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.Comment: Fixed the bug in the BBL file generated with bibliography management
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