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Computational mechanisms for temporal integration in the anterior claustrum.

Code ↔ Paper

12 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 12 matches · 11 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and methods › RNN population analysis ↔ matlab/run_01_population_clustering.m, the whole file · a weak match · score 0.82 · gap statistic, task epochs, t-SNE, embedded, optimal, exaggeration
  2. [2] § Materials and methods › Decoding analysis (for Figure 5A, B) › CS decoding ↔ matlab/helpers/train_linear_discriminant.m, the whole file · a weak match · score 0.73 · linear discriminant, kfoldLoss, cross validation, fitcdiscr, accuracy, trained
  3. [3] § Materials and methods › Decoding analysis (for Figure 5A, B) › CS decoding ↔ matlab/helpers/decode_by_time.m, the whole file · a weak match · score 0.71 · kfoldLoss, linear discriminant, cross validation, fitcdiscr, decoding, accuracy
  4. [4] § Materials and methods › Cross-temporal decoding analysis ↔ matlab/run_07_cross_temporal_decoding.m, the whole file · a weak match · score 0.69 · Cross temporal decoding, Decoding accuracy, heatmap, classify, predict, decoder
  5. [5] § Materials and methods › Visualization of PCA trajectories ↔ matlab/config_analysis.m, the whole file · a weak match · score 0.63 · smoothing window, pre open, post open, PCA, trajectories, cross
  6. [6] § Materials and methods › RNN population analysis ↔ matlab/config_analysis.m, the whole file · a weak match · score 0.61 · t-SNE, exaggeration, perplexity, RNG, dimensional, window
  7. [7] § Results › Trajectory-based dynamic coding underlies integration ↔ matlab/run_07_cross_temporal_decoding.m, the whole file · a weak match · score 0.61 · cross temporal decoding, training bin, discrimination, classification, decoder, accuracy
  8. [8] § Materials and methods › Visualization of PCA trajectories ↔ matlab/helpers/normalize_open_to_cross.m, the whole file · a weak match · score 0.60 · pre open, post open, cross latency, smoothed, segments, variable
  9. [9] § Materials and methods › Decoding analysis (for Figure 5A, B) › Door open decoding ↔ matlab/helpers/train_linear_discriminant.m, the whole file · a weak match · score 0.58 · cross validated linear, discriminant, predicted, accuracy, training
  10. [10] § Results › Principal component analysis-based trajectory analysis characterizes RNN dynamics ↔ matlab/run_06_nonlinear_trajectory_prediction.m, the whole file · a weak match · score 0.53 · linear regression, opening trajectory, Bar, MLP, RSS, predicted
  11. [11] § Materials and methods › Decoding analysis (for Figure 5A, B) › Door open decoding ↔ matlab/helpers/decode_by_time.m, the whole file · a weak match · score 0.52 · cross validated linear, discriminant, accuracy, decoding, activity
  12. [12] § Materials and methods › RNN modeling ↔ python/rnn_task.py, lines 237–244 · score 0.52 · inhibitory units, Dale, rows, uniformly, zeroed, excitatory

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

MATLAB · 38 lines · 1.5 KB · MIT · 2 matches

  1. function [trainedClassifier, validationAccuracy] = train_linear_discriminant(trainingData, responseData, varargin)
  2. %TRAIN_LINEAR_DISCRIMINANT Train a 5-fold cross-validated linear discriminant.
  3. %
  4. % This small wrapper replaces classifier-app-generated functions with a
  5. % generic implementation that works for any number of neurons/features.
  7. p = inputParser;
  8. addParameter(p, 'KFold', 5, @(x) isnumeric(x) && isscalar(x));
  9. parse(p, varargin{:});
  11. trainingData = double(trainingData);
  12. responseData = responseData(:);
  13. if size(trainingData, 1) ~= numel(responseData)
  14. error('trainingData rows must match responseData length.');
  15. end
  16. if numel(unique(responseData)) < 2
  17. error('responseData must contain at least two classes.');
  18. end
  20. nFeatures = size(trainingData, 2);
  21. predictorNames = cellstr(compose('x%d', 1:nFeatures));
  22. inputTable = array2table(trainingData, 'VariableNames', predictorNames);
  24. classificationDiscriminant = fitcdiscr( ...
  25. inputTable, responseData, ...
  26. 'DiscrimType', 'linear', ...
  27. 'Gamma', 0, ...
  28. 'FillCoeffs', 'on', ...
  29. 'ClassNames', unique(responseData));
  31. predictorExtractionFcn = @(x) array2table(double(x), 'VariableNames', predictorNames);
  32. discriminantPredictFcn = @(x) predict(classificationDiscriminant, x);
  33. trainedClassifier.predictFcn = @(x) discriminantPredictFcn(predictorExtractionFcn(x));
  34. trainedClassifier.ClassificationDiscriminant = classificationDiscriminant;
  36. partitionedModel = crossval(classificationDiscriminant, 'KFold', p.Results.KFold);
  37. validationAccuracy = 1 - kfoldLoss(partitionedModel, 'LossFun', 'ClassifError');
  38. end

