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)


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.


Spiking Neural Networks Supervised Learning Spatio- temporal patterns 


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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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