scholarly journals A small change in neuronal network topology can induce explosive synchronization transition and activity propagation in the entire network

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Zhenhua Wang ◽  
Changhai Tian ◽  
Mukesh Dhamala ◽  
Zonghua Liu
Keyword(s):  
Neuronal Network ◽  
Network Topology ◽  
Synchronization Transition ◽  
Small Change ◽  
Entire Network ◽  
Explosive Synchronization

scholarly journals The Synchronization and Synchronization Transition of Coupled Fractional-order Neuronal Networks

2021 ◽  
Author(s):  
Xin Yang ◽  
GuangJun Zhang ◽  
XueRen Li ◽  
Dong Wang
Keyword(s):  
Electromagnetic Radiation ◽  
Fractional Order ◽  
Neuronal Network ◽  
Neuronal Networks ◽  
Phase Synchronization ◽  
Chaotic Synchronization ◽  
Feedback Gain ◽  
Synchronization Transition ◽  
Conductance Parameter ◽  
Small Change

Abstract Different from the previous researches on the synchronization and synchronization transition of neuronal networks constructed by integer-order neuronal models, the synchronization and synchronization transition of fractional-order neuronal network are investigated in this paper. The fractional-order ring neuronal network constructed by fractional-order HindmarshRose (HR) neuronal models without electromagnetic radiation are proposed, and it’s synchronization behaviors are investigated numerically. The synchronization behaviors of two coupled fractional-order neuronal models and ring neuronal network under electromagnetic radiation are studied numerically. By research results, several novel phenomena and conclusions can be drawn. First, for the fractional-order HR model’s ring neuronal network without electromagnetic radiation, if the fractional-order q is changed, the threshold of the coupling strength when the network is in perfect synchronization will change. Furthermore, the change of fractional-order can induce the transition of periodic synchronization and chaotic synchronization. Second, for the two coupled neurons under electromagnetic radiation, the synchronization degree is influenced by fractional-order and the feedback gain parameter k1 . In addition, the fractional-order and parameter k1 can induce the synchronization transition of bursting synchronization, perfect synchronization and phase synchronization. For the perfect synchronization, the synchronization transition of chaotic synchronization and periodic synchronization induced by q and parameter k1 is also observed. Especially, When the fractionalorder is small, like 0.6, the synchronization behavior will be more complex. Third, for the ring neuronal network under electromagnetic radiation, with the change of memory-conductance parameter β, parameter k1 and fractional-order q of electromagnetic radiation, the synchronization behaviors are different. When β > 0.02 , the synchronization will be strengthened with the decreasing of fractional-order. The parameter k1 can induce the synchronization transition of perfect periodic10 synchronization, perfect periodic-7 synchronization, perfect periodic-5 synchronization and perfect periodic4 synchronization. It is hard for the system to synchronize and q has little effect on the synchronization when −0.06 < β < 0.02 . When β < −0.06 , the network moves directly from asynchronization to perfect synchronization, and the synchronization factor goes from 0.1 to 1 with the small change of fractional-order. Larger the factional-order is, larger the range of synchronization is. The synchronization degree increases with the increasing of k1.

Effects of Time Delay on Burst Synchronization Transition of Neuronal Networks

2018 ◽  
Vol 28 (12) ◽  
pp. 1850143 ◽  
Author(s):  
Xiaojuan Sun ◽  
Tianshu Xue
Keyword(s):  
Time Delay ◽  
Neuronal Network ◽  
Coupling Strength ◽  
Neuronal Networks ◽  
The Other ◽  
Electrical Synapses ◽  
Synchronization Transition ◽  
Burst Synchronization ◽  
Chemical Synapses ◽  
Bursting Activity

In this paper, we focus on investigating the effects of time delay on burst synchronization transitions of a neuronal network which is locally modeled by Hindmarsh–Rose neurons. Here, neurons inside the neuronal network are connected through electrical synapses or chemical synapses. With the numerical results, it is revealed that burst synchronization transitions of both electrically and chemically coupled neuronal networks could be induced by time delay just when the coupling strength is large enough. Meanwhile, it is found that, in electrically and excitatory chemically coupled neuronal networks, burst synchronization transitions are observed through change of spiking number per burst when coupling strength is large enough; while in inhibitory chemically coupled neuronal network, burst synchronization transitions are observed for large enough coupling strength through changing fold-Hopf bursting activity to fold-homoclinic bursting activity and vice versa. Namely, two types of burst synchronization transitions are observed. One type of burst synchronization transitions occurs through change of spiking numbers per burst and the other type of burst synchronization transition occurs through change of bursting types.

scholarly journals Delay-induced synchronization transition in a small-world neuronal network of FitzHugh–Nagumo neurons subjected to sine-Wiener bounded noise

