Stochastic Synchronization and Signal Rectification in the Crayfish Caudal Photoreceptor

2003 ◽  
Author(s):  
Sonya Bahar
Keyword(s):  
Stochastic Synchronization ◽  
Caudal Photoreceptor

scholarly journals Stochastic synchronization of dynamics on the human connectome

NeuroImage ◽  
2021 ◽  
Vol 229 ◽  
pp. 117738
Author(s):  
James C. Pang ◽  
Leonardo L. Gollo ◽  
James A. Roberts
Keyword(s):  
Stochastic Synchronization

scholarly journals Stochastic synchronization of genetic oscillator networks

2007 ◽  
Vol 1 (1) ◽  
Author(s):  
Chunguang Li ◽  
Luonan Chen ◽  
Kazuyuki Aihara
Keyword(s):  
Stochastic Synchronization ◽  
Oscillator Networks ◽  
Genetic Oscillator

Stochastic synchronization of complex networks via a novel adaptive composite nonlinear feedback controller

2014 ◽  
Vol 80 (1-2) ◽  
pp. 363-374 ◽  
Author(s):  
Weiping Wang ◽  
Lixiang Li ◽  
Haipeng Peng ◽  
Jürgen Kurths ◽  
Jinghua Xiao ◽  
...  
Keyword(s):  
Complex Networks ◽  
Feedback Controller ◽  
Nonlinear Feedback ◽  
Composite Nonlinear Feedback ◽  
Stochastic Synchronization ◽  
Adaptive Composite

scholarly journals Adaptive stochastic synchronization of delayed reaction–diffusion neural networks

2020 ◽  
Vol 53 (3-4) ◽  
pp. 378-389 ◽  
Author(s):  
Weiyuan Zhang ◽  
Junmin Li ◽  
Jinghan Sun ◽  
Minglai Chen
Keyword(s):  
Neural Networks ◽  
Feedback Control ◽  
Sufficient Conditions ◽  
Reaction Diffusion ◽  
Linear Matrix ◽  
Output Coupling ◽  
Delayed Reaction ◽  
Adaptive Feedback ◽  
Adaptive Feedback Control ◽  
Stochastic Synchronization

In this paper, we deal with the adaptive stochastic synchronization for a class of delayed reaction–diffusion neural networks. By combing Lyapunov–Krasovskii functional, drive-response concept, the adaptive feedback control scheme, and linear matrix inequality method, we derive some sufficient conditions in terms of linear matrix inequalities ensuring the stochastic synchronization of the addressed neural networks. The output coupling with delay feedback and the update laws of parameters for adaptive feedback control are proposed, which will be of significance in the real application. The novel Lyapunov–Krasovskii functional to be constructed is more general. The derived results depend on the measure of the space, diffusion effects, and the upper bound of derivative of time-delay. Finally, an illustrated example is presented to show the effectiveness and feasibility of the proposed scheme.

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