ArticleJournal of neurophysiology2014
Automatic parameter estimation of multicompartmental neuron models via minimization of trace error with control adjustment.
Article in Journal of neurophysiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Neuronal excitability and parameter variability in the Hodgkin-Huxley model.PLoS computational biology · 2026Article
- Correcting model error bias in estimations of neuronal dynamics from time series observations.Scientific reports · 2026Article
- Quantitative prediction of intracellular dynamics and synaptic currents in a small neural circuit.Frontiers in computational neuroscience · 2025Article
- Estimation of ionic currents and compensation mechanisms from recursive piecewise assimilation of electrophysiological data.Frontiers in computational neuroscience · 2025Article
- A universal workflow for creation, validation, and generalization of detailed neuronal models.Patterns (New York, N.Y.) · 2023Article
- Article
- Estimation of neuron parameters from imperfect observations.PLoS computational biology · 2020Article
- Illuminating dendritic function with computational models.Nature reviews. Neuroscience · 2020Review
- Data Assimilation Methods for Neuronal State and Parameter Estimation.Journal of mathematical neuroscience · 2018Review
- All-Optical Electrophysiology for High-Throughput Functional Characterization of a Human iPSC-Derived Motor Neuron Model of ALS.Stem cell reports · 2018Article
- Parameter Optimization Using Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES), an Approach to Investigate Differences in Channel Properties Between Neuron Subtypes.Frontiers in neuroinformatics · 2018Article
- A stepwise neuron model fitting procedure designed for recordings with high spatial resolution: Application to layer 5 pyramidal cells.Journal of neuroscience methods · 2018Article
- Automated evolutionary optimization of ion channel conductances and kinetics in models of young and aged rhesus monkey pyramidal neurons.Journal of computational neuroscience · 2016Article
- Computational implications of biophysical diversity and multiple timescales in neurons and synapses for circuit performance.Current opinion in neurobiology · 2016Review
- Computational models in the age of large datasets.Current opinion in neurobiology · 2015Review
- Silicon central pattern generators for cardiac diseases.The Journal of physiology · 2015Review
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We describe a new technique to fit conductance-based neuron models to intracellular voltage traces from isolated biological neurons. The biological neurons are recorded in current-clamp with pink (1/f) noise injected to perturb the activity of the neuron. The new algorithm finds a set of parameters that allows a multicompartmental model neuron to match the recorded voltage trace. Attempting to match a recorded voltage trace directly has a well-known problem: mismatch in the timing of action potentials between biological and model neuron is inevitable and results in poor phenomenological match between the model and data. Our approach avoids this by applying a weak control adjustment to the model to promote alignment during the fitting procedure. This approach is closely related to the control theoretic concept of a Luenberger observer. We tested this approach on synthetic data and on data recorded from an anterior gastric receptor neuron from the stomatogastric ganglion of the crab Cancer borealis. To test the flexibility of this approach, the synthetic data were constructed with conductance models that were different from the ones used in the fitting model. For both synthetic and biological data, the resultant models had good spike-timing accuracy.
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Registered trials
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