Dynamical estimation of neuron and network properties. I: Variational methods

DOI10.1007/s00422-011-0459-1zbMath1248.92015OpenAlexW2075691372WikidataQ45336508 ScholiaQ45336508MaRDI QIDQ714142

Daniel Margoliash, Mark Kostuk, Bryan A. Toth, C. Daniel Meliza, Henry D. I. Abarbanel

Publication date: 19 October 2012

Published in: Biological Cybernetics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc5759962

zbMATH Keywords

data assimilationneuronal dynamicsion channel properties

Mathematics Subject Classification ID

Related Items (8)

Reduced-Dimension, Biophysical Neuron Models Constructed From Observed Data ⋮ Predicting the behavior of sparsely-sampled systems across neurobiology and epidemiology ⋮ Nonlinear statistical data assimilation for \(\mathrm{HVC}_{\mathrm{RA}}\) neurons in the Avian song system ⋮ Dynamical estimation of neuron and network properties. II: Path integral Monte Carlo methods ⋮ Adaptive unscented Kalman filter for neuronal state and parameter estimation ⋮ On a framework of data assimilation for hyperparameter estimation of spiking neuronal networks ⋮ Data assimilation methods for neuronal state and parameter estimation ⋮ Dynamical estimation of neuron and network properties. I: Variational methods

Uses Software

• EnKF
• SNOPT
• NEURON
• Ipopt

Cites Work

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• Effective actions for statistical data assimilation
• State and parameter estimation in nonlinear systems as an optimal tracking problem
• Dynamical estimation of neuron and network properties. I: Variational methods
• HCIZ integral and 2D Toda lattice hierarchy
• On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
• Dynamical State and Parameter Estimation
• SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization
• Effects of Configuration Interaction on Intensities and Phase Shifts
• Data Assimilation

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