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

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 motivated a theoretical framework consisting of two layers of control loops—a fast, but inaccurate, reflexive layer that corrects for bumps and a slow, but accurate, planning layer that computes the trajectory to follow—each 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 are additive and shows 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,” so that both fast and accurate control is achieved using slow or inaccurate components.

Diversity deconstrains component limitations in sensorimotor control

“…In addition, there are benefits for diversity between control layers and subsystems. This is explored in a companion paper 32 that examines the layered control architectures used in mountain biking, including oculomotor control that combines a fast vestibular feedforward system for stabilizing against head motion and a slower cortical feedback loop through the visual cortex for tracking moving objects. Nature uses both diversity within and between layers and uses DESSs to deconstrain the limitations imposed on sensorimotor control imposed by severe resource and component constraints.…”

“…spike-based, spike-rate, etc.). Here, we summarize the SATs in spike-based encoding and refer interested readers to 32 for further details and other encoding schemes. In the spike-based encoding scheme, information is encoded in the presence or absence of a spike in specific time intervals, analogous to digital packet-switching networks 39,40 .…”

Fitts' Law for speed-accuracy trade-off describes a diversity-enabled sweet spot in sensorimotor control

“…spike-based, spike-rate, etc.). Here, we focus on spike-based encoding and present the discuss of alternative encoding strategies in our companion paper (31). In a spike-based encoding scheme, information is encoded in the presence or absence of a spike in specific time intervals, analogous to digital packet-switching networks (32,33).…”

“…5 in the main text. A more detailed description of the math tools applicable to a more general setting can be found in our companion paper (31).…”

“…More detail about DSSs in layered control architectures, like the visual system, is presented in a companion paper (31). More generally, DSSs may reveal a more general design principle for distributed control in brains and inspire the design of large-scale technological systems.…”