Yusoff, Nooraini and Grüning, André (2010) Supervised associative learning in spiking neural network. In: Artificial Neural Networks – ICANN 2010. Lecture Notes in Computer Science, 6352 (6352). Springer, pp. 224-229. ISBN 978-3-642-15818-6

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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:	Book Section
Additional Information:	Book Subtitle 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part I
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	College of Arts and Sciences
Depositing User:	Dr. Nooraini Yusoff
Date Deposited:	26 Oct 2014 02:45
Last Modified:	26 Oct 2014 02:45
URI:	https://repo.uum.edu.my/id/eprint/12487

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