About this Research Topic
A vast number of systems in nature display the peculiar ability to spontaneously enter synchrony across multiple spatial and temporal scales, and the study of natural synchronization has inspired numerous transdisciplinary applications in fields ranging from sensor networks and signal transmission to systems biology and physiological systems dynamics.
While synchronization between sub-units has been vastly investigated as a function of local as well as coupling dynamics, the recent advent and exponential growth of complex network science has provided novel, fertile ground for deepening our insight into synchronization phenomena as a function of intrinsic, possibly dynamical network properties.
For example, the study of synchronization in hierarchical networks, and in particular of how global dynamics can emerge from different network motifs and how mesoscale topology and time-delays due to propagation, as well as microscopic properties can influence whole-network synchronization, is beginning to provide solid stepping-stones for a better understanding of complex neuronal networks. In this context, recent neuroscience research has suggested that the synchronization of low-level elements in neural populations are instrumental to the dynamical emergence of higher-lever neural functional units, which in turn interact to generate and regulate complex behavioral patterns in health and disease. Also, recent simultaneous macro- (gross neural activities) and the micro- (single/multi-neuron activities) scale assessments are providing evidence for complex, cross-scale brain interactions which are not yet well understood.
Additionally, the study of synchronization in time-varying networks has very recently posited the existence of so-called “chimera-states”, whose appearance and disappearance in neuronal networks has been explained as an interplay of integration and segregation which gives rise to metastability. Chimera states have the potential to describe and explain spatiotemporal neuronal patterns in which phase-locked neural populations coexist with drifting ones. Similarly, the study of swarming in natural systems has very recently prompted ideas which exploit similarities with synchronization phenomena to define so-called “swarmalators”, i.e. units that are able to both swarm and synch, and are possibly governed by unifying physical principles such as energy conservation. Such systems exhibit rich spatiotemporal dynamics and may offer additional insight into mechanistic as well as statistical modeling for natural system, as well as potential technological applications such as bio-computing and swarm robotics. In this context, two emergent phenomena in the hippocampus, i.e. self-stabilizing maps as well as temporal reorganization through sharing oscillatory dynamics, have provided explanations for decentralized self-organization and distributed communication in the brain. In addition, studying emergent behaviors of such systems also has the potential to investigate the statistical physics of out-of-equilibrium systems, with possible time-varying and non-autonomous dynamics.
This Research Topic aims at collecting the latest theoretical as well as applicative developments in the emerging and overlapping fields of complex network science, synchronization, statistical physics and neuroscience. Review articles focused on these topics are also welcome. Potential topics include, but are not limited to, the following:
- Synchronization phenomena and topological substrate of interactions, including the delays due to propagation
- Evolving large-scale functional brain networks
- Cooperative phenomena underlying brain (dys)functioning
- Synchronization in models for biological (sub)systems
- Time-varying networks and underlying synchronization phenomena
- Hierarchical modeling and synchronization in neuronal populations
- Brain network synchronization across individuals (e.g. hyperscanning)
- Whole-network synchronization as a function of multiscale network topology
- Synchronization as marker of physiological aberrations
- Analyzing synchrony in artificial neural networks
- Generalized active control and synchronization
- Emergence of synchronization in chaotic and hyperchaotic systems
- Swarming and synching in oscillator networks
- Spectral properties of macroscopic signal as a function of microscopic synchronization
- Definition study and applications of different types of synchrony – e.g. complete- cluster- phase- impulsive- lag- or projective-synchronization
- Neural cross-frequency coupling functions and synchronization
- From neural to psychological and social structures through the assembling of synchronizing lower-level units
Keywords: Synchronization, Time-delayed coupling, Hyperscanning, Generalized active control, Swarmalators, Neural cross-frequency coupling, Coupling functions, Brain dynamics, Effective connectivity, Complex networks
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