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Distinct CA3 inputs differentially shape the learning-dependent evolution of right CA1 spatial maps.

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  1. [1] § Methods › Place field selection ↔ MATLAB/opto_align_with_beh.m, lines 97–205 · score 0.54 · PF width, boundaries, noise, shuffled, track, laps

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The authors' code

MATLAB · 264 lines · 9.7 KB · no license · 1 match

  1. %% Sig PF code updated for CA1 analysis and good behavior
  2. clear all
  3. close all
  5. [behavior_filepaths, beh_temp]=uigetfile('*cond.mat', 'Chose behavior files to load:','MultiSelect','on');
  6. load([beh_temp behavior_filepaths]);
  7. % start_frame = 258;
  8. % end_frame = 25000;
  9. % behavior.ybinned = behavior.ybinned(start_frame:end_frame);
  11. [cellsort_filepaths, cell_temp]=uigetfile('*suite2p.mat', 'Chose cellsort files to load:','MultiSelect','on');
  12. load([cell_temp cellsort_filepaths]);
  13. % beh_temp = '/home/sheffieldlab/Desktop/NoReward/PlaceFieldCodeTest/NR6/BehaviorData/';
  14. % behavior_filepaths = '/home/sheffieldlab/Desktop/NoReward/PlaceFieldCodeTest/NR6/BehaviorData/';
  15. % cellsort_filepaths = '/home/sheffieldlab/Desktop/NoReward/PlaceFieldCodeTest/NR6/';
  16. % load([behavior_filepaths, 'NR6_Task1_FamiliarReward1_beh_plain1.mat']);
  17. % load([cellsort_filepaths, 'NR6_famrew_famnorewnoclick_famrew_novrew_MotCor_converted_non_rigid_cellsorted_Area-83.25_850_mu-0.6_Th-2_Win-3_3.mat']);
  20. %% Important inputs
  23. % prompt = ['Is optogenetics on or off? 1 for on; 0 for off; 2 for all '];
  24. % opto = input(prompt);
  25. opto = 2;
  26. % startF=484; % behavior
  27. % endF= 30000;
  28. % startt = 10484; % imaging
  29. % endt = 40000;
  30. lick_stop_flag = 0;
  31. lick_stop_frame = 1361;
  32. remove_good_beh = 0;
  33. tunnellength= 200;
  35. % if lick_stop_flag
  36. % figure_folder = fullfile(beh_temp, ['PCFigures' behavior_filepaths(end-10:end-4)] );
  37. % mkdir(figure_folder);
  38. % else
  39. % figure_folder = fullfile(beh_temp,['PCFigures' behavior_filepaths(end-10:end-4)]);
  40. % mkdir(figure_folder);
  41. % end
  43. %Important place cell parameters
  44. %minfieldwidth=10; %in cm
  45. %maxfieldwidth = 125;
  46. minratio=10;%original =3
  47. thresh1=0.15; %only coninous parts of the mean place field above this value are included
  48. minDF=0.09; %orignal = 0.1 %peak of the mean PF must be larger than this value
  49. minrate=0.2; % Change from percetage to number of laps the cell should fire. changefrom 0.4, original =3 %percentage of times a tranisent must occur in the PF
  50. baselength=0.25; %number of lowest values used to calculate a mean baseline value
  51. PVAL_thresh=0.05; %only PF that have pvals lower than this generated by shuffle test will be included
  52. numrand=100;%num iterations for shuffle
  54. % select the corresponding frames in imaging
  55. ind_cell = cell(1,length(start_frame));
  56. for n = 1:length(start_frame)
  57. ind_cell{1,n} = start_frame(n):end_frame(n);
  58. end
  59. ind = cell2mat(ind_cell);
  61. onFrames_ind = zeros(length(onFrames),1);
  62. offFrames_ind = zeros(length(onFrames),1);
  63. for o = 1:length(onFrames)
  64. onFrames_ind(o) = min(find(ind>onFrames(o)),[],'all');
  65. offFrames_ind(o) = max(find(ind<offFrames(o)),[],'all');
  66. end
  68. F=data.F(ind, :);
  69. Fc3_DF=data.Fc3(ind,:);
  70. Fc2=data.Fc(ind,:);
  71. ybinned=behavior.ybinned;
  74. if remove_good_beh==1
  75. if isfield(behavior,'velocity') && max(behavior.velocity)>0.1
  76. good_behavior = remove_bad_behavior_ver2(behavior, behavior_filepaths, lick_stop_flag);
  77. else
