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Neural network model of the primary visual cortex: From functional architecture to lateral connectivity and back

The role of intrinsic cortical dynamics is a debatable issue. A recent optical imaging study (Kenet et al., 2003) found that activity patterns similar to orientation maps (OMs), emerge in the primary visual cortex (V1) even in the absence of sensory input, suggesting an intrinsic mechanism of OM act...

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Detalles Bibliográficos
Autores principales: Blumenfeld, Barak, Bibitchkov, Dmitri, Tsodyks, Misha
Formato: Texto
Lenguaje:English
Publicado: Kluwer Academic Publishers 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2784503/
https://www.ncbi.nlm.nih.gov/pubmed/16699843
http://dx.doi.org/10.1007/s10827-006-6307-y
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author Blumenfeld, Barak
Bibitchkov, Dmitri
Tsodyks, Misha
author_facet Blumenfeld, Barak
Bibitchkov, Dmitri
Tsodyks, Misha
author_sort Blumenfeld, Barak
collection PubMed
description The role of intrinsic cortical dynamics is a debatable issue. A recent optical imaging study (Kenet et al., 2003) found that activity patterns similar to orientation maps (OMs), emerge in the primary visual cortex (V1) even in the absence of sensory input, suggesting an intrinsic mechanism of OM activation. To better understand these results and shed light on the intrinsic V1 processing, we suggest a neural network model in which OMs are encoded by the intrinsic lateral connections. The proposed connectivity pattern depends on the preferred orientation and, unlike previous models, on the degree of orientation selectivity of the interconnected neurons. We prove that the network has a ring attractor composed of an approximated version of the OMs. Consequently, OMs emerge spontaneously when the network is presented with an unstructured noisy input. Simulations show that the model can be applied to experimental data and generate realistic OMs. We study a variation of the model with spatially restricted connections, and show that it gives rise to states composed of several OMs. We hypothesize that these states can represent local properties of the visual scene.
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spelling pubmed-27845032009-12-04 Neural network model of the primary visual cortex: From functional architecture to lateral connectivity and back Blumenfeld, Barak Bibitchkov, Dmitri Tsodyks, Misha J Comput Neurosci Article The role of intrinsic cortical dynamics is a debatable issue. A recent optical imaging study (Kenet et al., 2003) found that activity patterns similar to orientation maps (OMs), emerge in the primary visual cortex (V1) even in the absence of sensory input, suggesting an intrinsic mechanism of OM activation. To better understand these results and shed light on the intrinsic V1 processing, we suggest a neural network model in which OMs are encoded by the intrinsic lateral connections. The proposed connectivity pattern depends on the preferred orientation and, unlike previous models, on the degree of orientation selectivity of the interconnected neurons. We prove that the network has a ring attractor composed of an approximated version of the OMs. Consequently, OMs emerge spontaneously when the network is presented with an unstructured noisy input. Simulations show that the model can be applied to experimental data and generate realistic OMs. We study a variation of the model with spatially restricted connections, and show that it gives rise to states composed of several OMs. We hypothesize that these states can represent local properties of the visual scene. Kluwer Academic Publishers 2006-04-22 2006-04 /pmc/articles/PMC2784503/ /pubmed/16699843 http://dx.doi.org/10.1007/s10827-006-6307-y Text en © Springer Science + Business Media, LLC 2006
spellingShingle Article
Blumenfeld, Barak
Bibitchkov, Dmitri
Tsodyks, Misha
Neural network model of the primary visual cortex: From functional architecture to lateral connectivity and back
title Neural network model of the primary visual cortex: From functional architecture to lateral connectivity and back
title_full Neural network model of the primary visual cortex: From functional architecture to lateral connectivity and back
title_fullStr Neural network model of the primary visual cortex: From functional architecture to lateral connectivity and back
title_full_unstemmed Neural network model of the primary visual cortex: From functional architecture to lateral connectivity and back
title_short Neural network model of the primary visual cortex: From functional architecture to lateral connectivity and back
title_sort neural network model of the primary visual cortex: from functional architecture to lateral connectivity and back
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2784503/
https://www.ncbi.nlm.nih.gov/pubmed/16699843
http://dx.doi.org/10.1007/s10827-006-6307-y
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AT tsodyksmisha neuralnetworkmodeloftheprimaryvisualcortexfromfunctionalarchitecturetolateralconnectivityandback