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Method for Training a Spiking Neuron to Associate Input-Output Spike Trains


Mohemmed, A; Schliebs, S; Matsuda, S; Kasabov, N (2011). Method for Training a Spiking Neuron to Associate Input-Output Spike Trains. In: 12th INNS EANN-SIG International Conference, Corfu, Greece, 15 September 2011 - 18 September 2011, 219-228.

Abstract

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.

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.

Citations

3 citations in Web of Science®
4 citations in Scopus®
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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Uncontrolled Keywords:Spiking Neural Networks;Supervised Learning;Spatio;Temporal patterns
Language:English
Event End Date:18 September 2011
Deposited On:09 Mar 2012 14:39
Last Modified:05 Apr 2016 15:43
Publisher:Springer
Series Name:IFIP advances in information and communication technology
Number:363/2011
Number of Pages:9
ISSN:1868-4238
ISBN:978-3-642-23956-4;978-3-642-23957-1
Publisher DOI:10.1007/978-3-642-23957-1_25

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