  78. good_behavior = remove_bad_behavior_ver2_withoutvelocity(behavior, behavior_filepaths, lick_stop_flag);
  79. end
  80. ybinned_GoodBehav=good_behavior.good_runs;
  81. F_GoodBehav = F(good_behavior.good_runs_index, :);
  82. Fc3_DF_GoodBehav=Fc3_DF(good_behavior.good_runs_index, :);
  83. Fc2_GoodBehav = Fc2(good_behavior.good_runs_index, :);
  84. else
  85. ybinned_GoodBehav = ybinned;
  86. F_GoodBehav = F;
  87. Fc3_DF_GoodBehav=Fc3_DF;
  88. Fc2_GoodBehav = Fc2;
  89. end
  91. if lick_stop_flag
  92. Fc3_DF_GoodBehav=Fc3_DF_GoodBehav(lick_stop_frame:end, :);
  93. Fc2_GoodBehav = Fc2_GoodBehav(lick_stop_frame:end, :);
  94. ybinned_GoodBehav = ybinned_GoodBehav(lick_stop_frame:end);
  95. end
  97. %%%%%%%%%%%%%%%%%%%parameters%%%%%%%%%%%%%%%%%
  99. ybinned_GoodBehav=ybinned_GoodBehav';
  100. E=bwlabel(double_thresh(ybinned_GoodBehav,0.02,0.018)); %labels each traversal
  101. bad_E = bwlabel(double_thresh(ybinned,0.02,0.018));
  102. w=NaN(200,2);
  103. sig_PFs=cell(3,size(Fc2_GoodBehav,2));
  104. sig_PFs_with_noise=cell(3,size(Fc2_GoodBehav,2));
  105. numneurons=size(Fc3_DF_GoodBehav,2);
  106. trackstart=0.01+0.005; %track start location in quake units (+10 accounts for any noise in the track start location after teleportation)
  107. trackend=0.6-0.005; %track end location in quake units
  108. ZThreshDivisor=4;%used to further break up the data in cases of long silent stretches
  109. total_pos_randfields_shuffle=zeros(size(Fc2_GoodBehav,2), 1);
  110. PF_PVALS=NaN(size(Fc2_GoodBehav,2), 1);
  111. PF_width=NaN(3,size(Fc2_GoodBehav,2));
  112. PF_rate=NaN(3,size(Fc2_GoodBehav,2));
  113. PF_ratio=NaN(3,size(Fc2_GoodBehav,2));
  114. PF_dff=NaN(3,size(Fc2_GoodBehav,2));
  115. PF_start_bins=NaN(3,size(Fc2_GoodBehav,2));
  116. PF_end_bins=NaN(3,size(Fc2_GoodBehav,2));
  117. number_of_PFs=NaN(1,size(Fc2_GoodBehav,2));
  118. Allbinned_F = cell(1,size(Fc2_GoodBehav,2));
  119. long = 1:length(E); %check if the behavior and imaging data aligns well
  121. numbins=round(tunnellength/5);
  122. binsize=(trackend-trackstart)/numbins;%in Quake units
  124. width_M=zeros(1000,1000);
  125. % figure; plot(E); hold on; plot(ybinned_GoodBehav); title('good E')
  126. % figure; plot(bad_E); hold on; plot(ybinned); title('bad E')
  128. % correct E
  130. % construct savefile name
  131. % pat = '-' + wildcardPattern(3,4) + '_';
  132. % env = extract(behavior_filepaths,pat);
  133. % env = env{1,1}(2:end-1);
  134. % env = 'nov2';
  136. pat = textBoundary + lettersPattern + digitsPattern;
  137. mouse = extract(cellsort_filepaths, pat);
  138. mouse = mouse{1,1};
  139. %mouse = 'JR';
  140. pat = '_' + "day" + digitsPattern + '_';
  141. day = extract(cellsort_filepaths, pat);
  142. day = day{1,1}(2:end-1);
  144. wrong_lap=0;
  145. for i=1:max(E)
  146. onpoint=find(E==i,1);
  147. offpoint=find(E==i,1,'last');
  148. % end
  149. count=1;
  150. while max(ybinned_GoodBehav(onpoint:offpoint))<0.55
  151. count=count+1;
  152. if i~=max(E)
  153. offpoint=find(E==i+1,1,'last');
  154. E(onpoint:offpoint)=i;
  155. E(offpoint+1:end)=E(offpoint+1:end)-1;
  156. else
  157. E(onpoint:offpoint)=0;
  158. end
  160. end
  162. end
  164. wrong_lap=0;
  165. for i=1:max(bad_E)
  166. onpoint=find(bad_E==i,1);
  167. offpoint=find(bad_E==i,1,'last');
  168. % end
  169. count=1;
  170. while max(ybinned(onpoint:offpoint))<0.55
  171. count=count+1;
  172. if i~=max(bad_E)
  173. offpoint=find(bad_E==i+1,1,'last');
  174. bad_E(onpoint:offpoint)=i;
  175. bad_E(offpoint+1:end)=bad_E(offpoint+1:end)-1;
  176. else
  177. bad_E(onpoint:offpoint)=0;
  178. end
  180. end
  182. end
  184. % % Correct first one's jump
  185. % onpoint=find(E==1,1);
  186. % if ybinned_GoodBehav(onpoint)>0.02
  187. % start_correct = onpoint;
  188. % while ybinned_GoodBehav(start_correct)>0.02
