Statistical Analysis of Motor Unit Firing Patterns in a Human Skeletal Muscle

Abstract

A statistical analysis of the firing pattern of single motor units in the human brachial biceps muscle is presented. Single motor unit spike trains are recorded and analyzed. The statistical treatment of these spike trains is as stochastic point processes, the theory of which is briefly discussed. Evidence is presented that motor unit spike trains may be modelled by a renewal process with an underlying gaussian probability density. Statistical independence of successive interspike intervals is shown using scatter diagrams; the hypothesis of a gaussian distribution is accepted at the 99th percentile confidence limit, chi-square test, in 90% of the units tested. A functional relationship between the mean and standard deviation is shown and discussed; its implications in obtaining sample size are presented in an appendix.The results of higher order analysis in the form of autocorrelograms and grouped interval histograms are presented. Grouped interval histograms are discussed in the context of motor unit data, and used to confirm the hypothesis that a stable probability density function does not represent a good model of the data at this level of analysis.

Simulation system of spinal cord motor nuclei and associated nerves and muscles, in a Web-based architecture

“…Experimental measures of motor-unit ISI can be obtained from humans with special procedures (Clamann 1969;Andreassen and Rosenfalck 1980;Rosenfalck and Andreassen 1980;Halonen et al 1981). Different motor-unit ISI distributions have been found, according to the muscle and the contraction level (Clamann 1969;Person and Kudina 1972;De Luca and Forrest 1973;Poliakov et al 1995).…”

“…Different motor-unit ISI distributions have been found, according to the muscle and the contraction level (Clamann 1969;Person and Kudina 1972;De Luca and Forrest 1973;Poliakov et al 1995). In some cases, the distribution is Gaussian-like (usually for faster discharge rates), whereas in others, the distribution is asymmetric, with a prolonged right tail (usually for slower firing rates).…”

Minimum Principles in Motor Control

“…When participants are instructed to repeatedly produce identical movements of prescribed amplitude and duration, they instead produce a distribution of movements that is characterized by a scattering of endpoints whose standard deviation scales linearly with average movement speed (Schmidt et al, 1979;Meyer, Smith, 6 Wright 1982); quite apparently, participants do not and cannot produce identical movements across a series of trials. In addition to the behavioral evidence, there is physiological data suggesting that stochasticity is, in fact, a fundamental characteristic of neural information processing (Calvin 6 Stevens, 1968;Clamann, 1969).…”

Movement curvature planning through force field internal models

“…Another class of reaching models are stochastic models that take into account the noise inherent to the motor system. It has been consistently observed that the standard deviation of neuromotor commands increases with its mean (Sutton and Sykes 1967;Schmidt et al 1979;Clamman 1969;Matthews 1996;St-Amant et al 1998;Clancy and Hogan 1999;Osu et al 2004). In line with this evidence, it was suggested that the brain minimizes the variance of the final arm position in the presence of such signal-dependent motor noise (Harris and Wolpert 1998;Hamilton et al 2002).…”