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Method for Training a Spiking Neuron to Associate Input-Output Spike Trains

  • Ammar Mohemmed
  • Stefan Schliebs
  • Satoshi Matsuda
  • Nikola Kasabov
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 363)

Abstract

We propose a novel supervised learning rule allowing the training of a precise input-output behavior to a spiking neuron. A single neuron can be trained to associate (map) different output spike trains to different multiple input spike trains. Spike trains are transformed into continuous functions through appropriate kernels and then Delta rule is applied. The main advantage of the method is its algorithmic simplicity promoting its straightforward application to building spiking neural networks (SNN) for engineering problems. We experimentally demonstrate on a synthetic benchmark problem the suitability of the method for spatio-temporal classification. The obtained results show promising efficiency and precision of the proposed method.

Keywords

Spiking Neural Networks Supervised Learning Spatio- temporal patterns 

References

  1. 1.
    Bell, C.C., Han, V.Z., Sugawara, Y., Grant, K.: Synaptic plasticity in a cerebellum-like structure depends on temporal order. Nature 387, 278–281 (1997)CrossRefGoogle Scholar
  2. 2.
    Bi, G.q., Poo, M.m.: Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18(24), 10464–10472 (1998)Google Scholar
  3. 3.
    Bohte, S.M., Kok, J.N., Poutré, J.A.L.: SpikeProp: backpropagation for networks of spiking neurons. In: ESANN, pp. 419–424 (2000)Google Scholar
  4. 4.
    Florian, R.V.: The chronotron: a neuron that learns to fire temporally-precise spike patterns (November 2010) , http://precedings.nature.com/documents/5190/version/1
  5. 5.
    Gerstner, W., Kistler, W.M.: Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge (2002)zbMATHGoogle Scholar
  6. 6.
    Gewaltig, M.O., Diesmann, M.: Nest (neural simulation tool). Scholarpedia 2(4), 1430 (2007)CrossRefGoogle Scholar
  7. 7.
    Gutig, R., Sompolinsky, H.: The tempotron: a neuron that learns spike timing-based decisions. Nat. Neurosci. 9(3), 420–428 (2006)CrossRefGoogle Scholar
  8. 8.
    Kasiński, A.J., Ponulak, F.: Comparison of supervised learning methods for spike time coding in spiking neural networks. Int. J.of Applied Mathematics and Computer Science 16, 101–113 (2006)Google Scholar
  9. 9.
    Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14(11), 2531–2560 (2002)zbMATHCrossRefGoogle Scholar
  10. 10.
    Nordlie, E., Gewaltig, M.O., Plesser, H.E.: Towards reproducible descriptions of neuronal network models. PLoS Comput. Biol. 5(8), e1000456 (2009)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Ponulak, F.: ReSuMe – new supervised learning method for spiking neural networks. Tech. rep., Institute of Control and Information Engineering, Poznań University of Technology, Poznań, Poland (2005)Google Scholar
  12. 12.
    Ponulak, F.: Analysis of the resume learning process for spiking neural networks. Applied Mathematics and Computer Science 18(2), 117–127 (2008)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Ponulak, F., Kasiński, A.: Supervised learning in spiking neural networks with ReSuMe: sequence learning, classification, and spike shifting. Neural Computation 22(2), 467–510 (2010), PMID: 19842989 MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    van Rossum, M.C.: A novel spike distance. Neural Computation 13(4), 751–763 (2001)zbMATHCrossRefGoogle Scholar
  15. 15.
    Victor, J.D., Purpura, K.P.: Metric-space analysis of spike trains: theory, algorithms and application. Network: Computation in Neural Systems 8(2), 127–164 (1997)zbMATHCrossRefGoogle Scholar

Copyright information

© International Federation for Information Processing 2011

Authors and Affiliations

  • Ammar Mohemmed
    • 1
  • Stefan Schliebs
    • 1
  • Satoshi Matsuda
    • 1
    • 2
  • Nikola Kasabov
    • 1
  1. 1.Knowledge Engineering Discovery Research InstituteAucklandNew Zealand
  2. 2.Department of Mathematical Information EngineeringNihon UniversityJapan

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