The Importance of Spike Timing and Rate in Merkel Mechanoreceptor Model

Tactile sensing plays an important role in our understanding of the environment. Mechanical arms, robots, and nerve prostheses perform better, if they have the sense of touch. Microneurography studies in humans have shown that primary afferent neurons (e.g. fingertip mechanoreceptors play an important role in encoding and separability with various types of stimuli using spike train patterns.

We developed an experimental set up to simulate the responses of Merkel mechanoreceptors to force stimulation, with considering account receptor spiking behavior. Indeed, we used sensor data and spiking properties of Merkel mechanoreceptors to discriminate force. The analog tactile signals generated from sensor were fed as input to the Izhikevich neurons in order to obtain spike trains. The features of Spike trains were extracted with rate coding and timing coding. The desired features were assigned to the k-nearest neighbors (kNN), and support vector machine (SVM) to classify the types of forces. Findings: The highest classification accuracy achieved 100% with rate coding, 81.18% with inter-spike interval coefficient of variation (ISI CV), and 82% with victor-purpura distance (VPd). From the spike trains evoked during contact, we computed the information that rate and timing codes carried about applied force.

Rate coding carried more force information than spike timing for Merkel mechanoreceptors. Moreover, as the force increased, firing rate also increased