A combined method to estimate parameters of neuron from a heavily noise-corrupted time series of active potential
A combined method to estimate parameters of neuron from a heavily noise-corrupted time series of active potential
复制标题
一种从严重噪声破坏的活动电位时间序列中估计神经元参数的组合方法
DOI：
10.1063/1.3092907
复制
发表时间：
2009-03-01
期刊：
影响因子：
2.9
通讯作者：
Che, Yenqiu
中科院分区：
文献类型：
--
作者：
Deng, Bin;Wang, Jiang;Che, Yenqiu
提出了一种将无气味卡尔曼滤波（UKF）与同步参数估计技术相结合的方法，用于仅给定一个严重噪声破坏的活动电位时间序列时神经元的未知参数估计。与其他基于同步的方法相比，该方法使用UKF估计的状态变量而不是测量数据来驱动辅助系统。基于同步的方法为从时间序列中估计参数提供了一种系统的分析方法；然而，它仅对测量的弱噪声具有鲁棒性，因此使用UKF来估计状态变量，而基于同步的方法则使用UKF来估计神经元模型的所有未知参数。结果表明，当测量数据受到严重噪声干扰时，该方法的估计精度远高于单纯使用UKF或基于同步的方法。
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.