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Sleep strengthens successor representations of learned sequences in humans.

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 › Learning incorporates successor representations ↔ figS2.m, lines 68–83 · score 0.59 · interference sequence accuracy, post sleep memory, memory accuracy, successor representational strength, correlated
  2. [2] § Results › SO–spindle coupling predicts successor strength and representational shift ↔ fig4.m, lines 231–310 · score 0.52 · successor representational strength, Rayleigh, angles, histogram, vector, phase
  3. [3] § Results › SO–spindle coupling predicts successor strength and representational shift ↔ fig4.m, lines 231–310 · score 0.52 · spindle coupling, successor representational strength, representational shift, phase, PPC, rho
  4. [4] § Methods › Sleep architecture and its relation to successor representations ↔ fig4.m, lines 172–228 · score 0.51 · Sleep staging, NREM1, NREM2, epochs, SWS

Paper

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The authors' code

MATLAB · 351 lines · 12 KB · no license · 3 matches

  1. %% Figure 4: Correlation with Sleep Analysis
  2. % Author: Xianhui He, Staresina Lab, University of Oxford
  3. % Date: 18/03/2026
  4. %
  5. % This script analyzes and visualizes the correlation between successor representational changes and sleep characteristics, particularly focusing on slow-wave sleep and SO-spindle phase coupling.
  7. clear; close all; clc;
  9. %% 1. define folder and path
  10. [parentfolder, processed_data_folder, pathfolder] = defineParentFolderAndPath; % localise the data folder and the toolbox folder
  11. addpath([pathfolder filesep 'othercolor']);
  12. addpath('help_function')
  13. addpath(fullfile(pathfolder,'BrewerMap-master'));
  14. addpath(fullfile(pathfolder,'fieldtrip-20240916')); % add the fieldtrip toolbox to the path
  15. ft_defaults; % initialise the fieldtrip toolbox
  17. subs = 1:30;
  19. % load the behav_results
  20. load(fullfile(processed_data_folder,'behav_results','behav_results.mat'));
  21. sub_outlier = [behav_results.outliers_temporal behav_results.outliers_spatial]; % outlier detected in behav_check
  23. sub_used = setdiff(subs, sub_outlier);
  24. nsub = length(sub_used);
  26. fig_folder = fullfile(processed_data_folder,'figures','main');
  27. if ~exist(fig_folder,'dir')
  28. mkdir(fig_folder);
  29. end
  32. % load sleep stage information
  33. sleep_info_folder = fullfile(processed_data_folder,'behav_results');
  34. load(fullfile(sleep_info_folder,'sleep_stage_info.mat'));
  36. % compute sleep stage proportions (N1, N2, N3, NREM, REM)
  37. soi = {'N1','N2','N3','NREM','R'};
  38. sleep_result = zeros(nsub,5);
  39. for isoi = 1:5
  40. if isoi ~= 4
  41. sleep_result(:,isoi) = [sleep_stage_info(sub_used).([soi{isoi} '_proportion'])];
  42. else
  43. % NREM = N2 + N3
  44. sleep_result(:,isoi) = sum(sleep_result(:,2:3),2);
  45. end
  46. end
  48. % N3 (slow-wave sleep) proportion in percentage
  49. N3_proportion = 100*sleep_result(:,3);
  51. % ========================================================================
  52. %% 2. load the temporal decoding results
  54. load("data/time_sec.mat")
  55. ntime = length(time_sec);
  56. temporal_neural_result = zeros(nsub, ntime, ntime);
  57. for isub = 1:nsub
  58. subID = sub_used(isub);
  59. sublabel = sprintf('s%03d',subID);
  61. %% load the data
  62. decoding_folder = fullfile(processed_data_folder,'decoding',sublabel);
  63. decoding_file = fullfile(decoding_folder,[sublabel '_next_cate_4cate_decoder.mat']);
