Optimal control methods for nonlinear parameter estimation in biophysical neuron models

Nirag Kadakia

doi:10.1371/journal.pcbi.1010479

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Peer-reviewed

Research Article

Optimal control methods for nonlinear parameter estimation in biophysical neuron models

Nirag Kadakia

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Software, Validation, Writing – original draft, Writing – review & editing

* E-mail: nirag.kadakia@yale.edu

Affiliations Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, United States of America, Quantitative Biology Institute, Yale University, New Haven, CT, United States of America, Swartz Foundation for Theoretical Neuroscience, Yale University, New Haven, CT, United States of America

https://orcid.org/0000-0001-9978-6450

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Optimal control methods for nonlinear parameter estimation in biophysical neuron models

Nirag Kadakia

Published: September 15, 2022
https://doi.org/10.1371/journal.pcbi.1010479

About the Authors

Nirag Kadakia

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Software, Validation, Writing – original draft, Writing – review & editing

* E-mail: nirag.kadakia@yale.edu

Affiliations Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, United States of America, Quantitative Biology Institute, Yale University, New Haven, CT, United States of America, Swartz Foundation for Theoretical Neuroscience, Yale University, New Haven, CT, United States of America

https://orcid.org/0000-0001-9978-6450

Competing Interests

The authors have declared that no competing interests exist.

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Neurons
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Nonlinear dynamics
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Nonlinear systems
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Dynamical systems
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Optimization
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Action potentials
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Algorithms
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Kalman filter
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