Synergetic motor control paradigm for optimizing energy efficiency of multijoint reaching via tacit learning
A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs an ill-posed problem. The multijoint system involves complex interaction torques between joints. To produce optimal motion in terms of energy...
Gespeichert in:
| Veröffentlicht in: | Frontiers in computational neuroscience Jg. 8; S. 21 |
|---|---|
| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Switzerland
Frontiers Research Foundation
28.02.2014
Frontiers Frontiers Media S.A |
| Schlagworte: | |
| ISSN: | 1662-5188, 1662-5188 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs an ill-posed problem. The multijoint system involves complex interaction torques between joints. To produce optimal motion in terms of energy consumption, the so-called cost function based optimization has been commonly used in previous works.Even if it is a fact that an optimal motor pattern is employed phenomenologically, there is no evidence that shows the existence of a physiological process that is similar to such a mathematical optimization in our central nervous system.In this study, we aim to find a more primitive computational mechanism with a modular configuration to realize adaptability and optimality without prior knowledge of system dynamics.We propose a novel motor control paradigm based on tacit learning with task space feedback. The motor command accumulation during repetitive environmental interactions, play a major role in the learning process. It is applied to a vertical cyclic reaching which involves complex interaction torques.We evaluated whether the proposed paradigm can learn how to optimize solutions with a 3-joint, planar biomechanical model. The results demonstrate that the proposed method was valid for acquiring motor synergy and resulted in energy efficient solutions for different load conditions. The case in feedback control is largely affected by the interaction torques. In contrast, the trajectory is corrected over time with tacit learning toward optimal solutions.Energy efficient solutions were obtained by the emergence of motor synergy. During learning, the contribution from feedforward controller is augmented and the one from the feedback controller is significantly minimized down to 12% for no load at hand, 16% for a 0.5 kg load condition.The proposed paradigm could provide an optimization process in redundant system with dynamic-model-free and cost-function-free approach. |
|---|---|
| AbstractList | A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs an ill-posed problem. The multijoint system involves complex interaction torques between joints. To produce optimal motion in terms of energy consumption, the so-called cost function based optimization has been commonly used in previous works. Even if it is a fact that an optimal motor pattern is employed phenomenologically, there is no evidence that shows the existence of a physiological process that is similar to such a mathematical optimization in our central nervous system. In this study, we aim to find a more primitive computational mechanism with a modular configuration to realize adaptability and optimality without prior knowledge of system dynamics. We propose a novel motor control paradigm based on tacit learning with task space