Networks of spiking neurons underpin the extraordinary information-processing capabilities of the brain and have emerged as pillar models in neuromorphic intelligence. Despite extensive research on spiking neural networks (SNNs), most are established on deterministic models. Integrating noise into SNNs leads to biophysically more realistic neural dynamics and may benefit model performance. This work presents the noisy spiking neural network (NSNN) and the noise-driven learning rule (NDL) by introducing a spiking neuron model incorporating noisy neuronal dynamics. Our approach shows how noise may act as a resource for computation and learning and theoretically provides a framework for general SNNs. Moreover, NDL provides an insightful biological rationale for surrogate gradients. By incorporating various SNN architectures and algorithms, we show that our approach exhibits competitive performance and improved robustness against challenging perturbations than deterministic SNNs. Additionally, we demonstrate the utility of the NSNN model for neural coding studies. Overall, NSNN offers a powerful, flexible, and easy-to-use tool for machine learning practitioners and computational neuroscience researchers.

翻译：脉冲神经元网络支撑着大脑卓越的信息处理能力，并已成为神经形态智能的支柱模型。尽管对脉冲神经网络（SNN）进行了广泛研究，但大多数模型基于确定性框架。将噪声纳入SNN可产生更符合生物物理特性的神经动力学，并可能提升模型性能。本文通过引入包含噪声神经元动力学的脉冲神经元模型，提出了噪声脉冲神经网络（NSNN）与噪声驱动学习规则（NDL）。我们的方法揭示了噪声如何作为计算与学习资源，并从理论上为通用SNN提供了一种框架。此外，NDL为替代梯度方法提供了深刻的生物学理论依据。通过整合多种SNN架构与算法，我们验证了该方法相比确定性SNN具有竞争性性能与更强的鲁棒性，更能抵御挑战性扰动。同时，我们展示了NSNN模型在神经编码研究中的应用价值。总体而言，NSNN为机器学习实践者与计算神经科学研究者提供了一种强大、灵活且易于使用的工具。