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Evolutionary Spiking Neural Networks: A Survey

Authors :
Shen, Shuaijie
Zhang, Rui
Wang, Chao
Huang, Renzhuo
Tuerhong, Aiersi
Guo, Qinghai
Lu, Zhichao
Zhang, Jianguo
Leng, Luziwei
Source :
J Membr Comput (2024)
Publication Year :
2024

Abstract

Spiking neural networks (SNNs) are gaining increasing attention as potential computationally efficient alternatives to traditional artificial neural networks(ANNs). However, the unique information propagation mechanisms and the complexity of SNN neuron models pose challenges for adopting traditional methods developed for ANNs to SNNs. These challenges include both weight learning and architecture design. While surrogate gradient learning has shown some success in addressing the former challenge, the latter remains relatively unexplored. Recently, a novel paradigm utilizing evolutionary computation methods has emerged to tackle these challenges. This approach has resulted in the development of a variety of energy-efficient and high-performance SNNs across a wide range of machine learning benchmarks. In this paper, we present a survey of these works and initiate discussions on potential challenges ahead.

Details

Database :
arXiv
Journal :
J Membr Comput (2024)
Publication Type :
Report
Accession number :
edsarx.2406.12552
Document Type :
Working Paper
Full Text :
https://doi.org/10.1007/s41965-024-00156-x