OSCR

Thalamocortical bursts encode reward contingencies and drive associative learning.

Code ↔ Paper

11 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 11 matches
  1. [1] § Methods › Electrophysiological data ↔ ecephys_spike_sorting/scripts/create_input_json.py, lines 131–205 · score 0.90 · Ecephys spike sorting, quality metrics, noise templates, ISI threshold, distance, KiloSort
  2. [2] § Methods › Electrophysiological data ↔ ecephys_spike_sorting/modules/quality_metrics/metrics.py, lines 20–157 · score 0.89 · isolation distance, ISI violation, quality metrics, ISI threshold, Ecephys spike sorting, templates
  3. [3] § Results › BCNs track stimulus-outcome associations even after multiple rule reversals by inverting burst-encoding of the physical stimuli ↔ DataAndScripts/Licking_Behavior.m, lines 68–176 · score 0.70 · neutral trials, Lick rate, reversed rule, neutral aperture, Welch, go
  4. [4] § Methods › Neural decoding analysis ↔ Scripts/HelperFunctions/MatlabScripts/runNeuralDecodingToolbox.m, lines 2–79 · score 0.69 · Neural Decoding Toolbox, LIBSVM, bootstrapped, Classifier, validation, touch
  5. [5] § Methods › Neural response metrics › Tonic index ↔ Scripts/ApertureResponseTypes_StageProgression.m, lines 1–94 · score 0.62 · 0–200 ms, response window, 600 ms, baseline, tonic, 400 ms
  6. [6] § Results › Burst-coding neurons (BCNs) encode the aperture width through the presence or absence of bursts ↔ Scripts/ApertureResponseTypes_Comparison.m, lines 1–100 · score 0.59 · RS units, FS units, cohort, ZIv, BC, aperture
  7. [7] § Results › Burst coding scales with stimulus valence and predicts licking behavior ↔ DataAndScripts/Licking_Behavior.m, lines 68–176 · score 0.57 · lick behavior, lick rates, neutral aperture
  8. [8] § Results › Burst-coding neurons (BCNs) encode the aperture width through the presence or absence of bursts ↔ Scripts/ApertureResponseTypes_StageProgression.m, lines 1–94 · score 0.56 · RS units, FS units, cohort, ZIv, BC, POm
  9. [9] § Methods › Animals ↔ Scripts/HelperFunctions/Downloaded Scripts/brewermap.m, lines 352–474 · score 0.55 · 95 %, 65 %, 85 %, 45 %, 24, 20 deg
  10. [10] § Results › Thalamic burst inhibition impairs learning and task execution ↔ Scripts/HelperFunctions/MatlabScripts/runNeuralDecodingToolbox.m, lines 2–79 · score 0.54 · whisker contacts, tonic spiking, validated, rewarded, POm, VPM
  11. [11] § Methods › Neuropixels data analysis ↔ modules/spikeglx/spikeglxsettingsdialog.cpp, lines 30–124 · score 0.51 · SpikeGLX, OneBox, Imec

