Spiking neural networks (SNNs) exhibit superior energy efficiency but suffer from limited performance. In this paper, we consider SNNs as ensembles of temporal subnetworks that share architectures and weights, and highlight a crucial issue that affects their performance: excessive differences in initial states (neuronal membrane potentials) across timesteps lead to unstable subnetwork outputs, resulting in degraded performance. To mitigate this, we promote the consistency of the initial membrane potential distribution and output through membrane potential smoothing and temporally adjacent subnetwork guidance, respectively, to improve overall stability and performance. Moreover, membrane potential smoothing facilitates forward propagation of information and backward propagation of gradients, mitigating the notorious temporal gradient vanishing problem. Our method requires only minimal modification of the spiking neurons without adapting the network structure, making our method generalizable and showing consistent performance gains in 1D speech, 2D object, and 3D point cloud recognition tasks. In particular, on the challenging CIFAR10-DVS dataset, we achieved 83.20\% accuracy with only four timesteps. This provides valuable insights into unleashing the potential of SNNs.

本研究解决了脉冲神经网络(SNN)性能限制的问题，提出将其视为共享架构和权重的时间子网络集。通过改变膜电位的初始分布和输出，一方面增强了子网络的稳定性，另一方面通过膜电位平滑解决了时间梯度消失问题。我们的方案在多个识别任务中表现出一致的性能提升，特别是在CIFAR10-DVS数据集上达到了83.20%的准确率。 

从集成学习的角度重新思考脉冲神经网络