Spike Response Function weight and delay updating strategy using delay rules

Abdullah H. Almasri, Shahnorbanun Sahran, Eiad Yafi

Research output: Contribution to journalArticle

Abstract

Spike Response Function (SRF) plays an important role in the temporal coding Spiking Neural Network (SNN) as it has a significant role to determine when the neuron should fire. This paper studies the important role of the SRF in the SNN learning stability. It proposes a novel method to find out the rules to update delay for each class to make SRF stable, and then using these rules to update delay and weight simultaneously at the SNN learning rule. This method updates the delay depending on the local result to make SRF stable. The main issue of this paper is to put forward the idea that weight and delay parameters could and need to be updated simultaneously to make both SRF and SNN stable during the learning process. The delay rules strategy which have been found could be used for pattern recognition application which use SNN. The limitation of this work is that; getting the updating delay rules depends on a sample data from each class and the way of selecting the rules.

Original languageEnglish
Pages (from-to)173-177
Number of pages5
JournalInternational Journal of Engineering and Technology(UAE)
Volume7
Issue number4.29 Special Issue 29
Publication statusPublished - 1 Jan 2018

Fingerprint

Learning
Neural networks
Weights and Measures
Neurons
Pattern recognition
Fires
Recognition (Psychology)

Keywords

  • Classification
  • Delay
  • Pattern recognition
  • Spike Response Function
  • Spiking Neural Network
  • Weight

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science (miscellaneous)
  • Environmental Engineering
  • Chemical Engineering(all)
  • Engineering(all)
  • Hardware and Architecture

Cite this

Spike Response Function weight and delay updating strategy using delay rules. / Almasri, Abdullah H.; Sahran, Shahnorbanun; Yafi, Eiad.

In: International Journal of Engineering and Technology(UAE), Vol. 7, No. 4.29 Special Issue 29, 01.01.2018, p. 173-177.

Research output: Contribution to journalArticle

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