Models of Neural Networks III

1. Front Matter

   Pages i-xiii

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2. Global Analysis of Recurrent Neural Networks

   • Andreas V. M. Herz

   Pages 1-54

3. Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns

   • Kenneth D. Miller

   Pages 55-78

4. Associative Data Storage and Retrieval in Neural Networks

   • Günther Palm, Friedrich T. Sommer

   Pages 79-118

5. Inferences Modeled with Neural Networks

   • H.-O. Carmesin

   Pages 119-149

6. Statistical Mechanics of Generalization

   • Manfred Opper, Wolfgang Kinzel

   Pages 151-209

7. Bayesian Methods for Backpropagation Networks

   • David J. C. MacKay

   Pages 211-254

8. Penacée: A Neural Net System for Recognizing On-Line Handwriting

   • I. Guyon, J. Bromley, N. Matić, M. Schenkel, H. Weissman

   Pages 255-279

9. Topology Representing Network in Robotics

   • Kakali Sarkar, Klaus Schulten

   Pages 281-302

10. Back Matter

    Pages 303-311

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One of the most challenging and fascinating problems of the theory of neural nets is that of asymptotic behavior, of how a system behaves as time proceeds. This is of particular relevance to many practical applications. Here we focus on association, generalization, and representation. We turn to the last topic first. The introductory chapter, "Global Analysis of Recurrent Neural Net­ works," by Andreas Herz presents an in-depth analysis of how to construct a Lyapunov function for various types of dynamics and neural coding. It includes a review of the recent work with John Hopfield on integrate-and­ fire neurons with local interactions. The chapter, "Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns" by Ken Miller, explains how the primary visual cortex may asymptotically gain its specific structure through a self-organization process based on Hebbian learning. His argu­ ment since has been shown to be rather susceptible to generalization.

• algorithms
• artificial intelligence
• convergence
• cortex
• neural networks
• self-organizing map
• statistics
• training

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

  Eytan Domany

• Institut für Theoretische Physik, Technische Universität München, Garching bei München, Germany

  J. Leo Hemmen

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

  Klaus Schulten

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