Recalling temporal sequences of patterns using neurons with hysteretic property

Johan Sveholm, Yoshihiro Hayakawa, Koji Nakajima

Research output: Contribution to journalArticlepeer-review

Abstract

Further development of a network based on the Inverse Function Delayed (ID) model which can recall temporal sequences of patterns, is proposed. Additional advantage is taken of the negative resistanne region of the ID model and its hysteretic properties by widening the negative resistance region and letting the output of the ID neuron be almost instant. Calling this neuron limit ID neuron, a model with limit ID neurons connected pairwise with conventional neurons enlarges the storage capacity and increases it even further by using a weight matrix that is calculated to guarantee the storage after transforming the sequence of patterns into a linear separation problem. The network's tolerance, or the model's ability to recall a sequence, starting in a pattern with initial distortion is also investigated and by choosing a suitable value for the output delay of theconventional neuron, the distortion is gradually reduced and finallyvanishes.

Original languageEnglish
Pages (from-to)943-950
Number of pages8
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE91-A
Issue number4
DOIs
Publication statusPublished - 2008 Jan 1

Keywords

  • Limit inverse function delayed neuron
  • Negative resistance
  • Temporal sequences of patterns

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics

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