Inference of central nervous system input and its complexity for interactive arm movement

Abstract

This dissertation demonstrates a new method for inferring a representation of the motor command, generated by the central nervous system for interactive point-to-point, movements. This new tool, the input, inference neural network or IINN, allows estimation of the complexity of the motor command. The IINN was applied to experimental data, gathered from 7 volunteer subjects who performed point-to-point, tasks while interacting with a specially constructed haptic robot. The motor plan inference demonstrates that, for the point-to-point movement tasks executed during experiments, the motor command can be projected onto a low-dimensional manifold. This dimension is estimated to be 4 or 5 and far less than the degrees of freedom available in the arm. It is hypothesized that subjects simplify the problem of adapting to changing environments by projecting the motor control problem onto a motor manifold of low dimension. Reducing the dimension of the movement, optimization problem through the development of a motor manifold can explain rapid adaptation to new motor tasks.