Temporal sequences of patterns with an inverse function delayed Neural network

Johan Sveholm, Yoshihiro Hayakawa, Koji Nakajima

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

A network based on the Inverse Function Delayed (ID) model which can recall a temporal sequence of patterns, is proposed. The classical problem that the network is forced to make long distance jumps due to strong attractors that have to be isolated from each other, is solved by the introduction of the ID neuron. The ID neuron has negative resistance in its dynamics which makes a gradual change from one attractor to another possible. It is then shown that a network structure consisting of paired conventional and ID neurons, perfectly can recall a sequence.

Original languageEnglish
Pages (from-to)2818-2824
Number of pages7
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE89-A
Issue number10
DOIs
Publication statusPublished - 2006 Oct

Keywords

  • Inverse function delayed model
  • 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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