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Low dimensional models and synergies for simplified and effective motor control

A long history of examination has revealed many low-dimensional features in motor behavior from mammals (e.g. humans and rats), to reptiles and amphibians (e.g. turtles and frogs). Perhaps most prominent among these low-dimensional phenomena are muscle synergies: a fixed balance of activation across a range of muscles. Many have claimed that through the use of synergies motor behaviors ranging from basic reflexes to complex voluntary movements could be achieved with a relatively small number of motor commands. Despite the possible benefits, many fundamental questions remain. Taking a normative approach, we have examined what the nervous system might do, if it sought to simplify motor control. Borrowing from control theory, we propose a principle for the design of a low-dimensional controller: that it endeavors to control the natural dynamics of the limb, taking into account the nature of the task to be performed. Using this principle, we have obtained a low-dimensional model of the frog hindlimb and a set of muscle synergies to command it. We demonstrate that this set of synergies was similar to those found experimentally, and capable of producing effective commands. Furthermore, by combining the low-dimensional model and the muscle synergies we were able to build a relatively simple controller whose overall performance was close to that of the system's full-dimensional nonlinear controller. We suggest that the nervous system may employ a similar strategy to simplify control its own musculo-skeletal dynamics. The results have implications for both future neuroscience studies, and rehabilitative methods for restoring impaired movements.

Type of Seminar:
Public Seminar
Dr. Max Berniker
Postdoctoral Associate Rehabilitation Institute of Chicago Northwestern University
Sep 16, 2009   17:15 /

ETH Zentrum, Building ETZ, Room E 6
Contact Person:

Dr. Hartmut Geyer
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Biographical Sketch:
Max Berniker obtained a B.S. in mechanical engineering from the University of California, San Diego, and then received a Masters and Ph.D. from the Massachusetts Institute of Technology, both in mechanical engineering. Employing normative approaches, he examines computational models and uses psychophysical studies to explain many phenomena of human and animal motor behavior. He has pursued research ranging from largely theoretical issues of human motor control and learning, to practical issues of control and stability for robotic prosthetic devices.