Hippocampal formation trains independent components via forcing input reconstruction

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

2 Citations (Scopus)

Abstract

It is assumed that higher order concept formation utilizes independent components (ICs). It is argued that ICs require dynamic input reconstruction networks (RNs) to form a reliable internal representation. Input reconstruction, however, can be slow and poor with ICs on substrates with lossy dynamics. A model of the hippocampal formation is proposed that develops the ICs on lossy RNs by means of locking inputs to the internal representation and thus forcing fast reconstruction and cancelling losses. It is assumed that upon training ICs can lock themselves, thus hippocampal lesion mostly affects anterograde memories.

Original languageEnglish
Title of host publicationArtificial Neural Networks - ICANN 1997 - 7th International Conference, Proceeedings
EditorsWulfram Gerstner, Alain Germond, Martin Hasler, Jean-Daniel Nicoud
PublisherSpringer Verlag
Pages163-168
Number of pages6
ISBN (Print)3540636315, 9783540636311
DOIs
Publication statusPublished - Jan 1 1997
Event7th International Conference on Artificial Neural Networks, ICANN 1997 - Lausanne, Switzerland
Duration: Oct 8 1997Oct 10 1997

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1327
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Conference on Artificial Neural Networks, ICANN 1997
CountrySwitzerland
CityLausanne
Period10/8/9710/10/97

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Lörincz, A. (1997). Hippocampal formation trains independent components via forcing input reconstruction. In W. Gerstner, A. Germond, M. Hasler, & J-D. Nicoud (Eds.), Artificial Neural Networks - ICANN 1997 - 7th International Conference, Proceeedings (pp. 163-168). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1327). Springer Verlag. https://doi.org/10.1007/bfb0020150