RT Journal Article
T1 Physiological modules for generating discrete and rhythmic movements: 												Action identification by a dynamic recurrent neural network
A1 Bengoetxea, Ana
A1 Leurs, Françoise
A1 Hoellinger, Thomas
A1 Cebolla, Ana M.
A1 Dan, Bernard
A1 McIntyre, Joseph
A1 Cheron, Guy
AB In 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.
SN 1662-5188
YR 2014
FD 2014-09-17
LK https://hdl.handle.net/11556/3275
UL https://hdl.handle.net/11556/3275
LA eng
NO Bengoetxea , 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
NO Publisher Copyright: © 2014 Bengoetxea, Leurs, Hoellinger, Cebolla, Dan, McIntyre and Cheron.
DS TECNALIA Publications
RD 26 jul 2024