OSCR

Grid maps are disrupted after large-scale arena expansion and recover with experience.

Code ↔ Paper

8 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 8 matches · 5 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Analysis of single-unit spiking activity › Grid score ↔ +CMBHOME/@Session/Gridness.m, lines 1–60 · score 0.83 · inner circle, minor axis, major axis, rate map autocorrelogram, eccentricity, gridness
  2. [2] § Methods › Analysis of single-unit spiking activity › Grid score ↔ other/systemIndependent/GridnessScoreExternal/gridnessScoreCBM10_3.m, lines 9–145 · score 0.74 · outer radius, inner circle, classified, fewer, gridness, rate map
  3. [3] § Methods › Analysis of single-unit spiking activity › Grid score ↔ +CMBHOME/@Session/b_thetaIndex.m, the whole file · a weak match · score 0.69 · fast Fourier transform, power spectrum, autocorrelogram, peaks, spike
  4. [4] § Methods › Analysis of single-unit spiking activity › Spatial firing rate maps ↔ +CMBHOME/+PASS_INDEX/rate_map.m, the whole file · a weak match · score 0.66 · occupancy normalized, Gaussian kernel, firing rate maps, spent, smoothed, dimensional
  5. [5] § Methods › Analysis of single-unit spiking activity › Spatial firing rate maps ↔ +CMBHOME/+PASS_INDEX/pass_index.m, lines 1–128 · score 0.65 · occupancy normalized, Gaussian kernel, firing rate maps, smoothed, dimensional, animal
  6. [6] § Methods › Analysis of single-unit spiking activity › Spatial information ↔ +CMBHOME/@Session/SpatialInformation.m, the whole file · a weak match · score 0.55 · spatial information scores, Gaussian kernel, occupancy, bits, Rate maps, smoothed
  7. [7] § Methods › Analysis of single-unit spiking activity › Spatial information ↔ +CMBHOME/@Session/MyvarInformation.m, the whole file · a weak match · score 0.55 · spatial information scores, Gaussian kernel, occupancy, bits, Rate maps, smoothed
  8. [8] § Results › The formation of grid maps in large-scale expanded environments depends on experience ↔ +CMBHOME/@Session/plot_velocityrate.m, the whole file · a weak match · score 0.53 · linear regression, correlation coefficient, firing rates, cells

