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Models of Neural Systems

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Orientation Selective Cells Emerge in a Sparsely Coding Boltzmann Machine
Citation key Weber1999
Author Weber, C. and Obermayer, K.
Title of Book Artificial Neural Networks - ICANN 99
Pages 286 – 291
Year 1999
ISBN 0-85296-721-7
ISSN 0537-9989
DOI 10.1049/cp:19991123
Volume 1
Publisher IEEE
Abstract In our contribution we investigate a sparse coded Boltzmann machine as a model for the formation of orientation selective receptive fields in primary visual cortex. The model consists of two layers of neurons which are recurrently connected and which represent the lateral geniculate nucleus and primary visual cortex. Neurons have ternary activity values $+1$, $-1$, and $0$, where the $0$-state is degenerate being assumed with higher prior probability. The probability for a (stochastic) activation vector on the net obeys the Boltzmann distribution and maximum-likelihood leads to the standard Boltzmann learning rule. We apply a mean-field version of this model to natural image processing and find that neurons develop localized and oriented receptive fields.
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