OSCR

Continuous flash suppression of neural responses and population orientation coding in macaque V1.

Code ↔ Paper

4 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 4 matches
  1. [1] § Results ↔ Fig2CD.m, lines 89–176 · score 0.65 · orientation preferences, Gaussian fitting, tuning functions, slope, width, noise
  2. [2] § Results › Orientation classification and reconstruction under CFS ↔ Fig2CD.m, lines 89–176 · score 0.57 · Gaussian fittings, relative orientation, tuning functions, Box, OD, SE
  3. [3] § Results › Orientation classification and reconstruction under CFS ↔ Decoding_capacity_svm_ajacent.m, lines 8–58 · score 0.53 · population neural responses, contralateral eye, decoding, SVM, accuracies, orientation
  4. [4] § Results › Orientation classification and reconstruction under CFS ↔ Fig3A_calc.m, lines 8–67 · score 0.50 · population neural responses, contralateral eye, accuracies, V1, orientation, Figure 3

Paper

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The authors' code

MATLAB · 176 lines · 11 KB · MIT · 2 matches

  1. %% given a subset of population, draw it's pop ori tuning under different conditions (according to stimu/noise eye preference)
  2. %% with population orientation tuning fitting and params
  4. %% clear history variables
  5. clearvars;
  6. clear global;
  7. clc; close all;
  9. %% raw data pathes
  10. data_root = '/Volumes/TOSHIBA EXT/Research/CFS/';
  11. MA_1 = [data_root 'data/Data_repository/MA_1/'];
  12. MA_2 = [data_root 'data/Data_repository/MA_2/'];
  13. MB_1 = [data_root 'data/Data_repository/MB_1/'];
  14. MB_2 = [data_root 'data/Data_repository/MB_2/'];
  15. MA_3 = [data_root 'data/Data_repository/MA_3/'];
  16. MA_4 = [data_root 'data/Data_repository/MA_4/'];
  18. dataPathList = {MA_1, MA_2, MB_1, MB_2, MA_3, MA_4};
  19. dataNameList = {'MA_1', 'MA_2', 'MB_1', 'MB_2', 'MA_3', 'MA_4'};
  21. %% figure parameters
  22. fig = figure;
  23. tiledlayout(2,4);
  24. set(fig, 'Position', [100, 300, 1200, 550])
  26. pooled_list = {[1 2], [3 4], [5 6]};
  28. for iPool = [1 2]
  29. num_oris = 12;
  30. [bino_list, mono_contra_list, mono_ipis_list, cfs_contra_list, cfs_ipis_list] = deal([]);
  31. [noise_contra_list, noise_ipis_list, ODI_list, Y1_base, Y1_contra, Y1_ipis] = deal([]);
  33. icount = 0;
  34. for iPath = pooled_list{iPool}
  35. % load necessary datum
  36. load([dataPathList{iPath} 'ODI.mat'], "ODI");
  37. load([dataPathList{iPath} 'Y1_AnovaListTotal_base.mat'], "Y1_AnovaListTotal_base");
  38. load([dataPathList{iPath} 'Y1_AnovaListTotal_contra.mat'], "Y1_AnovaListTotal_contra");
  39. load([dataPathList{iPath} 'Y1_AnovaListTotal_ipis.mat'], "Y1_AnovaListTotal_ipis");
  40. load([dataPathList{iPath} 'G4_PeakOriListTotal_base.mat']); load([dataPathList{iPath} 'G4_PeakSfListTotal_base.mat']);
  41. load([dataPathList{iPath} 'G4_PeakOriListTotal_ipis.mat']); load([dataPathList{iPath} 'G4_PeakSfListTotal_ipis.mat']);
  42. load([dataPathList{iPath} 'G4_PeakOriListTotal_contra.mat']); load([dataPathList{iPath} 'G4_PeakSfListTotal_contra.mat']);
  43. load([dataPathList{iPath} 'G4_RspMeanTrialStdSeListTotal_base.mat']);
  45. % for each neuron, each condition, select its ori tuning under most
  46. % preferred sf, and align most preferred ori to the 1st element
  47. % [binocular, monocular_contralateral eye, monoocular ipislateral eye,
  48. % csf_grating on contralateral eye, cfs_grating on ipislateral eye]
  49. for ci = 1:size(G4_RspMeanTrialStdSeListTotal_base,1)
  50. icount = icount + 1;
  51. % extract sf and ori preference under each eye condition
  52. sflist = [G4_PeakSfListTotal_contra(ci) G4_PeakSfListTotal_ipis(ci) G4_PeakSfListTotal_base(ci)];
