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Categorical Representation of Numerosity in the Pigeon Entopallium: Coding Format and Temporal Dynamics.

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1 match 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.

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  1. [1] § 3. Results › 3.4. Numerosity Discriminability in QMI and QMD Neuron Populations ↔ DA_sAUROC_3D_UDs_lin_log_CV.m, lines 85–200 · score 0.56 · linear scale, logarithmic scale, sAUROC, CV, DA, bars

Paper

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The authors' code

MATLAB · 516 lines · 11 KB · no license · 1 match

  1. clear;
  2. close all
  3. clc;
  6. load('UDs_data.mat')
  14. %%
  16. Inc_LOG = mean(Inc_AUROC,3);
  17. Inc_LIN = mean(Inc_AUROC,3);
  18. Inc_POW12 = mean(Inc_AUROC,3);
  19. Inc_POW13 = mean(Inc_AUROC,3);
  20. LOG=[]
  21. LIN=[]
  22. POW12=[]
  23. POW13=[]
  26. for i=1:5
  27. for j=1:5
  28. if j>i
  30. Inc_LOG(i,j) = Inc_LOG(i,j) / (log2(j)-log2(i));
  31. LOG = [LOG Inc_LOG(i,j)];
  33. Inc_LIN(i,j) = Inc_LIN(i,j) / (j-i);
  34. LIN = [LIN Inc_LIN(i,j)];
  37. Inc_POW12(i,j) = Inc_POW12(i,j) / (j.^(1/2)-i.^(1/2));
  38. POW12 = [POW12 Inc_POW12(i,j)];
  40. Inc_POW13(i,j) = Inc_POW13(i,j) / (j.^(1/3)-i.^(1/3));
  41. POW13 = [POW13 Inc_POW13(i,j)];
  42. end
  43. end
  44. end
  46. Inc_4 = [std(LIN)/mean(LIN) std(POW12)/mean(POW12) std(POW13)/mean(POW13) std(LOG)/mean(LOG) ]
  51. %%
  53. Dec_LOG = mean(-Dec_AUROC,3);
  54. Dec_LIN = mean(-Dec_AUROC,3);
  55. Dec_POW12 = mean(-Dec_AUROC,3);
  56. Dec_POW13 = mean(-Dec_AUROC,3);
  57. LOG=[]
  58. LIN=[]
  59. POW12=[]
  60. POW13=[]
  63. for i=1:5
  64. for j=1:5
  65. if j>i
  67. Dec_LOG(i,j) = Dec_LOG(i,j) / (log2(j)-log2(i));
  68. LOG = [LOG Dec_LOG(i,j)];
  70. Dec_LIN(i,j) = Dec_LIN(i,j) / (j-i);
  71. LIN = [LIN Dec_LIN(i,j)];
  74. Dec_POW12(i,j) = Dec_POW12(i,j) / (j.^(1/2)-i.^(1/2));
  75. POW12 = [POW12 Dec_POW12(i,j)];
  77. Dec_POW13(i,j) = Dec_POW13(i,j) / (j.^(1/3)-i.^(1/3));
  78. POW13 = [POW13 Dec_POW13(i,j)];
  79. end
  80. end
  81. end
  83. Dec_4 = [ std(LIN)/mean(LIN) std(POW12)/mean(POW12) std(POW13)/mean(POW13) std(LOG)/mean(LOG)]
  85. %%
  89. Inc_AAA = mean(Inc_AUROC,3)
  90. Dec_AAA = mean(-Dec_AUROC,3)
  92. Inc_Dec_AAA = Inc_AAA;
  94. for i=1:5
  95. for j=1:5
  96. if j>i
  97. Inc_Dec_AAA(i,j)= Dec_AAA(i,j);
  99. end
  101. if j==i
  102. Inc_Dec_AAA(i,j)= nan;
  104. end
  105. end
  106. end
  109. Inc_Dec_AAA_log2=[]
  110. Inc_Dec_AAA_lin=[]
  111. for i=1:5
  112. for j=1:5
  113. if j~=i
  114. Inc_Dec_AAA_log2(i,j)= Inc_Dec_AAA(i,j) / abs(log2(j)-log2(i));
  115. Inc_Dec_AAA_lin(i,j)= Inc_Dec_AAA(i,j) / abs(j-i);
  117. end
  120. end
  121. end
  126. A=Inc_Dec_AAA
  127. triBar3_signed(A, coolwarm_mpl(256), 'DA-sAUROC');
  128. pause(0.5)
  130. A=Inc_Dec_AAA_log2
  131. triBar3_signed(A, coolwarm_mpl(256), ['logarithmic scale ' ] );
  132. pause(0.5)
  134. A=Inc_Dec_AAA_lin
  135. triBar3_signed(A, coolwarm_mpl(256), [' linear scale ' ] );
  136. pause(0.5)
  139. figure('Color','w');
  140. hold on;plot(Inc_4,'LineWidth',1,'LineStyle',':', 'Color',[215,48,39]/255)
  141. hold on;plot(Inc_4, '.','MarkerSize',24,'Color',[215,48,39]/255)
