From c0eac9b28d4c59150d731bb902e826aa5113ff67 Mon Sep 17 00:00:00 2001
From: Didi Hou <didi.hou@outlook.com>
Date: Thu, 24 Aug 2023 15:55:39 +0200
Subject: [PATCH] update multi-area-model.ipynb

---
 multi-area-model.ipynb | 584 ++++++++---------------------------------
 1 file changed, 105 insertions(+), 479 deletions(-)

diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index 62b8aad..75bd713 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -25,14 +25,16 @@
     "    * [2.2. Predict firing rates from theory](#section_2_2)\n",
     "    * [2.3. Extract interarea connectivity](#section_2_3)\n",
     "    * [2.4. Run the simulation](#section_2_4)\n",
-    "* [S3. Simulation results analysis and data processing](#section_3)\n",
-    "* [S4. Simulation results visualization](#section_4) \n",
-    "    * [4.1. Instantaneous firing rate and mean firing rate](#section_4_1)\n",
-    "    * [4.2. Raster plot of spiking activity for single area](#section_4_2)\n",
-    "    * [4.3. Population-averaged firing rate](#section_4_3)\n",
-    "    * [4.4. Average pairwise correlation coefficients of spiking activity](#section_4_4)\n",
-    "    * [4.5. Irregularity of spiking activity](#section_4_5)\n",
-    "    * [4.6. Time series of population- and area-averaged firing rates](#section_4_6)"
+    "* [S3. Simulation results validation and connection extraction](#section_3)\n",
+    "* [S4. Data loading and processing](#section_4)\n",
+    "* [S5. Simulation results visualization](#section_5) \n",
+    "    * [5.1. Instantaneous firing rate and mean firing rate across all populations](#section_5_1)\n",
+    "    * [5.2. Raster plot of spiking activity for single area](#section_5_2)\n",
+    "    * [5.3. Population-averaged firing rate](#section_5_3)\n",
+    "    * [5.4 Time-averaged population rates](#section_5_4)\n",
+    "    * [5.5. Average pairwise correlation coefficients of spiking activity](#section_5_5)\n",
+    "    * [5.6. Irregularity of spiking activity](#section_5_6)\n",
+    "    * [5.7. Time series of population- and area-averaged firing rates](#section_5_7)"
    ]
   },
   {
@@ -56,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "06e7f341-c226-4782-a913-4868027d7d06",
    "metadata": {},
    "outputs": [],
@@ -74,34 +76,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "96517739",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "              -- N E S T --\n",
-      "  Copyright (C) 2004 The NEST Initiative\n",
-      "\n",
-      " Version: 3.4\n",
-      " Built: May 17 2023 20:48:31\n",
-      "\n",
-      " This program is provided AS IS and comes with\n",
-      " NO WARRANTY. See the file LICENSE for details.\n",
-      "\n",
-      " Problems or suggestions?\n",
-      "   Visit https://www.nest-simulator.org\n",
-      "\n",
-      " Type 'nest.help()' to find out more about NEST.\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Import dependencies\n",
     "%matplotlib inline\n",
@@ -119,48 +99,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "7e07b0d0",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: nested_dict in /srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/._view/6axslmv6jvf4v2nte3uwlayg4vhsjoha/lib/python3.8/site-packages (1.61)\n",
-      "Requirement already satisfied: dicthash in /srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/._view/6axslmv6jvf4v2nte3uwlayg4vhsjoha/lib/python3.8/site-packages (0.0.2)\n",
-      "Requirement already satisfied: future in /srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/._view/6axslmv6jvf4v2nte3uwlayg4vhsjoha/lib/python3.8/site-packages (from dicthash) (0.18.2)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install nested_dict dicthash"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "1d440c07-9b69-4e52-8573-26b13493bc5a",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "table {float:left}\n",
-       "</style>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Jupyter notebook display format setting\n",
     "style = \"\"\"\n",
@@ -238,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "60265d52",
    "metadata": {},
    "outputs": [],
@@ -267,7 +221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "6e4bed8d",
    "metadata": {},
    "outputs": [],
@@ -359,70 +313,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "ab25f9f8",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initializing network from dictionary.\n",
-      "RAND_DATA_LABEL 7826\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/srv/main-spack-instance-2302/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.21.6-6fewtq7oarp3vtwlxrrcofz5sxwt55s7/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3440: RuntimeWarning:Mean of empty slice.\n",
-      "/srv/main-spack-instance-2302/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.21.6-6fewtq7oarp3vtwlxrrcofz5sxwt55s7/lib/python3.8/site-packages/numpy/core/_methods.py:189: RuntimeWarning:invalid value encountered in double_scalars\n",
-      "Error in library(\"aod\") : there is no package called ‘aod’\n",
-      "Execution halted\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No R installation or IndexError, taking hard-coded SLN fit parameters.\n",
-      "\n",
-      "\n",
-      "========================================\n",
-      "Customized parameters\n",
-      "--------------------\n",
-      "{'K_scaling': 0.005,\n",
-      " 'N_scaling': 0.005,\n",
-      " 'connection_params': {'K_stable': 'K_stable.npy',\n",
