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Journal of Computational Neuroscience
Erschienen in: Journal of Computational Neuroscience 2/2014

01.04.2014

Conductance-based refractory density model of primary visual cortex

verfasst von: Anton V. Chizhov

Erschienen in: Journal of Computational Neuroscience | Ausgabe 2/2014

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Abstract

A layered continual population model of primary visual cortex has been constructed, which reproduces a set of experimental data, including postsynaptic responses of single neurons on extracellular electric stimulation and spatially distributed activity patterns in response to visual stimulation. In the model, synaptically interacting excitatory and inhibitory neuronal populations are described by a conductance-based refractory density approach. Populations of two-compartment excitatory and inhibitory neurons in cortical layers 2/3 and 4 are distributed in the 2-d cortical space and connected by AMPA, NMDA and GABA type synapses. The external connections are pinwheel-like, according to the orientation of a stimulus. Intracortical connections are isotropic local and patchy between neurons with similar orientations. The model proposes better temporal resolution and more detailed elaboration than conventional mean-field models. In comparison to large network simulations, it excludes a posteriori statistical data manipulation and provides better computational efficiency and minimal parametrization.
Vorheriger Artikel Distribution of correlated spiking events in a population-based approach for Integrate-and-Fire networks

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Metadaten
Titel
Conductance-based refractory density model of primary visual cortex
verfasst von
Anton V. Chizhov
Publikationsdatum
01.04.2014
Verlag
Springer US
Erschienen in
Journal of Computational Neuroscience / Ausgabe 2/2014
Print ISSN: 0929-5313
Elektronische ISSN: 1573-6873
DOI
https://doi.org/10.1007/s10827-013-0473-5

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