Performance Limitations in Sensorimotor Control: Tradeoffs between Neural Computing and Accuracy in Tracking Fast Movements

Shreya Saxena, Columbia University


The ability to move fast and accurately track moving objects is fundamentally constrained by the biophysics of neurons and dynamics of the muscles involved. Yet, the corresponding tradeoffs between these factors and tracking motor commands have not been rigorously quantified. We use feedback control principles to identify performance limitations of the sensorimotor control system (SCS) to track fast periodic movements. We show that (i) linear models of the SCS fail to predict known undesirable phenomena produced when tracking signals in the "fast regime", while nonlinear pulsatile control models can predict such undesirable phenomena, and (ii) tools from nonlinear control theory allows us to characterize fundamental limitations in this fast regime. For a class of sinusoidal input signals, we identify undesirable phenomena at the output of the SCS, including skipped cycles, overshoot and undershoot. We then derive an analytical bound on the highest frequency that the SCS can track without producing such undesirable phenomena as a function of the neurons' computational complexity and muscle dynamics. Our modeling framework not only reproduces several characteristics of motor responses in both slow and fast regimes observed in humans and monkeys, but the performance limitations derived here have far-reaching implications in sensorimotor control. In particular, our analysis can be used to guide the design of therapies for movement disorders caused by neural damage by enhancing muscle performance with assistive neuroprosthetic devices.