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

Neural Computation

May 1994, Vol. 6, No. 3, Pages 441-458
(doi: 10.1162/neco.1994.6.3.441)
© 1994 Massachusetts Institute of Technology
Topology Learning Solved by Extended Objects: A Neural Network Model
Article PDF (946.71 KB)
Abstract

It is shown that local, extended objects of a metrical topological space shape the receptive fields of competitive neurons to local filters. Self-organized topology learning is then solved with the help of Hebbian learning together with extended objects that provide unique information about neighborhood relations. A topographical map is deduced and is used to speed up further adaptation in a changing environment with the help of Kohonen-type learning that teaches the neighbors of winning neurons as well.