Evidence map›Paper›PMID 39249120›Full record

ArticleBiological cybernetics2024

Variational analysis of sensory feedback mechanisms in powerstroke-recovery systems.

Zhuojun Yu, Peter J Thomas

Abstract read
In one paragraph

Article in Biological cybernetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Zhuojun YuDepartment of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, Cleveland, OH, 44106, USA. zhuojun.yu@case.edu.
Peter J ThomasDepartment of Mathematics, Applied Mathematics, and Statistics, Department of Biology, Department of Electrical, Control and Systems Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.

Funding

A novel approach to analyzing functional connectomics and combinatorial control in a tractable small-brain closed-loop systemRF1NS118606 · NINDS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI BYRNE, JOHN H, CHESTEK, CYNTHIA ANNE · 2020 to 2020
$3.0M
A novel approach to analyzing functional connectomics and combinatorial control in a tractable small-brain closed-loop systemR01NS118606 · NINDS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI BYRNE, JOHN H, CHESTEK, CYNTHIA ANNE · 2023 to 2024
$1.7M
National Institutes of Health BRAIN Initiative RF1 NS118606-01NINDS NIH HHS RF1 NS118606
6 · The paper itself

Abstract

Although the raison d'etre of the brain is the survival of the body, there are relatively few theoretical studies of closed-loop rhythmic motor control systems. In this paper we provide a unified framework, based on variational analysis, for investigating the dual goals of performance and robustness in powerstroke-recovery systems. To demonstrate our variational method, we augment two previously published closed-loop motor control models by equipping each model with a performance measure based on the rate of progress of the system relative to a spatially extended external substrate-such as a long strip of seaweed for a feeding task, or progress relative to the ground for a locomotor task. The sensitivity measure quantifies the ability of the system to maintain performance in response to external perturbations, such as an applied load. Motivated by a search for optimal design principles for feedback control achieving the complementary requirements of efficiency and robustness, we discuss the performance-sensitivity patterns of the systems featuring different sensory feedback architectures. In a paradigmatic half-center oscillator-motor system, we observe that the excitation-inhibition property of feedback mechanisms determines the sensitivity pattern while the activation-inactivation property determines the performance pattern. Moreover, we show that the nonlinearity of the sigmoid activation of feedback signals allows the existence of optimal combinations of performance and sensitivity. In a detailed hindlimb locomotor system, we find that a force-dependent feedback can simultaneously optimize both performance and robustness, while length-dependent feedback variations result in significant performance-versus-sensitivity tradeoffs. Thus, this work provides an analytical framework for studying feedback control of oscillations in nonlinear dynamical systems, leading to several insights that have the potential to inform the design of control or rehabilitation systems.

Indexed as

Feedback, SensoryAnimalsHumansLocomotionModels, BiologicalModels, NeurologicalCentral pattern generatorClosed-loop controlEfficiencyPower strokeRobustnessSensory feedback

Identifiers

PMID39249120
PMCPMC11588830

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.