Parameter Estimation of Hodgkin-Huxley Neuronal Model using Dual Extended Kalman Filter

Fitting biophysical models to real noisy data jointly with extracting fundamental biophysical parameters has recently stimulated tremendous studies in computational neuroscience. Hodgkin-Huxley (HH) neuronal model has been considered as the most detailed biophysical model for representing the dynamical behavior of the spiking neurons. In this paper, we derive, for the first time, the dual extended Kalman filtering (DEKF) approach for the HH neuronal model to track the dynamics and estimate the parameters of a single neuron from noisy recorded membrane voltage. As unscented Kalman filter (UKF) has been already applied to the HH model, a quantitative comparison between these methods is accomplished in our simulation for different signal to observation noise ratios. Our simulations demonstrate the high accuracy of DEKF in the prediction and estimation of hidden states and unknown parameters of the HH neuronal model. Faster implementation of DEKF (than UKF) makes it particularly useful in dynamic clamp technique.