Literature DB >> 21672856

Predictive and reactive tuning of the locomotor CPG.

Arthur Prochazka1, Sergiy Yakovenko.   

Abstract

The neural control of locomotion involves a constant interplay between the actions of a central pattern generator (CPG) and sensory input elicited by bodily movement. With respect to the CPG, recent analysis of fictive locomotion has shown that durations of flexion and extension tend to covary along specific lines in plots of phase duration versus cycle duration. The slopes of these lines evidently depend on internal states that vary among preparations, but, within a preparation, remain rather steady from one sequence to the next. These relationships can be reproduced in a simple oscillator model having two pairs of preset parameters, suggesting that steady internal drives to flexor and extensor half-centers determine how phase durations covary. Regarding the role of sensory inputs, previous experiments have revealed state-dependent rules that govern phase-switching independently of the CPG rhythm. In addition, sensory input is known to modulate motoneuronal activation through stretch reflexes. To explore how sensory input combines with the locomotor CPG, we used a neuromechanical model with muscle actuators, proprioceptive feedback, sensory phase-switching rules, and a CPG. Interestingly, sequences of stable locomotion were always associated with phase durations that conformed to an extensor-dominated phase-duration characteristic (where extension durations vary more than flexion durations). This is the characteristic seen in normal animals, but not necessarily in fictive locomotion, where movement and associated sensory input are absent. This suggests that to produce the biomechanical events required for stability, an extensor-dominated phase-duration characteristic is required. In the model, when the preset CPG phase durations were well matched to coincide the biomechanical requirements, CPG-mediated phase switching produced stable cycles. When CPG phase durations were too short, phases switched prematurely and the model soon fell. When CPG phase durations were too long, sensory rules fired and overrode the CPG, maintaining stability. We posit that under normal circumstances, descending input from higher centers continually adjusts the operating point of the CPG on the preset phase-duration characteristic according to anticipated biomechanical requirements. When the predictions are good, CPG-generated phase durations closely match those required by the kinetics and kinematics, and little or no sensory adjustment occurs. We propose the term "neuromechanical tuning" to describe this process of matching the CPG to the biomechanical requirements.

Year:  2007        PMID: 21672856     DOI: 10.1093/icb/icm065

Source DB:  PubMed          Journal:  Integr Comp Biol        ISSN: 1540-7063            Impact factor:   3.326


  10 in total

1.  Integration of intrinsic muscle properties, feed-forward and feedback signals for generating and stabilizing hopping.

Authors:  D F B Haeufle; S Grimmer; K-T Kalveram; A Seyfarth
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2.  Targeted stimulation of the spinal cord to restore locomotor activity.

Authors:  Arthur Prochazka
Journal:  Nat Med       Date:  2016-02       Impact factor: 53.440

Review 3.  Sensory control of normal movement and of movement aided by neural prostheses.

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Review 4.  Neurophysiology and neural engineering: a review.

Authors:  Arthur Prochazka
Journal:  J Neurophysiol       Date:  2017-05-31       Impact factor: 2.714

5.  Fast muscle responses to an unexpected foot-in-hole scenario, evoked in the context of prior knowledge of the potential perturbation.

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Journal:  Exp Brain Res       Date:  2010-04-23       Impact factor: 1.972

6.  The flexion synergy, mother of all synergies and father of new models of gait.

Authors:  Jacques Duysens; Friedl De Groote; Ilse Jonkers
Journal:  Front Comput Neurosci       Date:  2013-03-13       Impact factor: 2.380

7.  Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials.

Authors:  Anton Sobinov; Matthew T Boots; Valeriya Gritsenko; Lee E Fisher; Robert A Gaunt; Sergiy Yakovenko
Journal:  PLoS Comput Biol       Date:  2020-12-16       Impact factor: 4.475

8.  Maladaptive spinal plasticity opposes spinal learning and recovery in spinal cord injury.

Authors:  Adam R Ferguson; J Russell Huie; Eric D Crown; Kyle M Baumbauer; Michelle A Hook; Sandra M Garraway; Kuan H Lee; Kevin C Hoy; James W Grau
Journal:  Front Physiol       Date:  2012-10-10       Impact factor: 4.566

9.  Repeated Bout Rate Enhancement Is Elicited by Various Forms of Finger Tapping.

Authors:  Anders Emanuelsen; Michael Voigt; Pascal Madeleine; Pia Kjær; Sebastian Dam; Nikolaj Koefoed; Ernst A Hansen
Journal:  Front Neurosci       Date:  2018-07-31       Impact factor: 4.677

10.  Electrical spinal cord stimulation must preserve proprioception to enable locomotion in humans with spinal cord injury.

Authors:  Emanuele Formento; Karen Minassian; Fabien Wagner; Jean Baptiste Mignardot; Camille G Le Goff-Mignardot; Andreas Rowald; Jocelyne Bloch; Silvestro Micera; Marco Capogrosso; Gregoire Courtine
Journal:  Nat Neurosci       Date:  2018-10-31       Impact factor: 24.884

  10 in total

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