Conveying tactile feedback using a model of mechanotransduction

Abstract

In order to develop effective neural prostheses for the hand, it is necessary to characterize the tactile information conveyed by the hand to the brain. Here we present a model that predicts the neural activity evoked by vibratory stimuli in the three types of mechanoreceptive fibers that innervate the glabrous skin of the hand. The model takes as input the position of the stimulus as a function of time, along with its first (velocity), second (acceleration) and third (jerk) derivatives. This input is filtered and passed through an integrate-and-fire mechanism to generate a train of spikes as output. By fitting the model to the activity of the three fiber types, we found that activity in each fiber type is best accounted for by specific stimulus combinations. The major conclusion of this study is that the timing of individual spikes evoked in mechanoreceptive fibers innervating the hand can be accurately predicted using an integrate-and-fire model. This model constitutes an important first step towards tactile neural prostheses.