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Optimal control methods for nonlinear parameter estimation in biophysical neuron models

View ORCID ProfileNirag Kadakia
doi: https://doi.org/10.1101/2022.01.11.475951
Nirag Kadakia
1Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, USA
2Quantitative Biology Institute, Yale University, New Haven, CT, USA
3Swartz Foundation for Theoretical Neuroscience, Yale University, New Haven, CT, USA
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Article Information

doi 
https://doi.org/10.1101/2022.01.11.475951
History 
  • January 12, 2022.
Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.

Author Information

  1. Nirag Kadakia1,2,3,*
  1. 1Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, USA
  2. 2Quantitative Biology Institute, Yale University, New Haven, CT, USA
  3. 3Swartz Foundation for Theoretical Neuroscience, Yale University, New Haven, CT, USA
  1. * Corresponding author; email: niragkadakia{at}gmail.com
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Posted January 12, 2022.
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Optimal control methods for nonlinear parameter estimation in biophysical neuron models
Nirag Kadakia
bioRxiv 2022.01.11.475951; doi: https://doi.org/10.1101/2022.01.11.475951
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Optimal control methods for nonlinear parameter estimation in biophysical neuron models
Nirag Kadakia
bioRxiv 2022.01.11.475951; doi: https://doi.org/10.1101/2022.01.11.475951

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