The goal of the laboratory is to reverse engineer the neural circuits that drive adaptive motor behavior. We hope that by understanding the neural basis of adaptive motor control we can open new avenues in therapeutic research for neurological disease, help build better machine learning tools, and crucially, provide fundamental insights into brain function.
Here are questions that guide us:
How do animals adapt to their environment over short and long timescales?
What neural computations enable adaptive behavior?
Can we make more biologically-inspired artificial intelligence by studying natural intelligence?
Can we use these circuit-level and machine-learning advances to restore function in neurodegeneration and neurological injury?
We believe behavior is an essential component to understanding neural function. As part of our quest to better understand behavior, we develop new tools to study more complex and natural movements. Here is some technology that we utilize, and develop, to answer those questions:
We develop computer vision tools, like DeepLabCut™, to perform markerless pose estimation and behavioral analysis from any species in a multitude of settings. For our purposes, we use these key-points to study kinematics and motor learning during a variety of skills tasks, and during freely moving natural behaviors. We also will continue to collaborate with the Mathis Group on these types of tools.
We also have developed a set of skilled motor tasks where mice can learn from a dynamically changing sensory landscape.
By combining concepts from machine learning and optimal motor control with the power of the mouse's genetics and accessibility, our lab aims to uncover fundamental principles that guide motor adaptation, learning, and motor control.
We are using the latest techniques in 2-photon and deep brain imaging (including utilizing multi-area imaging with a 2-photon mesoscope), to uncover the neural correlates of adaptive behavior. We use optogenetics and chemogenetics to test what roles diverse areas have during behavior. Furthermore, we develop new computational models and tools to generate testable hypotheses and analyze our data.
Postdoctoral Fellows, Masters, & PhD Students
The laboratory will be moving to The Swiss Federal Institute of Technology, Lausanne (EPFL) in August 2020! We will be at Campus Biotech in Geneva. We will continue to integrate motor learning, artificial intelligence, & neurological disease-focused research with the generous support of the Bertarelli Foundation. Prof. Mackenzie Mathis was appointed as the Bertarelli Foundation Chair of Integrative Neuroscience.
June 2019: Bloomberg Businessweek Magazine covered work in our lab!
DeepLabCut 2.0 is published in Nature Protocols! Congratulations to Tanmay, Alex, Mackenzie & co-authors. Full python package, GUIs, 3D tools and more: https://www.nature.com/articles/s41596-019-0176-0 Plus, our team got the cover!
Jan 2019: We hosted a DeepLabCut workshop! Scientists from across the US, CAN & UK joined us for a hands-on tutorial on how to use the deep learning toolbox.
Kai Sandbrink (masters student) joins the lab!
Nov 2018: A new preprint on using DeepLabCut is posted, Congrats to Tanmay & Alexander!
Our work on extensions to DeepLabCut was accepted to Cosyne 2019, and Alex’s Team presentation on “deep learning in motor neuroscience” is accepted to NCM 2019!
Sept 2018: Adrian receives his master’s degree! Congrats, Adrian! We wish you the best in Zurich!
August 2018: Our work on building the DeepLabCut toolbox, a deep learning method to perform markerless pose estimation was published in Nature Neuroscience, congrats to Alexander Mathis and co-authors!: rdcu.be/4Rep
NVIDIA Developer Blog highlights DeepLabCut! https://news.developer.nvidia.com/ai-enables-markerless-animal-tracking/
July 2018: Our preprint on DeepLabCut was covered by The Atlantic! We are very happy that Ed Young was able to talk to some of the earlier adopters of DeepLabCut as well. For more information, see our page on DeepLabCut.
Gary Kane, PhD (Postdoctoral fellow) joins the lab!
June 2018: Our first paper from the lab is accepted! Stay tuned!
April 2018: Our first preprint from the lab, in collaboration with Matthias Bethge, is up on arXiv (check out our abstract page)!
Feb/March 2018: Congratulations to Melody Tong on completing her senior thesis! Adrian Hoffmann (masters student) joins the lab!
Jan 2018: Tanmay Nath, PhD (Postdoctoral fellow) joins the lab!
Dec 2017: NVIDIA GPU Grant awarded to the lab! We thank NVIDIA Corporation for supporting our research.
September 1st, 2017: The lab doors are open!