AI RESEARCH
Gradient-Free Continual Learning in Spiking Neural Networks via Inter-Spike Interval Regularization
arXiv CS.LG
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ArXi:2604.16496v1 Announce Type: cross Continual learning, the ability to acquire new tasks sequentially without forgetting prior knowledge, is essential for deploying neural networks in dynamic real-world environments, from nuclear digital twin monitoring to grid-edge fault detection. Existing synaptic importance methods, such as Elastic Weight Consolidation (EWC) and Synaptic Intelligence (SI), rely on gradient computation, making them incompatible with neuromorphic hardware that lacks backpropagation.