Spiking Neural Networks (SNNs) are biologically plausible models that have
been identified as potentially apt for the deployment for energy-efficient
intelligence at the edge, particularly for sequential learning tasks. However,
training of SNNs poses a significant challenge due to the necessity for precise
temporal and spatial credit assignment. Back-propagation through time (BPTT)
algorithm, whilst being the most widely used method for addressing these
issues, incurs a high computational cost due to its temporal dependency.
Moreover, BPTT and its approximations solely utilize causal information derived
from the spiking activity to compute the synaptic updates, thus neglecting
non-causal relationships. In this work, we propose S-TLLR, a novel three-factor
temporal local learning rule inspired by the Spike-Timing Dependent Plasticity
(STDP) mechanism, aimed at training SNNs on event-based learning tasks. S-TLLR
considers both causal and non-causal relationships between pre and
post-synaptic activities, achieving performance comparable to BPTT and
enhancing performance relative to methods using only causal information.
Furthermore, S-TLLR has low memory and time complexity, which is independent of
the number of time steps, rendering it suitable for online learning on
low-power devices. To demonstrate the scalability of our proposed method, we
have conducted extensive evaluations on event-based datasets spanning a wide
range of applications, such as image and gesture recognition, audio
classification, and optical flow estimation. In all the experiments, S-TLLR
achieved high accuracy with a reduction in the number of computations between
$1.1-10\times$.

提出了一种新的三因素时间局部学习规则 S-TLLR，该规则考虑了神经元之间的因果和非因果关系，可以在边缘设备上进行在线学习，并在图像和手势识别、音频分类和光流估计等应用中取得了高精度和计算复杂度降低的结果。