feedback. The motor command accumulation during repetitive environmental interactions, play a major role in the learning process. It is applied to a vertical cyclic reaching which involves complex interaction torques. We evaluated whether the proposed paradigm can learn how to optimize solutions with a 3-joint, planar biomechanical model. The results demonstrate that the proposed method was valid for acquiring motor synergy and resulted in energy efficient solutions for different load conditions. The case in feedback control is largely affected by the interaction torques. In contrast, the trajectory is corrected over time with tacit learning toward optimal solutions. Energy efficient solutions were obtained by the emergence of motor synergy. During learning, the contribution from feedforward controller is augmented and the one from the feedback controller is minimized down to 12% for no load at hand, 16% for a 0.5kg load condition. The proposed paradigm could provide an optimization process in redundant system with dynamic-model-free and cost-function-free approach. A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs an ill-posed problem. The multijoint system involves complex interaction torques between joints. To produce optimal motion in terms of energy consumption, the so-called cost function based optimization has been commonly used in previous works.Even if it is a fact that an optimal motor pattern is employed phenomenologically, there is no evidence that shows the existence of a physiological process that is similar to such a mathematical optimization in our central nervous system.In this study, we aim to find a more primitive computational mechanism with a modular configuration to realize adaptability and optimality without prior knowledge of system dynamics.We propose a novel motor control paradigm based on tacit learning with task space feedback. The motor command accumulation during repetitive environmental interactions, play a major role in the learning process. It is applied to a vertical cyclic reaching which involves complex interaction torques.We evaluated whether the proposed paradigm can learn how to optimize solutions with a 3-joint, planar biomechanical model. The results demonstrate that the proposed method was valid for acquiring motor synergy and resulted in energy efficient solutions for different load conditions. The case in feedback control is largely affected by the interaction torques. In contrast, the trajectory is corrected over time with tacit learning toward optimal solutions.Energy efficient solutions were obtained by the emergence of motor synergy. During learning, the contribution from feedforward controller is augmented and the one from the feedback controller is significantly minimized down to 12% for no load at hand, 16% for a 0.5 kg load condition.The proposed paradigm could provide an optimization process in redundant system with dynamic-model-free and cost-function-free approach.A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs an ill-posed problem. The multijoint system involves complex interaction torques between joints. To produce optimal motion in terms of energy consumption, the so-called cost function based optimization has been commonly used in previous works.Even if it is a fact that an optimal motor pattern is