 4 (scalar 102))",
+        "(membrane-capacitance 5 (scalar 0.01))",
+        "(ion-internal-concentration \"ca\" 6 (scalar 75.1))",
+        "(ion-external-concentration \"h\" 7 (scalar -50.1))",
+        "(ion-reversal-potential \"na\" 8 (scalar 30))"};
     auto paint_literals = {
         "(voltage-process (mechanism \"hh\"))",
         "(density (mechanism \"hh\"))",
@@ -791,25 +771,25 @@ TEST(decor_literals, round_tripping) {
 TEST(decor_expressions, round_tripping) {
     using namespace cable_s_expr;
     auto decorate_paint_literals = {
-        "(paint (region \"all\") (membrane-potential (scalar -65.1)))",
-        "(paint (tag 1) (temperature-kelvin (scalar 301)))",
-        "(paint (distal-interval (location 3 0)) (axial-resistivity (scalar 102)))",
-        "(paint (join (region \"dend\") (all)) (membrane-capacitance (scalar 0.01)))",
-        "(paint (radius-gt (tag 3) 1) (ion-internal-concentration \"ca\" (scalar 75.1)))",
-        "(paint (intersect (cable 2 0 0.5) (region \"axon\")) (ion-external-concentration \"h\" (scalar -50.1)))",
-        "(paint (region \"my_region\") (ion-reversal-potential \"na\" (scalar 30)))",
+        "(paint (region \"all\") (membrane-potential 1 (scalar -65.1)))",
+        "(paint (tag 1) (temperature-kelvin 2 (scalar 301)))",
+        "(paint (distal-interval (location 3 0)) (axial-resistivity 2 (scalar 102)))",
+        "(paint (join (region \"dend\") (all)) (membrane-capacitance 3 (scalar 0.01)))",
+        "(paint (radius-gt (tag 3) 1) (ion-internal-concentration \"ca\" 4 (scalar 75.1)))",
+        "(paint (intersect (cable 2 0 0.5) (region \"axon\")) (ion-external-concentration \"h\" 5 (scalar -50.1)))",
+        "(paint (region \"my_region\") (ion-reversal-potential \"na\" 6 (scalar 30)))",
         "(paint (cable 2 0.1 0.4) (density (mechanism \"hh\")))",
         "(paint (cable 2 0.1 0.4) (scaled-mechanism (density (mechanism \"pas\" (\"g\" 0.02))) (\"g\" (exp (add (distance 2.1 (region \"my_region\")) (scalar 3.2))))))",
         "(paint (all) (density (mechanism \"pas\" (\"g\" 0.02))))"
     };
     auto decorate_default_literals = {
-        "(default (membrane-potential (scalar -65.1)))",
-        "(default (temperature-kelvin (scalar 301)))",
-        "(default (axial-resistivity (scalar 102)))",
-        "(default (membrane-capacitance (scalar 0.01)))",
-        "(default (ion-internal-concentration \"ca\" (scalar 75.1)))",
-        "(default (ion-external-concentration \"h\" (scalar -50.1)))",
-        "(default (ion-reversal-potential \"na\" (scalar 30)))",
+        "(default (membrane-potential 1 (scalar -65.1)))",
+        "(default (temperature-kelvin 2 (scalar 301)))",
+        "(default (axial-resistivity 3 (scalar 102)))",
+        "(default (membrane-capacitance 4 (scalar 0.01)))",
+        "(default (ion-internal-concentration \"ca\" 5 (scalar 75.1)))",
+        "(default (ion-external-concentration \"h\" 6 (scalar -50.1)))",
+        "(default (ion-reversal-potential \"na\" 7 (scalar 30)))",
         "(default (ion-reversal-potential-method \"ca\" (mechanism \"nernst/ca\")))",
         "(default (cv-policy (max-extent 2 (region \"soma\") 2)))"
     };
@@ -875,14 +855,19 @@ TEST(morphology_literals, round_tripping) {
     }
 }
 
+TEST(decor, quantity) {
+    std::string q = "(quantity 10.0 \"Ohm\")";
+    parse_expression(q).value();
+}
+
 TEST(decor, round_tripping) {
     std::string component_str = "(arbor-component \n"
                                 "  (meta-data \n"
                                 "    (version \"" + arborio::acc_version() +"\"))\n"
                                 "  (decor \n"
                                 "    (default \n"
-                                "      (axial-resistivity \n"
-                                "        (scalar 100)))\n"
+                                "      (axial-resistivity 100.000000 \n"
+                                "        (scalar 1)))\n"
                                 "    (default \n"
                                 "      (ion-reversal-potential-method \"na\" \n"
                                 "        (mechanism \"nernst\")))\n"
@@ -915,7 +900,7 @@ TEST(decor, round_tripping) {
                                 "      (join \n"
                                 "        (tag 1)\n"
                                 "        (tag 2))\n"
-                                "      (ion-internal-concentration \"ca\" \n"
+                                "      (ion-internal-concentration \"ca\" 1.000000 \n"
                                 "        (scalar 0.5)))\n"
                                 "    (place \n"
                                 "      (location 0 0)\n"
@@ -1225,11 +1210,10 @@ TEST(doc_expressions, parse) {
                      "(mechanism \"hh\" (\"gl\" 0.5) (\"el\" 2))",
                      "(ion-reversal-potential-method \"ca\" (mechanism \"nernst/ca\"))",
                      "(current-clamp (envelope (0 10) (50 10) (50 0)) 40 0.25)",
-                     "(paint (tag 1) (membrane-capacitance 0.02))",
+                     "(paint (tag 1) (membrane-capacitance 0.02 (scalar 1)))",
                      "(place (locset \"mylocset\") (threshold-detector 10) \"mydetectors\")",
-                     "(default (membrane-potential -65))",
-                     "(segment 3 (point 0 0 0 5) (point 0 0 10 2) 1)"})
-    {
+                     "(default (membrane-potential -65.000000 (scalar 1)))",
+                     "(segment 3 (point 0 0 0 5) (point 0 0 10 2) 1)"}) {
         EXPECT_TRUE(arborio::parse_expression(expr));
     }
 
