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.
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