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

December, 2020

IEEE Symposium Series on Computational Intelligence (SSCI)

https://doi.org/10.1109/SSCI47803.2020.9308443

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Abstract

Spiking neural networks (SNNs), the class of neural networks used in neuromorphic computing, are difficult to train using traditional back-propagation techniques. Spike timing-dependent plasticity (STDP) is a biologically inspired learning mechanism that can be used to train SNNs. Evolutionary algorithms have also been demonstrated as a method for training SNNs. In this work, we explore the relationship between these two training methodologies. We evaluate STDP and evolutionary optimization as standalone methods for training networks, and also evaluate a combined approach where STDP weight updates are applied within an evolutionary algorithm. We also apply Bayesian hyperparameter optimization as a meta learner for each of the algorithms. We find that STDP by itself is not an ideal learning rule for randomly connected networks, while the inclusion of STDP within an evolutionary algorithm leads to similar performance, with a few interesting differences. This study suggests future work in understanding the relationship between network topology and learning rules.

Citation Information

Text

author       D. Elbrecht and M. Parsa and S. R. Kulkarni and J. P. Mitchell and C. D. Schuman
title        Training Spiking Neural Networks Using Combined Learning Approaches
booktitle    IEEE Symposium Series on Computational Intelligence (SSCI)
publisher    IEEE
pages        1995--2001
year         2020
url          https://doi.org/10.1109/SSCI47803.2020.9308443
doi          10.1109/SSCI47803.2020.9308443

Bibtex

@INPROCEEDINGS{epk:20:tsn,
    author = "D. Elbrecht and M. Parsa and S. R. Kulkarni and J. P. Mitchell and C. D. Schuman",
    title = "Training Spiking Neural Networks Using Combined Learning Approaches",
    booktitle = "IEEE Symposium Series on Computational Intelligence (SSCI)",
    publisher = "IEEE",
    pages = "1995--2001",
    year = "2020",
    url = "https://doi.org/10.1109/SSCI47803.2020.9308443",
    doi = "10.1109/SSCI47803.2020.9308443"
}