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The hippocampal CA3 area implements sequence learning of discontinuous episodes.

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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] § Methods › Simulation methods ↔ encoding.m, lines 51–99 · score 0.70 · exponential decay, synaptic delay, reset, arrive, pre, post
  2. [2] § Results › Simulation of sequence learning in the presence of intervening place cell firings ↔ showFigS4.m, lines 11–17 · score 0.52 · reward areas, place field, S4B, MF

Paper

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The authors' code

MATLAB · 237 lines · 7.1 KB · no license · 1 match

  1. clear
  2. set(0, 'DefaultFigureWindowStyle', 'docked');
  4. readprm;
  5. prepenc;
  6. initenc;
  8. %%
  9. %************************************************
  10. % Encoding Simulation Main
  11. %************************************************
  13. tic
  15. for t = 2:encnt
  16. % read izp from t and TppM (=Tmidpp)
  17. tdiff = Tmi; %Tp =Tm = interval btwn episodes
  18. for idx=1:nzm
  19. newdiff = abs(t-TppM(idx));
  20. if tdiff > newdiff
  21. tdiff = newdiff;
  22. izp = idx;
  23. end
  24. end
  26. %% take care fired neurons
  28. jf = find(v > 30); %just firing
  30. %if ~isempty(jf)
  31. nfire = length(jf);
  32. % append the list of newly firing neurons to [firings]
  33. firings = [firings; t*ones(nfire,1), jf]; %for storing the whole firing history, X(justfired,t) = 1;
  34. ejf = jf(jf < Ne+1); ijf = jf(jf > Ne);
  35. v(ejf) = Vrete; v(ijf) = Vreti;
  37. if bLIF == 1
  38. dvth(jf) = dVth;
  39. else
  40. uiz(ejf)=uiz(ejf)+ude; uiz(ijf)=uiz(ijf)+udi;
  41. end
  43. % ion conc
  44. dca(ejf) = 1;
  45. %end
  47. dzn = bzn.*(tmf<TmfZn);
  48. bzn(dzn==1) = 0; % bzn = 0 (zn in cleft disappear) if dzn ==1
  49. ca = ca + dca; zn = zn + dzn;
  51. %% A/C STDP
  53. stdpac = circshift(stdpac, [0 -1]); %left-shift of columns
  54. stdpac(:,Dlye+1) = dsac*stdpac(:,Dlye); %exponential decay of stdp
  55. stdpac(ejf,Dlye+1) = Adwac; %reset stdp of fired neurons
  57. dsw = zeros(Ne, Ne); %reset of dsw (= delta of synaptic weight)
  59. % STDP forward: ejf as a post -----------------------------
  60. nejf = length(ejf);
  61. if nejf > 0 %isempty(ejf)
  62. for idx=1:nejf
  63. dsw(ejf(idx),:) = stdpac(:,1)'; %forward synapse pre->ejf
  64. end
  65. end
  67. % consider synaptic delay ---------------------------
  68. pf = firings(firings(:,1)==t-Dlye,2); %pf = prevfired
  69. epfdx = pf < Ne+1;
  70. epf = pf(epfdx); %epf = exc_prevfired
