Gaussian-process-based Bayesian optimization for neurostimulation interventions in rats

被引：3

作者：

Choiniere, Leo ^{[1
,2
,5
]}

Guay-Hottin, Rose ^{[1
,2
,3
,4
,5
]}

Picard, Remi ^{[1
,2
,3
,4
]}

Lajoie, Guillaume ^{[5
,6
]}

Bonizzato, Marco ^{[1
,2
,3
,4
,5
]}

Dancause, Numa ^{[1
,2
]}

机构：

[1] Univ Montreal, Dept Neurosci, Montreal, PQ H3T 1J4, Canada

[2] Univ Montreal, Ctr Interdisciplinaire Rech Cerveau & Apprentissag, Montreal, PQ H3T 1J4, Canada

[3] Polytech Montreal, Dept Elect Engn, Montreal, PQ H3T 1J4, Canada

[4] Polytech Montreal, Inst Biomed Engn, Montreal, PQ H3T 1J4, Canada

[5] Mila Quebec Inst, Montreal, PQ H2S 3H1, Canada

[6] Univ Montreal, Math & Stat Dept, Montreal, PQ H3T 1J4, Canada

来源：

STAR PROTOCOLS | 2024年 / 5卷 / 01期

基金：

加拿大自然科学与工程研究理事会;

关键词：

PRIMARY MOTOR CORTEX; MICROSTIMULATION; ORGANIZATION;

D O I：

10.1016/j.xpro.2024.102885

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Effective neural stimulation requires adequate parametrization. Gaussian -process (GP) -based Bayesian optimization (BO) offers a framework to discover optimal stimulation parameters in real time. Here, we first provide a general protocol to deploy this framework in neurostimulation interventions and follow by exemplifying its use in detail. Specifically, we describe the steps to implant rats with multi -channel electrode arrays in the hindlimb motor cortex. We then detail how to utilize the GP -BO algorithm to maximize evoked target movements, measured as electromyographic responses. For complete details on the use and execution of this protocol, please refer to Bonizzato and colleagues (2023).1

引用

页数：29

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