Literature DB >> 15961421

Impedance control and internal model use during the initial stage of adaptation to novel dynamics in humans.

Theodore E Milner1, David W Franklin.   

Abstract

This study investigated the neuromuscular mechanisms underlying the initial stage of adaptation to novel dynamics. A destabilizing velocity-dependent force field (VF) was introduced for sets of three consecutive trials. Between sets a random number of 4-8 null field trials were interposed, where the VF was inactivated. This prevented subjects from learning the novel dynamics, making it possible to repeatedly recreate the initial adaptive response. We were able to investigate detailed changes in neural control between the first, second and third VF trials. We identified two feedforward control mechanisms, which were initiated on the second VF trial and resulted in a 50% reduction in the hand path error. Responses to disturbances encountered on the first VF trial were feedback in nature, i.e. reflexes and voluntary correction of errors. However, on the second VF trial, muscle activation patterns were modified in anticipation of the effects of the force field. Feedforward cocontraction of all muscles was used to increase the viscoelastic impedance of the arm. While stiffening the arm, subjects also exerted a lateral force to counteract the perturbing effect of the force field. These anticipatory actions indicate that the central nervous system responds rapidly to counteract hitherto unfamiliar disturbances by a combination of increased viscoelastic impedance and formation of a crude internal dynamics model.

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Year:  2005        PMID: 15961421      PMCID: PMC1474192          DOI: 10.1113/jphysiol.2005.090449

Source DB:  PubMed          Journal:  J Physiol        ISSN: 0022-3751            Impact factor:   5.182


  31 in total

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Authors:  J W Krakauer; M F Ghilardi; C Ghez
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2.  Computational nature of human adaptive control during learning of reaching movements in force fields.

Authors:  N Bhushan; R Shadmehr
Journal:  Biol Cybern       Date:  1999-07       Impact factor: 2.086

3.  Impedance control and internal model formation when reaching in a randomly varying dynamical environment.

Authors:  C D Takahashi; R A Scheidt; D J Reinkensmeyer
Journal:  J Neurophysiol       Date:  2001-08       Impact factor: 2.714

4.  Multiple paired forward and inverse models for motor control.

Authors:  D M Wolpert; M Kawato
Journal:  Neural Netw       Date:  1998-10

5.  Functional significance of stiffness in adaptation of multijoint arm movements to stable and unstable dynamics.

Authors:  David W Franklin; Etienne Burdet; Rieko Osu; Mitsuo Kawato; Theodore E Milner
Journal:  Exp Brain Res       Date:  2003-05-29       Impact factor: 1.972

6.  Adaptation to stable and unstable dynamics achieved by combined impedance control and inverse dynamics model.

Authors:  David W Franklin; Rieko Osu; Etienne Burdet; Mitsuo Kawato; Theodore E Milner
Journal:  J Neurophysiol       Date:  2003-11       Impact factor: 2.714

7.  Regulatory actions of human stretch reflex.

Authors:  P E Crago; J C Houk; Z Hasan
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8.  Temporal and amplitude generalization in motor learning.

Authors:  S J Goodbody; D M Wolpert
Journal:  J Neurophysiol       Date:  1998-04       Impact factor: 2.714

9.  Consolidation in human motor memory.

Authors:  T Brashers-Krug; R Shadmehr; E Bizzi
Journal:  Nature       Date:  1996-07-18       Impact factor: 49.962

10.  Human arm stiffness and equilibrium-point trajectory during multi-joint movement.

Authors:  H Gomi; M Kawato
Journal:  Biol Cybern       Date:  1997-03       Impact factor: 2.086

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  65 in total

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4.  Motor adaptation to a small force field superimposed on a large background force.

Authors:  Jiayin Liu; David J Reinkensmeyer
Journal:  Exp Brain Res       Date:  2006-11-08       Impact factor: 1.972

5.  Greater reliance on impedance control in the nondominant arm compared with the dominant arm when adapting to a novel dynamic environment.

Authors:  Christopher N Schabowsky; Joseph M Hidler; Peter S Lum
Journal:  Exp Brain Res       Date:  2007-07-05       Impact factor: 1.972

6.  The training schedule affects the stability, not the magnitude, of the interlimb transfer of learned dynamics.

Authors:  Wilsaan M Joiner; Jordan B Brayanov; Maurice A Smith
Journal:  J Neurophysiol       Date:  2013-05-29       Impact factor: 2.714

7.  Flexible Control of Safety Margins for Action Based on Environmental Variability.

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Journal:  J Neurosci       Date:  2015-06-17       Impact factor: 6.167

8.  The optimal neural strategy for a stable motor task requires a compromise between level of muscle cocontraction and synaptic gain of afferent feedback.

Authors:  Jakob L Dideriksen; Francesco Negro; Dario Farina
Journal:  J Neurophysiol       Date:  2015-07-22       Impact factor: 2.714

9.  Practice modulates motor-related beta oscillations differently in adolescents and adults.

Authors:  James E Gehringer; David J Arpin; Elizabeth Heinrichs-Graham; Tony W Wilson; Max J Kurz
Journal:  J Physiol       Date:  2019-05-15       Impact factor: 5.182

10.  A computational model of limb impedance control based on principles of internal model uncertainty.

Authors:  Djordje Mitrovic; Stefan Klanke; Rieko Osu; Mitsuo Kawato; Sethu Vijayakumar
Journal:  PLoS One       Date:  2010-10-26       Impact factor: 3.240

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