employed phenomenologically, there is no evidence that shows the existence of a physiological process that is similar to such a mathematical optimization in our central nervous system.In this study, we aim to find a more primitive computational mechanism with a modular configuration to realize adaptability and optimality without prior knowledge of system dynamics.We propose a novel motor control paradigm based on tacit learning with task space feedback. The motor command accumulation during repetitive environmental interactions, play a major role in the learning process. It is applied to a vertical cyclic reaching which involves complex interaction torques.We evaluated whether the proposed paradigm can learn how to optimize solutions with a 3-joint, planar biomechanical model. The results demonstrate that the proposed method was valid for acquiring motor synergy and resulted in energy efficient solutions for different load conditions. The case in feedback control is largely affected by the interaction torques. In contrast, the trajectory is corrected over time with tacit learning toward optimal solutions.Energy efficient solutions were obtained by the emergence of motor synergy. During learning, the contribution from feedforward controller is augmented and the one from the feedback controller is significantly minimized down to 12% for no load at hand, 16% for a 0.5 kg load condition.The proposed paradigm could provide an optimization process in redundant system with dynamic-model-free and cost-function-free approach. A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs an ill-posed problem. The multijoint system involves complex interaction torques between joints. To produce optimal motion in terms of energy consumption, the so-called cost function based optimization has been commonly used in previous works.Even if it is a fact that an optimal motor pattern is employed phenomenologically, there is no evidence that shows the existence of a physiological process that is similar to such a mathematical optimization in our central nervous system.In this study, we aim to find a more primitive computational mechanism with a modular configuration to realize adaptability and optimality without prior knowledge of system dynamics.We propose a novel motor control paradigm based on tacit learning with task space feedback. The motor command accumulation during repetitive environmental interactions, play a major role in the learning process. It is applied to a vertical cyclic reaching which involves complex interaction torques.We evaluated whether the proposed paradigm can learn how to optimize solutions with a 3-joint, planar biomechanical model. The results demonstrate that the proposed method was valid for acquiring motor synergy and resulted in energy efficient solutions for different load conditions. The case in feedback control is largely affected by the interaction torques. In contrast, the trajectory is corrected over time with tacit learning toward optimal solutions.Energy efficient solutions were obtained by the emergence of motor synergy. During learning, the contribution from feedforward controller is augmented and the one from the feedback controller is significantly minimized down to 12% for no load at hand, 16% for a 0.5 kg load condition.The proposed paradigm could provide an optimization process in redundant system with dynamic-model-free and cost-function-free approach. |
