OSCR

Cerebellar neural populations orchestrate dopamine reward signaling with single-trial precision.

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] § Methods › In vivo extracellular recording › Data recording and spike sorting ↔ Supportfunction.zip/F_openNEX.m, lines 70–132 · score 0.63 · spike waveforms, Open Ephys, Blackrock, analog, field, Channel
  2. [2] § Methods › In vivo extracellular recording › Data recording and spike sorting ↔ Supportfunction.zip/load_open_ephys_data.m, lines 1–71 · score 0.59 · Open Ephys, spike waveforms, Channel, events
  3. [3] § Methods › In vivo extracellular recording › Machine learning-based search for temporal features of reward codes ↔ s9_ML.zip/VTA/VTAdecodingSVM.py, lines 68–117 · score 0.54 · F1 score, SVM, classification, matrix, accuracy, trained
  4. [4] § Methods › In vivo extracellular recording › Machine learning-based search for temporal features of reward codes ↔ s9_ML.zip/DCN/DCNdecodingSVM.py, lines 69–118 · score 0.54 · F1 score, SVM, classification, matrix, accuracy, trained

Paper

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The authors' code

MATLAB · 344 lines · 15 KB · CC-BY-4.0 · 1 match

  1. function [NEV] = F_openNEX(fileName)
  3. % NEV = readNexFile(fileName) -- read .nex file and return file data in NEV File structure
  4. % Sorted with Plexon Offline Sorter first !!!!!!!!
  5. % V2.3
  6. %
  7. %%%%%%%%%%%%%%%%%%%%%%%%%%%
  8. % INPUT:
  9. % fileName - if empty string, will use File Open dialog
  10. %
  11. % OUTPUT:
  12. % NEV - a structure containing .nex file data
  13. % NEV.version - file version
  14. % NEV.comment - file comment
  15. % NEV.MetaTags - array of source file information
  16. % SampleRes - sampling rate
  17. % DataDuration - total timestamps
  18. % DataDurationSec - total time in sec
  19. % Filename - source file name
  20. % FilePath - source file path
  21. % FileExt - source file extension
  22. %
  23. % NEV.freq - file timestamp frequency (Hz)
  24. % NEV.tbeg - beginning of recording session (in seconds)
  25. % NEV.tend - end of recording session (in seconds)
  26. % NEV.metadata - file metadata as a string in json format
  27. %
  28. % NEV.neurons - array of neuron structures
  29. % neurons{i}.name - name of a neuron variable
  30. % neurons{i}.timestamps - array of neuron timestamps (in seconds)
  31. % to access timestamps for neuron 2 use {n} notation:
  32. % NEV.neurons{2}.timestamps
  33. %
  34. % NEV.waves - array of wave structures
  35. % waves{i}.name - name of waveform variable
  36. % waves{i}.NPointsWave - number of data points in each wave
  37. % waves{i}.WFrequency - A/D frequency for wave data points
  38. % waves{i}.timestamps - array of wave timestamps (in seconds)
  39. % waves{i}.waveforms - matrix of waveforms (in milliVolts), each
  40. % waveform is a column
  41. %
  42. % NEV.Data - structure containing spike time points
  43. % Data.Spikes.TimeStamp - array of
  44. % Data.Spikes.Electrode
  45. % Data.Spikes.Unit
  46. %
  47. % NEV.popvectors - array of population vector structures
  48. % popvectors{i}.name - name of population vector variable
  49. % popvectors{i}.weights - array of population vector weights
  50. %
  51. % NEV.markers - array of marker structures
  52. % markers{i}.name - name of marker variable
  53. % markers{i}.timestamps - array of marker timestamps (in seconds)
  54. % markers{i}.values - array of marker value structures
  55. % markers{i}.values{j}.name - name of marker value
  56. % markers{i}.values{j}.strings - array of marker value strings
  57. % (if values are stored as strings in the file)
  58. % markers{i}.values{j}.numericValues - numeric marker values
  59. % (if values are stored as numbers in the file)
  60. %
  61. % NEV.events - array of event structures
  62. % events{i}.name - name of event variable
  63. % events{i}.timestamps - array of event timestamps (in seconds)
  64. %