@@ -1241,9 +1225,9 @@ TEST(doc_expressions, parse) {
                      "  (region-def \"my_region\" (radius-ge (region \"my_soma\") 1.5))\n"
                      "  (locset-def \"terminal\" (terminal)))",
                      "(decor\n"
-                     "  (default (membrane-potential -55.000000))\n"
-                     "  (paint (region \"custom\") (temperature-kelvin 270))\n"
-                     "  (paint (region \"soma\") (membrane-potential -50.000000))\n"
+                     "  (default (membrane-potential -55.000000 (scalar 1)))\n"
+                     "  (paint (region \"custom\") (temperature-kelvin 270 (scalar 1)))\n"
+                     "  (paint (region \"soma\") (membrane-potential -50.000000 (scalar 1)))\n"
                      "  (paint (all) (density (mechanism \"pas\")))\n"
                      "  (paint (tag 4) (density (mechanism \"Ih\" (\"gbar\" 0.001))))\n"
                      "  (place (locset \"root\") (synapse (mechanism \"expsyn\")) \"root_synapse\")\n"
@@ -1274,9 +1258,9 @@ TEST(doc_expressions, parse) {
                      "    (region-def \"my_region\" (radius-ge (region \"my_soma\") 1.5))\n"
                      "    (locset-def \"terminal\" (terminal)))\n"
                      "  (decor\n"
-                     "    (default (membrane-potential -55.000000))\n"
-                     "    (paint (region \"my_soma\") (temperature-kelvin 270))\n"
-                     "    (paint (region \"my_region\") (membrane-potential -50.000000))\n"
+                     "    (default (membrane-potential -55.000000 (scalar 1)))\n"
+                     "    (paint (region \"my_soma\") (temperature-kelvin 270 (scalar 1)))\n"
+                     "    (paint (region \"my_region\") (membrane-potential -50.000000 (scalar 1)))\n"
                      "    (paint (tag 4) (density (mechanism \"Ih\" (\"gbar\" 0.001))))\n"
                      "    (place (locset \"root\") (synapse (mechanism \"expsyn\")) \"root_synapse\")\n"
                      "    (place (location 1 0.2) (junction (mechanism \"gj\")) \"terminal_gj\"))\n"
@@ -1315,9 +1299,9 @@ TEST(doc_expressions, parse) {
                             "(arbor-component\n"
                             "  (meta-data (version \"" + arborio::acc_version() +"\"))\n"
                             "  (decor\n"
-                            "    (default (membrane-potential -55.000000))\n"
+                            "    (default (membrane-potential -55.000000 (scalar 1)))\n"
                             "    (place (locset \"root\") (synapse (mechanism \"expsyn\")) \"root_synapse\")\n"
-                            "    (paint (region \"my_soma\") (temperature-kelvin 270))))",
+                            "    (paint (region \"my_soma\") (temperature-kelvin 270 (scalar 1)))))",
                             "(arbor-component\n"
                             "  (meta-data (version \"" + arborio::acc_version() +"\"))\n"
                             "  (morphology\n"
@@ -1332,9 +1316,9 @@ TEST(doc_expressions, parse) {
                             "      (region-def \"my_soma\" (tag 1))\n"
                             "      (locset-def \"root\" (root)))\n"
                             "    (decor\n"
-                            "      (default (membrane-potential -55.000000))\n"
+                            "      (default (membrane-potential -55.000000 (scalar 1)))\n"
                             "      (place (locset \"root\") (synapse (mechanism \"expsyn\")) \"root_synapse\")\n"
-                            "      (paint (region \"my_soma\") (temperature-kelvin 270)))\n"
+                            "      (paint (region \"my_soma\") (temperature-kelvin 270 (scalar 1))))\n"
                             "    (morphology\n"
                             "       (branch 0 -1\n"
                             "         (segment 0 (point 0 0 0 2) (point 4 0 0 2) 1)\n"
diff --git a/test/unit/test_schedule.cpp b/test/unit/test_schedule.cpp
index 8452385160443138b72cb01abf232613c7b42dce..6f6e98f30761da4267511e02fc892881ccf1f2e7 100644
--- a/test/unit/test_schedule.cpp
+++ b/test/unit/test_schedule.cpp
@@ -17,9 +17,8 @@ using namespace testing;
 
 using time_range = util::range<const time_type*>;
 
-// Pull events from n non-contiguous subintervals of [t0, t1)
-// and check for monotonicity and boundedness.
-
+// Pull events from n non-contiguous subintervals of [t0, t1) and check for
+// monotonicity and boundedness.
 void run_invariant_checks(schedule S, time_type t0, time_type t1, unsigned n, int seed=0) {
     if (!n) return;
 
@@ -45,14 +44,13 @@ void run_invariant_checks(schedule S, time_type t0, time_type t1, unsigned n, in
     }
 }
 
-// Take events from n contiguous intervals comprising [t0, t1), reset, and
-// then compare with events taken from a different set of contiguous
-// intervals comprising [t0, t1).
-
+// Take events from n contiguous intervals comprising [t0, t1), reset, and then
+// compare with events taken from a different set of contiguous intervals
+// comprising [t0, t1).
 void run_reset_check(schedule S, time_type t0, time_type t1, unsigned n, int seed=0) {
     if (!n) return;
 
-    std::minstd_rand R(seed);
+    engine_type R(seed);
     std::uniform_real_distribution<time_type> U(t0, t1);
 
     std::vector<time_type> first_div = {t0, t1};
@@ -87,7 +85,7 @@ TEST(schedule, regular) {
     // Use exact fp representations for strict equality testing.
     std::vector<time_type> expected = {0, 0.25, 0.5, 0.75, 1.0};
 
