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Parallel Distributed Processing, Vol. 2: Psychological and Biological Models Paperback – July 29, 1987

by James L. Mcclelland (Author), David E. Rumelhart (Author), PDP Research Group (Author)

4.5 15 ratings

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What makes people smarter than computers? These volumes by a pioneering neurocomputing group suggest that the answer lies in the massively parallel architecture of the human mind. They describe a new theory of cognition called connectionism that is challenging the idea of symbolic computation that has traditionally been at the center of debate in theoretical discussions about the mind.

The authors' theory assumes the mind is composed of a great number of elementary units connected in a neural network. Mental processes are interactions between these units which excite and inhibit each other in parallel rather than sequential operations. In this context, knowledge can no longer be thought of as stored in localized structures; instead, it consists of the connections between pairs of units that are distributed throughout the network.

Volume 1 lays the foundations of this exciting theory of parallel distributed processing, while 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.

Print length

632 pages
Language

English
Publication date

July 29, 1987
Reading age

18 years and up
Dimensions

6 x 1.42 x 9 inches
ISBN-10

9780262631105
ISBN-13

978-0262631105
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Editorial Reviews

Review

The most intense, most effective and most mind-stretching view of neurocomputing origins, theories and concerns to yet reach print.—Intelligence—

From the Back Cover

What makes people smarter than computers? These volumes by a pioneering neurocomputing group suggest that the answer lies in the massively parallel architecture of the human mind. They describe a new theory of cognition called connectionism that is challenging the idea of symbolic computation that has traditionally been at the center of debate in theoretical discussions about the mind.

Product details

ASIN ‏ : ‎ 0262631105
Publisher ‏ : ‎ Bradford Books (July 29, 1987)
Language ‏ : ‎ English
Paperback ‏ : ‎ 632 pages
ISBN-10 ‏ : ‎ 9780262631105
ISBN-13 ‏ : ‎ 978-0262631105
Reading age ‏ : ‎ 18 years and up
Item Weight ‏ : ‎ 13 ounces
Dimensions ‏ : ‎ 6 x 1.42 x 9 inches

Best Sellers Rank: #2,333,929 in Books (See Top 100 in Books)
- #3,487 in Medical Cognitive Psychology
- #5,172 in Cognitive Psychology (Books)
- #56,109 in Psychology & Counseling

Customer Reviews:
4.5 15 ratings

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David E. Rumelhart
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James L. McClelland
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4.5 out of 5

15 global ratings

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pythonguy

The classic and still the best book on the fundamentals of distributed computing

Reviewed in the United States on March 30, 2016

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The classic and still the best book on the fundamentals of distributed computing, neural networks, and biological models for computing. There are lots of good books out there that take a modern approach but this is much more fundamental and inspiring. Clearly written with excellent illustrations I have several copies of this book.

5 people found this helpful

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Lappoon Rupert Tang

Five Stars

Reviewed in the United States on December 23, 2014

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Great book arrived in perfect timing

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Zhe Chen

A Historic Milestone: A book for AI and machine learning

Reviewed in the United States on June 22, 2001

Though I believe I am not the first person writing the book review of this historic book, I still feel honored to encourage the new readers to read this one of most important AI research book published in 1986. The book edited by Rumlhart and McClelland was well organized and well written, comprised of a series of independent and interesting topics in neural network researches given by the dedicated authors. The editors themselves are also reputated authors in the connectionist community. The most results in that book never appeared in the past publications and represented the high-quality papers in the state-of-the-art research at that time. Many papers in that book rank the top position of citation rate even today, e.g. the paper about error backpropagation due to Rumelhart, Hinton and Williams. I also got to point out that the importance of the book not only lies in its scientific contribution, but also its philosophical meaning in the AI research (which is somehow influenced by the book 'Perceptrons' by Minsky and Pappert). The successful research results in that book showed people of the potential and new prospect of neural networks in different perspectives. From then on the second connectionist revolution has sprang and lasted today. Nowadays, people still can feel its leading influence by reading it. Upon reading the book again and again, you will always feel inspired at another new way (that is the value of a book!). Try it immediately.
In a word without exaggeration, the importance of this book to connectionist and AI researchers is like the Bible to Christians. Read it, enjoy it, once and again.

24 people found this helpful

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Touchstone

Classic and Influential

Reviewed in the United States on February 2, 2008

This book establishes the foundation mathematics and definitions of what are now called "neural networks". In 1986 these guys (on DARPA grants) figured out the basics of what is (in my opinion) the most significant advance in artifical intelligence since the 1960s. The book is a bit dry, as a fully rigorous academic text usually is, but the results speak for themselves - the techniques and approaches described in this book are used all over in some of the most challenging areas of AI - character, speech, and face recognition, surveilance, applicant screening, and so on.

Read it if you believe artifical intelligence is a bunch of hooey - I do, except this stuff.

8 people found this helpful

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Top reviews from other countries

ferginge

... the book on time and it was of very good quality, given that it was a second hand

Reviewed in the United Kingdom on December 17, 2017

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I got the book on time and it was of very good quality, given that it was a second hand product

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