Katic, Natalija, Siqueira, Rodrigo Kazu, Cleland, Luke et al. (4 more authors) (2023) Modelling foot sole cutaneous afferents: FootSim. iScience, 26 (1). 105874. ISSN 2589-0042
Abstract
While walking and maintaining balance, humans rely on cutaneous feedback from the foot sole. Electrophysiological recordings reveal how this tactile feedback is represented in neural afferent populations, but obtaining them is difficult and limited to stationary conditions. We developed the FootSim model, a realistic replication of mechanoreceptor activation in the lower limb. The model simulates neural spiking responses to arbitrary mechanical stimuli from the combined population of all four types of mechanoreceptors innervating the foot sole. It considers specific mechanics of the foot sole skin tissue and model internal parameters are fitted using human microneurography recording dataset. FootSim can be exploited for neuroscientific insights, to understand the overall afferent activation in dynamic conditions, overcoming the limitation of currently available recording techniques. Furthermore, neuroengineers can use the model as a robust in-silico tool for neuroprosthetic applications, for designing biomimetic stimulation patterns starting from the simulated afferent neural responses.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > Department of Psychology (Sheffield) |
Funding Information: | Funder Grant number WELLCOME TRUST (THE) 209998/Z/17/Z Medical Research Council 2441839 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 03 Jan 2023 11:39 |
Last Modified: | 21 Feb 2023 12:41 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.isci.2022.105874 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194719 |