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Protocol to build open-source Discrimin8 maze to study discrimination of reward-context associations in mice during open-field foraging.

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The authors' code

MATLAB · 211 lines · 6.5 KB · CC-BY-4.0

  1. %% Gergely Tarcsay, 2025 Ewell lab. MATLAB script to recreate learning curve and performance (Figure 1)
  2. close all
  3. clearvars -except behavior_struct
  4. cd("G:\Behavior only\")
  6. if ~exist("behavior_struct", "var")
  7. load BehaviorData
  8. behavior_struct.details.GPassed(5) =behavior_struct.details.GPassed(5)-1; % count the first day of > 70% for M99 because 2nd had behavior problems (cold room)
  9. end
  11. [N_days_d, N_mice] = size(behavior_struct.D_events);
  12. Performance_d = nan(N_days_d,N_mice+8, 3); % to store performance - total, ctxt A & ctxt B
  14. %% get Performance for discrimination
  15. for m = 1:N_mice
  16. disp(behavior_struct.details.Mouse(m))
  17. for d = 1:N_days_d
  18. event = behavior_struct.D_events{d,m};
  19. if isempty(event)
  20. break;
  21. end
  22. [TrialStart, TrialStop, ZoneTriggered, context] = ParseTrials(event);
  23. [Correct, Incorrect, Timeout, port, time, TrainingTrials, all_outcome] = GetTrialOutcome(event,TrialStart,TrialStop,ZoneTriggered);
  24. TrialType = ones(length(all_outcome),1);
  25. if ~isempty(TrainingTrials)
  26. TrialType([1; TrainingTrials(:,1)]) = 0; %1st trial is training when training trials are included in the session
  27. end
  28. Performance_d(d,m,:) = GetPerformance(TrialType, context, all_outcome);
  29. end
  31. end
  33. %% get performance for generalization
  34. [N_days_g, N_mice] = size(behavior_struct.G_events);
  35. Performance_g = nan(N_days_g,N_mice+8, 3); % to store performance - total, ctxt A & ctxt B
  37. for m = 1:N_mice
  38. disp(behavior_struct.details.Mouse(m))
  39. for d = 1:N_days_g
  40. event = behavior_struct.G_events{d,m};
  41. if isempty(event)
  42. break;
  43. end
  44. [TrialStart, TrialStop, ZoneTriggered, context] = ParseTrials(event);
  45. [Correct, Incorrect, Timeout, port, time, TrainingTrials, all_outcome] = GetTrialOutcome(event,TrialStart,TrialStop,ZoneTriggered);
  46. TrialType = ones(length(all_outcome),1);
  47. if ~isempty(TrainingTrials)
  48. TrialType([1; TrainingTrials(:,1)]) = 0; %1st trial is training when training trials are included in the session
  49. end
  50. Performance_g(d,m,:) = GetPerformance(TrialType, context, all_outcome);
  51. end
  53. end
  56. %% get performance for miniscope mice
  57. rootdir = strings(2,1);
  58. folders = strings(5,2);
  59. rootdir(1) = "F:\Included miniscope Mice\";
  60. folders(:,1) = ["\M119\" "\M120\" "\M292\" "M319\" "M210\"];
  61. rootdir(2) = "D:\Grouping First\";
  62. folders(:,2) = ["\M231\" "\M314\" "\M316\" "M318\" ""];
  63. nMice = [5 4];
  64. MiniscopeSex = ["f" "f" "f" "m" "m" "m", "f" "m" "m"];
  65. counter = 1;
  66. for r = 1:2
  67. for f = 1:nMice(r)
  68. load(strcat(rootdir(r), folders(f,r), "Performance_d.mat"))
  69. [days, ~,~] = size(Performance);
  70. Performance_d(1:days,counter+10,:) = Performance;
  71. clear Performance
  73. if exist(strcat(rootdir(r), folders(f,r), "Performance_g.mat"), "file")
  74. load(strcat(rootdir(r), folders(f,r), "Performance_g.mat"))
  75. [days, ~,~] = size(Performance);
  76. Performance_g(1:days,counter+10,:) = Performance;
  77. clear Performance
  78. else
  79. Performance_g(1:days,counter+10,:) = nan(days,3);
  80. end
  81. counter = counter +1;
  82. end
  83. end
  85. % plot results
  86. males = [find(behavior_struct.details.Sex == "m"); find(MiniscopeSex == "m")'+10];
