OSCR

Intrinsic properties link a network model to zebra finch song.

Overview

Authors: Nelson D Medina1,2,3, Dan Margoliash1,2,3
  1. Committee on Neurobiology, University of Chicago, Chicago, United States
  2. Department of Organismal Biology and Anatomy, University of Chicago, Chicago, United States
  3. The Neuroscience Institute, University of Chicago, Chicago, United States
Institutions: University of Chicago (United States)
Journal: eLife, volume 13, article RP99611
Dates: published online 25 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.99611 · PMID 42345369 · PMCID PMC13299593 · OpenAlex W4401360019
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), other (organism), computational (subfield)
Methods: Smoothing, state filtering, decompositions, Statistics, Evoked potentials, Connectivity
Keywords: Other
MeSH: Basal Ganglia*, Finches*, Models, Neurological*, Nerve Net*, Neurons*, Vocalization, Animal*, Action Potentials, Animals, Learning (* major topic)
Topic: Animal Vocal Communication and Behavior (Developmental Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIH HHS (1UF1NS115821)
Citations: cited by 1 paper (Europe PMC); 60 references in the paper

Abstract

Neuronal intrinsic excitability is a mechanism implicated in learning and memory that is distinct from synaptic plasticity. Prior work in songbirds established that intrinsic properties (IPs) of premotor basal-ganglia-projecting neurons (HVCX) relate to learned song. Here, we find that temporal song structure is related to specific HVCX IPs: HVCX from birds who sang longer songs, including longer invariant vocalizations (harmonic stacks), had IPs that reflected increased post-inhibitory rebound. This suggests a rebound excitation mechanism underlying the ability of HVCX neurons to integrate over long periods of time throughout the song and represent sequence information. To explore this, we constructed a network model of realistic neurons showing how in vivo HVC bursting properties link rebound excitation to network structure and behavior. These results demonstrate an explicit link between neuronal IPs and learned behavior. We propose that sequential behaviors exhibiting temporal regularity require IPs to be included in realistic network-level descriptions.

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

Code

No file of the authors' code could be read here: it is described below, and read at its source.

doi:10.5061/dryad.wdbrv162x

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)

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;
  • 0 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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 data and custom code supporting the findings of this study are publicly available in the Dryad Digital Repository: https://doi.org/10.5061/dryad.wdbrv162x. The deposit contains (i) extracted electrophysiological features and trace data for HVC-RA and HVC-X neurons (Python pickle format), (ii) per-bird summary statistics used to generate the figures (Excel files, "Cardinal 2.0"), and (iii) example MATLAB code for the Hodgkin-Huxley network simulations reported in Figure 5 and its supplements.

The following dataset was generated:

Medina N, Margliash D. 2026. Bursts from the past: Intrinsic properties link a network model to zebra finch song. Dryad Digital Repository.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 2 authors, 1 keyword, 9 MeSH terms, 1 funder, 57 references.

Cite

This paper

Medina, N. D., & Margoliash, D. (2026). Intrinsic properties link a network model to zebra finch song. eLife, 13, RP99611. https://doi.org/10.7554/elife.99611

BibTeX

@article{medina2026intrinsic,
author = {Medina, Nelson D and Margoliash, Dan},
title = {{Intrinsic properties link a network model to zebra finch song}},
journal = {eLife},
year = {2026},
month = jun,
volume = {13},
pages = {RP99611},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.99611},
url = {https://doi.org/10.7554/elife.99611},
pmid = {42345369},
pmcid = {PMC13299593}
}

RIS

TY - JOUR
AU - Medina, Nelson D
AU - Margoliash, Dan
TI - Intrinsic properties link a network model to zebra finch song
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/06/25
VL - 13
SP - RP99611
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.99611
UR - https://doi.org/10.7554/elife.99611
LA - en
ER -

CSL-JSON

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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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