PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Improved Spike-Timed Mappings using a Tri-Phasic Spike Timing-Dependent Plasticity Rule
Scott Notley and Andre Gruning
In: International Joint Conference on Neural Networks, June 2012, Brisbane.

Abstract

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

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:9575
Deposited By:Andre Gruning
Deposited On:22 September 2012