Neural Co-Processors for Restoring Brain Function: Results from a Cortical Model of Grasping

TL;DR

This paper demonstrates through simulations that neural co-processors using deep learning can learn and adapt stimulation policies to restore brain functions in a cortical model of grasping, paving the way for future neurorehabilitation applications.

Contribution

It introduces the concept of neural co-processors that adapt stimulation policies in real-time, showing their effectiveness in a simulated brain model with lesions.

Findings

Neural co-processors can learn stimulation policies via supervised learning.

They successfully adapt policies as neural conditions change.

They achieve recovery of grasping function after simulated lesions.

Abstract

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.