Nonstationary transition to phase synchronization of neural networks induced by the coupling architecture

Investigation of Details in the Transition to Synchronization in Complex Networks by Using Recurrence Analysis

“…In the scenario of complex networks, the neural system can be modeled on two scales. In the internal connection scheme, each network node can be understood as a neuron and their connections as the edges [8], which are able to simulate a single network, as used in many works [9][10][11][12][13]. On the other hand, considering the inter-networks connection scheme, it is possible to consider a neural system composed of different sub-areas, so each sub-network can be understood as a node and their connections as the edges, building a network of networks [14][15][16][17][18].…”

“…The connection architecture of complex networks is very important in the dynamical properties observed at a global level of behavior. Regarding neural networks, many topologies are considered, such as small-world, scale-free, and random ones [9,16,19] where a transition from unsynchronized to synchronized states is observed. The role of connection architecture is very important to the paths to synchronization, where different phenomena can be observed as a function of topology [20][21][22].…”

“…In some cases, in the transition region, on-off intermittency in the two states has been observed, where the network displays the existence of two locally stable states but globally unstable ones [18,26], as defined in [31]. In [9], the dynamical properties regarding synchronization and transition characteristics are studied as a function of the connection architecture, with both small-world and random topologies being considered. In the present paper, we extend the analysis and consider the scale-free topology, since there are topological differences between the connection schema.…”

Suppression of Phase Synchronization in Scale-Free Neural Networks Using External Pulsed Current Protocols

“…A similar approach have been used to analyze small-world neural networks [31,33,55]. However, the influence of topology plays an important role in the synchronization characteristics [22,35,38]. In this way, we simulated a scale-free network since there are great differences regarding the heterogeneity of the network in comparison to the small-world one [37,38].…”

“…In the theoretical point of view, a possible way to study coupled neurons is given by the computational simulation of complex networks, where each site of the network corresponds to a neuron and its connections are represented by the edges of the network [18]. In this scenario, distinct topologies or connection architectures have been successfully used to simulate the interconnections of the human brain, such as small-world, scale-free and random topologies [18][19][20][21][22][23].…”