The Language of Cortical Dynamics

  • Peter Andras
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4216)

Abstract

Cortical dynamics can be recorded in various ways. Theoretical works suggest that analyzing the dynamics of recorded activities might reveal the workings of the underlying neural system. Here we describe the extraction of an activity pattern language that characterizes the dynamics of high-resolution EEG data recorded. We show that the language can be formulated in terms of probabilistic continuation rules which predict reasonably well the dynamics of activity patterns in the data.

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References

  1. 1.
    Abeles, M., Bergman, H., Gat, I., Meilijson, I., Seidemann, E., Tishby, N., Vaadia, E.: Cortical activity flips among quasi-stationary states. PNAS 92, 8161–8620 (1995)CrossRefGoogle Scholar
  2. 2.
    Andras, P.: A model for emergent complex order in small neural networks. Journal of Integrative Neuroscience 2, 55–70 (2003)CrossRefGoogle Scholar
  3. 3.
    Andras, P.: Pattern languages: A new paradigm for neurocomputation. Neurocomputing 58, 223–228 (2004)CrossRefGoogle Scholar
  4. 4.
    Andras, P.: Computation with chaotic patterns. Biological Cybernetics 92, 452–460 (2005)MATHCrossRefGoogle Scholar
  5. 5.
    Fiser, J., Chiu, C., Weliky, M.: Small modulation of ongoing cortical dynamics by sensory input during natural vision. Nature 431, 703–718 (2004)CrossRefGoogle Scholar
  6. 6.
    Freeman, W.J.: Role of chaotic dynamics in neural plasticity. Progress in Brain Research 102, 319–333 (1994)CrossRefGoogle Scholar
  7. 7.
    Freeman, W.J., Burke, B.C.: A neurobiological theory of meaning in perception. Part IV: Multicortical patterns of amplitude modulation in gamma EEG. International Journal of Bifurcation and Chaos 13, 2857–2866 (2003)MATHCrossRefGoogle Scholar
  8. 8.
    Kay, L.M., Lancaster, L.R., Freeman, W.J.: Reafference and attractors in the olfactory system during odor recognition. International Journal of Neural Systems 7, 489–495 (1996)CrossRefGoogle Scholar
  9. 9.
    Kenet, T., Bibitchkov, D., Tsodyks, M., Grinvald, A., Arieli, A.: Spontaneously emerging cortical representations of visual attributes. Nature 425, 954–956 (2003)CrossRefGoogle Scholar
  10. 10.
    Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1995)Google Scholar
  11. 11.
    Ohl, F.W., Scheich, H., Freeman, W.J.: Change in pattern of ongoing cortical activity with auditory category learning. Nature 412, 733–736 (2001)CrossRefGoogle Scholar
  12. 12.
    Radons, G., Becker, J.D., Dulfer, B., Kruger, J.: Analysis, classification, and coding of multielectrode spike trains with hidden Markov models. Biological Cybernetics 71, 359–373 (1994)MATHCrossRefGoogle Scholar
  13. 13.
    Seidemann, E., Meilijson, I., Abeles, M., Bergman, H., Vaadia, E.: Simultaneously recorded single units in the frontal cortex go through sequences of discrete and stable states in monkeys performing a delayed localization task. The Journal of Neuroscience 16, 752–768 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Peter Andras
    • 1
  1. 1.School of Computing ScienceUniversity of NewcastleNewcastle upon TyneUK

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