Arthur Prochazka, M.D.
Division of Neuroscience
University of Alberta
Edmonton, Alberta, Canada
March 10, 2003
Our Growing Appreciation of the Control Mechanism Underlying Animal Movement
The range of motor capabilities of animals is truly astonishing. Not surprisingly, the underlying neural control mechanisms are highly sophisticated and varied. It seems that each time robotic technology "discovers" a new control system, we later realize that evolution also "discovered" it in the distant past. Furthermore, we have come to realize that unorthodox control schemes that would be unstable in most robots work well in animals because of specific unorthodox properties of muscle actuators. In this talk I described some of the control mechanisms we have come to recognize in animals and the quirky conclusions they have sometimes led us to.
Summary of Presentation
1. Activity of ensembles of sensors in muscle and skin. It has become possible in the last few years to "intercept" the activity of individual axons signaling sensory activity during movement of limbs. Thanks to a new type of implantable "hairbrush" microelectrode array (the Utah array), we have recently even been able to record from up to 20 sensory axons simultaneously during locomotion. From all of the data gathered in this and other ways over the last three decades, it has been possible to compile "look-up- charts" of the sensory signals from various parts of the limb that enter the spinal cord and get transmitted to the brain during stereotyped motor tasks such as locomotion. These look-up- charts have also allowed us to identify mathematical models of sensory activity, which predict sensory input from known muscle length and force variations.
Most recently, our group has used a mathematical matrix inversion method to predict locomotor movements of the whole limb from ensemble sensory recordings obtained with the Utah array (coworkers: Doug Weber, Dick Stein, Dick Normann). This corroborates the recent hypothesis of Bosco and Poppele at the University of Minnesota that ensemble sensory activity is used by the nervous system to derive limb end point position. From a clinical point of view, it may be possible to use implanted devices such as the Utah array to control neuroprostheses in paralyzed people.
2. One of the big unresolved questions in motor control physiology is whether simple reflex pathways (such as that underlying the tendon jerk) make a significant contribution to the control of movement and, in particular, to limb loading (such as occurs when a leg bears the weight of an animal). It may seem strange that such a fundamental question hasn't yet been answered. The problem is that when muscles are activated, e.g., by direct electrical stimulation, they develop an intrinsic resistance to stretch, even in the absence of stretch reflexes. The intrinsic stiffness alone could be capable of providing weight-bearing during locomotion. Stretch reflexes mediated by the spinal cord would add to this intrinsic property, but the question remains, how much?
Alternatively, the more significant role of sensory input may be to mediate higher-level control, such as state- dependent switching between the stance and swing phases of the locomotor step cycle, and the control of global variables such as locomotor speed, stability, and adaptation to the environment.
One of the ways to assess the role of sensory input is to study motor control in people in whom sensory input has been destroyed by disease. Experimental abolition of sensory input is another method. However, because sensory loss is rarely complete, and the brain has astonishing ways of developing coping strategies, sensory abolition studies are often quite hard to interpret.
Biomechanical modeling is another option that is growing in importance, thanks to the development of powerful software tools such as two- dimensional models. In the talk, I showed movies of robocats and robohorses: dynamic models approximating "animals." The models, developed by my graduate student Sergiy Yakovenko, incorporate intrinsic muscle stiffness and viscosity, as well as reflexes based on the mathematical models of sensory input I referred to above. The models "walk" in an extraordinarily life-like manner. Because they are just mathematical constructs, it is of course possible suddenly and completely to remove all sensory input during locomotion. The answer to the question "are reflexes important" turns out to be "it depends." If the underlying, centrally generated locomotor activity is weak, stretch reflexes can "rescue" a collapsing gait pattern. But if the underlying activity is strong, stretch reflexes merely alter the attitude of the body during locomotion, for example elevating the body such as in tip-toe walking or trotting.
The main thing the models have taught us is the importance of sensory input in implementing higher- level control. For example, incorporating some simple if-then rules can strongly influence the "read-out" of centrally generated muscle activity patterns, which in turn adapts cadence to the external requirements and "rescues" gait over a wide range of circumstances. Hazard "rules" that generate prepackaged reactions (such as the tripping response when a foot encounters an obstacle during forward swing) provide the models with a life-like ability to negotiate uneven terrain. In the next few years I predict that we will see some truly astonishing examples of life-like behavior of such models and the anthropoid robots they spawn.
Does this modeling tell us anything about the way real nervous systems control movements such as locomotion? I think the answer is yes, and I gave various examples during my talk, including the control of backwards, forwards, and sideways gait in infants supported over a treadmill, exploratory movements in locusts, and locomotor control in stick insects.