发表机构
University of Washington; Paul G. Allen School of Computer Science & Engineering; Center for Neurotechnology; Computational Neuroscience Center; Department of Electrical and Computer Engineering; Department of Bioengineering（华盛顿大学; 保罗·G·艾伦计算机科学与工程学院; 神经技术中心; 计算神经科学中心; 电气与计算机工程系; 生物工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究首次将元学习与预训练应用于神经刺激响应建模，通过改进时间基函数模型，大幅降低预测失效，减少校准需求，为闭环神经刺激的临床部署提供了可行方案。
AI 中文摘要
目标：基于模型的闭环神经刺激有望应用于从帕金森病到感觉恢复的治疗场景，但部署受限于两个障碍：1）预测刺激后果的模型在相当一部分实验会话中会出现灾难性失效；2）每会话的校准要求常与临床约束不兼容。我们首次证明元学习与预训练可应用于神经刺激响应建模，以此解决上述两个问题。方法：时间基函数模型（TBFMs）可预测依赖状态的神经对刺激的响应，我们采用基于模型无关元学习（MAML）的新型架构与算法，通过跨会话预训练扩展TBFMs，并在两只非人类灵长类动物初级感觉运动皮层的40个光遗传刺激会话上进行评估。结果：元学习大幅降低了灾难性预测失效：对于1000个校准集大小，测试R²<0.05的会话从40个中的16个（单会话训练）降至1个（MAML预训练），且预测区间显著更窄（p<0.05）；在匹配精度下，校准要求降低了50%-90%，可实现临床会话时间约束下原本不可行的实验。结论：我们的结果表明刺激响应的跨会话结构足够一致以支持预训练，提供了元学习方法适用于神经刺激的首个实证证据。意义：鲁棒性与样本效率的提升直接解决了基于模型的刺激控制器部署的已知障碍，我们的结果推动学界构建标准化多站点刺激数据集，并进一步探索用于鲁棒闭环刺激的元学习方法。
英文摘要
Objective: Model-based closed-loop neural stimulation holds promise for therapeutic applications ranging from Parkinson's disease to sensory restoration, but deployment has been limited by two obstacles: 1) forecasting models for predicting the consequences of stimulation fail catastrophically on a meaningful fraction of sessions, and 2) per-session calibration requirements are often incompatible with clinical constraints. We address both by demonstrating, for the first time, that meta-learning and pretraining can be applied to neural stimulation response modeling. Methods: Temporal basis function models (TBFMs) forecast state-dependent neural responses to stimulation. We extend TBFMs with cross-session pretraining using a novel architecture and algorithm based on model-agnostic meta-learning (MAML), evaluating them on 40 sessions of optogenetic stimulation in primary sensorimotor cortex of two non-human primates. Results: Meta-learning substantially reduces catastrophic forecast failure: for a 1k calibration set size, sessions with test R-squared < 0.05 drop from 16 of 40 (single-session training) to 1 (MAML-pretrained), and prediction intervals become significantly narrower (p < 0.05). Calibration requirements are reduced by 50-90% at matched accuracy, enabling experiments otherwise infeasible within clinical session-time constraints. Conclusion: Our results demonstrate that cross-session structure in stimulation responses is consistent enough to support pretraining, providing the first empirical evidence that meta-learning approaches are viable for neural stimulation. Significance: The robustness and sample efficiency gains directly address known obstacles to deploying model-based stimulation controllers. Our results motivate community efforts to assemble standardized multi-site stimulation datasets and to further explore meta-learning for robust closed-loop stimulation.
CommentsOpen source code available: github.com/mmattb/py-tbfm/tree/multisession. 15 pages, 10 figures