  64. load(decoding_file);
  66. temporal_neural_result(isub,:,:) = squeeze(perf(1,4,:,:)); % perf: task x 5 (-2,-1,0,1,2) x trainingtime x testtime
  67. end
  69. % ========================================================================
  70. %% 3. load the PPC data
  71. PPC_folder = fullfile(processed_data_folder,'behav_results');
  72. PPC_load = load(fullfile(PPC_folder,'PPC_data_2_3_envMaxTime.mat'));
  73. SO_spin_complex_PPC_all = PPC_load.SO_spin_complex_PPC_all(sub_used,:);
  74. coupled_spindle_phase_all_cz = PPC_load.coupled_spindle_phase_all_cz;
  75. coupled_spindle_phase_all = [coupled_spindle_phase_all_cz{:}];
  77. % ========================================================================
  78. %% 4. correlate the successor decoding accuracy with SO & spindle
  80. % load TOI
  81. TOI_folder = fullfile(processed_data_folder,'TOI');
  82. TOI_file = fullfile(TOI_folder, 'successor_decoding_accuracy.mat');
  83. TOI_results = load(TOI_file);
  84. clus_map = TOI_results.clus_map;
  85. time_sec_temporal = TOI_results.time_sec;
  86. test_time_used = TOI_results.test_time_used;
  87. toi_acc = clus_map{2}.stats.stim.pval < 0.05;
  89. data_stats = temporal_neural_result(:,test_time_used,test_time_used);
  91. % data_toi_mean = mean(mean(data_stats,2),3);
  92. data_toi_mean_acc = mean(data_stats(:,toi_acc),2);
  94. cz_id= 19;
  95. sleep_sub = find(~isnan(mean(SO_spin_complex_PPC_all(:,cz_id),2)));% & ~isnan(mean(SO_spin_complex_PPC_up,2))
  96. PPC_data = SO_spin_complex_PPC_all(sleep_sub,cz_id);
  98. %% 5. representational shift ~ SO & spindle
  100. % 5.1 load ANN successor RSA results
  101. RSA_folder = fullfile(processed_data_folder, 'ANN_RSA');
  103. ANN_RSA_file = fullfile(RSA_folder, 'ANN_RSA_successor.mat');
  104. ANN_RSA_successor = load(ANN_RSA_file); % 'rsa_results', 'time_sec', 'all_layers'
  106. all_layers = ANN_RSA_successor.all_layers;
  107. rsa_results = ANN_RSA_successor.rsa_results;
  108. time_sec = ANN_RSA_successor.time_sec;
  110. % 5.2 load TOI
  111. TOI_folder = fullfile(processed_data_folder,'TOI');
  112. TOI_file = fullfile(TOI_folder, 'successor_shift.mat');
  113. TOI_results = load(TOI_file);
  114. clus = TOI_results.clus_F{1};
  115. test_time_used = TOI_results.test_time_used;
  116. toi_shift = clus;
  118. shift_data = zeros(numel(sub_used), 1);
  119. shift_data_dynamic = zeros(numel(sub_used), numel(time_sec));
  120. for isub = 1:numel(sub_used)
  122. diff_data = zeros(7, 1);
  123. diff_data_dynamic = zeros(7, numel(time_sec));
  124. for ilayer = 1:7
  125. layer = all_layers{ilayer};
  127. data_layer = squeeze(rsa_results.(layer)(sub_used(isub), :, :));
  129. diff_data(ilayer,1) = mean(data_layer(2,test_time_used(toi_shift)) - data_layer(1,test_time_used(toi_shift)));
  130. diff_data_dynamic(ilayer,:) = data_layer(2,:) - data_layer(1,:);
  131. end
  133. shift_data(isub) = corr((1:7)', diff_data,"type","Spearman");
  134. shift_data_dynamic(isub,:) = corr((1:7)', diff_data_dynamic,"type","Spearman");
  135. end
  137. data_toi_mean_shift = shift_data;
  139. %% 6. get example sleep staging
  141. datapath = fullfile(processed_data_folder, 'behav_results');
  143. % example subject sleep stage (Xianhui scoring)
  144. sublabel = 's004';
  145. sleep_stage_file = fullfile(datapath, ['sub-' sublabel(2:4) '_memeyerena_scored_xh.mat']);
  146. load(sleep_stage_file);
  147. stages_xh = stageData.stages;
  149. stages_xh(stages_xh==0) = 100;
  150. stages_xh(stages_xh==1) = 102;
  151. stages_xh(stages_xh==2) = 103;
  152. stages_xh(stages_xh==3) = 104;
