Bibliograph

An introduction to neural networks, James A. Anderson

Type

Label

An introduction to neural networks, James A. Anderson

Language

eng

Bibliography note

Includes bibliographical references and index

Illustrations

illustrations

Index

index present

Literary Form

non fiction

Main title

An introduction to neural networks

Nature of contents

bibliography

Oclc number

30971691

Responsibility statement

James A. Anderson

Summary

Approaches 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 what the models might be used for. Intended for cognitive science and neuroscience students, and also for engineers who want to go beyond formal algorithms to applications and computing strategies

Table Of 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 Neighbor Models -- Adaptive Maps -- The BSB Model: A Simple Nonlinear Autoassociative Neural Network -- Associative Computation -- Teaching Arithmetic to a Neural Network

Classification