Paper

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The authors' code

MATLAB · 176 lines · 7.4 KB · no license · 2 matches

  1. %% Lick rates for initial rule, neutral stage and reversed rule
  2. % only works if animalData.m is loaded
  3. currentFolder = pwd;
  4. load(fullfile(currentFolder,'/RawData/animalData'))
  6. %% choose cohorts
  7. cohorts = arrayfun(@(x) num2str(x), 1:numel(animalData.cohort), 'UniformOutput', false);
  8. answer = listdlg('ListString',cohorts,'PromptString','Choose your cohort.');
  9. cohorts = cellfun(@str2double, cohorts(answer));
  10. cohortData = horzcat(animalData.cohort(cohorts).animal);
  12. %% choose stages
  13. stages = getstagenames(cohortData);
  14. answer = listdlg('ListString',stages,'PromptString','Choose stages.');
  15. stages = stages(answer);
  17. %% get lick rates
  18. numMice = length(cohortData);
  19. max_gosuc = max(arrayfun(@(m) length(cohortData(m).gogo_suc), 1:sum(numMice)));
  20. max_nogosuc = max(arrayfun(@(m) length(cohortData(m).nogo_suc), 1:sum(numMice)));
  21. allgosuc_initial = NaN(max_gosuc, numMice);
  22. allnogosuc_initial = NaN(max_nogosuc, numMice);
  23. allgosuc_switched = NaN(max_gosuc, numMice);
  24. allnogosuc_switched = NaN(max_nogosuc, numMice);
  26. all_ses_ini = []; all_ses_swi = [];
  27. for mouseIDX = 1:length(cohortData)
  28. for stageIDX = 1:length(stages)
  29. isStage = contains(cohortData(mouseIDX).session_names, stages(stageIDX));
  30. sesFlag_first = find(isStage, 1, 'first');
  31. sesFlag_last = find(isStage, 1, 'last');
  32. num_ses = sesFlag_last-sesFlag_first;
  34. if isempty(num_ses)
  35. continue
  36. else
  37. norm_ses = (1:num_ses)/num_ses;
  39. gosuc = cohortData(mouseIDX).gogo_suc;
  40. gosuc(sesFlag_last+1:end) = [];
  41. gosuc(1:sesFlag_first-1) = [];
  43. nogosuc = cohortData(mouseIDX).nogo_suc;
  44. nogosuc(sesFlag_last+1:end) = [];
  45. nogosuc(1:sesFlag_first-1) = [];
  47. if strcmp(stages{stageIDX}, 'P3.2')
  48. all_ses_ini = cat(1, all_ses_ini(:), {norm_ses});
  49. allgosuc_initial(1:length(gosuc),mouseIDX) = gosuc;
  50. allnogosuc_initial(1:length(nogosuc),mouseIDX) = nogosuc;
  51. elseif strcmp(stages{stageIDX}, 'P3.3')
  52. allgosuc_neu(1:length(gosuc),mouseIDX) = gosuc;
  53. allnogosuc_neu(1:length(nogosuc),mouseIDX) = nogosuc;
  55. neulick = cohortData(mouseIDX).medium_lick;
  56. neulick(sesFlag_last+1:end) = [];
  57. neulick(1:sesFlag_first-1) = [];
  58. allneutral(1:length(neulick),mouseIDX) = neulick;
  59. elseif strcmp(stages{stageIDX}, 'P3.4')
  60. all_ses_swi = cat(1, all_ses_swi(:), {norm_ses});
  61. allgosuc_switched(1:length(gosuc),mouseIDX) = gosuc;
  62. allnogosuc_switched(1:length(nogosuc),mouseIDX) = nogosuc;
  63. end
  64. end
  65. end
  66. end
  68. %% plot data
  69. % ---------- initial rule -------------------------------------------------
  70. [fig_1, int_nogo] = plot_patch(1-allnogosuc_initial,all_ses_ini,[0.6350 0.0780 0.1840],30);
  71. [~, int_go] = plot_patch(allgosuc_initial,all_ses_ini,[0.4660 0.6740 0.1880],30,fig_1);
  73. % welch test for statistical comparison
  74. [hi,pi] = ttest2(int_nogo, int_go); hi(isnan(hi)) = 0; hi = logical(hi);
  75. x_vals = (1:30)/30;
  76. y_vals = ones(1,length(hi))*0.05;
  78. if all(hi)
  79. % If h is all 1, plot the entire line
  80. plot(x_vals, y_vals, ':k', 'LineWidth', 1.5);
  81. else
  82. % Find where h changes (from 0 to 1 or 1 to 0)
  83. change_indices = find(diff([0 hi 0])); % Add 0s to start and end to detect transitions
  84. % Loop through each segment of consecutive h == 1 values and plot the line
  85. for i = 1:2:length(change_indices)-1
  86. start_idx = change_indices(i);
  87. end_idx = change_indices(i+1) - 1;
  88. plot(x_vals(start_idx:end_idx), y_vals(start_idx:end_idx), ':k', 'LineWidth', 1.5);
  89. end
  90. end
  92. title('Population lick rates (initial rule)')
  93. xlabel('Session proportion'); ylabel('Lick rate')
  94. legend({'No-go trials' '' 'Go trials'}, 'Box', 'off', 'Location', 'best')
