Chimera in a network of memristor-based Hopfield neural network

被引：0
|
作者：
Fatemeh Parastesh
Sajad Jafari
Hamed Azarnoush
Boshra Hatef
Hamidreza Namazi
Dawid Dudkowski
机构：
[1] Amirkabir University of Technology,Department of Biomedical Engineering
[2] Neuroscience Research Center,undefined
[3] Baqiyatallah University of Medical Sciences,undefined
[4] School of Engineering,undefined
[5] Monash University,undefined
[6] Division of Dynamics,undefined
[7] Lodz University of Technology,undefined
来源：
The European Physical Journal Special Topics | 2019年 / 228卷
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学科分类号：
摘要：
Memristors have shown great potential to yield novel features in various domains. Therefore, memristor-based systems are being studied in widespread applications. In this paper, a newly proposed hyperbolic-type memristor-based Hopfield neural network is studied, as a single unit of a coupled network. Particularly, the effects of the coupling between each state variable of the system on the network behavior is investigated. It is observed that changing the coupling variable leads to different patterns at each coupling strength, including partial chimera state, chimera state, synchronization, imperfect synchronization and oscillation death. When the memristor-based elements are coupled with each other, increasing the coupling strength causes a regular transition from asynchronization to chimera state and then toward synchronization.
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页码：2023 / 2033
页数：10
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