  Deep spiking neural networks (SNNs) are promising neural networks for their
model capacity from deep neural network architecture and energy efficiency from
SNNs' operations. To train deep SNNs, recently, spatio-temporal backpropagation
(STBP) with surrogate gradient was proposed. Although deep SNNs have been
successfully trained with STBP, they cannot fully utilize spike information. In
this work, we proposed gradient scaling with local spike information, which is
the relation between pre- and post-synaptic spikes. Considering the causality
between spikes, we could enhance the training performance of deep SNNs.
According to our experiments, we could achieve higher accuracy with lower
spikes by adopting the gradient scaling on image classification tasks, such as
CIFAR10 and CIFAR100.

Neural and Evolutionary Computing