Chimera states in neuronal networks: A review

Majhi, Soumen; Bera, Bidesh K.; Ghosh, Dibakar; Perc, Matjaž

doi:10.1016/j.plrev.2018.09.003

Cited by 385 publications

(153 citation statements)

References 110 publications

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“…Since their discovery, they have been extensively studied both theoretically [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] and experimentally [27][28][29][30][31] in a wide range of systems. For recent reviews see references [32][33][34][35]. For a long time, chimera states were believed to exist mostly for nonlocal coupling schemes.…”

Section: Introductionmentioning

confidence: 99%

Synchronization Patterns in Modular Neuronal Networks: A Case Study of C. elegans

Pournaki

Merfort

Ruiz

et al. 2019

Front. Appl. Math. Stat.

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We investigate synchronization patterns and chimera-like states in the modular multilayer topology of the connectome of Caenorhabditis elegans. In the special case of a designed network with two layers, one with electrical intra-community links and one with chemical inter-community links, chimera-like states are known to exist. Aiming at a more biological approach based on the actual connectivity data, we consider a network consisting of two synaptic (electrical and chemical) and one extrasynaptic (wireless) layers. Analyzing the structure and properties of this layered network using Multilayer-Louvain community detection, we identify modules whose nodes are more strongly coupled with each other than with the rest of the network. Based on this topology, we study the dynamics of coupled Hindmarsh-Rose neurons. Emerging synchronization patterns are quantified using the pairwise Euclidean distances between the values of all oscillators, locally within each community and globally across the network. We find a tendency of the wireless coupling to moderate the average coherence of the system: for stronger wireless coupling, the levels of synchronization decrease both locally and globally, and chimera-like states are not favored. By introducing an alternative method to define meaningful communities based on the dynamical correlations of the nodes, we obtain a structure that is dominated by two large communities. This promotes the emergence of chimera-like states and allows to relate the dynamics of the corresponding neurons to biological neuronal functions such as motor activities.

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Section: Introductionmentioning

confidence: 99%

Synchronization Patterns in Modular Neuronal Networks: A Case Study of C. elegans

Pournaki

Merfort

Ruiz

et al. 2019

Front. Appl. Math. Stat.

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“…The existence of chimera states on ring topologies has been verified in experiments with chemical and electrochemical oscillators [7,8], electronic units [9,10], laser systems [11,12] and hydrodynamically coupled colloids [13]. They are thought to play an important role in neurological disorders [14] and new metamaterials [15].…”

Section: Introductionmentioning

confidence: 91%

Chimera States on a Ring of Strongly Coupled Relaxation Oscillators

Rode

Totz

Fengler

et al. 2019

Front. Appl. Math. Stat.

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Weakly coupled oscillators can exhibit seemingly incongruous synchronization patterns comprised of coherent and incoherent spatial domains known as chimera states. However, the weak coupling approximation is invalid when the characteristic phase response curve of an oscillator does not scale linearly with the coupling strength and instead changes its shape. In chemical experiments with photo-coupled relaxation oscillators, we find that beyond weak coupling chimera patterns consist of different coexisting cluster states. Numerical modeling reveals that the observed cluster states result from a phase-dependent excitability that is also commonly observed in neural tissue and cardiac pacemaker cells.

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“…Complex networks are a popular topic that has attracted researchers' attention in many fields [14] because they can be used as a detailed model for many real-world complex systems such as brain networks [7], message networks [39,30], human lives [9], and social systems [25,27]. Many structural properties of complex networks are affected by some special nodes, e.g., the scale-free [18], self-similarity [22], and fractal [33] properties of complex networks [23].…”

Section: Introductionmentioning

confidence: 99%

Identification of influencers in complex networks by local information dimensionality

Wen

Deng

2020

Information Sciences

103

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The identification of influential spreaders in complex networks is a popular topic in studies of network characteristics. Many centrality measures have been proposed to address this problem, but most have limitations. In this paper, a method for identifying influencers in complex networks via the local information dimensionality is proposed. The proposed method considers the local structural properties around the central node; therefore, the scale of locality only increases to half of the maximum value of the shortest distance from the central node. Thus, the proposed method considers the quasilocal information and reduces the computational complexity. The information (number of nodes) in boxes is described via the Shannon entropy, which is more reasonable. A node is more influential when its local information dimensionality is higher. In order to show the effectiveness of the proposed method, five existing centrality measures are used as comparison methods to rank influential nodes in six real-world complex networks. In addition, a susceptible-infected (SI) model and Kendall's tau coefficient are applied to show the correlation between different methods. Experiment results show the superiority of the proposed method.

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Chimera states in neuronal networks: A review

Cited by 385 publications

References 110 publications

Synchronization Patterns in Modular Neuronal Networks: A Case Study of C. elegans

Synchronization Patterns in Modular Neuronal Networks: A Case Study of C. elegans

Chimera States on a Ring of Strongly Coupled Relaxation Oscillators

Identification of influencers in complex networks by local information dimensionality

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