Multimodule associative memory for many-to-many associations

Motonobu Hattori, Masafumi Hagiwara

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

In this paper, we propose a novel associative memory model called MultiModule Associative memory for Many-to-Many Associations, (MMA)2 for short. The proposed (MMA)2 consists of multiple modules and each module has a Hopfield type of associative memory. In the (MMA)2, a memory item is regarded as several divided patterns. Each pattern is assigned to each module and the patterns are related to each other in the learning. Unlike a single- layered conventional associative memory, the (MMA)2 can recall a complete memory item from even a single part of it owing to the multiple modules structure. Even if a part of a memory item that is common to the other memory items is given to the proposed (MMA)2, all items that relate to the input can be recalled: that is, the proposed (MMA)2 can deal with the set of memory items which includes one-to-many relations and many-to-many relations such as (A1,B1,C1, ....), (A1,B2,C2 ....),(A2,B2,C3 ....) ..... In order to memorize and recall such very complicated training data, the (MMA)2 employs pseudo-noise (PN) patterns, transformation of distributed patterns into locally represented patterns and the logical operations. These techniques contribute to avoid producing a mixed unknown pattern, which consists of a superimposed pattern of some stored patterns and the cross- talk noise and interferes with recalling correct patterns. A number of computer simulation results show the effectiveness of the proposed (MMA)2. Furthermore, we show that the (MMA)2 can deal with a knowledge processing.

Original languageEnglish
Pages (from-to)99-119
Number of pages21
JournalNeurocomputing
Volume19
Issue number1-3
DOIs
Publication statusPublished - 1998 Apr 21

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Keywords

  • Locally represented pattern
  • Many-to-Many Associations
  • Pseudo-Noise (PN) pattern

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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