Parallel Distributed Processing: Explorations in the Microstructure of Cognition : Psychological and Biological Models (Computational Models of Cogn)

McClelland, James L.; Rumelhart, David E.

ISBN 10: 0262132184 / ISBN 13: 9780262132183

Published by Mit Pr, 1986

About this Item

Very good, clean, tight condition. Blue cloth. No jacket. Text free of marks. Professional book dealer since 1999. All orders are processed promptly and carefully packaged with tracking.

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Bibliographic Details

• Title: Parallel Distributed Processing: Explorations in the Microstructure of Cognition : Psychological and Biological Models (Computational Models of Cogn)
• Publisher: Mit Pr
• Publication Date: 1986
• Language: English
• ISBN 10: 0262132184
• ISBN 13: 9780262132183
• Binding: Hardcover
• Condition: Very Good
• Dust Jacket Condition: Very Good

About this title

Synopsis

Volume 2 applies it to a number of specific issues in cognitive science and neuroscience, with chapters describing models of aspects of perception, memory, language, and thought.

Review

This two-volume work is now considered a classic in the field. It presents the results of the Parallel Distributed Processing (PDP) group's work in the early 1980s and provides a good overview of the earlier neural network research. The PDP approach (also known as connectionism among other things) is based on the conviction that various aspects of cognitive activity are thought of in terms of massively parallel processing. The first volume starts with the general framework and continues with an analysis of learning mechanisms and various mathematical and computational tools important in the analysis of neural networks. The chapter on backpropagation is written by Rumelhart, Hinton, and Williams, who codiscovered the algorithm in 1986. The second volume is written with a psychological and biological emphasis. It explores the relationship of PDP to various aspects of human cognition. The book is a comprehensive research survey of its time and most of the book's results and methods are still at the foundation of the neural network field.