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Observations on phase-locking within the response of primary muscle spindle afferents to pseudo-random stretch
Authors:J. Kröller  O.-J. Grüsser  L.-R. Weiss
Affiliation:(1) Physiologisches Institut der Freien Universität Berlin, Arnimallee 22, D-1000 Berlin 33, Germany
Abstract:In order to uncover encoder properties of primary muscle spindle afferent fibers, time coupling (phase-locking) of action potentials on cyclic muscle stretch was studied by means of pseudo-random noise. In cats Ia action potentials were recorded from dorsal root filaments and the gastrocnemius muscles of one hind leg were stretched. The stimulus time course was a determined sequence of randomly varying muscle length which could be applied repeatedly (sequence duration 0.6 or 20 s). The noise amplitude sgr (standard deviation of displacements) was varied between 5 and 300 mgrm, the upper cut-off frequency of noise fcwas varied between 20 and 100 Hz. The responses to the consecutive pseudo-random noise cycles were displayed as raster diagrams and cycle histograms. Phaselocking characterized the responses at all noise amplitudes outside the near threshold range (sgr>10 mgrm). The higher sgr and fc, the stronger was the phase-locking of impulses on the stretch. When sgr and fcwere selected to achieve high mean stretch velocities of about 500 mm/s, phase-locking was as precise as 0.15 ms, measured as the variability of spike occurrences with respect to stretch. The rasters obtained with low noise amplitudes (<40 mgrm) showed a loose phase-locking and this gave insight into underlying mechanisms: The elicitation of action potentials caused by dynamic stretch can be prevented by a post-spike depression of excitability. This disfacilitation was very effective in counteracting weak stretch components within the random sequence and less effective or even missing when relatively strong stretch components could force the spike elicitation. This led to the reestablishment of phase-locked patterns. The results were discussed in relation to the known encoder models.
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