  53. orilist = [G4_PeakOriListTotal_contra(ci) G4_PeakOriListTotal_ipis(ci) G4_PeakOriListTotal_base(ci)];
  55. bino_idx = 96+(sflist(3)-1)*12+1:96+(sflist(3)-1)*12+num_oris; % peak sf
  56. mono_contra_idx = 48+(sflist(1)-1)*12+1:48+(sflist(1)-1)*12+num_oris;
  57. mono_ipis_idx = 72+(sflist(2)-1)*12+1:72+(sflist(2)-1)*12+num_oris;
  58. cfs_contra_idx = 0+(sflist(1)-1)*12+1:0+(sflist(1)-1)*12+num_oris;
  59. cfs_ipis_idx = 24+(sflist(2)-1)*12+1:24+(sflist(2)-1)*12+num_oris;
  60. noise_contra_idx = 121;
  61. noise_ipis_idx = 122;
  63. bino_list(icount,:) = circshift(G4_RspMeanTrialStdSeListTotal_base(ci,bino_idx,1),12-orilist(3)+1);
  64. mono_contra_list(icount,:) = circshift(G4_RspMeanTrialStdSeListTotal_base(ci,mono_contra_idx,1),12-orilist(1)+1);
  65. mono_ipis_list(icount,:) = circshift(G4_RspMeanTrialStdSeListTotal_base(ci,mono_ipis_idx,1),12-orilist(2)+1);
  66. cfs_contra_list(icount,:) = circshift(G4_RspMeanTrialStdSeListTotal_base(ci,cfs_contra_idx,1),12-orilist(1)+1);
  67. cfs_ipis_list(icount,:) = circshift(G4_RspMeanTrialStdSeListTotal_base(ci,cfs_ipis_idx,1),12-orilist(2)+1);
  68. noise_contra_list(icount,:) = repmat(G4_RspMeanTrialStdSeListTotal_base(ci,noise_contra_idx,1), [1,12]);
  69. noise_ipis_list(icount,:) = repmat(G4_RspMeanTrialStdSeListTotal_base(ci,noise_ipis_idx,1), [1,12]);
  70. ODI_list(icount,:) = ODI(ci);
  71. Y1_base(icount) = Y1_AnovaListTotal_base(ci);
  72. Y1_contra(icount) = Y1_AnovaListTotal_contra(ci);
  73. Y1_ipis(icount) = Y1_AnovaListTotal_ipis(ci);
  74. end
  75. end
  77. linestoplot = {bino_list, mono_contra_list, mono_ipis_list,...
  78. cfs_contra_list, cfs_ipis_list};
  79. legends = {'bino','mono_contra','mono_ipis','cfs_contra','cfs_ipis'};
  80. neuron_idx = {find( ODI_list<-0.2 & (Y1_base'<.01 | Y1_contra'<.01 | Y1_ipis'<.01)), ...
  81. find(abs(ODI_list)<=0.2 & (Y1_base'<.01 | Y1_contra'<.01 | Y1_ipis'<.01)), ...
  82. find(ODI_list>0.2 & (Y1_base'<.01 | Y1_contra'<.01 | Y1_ipis'<.01)), ...
  83. find((Y1_base'<.01 | Y1_contra'<.01 | Y1_ipis'<.01))};
  84. num_of_lines = length(neuron_idx);
  85. colorlist = turbo(num_of_lines+2);
  86. colorlist = colorlist(2:size(colorlist,1), :);
  87. fitparams = zeros(num_of_lines,7);
  89. % plot ori tuning functions
  90. params_all = [];
  91. for j = [4 3 2 1]
  92. if j == 1 % prefer stimuli eye
  93. linestoplot = {[bino_list(neuron_idx{1},:); bino_list(neuron_idx{3},:)],... % binocular
  94. [mono_contra_list(neuron_idx{1},:); mono_ipis_list(neuron_idx{3},:)],... % monocular, grating at preferred eye
  95. [cfs_contra_list(neuron_idx{1},:); cfs_ipis_list(neuron_idx{3},:)]}; % cfs, grating at preferred eye
  96. dotstoplot = [noise_contra_list(neuron_idx{1},:); noise_ipis_list(neuron_idx{3},:)];
  97. neuro_num = length(neuron_idx{1}) + length(neuron_idx{3});
  98. ODImean = mean(abs(ODI_list([neuron_idx{1}; neuron_idx{3}])));
  99. elseif j == 2 % middle, no much preference
  100. linestoplot = {[bino_list(neuron_idx{2},:)],... % binocular
  101. [mono_contra_list(neuron_idx{2},:); mono_ipis_list(neuron_idx{2},:)],... % monocular, grating at either eye
  102. [cfs_contra_list(neuron_idx{2},:); cfs_ipis_list(neuron_idx{2},:)]}; % cfs, grating at either eye
  103. dotstoplot = [noise_contra_list(neuron_idx{2},:); noise_ipis_list(neuron_idx{2},:)];
  104. neuro_num = length(neuron_idx{2});
  105. ODImean = mean(abs(ODI_list(neuron_idx{2})));
  106. elseif j == 3 % prefer noise eye
  107. linestoplot = {[bino_list(neuron_idx{3},:); bino_list(neuron_idx{1},:)],... % binocular
  108. [mono_contra_list(neuron_idx{3},:); mono_ipis_list(neuron_idx{1},:)],... % monocular, grating at non-preferred eye
  109. [cfs_contra_list(neuron_idx{3},:); cfs_ipis_list(neuron_idx{1},:)]}; % cfs, grating at non-preferred eye
