跨脉冲传播的深度脉冲神经网络训练方法.

Backpropagation-based training methods for SNNs still face many problems and challenges, including that the spike firing process is non-differentiable and spike neurons have complex spatiotemporal dynamics processes. In addition, SNNs backpropagation training methods often do not consider the relationship of the error signal between adjacent spikes, greatly reducing the accuracy of the model. To this end, this paper proposed a cross-spike error backpropagation training method for deep spiking neural networks (CSBP), which divided the error backpropagation of neurons into two dependencies: the dependency of spike firing time with the postsynaptic membrane potential (DSFT) and the dependency between spike firing time (DBSFT). Among them, DSFT solved the problem of spike non-differentiability and DBSFT clarified the dependence between spikes, allowing error signals to propagate across spikes, improving biological rationality. In addition, this paper solved the problem of insufficient expressive ability in early spiking ResNet network architecture by modifying the structural order of the spike residual block. Experimental results show that the proposed method is significantly improved compared to the SOTA (state-of-the-art) training algorithms based on spike time. Under the same architecture, the improvement is 2.98% on the CIFAR10 dataset, and 2.26% on the DVS-CIFAR10 dataset. [ABSTRACT FROM AUTHOR]