Chaotic episodic associative memory

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