ArticleiScience2023
Controlling morpho-electrophysiological variability of neurons with detailed biophysical models.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Neuronal excitability and parameter variability in the Hodgkin-Huxley model.PLoS computational biology · 2026Article
- eFEL: electrophysiology feature extraction library.Bioinformatics (Oxford, England) · 2026Article
- Morphological details contribute to neuronal response variability within the same cell type.Frontiers in cellular neuroscience · 2026Article
- Signal-based differentiation of essential and dystonic tremor.Frontiers in neurology · 2026Article
- How to train your neuron: developing a detailed, up-to-date, multipurpose model of hippocampal CA1 pyramidal cells.Frontiers in computational neuroscience · 2026Article
- Combined statistical-biophysical modeling links ion channel genes to physiology of cortical neuron types.Patterns (New York, N.Y.) · 2025Article
- Higher-order interactions in neuronal function: From genes to ionic currents in biophysical models.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Biophysical modeling of thalamic reticular nucleus subpopulations and their differential contribution to spindle dynamics.iScience · 2025Article
- Generating brain-wide connectome using synthetic axonal morphologies.Nature communications · 2025Article
- A multiscale electro-metabolic model of a rat neocortical circuit reveals the impact of ageing on central cortical layers.PLoS computational biology · 2025Article
- Morphological variability may limit single-cell specificity to electric field stimulation.Frontiers in synaptic neuroscience · 2025Article
- A universal workflow for creation, validation, and generalization of detailed neuronal models.Patterns (New York, N.Y.) · 2023Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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