Neural Computation
May 1994, Vol. 6, No. 3, Pages 441-458
(doi: 10.1162/neco.1994.6.3.441)
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.