  $\textbf{Formal version available at}$
https://cell.com/patterns/fulltext/S2666-3899(23)00200-3
  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 computations in neural
coding. This study offers a powerful and easy-to-use tool for machine learning,
neuromorphic intelligence practitioners, and computational neuroscience
researchers.
