OSCR

Hippocampal place cells map terrain geometry independently of behavior.

Code ↔ Paper

15 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 15 matches
  1. [1] § MATERIALS AND METHODS › Generalized linear model ↔ utils/PIT_over_glm.m, lines 144–221 · score 0.84 · tilt directionality, tilt stability, speed score, terrain score, notation, fitglm
  2. [2] § MATERIALS AND METHODS › Place field characteristics ↔ plotting/PIT_fig_8_v1.m, lines 81–138 · score 0.77 · Place field detection, minor axis length, major axis length, weighted centroid, regionprops, orientation
  3. [3] § MATERIALS AND METHODS › Place field characteristics ↔ plotting/PIT_sup_1sess_v1.m, lines 662–719 · score 0.77 · Place field detection, minor axis length, major axis length, weighted centroid, regionprops, orientation
  4. [4] § MATERIALS AND METHODS › Generalized linear model ↔ utils/PIT_over_glm.m, lines 59–115 · score 0.70 · Partial R2, full model, Terrain score, repetition score, predictor, linear
  5. [5] § RESULTS › Terrain acts as a geometric input to place cells ↔ GIT_audit.m, lines 1–141 · score 0.70 · movement bias, behavioral anisotropy, place field elongation, place field anisotropy, S8, slopes
  6. [6] § RESULTS › Field repetition and elongation are explained by the BVC model ↔ GIT_audit.m, lines 1–141 · score 0.67 · BVC modeled place, Modeled place fields, place field elongation, S7, S13, S6
  7. [7] § MATERIALS AND METHODS › Place field detection ↔ plotting/PIT_fig_8_v1.m, lines 81–138 · score 0.61 · contiguous regions, peak firing rate, Firing rate maps, thresholded, detection, place fields
  8. [8] § MATERIALS AND METHODS › Place field detection ↔ plotting/PIT_sup_1sess_v1.m, lines 662–719 · score 0.61 · contiguous regions, peak firing rate, Firing rate maps, thresholded, detection, place fields
  9. [9] § MATERIALS AND METHODS › Firing rate maps ↔ klustest/unused/GIT_hilltest.m, lines 109–196 · score 0.57 · Gaussian kernel, Firing rate maps, histograms, interval, smoothed, bin
  10. [10] § MATERIALS AND METHODS › Firing rate maps ↔ klustest/unused/GIT_klustest3.m, lines 109–196 · score 0.57 · Gaussian kernel, Firing rate maps, histograms, interval, smoothed, bin
  11. [11] § MATERIALS AND METHODS › Boundary vector cells ↔ utils/pitch_tuning.m, lines 29–77 · score 0.56 · receptive field, tuning curves, angle, Arena, cells
  12. [12] § MATERIALS AND METHODS › Position tracking ↔ utils/PIT_pitch_tuning.m, lines 56–102 · score 0.56 · head roll, surface normal, LED, animals
  13. [13] § MATERIALS AND METHODS › Anisotropy simulations ↔ plotting/PIT_sup_fig_9_v1.m, lines 102–230 · score 0.53 · Gaussian weight, wall angle, normpdf, anisotropy, distance, axis
  14. [14] § MATERIALS AND METHODS › Anisotropy analyses ↔ GIT_sim.m, lines 189–229 · score 0.53 · weighted centroid, axis length, Firing rate maps, histogram, smoothed, place field
  15. [15] § MATERIALS AND METHODS › Position tracking ↔ utils/PIT_pitch_tuning.m, lines 56–102 · score 0.51 · norm, rx, front, cross, LEDs, azimuth

