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

• Published in: Frontiers in computational neuroscience Vol. 8; p. 100
• Main Authors: Bengoetxea, Ana, Leurs, Françoise, Hoellinger, Thomas, Cebolla, Ana M, Dan, Bernard, McIntyre, Joseph, Cheron, Guy
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 17.09.2014; Frontiers Media S.A
• Subjects: Arm; Biomechanics; Dynamic recurrent neuronal network; Electromyography; figure-eight; Hand; Kinematics; Muscles; muscular synergies; Musculoskeletal system; Neural networks; Neuroscience; Physiology; Posture; Preschool children; Principal Component Analysis; Principal components analysis; rhythmic movements; Rhythms; Spinal cord; upper-limb; Wrist; Belgium; Spain; figure-eight; upper limb; dynamic recurrent neuronal network; rhythmic movement; muscular synergy; principal component analysis
• ISSN: 1662-5188; 1662-5188
• DOI: 10.3389/fncom.2014.00100

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.