  72. pf = firings(firings(:,1)==t-Dlyi,2);
  73. ipfdx = pf > Ne;
  74. ipf = pf(ipfdx); %ipf = inh_prevfired
  76. % STDP reverse: epf as a pre (epf AP just arrived)-------
  77. nepf = length(epf);
  78. if nepf > 0 %isempty(ejf)
  79. for idx=1:nepf
  80. dsw(:,epf(idx)) = dsw(:,epf(idx))+stdpac(:,end); %reverse synapse epf->pre; Note that end = Dlye+1
  81. end
  82. end
  84. dsw = 1 + dsw*nRepeat;
  86. Jee = min(Jee.*dsw, Gacmax);
  87. WJee = Jee.*Wee;
  88. WJe = [WJee; WJie];
  90. % update Gsyn (ge & gi)
  92. ge = dke*ge;
  93. ge(:,epf) = ge(:,epf) + WJe(:,epf)*AchFactor;
  94. gesum(:,1) = sum(ge,2); % row sum of ge
  96. ipf = ipf - Ne;
  97. gi = dki*gi;
  98. gi(:, ipf) = gi(:, ipf) + WJi(:, ipf);
  99. gisum(:,1) = sum(gi,2); %gi = row sum of gi1
  101. %% PP synapse stdp ----------------------------------
  103. %decay of stdp
  104. stdppa = stdppa*dsppa; %stdp pp -> firing
  105. stdppb = stdppb*dsppb; %stdp firing -> pp
  107. if ~isempty(ejf)
  108. Jpp(ejf,izp) = Jpp(ejf,izp).*(stdppa(ejf)+1);
  109. stdppb(ejf) = Bdwpp;
  110. end
  112. ZppOn = find(Zpp(:,t));
  113. ZeppOn = find(Zpp(1:Ne,t));
  114. ZippOn = find(Zpp(Ne+1:Nt,t));
  116. if ~isempty(ZeppOn)
  117. Jpp(ZeppOn,izp) = Jpp(ZeppOn,izp).*(stdppb(ZeppOn)+1);
  118. stdppa(ZeppOn) = Adwpp;
  119. end
  121. Jpp = min(Gppmax, Jpp);
  123. %% update Gsyn (gmf and gpp)
  125. % gmf = mf input = zeros(Nt,1) -------------------
  126. gmf = gmf*dkmf;
  127. ZmfeOn = find(Zmf(1:Ne,t) == 1);
  128. ZmfiOn = find(Zmf(Ne+1:Nt,t) == 1)+Ne;
  129. gmf(ZmfeOn) = gmf(ZmfeOn) + Gmfe;
  130. gmf(ZmfiOn) = gmf(ZmfiOn) + Gmfi;
  132. bzn(ZmfeOn) = 1;
  133. tmf(ZmfeOn) = 0;
  135. gpp = gpp*dkpp;
  136. gpp(ZeppOn) = gpp(ZeppOn) + Jpp(ZeppOn,izp);
  137. gpp(ZippOn) = gpp(ZippOn) + Gppi;
  140. %% D-spike current
  141. % d-spike occurs if PP input arrives and IE>0.3
  142. Ids = zeros(Nt,1); %dvds = zeros(Nt,1);
  143. preT = max(1, t-150); % D spike occurs when more than 2 pp inputs arrive within 60 ms
  144. ZdsOn = find((IE(:,t-1)>0.5).*Zpp(1:Ne,t).*(sum(Zpp(1:Ne, preT:t), 2)>2).*(Idsidx>Rfrds)); % ZdsOn = ZppOn(IE(ZppOn, t)>0.5);
  145. Idsidx(ZdsOn) = 1;
  146. % dt should not be larger than tauds1(=0.1). To circumvent it, I repeat Ids calculations by ndtds times (= dt/dtds)
  147. for tdx=1:ndtds
  148. Idsidx = min(Idsidx+1, ntids);
  149. Ids(1:Ne,1) = Ids(1:Ne,1) + IdsTmplt(Idsidx); %Ids = IdsTmplt(Idsidx); % Ids = somatic current inj d/t dendritic spike
  150. % dvds = dvds + dtds*Ids/cm;
  151. end
  152. Ids = Ids/ndtds;
  153. % dsrec(:,t) = Ids; %dvds;
  155. %% calc vm
  156. if bLIF == 1
