Chaotic episodic associative memory

Junya Kitada, Yuko Osana, Masafumi Hagiwara

Research output: Contribution to journalArticlepeer-review


In this paper, we propose a chaotic associative memory (CEAM). It can deal with complex episodes which have common terms. The proposed CEAM is based on the conventional temporal associative memory and has connections in the input layer for autoassociation. Each scene of the episodes is memorized together with its own contextual information. The CEAM employs chaotic neurons in a part of the input layer corresponding to contextual information. The chaotic neurons change their states by chaos. As a result, the CEAM can associate plural episodes that have common terms.

Original languageEnglish
Pages (from-to)243-251
Number of pages9
JournalIntegrated Computer-Aided Engineering
Issue number3
Publication statusPublished - 2000 Jan 1

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Artificial Intelligence


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