I'm a research scientist at MIT studying the neural control of movement, the representations of learned skills as motor memories and, more recently, the relationship between motor memories and declarative memories. It appears that information storage via these two distinct modalities has more in common than previously thought. My main interests are: 1) Assessing, through pyschophysical experimentation, movement behavior in individuals with neurological disorders, 2) Deciphering the cortical codes by which movement commands are represented in the brain through analysis of neurophysiological data, 3) Elucidating neural principles of learning and self-organization as they pertain throughout the brain generally and the motor system specifically, and 4) Using knowledge of neural representation and learning to build neuroprosthetic devices (or brain-machine interfaces). 5) Developing a unifying framework capable of explaining how both motor memories and declarative memories are formed and persist through time. Recently, my efforts have been focused on two more specific problems. The first is developing a science of human athletic performance based on systems neuroscience principles -- a "sports neuroscience", if you will. For several decades, kinesiologists and sports scientists have made numerous observations on the practice/performance habits of athletes, and these observations have resulted in a set of heuristics on general sensorimotor skill enhancement. Yet these heuristics are relatively sparse and ill-defined, leaving most performance improvements, ultimately, to the whim of trial-and-error. If a codifying theory could be formulated at the systems level, our understanding of athletic performance could be deepened leading, hopefully, to more effective teaching/coaching interventions. So far, I have focused on a few specific heuristics, such as the puzzling problem of why a professional athlete (or musician or other expert practitioner of fine motor skills) needs to practice so extensively immediately prior to performance on the day of a competition, regardless of how much practice has occurred in the preceding days (article on practice effects). Here, I am borrowing on my own personal experience as a collegiate tennis player, because I couldn't play worth a damn without rallying from the baseline for at least 1/2 hour prior to a match. The second problem is how to train a Brain-Machine or Brain-Body interface device to perform at or near the performance level of an unimpaired human. Lately, there has been a lot of hype in the media (and even in scientific journals) about the long-term potential of recent developments in motor neuroprosthetics. However, none of these devices actually work very well. Some might say that tweaking the current methodologies will ultimately lead to better performance. I would diagree, arguing that there are fundamental flaws to the current paradigm. In particular, all current devices rely on a "Decoding" stage, whereby the brain is assumed to represent movement commands in a certain way, and control algorithms are employed to find the best-fit parameters for this presumed representation. However, because we really don't understand enough scientifically about how the brain generates movement commands, any representation we impose on the system for decoding purposes will lead to fundamental performance limitations. It makes far more sense to allow the brain to interact autonomously with the peripheral actuators without the interposition of a "decoding stage", so that the brain figures out its own means of control (just as a baby has to engage in motor babbling to develop coordination). Basically, I am simply saying that the brain is smarter than we are in terms of understanding movement control, so let it solve the problem. Of course, this approach exhibits the downside of a significantly increased learning time, but this is a limitation which can be addressed, unlike the fundamental limitations of the alternative approach.