  157. Isyn(:,t) = (gesum + gmf + gpp + Go).*(v-Ere) + gisum.*(v-Eri) - Ids; % nS*mV = pA
  158. v = v - Km.*((v - Vleak) + Rm.* Isyn(:,t)); % + dvds; % GOhm*pA = mV
  159. else % izh
  160. %{
  161. dv/dt [mV/ms]= Gs[nS] dE[mV] / Cm[pF] = [A/F] = [V/s] = [mV/ms]
  162. Since Cm [pF] = Tm [ms] Gm [nS],
  163. dv/dt [mV/ms] = (Gs/Gm) dE(mV) / Tm (ms)
  164. For izh, Cm is equivalent to Tm, and cminv = 1/Tm = 0.01
  165. %}
  166. Isyn(:,t) = cminv.*((gesum + gmf + gpp).*(v-Ere) + gisum.*(v-Eri)-Ids); % nS*mV / pF = pA/pF = V/s = mV/ms
  167. v=v+dthalf*(((0.04*v+5).*v+140-uiz)- Isyn(:,t));
  168. v=v+dthalf*(((0.04*v+5).*v+140-uiz)- Isyn(:,t));
  169. uiz=uiz+dt*aiz.*(biz.*v-uiz);
  170. %v = max(Vmin, v); %to suppress rebound AP firing by inh inputs
  171. end
  173. %% take care impending fire
  174. if bLIF == 1
  175. dvth = dvth.*dkrfp;
  176. v(v > (Vth+dvth) ) = 35;
  177. else
  178. v(v > Vth) = 35;
  179. end
  181. venc(:,t) = v;
  183. %% update IE & ions
  185. bIE(((ca>CaTh1).*(zn>ZnTh))==1) = 1;
  186. bIE(((ca>CaTh2).*(zn<ZnTh))==1) = 0;
  188. cIEdx = find(bIE==1);
  189. dIEdx = find(bIE==0);
  191. IE(cIEdx,t) = IE(cIEdx,t-1)*dincIE_ + dincIE;
  192. IE(dIEdx,t) = IE(dIEdx,t-1)*dkIE;
  194. ca = ca*dkca;
  195. zn = zn*dkzn;
  196. tmf = tmf + 1;
  198. dca(:) = 0;
  199. dzn(:) = 0;
  201. carec(:, t) = ca;
  202. znrec(:, t) = zn; %IErec(:,t) = IE;
  204. end
  206. toc
  207. beep;
  209. WJee_ = WJee;
  211. homeo;
  212. %or Rwmax=1/1.4; homeo2
  213. %% display
  214. Jpp = min(Gppmax, Jpp); %Jpp = max(0, Jpp);
  215. if app ~= 1
  216. JPP_ = zeros(Ne, nzm);
  217. for zpdx=1:nzm
  218. Zon = Zpcell{zpdx,1};
  219. JPP_(Zon,zpdx) = Jpp(Zon, zpdx);
  220. end
  221. Jpp = JPP_;
  222. clear JPP_;
  223. end
  224. figure(5); clf; image(Jpp, 'CdataMapping', 'scaled'); colorbar; caxis([0,Gppmax]); title('Jpp'); %ylim([0 Nam*nzm])
  226. showWJ %fig6
  227. showEnc %fig3,4
  228. ya = [1,Ne]; xa = [0, enctime];
  229. figure(7); clf; image(xa, ya, carec, 'CdataMapping', 'scaled'); title('Ca');
  230. colorbar; dcm = datacursormode(gcf); set(dcm,'DisplayStyle','window')
  231. xlabel("time (ms)"); ylabel("i.d. of neurons");
  232. figure(8); clf; image(xa, ya, znrec, 'CdataMapping', 'scaled'); title('Zn');
  233. colorbar;dcm = datacursormode(gcf); set(dcm,'DisplayStyle','window')
  234. xlabel("time (ms)"); ylabel("i.d. of neurons");
  235. figure(9); clf; image(xa, ya, IE, 'CdataMapping', 'scaled'); title('IE');
  236. colorbar; dcm = datacursormode(gcf); set(dcm,'DisplayStyle','window')
  237. xlabel("time (ms)"); ylabel("i.d. of neurons");