  95. set(gca,'Box','off','Color','none')
  97. % ---------- reversed rule ------------------------------------------------
  98. [fig_2, int_nogo] = plot_patch(1-allnogosuc_switched,all_ses_swi,[0.6350 0.0780 0.1840],40);
  99. [~, int_go] = plot_patch(allgosuc_switched,all_ses_swi,[0.4660 0.6740 0.1880],40,fig_2);
  101. % welch test for statistical comparison
  102. [hr,pr] = ttest2(int_nogo, int_go); hr(isnan(hr)) = 0; hr = logical(hr);
  103. x_vals = (1:40)/40;
  104. y_vals = ones(1,length(hr))*0.05;
  106. if all(hr)
  107. % If h is all 1, plot the entire line
  108. plot(x_vals, y_vals, ':k', 'LineWidth', 1.5);
  109. else
  110. % Find where h changes (from 0 to 1 or 1 to 0)
  111. change_indices = find(diff([0 hr 0])); % Add 0s to start and end to detect transitions
  112. % Loop through each segment of consecutive h == 1 values and plot the line
  113. for i = 1:2:length(change_indices)-1
  114. start_idx = change_indices(i);
  115. end_idx = change_indices(i+1) - 1;
  116. plot(x_vals(start_idx:end_idx), y_vals(start_idx:end_idx), ':k', 'LineWidth', 1.5);
  117. end
  118. end
  120. title('Population lick rates (reversed rule)')
  121. xlabel('Session proportion'); ylabel('Lick rate')
  122. legend({'No-go trials' '' 'Go trials'}, 'Box', 'off', 'Location', 'best')
  123. set(gca,'Box','off','Color','none')
  125. % ---------- neutral state ------------------------------------------------
  126. % as all animals had 8 sessions in stage 3 we don't necessarly need the proportion function
  127. numSes = 8; xvalues = 1:numSes;
  128. allrates_neu = [1-allnogosuc_neu; allgosuc_neu; allneutral];
  129. color_map = [[0.6350 0.0780 0.1840]; [0.4660 0.6740 0.1880]; [0.9290 0.6940 0.1250]];
  131. figure; hold on
  132. for trialIDX = 1:length(stages)
  133. sig_plot = std(allrates_neu((trialIDX*numSes)-(numSes-1):numSes*trialIDX,:),1,2,'omitnan');
  134. mu_plot = mean(allrates_neu((trialIDX*numSes)-(numSes-1):numSes*trialIDX,:),2,"omitnan");
  135. curve1 = mu_plot + sig_plot;
  136. curve2 = mu_plot - sig_plot;
  138. plot(xvalues, mu_plot, 'Color', color_map(trialIDX,:))
  139. fill([1:length(curve1) fliplr(1:length(curve1))], [curve1' fliplr(curve2')],[0 0 .85],...
  140. 'FaceColor',color_map(trialIDX,:), 'EdgeColor','none','FaceAlpha',0.1)
  141. end
  143. % welch test to neutral state
  144. [hg,pg] = ttest2(allgosuc_neu', allneutral'); h_struct.go = logical(hg);
  145. [hn,pn] = ttest2((1-allnogosuc_neu)', allneutral'); h_struct.nogo = logical(hn);
  146. x_vals = 1:8; y_vals = (ones(1,8))*0.05;
  148. struct_fields = fields(h_struct);
  149. for field_idx = 1:length(struct_fields)
  150. if all(h_struct.(struct_fields{field_idx}))
  151. % If h is all 1, stats on side
  152. if strcmp(struct_fields{field_idx}, 'nogo')
  153. line([8.25 8.25],[0.164608 0.72168],'Color','k')
  154. text(8.3,(0.164608+0.72168)/2 ,'*','HorizontalAlignment','left','VerticalAlignment','middle')
  155. elseif strcmp(struct_fields{field_idx}, 'go')
  156. line([8.25 8.25],[0.746168 0.965841],'Color','k')
  157. text(8.3,(0.965841+0.746168)/2 ,'*','HorizontalAlignment','left','VerticalAlignment','middle')
  158. end
  159. else
  160. % Find where h changes (from 0 to 1 or 1 to 0)
  161. change_indices = find(diff([0 h_struct.(struct_fields{field_idx}) 0])); % Add 0s to start and end to detect transitions
  162. % Loop through each segment of consecutive h == 1 values and plot the line
  163. for i = 1:2:length(change_indices)-1
  164. start_idx = change_indices(i);
  165. end_idx = change_indices(i+1) - 1;
  166. plot(x_vals(start_idx:end_idx), y_vals(start_idx:end_idx), ':k', 'LineWidth', 1.5);
  167. end
  168. end
  169. end
  171. % labels
  172. ylim([0,1])
  173. title('Population lick rates (neutral aperture state)')
  174. xlabel('Session'); ylabel('Lick rate')
  175. legend({'No-go trials' '' 'Go trials' '' 'Neutral trials'}, 'Box', 'off', 'Location', 'best')
  176. set(gca,'Box','off','Color','none')

