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

Authors
Type
Published Article
Journal
Chaos An Interdisciplinary Journal of Nonlinear Science
1089-7682
Publisher
American Institute of Physics
Publication Date
Volume
19
Issue
1
Pages
15105–15105
Identifiers
DOI: 10.1063/1.3092907
PMID: 19335009
Source
Medline
License
Unknown

Abstract

A method that combines the means of unscented Kalman filter (UKF) with the technique of synchronization-based parameter estimation is introduced for estimating unknown parameters of neuron when only a heavily noise-corrupted time series of active potential is given. Compared with other synchronization-based methods, this approach uses the state variables estimated by UKF instead of the measured data to drive the auxiliary system. The synchronization-based approach supplies a systematic and analytical procedure for estimating parameters from time series; however, it is only robust against weak noise of measurement, so the UKF is employed to estimate state variables which are used by the synchronization-based method to estimate all unknown parameters of neuron model. It is found out that the estimation accuracy of this combined method is much higher than only using UKF or synchronization-based method when the data of measurement were heavily noise corrupted.

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