Data assimilation methods for neuronal state and parameter estimation

DOI10.1186/s13408-018-0066-8zbMath1405.92050OpenAlexW2886172288WikidataQ57104059 ScholiaQ57104059MaRDI QIDQ1710228

Matthew J. Moye, Casey O. Diekman

Publication date: 16 January 2019

Published in: The Journal of Mathematical Neuroscience (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1186/s13408-018-0066-8

zbMATH Keywords

parameter estimationdata assimilationneuronal excitabilityconductance-based models

Mathematics Subject Classification ID

Neural biology (92C20)

Related Items (6)

Low-dimensional models of single neurons: a review ⋮ Adaptive unscented Kalman filter for neuronal state and parameter estimation ⋮ Special issue from the 2017 international conference on mathematical neuroscience ⋮ Robust design of inhibitory neuronal networks displaying rhythmic activity ⋮ Robust data assimilation with noise: Applications to cardiac dynamics ⋮ On temporal scale separation in coupled data assimilation with the ensemble Kalman filter

Uses Software

• Matlab
• L-BFGS-B
• EnKF
• L-BFGS
• LBFGS-B
• Neurofitter
• OPTI
• ADiGator
• GitHub
• L-BFGS-B-C

Cites Work

• Unnamed Item
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• Predicting the future. Completing models of observed complex systems
• Estimation in the partially observed stochastic Morris-Lecar neuronal model with particle filter and stochastic approximation methods
• Dynamical estimation of neuron and network properties. II: Path integral Monte Carlo methods
• Dynamical estimation of neuron and network properties. I: Variational methods
• Automated neuron model optimization techniques: a review
• Mathematical foundations of neuroscience
• Nonlinear statistical data assimilation for \(\mathrm{HVC}_{\mathrm{RA}}\) neurons in the Avian song system
• State and parameter estimation in stochastic dynamical models
• Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter
• A practical approach to parameter estimation applied to model predicting heart rate regulation
• An interior algorithm for nonlinear optimization that combines line search and trust region steps
• Identifiability of Linear and Linear-in-Parameters Dynamical Systems from a Single Trajectory
• Dynamical State and Parameter Estimation
• Inverse problems in systems biology
• Algorithm 778: L-BFGS-B
• A unifying view of synchronization for data assimilation in complex nonlinear networks
• Algorithm 984
• An Introduction to Trajectory Optimization: How to Do Your Own Direct Collocation
• NONLINEAR DYNAMICAL SYSTEM IDENTIFICATION FROM UNCERTAIN AND INDIRECT MEASUREMENTS
• An Interior Point Algorithm for Large-Scale Nonlinear Programming
• Data Assimilation
• Robustness of Solutions of the Inverse Problem for Linear Dynamical Systems with Uncertain Data
• Data Assimilation
• The ensemble Kalman filter for combined state and parameter estimation
• Data Assimilation
• A trust region method based on interior point techniques for nonlinear programming.

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