  189. % E(start_correct) = 0;
  190. % start_correct = start_correct+1;
  191. % end
  192. % E = E - 1;
  193. % end
  194. % figure; plot(E); hold on; plot(ybinned_GoodBehav); title('good E corrected')
  195. % figure; plot(bad_E); hold on; plot(ybinned); title('bad E corrected')
  197. nlaps = max(E);
  198. topEdge = trackend; % define limits
  199. botEdge = trackstart; % define limits
  200. binEdges = linspace(botEdge, topEdge, numbins+1);
  201. cell_binMean = zeros(numbins, nlaps, numneurons);
  202. %cell_mean_PF = zeros(numbins,numneurons);
  204. for ii = 1:numneurons
  205. %fprintf('Analysing Neuron %d \n', ii)
  206. %%%%%%%%now cut out transients from each lap and bin them%%%%%%%%%%%%%%%%%
  208. %% Plot data if required
  209. %combinedYpos_rew=[ybinned_GoodBehav Fc3_DF_GoodBehav(:,ii)];
  210. % Fc2_combined = [ybinned_GoodBehav F_GoodBehav(:,ii)];
  211. % figure; subplot(2, 1, 1); plot(combinedYpos_rew); subplot(2, 1,
  212. % 2); plot(Fc2_combined); pause(2)
  214. %% Separate data into laps and find mean
  215. [binMean, pos_Fpos_rew] = bindata_bylap(Fc3_DF_GoodBehav(:,ii), ybinned_GoodBehav, E, numbins, binEdges);
  216. cell_binMean(:,:,ii) = binMean;
  217. end
  219. if opto == 0
  220. optoCond = '_opto_off';
  221. elseif opto ==1
  222. optoCond = '_opto_on';
  223. elseif opto ==2
  224. optoCond = '_all';
  225. end
  227. [cell_pair_corr, mean_corr] = cell_corr_by_trial(Fc3_DF, E, 30, 300, 0.05);
  228. PCo = pop_coor(cell_pair_corr);
  229. hold on
  231. opto_on_lap = zeros(length(onFrames_ind),1);
  232. opto_off_lap = zeros(length(offFrames_ind),1);
  233. for n = 1:length(onFrames_ind)
  234. opto_on_lap(n) = max(E(1:onFrames_ind(n)));
  235. % line([0, opto_on_lap(n)],[opto_on_lap(n),opto_on_lap(n)],'Color','red','LineStyle','--')
  236. % line([opto_on_lap(n),opto_on_lap(n)],[0, opto_on_lap(n)],'Color','red','LineStyle','--')
  237. opto_off_lap(n) = max(E(1:offFrames_ind(n)));
  238. % line([0, opto_off_lap(n)],[opto_off_lap(n),opto_off_lap(n)],'Color','white','LineStyle','--')
  239. % line([opto_off_lap(n),opto_off_lap(n)],[0, opto_off_lap(n)],'Color','white','LineStyle','--')
  240. line([opto_on_lap(n),opto_on_lap(n)],[opto_on_lap(n),opto_off_lap(n)],'Color','red','LineStyle','--')
  241. line([opto_on_lap(n),opto_off_lap(n)],[opto_on_lap(n),opto_on_lap(n)],'Color','red','LineStyle','--')
  242. line([opto_off_lap(n),opto_off_lap(n)],[opto_on_lap(n),opto_off_lap(n)],'Color','red','LineStyle','--')
  243. line([opto_on_lap(n),opto_off_lap(n)],[opto_off_lap(n),opto_off_lap(n)],'Color','red','LineStyle','--')
  244. end
  246. switch_lap = zeros(length(start_frame),1);
  247. end_frame_ind = cumsum(cellfun(@length, ind_cell));
  248. for m = 1:length(start_frame)
  249. switch_lap(m) = max(E(1:end_frame_ind(m)));
  250. line([0, switch_lap(m)],[switch_lap(m),switch_lap(m)],'Color','black','LineStyle','--')
  251. line([switch_lap(m),switch_lap(m)],[0, switch_lap(m)],'Color','black','LineStyle','--')
  252. end
  253. env_switch_lap = switch_lap(1:end-1);
  254. switch_frame = end_frame_ind(1:end-1);
  256. saveas(gcf, ['PCo_all_conds_' day '.png'],'png')
  258. save([behavior_filepaths(1:end-4) optoCond '_align_cell_mean'],'cell_binMean', 'Fc3_DF', 'E', 'start_frame','opto_on_lap','opto_off_lap','onFrames_ind', 'offFrames_ind', 'env_switch_lap', 'switch_frame')
  259. %save([behavior_filepaths(1:end-4) optoCond '_align_cell_mean'],'cell_binMean', 'Fc3_DF', 'E', 'start_frame', 'env_switch_lap')
  260. %save([behavior_filepaths(1:end-4) optoCond '_align_cell_mean'],'cell_binMean', 'Fc3_DF', 'E', 'start_frame', 'env_switch_lap', 'switch_frame')
  263. % py_var = permute(cell_binMean, [2,1,3]);
  264. % save([mouse '_' day optoCond '_py_var'],'py_var')

