Supervised Associative Learning in Spiking Neural Network

  • Nooraini Yusoff
  • André Grüning
Conference paper

DOI: 10.1007/978-3-642-15819-3_30

Volume 6352 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Yusoff N., Grüning A. (2010) Supervised Associative Learning in Spiking Neural Network. In: Diamantaras K., Duch W., Iliadis L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6352. Springer, Berlin, Heidelberg

Abstract

In this paper, we propose a simple supervised associative learning approach for spiking neural networks. In an excitatory-inhibitory network paradigm with Izhikevich spiking neurons, synaptic plasticity is implemented on excitatory to excitatory synapses dependent on both spike emission rates and spike timings. As results of learning, the network is able to associate not just familiar stimuli but also novel stimuli observed through synchronised activity within the same subpopulation and between two associated subpopulations.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Nooraini Yusoff
    • 1
  • André Grüning
    • 1
  1. 1.Department of Computing, Faculty of Engineering and Physical SciencesUniversity of SurreySurreyUK