Episodic associative memory

Motonobu Hattori, Masafumi Hagiwara, Masao Nakagawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Episodic Associative Memory (EAM) is introduced and simulated. It uses Quick Learning for Bidirectional Associative Memory (QLBAM) and Pseudo-Noise (PN) sequences. The features of the proposed EAM are: 1) it can memorize and recall episodic associations; 2) it can store plural episodes; 3) it has high memory capacity.

Original languageEnglish
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
PublisherIEEE
Pages1062-1067
Number of pages6
Volume2
Publication statusPublished - 1994
EventProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
Duration: 1994 Jun 271994 Jun 29

Other

OtherProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period94/6/2794/6/29

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ASJC Scopus subject areas

  • Software

Cite this

Hattori, M., Hagiwara, M., & Nakagawa, M. (1994). Episodic associative memory. In IEEE International Conference on Neural Networks - Conference Proceedings (Vol. 2, pp. 1062-1067). IEEE.