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research article

A universal workflow for creation, validation and generalization of detailed neuronal models

Reva, Maria  
•
Rössert, Christian  
•
Arnaudon, Alexis  
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November 10, 2023
Patterns

Detailed single neuron modeling is widely used to study neuronal functions. While cellular and functional diversity across the mammalian cortex is vast, most of the available computational tools are dedicated to the reproduction of a small set of specific features characteristic of a single neuron. Here, we present a generalized automated workflow for the creation of robust electrical models and illustrate its performance by building cell models for the rat somatosensory cortex (SSCx). Each model is based on a 3D morphological reconstruction and a set of ionic mechanisms specific to the cell type. We use an evolutionary algorithm to optimize passive and active ionic parameters to match the electrophysiological features extracted from whole-cell patch-clamp recordings. To shed light on which parameters are constrained by experimental data and which could be degenerate, we perform a parameter sensitivity analysis. We also validate the optimized models against additional experimental stimuli and assess their generalizability on a population of morphologies with the same morphological type. With this workflow, we generate SSCx neuronal models producing the variability of neuronal responses. Due to its versatility, our workflow can be used to build robust biophysical models of any neuronal type.

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Type
research article
DOI
10.1016/j.patter.2023.100855
Web of Science ID

WOS:001111808600001

Author(s)
Reva, Maria  
Rössert, Christian  
Arnaudon, Alexis  
Damart, Tanguy  
Mandge, Darshan  
Tuncel, Anil  
Ramaswamy, Srikanth  
Markram, Henry  
Van Geit, Werner  
Date Issued

2023-11-10

Publisher

Cambridge

Published in
Patterns
Volume

4

Issue

11

Article Number

100855

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
BBP-CORE  
FunderGrant Number

Blue Brain Project

Swiss government's ETH Board of the Swiss Federal Institutes of Technology

Available on Infoscience
May 3, 2023
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/197258
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