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Incremental learning algorithm for spike pattern classification


Mohemmed, A; Kasabov, N (2012). Incremental learning algorithm for spike pattern classification. In: IEEE World Congress on Computational Intelligence (WCCI 2012) , Brisbane, Australia, 10 June 2012 - 15 June 2012, 1227-1232.

Abstract

In a previous work (Mohemmed et al.), the authors proposed a supervised learning algorithm to train a spiking neuron to associate input/output spike patterns. In this paper, the association learning rule is applied in training a single layer of spiking neurons to classify multiclass spike patterns whereby the neurons are trained to recognize an input spike pattern by emitting a predetermined spike train. The training is performed in incremental fashion, i.e. the synaptic weights are adjusted after each presentation of a training pattern. The individual neurons are trained independently from other neurons and on patterns from a single class. A spike train comparison criterion is used to decode the output spike trains into class labels. The results of the simulation experiments on a synthetic dataset of spike patterns show a high efficiency in solving the considered classification task.

Abstract

In a previous work (Mohemmed et al.), the authors proposed a supervised learning algorithm to train a spiking neuron to associate input/output spike patterns. In this paper, the association learning rule is applied in training a single layer of spiking neurons to classify multiclass spike patterns whereby the neurons are trained to recognize an input spike pattern by emitting a predetermined spike train. The training is performed in incremental fashion, i.e. the synaptic weights are adjusted after each presentation of a training pattern. The individual neurons are trained independently from other neurons and on patterns from a single class. A spike train comparison criterion is used to decode the output spike trains into class labels. The results of the simulation experiments on a synthetic dataset of spike patterns show a high efficiency in solving the considered classification task.

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Additional indexing

Item Type:Conference or Workshop Item (Speech), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:15 June 2012
Deposited On:28 Feb 2013 08:17
Last Modified:21 Nov 2017 16:36
Publisher:IEEE
Series Name:Proceedings of the International Joint Conference on Neural Networks
Number of Pages:6
ISSN:2161-4393
ISBN:978-1-4673-1489-3
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.
Publisher DOI:https://doi.org/10.1109/IJCNN.2012.6252533

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