ArticleScientific reports2016
Automatic Construction of Predictive Neuron Models through Large Scale Assimilation of Electrophysiological Data.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 43 citations in OpenAlex.
- Fast reconstruction of degenerate populations of conductance-based neuron models from spike times.PLoS computational biology · 2026Article
- Correcting model error bias in estimations of neuronal dynamics from time series observations.Scientific reports · 2026Article
- Estimation of ionic currents and compensation mechanisms from recursive piecewise assimilation of electrophysiological data.Frontiers in computational neuroscience · 2025Article
- Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework.PLoS computational biology · 2024Article
- Single shot detection of alterations across multiple ionic currents from assimilation of cell membrane dynamics.Scientific reports · 2024Article
- Cellular rules underlying psychedelic control of prefrontal pyramidal neurons.bioRxiv : the preprint server for biology · 2023Article
- Optimal control methods for nonlinear parameter estimation in biophysical neuron models.PLoS computational biology · 2022Article
- Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models.Frontiers in neuroinformatics · 2022Article
- Identifying the measurements required to estimate rates of COVID-19 transmission, infection, and detection, using variational data assimilation.Infectious Disease Modelling · 2021Article
- Estimation of neuron parameters from imperfect observations.PLoS computational biology · 2020Article
- Optimal solid state neurons.Nature communications · 2019Article
- Exploring the molecular basis of neuronal excitability in a vocal learner.BMC genomics · 2019Article
- Kernel Reconstruction for Delayed Neural Field Equations.Journal of mathematical neuroscience · 2018Article
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
Abstract
We report on the construction of neuron models by assimilating electrophysiological data with large-scale constrained nonlinear optimization. The method implements interior point line parameter search to determine parameters from the responses to intracellular current injections of zebra finch HVC neurons. We incorporated these parameters into a nine ionic channel conductance model to obtain completed models which we then use to predict the state of the neuron under arbitrary current stimulation. Each model was validated by successfully predicting the dynamics of the membrane potential induced by 20-50 different current protocols. The dispersion of parameters extracted from different assimilation windows was studied. Differences in constraints from current protocols, stochastic variability in neuron output, and noise behave as a residual temperature which broadens the global minimum of the objective function to an ellipsoid domain whose principal axes follow an exponentially decaying distribution. The maximum likelihood expectation of extracted parameters was found to provide an excellent approximation of the global minimum and yields highly consistent kinetics for both neurons studied. Large scale assimilation absorbs the intrinsic variability of electrophysiological data over wide assimilation windows. It builds models in an automatic manner treating all data as equal quantities and requiring minimal additional insight.
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