-    schedule S = regular_schedule(0.25);
+    schedule S = regular_schedule(0.25*arb::units::ms);
     EXPECT_EQ(expected, as_vector(S.events(0, 1.25)));
 
     S.reset();
@@ -100,12 +98,12 @@ TEST(schedule, regular) {
 
 TEST(schedule, regular_invariants) {
     SCOPED_TRACE("regular_invariants");
-    run_invariant_checks(regular_schedule(0.3), 3, 12, 7);
+    run_invariant_checks(regular_schedule(0.3*arb::units::ms), 3, 12, 7);
 }
 
 TEST(schedule, regular_reset) {
     SCOPED_TRACE("regular_reset");
-    run_reset_check(regular_schedule(0.3), 3, 12, 7);
+    run_reset_check(regular_schedule(0.3*arb::units::ms), 3, 12, 7);
 }
 
 TEST(schedule, regular_rounding) {
@@ -120,7 +118,7 @@ TEST(schedule, regular_rounding) {
     time_type t0 = t1-10*dt;
     time_type t2 = t1+10*dt;
 
-    schedule S = regular_schedule(t0, dt);
+    schedule S = regular_schedule(t0*arb::units::ms, dt*arb::units::ms);
     auto int_l = as_vector(S.events(t0, t1));
     auto int_r = as_vector(S.events(t1, t2));
 
@@ -144,10 +142,10 @@ TEST(schedule, regular_rounding) {
 }
 
 TEST(schedule, explicit_schedule) {
-    time_type times[] = {0.1, 0.3, 1.0, 1.25, 1.7, 2.2};
-    std::vector<time_type> expected = {0.1, 0.3, 1.0};
+    std::vector times{0.1, 0.3, 1.0, 1.25, 1.7, 2.2};
+    std::vector expected{0.1, 0.3, 1.0};
 
-    schedule S = explicit_schedule(times);
+    schedule S = explicit_schedule_from_milliseconds(times);
     EXPECT_EQ(expected, as_vector(S.events(0, 1.25)));
 
     S.reset();
@@ -161,47 +159,19 @@ TEST(schedule, explicit_schedule) {
 TEST(schedule, explicit_invariants) {
     SCOPED_TRACE("explicit_invariants");
 
-    time_type times[] = {0.1, 0.3, 0.4, 0.42, 2.1, 2.3, 6.01, 9, 9.1, 9.8, 10, 11.2, 13};
-    run_invariant_checks(explicit_schedule(times), 0.4, 10.2, 5);
+    std::vector<arb::time_type> times{0.1, 0.3, 0.4, 0.42, 2.1, 2.3, 6.01, 9, 9.1, 9.8, 10, 11.2, 13};
+    run_invariant_checks(explicit_schedule_from_milliseconds(times), 0.4, 10.2, 5);
 }
 
 TEST(schedule, explicit_reset) {
     SCOPED_TRACE("explicit_reset");
 
-    time_type times[] = {0.1, 0.3, 0.4, 0.42, 2.1, 2.3, 6.01, 9, 9.1, 9.8, 10, 11.2, 13};
-    run_reset_check(explicit_schedule(times), 0.4, 10.2, 5);
+    std::vector<arb::time_type> times{0.1, 0.3, 0.4, 0.42, 2.1, 2.3, 6.01, 9, 9.1, 9.8, 10, 11.2, 13};
+    run_reset_check(explicit_schedule_from_milliseconds(times), 0.4, 10.2, 5);
 }
 
-// A Uniform Random Bit Generator[*] adaptor that deliberately
-// skews the generated numbers by raising their quantile to
-// the given power.
-//
-// [*] Not actually uniform.
-
-template <typename RNG>
-struct skew_adaptor {
-    using result_type = typename RNG::result_type;
-    static constexpr result_type min() { return RNG::min(); }
-    static constexpr result_type max() { return RNG::max(); }
-
-    explicit skew_adaptor(double power): power_(power) {}
-    result_type operator()() {
-        constexpr double scale = (double)(max()-min());
-        constexpr double ooscale = 1./scale;
-
-        double x = ooscale*(G_()-min());
-        x = std::pow(x, power_);
-        return min()+scale*x;
-    }
-
-private:
-    RNG G_;
-    double power_;
-};
-
-template <typename RNG>
-double poisson_schedule_dispersion(int nbin, double rate_kHz, RNG& G) {
-    schedule S = poisson_schedule(rate_kHz, G);
+double poisson_schedule_dispersion(int nbin, double rate_kHz) {
+    schedule S = poisson_schedule(rate_kHz*arb::units::kHz);
 
     std::vector<int> bin(nbin);
     for (auto t: time_range(S.events(0, nbin))) {
@@ -238,8 +208,7 @@ TEST(schedule, poisson_uniformity) {
     constexpr double chi2_lb = 888.56352318146696;
     constexpr double chi2_ub = 1118.9480663231843;
 
-    std::mt19937_64 G;
-    double dispersion = poisson_schedule_dispersion(N, .813, G);
+    double dispersion = poisson_schedule_dispersion(N, .813);
     double test_value = N*dispersion;
     EXPECT_GT(test_value, chi2_lb);
     EXPECT_LT(test_value, chi2_ub);
@@ -247,34 +216,12 @@ TEST(schedule, poisson_uniformity) {
     // Run one sample K-S test for uniformity, with critical
     // value for the finite K-S statistic Dn of α=0.01.
 
