Abstract
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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References
Arbib, M. A.: The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge (1995).
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.
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.
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.
Kohonen, T.: Physiological interpretation of the self-organizing map algorithm. Neural Networks 6 (1993) 895–905.
Kohonen, T.: Self-organizing maps. Springer, Berlin (1995).
Maass, W.: Lower bounds for the computational power of networks of spiking neurons. Neural Computation 8 (1996) 1–40.
Maass, W.: Fast sigmoidal networks via spiking neurons. Neural Computation 9 (1997) 279–304.
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.
Murray, A., Tarassenko, L.: Analogue Neural VLSI: A Pulse Stream Approach. Chapman & Hall, London (1994).
Rieke, F., Warland, D., de Ruyter van Steveninck, R., and Bialek, W.: SPIKES: Exploring the Neural Code. MIT Press, Cambridge (1996).
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.
Sejnowski, T.: Time for a new neural code? Nature 376 (1995) 21–22.
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.
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© 1997 Springer-Verlag Berlin Heidelberg
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Ruf, B., Schmitt, M. (1997). Unsupervised learning in networks of spiking neurons using temporal coding. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020181
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DOI: https://doi.org/10.1007/BFb0020181
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