Topology Identification of the Hindmarsh-Rose Model via Deterministic Learning

Abstract

In this paper, the complex dynamic behavior of the Hindmarsh-Rose (HR) model which characterizes the neuron cell is analyzed numerically. And the unknown topology of the system in dynamic environment is locally accurately identified based on the deterministic learning (DL) algorithm. Firstly, the influence of different parameters on the dynamic behavior of the HR model are investigated. Then, the nonlinear dynamics of the HR model under unknown dynamic environment is locally accurately identified. In addition, the identified system dynamics can be stored in the form of constant neural network. The achievement of this work can provide more incentives and possibilities for the application of HR model in clinic and other related researches. Simulation studies are included to demonstrate the effectiveness.