Abstract

We present and apply a method that uses point process statistics to discriminate the forms of synergies in motor pattern data, prior to explicit synergy extraction. The method uses electromyogram (EMG) pulse peak timing or onset timing. Peak timing is preferable in complex patterns where pulse onsets may be overlapping. An interval statistic derived from the point processes of EMG peak timings distinguishes time-varying synergies from synchronous synergies (SS). Model data shows that the statistic is robust for most conditions. Its application to both frog hindlimb EMG and rat locomotion hindlimb EMG show data from these preparations is clearly most consistent with synchronous synergy models (p < 0.001). Additional direct tests of pulse and interval relations in frog data further bolster the support for synchronous synergy mechanisms in these data. Our method and analyses support separated control of rhythm and pattern of motor primitives, with the low level execution primitives comprising pulsed SS in both frog and rat, and both episodic and rhythmic behaviors.

Highlights

  • The efficient control of an organism’s motor architecture poses significant difficulties for the central nervous system

  • DIFFERENCE OF Q STATISTIC FOR SS AND time-varying synergy (TVS) MODEL DATA We constructed 25 time series for each parameter set as above (Poisson interval distributed events) and calculated Q values on simulated TVS and simulated SS models for each sampling on these series

  • Q statistics could not be discriminated between the uniform and Poisson generators using paired t-tests

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Summary

Introduction

The efficient control of an organism’s motor architecture poses significant difficulties for the central nervous system. Control of the limbs is an ill-posed problem: too many possible solutions are available to perform a particular motion for the nervous system to find the correct combinations of muscles in a timely manner. Modular control of motor structures reduces the number of independent points of control for the system and reduces the number of degrees of freedom available in the execution of a movement. Many examples of execution modularity have been reported in recent years and include such examples as central pattern generators (Grillner, 2006), half center oscillator models, blends (Stein et al, 1986; Stein, 1989), motor primitives (Giszter et al, 1991, 1993; Hart and Giszter, 2004, 2010), and time-varying synergies (d’Avella et al, 2006)

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