Broad range of neural dynamics from a time-varying FitzHugh-Nagumo model and its spiking threshold estimation.

We study the use of the FitzHugh-Nagumo (FHN) model for capturing neural spiking. The FHN model is a widely used approximation of the Hodgkin-Huxley model that has significant limitations. In particular, it cannot produce the key spiking behavior of bursting. We illustrate that by allowing time-varying parameters for the FHN model, these limitations can be overcome while retaining its low-order complexity. This extension has applications in modeling neural spiking behaviors in the thalamus and the respiratory center. We demonstrate the use of the FHN model from an estimation perspective by presenting a novel parameter estimation method that exploits its multiple time-scale properties, and compare the performance of this method with the extended Kalman filter in several illustrative examples. We demonstrate that the dynamics of the spiking threshold can be recovered even in the absence of complete specifications for the system.