Paper

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The authors' code

MATLAB · 631 lines · 19 KB · no license · 1 match

  1. function [gridness, props] = Gridness(self, cel, varargin)
  2. % Calculates the original gridness score of 'cel'. Continuizes all epochs.
  3. %
  4. % ARGUMENTS
  5. %
  6. % cel 1x2 vector indicating tetrode index and cell index
  7. %
  8. % RETURNS
  9. %
  10. % gridness a number indicating gridness score
  11. % props struct of grid cell properties (see below)
  12. %
  13. % GRID CELL PROPERTY STRUCT FIELDS:
  14. %
  15. % periodicity(n, 2) vector of the autocorrelogram correlation as a function of
  16. % rotation (see ALGORITHM below)
  17. %
  18. % rate_map ratemap used in analysis
  19. %
  20. %
  21. % auto_corr matrix of the rate map autocorrelogram with donut cut
  22. % out of it. There is a second matrix along the third
  23. % dimension indicating where cuts were made. This can be
  24. % used in the AlphaData property of an image plot
  25. %
  26. % auto_corr_mask binary matrix where 1 indicates values used in analysis
  27. % (donut)
  28. %
  29. % eccentricity eccentricity of ellipse created by first six peaks in
  30. % auto_corr around center
  31. %
  32. % e_angle angle of rotation of major axis of ellipse (useful when
  33. % setting correction)
  34. %
  35. % e_skew ratio of major:minor axis of ellipse. This is the
  36. % scaling along the major axis when performing correction
  37. % with grid3.
  38. %
  39. % r_out radius of outer circle (pixels)
  40. %
  41. % r_in radius of inner circle (pixels)
  42. %
  43. % spacing field spacing in pixels
  44. %
  45. % OPTIONAL PARAMETERS
  46. %
  47. % xdim vector of bin edges along x dimension
  48. % ydim vector of bin edges along y dimension
  49. % supress_plot 0 or 1 (1). If 0, plots autocorrelation, the cut
  50. % donut, and the periodicity of the donut
  51. % std_smooth_kernel STD of the gaussian kernel to smooth the rate map
  52. % binside The length in cm of the side of a bin when
  53. % calculating the rate map
  54. % rotate_inc (3 degrees). Change the angle increment for which
  55. % we calculate the correlation between rotated ac's
  56. % grid3 (0) if 1, solves for elipse that is grid field, and
  57. % then distorts it to create circle
  58. % r_out radius of the outer donut
  59. % r_in radius of the inner donut
  60. % continuize_epochs (0) if 1, returns the grid score for rate map of
  61. % cumulative data for all root.epoch(s). if 2,
  62. % returns grid score for each root.epoch separately
  63. % autocorr you can provide an autocorrelation to run gridness
  64. % on like this: CMBHOME.Session.Gridness([], [], 'autocorr', AC);
  65. %
  66. % DEPENDS ON
  67. %
  68. % image processessing toolbox (edge and padarray)
  69. %
  70. % CMBHOME.Utils (extrema2 and others)
  71. %
  72. % ALGORITHM
  73. %
  74. % 1. calculates the rate map, smooths it, sets no occupancy to zero
  75. % 2. gets the autocorrelation of the rate map
  76. % 3. cuts out the center peak from the autocorrelation, and finds the six
  77. % surrounding peaks, if they all exist, and cuts them out to make a
  78. % donut
  79. % 4. rotate the donut 3 (rotate inc) degrees at a time to 360, calculating
  80. % correlation at each step
  81. % 5. gridness score is the difference between the minimum peak at either
  82. % 60 or 120 degrees and the maximum trough at either 30, 90 or 150
