Retrieval property of associative memory with negative resistance

Yoshihiro Hayakawa, Hongge Li, Koji Nakajima

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

Abstract

The self-connection can enlarge the memory capacity of an associative memory based on the neural network, however, the basin size of the embedded memory state shrinks. The problem of basin size is related to undesirable stable states which are spurious states. If we can destabilize these spurious states, we expect to improve the basin size. The Inverse Function Delayed(ID) model which includes the BVP model has the negative resistance on its dynamics. The negative resistance of the ID model can destabilize the equilibrium states on some regions of conventional neural network. Hence, the associative memory based on the ID model has possibilities of improving the basin size of the network which has the self-connection in order to enlarge a memory capacity. In this paper, we show the improvement of performance compared with the conventional neural network by computer simulation.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages1187-1192
Number of pages6
DOIs
Publication statusPublished - 2005 Dec 1
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: 2005 Jul 312005 Aug 4

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2005
CountryCanada
CityMontreal, QC
Period05/7/3105/8/4

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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