Episodic associative memories

Motonobu Hattori, Masafumi Hagiwara

Research output: Contribution to journalArticle

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

Fingerprint Dive into the research topics of 'Episodic associative memories'. Together they form a unique fingerprint.

  • Cite this