SOURCE-LINKED INTELLIGENCE
Robust Neural Stimulation Response Modeling Through Meta-Learning and Pretraining
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 func
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T05:59:43.000Z
First collected: 2026-09-21T08:51:59.673Z. This is not the publication date.