  65. % NEV.intervals - array of interval structures
  66. % intervals{i}.name - name of interval variable
  67. % intervals{i}.intStarts - array of interval starts (in seconds)
  68. % intervals{i}.intEnds - array of interval ends (in seconds)
  69. %
  70. %%%%%%%%%%%%%%%%%%%%%%%%%%%
  71. % Version History
  72. % 1.0: January 22, 2020
  73. % - Added support for loading .nex files from Blackrock and OpenEphys
  74. % 2.0: Feb 9, 2020
  75. % - Added NEV.MetaTags
  76. % 2.1: Feb 11, 2020
  77. % - fix bug: non-existent "waves" field in NEV structure
  78. % - fix bug: non integer timestamp number in "NEV.Data.Spikes.TimeStamp"
  79. % - fix bug: analog labels conflict with electrode lables
  80. % 2.2: Mar 13, 2020
  81. % - add 'aux' input
  82. % 2.3: Mar 31, 2020
  83. % - fix bug: non-integer string in channel name
  85. % parameters
  86. show_sp=[1 100]; % extract representative spike waveforms
  88. % Parse '.nex' '.nex5' files
  89. NEV = [];
  90. if (nargin == 0 | isempty(fileName))
  91. [fname, pathname] = uigetfile('*.nex;*.nex5', 'NeuroExplorer files');
  92. if isequal(fname,0)
  93. error 'No file was selected'
  94. return
  95. end
  96. fileName = fullfile(pathname, fname);
  97. end
  99. [pathname,name,ext] = fileparts(fileName);
  100. if strcmp(ext, '.nex5') == 1
  101. NEV = readNex5File(fileName);
  102. return
  103. end
  104. NEV.MetaTags.Filename=name;
  105. NEV.MetaTags.FilePath=pathname;
  106. NEV.MetaTags.FileExt=ext;
  107. % note 'l' option when opening the file.
  108. % this options means that the file is 'little-endian'.
  109. % this should ensure that the files are read correctly
  110. % on big-endian systems, such as Mac G5.
  111. fid = fopen(fileName, 'r', 'l','US-ASCII');
  112. if(fid == -1)
  113. error 'Unable to open file'
  114. return
  115. end
  117. magic = fread(fid, 1, 'int32');
  118. if magic ~= 827868494
  119. error 'The file is not a valid .nex file'
  120. end
  121. NEV.version = fread(fid, 1, 'int32');
  122. comment = fread(fid, 256, '*char')';
  123. % remove first zero and all characters after the first zero
  124. comment(end+1) = 0;
  125. NEV.comment = comment(1:min(find(comment==0))-1);
  126. NEV.freq = fread(fid, 1, 'double');
  127. NEV.tbeg = fread(fid, 1, 'int32')./NEV.freq;
  128. NEV.tend = fread(fid, 1, 'int32')./NEV.freq;
  129. nvar = fread(fid, 1, 'int32');
  130. NEV.MetaTags.SampleRes=NEV.freq;
  131. NEV.MetaTags.DataDuration=(NEV.tend-NEV.tbeg)*NEV.freq;
  132. NEV.MetaTags.DataDurationSec=NEV.tend-NEV.tbeg;
  134. % skip location of next header fields and padding
  135. fseek(fid, 260, 'cof');
  137. neuronCount = 0;
  138. eventCount = 0;
  139. intervalCount = 0;
  140. waveCount = 0;
  141. popCount = 0;
  142. contCount = 0;
  143. markerCount = 0;
  145. % real all variables
  146. for variableIndex=1:nvar
  147. % read variable header
  148. type = fread(fid, 1, 'int32');
  149. varVersion = fread(fid, 1, 'int32');
  150. name = fread(fid, 64, '*char')';
  151. % remove first zero and all characters after the first zero
  152. name(end+1) = 0;
  153. name = name(1:min(find(name==0))-1);
  154. offset = fread(fid, 1, 'int32');
  155. n = fread(fid, 1, 'int32');
  156. wireNumber = fread(fid, 1, 'int32');
  157. unitNumber = fread(fid, 1, 'int32');
  158. gain = fread(fid, 1, 'int32');
  159. filter = fread(fid, 1, 'int32');
  160. xPos = fread(fid, 1, 'double');
  161. yPos = fread(fid, 1, 'double');
  162. WFrequency = fread(fid, 1, 'double'); % wf sampling fr.
  163. ADtoMV = fread(fid, 1, 'double'); % coeff to convert from AD values to Millivolts.
  164. NPointsWave = fread(fid, 1, 'int32'); % number of points in each wave
  165. NMarkers = fread(fid, 1, 'int32'); % how many values are associated with each marker
  166. MarkerLength = fread(fid, 1, 'int32'); % how many characters are in each marker value
  167. MVOfffset = fread(fid, 1, 'double'); % coeff to shift AD values in Millivolts: mv = raw*ADtoMV+MVOfffset
