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

IF 1.3 Q4 BIOCHEMICAL RESEARCH METHODS
STAR Protocols Pub Date : 2025-03-21 Epub Date: 2024-12-19 DOI:10.1016/j.xpro.2024.103496
Brandon S Coventry, Edward L Bartlett
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引用次数: 0

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.1 and Coventry and Bartlett.2.

基于深度强化学习和红外神经刺激的人工智能引导神经控制方案。
闭环神经控制是科学探索神经功能和减轻开环深部脑刺激(DBS)中发现的缺陷的有力工具。在这里,我们提出了一种使用深度强化学习(RL)和红外神经刺激(INS)的人工智能引导大鼠神经控制方案。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。有关本协议使用和执行的完整细节,请参阅Coventry et .1和Coventry and Bartlett.2。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
STAR Protocols
STAR Protocols Biochemistry, Genetics and Molecular Biology-General Biochemistry, Genetics and Molecular Biology
CiteScore
2.00
自引率
0.00%
发文量
789
审稿时长
10 weeks
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