| Author | Hayashibe, Mitsuhiro Shimoda, Shingo |
| AuthorAffiliation | 2 Brain Science Institute-Toyota Collaboration Center, RIKEN Nagoya, Japan 1 INRIA DEMAR Project and LIRMM, University of Montpellier Montpellier, France |
| AuthorAffiliation_xml | – name: 1 INRIA DEMAR Project and LIRMM, University of Montpellier Montpellier, France – name: 2 Brain Science Institute-Toyota Collaboration Center, RIKEN Nagoya, Japan |
| Author_xml | – sequence: 1 givenname: Mitsuhiro surname: Hayashibe fullname: Hayashibe, Mitsuhiro – sequence: 2 givenname: Shingo surname: Shimoda fullname: Shimoda, Shingo |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24616695$$D View this record in MEDLINE/PubMed https://hal-lirmm.ccsd.cnrs.fr/lirmm-00979632$$DView record in HAL |
| BookMark | eNp1kktrGzEUhYeS0jzafVdF0E0h2NV7pE0hhKYJGLpouxay5o4to5FcjWxwf31lOymJoSuJe79zdCWdy-YspghN857gKWNKf-6jS8OUYsKnGGNKXjUXREo6EUSps2f78-ZyHFcYSyoFftOcUy5rT4uLJvzYRcgLKN6hIZWUkUux5BTQ2mbb-cWA-lpM6-IH_8fHBYI9v0PQ9955iG6HUo-GTSh-lXwsKIN1yz249RYV63xBAWyOtfS2ed3bMMK7x_Wq-XX39eft_WT2_dvD7c1s4oSmZSJ6rDgGrWinWgkMg6Cy45RRiwl0XQtCcmUZ7-aUUwFzwVhLtZpTyi3hll01D0ffLtmVWWc_2LwzyXpzKKS8MDbXGwcwrbKd1Y44pwQHgS1Q2XYcMJurHpioXl-OXuvNfIDOQX0dG16YvuxEvzSLtDVMs1YSWg2ujwbLE9n9zcwEn4fBYKxbLRndkkp_ejwup98bGIsZ_OggBBshbUZDBJatpoywin48QVdpk2N9WEOp0rLFivBKfXg-_78JniJQAXkEXE7jmKE39cts8fsYWB8MwWafNXPImtlnzRyyVoX4RPjk_V_JX6K62IY |
| CitedBy_id | crossref_primary_10_3389_fncom_2024_1355855 crossref_primary_10_1109_LRA_2019_2924854 crossref_primary_10_1515_revneuro_2022_0120 crossref_primary_10_1080_00222895_2016_1247032 crossref_primary_10_3389_fnins_2014_00436 crossref_primary_10_1038_s41598_022_21261_w crossref_primary_10_1080_01691864_2019_1633402 crossref_primary_10_3389_fnbot_2018_00043 crossref_primary_10_3389_fpsyg_2015_01642 crossref_primary_10_1109_TSMC_2016_2560532 crossref_primary_10_1080_24748668_2019_1691814 crossref_primary_10_1016_j_cobeha_2022_101109 crossref_primary_10_1109_TCDS_2017_2697904 crossref_primary_10_3389_fnsyn_2020_00007 crossref_primary_10_1109_ACCESS_2020_2987095 crossref_primary_10_3390_brainsci13010039 crossref_primary_10_3389_fncom_2015_00126 crossref_primary_10_1109_LRA_2020_2970626 crossref_primary_10_3389_frobt_2021_632804 crossref_primary_10_1016_j_neunet_2022_03_002 crossref_primary_10_1109_LRA_2020_2968067 |
| ContentType | Journal Article |
| Copyright | 2014. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Distributed under a Creative Commons Attribution 4.0 International License Copyright © 2014 Hayashibe and Shimoda. 2014 |
| Copyright_xml | – notice: 2014. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: Distributed under a Creative Commons Attribution 4.0 International License – notice: Copyright © 2014 Hayashibe and Shimoda. 2014 |
| DBID | AAYXX CITATION NPM 3V. 7XB 88I 8FE 8FH 8FK ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO GNUQQ HCIFZ LK8 M2P M7P PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 1XC VOOES 5PM DOA |
| DOI | 10.3389/fncom.2014.00021 |