  83. % degrees
  84. %
  85. % v1 dec 2009 - based upon orig gridness score (conj grid cells by sargolini et al)
  86. % v2 may 6 2010 - added eliptical correction (grid 3 param)
  87. % syntax: [gridness, phi_v_cor, auto_corr, eccentricity, grid3rotate, grid3factor, r_out, r_in] = Gridness(self, cel, varargin)
  88. % v3 jan 10 2011 - created grid property struct to simplify output
  89. %
  90. % [gridness, props] = root.Gridness(cel)
  91. % [gridness, props] = root.Gridness(cel, params)
  92. p = inputParser;
  94. p.addRequired('self')
  95. p.addRequired('cel', @isnumeric)
  96. p.addParamValue('xdim', [], @isnumeric);
  97. p.addParamValue('ydim', [], @isnumeric);
  98. p.addParamValue('clims', [], @(c) numel(c)==1);
  99. p.addParamValue('continuize_epochs', 0, @(c) numel(c)==1 && (c==1 || c==0));
  100. p.addParamValue('supress_plot', 1, @(c) numel(c)==1 && (c==1 || c==0));
  101. p.addParamValue('figure_handle', [], @(c) numel(c)==1);
  102. p.addParamValue('std_smooth_kernel', 4, @isnumeric);
  103. p.addParamValue('binside', 3, @isnumeric)
  104. p.addParamValue('rotate_inc', 3, @isnumeric)
  105. p.addParamValue('grid3', 0, @isnumeric)
  106. p.addParamValue('r_out', 0, @isnumeric)
  107. p.addParamValue('r_in', 0, @isnumeric)
  108. p.addParamValue('thresh', .1, @isnumeric)
  109. p.addParamValue('grid3rotate', 0, @isnumeric)
  110. p.addParamValue('grid3factor', 0, @isnumeric)
  111. p.addParamValue('autocorr', [], @isnumeric)
  113. p.parse(self, cel, varargin{:});
  115. self = p.Results.self;
  116. cel = p.Results.cel;
  117. xdim = p.Results.xdim;
  118. ydim = p.Results.ydim;
  119. clims = p.Results.clims;
  120. continuize_epochs = p.Results.continuize_epochs;
  121. supress_plot = p.Results.supress_plot;
  122. figure_handle = p.Results.figure_handle;
  123. std_smooth_kernel = p.Results.std_smooth_kernel;
  124. binside = p.Results.binside;
  125. rotate_inc = p.Results.rotate_inc;
  126. grid3 = p.Results.grid3;
  127. r_out = p.Results.r_out;
  128. r_in = p.Results.r_in;
  129. thresh = p.Results.thresh;
  130. grid3rotate = p.Results.grid3rotate;
  131. grid3factor = p.Results.grid3factor;
  132. autocorr = p.Results.autocorr;
  134. import CMBHOME.Utils.* % we need the super cool extrema2 function
  136. gridness = [];
  138. props.periodicity = NaN; % initialize grid cell properties
  139. props.rate_map = NaN;
  140. props.rate_map_mask = NaN;
  141. props.orig_auto_corr = NaN;
  142. props.auto_corr = NaN;
  143. props.auto_corr_mask = NaN;
  144. props.eccentricity = NaN;
  145. props.e_angle = NaN;
  146. props.e_skew = NaN;
  147. props.r_out = NaN;
  148. props.r_in = NaN;
  149. props.angle = NaN;
  150. props.spacing = NaN;
  151. props.ellipse = NaN;
  153. if ~isempty(self)
  154. if size(self.epoch, 1)>1 && continuize_epochs==0 % use some recursion to do multiple epochs
  156. [gridness, props] = MultipleEpochs(self, xdim, ydim, binside, std_smooth_kernel,...
  157. rotate_inc, cel, grid3, r_out, r_in, thresh, grid3factor, grid3rotate);
  159. if ~supress_plot, PlotIt(props.auto_corr, props.periodicity, gridness); end
  161. return
  163. end
  164. end
  166. rot_ind_mins = round([30/rotate_inc, 90/rotate_inc, 150/rotate_inc])+1; % indexes in rotated ac to check
  168. rot_ind_maxs = round([60/rotate_inc, 120/rotate_inc])+1;
  170. if isempty(autocorr)