Paper

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

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The authors' code

MATLAB · 224 lines · 7.6 KB · MIT · 2 matches

  1. % PIT_over_glm
  2. % Fig S10 for Grieves, Duvelle and Jeffery (2026) Hippocampal place cells map
  3. % terrain geometry independently of behaviour
  4. % Reviewer requested, GLM, residuals and stats
  5. %
  6. % SEE ALSO GIT_audit
  8. % HISTORY
  9. %
  10. % version 1.0.0, Release 06/02/23 Code conception
  11. % version 2.0.0, Release 15/04/26 Publication release
  12. %
  13. % NOTES
  14. %
  15. % 1. This script require the summary dataset:
  16. % https://doi.org/10.5281/zenodo.17634454
  17. %
  18. % 2. This script is intended to be run via the control function GIT_audit
  19. %
  20. % AUTHOR
  21. %
  22. % Roddy Grieves
  23. % University of Glasgow, Sir James Black Building
  24. % Neuroethology and Spatial Cognition Lab
  25. % eMail: [email hidden]
  26. % Copyright 2026 Roddy Grieves
  28. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% INPUT ARGUMENTS CHECK
  29. % Create figure
  30. fig_now = figure('Units','pixels','Position',[50 50 210.*3 297.*3],'visible','on');
  31. set(gcf,'InvertHardCopy','off'); % gives the figure a grey background but means it will save white lines as white
  32. set(gcf,'color','w'); % makes the background colour white
  33. fs = [15 10];
  35. x = 0:10:3000;
  36. y = 0:10:1500;
  37. F = 0.299;
  38. wav = cos(2*pi*F*x+pi);
  39. wav = ((wav+1)./2)*450;
  40. [yy,xx] = ndgrid(y,x);
  41. zz = repmat(wav,size(xx,1),1);
  42. g = gradient(wav);
  43. gg = repmat(g,size(xx,1),1);
  45. clumaa.terrain_score = NaN(size(clumaa,1),1);
  46. for mm = 1:size(clumaa,1)
  47. if clumaa.partn(mm)~=2
  48. continue
  49. end
  50. mnow = clumaa.ratemap_surficial{mm};
  51. gnow = imresize(gg,size(mnow),"nearest");
  53. r = corr(mnow(:),gnow(:),"Type","Pearson","Rows","pairwise");
  54. clumaa.terrain_score(mm,1) = r;
  55. end
  57. clumaa = PIT_correlations(config,pidx,clumaa,posdata,0);
  59. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% FUNCTION BODY
  61. % Dependent variable
  62. repetition_score = clumaa.repetition_score(pidx & clumaa.partn==2,1); % repetition score, all place cells, ridges
  64. % Predictors
  65. speed_score = clumaa.speed_score(pidx & clumaa.partn==2,1); % Nx1 vector, correlation values
  66. azimuthal_directionality = clumaa.hd_3d_info(pidx & clumaa.partn==2,3); % Nx1 vector, spatial info content
  67. left_vs_right_stability = clumaa.azimuth_stability(pidx & clumaa.partn==2,1);
  68. pitch_directionality = clumaa.hd_3d_info(pidx & clumaa.partn==2,7); % Nx1 vector, spatial info content
  69. up_vs_down_stability = clumaa.pitch_stability(pidx & clumaa.partn==2,1);
  70. terrain_measure = abs(clumaa.terrain_score(pidx & clumaa.partn==2,1)); % Nx1 vector, terrain correlation
  73. % figure
  74. % scatter(repetition_score,speed_score,30,'k','filled','o');
  75. % refline
  76. % keyboard
  78. % Pack predictors and dependent variable into a table
  79. T = table(speed_score(:), azimuthal_directionality(:), left_vs_right_stability(:), pitch_directionality(:), up_vs_down_stability(:),terrain_measure(:), repetition_score(:),'VariableNames', {'speed_score','azimuthal_directionality','left_vs_right_stability','pitch_directionality','up_vs_down_stability','terrain_measure','repetition_score'});
  81. % Full GLM (linear regression)
  82. mdl = fitglm(T,'repetition_score ~ speed_score + azimuthal_directionality + left_vs_right_stability + pitch_directionality + up_vs_down_stability + terrain_measure','Distribution', 'normal', 'Link', 'identity');
  83. plotPartialRegression(fig_now,mdl);
  85. % Display outputs
  86. disp('--- Full Model Summary ---');
  87. disp(mdl);
  90. % Get full model R²
  91. R2_full = mdl.Rsquared.Ordinary;
  93. % Partial R² loop (from previous code)
  94. predictors = mdl.PredictorNames;
  95. partialR2 = zeros(numel(predictors),1);
  97. for i = 1:numel(predictors)
  98. reducedPredictors = setdiff(predictors, predictors{i});
