Our research focuses on the cortical basis of motor control and learning. We are investigating what features of motor behavior are encoded and how this information is represented in the collective activity of large neuronal ensembles in the motor, premotor, and somatosensory cortices. We are also interested in what way these representations change as motor learning occurs. Our approach has been to simultaneously record neural activity from large groups of neurons using multi-electrode arrays while performing detailed kinematic, kinetic, and muscle measurements of goal-directed, motor behaviors, and to develop mathematical models that relate neural activity with behavior. These mathematical models provide insights as to what aspects of motor behavior are being encoded in cortical neurons, but also can be used to decipher or “decode” neural activity in order to predict movement which has practical implications for brain-machine interface development. Ultimately, this research may lead to neural prosthetic technologies that will allow people with spinal cord injury, ALS, or amputation to use brain signals to voluntarily control a device so as to interact with the world.