Authors: Matthew J. Bryan, Linxing Preston Jiang and Rajesh P.N. Rao
Publication: Journal of Neural Engineering
Date: April 5, 2023
Abstract
Objective: A major challenge in designing closed-loop brain-computer interfaces (BCIs) is finding optimal stimulation patterns as a function of ongoing neural activity for different subjects and objectives. Traditional approaches, such as those currently used for deep brain stimulation (DBS), have largely followed a manual trial-and-error strategy to search for effective open-loop stimulation parameters, a strategy that is inefficient and does not generalize to closed-loop activity-dependent stimulation. Approach: To achieve goal-directed closed-loop neurostimulation, we propose the use of neural co-processors, devices which use artificial neural networks (ANNs) and deep learning to learn optimal closed-loop stimulation policies to shape neural activity and bridge injured neural circuits for targeted repair and restoration of function. The co-processor adapts the stimulation policy as the biological circuit itself adapts to the stimulation, achieving a form of brain-device co-adaptation. Here we use simulations to lay the groundwork for future in vivo tests of neural co-processors. We leverage a previously published cortical model of grasping, to which we applied various forms of simulated lesions. We used our simulations to develop the critical learning algorithms and study adaptations to non-stationarity. Main results: Our simulations show the ability of a neural co-processor to learn a stimulation policy using a supervised learning approach, and to adapt that policy as the underlying brain and sensors change. Our co-processor successfully co-adapted with the simulated brain to accomplish the reach-and-grasp task after a variety of lesions were applied. Significance: Our results provide the first proof-of-concept demonstration, using computer simulations, of a neural co-processor for adaptive activity-dependent closed-loop neurostimulation for optimizing a rehabilitation goal after injury. While a significant gap remains between simulations and in vivo applications, our results provide insights on how such co-processors may eventually be developed for learning complex adaptive stimulation policies for a variety of neural rehabilitation and neuroprosthetic applications.