  142. hold on;plot(Dec_4, 'LineWidth',1,'LineStyle',':','Color',[69,117,180]/255)
  143. hold on;plot(Dec_4, '.','MarkerSize',24,'Color',[69,117,180]/255)
  145. xticks([1 2 3 4]);
  148. xticklabels({'linear', 'power(1/2)', 'power(1/3)', 'log'})
  150. ylabel('CV');
  160. % Inc = Inc_4;
  161. % Dec = Dec_4;
  162. % scales = {'lin','Power(1/2)','Power(1/3)','log2'};
  163. %
  164. %
  165. % cA = [59 76 192]/255; % Inc
  166. % cB = [180 4 38]/255; % Dec
  167. %
  168. %
  169. % x1 = 1:4;
  170. % gap = 1;
  171. % x2 = (x1(end)+gap) + (1:4);
  172. %
  173. %
  174. % figure('Color','w','Position',[100 100 800 420]); hold on
  175. % b1 = bar(x1, Inc, 0.8, 'FaceColor','flat','EdgeColor','none'); b1.CData = repmat(cA,4,1);
  176. % b2 = bar(x2, Dec, 0.8, 'FaceColor','flat','EdgeColor','none'); b2.CData = repmat(cB,4,1);
  177. %
  178. % ax = gca;
  179. % ax.Box = 'off';
  180. % ax.YGrid = 'on'; ax.GridAlpha = 0.25; ax.LineWidth = 1;
  181. % ax.XTick = [x1 x2];
  182. % ax.XTickLabel = [scales scales];
  183. % xlabel('scale'); ylabel('CV');
  184. % legend([b1 b2], {'Inc','Dec'}, 'Location','northwest');
  185. %
  186. %
  187. %
  188. % for h = [b1 b2]
  189. % x = h.XEndPoints; y = h.YEndPoints;
  190. % text(x, y, compose('%.3f', y), 'HorizontalAlignment','center', ...
  191. % 'VerticalAlignment','bottom', 'FontSize',9);
  192. % end
  193. %
  194. %
  195. % yTop = max([Inc Dec]) * 1.12;
  196. % text(mean(x1), yTop, 'Inc', 'HorizontalAlignment','center','FontWeight','bold');
  197. % text(mean(x2), yTop, 'Dec', 'HorizontalAlignment','center','FontWeight','bold');
  198. %
  199. % xlim([0.25 x2(end)+0.75]);
  200. % ylim([0 yTop*1.05]);
  207. %%
  211. % % n = 5;
  212. % % a = 1.2;
  213. % % b = 0.9;
  214. % % theta = linspace(0,pi,n);
  215. % % x = a*cos(theta);
  216. % % y = b*sin(theta);
  217. %
  218. %
  219. % n = 5;
  220. % a = 1.2;
  221. % b = 0.9;
  222. % theta = linspace(-pi/2, pi/2, n);
  223. % x = a*cos(theta);
  224. % y = b*sin(theta);
  225. %
  226. % x=x(end:-1:1);
  227. % % ------------------------------------------------------------------
  228. % addpath cmocean
  229. %
  230. %
  231. %
  232. % cmap = bluewhitered(256);
  233. %
  234. %
  235. % nCol = size(cmap,1);
  236. % idxFun = @(v) round(((max(-1,min(1,v))+1)/2)*(nCol-1))+1;
  237. %
  238. %
  239. % % ---- --------------------------------------------------------------
  240. % figure
  241. % subplot(1,2,2)
  242. % % clf;
  243. % hold on;
  244. % axis equal off
  245. % for i = 1:n
  246. % for j = i+1:n
  247. % % if dist(i,j)>0.15
  248. %
  249. % c = cmap( idxFun(-Dec_AAA(i,j)), : );
  250. % plot([x(i) x(j)], [y(i) y(j)], 'Color', c, 'LineWidth', 2);
  251. %
  252. % % end
  253. % end
  254. % end
  255. %
  256. % Color_sum=[[220 18 20]/255; [173 76 167]/255; [0 177 63]/255; [0 127 187]/255; [250 127 0]/255];
  257. % for jm=1:5
  258. % % scatter(x(jm), y(jm), 80, Color_sum(jm,:), 'filled', 'MarkerEdgeColor','k', 'LineWidth',1.2)
  259. % scatter(x(jm), y(jm), 80, Color_sum(jm,:), 'filled', 'MarkerEdgeColor',Color_sum(jm,:), 'LineWidth',1.2)
  260. % end
  261. %
  262. % % scatter(x, y, 80, 'b', 'filled', 'MarkerEdgeColor','k', 'LineWidth',1.2)
  263. %
  264. % % for k = 1:n
  265. % % text(x(k), y(k)+0.05, sprintf('%d',k), ...
