EVENT DETAILS AND ABSTRACT

Title: Nonlinear Models of Motor Learning
Speaker: Professor S. Mussa-Ivaldi
Speaker Info: Northwestern U.
Brief Description:
Special Note:
Abstract:

I will 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 I will focus on 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 nervou system creates, updates and exploits internal representation of limb dynamics in order to deal with the complexity of inverse dynamics. I will discuss how such internal representation are likely to be built by combining the modules in the spinal cord as well as other building blocks found in higher brain structures.Experimental studies on spinalized frogs and rats have led to the conclusion that the premotor circuits within the spinal cord are organized 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. These force fields are computational primitives that the central nervous system may use to generate a rich grammar of motor behaviors.