Episodic associative memories

Motonobu Hattori, Masafumi Hagiwara

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

In this paper, we propose Episodic Associative Memories (EAMs). They use Quick Learning for Bidirectional Associative Memory (QLBAM), which enables high memory capacity, and Pseudo-Noise (PN) sequences. In the learning of the proposed EAMs, PN sequences are used as one side of training pairs. To store an episode, a scene of the episode is stored with a PN sequence, and the next scene is stored with the PN sequence which is shifted with one bit. Such a procedure enables episodic memory. The proposed EAMs can recall episodic associations by shifting the PN sequence one by one. The features of the proposed EAMs are: (1) they can memorize and recall episodic associations; (2) they can store plural episodes; (3) they have high memory capacity; (4) they are robust for noisy and incomplete inputs.

Original languageEnglish
Pages (from-to)1-18
Number of pages18
JournalNeurocomputing
Volume12
Issue number1
DOIs
Publication statusPublished - 1996 Jul 15

Keywords

  • Bidirectional Associative Memory (BAM)
  • Episodic Associative Memories (EAMs)
  • Pseudo-Noise (PN) sequences
  • Quick Learning for BAM (QLBAM)

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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