diff --git a/README.md b/README.md
index c7047d63c9106faa75107dcad6b1a307edd2a459..0d7d2ea3feb07ab51618aaf8126f9ffc700d224c 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,7 @@
This code implements the spiking network model of macaque visual cortex developed
-at the Institute of Neuroscience and Medicine (INM-6), Research Center Jülich.
+at the Institute of Neuroscience and Medicine (INM-6), Research Center Jülich.
The model has been documented in the following publications:
1. Schmidt M, Bakker R, Hilgetag CC, Diesmann M & van Albada SJ
@@ -15,7 +15,7 @@ The model has been documented in the following publications:
Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome.
PLOS Computational Biology, 13(2). [https://doi.org/10.1371/journal.pcbi.1005179](https://doi.org/10.1371/journal.pcbi.1005179)
-3. Schmidt M, Bakker R, Shen K, Bezgin B, Diesmann M & van Albada SJ (2018)
+3. Schmidt M, Bakker R, Shen K, Bezgin B, Diesmann M & van Albada SJ (2018)
A multi-scale layer-resolved spiking network model of
resting-state dynamics in macaque cortex. PLOS Computational Biology (accepted)
@@ -49,7 +49,7 @@ Furthermore, please add the path to the repository to your PYTHONPATH:
`MultiAreaModel`
The central class that initializes the network and contains all
-information about population sizes and network connectivity. This
+information about population sizes and network connectivity. This
enables reproducing all figures in [1]. Network parameters only
refer to the structure of the network and ignore any information on
its dynamical simulation or description via analytical theory.
@@ -84,7 +84,7 @@ The `figures` folder contains a subfolder with all scripts necessary to produce
the figures from [1]. The scripts for [2] and [3] will follow soon.
If snakemake is installed, the figures can be produced by executing
`snakemake` in the respective folder, e.g.:
-
+
cd figures/Schmidt2018/
snakemake
@@ -97,9 +97,9 @@ A simple simulation can be run in the following way:
custom_params = ...
custom_simulation_params = ...
2. Instantiate the model class together with a simulation class instance.
-
+
M = MultiAreaModel(custom_params, simulation=True, sim_spec=custom_simulation_params)
-
+
3. Start the simulation.
M.simulation.simulate()
@@ -113,13 +113,13 @@ The procedure is similar to a simple simulation:
custom_params = ...
custom_simulation_params = ...
2. Instantiate the model class together with a simulation class instance.
-
+
M = MultiAreaModel(custom_params, simulation=True, sim_spec=custom_simulation_params)
3. Start the simulation.
Call `start_job` to create a job file using the `jobscript_template` from the configuration file
and submit it to the queue with the user-defined `submit_cmd`.
-
-The file `run_example.py` provides an example.
+
+The file `run_example_fullscale.py` provides an example.
Be aware that, depending on the chosen parameters and initial conditions, the network can enter a high-activity state, which slows down the simulation drastically and can cost a significant amount of computing resources.
@@ -150,7 +150,7 @@ The multi-area model can be run in different modes.
- `hom_poisson_stat`: all non-simulated areas are replaced by Poissonian spike trains with the
same rate as the stationary background input (`rate_ext` in `input_params`).
- `het_poisson_stat`: all non-simulated areas are replaced by Poissonian spike trains with
- population-specific stationary rate stored in an external file.
+ population-specific stationary rate stored in an external file.
- `current_nonstat`: all non-simulated areas are replaced by stepwise constant currents with
population-specific, time-varying time series defined in an external file.
diff --git a/run_example.py b/run_example_downscaled.py
similarity index 51%
rename from run_example.py
rename to run_example_downscaled.py
index 9a16c376ae9b3c8ab8f38989b5c7d75135b34889..d5f278175460e345009752bf4e995ea2fc1e19e8 100644
--- a/run_example.py
+++ b/run_example_downscaled.py
@@ -2,56 +2,8 @@ import numpy as np
import os
from multiarea_model import MultiAreaModel
-from start_jobs import start_job
-from config import submit_cmd, jobscript_template
from config import base_path
-"""
-Example script showing how to simulate the multi-area model
-on a cluster.
