OSCR

Dataset of cortical and subcortical single neuron activity during value-based tasks in macaque monkey.

Code ↔ Paper

9 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 9 matches
  1. [1] § Data Records › Neural variables ↔ spk_qc.m, lines 92–175 · score 0.80 · firing_rate, peak amplitude, peak noise, noise overlap, SNR, isolation
  2. [2] § Data Records ↔ spk_qc.m, lines 92–175 · score 0.73 · peak amplitude, peak noise, noise overlap, firing rate, rounded, isolation
  3. [3] § Data Records ↔ bhv_qc.m, lines 50–110 · score 0.68 · proba_1FC, t_evt, AFC task, J2, J1, 2afc
  4. [4] § Data Records › Neural variables ↔ spk_peth.m, lines 48–184 · score 0.67 · avg_waveform, clust_id, firing rate, ch, timestamps, Spike
  5. [5] § Methods › Behavioral tasks ↔ bhv_qc.m, lines 1–48 · score 0.62 · AFC_dyn, AFC task, Behavioral
  6. [6] § Data Records › Behavioral variables ↔ bhv_qc.m, lines 50–110 · score 0.56 · proba_1FC, t_evt, variable, 2afc, trialtype, probability
  7. [7] § Methods › Behavioral tasks ↔ bhv_qc.m, lines 1–48 · score 0.55 · AFC_dyn, behavioral
  8. [8] § Usage Notes › Dynamic task trials ↔ spk_peth.py, lines 109–116 · score 0.50 · stimulus onset, t_evt, timestamps, event
  9. [9] § Methods › Defining neuroanatomical boundaries ↔ spk_qc.m, lines 1–90 · score 0.50 · VaVb, DC, DR

