The spiking neural network (SNN), as a promising brain-inspired computational model with binary spike information transmission mechanism, rich spatially-temporal dynamics, and event-driven characteristics, has received extensive attention. However, its intricately discontinuous spike mechanism brings difficulty to the optimization of the deep SNN. Since the surrogate gradient method can greatly mitigate the optimization difficulty and shows great potential in directly training deep SNNs, a variety of direct learning-based deep SNN works have been proposed and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these direct learning-based deep SNN works, mainly categorized into accuracy improvement methods, efficiency improvement methods, and temporal dynamics utilization methods. In addition, we also divide these categorizations into finer granularities further to better organize and introduce them. Finally, the challenges and trends that may be faced in future research are prospected.

翻译：脉冲神经网络（SNN）作为一种以二进制脉冲信息传递机制、丰富的时空动力学特性及事件驱动特性为特征的、具有前景的类脑计算模型，已受到广泛关注。然而，其复杂且不连续的脉冲机制给深度SNN的优化带来了困难。由于替代梯度方法能极大缓解优化难度，并在直接训练深度SNN方面展现出巨大潜力，近年来涌现出多种基于直接学习的深度SNN研究工作，并取得了令人满意的进展。本文对这些基于直接学习的深度SNN研究工作进行了全面综述，主要将其归纳为精度提升方法、效率提升方法以及时间动态利用方法。此外，为进一步优化组织与介绍，我们还对这些分类进行了更细粒度的划分。最后，对未来研究可能面临的挑战与趋势进行了展望。