Method for training a spiking neuron to associate input-output spike trains

Mohemmed, A
Schliebs, S
Matsuda, S
Kasabov, N
Item type
Conference Contribution
Degree name
Journal Title
Journal ISSN
Volume Title
AUT University

We propose a novel supervised learning rule allowing the training of a precise input-output behavior to a spiking neuron. A single neuron can be trained to associate (map) different output spike trains to different multiple input spike trains. Spike trains are transformed into continuous functions through appropriate kernels and then Delta rule is applied. The main advantage of the method is its algorithmic simplicity promoting its straightforward application to building spiking neural networks (SNN) for engineering problems. We experimentally demonstrate on a synthetic benchmark problem the suitability of the method for spatio-temporal classification. The obtained results show promising efficiency and precision of the proposed method.

Spiking neural networks , Supervised learning , Spatio-temporal patterns
Engineering Applications of Neural Networks (EANN), Greece, 15-18 September 2011
Rights statement
NOTICE: this is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in (see Citation). The original publication is available at (see Publisher's Version)