Title: Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks,
Abstract: Compared with artificial neural networks (ANNs), spiking neural networks
(SNNs) are promising to explore the brain-like behaviors since the spikes could
encode more spatio-temporal information. Although pre-training from ANN or
direct training based on backpropagation (BP) makes the supervised training of
SNNs possible, these methods only exploit the networks' spatial domain
information which leads to the performance bottleneck and requires many
complicated training skills. Another fundamental issue is that the spike
activity is naturally non-differentiable which causes great difficulties in
training SNNs. To this end, we build an iterative LIF model that is more
friendly for gradient descent training. By simultaneously considering the
layer-by-layer spatial domain (SD) and the timing-dependent temporal domain
(TD) in the training phase, as well as an approximated derivative for the spike
activity, we propose a spatio-temporal backpropagation (STBP) training
framework without using any complicated technology. We achieve the best
performance of multi-layered perceptron (MLP) compared with existing
state-of-the-art algorithms over the static MNIST and the dynamic N-MNIST
dataset as well as a custom object detection dataset. This work provides a new
perspective to explore the high-performance SNNs for future brain-like
computing paradigm with rich spatio-temporal dynamics.