Journal of the Korean Physical Society

, Volume 67, Issue 9, pp 1679–1685 | Cite as

Competitive learning behavior in a stochastic neural network

  • Myoung Won ChoEmail author


Stochastic behavior is a natural and inevitable property of biological neurons. The effect of stochastic behavior or thermal fluctuation in neural firings on the learning process in a neural system is investigated. A learning model, which is derived from the stochastic differential equation of the firing-rate model, is presented as an estimate of the gradient flow of free energy. The model reveals that the learning process becomes competitive owing to the effect of entropy even through the synapse modifications only follow the simple Hebbian rule.


Stochastic neural network Competitive learning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    D. O. Hebb, The organization of behavior: A neurophysiological theory (John Wiley and Sons, New York, 1949).Google Scholar
  2. [2]
    E. Oja, J. Math. Bio. 53, 267 (1982).CrossRefMathSciNetGoogle Scholar
  3. [3]
    E. L. Bienenstock, L. N. Cooper and P. W. Munro, J. Neurosci. 2, 32 (1982).Google Scholar
  4. [4]
    H. Markram, J. Lübke, M. Frotscher, and B. Sakmann, Science 275, 213 (1997).CrossRefGoogle Scholar
  5. [5]
    G.-q. Bi and M.-m. Poo, J. Neurosci 18, 10464 (1998).Google Scholar
  6. [6]
    L. F. Abbott and S. B. Nelson, Nat. Neurosci 3, 1173 (2000).Google Scholar
  7. [7]
    S. Song and L. F. Abbott, Neuron 32, 339 (2001).CrossRefGoogle Scholar
  8. [8]
    M. W. Cho and M. Y. Choi, Europhys. Lett 95, 58005 (2011).CrossRefADSGoogle Scholar
  9. [9]
    D. H. Ackley, G. Hinton and T. Sejnowski, Cognitive Science 9, 147 (1985).CrossRefGoogle Scholar
  10. [10]
    C. Peipenbrock, in Probabilistic Models of the Brain, edited by R. P. N. Rao, B. A. Olshausen and M. S. Lewicki (The MIT Press, Cambridge MA, 2002), p. 181.Google Scholar
  11. [11]
    B. A. Olshausen and D. J. Field, Nature 381, 607 (1996).CrossRefADSGoogle Scholar
  12. [12]
    P. Dayan and L. Fl. Abbott, Theoretical neuroscience (The MIT Press, London, 2001).zbMATHGoogle Scholar
  13. [13]
    H. Kleinert, Gauge Fields in Condensed Matter (World Scientific, Singapore, 1989).CrossRefzbMATHGoogle Scholar
  14. [14]
    A. J. Bell and T. J. Sejnowski, Neural Comput 7, 1004 (1995).CrossRefGoogle Scholar
  15. [15]
    T.-W. Lee, Independent component analysis (Kluwer Academic Publishers, Boston, 1998).zbMATHGoogle Scholar
  16. [16]
    M. W. Cho and M. Y. Choi, Neural Networks 31, 46 (2012).CrossRefGoogle Scholar

Copyright information

© The Korean Physical Society 2015

Authors and Affiliations

  1. 1.Department of Global Medical ScienceSungshin Women’s UniversitySeoulKorea

Personalised recommendations