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Activity-dependent neuromodulation and calcium homeostasis cooperate to produce robust and modulable neuronal function.

Code ↔ Paper

5 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 5 matches
  1. [1] § Results › Sharp neuromodulation interferes with calcium homeostasis whereas controlled neuromodulation naturally leads to robust and modulable neuronal function ↔ fig2_controlled/STG_utils.jl, lines 45–128 · score 0.73 · interspike interval, interburst frequency, intraburst frequency, spike bursts, burstiness, firing
  2. [2] § Materials and methods › The neuromodulation controller ↔ fig6/network_STG_DIC.jl, lines 323–386 · score 0.72 · sensitivity matrix, linear system, threshold voltage, gating variables, DICs, Vth
  3. [3] § Materials and methods › The neuromodulation controller ↔ fig2_controlled/DA_ODE.jl, lines 1–51 · score 0.62 · sensitivity matrix, threshold voltage, gating variables, Vth, model
  4. [4] § Materials and methods › Conductance-based model ↔ fig2_controlled/STG_models.jl, lines 1–54 · score 0.58 · delayed rectifier potassium, STG model, slow calcium, activated, sodium
  5. [5] § Materials and methods › Conductance-based model ↔ fig6/network_STG_animation.jl, lines 1–39 · score 0.53 · reversal potentials, membrane capacitance, synaptic, gated, variables, STG

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Julia · 128 lines · 3.5 KB · MIT · 1 match

  1. #=
  2. This file contains functions to extract characteristics of the firing pattern
  3. as well as some functions to plot complicated graphs
  4. =#
  6. using Statistics, Plots, StatsPlots, LaTeXStrings, Printf
  8. ## Functions extracting characteristics of the firing pattern
  10. # This function extracts the spiking frequency of a spiking firing pattern
  11. function extract_frequency(V, t)
  12. # Defining thresholds
  13. spike_up_threshold = 10.
  14. spike_down_threshold = 0.
  16. # Detecting spikes
  17. spike_detected = 0
  18. spike_times = []
  19. for i in 1:length(V)
  20. if V[i] > spike_up_threshold && spike_detected == 0 # Start of spike
  21. append!(spike_times, t[i])
  22. spike_detected = 1
  23. end
  24. if V[i] < spike_down_threshold && spike_detected == 1 # End of spike
  25. spike_detected = 0
  26. end
  27. end
  29. # If the neuron is silent
  30. if length(spike_times) < 2
  31. return NaN
  32. end
  34. # Calculating all interspike intervals
  35. ISI=[]
  36. for i in 2 : length(spike_times)
  37. append!(ISI, spike_times[i] - spike_times[i-1])
  38. end
  40. # If the neuron is silent
  41. if length(ISI) < 2
  42. return NaN
  43. end
  45. # Computing the spiking frequency
  46. T = mean(ISI) / 1000 # in seconds
  47. f = 1 / T # in Hz
  49. return f
  50. end
  52. # This function extracts characteristics of a bursting firing pattern
  53. function extract_burstiness(V, t)
  54. # Defining thresholds
  55. spike_up_threshold = 10.
  56. spike_down_threshold = 0.
  58. # Detecting spikes
  59. spike_detected = 0
  60. spike_times = []
  61. for i in 1 : length(V)
  62. if V[i] > spike_up_threshold && spike_detected == 0 # Start of spike
  63. append!(spike_times, t[i])
  64. spike_detected = 1
  65. end
  66. if V[i] < spike_down_threshold && spike_detected == 1 # End of spike
  67. spike_detected = 0
  68. end
  69. end
  71. # If the neuron is silent
  72. if length(spike_times) < 3
  73. return NaN, NaN, NaN, NaN
  74. end
  76. # Calculating all interspike intervals
  77. ISI = []
  78. for i in 2 : length(spike_times)
  79. append!(ISI, spike_times[i] - spike_times[i-1])
  80. end
  82. # Defining a threshold to separate intraburst from interburst ISI
  83. max_ISI = maximum(ISI)
  84. min_ISI = minimum(ISI)
  85. half_ISI = (max_ISI+min_ISI)/2
  87. # If ISI too constant, neuron is spiking
  88. if max_ISI - min_ISI < 25
  89. return NaN, NaN, NaN, NaN
  90. end
  92. # Detecting the first spike of a burst
  93. first_spike_burst = findall(x -> x > half_ISI, ISI)
  95. # Computing the interburst frequency
  96. Ts = ISI[first_spike_burst]
  97. interburst_T = mean(Ts) / 1000 # in seconds
  98. interburst_f = 1 / interburst_T # in Hz
  100. # Computing the number of spikes per burst
  101. nb_spike_burst = []
  102. for i in 2 : length(first_spike_burst)
  103. append!(nb_spike_burst, first_spike_burst[i] - first_spike_burst[i-1])
  104. end
  106. # If spiking
  107. if length(nb_spike_burst) < 2
  108. return NaN, NaN, NaN, NaN
  109. end
  110. nb_spike_per_burst = round(mean(nb_spike_burst))
  112. # If no bursting
  113. if nb_spike_per_burst < 1.5 || nb_spike_per_burst > 500
  114. burstiness = NaN
  115. intraburst_f = NaN
  116. nb_spike_per_burst = NaN
  117. interburst_f = NaN
  118. else # Else, bursting: computing the intraburst frequency
  119. intra_spike_burst = findall(x -> x < half_ISI, ISI)
  120. Ts_intraburst = ISI[intra_spike_burst]
  121. T_intraburst = mean(Ts_intraburst) / 1000 # in seconds
  122. intraburst_f = 1 / T_intraburst # in Hz
  124. burstiness = (nb_spike_per_burst * intraburst_f) / interburst_T
  125. end
  127. return burstiness, nb_spike_per_burst, intraburst_f, interburst_f
  128. end