  168. PrethresholdTimeInSeconds = fread(fid, 1, 'double'); % if waveform timestamp in seconds is t,
  169. % then the timestamp of the first point of waveform is t - PrethresholdTimeInSeconds
  170. filePosition = ftell(fid);
  171. switch type
  172. case 0 % neuron
  173. neuronCount = neuronCount+1;
  174. NEV.neurons{neuronCount,1}.name = name;
  175. NEV.neurons{neuronCount,1}.varVersion = varVersion;
  176. if varVersion > 100
  177. NEV.neurons{neuronCount,1}.wireNumber = wireNumber;
  178. NEV.neurons{neuronCount,1}.unitNumber = unitNumber;
  179. else
  180. NEV.neurons{neuronCount,1}.wireNumber = 0;
  181. NEV.neurons{neuronCount,1}.unitNumber = 0;
  182. end
  183. NEV.neurons{neuronCount,1}.xPos = xPos;
  184. NEV.neurons{neuronCount,1}.yPos = yPos;
  185. % go to the variable data position and read timestamps
  186. fseek(fid, offset, 'bof');
  187. NEV.neurons{neuronCount,1}.timestamps = fread(fid, [n 1], 'int32')./NEV.freq;
  189. case 1 % event
  190. eventCount = eventCount+1;
  191. NEV.events{eventCount,1}.name = name;
  192. NEV.events{eventCount,1}.varVersion = varVersion;
  193. fseek(fid, offset, 'bof');
  194. NEV.events{eventCount,1}.timestamps = fread(fid, [n 1], 'int32')./NEV.freq;
  196. case 2 % interval
  197. intervalCount = intervalCount+1;
  198. NEV.intervals{intervalCount,1}.name = name;
  199. NEV.intervals{intervalCount,1}.varVersion = varVersion;
  200. fseek(fid, offset, 'bof');
  201. NEV.intervals{intervalCount,1}.intStarts = fread(fid, [n 1], 'int32')./NEV.freq;
  202. NEV.intervals{intervalCount,1}.intEnds = fread(fid, [n 1], 'int32')./NEV.freq;
  204. case 3 % waveform
  205. waveCount = waveCount+1;
  206. NEV.waves{waveCount,1}.name = name;
  207. NEV.waves{waveCount,1}.varVersion = varVersion;
  208. NEV.waves{waveCount,1}.NPointsWave = NPointsWave;
  209. NEV.waves{waveCount,1}.WFrequency = WFrequency;
  210. if (varVersion > 101) && (NEV.version >= 106)
  211. NEV.waves{waveCount,1}.PrethresholdTimeInSeconds = PrethresholdTimeInSeconds;
  212. end
  213. if varVersion > 100
  214. NEV.waves{waveCount,1}.wireNumber = wireNumber;
  215. NEV.waves{waveCount,1}.unitNumber = unitNumber;
  216. else
  217. NEV.waves{waveCount,1}.wireNumber = 0;
  218. NEV.waves{waveCount,1}.unitNumber = 0;
  219. end
  220. NEV.waves{waveCount,1}.ADtoMV = ADtoMV;
  221. if NEV.version > 104
  222. NEV.waves{waveCount,1}.MVOfffset = MVOfffset;
  223. else
  224. NEV.waves{waveCount,1}.MVOfffset = 0;
  225. end
  226. fseek(fid, offset, 'bof');
  227. NEV.waves{waveCount,1}.timestamps = fread(fid, [n 1], 'int32')./NEV.freq;
  228. wf = fread(fid, [NPointsWave n], 'int16');
  229. NEV.waves{waveCount,1}.waveforms = wf.*ADtoMV + NEV.waves{waveCount,1}.MVOfffset;
  231. case 4 % population vector
  232. popCount = popCount+1;
  233. NEV.popvectors{popCount,1}.name = name;
  234. NEV.popvectors{popCount,1}.varVersion = varVersion;
  235. fseek(fid, offset, 'bof');
  236. NEV.popvectors{popCount,1}.weights = fread(fid, [n 1], 'double');
  238. case 5 % continuous variable
  239. %{
  240. contCount = contCount+1;
  241. NEV.ElectrodesInfo{contCount,1}.name = name;
  242. NEV.ElectrodesInfo{contCount,1}.varVersion = varVersion;
  243. NEV.ElectrodesInfo{contCount,1}.ADtoMV = ADtoMV;
  244. if NEV.version > 104
  245. NEV.ElectrodesInfo{contCount,1}.MVOfffset = MVOfffset;
  246. else
  247. NEV.ElectrodesInfo{contCount,1}.MVOfffset = 0;
  248. end
  249. NEV.ElectrodesInfo{contCount,1}.ADFrequency = WFrequency;
  250. fseek(fid, offset, 'bof');
  251. NEV.ElectrodesInfo{contCount,1}.timestamps = fread(fid, [n 1], 'int32')./NEV.freq;
  252. NEV.ElectrodesInfo{contCount,1}.fragmentStarts = fread(fid, [n 1], 'int32') + 1;
  253. NEV.ElectrodesInfo{contCount,1}.data = fread(fid, [NPointsWave 1], 'int16').*ADtoMV + NEV.ElectrodesInfo{contCount,1}.MVOfffset;
  254. %}
  256. case 6 % marker
  257. markerCount = markerCount+1;