  266. % % 'HorizontalAlignment','center','FontSize',11);
  267. % % end
  268. % colormap(cmap); caxis([-1 1])
  269. % cb = colorbar('southoutside');
  270. % cb.Label.String = '2(ROC_{area}-0.5)';
  271. % title('Category distinction coded by 2(AUC-0.5)');
  272. %
  273. %
  274. %
  275. % subplot(1,2,1)
  276. % % clf;
  277. % hold on;
  278. % axis equal off
  279. % for i = 1:n
  280. % for j = i+1:n
  281. % % if dist(i,j)>0.15
  282. %
  283. % c = cmap( idxFun(Inc_AAA(i,j)), : );
  284. % plot([x(i) x(j)], [y(i) y(j)], 'Color', c, 'LineWidth', 2);
  285. %
  286. % % end
  287. % end
  288. % end
  289. %
  290. % Color_sum=[[220 18 20]/255; [173 76 167]/255; [0 177 63]/255; [0 127 187]/255; [250 127 0]/255];
  291. % for jm=1:5
  292. % % scatter(x(jm), y(jm), 80, Color_sum(jm,:), 'filled', 'MarkerEdgeColor','k', 'LineWidth',1.2)
  293. % scatter(x(jm), y(jm), 80, Color_sum(jm,:), 'filled', 'MarkerEdgeColor',Color_sum(jm,:), 'LineWidth',1.2)
  294. % end
  295. %
  296. % % scatter(x, y, 80, 'b', 'filled', 'MarkerEdgeColor','k', 'LineWidth',1.2)
  297. %
  298. % % for k = 1:n
  299. % % text(x(k), y(k)+0.05, sprintf('%d',k), ...
  300. % % 'HorizontalAlignment','center','FontSize',11);
  301. % % end
  302. % colormap(cmap); caxis([-1 1])
  303. % cb = colorbar('southoutside');
  304. % cb.Label.String = '2(ROC_{area}-0.5)';
  305. % title('Category distinction coded by 2(AUC-0.5)');
  306. %
  307. %
  312. %%
  315. n = 5;
  316. a = 0.9;
  317. b = 0.83;
  318. ccm=20;
  319. theta = linspace(-pi/2 + pi/ccm, pi/2 - pi/ccm, n);
  322. x = a*cos(theta);
  323. y = b*sin(theta);
  325. x=x(end:-1:1);
  326. % -----------------------------------------------------------------
  327. addpath cmocean
  331. cmap = bluewhitered(256);
  334. nCol = size(cmap,1);
  335. idxFun = @(v) round(((max(-1,min(1,v))+1)/2)*(nCol-1))+1;
  338. % ------------------------------------------------------------------
  339. figure('Color','w');
  341. % subplot(1,2,2)
  342. % clf;
  343. hold on;
  344. axis equal off
  345. % axis equal
  346. % axis off
  347. for i = 1:n
  348. for j = i+1:n
  351. c = cmap( idxFun(-Dec_AAA(i,j)), : );
  352. plot([x(i) x(j)], [y(i) y(j)], 'Color', c, 'LineWidth', 2);
  353. pause(0.1)
  356. end
  357. end
  359. Color_sum=[[220 18 20]/255; [173 76 167]/255; [0 177 63]/255; [0 127 187]/255; [250 127 0]/255];
  360. for jm=1:5
  361. % scatter(x(jm), y(jm), 80, Color_sum(jm,:), 'filled', 'MarkerEdgeColor','k', 'LineWidth',1.2)
  362. scatter(x(jm), y(jm), 80, Color_sum(jm,:), 'filled', 'MarkerEdgeColor',Color_sum(jm,:), 'LineWidth',1.2)
  363. pause(0.1)
  365. end
  367. % scatter(x, y, 80, 'b', 'filled', 'MarkerEdgeColor','k', 'LineWidth',1.2)
  369. % for k = 1:n
  370. % text(x(k), y(k)+0.05, sprintf('%d',k), ...
  371. % 'HorizontalAlignment','center','FontSize',11);
  372. % end
  375. ax = subplot(1,1,1);
  381. colormap(ax,cmap); caxis(ax,[-1 1])
  382. cb = colorbar(ax,'southoutside');
  383. cb.Label.String = '2(ROC_{area}-0.5)';
  384. % title('Category distinction coded by 2(AUC-0.5)');
  386. %%
  389. theta = linspace(-pi/2 + pi/ccm, pi/2 - pi/ccm, n); % cos(theta) >= 0
  392. x = -a*cos(theta);
  393. y = -b*sin(theta);
  396. % subplot(1,2,1)
  397. % clf;
  398. hold on;
