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A computational model of altered neuronal activity in altered gravity.

Code ↔ Paper

2 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 2 matches
  1. [1] § Results › Observed synchronized bursts can be explained by spike frequency adaptation ↔ Fig2B.py, lines 16–41 · score 0.64 · threshold slope factor, effective threshold potential, leak, capacitance, adaptation, VT
  2. [2] § Results › Observed synchronized bursts can be explained by spike frequency adaptation ↔ Fig3A.py, lines 16–43 · score 0.64 · threshold slope factor, effective threshold potential, leak, capacitance, adaptation, VT

Paper

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

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The authors' code

Python · 104 lines · 2.4 KB · MIT · 1 match

  1. # -*- coding: utf-8 -*-
  2. """
  3. Created on Sat Dec 23 18:42:59 2023
  5. @author: CAG329
  6. """
  8. # Packages ####################################################################
  10. from brian2 import *
  11. # %matplotlib qt
  12. matplotlib.rcParams.update({'font.size': 13})
  14. # Parameters ##################################################################
  16. seed(4321)
  17. np.random.seed(4321)
  19. duration = 20*second
  20. # Neuronal population
  21. N_E = 1000
  22. # Total capacitance
  23. C = 200*pF
  24. # Total leak capacitance
  25. gL = 10*nS
  26. # Effective rest potential
  27. EL = -60*mV
  28. # Threshold slope factor
  29. DeltaT = 2*mV
  30. # Effective threshold potential
  31. VT = -50*mV
  32. # Conductance
  33. a = 2*nS
  34. # Time constant
  35. tauw = 800*msecond
  36. # Adaptation
  37. b = 30*pA
  38. # Reset potential
  39. Vr = -46*mV
  40. # External input
  41. I_values = 102*pA
  43. # Synaptic parameters
  44. connect_prob = 0.02
  45. we = 0.5*mV
  46. sigma = 1*mV
  47. tau_E = 0.01*second
  49. # Equations ###################################################################
  51. eqs_E = Equations('''
  52. dV/dt = (-gL*(V-EL) + gL*DeltaT*exp((V-VT)/DeltaT) + I - w)/C + sigma*sqrt(2/tau_E)*xi : volt
  53. dw/dt = (a*(V-EL) - w)/tauw : amp
  54. I : amp
  55. ''')
  57. G_E = NeuronGroup(N_E,
  58. model=eqs_E,
  59. threshold='V > 0*mV',
  60. reset='V=Vr; w+=b',
  61. )
  63. G_E.I = I_values
  65. S_EE = Synapses(G_E, G_E, on_pre='V += we')
  67. S_EE.connect(p=connect_prob)
  69. spikemon = SpikeMonitor(G_E)
  70. M = StateMonitor(G_E, 'V', record=True)
  71. P = StateMonitor(G_E, 'w', record=True)
  73. G_E.V = 'EL+EL*rand() * 0.1 '
  75. run(duration)
  77. print(np.mean(spikemon.count / duration))
  78. print(np.std(spikemon.count / duration))
  79. burst_results = []
  80. spike_trains = spikemon.spike_trains()
  81. for i in range(N_E):
  82. spike_trains_ = spike_trains[i]/msecond
  83. is_in_burst_idx = np.where(spike_trains_[1:] - spike_trains_[:-1] < 20)[0]
  84. if len(is_in_burst_idx)>0:
  85. burst_results.append((len(np.where(np.diff(is_in_burst_idx)>1)[0])+1) / duration)
  86. print(np.mean(burst_results))
  87. print(np.std(burst_results))
  89. # Results #####################################################################
  91. fig,axs=plt.subplots(nrows=2,ncols=1,sharex=True)
  92. axs[0].plot(0.001*M.t/ms,M.V[0]/mV,c='tab:blue')
  93. axs[0].axhline(y=VT/mV,linestyle='--',c='k')
  94. axs[1].axhline(y=VT/mV,linestyle='--',c='k')
  96. axs[1].plot(0.001*M.t/ms,M.V[1]/mV,c='tab:blue')
  97. axs[1].set_xlabel('Time [s]')
  98. axs[0].set_ylabel('V [mV]')
  99. axs[1].set_ylabel('V [mV]')
  100. axs[0].grid()
  101. axs[1].grid()
  102. fig.tight_layout()
  103. savefig("Fig2B.svg", dpi=300)