  110. dotstoplot = [noise_contra_list(neuron_idx{3},:); noise_ipis_list(neuron_idx{1},:)];
  111. neuro_num = length(neuron_idx{1}) + length(neuron_idx{3});
  112. ODImean = mean(abs(ODI_list([neuron_idx{1}; neuron_idx{3}])));
  113. elseif j == 4 % all neurons
  114. linestoplot = {[bino_list(neuron_idx{4},:)],... % binocular
  115. [mono_contra_list(neuron_idx{4},:); mono_ipis_list(neuron_idx{4},:)],... % monocular, grating at non-preferred eye
  116. [cfs_contra_list(neuron_idx{4},:); cfs_ipis_list(neuron_idx{4},:)]}; % cfs, grating at non-preferred eye
  117. dotstoplot = [noise_contra_list(neuron_idx{4},:); noise_ipis_list(neuron_idx{4},:)];
  118. neuro_num = length(ODI_list(neuron_idx{4}));
  119. ODImean = mean(abs(ODI_list(neuron_idx{4})));
  120. end
  121. legends = {'bino','mono','cfs'};
  122. neuron_legends = {'Preferring grating eye', 'Binocular', 'Preferring noise eye', 'All'};
  123. nexttile;
  124. % figure
  125. hold on
  126. p = [];
  127. colorlist = [115 180 77; 216 33 28; 41 155 207]/255; % colorlist = [255 0 0; 0 255 0; 0 0 255]/255;
  128. marker = {"d", "o", "^"};
  129. text(-95, 0.57, sprintf('Amp Width R^2 Slope'),'color', 'k')
  130. for i = 2:length(linestoplot)
  131. yValue = circshift(linestoplot{i}, 6, 2);
  133. % the Gaussian fitting
  134. [expect, params, R2, adjR2, slope] = GaussianOriFitting_centered(-90:15:75, mean(yValue,1), calcSE(yValue,1));
  135. if j ~= 4
  136. plot(-105:1:90, (params(1)*2.^(-(([-105:1:90]-params(2))/params(3)).^2)+params(4)), 'Color', colorlist(i,:), 'LineWidth', 1);
  137. p(i) = errorbar(-90:15:75,mean(yValue,1),calcSE(yValue,1), marker{i}, 'MarkerSize', 7, "MarkerEdgeColor",colorlist(i,:),'LineWidth',1,'Color',colorlist(i,:));
  138. else
  139. if i == 2 % mono
  140. plot(-105:1:90, (params(1)*2.^(-(([-105:1:90]-params(2))/params(3)).^2)+params(4)), 'Color', colorlist(i,:), 'LineWidth', 1.5, 'LineStyle','-');
  141. p(i) = errorbar(-90:15:75,mean(yValue,1),calcSE(yValue,1), marker{i}, 'MarkerSize', 7, "MarkerEdgeColor",colorlist(i,:),'LineWidth',1.5,'Color',colorlist(i,:));
  143. else % cfs
  144. plot(-105:1:90, (params(1)*2.^(-(([-105:1:90]-params(2))/params(3)).^2)+params(4)), 'Color', colorlist(i,:), 'LineWidth',1.5, 'LineStyle','-');
  145. % set(gcs, 'DashSpacing',2);
  146. p(i) = errorbar(-90:15:75,mean(yValue,1),calcSE(yValue,1), marker{i}, 'MarkerSize', 7, "MarkerEdgeColor",colorlist(i,:),'LineWidth',1.5,'Color',colorlist(i,:));
  147. end
  148. end
  149. text(-95, 0.57-(i)*0.04, sprintf('%.2f %.2f %.2f %5.4f\n', params(1), params(3), R2, slope),'color', colorlist(i,:))
  150. params_all((j-1)*3+i,:) = [params, R2, adjR2, slope];
  152. % the vonMises fitting
  153. % [expect, params, R2, adjR2] = vonMisesOriFitting(-90:15:75, mean(yValue,1), calcSE(yValue,1));
  154. % plot(-105:1:90, vmpdf(deg2rad(-105:1:90)', params(1), params(2), params(3), params(4)), 'Color', colorlist(i,:), 'LineWidth', 1);
  155. % text(-95, 0.6-i*0.03, sprintf('amp = %.2f, concen = %.2f, adjR^2 = %.2f\n', params(3), params(2), adjR2),'color', colorlist(i,:))
  156. end
  157. text(40, 0.65, [' N = ', num2str(neuro_num)], 'color', 'k')
  158. xlim([-105 90])
  159. xticks([-90, -45, 0, 45, 90])
  160. xticklabels({"-90","-45","0","45","90"})
  161. % if iPath == 8
  162. % xlabel("Relative orientation preference")
  163. % end
  164. ylim([0.05 0.7])
  165. yticks([0.1 0.3 0.5 0.7])
  166. if iPath == 3
  167. title(sprintf('%s', neuron_legends{j}))
  168. end
  169. hold off
  170. ax = gca;
  171. set(ax, 'Box', 'off', 'LineWidth', 1, 'FontSize', 12);
  172. end
  173. T = array2table(params_all);
  174. T.Properties.VariableNames = {'amp', 'peak', 'sigma', 'baseline', 'r2', 'adr2', 'slope'};
  176. end

