Topological Evolution of Spiking Neural Networks

Sam Slade, Li Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)
6 Downloads (Pure)

Abstract

Neuro-evolution is often used to generate the parameters, topology, and rules of artificial neural networks. This technique allows for automatic configuration of a neural network. In this paper we propose a method to generate Spiking Neural Networks (SNNs) automatically called NENG (Neuro-Evolutionary Network Generation). The aim was to help alleviate the manual construction and optimization of neural network implementations. The results show the algorithm is successful at generating and improving the design of SNNs for a Classification task. After 812 generations with a population size of 20 the algorithm converges to model the Xor gate with 100% accuracy. The results show improvements to the algorithm execution time and number of neurons over time.
Original languageEnglish
Title of host publicationIEEE World Congress on Computational Intelligence
Place of PublicationPiscataway
PublisherIEEE
Pages3135-3143
ISBN (Electronic)9781509060146
ISBN (Print)9781509060153
DOIs
Publication statusPublished - 8 Jul 2018
EventIEEE World Congress on Computational Intelligence 2018 - Windsor Barra Convention Centre, Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Conference

ConferenceIEEE World Congress on Computational Intelligence 2018
Abbreviated titleWCCI 2018
CountryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

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