  Objective: A major challenge in designing closed-loop brain-computer
interfaces is finding optimal stimulation patterns as a function of ongoing
neural activity for different subjects and objectives. Approach: To achieve
goal-directed closed-loop neurostimulation, we propose "neural co-processors"
which use artificial neural networks and deep learning to learn optimal
closed-loop stimulation policies, shaping neural activity and bridging injured
neural circuits for targeted repair and rehabilitation. 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 cortical model of grasping, to which we applied
various forms of simulated lesions, allowing us 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, achieving
recovery towards healthy function. Significance: Our results provide the first
proof-of-concept demonstration of a co-processor for adaptive
activity-dependent closed-loop neurostimulation, optimizing for a
rehabilitation goal. While a gap remains between simulations and applications,
our results provide insights on how co-processors may be developed for learning
complex adaptive stimulation policies for a variety of neural rehabilitation
and neuroprosthetic applications.
