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.

针对脉冲神经网络的在线学习问题，本研究提出了适用于在线学习的神经突触自适应空时学习算法（SOLSA）。相比传统的通过时间反向传播（BPTT）算法，SOLSA 在内存需求上更低，具有更平衡的时间工作负载分配，并且通过调度权重更新、提前停止训练和自适应突触滤波等增强技术，提高了收敛速度和学习性能。与其他非 BPTT 基于的脉冲神经网络学习方法相比，SOLSA 在平均学习准确度上表现出 14.2% 的提升。此外，SOLSA 相比于 BPTT，在平均学习准确度提高了 5% 的同时，内存成本减少了 72%。