-    schedule S = poisson_schedule(100., G);
+    schedule S = poisson_schedule(100.*arb::units::kHz);
     auto events = as_vector(S.events(0,1));
     int n = (int)events.size();
     double dn = ks::dn_statistic(events);
 
     EXPECT_LT(ks::dn_cdf(dn, n), 0.99);
-
-    // Check that these tests fail for a non-Poisson
-    // source.
-
-    skew_adaptor<std::mt19937_64> W(1.5);
-    dispersion = poisson_schedule_dispersion(N, .813, W);
-    test_value = N*dispersion;
-
-    EXPECT_FALSE(test_value>=chi2_lb && test_value<=chi2_ub);
-
-    S = poisson_schedule(100., W);
-    events = as_vector(S.events(0,1));
-    n = (int)events.size();
-    dn = ks::dn_statistic(events);
-
-    // This test is currently failing, because we can't
-    // use a sufficiently high `n` in the `dn_cdf` function
-    // to get enough discrimination from the K-S test at
-    // 1%. TODO: Fix this by implementing n>140 case in
-    // `dn_cdf`.
-
-    // EXPECT_GT(ks::dn_cdf(dn, n), 0.99);
 }
 
 TEST(schedule, poisson_rate) {
@@ -284,37 +231,26 @@ TEST(schedule, poisson_rate) {
     constexpr double alpha = 0.01;
     constexpr double lambda = 123.4;
 
-    std::mt19937_64 G;
-    schedule S = poisson_schedule(lambda, G);
+    schedule S = poisson_schedule(lambda*arb::units::kHz);
     int n = (int)time_range(S.events(0, 1)).size();
     double cdf = poisson::poisson_cdf_approx(n, lambda);
 
     EXPECT_GT(cdf, alpha/2);
-    EXPECT_LT(cdf, 1-alpha/2);
-
-    // Check that the test fails for a non-Poisson
-    // source.
-
-    skew_adaptor<std::mt19937_64> W(1.5);
-    S = poisson_schedule(lambda, W);
-    n = (int)time_range(S.events(0, 1)).size();
-    cdf = poisson::poisson_cdf_approx(n, lambda);
-
-    EXPECT_FALSE(cdf>=alpha/2 && cdf<=1-alpha/2);
+    EXPECT_LT(cdf, 1 - alpha/2);
 }
 
 TEST(schedule, poisson_invariants) {
     SCOPED_TRACE("poisson_invariants");
-    std::mt19937_64 G;
-    G.discard(100);
-    run_invariant_checks(poisson_schedule(0.81, G), 5.1, 15.3, 7);
+    auto sched = poisson_schedule(0.81*arb::units::kHz);
+    sched.discard(100);
+    run_invariant_checks(sched, 5.1, 15.3, 7);
 }
 
 TEST(schedule, poisson_reset) {
     SCOPED_TRACE("poisson_reset");
-    std::mt19937_64 G;
-    G.discard(200);
-    run_reset_check(poisson_schedule(.11, G), 1, 10, 7);
+    auto sched = poisson_schedule(0.11*arb::units::kHz);
+    sched.discard(200);
+    run_reset_check(sched, 1, 10, 7);
 }
 
 TEST(schedule, poisson_offset) {
@@ -322,43 +258,38 @@ TEST(schedule, poisson_offset) {
     // same sequence, after the offset, as a regular zero-based Poisson.
 
     const double offset = 3.3;
-
-    std::mt19937_64 G1;
-    G1.discard(300);
-
+    auto T = 100.0;
+    auto sched1 = poisson_schedule(.234*arb::units::kHz);
+    sched1.discard(300);
     std::vector<time_type> expected;
-    for (auto t: as_vector(poisson_schedule(.234, G1).events(0., 100.))) {
+    for (auto t: as_vector(sched1.events(0., T))) {
         t += offset;
-        if (t<100.) {
-            expected.push_back(t);
-        }
+        if (t < T) expected.push_back(t);
     }
 
-    std::mt19937_64 G2;
-    G2.discard(300);
-
+    auto sched2 = poisson_schedule(offset*arb::units::ms, .234*arb::units::kHz);
+    sched2.discard(300);
     EXPECT_TRUE(seq_almost_eq<time_type>(expected,
-        as_vector(poisson_schedule(offset, .234, G2).events(0., 100.))));
+                                         as_vector(sched2.events(0., 100.))));
 }
 
 TEST(schedule, poisson_offset_reset) {
     SCOPED_TRACE("poisson_reset");
-    std::mt19937_64 G;
-    G.discard(400);
-    run_reset_check(poisson_schedule(3.3, 9.1, G), 1, 10, 7);
+    auto sched = poisson_schedule(3.3*arb::units::ms, 0.81*arb::units::kHz);
+    sched.discard(400);
+    run_reset_check(sched, 1, 10, 7);
 }
 
 TEST(schedule, poisson_tstop) {
     SCOPED_TRACE("poisson_tstop");
-    std::mt19937_64 G;
-    G.discard(500);
-
-    const double tstop = 50;
 
-    auto const times = as_vector(poisson_schedule(0, .234, G, tstop).events(0., 100.));
+    auto T = 50.0;
+    auto sched = poisson_schedule(0*arb::units::ms, 0.234*arb::units::kHz, default_seed, T*arb::units::ms);
+    sched.discard(500);
+    auto const times = as_vector(sched.events(0., 100.));
     auto const max = std::max_element(begin(times), end(times));
 
     EXPECT_TRUE(max != end(times));
-    EXPECT_TRUE(*max <= tstop);
+    EXPECT_TRUE(*max <= T);
 }
 
diff --git a/test/unit/test_sde.cpp b/test/unit/test_sde.cpp
index 7383b7ef02b9b193b5a1c47d3b56aaf15f46b881..87f19824f35b9a4fe3a19093d442c46c7aef870a 100644
--- a/test/unit/test_sde.cpp
+++ b/test/unit/test_sde.cpp
@@ -1,9 +1,9 @@
-
 #include <gtest/gtest.h>
 
