  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.
