Spiking Neural Networks (SNNs) use spatio-temporal spike patterns to
represent and transmit information, which is not only biologically realistic
but also suitable for ultra-low-power event-driven neuromorphic implementation.
Motivated by the success of deep learning, the study of Deep Spiking Neural
Networks (DeepSNNs) provides promising directions for artificial intelligence
applications. However, training of DeepSNNs is not straightforward because the
well-studied error back-propagation (BP) algorithm is not directly applicable.
In this paper, we first establish an understanding as to why error
back-propagation does not work well in DeepSNNs. To address this problem, we
propose a simple yet efficient Rectified Linear Postsynaptic Potential function
(ReL-PSP) for spiking neurons and propose a Spike-Timing-Dependent
Back-Propagation (STDBP) learning algorithm for DeepSNNs. In STDBP algorithm,
the timing of individual spikes is used to convey information (temporal
coding), and learning (back-propagation) is performed based on spike timing in
an event-driven manner. Our experimental results show that the proposed
learning algorithm achieves state-of-the-art classification accuracy in single
spike time based learning algorithms of DeepSNNs. Furthermore, by utilizing the
trained model parameters obtained from the proposed STDBP learning algorithm,
we demonstrate the ultra-low-power inference operations on a recently proposed
neuromorphic inference accelerator. Experimental results show that the
neuromorphic hardware consumes 0.751~mW of the total power consumption and
achieves a low latency of 47.71~ms to classify an image from the MNIST dataset.
Overall, this work investigates the contribution of spike timing dynamics to
information encoding, synaptic plasticity and decision making, providing a new
perspective to design of future DeepSNNs and neuromorphic hardware systems.

本文介绍了一个针对 DeepSNNs 设计的 Spike-Timing-Dependent Back-Propagation 学习算法，并提供了一种新的思路，探讨了时序动态、突触可塑性和决策等方面对信息编码的贡献，为未来 DeepSNNs 和神经形态硬件系统的设计提供了新的视角和方法。