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JHU Biomedical Engineering Primary Faculty

Reza Shadmehr, Ph.D.

Professor of Biomedical Engineering
Professor of Neuroscience
Johns Hopkins University
Co-Director, Ph.D Program

Laboratory for Computational Motor Control
Traylor 410
(410) 614-2458
shadmehr AT jhu.edu
Website

Research Interests

The Lab members are engineers, physicists, and physicians, working together to understand the brain. We are intrigued by how the brain controls movements of the arm. In particular, how does it learn this control? When it learns a new task, how is the information represented? What parts of the brain are involved in storing the representation? With the passage of time, does the representation change? When there is damage to the brain, can it affect the ability to learn control? If so, can we aid in the process of recovery? We use tools from robotics, computational neuroscience, and functional imaging of the brain to discover the principles of motor control in humans.

Our approach stresses a close integration of viewpoints from robotics and control theory with neuroscience. We are driven to understand the nature of the biological computations that underlie the control of movements. We couple this effort with brain imaging studies and the study of motor disorders in patient populations in order to discover the functional anatomy of the control system and the cause of neurological motor disorders.

For example, what aspect of information processing in the motor control system of patients with Huntington's Disease, a neurological disorder that affects the basal ganglia, has been disrupted? What does this disruption tell us about the normal function of the basal ganglia in motor control? How do the characteristics of the control system in patients with basal ganglia disorders differ from those of patients with cerebellar disease?

Because the brain is fundamentally a learning system, understanding the systems architecture of how it learns motor control is a central theme in our laboratory. 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.

Selected Publications

Thoroughman KA, Shadmehr R (2000) Learning of action through adaptive combination of motor primitives. Nature, 407:742-747

Smith MA, Brandt J, Shadmehr R (2000) Motor disorder in Huntington's disease begins as a dysfunction in error feedback control. Nature, 403:544-549

Shadmehr R, Holcomb HH (1997) Neural correlates of motor memory consolidation. Science, 277:821- 825

Brashers-Krug T, Shadmehr R, Bizzi E (1996) Consolidation in human motor memory. Nature, 382:252- 255

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