Internal representations for associative memory
type: Item
instance of: scholarly article
title: Internal representations for associative memory
zbMATH Open document ID: 0655.68022
DOI: 10.1007/BF00332910
author: S. H. Smith
published in: Biological Cybernetics
publication date: 1988-01-01T00:00:00Z
review text: 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.
Mathematics Subject Classification ID: 68P05
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 92F05
zbMATH DE Number: 4070288
zbMATH Keywords: feed forward neural network models
zbMATH Keywords: associative content addressable memory
zbMATH Keywords: internal label representations
MaRDI profile type: Publication
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full work available at URL: https://doi.org/10.1007/bf00332910
OpenAlex ID: W2066340926
description: scientific article; zbMATH DE number 4070288