Before revision:  We compare the proposed method to an EnKF-based parameter drift algorithm, where periodicity is not guaranteed , using synthetic data generated from the  FitzHugh-Nagumo system which models the spiking dynamics of a neuron.
After revision:  We employ nonlinear filtering, more specifically, a version of the augmented ensemble Kalman filter (EnKF), to estimate the unknown coefficients comprising the piecewise approximation of the periodic, time-varying parameter. This allows for straightforward comparison of the proposed method with an EnKF-based parameter drift algorithm, where periodicity is not guaranteed , using synthetic data generated from the FitzHugh-Nagumo system which models the spiking dynamics of a neuron.