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This study applies meta-learning and pretraining to improve the forecasting of neural responses to stimulation, addressing critical challenges in model-based closed-loop neural stimulation for therapeutic applications. By utilizing temporal basis function models (TBFMs) enhanced with a model-agnostic meta-learning (MAML) approach, the authors demonstrate a significant reduction in catastrophic forecast failures and calibration requirements across multiple sessions in non-human primates. The findings reveal that pretraining can enhance prediction accuracy and efficiency, paving the way for more effective clinical implementations of neural stimulation therapies.
Meta-learning slashes catastrophic forecast failures in neural stimulation models from 40% to just 2.5%, revolutionizing their clinical viability.
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.