Paper

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

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The authors' code

MATLAB · 239 lines · 11 KB · no license · 4 matches

  1. %% behavioral analyses for 1FC and 2AFC tasks
  3. clear
  5. pathspk = 'path\of\your\directory\spk\';
  6. list = dir([pathspk '*_spk.mat']);
  7. % reorder files by dates
  8. for i = 1 : length(list)
  9. dates(i) = datenum(list(i).name(2:7),'mmddyy');
  10. end
  11. [~,idx] = sort(dates);
  12. list = list(idx);
  14. all_pbs = 10:10:90;
  15. tasks = {'1FC' '2AFC'};
  17. norm = @(data) -1+((data-min(data))*2)/(max(data)-min(data)) ;
  19. prop_completed = NaN(length(list),2);
  20. nb_trials = NaN(length(list),4);
  21. rt_probas = NaN(length(list),length(all_pbs));
  22. rt_lm = table(); rt_tstat=table();
  23. perf_2AFC = table();
  24. for sess = 1:length(list)
  25. clearvars -except sess list pathspk prop_completed all_pbs rt_probas rt_lm rt_tstat names tasks nb_trials norm perf_2AFC all_task_patterns
  26. %- load behav data for that session
  27. disp(['Processing session ' num2str(sess) ' of ' num2str(length(list)) '...'])
  28. load([pathspk list(sess).name],'t_evt','trialtype');
  30. completed_tr = ~trialtype.brk; %- completed trials
  31. task = trialtype.task; %- task id (1FC or 2AFC)
  32. nb_trials(sess,:) = [sum(completed_tr & task==tasks(1)), sum(completed_tr & task==tasks(2)) , sum(completed_tr & task=='1FC_dyn') , sum(completed_tr & task=='2AFC_dyn')];
  33. names(sess,1) = list(sess).name(1);
  35. % find the patterns of trials performed (count number of trials performed IN A ROW for each task)
  36. % For each change in task, count number of trials performed and combine into a string (e.g., '1FC_99')
  37. change_task = [1 ; find(diff(double(task))~=0)+1 ; length(task)+1];
  38. task_pattern = cell(length(change_task)-1,1); % Preallocate cell array for combined strings
  39. for c = 1:length(change_task)-1
  40. curr_task = task(change_task(c)); % Get current task name
  41. n_trials = sum(completed_tr(change_task(c):change_task(c+1)-1));
  42. task_pattern{c} = sprintf('%s_%d', curr_task, n_trials);
  43. end
  44. % Store task pattern for each session
  45. if sess == 1
  46. all_task_patterns = cell(length(list),1);
  47. end
  48. all_task_patterns{sess} = task_pattern;
  50. %- proportion of completed trials for each task
  51. for t = 1 : length(tasks)
  52. if sum(task==tasks(t))<50
  53. continue
  54. end
  55. prop_completed(sess,t) = mean(completed_tr(task==tasks(t)));
  56. end
  58. if sum(task==tasks(1))>50 % for 1FC task
  59. %- reaction times depending on probability/flavor for 1FC task
  60. rt = t_evt.resp_fix - t_evt.resp_on;
  61. rt = rt(completed_tr & task==tasks(1)) ; %- keep only completed trials of 1FC
  62. proba = trialtype.proba_1FC(completed_tr & task==tasks(1)); %- get proba
  63. flavor = trialtype.flavor_1FC(completed_tr & task==tasks(1)); %- get flavor
  65. % avg rt per proba
  66. for pb = 1 : length(all_pbs)
  67. rt_probas(sess,pb) = mean(log(1000*rt(proba==all_pbs(pb))));
  68. end
  69. %- anova explaining rt with proba and flavor
  70. tbl = table(rt,proba,flavor,'VariableNames',{'rt','proba','flavor'});
  71. lm = fitglm(tbl,'rt ~ proba + flavor');
  73. rt_lm = [rt_lm ; array2table([lm.Coefficients.pValue' strcmp(list(sess).name(1),'M')],"VariableNames",[lm.CoefficientNames {'monkey'}])];
  74. rt_tstat = [rt_tstat ; array2table([lm.Coefficients.tStat' strcmp(list(sess).name(1),'M')],"VariableNames",[lm.CoefficientNames {'monkey'}])];
  75. end
  78. if sum(task==tasks(2))>50 % for 2AFC task
  79. %- performance depending on probability/flavor for 2AFC task
  81. % Identify trials with different flavors
  82. diff_fl = trialtype.flavorL_2AFC ~= trialtype.flavorR_2AFC;
