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 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非凡的信息处理能力，并已成为神经形态智能领域的支柱模型。尽管对脉冲神经网络（SNN）已有广泛研究，但大多数模型均建立在确定性框架上。将噪声整合到SNN中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）和噪声驱动学习规则（NDL）。我们的方法展示了噪声如何作为计算与学习的资源，并在理论上为通用SNN提供了框架。此外，NDL为替代梯度方法提供了深刻的原理依据。通过整合多种SNN架构与算法，我们证明该方法相比确定性SNN展现出具有竞争力的性能及更强的鲁棒性以应对挑战性扰动。同时，我们验证了NSNN模型在神经编码研究中的实用性。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个强大、灵活且易于使用的工具。