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Dynamic population coding of kinematic structure across executed and observed actions in primate premotor cortex.

Code ↔ Paper

3 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 3 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § RESULTS › Neural population encoding of grasp configurations ↔ Fig_3.m, lines 1–122 · score 0.60 · consecutive bins, neurons selective, selectivity stability, events, Figure 3, Population
  2. [2] § MATERIALS AND METHODS › Canonical correlation analysis ↔ Fig_4.m, the whole file · a weak match · score 0.57 · canonical correlations, canonical dimension, shuffling, trajectories
  3. [3] § RESULTS › Encoding of action kinematics ↔ Fig_9.m, the whole file · a weak match · score 0.56 · cross predicted kinematics, Human kinematics predicted, Monkey kinematics predicted, model, Figure 9, contours

Paper

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The authors' code

MATLAB · 261 lines · 11 KB · CC-BY-4.0 · 1 match

  1. clear
  3. % Load data
  4. data_dir = 'data\neuronal_selectivity.mat';
  5. load(data_dir)
  6. names_to_plot = who('-file', data_dir);
  8. load('data\event_times.mat');
  10. graymap = [linspace(0,0.9,100)', linspace(0,0.9,100)', linspace(0,0.9,100)'; % colormap gray
  11. 1, 1, 1];
  12. cmap = [linspace(0, 0.3, 26)', linspace(0.2, 0.5, 26)', linspace(0, 0, 26)'; % colormap green-yellow-red
  13. linspace(0.3, 0.95, 12)' linspace(0.5, 0.9, 12)' linspace(0, 0.1, 12)';
  14. linspace(0.95, 1, 30)' linspace(0.9, 0.5, 30)' linspace(0.1, 0, 30)';
  15. linspace(1, 0.9, 35)' linspace(0.5, 0, 35)' linspace(0, 0, 35)'];
  17. time_points = -550:25:3350;
  18. time_0 = find(time_points==0);
  20. % Run for different conditions and populations
  21. for i = 1:length(names_to_plot)
  22. % Sort normalized activity
  23. eval(['curr_result =' names_to_plot{i} ';']);
  25. isSelective = curr_result.selectivity_p < 0.05;
  26. [isSelective,order] = keep_only_consecutive_bins(isSelective);
  28. curr_mat_to_plot = curr_result.selectivity_p .* isSelective;
  29. curr_mat_to_plot(curr_mat_to_plot==0) = 1;
  30. percent_selective_units = sum(isSelective, 1)./size(isSelective,1);
  32. % Plots
  33. f = figure();
  34. pos = get(f, 'Position');
  35. set(f, 'Position', [0, pos(2), 3*pos(3), pos(4)]);
  36. sgtitle(names_to_plot{i}, 'Interpreter', 'none')
  38. subplot(1, 3, 1); % per neuron selectivity
  40. hh=imagesc(curr_mat_to_plot(order,:));
  42. set(hh, 'AlphaData', ~isnan(curr_mat_to_plot))
  43. caxis([0 0.05])
  44. c = colorbar();
  45. colormap(graymap)
  47. xlabel('Time (s)')
  48. ylabel('# Neurons')
  50. xticks = time_0:20:length(time_points);
  51. set(gca, 'XTick', xticks);
  52. set(gca, 'XTickLabel', time_points(xticks)/1000);
  53. set(gca, 'tickdir', 'out')
  54. c.Label.String = 'selectivity p-value';
  56. line([find(time_points==0), find(time_points==0)],get(gca, 'YLim'),'color', [.5 .5 .5],'LineWidth',1, 'LineStyle', '--')
  57. line([find(time_points==1250), find(time_points==1250)],get(gca, 'YLim'),'color', [.5 .5 .5],'LineWidth',1, 'LineStyle', '--')
  59. line([find(time_points==800), find(time_points==800)],get(gca, 'YLim'),'color', [.9 .9 .9],'LineWidth',3)
  61. [~,endMoveIndex] = min(abs(time_points-endMove(i)));
  62. line([endMoveIndex, endMoveIndex], get(gca, 'YLim'),'color',[.5 .5 .5], 'LineStyle', '--' )
  63. [~,endHoldIndex] = min(abs(time_points-endHold(i)));
  64. line([endHoldIndex, endHoldIndex], get(gca, 'YLim'),'color',[.5 .5 .5], 'LineStyle', '--' )
  66. subplot(1, 3, 2); % selective units per time bin
  68. plot(time_points/1000, imgaussfilt(percent_selective_units,2), 'color', 'k','LineWidth',2)
  70. xlim([-0.600 3.400])
  71. ylim(get(gca, 'YLim'))
  73. line([0, 0],get(gca, 'YLim'),'color', [.5 .5 .5],'LineWidth',1, 'LineStyle', '--')
  74. line([1.250, 1.250],get(gca, 'YLim'),'color', [.5 .5 .5],'LineWidth',1, 'LineStyle', '--')
  76. line([0.800, 0.800],get(gca, 'YLim'),'color', [.9 .9 .9],'LineWidth',3)