  258. NEV.markers{markerCount,1}.name = name;
  259. NEV.markers{markerCount,1}.varVersion = varVersion;
  260. fseek(fid, offset, 'bof');
  261. NEV.markers{markerCount,1}.timestamps = fread(fid, [n 1], 'int32')./NEV.freq;
  262. for markerFieldIndex=1:NMarkers
  263. markerName = fread(fid, 64, '*char')';
  264. % remove first zero and all characters after the first zero
  265. markerName(end+1) = 0;
  266. markerName = markerName(1:min(find(markerName==0))-1);
  267. NEV.markers{markerCount,1}.values{markerFieldIndex,1}.name = markerName;
  268. for markerValueIndex = 1:n
  269. markerValue = fread(fid, MarkerLength, '*char')';
  270. % remove first zero and all characters after the first zero
  271. markerValue(end+1) = 0;
  272. markerValue = markerValue(1:min(find(markerValue==0))-1);
  273. NEV.markers{markerCount,1}.values{markerFieldIndex,1}.strings{markerValueIndex, 1} = markerValue;
  274. end
  275. end
  277. otherwise
  278. disp (['unknown variable type ' num2str(type)]);
  279. end
  280. % return to file position that was after reading the variable header
  281. fseek(fid, filePosition, 'bof');
  282. dummy = fread(fid, 52, 'char');
  283. end
  285. % plot representative waves
  286. %{
  287. for i=1:1:length(NEV.waves)
  288. figure(i);
  289. plot(NEV.waves{i,1}.waveforms(1:32,show_sp(1):show_sp(2)),'-r');
  290. % unfinish: title axis scale color
  291. end
  292. %}
  293. % reform (NEV.Data.Spikes.TimeStamp)(NEV.MetaTags)
  294. NEV.Data.Spikes.TimeStamp=[];
  295. NEV.Data.Spikes.Electrode=[];
  296. NEV.Data.Spikes.Unit=[];
  297. % reform Electrode
  298. for i=1:length(NEV.neurons)
  299. if ~isempty(strfind(NEV.neurons{i,1}.name,'chan'))
  300. Electrode(i,1:length(NEV.neurons{i,1}.timestamps))=str2double(regexp(NEV.neurons{i,1}.name,'\d+','match','once'));
  301. elseif ~isempty(strfind(NEV.neurons{i,1}.name,'ainp'))
  302. Electrode(i,1:length(NEV.neurons{i,1}.timestamps))=str2double(regexp(NEV.neurons{i,1}.name,'\d+','match','once'))+128;
  303. elseif ~isempty(strfind(NEV.neurons{i,1}.name,'aux'))
  304. Electrode(i,1:length(NEV.neurons{i,1}.timestamps))=str2double(regexp(NEV.neurons{i,1}.name,'\d+','match','once'))+900;
  305. else
  306. end
  307. end
  308. Electrode(Electrode==0)=NaN; % replace '0' label
  309. Electrode=reshape(Electrode.',1,[]);
  310. NEV.Data.Spikes.Electrode=Electrode(~isnan(Electrode));
  311. % reform Unit
  312. for i=1:length(NEV.neurons)
  313. Unit(i,1:length(NEV.neurons{i,1}.timestamps))=NEV.neurons{i,1}.unitNumber;
  314. end
  315. Unit(Unit==0)=NaN; % replace '0' label
  316. Unit=reshape(Unit.',1,[]);
  317. NEV.Data.Spikes.Unit=Unit(~isnan(Unit));
  318. % reform TimeStamp
  319. for i=1:length(NEV.neurons)
  320. NEV.Data.Spikes.TimeStamp= cat(2,NEV.Data.Spikes.TimeStamp,round((NEV.neurons{i,1}.timestamps')*(NEV.freq)));
  321. end
  324. %%supplement (optional)
  325. % NEV.ElectrodesInfo - array of continuous variable structures
  326. % ElectrodesInfo{i}.name - name of continuous variable
  327. % ElectrodesInfo{i}.ADFrequency - A/D frequency for data points
  328. %
  329. % Continuous (a/d) data for one channel is allowed to have gaps
  330. % in the recording (for example, if recording was paused, etc.).
  331. % Therefore, continuous data is stored in fragments.
  332. % Each fragment has a timestamp and an index of the first data
  333. % point of the fragment (data values for all fragments are stored
  334. % in one array and the index indicates the start of the fragment
  335. % data in this array).
  336. % The timestamp corresponds to the time of recording of
  337. % the first a/d value in this fragment.
  338. %
  339. % ElectrodesInfo{i}.timestamps - array of timestamps (fragments start times in seconds)
  340. % ElectrodesInfo{i}.fragmentStarts - array of start indexes for fragments in ElectrodesInfo.data array
  341. % ElectrodesInfo{i}.data - array of data points (in milliVolts)
  344. fclose(fid);

