  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.
