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Towards model-based characterization of individual electrically stimulated nerve fibers.

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Paper

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The authors' code

Markdown · 96 lines · 5.1 KB · CC-BY-4.0

  1. -------------------------------------------------------------------------------------------------
  2. (c) 2025 Rebecca C. Felsheim, David J. Sly, Stephen J. O'Leary, Mathias Dietz
  4. This file is part of the repository providing the code for optimizing the parameters the aLIFP model
  5. such that the behavior of an individual nerve fiber can be simulated.
  7. This code is free software: you can redistribute it and/or modify it under the terms of the
  8. CC BY 4.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
  10. The code is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
  11. without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
  13. Please cite
  14. Felsheim RC, Sly DJ, O'Leary SJ, Dietz M (2026), Towards model-based characterization of
  15. individual electrically stimulated nerve fibers, PLoS Computational Biology
  18. -------------------------------------------------------------------------------------------------
  20. This repository contains the code for the work of Felsheim et al. (2026). It is an optimization
  21. procedure that adjusts the parameters of the adaptive leaky-integrate and firing probability (aLIFP)
  22. model (Felsheim and Dietz, 2024) such that the behavior of every individual nerve fiber in the dataset
  23. from Heffer et al. (2010) is simulated. This results in one parameter set per nerve fiber. This
  24. repository contains the code to run the parameter optimization (fit_main, fit_stages/) and code to
  25. evaluate the sucess of the fits (plot_functions/, support_functions/). Additionally the parameters
  26. which form the basis of the evaluation in the publication of Felsheim et al. (2026) are provided
  27. in optimized_parameters/.
  29. To run the evaluations the aLIFP model and the data are required. For the optimization, additionally,
  30. the optimizer BADS needs to be downloaded. A list pointing to the respective repositories can be
  31. found below.
  33. For questions please contact [email hidden]
  35. # Repository structure
  36. - fit_main: main fit function
  38. - fit_stages/ contains the individual stages required for the fitting, they need to be run
  39. in the following order
  40. 1. fit_spike_probability_data.m
  41. 2. fit_latency_jitter_data.m
  42. 3. fit_short_term_interaction_data.m
  43. 4. refit_latency.m
  44. 5. fit_long_term_interaction_data.m
  46. - optimized_parameters/ contains the optimized parameters in four folders
  47. - default_fit/ the default fitted parameters (fitted on 200pps, 1000pps, 2000pps)
  48. - refit_parameter_certainty/ the parameters 20 fit repetitiions of the two fibers, these were
  49. used to evaluate the parameter certainty
  50. - refit_with_5000/ fit of 10 fibers that includes also the 5000 pps data
  51. - refit_with_5000_without_1000/ fit of the same 10 fibers as above but this time fitted on
  52. 200 pps, 2000 pps, 5000 pps
  53. naming scheme for all fitted parameters: YYDDMM_hhmm_parameters_LH-AAA_FFF_repX.mat
  54. - the date and time give the time point when the optimization script was started
  55. - the fiber name starts with LH and is then followed by three digits indicating the animal
  56. and another three digits indicating the fiber
  57. - the last part indiciates the repetition number
  58. Each folder also contains the saved aLIFP simulations for each parameter set, a table where the
  59. pre-calculated error values are saved (curr_error_table.mat). The default_fit contains also a
  60. table containing the error values for the default parameters are saved (curr_error_table_default_params.mat)
  62. - plot_functions/ contains the scripts used to create the results figures
  63. - plot_error_values: Figure 3
  64. - plot_error_values_5000pps: Figure 7
  65. - plot_examples: Figure 1, 2, 6
  66. - plot_phenomena_histogram: Figure 8
  67. - plot_phenomena_interaction_matrix: Figure 9
  68. - plot_relative_parameters: Figure 4 A-H, Figure 5 A-H
  69. - plot_relative_phenomena: Figure 4 I-N, Figure 5 I-N
  71. - support_functions/ contains functions supporting the fitting process or the result visualization
  75. # Dependencies
  76. - BADS (https://github.com/acerbilab/bads)
  77. - aLIFP model (doi.org/10.5281/zenodo.11198029)
  78. - Data (doi.org/10.5281/zenodo.15827115)
  79. - MATLAB toolboxes:
  80. - parallel computing toolbox
  81. - signal processing toolbox
  82. - curve fitting toolbox
  83. - optimization toolbox
  84. - statistics and machine learing toolbox
  85. The code has been run and tested using MATLAB 2024a.
  87. # References
  88. Felsheim RC, Sly DJ, O'Leary SJ, Dietz M, Towards model-based characterization of
  89. individual electrically stimulated nerve fibers, PLoS Computational Biology. (2026)
  91. Felsheim RC, Dietz M (2024). An Adaptive Leaky Integrate and Firing Probability Model of an
  92. Electrically Stimulated Auditory Nerve Fiber. Trends in Hearing. 2024
  94. Heffer LF, Sly DJ, Fallon JB, White MW, Shepherd RK, O'Leary SJ. Examining the auditory nerve fiber
  95. response to high rate cochlear implant stimulation: chronic sensorineural hearing loss and facilitation.
  96. J Neurophysiol. 2010

