Topology learning solved by extended objects: A neural network model

Authors:
Csaba Szepesvári;László Balázs;András Lőrincz
Affiliations:
Department of Photophysics, Institute of Isotopes of the Hungarian Academy of Sciences, P.O. Box 77, Budapest, Hungary H-1525;Department of Photophysics, Institute of Isotopes of the Hungarian Academy of Sciences, P.O. Box 77, Budapest, Hungary H-1525;Department of Photophysics, Institute of Isotopes of the Hungarian Academy of Sciences, P.O. Box 77, Budapest, Hungary H-1525
Venue:
Neural Computation
Year:
1994

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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.