Characterization of the mechanosensitivity of tactile receptors using multivariate logistical regression

Our initial objective was to establish a framework for modeling afferent mechanoreceptor behavior under dynamic compressive loads using multivariate regression techniques. A multivariate logistical model of the system was chosen because the system contains continuous input variables and a singular binary output variable corresponding to an "all-or-nothing" nerve action potential. Subsequently, this method was used to quantitatively assess the sensitivity of rapidly adapting afferents in rat hairy skin to the stimulus metrics stress, strain, and their time derivatives. In-vitro experiments involving compressive stimulation of isolated afferents using pseudorandom and non-repeating noise sequences were completed and an analysis of the data was performed using multivariate logistical regression.