Summary
In this chapter we describe the behavior of the muscle spindle by using a logistic regression model. The system receives input from a motoneuron and fires through the Ia sensory axon that transfers the information to the spinal cord and from there to the brain. Three functions which are of special interest are included in the model: the threshold, the recovery and the summation functions. The most favorable method of estimating the parameters of the muscle spindle is the maximum likelihood approach. However, there are cases when this approach fails to converge because some of the model’s parameters are considered to be perfect predictors. In this case, the exact likelihood can be used, which succeeds in finding the estimates and the exact confidence intervals for the unknown parameters. This method has a main drawback: it is computationally very demanding, especially with large data sets. A good alternative in this case is a specific application of the Monte Carlo technique.
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Kotti, V.K., Rigas, A.G. (2008). A Monte Carlo Method Used for the Identification of the Muscle Spindle. In: Deutsch, A., et al. Mathematical Modeling of Biological Systems, Volume II. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4556-4_21
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DOI: https://doi.org/10.1007/978-0-8176-4556-4_21
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