Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Compared to conventional deep Artificial Neural Networks (ANNs), SNNs exhibit superior efficiency and capability to process temporal information. However, it remains a challenge to train SNNs due to their undifferentiable spiking mechanism. The surrogate gradients method is commonly used to train SNNs, but often comes with an accuracy disadvantage over ANNs counterpart. We link the degraded accuracy to the vanishing of gradient on the temporal dimension through the analytical and experimental study of the training process of Leaky Integrate-and-Fire (LIF) Neuron-based SNNs. Moreover, we propose the Complementary Leaky Integrate-and-Fire (CLIF) Neuron. CLIF creates extra paths to facilitate the backpropagation in computing temporal gradient while keeping binary output. CLIF is hyperparameter-free and features broad applicability. Extensive experiments on a variety of datasets demonstrate CLIF's clear performance advantage over other neuron models. Moreover, the CLIF's performance even slightly surpasses superior ANNs with identical network structure and training conditions.

翻译：脉冲神经网络（SNNs）是一种有前景的脑启发式节能模型。与传统深度人工神经网络（ANNs）相比，SNNs在时间信息处理方面展现出卓越的效率和能力。然而，由于其不可微的脉冲机制，训练SNNs仍是一个挑战。代理梯度法常用于训练SNNs，但通常其准确性不及ANNs。通过对基于泄漏积分激发（LIF）神经元的SNNs训练过程的分析与实验研究，我们将准确性下降归因于时间维度上的梯度消失。此外，我们提出了互补泄漏积分激发（CLIF）神经元。CLIF在保持二进制输出的同时，通过引入额外路径来促进时间梯度计算中的反向传播。CLIF无需超参数且具有广泛适用性。多种数据集上的大量实验表明，CLIF在性能上明显优于其他神经元模型。此外，在相同网络结构和训练条件下，CLIF的性能甚至略微超过了优越的ANNs。