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使用深度强化学习和红外神经刺激进行人工智能引导神经控制的方案。

Protocol for artificial intelligence-guided neural control using deep reinforcement learning and infrared neural stimulation.

作者信息

Coventry Brandon S, Bartlett Edward L

机构信息

Weldon School of Biomedical Engineering, the Center for Implantable Devices, and the Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN 47907, USA.

Weldon School of Biomedical Engineering, the Center for Implantable Devices, and the Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN 47907, USA; Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA.

出版信息

STAR Protoc. 2025 Mar 21;6(1):103496. doi: 10.1016/j.xpro.2024.103496. Epub 2024 Dec 19.

Abstract

Closed-loop neural control is a powerful tool for both the scientific exploration of neural function and for mitigating deficiencies found in open-loop deep brain stimulation (DBS). Here, we present a protocol for artificial intelligence-guided neural control in rats using deep reinforcement learning (RL) and infrared neural stimulation (INS). We describe steps for integrating RL closed-loop control into neuroscience and neuromodulation studies. We then detail procedures for using and deploying infrared INS in chronic DBS applications. For complete details on the use and execution of this protocol, please refer to Coventry et al. and Coventry and Bartlett..

摘要

闭环神经控制对于神经功能的科学探索以及减轻开环深部脑刺激(DBS)中发现的缺陷而言,都是一种强大的工具。在此,我们展示了一种使用深度强化学习(RL)和红外神经刺激(INS)在大鼠中进行人工智能引导神经控制的方案。我们描述了将RL闭环控制整合到神经科学和神经调节研究中的步骤。然后,我们详细介绍了在慢性DBS应用中使用和部署红外INS的程序。有关此方案使用和执行的完整详细信息,请参考考文垂等人以及考文垂和巴特利特的研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6943/11728987/a6c2bb08bd40/fx1.jpg

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