Computational neuroscience and brain-machine interfaces

 

At the University of Chicago’s Department of Organismal Biology and Anatomy, chips implanted in their motor corticies of rhesus monkeys record the discharge rates of over 100 neurons at a time, while the monkeys manipulate a robotic exoskeleton known as the KINARM (figure on right), by BKIN Technologies.  This machine records the positions of the monkey’s shoulder and elbow joints, for the purpose of investigating the relationships between brain activity and arm motion.  As shown in Figure 19, the KINARM has jointed sections in addition to those that directly correspond to the shoulder and elbow joints of the monkey.  Dr. Ojakangas developed the original, correct equations of motion for the KINARM, relating torques applied at the shoulder and elbow joints by the monkey to the motion of the entire apparatus.  For this contribution, Ojakangas is credited in the KINARM User’s Manual (link).

It was discovered over 15 years ago that the Cartesian position of a monkey’s hand can be determined fairly well using a hyper-dimensional linear regression, using, as independent variables, the discharge rates of over 100 neurons and their histories over the one second prior to the time of the attempted prediction.  This “brute force” methodology, completely lacking in the laws of physics, led Dr. Ojakangas and his colleagues to wonder whether a better fit would be attained by using the more physically reasonable assumption that neuronal discharge rates are proportional to muscle activation, and consequently, to torque generated at the shoulder and elbow of a monkey using the KINARM.  This assumption was investigated, leading to this publication in the Journal of Neural Rehabilitation.  Ironically, although this approach is almost certainly more closely related to the actual physics involved in arm motion, the fact that Newton’s Laws of motion involve two time derivatives, each of which introduce noise to the system, led to a result that is only comparable to “brute force” models, rather than clearly superior to them.  Dr. Ojakangas spent a few semesters working with a group of Drury students to test a new proposed model of his, relating activity in the motor cortex to the motion of the arm.  This new model is still to be published.

At the left, a person sits in the KINARM exoskeletal apparatus, for neurological assessment.  The subject can move her arm, while the motion of both the elbow and shoulder joints, in the horizontal plane, is measured by the KINARM.  Prescribed torques may be applied (by motors) at the joints of the device, and the subject’s simultaneous muscle-generated torques are computed, through the corresponding motions of the KINARM segments.  Simultaneously, neural activity in the appropriate region of the motor cortex is measured, by a cortical implant or other device.  Thereby, relationships between brain activity and joint motion are determined. On the right is a schematic diagram of the KINARM and its segments and relevant angles.

In another work, I developed a new theoretical model describing a simple type of linkage between neural activity in the motor cortex and subsequent arm motion.  I spent a few semesters working with a group of Drury students to test this proposed model.  This new model is still to be published, but I presented the work as a poster (below) at the Neural Control of Movement conference in 2011.