  99. formula = sprintf('repetition_score ~ %s', strjoin(reducedPredictors, ' + '));
  100. mdl_reduced = fitglm(mdl.Variables, formula);
  102. SSE_reduced = mdl_reduced.SSE;
  103. SSE_full = mdl.SSE;
  105. % Partial R² (unique contribution of predictor i)
  106. partialR2(i) = (SSE_reduced - SSE_full) / SSE_reduced;
  107. end
  109. % % of total R² explained
  110. percentR2 = (partialR2 / R2_full) * 100;
  112. % Results table
  113. T_contrib = table(predictors, partialR2, percentR2,'VariableNames', {'Predictor','PartialR2','PercentOfTotalR2'})
  115. % keyboard
  116. %%%%%%%%%%%%%%%% Save the overall figure
  117. if 1
  118. fname = [config.fig_dir '\Fig S11a.png'];
  119. if fast_figs
  120. frame = getframe(gcf); % fig is the figure handle to save
  121. [raster, raster_map] = frame2im(frame); % raster is the rasterized image, raster_map is the colormap
  122. if isempty(raster_map)
  123. imwrite(raster, fname);
  124. else
  125. imwrite(raster, raster_map, fname); % fig_file is the path to the image
  126. end
  127. else
  128. exportgraphics(gcf,fname,'BackgroundColor',[1 1 1],'Colorspace','rgb','Resolution',res);
  129. end
  130. close(gcf);
  131. end
  144. function plotPartialRegression(fig,mdl)
  145. % mdl: fitglm or fitlm object
  147. X = mdl.Variables; % full table of predictors + dependent
  148. y = mdl.Variables{:, mdl.ResponseName}; % dependent variable
  149. predictors = mdl.PredictorNames; % list of predictor names
  150. predictor_plot_names = {'Speed score','Directionality','Directional stability','Tilt stability','Tilt directionality','Terrain score'};
  151. coefTable = mdl.Coefficients; % GLM coefficient table
  153. figure(fig);
  154. tiledlayout(4,3)
  155. nPred = numel(predictors);
  157. for i = 1:nPred
  158. % Current predictor
  159. x = X.(predictors{i});
  161. % Regress y on all other predictors (excluding current one)
  162. otherPreds = setdiff(predictors, predictors{i});
  163. tbl_y = X(:, otherPreds);
  164. lm_y = fitlm(tbl_y, y);
  165. x_resid = lm_y.Residuals.Raw;
  167. % Regress current predictor on all others
  168. lm_x = fitlm(tbl_y, x);
  169. y_resid = lm_x.Residuals.Raw;
  171. % Plot residuals
  172. nexttile
  173. % subplot(ceil(sqrt(nPred)), ceil(sqrt(nPred)), i);
  174. alpha = 0.5;
  175. scatter(x_resid, y_resid, 20,'k','filled','MarkerFaceAlpha',alpha,'MarkerEdgeColor','none');
  176. ylabel([predictor_plot_names{i} ' (residuals)'],'Interpreter',"none");
  177. if i>3
  178. xlabel(['Repetition score (residuals)'],'Interpreter',"none");
  179. else
  180. xlabel(' ')
  181. end
  182. % title(['Partial regression: ' predictors{i}]);
  183. hold on;
  185. % Fit line to residuals
  186. ax = gca;
  187. ax.XLim = [-0.5 1];
  188. ax.YLim(2) = ax.YLim(2)*1.15;
  189. p = polyfit(x_resid, y_resid, 1);
  190. x_fit = linspace(ax.XLim(1), ax.XLim(2), 100);
  191. y_fit = polyval(p, x_fit);
  192. plot(x_fit, y_fit, 'r-', 'LineWidth', 2);
  194. % Extract GLM results for this predictor
  195. row = coefTable(strcmp(coefTable.Row, predictors{i}), :);
  196. beta = row.Estimate;
  197. se = row.SE;
  198. tval = row.tStat;
  199. pval = row.pValue;
  201. if pval < .05
  202. % Scientific notation: mantissa × 10^{exponent}
  203. [mantissa, exponent] = sprintf('%.2e', pval); % format in scientific
  204. parts = regexp(sprintf('%.2e', pval), '([-+]?\d*\.\d+)e([-+]?\d+)', 'tokens');
  205. mantissa = parts{1}{1};
  206. exponent = str2double(parts{1}{2});
  207. pStr = sprintf('\\it{p} = %s \\times 10^{%d}', mantissa, exponent);
  208. else
  209. % Normal decimal, strip leading zero
  210. pStr = sprintf('\\it{p} = %.3f', pval);
  211. pStr = regexprep(pStr, '0\.', '.');
  212. end
  214. % Annotation string
  215. txt = sprintf('\\beta = %.3f, SE = %.3f\nt = %.2f, %s', beta, se, tval, pStr);
  217. % Place annotation at the very top of the plot
  218. text(0.5, 1, txt, 'HorizontalAlignment', 'center', 'VerticalAlignment','middle','FontSize', 7, 'Interpreter','tex','Units','normalized');
  220. end
  221. end