  153. stages_xh(stages_xh==5) = 101;
  155. %% 7. corr with sleep
  156. % Define figure parameters
  157. w_scale = 26;
  158. h_scale = 5;
  159. left_idx = [2 10 18];
  160. width_idx = [6 6 6];
  161. pos_used = {[left_idx(1)/w_scale, 1/h_scale, width_idx(1)/w_scale, 3/h_scale],...
  162. [left_idx(2)/w_scale, 1/h_scale, width_idx(2)/w_scale, 3/h_scale],...
  163. [left_idx(3)/w_scale, 1/h_scale, width_idx(3)/w_scale, 3/h_scale]};
  165. % Set visualization parameters
  166. fz = 12;
  168. % Prepare data for plotting
  169. data_neural = data_toi_mean_acc(sleep_sub)*100;
  170. data_ANN = data_toi_mean_shift(sleep_sub);
  172. %% 8. plot the example sleep stage & neural/ANN-sleep correlation (N3 proportion)
  173. data_x = N3_proportion(sleep_sub);
  175. x_label = 'slow-wave sleep proportion (%)';
  177. figure('Position', [100, 100, w_scale*50, h_scale*1.5*50]);
  179. % 8.1 example sleep staging
  180. subplot('Position', pos_used{1});
  182. soi = 100:104;
  183. soi_name = {'Wake','REM','NREM1','NREM2','SWS'};
  184. hold on
  186. stairs(stages_xh,'Color',[0.3 0.3 0.3],'LineWidth',1.5)
  188. N3_epochs = find(stages_xh == 104);
  189. if ~isempty(N3_epochs)
  190. % Find where the difference between consecutive indices is greater than 1
  191. d = diff(N3_epochs);
  192. split_points = [1; find(d > 1) + 1; numel(N3_epochs) + 1];
  194. % Plot each contiguous segment separately
  195. for i = 1:(numel(split_points)-1)
  196. idx_range = split_points(i):(split_points(i+1)-1);
  197. seg_idx = N3_epochs(idx_range);
  198. plot(seg_idx(1):seg_idx(end)+1, 104*ones(numel(seg_idx)+1,1), 'Color', [178, 54, 54] / 255, 'LineWidth', 1.5);
  199. hold on
  200. end
  201. end
  203. set(gca, 'YDir','reverse')
  205. ax=gca;
  206. ax.YTick = soi;
  207. ax.YTickLabel = soi_name;
  209. ylim([99.5,104.5]);
  210. xlim([0,numel(stages_xh)]);
  212. ylabel('sleep stage')
  213. xlabel('epochs (30s per epoch)')
  215. set(findall(gcf,'-property','FontSize'),'FontSize',12, 'fontname', 'calibri');
  217. % 8.2 N3 proportion vs successor strength
  218. successor_color = [178, 54, 54] / 255;
  219. plot_corr_panel(pos_used{2}, data_x, data_neural, successor_color, ...
  220. x_label, 'successor representational strength (%)', 0:20:60, 15:5:35, [20 32], fz);
  222. % 8.3 N3 proportion vs successor shift
  223. ANN_color = [0 93 138]/255;
  224. plot_corr_panel(pos_used{3}, data_x, data_ANN, ANN_color, ...
  225. x_label, 'successor representational shift (rho)', 0:20:60, -1:0.5:1, [-1 1], fz);
  227. print(fullfile(fig_folder, 'Figure4_corr_N3_proportion_TOI'), '-dsvg', '-r600');
  228. print(fullfile(fig_folder, 'Figure4_corr_N3_proportion_TOI'), '-dpng', '-r600');
  231. %% 9. Visualization: SO-spindle phase coupling
  232. data_x = PPC_data;
  234. figure('Position', [100, 100, w_scale*50, h_scale*1.5*50]);
  236. % 9.1 SO-spindle phase coupling example
  238. subplot('Position', pos_used{1});
  240. phase_vec = coupled_spindle_phase_all(:);
  241. polarhistogram(phase_vec, 18,"FaceColor",[1 1 1],'LineWidth',1.5); % 18 bins
  242. ax = gca;
  243. ax.ThetaAxisUnits = 'radians';
  244. ax.ThetaTick = 0:pi/6:2*pi;
  245. ax.ThetaTickLabel = {'0','','','\pi/2','','','±\pi','','','-\pi/2','','','2\pi'};
  246. ax.FontSize = fz+2;
  247. ax.FontName = 'calibri';
  249. ax.ThetaDir = 'counterclockwise';
  250. ax.RTick = [100 200];
  251. ax.RAxisLocation = 90;
  253. hold on;
  255. % per-subject mean vectors
  256. nSub_used = numel(sub_used);
  257. sub_mean_angle = nan(nSub_used,1);
  258. sub_mean_r = nan(nSub_used,1);
  259. sub_mean_vec = nan(nSub_used,1);