  84. trialtype.unchosenflavor_2AFC(trialtype.chosenside_2AFC=='right') = trialtype.flavorL_2AFC(trialtype.chosenside_2AFC=='right');
  85. trialtype.unchosenflavor_2AFC(trialtype.chosenside_2AFC=='left') = trialtype.flavorR_2AFC(trialtype.chosenside_2AFC=='left');
  87. probaJ1 = NaN(numel(trialtype.task),1);
  88. probaJ2 = NaN(numel(trialtype.task),1);
  89. probaJ1(trialtype.chosenflavor_2AFC=='J1') = trialtype.chosenproba_2AFC(trialtype.chosenflavor_2AFC=='J1');
  90. probaJ1(trialtype.unchosenflavor_2AFC=='J1') = trialtype.unchosenproba_2AFC(trialtype.unchosenflavor_2AFC=='J1');
  91. probaJ2(trialtype.chosenflavor_2AFC=='J2') = trialtype.chosenproba_2AFC(trialtype.chosenflavor_2AFC=='J2');
  92. probaJ2(trialtype.unchosenflavor_2AFC=='J2') = trialtype.unchosenproba_2AFC(trialtype.unchosenflavor_2AFC=='J2');
  94. choice = trialtype.chosenflavor_2AFC == 'J1'; % choice 1 or 2
  95. keepme = ~isnan(probaJ1) & ~isnan(probaJ2) & diff_fl; % remove NaN values and same flavor trials
  97. probaJ2 = probaJ2(keepme);
  98. probaJ1 = probaJ1(keepme);
  99. choice = choice(keepme);
  100. logpb = log(probaJ1 ./ probaJ2);
  102. T = table(choice==1,logpb,norm(probaJ1),norm(probaJ2),'VariableNames',{'choice','prob','probaJ1','probaJ2'}); % create a table with choice and log odds of the probabilities
  104. nTr2keep = 200; %- to have similar range of values for tstat
  105. if length(T.choice)>nTr2keep
  106. r = randperm(length(T.choice),nTr2keep);
  107. T = T(r,:);
  108. end
  110. mdl = fitglm(T,'choice ~ 1 + prob','Distribution','binomial','Link','logit','LikelihoodPenalty','jeffreys-prior');
  112. % get proba influence on choice
  113. proba_tstat = mdl.Coefficients.tStat(2);
  114. proba_r2 = mdl.Rsquared.Adjusted;
  115. proba_est = mdl.Coefficients.Estimate(2);
  116. proba_p = mdl.Coefficients.pValue(2);
  118. %- preference from log ratio model
  119. bias_point = -mdl.Coefficients{'(Intercept)','Estimate'} / mdl.Coefficients{'prob','Estimate'};
  121. %- predicted values
  122. xrange = linspace(-log(90/10),log(90/10),200)'; % covers proba range (log(90/10) ≈ 2.197)
  123. tab = table(xrange,'VariableNames',{'prob'});
  124. [predictedP,~] = predict(mdl,tab);
  126. % make a table with session name, choice bias, probability model metrics, and predictedP
  127. perf_2AFC = [perf_2AFC ; table({list(sess).name(1:7)}, {names(sess)}, proba_r2 , proba_p, proba_tstat, proba_est, bias_point, {predictedP}, ...
  128. 'VariableNames', {'session', 'monkey','proba_r2','proba_p', 'proba_tstat', 'proba_est', 'bias_point','predictedP'})];
  129. end
  130. end
  132. %- boxplot the proportion of completed trials
  133. col = [51 160 44 ; 106 61 154]/255;
  134. mks={'M' 'X'};
  135. figure;hold on
  136. for t = 1 : 2
  137. for m=1:length(mks)
  138. wdth = 0.3;
  139. perf2plot = prop_completed(ismember(names,mks(m)),t);
  140. yl = t+(m-1)/3-((1/3)/2);
  142. quartiles = quantile(perf2plot, [0.25 0.75 0.5]);
  143. iqr = quartiles(2) - quartiles(1);
  144. Xs = sort(perf2plot);
  145. whiskers(1) = min(Xs(Xs > (quartiles(1) - (1.5 * iqr))));
  146. whiskers(2) = max(Xs(Xs < (quartiles(2) + (1.5 * iqr))));
  147. Y = [quartiles whiskers];
  148. jit = (rand(size(perf2plot)) - 0.5) * (0.65*wdth);
  149. drops_pos = jit + yl ;
  150. box_pos = [Y(1) yl(1)-(wdth * 0.5) Y(2)-Y(1) wdth];
  152. curr_col = col(m,:);
  154. h{2} = scatter(perf2plot, drops_pos,'SizeData',10,'MarkerEdgeColor','none','MarkerFaceColor',curr_col);
  155. h{3} = rectangle('Position', box_pos,'EdgeColor', curr_col(1,:),'LineWidth', 1.5);
  156. h{4} = line([Y(3) Y(3)], [yl(1)-(wdth * 0.5) yl(1) + (wdth * 0.5)], 'col', curr_col(1,:), 'LineWidth', 2);
  157. h{5} = line([Y(2) Y(5)], [yl(1) yl(1)], 'col', curr_col(1,:), 'LineWidth', 1);
  158. h{6} = line([Y(1) Y(4)], [yl(1) yl(1)], 'col', curr_col(1,:), 'LineWidth', 1);
  160. end
  161. end
  162. xlim([0.5 1])
  163. view([90 -90])
