A hierarchical neural network model for associative memory
type: Item
instance of: scholarly article
title: A hierarchical neural network model for associative memory
zbMATH Open document ID: 0538.92008
DOI: 10.1007/BF00337157
author: Kunihiko Fukushima
published in: Biological Cybernetics
publication date: 1984-01-01T00:00:00Z
review text: A hierarchical neural network model with feedback interconnections, which has the function of associative memory and the ability to recognize patterns, is proposed. The model consists of a hierarchical multi-layered network to which different connections are added, so as to make positive feedback loops in pairs with afferent connections. The cell-layer at the initial stage of the network is the input layer which receives the stimulus input and at the same time works as an output layer for associative recall. The deepest layer is the output layer for pattern-recognition. Pattern-recognition is performed hierarchically by integrating information by converging afferent paths in the network. For the purpose of associative recall, the integrated information is again distributed to lower-order cells by diverging efferent paths. These two operations progress simultaneously in the network.
Mathematics Subject Classification ID: 92Cxx
Mathematics Subject Classification ID: 92F05
Mathematics Subject Classification ID: 68U99
zbMATH DE Number: 3856879
zbMATH Keywords: hierarchical neural network model
zbMATH Keywords: associative memory
zbMATH Keywords: multi-layered network
zbMATH Keywords: positive feedback loops
zbMATH Keywords: afferent connections
zbMATH Keywords: pattern- recognition
zbMATH Keywords: associative recall
Wikidata QID: Q52212413
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 3856879