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Cited by 3Neural dynamics and brain functionIon channel regulation and functionstochastic dynamics and bifurcation

Higher-order interactions in neuronal function: from genes to ionic currents in biophysical models

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Reva, Maria, Arnaudon, Alexis, Zbili, Mickael, Makkeh, Abdullah, Markram, Henry, Goaillard, Jean-Marc, Van Geit, Werner·2025·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.2500048122PMC PMC12519081
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Abstract

Neuronal firing patterns are the consequence of precise variations in neuronal membrane potential, which are themselves shaped by multiple ionic currents. In this study, we use biophysical models, statistical methods, and information theory to explore the interaction between these ionic currents and neuron electrophysiological phenotype. We created numerous electrical models with diverse firing patterns. By analyzing these models, we identified intricate relationships between model parameters and electrical features. Our findings show that neuronal activity is often influenced by multiple biophysical model parameters, in a nonadditive (i.e. synergistic) fashion. When comparing this with single-cell RNAseq data, we found a contrasting structure: gene expression profiles were dominated by redundancy, reflecting differences in regulatory constraints and sampling diversity. This research sheds light on the complex links between biophysical parameters and neuronal phenotypes.

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  • Similar physiological outputs can be produced by variable sets of component parameters.

    “similar physiological outputs can be produced by variable sets of component parameters [17][18][19][20]”

    From A feature and not a bug: Replication failures can lead to new biology (2026) · cites this paper as [20]

  • Populations of excitatory and inhibitory cortical neuron models replicate similar electrophysiological features despite their variable underlying parameters.

    “They generated populations of excitatory and inhibitory cortical neuron models that replicate similar electrophysiological features despite their variable underlying parameters.”

    From A feature and not a bug: Replication failures can lead to new biology (2026) · cites this paper as [20]

