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Astrocyte-induced dynamics of a pyramidal cell with a dendrite-connected astrocyte.

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  1. [1] § Methods › Methods used for analyses › Numerical simulations and spectral analysis ↔ scripts/script_Fig_04_05_06.m, lines 27–71 · score 0.58 · Lomb Scargle periodogram, plomb, dominant

Paper

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The authors' code

MATLAB · 203 lines · 6.1 KB · MIT · 1 match

  1. clear
  2. close
  4. PATH = './../Systems/Pyramid_astro/transients/';
  6. % FIG. 4
  7. fileName = 'theta_gamma'; tlims = [0 5.5]; tticks = 0:0.5:5.5;
  8. clims1 = [0,75]; cticks1 = 0:15:75; ctickslabels1 = {'0','15','30','45','60','75+'};
  9. clims2 = [0,1000]; cticks2 = 0:250:1000; ctickslabels2 = {'0','250','500','750','1000+'};
  10. flims = [0,100]; fticks = 0:20:100;
  12. % FIG. 5
  13. % fileName = 'epileptiform_activity'; tlims = [0 7]; tticks = 0:7;
  14. % clims1 = [0,40]; cticks1 = 0:10:40; ctickslabels1 = {'0','10','20','30','40+'};
  15. % clims2 = [0,400]; cticks2 = 0:100:400; ctickslabels2 = {'0','100','200','300','400+'};
  16. % flims = [0,150]; fticks = 0:30:150;
  18. % FIG. 6
  19. % fileName = 'ripples_reduction'; tlims = [0 10]; tticks = 0:1.5:10;
  20. % clims1 = [0,60]; cticks1 = 0:20:60; ctickslabels1 = {'0','20','40','60+'};
  21. % clims2 = [0,750]; cticks2 = 0:250:750; ctickslabels2 = {'0','250','500','750+'};
  22. % flims = [0,200]; fticks = 0:50:200;
  24. data = load(strcat(PATH,fileName));
  27. %% EXAMPLE
  28. % Frequencies
  29. frequencies_Vs = linspace(0.5, 200, 500);
  30. frequencies_Ca = linspace(0.5, 200, 500);
  32. % Lomb-Scargle periodogram
  33. interval=(1000<data.t)&(data.t<1500);
  34. %interval=(2500<data.t)&(data.t<3500);
  35. power_Vs = plomb(data.y(interval,1), data.t(interval,1)/1000, frequencies_Vs);
  36. power_Ca = plomb(data.y(interval,3), data.t(interval,1)/1000, frequencies_Ca);
  37. [~, idx] = max(power_Vs);
  38. main_frequency_Vs = frequencies_Vs(idx);
  40. fprintf('Dominant spike frequency: %.2f Hz\n', main_frequency_Vs);
  41. [~, idx] = max(power_Ca);
  43. main_frequency_Ca = frequencies_Ca(idx);
  44. fprintf('Dominant calcium frequency: %.2f Hz\n', main_frequency_Ca);
  46. % Plotting
  47. figure
  48. tlo = tiledlayout(1,2,'TileSpacing','compact');
  49. title(tlo,'Lomb–Scargle Periodogram')
  51. nexttile
  52. plot(frequencies_Vs, power_Vs)
  53. xlabel('Frequency (Hz)','Interpreter','latex')
  54. ylabel('$V_s$ power','Interpreter','latex')
  55. xaxisproperties = get(gca, 'XAxis');
  56. xaxisproperties.TickLabelInterpreter = 'latex';
  57. yaxisproperties = get(gca, 'YAxis');
  58. yaxisproperties.TickLabelInterpreter = 'latex';
  59. grid on
  61. nexttile
  62. plot(frequencies_Ca, power_Ca)
  63. xlabel('Frequency (Hz)','Interpreter','latex')
  64. ylabel('$Ca_{pc}$ power','Interpreter','latex')
  65. xaxisproperties = get(gca, 'XAxis');
  66. xaxisproperties.TickLabelInterpreter = 'latex';
  67. yaxisproperties = get(gca, 'YAxis');
  68. yaxisproperties.TickLabelInterpreter = 'latex';
  69. grid on
  71. set(gcf, 'PaperPositionMode', 'Auto', 'PaperUnits', 'Inches', 'Units', 'Inches', 'Position', [2, 2, 10, 5]);
  74. %% LOMB-SCARGLE SPECTROGRAM
  75. frequencies = linspace(0.1, 200, 500);
  77. % Window setting
  78. window_size = 400;
  79. step_size = 19;
  81. time_windows = 0:step_size:10000-window_size; % window intervals
  82. power_Vs = zeros(length(time_windows), length(frequencies)); % initialization
  83. power_Ca = zeros(length(time_windows), length(frequencies)); % initialization
  85. % Computation
  86. parfor i = 1:length(time_windows)
  87. t_start = time_windows(i);
  88. t_end = t_start + window_size;
  89. interval = (t_start < data.t) & (data.t < t_end);
