TY - JOUR

T1 - Parameter Estimation for Hindmarsh–Rose Neurons

AU - Fradkov, Alexander L.

AU - Kovalchukov, Aleksandr

AU - Andrievsky, Boris

N1 - Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2022/3/11

Y1 - 2022/3/11

N2 - 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.

AB - 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.

KW - Adaptation

KW - Hindmarsh–Rose neuron

KW - Modeling

KW - Parameter estimation

KW - Persistent excitation

KW - Speed gradient

UR - http://www.scopus.com/inward/record.url?scp=85128473487&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/05f5fb4c-d983-3d7d-90d2-dcc163e58a33/

U2 - 10.3390/electronics11060885

DO - 10.3390/electronics11060885

M3 - Article

AN - SCOPUS:85128473487

VL - 11

JO - Electronics (Switzerland)

JF - Electronics (Switzerland)

SN - 2079-9292

IS - 6

M1 - 885

ER -