| DatabaseName | CrossRef PubMed ProQuest Central (Corporate) ProQuest Central (purchase pre-March 2016) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One ProQuest Central Korea ProQuest Central Student SciTech Premium Collection Biological Sciences Science Database Biological Science Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic Hyper Article en Ligne (HAL) Hyper Article en Ligne (HAL) (Open Access) PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences Natural Science Collection ProQuest Central Korea Biological Science Collection ProQuest Central (New) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition Biological Science Database ProQuest SciTech Collection ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic PubMed Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Anatomy & Physiology Computer Science |
| EISSN | 1662-5188 |
| ExternalDocumentID | oai_doaj_org_article_78ada9c1cc854e50ae267d4e03b8fe35 PMC3937612 oai:HAL:lirmm-00979632v1 24616695 10_3389_fncom_2014_00021 |
| Genre | Journal Article |
| GroupedDBID | --- 29H 2WC 53G 5GY 5VS 88I 8FE 8FH 9T4 AAFWJ AAYXX ABUWG ACGFO ACGFS ADBBV ADMLS ADRAZ AEGXH AENEX AFFHD AFKRA AFPKN AIAGR ALMA_UNASSIGNED_HOLDINGS AOIJS ARCSS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ C1A CCPQU CITATION CS3 DIK DWQXO E3Z F5P GNUQQ GROUPED_DOAJ GX1 HCIFZ HYE IPNFZ KQ8 LK8 M2P M48 M7P M~E O5R O5S OK1 OVT P2P PGMZT PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC RIG RNS RPM TR2 ACXDI NPM 3V. 7XB 8FK PKEHL PQEST PQUKI PRINS Q9U 7X8 1XC VOOES 5PM |
| ID | FETCH-LOGICAL-c592t-5f0840e982d876e30e526d4232a01edd7e5648a34db2425eb5337298b224a14a3 |
| IEDL.DBID | BENPR |
| ISICitedReferencesCount | 24 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000332514900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1662-5188 |
| IngestDate | Mon Nov 10 04:32:22 EST 2025 Tue Nov 04 01:41:27 EST 2025 Tue Oct 14 20:37:01 EDT 2025 Sun Nov 09 10:42:55 EST 2025 Fri Jul 25 11:43:39 EDT 2025 Mon Jul 21 05:50:36 EDT 2025 Sat Nov 29 02:11:10 EST 2025 Tue Nov 18 22:30:59 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | feedback error learning Bernstein problem interaction torques motor synergy tacit learning optimality redundancy Motor synergy Redundancy Interaction torques Optimality Tacit learning Feedback error learning |
| Language | English |
| License | Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0 This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c592t-5f0840e982d876e30e526d4232a01edd7e5648a34db2425eb5337298b224a14a3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Edited by: Andrea d'Avella, IRCCS Fondazione Santa Lucia, Italy Reviewed by: Amir Karniel, Ben-Gurion University, Israel; Elmar A. Rückert, Graz University of Technology, Austria; J. Lucas McKay, Georgia Tech/Emory University, USA This article was submitted to the journal Frontiers in Computational Neuroscience. |
| ORCID | 0000-0001-6179-5706 |
| OpenAccessLink | https://www.proquest.com/docview/2289670814?pq-origsite=%requestingapplication% |
| PMID | 24616695 |
| PQID | 2289670814 |
| PQPubID | 4424409 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_78ada9c1cc854e50ae267d4e03b8fe35 pubmedcentral_primary_oai_pubmedcentral_nih_gov_3937612 hal_primary_oai_HAL_lirmm_00979632v1 proquest_miscellaneous_1506792313 proquest_journals_2289670814 pubmed_primary_24616695 crossref_citationtrail_10_3389_fncom_2014_00021 crossref_primary_10_3389_fncom_2014_00021 |
| PublicationCentury | 2000 |
| PublicationDate | 2014-02-28 |
