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Multi-DL-ReSuMe : multiple neurons delay learning remote supervised method

Taherkhani, A, Belatreche, A, Li, Y and Maguire, L 2015, 'Multi-DL-ReSuMe : multiple neurons delay learning remote supervised method' , in: Proceedings of the International Joint Conference on Neural Networks , Institute of Electrical and Electronics Engineers (IEEE), Article number 7280743.

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Abstract

Spikes are an important part of information transmission between neurons in the biological brain. Biological evidence shows that information is carried in the timing of individual action potentials, rather than only the firing rate. Spiking neural networks are devised to capture more biological characteristics of the brain to construct more powerful intelligent systems. In this paper, we extend our newly proposed supervised learning algorithm called DL-ReSuMe (Delay learning Remote Supervised Method) to train multiple neurons to classify spatiotemporal spiking patterns. In this method, several neurons instead of a single neuron are trained to perform the classification task. The simulation results show that a population of neurons has significantly higher processing ability compared to a single neuron. It is also shown that the performance of Multi-DL-ReSuMe (Multiple DL-ReSuMe) is increased when the number of desired spikes is increased in the desired spike trains to an appropriate number.

Item Type: Book Section
Additional Information: International Joint Conference on Neural Networks, IJCNN 2015; Killarney; Ireland; 12 July 2015 through 17 July 2015
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: International Joint Conference on Neural Networks (IJCNN) 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
ISBN: 9781479919604
Related URLs:
Funders: Non funded research
Depositing User: Yuhua Li
Date Deposited: 27 Jul 2015 10:57
Last Modified: 19 Jan 2016 16:07
URI: http://usir.salford.ac.uk/id/eprint/35772

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