Validating Stochastic Models: Invariance Criteria for Systems of Stochastic Differential Equations and the Selection of a Stochastic Hodgkin-Huxley Type Model

Abstract: In recent years, many difficulties appeared when taking into account the inherent stochastic behavior of neurons and voltage-dependent ion channels in Hodgking-Huxley type models. In particular, an open problem for a stochastic model of cerebellar granule cell excitability was to ensure that the values of the gating variables remain within the unit interval. In this paper, we provide an answer to this modeling issue and obtain a class of viable stochastic models. We select the stochastic models thanks to a general criterion for the flow invariance of rectangular subsets under systems of stochastic differential equations. We formulate explicit necessary and sufficient conditions, that are valid for both, It^o's and Stratonovich's interpretation of stochastic differential equations, improving a previous result obtained by A. Milian [A.Milian, Coll. Math. 1995] in the It^o case. These invariance criteria allow to validate stochastic models in many applications. To illustrate our results we present numerical simulations for a stochastic Hodgkin-Huxley model.