Spiking neural networks (SNNs) are bio-inspired neural networks with
asynchronous discrete and sparse characteristics, which have increasingly
manifested their superiority in low energy consumption. Recent research is
devoted to utilizing spatio-temporal information to directly train SNNs by
backpropagation. However, the binary and non-differentiable properties of spike
activities force directly trained SNNs to suffer from serious gradient
vanishing and network degradation, which greatly limits the performance of
directly trained SNNs and prevents them from going deeper. In this paper, we
propose a multi-level firing (MLF) method based on the existing spatio-temporal
back propagation (STBP) method, and spiking dormant-suppressed residual network
(spiking DS-ResNet). MLF enables more efficient gradient propagation and the
incremental expression ability of the neurons. Spiking DS-ResNet can
efficiently perform identity mapping of discrete spikes, as well as provide a
more suitable connection for gradient propagation in deep SNNs. With the
proposed method, our model achieves superior performances on a non-neuromorphic
dataset and two neuromorphic datasets with much fewer trainable parameters and
demonstrates the great ability to combat the gradient vanishing and degradation
problem in deep SNNs.

本文提出一种基于现有的时空反向传播（STBP）方法和脉冲沉睡抑制残余网络（spiking DS-ResNet）的多层发射（MLF）方法，可以更有效地传播梯度和提高神经元的增量表达能力，解决深度 SNNs 的梯度消失和退化问题，并且在非神经形态数据集和两个神经形态数据集上展示了出色的性能。