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.

本文综述了基于直接学习的深度脉冲神经网络研究，重点讨论了提高准确性、提高效率和利用时间动态的方法，并进一步分类和总结。同时，展望了未来研究中可能面临的挑战和趋势。