Licking_Behavior.m at commit 76cca52, no license · at the source

Overview

Authors: Filippo Heimburg1, Nadin Mari Saluti1, Lars-Lennart Oettl1, Josephine Timm1,2, Katharina Ziegler1, Maria Helena Bortolozzo-Gleich1, Thomas Kuner3, Alexander Groh1
ORCID iDs: Filippo Heimburg, Nadin Mari Saluti, Lars-Lennart Oettl, Josephine Timm, Katharina Ziegler, Maria Helena Bortolozzo-Gleich, Thomas Kuner, Alexander Groh
  1. Medical Biophysics, Institute for Physiology and Pathophysiology, Heidelberg University, Heidelberg, Germany
  2. Present Address: Institute for Experimental Epileptology and Cognition Research, University of Bonn, Bonn, Germany
  3. Institute for Anatomy and Cell Biology, Heidelberg University, Heidelberg, Germany
Institutions: Heidelberg University (Germany); University of Bonn (Germany)
Journal: Nature communications, volume 17, issue 1, article 8170
Dates: received 7 January 2026; accepted 3 August 2026; published online 11 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-76581-6 · PMID 42581298 · PMCID PMC13463074 · OpenAlex W7202123491
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), systems (subfield)
Methods: Statistics, Machine learning, Preprocessing, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: Learning and memory, Neural circuits, Cellular neuroscience, Ion channels in the nervous system
MeSH: Association Learning*, Cerebral Cortex*, Neurons*, Reward*, Thalamus*, Action Potentials, Animals, Male, Mice, Mice, Inbred C57BL (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (German Research Foundation) (GR3757/4-1, INST 35/1503-1 FUGG)
Citations: cited by 1 paper (Europe PMC); 58 references in the paper

Abstract

Learning requires adaptive changes in neuronal circuits, but how neurons encode learning content in their activity patterns to construct memories remains poorly understood. Using longitudinal multi-site recordings in freely moving male mice performing a sensory discrimination task, we discover the emergence of burst-coding neurons (BCNs) across cortical, thalamic, and extrathalamic regions. BCNs encoded task rules through the presence or absence of bursts, with their proportion increasing as learning progressed. Decoding analyses reveal that BCNs act as the principal carriers of rule information within the thalamocortical system. BCN burst rates scaled with stimulus valence, collapsed when contingencies were degraded, and inverted after repeated rule reversals, demonstrating that bursts dynamically track associative context during learning. Indeed, pharmacological and focal genetic suppression of thalamocortical bursting disrupted learning and task performance, establishing neuronal bursts as context-sensitive drivers of associative learning. These findings identify a burst-based neural code for stimulus–outcome associations in the thalamocortical system and provide causal evidence linking cellular firing dynamics to reward contingency learning.

