Published December 27, 2025 | Version 1.1.0

Biomechanical filtering supports efficient tactile encoding in the human hand

  • 1. ROR icon University of California, Santa Barbara
  • 2. ROR icon Technische Universität Dresden
  • 3. ROR icon University of Sheffield

Description

This repository contains the data and code used to produce the results in the publication "Biomechanical filtering supports efficient tactile encoding in the human hand." If you use these data or code, please cite our publication and the publication presenting Touchsim:

N. Tummala, G. Reardon, B. Dandu, Y. Shao, H. P. Saal, and Y. Visell. (2024) "Biomechanical filtering supports efficient tactile encoding in the human hand." bioRxiv. doi: 10.1101/2023.11.10.565040

H. P. Saal, B. P. Delhaye, B. C. Rayhaun, and S. J. Bensmaia. (2017). Simulating tactile signals from the whole hand with millisecond precision. Proceedings of the National Academy of Sciences, 114(28), E5693–E5702, 2017. doi: 10.1073/pnas.1704856114
 
The original Python implementation of Touchsim, of which a modified version is included in this repository, is located here: https://github.com/hsaal/touchsim.
 

Abstract From Manuscript

Touching an object elicits skin oscillations that are biomechanically transmitted throughout the hand, driving responses in thousands of tactile receptors, including numerous exquisitely sensitive Pacinian corpuscles (PCs). Accepted descriptions of PC functionality characterize their response properties as highly stereotyped, based on experimental data gathered when stimuli are applied near the receptor. However, during natural touch, spiking activity in the majority of PCs is evoked by transmitted skin oscillations that are modified by biomechanical filtering. This filtering mechanism, stemming from dispersive wave dynamics in the skin, bears some similarity to the pre-neuronal filtering of auditory signals by the basilar membrane, a mechanical process that is instrumental to perception. Thus, we sought to clarify how skin biomechanics might influence tactile information encoding in the periphery. We used vibrometry imaging and computational neural experiments to examine the influence of biomechanical filtering on neural activity in whole-hand PC populations. We observed complex, location- and frequency-dependent patterns of filtering that were shaped by tissue mechanics and hand morphology. This source of biomechanical modulation diversified PC population spiking activity and enhanced tactile information encoding efficiency. These findings indicate that biomechanics furnishes a pre-neuronal mechanism that facilitates efficient tactile encoding and processing.

 

Files

Tummala_BiomechanicalFiltering_ZenodoDocumentation.pdf

Additional details

Related works

Is part of
Preprint: 10.1101/2023.11.10.565040 (DOI)

Funding

U.S. National Science Foundation
CAREER: Making Tactile Waves: Somatosensation as Elastic Wave Propagation 1751348
Leverhulme Trust
The computational origins of the cortical homunculus RPG-2022-031
Link Foundation
University of California, Santa Barbara

Software

Programming language
Python , MATLAB