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 (see Mathis et al 2017). We hope that by understanding the neural basis of adaptive motor control we can open new avenues in therapeutic research for neurological disease and provide fundamental insights into brain function.  

Novel mouse paradigms for studying adaptive motor control

We believe behavior is an essential component to understanding neural function.

We have developed a set of skilled motor tasks where mice can learn from a dynamically changing sensory landscape. By combining concepts from 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 control.

Technology

We are using and building on 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 computational models to generate testable hypotheses and analyze our data. Lastly, we aim to develop new robotics for the mouse community. Taken together, we aim to better understand how multiple areas interact to facilitate adaptive motor control.

People

Mackenzie W. Mathis, PhD
Principal Investigator
mathis@rowland.harvard.edu

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 ('07-'12). I then attended Harvard University for my PhD ('12-'17), where I worked in the laboratory of Nao Uchida investigating the role of reward and sensory prediction errors in guiding motor learning. Currently, I am a WATB/Project ALS post-doctoral fellow in the laboratory of Matthias Bethge (University of Tübingen).

Google Scholar 
In the News: RJF position, Peralta Prize, NSF Fellowship

Alexander Mathis, PhD
Marie-Curie Fellow, lab mathematician
amathis@fas.harvard.edu
Publications

Alexander studied Mathematics at the Ludwig Maximilians University in Munich. For his dissertation he derived properties of grid cells from optimal coding principles. As a post-doctoral fellow he is working on trail tracking in mice, principles of olfactory learning with Venki Murthy (Harvard University) and Matthias Bethge (University of Tübingen, Germany), and motor adaptation with Mackenzie and Nao Uchida.  He strives to understand how internal representations guide behavior and how internal models are implemented in the brain.

News

January 2017

Please consider joining! http://www2.rowland.harvard.edu/employment

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