Fields of expertise
Measuring and modeling behavior, machine learning, computational neuroscience
Alexander studied pure mathematics with a minor in logic and theory of science at the Ludwig Maximilians University in Munich. For his PhD also at LMU, he worked on optimal coding approaches to elucidate the properties of grid cells. As a postdoctoral fellow with Prof. Venkatesh N. Murthy at Harvard University and Prof. Matthias Bethge at Tuebingen AI, he decided to study olfactory behaviors such as odor-guided navigation, social behaviors and the cocktail party problem in mice. During this time, he increasingly got interested sensorimotor behaviors beyond olfaction and started working on proprioception, motor adaption, as well as computer vision tools for measuring animal behavior.
In his group, he is interested in elucidating how the brain gives rise to adaptive behavior. One of the major goals is to synthesize large datasets into computationally useful information. For those purposes, he develops algorithms and systems to analyze animal behavior (e.g. DeepLabCut), neural data, as well as creates experimentally testable computational models.
|Alexander Mathis, Pranav Mamidanna, Kevin M. Cury, Taiga Abe, Venkatesh N. Murthy, Mackenzie Weygandt Mathis* & Matthias Bethge*
|DeepLabCut: Markerless tracking of user-defined features with deep learning|
|Alexander Mathis, Andreas V.M. Herz, Martin Stemmler
|Optimal Population Codes for Space: Grid Cells Outperform Place Cells|
|Ying Li, Alexander Mathis, Benjamin Grewe, Jessica A. Osterhout, Biafra Ahanonu, Mark J. Schnitzer, Venkatesh N. Murthy, Catherine Dulac
|Neuronal Representation of Social Information in the Medial Amygdala of Awake Behaving Mice|
|Kai J. Sandbrink*, Pranav Mamidanna*, Claudio Michaelis, Mackenzie W. Mathis*, Matthias Bethge*, Alexander Mathis*
|Task-driven hierarchical deep neural network models of the proprioceptive pathway|
Teaching & PhD
Life Sciences Engineering