Motor learning through the combination of primitives.

F.A. Mussa-Ivaldi (+) and E. Bizzi (*)

(+) Department of Physiology, Northwestern University Medical School, Chicago, Illinois, USA

(*) Massachusetts Institute of Technology. Cambridge. Massachusetts. USA

To appear in the Philosophical Transactions of the Royal Society: Biological Sciences. 2000

SUMMARY

In this paper we discuss a new perspective on how the central nervous system represents and solves some of the most fundamental computational problems of motor control. In particular, we consider the task of transforming a planned limb movement into an adequate set of motor commands. To carry out this task the central nervous system must solve a complex inverse dynamic problem. This problem involves the transformation from a desired motion to the forces that are needed to drive the limb. The inverse dynamic problem is a hard computational challenge because of the need to coordinate multiple limb segments and because of the continuous changes in the mechanical properties of the limbs and of the environment with which they come in contact. A number of studies of motor learning have provided support to the idea that the central nervous system creates, updates and exploits internal representation of limb dynamics in order to deal with the complexity of inverse dynamics. Here we discuss how such internal representation are likely to be built by combining the modular primitives in the spinal cord as well as other building blocks found in higher brain structures. Experimental studies on spinalised frogs and rats have led to the conclusion that the premotor circuits within the spinal cord are organised into a set of discrete modules. Each module, when activated, induces a specific force field and the simultaneous activation of multiple modules leads to the vectorial combination of the corresponding fields. We regard these force fields as computational primitives that are used by the central nervous system for generating a rich grammar of motor behaviours.

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