HLML-SNN：Fast Continual Learning in Spiking Neural Networks Achieved via Hebbian Learning-Driven Meta-Learning

Jiangshuai Xu; Peiyun Xue; Jiacheng Song; Xuhui Huang; Qingshan Hou

AAAI 2026technical0 citations

HLML-SNN：Fast Continual Learning in Spiking Neural Networks Achieved via Hebbian Learning-Driven Meta-Learning

Jiangshuai Xu, Peiyun Xue, Jiacheng Song, Xuhui Huang, Qingshan Hou

Abstract

Catastrophic forgetting remains a fundamental barrier to artificial continual learning (CL) - a capability innate to humans. Existing CL methods often incur prohibitive computational costs in resource-constrained scenarios. Spiking neural networks (SNNs), with their biological plausibility and energy efficiency, offer distinct advantages for CL. Inspired by cortico-hippocampal memory mechanisms, we propose a spiking neural network framework integrating Hebbian plasticity with meta-learning, named HLML-SNN. This architecture emulates a dual-phase CL process: (1) In the short-term phase, sample-level Hebbian learning rapidly adapts to new inputs through local synaptic updates; (2) In the long-term phase, task-level meta-learning optimizes cross-task parameters using consolidated synaptic weights, mimicking cortical memory integration to refine shared representations and initialize subsequent Hebbian learning. HLML-SNN incrementally transforms short-term adaptations into stable long-term knowledge, where the synergy of rapid synaptic updates and meta-driven global optimization enables efficient continual learning while balancing stability and plasticity. Empirical results establish HLML-SNN

BibTeX

@inproceedings{aaai2026_hlmlsnnfastconti,
  title = {HLML-SNN：Fast Continual Learning in Spiking Neural Networks Achieved via Hebbian Learning-Driven Meta-Learning},
  author = {Jiangshuai Xu and Peiyun Xue and Jiacheng Song and Xuhui Huang and Qingshan Hou},
  booktitle = {AAAI 2026},
  year = {2026}
}