 #include <atomic>
 #include <algorithm>
 #include <cmath>
+#include <iostream>
 
 #include <arborio/label_parse.hpp>
 
@@ -14,6 +14,8 @@
 #include <arbor/schedule.hpp>
 #include <arbor/mechanism.hpp>
 #include <arbor/util/any_ptr.hpp>
+#include <arbor/units.hpp>
+
 #ifdef ARB_GPU_ENABLED
 #include "memory/gpu_wrappers.hpp"
 #endif
@@ -23,6 +25,8 @@
 #include "unit_test_catalogue.hpp"
 #include "../simple_recipes.hpp"
 
+namespace U = arb::units;
+
 // ============================
 // helper classes and functions
 // ============================
@@ -409,7 +413,7 @@ TEST(sde, reproducibility) {
     // simulation parameters
     unsigned ncells = 4;
     unsigned ncvs = 2;
-    double const dt = 0.5;
+    auto const dt = 0.5*arb::units::ms;
     unsigned nsteps = 6;
 
     // Decorations with a bunch of stochastic processes
@@ -490,7 +494,7 @@ TEST(sde, normality) {
     unsigned ncells = 4;
     unsigned nsynapses = 100;
     unsigned ncvs = 100;
-    double const dt = 0.5;
+    auto dt = 0.5*arb::units::ms;;
     unsigned nsteps = 50;
 
     // make labels (and locations for synapses)
@@ -647,7 +651,7 @@ TEST(sde, solver) {
     unsigned ncells = 4;
     unsigned nsynapses = 2000;
     unsigned ncvs = 1;
-    double const dt = 1.0/512; // need relatively small time steps due to low accuracy
+    auto dt = 1.0/512*arb::units::ms; // need relatively small time steps due to low accuracy
     unsigned nsteps = 100;
     unsigned nsims = 4;
 
@@ -773,7 +777,7 @@ TEST(sde, solver) {
 
     auto test = [&] (auto func, const auto& stats) {
         for (unsigned int i=1; i<nsteps; ++i) {
-            auto [mu, sigma_squared] = func(i*dt);
+            auto [mu, sigma_squared] = func(i*dt.value());
             double const mean = stats[i].mean();
             double const var = stats[i].variance();
 
@@ -802,7 +806,7 @@ TEST(sde, coupled) {
     unsigned ncells = 4;
     unsigned nsynapses = 2000;
     unsigned ncvs = 1;
-    double const dt = 1.0/512; // need relatively small time steps due to low accuracy
+    auto dt = 1.0/512*arb::units::ms; // need relatively small time steps due to low accuracy
     unsigned nsteps = 100;
     unsigned nsims = 4;
 
@@ -908,7 +912,7 @@ TEST(sde, coupled) {
     };
 
     for (unsigned int i=1; i<nsteps; ++i) {
-        auto ex = expected(i*dt, 0.1, 0.1, 0.1, 0.1, 1, 0.2);
+        auto ex = expected(i*dt.value(), 0.1, 0.1, 0.1, 0.1, 1, 0.2);
 
         const double E_P = ex[0];
         const double E_sigma = ex[1];
@@ -1030,7 +1034,7 @@ TEST(sde, gpu) {
     unsigned ncells = 4;
     unsigned nsynapses = 100;
     unsigned ncvs = 100;
-    double const dt = 0.5;
+    auto dt = 0.5*U::ms;
     unsigned nsteps = 50;
 
     // make labels (and locations for synapses)
diff --git a/test/unit/test_serdes.cpp b/test/unit/test_serdes.cpp
index cd0e53b15a4f7e3551d4bee12bc29e388a34e242..546362daac9d870d93215adad50487214f8b7074 100644
--- a/test/unit/test_serdes.cpp
+++ b/test/unit/test_serdes.cpp
@@ -19,6 +19,8 @@
 
 using arb::serialize;
 
+namespace U = arb::units;
+
 using json = nlohmann::json;
 using io = arborio::json_serdes;
 using serdes = arb::serializer;
@@ -143,7 +145,7 @@ struct serdes_recipe: public arb::recipe {
             .paint(arb::join(arb::reg::tagged(2), arb::reg::tagged(3)),
                    arb::density("pas"))
             .place(arb::mlocation{0, 0.0},
-                   arb::threshold_detector{10},
+                   arb::threshold_detector{10*arb::units::mV},
                    "detector")
             .place(arb::mlocation{0, 1.0},
                    arb::synapse("exp2syn"),
@@ -162,12 +164,12 @@ struct serdes_recipe: public arb::recipe {
         return {{{src, "detector"},
                  {"synapse"},
                  0.5,
-                 0.125}};
+                 0.125*U::ms}};
     }
 
     std::vector<arb::event_generator> event_generators(arb::cell_gid_type gid) const override {
         std::vector<arb::event_generator> res;
-        if (!gid) res.push_back(arb::regular_generator({"synapse"}, 1, 0.5, 0.73));
+        if (!gid) res.push_back(arb::regular_generator({"synapse"}, 1, 0.5*arb::units::ms, 0.73*arb::units::ms));
         return {};
     }
 
@@ -190,8 +192,8 @@ void sampler(arb::probe_metadata pm,
 }
 
 TEST(serdes, single_cell) {
-    double dt = 0.5;
-    double T  = 5;
+    auto dt = 0.5*arb::units::ms;
+    auto T  = 5*arb::units::ms;
 
     // Result
     std::vector<double> result_pre;
@@ -231,8 +233,8 @@ TEST(serdes, single_cell) {
 }
 
 TEST(serdes, network) {
-    double dt = 0.5;
-    double T  = 5;
+    auto dt = 0.5*arb::units::ms;
+    auto T  = 5*arb::units::ms;
 
     // Result
     std::vector<double> result_pre;
@@ -300,8 +302,8 @@ TEST(serdes, host_device_arrays) {
 }
 
 TEST(serdes, single_cell_gpu) {
-    double dt = 0.5;
-    double T  = 5;
+    auto dt = 0.5*arb::units::ms;
+    auto T  = 5*arb::units::ms;
 
     // Result
     std::vector<double> result_pre;
@@ -340,8 +342,8 @@ TEST(serdes, single_cell_gpu) {
 }
 
