SNNVis: Visualizing Graph Embedding of Evolutionary Optimization for Spiking Neural Networks

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Abstract

While Spiking Neural Networks (SNNs) show a lot of promise, it is difficult to optimize them because applying traditional gradient-based optimization techniques is difficult. Even though evolutionary algorithms (EAs) have been shown to promise to optimize SNNs, understanding the relationship between evolving the characteristics of SNNs and their performance to improve the optimization algorithm is challenging because of the complex characteristics and huge population size. We propose visual analytics with novel graph embedding for evolutionary SNNs to address the challenges. While existing graph embedding techniques have limitations in preserving the specific features of the nodes and edges, our approach maintains them. Also, we develop visual analytics for understanding the relationship between the network performance and the features of nodes and edges and exploring and analyzing the evolving SNNs to build insights into improving the EA.

Original languageEnglish
Title of host publicationProceedings - 2024 International Conference on Neuromorphic Systems, ICONS 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages327-330
Number of pages4
ISBN (Electronic)9798350368659
DOIs
StatePublished - 2024
Event2024 International Conference on Neuromorphic Systems, ICONS 2024 - Arlington, United States
Duration: Jul 30 2024Aug 2 2024

Publication series

NameProceedings - 2024 International Conference on Neuromorphic Systems, ICONS 2024

Conference

Conference2024 International Conference on Neuromorphic Systems, ICONS 2024
Country/TerritoryUnited States
CityArlington
Period07/30/2408/2/24

Keywords

  • evolutionary algorithm
  • SNN
  • Visual analytics

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