-      "                       'av_indegree_V1': 3950.0,\n",
-      "                       'fac_nu_ext_5E': 1.125,\n",
-      "                       'fac_nu_ext_6E': 1.41666667,\n",
-      "                       'fac_nu_ext_TH': 1.2,\n",
-      "                       'g': -11.0,\n",
-      "                       'replace_non_simulated_areas': 'het_poisson_stat'},\n",
-      " 'fullscale_rates': 'tests/fullscale_rates.json',\n",
-      " 'input_params': {'rate_ext': 10.0},\n",
-      " 'neuron_params': {'V0_mean': -150.0, 'V0_sd': 50.0}}\n",
-      "========================================\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/dicthash/dicthash.py:47: UserWarning:Float too small for safe conversion tointeger. Rounding down to zero.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Simulation label: 27d81076e6d6e9e591684be053078477\n",
-      "Copied files.\n",
-      "Initialized simulation class.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "M = MultiAreaModel(network_params, \n",
     "                   simulation=True,\n",
@@ -441,19 +335,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "6a7ddf0e",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iteration: 0\n",
-      "Mean-field theory predicts an average firing rate of 29.588 spikes/s across all populations.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "p, r = M.theory.integrate_siegert()\n",
     "print(\"Mean-field theory predicts an average \"\n",
@@ -486,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "6316ac24",
    "metadata": {},
    "outputs": [],
@@ -506,7 +391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "445a722a",
    "metadata": {},
    "outputs": [],
@@ -542,85 +427,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "15778e9c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Prepared simulation in 0.00 seconds.\n",
-      "Rank 0: created area V1 with 0 local nodes\n",
-      "Memory after V1 : 1911.93 MB\n",
-      "Rank 0: created area V2 with 0 local nodes\n",
-      "Memory after V2 : 1938.52 MB\n",
-      "Rank 0: created area VP with 0 local nodes\n",
-      "Memory after VP : 1967.71 MB\n",
-      "Rank 0: created area V3 with 0 local nodes\n",
-      "Memory after V3 : 1996.08 MB\n",
-      "Rank 0: created area V3A with 0 local nodes\n",
-      "Memory after V3A : 2015.90 MB\n",
-      "Rank 0: created area MT with 0 local nodes\n",
-      "Memory after MT : 2041.53 MB\n",
-      "Rank 0: created area V4t with 0 local nodes\n",
-      "Memory after V4t : 2066.51 MB\n",
-      "Rank 0: created area V4 with 0 local nodes\n",
-      "Memory after V4 : 2093.57 MB\n",
-      "Rank 0: created area VOT with 0 local nodes\n",
-      "Memory after VOT : 2118.73 MB\n",
-      "Rank 0: created area MSTd with 0 local nodes\n",
-      "Memory after MSTd : 2140.20 MB\n",
-      "Rank 0: created area PIP with 0 local nodes\n",
-      "Memory after PIP : 2161.71 MB\n",
-      "Rank 0: created area PO with 0 local nodes\n",
-      "Memory after PO : 2183.06 MB\n",
-      "Rank 0: created area DP with 0 local nodes\n",
-      "Memory after DP : 2203.29 MB\n",
-      "Rank 0: created area MIP with 0 local nodes\n",
-      "Memory after MIP : 2224.94 MB\n",
-      "Rank 0: created area MDP with 0 local nodes\n",
-      "Memory after MDP : 2246.42 MB\n",
-      "Rank 0: created area VIP with 0 local nodes\n",
-      "Memory after VIP : 2268.23 MB\n",
-      "Rank 0: created area LIP with 0 local nodes\n",
-      "Memory after LIP : 2292.30 MB\n",
-      "Rank 0: created area PITv with 0 local nodes\n",
-      "Memory after PITv : 2317.64 MB\n",
-      "Rank 0: created area PITd with 0 local nodes\n",
-      "Memory after PITd : 2342.73 MB\n",
-      "Rank 0: created area MSTl with 0 local nodes\n",
-      "Memory after MSTl : 2364.20 MB\n",
-      "Rank 0: created area CITv with 0 local nodes\n",
-      "Memory after CITv : 2383.38 MB\n",
-      "Rank 0: created area CITd with 0 local nodes\n",
-      "Memory after CITd : 2402.79 MB\n",
-      "Rank 0: created area FEF with 0 local nodes\n",
-      "Memory after FEF : 2424.29 MB\n",
-      "Rank 0: created area TF with 0 local nodes\n",
-      "Memory after TF : 2439.94 MB\n",
-      "Rank 0: created area AITv with 0 local nodes\n",
-      "Memory after AITv : 2462.49 MB\n",
-      "Rank 0: created area FST with 0 local nodes\n",
-      "Memory after FST : 2479.19 MB\n",
-      "Rank 0: created area 7a with 0 local nodes\n",
-      "Memory after 7a : 2500.40 MB\n",
-      "Rank 0: created area STPp with 0 local nodes\n",
-      "Memory after STPp : 2519.23 MB\n",
-      "Rank 0: created area STPa with 0 local nodes\n",
-      "Memory after STPa : 2538.30 MB\n",
-      "Rank 0: created area 46 with 0 local nodes\n",
-      "Memory after 46 : 2553.66 MB\n",
-      "Rank 0: created area AITd with 0 local nodes\n",
-      "Memory after AITd : 2576.35 MB\n",
-      "Rank 0: created area TH with 0 local nodes\n",
-      "Memory after TH : 2588.97 MB\n",
-      "Created areas and internal connections in 2.20 seconds.\n",
-      "Created cortico-cortical connections in 22.63 seconds.\n",
-      "Simulated network in 77.48 seconds.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# run the simulation, depending on the model parameter and downscale ratio, the running time varies largely.\n",
     "M.simulation.simulate()"
@@ -650,7 +460,7 @@
     "tags": []
    },
    "source": [
-    "## S3. Simulation results analysis <a class=\"anchor\" id=\"section_3\"></a>"
