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.
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.