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A direction-dependent mechanotransduction model to convert fingertip forces into neural spike trains for tactile feedback

A direction-dependent mechanotransduction model to convert fingertip forces into neural spike trains for tactile feedback
A direction-dependent mechanotransduction model to convert fingertip forces into neural spike trains for tactile feedback
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.
Haptics, ouch-based properties and capabilities of the human user, Neuroscience, Haptics Applications, Prosthetics
2329-4051
Shaw, Hope O.
b98622aa-8c92-4912-9bf7-78f88d30bed6
McBride, John
d9429c29-9361-4747-9ba3-376297cb8770
Jiang, Liudi
374f2414-51f0-418f-a316-e7db0d6dc4d1
Shaw, Hope O.
b98622aa-8c92-4912-9bf7-78f88d30bed6
McBride, John
d9429c29-9361-4747-9ba3-376297cb8770
Jiang, Liudi
374f2414-51f0-418f-a316-e7db0d6dc4d1

Shaw, Hope O., McBride, John and Jiang, Liudi (2026) A direction-dependent mechanotransduction model to convert fingertip forces into neural spike trains for tactile feedback. IEEE Transactions on Haptics. (doi:10.1109/TOH.2026.3676160).

Record type: Article

Abstract

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.

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e-pub ahead of print date: 20 March 2026
Keywords: Haptics, ouch-based properties and capabilities of the human user, Neuroscience, Haptics Applications, Prosthetics

Identifiers

Local EPrints ID: 510968
URI: http://eprints.soton.ac.uk/id/eprint/510968
ISSN: 2329-4051
PURE UUID: f095d424-4ea7-46ac-a446-b34841e107ac
ORCID for Hope O. Shaw: ORCID iD orcid.org/0000-0002-5211-2501
ORCID for John McBride: ORCID iD orcid.org/0000-0002-3024-0326
ORCID for Liudi Jiang: ORCID iD orcid.org/0000-0002-3400-825X

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Date deposited: 28 Apr 2026 16:42
Last modified: 29 Apr 2026 02:03

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Contributors

Author: Hope O. Shaw ORCID iD
Author: John McBride ORCID iD
Author: Liudi Jiang ORCID iD

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