Discrete Neural Computation: A Theoretical Foundation
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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Linear Threshold Element
Computing Symmetric Functions
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