Parameter Estimation for Hindmarsh-Rose Neurons

In the paper, a new adaptive model of a neuron based on the Hindmarsh-Rose third-order model of a single neuron is proposed. The learning algorithm for adaptive identification of the neuron parameters is proposed and analyzed both theoretically and by computer simulation. The proposed algorithm is based on the Lyapunov functions approach and reduced adaptive observer. It allows one to estimate parameters of the population of the neurons if they are synchronized. The rigorous stability conditions for synchronization and identification are presented.

Automated summary

In this paper, a new adaptive model for neurons based on the Hindmarsh-Rose third-order model is proposed. The learning algorithm for adaptive identification of neuron parameters is analyzed theoretically and through computer simulation. The algorithm is based on the Lyapunov functions approach and a reduced adaptive observer, allowing for parameter estimation of synchronized neuron populations. Rigorous stability conditions for synchronization and identification are presented.