 TEST(serdes, network_gpu) {
-    double dt = 0.5;
-    double T  = 5;
+    auto dt = 0.5*arb::units::ms;
+    auto T  = 5*arb::units::ms;
 
     // Result
     std::vector<double> result_pre;
diff --git a/test/unit/test_simulation.cpp b/test/unit/test_simulation.cpp
index 88bdec4072d35f0d3ba23420a8345a154b9b1944..8fa4c08d572f41c224baca7ba02eecdfc558a969 100644
--- a/test/unit/test_simulation.cpp
+++ b/test/unit/test_simulation.cpp
@@ -1,6 +1,5 @@
 #include <gtest/gtest.h>
 
-#include <random>
 #include <vector>
 #include <any>
 
@@ -17,18 +16,15 @@
 #include "util/rangeutil.hpp"
 #include "util/transform.hpp"
 
-#include "common.hpp"
 using namespace arb;
+namespace U = arb::units;
 
 struct play_spikes: public recipe {
     play_spikes(std::vector<schedule> spike_times): spike_times_(std::move(spike_times)) {}
 
     cell_size_type num_cells() const override { return spike_times_.size(); }
     cell_kind get_cell_kind(cell_gid_type) const override { return cell_kind::spike_source; }
-    util::unique_any get_cell_description(cell_gid_type gid) const override {
-        return spike_source_cell("src", spike_times_.at(gid));
-    }
-
+    util::unique_any get_cell_description(cell_gid_type gid) const override { return spike_source_cell("src", spike_times_.at(gid)); }
     std::vector<schedule> spike_times_;
 };
 
@@ -52,7 +48,7 @@ TEST(simulation, null) {
     auto c = arb::make_context();
     auto d = arb::partition_load_balance(r, c);
     auto s = arb::simulation(r, c, d);
-    s.run(0.05, 0.01);
+    s.run(0.05*arb::units::ms, 0.01*arb::units::ms);
 }
 
 // Test with simulation builder
@@ -60,22 +56,23 @@ TEST(simulation, null_builder) {
     auto r = null_recipe{};
     {
         arb::simulation s = arb::simulation::create(r);
-        s.run(0.05, 0.01);
+        s.run(0.05*arb::units::ms,
+              0.01*arb::units::ms);
     }
     {
         arb::simulation s = arb::simulation::create(r).set_seed(42);
-        s.run(0.05, 0.01);
+        s.run(0.05*arb::units::ms, 0.01*arb::units::ms);
     }
     {
         auto c = arb::make_context();
         arb::simulation s = arb::simulation::create(r).set_context(c);
-        s.run(0.05, 0.01);
+        s.run(0.05*arb::units::ms, 0.01*arb::units::ms);
     }
     {
         auto c = arb::make_context();
         auto d = arb::partition_load_balance(r, c);
         arb::simulation s = arb::simulation::create(r).set_context(c).set_decomposition(d);
-        s.run(0.05, 0.01);
+        s.run(0.05*arb::units::ms, 0.01*arb::units::ms);
     }
 }
 
@@ -85,7 +82,7 @@ TEST(simulation, spike_global_callback) {
 
     std::vector<schedule> spike_times;
     for (unsigned i = 0; i<n; ++i) {
-        spike_times.push_back(poisson_schedule(0., 20./t_max, std::minstd_rand(1000+i)));
+        spike_times.push_back(poisson_schedule(0.*arb::units::ms, 20./t_max*arb::units::kHz, 1000 + i));
     }
 
     std::vector<spike> expected_spikes;
@@ -107,7 +104,7 @@ TEST(simulation, spike_global_callback) {
 
     double tfinal = 0.7*t_max;
     constexpr double dt = 0.01;
-    sim.run(tfinal, dt);
+    sim.run(tfinal*arb::units::ms, dt*arb::units::ms);
 
     auto spike_lt = [](spike a, spike b) { return a.time<b.time || (a.time==b.time && a.source<b.source); };
     std::sort(expected_spikes.begin(), expected_spikes.end(), spike_lt);
@@ -128,28 +125,20 @@ struct lif_chain: public recipe {
     util::unique_any get_cell_description(cell_gid_type) const override {
         // A hair-trigger LIF cell with tiny time constant and no refractory period.
         lif_cell lif("src", "tgt");
-        lif.tau_m = 0.01;           // time constant (ms)
-        lif.t_ref = 0;              // refactory period (ms)
-        lif.V_th = lif.E_L + 0.001; // threshold voltage 1 µV higher than resting
+        lif.tau_m = 0.01*U::ms;           // time constant (ms)
+        lif.t_ref = 0*U::ms;              // refactory period (ms)
+        lif.V_th = lif.E_L + 0.001*U::mV; // threshold voltage 1 µV higher than resting
         return lif;
     }
 
     std::vector<cell_connection> connections_on(cell_gid_type target) const override {
-        if (target) {
-            return {cell_connection({target-1, "src"}, {"tgt"}, weight_, delay_)};
-        }
-        else {
-            return {};
-        }
+        if (target) return {cell_connection({target-1, "src"}, {"tgt"}, weight_, delay_*U::ms)};
+        return {};
     }
 
     std::vector<event_generator> event_generators(cell_gid_type target) const override {
-        if (target) {
-            return {};
-        }
-        else {
-            return {event_generator({"tgt"}, weight_, triggers_)};
-        }
+        if (target) return {};
+        return {event_generator({"tgt"}, weight_, triggers_)};
     }
 
     static constexpr double weight_ = 2.0;
@@ -162,7 +151,7 @@ TEST(simulation, restart) {
     std::vector<double> trigger_times = {1., 2., 3.};
     double delay = 10;
     unsigned n = 5;
-    lif_chain rec(n, delay, explicit_schedule(trigger_times));
+    lif_chain rec(n, delay, explicit_schedule_from_milliseconds(trigger_times));
 
