Assistant Professor, Bertarelli Foundation Chair of Integrative Neuroscience
Swiss Federal Institute of Technology, Lausanne (EPFL)
Principal Investigator, Rowland Fellow
The Rowland Institute at Harvard, Harvard University
With Students Sebastien Hausmann and Steffen Schneider
Quantifying behavior is crucial for many applications across the life sciences and engineering. 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 and computationally challenging. I will present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. I will show that for both pretrained and networks trained from random initializations, better ImageNet-performing architectures perform better for pose estimation, with a substantial improvement on out-of-domain data when pretrained on ImageNet. I will illustrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors from egg-laying flies to hunting cheetahs.
 Alexander Mathis, Pranav Mamidanna, Kevin N. Cury, Taiga Abe, Venkatesh N. Murthy, Mackenzie W. Mathis & Matthias Bethge. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning.Nature Neuroscience (2018) 10.1038/s41593-018-0209
 Mackenzie W. Mathis & Alexander Mathis. Deep learning tools for the measurement of animal behavior in neuroscience. Current Opinion in Neurobiology. Vol 60.
Zoom link to be shared with registrants on the day of the event.