From Skin Mechanics to Tactile Neural Coding: Predicting Afferent Neural Dynamics During Active Touch and Perception

Abstract

First order cutaneous neurons allow object 2 recognition, texture discrimination, and sensorimotor 3 feedback. Their function is well-investigated under passive 4 stimulation while their role during active touch or 5 sensorimotor control is understudied. To understand how 6 human perception and sensorimotor controlling strategy 7 depend on cutaneous neural signals under active tactile 8 exploration, the finite element (FE) hand and Izhikevich 9 neural dynamic model were combined to predict the 10 cutaneous neural dynamics and the resulting perception 11 during a discrimination test. Using in-vivo microneurography 12 generated single afferent recordings, 75% of the data was 13 applied for the model optimization and another 25% was 14 used for validation. By using this integrated numerical model, 15 the predicted tactile neural signals of the single afferent fibers 16 agreed well with the microneurography test results, achieving 17 the out-of-sample values of 0.94 and 0.82 for slowly adapting 18 type I (SAI) and fast adapting type I unit (FAI) respectively. 19 Similar discriminating capability with the human subject was 20 achieved based on this computational model. Comparable 21 performance with the published numerical model on 22 predicting the cutaneous neural response under passive 23 stimuli was also presented, ensuring the potential 24 applicability of this multi-level numerical model in studying 25 the human tactile sensing mechanisms during active touch. 26 The predicted population-level 1st order afferent neural 27 signals under active touch suggest that different coding 28 strategies might be applied to the afferent neural signals 29 elicited from different cutaneous neurons simultaneously. 30 31

Finite Element Modeling of the Human Wrist: A Review

“…The bone is a stiff connective tissue that supports body parts and is the mechanical basis for movement. 37 38 Bone is often simplified into a macroscopic solid and modeled using elastic isotropic constitutive laws. In most simulations, bone is divided into two different types: cortical/compact bone and cancellous/trabecular bone.…”

Human tactile sensing and sensorimotor mechanism: from afferent tactile signals to efferent motor control

“…The hit rate of the ATSS increased with the curvature of the object because the firing rate and Victor-Purpura distance are larger than those initiated by being in contact with the baseline object. Our previous research demonstrated that a more intense variation of hand contact strain/stress could be imitated when touching the object with a larger surface curvature 40 . This leads to a larger membrane current through cutaneous receptors and more intense neural spiking.…”

“…The tactile signals elicited from the first-order neurons were then computed through the Izhikevich neural dynamic model based on the membrane current flow over the cutaneous receptor. The detailed process of predicting the human afferent tactile signals was illustrated in our previous research 40 .…”

Mechano-neurophysiological model of fingertip to simulate tactile response during Braille reading under multiple frictional conditions

“…However, the FE models alone cannot prescribe whether the tactile receptors are activated due to the stimulus. Therefore, certain studies employed a FE mechanical model combined with a neurophysiological model, i.e., mechano-neurophysiological model (Gerling et al, 2014;Hamasaki and Yamada, 2021;Wei et al, 2022). Gerling et al (2014) evaluated the neural responses of slowly adapting type-1 (SA1) afferents against the contact of an indentor with a finger pad.…”