encoding.m at commit db17e7b, no license · at the source

Overview

Authors: Kisang Eom1,2,3, Yujin Kim1,2, Hyoung-Ro Lee1, Yolguk Lee2, Young-Eun Han1, Jiwoo Shin1,2, Jae Sung Lee4, Jung Ho Hyun3, Alan J. Park1, Suk-Ho Lee1,2
ORCID iDs: Kisang Eom, Jung Ho Hyun, Alan J. Park, Suk-Ho Lee
  1. Department of Physiology, Seoul National University College of Medicine,Seoul, Republic of Korea
  2. Department of Brain and Cognitive Science, Seoul National University College of Natural Science,Seoul, Republic of Korea
  3. Department of Brain Sciences, DGIST,Daegu, Republic of Korea
  4. Beth Israel Deaconess Medical Center, Howard Hughes Medical Institute,Boston, MA USA
Institutions: Seoul National University (South Korea); Daegu Gyeongbuk Institute of Science and Technology (South Korea); Howard Hughes Medical Institute (United States)
Journal: Communications biology, volume 9, issue 1, article 1046
Dates: received 14 August 2025; accepted 2 April 2026; published online 11 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-10064-8 · PMID 42115760 · PMCID PMC13443506 · OpenAlex W7160874317
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Graphs
Keywords: Network models, Hippocampus
MeSH: CA3 Region, Hippocampal*, Learning*, Animals, Kv1.2 Potassium Channel, Male, Memory, Mice, Pyramidal Cells (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Research Foundation of Korea (RS-2024-00333669, 2020R1A2C2006438, 2021R1I1A1A01059646)
Citations: not cited yet (Europe PMC); 70 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

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bobazok/Sequence-learning-in-the-hippocampal-CA3

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: db17e7bafd4748edb4d7b49e9eaff18a081184d4, 27 March 2026
Languages: MATLAB (19)
Size: 24 files, 19 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
20 files
At the source: github.com/bobazok/Sequence-learning-in-the-hippocampal-CA3

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

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Read it in the paper: doi.org/10.1038/s42003-026-10064-8.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 2 keywords, 8 MeSH terms, 1 funder, 69 references.

Cite

This paper

Eom, K., Kim, Y., Lee, H.-R., Lee, Y., Han, Y.-E., Shin, J., Lee, J. S., Hyun, J. H., Park, A. J., & Lee, S.-H. (2026). The hippocampal CA3 area implements sequence learning of discontinuous episodes. Communications biology, 9(1), 1046. https://doi.org/10.1038/s42003-026-10064-8

BibTeX

@article{eom2026hippocampal,
author = {Eom, Kisang and Kim, Yujin and Lee, Hyoung-Ro and Lee, Yolguk and Han, Young-Eun and Shin, Jiwoo and Lee, Jae Sung and Hyun, Jung Ho and Park, Alan J. and Lee, Suk-Ho},
title = {{The hippocampal CA3 area implements sequence learning of discontinuous episodes}},
journal = {Communications biology},
year = {2026},
month = may,
volume = {9},
number = {1},
pages = {1046},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10064-8},
url = {https://doi.org/10.1038/s42003-026-10064-8},
pmid = {42115760},
pmcid = {PMC13443506}
}

RIS

TY - JOUR
AU - Eom, Kisang
AU - Kim, Yujin
AU - Lee, Hyoung-Ro
AU - Lee, Yolguk
AU - Han, Young-Eun
AU - Shin, Jiwoo
AU - Lee, Jae Sung
AU - Hyun, Jung Ho
AU - Park, Alan J.
AU - Lee, Suk-Ho
TI - The hippocampal CA3 area implements sequence learning of discontinuous episodes
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/05/11
VL - 9
IS - 1
SP - 1046
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10064-8
UR - https://doi.org/10.1038/s42003-026-10064-8
LA - en
ER -

CSL-JSON

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"container-title": "Communications biology",
"author": [
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"family": "Eom",
"given": "Kisang"
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"family": "Kim",
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"page": "1046",
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"PMCID": "PMC13443506",
"ISSN": "2399-3642",
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"issued": {
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