Bova Alexandra, Kernodle Krista, Mulligan Kaitlyn, Leventhal Daniel
Neuroscience Graduate Program, University of Michigan.
Department of Neurology, University of Michigan.
J Vis Exp. 2019 Jul 10(149). doi: 10.3791/59979.
Rodent skilled reaching is commonly used to study dexterous skills, but requires significant time and effort to implement the task and analyze the behavior. Several automated versions of skilled reaching have been developed recently. Here, we describe a version that automatically presents pellets to rats while recording high-definition video from multiple angles at high frame rates (300 fps). The paw and individual digits are tracked with DeepLabCut, a machine learning algorithm for markerless pose estimation. This system can also be synchronized with physiological recordings, or be used to trigger physiologic interventions (e.g., electrical or optical stimulation).
啮齿动物的熟练抓握常用于研究灵巧技能，但实施该任务并分析行为需要大量时间和精力。最近已开发出几种熟练抓握的自动化版本。在此，我们描述一种版本，它在以高帧率（300帧/秒）从多个角度记录高清视频的同时，自动向大鼠提供食丸。使用DeepLabCut（一种用于无标记姿态估计的机器学习算法）跟踪爪子和各个手指。该系统还可以与生理记录同步，或用于触发生理干预（例如，电刺激或光刺激）。