  91. if sum(interval) > 100 % enough points?
  92. power_Vs(i, :) = plomb(data.y(interval, 1), data.t(interval)/1000, frequencies);
  93. power_Ca(i, :) = plomb(data.y(interval, 3), data.t(interval)/1000, frequencies);
  94. end
  95. end
  98. %% PLOTTING
  99. lwd = 0.3;
  100. lwd2 = 0.7;
  102. figure;
  103. t = tiledlayout(2,2,'TileSpacing','compact','Padding','none');
  105. % TOP LEFT (Vs, Iast)
  106. Vs_x = data.t/1000; Vs_y = data.y(:,1);
  107. Ca_ast = data.y(:,10);
  108. IA_x = data.t/1000; IA_y = 2.11*1/2.*log((1000.*Ca_ast-196.69).^2+1).*1./(1+exp(-10.*(1000.*Ca_ast-196.69)));
  110. nexttile
  111. hold on
  113. % Y axis for Vs (left)
  114. yyaxis left
  115. plot(Vs_x, Vs_y, 'b', 'LineWidth', lwd) % Vs blue
  116. ylabel('$V_s$ (mV)','Interpreter','latex')
  117. set(gca, 'YColor', 'b')
  118. yaxisproperties = get(gca, 'YAxis');
  119. yaxisproperties(1).TickLabelInterpreter = 'latex';
  120. ylim([-70,30]), yticks(-70:25:30)
  122. % Y axis for IA (right)
  123. yyaxis right
  124. plot(IA_x, IA_y, 'r', 'LineWidth', lwd2) % IA red
  125. ylabel('$I_{ast} \,(\mu\mathrm{A}/\mathrm{cm}^2)$','Interpreter','latex')
  126. set(gca, 'YColor', 'r')
  127. yaxisproperties = get(gca, 'YAxis');
  128. yaxisproperties(2).TickLabelInterpreter = 'latex';
  129. ylim([-0.25,15]), yticks(0:5:15)
  131. % X axis
  132. xlabel('$t$ (s)','Interpreter','latex')
  133. xlim(tlims), xticks(tticks)
  134. xaxisproperties = get(gca, 'XAxis');
  135. xaxisproperties.TickLabelInterpreter = 'latex';
  136. hold off
  140. % TOP RIGHT (Ca)
  141. Ca_x = data.t/1000; Ca_y = data.y(:,3);
  143. nexttile
  144. hold on
  146. % Y axis for Ca
  147. plot(Ca_x, Ca_y, 'b', 'LineWidth', lwd) % Ca blue
  148. ylabel('$Ca_{pc}$ (nM)','Interpreter','latex')
  149. set(gca, 'YColor', 'b')
  150. yaxisproperties = get(gca, 'YAxis');
  151. yaxisproperties.TickLabelInterpreter = 'latex';
  152. ylim([0,400]), yticks(0:100:400)
  154. % X axis
  155. xlabel('$t$ (s)','Interpreter','latex');
  156. xlim(tlims), xticks(tticks)
  157. xaxisproperties = get(gca, 'XAxis');
  158. xaxisproperties.TickLabelInterpreter = 'latex';
  162. % BOTTOM LEFT (Vs)
  163. nexttile
  164. imagesc(time_windows/1000, frequencies, power_Vs')
  165. axis xy
  166. xlabel('$t$ (s)','Interpreter','latex')
  167. ylabel('$V_s$ frequency (Hz)','Interpreter','latex')
  168. xaxisproperties = get(gca, 'XAxis');
  169. xaxisproperties.TickLabelInterpreter = 'latex';
  170. yaxisproperties = get(gca, 'YAxis');
  171. yaxisproperties.TickLabelInterpreter = 'latex';
  172. xlim(tlims), xticks(tticks)
  173. ylim(flims), yticks(fticks)
  175. cb1 = colorbar('Ticks',cticks1,'TickLabels',ctickslabels1); clim(clims1);
  176. cb1.Label.Interpreter = 'latex';
  177. %cb1.Label.String = 'Power';
  178. cb1.TickLabelInterpreter = 'latex';
  182. % BOTTOM RIGHT (Ca)
  183. nexttile
  184. imagesc(time_windows/1000, frequencies, power_Ca')
  185. axis xy
  186. xlabel('$t$ (s)','Interpreter','latex')
  187. ylabel('$Ca_{pc}$ frequency (Hz)','Interpreter','latex')
  188. xaxisproperties = get(gca, 'XAxis');
  189. xaxisproperties.TickLabelInterpreter = 'latex';
  190. yaxisproperties = get(gca, 'YAxis');
  191. yaxisproperties.TickLabelInterpreter = 'latex';
  192. xlim(tlims), xticks(tticks)
  193. ylim(flims), yticks(fticks)
  195. cb2 = colorbar('Ticks',cticks2,'TickLabels',ctickslabels2); clim(clims2);
  196. cb2.Label.Interpreter = 'latex';
  197. cb2.Label.String = 'Power';
  198. cb2.TickLabelInterpreter = 'latex';
  201. % set(gcf, 'Position', [100, 100, 1200, 500]);
  202. set(gcf, 'PaperPositionMode', 'Auto', 'PaperUnits', 'Inches', 'Units', 'Inches', 'Position', [1, 1, 8, 3.25])
  203. exportgraphics(gcf,strcat('./../figures/',fileName,'.pdf'),'ContentType','vector')

