Diversity-enabled sweet spots in layered architectures and speed-accuracy trade-offs in sensorimotor control

TL;DR

This paper investigates how layered control architectures with diverse component speeds and accuracies enable fast, accurate, and robust sensorimotor control, exemplified through a mountain biking task and a theoretical model.

Contribution

It introduces a theoretical framework explaining how diversity in control loop properties creates sweet spots for speed and accuracy in sensorimotor systems.

Findings

Layered control with diverse speeds and accuracies explains observed trade-offs.

Fast, inaccurate reflexes combined with slow, accurate planning improve control.

Diversity in neural properties enables optimal speed-accuracy balance.

Abstract

Nervous systems sense, communicate, compute and actuate movement using distributed components with severe trade-offs in speed, accuracy, sparsity, noise and saturation. Nevertheless, brains achieve remarkably fast, accurate, and robust control performance due to a highly effective layered control architecture. Here we introduce a driving task to study how a mountain biker mitigates the immediate disturbance of trail bumps and responds to changes in trail direction. We manipulated the time delays and accuracy of the control input from the wheel as a surrogate for manipulating the characteristics of neurons in the control loop. The observed speed-accuracy trade-offs (SATs) motivated a theoretical framework consisting of layers of control loops with components having diverse speeds and accuracies within each physical level, such as nerve bundles containing axons with a wide range of sizes. Our model explains why the errors from two control loops -- one fast but inaccurate reflexive layer that corrects for bumps, and a planning layer that is slow but accurate -- are additive, and show how the errors in each control loop can be decomposed into the errors caused by the limited speeds and accuracies of the components. These results demonstrate that an appropriate diversity in the properties of neurons across layers helps to create "diversity-enabled sweet spots" (DESSs) so that both fast and accurate control is achieved using slow or inaccurate components.