Nonlinear Observer-based Synchronization of Neuron Models
Synchronization is the asymptotic coincidence of the state vectors of two (or more) systems.
Synchronization phenomena among multiple subsystems have been studied in various publications using many kinds of models for a long time.
The Hindmarsh-Rose (HR) model is commonly used for neuron research. In this paper, a nonlinear observer-based synchronization is proposed for multiple HR models using observer error linearization method and a nonlinear separation principle.