Journal of Statistical Physics

, Volume 51, Issue 5–6, pp 743–775 | Cite as

Neural networks: A biased overview

  • Eytan Domany
Articles

Abstract

An overview of recent activity in the field of neural networks is presented. The long-range aim of this research is to understand how the brain works. First some of the problems are stated and terminology defined; then an attempt is made to explain why physicists are drawn to the field, and their main potential contribution. In particular, in recent years some interesting models have been introduced by physicists. A small subset of these models is described, with particular emphasis on those that are analytically soluble. Finally a brief review of the history and recent developments of single- and multilayer perceptrons is given, bringing the situation up to date regarding the central immediate problem of the field: search for a learning algorithm that has an associated convergence theorem.

Key words

Neural networks perceptron memory learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    J. L. van Hemmen and I. Morgenstern, eds.,Heidelberg Colloquium on Glassy Dynamics (Springer-Verlag, 1986); E. Bienenstock, F. Fogelman Soulie, and G. Weisbuch, eds.,Disordered Systems and Biological Organization (NATO ASI Series, Springer-Verlag, 1986); J. W. Clark, J. Rafaelski, and J. V. Winston,Phys. Rep. 123:215 (1985); D. Farmer, A. Lapedes, N. Packard, and B. Wendroff, eds., Evolution, Games and Learning,Physica 22D (1986); T. Hogg and B. A. Huberman, Artificial intelligence and large scale computation: A physics perspective,Phys. Rep. (1987), to appear.Google Scholar
  2. 2.
    T. Kohonen,Self-Organization and Associative Memory (Springer-Verlag, 1984); G. E. Hinton and J. A. Anderson, eds.,Parallel Models of Associative Memory (Erlbaum, Hillsdale, New Jersey, 1981); D. E. Rumelhart and J. L. McClelland,Parallel Distributed Processing: Explorations in the Microstructure of Cognition (MIT Press, 1986); R. Lippmann, An introduction to computing with neural nets,IEEE ASSP Mag. (April 1987).Google Scholar
  3. 3.
    A. I. Selverston, ed.,Model Neural Networks and Behavior (Plenum Press, New York, 1985).Google Scholar
  4. 4.
    J.-P. Changeux,Neuronal Man (Pantheon Books, New York, 1985); J. S. Albus,Brains, Behavior, Robotics (Byte Books, Peterborough, 1981).Google Scholar
  5. 5.
    E. R. Kandel and J. H. Schwartz,Principles of Neuroscience (Elsevier, New York, 1985).Google Scholar
  6. 6.
    J. Maddox,Nature 328:571 (1987).Google Scholar
  7. 7.
    W. S. McCulloch and W. Pitts,Bull. Math. Biophys. 5:115 (1943).Google Scholar
  8. 8.
    K. Binder and A. P. Young,Rev. Mod. Phys. 58:801 (1986).Google Scholar
  9. 9.
    J. J. Hopfield,Proc. Natl. Acad. Sci. USA 79:2554 (1982).Google Scholar
  10. 10.
    D. C. Hebb,The Organization of Behavior: A Neurophysiological Theory (Wiley, New York, 1957); L. N. Cooper, F. Liberman, and E. Oja,Biol. Cybernet. 33:9 (1979).Google Scholar
  11. 11.
    W. Kinzel,Z. Phys. B 60:205 (1985).Google Scholar
  12. 12.
    D. J. Amit, H. Gutfreund, and H. Sompolinsky,Phys. Rev. Lett. 55:1530 (1985);Phys. Rev. A 32:1007 (1985);Phys. Rev. A 35:2293 (1987);Ann. Phys. 173:30 (1987).Google Scholar
  13. 13.
    W. A. Little,Math. Biosci. 19:101 (1975).Google Scholar
  14. 14.
    G. Toulouse,Nature 327:662 (1987).Google Scholar
  15. 15.
    J. Hertz, G. Grinstein, and S. Solla, in L. Van Hemmen and I. Morgenstern, eds.,Glassy Dynamics (Springer-Verlag, Berlin, 1987), p. 538.Google Scholar
  16. 16.
    B. Derrida, E. Gardner, and A. Zippelius,Europhys. Lett. 4:167–173 (1987).Google Scholar
  17. 17.
    E. Domany, R. Meir, and W. Kinzel,Europhys. Lett. 2:175 (1986).Google Scholar
  18. 18.
    R. Meir and E. Domany,Phys. Rev. Lett. 59:359 (1987);Europhys. Lett. 4:645 (1987);Phys. Rev. A 37:608 (1988).Google Scholar
  19. 19.
    I. Kanter and H. Sompolinsky,Phys. Rev. A 35:380 (1987).Google Scholar
  20. 20.
    E. Gardner, B. Derrida, and P. Mottishaw,J. Phys. (Paris) 48:741 (1987).Google Scholar
  21. 21.
    J. L. van Hemmen, in J. L. van Hemmen and I. Morgenstern, eds.,Heidelberg Colloquium on Glassy Dynamics (Springer-Verlag, 1986), p. 547.Google Scholar
  22. 22.
    F. Rosenblatt,Principles of Neurodynamics (Spartan, Washington, D.C., 1961).Google Scholar
  23. 23.
    A. K. Dewdney,Sci. Am. 1984 (September).Google Scholar
  24. 24.
    M. Minsky and S. Papert,Perceptrons (MIT Press, 1969).Google Scholar
  25. 25.
    D. E. Rumelhart, G. E. Hinton, and R. J. Williams, in D. E. Rumelhart and J. L. McClelland,Parallel Distributed Processing: Explorations in the Microstructure of Cognition (MIT Press, 1986), Vol. 1, p. 318.Google Scholar
  26. 26.
    D. E. Rumelhart, G. E. Hinton, and R. J. Williams,Nature 323:533–536 (1986).Google Scholar
  27. 27.
    D. B. Parker, Learning Logic, Invention Report S81-64, Stanford University (1982).Google Scholar

Copyright information

© Plenum Publishing Corporation 1988

Authors and Affiliations

  • Eytan Domany
    • 1
  1. 1.Department of ElectronicsWeizmann Institute of ScienceRehovotIsrael

Personalised recommendations