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Lookup NU author(s): Dr Srikanth RamaswamyORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2023 The Author(s). 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 focus on a limited 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. Each model is based on a 3D morphological reconstruction and a set of ionic mechanisms. We use an evolutionary algorithm to optimize neuronal parameters to match the electrophysiological features extracted from experimental data. Then we validate the optimized models against additional stimuli and assess their generalizability on a population of similar morphologies. Compared to the state-of-the-art canonical models, our models show 5-fold improved generalizability. This versatile approach can be used to build robust models of any neuronal type.
Author(s): Reva M, Rossert C, Arnaudon A, Damart T, Mandge D, Tuncel A, Ramaswamy S, Markram H, Van Geit W
Publication type: Article
Publication status: Published
Journal: Patterns
Year: 2023
Volume: 4
Issue: 11
Print publication date: 10/11/2023
Online publication date: 04/10/2023
Acceptance date: 12/09/2023
Date deposited: 05/03/2025
ISSN (electronic): 2666-3899
Publisher: Cell Press
URL: https://doi.org/10.1016/j.patter.2023.100855
DOI: 10.1016/j.patter.2023.100855
Data Access Statement: To illustrate the usage of our workflow, we prepared a set of Python notebooks: https://github.com/BlueBrain/SSCxEModelExamples. [See article for full data access statement.]
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