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Improving Learning Algorithm Performance for Spiking Neural Networks

Fu, Q, Luo, F, Liu, J, Bi, J, Qui, S, Cao, Yi and Ding, X (2018) Improving Learning Algorithm Performance for Spiking Neural Networks In: 17th IEEE International Conference on Communication Technology (ICCT 2017), October 27 - 30 2017, Chengdu, China.

2017-ICCT-Improving Learning Algorithm Performance for Spiking Neural Networks.pdf - Accepted version Manuscript

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This paper proposes three methods to improve the learning algorithm for spiking neural networks (SNNs). The aim is to improve learning performance in SNNs where neurons are allowed to fire multiple times. The performance is analyzed based on the convergence rate, the concussion condition in the training period and the error between actual output and desired output. The exclusive-or (XOR) and Wisconsin breast cancer (WBC) classification tasks are employed to validate the proposed optimized methods. Experimental results demonstrate that compared to original learning algorithm, all three methods have less iterations, higher accuracy, and more stable in the training period.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Arts and Social Sciences > Surrey Business School
Authors :
Fu, Q
Luo, F
Liu, J
Bi, J
Qui, S
Ding, X
Date : 17 May 2018
DOI : 10.1109/ICCT.2017.8359963
Copyright Disclaimer : © 2017 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.
Uncontrolled Keywords : spiking neural network; optimization method; learning performance
Depositing User : Melanie Hughes
Date Deposited : 12 Oct 2017 11:09
Last Modified : 27 Jun 2018 11:51

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