Merge pull request #3 from AlexVanMeegen/master

Split run_example into run_example_downscaled and run_example_fullscale

README.md

@@ -4,7 +4,7 @@
 ![Model overview](model_construction.png)
 
 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.
 

run_example.py → 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 %.

run_example_fullscale.py

+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)