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Spiking neural networks (SNNs) are different from the classical networks usedin deep learning: the neurons communicate using electrical impulses calledspikes, just like biological neurons. SNNs are appealing for AI technology,because they could be implemented on low power neuromorphic chips. However,SNNs generally remain less accurate than their analog counterparts. In thisreport, we examine various regularization and normalization techniques with thegoal of improving surrogate gradient learning in SNNs.

Improving Surrogate Gradient Learning in Spiking Neural Networks via Regularization and Normalization