Fig2B.py at commit 76f9070, under MIT · at the source

Overview

Authors: Camille Gontier1,2, Laura Drouvé3, Johannes Striebel4, Maximilian Sturm3, Zoe Meerholz3, Sarah Schunk3,4, Yannick Lichterfeld3, Christian Liemersdorf3
ORCID iDs: Christian Liemersdorf
  1. LIDE Space, Louvain-la-Neuve, Belgium
  2. INRIA Center at University of Lorraine,Strasbourg, France
  3. Department of Applied Aerospace Biology, Institute of Aerospace Medicine, German Aerospace Center,Cologne, Germany
  4. Department of Ophthalmology, Medical Faculty, University of Bonn,Bonn, Germany
Institutions: Université de Lorraine (France); Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (Germany); University of Bonn (Germany)
Journal: NPJ microgravity, volume 12, issue 1, article 70
Dates: received 2 September 2024; accepted 23 July 2026; published online 7 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41526-026-00644-7 · PMID 42567862 · PMCID PMC13451429 · OpenAlex W4401181003
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.07.30.605832
Status: code verified
Categories: computational modeling (no new data) (modality), none (in silico) (organism), computational (subfield)
Keywords: Computational biology and bioinformatics, Neuroscience
Topic: Spaceflight effects on biology (Physiology, Medicine), according to OpenAlex
Funding: German Aerospace Center
Citations: not cited yet (Europe PMC); 78 references in the paper

Abstract

Electrophysiological experiments have shown that neuronal activity changes upon exposure to altered gravity. More specifically, neurons’ firing rates increase during microgravity and decrease during centrifugal-induced hypergravity. Different biophysical explanations have been proposed for this phenomenon; however, they have not been backed by quantitative analyses or simulations. More generally, classical computational models of neurons and networks do not account for the effect of altered gravity, which limits the possibility to perform in-silico experiments and simulations. Here, we propose computational implementations for different effects of altered gravity on cellular functions, and modify existing models to account for the effect of micro- and hyper-gravity. Firstly, in line with previous experiments, we suggest that microgravity could be modeled as an increase in the voltage-dependent channel transition rates, which is assumed to be the result of a higher membrane fluidity and can be readily implemented into the Hodgkin–Huxley model. Using in silico simulations of single neurons, we show that this model of the influence of gravity on neuronal activity allows for reproducing the observed increased firing and burst rates. Secondly, we explore the role of mechano-gated (MG) ion channels on population activity. We show that recordings can be fitted by a network of connected excitatory neurons, whose activity is balanced by firing rate adaptation. Adding a small depolarizing current to account for the activation of MG channels also reproduces the observed increased firing and burst rates. Overall, our results fill an important gap in the literature, by providing a computational link between altered gravity and neuronal activity. Starting from historical observations of the effects of gravity on cellular functions, we derived gravity-sensitive models of neurons and networks, whose predictions could be refined using future experiments.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

camillegontier/neuron_microgravity

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 76f907083a06972d631a9a295271a08ef5099334, 9 November 2025
Languages: Python (11)
Size: 14 files, 11 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Brian 2 (10 files), Matplotlib (2 files), h5py (1 file), NumPy (1 file), SpikeInterface (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
13 files
At the source: github.com/camillegontier/neuron_microgravity

Code availability

Analysis code is available from the following repository: https://github.com/camillegontier/neuron_microgravity.

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

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 11 scripts, each with its path and the digest of its content;
  • 2 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

Data shown in Fig. 2A are not publicly available due to them being currently used in yet unpublished studies but are available from the corresponding author on reasonable request.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 2 keywords, 1 funder, 72 references.

Cite

This paper

Gontier, C., Drouvé, L., Striebel, J., Sturm, M., Meerholz, Z., Schunk, S., Lichterfeld, Y., & Liemersdorf, C. (2026). A computational model of altered neuronal activity in altered gravity. NPJ microgravity, 12(1), 70. https://doi.org/10.1038/s41526-026-00644-7

BibTeX

@article{gontier2026computational,
author = {Gontier, Camille and Drouvé, Laura and Striebel, Johannes and Sturm, Maximilian and Meerholz, Zoe and Schunk, Sarah and Lichterfeld, Yannick and Liemersdorf, Christian},
title = {{A computational model of altered neuronal activity in altered gravity}},
journal = {NPJ microgravity},
year = {2026},
month = aug,
volume = {12},
number = {1},
pages = {70},
publisher = {Nature Publishing Group},
issn = {2373-8065},
doi = {10.1038/s41526-026-00644-7},
url = {https://doi.org/10.1038/s41526-026-00644-7},
pmid = {42567862},
pmcid = {PMC13451429}
}

RIS

TY - JOUR
AU - Gontier, Camille
AU - Drouvé, Laura
AU - Striebel, Johannes
AU - Sturm, Maximilian
AU - Meerholz, Zoe
AU - Schunk, Sarah
AU - Lichterfeld, Yannick
AU - Liemersdorf, Christian
TI - A computational model of altered neuronal activity in altered gravity
T2 - NPJ microgravity
J2 - NPJ Microgravity
PY - 2026
DA - 2026/08/07
VL - 12
IS - 1
SP - 70
SN - 2373-8065
PB - Nature Publishing Group
DO - 10.1038/s41526-026-00644-7
UR - https://doi.org/10.1038/s41526-026-00644-7
LA - en
ER -

CSL-JSON

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