  399. % axis equal off
  400. % axis equal
  401. % axis off
  403. for i = 1:n
  404. for j = i+1:n
  405. % if dist(i,j)>0.15
  407. c = cmap( idxFun(Inc_AAA(i,j)), : );
  408. plot([x(i) x(j)], [y(i) y(j)], 'Color', c, 'LineWidth', 2);
  409. pause(0.1)
  411. % end
  412. end
  413. end
  415. Color_sum=[[220 18 20]/255; [173 76 167]/255; [0 177 63]/255; [0 127 187]/255; [250 127 0]/255];
  416. for jm=1:5
  417. % scatter(x(jm), y(jm), 80, Color_sum(jm,:), 'filled', 'MarkerEdgeColor','k', 'LineWidth',1.2)
  418. scatter(x(jm), y(jm), 80, Color_sum(jm,:), 'filled', 'MarkerEdgeColor',Color_sum(jm,:), 'LineWidth',1.2)
  419. pause(0.1)
  420. end
  422. % scatter(x, y, 80, 'b', 'filled', 'MarkerEdgeColor','k', 'LineWidth',1.2)
  424. % for k = 1:n
  425. % text(x(k), y(k)+0.05, sprintf('%d',k), ...
  426. % 'HorizontalAlignment','center','FontSize',11);
  427. % end
  428. % colormap(cmap); caxis([-1 1])
  429. % cb = colorbar('southoutside');
  431. colormap(ax,cmap); caxis(ax,[-1 1])
  432. cb = colorbar(ax,'southoutside');
  433. cb.Label.String = '2(ROC_{area}-0.5)';
  434. % title('Category distinction coded by 2(AUC-0.5)');
  440. % if ispc
  441. % fontCN = 'Microsoft YaHei';
  442. % elseif ismac
  443. % fontCN = 'PingFang SC';
  444. % else
  445. % fontCN = 'SimHei';
  446. % end
  447. %
  448. %
  449. % load('cmapL.mat')
  450. %
  451. %
  452. % Reds = cmapL(6:end,:)
  453. %
  454. % Blues = cmapL(4:-1:1,:)
  455. % % Reds = [254 229 217;
  456. % % 252 174 145;
  457. % % 251 106 74;
  458. % % 203 24 29] / 255;
  459. % % Blues = [222 235 247;
  460. % % 158 202 225;
  461. % % 66 146 198;
  462. % % 8 81 156] / 255;
  463. %
  464. % figure('Color','w','Units','centimeters','Position',[2 2 14 8]);
  465. %
  466. % % ax = axes('Position',[0.06 0.12 0.90 0.80]);
  467. % ax = subplot(1,1,1);
  468. % hold(ax,'on');
  469. % xlim([0 5]); ylim([0 3]);
  470. % axis ij;
  471. % axis equal;
  472. % % axis off;
  473. %
  474. % % for r = 0:2
  475. % % for c = 0:4
  476. % % rectangle('Position',[c r 1 1], 'EdgeColor',[0 0 0], ...
  477. % % 'LineWidth',0.8, 'FaceColor','none');
  478. % % end
  479. % % end
  480. % % rectangle('Position',[0 0 5 3], 'EdgeColor','k', 'LineWidth',1.0, 'FaceColor','none');
  481. %
  482. %
  483. % text(0+0.08, 0+0.5, 'Large-preferring neurons', 'FontSize',12, ...
  484. % 'HorizontalAlignment','left','VerticalAlignment','middle');
  485. %
  486. %
  487. %
  488. %
  489. % text(0+0.08, 1+0.5, 'Small-preferring neurons', 'FontSize',12, ...
  490. % 'HorizontalAlignment','left','VerticalAlignment','middle');
  491. % text(0+0.08, 2+0.5, 'Numerical distance', 'FontSize',12, ...
  492. % 'HorizontalAlignment','left','VerticalAlignment','middle');
  493. %
  494. %
  495. % for k = 1:4
  496. % rectangle('Position',[k+0.2 0.2 0.6 0.6], 'FaceColor',Reds(k,:), ...
  497. % 'EdgeColor','k', 'LineWidth',0.6);
  498. % end
  499. %
  500. % for k = 1:4
  501. % rectangle('Position',[k+0.2 1+0.2 0.6 0.6], 'FaceColor',Blues(k,:), ...
  502. % 'EdgeColor','k', 'LineWidth',0.6);
  503. % end
  504. %
  505. % for k = 1:4
  506. %
  507. % text(k+0.5, 2+0.5, num2str(k), 'HorizontalAlignment','center', ...
  508. % 'VerticalAlignment','middle', 'FontSize',12, 'FontName','Arial');
  509. % end

