We strive to develop tools for the analysis of animal behavior. Behavior is a complex reflexion of an animal’s goals, state and character. Thus, accurately measuring behavior is crucial for advancing basic neuroscience, as well as the study of various neural and psychiatric disorders. However, measuring behavior (from video) is also a challenging computer vision and machine learning problem. Thus, our work will build on advances in machine learning and computer vision to push the envelope for the analysis of behavior. Published work in this field includes DeepLabCut, a popular open-source software tool for pose estimation.

We develop normative theories for sensorimotor transformations and learning. Recent work has demonstrated that networks trained on object-recognition tasks provide excellent models for the visual system. Yet, for sensorimotor circuits this fruitful approach is less explored, perhaps due to the lack of datasets like ImageNet. Thus, we will explore task-demands, like controlling an arm or learning motor skills, and investigate the emerging representations and computations. We plan to test & improve those models in collaboration with experimental labs. One first model in this direction is DeepDraw.