Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal dynamics, and event-driven computation. The direct training algorithms based on the surrogate gradient method provide sufficient flexibility to design novel SNN architectures and explore the spatial-temporal dynamics of SNNs. According to previous studies, the performance of models is highly dependent on their sizes. Recently, direct training deep SNNs have achieved great progress on both neuromorphic datasets and large-scale static datasets. Notably, transformer-based SNNs show comparable performance with their ANN counterparts. In this paper, we provide a new perspective to summarize the theories and methods for training deep SNNs with high performance in a systematic and comprehensive way, including theory fundamentals, spiking neuron models, advanced SNN models and residual architectures, software frameworks and neuromorphic hardware, applications, and future trends. The reviewed papers are collected at https://github.com/zhouchenlin2096/Awesome-Spiking-Neural-Networks

翻译：脉冲神经网络（SNNs）凭借其高度的生物合理性、丰富的时空动态特性以及事件驱动计算方式，为人工神经网络（ANNs）提供了一种极具前景的高效能替代方案。基于代理梯度法的直接训练算法为设计新型SNN架构和探索其时空动态特性提供了充分的灵活性。已有研究表明，模型性能高度依赖于其规模。近年来，直接训练的深度SNN在神经形态数据集和大规模静态数据集上均取得了重大进展。值得注意的是，基于Transformer的SNN模型已展现出与其ANN对应模型相当的性能。本文从新的视角出发，系统而全面地综述了训练高性能深度SNN的理论与方法，涵盖理论基础、脉冲神经元模型、先进SNN模型与残差架构、软件框架与神经形态硬件、应用场景及未来发展趋势。相关文献汇总于 https://github.com/zhouchenlin2096/Awesome-Spiking-Neural-Networks。