The Science of Mnemonics, Aug. 2 2017

      Celtics game, Jan. 2015           Team Reunion, June 2015

McGovern Institute for Brain Research
Building 46, Room 6193
77 Massachusetts Avenue
Cambridge, MA 02139

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.

Some of my publications (and CV ) with links are listed at the bottom.

I also teach a class entitled "Emergent computation in distributed neural circuits". The class illustrates the different principles that underlie computation by a computer vs. computation by the brain. The stark differences between these two systems tend to be underappreciated often resulting in gross misconceptions about how cognitive, sensory, and motor functions are implemented in neural circuits. A syllabus for the class can also be found below.

What follows is the semi-facetious, semi-serious introduction to my dissertation. It's facetious in the sense of being wildly bombastic and quite incongruous with the remainder of my computationally-oriented dissertation. But it's serious in the sense of touching upon some of the philosophical and epistemological issues that have attracted me and many others to the study of the mind. (It's also serious in the sense that I managed to slip it past my readers into the final archived version.)

Chimera of Mind from Chaos of Brain

In an eternally noble quest to comprehend the grand cosmic millieu into which it has been inseparably thrust, mankind has acquired the capacity to perceive universal phenomena at manifold spatial and temporal scales. Yet despite a divergence of specific observations, a recurrent theme ineluctably emerges: structure always arises and patterns always exist. From the gravitationally choreographed rhythms of a spawning solar system to the ironclad rules of chemical bond formation to the irrepressible non-locality of sub-atomic particles, order has always been found, if not often the anthropomorphic order that an inherently solipsistic species prefers. But of all conglomerations of matter known to exist in the universe at any level, perhaps none exceed the human brain in complexity of structure, diversity of function, and inscrutability of operation. Indeed, while the state of the universe has been traced back to the first few microseconds after its fiery birth, while cloning capacity rockets up the phylogenetic ladder to its inevitably hominid end, and while molecular biologists produce antibodies that seek out and deactivate invading micro-organisms as if they were immunological heat seeking missiles, modern neuroscience exists in a state of infancy relative to scientific disciplines such as organic chemistry or particle physics.

The source of difficulty in unravelling the mystery of how the brain gives rise to the mind is not difficult to pinpoint. Chemical neurotransmission, the signalling of one neuron via chemical transmitters released by another, depends upon biochemical processes that occur on a spatial scale on the order of 10^-7 meters. Behavior occurs at a spatial scale on the order of meters. Entailed, then, in the actualization of purposive human behavior is continuous control of a dynamical process which perpetually spans 7 orders of magnitude in spatial scale despite massive fluctuations in the embedding environment at both the macroscale (e.g., a temperature change or the approach of a predator) and the microscale (radically different blood sugar levels, for example). Given the extraordinary computational burdens of maintaining this level of order - such a life process by its very existence perpetuates an almost unimagineable oasis of minimal entropy amidst the perpetually raging winds of the Second Law of Thermodynamics - we are perhaps less surprised by the brain's indelibly intricate structure or its almost unfathomable complexity. Nevertheless mankind is nothing if not collectively dauntless, even as individual humans succumb to frailty and fear, and so has fully immersed itself in the scientific struggle to elucidate the secrets of the mind. While some time may pass before the quest arrives at a triumphant conclusion, impressive progress has already been made on numerous fronts, and herein we explore one minuscule contribution to the field of brain science.

Classes Taught

9.53 Emergent computation in distributed neural circuits

Selected Publications

Ajemian R, Bizzi E, Schachter S, Edgerton R, Winograd J, Malik W. Gentler alternatives to chips in the brain. Nature. 2017 April 27;544(7651):416.

Bizzi E, Ajemian R. A hard scientific quest: understanding voluntary movements. Daedalus. 2015 Jan 16;144(1):83-95.

Ajemian R, D'Ausilio A, Moorman H, Bizzi E. A theory for how sensorimotor skills are learned in noisy and non-stationary neural circuits. Proceedings of the National Academy of Sciences. 2013 Dec 9;110(52):E5078-87.

Ajemian R, D'Ausilio A, Moorman H, Bizzi E. Why professional athletes need a prolonged period of warm-up and other peculiarities of human motor learning. Journal of Motor Behavior. 2010 Nov;42(6):381-8.

Ajemian R, Hogan N. Experimenting with theoretical motor neuroscience. Journal of Motor Behavior. 2010 Nov;42(6):333-42.

Ajemian R, Green A, Bullock D, Sergio L, Kalaska J, Grossberg S. Assessing the function of motor cortex: single-neuron models of how neural response is modulated by arm biomechanics. Neuron. 2008 May 8;58(3):414-28.

Ajemian R, Bullock D, Grossberg S. A model of movement coordinates in the motor cortex: posture-dependent changes in the gain and direction of single cell tuning curves. Cerebral Cortex. 2001 Dec;11(12):1124-35.

Ajemian R, Bullock D, Grossberg S. Kinematic coordinates in which motor cortical cells encode movement direction. Journal of Neurophysiology. 2000 Nov;84(5):2191-203.