The problem of training spiking neural networks (SNNs) is a necessary
precondition to understanding computations within the brain, a field still in
its infancy. Previous work has shown that supervised learning in multi-layer
SNNs enables bio-inspired networks to recognize patterns of stimuli through
hierarchical feature acquisition. Although gradient descent has shown
impressive performance in multi-layer (and deep) SNNs, it is generally not
considered biologically plausible and is also computationally expensive. This
paper proposes a novel supervised learning approach based on an event-based
spike-timing-dependent plasticity (STDP) rule embedded in a network of
integrate-and-fire (IF) neurons. The proposed temporally local learning rule
follows the backpropagation weight change updates applied at each time step.
This approach enjoys benefits of both accurate gradient descent and temporally
local, efficient STDP. Thus, this method is able to address some open questions
regarding accurate and efficient computations that occur in the brain. The
experimental results on the XOR problem, the Iris data, and the MNIST dataset
demonstrate that the proposed SNN performs as successfully as the traditional
NNs. Our approach also compares favorably with the state-of-the-art multi-layer
SNNs.

本文提出一种基于事件驱动的脉冲时序依赖可塑性 (Spike-Timing-Dependent Plasticity, STDP) 规则的监督学习方法，可以在保持计算精度的同时提高计算效率，特别适用于脉冲神经网络 (Spiking Neural Networks, SNNs)。实验结果表明，该方法可以较好地处理 XOR 问题、鸢尾花数据以及 MNIST 数据集，表现与传统神经网络相当，甚至比目前最先进的多层 SNNs 效果更好。