| PublicationDateYYYYMMDD | 2014-02-28 |
| PublicationDate_xml | – month: 02 year: 2014 text: 2014-02-28 day: 28 |
| PublicationDecade | 2010 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland – name: Lausanne |
| PublicationTitle | Frontiers in computational neuroscience |
| PublicationTitleAlternate | Front Comput Neurosci |
| PublicationYear | 2014 |
| Publisher | Frontiers Research Foundation Frontiers Frontiers Media S.A |
| Publisher_xml | – name: Frontiers Research Foundation – name: Frontiers – name: Frontiers Media S.A |
| References | 11058820 - Trends Cogn Sci. 2000 Nov 1;4(11):423-431 8182467 - J Neurosci. 1994 May;14(5 Pt 2):3208-24 4338265 - Brain Res. 1972 May 12;40(1):81-4 4020415 - J Neurosci. 1985 Jul;5(7):1688-703 9116080 - Biol Cybern. 1997 Feb;76(2):97-105 9723616 - Nature. 1998 Aug 20;394(6695):780-4 8836239 - J Neurophysiol. 1996 Jul;76(1):492-509 10607637 - Curr Opin Neurobiol. 1999 Dec;9(6):718-27 12563264 - Nat Neurosci. 2003 Mar;6(3):300-8 9548253 - Nature. 1998 Apr 2;392(6675):494-7 10561408 - J Neurophysiol. 1999 Nov;82(5):2310-26 1281352 - Trends Neurosci. 1992 Nov;15(11):445-53 3676355 - Biol Cybern. 1987;57(3):169-85 17005621 - J Neurophysiol. 2007 Jan;97(1):331-47 19651559 - IEEE Trans Syst Man Cybern B Cybern. 2010 Feb;40(1):77-90 8872282 - J Biomech. 1996 Sep;29(9):1223-30 19458218 - J Neurosci. 2009 May 20;29(20):6472-8 15332089 - Nat Neurosci. 2004 Sep;7(9):907-15 15541947 - Neural Netw. 2004 Dec;17(10):1453-65 24133444 - Front Comput Neurosci. 2013 Oct 15;7:136 9753117 - Eur J Neurosci. 1998 Jan;10(1):95-105 21227230 - Trends Cogn Sci. 1998 Sep 1;2(9):338-47 12404008 - Nat Neurosci. 2002 Nov;5(11):1226-35 2742921 - Biol Cybern. 1989;61(2):89-101 15541316 - Neuron. 2004 Nov 18;44(4):691-700 21487784 - Cogn Process. 2011 Nov;12(4):319-40 1486143 - Biol Cybern. 1992;68(2):95-103 22654062 - Science. 2012 Jun 1;336(6085):1182-5 1857964 - Science. 1991 Jul 19;253(5017):287-91 |
| References_xml | – reference: 9753117 - Eur J Neurosci. 1998 Jan;10(1):95-105 – reference: 9723616 - Nature. 1998 Aug 20;394(6695):780-4 – reference: 24133444 - Front Comput Neurosci. 2013 Oct 15;7:136 – reference: 21227230 - Trends Cogn Sci. 1998 Sep 1;2(9):338-47 – reference: 2742921 - Biol Cybern. 1989;61(2):89-101 – reference: 12563264 - Nat Neurosci. 2003 Mar;6(3):300-8 – reference: 10561408 - J Neurophysiol. 1999 Nov;82(5):2310-26 – reference: 17005621 - J Neurophysiol. 2007 Jan;97(1):331-47 – reference: 4338265 - Brain Res. 1972 May 12;40(1):81-4 – reference: 1857964 - Science. 1991 Jul 19;253(5017):287-91 – reference: 11058820 - Trends Cogn Sci. 2000 Nov 1;4(11):423-431 – reference: 1281352 - Trends Neurosci. 1992 Nov;15(11):445-53 – reference: 4020415 - J Neurosci. 1985 Jul;5(7):1688-703 – reference: 8182467 - J Neurosci. 1994 May;14(5 Pt 2):3208-24 – reference: 15541316 - Neuron. 2004 Nov 18;44(4):691-700 – reference: 10607637 - Curr Opin Neurobiol. 1999 Dec;9(6):718-27 – reference: 19651559 - IEEE Trans Syst Man Cybern B Cybern. 2010 Feb;40(1):77-90 – reference: 8872282 - J Biomech. 1996 Sep;29(9):1223-30 – reference: 9116080 - Biol Cybern. 1997 Feb;76(2):97-105 – reference: 9548253 - Nature. 1998 Apr 2;392(6675):494-7 – reference: 12404008 - Nat Neurosci. 2002 Nov;5(11):1226-35 – reference: 15541947 - Neural Netw. 2004 Dec;17(10):1453-65 – reference: 22654062 - Science. 2012 Jun 1;336(6085):1182-5 – reference: 1486143 - Biol Cybern. 1992;68(2):95-103 – reference: 15332089 - Nat Neurosci. 2004 Sep;7(9):907-15 – reference: 8836239 - J Neurophysiol. 1996 Jul;76(1):492-509 – reference: 21487784 - Cogn Process. 2011 Nov;12(4):319-40 – reference: 19458218 - J Neurosci. 2009 May 20;29(20):6472-8 – reference: 3676355 - Biol Cybern. 1987;57(3):169-85 |
