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Post-synaptic facilitation and network dynamics underlying stimulus-specific combination sensitivity.

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The authors' code

MATLAB · 298 lines · 9.5 KB · MIT

  1. function results = Figure_2()
  2. P = get_common_params();
  4. cases(1) = struct('title','(A) AB-0 ms delay','from1',100,'to1',150,'from2',150,'to2',200,'reverse_order',false);
  5. cases(2) = struct('title','(B) AB-early B','from1',100,'to1',150,'from2',120,'to2',170,'reverse_order',false);
  6. cases(3) = struct('title','(C) AB-50 ms delay','from1',100,'to1',150,'from2',200,'to2',250,'reverse_order',false);
  7. cases(4) = struct('title','(D) BA-0 ms delay','from1',100,'to1',150,'from2',150,'to2',200,'reverse_order',true);
  9. figure('Color','w');
  10. tiledlayout(2,2,'Padding','compact','TileSpacing','compact');
  12. results = struct([]);
  14. for k = 1:4
  15. [t, sol] = run_case(P, cases(k));
  16. nexttile;
  17. plot_case(t, sol, cases(k).title);
  18. results(k).t = t;
  19. results(k).sol = sol;
  20. results(k).case = cases(k);
  21. end
  22. end
  24. function P = get_common_params()
  25. P.Iapp1 = 200;
  26. P.Iapp2 = 200;
  28. P.gCaTINT1 = 0.5;
  29. P.gCaTX2 = 0.5;
  30. P.gCaTCSN = 0.8;
  32. P.gSKX2 = 2;
  33. P.gSKCSN = 2;
  35. P.gGAi1csn = 10;
  36. P.gAMx2csn = 1.2;
  37. P.gAMCNCN = 5;
  39. P.gNaINT = 800;
  40. P.gKINT = 1200;
  41. P.gHINT = 4;
  42. P.CINT = 20;
  43. P.krINT = 0.01;
  45. P.gNaX = 450;
  46. P.gKX = 60;
  47. P.gHX = 6;
  48. P.CX = 20;
  49. P.krX = 0.3;
  51. P.taur0 = 5;
  52. P.taur1 = 15;
  53. P.thetarrT = 68;
  54. P.sigmarrT = 2;
  55. P.thetarT = -67;
  56. P.sigmarT = 2;
  57. P.thetaaT = -85;
  58. P.sigmaaT = -8;
  59. P.thetab = 0.4;
  60. P.sigmab = -0.1;
  61. P.phirT = 0.2;
  63. P.VL = -70;
  64. P.VK = -90;
  65. P.VNa = 70;
  66. P.VH = -30;
  67. P.Tmax = 1;
  68. P.VT = 2;
  69. P.Kps = 5;
  70. P.gL = 2;
  71. P.gCa = 19;
  72. P.arGA = 5;
  73. P.adGA = 0.18;
  74. P.VGA = -100;
  75. P.arAM = 1.1;
  76. P.adAM = 0.19;
  77. P.VAM = 0;
  79. P.taunbar = 10;
  80. P.tauh = 1;
  81. P.Ca_ex = 2.5;
  82. P.RTF = 26.7;
  83. P.thetam = -35;
  84. P.thetan = -30;
  85. P.thetas = -20;
  86. P.sigmam = -5;
  87. P.sigman = -5;
  88. P.sigmas = -0.05;
  89. P.f = 0.1;
  90. P.eps = 0.0015;
  91. P.kCa = 0.3;
  92. P.bCa = 0.1;
  93. P.ks = 0.5;
  94. P.taurs = 1500;
  95. P.thetarf = -105;
  96. P.thetars = -105;
  97. P.sigmarf = 5;
  98. P.sigmars = 25;
  99. P.prf = 100;
  101. P.tspan = 0:0.01:900;
  102. P.INTinit = [-67.6; 0.5e-03; 0.996; 0.1355; 0.1; 5.5e-04; 0.034];
  103. P.Xinit = [-75; 0.01; 0.01; 0.01; 0.01; 0.01; 0.01];
  104. P.init_cond = [P.INTinit; P.Xinit; P.Xinit; 0.01; 0.01; 0.01];
