模仿大脑突触稳态，实现持续学习下的高效脑电解码
SPICED: A Synaptic Homeostasis-Inspired Framework for Unsupervised Continual EEG Decoding
- 借鉴突触稳态机制，动态扩展神经网络以适应新用户
- 在三个脑电数据集上显著缓解灾难性遗忘，保持解码性能
- 适合持续更新用户、跨个体差异大的脑机接口场景
人类大脑通过突触稳态实现动态稳定与可塑性的平衡。受此生物机制启发，我们提出 SPICED：一种类脑框架，融合突触稳态机制用于无监督持续脑电解码，尤其应对新个体不断出现且存在显著个体差异的现实场景。SPICED 包含一种新型突触网络，通过三种生物启发式神经机制——关键记忆重激活、突触巩固与突触归一化——实现在持续适应中的动态扩展。突触稳态内部作用机制动态强化任务区分性记忆痕迹，弱化有害记忆。结合持续学习系统，SPICED 优先回放与新出现个体强关联的任务区分性记忆，实现稳健适应；同时通过抑制有害记忆的回放优先级，有效缓解长期持续学习中的灾难性遗忘。在三个脑电数据集上的验证表明其有效性。
原文摘要 · Abstract (English)
Human brain achieves dynamic stability-plasticity balance through synaptic homeostasis. Inspired by this biological principle, we propose SPICED: a neuromorphic framework that integrates the synaptic homeostasis mechanism for unsupervised continual EEG decoding, particularly addressing practical scenarios where new individuals with inter-individual variability emerge continually. SPICED comprises a novel synaptic network that enables dynamic expansion during continual adaptation through three bio-inspired neural mechanisms: (1) critical memory reactivation; (2) synaptic consolidation and (3) synaptic renormalization. The interplay within synaptic homeostasis dynamically strengthens task-discriminative memory traces and weakens detrimental memories. By integrating these mechanisms with continual learning system, SPICED preferentially replays task-discriminative memory traces that exhibit strong associations with newly emerging individuals, thereby achieving robust adaptations. Meanwhile, SPICED effectively mitigates catastrophic forgetting by suppressing the replay prioritization of detrimental memories during long-term continual learning. Validated on three EEG datasets, SPICED show its effectiveness.
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