Spiking neural networks (SNNs) are receiving increasing attention due to their low power consumption and strong bio-plausibility. Optimization of SNNs is a challenging task. Two main methods, artificial neural network (ANN)-to-SNN conversion and spike-based backpropagation (BP), both have their advantages and limitations. For ANN-to-SNN conversion, it requires a long inference time to approximate the accuracy of ANN, thus diminishing the benefits of SNN. With spike-based BP, training high-precision SNNs typically consumes dozens of times more computational resources and time than their ANN counterparts. In this paper, we propose a novel SNN training approach that combines the benefits of the two methods. We first train a single-step SNN(T=1) by approximating the neural potential distribution with random noise, then convert the single-step SNN(T=1) to a multi-step SNN(T=N) losslessly. The introduction of Gaussian distributed noise leads to a significant gain in accuracy after conversion. The results show that our method considerably reduces the training and inference times of SNNs while maintaining their high accuracy. Compared to the previous two methods, ours can reduce training time by 65%-75% and achieves more than 100 times faster inference speed. We also argue that the neuron model augmented with noise makes it more bio-plausible.

翻译：脉冲神经网络（SNNs）因其低功耗和强生物合理性而受到日益关注。SNN的优化是一项具有挑战性的任务。两种主流方法——人工神经网络（ANN）到SNN的转换方法和基于脉冲的反向传播（BP）方法——各有优势与局限。对于ANN到SNN的转换，需要较长的推理时间来逼近ANN的精度，从而削弱了SNN的优势；而基于脉冲的反向传播方法中，训练高精度SNN通常比训练同等ANN消耗数十倍的计算资源和时间。本文提出一种融合两种方法优势的新型SNN训练方法。我们首先通过随机噪声逼近神经电位分布，训练单步SNN（T=1），随后将单步SNN（T=1）无损转换为多步SNN（T=N）。高斯分布噪声的引入使转换后精度显著提升。结果表明，该方法在保持高精度的同时，大幅缩短了SNN的训练和推理时间。相较于先前两种方法，本方法可将训练时间减少65%-75%，并实现超过100倍的推理速度提升。此外，我们认为噪声增强的神经元模型具有更强的生物合理性。