A Direction-Dependent Mechanotransduction Model to Convert Fingertip Forces Into Neural Spike Trains for Tactile...

Microneurography studies have shown that human mechanoreceptor (MR) activity is directionally sensitive to shear forces, enabling fine tactile perception and object manipulation. However, existing computational mechanotransduction models largely neglect this directional tuning, limiting their biological realism and effectiveness for tactile feedback systems such as prosthetic hands. This paper presents a Direction-Dependent Mechanotransduction Model (DDMM) that replicates the direction-specific encoding behavior observed in human tactile afferents. The model integrates multidirectional pressure and shear forces to modulate neural spiking according to the alignment between resultant shear vectors and neuron-specific attenuation profiles. Force inputs are first transformed into afferent-specific currents (SAI, RAI, RAII), which are then converted into spike trains using an Izhikevich neuron model. Simulated fingertip interactions produced directionally selective spiking frequencies ranging from 0 to 47.5 pulses per second, consistent with biological firing ranges. Directional tuning, quantified using the profile-resolved sensitivity index (PRSI), yielded values of 0.31-0.45 for selective and broad profiles, comparable with those experimentally measured directional sensitivity indices (DSI; 0.23 ± 0.18) as reported in the literature. Further experimental validation using triaxial force measurements from human fingertip press-push-lift actions confirm the model's directional sensitivity, with aligned neural attenuation profiles and shear force direction yielding a mean spiking frequency increase of approximately 350% relative to misaligned conditions. These findings establish the DDMM as a biologically inspired and computationally efficient framework for encoding tactile force direction, with potential applications in neuroprosthetics, robotic manipulation, and somatosensory modeling.