  164. set(gca,'YTick',1:2,'YTickLabel',{'1FC' '2AFC'},'FontSize',14)
  165. ylabel('task')
  166. xlabel('proportion of completed trials')
  167. title('Proportion of completed trials in 1FC and 2AFC tasks')
  169. % plot average rt over probas with error bars
  170. figure;hold on
  171. subplot(3,2,1);plot(all_pbs,nanmean(rt_probas(names(:,1)=='M',:)),'.-','Color',[51 160 44]/255,'LineWidth',2,'MarkerSize',20);hold on
  172. errorbar(all_pbs,nanmean(rt_probas(names(:,1)=='M',:)),nanstd(rt_probas(names(:,1)=='M',:))/sqrt(sum(names(:,1)=='M')),'Color',[51 160 44]/255)
  173. set(gca,'FontSize',14)
  174. xlabel('probability');ylabel('log(RT)')
  175. subplot(3,2,2);plot(all_pbs,nanmean(rt_probas(names(:,1)=='X',:)),'.-','Color',[106 61 154]/255,'LineWidth',2,'MarkerSize',20);hold on
  176. errorbar(all_pbs,nanmean(rt_probas(names(:,1)=='X',:)),nanstd(rt_probas(names(:,1)=='X',:))/sqrt(sum(names(:,1)=='X')),'Color',[106 61 154]/255)
  177. set(gca,'FontSize',14)
  178. xlabel('probability');ylabel('log(RT)')
  180. %- plot influence of proba on RT
  181. [cnts_M,xax] = hist(rt_tstat.proba(rt_tstat.monkey==true),[-5:0.5:5],'Normalization','probability');
  182. [cnts_X,xax] = hist(rt_tstat.proba(rt_tstat.monkey==false),[-5:0.5:5],'Normalization','probability');
  183. subplot(3,2,[3:6])
  184. line([0 0],[0 0.25],'Color','k','LineStyle','--','LineWidth',1); hold on ;box on
  185. bar(xax-0.1,cnts_M/sum(cnts_M),'FaceColor',[51 160 44]/255,'LineWidth',1,'BarWidth',0.35); hold on
  186. bar(xax+0.1,(cnts_X/sum(cnts_X)),'FaceColor',[106 61 154]/255,'LineWidth',1,'BarWidth',0.35); hold on
  187. set(gca,'FontSize',14)
  188. xlabel('t-stat')
  189. ylabel('proportion of sessions')
  190. title('t-value of proba effect on RT in 1FC task')
  192. % how many sessions in each monkey showed a sig rt effect of proba/flavor
  193. sum(rt_lm(rt_lm.monkey==true,:)<0.05)
  194. sum(rt_lm(rt_lm.monkey==false,:)<0.05)
  196. % plot perf_2AFC proba_tstat like previous figure as subplot(3,1,3)
  197. figure;
  198. monkey_names = {'M', 'X'};
  199. main_colors = containers.Map({'M','X'}, {[51 160 44]/255, [106 61 154]/255});
  201. % PredictedP curves and bias points for each monkey in subplot(3,1,[1 2])
  202. subplot(6,1,[1 2 3]);
  203. for i = 1:2
  204. hold on;
  205. idx = strcmp(perf_2AFC.monkey, monkey_names{i});
  206. if any(idx)
  207. preds = cat(2,perf_2AFC.predictedP{idx});
  208. plot(xrange, preds, '-', 'Color', [0.7 0.7 0.7], 'LineWidth', 1);
  209. plot(xrange, mean(preds,2), '-', 'Color', main_colors(monkey_names{i}), 'LineWidth', 2.5);
  210. ylim([0 1]);
  211. set(gca, 'YTick', [0 0.5 1], 'YTickLabel', {'0','0.5','1'}, 'FontSize', 16);
  212. end
  213. end
  214. hold off;
  215. xlim([-log(90/10), log(90/10)]);
  216. xlabel('log(probaJ1/probaJ2)', 'FontSize', 16);
  217. ylabel('P(choice J1)', 'FontSize', 16);
  219. % Histogram in subplot(3,1,3) using hist
  220. subplot(6,1,[5 6]);
  221. hold on; box on;
  222. edges = -5:0.5:10;
  223. [cnts_M,xax] = hist(perf_2AFC.proba_tstat(ismember(perf_2AFC.monkey,'M')), edges);
  224. [cnts_X,xax] = hist(perf_2AFC.proba_tstat(ismember(perf_2AFC.monkey,'X')), edges);
  225. bar(xax-0.1, cnts_M/sum(cnts_M), 'FaceColor', [51 160 44]/255, 'LineWidth', 1, 'BarWidth', 0.35);
  226. bar(xax+0.1, cnts_X/sum(cnts_X), 'FaceColor', [106 61 154]/255, 'LineWidth', 1, 'BarWidth', 0.35);
  227. line([0 0],[0 0.5],'Color','k','LineStyle','--','LineWidth',1);
  228. set(gca,'FontSize',14)
  229. xlabel('t-stat - log(probaJ1/probaJ2)')
  230. ylabel('proportion of sessions')
  231. hold off;
  232. ylim([0 0.5]);
  234. % # of sessions with sig effect of proba on 2AFC choice
  235. sum(perf_2AFC.proba_p(ismember(perf_2AFC.monkey,'M'))<0.05)
  236. sum(perf_2AFC.proba_p(ismember(perf_2AFC.monkey,'X'))<0.05)
  237. % highest pvalue
  238. max(perf_2AFC.proba_p)