script_Fig_04_05_06.m at commit 55036f4, under MIT · at the source

Overview

Authors: Lenka Přibylová1, Jan Ševčík1, Anastasia Egorova1, Štěpán Husa1, Lucia Kajanová1, Eva Kopřivová1, Lucie Alexandra Mega1, Veronika Eclerová1
ORCID iDs: Lenka Přibylová, Jan Ševčík, Lucia Kajanová, Veronika Eclerová
  1. Department of Mathematics and Statistics, Faculty of Science, Masaryk University,Kotlarska 2, Brno, 61137 Czech Republic
Institutions: Masaryk University (Czechia)
Journal: Journal of computational neuroscience, volume 54, issue 2, pages 153-176
Dates: received 6 June 2025; accepted 26 January 2026; published online 10 March 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1007/s10827-026-00924-x · PMID 41806289 · PMCID PMC13233653 · OpenAlex W7134973165
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: none (in silico) (organism), epilepsy (population), cellular / molecular (subfield)
Methods: Spectral & time-frequency
Keywords: Pyramidal cell, Astrocyte, Epileptiform activity, Pinsky–Rinzel model, Li–Rinzel-type model
MeSH: Astrocytes*, Dendrites*, Models, Neurological*, Pyramidal Cells*, Action Potentials, Animals, Calcium Signaling, Computer Simulation, Membrane Potentials (* major topic)
Topic: Neuroscience and Neuropharmacology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Masarykova Univerzita (MUNI/G/1213/2022)
Citations: not cited yet (Europe PMC); 91 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

