As a new brain-inspired computational model of the artificial neural network, a spiking neural network encodes and processes neural information through precisely timed spike trains. Spiking neural networks are composed of biologically plausible spiking neurons, which have become suitable tools for processing complex temporal or spatiotemporal information. However, because of their intricately discontinuous and implicit nonlinear mechanisms, the formulation of efficient supervised learning algorithms for spiking neural networks is difficult, and has become an important problem in this research field. This article presents a comprehensive review of supervised learning algorithms for spiking neural networks and evaluates them qualitatively and quantitatively. First, a comparison between spiking neural networks and traditional artificial neural networks is provided. The general framework and some related theories of supervised learning for spiking neural networks are then introduced. Furthermore, the state-of-the-art supervised learning algorithms in recent years are reviewed from the perspectives of applicability to spiking neural network architecture and the inherent mechanisms of supervised learning algorithms. A performance comparison of spike train learning of some representative algorithms is also made. In addition, we provide five qualitative performance evaluation criteria for supervised learning algorithms for spiking neural networks and further present a new taxonomy for supervised learning algorithms depending on these five performance evaluation criteria. Finally, some future research directions in this research field are outlined.

作为一种新的受大脑启发的人工神经网络计算模型，尖峰神经网络通过精确定时的尖峰序列对神经信息进行编码和处理。尖峰神经网络由生物学上合理的尖峰神经元组成，它们已成为处理复杂的时空信息的合适工具。然而，由于它们复杂的不连续和隐含的非线性机制，难以为尖峰神经网络建立有效的监督学习算法，这已成为该研究领域的重要问题。本文全面介绍了用于尖刺神经网络的监督学习算法，并对它们进行了定性和定量评估。第一，尖峰神经网络和传统的人工神经网络之间的比较。然后介绍了用于尖峰神经网络的监督学习的一般框架和一些相关理论。此外，从适用于尖峰神经网络体系结构的适用性和监督学习算法的内在机制的角度，回顾了近年来的最新监督学习算法。还进行了一些代表性算法的尖峰训练学习的性能比较。另外，我们为尖峰神经网络的监督学习算法提供了五个定性性能评估标准，并根据这五个绩效评估标准，进一步提出了监督学习算法的新分类法。最后，