Prototype Replay for Cold-Start Online Class-Incremental Learning in Spiking Networks

Abstract

Online class-incremental learning is a challenging setting in which the model continually learns new classes from a single pass over the data while retaining discrimination among previously seen classes. Although exemplar replay performs well in this setting, it incurs substantial memory and computational cost. Prototype replay offers a compact alternative by storing a single representative feature vector per class, but in cold-start settings it faces a stability-plasticity dilemma: limited pretraining can reduce the usefulness of the learned representation for downstream tasks, while continued adaptation through finetuning can disrupt the stability of the feature space and undermine the reliability of stored prototypes. To address this dilemma, we propose a continual learning mechanism that combines prototype replay with a feature extractor enhanced with activity-dependent metaplasticity in a spiking network. During online learning, metaplasticity selectively reduces the plasticity of important synaptic weights, limiting feature drift while preserving adaptability in the feature extractor. This is followed by a sleep phase in which stored class prototypes are replayed to improve discrimination across all seen classes. Evaluations on two continual learning benchmarks show that the proposed approach achieves a more favorable stability-plasticity tradeoff than either fully freezing or fully finetuning the feature extractor, while providing more favorable replay memory scaling than exemplar feature replay.