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Discrete Neural Computation: A Theoretical Foundation

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Discrete Neural Computation: A Theoretical Foundation


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  • addresses issues in the context of discrete models of neural networks such as threshold circuits and discrete time Hopfield networks.
  • compares the neural network model with conventional models.
  • outlines systematic procedures for designing neural networks in a given model.
  • explains the computational limitations of each model.
  • contains exercises that represent contemporary research.


  • Copyright 1995
  • Dimensions: 7 X 9-1/4
  • Pages: 432
  • Edition: 1st
  • Book
  • ISBN-10: 0-13-300708-1
  • ISBN-13: 978-0-13-300708-4

Written by the three leading authorities in the field, this book brings together — in one volume — the recent developments in discrete neural computation, with a focus on neural networks with discrete inputs and outputs. It integrates a variety of important ideas and analytical techniques, and establishes a theoretical foundation for discrete neural computation. KEY TOPICS: Discusses the basic models for discrete neural computation and the fundamental concepts in computational complexity; establishes efficient designs of threshold circuits for computing various functions; develops techniques for analyzing the computational power of neural models. MARKET: A reference/text for computer scientists and researchers involved with neural computation and related disciplines.

Sample Content

Table of Contents

 1. Introduction.

 2. Linear Threshold Element.

 3. Computing Symmetric Functions.

 4. Depth Efficient Arithmetic Circuits.

 5. Depth-Size Tradeoffs.

 6. Computing with Small Weights.

 7. Rational Approximation and Optimal Size Circuits.

 8. Geometric Framework and Spectral Analysis.

 9. Limitations of AND-OR Circuits.

10. Lower Bounds via Communication Complexity.

11. Hopfield Network.





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