Data Assimilation Methods for Neuronal State and Parameter Estimation Article Swipe
Matthew Moye
,
Casey O. Diekman
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1186/s13408-018-0066-8
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1186/s13408-018-0066-8
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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- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1186/s13408-018-0066-8
- https://mathematical-neuroscience.springeropen.com/track/pdf/10.1186/s13408-018-0066-8
- OA Status
- hybrid
- Cited By
- 38
- References
- 78
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2886172288
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https://openalex.org/W2886172288Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s13408-018-0066-8Digital Object Identifier
- Title
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Data Assimilation Methods for Neuronal State and Parameter EstimationWork title
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-08-09Full publication date if available
- Authors
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Matthew Moye, Casey O. DiekmanList of authors in order
- Landing page
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https://doi.org/10.1186/s13408-018-0066-8Publisher landing page
- PDF URL
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https://mathematical-neuroscience.springeropen.com/track/pdf/10.1186/s13408-018-0066-8Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://mathematical-neuroscience.springeropen.com/track/pdf/10.1186/s13408-018-0066-8Direct OA link when available
- Concepts
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Computer science, Data assimilation, Kalman filter, Nonlinear system, Estimation theory, Bifurcation, Artificial intelligence, Filter (signal processing), State (computer science), Machine learning, Algorithm, Computer vision, Quantum mechanics, Meteorology, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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38Total citation count in OpenAlex
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2025: 4, 2024: 2, 2023: 5, 2022: 11, 2021: 5Per-year citation counts (last 5 years)
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78Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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