Evolutionary Spiking Neural Networks: A Survey
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.
Cite
@article{arxiv.2406.12552,
title = {Evolutionary Spiking Neural Networks: A Survey},
author = {Shuaijie Shen and Rui Zhang and Chao Wang and Renzhuo Huang and Aiersi Tuerhong and Qinghai Guo and Zhichao Lu and Jianguo Zhang and Luziwei Leng},
journal= {arXiv preprint arXiv:2406.12552},
year = {2024}
}