PIT_over_glm.m at commit d3a03d4, under MIT · at the source

Overview

Authors: Roddy M. Grieves1,2, Éléonore Duvelle1,2, Jeffrey S. Taube2
ORCID iDs: Roddy M. Grieves, Éléonore Duvelle, Jeffrey S. Taube
  1. School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
  2. Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
Institutions: Dartmouth College (United States); University of Glasgow (United Kingdom)
Journal: Science advances, volume 12, issue 23, article eadz9893
Dates: received 22 June 2025; accepted 24 April 2026; published online 3 June 2026; in print June 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1126/sciadv.adz9893 · PMID 42234755 · PMCID PMC13232613 · OpenAlex W7163332544
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: extracellular electrophysiology (units, LFP) (modality), rat (organism)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Single-unit activity, calcium imaging
MeSH: Behavior, Animal*, Hippocampus*, Place Cells*, Animals, Neurons, Rats, Space Perception (* major topic)
Journal subjects: Neuroscience, Cognitive Neuroscience
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institutes of Health (NS053907); Royal Society (RG\R1\251083)
Citations: not cited yet (Europe PMC); 111 references in the paper

Abstract

How does the brain map uneven terrain? While spatial neurons such as hippocampal place cells and entorhinal grid cells have been extensively studied in flat, horizontal environments, the natural world is hilly and irregular. To investigate how place cells represent irregular terrain, we recorded from the hippocampus of rats foraging across either flat or ridged terrain. Place cell activity reflected terrain shape, consistent with a surface-bound cognitive map rather than a volumetric one. Place fields were elongated parallel to terrain contours, which contrasted with the movement biases of the rats, and are inconsistent with a predictive coding model. Reflecting the importance of terrain information, a third of the place cells exhibited repeating fields on each ridge. A boundary vector cell model of place cell firing replicated these results. These findings demonstrate that the cognitive map is sensitive to topography, bringing our understanding of spatial cognition closer to the real world.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 15 matches between paragraphs and lines of code.

Neuroesc/Hillscape_analyses

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d3a03d4bc00e9a3ac94af3a8406ebc992f85f09b, 25 September 2026
Languages: MATLAB (91)
Size: 98 files, 91 scripts
Software Heritage: not archived
Found in: “Data, code, and materials availability:”
Holds: README, license file, tests
Not found: CITATION.cff, environment file, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (28 files), Image Processing Toolbox (27 files), CircStat (12 files), boundedline (9 files), Signal Processing Toolbox (4 files), Curve Fitting Toolbox (3 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
93 files
At the source: github.com/Neuroesc/Hillscape_analyses

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;
  • 91 scripts, each with its path and the digest of its content;
  • 15 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, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. This study did not generate new materials. Data, annotated files, and MATLAB code are permanently accessible and can be downloaded via the independent, nonprofit, online open-access repository Zenodo using the following link: https://doi.org/10.5281/zenodo.17634455. MATLAB code can also be downloaded from GitHub at https://github.com/Neuroesc/Hillscape_analyses.

Reproduced under the paper's license (CC BY-NC), 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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 7 MeSH terms, 2 funders, 100 references.

Cite

This paper

Grieves, R. M., Duvelle, É., & Taube, J. S. (2026). Hippocampal place cells map terrain geometry independently of behavior. Science advances, 12(23), eadz9893. https://doi.org/10.1126/sciadv.adz9893

BibTeX

@article{grieves2026hippocampal,
author = {Grieves, Roddy M. and Duvelle, Éléonore and Taube, Jeffrey S.},
title = {{Hippocampal place cells map terrain geometry independently of behavior}},
journal = {Science advances},
year = {2026},
month = jun,
volume = {12},
number = {23},
pages = {eadz9893},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.adz9893},
url = {https://doi.org/10.1126/sciadv.adz9893},
pmid = {42234755},
pmcid = {PMC13232613}
}

RIS

TY - JOUR
AU - Grieves, Roddy M.
AU - Duvelle, Éléonore
AU - Taube, Jeffrey S.
TI - Hippocampal place cells map terrain geometry independently of behavior
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/06/03
VL - 12
IS - 23
SP - eadz9893
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.adz9893
UR - https://doi.org/10.1126/sciadv.adz9893
LA - en
ER -

CSL-JSON

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"container-title-short": "Sci Adv",
"volume": "12",
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