OSCR

Hierarchical learning creates invariant schema within plastic neural networks.

Code ↔ Paper

6 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 6 matches
  1. [1] § Methods › Trial firing ↔ scripts/PlotMaker.py, lines 231–303 · score 0.69 · background firing, peak firing, activated firings, steepness, boundary, neurons
  2. [2] § Methods › Top models › Backpropagation networks ↔ scripts/PlotMaker.py, lines 545–608 · score 0.65 · MLPClassifier, max_iter, tanh activation, hidden layers, model, neuron
  3. [3] § Results › Distinct populations of hidden neurons segregate into sequential layers in hierarchical models ↔ scripts/PlotMaker.py, lines 545–608 · score 0.60 · peak firing, activated firing, event cells, hidden layer, NB, HB
  4. [4] § Results › Hierarchical networks converge to a stable schema robust to further training ↔ scripts/PlotMaker.py, lines 1–34 · score 0.60 · Concept space, Network robustness, event cell, Boundary cell, firing, ENN
  5. [5] § Results › Hierarchical schema is distillable into a concise interpretable circuit ↔ scripts/PlotMaker.py, lines 231–303 · score 0.56 · background firing, peak firing, Boundary cell, steepness, Event, activated
  6. [6] § Methods › Top models › Essence neural networks ↔ enn/network.py, lines 32–152 · score 0.55 · subconcept neurons, activation function, artificial, biases, tanh, class

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

Python · 723 lines · 26 KB · no license · 5 matches

The registry keeps no copy of this file: its repository has no license, so its authors keep all their rights to it. Your browser shows it from its source, with JavaScript.

It can be read at the source: scripts/PlotMaker.py.

Overview

Authors: James R. Elder1,2,3, Jie Zheng4,5, Lydia B. Shimelis6, Ueli Rutishauser7,8,9,10, Milo M. Lin1,2,11,12
ORCID iDs: Milo M. Lin
  1. Green Center for Systems Biology, University of Texas Southwestern Medical Center,Dallas, TX 75390 USA
  2. Lyda Hill Dept. of Bioinformatics, University of Texas Southwestern Medical Center,Dallas, TX 75390 USA
  3. Molecular Biophysics Program, University of Texas Southwestern Medical Center,Dallas, TX 75390 USA
  4. Department of Biomedical Engineering, University of California at Davis,Davis, CA 95616 USA
  5. Department of Neurological Surgery, UC Davis Health,Davis, CA 95616 USA
  6. Biomedical Engineering and Neuroscience, Harvard University,Cambridge, MA 02138 USA
  7. Department of Neurosurgery, Cedars-Sinai Medical Center,Los Angeles, CA 90048 USA
  8. Department of Neurology, Cedars-Sinai Medical Center,Los Angeles, CA 90048 USA
  9. Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center,Los Angeles, CA 90048 USA
  10. Division of Biology and Biological Engineering, California Institute of Technology,Pasadena, CA 91125 USA
  11. Department of Biophysics, University of Texas Southwestern Medical Ctr.,Dallas, TX 75390 USA
  12. Center for Alzheimer’s and Neurodegenerative Diseases, University of Texas Southwestern Medical Center,Dallas, TX 75390 USA
Institutions: The University of Texas Southwestern Medical Center (United States); University of California, Davis (United States); UC Davis Health (United States); Harvard University (United States); Cedars-Sinai Medical Center (United States); California Institute of Technology (United States)
Journal: Journal of computational neuroscience, volume 54, issue 3, pages 503-514
Dates: received 5 December 2025; accepted 18 June 2026; published online 30 June 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1007/s10827-026-00940-x · PMID 42377689 · PMCID PMC13588848 · OpenAlex W7166740900
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism)
Methods: Statistics, Machine learning
Keywords: cognitive neuroscience, schema formation
MeSH: Learning*, Models, Neurological*, Neural Networks, Computer*, Neuronal Plasticity*, Neurons*, Algorithms, Animals, Humans, Nerve Net, Soft Computing (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institutes of Health,United States (5T32GM131963, R00NS126233, R01GM125748)
Citations: not cited yet (Europe PMC); 50 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

jre411/bdENN

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 8aee36c26295ac90ceec784a66da1925c225d34a, 4 February 2025
Languages: Python (24)
Size: 3,643 files, 24 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (20 files), Matplotlib (15 files), scikit-learn (10 files), SciPy (4 files), pandas (2 files), Keras (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
25 files, not copied: shown from their source

OSCR keeps no copy of these files: this repository has no license that allows it. The reader above shows each one from its source, fetched by your browser at commit 8aee36c, when its fingerprint is the one OSCR verified. How this works.

At the source: github.com/jre411/bdENN

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;
  • 24 scripts, each with its path and the digest of its content;
  • 6 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.

Code and data availability statement

The paper has a code and 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/10.1007/s10827-026-00940-x.

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 2, 28 September 2026

  • Publisher: — → Springer Science+Business Media

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 2 keywords, 10 MeSH terms, 1 funder, 37 references.

