ReviewJournal of mathematical neuroscience2018
Data Assimilation Methods for Neuronal State and Parameter Estimation.
Review in Journal of mathematical neuroscience, 2018. 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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13 citing papers in PubMed.
- Deterministic, stochastic, and mean-field PDE models in neuroscience.Frontiers in computational neuroscience · 2026Review
- Digital twin brain simulator for real-time consciousness monitoring and virtual intervention using primate electrocorticogram data.NPJ digital medicine · 2025Article
- Reconstructing ventricular cardiomyocyte dynamics and parameter estimation using data assimilation.Biophysical journal · 2024Article
- Low-dimensional models of single neurons: a review.Biological cybernetics · 2023Review
- Adaptive unscented Kalman filter for neuronal state and parameter estimation.Journal of computational neuroscience · 2023Article
- Optimal control methods for nonlinear parameter estimation in biophysical neuron models.PLoS computational biology · 2022Article
- A data-assimilation approach to predict population dynamics during epithelial-mesenchymal transition.Biophysical journal · 2022Article
- The brainstem connectome database.Scientific data · 2022Article
- The E3 ubiquitin ligase adaptorProceedings of the National Academy of Sciences of the United States of America · 2021Article
- Robust data assimilation with noise: Applications to cardiac dynamics.Chaos (Woodbury, N.Y.) · 2021Article
- Using Data Assimilation for Quantitative Electroencephalography Analysis.Brain sciences · 2020Article
- Cell Fate Forecasting: A Data-Assimilation Approach to Predict Epithelial-Mesenchymal Transition.Biophysical journal · 2020Article
- Special Issue from the 2017 International Conference on Mathematical Neuroscience.Journal of mathematical neuroscience · 2019Article
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Abstract
This tutorial illustrates the use of data assimilation algorithms to estimate unobserved variables and unknown parameters of conductance-based neuronal models. Modern data assimilation (DA) techniques are widely used in climate science and weather prediction, but have only recently begun to be applied in neuroscience. The two main classes of DA techniques are sequential methods and variational methods. We provide computer code implementing basic versions of a method from each class, the Unscented Kalman Filter and 4D-Var, and demonstrate how to use these algorithms to infer several parameters of the Morris-Lecar model from a single voltage trace. Depending on parameters, the Morris-Lecar model exhibits qualitatively different types of neuronal excitability due to changes in the underlying bifurcation structure. We show that when presented with voltage traces from each of the various excitability regimes, the DA methods can identify parameter sets that produce the correct bifurcation structure even with initial parameter guesses that correspond to a different excitability regime. This demonstrates the ability of DA techniques to perform nonlinear state and parameter estimation and introduces the geometric structure of inferred models as a novel qualitative measure of estimation success. We conclude by discussing extensions of these DA algorithms that have appeared in the neuroscience literature.
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