An extended algorithm using adaptation of momentum and learning rate for spiking neurons emitting multiple spikes

Yuling Luo, Qiang Fu, Junxiu Liu*, Jim Harkin, Liam McDaid, Yi Cao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents two methods of using the dynamic momentum and learning rate adaption, to improve learning performance in spiking neural networks where neurons are modelled as spiking multiple times. The optimum value for the momentum factor is obtained from the mean square error with respect to the gradient of synaptic weights in the proposed algorithm. The delta-bar-delta rule is employed as the learning rate adaptation method. The XOR and Wisconsin breast cancer (WBC) classification tasks are used to validate the proposed algorithms. Results demonstrate no error and a minimal error of 0.08 are achieved for the XOR and WBC classification tasks respectively, which are better than the original Booij’s algorithm. The minimum number of epochs for XOR and Wisconsin breast cancer tasks are 35 and 26 respectively, which are also faster than the original Booij’s algorithm - i.e. 135 (for XOR) and 97 (for WBC). Compared with the original algorithm with static momentum and learning rate, the proposed dynamic algorithms can control the convergence rate and learning performance more effectively.

Original languageEnglish
Title of host publicationAdvances in Computational Intelligence - 14th International Work-Conference on Artificial Neural Networks, IWANN 2017, Proceedings
EditorsAndreu Catala, Ignacio Rojas, Gonzalo Joya
PublisherSpringer-Verlag
Pages569-579
Number of pages11
ISBN (Print)9783319591520
DOIs
Publication statusPublished - 2017
Event14th International Work-Conference on Artificial Neural Networks, IWANN 2017 - Cadiz, Spain
Duration: 14 Jun 201716 Jun 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10305 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Work-Conference on Artificial Neural Networks, IWANN 2017
Country/TerritorySpain
CityCadiz
Period14/06/1716/06/17

Keywords

  • learning rate
  • momentum
  • self-adaptation
  • spiking neural networks

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