计算机科学
任务(项目管理)
数字
帧(网络)
帧速率
人工智能
计算机视觉
模式识别(心理学)
啮齿动物模型
模拟
算术
数学
医学
电信
内科学
经济
管理
作者
Alexandra Bova,Krista Kernodle,Kaitlyn Mulligan,Daniel K. Leventhal
摘要
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).
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