Networks of spiking neurons underpin the extraordinary information-processing capabilities of the brain and have become pillar models in neuromorphic artificial intelligence. Despite extensive research on spiking neural networks (SNNs), most studies are established on deterministic models, overlooking the inherent non-deterministic, noisy nature of neural computations. This study introduces the noisy spiking neural network (NSNN) and the noise-driven learning rule (NDL) by incorporating noisy neuronal dynamics to exploit the computational advantages of noisy neural processing. NSNN provides a theoretical framework that yields scalable, flexible, and reliable computation. We demonstrate that NSNN leads to spiking neural models with competitive performance, improved robustness against challenging perturbations than deterministic SNNs, and better reproducing probabilistic neural computation in neural coding. This study offers a powerful and easy-to-use tool for machine learning, neuromorphic intelligence 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，并已成为神经形态人工智能的支柱模型。尽管对脉冲神经网络（SNN）的研究广泛深入，但大多数研究基于确定性模型，忽视了神经计算固有的非确定性、噪声特性。本研究通过引入噪声神经元动力学，提出了噪声脉冲神经网络（NSNN）和噪声驱动学习规则（NDL），以利用噪声神经处理的计算优势。NSNN提供了一个可扩展、灵活且可靠的理论计算框架。我们证明，与确定性SNN相比，NSNN在实现具有竞争力的性能的同时，增强了对挑战性扰动的鲁棒性，并能更好地再现神经编码中的概率性神经计算。本研究为机器学习、神经形态智能实践者以及计算神经科学研究人员提供了一种强大且易用的工具。