+    "## S3. Simulation results validation and connection extraction <a class=\"anchor\" id=\"section_3\"></a>"
    ]
   },
   {
@@ -663,7 +473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "dc3b1820",
    "metadata": {},
    "outputs": [],
@@ -697,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "e7eb052e",
    "metadata": {},
    "outputs": [],
@@ -720,7 +530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "902f2800",
    "metadata": {},
    "outputs": [],
@@ -757,12 +567,12 @@
     "tags": []
    },
    "source": [
-    "## S4. Data processing and simulation results visualization <a class=\"anchor\" id=\"section_4\"></a>"
+    "## S4. Data loading and processing <a class=\"anchor\" id=\"section_4\"></a>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "f5b58845-4d1a-430f-83f4-402fdf918aef",
    "metadata": {},
    "outputs": [],
@@ -773,18 +583,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "6607a73d-1c74-4848-9603-081ad0e7cae8",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "loading spikes\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "\"\"\"\n",
     "Analysis class.\n",
@@ -806,23 +608,16 @@
     "    Default value is None and leads to loading of data for all\n",
     "    simulated areas.\n",
     "\"\"\"\n",
+    "# Instantiate an analysis class and load spike data\n",
     "A = Analysis(network=M, \n",
     "             simulation=M.simulation, \n",
     "             data_list=['spikes'],\n",
     "             load_areas=None)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "4885512d-8cc4-4b14-9090-723b56f5e872",
-   "metadata": {},
-   "source": [
-    "### 4.1. Data loading and processing <a class=\"anchor\" id=\"section_4_1\"></a>"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "c471e9c8-b1e4-43e4-a6a1-443b8b8963be",
    "metadata": {},
    "outputs": [],
@@ -834,34 +629,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "6806564b-d5d4-47d4-afa9-5da0df83e025",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "loading spikes\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\"\n",
-    "Loads simulation data of the requested type either from hdf5 files.\n",
-    "\n",
-    "Parameters\n",
-    "----------\n",
-    "\n",
-    "data_list : list\n",
-    "    list of observables to be loaded. Can contain 'spikes' and 'vm'\n",
-    "\"\"\"\n",
-    "A.load_data(data_list=['spikes'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "1870cf34-ee62-4614-bc25-c36bc9a7377c",
    "metadata": {
     "jupyter": {
@@ -869,18 +637,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\\nCalculate time-averaged population rates and store them in member pop_rates.\\nIf the rates had previously been stored with the same\\nparameters, they are loaded from file.\\n\\nParameters\\n----------\\nt_min : float, optional\\n    Minimal time in ms of the simulation to take into account\\n    for the calculation. Defaults to 500 ms.\\nt_max : float, optional\\n    Maximal time in ms of the simulation to take into account\\n    for the calculation. Defaults to the simulation time.\\ncompute_stat : bool, optional\\n    If set to true, the mean and variance of the population rate\\n    is calculated. Defaults to False.\\n    Caution: Setting to True slows down the computation.\\nareas : list, optional\\n    Which areas to include in the calculcation.\\n    Defaults to all loaded areas.\\npops : list or {'complete'}, optional\\n    Which populations to include in the calculation.\\n    If set to 'complete', all populations the respective areas\\n    are included. Defaults to 'complete'.\\n\""
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "\"\"\"\n",
     "Calculate time-averaged population rates and store them in member pop_rates.\n",
@@ -907,47 +664,50 @@
     "    If set to 'complete', all populations the respective areas\n",
     "    are included. Defaults to 'complete'.\n",
     "\"\"\"\n",
-    "# A.create_pop_rates()\n",
-    "# print(\"Computing population rates done!\")"
+    "A.create_pop_rates()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "fce93688-01ef-4042-ab4b-b1cdde65937a",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    },
-    "tags": []
-   },
+   "execution_count": null,
+   "id": "50b7df89",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# compute_corrcoeff.py"
+    "\"\"\"\n",
+    "Calculate synchrony as the coefficient of variation of the population rate\n",
+    "and store in member synchrony. Uses helper function synchrony.\n",
+    "If the synchrony has previously been stored with the\n",
+    "same parameters, they are loaded from file.\n",
+    "\n",
+    "\n",
+    "Parameters\n",
+    "----------\n",
+    "t_min : float, optional\n",
+    "    Minimal time in ms of the simulation to take into account\n",
+    "    for the calculation. Defaults to 500 ms.\n",
+    "t_max : float, optional\n",
+    "    Maximal time in ms of the simulation to take into account\n",
+    "    for the calculation. Defaults to the simulation time.\n",
+    "areas : list, optional\n",
+    "    Which areas to include in the calculcation.\n",
+    "    Defaults to all loaded areas.\n",
+    "pops : list or {'complete'}, optional\n",
+    "    Which populations to include in the calculation.\n",
+    "    If set to 'complete', all populations the respective areas\n",
+    "    are included. Defaults to 'complete'.\n",
+    "resolution : float, optional\n",
+    "    Resolution of the population rate. Defaults to 1 ms.\n",
+    "\"\"\"\n",
