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Biological complexity facilitates tuning of the neuronal parameter space

Marius Schneider, View ORCID ProfileAlbert Gidon, View ORCID ProfileJochen Triesch, View ORCID ProfilePeter Jedlicka, View ORCID ProfileHermann Cuntz
doi: https://doi.org/10.1101/2021.05.04.442120
Marius Schneider
aFrankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany
bErnst Strü ngmann Institute (ESI) for Neuroscience in cooperation with the Max Planck Society, 60528 Frankfurt am Main, Germany
cICAR3R – Interdisciplinary Centre for 3Rs in Animal Research, Justus Liebig University Giessen, 35390 Giessen, Germany
dFaculty of Physics, Goethe University, Frankfurt/Main, 60438 Frankfurt am Main, Germany
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  • For correspondence: mschneider@fias.uni-frankfurt.de cuntz@fias.uni-frankfurt.de
Albert Gidon
eInstitute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
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Jochen Triesch
aFrankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany
dFaculty of Physics, Goethe University, Frankfurt/Main, 60438 Frankfurt am Main, Germany
fFaculty of Computer Science and Mathematics, Goethe University, 60438 Frankfurt am Main, Germany
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Peter Jedlicka
aFrankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany
cICAR3R – Interdisciplinary Centre for 3Rs in Animal Research, Justus Liebig University Giessen, 35390 Giessen, Germany
gInstitute of Clinical Neuroanatomy, Neuroscience Center, Goethe University, 60528 Frankfurt am Main, Germany
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Hermann Cuntz
aFrankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany
bErnst Strü ngmann Institute (ESI) for Neuroscience in cooperation with the Max Planck Society, 60528 Frankfurt am Main, Germany
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  • ORCID record for Hermann Cuntz
  • For correspondence: mschneider@fias.uni-frankfurt.de cuntz@fias.uni-frankfurt.de
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Abstract

The electrical and computational properties of neurons in our brains are determined by a rich repertoire of membrane-bound ion channels and elaborate dendritic trees. However, the precise reason for this inherent complexity remains unknown. Here, we generated large stochastic populations of biophysically realistic hippocampal granule cell models comparing those with all 15 ion channels to their reduced but functional counterparts containing only 5 ion channels. Strikingly, valid parameter combinations in the full models were more frequent and more stable in the face of perturbations. Scaling up the numbers of ion channels artificially in the reduced models recovered these advantages confirming the key contribution of the actual number of ion channels. We conclude that the diversity of ion channels allows a neuron to achieve a target excitability through random channel expression with increased robustness and higher flexibility.

Significance statement Over the course of billions of years, evolution has led to a wide variety of biological systems. The emergence of the more complex among these seems surprising in the light of the high demands on searching viable solutions in a correspondingly high-dimensional parameter space. In realistic neuron models with their inherently complex ion channel composition, we find a surprisingly large number of viable solutions when selecting parameters randomly. This effect is strongly reduced in models with less ion channel types but is recovered when inserting additional artificial ion channels. Because concepts from probability theory provide a plausible explanation for such an improved arrangement of valid model parameters, we propose that this generalises to evolutionary selection in other complex biological systems.

In brief Studying ion channel diversity in neuronal models we show how robust biological systems may evolve not despite but through their complexity.

Highlights

  • 15 channel model of hippocampal granule cells (GCs) reduces to 5 ion channels without loss of spiking behaviour.

  • But knocking out ion channels can be compensated only in the full model.

  • Random sampling leads to ∼ 6% solutions in full but only ∼ 1% in reduced model.

  • Law of large numbers generalises our observations to other complex biological systems.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵1 Joint senior authors

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 04, 2021.
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Biological complexity facilitates tuning of the neuronal parameter space
Marius Schneider, Albert Gidon, Jochen Triesch, Peter Jedlicka, Hermann Cuntz
bioRxiv 2021.05.04.442120; doi: https://doi.org/10.1101/2021.05.04.442120
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Biological complexity facilitates tuning of the neuronal parameter space
Marius Schneider, Albert Gidon, Jochen Triesch, Peter Jedlicka, Hermann Cuntz
bioRxiv 2021.05.04.442120; doi: https://doi.org/10.1101/2021.05.04.442120

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