Pereira Sanabria Leo F, Voutour Luciano S, Kaufman Victoria J, Reeves Christopher A, Bal Aneesh S, Maureira Fidel, Arguello Amy A
Department of Psychology, Michigan State University, East Lansing, Michigan 48823.
Department of Earth and Environmental Sciences, Michigan State University, East Lansing, Michigan 48823.
eNeuro. 2025 Feb 6;12(2). doi: 10.1523/ENEURO.0031-24.2024. Print 2025 Feb.
The use of supervised machine learning to approximate poses in video recordings allows for rapid and efficient analysis of complex behavioral profiles. Currently, there are limited protocols for automated analysis of operant self-administration behavior. We provide a methodology to (1) obtain videos of training sessions via Raspberry Pi microcomputers or GoPro cameras, (2) obtain pose estimation data using the supervised machine learning software packages DeepLabCut (DLC) and Simple Behavioral Analysis (SimBA) with a local high-performance computer cluster, (3) compare standard Med-PC lever response versus quadrant time data generated from pose estimation regions of interest, and (4) generate predictive behavioral classifiers. Overall, we demonstrate proof of concept to use pose estimation outputs from DLC to both generate quadrant time results and obtain behavioral classifiers from SimBA during operant training phases.
使用监督式机器学习来近似视频记录中的姿势，能够对复杂的行为概况进行快速且高效的分析。目前，用于操作性自我给药行为自动分析的方案有限。我们提供了一种方法，用于：(1) 通过树莓派微型计算机或GoPro相机获取训练 session 的视频；(2) 使用监督式机器学习软件包DeepLabCut (DLC) 和简单行为分析 (SimBA) 以及本地高性能计算机集群获取姿势估计数据；(3) 比较标准的Med-PC杠杆反应与从姿势估计感兴趣区域生成的象限时间数据；(4) 生成预测性行为分类器。总体而言，我们展示了概念验证，即使用DLC的姿势估计输出在操作性训练阶段生成象限时间结果并从SimBA获得行为分类器。