  172. [rate_map, ~, ~, ~, rmmask] = self.RateMap(cel, 'supress_plot', 1, 'xdim', xdim, 'ydim', ydim, 'binside', binside, 'std_smooth_kernel', std_smooth_kernel, 'continuize_epochs', 1); % step 1
  174. autocorr = moserac(rate_map); % step 2: get autocorrelogram, make sure its a square image for later processing...
  176. else
  178. rate_map = [];
  180. rmmask = [];
  182. end
  184. auto_corr = MakeSquare(autocorr); % make autocorrelation matrix square
  186. props.orig_auto_corr = auto_corr;
  188. [auto_corr, peak_radius, mask, no_center_peak] = RemoveCenterPeak(auto_corr, r_in, thresh);
  190. if no_center_peak, disp('No center peak'), return, end
  192. [succ, props] = RemoveOuterCircle(auto_corr, peak_radius, mask, thresh, grid3, binside, r_out, grid3rotate, grid3factor, props);
  194. [~, ~, props.periodicity] = AutoCorrRotation(rot90(props.auto_corr, 3), props.auto_corr, 'cut_circle', 0, 'supress_plot', 1, 'rotate_inc', rotate_inc);
  196. gridness = min(props.periodicity(rot_ind_maxs, 2))-max(props.periodicity(rot_ind_mins, 2)); % solve for gridness
  198. props.periodicity(:,1) = props.periodicity(:,1) + 90; % shift correlation angles to 0-180 degree
  200. props.rate_map = rate_map;
  201. props.rate_map_mask = rmmask;
  202. props.r_in = peak_radius;
  204. if ~supress_plot, PlotIt(props.auto_corr, props.periodicity, gridness); end
  206. end
  208. function PlotIt(auto_corr, phi_v_cor, gridness)
  210. figure
  212. n_epochs = length(gridness);
  214. for i = 1:n_epochs
  216. if iscell(auto_corr)
  217. subplot(n_epochs, 2, (i-1)*2+1), imagesc(auto_corr{i}(:,:,1)), axis square, axis off
  218. else
  219. subplot(n_epochs, 2, (i-1)*2+1), imagesc(auto_corr(:,:,1)), axis square, axis off
  220. end
  222. subplot(n_epochs, 2, (i-1)*2+2), plot(phi_v_cor(:,(i-1)*2+1), phi_v_cor(:,(i-1)*2+2));
  224. end
  226. end
  228. function [succ, props] = RemoveOuterCircle(auto_corr, peak_width, mask, thresh, grid3, binside, r_out, grid3rotate, grid3factor, props)
  229. %
  230. % auto_corr matrix of autocorrelation
  231. % d half the width of peaks (radius of peak edge)
  232. % mask marks where cuts in auto_corr were made
  233. % thresh threshold for peaks
  235. import CMBHOME.Utils.* % for extrema2 function
  237. eccentricity = NaN;
  239. spacing = NaN;
  241. succ = 0;
  243. s = size(auto_corr, 1)-1; % square matrix
  245. [col, row] = meshgrid(-s/2:s/2, -s/2:s/2); % indices on grid
  247. [~, inds] = extrema2(auto_corr);
  249. peak_centers = [col(inds), row(inds)];
  251. d2 = sqrt(sum(peak_centers.^2,2));
  253. peak_centers(d2<1.2*peak_width, :) = [];
  255. d2(d2<1.2*peak_width) = [];
  257. if r_out % if outter radius is given, find peaks within radius
  259. peak_centers(d2>r_out,:) = [];
  261. d2(d2>r_out) = [];
  263. l = length(d2);
  265. d2 = d2(1:min([l, 6]));
  267. peak_centers = peak_centers(1:min([l, 6]), :);
  269. if isempty(d2), d2 = s/2; end
  271. elseif ~isempty(d2)
  273. succ = 1;
  275. [d2, ind] = sort(d2(:));
  277. peak_centers = peak_centers(ind, :);
  279. peak_centers(d2-d2(1)>.6*d2(1),:) = []; % clear peaks much farther away from the first
  281. d2(d2-d2(1)>.6*d2(1)) = [];
  283. l = length(d2);
  285. d2 = d2(1:min([l, 6]));
  287. peak_centers = peak_centers(1:min([l, 6]), :);
  289. if isempty(d2), d2 = s/2; end
  291. else
  293. disp('No surrounding peaks.');
  295. d2 = s/2;
  297. end
  299. if (grid3 & length(d2)==6) || (grid3 & r_out & grid3rotate & grid3factor)