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  1. [1]Y. Bernaerts, Combined statistical-mechanistic modeling links ion channel genes to physiology of cortical neuron types. bioRxiv [Preprint] (2023). 10.1101/2023.03.02.530774 (Accessed 20 March 2024). · DOI
  2. [2]J. Eberwine, J. Kim, Cellular deconstruction: Finding meaning in individual cell variation. Trends Cell Biol. 25, 569– 578 (2015). 26410403 PMC4584424
  3. [3]R. M. Kendrick, S. Linderman, S. F. Owen, Transcriptomically-measured gene expression predicts physiological variation across single neurons in humans and mice. bioRxiv [Preprint] (2024). 10.1101/2024.08.26.609746 (Accessed 5 October 2024). · DOI
  4. [4]A. M. Swensen, B. P. Bean, Robustness of burst firing in dissociated purkinje neurons with acute or long-term reductions in sodium conductance. J. Neurosci. 25, 3509– 3520 (2005). 15814781 PMC6725377
  5. [5]D. J. Schulz, J. M. Goaillard, E. Marder, Variable channel expression in identified single and electrically coupled neurons in different animals. Nat. Neurosci. 9, 356– 362 (2006). 16444270
  6. [6]T. Kodama, Graded coexpression of ion channel, neurofilament, and synaptic genes in fast-spiking vestibular nucleus neurons. J. Neurosci. 40, 496– 508 (2020). 31719168 PMC6961989
  7. [7]M. Tapia, Neurotransmitter identity and electrophysiological phenotype are genetically coupled in midbrain dopaminergic neurons. Sci. Rep. 8, 13637 (2018). 30206240 PMC6134142
  8. [8]D. J. Schulz, J. M. Goaillard, E. E. Marder, Quantitative expression profiling of identified neurons reveals cell-specific constraints on highly variable levels of gene expression. Proc. Natl. Acad. Sci. U.S.A. 104, 13187– 13191 (2007). 17652510 PMC1933263
  9. [9]J. M. Goaillard, A. L. Taylor, D. J. Schulz, E. Marder, Functional consequences of animal-to-animal variation in circuit parameters. Nat. Neurosci. 12, 1424– 1430 (2009). 19838180 PMC2826985
  10. [10]M. Toledo-Rodriguez, Correlation maps allow neuronal electrical properties to be predicted from single-cell gene expression profiles in rat neocortex. Cereb. Cortex 14, 1310– 1327 (2004). 15192011
  11. [15]C. Bomkamp, Transcriptomic correlates of electrophysiological and morphological diversity within and across excitatory and inhibitory neuron classes. PLoS Comput. Biol. 15, e1007113 (2019). 31211786 PMC6599125
  12. [16]T. E. Chan, M. P. Stumpf, A. C. Babtie, Gene regulatory network inference from single-cell data using multivariate information measures. Cell Syst. 5, 251– 267 (2017). 28957658 PMC5624513
  13. [17]J. Wildenhain, Prediction of synergism from chemical-genetic interactions by machine learning. Cell Syst. 1, 383– 395 (2015). 27136353 PMC5998823
  14. [18]D. Anastassiou, Computational analysis of the synergy among multiple interacting genes. Mol. Syst. Biol. 3, 83 (2007). 17299419 PMC1828751
  15. [19]M. Reva, A universal workflow for creation, validation, and generalization of detailed neuronal models. Patterns 4, 100855 (2023). 38035193 PMC10682753
  16. [20]A. Arnaudon, Controlling morpho-electrophysiological variability of neurons with detailed biophysical models. Iscience 26, 108222 (2023). 37953946 PMC10638024
  17. [21]F. E. Rosas, P. A. Mediano, M. Gastpar, H. J. Jensen, Quantifying high-order interdependencies via multivariate extensions of the mutual information. Phys. Rev. E 100, 032305 (2019). 31640038
  18. [22]G. Chechik, Group redundancy measures reveal redundancy reduction in the auditory pathway. Adv. Neural Inf. Process. Syst. 14, 173– 180 (2001).
  19. [23]B. Tasic, Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335– 346 (2016). 26727548 PMC4985242
  20. [24]W. Hu, B. P. Bean, Differential control of axonal and somatic resting potential by voltage-dependent conductances in cortical layer 5 pyramidal neurons. Neuron 97, 1315– 1326 (2018). 29526554 PMC5866248
  21. [25]H. Markram, Interneurons of the neocortical inhibitory system. Nat. Rev. Neurosci. 5, 793– 807 (2004). 15378039
  22. [26]A. Nandi, Single-neuron models linking electrophysiology, morphology, and transcriptomics across cortical cell types. Cell Rep. 40, 111176 (2022). 35947954 PMC9793758
  23. [27]W. Van Geit, Bluepyopt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience. Front. Neuroinf. 10, 17 (2016). PMC4896051 27375471
  24. [31]B. P. Bean, The action potential in mammalian central neurons. Nat. Rev. Neurosci. 8, 451– 465 (2007). 17514198
  25. [33]P. K. Raval, W. Y. Ngan, J. Gallie, D. Agashe, The layered costs and benefits of translational redundancy. eLife 12, e81005 (2023). 36862572 PMC9981150
  26. [34]B. Klein, A computational exploration of resilience and evolvability of protein-protein interaction networks. Commun. Biol. 4, 1352 (2021). 34857859 PMC8639913
  27. [35]P. Hunter, Understanding redundancy and resilience: Redundancy in life is provided by distributing functions across networks rather than back-up systems. EMBO Rep. 23, e54742 (2022). 35156768 PMC8892264
  28. [36]A. A. Prinz, D. Bucher, E. Marder, Similar network activity from disparate circuit parameters. Nat. Neurosci. 7, 1345– 1352 (2004). 15558066
  29. [37]J. Golowasch, M. S. Goldman, L. Abbott, E. Marder, Failure of averaging in the construction of a conductance-based neuron model. J. Neurophysiol. 87, 1129– 1131 (2002). 11826077
  30. [38]G. Drion, T. O’Leary, E. Marder, Ion channel degeneracy enables robust and tunable neuronal firing rates. Proc. Natl. Acad. Sci. U.S.A. 112, E5361– E5370 (2015). 26354124 PMC4586887
  31. [39]A. Fyon, A. Franci, P. Sacré, G. Drion, Dimensionality reduction of neuronal degeneracy reveals two interfering physiological mechanisms. PNAS Nexus 3, pgae415 (2024). 39359396 PMC11443964
  32. [40]A. Jain, R. Narayanan, Degeneracy in the emergence of spike-triggered average of hippocampal pyramidal neurons. Sci. Rep. 10, 374 (2020). 31941985 PMC6962224
  33. [41]R. K. Rathour, R. Narayanan, Homeostasis of functional maps in active dendrites emerges in the absence of individual channelostasis. Proc. Natl. Acad. Sci. U.S.A. 111, E1787– E1796 (2014). 24711394 PMC4035944
  34. [42]T. O’Leary, A. H. Williams, A. Franci, E. Marder, Cell types, network homeostasis, and pathological compensation from a biologically plausible ion channel expression model. Neuron 82, 809– 821 (2014). 24853940 PMC4109293
  35. [43]N. T. Carnevale, M. L. Hines, The NEURON Book (Cambridge University Press, 2006).
  36. [45]F. A. Wolf, P. Angerer, F. J. Theis, Scanpy: Large-scale single-cell gene expression data analysis. Genome Biol. 19, 1– 5 (2018). 29409532 PMC5802054

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Authors
Reva, M., Arnaudon, A., Zbili, M., Makkeh, A., Markram, H., Goaillard, J.-M., & Van Geit, W.
Affiliations
  • École Polytechnique Fédérale de Lausanne (CH)
  • Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL)
  • Max Planck Institute for Dynamics and Self-Organization (DE)
  • Centre National de la Recherche Scientifique (FR)
Corresponding author
  • Reva, Maria
  • Goaillard, Jean-Marc
ORCID
Venue
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Published
2025-09-29
Volume
122
Issue
40
Pages
e2500048122-e2500048122
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
DOI
10.1073/pnas.2500048122
PMC
PMC12519081
OpenAlex
W4414601442
Cited by
3
References
9
Citations by year
2026: 3
FWCI
1.70
Type
journal article
Language
en
Update notices
correction (2025-11-06)
Open access
Licence
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, cc-by)
  • GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, cc-by)
Funders
ETH Board (-)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Ion channel regulation and function
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
Keywords
neuronal firing patterns, ionic currents, higher-order interactions, synergistic interaction, non-additive interactions, biophysical neuron models, single-cell RNA sequencing, information theory, neuronal membrane potential
MeSH terms
Action Potentials, Animals, Humans, Ion Channels, Membrane Potentials, Models, Neurological, Neurons, Biophysical Phenomena, Single-Cell Analysis

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