  78. line([endMove(i)/1000, endMove(i)/1000], get(gca, 'YLim'),'color',[.5 .5 .5], 'LineStyle', '--' )
  79. line([endHold(i)/1000, endHold(i)/1000], get(gca, 'YLim'),'color',[.5 .5 .5], 'LineStyle', '--' )
  81. xlabel('Time (s)')
  82. ylabel('selective neurons (%)')
  84. ax3 = subplot(1, 3, 3); % selectivity stability map
  85. imagesc(curr_result.stability_map ./ (diag(curr_result.stability_map)+12))
  86. hold on;
  88. line([find(time_points==0), find(time_points==0)], get(gca, 'YLim'), 'color', [1 1 1],'LineWidth',1, 'LineStyle', '--')
  89. line(get(gca, 'XLim'), [find(time_points==0), find(time_points==0)], 'color', [1 1 1],'LineWidth',1, 'LineStyle', '--')
  90. line([find(time_points==1250), find(time_points==1250)], get(gca, 'YLim'), 'color', [1 1 1],'LineWidth',1, 'LineStyle', '--')
  91. line(get(gca, 'XLim'), [find(time_points==1250), find(time_points==1250)], 'color', [1 1 1],'LineWidth',1, 'LineStyle', '--')
  93. line([find(time_points==800), find(time_points==800)], get(gca, 'YLim'), 'color', [1 1 1],'LineWidth',3)
  94. line(get(gca, 'XLim'), [find(time_points==800), find(time_points==800)], 'color', [1 1 1],'LineWidth',3)
  96. [~,endMoveIndex] = min(abs(time_points-endMove(i)));
  97. line([endMoveIndex, endMoveIndex], get(gca, 'YLim'), 'color',[1 1 1], 'LineStyle', '--' )
  98. line(get(gca, 'XLim'), [endMoveIndex, endMoveIndex], 'color',[1 1 1], 'LineStyle', '--' )
  99. [~,endHoldIndex] = min(abs(time_points-endHold(i)));
  100. line([endHoldIndex, endHoldIndex], get(gca, 'YLim'), 'color',[1 1 1], 'LineStyle', '--' )
  101. line(get(gca, 'XLim'), [endHoldIndex, endHoldIndex], 'color',[1 1 1], 'LineStyle', '--' )
  103. set(gca,'YDir','normal')
  104. axis image
  106. ylabel('reference time (s)')
  107. xlabel('test time (s)')
  109. ticks = time_0:20:length(time_points);
  110. set(gca, 'XTick', ticks);
  111. set(gca, 'XTickLabel', time_points(ticks)/1000);
  112. set(gca, 'YTick', ticks);
  113. set(gca, 'YTickLabel', time_points(ticks)/1000);
  114. set(gca, 'tickdir', 'out')
  115. box on
  117. colormap(ax3, cmap)
  118. caxis([0 0.8])
  119. cc = colorbar(ax3);
  120. cc.Label.String = 'selectivity stability index (population average)';
  122. end
  124. %% plot congruency map
  125. data_dir = 'data\selectivity_congruence.mat';
  126. load(data_dir)
  128. f = figure();
  129. pos = get(f, 'Position');
  130. set(f, 'Position', [0, pos(2), 2*pos(3), pos(4)]);
  132. subplot(1, 2, 1); % sum of common selectivity periods
  133. imagesc(common_selectivity)
  134. hold on;
  136. line([find(time_points==0), find(time_points==0)], get(gca, 'YLim'), 'color', [1 1 1],'LineWidth',1, 'LineStyle', '--')
  137. line(get(gca, 'XLim'), [find(time_points==0), find(time_points==0)], 'color', [1 1 1],'LineWidth',1, 'LineStyle', '--')
  138. line([find(time_points==1250), find(time_points==1250)], get(gca, 'YLim'), 'color', [1 1 1],'LineWidth',1, 'LineStyle', '--')
  139. line(get(gca, 'XLim'), [find(time_points==1250), find(time_points==1250)], 'color', [1 1 1],'LineWidth',1, 'LineStyle', '--')
  141. line([find(time_points==800), find(time_points==800)], get(gca, 'YLim'), 'color', [1 1 1],'LineWidth',3)
  142. line(get(gca, 'XLim'), [find(time_points==800), find(time_points==800)], 'color', [1 1 1],'LineWidth',3)
  144. [~,endMoveIndex] = min(abs(time_points-endMove(1)));
  145. line([endMoveIndex, endMoveIndex], get(gca, 'YLim'), 'color',[1 1 1], 'LineStyle', '--' )
  146. [~,endMoveIndex] = min(abs(time_points-endMove(2)));
  147. line(get(gca, 'XLim'), [endMoveIndex, endMoveIndex], 'color',[1 1 1], 'LineStyle', '--' )
  148. [~,endHoldIndex] = min(abs(time_points-endHold(1)));
  149. line([endHoldIndex, endHoldIndex], get(gca, 'YLim'), 'color',[1 1 1], 'LineStyle', '--' )
  150. [~,endHoldIndex] = min(abs(time_points-endHold(2)));
  151. line(get(gca, 'XLim'), [endHoldIndex, endHoldIndex], 'color',[1 1 1], 'LineStyle', '--' )
  153. ylabel('time (s) - GO')
  154. xlabel('time (s) - GE')
  155. title('common selectivity')
  157. set(gca,'YDir','normal')
  158. axis image