  301. [auto_corr, grid3factor, mask, eccentricity, grid3rotate, cs, r_out] = Ovalate(auto_corr, d2, peak_width, peak_centers, mask, r_out, grid3rotate, grid3factor);
  303. props.ellipse = cs; % assign ellipse coefficients
  304. props.r_out = r_out;
  306. elseif r_out % user spec outer radius but 6 peaks were not detected, or
  308. [auto_corr, mask ] = MakeDonutAndMask(auto_corr, mask, r_out);
  309. props.r_out = r_out;
  311. else
  313. [auto_corr, mask ] = MakeDonutAndMask(auto_corr, mask, d2(end)+peak_width);
  314. props.r_out = d2(end)+peak_width;
  316. end
  318. props.auto_corr = auto_corr;
  319. props.auto_corr_mask = ~mask;
  320. props.eccentricity = eccentricity;
  321. props.e_angle = grid3rotate;
  322. props.e_skew = grid3factor;
  324. end
  326. function [auto_corr, grid3factor, mask, eccentricity, grid3rotate, cs, r_out] = Ovalate(auto_corr, d2, peak_width, peak_centers, mask, r_out, grid3rotate, grid3factor)
  327. % This function takes the 6 peaks in the autocorrelogram, fits an adapted
  328. % oval equation to it, and then squishes the autocorrelation to make the
  329. % pattern roouunund
  331. %figure('position', [0 0 1300 600])
  333. import CMBHOME.Utils.*
  335. rot_ind_mins = round([30/30, 90/30, 150/30])+1; % indexes in rotated ac to check
  337. rot_ind_maxs = round([60/30, 120/30])+1;
  339. s = size(auto_corr, 1);
  341. if r_out & grid3rotate & grid3factor % if we are to assume an elliptical correction
  343. auto_corr = imrotate(auto_corr, grid3rotate, 'bicubic', 'crop'); % rotate auto_corr so that major axis is up
  344. mask = imrotate(mask, grid3rotate - 90, 'bicubic', 'crop');
  345. auto_corr = imresize(auto_corr, [ceil(s*grid3factor) s], 'bicubic'); % resize it
  346. mask = imresize(mask, [ceil(s*grid3factor) s], 'bicubic');
  348. [auto_corr, mask] = MakeDonutAndMask(auto_corr, mask, r_out, peak_width);
  350. eccentricity = sqrt(1 - factor^2);
  352. else
  354. % Try without correction
  356. [nocorrect.auto_corr, nocorrect.mask] = MakeDonutAndMask(auto_corr, mask, min([d2(end)+peak_width/2 s/2]));
  357. [~, ~, nocorrect.phi_v_cor] = AutoCorrRotation(rot90(nocorrect.auto_corr, 3), nocorrect.auto_corr, 'cut_circle', 0, 'supress_plot', 1, 'rotate_inc', 30);
  358. nocorrect.gridness = min(nocorrect.phi_v_cor(rot_ind_maxs, 2))-max(nocorrect.phi_v_cor(rot_ind_mins, 2));
  360. % Fit ellipse and solve for minor and major axis, etc
  362. cs = EllipseDirectFit(peak_centers); % coefficients for equation for an ellipse
  364. [A, B, phi, x, y] = EllipseFromCoef(cs, peak_centers);
  366. ec.factor = min(A, B)/max(A,B);
  368. if ec.factor >.5 % if major axis is less than twice the length of the minor axis
  370. ec.auto_corr = imrotate(auto_corr, rad2deg(phi)+90, 'bicubic', 'crop'); % rotate auto_corr so that major axis is up
  371. ec.mask = imrotate(mask, 90+rad2deg(phi), 'bicubic', 'crop');
  372. ec.auto_corr = imresize(ec.auto_corr, [ceil(s*ec.factor) s], 'bicubic'); % resize it
  373. ec.mask = imresize(ec.mask, [ceil(s*ec.factor) s], 'bicubic');
  375. [ec.auto_corr, ec.mask ] = MakeDonutAndMask(ec.auto_corr, ec.mask, B+peak_width/2, peak_width);
  376. [~, ~, ec.phi_v_cor] = AutoCorrRotation(rot90(ec.auto_corr, 3), ec.auto_corr, 'cut_circle', 0, 'supress_plot', 1, 'rotate_inc', 30);
  377. ec.gridness = min(ec.phi_v_cor(rot_ind_maxs, 2))-max(ec.phi_v_cor(rot_ind_mins, 2));
  378. ec.eccentricity = sqrt(1-(B/A)^2);
