Presentation 2006-03-16
A computational V1 model derived from multi-resolution image-reconstruction
Shunji SATOH,
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Abstract(in English) Neurophysiological experiments have revealed that post-synaptic effect conveyed by long-range horizontal connections (LHC) in V1 can be excitatory or inhibitory, and the effect highly depends on the activity of pre-synaptic neurons (non-linearity of horizontal connections). Almost all of computational studies about LHC have been based on contour integration. However, contour integration cannot explain the non-linearity of LHC. In this study, the author proposes image-reconstruction as the computational role of LHC in V1 instead of contour integration. The spatial distribution of LHC and the non-linear property are theoretically derived from the dynamics for image-reconstruction. It is shown that the theoretically obtained distribution of LHC and the non-linear property are qualitatively consistent with neurophysiological findings. Numerical simulations show the effectiveness of the proposed model from engineering points of view.
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Keyword(in English) V1 / long-range horizontal connection / image reconstruction / standard regularization theory
Paper # NC2005-134
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Committee NC
Conference Date 2006/3/9(1days)
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Registration To Neurocomputing (NC)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A computational V1 model derived from multi-resolution image-reconstruction
Sub Title (in English)
Keyword(1) V1
Keyword(2) long-range horizontal connection
Keyword(3) image reconstruction
Keyword(4) standard regularization theory
1st Author's Name Shunji SATOH
1st Author's Affiliation Department of Information Science for Human Welfare, Tohoku Fukushi University()
Date 2006-03-16
Paper # NC2005-134
Volume (vol) vol.105
Number (no) 658
Page pp.pp.-
#Pages 6
Date of Issue