  160. ticks = time_0:20:length(time_points);
  161. set(gca, 'XTick', ticks);
  162. set(gca, 'XTickLabel', time_points(ticks)/1000);
  163. set(gca, 'YTick', ticks);
  164. set(gca, 'YTickLabel', time_points(ticks)/1000);
  165. set(gca, 'tickdir', 'out')
  166. box on
  168. subplot(1, 2, 2); % sum of congruent selectivity periods
  169. imagesc(congruence)
  170. hold on;
  172. line([find(time_points==0), find(time_points==0)], get(gca, 'YLim'), 'color', [1 1 1],'LineWidth',1, 'LineStyle', '--')
  173. line(get(gca, 'XLim'), [find(time_points==0), find(time_points==0)], 'color', [1 1 1],'LineWidth',1, 'LineStyle', '--')
  174. line([find(time_points==1250), find(time_points==1250)], get(gca, 'YLim'), 'color', [1 1 1],'LineWidth',1, 'LineStyle', '--')
  175. line(get(gca, 'XLim'), [find(time_points==1250), find(time_points==1250)], 'color', [1 1 1],'LineWidth',1, 'LineStyle', '--')
  177. line([find(time_points==800), find(time_points==800)], get(gca, 'YLim'), 'color', [1 1 1],'LineWidth',3)
  178. line(get(gca, 'XLim'), [find(time_points==800), find(time_points==800)], 'color', [1 1 1],'LineWidth',3)
  180. [~,endMoveIndex] = min(abs(time_points-endMove(1)));
  181. line([endMoveIndex, endMoveIndex], get(gca, 'YLim'), 'color',[1 1 1], 'LineStyle', '--' )
  182. [~,endMoveIndex] = min(abs(time_points-endMove(2)));
  183. line(get(gca, 'XLim'), [endMoveIndex, endMoveIndex], 'color',[1 1 1], 'LineStyle', '--' )
  184. [~,endHoldIndex] = min(abs(time_points-endHold(1)));
  185. line([endHoldIndex, endHoldIndex], get(gca, 'YLim'), 'color',[1 1 1], 'LineStyle', '--' )
  186. [~,endHoldIndex] = min(abs(time_points-endHold(2)));
  187. line(get(gca, 'XLim'), [endHoldIndex, endHoldIndex], 'color',[1 1 1], 'LineStyle', '--' )
  189. ylabel('reference time (s) - GO')
  190. xlabel('test time (s) - GE')
  191. title('congruent selectivity')
  193. set(gca,'YDir','normal')
  194. axis image
  196. ticks = time_0:20:length(time_points);
  197. set(gca, 'XTick', ticks);
  198. set(gca, 'XTickLabel', time_points(ticks)/1000);
  199. set(gca, 'YTick', ticks);
  200. set(gca, 'YTickLabel', time_points(ticks)/1000);
  201. set(gca, 'tickdir', 'out')
  202. box on
  204. c = colorbar();
  205. c.Label.String = 'sum of common/congruent selectivity periods';
  206. colormap(cmap)
  207. caxis([0 35])
  209. %%
  210. function [isSig, order] = keep_only_consecutive_bins(isSig)
  212. first_significance = nan(1,size(isSig,1));
  214. for iUnit= 1:size(isSig,1)
  215. %%%% keep only consecutive bin significant periods
  216. bin = 1;
  217. min_num_of_bins = 7; % choose minimum number of significant bins
  218. while bin <= size(isSig,2)
  219. if isSig(iUnit,bin) == 1
  220. cond = 1;
  221. k = 0;
  222. while cond % check the next bins
  223. k = k+1;
  224. if bin+k>size(isSig,2) % dont let the bin go above limit
  225. if k<min_num_of_bins
  226. isSig(iUnit,bin:bin+k-1) = 0;
  227. end
  228. break
  229. end
  230. if isSig(iUnit,bin+k) == 0 % find the end of the significant period
  231. cond = 0;
  232. if k<min_num_of_bins
  233. isSig(iUnit,bin:bin+k) = 0; % delete short significant periods
  234. end
  235. end
  236. end
  237. bin = bin+k+1;
  238. else
  239. bin = bin+1;
  240. end
  241. end % for all bins
  243. %%% find first significance after movement preparation
  244. if max(isSig(iUnit,60:157))==1
  245. first_index = 59+find(isSig(iUnit,60:157),1);
  246. % [max_pev, max_index] = max(omegaSq{iUnit}{iCond}.*significant{iUnit}{iCond});
  247. % first_significance(iUnit,iCond) = max_index;
  248. ind = 0;
  249. while isnan(first_significance(iUnit))
  250. ind = ind+1;
  251. if isSig(iUnit,first_index-ind)==0 && isSig(iUnit,first_index-ind-1)==0 && isSig(iUnit,first_index-ind-2)==0 && isSig(iUnit,first_index-ind-3)==0
  252. first_significance(iUnit) = first_index-ind+1;
  253. elseif first_index-ind-3==1
  254. first_significance(iUnit) = first_index-ind+1;
  255. end
  256. end
  257. end
  258. end
  260. [~,order] = sort(first_significance);
  261. end