Fig2CD.m at commit 89e2713, under MIT · at the source

Overview

Authors: Cai-Xia Chen1, Xin Wang1, Dan-Qing Jiang1, Shi-Ming Tang2,3, Cong Yu4,5
ORCID iDs: Cai-Xia Chen, Xin Wang, Shi-Ming Tang, Cong Yu
  1. School of Psychological and Cognitive Sciences, Peking University Beijing China
  2. School of Life Sciences, Peking University Beijing China
  3. IDG-McGovern Institute for Brain Research, Peking University Beijing China
  4. Department of Psychology and Behavioral Sciences, Zhejiang University Hangzhou China
  5. Zhejiang Key Laboratory of Neurocognitive Development and Mental Health, Zhejiang University Hangzhou China
Institutions: Peking University (China); Zhejiang University (China)
Journal: eLife, volume 14, article RP107518
Dates: published online 6 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.107518 · PMID 42089882 · PMCID PMC13148820 · OpenAlex W4417162375
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: optical imaging (calcium, voltage, 2-photon) (modality), non-human primate (organism)
Methods: Spectral & time-frequency, Machine learning, Statistics, fMRI & imaging, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: continuous flash suppression, orientation, subconsciousness, two-photon calcium imaging, macaque V1, Rhesus macaque
MeSH: Neurons*, Orientation*, Visual Cortex*, Visual Perception*, Animals, Macaca mulatta, Male, Photic Stimulation (* major topic)
Journal subjects: Neuroscience
Topic: Visual perception and processing mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (2022ZD0204600)
Citations: cited by 2 papers (Europe PMC); 49 references in the paper
Research resources: MATLAB RRID:SCR_001622, The data can be found at Zenodo RRID:SCR_004129

