Classification of human muscle stretch receptor afferents: a Bayesian approach

Author:

Edin B. B.1,Vallbo A. B.1

Affiliation:

1. Department of Physiology, University of Umea, Sweden.

Abstract

1. A sample of 124 human muscle afferents originating from the finger extensor muscles were recorded from the radial nerve in the upper arm. A method is described to formalize the classification of units in muscle spindle primary and secondary afferents and Golgi tendon organ afferents on the basis of a few, nonrigorous assumptions. The classification was based on experimental data that largely have been described in a series of previous papers, although some additional data were collected in the present study. 2. The units were subjected to five tests providing identification data: twitch contraction test, ramp-and-hold stretch, small-amplitude sinusoidal stretches superimposed on ramp stretch, stretch sensitization, and isometric contraction/relaxation. From these five tests the following eight response features were extracted: response to maximal isometric twitch contractions, type of stretch sensitization, correlation between discharge rate and contractile force, response to sudden isometric relaxation, presence or absence of an initial burst, deceleration response, prompt silencing at slow muscle shortening, and driving by small-amplitude sinusoidal stretches. 3. A Bayesian decision procedure was adopted to classify the units on the basis of the eight discriminators. As a first step, units were provisionally classified into muscle spindle primary and secondary afferents, and Golgi tendon organ afferents, by intuitively weighting their responses to the identification tests. Prior probabilities were estimated on the basis of the provisional classification. The eight response features were analyzed and tabulated for all afferents, and the likelihood functions of the tests were directly calculated on the basis of these data.(ABSTRACT TRUNCATED AT 250 WORDS)

Publisher

American Physiological Society

Subject

Physiology,General Neuroscience

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