Introduction to Neural Networks

ISBN-10: 0262510812
ISBN-13: 9780262510813
Edition: 1995
List price: $75.00
30 day, 100% satisfaction guarantee

If an item you ordered from TextbookRush does not meet your expectations due to an error on our part, simply fill out a return request and then return it by mail within 30 days of ordering it for a full refund of item cost.

Learn more about our returns policy

Description: 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  More...

what's this?
Rush Rewards U
Members Receive:
coins
coins
You have reached 400 XP and carrot coins. That is the daily max!
You could win $10,000

Get an entry for every item you buy, rent, or sell.

Study Briefs

Limited time offer: Get the first one free! (?)

All the information you need in one place! Each Study Brief is a summary of one specific subject; facts, figures, and explanations to help you learn faster.

Add to cart
Study Briefs
History of Western Art Online content $4.95 $1.99
Add to cart
Study Briefs
History of World Philosophies Online content $4.95 $1.99
Add to cart
Study Briefs
American History Volume 1 Online content $4.95 $1.99
Add to cart
Study Briefs
History of Western Music Online content $4.95 $1.99

Customers also bought

Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading

Book details

List price: $75.00
Copyright year: 1995
Publisher: MIT Press
Publication date: 3/16/1995
Binding: Paperback
Pages: 672
Size: 7.50" wide x 9.25" long x 1.75" tall
Weight: 3.080
Language: English

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.

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

Introduction
Acknowledgements
Properties of Single Neurons
Synaptic Integration and Neuron Models
Essential Vector Operations
Lateral Inhibition and Sensory Processing
Simple Matrix Operations
The Linear Associator: Background and Foundations
The Kinear Associator: Simulations
Early Network Models: The Perceptron
Gradient Descent Algorithms
Representation of Information
Applications of Simple Associators: Concept Formation and Object Motion
Energy and Neural Networks: Hopfield Networks and Boltzmann Machines
Nearest Neighbor Models
Adaptive Maps
The BSB Model: A Simple Nonlinear Autoassociative Neural Network
Associative Computation
Teaching Arithmetic to a Neural Network
Afterword
Index

×
Free shipping on orders over $35*

*A minimum purchase of $35 is required. Shipping is provided via FedEx SmartPost® and FedEx Express Saver®. Average delivery time is 1 – 5 business days, but is not guaranteed in that timeframe. Also allow 1 - 2 days for processing. Free shipping is eligible only in the continental United States and excludes Hawaii, Alaska and Puerto Rico. FedEx service marks used by permission."Marketplace" orders are not eligible for free or discounted shipping.

Learn more about the TextbookRush Marketplace.

×