  380. else
  382. ec.gridness = -100; % some arbitrarily low number
  383. ec.auto_corr = auto_corr;
  384. ec.mask = mask;
  385. cs = NaN;
  387. end
  389. % tmph = gcf;
  390. %
  391. % figure, subplot(1, 2, 1),
  392. % imagesc(auto_corr), axis equal, set(gca, 'ydir', 'normal'), hold on
  393. % scatter(peak_centers(:,1)+s/2+.5, peak_centers(:,2)+s/2+.5, 'g*'),
  394. % line([s/2+.5, s/2+A*cos(phi)+.5], [s/2+.5, s/2+A*sin(phi)+.5], 'Color', 'r', 'LineWidth', 2);
  395. % line([s/2+.5, s/2+B*cos(phi+pi/2)+.5], [s/2+.5, s/2+B*sin(phi+pi/2)+.5], 'Color', 'b', 'LineWidth', 2); title('major app'), axis equal, hold off
  396. % line(x+s/2+.5, y+s/2+.5, 'color', 'k', 'linewidth', 1.5)
  397. %
  398. % subplot(1, 2, 2), imagesc(ec.auto_corr), title(['g: ' num2str(ec.gridness)]), set(gca, 'ydir', 'normal'), axis equal
  399. %
  400. % %print(gcf, '-dpsc2', 'grid3.ps', '-append')
  401. % %close(gcf)
  402. % figure(tmph)
  404. if ec.gridness < nocorrect.gridness
  406. grid3rotate = 90; % no rotation
  407. grid3factor = 1;
  408. auto_corr = nocorrect.auto_corr;
  409. mask = nocorrect.mask;
  410. eccentricity = 0;
  411. r_out = min([d2(end)+peak_width/2 s/2]);
  413. else
  415. grid3rotate = phi;
  416. grid3factor = ec.factor;
  417. auto_corr = ec.auto_corr;
  418. mask = ec.mask;
  419. eccentricity = ec.eccentricity;
  420. r_out = B+peak_width/2;
  422. end
  424. end
  426. end
  428. function [matout, d, mask, no_center_peak] = RemoveCenterPeak(matin, r_in, thresh)
  429. % [centercut, 2stdmajoraxis] = RemoveCenterPeak(matin);
  430. %
  431. % Removes center peak of autocorrelogram by finding point furthest from
  432. % center that is half the peak (which is == 1 if using moserac).
  433. %
  434. % The above is the radius of center cutout. The since we are using an
  435. % autocorr, the peak will be either round or an oblong shape. The ratio of
  436. % the half width at the minor axis to the major axis could be the skewedness of
  437. % fields to be added in a future version.
  438. %
  439. % andrew nov 10 2010
  441. matout = [];
  442. d = [];
  443. mask = [];
  445. import CMBHOME.Utils.*
  447. s = size(matin, 1)-1; % square matrix
  449. no_center_peak = 0;
  451. [col, row] = meshgrid(-s/2:s/2, -s/2:s/2);
  453. if r_in % if inner radius is given
  455. d = r_in;
  457. matin(sqrt(col.^2 + row.^2) <= d) = 0; % set center peak to zero
  459. matout = matin;
  461. mask = zeros(size(matin)); % build mask for plotting
  463. mask(sqrt(col.^2 + row.^2) <= d) = 1;
  465. return
  467. end
  469. if all(isnan(matin(:))), no_center_peak = 1; return; end
  471. [~, inds] = extrema2(matin);
  473. peak_centers = [col(inds), row(inds)];
  475. d2 = sqrt(sum(peak_centers.^2,2));
  477. d2 = sort(d2);
  479. if length(d2)>1
  481. d2(1) = [];
  483. d2(d2-d2(1)>1.5*d2(1)) = [];
  485. end
  487. if ~isempty(d2)
  489. d = mean(d2(1:min([length(d2) 6])))/2;
  491. end
  493. % peaks = sqrt(col(matin >= thresh).^2 + row(matin>= thresh).^2);
  494. %
  495. % d = sort(peaks(:));
  496. %
  497. % d = d(find(diff(d)>1, 1, 'first')); % farthest radius of center peak
  499. % edge_inds = edge(matin>=.1);
  500. %
  501. % d = min(sqrt(col(edge_inds).^2 + row(edge_inds).^2));
  502. % below used to work OK
  503. % img_prop = regionprops(matin>thresh, {'centroid', 'minoraxislength', 'majoraxislength', 'FilledArea'});
  504. %
