Chaotic pseudo-orthogonalized Hopfield associative memory

Abstract

A Hopfield associative memory (HAM) is a major model of associative memory using neural networks. The classical HAM model has a low storage capacity. A pseudo-orthogonalized HAM (POHAM) was therefore proposed to improve storage capacity by encoding the training patterns to be pseudo-orthogonal. In the present work, we examine a different property of POHAM. A chaotic HAM (CHAM) can explore embedded patterns, including training patterns. Although it is not desirable to recall patterns other than training patterns, at a minimum, the reversed patterns are recalled. A POHAM regards the training and reversed patterns as equivalent. To take advantage of this property, we propose a chaotic POHAM (CPOHAM). We evaluated a CHAM and a CPOHAM using computer simulations. The CPOHAM never recalled the reversed patterns. In addition, the CPOHAM recalled very few other pseudo-memories, such as mixture patterns, due to the orthogonality of the encoded training patterns, while the CHAM frequently recalled the mixture patterns.