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

Overview

Authors: Liang-Yin Lu1,2,3, Peng Chen3,4, Ting-Yu Liang3,4, Liang-Ying Chen3,4, Wen-Chuan Liu3,4,5, Wei-Xiang Chen3,4, Jye-Chang Lee3,5, Wen-Sung Lai1,6, Ming-Kai Pan1,2,3,4,5,7,8
ORCID iDs: Liang-Yin Lu, Peng Chen, Ting-Yu Liang, Liang-Ying Chen, Wei-Xiang Chen, Jye-Chang Lee, Wen-Sung Lai, Ming-Kai Pan
  1. Taiwan International Graduate Program in Interdisciplinary Neuroscience, National Taiwan University and Academia Sinica,Taipei, Taiwan, ROC
  2. Institute of Biomedical Sciences, Academia Sinica,Taipei, Taiwan, ROC
  3. Molecular Imaging Center, National Taiwan University,Taipei, Taiwan, ROC
  4. Department and Graduate Institute of Pharmacology, National Taiwan University College of Medicine,Taipei, Taiwan, ROC
  5. Department of Medical Research, National Taiwan University Hospital,Taipei, Taiwan, ROC
  6. Department of Psychology, National Taiwan University,Taipei, Taiwan, ROC
  7. Cerebellar Research Center, National Taiwan University Hospital, Yun-Lin Branch,Yun-Lin, Taiwan, ROC
  8. Research Center for Developmental Biology and Regenerative Medicine, National Taiwan University,Taipei, Taiwan, ROC
Institutions: National Taiwan University (Taiwan); Institute of Biomedical Sciences, Academia Sinica (Taiwan); National Taiwan University Hospital (Taiwan)
Journal: Nature communications, volume 17, issue 1, article 8602
Dates: received 1 October 2025; accepted 25 June 2026; published online 13 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-75371-4 · PMID 42443192 · PMCID PMC13486697 · OpenAlex W7168185069
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), systems (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Decision, Neural circuits, Reward
MeSH: Cerebellar Nuclei*, Dopamine*, Dopaminergic Neurons*, Purkinje Cells*, Reward*, Animals, Choice Behavior, Female, Male, Mice, Mice, Inbred C57BL, Optogenetics, Single-Cell Analysis, Ventral Tegmental Area (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Taiwan University Hospital (NSC-145-11, 113-UN0013, 112-UN0024, 113-E0001, 108-039); NTU | College of Medicine, National Taiwan University (College of Medicine, National Taiwan University) (110C101-011); Academia Sinica (AS-TM-112-01-02); National Health Research Institutes (NHRI-EX113-11303NI)
Citations: not cited yet (Europe PMC); 92 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