  505. % [~, ind] = min(sum((vertcat(img_prop(:).Centroid)-repmat((s+1)/2, length(img_prop), 2)).^2, 2)); % ind to center region
  506. %
  507. % d = img_prop(ind).MinorAxisLength/2;
  509. %d = img_prop(ind).FilledArea * (img_prop(ind).MinorAxisLength/2) / ((img_prop(ind).MinorAxisLength/2)^2*pi);
  511. if isempty(d) || 2*d>=.8 * (s+1) % if center peak is not a discrete thing, redefine thresh and look for closest non-thresh value
  512. %thresh = mean([1, min(min(matin(round(.2*end):round(.8*end),
  513. %round(.2*end):round(.8*end))))]); % half width
  514. thresh = .1; % half width
  516. peaks = sqrt(col(matin < thresh).^2 + row(matin < thresh).^2);
  518. d = min(peaks(:));
  520. end
  522. if 2*d>=.8 * (s+1) % if the detected center peak diameter is more than 80% of the autocorrelogram side dim
  524. no_center_peak = 1;
  526. d = 0;
  528. end
  530. matin(sqrt(col.^2 + row.^2) <= d) = 0; % set center peak to zero
  532. matout = matin;
  534. mask = zeros(size(matin)); % build mask for plotting
  536. mask(sqrt(col.^2 + row.^2) <= d) = 1;
  538. end
  540. function [auto_corr, mask] = MakeDonutAndMask(auto_corr, mask, r, recut_center)
  542. if ~exist('recut_center', 'var'), recut_center = 0; end
  544. s1 = size(auto_corr, 1)-1;
  545. s2 = size(auto_corr, 2)-1;
  547. [col, row] = meshgrid(-s2/2:s2/2, -s1/2:s1/2); % indices on grid % cut the donut
  549. r = min([ (s1-1)/2, r]); % set outer radius
  551. auto_corr(sqrt(col.^2 + row.^2) > r) = 0;
  553. if recut_center
  554. auto_corr(sqrt(col.^2 + row.^2)<=recut_center) = 0;
  556. mask(sqrt(col.^2 + row.^2)<=recut_center) = 1;
  557. end
  559. mask(sqrt(col.^2 + row.^2) > r) = 1;
  561. r = ceil(r); % reshape to square
  563. ds = size(auto_corr, 1)/2-r;
  565. ds2 = size(auto_corr, 2)/2-r;
  567. if ds<1, ds = .9; end
  568. if ds2<1, ds2 = .9; end
  570. auto_corr = auto_corr(ceil(ds):end-floor(ds),ceil(ds2):end-floor(ds2));
  572. mask = mask(ceil(ds):end-floor(ds),ceil(ds2):end-floor(ds2));
  574. auto_corr = MakeSquare(auto_corr);
  576. mask = MakeSquare(mask);
  578. mask = round(mask);
  580. end
  582. function matout = MakeSquare(matin)
  583. % no longer pads, but instead cuts
  585. a = diff(size(matin));
  587. if a<0 % if there are more rows than cols
  589. matout = matin(1+floor(-a/2):end-ceil(-a/2), :);
  591. elseif a>0 % if there are more cols than rows
  593. matout = matin(:, 1+floor(a/2):end-ceil(a/2));
  595. else
  596. matout = matin;
  598. end
  600. end
  602. function [gridness, props] = MultipleEpochs(self, xdim, ydim,...
  603. binside, std_smooth_kernel, rotate_inc,...
  604. cel, grid3, r_out, r_in, thresh,...
  605. grid3factor, grid3rotate)
  607. import CMBHOME.Utils.*
  609. if isempty(xdim) || isempty(ydim), % if binning dimensions are not specified, create them
  611. [x, y] = ContinuizeEpochs(self.x, self.y);
  613. xdim = min(x):self.spatial_scale^-1*binside:max(x); %edges of x and y dimensions
  615. ydim = min(y):self.spatial_scale^-1*binside:max(y);
  617. end
  619. gridness = nan(size(self.epoch,1), 1);
  621. epochs = self.epoch;
  623. for i = 1:size(epochs,1)
  625. self.epoch = epochs(i,:);
  627. [gridness(i), props(i)] = self.Gridness(cel, 'xdim', xdim, 'ydim', ydim, 'std_smooth_kernel', std_smooth_kernel, 'rotate_inc', rotate_inc, 'grid3', grid3', 'r_out', r_out, 'r_in', r_in, 'thresh', thresh, 'grid3factor', grid3factor, 'grid3rotate', grid3rotate);
  629. end
  631. end

