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6 x 9, illustrated
ISSN
0899-7667
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1530-888X
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2.21

Neural Computation

March 1, 1997, Vol. 9, No. 3, Pages 595-606
(doi: 10.1162/neco.1997.9.3.595)
© 1997 Massachusetts Institute of Technology
The Formation of Topographic Maps That Maximize the Average Mutual Information of the Output Responses to Noiseless Input Signals
Article PDF (104.74 KB)
Abstract

This article introduces an extremely simple and local learning rule for to pographic map formation. The rule, called the maximum entropy learning rule (MER), maximizes the unconditional entropy of the map's output for any type of input distribution. The aim of this article is to show that MER is a viable strategy for building topographic maps that maximize the average mutual information of the output responses to noiseless input signals when only input noise and noise-added input signals are available.