| SSID | ssj0062650 |
| Score | 2.0944655 |
| Snippet | A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs... |
| SourceID | doaj pubmedcentral hal proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 21 |
| SubjectTerms | Adaptability Adaptation Automatic Control Engineering Behavior Bernstein problem Bioengineering Central nervous system Computer applications Computer Science Energy efficiency Feedback Feedback Error Learning Kinematics Life Sciences Mechanical properties Motor skill learning Motor synergies Motor task performance Neuroscience Neurosciences optimality Optimization redundancy Robotics Robots Tacit Learning Trends |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwELVQxYELopSPQEFGqpA4RJs4Tmwfl2qrHqBUUKTeLMd22qBNgnbTldpfz4yTXTUgwYVTpMRxLM94Zl4880zIUclMJgtp48SaMuauAjtoZBKzwiGvdVoKz8NhE-LsTF5eqvN7R31hTthADzxM3ExI44yyqbUy5z5PjGeFcNwnWSkrnwX2Uuh0C6YGGwxRep4Mm5IAwdSsajE1BHwdUmUnLJ04ocDVD67lGjMh_wwzf8-WvOd-Tp6Qx2PcSOfDePfJA98-JQfzFjBzc0vf05DJGX6RH5Dlt1us6MPyRPq5A1BNj4eEdHpuVsbVVw2FUJV-AWvR1Hfgu-giVADSReCTwGJM2lU01Ob-6Oq2p1_HnEu6qQ29MLbu6UjMevWMfD9ZXByfxuOpCrHNFevjvEoA1HklmQNL6LPE5yAY3K81SeqdEz4vuDQZdyXCEV9CQAgRuCzB2ZuUm-w52Wu71r8kVEEHecVF7sqKw1UxZjigc2Gx4FeoiMy206ztSDmOJ18sNUAPFIwOgtEoGB0EE5EPuzd-DnQbf2n7ESW3a4dE2eEGqI8e1Uf_S30icgRyn_RxOv-kl_WqaTQWuYCBYhv41uFWMfS4yNeaAVgtBMRUPCLvdo9heeKei2l9d7PWSOCIFI1pFpEXgx7tPoZUfkWhYAxiomGT0UyftPV1oABHGkOITV_9jyl4TR7hpA51-odkr1_d-Dfkod309Xr1NqyrX2_pKWg priority: 102 providerName: Directory of Open Access Journals |
| Title | Synergetic motor control paradigm for optimizing energy efficiency of multijoint reaching via tacit learning |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/24616695 https://www.proquest.com/docview/2289670814 https://www.proquest.com/docview/1506792313 https://hal-lirmm.ccsd.cnrs.fr/lirmm-00979632 https://pubmed.ncbi.nlm.nih.gov/PMC3937612 https://doaj.org/article/78ada9c1cc854e50ae267d4e03b8fe35 |
| Volume | 8 |
| WOSCitedRecordID | wos000332514900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1662-5188 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062650 issn: 1662-5188 databaseCode: DOA dateStart: 20070101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1662-5188 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062650 issn: 1662-5188 databaseCode: M~E dateStart: 20070101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1662-5188 dateEnd: 20211231 omitProxy: false ssIdentifier: ssj0062650 issn: 1662-5188 databaseCode: M7P dateStart: 20071102 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1662-5188 dateEnd: 20211231 omitProxy: false ssIdentifier: ssj0062650 issn: 1662-5188 databaseCode: BENPR dateStart: 20071102 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1662-5188 dateEnd: 20211231 omitProxy: false ssIdentifier: ssj0062650 issn: 1662-5188 databaseCode: PIMPY dateStart: 20071102 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 1662-5188 dateEnd: 