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Multi-area layer-resolved spiking network model of resting-state dynamics in macaque visual cortex
 
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Model Information
Model File
Accession:
262457
See https://inm-6.github.io/multi-area-model/ for any updates.
Reference:
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
]
Citations
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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];
/
multi-area-model-master
multiarea_model
data_multiarea
__init__.py
analysis.py
analysis_helpers.py
default_params.py
multiarea_helpers.py
multiarea_model.py
simulation.py
stabilize.py
sumatra_helpers.py
theory.py
theory_helpers.py
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