DA_sAUROC_3D_UDs_lin_log_CV.m at commit ae87aa6, no license · at the source

Overview

Authors: Peng Wu1,2, Yanyan Peng1,2, Juncai Zhu1,2, Qingzhi He1,2, Jiangtao Wang1,2, Xiaoke Niu1,2, Songwei Wang1,2, Zhizhong Wang1,2, Li Shi2,3
ORCID iDs: Yanyan Peng, Jiangtao Wang, Xiaoke Niu
  1. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; (P.W.); (Y.P.); (J.Z.); (Q.H.); (J.W.); (X.N.); (S.W.)
  2. Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou 450001, China
  3. Department of Automation, Tsinghua University, Beijing 100084, China
Institutions: Zhengzhou University (China); Tsinghua University (China)
Journal: Animals : an open access journal from MDPI, volume 16, issue 16, article 2494
Dates: received 16 June 2026; accepted 8 August 2026; published online 11 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/ani16162494 · PMID 42651898 · PMCID PMC13508866 · OpenAlex W7202193379
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: extracellular electrophysiology (units, LFP) (modality), other (organism), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, Evoked potentials, Single-unit activity, calcium imaging
Keywords: numerosity, single-unit recording, pigeon, entopallium, neural coding, temporal dynamics
Topic: Cognitive and developmental aspects of mathematical skills (Statistics and Probability, Mathematics), according to OpenAlex
Funding: China Postdoctoral Science Foundation (2024M752934); Henan Science and Technology Department (262102211044); National Natural Science Foundation of China (62206253)
Citations: not cited yet (Europe PMC); 65 references in the paper

Abstract

Numerosity perception is an evolutionarily conserved ability observed across diverse taxa, from insects and fish to birds and primates. Unlike traditional visual categories, numerosity possesses an intrinsic metric structure, making it well suited for quantitatively investigating how categorical representations emerge along the visual hierarchy. While numerical representations are well characterized in high-level associative areas of non-human primates and avian species, neuronal processing of numerosity in upstream regions remains largely unexplored. To address this gap, we recorded single-unit activity in the pigeon entopallium during a delayed match-to-numerosity task. We found a subset of neurons that encoded numerosity independently of the non-numerical feature controlled in the corresponding recording session, exhibiting quasi-monotonic increasing (QMI) or decreasing (QMD) response profiles. Compared with QMI neurons, QMD neurons exhibited a markedly later coding window and stronger category discriminability. Moreover, in both neuronal populations, the normalized response functions and category discriminability were better described by compressed numerosity scales than by a linear scale, with the logarithmic scale showing a modest overall advantage. These findings help refine current theoretical frameworks for how numerosity information is extracted and represented along the avian visual processing hierarchy, providing a comparative basis for cross-species research.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

