Identification of a stochastic neuroelectric system using the maximum likelihood approach

In this work we use stochastic models in order to describe the behaviour of a neuroelectric system, called muscle spindle that involves binary time series. Three parameters are of interest: the threshold, the recovery function and the summation function, because they describe the intrinsic properties of the system. Generalized Linear Models (GLMs) are used for modelling the system and the parameters involved are estimated by employing the maximum likelihood approach. In the work a case is examined, where the system is affected by the presence of a single input. It is shown that there is no spontaneous activity, since the level of the estimated recovery function does not cross the level of the estimated threshold. The estimate of the summation function is positive, implying that the system is excitatory.