Chaotic episodic associative memory

Junya Kitada, Yuko Osana, Masafumi Hagiwara

Research output: Contribution to journalConference article

Abstract

In this paper, we propose a chaotic episodic associative memory (CEAM). It can deal with complex episodes which have common terms. Temporal associative memory (TAM) and episodic associative memory (EAM) have been proposed as models for episodic memory. However these models cannot deal with association of plural episodes that have common terms because the stored common patterns cause superimposed patterns. The proposed CEAM is based on the conventional TAM and has connections in the input layer for autoassociation. It also employs chaotic neurons in a part of the input layer. Each scene of the episodes is memorized together with its own contextual information. That is, the training set including common terms is converted into a form which doesn't include any common terms. The chaotic neurons in the input layer corresponding to contextual information change their states by chaos. As a result, the contextual information changes dynamically, which enables the CEAM to recall plural episodes that have common terms. A series of computer simulations shows the effectiveness of the proposed model.

Original languageEnglish
Pages (from-to)3629-3634
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume4
Publication statusPublished - 1998 Dec 1
EventProceedings of the 1998 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5) - San Diego, CA, USA
Duration: 1998 Oct 111998 Oct 14

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture

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