  260. sel_mask = false(nSub_used,1);
  261. sel_mask(sleep_sub) = true;
  262. for ii = 1:nSub_used
  263. phases_sub = coupled_spindle_phase_all_cz{sub_used(ii)};
  264. if ~isempty(phases_sub)
  265. v = mean(exp(1i * phases_sub(:)));
  266. sub_mean_vec(ii) = v;
  267. sub_mean_angle(ii) = angle(v);
  268. sub_mean_r(ii) = abs(v);
  269. end
  270. end
  272. % green line: mean of all subject vectors
  273. group_vec = mean(sub_mean_vec(sel_mask), 'omitnan');
  274. group_r = abs(group_vec);
  275. r_lim_max = max(ax.RLim);
  276. max_r_val = max([sub_mean_r(sel_mask); group_r], [], 'omitnan');
  277. r_scale = r_lim_max / max_r_val; % scale so max aligns with histogram
  278. if ~isnan(group_vec)
  279. group_angle = angle(group_vec);
  280. polarplot([group_angle group_angle], [0 group_r * r_scale], 'color', [0 0.6 0], 'LineWidth', 3);
  281. end
  283. % gray dots: each subject's mean vector
  284. if any(sel_mask)
  285. polarscatter(sub_mean_angle(sel_mask), sub_mean_r(sel_mask) * r_scale, 25, [0.5 0.5 0.5], 'filled', 'MarkerFaceAlpha', 0.8);
  286. end
  288. % stats: Rayleigh test on preferred phases (gray dots)
  289. phase_preferred = sub_mean_angle(sel_mask);
  290. phase_preferred = phase_preferred(~isnan(phase_preferred));
  291. [p_rayleigh_pref, z_rayleigh] = circ_rtest(phase_preferred);
  293. text(0.5, 1.2, {'spindle-coupled SO phase histogram'; ...
  294. sprintf('Rayleigh test: z = %.2f, p < 0.001', z_rayleigh)}, ...
  295. 'Units', 'normalized', 'FontSize', fz, 'HorizontalAlignment', 'center');
  297. % 9.2 SO-spindle coupling vs successor strength
  298. plot_corr_panel(pos_used{2}, data_x, data_neural, successor_color, ...
  299. 'SO-spindle PPC (a.u.)', 'successor representational strength (%)', 0:0.1:0.5, 15:5:35, [20 32], fz);
  300. xlim([-0.056,0.51])
  302. % 9.3 SO-spindle coupling vs successor shift
  303. plot_corr_panel(pos_used{3}, data_x, data_ANN, ANN_color, ...
  304. 'SO-spindle PPC (a.u.)', 'successor representational shift (rho)', 0:0.1:0.5, -1:0.5:1, [-1 1], fz);
  305. xlim([-0.056,0.51])
  307. % ========================================================================
  309. print(fullfile(fig_folder, 'Figure4_corr_SO_spindle_phase_coupling'), '-dsvg', '-r600');
  310. print(fullfile(fig_folder, 'Figure4_corr_SO_spindle_phase_coupling'), '-dpng', '-r600');
  313. %% local function
  314. function plot_corr_panel(pos, x, y, color, x_label, y_label, x_ticks, y_ticks, y_lim, fz)
  316. subplot('Position', pos);
  318. scatter(x,y,40,color,"filled",'MarkerEdgeColor','none','MarkerFaceAlpha',0.5);
  319. hold on
  321. brob = fitlm(x,y);
  323. % correlation
  324. [r_tmp, p_tmp] = corr(x,y,"type","Spearman");
  326. h = plot(brob,'marker','none');
  327. % h(1) = data points, h(2) = fitted line, h(3) = lower CI, h(4) = upper CI
  328. % Only modify the fitted line (h(2)), keep CI lines as default
  329. if p_tmp < 0.05
  330. set(h(2), 'Color', color * 0.8, 'LineWidth', 1.5, 'LineStyle', '-');
  331. set(h(3), 'Color', color * 0.8, 'LineWidth', 1.5, 'LineStyle', ':');
  333. else
  334. set(h(3), 'Color', color * 0.8, 'LineWidth', 1.5, 'LineStyle', ':');
  335. set(h(2), 'Color', color * 0.8, 'LineWidth', 1.5, 'LineStyle', '--');
  336. end
  338. ylabel(y_label);
  339. xlabel(x_label);
  341. legend off
  342. title([])
  344. set(gca,'xtick',x_ticks,'ytick',y_ticks,'fontsize',fz, 'fontname', 'calibri');
  345. ylim(y_lim)
  347. text(0.03, 1.08, {''; ...
  348. sprintf('Spearman''s rho = %.2f, p = %.3f', r_tmp, p_tmp)}, ...
  349. 'Units', 'normalized', 'FontSize', fz-2);
  351. end

