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An Introduction to Neural Networks

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Synopses & Reviews

Publisher Comments:

An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain function in terms of computational modeling, and at engineers who want to go beyond formal algorithms to applications and computing strategies. It is the only current text to approach networks from a broad neuroscience and cognitive science perspective, with an emphasis on the biology and psychology behind the assumptions of the models, as well as on what the models might be used for. It describes the mathematical and computational tools needed and provides an account of the author's own ideas.

Students learn how to teach arithmetic to a neural network and get a short course on linear associative memory and adaptive maps. They are introduced to the author's brain-state-in-a-box (BSB) model and are provided with some of the neurobiological background necessary for a firm grasp of the general subject.

The field now known as neural networks has split in recent years into two major groups, mirrored in the texts that are currently available: the engineers who are primarily interested in practical applications of the new adaptive, parallel computing technology, and the cognitive scientists and neuroscientists who are interested in scientific applications. As the gap between these two groups widens, Anderson notes that the academics have tended to drift off into irrelevant, often excessively abstract research while the engineers have lost contact with the source of ideas in the field. Neuroscience, he points out, provides a rich and valuable source of ideas about data representation and setting up the data representation is the major part of neural network programming. Both cognitive science and neuroscience give insights into how this can be done effectively: cognitive science suggests what to compute and neuroscience suggests how to compute it.

Synopsis:

This text is aimed at cognitive science and neuroscience students who need to understand brain function in terms of computational modelling and at engineers who want to go beyond formal algorithms to applications and computing strategies.

Synopsis:

(Paperback not available in U.S. and Canada.)

About the Author

James A. Anderson is Professor in the Department of Cognitive and Linguistic Sciences at Brown University.

Product Details

ISBN:
9780262510813
Author:
Anderson, James A.
Publisher:
Bradford Book
Location:
Cambridge
Subject:
Computer Science
Subject:
Artificial Intelligence - General
Subject:
Artificial Intelligence
Subject:
Intelligence (AI) & Semantics
Subject:
Computers-Reference - General
Edition Description:
Trade paper
Series:
An Introduction to Neural Networks
Publication Date:
19950331
Binding:
TRADE PAPER
Grade Level:
Professional and scholarly
Language:
English
Illustrations:
367
Pages:
672
Dimensions:
10 x 8 in
Age Level:
from 18

Related Subjects

Business » Communication
Business » General
Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
Computers and Internet » Personal Computers » General

An Introduction to Neural Networks New Trade Paper
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Product details 672 pages Bradford Book - English 9780262510813 Reviews:
"Synopsis" by , This text is aimed at cognitive science and neuroscience students who need to understand brain function in terms of computational modelling and at engineers who want to go beyond formal algorithms to applications and computing strategies.
"Synopsis" by , (Paperback not available in U.S. and Canada.)
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