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Modeling Brain Function : The World of Attractor Neural Networks by Daniel J. Amit (1989, Hardcover)
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About this product
Product Identifiers
PublisherCambridge University Press
ISBN-100521361001
ISBN-139780521361002
eBay Product ID (ePID)306516
Product Key Features
Number of Pages528 Pages
LanguageEnglish
Publication NameModeling Brain Function : the World of Attractor Neural Networks
SubjectLife Sciences / Neuroscience, Life Sciences / Biophysics
Publication Year1989
TypeTextbook
Subject AreaScience
AuthorDaniel J. Amit
FormatHardcover
Dimensions
Item Height1.3 in
Item Weight30.6 Oz
Item Length9.3 in
Item Width6.3 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN89-015741
Dewey Edition20
Reviews"...of interest to those following the neural net field...takes off from discoveries that link areas of physics with the emerging neural network paradigm." Intelligence Monthly, "...regard this book as an opening of a discussion--undoubtedly a very qualified one." Journal of Mathematical Psychology
IllustratedYes
Dewey Decimal591.1/88
Table Of ContentPreface; 1. Introduction; 2. The basic attractor neural network; 3. General ideas concerning dynamics; 4. Symmetric neural networks at low memory loading; 5. Storage and retrieval of temporal sequences; 6. Storage capacity of ANNs; 7. Robustness - getting closer to biology; 8. Memory data structures; 9. Learning; 10. Hareware implementations of neural networks; Glossary; Index.
SynopsisOne of the most exciting and potentially rewarding areas of scientific research is the study of the principles and mechanisms underlying brain function. It is also of great promise to future generations of computers. A growing group of researchers, adapting knowledge and techniques from a wide range of scientific disciplines, have made substantial progress understanding memory, the learning process, and self organization by studying the properties of models of neural networks - idealized systems containing very large numbers of connected neurons, whose interactions give rise to the special qualities of the brain. This book introduces and explains the techniques brought from physics to the study of neural networks and the insights they have stimulated. It is written at a level accessible to the wide range of researchers working on these problems - statistical physicists, biologists, computer scientists, computer technologists and cognitive psychologists. The author presents a coherent and clear nonmechanical presentation of all the basic ideas and results. More technical aspects are restricted, wherever possible, to special sections and appendices in each chapter. The book is suitable as a text for graduate courses in physics, electrical engineering, computer science and biology., Exploring one of the most exciting and potentially rewarding areas of scientific research, the study of the principles and mechanisms underlying brain function, this book introduces and explains the techniques brought from physics to the study of neural networks and the insights they have stimulated. Substantial progress in understanding memory, the learning process, and self-organization by studying the properties of models of neural networks have resulted in discoveries of important parallels between the properties of statistical, nonlinear cooperative systems in physics and neural networks. The author presents a coherent and clear, nontechnical view of all the basic ideas and results. More technical aspects are restricted to special sections and appendices in each chapter., A group of researchers, adapting knowledge and techniques from a wide range of scientific disciplines, have made progress understanding memory by studying the properties of models of neural networks. This book introduces and explains techniques bought from physics to the study of neural networks and the insights they have stimulated.
LC Classification NumberQP376 .A427 1989
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Most relevant reviews
- Dec 10, 2018
Great book, fantastic value
Great book, hard to find at this price. Great value!Verified purchase: YesCondition: Pre-owned
