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, yet markers are intrusive (especially for smaller animals), and the number and location of the markers must be determined a priori. Here, we present a highly efficient method for markerless tracking 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 a broad collection of experimental settings: mice odor trail-tracking, egg-laying behavior in drosophila, and mouse hand articulation in a skilled forelimb task. For example, during the skilled reaching behavior, individual joints can be automatically tracked (and a confidence score is reported).
Remarkably, even when a small number of frames are labeled (≈200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.
Biography: My main research interests comprise the neural basis of olfactory and motor behaviors. More specifically, I am currently interested in odor-guided navigation, the olfactory cocktail party problem as well as motor control and adaptation. I have been awarded a Marie-Curie fellowship (Department news) .
I work as a postdoctoral fellow in the group of Matthias Bethge at the Bernstein Center for Computational Neuroscience in Tuebingen and the University of Tuebingen and the lab of Venkatesh N. Murthy at the Department of Molecular and Cellular Biology at Harvard University.