A multiphysics model of the Pacinian corpuscle

Julia C. Quindlen, Henryk K. Stolarski, Matthew D. Johnson, Victor H. Barocas

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


The Pacinian corpuscle (PC) is a dermal mechanoreceptor that responds to high-frequency (20-1000 Hz) vibrations. The PC's structure allows transmission of vibrations through its layers (lamellae) to the centrally-located nerve fiber (neurite). This work combines mechanical models of the PC with an electrochemical model of peripheral nerves to simulate the tactile response of the entire system. A three-stage model of response to a vibratory input was developed, consisting of (1) outer core mechanics, (2) inner core mechanics, and (3) neurite electrochemistry. The model correctly predicts the band-pass nature of the PC's frequency response, showing that the PC structure can amplify oscillatory strains within its target frequency band. Specifically, strain induced by a vibratory stimulus is amplified by a factor of 8-12 from the PC surface to the neurite. Our results also support the hypothesis that PC rapid adaptation is affected by the lamellar structures without requiring neuronal adaptivity. Simulated different-sized PCs showed a shift in frequency response, suggesting that clusters of different-sized PCs could enable more nuanced tactile encoding than uniform clusters. By modeling the PC's mechano-to-neural transduction, we can begin to characterize the mechanosensation of other receptors to understand how multiple receptors interact to create our sensation of touch.

Original languageEnglish (US)
Pages (from-to)1111-1125
Number of pages15
JournalIntegrative Biology (United Kingdom)
Issue number11
StatePublished - Nov 2016

Bibliographical note

Funding Information:
The authors thank Martha Flanders for her advice and support of the project and Philip Bayly and Dennis Tweten for their COMSOL guidance. This research was financially supported by an NSF IGERT fellowship (Systems Neuroengineering, DGE-1069104) and the University of Minnesota Interdisciplinary Doctoral Fellowship. The authors acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing resources that contributed to the research results reported within this paper.

Publisher Copyright:
© 2016 The Royal Society of Chemistry.


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