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Published June 1, 2021 | Supplemental Material + Submitted + Published
Journal Article Open

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.

Additional Information

© 2021 National Academy of Sciences. Published under the PNAS license. Contributed by Terrence J. Sejnowski, March 29, 2021 (sent for review March 29, 2019; reviewed by Terry Sanger and Rodolphe Sepulchre). This research was supported by NSF Grants NCS-FO (Integrative Strategies for Understanding Neural and Cognitive Systems) 1735004 and 1735003 and the Swartz Foundation. Q.L. was supported by a Boswell fellowship. This paper is based on the theoretical doctoral research of Y.N. and on the experimental research of Q.L. Data Availability: All data and programs used to analyze the data are available at GitHub (https://github.com/ncclabsustech/SAT-in-sensorimotor-control). Author contributions: Y.N., Q.L., T.J.S., and J.C.D. designed research; Y.N. and Q.L. performed research; Y.N. and Q.L. analyzed data; and Y.N., Q.L., T.J.S., and J.C.D. wrote the paper. Reviewers: T.S., University of Southern California; and R.S., University of Cambridge. The authors declare no competing interest. This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1916367118/-/DCSupplemental.

Attached Files

Published - e1916367118.full.pdf

Submitted - 1909.08601.pdf

Supplemental Material - pnas.1916367118.sapp.pdf

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Additional details

Created:
August 22, 2023
Modified:
October 23, 2023