Efferent feedback in a spinal-like controller: Reaching with perturbations

We use simulations of a controller that adopts a spinal-like network topology for goal-oriented reaching and assess its sensitivity to the dynamics of internal elements that allow context-independent performance. Such internal elements are often referred to as inverse or forward models of the periphery dynamics, depending on the proposed controller theory. Here, the "models" are used in a forward implementation, and we evaluate how the controller's performance would be affected by the nature of the model. For each point-to-point reaching motion experiment, we use forms of internal "efference models" (e.g., full mathematical representations of peripheral dynamics, simple spindle feedback, etc.) driven by motor reafference, then compare hand trajectories and hand path speeds in the presence or absence of external perturbations. It is demonstrated that a simple velocity- based model reduced the effects of dynamic perturbations by as much as 66%. In addition, the 2D hand trajectories varied from a biological reference by only 0.05 cm. Thus, the controller facilitated biological like motions while providing response to dynamic events which are omitted in earlier biomimetic controllers. This research suggests that these spinal-like systems are robust and tunable via gain-fields without the need of context dependent pre-planning.