Spiking neural networks (SNNs) are bio-plausible computing models with high energy efficiency. The temporal dynamics of neurons and synapses enable them to detect temporal patterns and generate sequences. While Backpropagation Through Time (BPTT) is traditionally used to train SNNs, it is not suitable for online learning of embedded applications due to its high computation and memory cost as well as extended latency. Previous works have proposed online learning algorithms, but they often utilize highly simplified spiking neuron models without synaptic dynamics and reset feedback, resulting in subpar performance. In this work, we present Spatiotemporal Online Learning for Synaptic Adaptation (SOLSA), specifically designed for online learning of SNNs composed of Leaky Integrate and Fire (LIF) neurons with exponentially decayed synapses and soft reset. The algorithm not only learns the synaptic weight but also adapts the temporal filters associated to the synapses. Compared to the BPTT algorithm, SOLSA has much lower memory requirement and achieves a more balanced temporal workload distribution. Moreover, SOLSA incorporates enhancement techniques such as scheduled weight update, early stop training and adaptive synapse filter, which speed up the convergence and enhance the learning performance. When compared to other non-BPTT based SNN learning, SOLSA demonstrates an average learning accuracy improvement of 14.2%. Furthermore, compared to BPTT, SOLSA achieves a 5% higher average learning accuracy with a 72% reduction in memory cost.

翻译：脉冲神经网络（SNN）是一种具有高能效优势的生物合理性计算模型。神经元与突触的时域动态特性使其能够检测时间模式并生成序列。尽管传统的脉冲时间反向传播（BPTT）算法被用于训练SNN，但其计算与存储成本高昂且延迟较大，不适用于嵌入式应用的在线学习场景。现有在线学习算法虽被提出，但常采用高度简化的脉冲神经元模型，未考虑突触动态特征与重置反馈机制，导致性能欠佳。本文提出面向突触适应的时空在线学习（SOLSA）算法，专为基于带指数衰减突触与软重置机制的泄露积分点火（LIF）神经元构建的SNN在线学习而设计。该算法不仅学习突触权重，还能自适应调整与突触相关的时间滤波器。与BPTT相比，SOLSA显著降低内存需求，并实现更均衡的时域工作负载分布。此外，SOLSA融合了计划权重更新、早停训练与自适应突触滤波器等增强技术，加速收敛并提升学习性能。在非BPTT类SNN学习方法对比中，SOLSA平均学习准确率提升14.2%；与BPTT相比，SOLSA在降低72%内存成本的同时，平均学习准确率提升5%。