  87. females = [find(behavior_struct.details.Sex == "f"); find(MiniscopeSex == "f")'+10];
  90. %% get when they reached 70%
  91. Main_Perform_D = Performance_d(:,:,1);
  92. Main_Perform_G = Performance_g(:,:,1);
  94. Discrimination_70 = nan(19,2);
  95. Grouping_70 = nan(19,2);
  97. for i = 1:19
  98. idx = find(Main_Perform_D(:,i) >= 70);
  99. Discrimination_70(i,1) = idx(1);
  100. Discrimination_70(i,2) = Main_Perform_D(idx(1),i);
  101. idx = find(Main_Perform_G(:,i) >= 70);
  102. if ~isempty(idx)
  103. Grouping_70(i,1) = idx(1);
  104. Grouping_70(i,2) = Main_Perform_G(idx(1),i);
  105. end
  106. end
  108. %% plot learning curves - separate for males and females
  109. figure
  110. tiledlayout(1,2)
  111. nexttile;
  112. hold on
  113. box off
  114. axis square
  115. for i = 1:length(females)
  116. plot(Main_Perform_D(1:Discrimination_70(females(i),1),females(i)),"o-", "MarkerSize",5, "Color","#a8cfc3")
  117. end
  119. for i = 1:length(males)
  120. plot(Main_Perform_D(1:Discrimination_70(males(i),1),males(i)),"o-", "MarkerSize",5, "Color","#27a37d")
  121. end
  122. ylim([0 100])
  123. xlim([0 11])
  125. nexttile;
  126. hold on
  127. box off
  128. axis square
  129. for i = 1:length(females)
  130. disp(i)
  131. if ~isnan(Grouping_70(females(i),1))
  132. plot(Main_Perform_G(1:Grouping_70(females(i),1),females(i)),"o-", "MarkerSize",5, "Color","#bea7d9")
  133. end
  134. end
  136. for i = 1:length(males)
  137. if ~isnan(Grouping_70(males(i),1))
  138. plot(Main_Perform_G(1:Grouping_70(males(i),1),males(i)),"o-", "MarkerSize",5, "Color","#854bc9")
  139. end
  140. end
  141. xlim([0 7])
  142. ylim([0 100])
  144. %% make violin plots for grouping and discrimination
  145. figure
  146. tiledlayout(1,2)
  147. Y_D = nan(10,2);
  148. Y_G = nan(10,2);
  150. Y_D(1:length(females),1) = Discrimination_70(females,2);
  151. Y_D(1:length(males),2) = Discrimination_70(males,2);
  153. nexttile;
  154. violin(Y_D, 'x', [1 2], 'facecolor', [.85 .85 .85; 0 0 0; .85 .85 .85; 0 0 0], 'edgecolor', 'k', 'mc', [], 'medc', [])
  155. box off
  156. hold on
  157. plot(repmat(1,10,1), Y_D(:,1), 'o', 'MarkerEdgeColor', [.5 .5 .5], 'MarkerFaceColor',[.5 .5 .5], 'MarkerSize', 3)
  158. plot(repmat(2,10,1), Y_D(:,2), 'o', 'MarkerEdgeColor', [.5 .5 .5].*0, 'MarkerFaceColor',[.5 .5 .5].*0, 'MarkerSize', 3)
  159. xlim([0.5 2.5])
  160. ylim([40 120])
  161. yticks(10:20:90)
  162. ylabel("Performance (%)")
  163. box off
  166. Y_G(1:length(females),1) = Grouping_70(females,2);
  167. Y_G(1:length(males),2) = Grouping_70(males,2);
  169. nexttile;
  170. violin(Y_G, 'x', [1 2], 'facecolor', [.85 .85 .85; 0 0 0; .85 .85 .85; 0 0 0], 'edgecolor', 'k', 'mc', [], 'medc', [])
  171. box off
  172. hold on
  173. plot(repmat(1,10,1), Y_G(:,1), 'o', 'MarkerEdgeColor', [.5 .5 .5], 'MarkerFaceColor',[.5 .5 .5], 'MarkerSize', 3)
  174. plot(repmat(2,10,1), Y_G(:,2), 'o', 'MarkerEdgeColor', [.5 .5 .5].*0, 'MarkerFaceColor',[.5 .5 .5].*0, 'MarkerSize', 3)
  175. xlim([0.5 2.5])
  176. ylim([40 120])
  177. yticks(10:20:90)
  178. ylabel("Performance (%)")
  179. box off
  181. %% statistics
  182. [~,p_D, ci_D, stats_D] = ttest2(Y_D(:,1), Y_D(:,2));
  183. [~,p_G,ci_G,stats_G] = ttest2(Y_G(:,1), Y_G(:,2));
  186. %% plot days to reach criteria
  187. Y_D = nan(10,2);
  188. Y_G = nan(10,2);
  190. Y_D(1:length(females),1) = Discrimination_70(females,1);
  191. Y_D(1:length(males),2) = Discrimination_70(males,1);
  193. Y_G(1:length(females),1) = Grouping_70(females,1);
  194. Y_G(1:length(males),2) = Grouping_70(males,1);
  197. figure
  198. tiledlayout(1,2)
  199. nexttile;
  200. boxplot(Y_D)
  201. box off
  202. ylim([0 12])
  203. nexttile;
  204. boxplot(Y_G)
  205. box off
  206. ylim([0 12])