BrainSystemsLab/Data_and_Code_for_ENTO

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: ae87aa6c9a2418260ddaa7e46872f3d909a39a8a, 11 August 2026
Languages: MATLAB (10)
Size: 17 files, 10 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (3 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
11 files
At the source: github.com/BrainSystemsLab/Data_and_Code_for_ENTO

The paper's code and data availability statement is in the Data section.

Tracing map

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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;
  • 10 scripts, each with its path and the digest of its content;
  • 1 match 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

No dataset and no data link were found in the paper.

Data Availability Statement

The data and analysis code supporting this study are available in the following GitHub repository: https://github.com/BrainSystemsLab/Data_and_Code_for_ENTO (accessed on 1 August 2026).

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 6 keywords, 3 funders, 64 references.

Cite

This paper

Wu, P., Peng, Y., Zhu, J., He, Q., Wang, J., Niu, X., Wang, S., Wang, Z., & Shi, L. (2026). Categorical Representation of Numerosity in the Pigeon Entopallium: Coding Format and Temporal Dynamics. Animals : an open access journal from MDPI, 16(16), 2494. https://doi.org/10.3390/ani16162494

BibTeX

@article{wu2026categorical,
author = {Wu, Peng and Peng, Yanyan and Zhu, Juncai and He, Qingzhi and Wang, Jiangtao and Niu, Xiaoke and Wang, Songwei and Wang, Zhizhong and Shi, Li},
title = {{Categorical Representation of Numerosity in the Pigeon Entopallium: Coding Format and Temporal Dynamics}},
journal = {Animals : an open access journal from MDPI},
year = {2026},
month = aug,
volume = {16},
number = {16},
pages = {2494},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2076-2615},
doi = {10.3390/ani16162494},
url = {https://doi.org/10.3390/ani16162494},
pmid = {42651898},
pmcid = {PMC13508866}
}

RIS

TY - JOUR
AU - Wu, Peng
AU - Peng, Yanyan
AU - Zhu, Juncai
AU - He, Qingzhi
AU - Wang, Jiangtao
AU - Niu, Xiaoke
AU - Wang, Songwei
AU - Wang, Zhizhong
AU - Shi, Li
TI - Categorical Representation of Numerosity in the Pigeon Entopallium: Coding Format and Temporal Dynamics
T2 - Animals : an open access journal from MDPI
J2 - Animals (Basel)
PY - 2026
DA - 2026/08/11
VL - 16
IS - 16
SP - 2494
SN - 2076-2615
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/ani16162494
UR - https://doi.org/10.3390/ani16162494
LA - en
ER -

CSL-JSON

{
"id": "10.3390/ani16162494",
"type": "article-journal",
"title": "Categorical Representation of Numerosity in the Pigeon Entopallium: Coding Format and Temporal Dynamics",
"container-title": "Animals : an open access journal from MDPI",
"author": [
{
"family": "Wu",
"given": "Peng"
},
{
"family": "Peng",
"given": "Yanyan"
},
{
"family": "Zhu",
"given": "Juncai"
},
{
"family": "He",
"given": "Qingzhi"
},
{
"family": "Wang",
"given": "Jiangtao"
},
{
"family": "Niu",
"given": "Xiaoke"
},
{
"family": "Wang",
"given": "Songwei"
},
{
"family": "Wang",
"given": "Zhizhong"
},
{
"family": "Shi",
"given": "Li"
}
],
"container-title-short": "Animals (Basel)",
"volume": "16",
"issue": "16",
"page": "2494",
"DOI": "10.3390/ani16162494",
"PMID": "42651898",
"PMCID": "PMC13508866",
"ISSN": "2076-2615",
"publisher": "Multidisciplinary Digital Publishing Institute (MDPI)",
"URL": "https://doi.org/10.3390/ani16162494",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
11
]
]
}
}

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