Sleep-Inspired Two-Stage Continual Learning for Spiking Neural Networks

Continual learning systems are often plagued by catastrophic forgetting across sequential tasks, whereas the human brain retains prior knowledge through neural plasticity and memory consolidation. Spiking neural networks (SNNs), which emulate the brain’s communication and neuronal dynamics, provide a promising substrate for brain-inspired continual learning. However, a pronounced stability and plasticity dilemma is still encountered in continual learning with SNNs. Neuronal dynamics are altered by incoming tasks, while current and historical samples are usually treated symmetrically by conventional replay. To overcome these limitations, a sleep inspired framework is proposed. Rapid daytime adaptation is separated from nighttime knowledge consolidation. Three complementary mechanisms are integrated, including sample-wise coefficients derived from membrane potential and spike statistics, source-dependent optimization for current and replay samples, and class-balanced consolidation via old class response distillation. Experiments on MNIST, DVS128 Gesture, CIFAR-10, and CIFAR-100 under class incremental and task incremental settings show consistent improvements in final accuracy over experience replay. These results demonstrate that temporally separating acquisition and consolidation can effectively coordinate stability and plasticity in SNN continual learning.