bhv_qc.m at commit e0c7d4e, no license · at the source

Overview

Authors: Liza London1, Marques Love1, Zachary R Zeisler1, Peter H Rudebeck1, Frederic M Stoll1
ORCID iDs: Frederic M Stoll
  1. Nash Family Department of Neuroscience, Lipschultz Center for Cognitive Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
Institutions: Icahn School of Medicine at Mount Sinai (United States)
Journal: Scientific data, volume 13, issue 1, article 989
Dates: received 4 December 2025; accepted 25 March 2026; published online 1 April 2026
Type: Data paper · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41597-026-07129-y · PMID 41922368 · PMCID PMC13338295 · OpenAlex W7147624840
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: non-human primate (organism)
Methods: Spectral & time-frequency, Smoothing, state filtering, decompositions, Single-unit activity, calcium imaging
MeSH: Decision Making*, Frontal Lobe*, Neurons*, Animals, Macaca, Reward (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Brain and Behavior Research Foundation (NARSAD Young Investigator); Icahn School of Medicine at Mount Sinai; National Institute of Mental Health (MH110822); NIMH NIH HHS (MH110822); Philippe Foundation
Citations: cited by 2 papers (Europe PMC); 37 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 9 matches between paragraphs and lines of code.

RudebeckLab/FlavorProba-dataset

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: e0c7d4ec49abf35b334d1b8fddf7b84ebd7c79f3, 10 November 2025
Languages: MATLAB (4), Python (2)
Size: 7 files, 6 scripts
Software Heritage: not archived
Found in: the text, “Code availibility”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (3 files), pandas (2 files), SciPy (2 files), Matplotlib (1 file), NumPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
7 files
At the source: github.com/RudebeckLab/FlavorProba-dataset

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;
  • 6 scripts, each with its path and the digest of its content;
  • 9 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

Datasets cited

Data availability statement

The paper has a 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/10.1038/s41597-026-07129-y.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 6 MeSH terms, 5 funders, 33 references.

Cite

This paper

London, L., Love, M., Zeisler, Z. R., Rudebeck, P. H., & Stoll, F. M. (2026). Dataset of cortical and subcortical single neuron activity during value-based tasks in macaque monkey. Scientific data, 13(1), 989. https://doi.org/10.1038/s41597-026-07129-y

BibTeX

@article{london2026dataset,
author = {London, Liza and Love, Marques and Zeisler, Zachary R and Rudebeck, Peter H and Stoll, Frederic M},
title = {{Dataset of cortical and subcortical single neuron activity during value-based tasks in macaque monkey}},
journal = {Scientific data},
year = {2026},
month = apr,
volume = {13},
number = {1},
pages = {989},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-026-07129-y},
url = {https://doi.org/10.1038/s41597-026-07129-y},
pmid = {41922368},
pmcid = {PMC13338295}
}

RIS

TY - JOUR
AU - London, Liza
AU - Love, Marques
AU - Zeisler, Zachary R
AU - Rudebeck, Peter H
AU - Stoll, Frederic M
TI - Dataset of cortical and subcortical single neuron activity during value-based tasks in macaque monkey
T2 - Scientific data
J2 - Sci Data
PY - 2026
DA - 2026/04/01
VL - 13
IS - 1
SP - 989
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-026-07129-y
UR - https://doi.org/10.1038/s41597-026-07129-y
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41597-026-07129-y",
"type": "article-journal",
"title": "Dataset of cortical and subcortical single neuron activity during value-based tasks in macaque monkey",
"container-title": "Scientific data",
"author": [
{
"family": "London",
"given": "Liza"
},
{
"family": "Love",
"given": "Marques"
},
{
"family": "Zeisler",
"given": "Zachary R"
},
{
"family": "Rudebeck",
"given": "Peter H"
},
{
"family": "Stoll",
"given": "Frederic M"
}
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"container-title-short": "Sci Data",
"volume": "13",
"issue": "1",
"page": "989",
"DOI": "10.1038/s41597-026-07129-y",
"PMID": "41922368",
"PMCID": "PMC13338295",
"ISSN": "2052-4463",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41597-026-07129-y",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
1
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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Journal: Nature communications
In common: pandas, SciPy, Matplotlib, 1 other tool, non-human primate, 2 references
[9] doi:10.1002/hbm.70520
Bridging Histology and Tractography: First In Vivo Visualization of Short-Range Prefrontal Connections Informed by Primate Tract-Tracing.
Journal: Human brain mapping
In common: 4 references
[10] doi:10.1126/sciadv.aef0343 [code]
Learning induces activation-mechanism-dependent neural plasticity in an intracortical microstimulation task.
Journal: Science advances
In common: Statistics and Machine Learning Toolbox, pandas, SciPy, 2 other tools, 1 reference

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