Abstract
In this paper, we propose a chaotic episodic associative memory (CEAM). It can deal with complex episodes which have common terms. Temporal associative memory (TAM) and episodic associative memory (EAM) have been proposed as models for episodic memory. However these models cannot deal with association of plural episodes that have common terms because the stored common patterns cause superimposed patterns. The proposed CEAM is based on the conventional TAM and has connections in the input layer for autoassociation. It also employs chaotic neurons in a part of the input layer. Each scene of the episodes is memorized together with its own contextual information. That is, the training set including common terms is converted into a form which doesn't include any common terms. The chaotic neurons in the input layer corresponding to contextual information change their states by chaos. As a result, the contextual information changes dynamically, which enables the CEAM to recall plural episodes that have common terms. A series of computer simulations shows the effectiveness of the proposed model.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |
| Editors | Anon |
| Publisher | IEEE |
| Pages | 3629-3634 |
| Number of pages | 6 |
| Volume | 4 |
| Publication status | Published - 1998 |
| Event | Proceedings of the 1998 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5) - San Diego, CA, USA Duration: 1998 Oct 11 → 1998 Oct 14 |
Other
| Other | Proceedings of the 1998 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5) |
|---|---|
| City | San Diego, CA, USA |
| Period | 98/10/11 → 98/10/14 |
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ASJC Scopus subject areas
- Hardware and Architecture
- Control and Systems Engineering
Cite this
Chaotic episodic associative memory. / Kitada, Junya; Osana, Yuko; Hagiwara, Masafumi.
Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. ed. / Anon. Vol. 4 IEEE, 1998. p. 3629-3634.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Chaotic episodic associative memory
AU - Kitada, Junya
AU - Osana, Yuko
AU - Hagiwara, Masafumi
PY - 1998
Y1 - 1998
N2 - In this paper, we propose a chaotic episodic associative memory (CEAM). It can deal with complex episodes which have common terms. Temporal associative memory (TAM) and episodic associative memory (EAM) have been proposed as models for episodic memory. However these models cannot deal with association of plural episodes that have common terms because the stored common patterns cause superimposed patterns. The proposed CEAM is based on the conventional TAM and has connections in the input layer for autoassociation. It also employs chaotic neurons in a part of the input layer. Each scene of the episodes is memorized together with its own contextual information. That is, the training set including common terms is converted into a form which doesn't include any common terms. The chaotic neurons in the input layer corresponding to contextual information change their states by chaos. As a result, the contextual information changes dynamically, which enables the CEAM to recall plural episodes that have common terms. A series of computer simulations shows the effectiveness of the proposed model.
AB - In this paper, we propose a chaotic episodic associative memory (CEAM). It can deal with complex episodes which have common terms. Temporal associative memory (TAM) and episodic associative memory (EAM) have been proposed as models for episodic memory. However these models cannot deal with association of plural episodes that have common terms because the stored common patterns cause superimposed patterns. The proposed CEAM is based on the conventional TAM and has connections in the input layer for autoassociation. It also employs chaotic neurons in a part of the input layer. Each scene of the episodes is memorized together with its own contextual information. That is, the training set including common terms is converted into a form which doesn't include any common terms. The chaotic neurons in the input layer corresponding to contextual information change their states by chaos. As a result, the contextual information changes dynamically, which enables the CEAM to recall plural episodes that have common terms. A series of computer simulations shows the effectiveness of the proposed model.
UR - http://www.scopus.com/inward/record.url?scp=0032312937&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0032312937&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0032312937
VL - 4
SP - 3629
EP - 3634
BT - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
A2 - Anon, null
PB - IEEE
ER -