Gridness.m at commit 01ddb2e, no license · at the source

Overview

Authors: Blanca E. Gutiérrez-Guzmán1, J Jesús Hernández-Pérez1, Holger Dannenberg1,2
ORCID iDs: Blanca E. Gutiérrez-Guzmán, J Jesús Hernández-Pérez, Holger Dannenberg
  1. Department of Bioengineering, George Mason University,Fairfax, VA USA
  2. Interdisciplinary Program in Neuroscience, George Mason University,Fairfax, VA USA
Institutions: George Mason University (United States)
Journal: Nature communications, volume 17, issue 1, article 7035
Dates: received 14 March 2025; accepted 21 April 2026; published online 8 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-72620-4 · PMID 42103711 · PMCID PMC13391489 · OpenAlex W7160718492
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: extracellular electrophysiology (units, LFP) (modality), mouse (organism), systems (subfield)
Methods: Statistics, Spectral & time-frequency, Connectivity, Single-unit activity, calcium imaging
Keywords: Neuroscience, Neural circuits
MeSH: Entorhinal Cortex*, Grid Cells*, Action Potentials, Animals, Male, Mice, Mice, Inbred C57BL, Space Perception (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NINDS (R00NS116129)
Citations: cited by 1 paper (Europe PMC); 46 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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

hasselmonians/CMBHOME

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 01ddb2e967f0d75fca5e9f76459f53f59f3ffbc1, 5 January 2023
Languages: MATLAB (641), C (23), C/C++ (13)
Size: 1,409 files, 677 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, tests, documentation
Not found: license file, CITATION.cff, environment file, continuous integration
Tools: Chronux (75 files), Statistics and Machine Learning Toolbox (31 files), CircStat (28 files), Signal Processing Toolbox (15 files), Image Processing Toolbox (14 files), Curve Fitting Toolbox (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
678 files
At the source: github.com/hasselmonians/CMBHOME

cnc-ntnu/bnt

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 648698c8b1985ee7968b1cdbcc37d1ff2ab8cc6f, 22 September 2021
Languages: MATLAB (482), Java (1)
Size: 576 files, 483 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: license file
Not found: README, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: CircStat (31 files), Statistics and Machine Learning Toolbox (26 files), Image Processing Toolbox (8 files), Signal Processing Toolbox (7 files), export_fig (1 file), Optimization Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
484 files
At the source: bitbucket.org/cnc-ntnu/bnt/src/master/

dannenberglab/Tiling-of-large-scaled-environments-by-grid-cells

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: ef4a0dbe374331ca3745fc0ad6eaae8a2aeb8f04, 13 March 2026
Languages: MATLAB (9)
Size: 11 files, 9 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
11 files
At the source: github.com/dannenberglab/Tiling-of-large-scaled-environments-by-grid-cells

Zenodo 19053822

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
11 files
At the source:

Code availability statement

The paper has a code 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/s41467-026-72620-4.

Tracing map

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What the map holds:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1,178 scripts, each with its path and the digest of its content;
  • 8 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Code and data availability statement

The paper has a code and 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/s41467-026-72620-4.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 2 keywords, 8 MeSH terms, 1 funder, 44 references.

Cite

This paper

Gutiérrez-Guzmán, B. E., Hernández-Pérez, J. J., & Dannenberg, H. (2026). Grid maps are disrupted after large-scale arena expansion and recover with experience. Nature communications, 17(1), 7035. https://doi.org/10.1038/s41467-026-72620-4

BibTeX

@article{gutierrezguzman2026grid,
author = {Gutiérrez-Guzmán, Blanca E. and Hernández-Pérez, J Jesús and Dannenberg, Holger},
title = {{Grid maps are disrupted after large-scale arena expansion and recover with experience}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {7035},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-72620-4},
url = {https://doi.org/10.1038/s41467-026-72620-4},
pmid = {42103711},
pmcid = {PMC13391489}
}

RIS

TY - JOUR
AU - Gutiérrez-Guzmán, Blanca E.
AU - Hernández-Pérez, J Jesús
AU - Dannenberg, Holger
TI - Grid maps are disrupted after large-scale arena expansion and recover with experience
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/08
VL - 17
IS - 1
SP - 7035
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-72620-4
UR - https://doi.org/10.1038/s41467-026-72620-4
LA - en
ER -

CSL-JSON

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"container-title": "Nature communications",
"author": [
{
"family": "Gutiérrez-Guzmán",
"given": "Blanca E."
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"given": "J Jesús"
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{
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}
],
"container-title-short": "Nat Commun",
"volume": "17",
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"PMCID": "PMC13391489",
"ISSN": "2041-1723",
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"URL": "https://doi.org/10.1038/s41467-026-72620-4",
"language": "en",
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2026,
5,
8
]
]
}
}

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

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