leomol/FPA

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 88a6278332009bc2f5bd71387cc8b0315c0a687a, 20 April 2026
Languages: MATLAB (30), C/C++ (1), C++ (1)
Size: 39 files, 32 scripts
Software Heritage: not archived
Found in: the text, “Fluorescence trace extraction”
Holds: README, license file, CITATION.cff
Not found: environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (3 files), Curve Fitting Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
34 files
At the source: github.com/leomol/FPA

cnchi/HappyML

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 776f2917a47c8a7571950ed2b8ef4f15e177537b, 6 August 2024
Languages: Python (9)
Size: 13 files, 9 scripts
Software Heritage: not archived
Found in: the text, “Machine learning-based search for temporal featu”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (6 files), pandas (6 files), scikit-learn (6 files), Matplotlib (4 files), statsmodels (2 files), Keras (1 file), PyTorch (1 file), SciPy (1 file), seaborn (1 file), TensorFlow (1 file)
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/cnchi/HappyML

Zenodo 19331923

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 11 files
Software Heritage: not checked
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (3 files), Statistics and Machine Learning Toolbox (3 files), NumPy (2 files), SciPy (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
20 files
At the source:

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it points to the authors' code: Zenodo 19331923

Read it in the paper: doi.org/10.1038/s41467-026-75371-4.

Tracing map

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What the map holds:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 61 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 statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-75371-4.

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, 3 keywords, 14 MeSH terms, 4 funders, 92 references.

Cite

This paper

Lu, L.-Y., Chen, P., Liang, T.-Y., Chen, L.-Y., Liu, W.-C., Chen, W.-X., Lee, J.-C., Lai, W.-S., & Pan, M.-K. (2026). Cerebellar neural populations orchestrate dopamine reward signaling with single-trial precision. Nature communications, 17(1), 8602. https://doi.org/10.1038/s41467-026-75371-4

BibTeX

@article{lu2026cerebellar,
author = {Lu, Liang-Yin and Chen, Peng and Liang, Ting-Yu and Chen, Liang-Ying and Liu, Wen-Chuan and Chen, Wei-Xiang and Lee, Jye-Chang and Lai, Wen-Sung and Pan, Ming-Kai},
title = {{Cerebellar neural populations orchestrate dopamine reward signaling with single-trial precision}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {8602},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-75371-4},
url = {https://doi.org/10.1038/s41467-026-75371-4},
pmid = {42443192},
pmcid = {PMC13486697}
}

RIS

TY - JOUR
AU - Lu, Liang-Yin
AU - Chen, Peng
AU - Liang, Ting-Yu
AU - Chen, Liang-Ying
AU - Liu, Wen-Chuan
AU - Chen, Wei-Xiang
AU - Lee, Jye-Chang
AU - Lai, Wen-Sung
AU - Pan, Ming-Kai
TI - Cerebellar neural populations orchestrate dopamine reward signaling with single-trial precision
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/13
VL - 17
IS - 1
SP - 8602
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-75371-4
UR - https://doi.org/10.1038/s41467-026-75371-4
LA - en
ER -

CSL-JSON

{
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"type": "article-journal",
"title": "Cerebellar neural populations orchestrate dopamine reward signaling with single-trial precision",
"container-title": "Nature communications",
"author": [
{
"family": "Lu",
"given": "Liang-Yin"
},
{
"family": "Chen",
"given": "Peng"
},
{
"family": "Liang",
"given": "Ting-Yu"
},
{
"family": "Chen",
"given": "Liang-Ying"
},
{
"family": "Liu",
"given": "Wen-Chuan"
},
{
"family": "Chen",
"given": "Wei-Xiang"
},
{
"family": "Lee",
"given": "Jye-Chang"
},
{
"family": "Lai",
"given": "Wen-Sung"
},
{
"family": "Pan",
"given": "Ming-Kai"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "8602",
"DOI": "10.1038/s41467-026-75371-4",
"PMID": "42443192",
"PMCID": "PMC13486697",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-75371-4",
"language": "en",
"issued": {
"date-parts": [
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2026,
7,
13
]
]
}
}

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