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DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons

Taherkhani, Aboozar, Belatreche, Ammar, Li, Yuhua and Maguire, Liam (2015) DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons. IEEE Transactions on Neural Networks and Learning Systems, 26 (12). pp. 3137-3149. [Journal article]

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URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7063227

DOI: doi: 10.1109/TNNLS.2015.2404938

Abstract

Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.

Item Type:Journal article
Keywords:Delay shift learning, spiking neuron, supervised learning, synaptic delay
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:32879
Deposited By: Dr Ammar Belatreche
Deposited On:18 Dec 2015 15:32
Last Modified:18 Dec 2015 15:32

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