Abstract

Continuous flash suppression (CFS), in which a dynamic masker presented to one eye suppresses awareness of a stimulus in the other eye, is widely used to study visual subconsciousness. Although some studies report preserved high-level processing under CFS, these effects have been increasingly questioned and may partly reflect residual low-level feature processing. A key unresolved issue is how strongly neuronal responses in V1, where inputs from the two eyes first converge, are affected by CFS, and how much the remaining signals can support downstream processing. Here, we used two-photon calcium imaging to record large populations of V1 neurons in awake, fixating macaques while presenting grating stimuli under CFS. CFS strongly suppressed V1 orientation responses in an ocular-dominance-dependent manner, nearly abolishing responses in neurons preferring the masker eye or both eyes, and significantly reducing responses in neurons preferring the grating eye. Modeling analyses further indicated that V1 population activity under CFS may still support coarse orientation classification but not accurate stimulus reconstruction. These results suggest that CFS substantially degrades orientation information in V1. The residual signals may support limited low-level processing but are likely insufficient for downstream higher-level visual and cognitive tasks.

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 4 matches between paragraphs and lines of code.

caviaryusi/CFS_2p

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 89e271380f693777d03d4701a279ac7a04b22288, 29 April 2026
Languages: MATLAB (14)
Size: 16 files, 14 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (3 files), Curve Fitting Toolbox (1 file), Optimization Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
16 files
At the source: github.com/caviaryusi/CFS_2p

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;
  • 14 scripts, each with its path and the digest of its content;
  • 4 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 availability

The code can be found at GitHub: https://github.com/caviaryusi/CFS_2p (copy archived at Chen, 2026). The data can be found at Zenodo (Zenodo, RRID:SCR_004129 (https://identifiers.org/RRID:SCR_004129)): https://doi.org/10.5281/zenodo.20053907.

The following dataset was generated:

ChenC WangX JiangD-Q Shi-MingT YuC 2026Data for "Continuous flashing suppression of neural responses and population orientation coding in macaque V1"Zenodo10.5281/zenodo.20053907PMC1314882042089882

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 6 keywords, 8 MeSH terms, 1 funder, 47 references, 2 RRIDs.

Cite

This paper

Chen, C.-X., Wang, X., Jiang, D.-Q., Tang, S.-M., & Yu, C. (2026). Continuous flash suppression of neural responses and population orientation coding in macaque V1. eLife, 14, RP107518. https://doi.org/10.7554/elife.107518

BibTeX

@article{chen2026continuous,
author = {Chen, Cai-Xia and Wang, Xin and Jiang, Dan-Qing and Tang, Shi-Ming and Yu, Cong},
title = {{Continuous flash suppression of neural responses and population orientation coding in macaque V1}},
journal = {eLife},
year = {2026},
month = may,
volume = {14},
pages = {RP107518},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.107518},
url = {https://doi.org/10.7554/elife.107518},
pmid = {42089882},
pmcid = {PMC13148820}
}

RIS

TY - JOUR
AU - Chen, Cai-Xia
AU - Wang, Xin
AU - Jiang, Dan-Qing
AU - Tang, Shi-Ming
AU - Yu, Cong
TI - Continuous flash suppression of neural responses and population orientation coding in macaque V1
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/05/06
VL - 14
SP - RP107518
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.107518
UR - https://doi.org/10.7554/elife.107518
LA - en
ER -

CSL-JSON

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"id": "10.7554/elife.107518",
"type": "article-journal",
"title": "Continuous flash suppression of neural responses and population orientation coding in macaque V1",
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"author": [
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"family": "Chen",
"given": "Cai-Xia"
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{
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"given": "Dan-Qing"
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{
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},
{
"family": "Yu",
"given": "Cong"
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],
"container-title-short": "eLife",
"volume": "14",
"page": "RP107518",
"DOI": "10.7554/elife.107518",
"PMID": "42089882",
"PMCID": "PMC13148820",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
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"issued": {
"date-parts": [
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2026,
5,
6
]
]
}
}

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

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