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：尖峰神经网络（SNN）作为一种具有二进制尖峰信息传递机制、丰富时空动力学及事件驱动特性的脑启发计算模型，已受到广泛关注。然而，其非连续尖峰机制给深层SNN的优化带来了困难。由于替代梯度方法能显著缓解优化难题，并展现出直接训练深度SNN的巨大潜力，近年来涌现出大量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研究进行了全面综述，主要将其归类为精度提升方法、效率提升方法以及时间动态利用方法。此外，我们进一步对这些类别进行了更细粒度的划分，以便更好地组织与介绍。最后，展望了未来研究中可能面临的挑战与发展趋势。