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Cited by 1Neural dynamics and brain functionstochastic dynamics and bifurcationIon channel regulation and function

Fast reconstruction of degenerate populations of conductance-based neuron models from spike times

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Brandoit, Julien, Ernst, Damien, Drion, Guillaume, Fyon, Arthur·2026·PLOS Computational Biology
DOI 10.1371/journal.pcbi.1014337PMC PMC13241015
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Abstract

Inferring the biophysical parameters of conductance-based models (CBMs) from experimentally accessible recordings remains a central challenge in computational neuroscience. Spike times are the most widely available data, yet they reveal little about which combinations of ion channel conductances generate the observed activity. This inverse problem is further complicated by neuronal degeneracy, where multiple distinct conductance sets yield similar spiking patterns. We introduce a method that addresses this challenge by combining deep learning with Dynamic Input Conductances (DICs), a theoretical framework that reduces complex CBMs to three interpretable feedback components governing excitability and firing patterns. Our approach first maps spike times directly to DIC densities at threshold using a lightweight neural network that learns a low-dimensional representation of neuronal activity. The predicted DIC values are then used to generate degenerate CBM populations via an iterative compensation algorithm, ensuring compatibility with the intermediate target DICs, and thereby reproducing the corresponding firing patterns, even in high-dimensional models. Applied to two neuronal models, this algorithmic pipeline reconstructs spiking, bursting, and irregular regimes with high accuracy and robustness to variability, including spike trains generated under noisy current injection mimicking physiological stochasticity. It produces diverse degenerate populations within milliseconds on standard hardware, enabling scalable and efficient inference from spike recordings alone. Beyond methodological advances, we provide an open-source software package with a graphical interface that allows experimentalists to generate and explore CBM populations directly from spike trains without requiring programming expertise. Together, this work positions DICs as a practical and interpretable link between experimentally observed activity and mechanistic models. By enabling fast and scalable reconstruction of degenerate populations directly from spike times, our approach provides a powerful way to investigate how neurons exploit conductance variability to achieve reliable computation and provides the foundation for experimental applications that span from neuromodulation studies to real-time model-guided interventions.

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  • There are attempts to develop new methods to characterize the parameters of biological neurons to estimate or evaluate their contributions to neuronal dynamics.

    “Therefore, there are attempts to develop new methods to characterize the parameters of biological neurons to estimate or evaluate their contributions to neuronal dynamics [48][49][50].”

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Cited by

Sorted by citations

Authors
Brandoit, J., Ernst, D., Drion, G., & Fyon, A.
Affiliations
  • University of Liège (BE)
  • Télécom Paris (FR)
Corresponding author
Fyon, Arthur
ORCID
Venue
PLOS Computational Biology
Publisher
Public Library of Science (PLoS)
Published
2026-05-21
Volume
22
Issue
5
Pages
e1014337
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
DOI
10.1371/journal.pcbi.1014337
PMC
PMC13241015
OpenAlex
W4415316636
Cited by
1
References
0
Citations by year
2026: 1
FWCI
3.05
Type
journal article
Language
en
Update notices
new_version (2026-06-05)
Open access
Licence
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
  • ORBi (University of Liège) (submittedVersion, cc-by-sa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
  • Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA40038025)
  • Fonds De La Recherche Scientifique - FNRS (ASP-REN40024838)
  • Belgian Federal Science Policy Office (NEMODEI2)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Ion channel regulation and function
Keywords
conductance-based neuron model, inverse problems, deep learning, burst firing, spiking patterns, neuromodulation
MeSH terms
Deep Learning, Action Potentials, Algorithms, Animals, Computer Simulation, Humans, Ion Channels, Models, Neurological, Neurons, Neural Networks, Computer, Computational Biology

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