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.

翻译：摘要：目的：在设计闭环脑机接口时，一个主要挑战是找到能够根据持续神经活动为不同受试者和目标提供最优刺激模式的方法。方法：为实现目标导向的闭环神经刺激，我们提出"神经协处理器"，它利用人工神经网络和深度学习来学习最优闭环刺激策略，从而塑造神经活动并桥接受损神经回路，实现定向修复与康复。协处理器会随着生物回路对刺激的适应而调整刺激策略，实现脑-设备共同适应。本研究通过仿真为未来神经协处理器的体内实验奠定基础。我们采用抓取皮质模型，对其施加多种模拟损伤模式，从而开发关键学习算法并研究非平稳性适应过程。主要结果：仿真表明，神经协处理器能够通过监督学习方法学习刺激策略，并在底层脑组织和传感器变化时自适应调整该策略。在多种模拟损伤后，我们的协处理器成功与模拟大脑共同适应并完成触达-抓取任务，实现趋于健康功能的恢复。意义：本研究首次概念验证了面向康复目标的适应性活动依赖性闭环神经刺激协处理器。尽管仿真与实际应用之间仍存在差距，但我们的结果为如何开发用于学习复杂自适应刺激策略的协处理器提供了重要启示，可适用于多种神经康复和神经假体应用。