Arnaudon, AlexisReva, MariaZbili, Mickael MauriceMarkram, HenryVan Geit, WernerKanari, Lida2024-02-192024-02-192024-02-192023-10-3010.1016/j.isci.2023.108222https://infoscience.epfl.ch/handle/20.500.14299/204278WOS:001101354300001Variability, 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.Gabaergic InterneuronsDiversityCompensationConductancesDegeneracyModulationSimulationVarianceFeaturesImpacttext::journal::journal article::research article