“We are lucky to live in an age in which we
are still making discoveries. It
is like the discovery of
Richard Feynman, 1964
The brain sciences are in their infancy. The fundamental properties of the brain, how it controls our movements, how it learns, are just starting to be understood. Our goal is to help discover these laws, and through it learn more about ourselves and diseases that affect our ability to control our actions.
Computational and neural mechanisms of human motor control
The lab members are engineers, physicists, and physicians, working together to understand the brain. We are intrigued by how the brain controls movements. We find it amazing that despite the fact that neurons are noisy, slow transducers of information, we exhibit effortless grace in how we move our arm, our eyes, and use our hands to interact with objects. As engineers who build robots, we appreciate the enormous complexity of these graceful acts: our environment and our body are constantly changing, which means that our brain must be constantly adapting to maintain the control the makes the graceful movements possible. How is this adaptation, this learning that occurs below our level of consciousness, done? What parts of the brain are involved in storing the representation? With the passage of time, does the representation change? What kind of mathematics should we use to describe the computations? When there is damage to the brain, how does it affect control? Can we accelerate the process of relearning control?
We use tools from
robotics, computational neuroscience, neurophysiology, and brain imaging to
discover the principles of motor control in humans. Our approach stresses a
close integration between control theory and neuroscience. We are driven to
understand the nature of the biological computations that underlie the
control of movements. We use the following tools to understand human motor
We couple this effort with the study of motor disorders in patient populations in order to discover the functional anatomy of the motor control system. One of our more recent projects involves the design and engineering of a new class of robotic arms. This robot allows us to investigate the neural basis of motor control of arm movements in humans both during neurosurgery and during FMRI experiments. During neurosurgery, we investigate motor control and learning by recording from neurons in patients who are undergoing a brain mapping study.
Because the brain is fundamentally a learning system, understanding the systems architecture of how it learns motor control is a central theme in our lab. We use robots to produce novel dynamical systems that subjects learn to control. We program the mechanical impedance of the robot and produce force-field based environments. Subjects explore these environments by moving the handle of the robot. With practice, their brain builds an internal model of the robot's dynamics and adapts to the environment. One goal is to understand the computational properties of this adaptive controller and implicate the neural systems responsible for its representation.
As we make progress in
discovering how a specific neurological disorder affects information
processing in the motor control system, we gain insight into methods of
redirecting the learning capabilities of the brain through focused