iScience (Nov 2023)

Controlling morpho-electrophysiological variability of neurons with detailed biophysical models

  • Alexis Arnaudon,
  • Maria Reva,
  • Mickael Zbili,
  • Henry Markram,
  • Werner Van Geit,
  • Lida Kanari

Journal volume & issue
Vol. 26, no. 11
p. 108222

Abstract

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Summary: Variability, which is known to be a universal feature among biological units such as neuronal cells, holds significant importance, as, for example, it enables a robust encoding of a high volume of information in neuronal circuits and prevents hypersynchronizations. While most computational studies on electrophysiological variability in neuronal circuits were done with single-compartment neuron models, we instead focus on the variability of detailed biophysical models of neuron multi-compartmental morphologies. We leverage a Markov chain Monte Carlo method to generate populations of electrical models reproducing the variability of experimental recordings while being compatible with a set of morphologies to faithfully represent specifi morpho-electrical type. We demonstrate our approach on layer 5 pyramidal cells and study the morpho-electrical variability and in particular, find that morphological variability alone is insufficient to reproduce electrical variability. Overall, this approach provides a strong statistical basis to create detailed models of neurons with controlled variability.

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