D. Elbrecht and S. R. Kulkarni and M. Parsa and J. P. Mitchell and C. D. Schuman

December, 2020

IEEE Symposium Series on Computational Intelligence (SSCI)

https://doi.org/10.1109/SSCI47803.2020.9308568

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Abstract

Evolutionary algorithms have been proposed as a solution to overcome many of the challenges associated with training spiking neural networks. While evolutionary optimization for spiking neural networks is very flexible, its performance has difficulty scaling to complex tasks and correspondingly complex network structures. Here we propose a method for evolving ensembles of spiking neural networks. By using ensemble learning, the flexibility of evolutionary optimization is fully preserved while scaling to more challenging tasks. We test the performance of the proposed method using handwritten digit classification. We investigate multiple strategies for constructing ensembles of spiking neural networks, and demonstrate that evolving ensembles of SNNs offers significant performance advantages over evolutionary optimization.

Citation Information

Text

author       D. Elbrecht and S. R. Kulkarni and M. Parsa and J. P. Mitchell and C. D. Schuman
title        Evolving Ensembles of Spiking Neural Networks for Neuromorphic Systems
booktitle    IEEE Symposium Series on Computational Intelligence (SSCI)
publisher    IEEE
pages        1989-1994
year         2020
url          https://doi.org/10.1109/SSCI47803.2020.9308568
doi          10.1109/SSCI47803.2020.9308568

Bibtex

@INPROCEEDINGS{ekp:20:ees,
    author = "D. Elbrecht and S. R. Kulkarni and M. Parsa and J. P. Mitchell and C. D. Schuman",
    title = "Evolving Ensembles of Spiking Neural Networks for Neuromorphic Systems",
    booktitle = "IEEE Symposium Series on Computational Intelligence (SSCI)",
    publisher = "IEEE",
    pages = "1989-1994",
    year = "2020",
    url = "https://doi.org/10.1109/SSCI47803.2020.9308568",
    doi = "10.1109/SSCI47803.2020.9308568"
}