  Tactile afferents such as (RA), and Pacinian (PC) afferents that respond to
external stimuli enable complicated actions such as grasping, stroking and
identifying an object. To understand the tactile sensation induced by these
actions deeply, the activities of the tactile afferents need to be revealed.
For this purpose, we develop a computational model for each tactile afferent
for vibration stimuli, combining finite element analysis finite element method
(FEM) analysis and a leaky integrate-and-fire model that represents the neural
characteristics. This computational model can easily estimate the neural
activities of the tactile afferents without measuring biological data. Skin
deformation calculated using FEM analysis is substituted into the
integrate-and-fire model as current input to calculate the membrane potential
of each tactile afferent. We optimized parameters in the integrate-and-fire
models using reported biological data. Then, we calculated the responses of the
numerical models to sinusoidal, diharmonic, and white-noise-like mechanical
stimuli to validate the proposed numerical models. From the result, the
computational models well reproduced the neural responses to vibration stimuli
such as sinusoidal, diharmonic, and noise stimuli and compare favorably with
the similar computational models that can simulate the responses to vibration
stimuli.