gitlab.ics.muni.cz/ndteam/gamu/pc-astrocyte

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 55036f470e5f35e9f85b67af0ccadf3d60a19406, 9 November 2025
Languages: MATLAB (12)
Size: 74 files, 12 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
14 files
At the source: gitlab.ics.muni.cz/ndteam/gamu/PC-astrocyte

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:

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

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1007/s10827-026-00924-x.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 5 keywords, 9 MeSH terms, 1 funder, 85 references.

Cite

This paper

Přibylová, L., Ševčík, J., Egorova, A., Husa, Š., Kajanová, L., Kopřivová, E., Mega, L. A., & Eclerová, V. (2026). Astrocyte-induced dynamics of a pyramidal cell with a dendrite-connected astrocyte. Journal of computational neuroscience, 54(2), 153-176. https://doi.org/10.1007/s10827-026-00924-x

BibTeX

@article{pribylova2026astrocyte,
author = {Přibylová, Lenka and Ševčík, Jan and Egorova, Anastasia and Husa, Štěpán and Kajanová, Lucia and Kopřivová, Eva and Mega, Lucie Alexandra and Eclerová, Veronika},
title = {{Astrocyte-induced dynamics of a pyramidal cell with a dendrite-connected astrocyte}},
journal = {Journal of computational neuroscience},
year = {2026},
month = mar,
volume = {54},
number = {2},
pages = {153--176},
publisher = {Springer Science+Business Media},
issn = {0929-5313},
doi = {10.1007/s10827-026-00924-x},
url = {https://doi.org/10.1007/s10827-026-00924-x},
pmid = {41806289},
pmcid = {PMC13233653}
}

RIS

TY - JOUR
AU - Přibylová, Lenka
AU - Ševčík, Jan
AU - Egorova, Anastasia
AU - Husa, Štěpán
AU - Kajanová, Lucia
AU - Kopřivová, Eva
AU - Mega, Lucie Alexandra
AU - Eclerová, Veronika
TI - Astrocyte-induced dynamics of a pyramidal cell with a dendrite-connected astrocyte
T2 - Journal of computational neuroscience
J2 - J Comput Neurosci
PY - 2026
DA - 2026/03/10
VL - 54
IS - 2
SP - 153
EP - 176
SN - 0929-5313
PB - Springer Science+Business Media
DO - 10.1007/s10827-026-00924-x
UR - https://doi.org/10.1007/s10827-026-00924-x
LA - en
ER -

CSL-JSON

{
"id": "10.1007/s10827-026-00924-x",
"type": "article-journal",
"title": "Astrocyte-induced dynamics of a pyramidal cell with a dendrite-connected astrocyte",
"container-title": "Journal of computational neuroscience",
"author": [
{
"family": "Přibylová",
"given": "Lenka"
},
{
"family": "Ševčík",
"given": "Jan"
},
{
"family": "Egorova",
"given": "Anastasia"
},
{
"family": "Husa",
"given": "Štěpán"
},
{
"family": "Kajanová",
"given": "Lucia"
},
{
"family": "Kopřivová",
"given": "Eva"
},
{
"family": "Mega",
"given": "Lucie Alexandra"
},
{
"family": "Eclerová",
"given": "Veronika"
}
],
"container-title-short": "J Comput Neurosci",
"volume": "54",
"issue": "2",
"page": "153-176",
"DOI": "10.1007/s10827-026-00924-x",
"PMID": "41806289",
"PMCID": "PMC13233653",
"ISSN": "0929-5313",
"publisher": "Springer Science+Business Media",
"URL": "https://doi.org/10.1007/s10827-026-00924-x",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
10
]
]
}
}

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