fig4.m, no license · at the source

Overview

Authors: Xianhui He1, Philipp K. Büchel2, Simon Faghel-Soubeyrand1, Janina Klingspohr3, Marcel S. Kehl1, Bernhard P. Staresina1,4
ORCID iDs: Xianhui He, Marcel S. Kehl, Bernhard P. Staresina
  1. Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
  2. Department of Epileptology, University Hospital Bonn, Venusberg Campus, Bonn, Germany
  3. Department of Systems Neuroscience, Universitaetsklinikum Hamburg Eppendorf, Hamburg, Germany
  4. Oxford Centre for Human Brain Activity, Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
Institutions: University of Oxford (United Kingdom); University Hospital Bonn (Germany); University Medical Center Hamburg-Eppendorf (Germany); Wellcome Centre for Integrative Neuroimaging (United Kingdom)
Journal: PLoS biology, volume 24, issue 4, article e3003740
Dates: received 13 January 2026; accepted 20 March 2026; published online 7 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pbio.3003740 · PMID 41945608 · PMCID PMC13095118 · OpenAlex W7151268381
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials, Physiology & signal measures
MeSH: Learning*, Sleep*, Adult, Brain, Electroencephalography, Female, Humans, Male, Memory, Visual Perception, Young Adult (* major topic)
Journal subjects: Biology and Life Sciences, Neuroscience, Cognitive Science, Cognitive Psychology, Learning, Psychology, Social Sciences, Learning and Memory, Physiology, Physiological Processes, Sleep, Research and Analysis Methods, Bioassays and Physiological Analysis, Electrophysiological Techniques, Brain Electrophysiology, Electroencephalography, Electrophysiology, Neurophysiology, Brain Mapping, Medicine and Health Sciences, Clinical Medicine, Clinical Neurophysiology, Imaging Techniques, Neuroimaging, Cognition, Memory, Perception, Sensory Perception, Physical Sciences, Mathematics, Discrete Mathematics, Combinatorics, Permutation, Vision, Engineering and Technology, Signal Processing, Signal Filtering
Topic: Sleep and Wakefulness Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: HORIZON EUROPE European Research Council (101001121); Royal Society (The Royal Society) (NIF\R1\221006)
Citations: not cited yet (Europe PMC); 65 references in the paper

