Physiological modules for generating discrete and rhythmic movements: Action identification by a dynamic recurrent neural network

Simple item page
dc.contributor.authorBengoetxea, Ana
dc.contributor.authorLeurs, Françoise
dc.contributor.authorHoellinger, Thomas
dc.contributor.authorCebolla, Ana M.
dc.contributor.authorDan, Bernard
dc.contributor.authorMcIntyre, Joseph
dc.contributor.authorCheron, Guy
dc.contributor.institutionMedical Technologies
dc.date.accessioned2024-07-24T12:02:43Z
dc.date.available2024-07-24T12:02:43Z
dc.date.issued2014-09-17
dc.descriptionPublisher Copyright: © 2014 Bengoetxea, Leurs, Hoellinger, Cebolla, Dan, McIntyre and Cheron.
dc.description.abstractIn this study we employed a dynamic recurrent neural network (DRNN) in a novel fashion to reveal characteristics of control modules underlying the generation of muscle activations when drawing figures with the outstretched arm. We asked healthy human subjects to perform four different figure-eight movements in each of two workspaces (frontal plane and sagittal plane). We then trained a DRNN to predict the movement of the wrist from information in the EMG signals from seven different muscles. We trained different instances of the same network on a single movement direction, on all four movement directions in a single movement plane, or on all eight possible movement patterns and looked at the ability of the DRNN to generalize and predict movements for trials that were not included in the training set. Within a single movement plane, a DRNN trained on one movement direction was not able to predict movements of the hand for trials in the other three directions, but a DRNN trained simultaneously on all four movement directions could generalize across movement directions within the same plane. Similarly, the DRNN was able to reproduce the kinematics of the hand for both movement planes, but only if it was trained on examples performed in each one. As we will discuss, these results indicate that there are important dynamical constraints on the mapping of EMG to hand movement that depend on both the time sequence of the movement and on the anatomical constraints of the musculoskeletal system. In a second step, we injected EMG signals constructed from different synergies derived by the PCA in order to identify the mechanical significance of each of these components. From these results, one can surmise that discrete-rhythmic movements may be constructed from three different fundamental modules, one regulating the co-activation of all muscles over the time span of the movement and two others elliciting patterns of reciprocal activation operating in orthogonal directions.en
dc.description.statusPeer reviewed
dc.identifier.citationBengoetxea , A , Leurs , F , Hoellinger , T , Cebolla , A M , Dan , B , McIntyre , J & Cheron , G 2014 , ' Physiological modules for generating discrete and rhythmic movements : Action identification by a dynamic recurrent neural network ' , Frontiers in Computational Neuroscience , vol. 8 , 100 . https://doi.org/10.3389/fncom.2014.00100
dc.identifier.doi10.3389/fncom.2014.00100
dc.identifier.issn1662-5188
dc.identifier.urihttps://hdl.handle.net/11556/3275
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=84907287349&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofFrontiers in Computational Neuroscience
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsDynamic recurrent neuronal network
dc.subject.keywordsFigure-eight
dc.subject.keywordsMuscular synergy
dc.subject.keywordsPrincipal component analysis
dc.subject.keywordsRhythmic movement
dc.subject.keywordsUpper limb
dc.subject.keywordsNeuroscience (miscellaneous)
dc.subject.keywordsCellular and Molecular Neuroscience
dc.titlePhysiological modules for generating discrete and rhythmic movements: Action identification by a dynamic recurrent neural networken
dc.typejournal article
Files