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. 

Mathis Lab-logo-black (1).png


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.  

Here are some questions that guide us:

  • how are internal models represented in the neural code?

  • what are the sensory and motor cortical contributions to motor adaptation?

  • how are multiple areas across the brain efficiently sharing information during learning?

  • how do (biological) neural networks enable lifelong learning?

behavior, models, & neural data

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. We develop tools, like DeepLabCut, to perform markerless pose estimation and behavioral analysis 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.

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.





I received my BSc from the University of Oregon, then worked in the labs of Hynek Wichterle and Christopher Henderson at Columbia University to build  in vitro  models of ALS. I then attended Harvard University for my PhD, where I worked in the laboratory of  Nao Uchida  investigating the role of reward and sensory prediction errors in guiding motor learning. Before starting at the Rowland, I was a postdoctoral fellow in the group of  Matthias Bethge  (University of Tübingen). In the News:  RJF position ,  Peralta Prize ,  NSF Fellowship

Mackenzie Mathis, PhD | Principal Investigator
Office Location: 3rd Floor, 308
Google Scholar | CV

In the News: RJF position, Peralta Prize,
NSF Fellowship, The Atlantic, Nature


Gary Kane, PhD | Postdoctoral Fellow, Office Location: 3rd Floor, 309
Google Scholar

Gary completed his PhD at Princeton, working on neural circuits of decision-making in rats. He joined the lab in July 2018.


Tanmay Nath, PhD | Postdoctoral Fellow, Office location: 3rd Floor, 309

Tanmay completed his PhD focused on developing machine learning tools for biological systems. He joined the Mathis Lab in January of 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
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).


Matthias Bethge, PhD


Travis DeWolf, PhD
Applied Brain Research - Publications & Blog


Adrian Hoffmann
Masters student | University of Tübingen, Neural Information Processing

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:



2018 - 19 | Mind, Brain Behavior Faculty Award

2017 - '22 | Rowland Fellowship to M. Mathis

2018 | NVIDIA GPU Grant Award



2017 | WATB/Project ALS - Postdoctoral Fellowship to M. Mathis


2013 - '17 | NSF Graduate Research Fellowship to M. Mathis


The Rowland Institute at Harvard
Mind Brain Behavior - Interfaculty Initiative
The Center for Brain Science, Harvard University 






January 2019

Nov 2018

Sept 2018

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.

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!

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!:

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! 

Screenshot from 2018-07-04 17-12-25.png

June 2018

Our first paper 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)

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!

August 2017

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!

May 2017

We will be presenting new work at NCMDub this week! Stop by our poster cluster to learn more about our past and future work.

March 22, 2017

March 2017

L to R: Prof. Nao Uchida, Dr. Mackenzie Mathis, Prof. Venki Murthy

L to R: Prof. Nao Uchida, Dr. Mackenzie Mathis, Prof. Venki Murthy

Here is a story from MCB Harvard about the new lab:



January 2017

Please consider joining!

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!

December 2016