Supervised Associative Learning in Spiking Neural Network

Yusoff, N and Grüning, A (2010) Supervised Associative Learning in Spiking Neural Network In: Artificial Neural Networks – ICANN 2010, 2010-09-15 - 2010-09-18, Thessaloniki, Greece.

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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.

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Divisions :	Faculty of Engineering and Physical Sciences > Computing Science
Authors :	Name Email ORCID Yusoff, N Grüning, A
Date :	2010
DOI :	10.1007/978-3-642-15819-3_30
Contributors :	Contribution Name Email ORCID http://www.loc.gov/loc.terms/relators/EDT Diamantaras, KI http://www.loc.gov/loc.terms/relators/EDT Duch, W http://www.loc.gov/loc.terms/relators/EDT Iliadis, LS http://www.loc.gov/loc.terms/relators/PBL Springer,
Related URLs :	Publisher
Additional Information :	The original publication is available at http://www.springerlink.com
Depositing User :	Symplectic Elements
Date Deposited :	14 Dec 2011 11:24
Last Modified :	31 Oct 2017 14:14
URI:	http://epubs.surrey.ac.uk/id/eprint/22853

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