Synchronization of two coupled hindmarsh-Rose neurons
Author(s)
Ding, Ke
Han, Qing-Long
Griffith University Author(s)
Year published
2015
Metadata
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This paper is concerned with synchronization of two coupled Hind-marsh-Rose (HR) neurons. Two synchronization criteria are derived by using nonlinear feedback control and linear feedback control, respectively. A synchronization criterion for FitzHugh-Nagumo (FHN) neurons is derived as the application of control method of this paper. Compared with some existing synchronization results for chaotic systems, the contribution of this paper is that feedback gains are only dependent on system parameters, rather than dependent on the norm bounds of state variables of uncontrolled and controlled HR neurons. The effectiveness of our ...
View more >This paper is concerned with synchronization of two coupled Hind-marsh-Rose (HR) neurons. Two synchronization criteria are derived by using nonlinear feedback control and linear feedback control, respectively. A synchronization criterion for FitzHugh-Nagumo (FHN) neurons is derived as the application of control method of this paper. Compared with some existing synchronization results for chaotic systems, the contribution of this paper is that feedback gains are only dependent on system parameters, rather than dependent on the norm bounds of state variables of uncontrolled and controlled HR neurons. The effectiveness of our results are demonstrated by two simulation examples.
View less >
View more >This paper is concerned with synchronization of two coupled Hind-marsh-Rose (HR) neurons. Two synchronization criteria are derived by using nonlinear feedback control and linear feedback control, respectively. A synchronization criterion for FitzHugh-Nagumo (FHN) neurons is derived as the application of control method of this paper. Compared with some existing synchronization results for chaotic systems, the contribution of this paper is that feedback gains are only dependent on system parameters, rather than dependent on the norm bounds of state variables of uncontrolled and controlled HR neurons. The effectiveness of our results are demonstrated by two simulation examples.
View less >
Journal Title
Kybernetika
Volume
51
Issue
5
Subject
Statistics not elsewhere classified
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