Models of Neural Networks

1. Front Matter

   Pages I-XVI

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2. Collective Phenomena in Neural Networks

   • J. Leo van Hemmen, Reimer Kühn

   Pages 1-105

3. Information from Structure: A Sketch of Neuroanatomy

   • Valentino Braitenberg

   Pages 107-120

4. Storage Capacity and Learning in Ising-Spin Neural Networks

   • Bruce M. Forrest, David J. Wallace

   Pages 121-148

5. Dynamics of Learning

   • Wolfgang Kinzel, Manfred Opper

   Pages 149-171

6. Hierarchical Organization of Memory

   • Michail V. Feigel’man, Lev B. Ioffe

   Pages 173-192

7. Asymmetrically Diluted Neural Networks

   • Reiner Kree, Annette Zippelius

   Pages 193-212

8. Temporal Association

   • Reimer Kühn, J. Leo van Hemmen

   Pages 213-280

9. Self-organizing Maps and Adaptive Filters

   • Helge Ritter, Klaus Obermayer, Klaus Schulten, Jeanne Rubner

   Pages 281-306

10. Layered Neural Networks

    • Eytan Domany, Ronny Meir

    Pages 307-334

11. Back Matter

    Pages 335-345

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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.

• Hirnforschung
• Mustererkennung
• Nervennetz
• Neuron
• artificial intelligence
• brain research
• künstliche Intelligenz
• neural modeling
• neural network
• neuroscience

• Department of Electronics, Weizmann Institute of Science, Rehovot, Israel

  Eytan Domany

• Institut für Theoretische Physik, Technische Universität München, Garching bei München, Fed. Rep. of Germany

  J. Leo Hemmen

• Department of Physics and Beckman Institute, University of Illinois, Urbana, USA

  Klaus Schulten

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