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Improved Spike-Timed Mappings using a Tri-Phasic Spike Timing-Dependent Plasticity Rule

Notley, S and Grüning, A (2012) Improved Spike-Timed Mappings using a Tri-Phasic Spike Timing-Dependent Plasticity Rule In: The 2012 International Joint Conference on Neural Networks (IJCNN), 2012-06-10 - 2012-06-15.

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Reservoir computing and the liquid state machine models have received much attention in the literature in recent years. In this paper we investigate using a reservoir composed of a network of spiking neurons, with synaptic delays, whose synapses are allowed to evolve using a tri-phasic spike timing- dependent plasticity (STDP) rule. The networks are trained to produce specific spike trains in response to spatio-temporal input patterns. The results of using a tri-phasic STDP rule on the network properties are compared to those found using the more common exponential form of the rule. It is found that each rule causes the synaptic weights to evolve in significantly different fashions giving rise to different network dynamics. It is also found that the networks evolved with the tri-phasic rule are more capable of mapping input spatio-temporal patterns to the output spike trains.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Notley, S
Grüning, A
Date : 2012
DOI : 10.1109/IJCNN.2012.6252773
Contributors :
Additional Information : © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 19 Nov 2012 10:45
Last Modified : 31 Oct 2017 14:41

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