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

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bothlab/syntalos

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Languages: C/C++ (394), C++ (364), Python (24), Shell (8), C (2)
Size: 1,245 files, 792 scripts
Software Heritage: archived
Found in: the text, “Behavioral setup”
Holds: README, license file, CITATION.cff, environment (modules/deeplabcut-live/requirements.txt, contrib/flatpak/modules/python/requirements.txt), tests, continuous integration, documentation
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grohlab/thalamocortical-bursts-encode-reward-contingencies-and-drive-associative-learning

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At the source: github.com/GrohLab/Thalamocortical-bursts-encode-reward-contingencies-and-drive-associative-learning/tree/main/Data/Reconstructions

doi:10.5061/dryad.hdr7sqvt1

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Zenodo 21249852

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alleninstitute/ecephys_spike_sorting

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Size: 216 files, 114 scripts
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At the source: github.com/alleninstitute/ecephys_spike_sorting

Zenodo 13369686

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Zenodo 21429439

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grohlab/a-tactile-discrimination-task-to-study-neuronal-dynamics-in-freely-moving-mice

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At the source: github.com/GrohLab/A-tactile-discrimination-task-to-study-neuronal-dynamics-in-freely-moving-mice/tree/v1.0

Code availability

The code used in this study is available in both a Dryad repository58(10.5061/dryad.hdr7sqvt1) and a GitHub repository (10.5281/zenodo.21249852).

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

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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

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Data Availability Statement

The datasets generated and analyzed in this study have been deposited in the Dryad Digital Repository under the following accession code: 10.5061/dryad.hdr7sqvt1. Source data are provided with this paper.

The code used in this study is available in both a Dryad repository58(10.5061/dryad.hdr7sqvt1) and a GitHub repository (10.5281/zenodo.21249852).

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

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 4 keywords, 10 MeSH terms, 1 funder, 55 references.

Cite

This paper

Heimburg, F., Mari Saluti, N., Oettl, L.-L., Timm, J., Ziegler, K., Bortolozzo-Gleich, M. H., Kuner, T., & Groh, A. (2026). Thalamocortical bursts encode reward contingencies and drive associative learning. Nature communications, 17(1), 8170. https://doi.org/10.1038/s41467-026-76581-6

BibTeX

@article{heimburg2026thalamocortical,
author = {Heimburg, Filippo and Mari Saluti, Nadin and Oettl, Lars-Lennart and Timm, Josephine and Ziegler, Katharina and Bortolozzo-Gleich, Maria Helena and Kuner, Thomas and Groh, Alexander},
title = {{Thalamocortical bursts encode reward contingencies and drive associative learning}},
journal = {Nature communications},
year = {2026},
month = aug,
volume = {17},
number = {1},
pages = {8170},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-76581-6},
url = {https://doi.org/10.1038/s41467-026-76581-6},
pmid = {42581298},
pmcid = {PMC13463074}
}

RIS

TY - JOUR
AU - Heimburg, Filippo
AU - Mari Saluti, Nadin
AU - Oettl, Lars-Lennart
AU - Timm, Josephine
AU - Ziegler, Katharina
AU - Bortolozzo-Gleich, Maria Helena
AU - Kuner, Thomas
AU - Groh, Alexander
TI - Thalamocortical bursts encode reward contingencies and drive associative learning
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/08/11
VL - 17
IS - 1
SP - 8170
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-76581-6
UR - https://doi.org/10.1038/s41467-026-76581-6
LA - en
ER -

CSL-JSON

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"family": "Heimburg",
"given": "Filippo"
},
{
"family": "Mari Saluti",
"given": "Nadin"
},
{
"family": "Oettl",
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},
{
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{
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"issue": "1",
"page": "8170",
"DOI": "10.1038/s41467-026-76581-6",
"PMID": "42581298",
"PMCID": "PMC13463074",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-76581-6",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
11
]
]
}
}

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