+    "A.create_synchrony()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "6ef8bdb6-a9f2-4eee-af85-d65b2b300a55",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\\nCalculate poulation-averaged LvR (see Shinomoto et al. 2009) and\\nstore as member pop_LvR. Uses helper function LvR.\\n\\nParameters\\n----------\\nt_min : float, optional\\n    Minimal time in ms of the simulation to take into account\\n    for the calculation. Defaults to 500 ms.\\nt_max : float, optional\\n    Maximal time in ms of the simulation to take into account\\n    for the calculation. Defaults to the simulation time.\\nareas : list, optional\\n    Which areas to include in the calculcation.\\n    Defaults to all loaded areas.\\npops : list or {'complete'}, optional\\n    Which populations to include in the calculation.\\n    If set to 'complete', all populations the respective areas\\n    are included. Defaults to 'complete'.\\n\""
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "d43b493c",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "\"\"\"\n",
     "Calculate poulation-averaged LvR (see Shinomoto et al. 2009) and\n",
@@ -969,12 +729,12 @@
     "    If set to 'complete', all populations the respective areas\n",
     "    are included. Defaults to 'complete'.\n",
     "\"\"\"\n",
-    "# A.create_pop_cv_isi()"
+    "A.create_pop_LvR()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "401ece2d-47c8-4775-80ae-92a8e432520c",
    "metadata": {
     "jupyter": {
@@ -982,18 +742,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\\nCalculate time series of population- and area-averaged firing rates.\\nUses ah.pop_rate_time_series.\\nIf the rates have previously been stored with the\\nsame parameters, they are loaded from file.\\n\\n\\nParameters\\n----------\\nt_min : float, optional\\n    Minimal time in ms of the simulation to take into account\\n    for the calculation. Defaults to 500 ms.\\nt_max : float, optional\\n    Maximal time in ms of the simulation to take into account\\n    for the calculation. Defaults to the simulation time.\\nareas : list, optional\\n    Which areas to include in the calculcation.\\n    Defaults to all loaded areas.\\npops : list or {'complete'}, optional\\n    Which populations to include in the calculation.\\n    If set to 'complete', all populations the respective areas\\n    are included. Defaults to 'complete'.\\nkernel : {'gauss_time_window', 'alpha_time_window', 'rect_time_window'}, optional\\n    Specifies the kernel to be convolved with the spike histogram.\\n    Defaults to 'binned', which corresponds to no convolution.\\nresolution: float, optional\\n    Width of the convolution kernel. Specifically it correponds to:\\n    - 'binned' : bin width of the histogram\\n    - 'gauss_time_window' : sigma\\n    - 'alpha_time_window' : time constant of the alpha function\\n    - 'rect_time_window' : width of the moving rectangular function\\n\""
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "\"\"\"\n",
     "Calculate time series of population- and area-averaged firing rates.\n",
@@ -1027,8 +776,16 @@
     "    - 'alpha_time_window' : time constant of the alpha function\n",
     "    - 'rect_time_window' : width of the moving rectangular function\n",
     "\"\"\"\n",
-    "# A.create_rate_time_series()\n",
-    "# A.save()"
+    "A.create_rate_time_series()\n",
+    "A.save()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb71c922",
+   "metadata": {},
+   "source": [
+    "## S5. Simulation results visualziation <a class=\"anchor\" id=\"section_5\"></a>"
    ]
   },
   {
@@ -1038,38 +795,15 @@
     "tags": []
    },
    "source": [
-    "### 4.2. Visualization: Instantaneous firing rate and mean firing rate <a class=\"anchor\" id=\"section_4_2\"></a>"
+    "### 5.1. Instantaneous firing rate and mean firing rate across all populations <a class=\"anchor\" id=\"section_5_1\"></a>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "bea30fc8",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f2d8f4e1fd0>"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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hFEVRlKioslAURVGiospCURRFiYoqC0VRFCUqqiwURVGUqKiyUBRFUaKiykJRFEWJiioLRVEUJSqqLBRFUZSoqLJQFEVRoqLKQlEURYmKKgtFURQlKqosFEVRlKioslAURVGiospCURRFiYoqC0VRFCUqvisLEckUkVki8qX9v6GIjBORZfZ3A79lUBRFUcpHMnoWdwGLHP8HAeONMe2B8fZ/RVEUJY3xVVmISEtgIPC64/AFwDD79zDgQj9lUBRFUcqP3z2L54D7gRLHsWbGmPUA9ndTtwtF5CYRmS4i04uKin0WU1EURYmEb8pCRM4FNhljZsRzvTHmVWNMT2NMz2rVshIsnaIoihILftbCfYDzReQcIBuoKyLvARtFJNcYs15EcoFNPsqgKIqiJADfehbGmAeNMS2NMXnAZcB3xpgrgS+Aq+1gVwMj/ZJBURRFSQypmGcxFDhdRJYBp9v/FUVRlDQmKYMBxpiJwET791agfzLSVRRFURKDzuBWFEVRoqLKQlEURYmKKgtFURQlKqosFEVRlKioslAURVGiospCURRFiYoqC0VRFCUqnpWFiNQWkUw/hVEURVHSk7DKQkQyROSPIjJaRDYBi4H1IrJARJ4UkfbJE1NRFEVJJZF6FhOAtsCDwGHGmFbGmKbAScBUYKiIXJkEGTEmGakoiqIo4Yi03McAY0xR6EFjzDZgBDBCRKr5JpmiKIqSNoTtWQQUhYi0FZEa9u9+InKniNR3hlEURVEqN14GuEcAB0WkHfAG0AYY7qtUiqIoSlrhRVmUGGOKgYuA54wxfwFy/RVLURRFSSe8KIsiEbkca6OiL+1jOlahKIpShfCiLK4FTgD+aYxZJSJtgPf8FUtRFEVJJ8RUAL/U+od3MDt+W5xqMRRFUSoUIjLDGNMzEXHpch+KoihKVFRZKIqiKFGJtNzHgyLSPZnCKBZ7DxxkzuodqRZDURSllEg9i1XAXSIyS0TeFpE/iEiDZAlWlbnv0zlc8NIUNhfuT7UoiqIoQITlPowxHwIfAtg9jLOAz+yVZ78FxhhjpiVFyirG3DUFAOw5UAzUSK0wiqIoRF4bqhRjzCxgFvC4iNQFTgduAFRZKIqiVAE8KQsnxpid2AsJJl4cJdHsOVBMzWqZiEiqRVEUpQKj3lBpiCExc19WbdlNx7+N5ePpqxMSn6IoVRdVFmmMUL7ewLKNhQCMW7gpEeIoilKFiaosRKSPiNS2f18pIs+ISGv/RVMSR/rP0lcUJb3x0rN4GdgjIscA9wO/Au/4KpWSEHScQlGUROFFWRQbawGpC4DnjTHPAzn+iqUkkgqw/JeiKGmOF2+oQhF5EPg/4CR7noUuUV4BCPQrVFcoilJevPQs/gDsB64zxmwAWgBP+iqVkhDUCqUoSqKIqixsBTGCQ1OJtwCf+ymUklgqwjL0iqKkN168oW4EPgVesQ+1AP7no0xKgtCeRfL5JX8b/5m4PNViKErC8WKGuh3oA+wEMMYsA5r6KVRVJ9EdAe1XJI9L//sTT4xZkmoxFCXheFEW+40xBwJ/RCQLD/WPiGSLyDQRmSMiC0TkUft4QxEZJyLL7O9KsZLt21NWsXrbnoTGWd6eQXkn9Snpw+i565nx6/ZUi6FUYbwoi+9F5CGgpoicDnwCjPJw3X7gNGPMMUA34CwROR4YBIw3xrQHxtv/KzQFe4oYPGohV7z+c6pFUSoptw+fye9e/jHVYihVGC/KYhCwGZgH3Ax8ZYx5ONpFxmKX/bea/QnM1xhmHx8GXBijzGnHQdtuVLivKMWSKKGs27GXcQs3Jj3dbbsPRA+kKBUIL8pisDHmNWPMpcaYS4A3ReR9L5GLSKaIzAY2AeOMMT8DzYwx6wHsb9fxDxG5SUSmi8j04uJiTzeTKtLd2yjNxfOV81+cwo3vTE96uje/m/w0FcVPvCiLw+1JeYhIdeAzYJmXyI0xB40x3YCWQG8R6exVMGPMq8aYnsaYnllZMa+knhLSbnkNW5ytu/czu4pu07plV2p2G1y3Y19K0lXSn227DzDrt4o3/uRFWVwLdLEVxpfARGPM4FgSMcbsACZi7ba3UURyAezvCr8karo23AOqa/7anVz40pSUyqIoisUl//2Ri/5T8cafwioLEekhIj2A7sDzWDO5l2ENePeIFrGINBGR+vbvmsAAYDHwBXC1HexqYGR5biDRlJQYFm/YGdM1ATNPmvUrKgWrt+1hpz0WtHLzLvYVHfQlnfwtu+1tbBNDunUylfRh5ebdvsW9dGMhxQdLfIk7Us/iacdnKLAd6Gj/f8pD3LnABBGZC/yCNWbxpR3X6SKyDGt71qHxi5943pi8irOe+yEuN8V0qyDSziwWByc9MYGB//6BfUUHOe3p77nrw1m+pNPvqYkpGdtQlESRv2U3Zzw7iSfG+jPPJ+xggDHm1PJEbIyZi9UrCT2+FehfnrhD2Vd0kIMlhto1yj+2MXdtAQBrtu/h2NbepoBE29mupMSwdsdemuTUILtaZvT4IkRXuK+IapkZnuKpLKzetpcDdmvpx+VbfUtnSgLjrgR6WqlgBMbn/JqPE7Z2FZErjTHvicjdbueNMc/4IlEcnPD4eLbvKSJ/6MAUS+JeQ7zw3XKe/XYpOdlZzBt8ZrlS6DL4G45sVodv/nJKnNIooazbsTfhceqESKWyEakpXtv+Tvu9K7bvSfH8higj3N8usvz8C/clxia+dOOu6IEUz6wvUM8lRYlG2DELY8wr9vejbp/kiVhx8GJ6WL6pkLxBo/nWMVFsxeZd5A0azTcLNiRdnqrOY18ujDoz+h+jFpI3aDSv/7DSc7zpnvffL91M3qDRLFofmzNHRWTrrv3kDRrNu1N/9SX+d3/KJ2/Q6Eo/EdPLqrNHiMgoEdksIptEZKSIHJEM4SoKgY6Fl/ph5m87ABjjUAxz7DkQX89PrLJQovP65FVRw7w5xQrzhoewFYVAw2R6FVhvas12y8z4yfTVvsT/kR3v2u2JN2emE17mWQwHPsbybmqOtTbUB34KlUriaRCWus6GudjtuJdZ1bF651zw0hRuHz4zarjlm6yezPy1BZz61EQe/GxeTOmUh31FB8kbNJqPf1ldKse8NZZTQee/j+WZcUuTJks4bh8+s9zzUn7duocflm32HP7at6Zx+atTPYfv9+QEHvrc23Pbua+IvEGjGT13vef4IzFh8SbyBo1ma8iEx/em/kreoNG+uW6G8v7PVnpFUdIrzzyov3w0m7Of/8H1XN6g0bw6aUU5Yk8sfs/38qIsxBjzrjGm2P68lwS5UkY8NxbNGypeFm8ojCn8nNU7giqEcIOs4+0xlC/mrGPVlt18MO23+IWMkUBX/dlvlzrkWAvArv3F/Hu8p8UBfGX03PWuM95jbUi8+5N3s8eEJZv5aaV3b6z8rXsY/rO35/brFms15Je/T8w+G4Ee1sIQE9a/vl4MwB6f5sKEUprefm/pxdMQ/HzW2oimuiFfLY4jVn/xywLqRVlMEJFBIpInIq1F5H5gtL3UeEOf5Eor/vjaVE8VarjKOdrDc/YyDhSXsNYH7xywWkIFSXQGuPL1n3n/50MV5oaCfZw49LvS/4Ee19s/5nPd27+UuT6aPd0Yw/kvTk5Yi7k8fLNgA2c9Nyno2PjFsS9OsGt/bE4QJz3xXcwLWK7aspv3PSqaAP+ZuLxMr/WxLxfxt5Hzy4T1ay2yTTv3ccLj41mxOb0cPIqK47vhqSu3cupTE2OaaHrl6z/z7k/5nPXcpKBxT4AVm6x82ezTEjde9+C+GZiAtWTHrcB1wAygSsxi+nHF1oSZaqIpjg0J9MxxM385W69+L4A4efkWHv78UGUycvZa13BFBw3fuVSs/5lYtovvlLjEwNw1Bfzpg+imN7+5++M5ZXqCB0tiz9+5Ma7htXrb3tJxsHCE9nyH/Zgfm1DAE2OWlCrlQHxLNhbyjrP35POg/tfzN7C+YF9c8vvJpkLrnY3VwvCPUQtZtWU3yzd5V36Tl2/hkZELWLyhkPtHzA06Fxhb+3VrYvfVCeBlD+42ET6VbqA7WnnfvvsAN787PaiFvqXQMq3sdmkVjpqzjjm2TR5gy66yHhNePWfC2fPHL9rI0994n7UZSK/oYGqtieF6Yn/+cBZFB0vKrcxmxrFY27RV20p/fzFnXdC5SDPiw8k6Yckm/jUmtaaKQCUfae7HTyu28uioBezYc4Bb3p3B+oK93PreDDbu9MeteM+BYm59b0ZCGkd/+nAWv0WoINNhVei8QaO595M5vsX/+aw1QS71ew4Uc9v7MxKahhdvqEtFJMf+/VcR+UxEyszMriq8PnklYxds5N2p+aXHnrft7IUuyuJPHwQvT/Hid/Hb5MPZ868fNp0Xvitrj46mg6bGYCNPBKGvbLi693+z1zHXoWCDrokhvYvjWKzt96/8VPr7zpBnFw/XvvULL7v0kMLiQ+v8lUnRXX4vf20qb03J580p+YxZsIHf/edHvp6/IaZGSCke6uYv567n6/kbeCqe+EOYtHQzj7iYw8qIk2J/5k9nrPEt7r98FKyIvpq3ga/mJda70osZ6hFjTKGI9AXOxNqw6L8JlcInCvYUcfdHs7npnem8NCExg3vuuL8dkQrHiJlrWL5pF09/syTIxhtrec7f4r4o2YaCfa49Eae9N9oA+quTVrBso7dB9glLNvHl3HXRA3pk+M+/McthXnnmG+teEtlG/NiDK+VSx/2vKzg0lnSwxDD068WlHkHR5Nq4cx9PjV3C6z+s5LwXJocN99nMtZSUGLoOHsspT04oXUQxgDEmagU+as46Ji4pa9abt7aA10IUx9j5G0odDdwwBn7d6m3hu8Ck0/en/cqPK7bwz9ELXe3xSzYUxjRnBaxZ9s9+u7RUpnAsWr8zyMV5/KKNjHW4pP+2dQ//Hr8s5t7G6c98z90fzQ5rSgV4cuySUpOUF0Lf9ZISwxNjFrO50H3MYc127+YlP3pTXhZTCjztgcDLxpiRIjI44ZL4wJCvFvHZLOvhfrNwI1efmEedBKwfFUq45+LW7XQGHfDM9wBcc2Je3Glf9eY01+N3fjjL1Yf+ybFLePico6PGa4xhyFeLeXbcMhb9v7Oihr/2LWuA+tyuzaOGDRDJrDNiZrCifdvFTh3LC7FzXxF1s6sFHbv/07lhQh/ijGcPDVo7k/t+6Sb++/0KVm/bw0tX9Ig6qHvXh7OYuvKQiWv77gM0qF0dCB7b+HTGGi7v3Yqd+4rZua+Yp8cu4dELDm0DM29tgWsv0kmgN+u2/M0/v1oUVN4mL9/C5OVbyoQLPBsDXB2mjDlxTkh7YswhZdagdnVu69cuKOy5L/wQswn0juEz2eHBOSPg5np93zbW97DgYdVr357Gis27+d2xLWlRv6bn9Jdt2sWyTbv4bNZaLujWwjXMD8u2cO8nc3nnut6e43UydeVW/jNxBYs3FPLmNb3KnL/53cSalWLFS89irYi8Avwe+EpEani8LuUciOB/XbC3iHd/ymfpxkLGLtjAOz/lM2rOujJ2aideupGrt+2J2PpwIzDWUeJS4+zeX8zbU1aVqRgDreLftrm3NiJ5WAQWS3Ty1bz1PPblQrbbL32gAtsboxvkm5NXUVJiXCvyREw63LW/mLOem+TqWz9u4UaWxOhuHA8zft3G5GWWCW9/sSVHtMHNvUXB8n74S/hejfPWhv30K/Mdz+v1H8pODPx85pqw61vNXbOjzLFIvdeCPcFjasYEl4GCvUVlFlycsGQT81zKFECxi1JwKooF6w55vD38+TzOfHYSq7bsLvMeug3iOxsbXrYVEGCf/Rz8GsfYH8P7Erj3NyavYsG6gtL3I+DsMWf1Du79ZE5pjyLcO/2xS1ny4+68NLN/j7Vp0VPGmB32hkX3+SBLUnno83kxu1ze+8kcrjqhdZnjzgdz4UtT2Lr7QNjWhxuf2Epo7IIN3HvGUUHnHhu9kA+mraZ1o9pBx+//dC79jmriXXgHo1wU4m3vWx5Fyzfv4u1rewfNMI+Ff3y5kGZ1s2lat0bpsYI9RdSrVa10pjpYlVC8FuTFGwp5cuwS7j8zOK8Ckxj9XlDydy//5PhnPf1odU9o5fSvMYsZ2CWXwxvVKhM2tDI/12G2cmvM/G/2Omat3sH395VdKPr8F2ObXDjM9m4Kp1DcJgIGepXx4HSPDrjznvrURADOP8bqpa4McZV1U8wbd6ZmR0Qof8X8+ay1fD4ruIG5ZvseLrAnhk5auplpDw9wvXbjzn1lvKL8ItLmR3UAjDF7jDGfGWOW2f/XG2O+cYZJNss2FrJwXfiWxMzftrN62x7XymjX/mLGzF8ft29+oMW9dfcBptjdd2dFsLUc68PsKyop40O+fbfV9XZr4UdyzdxzIL6JUYGehdOza0yMPYJ1O/YGtYLcekwbdu4r13jj9t0HYnpJ12zb69vSzV5wG7BfuL6ARet38tW88s8T2eRSWc4P09r3QmCJjNCKebsP6x/NW1PgOv9nzPz17Cs6GNFCEInQcZs5a3YEzWFauG4nb05eFfM9rd2xl5vCrK7w86ptXPPWtLA9l3lhHDdCcZbVTfYYhtvrHm4cY+fexM+nitSzGCkis7F2spthjNkN1lpRwKlYPY7XgE8TLlUUTrftyOFakAEvmIu7l23d3/PxbMYuCD+gF43A83prSj5vTcknf+jAhHb5rglppcVToS7ZUBiT73Y0bnlvBh/edDzHH9HIU/h/frUoyG4bLn+SOZnunH9btmy/eh3xlIFb3nOfH1JUHHvl6NaYODfCQHo6cd6Lk+ndpuz83lvem8k1J+ZxWe9WrtdFMiXt2HOgzLvkDG7MoTIxcvZaRt7Rt0wc4ZRtH8fE0tB4ASYu2cwL3y3nzv7ty1x73ovensldH84uc2yVizNLcC/3EI+NXuQpnViItOpsf2A81oS8BSJSICJbgfeAw4CrjTFJVxRORs5ey4J1sbWe3DK8POzcV8TSJNjJYzGxlmv1y8DAZkh6O0Js2Us2FJbx1HHiXI49XC+wvIvYOb10YtkStWBPUVLGNuJlSxqtXlp00ASZeLa6zBOKhLMc7Ss6GLYCXhGmcbPGZXE+L+/CgSgK1zkHZ8Xm3RwoLmHumh0cKC4pNZdGKt9OClxa8avtscRtuw8kZMb5Jp/mu8RCxDELY8xXwFdJkiVm3LRvEFL2r9dKN1y4BSGFvevgb7xFGCeBnkUss0MfHbUg4XKEdoHPfG4SnZrXZfSdJ7mGdy4NceUbPzNv8BkJl2nAM4c8lWKZE3H+S5MTP8s1gd3LRMzvSBSh41tLPLpSu3Hfp3Ndx8ugfObbeHDWHbv2F/PoqAW8//NvnHJkE75fupnv7om+uVgkAsWh/9MTE7LfTu8h48sdR3mpEF5N8eLmieHWCgjHuh17y4wLeB1IS9Tua7siLJIWrpUX6wKETgL6NTTn1u3YW2Y10QURxo1CidbSKy/OXsqB4pLSll0om3buK5eiCDV9TP91O/lbdsdtV08F63ckt5W6oWAfRQdLmBXHjHo39hw4yPbdByK6Xsc64P2Nvc7S90utlYKn/7o9IQ0APzdmcxsL9JPETzpIIXsOFFOr+qFbcvMc2RRmwksoG3Zai97d2q9tXLKcGGLXDBDr851kF1636/y0SYem99joRazZvpfB53fyLc1YiJSNxw35NuxLWt4WWqjL6449RfSzvXcqCvF6usXD7gPFHP/4eC7r5T7uEA9u3kOheB0bCBA6Ee7+T+cy/IbjYpYtmXiZd5JIKlXPYn9R5NZdtHXvnQRapmNDPIFinXcQSrytgWS1IQKNtf3FZe8z4F3i9HQ6UFziacG8gwluBTmjKwlxw/WzNTc9P303C9pzoDimFUyTQcCrbsyCDb6tRpuOVMZ79dSzsJf6aG+MeUtEmgB1jDFlZwelmGjPp9s/xnmOK+DzvTJkQDwWM5Yb++M0xyRrgGvtdsvt9dFRC8ucM1jmKGev6ci/fu3qyRKKcyZ0Igid4Z2sfdjTYVG6cHT829hUi1CGQONjx54ialeP3ZARSwMvnUi2iSgZeFlI8O/AA8CD9qFqWB5RaUc6v8jlJZwNPtFsKtzvunouWK0lt7WonCu1hiPZXWa/qLwlLD3ZW3Qw4mq56UpxHMvTpzteVP1FQHdgJoAxZl1gFVoleQyLYde18rI+wrLRle8ViI3K3CDxm3g29Zq2apvn3vzdH82OOf5I/PH1n+O+dtScdWE9vyoqXsYsDhjrDTEAIlI7SnilghNp7kpVrysrYYMx7Qm3/lkon0UZ9FbKh5eexcf2QoL1ReRGrF3yXvdXrPi479O5Cd1prqrywIjwuwJ+Nsu/NfkrApEWmlTK8t7U8u/v7uemQYp3oioLY8xTInI6sBM4CvibMcb7SHEScduaU0ksn83U1puiVEWiKgsR+Zcx5gFgnMsxpQrh1RygKErlw8uYxekux85OtCCKoihK+hK2ZyEitwK3AUeIiHPB9BwgtkXyFUVRlApNJDPUcOBr4HFgkON4oTEmumO9oiiKUmkIqyyMMQVAAXA5gIg0BbKBOiJSxxhTfjcHRVEUpULgZQb3eSKyDFgFfA/kY/U4FEVRlCqClwHux4DjgaXGmDZAf1I4ZlGis6IURVGSjhdlUWSM2QpkiEiGMWYC0M1fscLz8fTV0QMpiqIoCcWLstghInWAScD7IvI8EHUPSxFpJSITRGSRiCwQkbvs4w1FZJyILLO/G8Qi8JQVW2MJriiKoiQAL8riAmAP8BdgDLACOM/DdcXAPcaYo7HMWLeLSEcsz6rxxpj2WHt8D4oQRxkq2+JciqIoFYGIM7hFJBMYaYwZAJQAw7xGbIxZD6y3fxeKyCKgBZby6WcHGwZMxFoCXVEURUlTIvYsjDEHgT0iUq88iYhIHtYy5z8DzWxFElAoTcNcc5OITBeR6UXFUa1eiqIoio94WXV2HzBPRMYBpTvfGGPu9JKAPd4xAvizMWZnpE3WnRhjXgVeBah3eAd1gVIURUkhXpTFaPsTMyJSDUtRvG+M+cw+vFFEco0x60UkF9ClYhVFUdIcL0uUex6ncCJWF+INYJEx5hnHqS+Aq4Gh9vfIeOJXFEVRkkfsO6h7pw/wf1gmrNn2sYewlMTHInI98BtwqY8yKIqiKAnAN2VhjJkMYXda7+9XuoqiKEri8TLPQlEURanieNkpbxQQ6o1UAEwHXjHG+L7ptWDgYBEAWdEnjyuKoigJxosZaiXQBPjA/v8HYCNwJPAa1riEr7QrXg7/rzEAy7P9Tk1RFKVy4G2igje8KIvuxpiTHf9HicgkY8zJIrIggbKEZWtGIzjtPp4cuzQZySmKolQSXk9YTF6URRMROTyw2ZGIHA40ts8dSJgkEdiW0RBOvo+XvopruoeiKEoVJbnK4h5gsoiswOrVtAFuE5HaxLBWlKIoilJx8TIp7ysRaQ90wFIWix2D2s/5KJuiKIqSJnidZ3EskGeH7yoiGGPe8U0qRVEUJa3w4jr7LtAWmA0ctA8bQJWFoihKFcFLz6In0NEYoyu/KoqiVFG8zOCeDxzmtyCKoihK+uKlZ9EYWCgi04D9gYPGmPN9k0pRFEVJK7woi8F+C6EoiqKkN15cZ79PhiCKoihK+hJWWYjIZGNMXxEpJHghQQGMMaau79IpiqIoaUFYZWGM6Wt/5yRPHEVRFCUdiegNJSIZIjI/WcIoiqIo6UlEZWGMKQHm2IsHKoqiKFUUL95QucAC23V2d+Cgus4qiqJUHbwoi0d9l0JRFEVJa9R1VlGUIFo1rMnqbXtTLYaSZoQdsxCRyfZ3oYjsdHwKRWRn8kSsmhydq57JSmr406ntUy2CkoZEGuC+AizXWWNMXccnR+dY+M9b1/RKtQhVmuv6tEm1CKkjkRs3K0mnW6v6vsQbSVl8HvghIiN8SV0Jy2H1slMtQpXmxLaNUi1CysiUyqUt6tTwum1P+nP/WUdFDfPH3v44r0ZSFs4Sc4QvqStKJeTu04+kfdM6Qce6tqyXImlgyEVdoob54o4+pb8v7N7CT3GiMuqOvgmNr17NagmNL5Uc07J+1DDN6mVz6lFNEp52JGVhwvyu9FTP9LJye8WhcZ0a5A8dmGoxqgx39m9P2ybByuLMTslf5T9/6EDyhw7kj8dFb2l2dVRCmRmp7Vl0SaFiTTWnd2wW8byXTp8Afx5wZGIEchCpVjwmMKCNtZWqDnCH4do+eb7E+58rengO++/Lu3PLKW1dz917RuILjhvDbzwuKekkmgfP7lDmWLItMSccUdbsdXnvwxl2Xe+Y4rm4R2p7BelIRdm37Zwuh5EVRVEf27qBp7g6Nq/LWQluoIRVFsaYTMeAdlZVGuAu8VC4erdpWPr77+d18kWOc7rkeg57/jHNGeRS6QE0r18TgMt6tSpzznkf5eXEto0TFleiieRddnMYJZtMTj6yrNng8Yu7cIrL8Ui0alArIfLcfXpyGhjJoGKoCnjq0mPKNFLq1wo2odXIyowajwhUy8zgv/93bCLF87RTXpXA2f3zUrjuPSP6QFNVZGAMCs7JxUm2k191QuuI58vbs7jt1EMKqE6NLM4/pnn5IkwyF8X4PI6J0XR052ntYgpfHvzoWPzt3I4AHFb3kCPKg2d3oG+7xlHLViRuPaUdDWtXL/0/9OLo403JQpWFzXmOlzlatzV/6MCILfIVQ84hf+hAfnl4AACN61QPGzYRDDja3c7Zt13yW/ovXdEjrvGRPx53OD0Or594gcJw1Ql5CY3v+cu6Bf3v2rJ+6ZjB/EfPpFVDby3+m09JD18SN3nzhw6kVnX3lm2D2t7KeL2a1cgfOpC7HY2tRIyn9e/QtNxxxMJ1fduQP3QgUx/qX3rs5lPa8t4Nx/GPCzrHHW+XlvWY+cjppf/P6hy58dUrz5tZKhFUDGXhUz8yXCVe3uRCzY6xtmz+EuPg1BXHuw9gGvtOIrWSr4sw3lItM/yF8ba8w7Ww24V4DwFccmzL+BJx4ahmofEbBnbNpW2T2gB0OCyHMxy9S4lxsoEXL5VImAQVcq+xOBVzwLZ9a7+2YW3mgZ6Dsyxf1qtVacs61c62/cM0mCBy3rqVu4rE9X3LzgfqcJg/owQVQ1kkgDv7ty/jkte+aQ7ndrU0t7M3UZ5ua/7QgYhdk8Zbod41wH0G7RF2xRbKqUdFblW5VXzVMzPIHzowYsulZjX3VmTLBjW56vj4utrX9snjgbOCx1b+OvBo6tcKVty92zTk8TBd8BPbNuJwu+U78d5+pXL+vwvDt+jq16pepgX70h97MP6efgCM+fPJvHpVT9drlz52dtTWb9O6NSKed8NtPMLtWR3n0ot1ytPCHpPyyohbT+Cz2/pwZiergr2wu6XAHzirA8uHnON6zUj73XFWvEN/15V/XhR/K9qNSPkc6dyRZRoDwTSuYz2faQ/3Dzp+yyltubN/4mesf3XnSZ7Dlsf77KzOuaWN04BFo0lO7GXRC5VeWQRaTX3aNqJ5fasV5DTbiEuNHmo/j5T5gdboH3qWHTwOEE73dDgs8ftKXdDNaU4LPuc2iArQPMwEwMDloRPUymPPd+Z3R3vQuVeeVRn+ztGTuLBbi7CTw5z3JRJfq7xJncRPeozHtz2eyX81ssK/tn08xOe1MZRdzUqntsP0VBJy7ZHNrDJ8nscxmUhl5/LeZd+hcGYvJ9f1aVPaeHDD7X5z7Il6XVvW88VcmxvDpNpA775anC7715xo9S787t1VnqmNLqwYcg4ZYhXwgPZeMeQcflqxlW8XbQxbcK8+MY/PZq0FYOWQcxA7jrYPfRUULnAOYOjvupRpCUd7eJ1b1GPxhkIev7gLD342L/oNOQr9mZ2aMXbBxjJBnrikKyNnrwuaKxKQ0c09E6Bj83qsK9jHg2d34PGvFx9Kzk5v0NkdmLZqG4+NXhRWtJdD3HxXDjmHEmNo9/DXYa85qX1jRv2pb+mz+WPvw/l9z1ZkiJAhlmI5t2suX85dHzYOJ7G8LPVqRZmo5YjMizIShDev6eWpIn7j6p5cP2w6Bnj/huMwBl7+fkX4uB2yrHRp+QfOT7y3H3mN3XufTgIiRpN14aNnYQjJ15BrWjWsxYoh55CZIUxduZWPp68pE8871/WmQ24Ovf85PmxaznfJyfzBZ3LQmIizyh8592hEhAu7Ned/s9dFvikT/E4Hyt41J+bx9o/5gFVHON/1uYPP4KKXprBi8263GF1pULs6K4acw3FDxrNl1/6w4QKyPHj20WTE2MMIlIVHzj2avw6M/fpY8a1nISJvisgm5057ItJQRMaJyDL729fRmcwMQUSCunmZGRLVlu98HzJc4gg9Z8UlZR5WreqWLg43IStgXjg6ty5HNYutl5ERRvjASzWwa25pTyJgpnBeMrDrIfPTSe2tllXomjIBn/2mOdlBPYImdWrQMy/YNNI2xPabkSFkubSUMpw9AQnugosI1TIzSp9buPs86cjGpd5rXmfnevVPLw9uZcCNNnaF3rddozLXBG7X2Qt0/s7IKJtGoNL3as4oqyTcr8vIsMq9M71zu5Y1WwbSDTfOc3jDWtTNtp7T2Z3d3wXnuxR6vFpmRsR8LS0rYcKE3q7bO+10WAnNx+ysTLIyDpXlwPsSjcwMKXO/3UOcOAKyxFLRB8aJAtfEen28+NmzeBt4EXjHcWwQMN4YM1REBtn/HyhvQt/f1496Natx/otT+G3bHs/XhSvcXuZZeKFm9Uym/3UA9WtW44NpvwEw7aH+9B5itbAuObYlpxzZhKZ1sxl5Rx86PDImbFwzHzmdS17+MWqaWZkZzPjrAOrWrEZWhnBx9xY0rVu2S/zEJV1Lf191QmvO7nwYTetm88vDA6hXsxoFe4toVLs6d5zWjqY5wdcfVi+b845pzj++XMjmwv28cXXPUnNEKB0Oy2HxhsLS/4KUVlaxDiIHuOXkthgse7NzrCPw1Pp3aMpjF3VmwuLNPPT5PLq2rOfZTHJITv84okkdpj3UP6x5c+Yjp1O7xiHzy62ntOWJMUsSln55Jqn965Kupb1uL/z04Gnk1rMaKzP+OiBhS2/88vAAev3z29gvDPNgz+mSy6T7TnWVr7rD7PfBjcfTo3V9z8n9/byO3DWgPT0f+7b0+tcmreTpcUs9x3Fx9xZ8Nmst951peZBNuLcfBw6WeL4+UfjWszDGTAK2hRy+ABhm/x4GXJiItFo3qk39WtUj2i2DZTv0u1Nzy27uHCQsCTXMloPGdWqUtrCPaFI7qOIWkdL/2WEGkwM0rF3dtXXsNrDXqE4NqmVmBMUPhyrnutlZQfZRZ7gmOTWonpVBk5waZGRIGUUB0Km55RkTaDWFUxRuOCcZeVq6ICRMm8a1S1u84SrberWqkVuvJl1aWHKeFqNbZU7IwnPhenGR5AxHwKuoad3g3lrAK6tdkzo0rF09aPKVW4s7GpEWz2tkD/Z2tvMnFvt6oNy0bFB2UN1NzICiCKTr1tt0I9pYRZOcGvQO6d12DDPx0qtuPLxRrbDmyR72u5fXuFaZiXGRlgfKyswoHVwH6z3vEWMvt66twAKOHDWrZ6Zkvatkj1k0M8asBzDGrBeRsG+xiNwE3ARQKzf8DFtnizHwoP943OHcFcHDIVB2ROCmk46gb7vGpS8OlB3ESwQT7+1Hw3LOt3jsos7k1svm398tR8RqYTTyeQ5HKL3zGnKrPeP5rwM7cnnvwz3NIfjgxuPZunu/5/kGAQIv+sPnHE3nFvXo2Ny7W2CXlvX48k99Y9ob5If7T6VOjSzmri0ArMH/QAX5w/2nUrC3iHNfmOz9BhyMv+cUmoZRcGd1zmXUHX3p3KL8bo+Be3DjqztPKnUXvf3UdpzWoWlQ2ffCxHv7eZ5XES+THziN3fuLyxz/5eEBFNmt6reu7cX6gn2l567r04bjj2jE9j0H+L83prlHHOe7/ej5nbjqhNZByi/A1If6u8rqZPpfB7C/2JK7T7vGPPuHYzyvFxZQwomyeMRL2npDGWNeNcb0NMb0zMrMYtvuA67hejsmpQR6Fud2yaWZi+klQH1bK+c1slqpoS9LTnbidWhe49qldtt4qZGVyQn2khqtGtaiTYxxZtnzJuLxww5Ucmd0alZqH62elRG1Ig5UTM3rZ3NuV0uxB8wgsbSXm+TU4IS2jSK3qFxeps4t6sXkmtiqYS0a1K5eWkac/vutGtYKW7F6afy3bVKHnAjPq0vLenH1IkIvCdyDk9aNrHfDqWwzXcq+F/Ia13Z9Dm69jXhpWLu6a8OiSU6N0uVratfICponEXiXAxV6YB7Vkc3qlHtGfqSyHk5WJ43r1AiyXlzUvWXpmGY04jXXJppk9yw2ikiu3avIBTZ5vTB/a7AnwvV9rVaEc+bmXwYcSa+8BpwYxRXumFb1eeuaXpzYzt07KJ13qTuhbSPeuLpnWDfYSNSukcXwG4+jU27sFcS5XXPJrpYZs0nniUu68vuerWjd6JCXTumYRRxmqGQRKCMnRHFFffmKHjQIMRsliy//1Jf6tarxh1emRg074tYTyd/i3ZsnHm45pS3//m45B4pL+McFnaK+R6Pv7FvuBlQk6mZX49+Xdadzy3r0f7pi7Q79/X392Ft0MNViBJFsZfEFcDUw1P4e6fXCDY7uJlg22dDlfKtnZXBah8hL/AY4NcnLAySSSLNVoxHvYn8iEnX5ZDdqVc8qo9hKzYAeWkyBdXIi2bCN066YQLyUkWb1sulxePKWXHASS6+gcZ0aQbZzP8jKzGDA0U35at4GGtWuUTp/JhyBsa9E4ywGgYZjbr1sNhfuT/ny615xNq4a1rYUaqo3cfItdRH5AOgHNBaRNcDfsZTExyJyPfAbcKnX+H5YtsUPMYN457repTvUfX7bib7ZCN+4umep+6STf17UmR17inxJMx3xUrc/cFYH2jWtE5ei8ovx95zCgnU72XugmO4+bWEZyri/nMy6kAZTgEq2sZ0vvHF1L35csaV0cD8a3959MgOemeSzVN646eS21KtVnUsjTPxNBr4pC2PM5WFO9Q9zPLb4ExFJCM4WcHcfW4vhegZXHBf/apUViVh0cHa1zJjzxW8bb9smdcpsbuQ37Zvl0D7GuThVHWcxa5JTgwu6eV9Jt13T9Mnr6lkZ/F+cy+skkgo0g7uirEqvRKN0UmSK5VAqJ4kqVx/ddDybCsPPvq5qVBhlUaY1WkF2v1LK4tcYQ1UvE+l0+4laRbdcMpQzQ44LszxOVaXCKAsnjWpX54aT02Pdf795+YoebA3jNlxROTTAnRhG3Hoio+asK/VIqmo2/Lev7cV7U3+jVcPEua7Gy+DzOrk6nySTeNyPlehUGGVRsPfQwO+Tl3b11eUunTg7zp3nKgKJeqc7t6hH5xb1+Hj66sREWMFo1zSHwef7s7VvrDStm80TlxyTajEUH6gwyuLr+RtSLQIXd29B9yQsSFfZubx3K8bMX88fXPYELw9ndGzG683qcGu/1O+praSOlg1q0q1V/TL7pijlo8Ioi3TgmT90S7UIlYLcejX55i+nJDze+rWq+xKvUrGolpnB/27vk2oxKh1pu9yHoiiKkj6oslAURVGiospCURRFiYoqC0VRFCUqFVJZtGmc3KUWlMrDMUlay0lRKhsV0hvKbRE+RfHCJzefQHFJ8rekVJSKToVUFooSL9WzMqheMTvUipJS9K1RFEVRoqLKQlEURYmKKgtFURQlKqosFEVRlKioslAURVGiospCURRFiYoqC0VRFCUqFUJZ6CQqRVGU1FJBlMWhvXR1uQZFUZTkUyGUhZPqmbq/rqIoSrKpcMpCUGWhKIqSbCqcslAURVGST4VTFgYTPZCiKIqSUCqeslBdoSiKknQqnLK4/bR2qRZBURSlylHhlMWpRzVNtQiKoihVjgqnLBRFUZTko8pCURRFiYoqC0VRFCUqqiwURVGUqKiyUBRFUaKiykJRFEWJSkqUhYicJSJLRGS5iAxKhQyKoiiKd5KuLEQkE3gJOBvoCFwuIh29XPvxzSf4KZqiKIoShqwUpNkbWG6MWQkgIh8CFwALw13QpUU9pg8dmCTxFEVRlFBSoSxaAKsd/9cAx4UGEpGbgJvsv/tFZH4SZCsvjYEtqRbCAypn4qgIMoLKmWgqipxHJSqiVCgLtw0pyiwPaIx5FXgVQESmG2N6+i1YeVE5E0tFkLMiyAgqZ6KpSHImKq5UDHCvAVo5/rcE1qVADkVRFMUjqVAWvwDtRaSNiFQHLgO+SIEciqIoikeSboYyxhSLyB3AWCATeNMYsyDKZa/6L1lCUDkTS0WQsyLICCpnoqlycorR3YQURVGUKOgMbkVRFCUqqiwURVGUqKS1skinZUFEpJWITBCRRSKyQETuso8PFpG1IjLb/pzjuOZBW/YlInJmEmXNF5F5tjzT7WMNRWSciCyzvxukUk4ROcqRZ7NFZKeI/Dkd8lNE3hSRTc65PfHkn4gcaz+H5SLybxFxcxtPtJxPishiEZkrIp+LSH37eJ6I7HXk63+TIWcYGWN+xinKy48cMuaLyGz7eEry0o4/XD3kf/k0xqTlB2vwewVwBFAdmAN0TKE8uUAP+3cOsBRruZLBwL0u4TvaMtcA2tj3kpkkWfOBxiHHngAG2b8HAf9KtZwhz3oD0Dod8hM4GegBzC9P/gHTgBOw5hZ9DZydBDnPALLs3/9yyJnnDBcSj29yhpEx5mecirwMOf808LdU5qUdf7h6yPfymc49i9JlQYwxB4DAsiApwRiz3hgz0/5dCCzCmo0ejguAD40x+40xq4DlWPeUKi4Ahtm/hwEXOo6nWs7+wApjzK8RwiRNTmPMJGCbS/qe809EcoG6xpifjPVmvuO4xjc5jTHfGGOK7b9TseYxhcVvOcPkZTjSKi8D2C3u3wMfRIojSXKGq4d8L5/prCzclgWJVDknDRHJA7oDP9uH7rC7/W86un+plN8A34jIDLGWTQFoZoxZD1aBA5qmgZwBLiP4RUy3/ITY86+F/Tv0eDK5DqvFGKCNiMwSke9F5CT7WKrkjOUZpzovTwI2GmOWOY6lPC9D6iHfy2c6KwtPy4IkGxGpA4wA/myM2Qm8DLQFugHrsbqrkFr5+xhjemCt7Hu7iJwcIWxK81msiZnnA5/Yh9IxPyMRTq5U5+vDQDHwvn1oPXC4MaY7cDcwXETqkho5Y33GqX72lxPcmEl5XrrUQ2GDhpEpZlnTWVmk3bIgIlIN6wG9b4z5DMAYs9EYc9AYUwK8xiHTSMrkN8ass783AZ/bMm20u56B7vKmVMtpczYw0xizEdIzP21izb81BJuAkiaviFwNnAtcYZsYsM0QW+3fM7Bs10emQs44nnEq8zILuBj4KHAs1XnpVg+RhPKZzsoirZYFse2WbwCLjDHPOI7nOoJdBAS8Kb4ALhORGiLSBmiPNaDkt5y1RSQn8BtrwHO+Lc/VdrCrgZGplNNBUKst3fLTQUz5Z5sCCkXkeLvsXOW4xjdE5CzgAeB8Y8wex/EmYu0lg4gcYcu5MhVyxvqMU5WXNgOAxcaYUpNNKvMyXD1EMspnIkfqE/0BzsEa7V8BPJxiWfpiddPmArPtzznAu8A8+/gXQK7jmodt2ZeQYK+ICHIegeX9MAdYEMg3oBEwHlhmfzdMpZx2urWArUA9x7GU5yeW8loPFGG1wK6PJ/+AnlgV4QrgRewVE3yWczmWjTpQRv9rh/2dXR7mADOB85IhZxgZY37GqchL+/jbwC0hYVOSl3b84eoh38unLvehKIqiRCWdzVCKoihKmqDKQlEURYmKKgtFURQlKqosFEVRlKioslAURVGiospCqdSISH0Ruc3xv7mIfOpTWheKyN8SEM9TInJaImRSlEShrrNKpcZeP+dLY0znJKT1I9ZkuC3ljKc18Jox5ozESKYo5Ud7FkplZyjQVqx9B54Uay+C+QAico2I/E9ERonIKhG5Q0TutheImyoiDe1wbUVkjL0w4w8i0iE0ERE5EtgfUBQi8raIvCzW3gMrReQUe9G8RSLyth0m0w43X6x9Bf4CYKzVdxuJyGHJySJFiU5WqgVQFJ8ZBHQ2xnSD0p6Gk85YK3dmY81+fsAY011EnsVaAuE5rE3vbzHGLBOR44D/AKFmoj5Ys3mdNLDDnQ+MssPcAPwiIt2w9vFoEej1iL1Rkc1MO/yIOO5ZURKOKgulqjPBWPsCFIpIAValDtZyFF3t1T1PBD6RQxuJ1XCJJxfYHHJslDHGiMg8rCWu5wGIyAKsDXS+B44QkReA0cA3jms3Ac3Le3OKkihUWShVnf2O3yWO/yVY70cGsCPQM4nAXqBemLid8ZbGbYzZLiLHAGcCt2NtsHOdHSbbjlNR0gIds1AqO4VY20/GhbH2ClglIpeCteqnXcGHsghoF0vcItIYyDDGjAAewdrWM8CRHFqNVVFSjioLpVJjrH0HptiDyE/GGc0VwPUiEljJ121730lAd5Eom94H0wKYKCKzsVY3fRBK9ytoB0yPU15FSTjqOqsoCUJEnscap/i2nPFcBPQwxjySGMkUpfxoz0JREscQrD06yksWh7YaVZS0QHsWiqIoSlS0Z6EoiqJERZWFoiiKEhVVFoqiKEpUVFkoiqIoUVFloSiKokTl/wPrm6n/proIsgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots()\n",
