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Statistical Mechanics of Neural Networks

Proceedings of the XIth Sitges Conference Sitges, Barcelona, Spain, 3–7 June 1990

  • Conference proceedings
  • © 1990

Overview

Editors:
  1. Luis Garrido
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Part of the book series: Lecture Notes in Physics (LNP, volume 368)

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Table of contents (35 papers)

  1. Front Matter

    Download chapter PDF

  2. Introduction On the statistical-mechanical formulation of neural networks

    • L. Garrido, M. Rubi
    Pages 1-3

  3. Model neurons: From Hodgkin-Huxley to hopfield

    • L. F. Abbott, Thomas B. Kepler
    Pages 5-18

  4. Statistical mechanics for networks of analog neurons

    • R. Kühn
    Pages 19-32

  5. Properties of neural networks with multi-state neurons

    • H. Rieger
    Pages 33-47

  6. Adaptive recurrent neural networks and dynamic stability

    • B. Schürmann, J. Hollatz, U. Ramacher
    Pages 49-63

  7. Neuronal oscillators: Experiments and models

    • Carme Torras i Genís
    Pages 65-79

  8. Neuronal networks in the hippocampus involved in memory

    • Alessandro Treves, Edmund T Rolls
    Pages 81-95

  9. Basins of attraction and spurious states in neural networks

    • L. Viana, E. Cota, C. Martinez
    Pages 97-104

  10. Tailoring the performance of attractor neural networks

    • K. Y. M. Wong, D Sherrington
    Pages 105-119

  11. Learning and optimization

    • J. Bernasconi
    Pages 121-136

  12. Statistical dynamics of learning

    • J. A. Hertz
    Pages 137-153

  13. Learning and retrieving marked patterns

    • Stam Nicolis
    Pages 155-166

  14. Learning algorithm for binary synapses

    • C. J. Pérez Vicente
    Pages 167-174

  15. Statistical mechanics of the perceptron with maximal stability

    • Wolfgang Kinzel
    Pages 175-188

  16. Simulation and hardware implementation of competitive learning neural networks

    • Prieto A., Martin-Smith P., Merelo J. J., Pelayo F. J., Ortega J., Fernandez F. J. et al.
    Pages 189-204

  17. Learning in multilayer networks: A geometric computational approach

    • Pá1 Ruján
    Pages 205-224

  18. Storage capacity of diluted neural networks

    • M. Bouten
    Pages 225-236

  19. Dynamics and storage capacity of neural networks with sign-constrained weights

    • C. Campbell, K. Y. M. Wong
    Pages 237-252

  20. The neural basis of the locomotion of nematodes

    • Paul Erdös, Ernst Niebur
    Pages 253-267
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Keywords

About this book

Combined for researchers and graduate students the articles from the Sitges Summer School together form an excellent survey of the applications of neural-network theory to statistical mechanics and computer-science biophysics. Various mathematical models are presented together with their interpretation, especially those to do with collective behaviour, learning and storage capacity, and dynamical stability.

Bibliographic Information

  • Book Title: Statistical Mechanics of Neural Networks

  • Book Subtitle: Proceedings of the XIth Sitges Conference Sitges, Barcelona, Spain, 3–7 June 1990

  • Editors: Luis Garrido

  • Series Title: Lecture Notes in Physics

  • DOI: https://doi.org/10.1007/3-540-53267-6

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Berlin Heidelberg 1990

  • Softcover ISBN: 978-3-662-13785-7Published: 23 August 2014

  • eBook ISBN: 978-3-540-46808-0Published: 05 August 2005

  • Series ISSN: 0075-8450

  • Series E-ISSN: 1616-6361

  • Edition Number: 1

  • Number of Pages: VI, 477

  • Topics: Thermodynamics, Complex Systems, Artificial Intelligence, Statistical Physics and Dynamical Systems

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