  105. end
  107. function [t, sol] = run_case(P, caseDef)
  108. P.from1 = caseDef.from1;
  109. P.to1 = caseDef.to1;
  110. P.from2 = caseDef.from2;
  111. P.to2 = caseDef.to2;
  112. P.reverse_order = caseDef.reverse_order;
  113. [t, sol] = ode113(@(t,sol) SimpleFTN_local(t, sol, P), P.tspan, P.init_cond);
  114. end
  116. function plot_case(t, sol, titleStr)
  117. vINT1 = sol(:,1);
  118. vX2 = sol(:,8);
  119. vCSN = sol(:,15);
  121. step = 150;
  122. i = 1;
  124. hold on
  125. plot(t, vINT1-step*i, 'Color', [0.46,0.01,0.65], 'LineWidth', 1);
  126. text(60, -step*i, 'A');
  127. i = i + 1;
  129. plot(t, vX2-step*i, 'Color', [0.61,0.62,0.67], 'LineWidth', 1);
  130. text(60, -step*i, 'B');
  131. i = i + 1;
  133. plot(t, vCSN-step*i, 'Color', [0.04,0.17,0.55], 'LineWidth', 1);
  134. text(60, -step*i, 'CSN');
  135. i = i + 1;
  137. yMin = min([vINT1; vX2; vCSN]) - (i * step) - 100;
  138. yMax = max([vINT1; vX2; vCSN]) + 100;
  140. xlim([50 300]);
  141. ylim([yMin yMax]);
  143. scaleLengthX = 10;
  144. scaleLengthY = 50;
  145. x_start = 270;
  146. y_start = yMin + 200;
  148. line([x_start, x_start + scaleLengthX], [y_start, y_start], 'Color', 'k', 'LineWidth', 2);
  149. line([x_start, x_start], [y_start, y_start + scaleLengthY], 'Color', 'k', 'LineWidth', 2);
  151. text(x_start + scaleLengthX/2, y_start - 30, '10 ms', 'HorizontalAlignment', 'center', 'FontSize', 9);
  152. text(x_start - 5, y_start + scaleLengthY/2, '50 mV', 'HorizontalAlignment', 'right', 'FontSize', 9);
  154. title(titleStr, 'FontWeight', 'normal');
  155. set(gca, 'YTick', [], 'XTick', [], 'XColor', 'none', 'YColor', 'none');
  156. box off
  157. hold off
  158. end
  160. function output = SimpleFTN_local(t, sol, P)
  161. VINT1 = sol(1); nINT1 = sol(2); hINT1 = sol(3); rTINT1 = sol(4); CaiINT1 = sol(5); rfINT1 = sol(6); rsINT1 = sol(7);
  162. VX2 = sol(8); nX2 = sol(9); hX2 = sol(10); rTX2 = sol(11); CaiX2 = sol(12); rfX2 = sol(13); rsX2 = sol(14);
  163. VCSN = sol(15); nCSN = sol(16); hCSN = sol(17); rTCSN = sol(18); CaiCSN = sol(19); rfCSN = sol(20); rsCSN = sol(21);
  165. sGAi1csn = sol(22);
  166. sAMx2csn = sol(23);
  167. sAMCNCN = sol(24);
  169. sinfINT1 = 1/(1 + exp((VINT1-P.thetas)/P.sigmas));
  170. iCaLINT1 = P.gCa*(sinfINT1^2)*VINT1*(P.Ca_ex/(1-exp((2*VINT1)/P.RTF)));
  171. aTinfINT1 = 1/(1 + exp((VINT1-P.thetaaT)/P.sigmaaT));
  172. bTinfINT1 = 1/(1 + exp((rTINT1-P.thetab)/P.sigmab)) - 1/(1 + exp(-P.thetab/P.sigmab));
  173. rTinfINT1 = 1/(1 + exp((VINT1-P.thetarT)/P.sigmarT));
  174. taurTINT1 = P.taur0 + P.taur1/(1 + exp((VINT1-P.thetarrT)/P.sigmarrT));
  175. iCaTINT1 = P.gCaTINT1*(aTinfINT1^3)*(bTinfINT1^2)*VINT1*(P.Ca_ex/(1-exp((2*VINT1)/P.RTF)));
  176. iLINT1 = P.gL*(VINT1-P.VL);
