Discrete Neural Computation: A Theoretical Foundation

Paperback | January 18, 1995

byKai-yeung Siu, Vwani Roychowdhury, Thomas Kailath

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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. 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. A reference/text for computer scientists and researchers involved with neural computation and related disciplines.

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From Our Editors

Interest in artificial neural networks has sparked research efforts in many disciplines, including neurobiology, physics, mathematics, computer science, and engineering. To help readers keep pace, this book brings together in one volume the recent developments in discrete neural computation.

From the Publisher

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 foundati...

From the Jacket

Interest in artificial neural networks has sparked research efforts in many disciplines, including neurobiology, physics, mathematics, computer science, and engineering. To help readers keep pace, this book brings together in one volume the recent developments in discrete neural computation.

Format:PaperbackDimensions:432 pages, 9 × 7 × 1 inPublished:January 18, 1995Publisher:Pearson Education

The following ISBNs are associated with this title:

ISBN - 10:0133007081

ISBN - 13:9780133007084

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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.


Glossary.


Bibliography.


Index.

From Our Editors

Interest in artificial neural networks has sparked research efforts in many disciplines, including neurobiology, physics, mathematics, computer science, and engineering. To help readers keep pace, this book brings together in one volume the recent developments in discrete neural computation.