GLMCC validation neural network model (Kobayashi et al. 2019)

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Accession:258807
Network model of two populations of randomly connected inhibitory and excitatory neurons to validate method for reconstructing the neural circuitry developed in "Reconstructing Neuronal Circuitry from Parallel Spike Trains" by Ryota Kobayashi, Shuhei Kurita, Anno Kurth, Katsunori Kitano, Kenji Mizuseki, Markus Diesmann, Barry J. Richmond and Shigeru Shinomoto.
Reference:
1 . Kobayashi R, Kurita S, Kurth A, Kitano K, Mizuseki K, Diesmann M, Richmond BJ, Shinomoto S (2019) Reconstructing neuronal circuitry from parallel spike trains. Nat Commun 10:4468 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
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): Methods;
Implementer(s): Kurth, Anno [a.kurth at fz-juelich.de];
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GLMCC_validation_network
README.md
glmcc_validation_network.py
LICENSE.md
                            
# GLMCC validation network model

License: CC BY-NC-SA 4.0 (https://creativecommons.org/licenses/by-nc-sa/4.0/, see LICENSE.md)  

Author: Anno Kurth  
Contact: a.kurth@fz-juelich.de  

Network model to validate method for reconstructing the neural circuitry developed in 
"Reconstructing Neuronal Circuitry from Parallel Spike Trains" by Ryota Kobayashi, 
Shuhei Kurita, Anno Kurth, Katsunori Kitano, Kenji Mizuseki, Markus Diesmann, Barry J. Richmond 
and Shigeru Shinomoto to appear in Nature Communications in 2019 using NEST (https://www.nest-simulator.org/).  

For further information see Supplementary Note 5 of above paper.