     "ax.plot(tsteps, rate)\n",
@@ -1089,53 +823,16 @@
     "tags": []
    },
    "source": [
-    "### 4.2 Raster plot of spiking activity for single area <a class=\"anchor\" id=\"section_4_2\"></a>\n",
-    "Raster plot of spiking activity of 3% of the neurons in area V1 (A), V2 (B), and FEF (C). Blue: excitatory neurons, red: inhibitory neurons. (D-F) Spiking statistics across all 32 areas for the respective populations shown as area-averaged box plots. Crosses: medians, boxes: interquartile range (IQR), whiskers extend to the most extremeobservat ions within 1.5×IQR beyond the IQR."
+    "### 5.2 Raster plot of spiking activity for single area <a class=\"anchor\" id=\"section_5_2\"></a>\n",
+    "Fig. 3 (A-C) Raster plot of spiking activity of 3% of the neurons in area V1 (A), V2 (B), and FEF (C). Blue: excitatory neurons, red: inhibitory neurons. (D-F) Spiking statistics across all 32 areas for the respective populations shown as area-averaged box plots. Crosses: medians, boxes: interquartile range (IQR), whiskers extend to the most extremeobservat ions within 1.5×IQR beyond the IQR."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "1da18fee",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "\"\"\"\n",
     "Create raster display of a single area with populations stacked onto each other. Excitatory neurons in blue, inhibitory neurons in red.\n",
@@ -1169,96 +866,24 @@
     "tags": []
    },
    "source": [
-    "### 4.3 Population-averaged firing rate <a class=\"anchor\" id=\"section_4_3\"></a>"
+    "### 5.3 Population-averaged firing rate <a class=\"anchor\" id=\"section_5_3\"></a>\n",
+    "Fig 3. (D) Population-averaged firing rates"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "id": "473d0882-8e45-4330-bfa2-2c7e1af0dac4",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "### 4.4 Time-averaged population rates <a class=\"anchor\" id=\"section_4_3\"></a>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "56930a25-0259-4504-bf3c-9f4d2718d9d7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Stored data have been computed with different parameters\n",
-      "Computing population rates\n"
-     ]
-    }
-   ],
    "source": [
-    "A.create_pop_rates()"
+    "### 5.4 Time-averaged population rates <a class=\"anchor\" id=\"section_5_4\"></a>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "id": "721d1f03-df25-468d-8075-a807025a9c58",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 V1\n",
-      "1 V2\n",
-      "2 VP\n",
-      "3 V3\n",
-      "4 PIP\n",
-      "5 V3A\n",
-      "6 MT\n",
-      "7 V4t\n",
-      "8 V4\n",
-      "9 PO\n",
-      "10 VOT\n",
-      "11 DP\n",
-      "12 MIP\n",
-      "13 MDP\n",
-      "14 MSTd\n",
-      "15 VIP\n",
-      "16 LIP\n",
-      "17 PITv\n",
-      "18 PITd\n",
-      "19 AITv\n",
-      "20 MSTl\n",
-      "21 FST\n",
-      "22 CITv\n",
-      "23 CITd\n",
-      "24 7a\n",
-      "25 STPp\n",
-      "26 STPa\n",
-      "27 FEF\n",
-      "28 46\n",
-      "29 TF\n",
-      "30 TH\n",
-      "31 AITd\n",
-      "['23E', '23I', '4E', '4I', '5E', '5I', '6E', '6I']\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "\"\"\"\n",
     "Plot overview over time-averaged population rates encoded in colors\n",
@@ -1282,7 +907,8 @@
     "tags": []
    },
    "source": [
-    "### 4.4 Average pairwise correlation coefficients of spiking activity <a class=\"anchor\" id=\"section_4_4\"></a>"
+    "### 5.5 Average pairwise correlation coefficients of spiking activity <a class=\"anchor\" id=\"section_5_5\"></a>\n",
+    "Fig 5. (E) Average pairwise correlation coefficients of spiking activity"
    ]
   },
   {
@@ -1344,8 +970,8 @@
     "tags": []
    },
    "source": [
-    "### 4.5 Irregularity of spiking activity <a class=\"anchor\" id=\"section_4_5\"></a>\n",
-    "Irregularity is measured by revised local variation LvR averaged across neurons"
+    "### 5.6 Irregularity of spiking activity <a class=\"anchor\" id=\"section_5_6\"></a>\n",
+    "Fig 5. (F) Irregularity measured by revised local variation LvR averaged across neurons"
    ]
   },
   {
@@ -1418,8 +1044,8 @@
     "tags": []
    },
    "source": [
-    "### 4.6 Time series of population- and area-averaged firing rates <a class=\"anchor\" id=\"section_4_6\"></a>\n",
-    "Area-averaged firing rates, shown as raw binned spike histograms with 1ms bin width (gray) and convolved histograms, with aGaussian kernel (black) of optimal width"
+    "### 5.7 Time series of area-averaged firing rates <a class=\"anchor\" id=\"section_5_7\"></a>\n",
+    "Area-averagedfiring rates, shown as raw binned spike histograms with 1ms bin width (gray) and convolvedhistograms, with aGaussia nkernel (black) of optimal width"
    ]
   },
   {
-- 
GitLab