  177. ninfINT1 = 1/(1 + exp((VINT1-P.thetan)/P.sigman));
  178. taunINT1 = P.taunbar/cosh((VINT1-P.thetan)/(2*P.sigman));
  179. iKINT1 = P.gKINT*(nINT1^4)*(VINT1-P.VK);
  180. minfINT1 = 1/(1 + exp((VINT1-P.thetam)/P.sigmam));
  181. alphahINT1 = 0.128*exp(-(VINT1+50)/18);
  182. betahINT1 = 4/(1 + exp(-(VINT1+27)/5));
  183. hinfINT1 = alphahINT1/(alphahINT1 + betahINT1);
  184. iNaINT1 = P.gNaINT*(minfINT1^3)*hINT1*(VINT1-P.VNa);
  185. rfinfINT1 = 1/(1 + exp((VINT1-P.thetarf)/P.sigmarf));
  186. taurfINT1 = P.prf/(-7.4*(VINT1+70)/(exp(-(VINT1+70)/0.8)-1) + 65*exp(-(VINT1+56)/23));
  187. rsinfINT1 = 1/(1 + exp((VINT1-P.thetars)/P.sigmars));
  188. iHINT1 = P.gHINT*(P.krINT*rfINT1 + (1-P.krINT)*rsINT1)*(VINT1-P.VH);
  189. TINT1 = P.Tmax/(1 + exp(-(VINT1-P.VT)/P.Kps));
  191. minfX2 = 1/(1 + exp((VX2-P.thetam)/P.sigmam));
  192. ninfX2 = 1/(1 + exp((VX2-P.thetan)/P.sigman));
  193. tauNX2 = P.taunbar./cosh((VX2-P.thetan)/(2*P.sigman));
  194. alphaHX2 = 0.128*exp(-(VX2+50)/18);
  195. betaHX2 = 4/(1 + exp(-(VX2+27)/5));
  196. hinfX2 = alphaHX2/(alphaHX2 + betaHX2);
  197. iNaX2 = P.gNaX*(minfX2^3)*hX2*(VX2-P.VNa);
  198. iKX2 = P.gKX*(nX2^4)*(VX2-P.VK);
  199. sinfX2 = 1/(1 + exp((VX2-P.thetas)/P.sigmas));
  200. iCaLX2 = P.gCa*(sinfX2^2)*VX2*(P.Ca_ex/(1-exp((2*VX2)/P.RTF)));
  201. aTinfX2 = 1/(1 + exp((VX2-P.thetaaT)/P.sigmaaT));
  202. bTinfX2 = 1/(1 + exp((rTX2-P.thetab)/P.sigmab)) - 1/(1 + exp(-P.thetab/P.sigmab));
  203. rTinfX2 = 1/(1 + exp((VX2-P.thetarT)/P.sigmarT));
  204. taurTX2 = P.taur0 + P.taur1/(1 + exp((VX2-P.thetarrT)/P.sigmarrT));
  205. iCaTX2 = P.gCaTX2*(aTinfX2^3)*(bTinfX2^2)*VX2*(P.Ca_ex/(1-exp((2*VX2)/P.RTF)));
  206. kinfX2 = (CaiX2^2)/(CaiX2^2 + (P.ks^2));
  207. iSKX2 = P.gSKX2*kinfX2*(VX2-P.VK);
  208. rinffX2 = 1/(1 + exp((VX2-P.thetarf)/P.sigmarf));
  209. rinfsX2 = 1/(1 + exp((VX2-P.thetars)/P.sigmars));
  210. taurfX2 = P.prf./(-7.4*(VX2+70)./(exp(-(VX2+70)/0.8)-1) + 65*exp(-(VX2+56)/23));
  211. iHX2 = P.gHX*(P.krX*rfX2 + (1-P.krX)*rsX2)*(VX2-P.VH);
  212. iLX2 = P.gL*(VX2-P.VL);
  213. TX2 = P.Tmax/(1 + exp(-(VX2-P.VT)/P.Kps));
  215. minfCSN = 1/(1 + exp((VCSN-P.thetam)/P.sigmam));
  216. ninfCSN = 1/(1 + exp((VCSN-P.thetan)/P.sigman));
  217. tauNCSN = P.taunbar./cosh((VCSN-P.thetan)/(2*P.sigman));
  218. alphaHCSN = 0.128*exp(-(VCSN+50)/18);
  219. betaHCSN = 4/(1 + exp(-(VCSN+27)/5));
  220. hinfCSN = alphaHCSN/(alphaHCSN + betaHCSN);
  221. iNaCSN = P.gNaX*(minfCSN^3)*hCSN*(VCSN-P.VNa);
  222. iKCSN = P.gKX*(nCSN^4)*(VCSN-P.VK);
  223. sinfCSN = 1/(1 + exp((VCSN-P.thetas)/P.sigmas));
  224. iCaLCSN = P.gCa*(sinfCSN^2)*VCSN*(P.Ca_ex/(1-exp((2*VCSN)/P.RTF)));
  225. aTinfCSN = 1/(1 + exp((VCSN-P.thetaaT)/P.sigmaaT));