2019 ◽  
Vol 33 (08) ◽  
pp. 1950053 ◽  
Author(s):  
Yuangen Yao ◽  
Ming Yi ◽  
Dejia Hou
Keyword(s):  
Neuronal Network ◽  
Correlation Time ◽  
Small World ◽  
Nearest Neighbors ◽  
Spatiotemporal Patterns ◽  
Small World Network ◽  
Bounded Noise ◽  
Future Studies ◽  
Synchronization Transition ◽  
Brain Functions

Noise and delay are ubiquitous in brain and they have significant effects on neuronal network synchronization and even brain functions. Based on a small-world neuronal network of delayed FitzHugh–Nagumo (FHN) neurons subjected to sine-Wiener (SW) bounded noise, the effects of delay and SW noise on synchronization and synchronization transition are numerically investigated by calculating a synchronization measure R and plotting spatiotemporal patterns. The phenomenon of delay-induced synchronization transition is observed as delay [Formula: see text] is increased. And large self-correlation time and strength of SW noise can increase the number of delay-induced synchronization transition. In addition, delay-induced synchronization transition is robust against the change of topology structure of neuronal network and this phenomenon becomes much easier to see for small nearest neighbors k in the small-world network. Since synchronization transition may imply functional switch, our results may have important implications, and inspire future studies.

Discuss the Design Method of Topology Discovery System Based on SNMP

2011 ◽  
Vol 267 ◽  
pp. 594-598
Author(s):  
Su Jing Xue
Keyword(s):  
Network Management ◽  
Network Topology ◽  
Computer Network ◽  
Design Method ◽  
Topology Discovery ◽  
Safe Operation ◽  
Effect Factor ◽  
Effective Network ◽  
Discovery Method ◽  
Entire Network

Since the network was born, the network management has been the material effect factor which the computer network develops. Taking the reasonable network topology technology has already become the entire network management foundation,and for the isomerism, diverse and changeable network, the importance of network topology survey discovery is also enhancing. studying the highly effective network topology discovery method has the quite vital significance and the value to guarantee the network effective and safe operation.

scholarly journals Hiding in Multilayer Networks

2020 ◽  
Vol 34 (01) ◽  
pp. 1021-1028
Author(s):  
Marcin Waniek ◽  
Tomasz Michalak ◽  
Talal Rahwan
Keyword(s):  
Social Networks ◽  
Network Topology ◽  
Centrality Measures ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Strategic Manipulation ◽  
Np Complete ◽  
Complex Relationships ◽  
Entire Network

Multilayer networks allow for modeling complex relationships, where individuals are embedded in multiple social networks at the same time. Given the ubiquity of such relationships, these networks have been increasingly gaining attention in the literature. This paper presents the first analysis of the robustness of centrality measures against strategic manipulation in multilayer networks. More specifically, we consider an “evader” who strategically chooses which connections to form in a multilayer network in order to obtain a low centrality-based ranking—thereby reducing the chance of being highlighted as a key figure in the network—while ensuring that she remains connected to a certain group of people. We prove that determining an optimal way to “hide” is NP-complete and hard to approximate for most centrality measures considered in our study. Moreover, we empirically evaluate a number of heuristics that the evader can use. Our results suggest that the centrality measures that are functions of the entire network topology are more robust to such a strategic evader than their counterparts which consider each layer separately.

IN-PHASE BURST SYNCHRONIZATION AND RHYTHM DYNAMICS OF COMPLEX NEURONAL NETWORKS

2012 ◽  
Vol 22 (05) ◽  
pp. 1250101 ◽  
Author(s):  
XIA SHI ◽  
QISHAO LU ◽  
HAIXIA WANG
Keyword(s):  
Neuronal Network ◽  
Network Topology ◽  
Coupling Strength ◽  
Neuronal Networks ◽  
Quantitative Characteristic ◽  
Coupling Scheme ◽  
Average Width ◽  
Bursting Neurons ◽  
Burst Synchronization ◽  
Width Factor

In-phase burst synchronization, spatiotemporal order and rhythm dynamics of a complex neuronal network with electrical or chemically excitatory synapses are studied in this paper. A quantitative characteristic, the width factor, is introduced to describe the rhythm dynamics of an individual neuron, and the average width factor is used to characterize the rhythm dynamics of a neural network. The in-phase burst synchronization is studied in terms of the burst phase order parameter. We also study the effects of the coupling schemes, the intrinsic neuronal property and the network topology on the rhythm dynamics of the network. It is found that the neuronal network with electrical coupling is easier to realize the in-phase burst synchronization than that with the chemically excitatory coupling. The bursting type of short bursting neuronal networks is unchanged for different coupling schemes with the coupling strength increasing. Moreover, the short bursting type is robust both to the coupling strength and the coupling scheme. As for the network topology, more links can only change the bursting type of long bursting neurons, but short bursting neurons are robust to the link numbers.

Sign in / Sign up

Export Citation Format

Share Document