Supervised associative learning in spiking neural network

Authors:
Nooraini Yusoff;André Grüning
Affiliations:
Department of Computing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, UK;Department of Computing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, UK
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Year:
2010

Citing 5
Cited 0

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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.