Chapter

Artificial Neural Networks – ICANN 2010

Volume 6352 of the series Lecture Notes in Computer Science pp 224-229

Supervised Associative Learning in Spiking Neural Network

  • Nooraini YusoffAffiliated withLancaster UniversityDepartment of Computing, Faculty of Engineering and Physical Sciences, University of Surrey
  • , André GrüningAffiliated withLancaster UniversityDepartment of Computing, Faculty of Engineering and Physical Sciences, University of Surrey

* Final gross prices may vary according to local VAT.

Get Access

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.