Catalytic feed-forward explosive synchronization in multilayer networks

2021 ◽  
Vol 31 (12) ◽  
pp. 123130
Author(s):  
Vasundhara Rathore ◽  
Ajay Deep Kachhvah ◽  
Sarika Jalan
Keyword(s):  
Multilayer Networks ◽  
Feed Forward ◽  
Explosive Synchronization

scholarly journals Explosive Synchronization in Adaptive and Multilayer Networks

2015 ◽  
Vol 114 (3) ◽  
Author(s):  
Xiyun Zhang ◽  
Stefano Boccaletti ◽  
Shuguang Guan ◽  
Zonghua Liu
Keyword(s):  
Multilayer Networks ◽  
Explosive Synchronization

scholarly journals Explosive synchronization in mono and multilayer networks

2018 ◽  
Vol 23 (5) ◽  
pp. 1931-1944 ◽  
Author(s):  
Inmaculada Leyva ◽  
◽  
Irene Sendiña-Nadal ◽  
Stefano Boccaletti ◽  
◽  
...  
Keyword(s):  
Multilayer Networks ◽  
Explosive Synchronization

Artificial NeuroGlial Networks

2011 ◽  
pp. 167-171 ◽  
Author(s):  
Ana Belén Porto Pazos ◽  
Alberto Alvarellos González ◽  
Félix Montañés Pazos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Classification Problems ◽  
Multilayer Networks ◽  
Process Information ◽  
Feed Forward ◽  
Complex Problems ◽  
Artificial Neural ◽  
The Brain

More than 50 years ago connectionist systems (CSs) were created with the purpose to process information in the computers like the human brain (McCulloch & Pitts, 1943). Since that time these systems have advanced considerably and nowadays they allow us to resolve complex problems in many disciplines (classification, clustering, regression, etc.). But this advance is not enough. There are still a lot of limitations when these systems are used (Dorado, 1999). Mostly the improvements were obtained following two different ways. Many researchers have preferred the construction of artificial neural networks (ANNs) based in mathematic models with diverse equations which lead its functioning (Cortes & Vapnik, 1995; Haykin, 1999). Otherwise other researchers have pretended the most possibly to make alike these systems to human brain (Rabuñal, 1999; Porto, 2004). The systems included in this article have emerged following the second way of investigation. CSs which pretend to imitate the neuroglial nets of the brain are introduced. These systems are named Artificial NeuroGlial Networks (ANGNs) (Porto, 2004). These CSs are not only made of neuron, but also from elements which imitate glial neurons named astrocytes (Araque, 1999). These systems, which have hybrid training, have demonstrated efficacy when resolving classification problems with totally connected feed-forward multilayer networks, without backpropagation and lateral connections.

A Feed-Forward Time Amplifier Using a Phase Detector and Variable Delay Lines

2013 ◽  
Vol E96.C (6) ◽  
pp. 920-922 ◽  
Author(s):  
Kiichi NIITSU ◽  
Naohiro HARIGAI ◽  
Takahiro J. YAMAGUCHI ◽  
Haruo KOBAYASHI
Keyword(s):  
Phase Detector ◽  
Variable Delay ◽  
Delay Lines ◽  
Feed Forward ◽  
Time Amplifier
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