Average Reviews:
(More customer reviews)This book is more aimed towards researchers in the field of computational complexity, who are studying the computational limitations of discrete neural nets, in terms of depth, size, and connection weights. From this perspective it seems very good, if not excellent. On the other hand, aside from coverage of the perceptron learning algorithm and Hopfield networks the book has little in terms of applications.
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Written by the three leading authorities inthe field, this book brings together — in one volume — therecent developments in discrete neural computation, with a focus onneural networks with discrete inputs and outputs. It integrates avariety of important ideas and analytical techniques, and establishesa theoretical foundation for discrete neural computation. Discusses the basic models for discrete neural computation and thefundamental concepts in computational complexity; establishes efficientdesigns of threshold circuits for computing various functions; developstechniques for analyzing the computational power of neural models. A reference/text for computer scientists and researchersinvolved with neural computation and related disciplines.
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