  226. bTinfCSN = 1/(1 + exp((rTCSN-P.thetab)/P.sigmab)) - 1/(1 + exp(-P.thetab/P.sigmab));
  227. rTinfCSN = 1/(1 + exp((VCSN-P.thetarT)/P.sigmarT));
  228. taurTCSN = P.taur0 + P.taur1/(1 + exp((VCSN-P.thetarrT)/P.sigmarrT));
  229. iCaTCSN = P.gCaTCSN*(aTinfCSN^3)*(bTinfCSN^3)*VCSN*(P.Ca_ex/(1-exp((2*VCSN)/P.RTF)));
  230. kinfCSN = (CaiCSN^2)/(CaiCSN^2 + (P.ks^2));
  231. iSKCSN = P.gSKCSN*kinfCSN*(VCSN-P.VK);
  232. rinffCSN = 1/(1 + exp((VCSN-P.thetarf)/P.sigmarf));
  233. rinfsCSN = 1/(1 + exp((VCSN-P.thetars)/P.sigmars));
  234. taurfCSN = P.prf./(-7.4*(VCSN+70)./(exp(-(VCSN+70)/0.8)-1) + 65*exp(-(VCSN+56)/23));
  235. iHCSN = P.gHX*(P.krX*rfCSN + (1-P.krX)*rsCSN)*(VCSN-P.VH);
  236. iLCSN = P.gL*(VCSN-P.VL);
  237. TCSN = P.Tmax/(1 + exp(-(VCSN-P.VT)/P.Kps));
  239. iGAi1csn = P.gGAi1csn*sGAi1csn*(VCSN-P.VGA);
  240. iAMx2csn = P.gAMx2csn*sAMx2csn*(VCSN-P.VAM);
  241. iAMCNCN = P.gAMCNCN*sAMCNCN*(VCSN-P.VAM);
  243. if ~P.reverse_order
  244. if (t > P.from1 && t < P.to1)
  245. curr1 = P.Iapp1;
  246. else
  247. curr1 = 0;
  248. end
  250. if (t > P.from2 && t < P.to2)
  251. curr2 = P.Iapp2;
  252. else
  253. curr2 = 0;
  254. end
  255. else
  256. if (t > P.from1 && t < P.to1)
  257. curr2 = P.Iapp2;
  258. else
  259. curr2 = 0;
  260. end
  262. if (t > P.from2 && t < P.to2)
  263. curr1 = P.Iapp1;
  264. else
  265. curr1 = 0;
  266. end
  267. end
  269. output = zeros(24,1);
  271. output(1) = (-iNaINT1-iKINT1-iCaLINT1-iCaTINT1-iHINT1-iLINT1+curr1)/P.CINT;
  272. output(2) = (ninfINT1-nINT1)/taunINT1;
  273. output(3) = (hinfINT1-hINT1)/P.tauh;
  274. output(4) = P.phirT*(rTinfINT1-rTINT1)/taurTINT1;
  275. output(5) = -P.f*(P.eps*(iCaLINT1+iCaTINT1) + P.kCa*(CaiINT1-P.bCa));
  276. output(6) = (rfinfINT1-rfINT1)/taurfINT1;
  277. output(7) = (rsinfINT1-rsINT1)/P.taurs;
  279. output(8) = (-iNaX2-iKX2-iCaLX2-iCaTX2-iSKX2-iHX2-iLX2+curr2)/P.CX;
  280. output(9) = (ninfX2-nX2)/tauNX2;
  281. output(10) = (hinfX2-hX2)/P.tauh;
  282. output(11) = P.phirT*(rTinfX2-rTX2)/taurTX2;
  283. output(12) = -P.f*(P.eps*(iCaLX2+iCaTX2) + P.kCa*(CaiX2-P.bCa));
  284. output(13) = (rinffX2-rfX2)/taurfX2;
  285. output(14) = (rinfsX2-rsX2)/P.taurs;
  287. output(15) = (-iNaCSN-iKCSN-iCaLCSN-iCaTCSN-iSKCSN-iHCSN-iLCSN-iGAi1csn-iAMx2csn-iAMCNCN)/P.CX;
  288. output(16) = (ninfCSN-nCSN)/tauNCSN;
  289. output(17) = (hinfCSN-hCSN)/P.tauh;
  290. output(18) = P.phirT*(rTinfCSN-rTCSN)/taurTCSN;
  291. output(19) = -P.f*(P.eps*(iCaLCSN+iCaTCSN) + P.kCa*(CaiCSN-P.bCa));
  292. output(20) = (rinffCSN-rfCSN)/taurfCSN;
  293. output(21) = (rinfsCSN-rsCSN)/P.taurs;
  295. output(22) = P.arGA*TINT1*(1-sGAi1csn) - P.adGA*sGAi1csn;
  296. output(23) = P.arAM*TX2*(1-sAMx2csn) - P.adAM*sAMx2csn;
  297. output(24) = P.arAM*TCSN*(1-sAMCNCN) - P.adAM*sAMCNCN;
  298. end