opto_align_with_beh.m at commit 57b7111, no license · at the source

Overview

Authors: Anqi Jiang1, Douglas GoodSmith1, Julliana Ramirez-Matias1, Ariana F. Tortolani1, Mark E. J. Sheffield1
ORCID iDs: Douglas GoodSmith, Julliana Ramirez-Matias, Mark E. J. Sheffield
  1. Department of Neurobiology, Neuroscience Institute, University of Chicago,Chicago, IL USA
Institutions: University of Chicago (United States)
Journal: Nature communications, volume 17, issue 1, article 5682
Dates: received 9 April 2025; accepted 9 April 2026; published online 28 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-72275-1 · PMID 42049756 · PMCID PMC13319192 · OpenAlex W7158193347
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism)
Methods: Connectivity, Statistics, Machine learning, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Spatial memory, Hippocampus, Neural circuits
MeSH: CA1 Region, Hippocampal*, CA3 Region, Hippocampal*, Learning*, Animals, Mice, Mice, Inbred C57BL, Optogenetics (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Whitehall Foundation (Whitehall Foundation, Inc.); Alfred P. Sloan Foundation; NINDS (1R21NS128822-01, 4R01NS127123-02); National Institute of Mental Health (1R01MH136274-01)
Citations: cited by 1 paper (Europe PMC); 64 references in the paper
Research resources: RRID:SCR_019197

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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anqijiang/opto_analysis

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 57b711134b3421d3e51e44ac607fa88c1fc4a5b4, 17 March 2025
Languages: Python (15), Jupyter (7), MATLAB (2)
Size: 44 files, 24 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (environment.yml, setup.py), 4 notebooks
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: Matplotlib (5 files), NumPy (5 files), pandas (5 files), SciPy (5 files), seaborn (5 files), scikit-learn (4 files), statannotations (2 files), EEGLAB (1 file), Image Processing Toolbox (1 file), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
8 files
At the source: github.com/anqijiang/opto_analysis

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Data

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 3 keywords, 7 MeSH terms, 4 funders, 63 references, 1 RRID.

Cite

This paper

Jiang, A., GoodSmith, D., Ramirez-Matias, J., Tortolani, A. F., & Sheffield, M. E. J. (2026). Distinct CA3 inputs differentially shape the learning-dependent evolution of right CA1 spatial maps. Nature communications, 17(1), 5682. https://doi.org/10.1038/s41467-026-72275-1

BibTeX

@article{jiang2026distinct,
author = {Jiang, Anqi and GoodSmith, Douglas and Ramirez-Matias, Julliana and Tortolani, Ariana F. and Sheffield, Mark E. J.},
title = {{Distinct CA3 inputs differentially shape the learning-dependent evolution of right CA1 spatial maps}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5682},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-72275-1},
url = {https://doi.org/10.1038/s41467-026-72275-1},
pmid = {42049756},
pmcid = {PMC13319192}
}

RIS

TY - JOUR
AU - Jiang, Anqi
AU - GoodSmith, Douglas
AU - Ramirez-Matias, Julliana
AU - Tortolani, Ariana F.
AU - Sheffield, Mark E. J.
TI - Distinct CA3 inputs differentially shape the learning-dependent evolution of right CA1 spatial maps
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/28
VL - 17
IS - 1
SP - 5682
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-72275-1
UR - https://doi.org/10.1038/s41467-026-72275-1
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Distinct CA3 inputs differentially shape the learning-dependent evolution of right CA1 spatial maps",
"container-title": "Nature communications",
"author": [
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"family": "Jiang",
"given": "Anqi"
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{
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{
"family": "Ramirez-Matias",
"given": "Julliana"
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"container-title-short": "Nat Commun",
"volume": "17",
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"page": "5682",
"DOI": "10.1038/s41467-026-72275-1",
"PMID": "42049756",
"PMCID": "PMC13319192",
"ISSN": "2041-1723",
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"URL": "https://doi.org/10.1038/s41467-026-72275-1",
"language": "en",
"issued": {
"date-parts": [
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2026,
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]
}
}

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