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- C. Weber and
K. Obermayer. Orientation Selective Cells Emerge in a Sparsely Coding
Boltzmann Machine.
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In Artificial Neural Networks - ICANN 99, pages 286-291, 1999.
(FTP Gzipped PostScript, 6 pages, 61 kb)
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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