Table of Contents
Advances in Artificial Neural Systems
Volume 2016, Article ID 1746514, 12 pages
http://dx.doi.org/10.1155/2016/1746514
Research Article

Modified STDP Triplet Rule Significantly Increases Neuron Training Stability in the Learning of Spatial Patterns

Faculty of Mathematics and Informatics, Vilnius University, Naugarduko Street 24, 03225 Vilnius, Lithuania

Received 25 February 2016; Revised 2 May 2016; Accepted 27 June 2016

Academic Editor: Christian Georg Mayr

Copyright © 2016 Dalius Krunglevicius. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules which are based firmly on biological evidence. STDP learning is capable of detecting spatiotemporal patterns highly obscured by noise. This feature appears attractive from the point of view of machine learning. In this paper three different additive STDP models of spike interactions were compared in respect to training performance when the neuron is exposed to a recurrent spatial pattern injected into Poisson noise. The STDP models compared were all-to-all interaction, nearest-neighbor interaction, and the nearest-neighbor triplet interaction. The parameters of the neuron model and STDP training rules were optimized for a range of spatial patterns of different sizes by the means of heuristic algorithm. The size of the pattern, that is, the number of synapses containing the pattern, was gradually decreased from what amounted to a relatively easy task down to a single synapse. Optimization was performed for each size of the pattern. The parameters were allowed to evolve freely. The triplet rule, in most cases, performed better by far than the other two rules, while the evolutionary algorithm immediately switched the polarity of the triplet update. The all-to-all rule achieved moderate results.