The rapid expansion of spiking neural networks (SNNs) has led to a proliferation of training algorithms that differ widely in biological inspiration, computational structure, and hardware suitability. Despite this progress, the field lacks a unified, fine-grained taxonomy that systematically organizes these approaches and clarifies their conceptual relationships. This survey provides a comprehensive taxonomy of SNN training algorithms, spanning surrogate-gradient backpropagation, local and three-factor learning rules, biologically inspired plasticity mechanisms, ANN-to-SNN conversion pipelines, and non-standard optimization strategies. We analyze each class in terms of its computational principles, learning signals, and locality properties. To support reproducible research, we release NeuroTrain, an open-source snnTorch-based framework that implements a representative set of these algorithms within a unified, modular, and extendable framework, enabling consistent benchmarking across datasets, architectures, and training regimes. By consolidating fragmented literature and providing a reusable benchmarking framework, this survey identifies common patterns, highlights open challenges, and outlines promising directions for future work on scalable, efficient SNN training.

翻译：脉冲神经网络（SNNs）的快速发展催生了众多训练算法，这些算法在生物学灵感、计算结构和硬件适配性方面存在显著差异。尽管取得了进展，该领域仍缺乏一个统一的、细粒度的分类体系来系统性地组织这些方法并阐明其概念关联。本综述提供了SNN训练算法的全面分类，涵盖替代梯度反向传播、局部学习和三因子学习规则、生物启发可塑性机制、ANN-to-SNN转换流程以及非标准优化策略。我们从计算原理、学习信号和局部特性等角度对每类算法进行分析。为支持可重复研究，我们发布了NeuroTrain——一个基于snnTorch的开源框架，以统一、模块化且可扩展的方式实现代表性算法集，支持跨数据集、架构和训练体制的一致基准测试。通过整合零散文献并提供可复用的基准测试框架，本综述识别了共性模式，揭示了开放挑战，并指出了可扩展、高效SNN训练领域的未来研究方向。