A combined method to estimate parameters of neuron from a heavily noise-corrupted time series of active potential

DOI10.1063/1.3092907zbMath1311.92044OpenAlexW2031195633WikidataQ46723179 ScholiaQ46723179MaRDI QIDQ5251428

Bin Deng, Yenqiu Che, Jiang Wang

Publication date: 20 May 2015

Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1063/1.3092907

zbMATH Keywords

unscented Kalman filternoise-corrupted time seriessynchronization-based parameter estimation

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Neural biology (92C20)

Related Items (6)

Robust stabilization control of bifurcations in Hodgkin-Huxley model with aid of unscented Kalman filter ⋮ A combined method to estimate parameters of the thalamocortical model from a heavily noise-corrupted time series of action potential ⋮ Parameter estimation of the FitzHugh-Nagumo model using noisy measurements for membrane potential ⋮ Parameter estimation of slow potassium dynamics in a neuron model for seizure-like activity via adaptive lag synchronization and unscented Kalman filter ⋮ Estimating and adjusting abnormal networks with unknown parameters and topology ⋮ Parameter Estimation in Multiple Dynamic Synaptic Coupling Model Using Bayesian Point Process State-Space Modeling Framework

Cites Work

• Parametric, nonparametric and parametric modelling of a chaotic circuit time series
• Adaptive-feedback control algorithm
• Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems
• NONLINEAR DYNAMICAL SYSTEM IDENTIFICATION FROM UNCERTAIN AND INDIRECT MEASUREMENTS
• THE UNSCENTED KALMAN FILTER, A POWERFUL TOOL FOR DATA ANALYSIS

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