SPAN: A Neuron for Precise-Time Spike Pattern Association

  • Ammar Mohemmed
  • Stefan Schliebs
  • Nikola Kasabov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7063)

Abstract

In this paper we propose SPAN, a LIF spiking neuron that is capable of learning input-output spike pattern association using a novel learning algorithm. The main idea of SPAN is transforming the spike trains into analog signals where computing the error can be done easily. As demonstrated in an experimental analysis, the proposed method is both simple and efficient achieving reliable training results even in the context of noise.

Keywords

Spiking Neural Networks Supervised Learning Nuerocomputing Spatiotemporal pattern recognition 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ammar Mohemmed
    • 1
  • Stefan Schliebs
    • 1
  • Nikola Kasabov
    • 1
    • 2
    • 3
  1. 1.Knowledge Engineering Discovery Research InstituteAucklandNew Zealand
  2. 2.Institute for NeuroinformaticsETHSwitzerland
  3. 3.University of ZurichSwitzerland

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