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.

翻译：脉冲神经网络（SNNs）作为传统人工神经网络（ANNs）潜在的高计算效率替代方案，正受到越来越多的关注。然而，SNN独特的信息传播机制及其神经元模型的复杂性，使得将为ANNs开发的传统方法应用于SNNs面临挑战。这些挑战包括权重学习和架构设计两方面。尽管代理梯度学习在解决前一个挑战方面已取得一定成功，但后一个挑战的探索仍相对不足。最近，一种利用进化计算方法的新范式应运而生，以应对这些挑战。该方法已在广泛的机器学习基准测试中催生出多种高能效、高性能的SNNs。本文对这些研究工作进行了综述，并对未来潜在的挑战展开了初步探讨。