Stabilizing competitive learning during on-line training with an anti-Hebbian weight modulation

Stephane Tavitian, Tibor Fomin, András Lörincz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Competitive learning algorithms are statistically driven schemes requiring that the training samples are both representative and randomly ordered. Within the frame of self-organization, the latter condition appears as a paradoxical unrealistic assumption about the temporal structure of the environment. In this paper, the resulting vulnerability to continuously changing inputs is illustrated in the case of a simple space discretization task. A biologically motivated local anti-Hebbian modulation of the Hebbian weights is introduced, and successfully used to stabilize this network under real-time-like conditions.

Original languageEnglish
Title of host publicationArtificial Neural Networks, ICANN 1996 - 1996 International Conference, Proceedings
PublisherSpringer Verlag
Pages697-702
Number of pages6
ISBN (Print)3540615105, 9783540615101
DOIs
Publication statusPublished - Jan 1 1996
Event1996 International Conference on Artificial Neural Networks, ICANN 1996 - Bochum, Germany
Duration: Jul 16 1996Jul 19 1996

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1112 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1996 International Conference on Artificial Neural Networks, ICANN 1996
CountryGermany
CityBochum
Period7/16/967/19/96

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Tavitian, S., Fomin, T., & Lörincz, A. (1996). Stabilizing competitive learning during on-line training with an anti-Hebbian weight modulation. In Artificial Neural Networks, ICANN 1996 - 1996 International Conference, Proceedings (pp. 697-702). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1112 LNCS). Springer Verlag. https://doi.org/10.1007/3-540-61510-5_118