Unsupervised learning in networks of spiking neurons using temporal coding

Part III: Learning: Theory and Algorithms
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1327)


We propose a mechanism for unsupervised learning in networks of spiking neurons which is based on the timing of single firing events. Our results show that a topology preserving behaviour quite similar to that of Kohonen's self-organizing map can be achieved using temporal coding. In contrast to previous approaches, which use rate coding, the winner among competing neurons can be determined fast and locally. Hence our model is a further step towards a more realistic description of unsupervised learning in biological neural systems.


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  1. 1.
    Arbib, M. A.: The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge (1995).Google Scholar
  2. 2.
    Choe, Y., Miikkulainen, R.: Self-organization and segmentation with laterally connected spiking neurons. Technical Report AI TR 96-251, Department of Computer Science, University of Texas at Austin, September 1996.Google Scholar
  3. 3.
    Gerstner, W., van Hemmen, L. H.: How to describe neuronal activity: spikes, rates, or assemblies? In Advances in Neural Information Processing Systems 6, Morgan Kaufmann, San Mateo (1994) 463–470.Google Scholar
  4. 4.
    Goodhill, G. J., Sejnowski, T. J.: Quantifying neighbourhood preservation in topographic mappings. Proceedings of the 3rd Joint Symposium on Neural Computation, San Diego, CA (1996) 61–82.Google Scholar
  5. 5.
    Kohonen, T.: Physiological interpretation of the self-organizing map algorithm. Neural Networks 6 (1993) 895–905.Google Scholar
  6. 6.
    Kohonen, T.: Self-organizing maps. Springer, Berlin (1995).Google Scholar
  7. 7.
    Maass, W.: Lower bounds for the computational power of networks of spiking neurons. Neural Computation 8 (1996) 1–40.Google Scholar
  8. 8.
    Maass, W.: Fast sigmoidal networks via spiking neurons. Neural Computation 9 (1997) 279–304.Google Scholar
  9. 9.
    Maass, W.: Networks of spiking neurons: the third generation of neural network models. Neural Networks, to appear; extended abstract in Proceedings of the Seventh Australian Conference on Neural Networks, Canberra (1996) 1–10.Google Scholar
  10. 10.
    Murray, A., Tarassenko, L.: Analogue Neural VLSI: A Pulse Stream Approach. Chapman & Hall, London (1994).Google Scholar
  11. 11.
    Rieke, F., Warland, D., de Ruyter van Steveninck, R., and Bialek, W.: SPIKES: Exploring the Neural Code. MIT Press, Cambridge (1996).Google Scholar
  12. 12.
    Ruf, B.: Computing functions with spiking neurons in temporal coding. In J. Mira, R. Moreno-Díaz and J. Cabestany (eds.). Biological and Artificial Computation: From Neuroscience to Technology. Proceedings of the International Work Conference on Artificial and Natural Neural Networks IWANN'97, Lecture Notes in Computer Science, vol. 1240, Springer, Berlin (1997) 265–272.Google Scholar
  13. 13.
    Sejnowski, T.: Time for a new neural code? Nature 376 (1995) 21–22.Google Scholar
  14. 14.
    Sirosh, J., Miikkulainen, R.: Topographic receptive fields and patterned lateral interaction in a self-organizing model of the primary visual cortex. Neural Computation 9 (1997) 577–594.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  1. 1.Institute for Theoretical Computer ScienceTechnische Universität GrazGrazAustria

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