20211231 omitProxy: false ssIdentifier: ssj0062650 issn: 1662-5188 databaseCode: M2P dateStart: 20071102 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3Nb9MwFLdYx4ELX4NRGJWRJiQOURPHiZMT6lCnIbGq4kMqJ8uxnS6oTUaaTRoH_nbec91CQdqFiyPF-XDynt-H_d7vEXJcMBVnaaaDUKsi4KYEOaiyMGCpQVzrqBCWu2ITYjLJZrN86hfcVj6sciMTnaA2jcY18iEDzyAVoMD428vvAVaNwt1VX0Jjj-wjUhnvkf2T8WT6cSOLwVpPwvXmJLhi-bCsMUQEdB5CZocs2lFGDrMfVMwFRkT-a27-HTX5hxo6ffC_H_CQ3PcGKB2tOeYRuWPrx-RgVIPzvbyhr6kLCXVr7Qdk8ekGUwMxz5ECSZuW-sh2iojhppovKdi8tAGxs6x-gBKk1qUSUuuAKTCrkzYldUGL35qq7mjrgzfpdaVop3TVUV-3Yv6EfDkdf353FvjyDIFOctYFSRmCd2jzjBkQqTYObQIUxo1fFUbWGGGTlGcq5qZAv8YWYFmCKZ8VYDWoiKv4KenVTW2fEZrDA5KSi8QUJYdjzpji4OYLjZnDIu-T4YZOUnvsciyhsZDgwyBlpaOsRMpKR9k-ebO943KN23HLtSdI-u11iLjtTjTtXPoJLEWmjMp1pHWWcJuEyrJUGG7DuMhKGyd9cgyMs_OMs9EHuaja5VJitgxIOnYN7zracIf00mIlf7NGn7zadsM8x80bVdvmaiURCRKxHqO4Tw7XjLh9GWICpmkOYxA7LLozmt2eurpwWOKIhwhG7vPbh_WC3MPftU7lPyK9rr2yL8ldfd1Vq3ZA9sQsG_ipN3CrGtCesym2wrU_x9A_fX8-_foLIG4_5g |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEF6VggQXXuURKLBIBYmDFXu9fh0QCo8qVUNUiSL1tqx316lRbBfHDQo_it_IzNoOBKTeeuAUKbbXG_vbb2ayM98Qspcy6cdhrBxXydThOgMelLHrsFCjrrWXRobbZhPRdBqfnCRHW-RnXwuDaZU9J1qi1pXC_8iHDCKDMAIDxt-cfXOwaxTurvYtNFpYHJrVdwjZFq8P3sP7fcHY_ofjd2On6yrgqCBhjRNkLgQ1JomZBiYwvmsCmBjuV0rXM1pHJgh5LH2uU3THTQoOEXigcQrGTnpc-jDuFXIV3AgW21TBo575ITYI3HYrFAK_ZJiVmJACFhYFul3mbZg-2yEADNop5l_-69z-naP5h9Hbv_W_Pa7b5GbnXtNRux7ukC1T3iU7o1I2VbGiL6lNeLU7CTtk_mmFhY9YxUkBsFVNu7x9inroOp8VFDx6WgGpFvkPMPHU2EJJaqzsBtas0iqjNiXza5WXDa271FS6zCVtpMob2nXlmN0jny_ld98n22VVmoeEJjBAkPEo0GnG4TNhTHKT-pHCuugoGZBhjwuhOmV2bBAyFxChIZKERZJAJAmLpAF5tb7irFUlueDctwi19XmoJ26_qOqZ6OhJRLHUMlGeUnHATeBKw8JIc-P6aZwZPxiQPQDqxhjj0UTM87ooBNYCAY-zJdxrt0ej6LhwIX5DcUCerw8Di-HWlCxNdb4QqHOJSpaePyAPWuCvb4aKh2GYwByijSWxMZvNI2V-apXSUe0RXPhHF0_rGbk-Pv44EZOD6eFjcgMfXStasEu2m_rcPCHX1LLJF_VTu9wp-XLZC-YXa7aRsA |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEF6VghAXXuURKLBIBYmDFXu968cBoUCJWrVEkQCpt2W9XqdGsV0cNyj8NH4dM2s7EJB664FTpNheb-xvXpmZbwjZS5jyoyDSjqtV4vA0Az2oItdhQYq81l4SGm6HTYSTSXRyEk-3yM--FwbLKnudaBV1Wmn8j3zIIDIIQjBgfJh1ZRHT_fGbs28OTpDCTGs_TqOFyJFZfYfwbfH6cB_e9QvGxu8_vTtwugkDjhYxaxyRuRDgmDhiKWgF47tGwCYxd6lcz6RpaETAI-XzNEHX3CTgHIE3GiVg-JTHlQ_rXiFXQy4EStcHNu2tAMQJwm3TohAExsOsxOIUsLZI1u0yb8MM2mkBYNxOsRbzX0f373rNPwzg-Nb__Ohuk5ud201HrZzcIVumvEt2RqVqqmJFX1JbCGszDDtk_nGFDZHY3UkByFVNu3p-ijzpaT4rKHj6tAJlW-Q_wPRTYxsoqbF0HNjLSquM2lLNr1VeNrTuSlbpMle0UTpvaDetY3aPfL6U332fbJdVaR4SGsMCIuOhSJOMw2fMmOIm8UON_dJhPCDDHiNSd4ztODhkLiFyQ1RJiyqJqJIWVQPyan3FWctWcsG5bxF26_OQZ9x-UdUz2aktGUYqVbH2tI4EN8JVhgVhyo3rJ1FmfDEgewDajTUORsdyntdFIbFHCPQ7W8K9dntkyk5HLuRvWA7I8_Vh0G6YslKlqc4XEvkvkeHS8wfkQSsE65shE2IQxLCHcEM8NnazeaTMTy2DOrJAgmv_6OJtPSPXQU7k8eHk6DG5gU-u5TLYJdtNfW6ekGt62eSL-qmVfEq-XLa8_ALhnZps |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Synergetic+motor+control+paradigm+for+optimizing+energy+efficiency+of+multijoint+reaching+via+tacit+learning&rft.jtitle=Frontiers+in+computational+neuroscience&rft.au=Hayashibe%2C+Mitsuhiro&rft.au=Shimoda%2C+Shingo&rft.date=2014-02-28&rft.issn=1662-5188&rft.eissn=1662-5188&rft.volume=8&rft.spage=21&rft_id=info:doi/10.3389%2Ffncom.2014.00021&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1662-5188&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1662-5188&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1662-5188&client=summon |