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];
"""
This script is used to run a simulation from the given command-line
arguments:
1. Label of the simulation
2. Label of the network to be simulated

It initializes the network class and then runs the simulate method of
the simulation class instance.

This script should be used in the `jobscript_template` defined in the
config.py file. See config_template.py.
"""

import json
import nest
import os
import sys

from config import data_path
from multiarea_model import MultiAreaModel

label = sys.argv[1]
network_label = sys.argv[2]
fn = os.path.join(data_path,
                  label,
                  '_'.join(('custom_params',
                            label,
                           str(nest.Rank()))))
with open(fn, 'r') as f:
    custom_params = json.load(f)

os.remove(fn)

M = MultiAreaModel(network_label,
                   simulation=True,
                   sim_spec=custom_params['sim_params'])
M.simulation.simulate()

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