Table of Contents

Part I - Traces of History and A Neuroscience Briefer
1. Brain Style Computing: Origins and Issues
2. Lessons from Neuroscience

Part II : Feedforward Neural Networks and Supervised Learning
3. Artificial Neurons, Neural Networks and Architectures
4. Geometry of Binary Threshold Neurons and Their Networks
5. Supervised Learning I: Perceptrons and LMS
6. Supervised Learning II: Backpropagation and Beyond
7. Neural Networks: A Statistical Pattern Recognition Perspective
8. Focussing on Generalization: Support Vector Machines and Radial Basis Function Networks

Part III : Recurrent Neurodynamical Systems
9. Dynamical Systems Review
10. Attractor Neural Networks
11. Adaptive Resonance Theory
12. Towards the Self-organizing Feature Map

Part IV : Contemporary Topics
13. Pulsed Neuron Models: The New Generation
14. Fuzzy Sets, Fuzzy Systems and Applications
15. Neural Networks and the Soft Computing Paradigm

Appendix A: Neural Network Hardware

A.1 Motivation and Issues
A.2 Analog Building Blocks for Neuromorphic Networks
A.3 Digital Techniques
A.4 Bibliographic Remarks

Appendix B: Web Pointers

Bibliography
Index

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