Literature DB >> 21184354

Modularity for sensorimotor control: evidence and a new prediction.

Andrea d'Avella1, Dinesh K Pai.   

Abstract

By combining a few modules, the CNS may learn new control policies quickly and efficiently. Support for a modular organization of the motor system has recently come from the observation of low dimensionality in the motor commands. However, stronger evidence would come from testing the predictions on the effect of an intervention on the mechanisms required to implement a modular controller. Thus, the authors propose to test the predictions of modularity on motor adaptation. They argue that unlike a nonmodular controller, a modular controller must adapt faster to a perturbation that is compatible with the modules (i.e., one that can be compensated by reusing the same modules), than to an incompatible perturbation (i.e., one that requires new modules).

Mesh:

Year:  2010        PMID: 21184354     DOI: 10.1080/00222895.2010.526453

Source DB:  PubMed          Journal:  J Mot Behav        ISSN: 0022-2895            Impact factor:   1.328


  14 in total

1.  Muscle Synergies of Untrained Subjects during 6 min Maximal Rowing on Slides and Fixed Ergometer.

Authors:  Shazlin Shaharudin; Damiano Zanotto; Sunil Agrawal
Journal:  J Sports Sci Med       Date:  2014-12-01       Impact factor: 2.988

2.  Control of reaching movements by muscle synergy combinations.

Authors:  Andrea d'Avella; Francesco Lacquaniti
Journal:  Front Comput Neurosci       Date:  2013-04-19       Impact factor: 2.380

3.  Learned graphical models for probabilistic planning provide a new class of movement primitives.

Authors:  Elmar A Rückert; Gerhard Neumann; Marc Toussaint; Wolfgang Maass
Journal:  Front Comput Neurosci       Date:  2013-01-02       Impact factor: 2.380

4.  Optimization of muscle activity for task-level goals predicts complex changes in limb forces across biomechanical contexts.

Authors:  J Lucas McKay; Lena H Ting
Journal:  PLoS Comput Biol       Date:  2012-04-12       Impact factor: 4.475

5.  Robustness of muscle synergies during visuomotor adaptation.

Authors:  Reinhard Gentner; Timothy Edmunds; Dinesh K Pai; Andrea d'Avella
Journal:  Front Comput Neurosci       Date:  2013-09-03       Impact factor: 2.380

6.  Effort minimization and synergistic muscle recruitment for three-dimensional force generation.

Authors:  Daniele Borzelli; Denise J Berger; Dinesh K Pai; Andrea d'Avella
Journal:  Front Comput Neurosci       Date:  2013-12-20       Impact factor: 2.380

7.  Feedback of mechanical effectiveness induces adaptations in motor modules during cycling.

Authors:  Cristiano De Marchis; Maurizio Schmid; Daniele Bibbo; Anna Margherita Castronovo; Tommaso D'Alessio; Silvia Conforto
Journal:  Front Comput Neurosci       Date:  2013-04-17       Impact factor: 2.380

8.  The neural origin of muscle synergies.

Authors:  Emilio Bizzi; Vincent C K Cheung
Journal:  Front Comput Neurosci       Date:  2013-04-29       Impact factor: 2.380

9.  Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems.

Authors:  Elmar Rückert; Andrea d'Avella
Journal:  Front Comput Neurosci       Date:  2013-10-17       Impact factor: 2.380

10.  A computational analysis of motor synergies by dynamic response decomposition.

Authors:  Cristiano Alessandro; Juan Pablo Carbajal; Andrea d'Avella
Journal:  Front Comput Neurosci       Date:  2014-01-16       Impact factor: 2.380

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