Internal representations for associative memory

E. B. Baum, J. Moody, F. Wilczek

Research output: Contribution to journalArticlepeer-review

71 Scopus citations

Abstract

We describe a class of feed forward neural network models for associative content addressable memory (ACAM) which utilize sparse internal representations for stored data. In addition to the input and output layers, our networks incorporate an intermediate processing layer which serves to label each stored memory and to perform error correction and association. We study two classes of internal label representations: the unary representation and various sparse, distributed representations. Finally, we consider storage of sparse data and sparsification of data. These models are found to have advantages in terms of storage capacity, hardware efficiency, and recall reliability when compared to the Hopfield model, and to possess analogies to both biological neural networks and standard digital computer memories.

Original languageEnglish (US)
Pages (from-to)217-228
Number of pages12
JournalBiological Cybernetics
Volume59
Issue number4-5
DOIs
StatePublished - Sep 1988
Externally publishedYes

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science(all)

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