Models Of Neural Networks (physics Of Neural Networks)

This book by internationally renowned experts gives an ex- cellent overview of a hot research field. It is equally im- portant for graduate students andactive researchers in physics, computer science, neuroscience, AI, and brainre- search. One of the great inteJlectual cha1lenges for the next few decades is the question of brain organization. What is the basic mechanism for storage of memory? What are the processes that serve as the interphase between the basically chemical processes of the body and the very specific and nonstatistical operations in the brain? Above all. how is concept formation achieved in the human brain? I wonder whether the spirit of the physics that will be involved in these studies will not be akin to that which moved the founders of the''rational foundation of thermodynamics'. CN. Yangl 10 The human brain is said 10 have roughly 10 neurons connected through about 14 10 synapses. Each neuron is itself a complex device which compares and integrates incoming electrical signals and relays a nonlinear response to other neurons. The brain certainly exceeds in complexity any system which physicists have studied in the past. Nevertheless, there do exist many analogies of the We have witnessed during the last decade brain to simpler physical systems. This collection of articles responds to the urgent need for timely and comprehensive reviews in a multidisciplinary, rapidly developing field of research. The book starts out with an extensive introduction to the ideas used in the subsequent chapters, which are all centered around the theme of collective phenomena in neural netwerks: dynamics and storage capacity of networks of formal neurons with symmetric or asymmetric couplings, learning algorithms, temporal association, structured data (software), and structured nets (hardware). The style and level of this book make it particularly useful for advanced students and researchers looking for an accessible survey of today's theory of neural networks Front Matter....Pages I-XVI Collective Phenomena in Neural Networks....Pages 1-105 Information from Structure: A Sketch of Neuroanatomy....Pages 107-120 Storage Capacity and Learning in Ising-Spin Neural Networks....Pages 121-148 Dynamics of Learning....Pages 149-171 Hierarchical Organization of Memory....Pages 173-192 Asymmetrically Diluted Neural Networks....Pages 193-212 Temporal Association....Pages 213-280 Self-organizing Maps and Adaptive Filters....Pages 281-306 Layered Neural Networks....Pages 307-334 Back Matter....Pages 335-345