University of Surrey

Test tubes in the lab Research in the ATI Dance Research

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.

icann_2010_preprint.pdf - Accepted version Manuscript

Download (77kB)


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 > Computer Science
Authors : Yusoff, N and Grüning, A
Date : 2010
DOI : 10.1007/978-3-642-15819-3_30
Contributors :
ContributionNameEmailORCID, KI, W, LS,
Related URLs :
Additional Information : The original publication is available at
Depositing User : Symplectic Elements
Date Deposited : 14 Dec 2011 11:24
Last Modified : 06 Jul 2019 05:08

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800