Abstract

Experiences reshape our internal representations of the world. However, the neural and cognitive dynamics of this process are largely unknown. Here, we investigated how sequence learning reorganizes neural representations and how sleep-related consolidation mechanisms contribute to this transformation. Using high-density electroencephalography and multivariate decoding, we found that learning temporal sequences of visual information led to the incorporation of successor representations during a subsequent perceptual task, despite temporal information being task-irrelevant. Importantly, individuals with better sequence memory performance exhibited stronger successor incorporation during the perceptual task. Representational similarity analyses comparing neural patterns with different layers of a deep neural network revealed a learning-induced shift in representational format, from low-level visual features to higher-level abstract properties. Critically, both the strength and transformation of successor representations correlated with the neurophysiological hallmarks of slow-wave sleep during a post-learning nap, particularly the coupling between slow oscillations and spindles. These findings support the idea that sequence learning induces lasting changes in visual representational geometry and that sleep physiology strengthens these changes, providing mechanistic insights into how the brain updates internal models after exposure to environmental regularities.

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

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OSF sn9kv

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: MATLAB (11)
Size: 14 files, 11 scripts
Software Heritage: not checked
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (8 files), FieldTrip (7 files), CircStat (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
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12 files
At the source: doi.org/10.17605/OSF.IO/SN9KV

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

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Data Availability

The individual numerical data for figures within this paper is available in the Supporting information files (S1 Data). Pre-processed data and the code supporting the conclusions of this study are available in the Open Science Framework at DOI: https://doi.org/10.17605/OSF.IO/SN9KV.

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Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 11 MeSH terms, 2 funders, 63 references.

Cite

This paper

He, X., Büchel, P. K., Faghel-Soubeyrand, S., Klingspohr, J., Kehl, M. S., & Staresina, B. P. (2026). Sleep strengthens successor representations of learned sequences in humans. PLoS biology, 24(4), e3003740. https://doi.org/10.1371/journal.pbio.3003740

BibTeX

@article{he2026sleep,
author = {He, Xianhui and Büchel, Philipp K. and Faghel-Soubeyrand, Simon and Klingspohr, Janina and Kehl, Marcel S. and Staresina, Bernhard P.},
title = {{Sleep strengthens successor representations of learned sequences in humans}},
journal = {PLoS biology},
year = {2026},
month = apr,
volume = {24},
number = {4},
pages = {e3003740},
publisher = {PLOS},
issn = {1544-9173},
doi = {10.1371/journal.pbio.3003740},
url = {https://doi.org/10.1371/journal.pbio.3003740},
pmid = {41945608},
pmcid = {PMC13095118}
}

RIS

TY - JOUR
AU - He, Xianhui
AU - Büchel, Philipp K.
AU - Faghel-Soubeyrand, Simon
AU - Klingspohr, Janina
AU - Kehl, Marcel S.
AU - Staresina, Bernhard P.
TI - Sleep strengthens successor representations of learned sequences in humans
T2 - PLoS biology
J2 - PLoS Biol
PY - 2026
DA - 2026/04/07
VL - 24
IS - 4
SP - e3003740
SN - 1544-9173
PB - PLOS
DO - 10.1371/journal.pbio.3003740
UR - https://doi.org/10.1371/journal.pbio.3003740
LA - en
ER -

CSL-JSON

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"family": "He",
"given": "Xianhui"
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{
"family": "Büchel",
"given": "Philipp K."
},
{
"family": "Faghel-Soubeyrand",
"given": "Simon"
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{
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"volume": "24",
"issue": "4",
"page": "e3003740",
"DOI": "10.1371/journal.pbio.3003740",
"PMID": "41945608",
"PMCID": "PMC13095118",
"ISSN": "1544-9173",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pbio.3003740",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
7
]
]
}
}

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