Comparing data assimilation filters for parameter estimation in a neuron model

Data assimilation (DA) has proved to be an efficient framework for estimation problems in real-world complex dynamical systems arising in geoscience, and it has also begun to show its power in computational neuroscience. The ensemble Kalman filter (EnKF) is believed to be a powerful tool of DA in practice. In comparison to the other filtering methods of DA, such as the bootstrap filter (BF) and optimal sequential importance re-sampling (OPT-SIRS), it is considered more convenient in many applications, but with the theoretical flaw of Gaussian assumption. In this paper, we apply the EnKF, the BF and the OPT-SIRS to the estimation and prediction of a single computational neuron model with ten parameters and conduct a comparison study of these three DA filtering methods on this model. It is numerically shown that the EnKF presents the best performance in both accuracy and computation load. We argue that the EnKF will be a promising tool in the large-scale DA problem occurring in computational neuroscience with experimental data.