Fig_3.m, under CC-BY-4.0 · at the source

Overview

Authors: Konstantinos Chatzimichail1,2, Christos Paschalidis1,2, Eleftheria Tzamali1, Vassilis Papadourakis1, Vassilis Raos1,2,3
ORCID iDs: Konstantinos Chatzimichail, Eleftheria Tzamali, Vassilis Papadourakis, Vassilis Raos
  1. Lab of Movement Physiology, Medical School, University of Crete, Heraklion, Greece
  2. Graduate Program in the Brain and Mind Sciences, University of Crete, Heraklion, Greece
  3. Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas, Heraklion, Greece
Institutions: University of Crete (Greece); Foundation for Research and Technology Hellas (Greece)
Journal: Science advances, volume 12, issue 25, article eaed9309
Dates: received 14 November 2025; accepted 8 May 2026; published online 19 June 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.aed9309 · PMID 42319945 · PMCID PMC13281793 · OpenAlex W7165376055
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other (modality), non-human primate (organism), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Single-unit activity, calcium imaging
MeSH: Motor Cortex*, Animals, Biomechanical Phenomena, Hand Strength, Macaca mulatta, Male, Movement, Neurons (* major topic)
Topic: Action Observation and Synchronization (Social Psychology, Psychology), according to OpenAlex
Funding: European Regional Development Fund (MIS 5048512)
Citations: not cited yet (Europe PMC); 91 references in the paper

