(Credit: Sonya Todorova)

Our research focuses on the representation of information within neural networks. How do trains of action potentials encode percepts and intents? How do these representations change across brain areas? How do networks of neurons interact to perform computation? In our lab, we use a combination of theoretical and experimental approaches to answer these questions.

Our current thrust is in the area of motor control. Using a variety of learning experiments, we are investigating how sensory feedback impacts the neural representation of motor intent at multiple levels. One of the major tools we use is the brain-computer interface. These devices allow us to tap directly into the output of a network of neurons and use that recorded activity to drive a device, like a computer cursor. By creating a defined link between neural activity and behavior, brain-computer interfaces provide a unique window for probing brain processes that would otherwise remain covert, like learning, adaptation, and the dynamic evolution of the intent signal. Ultimately, a basic understanding of these phenomena will not only inform us about the fundamental limits of motor control processes, but will also help propel the development of new neural prosthetic devices.

Funding provided by DARPA and the PA Department of Health Commonwealth Universal Research Enhancement (CURE) Program