Neural co-processors for restoring brain function: results from a cortical model of grasping
Neural co-processors for restoring brain function: results from a cortical model of grasping
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用于恢复大脑功能的神经协处理器：抓取皮质模型的结果
DOI：
10.1088/1741-2552/accaa9
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发表时间：
2023
影响因子：
4
通讯作者：
P N Rao, Rajesh
中科院分区：
文献类型：
--
作者：
Bryan, Matthew J.;Preston Jiang, Linxing;P N Rao, Rajesh
设计闭环脑机接口的一个主要挑战是找到最佳刺激模式，作为不同受试者和不同目标的持续神经活动的函数。传统的方法，例如目前用于深部脑刺激的方法，在很大程度上遵循手动试错策略来搜索有效的开环刺激参数，这种策略效率低下，并且不能推广到闭环活动依赖性刺激。方法为了实现目标导向的闭环神经刺激，我们提出使用大脑协处理器，这些设备利用人工智能来塑造神经活动并桥接受损的神经回路，以进行有针对性的修复和功能恢复。在这里，我们研究了一种称为“神经协处理器”的特定类型的协处理器，它使用人工神经网络和深度学习来学习最佳闭环刺激策略。当生物电路本身适应刺激时，协处理器适应刺激策略，从而实现一种形式的大脑-设备协同适应。在这里，我们使用模拟为神经协处理器的未来体内测试奠定基础。我们利用以前发表的皮质模型的把握，我们应用了各种形式的模拟病变。我们用我们的模拟来开发关键的学习算法和研究适应非平稳性，为未来的体内tests.Main resultsOur模拟显示的能力的神经协处理器学习的刺激政策，使用监督学习方法，并适应该政策作为底层的大脑和传感器的变化。我们的协处理器成功地与模拟大脑共同适应，以完成各种损伤后的伸手和抓取任务，实现恢复到健康的功能范围在75%-90%.SignificanceOur结果提供了第一个概念验证演示，使用计算机模拟，神经协处理器的自适应活动依赖性闭环神经刺激，以优化损伤后的康复目标。虽然模拟和体内应用之间仍然存在显着的差距，但我们的研究结果提供了有关如何最终开发此类协处理器以学习各种神经康复和神经假体应用的复杂自适应刺激策略的见解。
ObjectiveA 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 different objectives. Traditional approaches, such as those currently used for deep brain stimulation, 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.ApproachTo achieve goal-directed closed-loop neurostimulation, we propose the use of brain co-processors, devices which exploit artificial intelligence to shape neural activity and bridge injured neural circuits for targeted repair and restoration of function. Here we investigate a specific type of co-processor called a'neural co-processor'which uses artificial neural networks and deep learning to learn optimal closed-loop stimulation policies. 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 in preparation for future in vivo tests.Main resultsOur 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 in the range 75%–90%.SignificanceOur 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.
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DOI：
--
发表时间：
2012
期刊：
Annual International Conference of the IEEE Engineering in Medicine and Biology Society
影响因子：
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作者：
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DOI：
10.1007/978-981-15-2848-4_32-1
发表时间：
2020
期刊：
ArXiv
影响因子：
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作者：
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影响因子：
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作者：
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影响因子：
25
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Tolias, Andreas S.