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.
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.
We are using the latest techniques in 2-photon and deep brain imaging, optogenetics, chemogenetics, anatomical tracing, electrophysiology, computational modeling and robotics to better understand how multiple areas interact to facilitate adaptive motor control.