Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27848
Title: A combined method to estimate parameters of the thalamocortical model from a heavily noise-corrupted time series of action potential
Authors: Wang, R
Wang, J
Deng, B
Liu, C
Wei, X
Tsang, KM 
Chan, WL 
Issue Date: 2014
Publisher: American Institute of Physics
Source: Chaos, 2014, v. 24, no. 1, e5415 How to cite?
Journal: Chaos 
Abstract: A combined method composing of the unscented Kalman filter (UKF) and the synchronization-based method is proposed for estimating electrophysiological variables and parameters of a thalamocortical (TC) neuron model, which is commonly used for studying Parkinson's disease for its relay role of connecting the basal ganglia and the cortex. In this work, we take into account the condition when only the time series of action potential with heavy noise are available. Numerical results demonstrate that not only this method can estimate model parameters from the extracted time series of action potential successfully but also the effect of its estimation is much better than the only use of the UKF or synchronization-based method, with a higher accuracy and a better robustness against noise, especially under the severe noise conditions. Considering the rather important role of TC neuron in the normal and pathological brain functions, the exploration of the method to estimate the critical parameters could have important implications for the study of its nonlinear dynamics and further treatment of Parkinson's disease.
URI: http://hdl.handle.net/10397/27848
ISSN: 1054-1500
EISSN: 1089-7682
DOI: 10.1063/1.4867658
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