SPICED：一种受突触稳态启发的无监督连续脑电解码框架
SPICED: A Synaptic Homeostasis-Inspired Framework for Unsupervised Continual EEG Decoding
AI总结：
本文提出受突触稳态启发的SPICED框架，通过关键记忆再激活、突触巩固与重归一化三种机制实现无监督连续脑电解码，在缓解灾难性遗忘的同时适应新个体。
摘要：
人脑通过突触稳态实现动态的稳定性-可塑性平衡。受这一生物学原理启发，我们提出SPICED：一种神经形态框架，该框架整合突触稳态机制用于无监督连续脑电解码，特别针对具有个体间差异的新个体不断出现的实际场景。SPICED包含一个新颖的突触网络，通过三种受生物启发的神经机制在持续适应过程中实现动态扩展：（1）关键记忆再激活；（2）突触巩固；（3）突触重归一化。突触稳态内部的相互作用动态地强化任务判别性记忆痕迹，并弱化有害记忆。通过将这些机制与持续学习系统相结合，SPICED优先重放与新出现个体具有强关联的任务判别性记忆痕迹，从而实现稳健适应。同时，SPICED通过在长期持续学习过程中抑制有害记忆的重放优先级，有效缓解灾难性遗忘。在三个脑电数据集上的验证表明，SPICED具有有效性。
英文摘要：
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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