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An introduction to neural networks James A. Anderson.

By: Material type: TextTextPublication details: New Delhi : Prentice-Hall of India, c1995. [reprinted 2001]Edition: Eastern economy edDescription: xi, 650 p. : ill., figs., tables ; 26 cmISBN:
  • 0262011441
Subject(s): DDC classification:
  • 612.8 22
LOC classification:
  • QP363.3 .A534 1995
Contents:
1. Properties of Single Neurons -- 2. Synaptic Integration and Neuron Models -- 3. Essential Vector Operations -- 4. Lateral Inhibition and Sensory Processing -- 5. Simple Matrix Operations -- 6. The Linear Associator: Background and Foundations -- 7. The Linear Associator: Simulations -- 8. Early Network Models: The Perceptron -- 9. Gradient Descent Algorithms -- 10. Representation of Information -- 11. Applications of Simple Associators: Concept Formation and Object Motion -- 12. Energy and Neural Networks: Hopfield Networks and Boltzmann Machines -- 13. Nearest Neighbor Models -- 14. Adaptive Maps -- 15. The BSB Model: A Simple Nonlinear Autoassociative Neural Network -- 16. Associative Computation -- 17. Teaching Arithmetic to a Neural Network.
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"A Bradford book."

Includes bibliographical references and index.

1. Properties of Single Neurons -- 2. Synaptic Integration and Neuron Models -- 3. Essential Vector Operations -- 4. Lateral Inhibition and Sensory Processing -- 5. Simple Matrix Operations -- 6. The Linear Associator: Background and Foundations -- 7. The Linear Associator: Simulations -- 8. Early Network Models: The Perceptron -- 9. Gradient Descent Algorithms -- 10. Representation of Information -- 11. Applications of Simple Associators: Concept Formation and Object Motion -- 12. Energy and Neural Networks: Hopfield Networks and Boltzmann Machines -- 13. Nearest Neighbor Models -- 14. Adaptive Maps -- 15. The BSB Model: A Simple Nonlinear Autoassociative Neural Network -- 16. Associative Computation -- 17. Teaching Arithmetic to a Neural Network.

CSE, CIS, CS, ETE

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