Abstract

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.

Highlights

  • The concept of synergy, associated with basic motor modules of activity, refers to two distinct notions

  • When a dynamic recurrent neural network (DRNN) was applied to EMG and kinematic data acquired from infants and toddlers (Cheron et al, 2001) we showed that it is only when behaviors have been practiced sufficiently by the children and when the task and the context are unchanging that patterns emerged were sufficiently stable to allow the DRNN to generalize (Cheron et al, 2011)

  • In our previous study we showed that the DRNN could recognize the preferential direction of the muscles based on a single movement, it was not able to generalize from training on one movement to reproduce movements based on EMG signals from trials with different initial directions of the movement

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Summary

Introduction

The concept of synergy, associated with basic motor modules of activity, refers to two distinct notions. Non-invasive recording of the electromyographic (EMG) signals are widely used to extract muscular synergies (d’Avella et al, 2003, 2008; Ivanenko et al, 2004; Klein Breteler et al, 2007; Cheung et al, 2012; Frère and Hug, 2012). These muscular synergies seem to be structured in the brain stem and spinal cord (Cheung et al, 2009; Clark et al, 2010) and even in the motor cortex for highly skilled movements (Gentner and Classen, 2006; Rathelot and Strick, 2006)

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