Chaotic episodic associative memory

Authors:
Junya Kitada;Yuko Osana;Masafumi Hagiwara
Affiliations:
Department of Information and Computer Science, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kouhoku-Ku, Yokohama 223-8522, Japan. E-mail: kita@soft.ics.keio.ac.jp;Department of Information and Computer Science, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kouhoku-Ku, Yokohama 223-8522, Japan. E-mail: kita@soft.ics.keio.ac.jp;Department of Information and Computer Science, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kouhoku-Ku, Yokohama 223-8522, Japan. E-mail: kita@soft.ics.keio.ac.jp
Venue:
Integrated Computer-Aided Engineering
Year:
2000

Citing 3
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Abstract

In this paper, we propose a chaotic episodic 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.