An information-theoretic analysis of temporal coding strategies by spiking central neurons

  • Gustavo Deco
  • Bernd Schürmann
Part I: Coding and Learning in Biology
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1327)


The brain encodes information in the intervals between the spikes which characterize neural firing events. Therefore it is relevant to study in a timing code how many spikes are necessary for reliably encoding input signals. We analyze the transmission of information, the reliability of signal detection and the coding strategy for the case of central neurons which contrary to peripheral sensory neurons handle input signals assumed to be given by a combination of Poisson spike trains. We consider an integrate-and-fire model of a central neuron which combines diffusion and jump processes. In order to obtain analytical results, we introduce in addition a new Rényi-Information based measure for the discrimination ability of single neurons, which is investigated in the framework of a simple spike response model.


Input Signal Mutual Information Spike Train Jump Process Central Neuron 
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  1. [1]
    H. Tuckwell, Introduction to Theoretical Neurobiology (Cambridge Press. 1988).Google Scholar
  2. [2]
    F. Rieke, D. Warland, R. de Ruyter van Steveninck, and W. Bialek, Spikes: Exploring the Neural Code (The MIT Press, Cambridge, 1997).Google Scholar
  3. [3]
    E. Adrian, The Physical Background of Perception; (Oxford Univ. Press, 1947).Google Scholar
  4. [4]
    W. Softky and C. Koch, J. Neuroscience 13, 334 (1993).Google Scholar
  5. [5]
    M. Abeles, Israel J. Med. Sci. 18, 83 (1982).Google Scholar
  6. [6]
    W. Softky, Current Opinion in Neurobiology 5, 239 (1995).Google Scholar
  7. [7]
    Z. Mainen and T. Sejnowski, Science 268, 1503 (1995).Google Scholar
  8. [8]
    G. Deco and B. Schürmann, Physical Review E 51, 1780 (1995a).Google Scholar
  9. [9]
    G. Deco and B. Schürmann, Physical Review E 52, 6580 (1995b).Google Scholar
  10. [10]
    G. Deco and D. Obradovic, An Information Theoretic Approach to Neural Computing (Springer, New York, 1996).Google Scholar
  11. [11]
    M. Musila and P. Lánsky, Int. J. Biomed. Comput. 31, 233 (1992).Google Scholar
  12. [12]
    B. Pompe, Chaos, Solitons and Fractals, 4, 83 (1994).Google Scholar
  13. [13]
    W. Gerstner and J. van Hemmen, Phys. Rev. Lett., 71, 312 (1993).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Gustavo Deco
    • 1
  • Bernd Schürmann
    • 1
  1. 1.MunichGermany

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