Gradient-Free Continual Learning in Spiking Neural Networks via Inter-Spike Interval Regularization
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 support. We propose ISI-CV, the first gradient-free synaptic importance metric for SNN continual learning, derived from the Coefficient of Variation (CV) of Inter-Spike Intervals (ISIs). Neurons that fire regularly (low CV) enc...
| Authors | Samrendra Roy, Kazuma Kobayashi, Souvik Chakraborty et al. |
|---|---|
| Primary category | cs.NE |
| Published | 14 April 2026 |
| Last updated at source | 14 April 2026 |
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