RESEARCH
RESEARCH
Welcome! We are a research team at the Rowland Institute at Harvard University lead by Mackenzie Mathis, PhD. Using motor behaviors, machine learning techniques, and mice as a model system, we aim to understand how neural circuits contribute to adaptive motor behaviors.
Research
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.
People
People
People
Mackenzie Mathis, PhD
Principal Investigator
mathis@rowland.harvard.edu
Office Location: 3rd Floor, 308
Google Scholar | CV
In the News: RJF position, Peralta Prize, NSF Fellowship, The Atlantic
Tanmay Nath, PhD
Postdoctoral Fellow
nath@rowland.harvard.edu, 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.
Gary Kane, PhD
Postdoctoral Fellow
gkane@rowland.harvard.edu, 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 of 2018.
Kai Sandbrink
Masters student
sandbrink@rowland.harvard.edu, Office Location: 3rd Floor, 309
Kai is completing his masters thesis from ETH in the lab. He joined in March of 2019.
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!
Tom Biasi
Undergraduate student
He is a freshman at Harvard studying computer science. He joined the lab in March of 2019.
Michael Beauzile
Undergraduate student
He is an undergraduate sophomore pre-medical student at Boston University studying Biomedical and Electrical engineering. His interests include psychology, neuroanatomy and nanotechnology. He joined the lab in March of 2019.
Collaborators:
Alexander Mathis, PhD | http://www.people.fas.harvard.edu/~amathis/
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 | Bethgelab.org
Matthias and his group collaborate with us on using deep learning tools for pose estimation, neural data analysis, and predictive modeling.
Travis DeWolf, PhD | Applied Brain Research - Publications & Blog
Travis works with us on multi-area brain models
Alumni:
Adrian Hoffmann
Masters student | University of Tübingen, Neural Information Processing.
hoffmann@rowland.harvard.edu; 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.
News
News
News
June 2019: Bloomberg Businessweek covered work in our lab: https://www.bloomberg.com/news/features/2019-06-18/apple-google-and-facebook-are-raiding-animal-research-labs
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!
“A running cheetah with DeepLabCut Image of a cheetah ‘in the wild’ with markerless tracking of user-defined parts made with DeepLabCut, an open-source toolbox for deep-learning-based animal-pose estimation provided by Nath et al.”
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!
Funding
Funding
Funding:
We gratefully acknowledge the funding sources that make our research possible:
Current:
2018 - 19 | Mind, Brain Behavior Faculty Award
2017 - '22 | Rowland Fellowship to M. Mathis
2018 | NVIDIA GPU Grant Award
Past:
2017 | WATB/Project ALS - Postdoctoral Fellowship to M. Mathis