     // Expect spike times to be almost exactly according to trigger times,
     // plus delays along the chain of cells.
@@ -203,7 +192,7 @@ TEST(simulation, restart) {
         double t = 0;
         do {
             double run_to = std::min(tfinal, t + run_time);
-            t = sim.run(run_to, dt);
+            t = sim.run(run_to*arb::units::ms, dt*arb::units::ms);
             ASSERT_EQ(t, run_to);
         } while (t<tfinal);
         ASSERT_EQ(expected_spikes.size(), collected.size());
diff --git a/test/unit/test_spike_source.cpp b/test/unit/test_spike_source.cpp
index 7dc592ac8b9fa761f79d0d002e4d642c0ebf595f..8e77af7c93fdb5d6352e7d3e957aaa699a607b7e 100644
--- a/test/unit/test_spike_source.cpp
+++ b/test/unit/test_spike_source.cpp
@@ -14,8 +14,7 @@ using ss_recipe = homogeneous_recipe<cell_kind::spike_source, spike_source_cell>
 
 // Test that a spike_source_cell_group identifies itself with the correct
 // cell_kind enum value.
-TEST(spike_source, cell_kind)
-{
+TEST(spike_source, cell_kind) {
     ss_recipe rec(1u, spike_source_cell("src", explicit_schedule({})));
     cell_label_range srcs, tgts;
     spike_source_cell_group group({0}, rec, srcs, tgts);
@@ -37,8 +36,7 @@ static std::vector<time_type> spike_times(const std::vector<spike>& evs) {
 
 // Test that a spike_source_cell_group produces a sequence of spikes with spike
 // times corresponding to the underlying time_seq.
-TEST(spike_source, matches_time_seq)
-{
+TEST(spike_source, matches_time_seq) {
     auto test_seq = [](schedule seq) {
         ss_recipe rec(1u, spike_source_cell("src", seq));
         cell_label_range srcs, tgts;
@@ -57,16 +55,14 @@ TEST(spike_source, matches_time_seq)
         EXPECT_EQ(spike_times(group.spikes()), as_vector(seq.events(10, 20)));
     };
 
-    std::mt19937_64 G;
-    test_seq(regular_schedule(0, 1));
-    test_seq(poisson_schedule(10, G));   // produce many spikes in each interval
-    test_seq(poisson_schedule(1e-6, G)); // very unlikely to produce any spikes in either interval
+    test_seq(regular_schedule(1*arb::units::ms));
+    test_seq(poisson_schedule(10*arb::units::kHz));   // produce many spikes in each interval
+    test_seq(poisson_schedule(1e-6*arb::units::kHz)); // very unlikely to produce any spikes in either interval
 }
 
 // Test that a spike_source_cell_group will produce the same sequence of spikes
 // after being reset.
-TEST(spike_source, reset)
-{
+TEST(spike_source, reset) {
     auto test_seq = [](schedule seq) {
         ss_recipe rec(1u, spike_source_cell("src", seq));
         cell_label_range srcs, tgts;
@@ -87,16 +83,14 @@ TEST(spike_source, reset)
         EXPECT_EQ(spikes1, spikes2);
     };
 
-    std::mt19937_64 G;
-    test_seq(regular_schedule(0, 1));
-    test_seq(poisson_schedule(10, G));   // produce many spikes in each interval
-    test_seq(poisson_schedule(1e-6, G)); // very unlikely to produce any spikes in either interval
+    test_seq(regular_schedule(10*arb::units::ms));
+    test_seq(poisson_schedule(100*arb::units::kHz));   // produce many spikes in each interval
+    test_seq(poisson_schedule(1e-6*arb::units::kHz)); // very unlikely to produce any spikes in either interval
 }
 
 // Test that a spike_source_cell_group will produce the expected
 // output when the underlying time_seq is finite.
-TEST(spike_source, exhaust)
-{
+TEST(spike_source, exhaust) {
     // This test assumes that seq will exhaust itself before t=10 ms.
     auto test_seq = [](schedule seq) {
         ss_recipe rec(1u, spike_source_cell("src", seq));
@@ -112,12 +106,11 @@ TEST(spike_source, exhaust)
         EXPECT_LT(group.spikes().back().time, time_type(10));
     };
 
-    test_seq(regular_schedule(0, 1, 5));
-    test_seq(explicit_schedule({0.3, 2.3, 4.7}));
+    test_seq(regular_schedule(0*arb::units::ms, 1*arb::units::ms, 5*arb::units::ms));
+    test_seq(explicit_schedule_from_milliseconds(std::vector{0.3, 2.3, 4.7}));
 }
 
-TEST(spike_source, multiple)
-{
+TEST(spike_source, multiple) {
     // This test assumes that seq will exhaust itself before t=10 ms.
     auto test_seq = [](auto&&... seqs) {
         std::vector<schedule> schedules{seqs...};
@@ -145,13 +138,7 @@ TEST(spike_source, multiple)
         EXPECT_LT(group.spikes().back().time, time_type(10));
     };
 
-    auto seqs = std::vector<schedule>{regular_schedule(0, 1, 5),
-                                      explicit_schedule({0.3, 2.3, 4.7})};
+    std::vector seqs{regular_schedule(0*arb::units::ms, 1*arb::units::ms, 5*arb::units::ms),
+                     explicit_schedule_from_milliseconds(std::vector{0.3, 2.3, 4.7})};
     test_seq(seqs);
-    test_seq(std::vector<schedule>{regular_schedule(0, 1, 5),
-                                   explicit_schedule({0.3, 2.3, 4.7})});
-    test_seq(regular_schedule(0, 1, 5),
-             explicit_schedule({0.3, 2.3, 4.7}));
-    auto reg_sched = regular_schedule(0, 1, 5);
-    test_seq(reg_sched, explicit_schedule({0.3, 2.3, 4.7}));
 }
diff --git a/test/unit/test_spikes.cpp b/test/unit/test_spikes.cpp
index c27b6d1990a9c68a49cd094b7d1247eac96206b2..031b9bce8910a892bf3fb5e348884ed4b803521e 100644
--- a/test/unit/test_spikes.cpp
+++ b/test/unit/test_spikes.cpp
@@ -203,8 +203,8 @@ TEST(SPIKES_TEST_CLASS, threshold_watcher) {
 }
 