Cite

This paper

Elder, J. R., Zheng, J., Shimelis, L. B., Rutishauser, U., & Lin, M. M. (2026). Hierarchical learning creates invariant schema within plastic neural networks. Journal of computational neuroscience, 54(3), 503-514. https://doi.org/10.1007/s10827-026-00940-x

BibTeX

@article{elder2026hierarchical,
author = {Elder, James R. and Zheng, Jie and Shimelis, Lydia B. and Rutishauser, Ueli and Lin, Milo M.},
title = {{Hierarchical learning creates invariant schema within plastic neural networks}},
journal = {Journal of computational neuroscience},
year = {2026},
month = jun,
volume = {54},
number = {3},
pages = {503--514},
publisher = {Springer Science+Business Media},
issn = {0929-5313},
doi = {10.1007/s10827-026-00940-x},
url = {https://doi.org/10.1007/s10827-026-00940-x},
pmid = {42377689},
pmcid = {PMC13588848}
}

RIS

TY - JOUR
AU - Elder, James R.
AU - Zheng, Jie
AU - Shimelis, Lydia B.
AU - Rutishauser, Ueli
AU - Lin, Milo M.
TI - Hierarchical learning creates invariant schema within plastic neural networks
T2 - Journal of computational neuroscience
J2 - J Comput Neurosci
PY - 2026
DA - 2026/06/30
VL - 54
IS - 3
SP - 503
EP - 514
SN - 0929-5313
PB - Springer Science+Business Media
DO - 10.1007/s10827-026-00940-x
UR - https://doi.org/10.1007/s10827-026-00940-x
LA - en
ER -

CSL-JSON

{
"id": "10.1007/s10827-026-00940-x",
"type": "article-journal",
"title": "Hierarchical learning creates invariant schema within plastic neural networks",
"container-title": "Journal of computational neuroscience",
"author": [
{
"family": "Elder",
"given": "James R."
},
{
"family": "Zheng",
"given": "Jie"
},
{
"family": "Shimelis",
"given": "Lydia B."
},
{
"family": "Rutishauser",
"given": "Ueli"
},
{
"family": "Lin",
"given": "Milo M."
}
],
"container-title-short": "J Comput Neurosci",
"volume": "54",
"issue": "3",
"page": "503-514",
"DOI": "10.1007/s10827-026-00940-x",
"PMID": "42377689",
"PMCID": "PMC13588848",
"ISSN": "0929-5313",
"publisher": "Springer Science+Business Media",
"URL": "https://doi.org/10.1007/s10827-026-00940-x",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
30
]
]
}
}

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

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s42003-026-10169-0 [code]
Shared representations in brains and models reveal a two-route cortical organization during scene perception.
Journal: Communications biology
In common: scikit-learn, pandas, SciPy, 2 other tools, 3 references
[2] doi:10.1186/s13321-026-01177-7 [code]
A pipeline for developing AI-driven models to predict molecular initiating events: a case study on neural tube defects.
Journal: Journal of cheminformatics
In common: scikit-learn, pandas, SciPy, 2 other tools, 2 references
[3] doi:10.1371/journal.pcbi.1014656 [code]
Contrastive learning to fine-tune feature extraction models for the visual cortex.
Journal: PLoS computational biology
In common: scikit-learn, pandas, SciPy, 2 other tools, 2 references
[4] doi:10.1016/j.isci.2026.116439 [code]
Decoding the role of transcriptomic clocks in the human prefrontal cortex.
Journal: iScience
In common: Keras, scikit-learn, pandas, 3 other tools, 1 reference
[5] doi:10.1038/s41597-026-07248-6 [code]
A large-scale fMRI dataset for vision-language semantic association.
Journal: Scientific data
In common: scikit-learn, pandas, SciPy, 2 other tools, 2 references
[6] doi:10.1016/j.bpj.2026.06.005 [code]
Curvature-based machine-learning method for automated segmentation of dendritic spines.
Journal: Biophysical journal
In common: Keras, scikit-learn, pandas, 3 other tools, 1 reference
[7] doi:10.1038/s41467-026-72687-z [code]
Chordless cycle filtrations for dimensionality detection in complex networks via topological data analysis.
Journal: Nature communications
In common: Keras, scikit-learn, pandas, 3 other tools, 1 reference
[8] doi:10.1038/s41598-026-52672-8 [code]
Validation of portable, semi-dry electrode-based electroencephalography device for its application in brain-computer interface solutions.
Journal: Scientific reports
In common: Keras, scikit-learn, SciPy, 2 other tools, 1 reference
[9] doi:10.1038/s41598-026-51212-8 [code]
Uncertainty aware machine learning for bridging simulation and experiment in high throughput materials characterization.
Journal: Scientific reports
In common: Keras, scikit-learn, pandas, 2 other tools, 1 reference
[10] doi:10.1371/journal.pcbi.1014458 [code]
Neuronal excitability and parameter variability in the Hodgkin-Huxley model.
Journal: PLoS computational biology
In common: pandas, SciPy, Matplotlib, 1 other tool, 2 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.