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Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks
Based on the introduced finite difference gradient, we propose a new family of Differentiable Spike (Dspike) functions that can adaptively evolve during training to find the optimal shape and smoothness for gradient estimation.
Ranked #4 on
Event data classification
on CIFAR10-DVS
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