Spiking Neural Networks (SNNs) provide an efficient framework for processing dynamic spatio-temporal signals and for investigating the learning principles underlying biological neural systems. A key challenge in training SNNs is to solve both spatial and temporal credit assignment. The dominant approach for training SNNs is Backpropagation Through Time (BPTT) with surrogate gradients. However, BPTT is in stark contrast with the spatial and temporal locality observed in biological neural systems and leads to high computational and memory demands, limiting efficient training strategies and on-device learning. Although existing local learning rules achieve local temporal credit assignment by leveraging eligibility traces, they fail to address the spatial credit assignment without resorting to auxiliary layer-wise matrices, which increase memory overhead and hinder scalability, especially on embedded devices. In this work, we propose Traces Propagation (TP), a forward-only, memory-efficient, scalable, and fully local learning rule that combines eligibility traces with a layer-wise contrastive loss without requiring auxiliary layer-wise matrices. TP outperforms other fully local learning rules on NMNIST and SHD datasets. On more complex datasets such as DVS-GESTURE and DVS-CIFAR10, TP showcases competitive performance and scales effectively to deeper SNN architectures such as VGG-9, while providing favorable memory scaling compared to prior fully local scalable rules, for datasets with a significant number of classes. Finally, we show that TP is well suited for practical fine-tuning tasks, such as keyword spotting on the Google Speech Commands dataset, thus paving the way for efficient learning at the edge.

翻译：脉冲神经网络（SNNs）为处理动态时空信号及探索生物神经系统背后的学习原理提供了一个高效框架。训练SNNs的核心挑战在于同时解决空间与时间信用分配问题。目前主流的SNN训练方法是基于替代梯度的沿时间反向传播（BPTT）。然而，BPTT与生物神经系统中观察到的空间和时间局部性存在显著差异，且会导致高昂的计算与内存开销，限制了高效训练策略及设备端学习的实现。尽管现有的局部学习规则通过利用资格痕迹实现了局部时间信用分配，但它们在处理空间信用分配时仍需依赖逐层的辅助矩阵，这不仅增加了内存负担，也阻碍了算法的可扩展性，尤其在嵌入式设备上。本工作提出痕迹传播（TP），一种仅需前向传播、内存高效、可扩展且完全局部化的学习规则，它结合了资格痕迹与逐层对比损失，无需任何逐层辅助矩阵。TP在NMNIST和SHD数据集上优于其他完全局部学习规则。在更复杂的数据集如DVS-GESTURE和DVS-CIFAR10上，TP展现出具有竞争力的性能，并能有效扩展到更深的SNN架构（如VGG-9），同时在类别数量显著的数据集上，相较于先前完全局部可扩展的规则，TP提供了更优的内存扩展性。最后，我们证明TP非常适用于实际微调任务，例如在Google Speech Commands数据集上的关键词检测，从而为边缘设备的高效学习铺平了道路。