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 $1.1-10\times$.

翻译：脉冲神经网络（SNNs）作为生物可解释模型，被认为适用于边缘端节能智能部署，尤其在时序学习任务中。然而，由于需要精确的时序和空间信用分配，SNNs的训练面临重大挑战。时间反向传播算法（BPTT）虽是解决这些问题的最广泛使用的方法，但其时间依赖性导致计算成本高昂。此外，BPTT及其近似方法仅利用脉冲活动产生的因果信息计算突触更新，从而忽略了非因果关系。本文提出S-TLLR——一种受脉冲时序依赖可塑性（STDP）机制启发的全新三因子时序局部学习规则，旨在训练SNNs处理基于事件的学习任务。S-TLLR同时考虑突触前与突触后活动之间的因果与非因果关系，在性能上达到与BPTT相当的水平，并优于仅使用因果信息的方法。此外，S-TLLR具有独立于时间步数的低内存和时间复杂度，适用于低功耗设备的在线学习。为展示所提出方法的可扩展性，我们跨越图像与手势识别、音频分类及光流估计等多种应用场景，在事件基准数据集上进行了全面评估。在所有实验中，S-TLLR均以降低$1.1-10\times$的计算量实现了高精度。