-
-We choose the same configuration as in
-Fig. 3 of Schmidt et al. (2018).
-
-"""
-
-"""
-Full model. Needs to be simulated with sufficient
-resources, for instance on a compute cluster.
-"""
-d = {}
-conn_params = {'g': -11.,
- 'K_stable': os.path.join(base_path, 'K_stable.npy'),
- 'fac_nu_ext_TH': 1.2,
- 'fac_nu_ext_5E': 1.125,
- 'fac_nu_ext_6E': 1.41666667,
- 'av_indegree_V1': 3950.}
-input_params = {'rate_ext': 10.}
-neuron_params = {'V0_mean': -150.,
- 'V0_sd': 50.}
-network_params = {'N_scaling': 1.,
- 'K_scaling': 1.,
- 'connection_params': conn_params,
- 'input_params': input_params,
- 'neuron_params': neuron_params}
-
-sim_params = {'t_sim': 2000.,
- 'num_processes': 720,
- 'local_num_threads': 1,
- 'recording_dict': {'record_vm': False}}
-
-theory_params = {'dt': 0.1}
-
-M = MultiAreaModel(network_params, simulation=True,
- sim_spec=sim_params,
- theory=True,
- theory_spec=theory_params)
-p, r = M.theory.integrate_siegert()
-print("Mean-field theory predicts an average "
- "rate of {0:.3f} spikes/s across all populations.".format(np.mean(r[:, -1])))
-start_job(M.simulation.label, submit_cmd, jobscript_template)
-
-
"""
Down-scaled model.
Neurons and indegrees are both scaled down to 10 %.
diff --git a/run_example_fullscale.py b/run_example_fullscale.py
new file mode 100644
index 0000000000000000000000000000000000000000..30e0c8bcbf9877a52a65a8ca699c889c5011b0bc
--- /dev/null
+++ b/run_example_fullscale.py
@@ -0,0 +1,52 @@
+import numpy as np
+import os
+
+from multiarea_model import MultiAreaModel
+from start_jobs import start_job
+from config import submit_cmd, jobscript_template
+from config import base_path
+
+"""
+Example script showing how to simulate the multi-area model
+on a cluster.
+
+We choose the same configuration as in
+Fig. 3 of Schmidt et al. (2018).
+
+"""
+
+"""
+Full model. Needs to be simulated with sufficient
+resources, for instance on a compute cluster.
+"""
+d = {}
+conn_params = {'g': -11.,
+ 'K_stable': os.path.join(base_path, 'K_stable.npy'),
+ 'fac_nu_ext_TH': 1.2,
+ 'fac_nu_ext_5E': 1.125,
+ 'fac_nu_ext_6E': 1.41666667,
+ 'av_indegree_V1': 3950.}
+input_params = {'rate_ext': 10.}
+neuron_params = {'V0_mean': -150.,
+ 'V0_sd': 50.}
+network_params = {'N_scaling': 1.,
+ 'K_scaling': 1.,
+ 'connection_params': conn_params,
+ 'input_params': input_params,
+ 'neuron_params': neuron_params}
+
+sim_params = {'t_sim': 2000.,
+ 'num_processes': 720,
+ 'local_num_threads': 1,
+ 'recording_dict': {'record_vm': False}}
+
+theory_params = {'dt': 0.1}
+
+M = MultiAreaModel(network_params, simulation=True,
+ sim_spec=sim_params,
+ theory=True,
+ theory_spec=theory_params)
+p, r = M.theory.integrate_siegert()
+print("Mean-field theory predicts an average "
+ "rate of {0:.3f} spikes/s across all populations.".format(np.mean(r[:, -1])))
+start_job(M.simulation.label, submit_cmd, jobscript_template)