Abstract：
:Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.
journal_name
Nat Neuroscijournal_title
Nature neuroscienceauthors
Mathis A,Mamidanna P,Cury KM,Abe T,Murthy VN,Mathis MW,Bethge Mdoi
10.1038/s41593-018-0209-ysubject
Has Abstractpub_date
2018-09-01 00:00:00pages
1281-1289issue
9eissn
1097-6256issn
1546-1726pii
10.1038/s41593-018-0209-yjournal_volume
21pub_type
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