Multi-area layer-resolved spiking network model of resting-state dynamics in macaque visual cortex

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Accession:262457
See https://inm-6.github.io/multi-area-model/ for any updates.
References:
1 . Schmidt M, Bakker R, Shen K, Bezgin G, Diesmann M, van Albada SJ (2018) A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas. PLoS Comput Biol 14:e1006359 [PubMed]
2 . Schmidt M, Bakker R, Hilgetag CC, Diesmann M, van Albada SJ (2018) Multi-scale account of the network structure of macaque visual cortex. Brain Struct Funct 223:1409-1435 [PubMed]
3 . Schuecker J, Schmidt M, van Albada SJ, Diesmann M, Helias M (2017) Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome. PLoS Comput Biol 13:e1005179 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Connectionist Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Abstract integrate-and-fire leaky neuron with exponential post-synaptic current;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEST;
Model Concept(s): Spatio-temporal Activity Patterns; Activity Patterns; Connectivity matrix; Synchronization; Multiscale;
Implementer(s): Schmidt, Maximilian [schmidt.maximilian at posteo.de]; Schuecker, Jannis ; van Albada, Sacha Jennifer [s.van.albada at fz-juelich.de];
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)

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