Welcome! We are a research team at the Rowland Institute at Harvard University lead by Mackenzie Mathis, PhD. Using machine learning techniques and mice as a model system, we aim to understand how neural circuits contribute to adaptive motor behaviors.
Executing successful movements requires the brain to predict the consequences of actions. It is believed that the brain builds internal models of our body and the environment in order to simulate the sensory and motor outcomes of movements.
Due to the constant changes in our body and environment (for instance, those due to fatigue, tool-use, or disease) these models require constant re-calibration, called motor adaptation, to keep us moving in predictable ways.
Where in the brain these models reside, how they are formed, and how they are updated following bodily or environmental changes remains unclear.
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.
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. With the Bethge lab, we developed a deep learning toolbox called DeepLabCut to perform markerless pose estimation from any species in a multitude of settings.
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.
Tools + Technology
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.
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!
A new preprint on using DeepLabCut is posted, Congrats to Tanmay & Alexander!
Adrian receives his master’s degree! Congrats, Adrian! We wish you the best in Zurich!
Our work on building the DeepLabCut toolbox, a deep learning method to perform markerless pose estimation was published in Nature Neuroscience, congrats to Alexander!: rdcu.be/4Rep
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!
Our first paper is accepted! Stay tuned!
Our first preprint from the lab, in collaboration with Matthias Bethge, is up on arxiv (check out our abstract page)
Congratulations to Melody Tong on completing her senior thesis!
Adrian Hoffmann (masters student) joins the lab!
Tanmay Nath, PhD (Postdoctoral fellow) joins the lab!
NVIDIA GPU Grant awarded to the lab! We thank NVIDIA Corporation for supporting our research.
September 1st, 2017
The lab doors are open!
Our very talented friend, Taiga Abe, who completed his Harvard College thesis (Analysis and modeling of movement kinematics in a mouse model of motor adaptation) with Mackenzie, Alexander, and Nao, started his PhD graduate studies at Columbia University today! Congratulations!
We will be presenting new work at NCMDub this week! Stop by our poster cluster to learn more about our past and future work.
come see our poster at COSYNE 2017! Somatosensory cortex plays an essential role in forelimb motor adaptation in mice Mathis, M.W., Mathis, A., Uchida, N. (2017). Cosyne Abstracts 2017, Salt Lake City USA
It's official! The lab will open at the Rowland Sept 1st!
Mackenzie Mathis, PhD Principal Investigator firstname.lastname@example.org Office Location: 3rd Floor, 308 Google Scholar | CV
Gary completed his PhD at Princeton, working on decision-making in rats. He joined the lab in July 2018.
Eric Hepler Animal Technician
Eric joined the Rowland in the fall of 2017, and provides technical support to several Fellows labs. He works with us on maintaining our precious mouse colony!
Alexander Mathis, PhD Lab Mathematician (our “in house“ collaborator) Postdoctoral Fellow in the Bethge & Murthy and labs email@example.com Office Location: 3rd Floor, 310 Publications & website Alexander collaborates closely with us on computational models of motor adaptation and learning, and using deep learning to quantify animal behavior (see DeepLabCut).
Adrian Hoffmann Masters student | University of Tübingen, Neural Information Processing firstname.lastname@example.org
Adrian completed his master's thesis in the lab through the University of Tübingen in 2018. He received a BS in Physics from Heidelberg University. Adrian is now a PhD student in the group of Prof. Helmchen in Zurich.
Melody Tong - Undergraduate Researcher|Harvard College Class of '18 - Melody was co-mentored by Mackenzie and Nao Uchida. Her thesis work was focused on characterizing a rapidly learned freely-moving reaching & pulling task in mice. She is now attending medical school at NYU.
We gratefully acknowledge the funding sources that make our research possible: