Add script to run network simulations

figures/Schmidt2018_dyn/run_network_simulations.py

+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
+
+"""
+This script provides the code to execute all simulations presented in
+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.
+
+Needs to be simulated with sufficient
+resources, for instance on a compute cluster.
+"""
+
+# Common parameter settings
+input_params = {'rate_ext': 10.}
+neuron_params = {'V0_mean': -150.,
+                 'V0_sd': 50.}
+sim_params = {'num_processes': 20,  # Needs to be adapted to the HPC system used
+              'local_num_threads': 24,  # Needs to be adapted to the HPC system used
+              'recording_dict': {'record_vm': False}}
+
+
+"""
+Simulation with  kappa = 1. leading to the low-activity fixed point
+shown in Fig. 2.
+"""
+d = {}
+
+sim_params.update({'t_sim': 10500.})
+conn_params = {'g': -16.,
+               'fac_nu_ext_TH': 1.2,
+               'fac_nu_ext_5E': 1.,
+               'fac_nu_ext_6E': 1.,
+               'av_indegree_V1': 3950.}
+network_params = {'N_scaling': 1.,
+                  'K_scaling': 1.,
+                  'connection_params': conn_params,
+                  'neuron_params': neuron_params,
+                  'input_params': input_params}
+
+M_LA = MultiAreaModel(network_params, simulation=True,
+                      sim_spec=sim_params)
+
+# start_job(M_LA.simulation.label, submit_cmd, jobscript_template)
+
+
+"""
+Simulation with kappa = 1.125 leading to the high-activity fixed point
+shown in Fig. 2.
+"""
+sim_params.update({'t_sim': 10500.})
+conn_params = {'g': -16.,
+               'fac_nu_ext_TH': 1.2,
+               'fac_nu_ext_5E': 1.125,
+               'fac_nu_ext_6E': 1.41666667,
+               'av_indegree_V1': 3950.}
+network_params = {'N_scaling': 1.,
+                  'K_scaling': 1.,
+                  'connection_params': conn_params,
+                  'neuron_params': neuron_params}
+
+M_HA = MultiAreaModel(network_params, simulation=True,
+                      sim_spec=sim_params)
+p, r_HA = M_HA.theory.integrate_siegert()
+# start_job(M_HA.simulation.label, submit_cmd, jobscript_template)
+
+
+"""
+Simulation with kappa = 1.125, chi=1
+Presented in Fig. 2, 4, and 8.
+"""
+sim_params.update({'t_sim': 10500.})
+conn_params = {'g': -11.,
+               'fac_nu_ext_TH': 1.2,
+               'fac_nu_ext_5E': 1.125,
+               'fac_nu_ext_6E': 1.41666667,
+               'av_indegree_V1': 3950.,
+               'K_stable': '../SchueckerSchmidt2018/K_prime_original.npy'}
+network_params = {'N_scaling': 1.,
+                  'K_scaling': 1.,
+                  'connection_params': conn_params,
+                  'neuron_params': neuron_params}
+
+M_stab = MultiAreaModel(network_params, simulation=True,
+                        sim_spec=sim_params)
+p, r_stab = M_stab.theory.integrate_siegert()
+# start_job(M_stab.simulation.label, submit_cmd, jobscript_template)
+
+
+"""
+Simulation with kappa = 1.125, chi=1.9
+Presented in Fig. 4 and all following.
+"""
+sim_params.update({'t_sim': 100500.})
+conn_params = {'g': -11.,
+               'fac_nu_ext_TH': 1.2,
+               'fac_nu_ext_5E': 1.125,
+               'fac_nu_ext_6E': 1.41666667,
+               'av_indegree_V1': 3950.,
+               'K_stable': '../SchueckerSchmidt2018/K_prime_original.npy',
+               'cc_weights_factor': 1.9,
+               'cc_weights_I_factor': 2.}
+network_params = {'N_scaling': 1.,
+                  'K_scaling': 1.,
+                  'connection_params': conn_params,
+                  'neuron_params': neuron_params}
+
+M_stab = MultiAreaModel(network_params, simulation=True,
+                        sim_spec=sim_params)
+p, r_stab = M_stab.theory.integrate_siegert()
+# start_job(M_stab.simulation.label, submit_cmd, jobscript_template)
+
+
+"""
+Simulations with kappa = 1.125
+and varying chi, presented in Fig. 4 and 8.
+"""
+for cc_weights_factor in [1.4, 1.5, 1.6, 1.7, 1.75, 1.8, 2., 2.1, 2.5]:
+    sim_params.update({'t_sim': 10500.})
+    conn_params = {'g': -11.,
+                   'fac_nu_ext_TH': 1.2,
+                   'fac_nu_ext_5E': 1.125,
+                   'fac_nu_ext_6E': 1.41666667,
+                   'av_indegree_V1': 3950.,
+                   'K_stable': '../SchueckerSchmidt2018/K_prime_original.npy',
+                   'cc_weights_factor': cc_weights_factor,
+                   'cc_weights_I_factor': 2.}
+
+    network_params = {'N_scaling': 1.,
+                      'K_scaling': 1.,
+                      'connection_params': conn_params,
+                      'neuron_params': neuron_params}
+
+    M_stab = MultiAreaModel(network_params, simulation=True,
+                            sim_spec=sim_params)
+    p, r_stab = M_stab.theory.integrate_siegert()
+    # start_job(M_stab.simulation.label, submit_cmd, jobscript_template)