train_linear_discriminant.m at commit 6838dad, under MIT · at the source

Overview

Authors: Kuenbae Sohn1, Donghyeon Yoon1, Junghwa Lee1, Sukwoo Choi1
ORCID iDs: Kuenbae Sohn, Donghyeon Yoon, Junghwa Lee, Sukwoo Choi
  1. School of Biological Sciences, College of Natural Sciences, Seoul National University Seoul Republic of Korea
Institutions: Seoul National University (South Korea)
Journal: eLife, volume 15, article RP109539
Dates: published online 28 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.109539 · PMID 42206688 · PMCID PMC13218724 · OpenAlex W7135088909
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: rat (organism), computational (subfield)
Methods: Smoothing, state filtering, decompositions, Machine learning, Statistics, Preprocessing, Connectivity, Single-unit activity, calcium imaging
Keywords: claustrum, recurrent neural network, integration, trajectory, dynamic coding, Rat
MeSH: Claustrum*, Neurons*, Animals, Models, Neurological, Neural Networks, Computer, Rats, Recurrent Neural Networks (* major topic)
Journal subjects: Neuroscience
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Research Foundation of Korea (NRF-2021R1A2B5B03002345)
Citations: cited by 1 paper (Europe PMC); 49 references in the paper

Abstract

The claustrum, with its extensive reciprocal connections to nearly all cortical regions, has long been hypothesized as a key hub for integrating diverse cognitive, sensory and motor information. However, despite its anatomical connectivity, whether and how it functionally integrates different inputs to generate coherent representations has remained unclear. Here, we developed a recurrent neural network (RNN) trained via supervised learning on behavioral metrics of delayed escape—a behavioral paradigm that requires integration of temporally separated task-relevant signals. A subset of RNN neurons exhibited dynamics similar to those of anterior claustral neurons during this behavior. These neurons formed a recurrent cluster, a structure supported by in vitro stimulation experiments in claustral brain slices. We analyzed the computational properties of this claustrum-like cluster via dimensionality reduction of population activity. The network showed nonlinear integration of temporally distributed inputs and increased synergistic information. Rather than settling into attractors, integrated information was dynamically encoded along continuously evolving neural trajectories. Notably, similar trajectory patterns associated with dynamic integration were observed in claustral recordings, suggesting the model’s biological plausibility. We propose that the anterior claustrum dynamically integrates task-relevant input signals over time and broadcasts the evolving representation to downstream brain regions capable of reading and interpreting it in a context-dependent manner.

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 12 matches between paragraphs and lines of code.

Kuenbae/claustrum_computation

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 6838dad3216b7a7c4cbf35680360f17ea62c33fc, 28 May 2026
Languages: MATLAB (30), Python (3)
Size: 41 files, 33 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, CITATION.cff, environment (requirements.txt)
Not found: tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (6 files), NumPy (3 files), TensorFlow (3 files), SciPy (2 files), Deep Learning Toolbox (1 file), Parallel Computing Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
35 files
At the source: github.com/Kuenbae/claustrum_computation

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;
  • 33 scripts, each with its path and the digest of its content;
  • 12 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 availability

All custom code and trained models used for RNN simulations and downstream analyses in this study are publicly available at GitHub: https://github.com/Kuenbae/claustrum_computation (copy archived at Sohn and Yoon, 2026).

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, 4 authors, 6 keywords, 7 MeSH terms, 1 funder, 48 references.

Cite

This paper

Sohn, K., Yoon, D., Lee, J., & Choi, S. (2026). Computational mechanisms for temporal integration in the anterior claustrum. eLife, 15, RP109539. https://doi.org/10.7554/elife.109539

BibTeX

@article{sohn2026computational,
author = {Sohn, Kuenbae and Yoon, Donghyeon and Lee, Junghwa and Choi, Sukwoo},
title = {{Computational mechanisms for temporal integration in the anterior claustrum}},
journal = {eLife},
year = {2026},
month = may,
volume = {15},
pages = {RP109539},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.109539},
url = {https://doi.org/10.7554/elife.109539},
pmid = {42206688},
pmcid = {PMC13218724}
}

RIS

TY - JOUR
AU - Sohn, Kuenbae
AU - Yoon, Donghyeon
AU - Lee, Junghwa
AU - Choi, Sukwoo
TI - Computational mechanisms for temporal integration in the anterior claustrum
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/05/28
VL - 15
SP - RP109539
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.109539
UR - https://doi.org/10.7554/elife.109539
LA - en
ER -

CSL-JSON

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"container-title-short": "Elife",
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"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
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"language": "en",
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"date-parts": [
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}
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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