Supervised Associative Learning in Spiking Neural Network

  • Nooraini Yusoff
  • André Grüning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6352)


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.


Synaptic Plasticity Excitatory Synapse Excitatory Neuron Spike Rate Learning Trial 
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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

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