Abstract

Neurons active during both action execution and observation [mirror neurons (MirNs)] are central to theories of action understanding, yet what they represent remains debated: abstract goals, static grips, or movement kinematics? We recorded 433 neurons from macaque premotor cortex during execution and observation of reach-to-grasp actions. Population analyses revealed grasp-specific information in both conditions, broadly distributed across neurons and dynamically reconfigured over time. Generalization across task phases was limited, indicating time-specific and evolving population codes rather than static representations. Execution and observation were linked by a shared, partially overlapping population geometry that supported reliable cross-condition classification, with the strongest alignment emerging during movement and hold. Neural activity was systematically related to multidimensional hand kinematics, and these relationships generalized across neuronal populations and across agents. Together, these findings support a dynamic population-level account of MirN function in which premotor circuits integrate visual and motor signals to represent and anticipate the unfolding structure of others’ actions.

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 3 matches between paragraphs and lines of code.

Zenodo 18983284

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (9)
Size: 20 files, 9 scripts
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (7 files), Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
9 files
At the source: doi.org/10.5281/zenodo.18983284

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;
  • 9 scripts, each with its path and the digest of its content;
  • 3 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

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

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. The data and code used to generate the figures have been deposited to Zenodo (https://doi.org/10.5281/zenodo.18983284). This study did not generate new materials.

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 2, 28 September 2026

  • Funding: added European Social Fund: MIS 5048512

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 8 MeSH terms, 86 references.

Cite

This paper

Chatzimichail, K., Paschalidis, C., Tzamali, E., Papadourakis, V., & Raos, V. (2026). Dynamic population coding of kinematic structure across executed and observed actions in primate premotor cortex. Science advances, 12(25), eaed9309. https://doi.org/10.1126/sciadv.aed9309

BibTeX

@article{chatzimichail2026dynamic,
author = {Chatzimichail, Konstantinos and Paschalidis, Christos and Tzamali, Eleftheria and Papadourakis, Vassilis and Raos, Vassilis},
title = {{Dynamic population coding of kinematic structure across executed and observed actions in primate premotor cortex}},
journal = {Science advances},
year = {2026},
month = jun,
volume = {12},
number = {25},
pages = {eaed9309},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aed9309},
url = {https://doi.org/10.1126/sciadv.aed9309},
pmid = {42319945},
pmcid = {PMC13281793}
}

RIS

TY - JOUR
AU - Chatzimichail, Konstantinos
AU - Paschalidis, Christos
AU - Tzamali, Eleftheria
AU - Papadourakis, Vassilis
AU - Raos, Vassilis
TI - Dynamic population coding of kinematic structure across executed and observed actions in primate premotor cortex
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/06/19
VL - 12
IS - 25
SP - eaed9309
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aed9309
UR - https://doi.org/10.1126/sciadv.aed9309
LA - en
ER -

CSL-JSON

{
"id": "10.1126/sciadv.aed9309",
"type": "article-journal",
"title": "Dynamic population coding of kinematic structure across executed and observed actions in primate premotor cortex",
"container-title": "Science advances",
"author": [
{
"family": "Chatzimichail",
"given": "Konstantinos"
},
{
"family": "Paschalidis",
"given": "Christos"
},
{
"family": "Tzamali",
"given": "Eleftheria"
},
{
"family": "Papadourakis",
"given": "Vassilis"
},
{
"family": "Raos",
"given": "Vassilis"
}
],
"container-title-short": "Sci Adv",
"volume": "12",
"issue": "25",
"page": "eaed9309",
"DOI": "10.1126/sciadv.aed9309",
"PMID": "42319945",
"PMCID": "PMC13281793",
"ISSN": "2375-2548",
"publisher": "American Association for the Advancement of Science",
"URL": "https://doi.org/10.1126/sciadv.aed9309",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
19
]
]
}
}

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Sensorimotor transformation of number in the primate parietal cortex.
Journal: Nature communications
In common: Statistics and Machine Learning Toolbox, non-human primate, 2 references

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