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An introduction to neural networks

Author: James A Anderson
Publisher: Cambridge Mass. : MIT Press, 1997.
Series: Bradford book.
Edition/Format:   Print book : English : [3rd print.]View all editions and formats
Database:WorldCat
Summary:

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  Read more...

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Document Type: Book
All Authors / Contributors: James A Anderson
ISBN: 0262011441 9780262011440
OCLC Number: 717253657
Description: XI, 650 p. : ill. ; 27 cm.
Contents: 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 linear 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 neighbour models; adaptive maps; the BSB model - a simple nonlinear autoassociative neural network; associative computation; teaching arithmetic to a neural network.
Series Title: Bradford book.
Responsibility: James A. Anderson.

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