Literature DB >> 22868500

Conclusions on motor control depend on the type of model used to represent the periphery.

Ilona J Pinter1, Arthur J van Soest, Maarten F Bobbert, Jeroen B J Smeets.   

Abstract

Within the field of motor control, there is no consensus on which kinematic and kinetic aspects of movements are planned or controlled. Perturbing goal-directed movements is a frequently used tool to answer this question. To be able to draw conclusions about motor control from kinematic responses to perturbations, a model of the periphery (i.e., the skeleton, muscle-tendon complexes, and spinal reflex circuitry) is required. The purpose of the present study was to determine to what extent such conclusions depend on the level of simplification with which the dynamical properties of the periphery are modeled. For this purpose, we simulated fast goal-directed single-joint movement with four existing types of models. We tested how three types of perturbations affected movement trajectory if motor commands remained unchanged. We found that the four types of models of the periphery showed different robustness to the perturbations, leading to different predictions on how accurate motor commands need to be, i.e., how accurate the knowledge of external conditions needs to be. This means that when interpreting kinematic responses obtained in perturbation experiments the level of error correction attributed to adaptation of motor commands depends on the type of model used to describe the periphery.

Mesh:

Year:  2012        PMID: 22868500     DOI: 10.1007/s00422-012-0505-7

Source DB:  PubMed          Journal:  Biol Cybern        ISSN: 0340-1200            Impact factor:   2.086


  6 in total

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Journal:  Biol Cybern       Date:  2021-02-15       Impact factor: 2.086

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Journal:  PLoS Comput Biol       Date:  2022-06-08       Impact factor: 4.779

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Authors:  Frédéric Crevecoeur; Stephen H Scott
Journal:  PLoS Comput Biol       Date:  2014-10-09       Impact factor: 4.475

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Journal:  Front Physiol       Date:  2016-07-19       Impact factor: 4.566

5.  Morphological Computation Increases From Lower- to Higher-Level of Biological Motor Control Hierarchy.

Authors:  Daniel F B Haeufle; Katrin Stollenmaier; Isabelle Heinrich; Syn Schmitt; Keyan Ghazi-Zahedi
Journal:  Front Robot AI       Date:  2020-10-21

6.  Muscles Reduce Neuronal Information Load: Quantification of Control Effort in Biological vs. Robotic Pointing and Walking.

Authors:  Daniel F B Haeufle; Isabell Wochner; David Holzmüller; Danny Driess; Michael Günther; Syn Schmitt
Journal:  Front Robot AI       Date:  2020-06-24
  6 in total

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