README.md, under CC-BY-4.0 · at the source

Overview

Authors: Rebecca C Felsheim1,2, David J Sly3,4,5, Stephen J O’Leary3, Mathias Dietz1,2
ORCID iDs: Rebecca C Felsheim, David J Sly
  1. Department of Medical Physics and Acoustics, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
  2. Cluster of Excellence “Hearing4All”, Oldenburg, Germany
  3. Department of Surgery (Otolaryngology), The University of Melbourne, Parkville, Victoria, Australia
  4. Ear Science Institute Australia, Perth, Western Australia, Australia
  5. School of Translational Medicine, Monash University, Clayton, Victoria, Australia
Institutions: Carl von Ossietzky Universität Oldenburg (Germany); Hearing4all (Germany); The University of Melbourne (Australia); Ear Science Institute Australia (Australia); Monash University (Australia)
Journal: PLoS computational biology, volume 22, issue 3, article e1013342
Dates: received 17 July 2025; accepted 24 February 2026; published online 13 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pcbi.1013342 · PMID 41824532 · PMCID PMC12987586 · OpenAlex W7135204039
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), other (organism), computational (subfield)
Methods: Connectivity, Machine learning, Single-unit activity, calcium imaging, fMRI & imaging
MeSH: Electric Stimulation*, Models, Neurological*, Nerve Fibers*, Action Potentials, Animals, Cochlear Nerve, Computational Biology, Computer Simulation, Guinea Pigs (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: cited by 1 paper (Europe PMC); 42 references in the paper

Abstract

Neuroprosthetics can partially restore the impaired system’s functionality. To improve such prosthetics, a quantitative understanding of the electrical stimulation of nerve fibers is required. This knowledge can best be represented by computational models of the process. Currently, most models of electrically stimulated nerve fibers are based on many different datasets, which mainly consist of the average analysis values of recordings of many nerve fibers. While this is a valid approach for understanding the fundamental neurophysiological response properties, both the combination of many different datasets and the average analysis can confound details in the response of the nerve fiber. To improve computational models of electrically stimulated nerve fibers further, we propose an optimization procedure that can fit the parameters of a neuron model to the response of a single nerve fiber to pulse-train stimulation. We show that in this way, the model can reproduce a wide variety of fiber responses of electrically stimulated auditory nerve fibers of guinea pigs in a remarkably detailed way on a scale of less than 1 ms. This not only illustrates the ability of the model, but also the quality and consistency of the data. We analyze and discuss the certainty and generalizability of the parameter sets thus exposed. The model parameters found by the optimization procedure can then form the basis for a detailed fiber-by-fiber analysis, which we illustrate by a correlation analysis of the predicted response properties (e.g., spike latency and refractory period) in the fiber response.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

Zenodo 15848230

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 2 files
Software Heritage: not checked
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
1 file
At the source: doi.org/10.5281/zenodo.15848230

The paper's code and data availability statement is in the Data section.

Tracing map

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Data

Datasets cited

Data Availability

The nerve fiber data used in this work was published on Zenodo (DOI: https://doi.org/10.5281/zenodo.15827115). All code written for this paper is also available on Zenodo (DOI: https://doi.org/10.5281/zenodo.15848230).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 9 MeSH terms, 40 references.

Cite

This paper

Felsheim, R. C., Sly, D. J., O’Leary, S. J., & Dietz, M. (2026). Towards model-based characterization of individual electrically stimulated nerve fibers. PLoS computational biology, 22(3), e1013342. https://doi.org/10.1371/journal.pcbi.1013342

BibTeX

@article{felsheim2026towards,
author = {Felsheim, Rebecca C and Sly, David J and O’Leary, Stephen J and Dietz, Mathias},
title = {{Towards model-based characterization of individual electrically stimulated nerve fibers}},
journal = {PLoS computational biology},
year = {2026},
month = mar,
volume = {22},
number = {3},
pages = {e1013342},
publisher = {PLOS},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1013342},
url = {https://doi.org/10.1371/journal.pcbi.1013342},
pmid = {41824532},
pmcid = {PMC12987586}
}

RIS

TY - JOUR
AU - Felsheim, Rebecca C
AU - Sly, David J
AU - O’Leary, Stephen J
AU - Dietz, Mathias
TI - Towards model-based characterization of individual electrically stimulated nerve fibers
T2 - PLoS computational biology
J2 - PLoS Comput Biol
PY - 2026
DA - 2026/03/13
VL - 22
IS - 3
SP - e1013342
SN - 1553-734X
PB - PLOS
DO - 10.1371/journal.pcbi.1013342
UR - https://doi.org/10.1371/journal.pcbi.1013342
LA - en
ER -

CSL-JSON

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