A combined method to estimate parameters of the thalamocortical model from a heavily noise-corrupted time series of action potential.
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.
Department of Electrical and Automation Engineering, Tianjin University, Tianjin, China.
Ruofan
R
Wang
Department of Electrical and Automation Engineering, Tianjin University, Tianjin, China.
Jiang
J
Wang
Department of Electrical and Automation Engineering, Tianjin University, Tianjin, China.
Bin
B
Deng
Department of Electrical and Automation Engineering, Tianjin University, Tianjin, China.
Chen
C
Liu
Department of Electrical and Automation Engineering, Tianjin University, Tianjin, China.
Xile
X
Wei
Department of Electrical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
K M
KM
Tsang
Department of Electrical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
W L
WL
Chan
D000200:Action Potentials; D002540:Cerebral Cortex; D006801:Humans; D008959:Models, Neurological; D009474:Neurons; D017711:Nonlinear Dynamics; D010300:Parkinson Disease / Q000503:physiopathology*; D013788:Thalamus