A model of the feline medial gastrocnemius motoneuron-muscle system subjected to recurrent inhibition

Takanori Uchiyama, Håkan Johansson, Uwe Windhorst

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Recurrent inhibition in the mammalian spinal cord is complex, and its functions are not yet well understood. Skeletomotoneurons (α-MNs) excite, via recurrent axon collaterals, inhibitory Renshaw cells (RCs), which in turn inhibit α-MNs and other neurons. The anatomical and functional structure of the recurrent inhibitory network is nonhomogeneous, and the gain and filtering characteristics of RCs are modulated by inputs circumventing α-MNs. This complex organization is likely to play important roles for the discharge and recruitment properties of α-MNs. Modeling this system is a way of investigating hypothesized roles for normal functioning including muscle fatigue and different forms of physiological pathological tremor. In this paper, a detailed model including α-MNs, RCs, and the muscle fibers innervated by the α-MNs is presented. Outlines of the experimental data underlying the model and the modeling philosophy and procedure are presented. Then the behavior of a RC model is compared with experimental data reported in the literature. Model and experimental data agree well for burst responses elicited by synchronous single-pulse activation of different numbers of motor axons. In addition, the static relation between motor-axon activation rate and RC firing rate agree fairly well in model and experiment, and the same applies to the dynamic responses to step changes in motor-axon rate. The ultimate objective is to use this model in probing the role of recurrent inhibition in the control and stability of (isometric) muscular force under normal and altered conditions occurring during fatigue and muscle pain.

Original languageEnglish
Pages (from-to)139-151
Number of pages13
JournalBiological Cybernetics
Volume89
Issue number2
DOIs
Publication statusPublished - 2003 Aug 1

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science(all)

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