Figure_2.m at commit 873b631, under MIT · at the source

Overview

Authors: Zeina Merabi1, Arij Daou1
ORCID iDs: Arij Daou
  1. Neurophysiology and Computational Neuroscience Group, Biomedical Engineering Program, American University of Beirut, Beirut, Lebanon
Institutions: American University of Beirut (Lebanon)
Journal: iScience, volume 29, issue 6, article 116125
Dates: received 26 August 2025; accepted 11 May 2026; published online 27 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.116125 · PMID 42256266 · PMCID PMC13235528 · OpenAlex W4413716464
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.08.25.672194
Status: code verified
Categories: cellular / molecular (subfield)
Keywords: neuroscience, sensory neuroscience, systems neuroscience
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: University Research Board, American University of Beirut; American University of Beirut
Citations: not cited yet (Europe PMC); 99 references in the paper

Abstract

Combination-sensitive neurons (CSNs) transform specific combinations of sensory features into selective neural responses, supporting the temporal organization of complex signals. We developed a biophysically realistic computational model, constrained by intrinsic and synaptic properties, to examine how temporally ordered stimulus pairs can be associated across delays of hundreds of milliseconds. The model shows that upstream inhibitory-delay circuits can transiently preserve information about the first stimulus while progressively shaping the excitability of an output neuron. Together with post-inhibitory rebound and facilitation, these dynamics create a delay-dependent primed state that funnels separated inputs into a narrow coincidence-detection window. This mechanism enables sharply timed, selective responses to stimulus pairs without requiring sustained spiking in the output neuron. Our results provide a circuit-level link between coincidence detection and longer-timescale temporal integration, offering a general framework for temporal coding in sensory systems.

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

Repository

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

daoulab/combination-sensitivity-model

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 873b631e0e6cc7156ea34a2f84c353e46747ad94, 13 April 2026
Languages: MATLAB (13)
Size: 16 files, 13 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, license file, CITATION.cff
Not found: environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
15 files
At the source: github.com/daoulab/combination-sensitivity-model

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;
  • 13 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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 and code availability

• Data: This study did not generate new unique datasets. All results reported in this paper were produced from computational simulations and can be reproduced from the code provided below. • Code: MATLAB source codes that generate every figure and supplementary figure in the paper is available on GitHub at https://github.com/daoulab/combination-sensitivity-model. • All other items: Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon 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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 3 keywords, 2 funders, 92 references.

Cite

This paper

Merabi, Z., & Daou, A. (2026). Post-synaptic facilitation and network dynamics underlying stimulus-specific combination sensitivity. iScience, 29(6), 116125. https://doi.org/10.1016/j.isci.2026.116125

BibTeX

@article{merabi2026post,
author = {Merabi, Zeina and Daou, Arij},
title = {{Post-synaptic facilitation and network dynamics underlying stimulus-specific combination sensitivity}},
journal = {iScience},
year = {2026},
month = may,
volume = {29},
number = {6},
pages = {116125},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116125},
url = {https://doi.org/10.1016/j.isci.2026.116125},
pmid = {42256266},
pmcid = {PMC13235528}
}

RIS

TY - JOUR
AU - Merabi, Zeina
AU - Daou, Arij
TI - Post-synaptic facilitation and network dynamics underlying stimulus-specific combination sensitivity
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/05/27
VL - 29
IS - 6
SP - 116125
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116125
UR - https://doi.org/10.1016/j.isci.2026.116125
LA - en
ER -

CSL-JSON

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"id": "10.1016/j.isci.2026.116125",
"type": "article-journal",
"title": "Post-synaptic facilitation and network dynamics underlying stimulus-specific combination sensitivity",
"container-title": "iScience",
"author": [
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"family": "Merabi",
"given": "Zeina"
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"family": "Daou",
"given": "Arij"
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],
"container-title-short": "iScience",
"volume": "29",
"issue": "6",
"page": "116125",
"DOI": "10.1016/j.isci.2026.116125",
"PMID": "42256266",
"PMCID": "PMC13235528",
"ISSN": "2589-0042",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.isci.2026.116125",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
27
]
]
}
}

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