BehaviorLearning.m, under CC-BY-4.0 · at the source

Overview

Authors: Gergely Tarcsay1, Laura A Ewell1,2,3
ORCID iDs: Gergely Tarcsay, Laura A Ewell
  1. Anatomy & Neurobiology, School of Medicine, University of California, Irvine, Irvine, CA, USA
  2. Neurobiology of Brain and Behavior, Charlie Dunlop School of Biological Sciences, University of California, Irvine, Irvine, CA, USA
  3. Center for Learning and Memory, University of California, Irvine, Irvine, CA, USA
Institutions: University of California, Irvine (United States)
Journal: STAR protocols, volume 7, issue 1, article 104415
Dates: published online 5 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.xpro.2026.104415 · PMID 41790546 · PMCID PMC12969727 · OpenAlex W7133651954
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: behavior only (modality), mouse (organism), methods / tools (subfield)
Methods: Statistics
Keywords: neuroscience, behavior, computer sciences
MeSH: Maze Learning*, Reward*, Animals, Hippocampus, Memory, Mice (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute of Neurological Disorders and Stroke (R01 1R01NS128222); National Institutes of Health
Citations: not cited yet (Europe PMC); 12 references in the paper

Abstract

The hippocampus is known to process context-specific memories. We provide a protocol for studying context discrimination in mice utilizing the open-source Discrimin8 maze. We describe the steps for building the maze and using custom-written codes implementing the task. Finally, instructions are provided to train mice on the task. This protocol implements a low-cost, automated maze allowing for investigation of context discrimination in freely moving mice.

For complete details on the use and execution of this protocol, please refer to Tarcsay et al.1

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

Repositories

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

Zenodo 17488388

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the text, “Setting up the Arduino code”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (18 files), Image Processing Toolbox (3 files), CircStat (1 file), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
115 files
At the source: doi.org/10.5281/zenodo.17488388

Zenodo 17488387

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (18 files), Image Processing Toolbox (3 files), CircStat (1 file), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
115 files
At the source:

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 228 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

The code generated during this study is available on GitHub (https://doi.org/10.5281/zenodo.17488387).

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 3 keywords, 6 MeSH terms, 2 funders, 12 references.

Cite

This paper

Tarcsay, G., & Ewell, L. A. (2026). Protocol to build open-source Discrimin8 maze to study discrimination of reward-context associations in mice during open-field foraging. STAR protocols, 7(1), 104415. https://doi.org/10.1016/j.xpro.2026.104415

BibTeX

@article{tarcsay2026protocol,
author = {Tarcsay, Gergely and Ewell, Laura A},
title = {{Protocol to build open-source Discrimin8 maze to study discrimination of reward-context associations in mice during open-field foraging}},
journal = {STAR protocols},
year = {2026},
month = mar,
volume = {7},
number = {1},
pages = {104415},
publisher = {Elsevier},
issn = {2666-1667},
doi = {10.1016/j.xpro.2026.104415},
url = {https://doi.org/10.1016/j.xpro.2026.104415},
pmid = {41790546},
pmcid = {PMC12969727}
}

RIS

TY - JOUR
AU - Tarcsay, Gergely
AU - Ewell, Laura A
TI - Protocol to build open-source Discrimin8 maze to study discrimination of reward-context associations in mice during open-field foraging
T2 - STAR protocols
J2 - STAR Protoc
PY - 2026
DA - 2026/03/05
VL - 7
IS - 1
SP - 104415
SN - 2666-1667
PB - Elsevier
DO - 10.1016/j.xpro.2026.104415
UR - https://doi.org/10.1016/j.xpro.2026.104415
LA - en
ER -

CSL-JSON

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"type": "article-journal",
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"page": "104415",
"DOI": "10.1016/j.xpro.2026.104415",
"PMID": "41790546",
"PMCID": "PMC12969727",
"ISSN": "2666-1667",
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"URL": "https://doi.org/10.1016/j.xpro.2026.104415",
"language": "en",
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