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Evolving Ensembles of Spiking Neural Networks for Neuromorphic Systems...

by Daniel Elbrecht, Shruti R Kulkarni, Maryam Parsa, John P Mitchell, Catherine D Schuman
Publication Type
Conference Paper
Book Title
2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Publication Date
Page Numbers
1989 to 1994
Conference Name
IEEE Symposium Series on Computational Intelligence (SSCI)
Conference Location
Canberra, Australia
Conference Sponsor
Conference Date

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.