ArticleFrontiers in neuroscience2007
A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data.
Article in Frontiers in neuroscience, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 128 papers.
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128 citing papers in PubMed.
- Accurately fitting biophysical neuron models to experimental voltage data enabled by meta-learning.Research square · 2026Article
- Neuronal excitability and parameter variability in the Hodgkin-Huxley model.PLoS computational biology · 2026Article
- eFEL: electrophysiology feature extraction library.Bioinformatics (Oxford, England) · 2026Article
- Fast reconstruction of degenerate populations of conductance-based neuron models from spike times.PLoS computational biology · 2026Article
- Towards model-based characterization of individual electrically stimulated nerve fibers.PLoS computational biology · 2026Article
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- How to train your neuron: developing a detailed, up-to-date, multipurpose model of hippocampal CA1 pyramidal cells.Frontiers in computational neuroscience · 2026Article
- A genetic algorithm for self-supervised models of oscillatory neurodynamics.bioRxiv : the preprint server for biology · 2025Article
- Automated inference of disease mechanisms in patient-hiPSC-derived neuronal networks.Communications biology · 2025Article
- Visual activity enhances neuronal excitability in thalamic relay neurons.Science advances · 2025Article
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- Neuroevolution insights into biological neural computation.Science (New York, N.Y.) · 2025Article
- Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework.PLoS computational biology · 2024Article
- Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference.PLoS computational biology · 2024Article
- Zebrafish brain atlases: a collective effort for a tiny vertebrate brain.Neurophotonics · 2023Article
- KCNQ2/3 Gain-of-Function Variants and Cell Excitability: Differential Effects in CA1 versus L2/3 Pyramidal Neurons.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Ion-channel degeneracy and heterogeneities in the emergence of complex spike bursts in CA3 pyramidal neurons.The Journal of physiology · 2023Article
- Simulations of cortical networks using spatially extended conductance-based neuronal models.The Journal of physiology · 2023Review
- Biological complexity facilitates tuning of the neuronal parameter space.PLoS computational biology · 2023Article
- Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference.bioRxiv : the preprint server for biology · 2023Article
68 more citing papers are in PubMed but not listed here.
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6 authors.
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Abstract
We present a novel framework for automatically constraining parameters of compartmental models of neurons, given a large set of experimentally measured responses of these neurons. In experiments, intrinsic noise gives rise to a large variability (e.g., in firing pattern) in the voltage responses to repetitions of the exact same input. Thus, the common approach of fitting models by attempting to perfectly replicate, point by point, a single chosen trace out of the spectrum of variable responses does not seem to do justice to the data. In addition, finding a single error function that faithfully characterizes the distance between two spiking traces is not a trivial pursuit. To address these issues, one can adopt a multiple objective optimization approach that allows the use of several error functions jointly. When more than one error function is available, the comparison between experimental voltage traces and model response can be performed on the basis of individual features of interest (e.g., spike rate, spike width). Each feature can be compared between model and experimental mean, in units of its experimental variability, thereby incorporating into the fitting this variability. We demonstrate the success of this approach, when used in conjunction with genetic algorithm optimization, in generating an excellent fit between model behavior and the firing pattern of two distinct electrical classes of cortical interneurons, accommodating and fast-spiking. We argue that the multiple, diverse models generated by this method could serve as the building blocks for the realistic simulation of large neuronal networks.
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