 TEST(SPIKES_TEST_CLASS, threshold_watcher_interpolation) {
-    double dt = 0.025;
-    double duration = 1;
+    auto dt = 0.025*arb::units::ms;
+    auto duration = 1*arb::units::ms;
 
     arb::segment_tree tree;
     tree.append(arb::mnpos, { -6.3, 0.0, 0.0, 6.3}, {  6.3, 0.0, 0.0, 6.3}, 1);
@@ -223,8 +223,8 @@ TEST(SPIKES_TEST_CLASS, threshold_watcher_interpolation) {
     for (unsigned i = 0; i < 8; i++) {
         arb::decor decor;
         decor.set_default(arb::cv_policy_every_segment());
-        decor.place("mid"_lab, arb::threshold_detector{10}, "detector");
-        decor.place("mid"_lab, arb::i_clamp::box(0.01+i*dt, duration, 0.5), "clamp");
+        decor.place("mid"_lab, arb::threshold_detector{10*arb::units::mV}, "detector");
+        decor.place("mid"_lab, arb::i_clamp::box(0.01*arb::units::ms + i*dt, duration, 0.5*arb::units::nA), "clamp");
         decor.place("mid"_lab, arb::synapse("expsyn"), "synapse");
 
         arb::cable_cell cell(morpho, decor, dict);
@@ -243,7 +243,7 @@ TEST(SPIKES_TEST_CLASS, threshold_watcher_interpolation) {
     }
 
     for (unsigned i = 1; i < spikes.size(); ++i) {
-        EXPECT_NEAR(dt, spikes[i].time - spikes[i-1].time, 1e-4);
+        EXPECT_NEAR(dt.value(), spikes[i].time - spikes[i-1].time, 1e-4);
     }
 }
 
diff --git a/test/unit/test_synapses.cpp b/test/unit/test_synapses.cpp
index 2c00ce0382933a3ffa8174d4e0e1830ed7969da7..8e56151faa8800fc6e3ebe5d367d532c6dc7593b 100644
--- a/test/unit/test_synapses.cpp
+++ b/test/unit/test_synapses.cpp
@@ -1,7 +1,6 @@
 #include <gtest/gtest.h>
 
 #include <cmath>
-#include <tuple>
 #include <vector>
 
 #include <arbor/constants.hpp>
@@ -10,7 +9,6 @@
 #include <arbor/cable_cell.hpp>
 
 #include "backends/multicore/fvm.hpp"
-#include "util/maputil.hpp"
 #include "util/range.hpp"
 
 #include "../common_cells.hpp"
diff --git a/test/unit/test_v_clamp.cpp b/test/unit/test_v_clamp.cpp
index 2142d45ce6109270ca20707d90e17a6455770036..cb5dd6d460e543c77ea5d84cda930bbaa5342427 100644
--- a/test/unit/test_v_clamp.cpp
+++ b/test/unit/test_v_clamp.cpp
@@ -41,7 +41,7 @@ struct v_proc_recipe: public arb::recipe {
                  {arb::cable_probe_membrane_voltage{"(location 0 0.625)"_ls}, "Um-(0, 0.625)"}}; // dend center: 0.75/2 + 0.25
     }
     std::vector<arb::event_generator> event_generators(arb::cell_gid_type) const override {
-        return {arb::regular_generator({"tgt"}, 5.0, 0.2, 0.05)};
+        return {arb::regular_generator({"tgt"}, 5.0, 0.2*arb::units::ms, 0.05*arb::units::ms)};
     }
     std::any get_global_properties(arb::cell_kind) const override { return gprop; }
 
@@ -90,8 +90,8 @@ TEST(v_process, clamp) {
         }
     };
     auto sim = arb::simulation(v_proc_recipe{true, false});
-    sim.add_sampler(arb::all_probes, arb::regular_schedule(0.05), fun);
-    sim.run(1.0, 0.005);
+    sim.add_sampler(arb::all_probes, arb::regular_schedule(0.05*arb::units::ms), fun);
+    sim.run(1.0*arb::units::ms, 0.005*arb::units::ms);
 
     um_s_type exp_soma{{ 0, -65 },
                        { 0.05, -42 },
@@ -158,8 +158,8 @@ TEST(v_process, limit) {
         }
     };
     auto sim = arb::simulation(v_proc_recipe{false, true});
-    sim.add_sampler(arb::all_probes, arb::regular_schedule(0.05), fun);
-    sim.run(1.0, 0.005);
+    sim.add_sampler(arb::all_probes, arb::regular_schedule(0.05*arb::units::ms), fun);
+    sim.run(1.0*arb::units::ms, 0.005*arb::units::ms);
 
     um_s_type exp_soma{{ 0, -65 },
                        { 0.05, -60 },
@@ -228,8 +228,8 @@ TEST(v_process, clamp_fine) {
     auto rec = v_proc_recipe{true, false};
     rec.gprop.default_parameters.discretization = arb::cv_policy_max_extent(0.5);
     auto sim = arb::simulation(rec);
-    sim.add_sampler(arb::all_probes, arb::regular_schedule(0.05), fun);
-    sim.run(1.0, 0.005);
+    sim.add_sampler(arb::all_probes, arb::regular_schedule(0.05*arb::units::ms), fun);
+    sim.run(1.0*arb::units::ms, 0.005*arb::units::ms);
 
     um_s_type exp_soma{{ 0, -65 },
                        { 0.05, -42 },