A Multivariate Logistical Model for Identifying the Compressive Sensitivity of Single Rat Tactile Receptors as Nanobiosensors

Abstract

Tactile sensation is a complex manifestation of mechanical stimuli applied to the skin. At the most fundamental level of the somatosensory system is the cutaneous mechanoreceptor. The objective here was to establish a framework for modeling afferent mechanoreceptor behavior as a nanoscale biosensor under dynamic compressive loads using multivariate regression techniques. A multivariate logistical model was chosen because the system contains continuous input variables and a singular binary-output variable corresponding to the nerve action potential. Subsequently, this method was used to quantify the sensitivity of ten rapidly adapting afferents from rat hairy skin due to the stimulus metrics of compressive stress, strain, their respective time derivatives, and interactions. In vitro experiments involving compressive stimulation of isolated afferents using pseudorandom and nonrepeating noise sequences were completed. An analysis of the data was performed using multivariate logistical regression producing odds ratios (ORs) as a metric associated with mechanotransduction. It was determined that cutaneous mechanoreceptors are preferentially sensitive to stress (mean ORmax=26.10), stress rate (mean ORmax=15.03), strain (mean ORmax=12.01), and strain rate (mean ORmax=7.29) typically occurring within 7.3 ms of the nerve response. As a novel approach to receptor characterization, this analytical framework was validated for the multiple-input, binary-output neural system.