sLoTh: An Efficient Framework for Continual Learning in Spiking Transformers

Physical AI systems operate in dynamic environments where data arrives continuously, requiring models to adapt while preserving previously learned knowledge under strict memory and energy constraints. While parameter-efficient fine-tuning (PEFT) has shown promise for continual learning with vision transformers, conventional architectures rely on dense matrix multiplication and remain costly for real-world deployment. Spiking transformers provide energy-efficient event-driven computation, yet their continual learning capabilities remain largely unexplored, specifically from a PEFT perspective. We here introduce and study sLoTh, a parameter-efficient continual learning framework for pretrained spiking vision transformers. sLoTh freezes the backbone and restricts plasticity to scalable-efficient low-rank attention updates (seLoRA) and shared neuronal threshold modulation, enabling adaptation without replay buffers by updating fewer than 1% of model parameters. Experiments across CIFAR-100, Tiny-ImageNet, ImageNet-100, and ImageNet-R with up to 100 tasks demonstrate competitive rehearsal-free performance in class-incremental learning and online continual learning, while enabling $\sim$17$\times$ lower inference energy consumption than conventional dense vision transformers.