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$\textbf{Formal version available at}$https://cell.com/patterns/fulltext/S2666-3899(23)00200-3  Networks of spiking neurons underpin the extraordinary information-processingcapabilities of the brain and have become pillar models in neuromorphicartificial intelligence. Despite extensive research on spiking neural networks(SNNs), most studies are established on deterministic models, overlooking theinherent non-deterministic, noisy nature of neural computations. This studyintroduces the noisy spiking neural network (NSNN) and the noise-drivenlearning rule (NDL) by incorporating noisy neuronal dynamics to exploit thecomputational advantages of noisy neural processing. NSNN provides atheoretical framework that yields scalable, flexible, and reliable computation.We demonstrate that NSNN leads to spiking neural models with competitiveperformance, improved robustness against challenging perturbations thandeterministic SNNs, and better reproducing probabilistic computations in neuralcoding. This study offers a powerful and easy-to-use tool for machine learning,neuromorphic intelligence practitioners, and computational neuroscienceresearchers.

Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networks