Rehearsal-based Continual Learning (CL) has been intensely investigated in Deep Neural Networks (DNNs). However, its application in Spiking Neural Networks (SNNs) has not been explored in depth. In this paper we introduce the first memory-efficient implementation of Latent Replay (LR)-based CL for SNNs, designed to seamlessly integrate with resource-constrained devices. LRs combine new samples with latent representations of previously learned data, to mitigate forgetting. Experiments on the Heidelberg SHD dataset with Sample and Class-Incremental tasks reach a Top-1 accuracy of 92.5% and 92%, respectively, without forgetting the previously learned information. Furthermore, we minimize the LRs' requirements by applying a time-domain compression, reducing by two orders of magnitude their memory requirement, with respect to a naive rehearsal setup, with a maximum accuracy drop of 4%. On a Multi-Class-Incremental task, our SNN learns 10 new classes from an initial set of 10, reaching a Top-1 accuracy of 78.4% on the full test set.

基于回顾的持续学习在深度神经网络中被广泛研究，然而在脉冲神经网络中的应用尚未深入探索。本文介绍了第一种用于脉冲神经网络的内存高效实现的潜在重放（LR）-基于回顾持续学习，旨在与资源受限设备无缝集成。实验结果表明，在Heidelberg SHD数据集上进行的示例和类增量任务中，Top-1准确率分别达到92.5％和92％，而不会忘记先前学到的信息。此外，通过应用时间域压缩，我们将LR的存储要求减少了两个数量级，并且与简单的回顾设置相比，最大准确率下降仅为4％。在多类增量任务中，我们的脉冲神经网络从初始的10个类别学习了10个新类别，最终在完整测试集上达到了78.4％的Top-1准确率。

基于压缩潜在重放的轻量级脉冲神经网络的持续学习