STG_utils.jl at commit 4b83c2d, under MIT · at the source

Overview

Authors: Arthur Fyon1, Guillaume Drion1
ORCID iDs: Arthur Fyon
  1. Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium
Institutions: University of Liège (Belgium)
Journal: PLoS computational biology, volume 22, issue 4, article e1014177
Dates: received 5 December 2025; accepted 28 March 2026; published online 15 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pcbi.1014177 · PMID 41984991 · PMCID PMC13102308 · OpenAlex W4405095177
Open access: gold, a free copy (OpenAlex)
Preprint: arxiv:2412.04172
Status: code verified
Categories: computational modeling (no new data) (modality), none (in silico) (organism)
Methods: Source localization
MeSH: Calcium*, Homeostasis*, Models, Neurological*, Neurons*, Neurotransmitter Agents*, Action Potentials, Animals, Calcium Signaling, Computational Biology, Computer Simulation (* major topic)
Topic: Vagus Nerve Stimulation Research (Neurology, Neuroscience), according to OpenAlex
Funding: Belgian Federal Science Policy Office (NEMODEI2); Fonds De La Recherche Scientifique - FNRS (ASP-REN40024838)
Citations: not cited yet (Europe PMC); 68 references in the paper

Abstract

Neurons rely on two interdependent mechanisms — homeostasis and neuromodulation — to maintain robust and adaptable functionality. Calcium homeostasis stabilizes neuronal activity by adjusting ionic conductances, whereas neuromodulation dynamically modifies ionic properties in response to external signals carried by neuromodulators. Combining these mechanisms in conductance-based models often produces unreliable outcomes, particularly when sharp neuromodulation interferes with calcium-homeostatic tuning. This study explores how a biologically inspired neuromodulation controller can harmonize with calcium homeostasis to ensure reliable neuronal function. Using computational models of stomatogastric ganglion and dopaminergic neurons, we demonstrate that controlled neuromodulation preserves neuronal firing patterns while calcium homeostasis simultaneously maintains target intracellular calcium levels. Unlike sharp neuromodulation, the neuromodulation controller integrates activity-dependent feedback through mechanisms mimicking G-protein-coupled receptor cascades. The interaction between these controllers critically depends on the existence of an intersection in conductance space, representing a balance between target calcium levels and neuromodulated firing patterns. Maximizing neuronal degeneracy enhances the likelihood of such intersections, enabling robust modulation and compensation for channel blockades. We further show that this controller pairing extends to network-level activity, reliably modulating the rhythmic activity of central pattern generators. This study highlights the complementary roles of calcium homeostasis and neuromodulation, proposing a unified control framework for maintaining robust and adaptive neural activity under physiological and pathological conditions.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

arthur-fyon/BIOCONTROL_2025

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 4b83c2ddce05461bdadca66c1bb6352527ebf8fe, 18 May 2026
Languages: Julia (46), Jupyter (21)
Size: 426 files, 67 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, license file, 21 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Plots.jl (29 files), DifferentialEquations.jl (25 files), Makie (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
69 files
At the source: github.com/arthur-fyon/BIOCONTROL_2025

Zenodo 17827990

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.5281/zenodo.17827990

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 67 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability

There are no primary data in the paper; all materials are available at https://github.com/arthur-fyon/BIOCONTROL_2025 and we have archived our code on Zenodo (DOI: 10.5281/zenodo.17827990 (https://doi.org/10.5281/zenodo.17827990)).

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 10 MeSH terms, 2 funders, 67 references.

Cite

This paper

Fyon, A., & Drion, G. (2026). Activity-dependent neuromodulation and calcium homeostasis cooperate to produce robust and modulable neuronal function. PLoS computational biology, 22(4), e1014177. https://doi.org/10.1371/journal.pcbi.1014177

BibTeX

@article{fyon2026activity,
author = {Fyon, Arthur and Drion, Guillaume},
title = {{Activity-dependent neuromodulation and calcium homeostasis cooperate to produce robust and modulable neuronal function}},
journal = {PLoS computational biology},
year = {2026},
month = apr,
volume = {22},
number = {4},
pages = {e1014177},
publisher = {PLOS},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1014177},
url = {https://doi.org/10.1371/journal.pcbi.1014177},
pmid = {41984991},
pmcid = {PMC13102308}
}

RIS

TY - JOUR
AU - Fyon, Arthur
AU - Drion, Guillaume
TI - Activity-dependent neuromodulation and calcium homeostasis cooperate to produce robust and modulable neuronal function
T2 - PLoS computational biology
J2 - PLoS Comput Biol
PY - 2026
DA - 2026/04/15
VL - 22
IS - 4
SP - e1014177
SN - 1553-734X
PB - PLOS
DO - 10.1371/journal.pcbi.1014177
UR - https://doi.org/10.1371/journal.pcbi.1014177
LA - en
ER -

CSL-JSON

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