Spiking neural networks have gained significant attention due to their brain-like information processing capabilities. The use of surrogate gradients has made it possible to train spiking neural networks with backpropagation, leading to impressive performance in various tasks. However, spiking neural networks trained with backpropagation typically approximate actual labels using the average output, often necessitating a larger simulation timestep to enhance the network's performance. This delay constraint poses a challenge to the further advancement of SNNs. Current training algorithms tend to overlook the differences in output distribution at various timesteps. Particularly for neuromorphic datasets, inputs at different timesteps can cause inconsistencies in output distribution, leading to a significant deviation from the optimal direction when combining optimization directions from different moments. To tackle this issue, we have designed a method to enhance the temporal consistency of outputs at different timesteps. We have conducted experiments on static datasets such as CIFAR10, CIFAR100, and ImageNet. The results demonstrate that our algorithm can achieve comparable performance to other optimal SNN algorithms. Notably, our algorithm has achieved state-of-the-art performance on neuromorphic datasets DVS-CIFAR10 and N-Caltech101, and can achieve superior performance in the test phase with timestep T=1.

翻译：脉冲神经网络因其类脑信息处理能力而受到广泛关注。替代梯度的应用使得通过反向传播训练脉冲神经网络成为可能，从而在各类任务中取得了显著性能。然而，利用反向传播训练的脉冲神经网络通常使用平均输出逼近真实标签，往往需要更大的仿真时间步长以提升网络性能。这一延迟限制对SNN的进一步发展构成了挑战。现有训练算法往往忽视不同时间步下输出分布的差异。特别是在神经形态数据集中，不同时间步的输入会导致输出分布的不一致性，当融合不同时刻的优化方向时，会使其显著偏离最优方向。为解决这一问题，我们设计了一种增强不同时间步输出时间一致性的方法。我们在CIFAR10、CIFAR100和ImageNet等静态数据集上进行了实验。结果表明，我们的算法能够达到与其他最优SNN算法相当的性能。尤为重要的是，我们的算法在神经形态数据集DVS-CIFAR10和N-Caltech101上取得了最先进的性能，并且能够在时间步T=1的测试阶段实现优越表现。