S
Stefano Boccaletti
Researcher at Moscow Institute of Physics and Technology
Publications - 361
Citations - 29686
Stefano Boccaletti is an academic researcher from Moscow Institute of Physics and Technology. The author has contributed to research in topics: Complex network & Synchronization (computer science). The author has an hindex of 60, co-authored 348 publications receiving 25776 citations. Previous affiliations of Stefano Boccaletti include King Juan Carlos University & Istituto Nazionale di Fisica Nucleare.
Papers
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Concurrent enhancement of percolation and synchronization in adaptive networks.
TL;DR: In this article, an adaptive network of oscillators with a stochastic, fitness-based, rule of connectivity is considered, and it self-organizes from fragmented and incoherent states to connected and synchronized ones.
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Experimental targeting and control of spatiotemporal chaos in nonlinear optics.
TL;DR: This work demonstrates targeting and control over spatiotemporal chaos in an optical feedback loop experiment by means of a two dimensional space extended perturbation field driven by an interfaced computer.
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Interlayer Hebbian plasticity induces first-order transition in multiplex networks
TL;DR: This work investigates the impact of adaptive inter-layer processes on intra-layer synchronization in multiplex networks by providing rigorous analytic predictions of the critical coupling strengths for the onset of synchronization and de-synchronization.
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Synchronization clusters emerge as the result of a global coupling among classical phase oscillators
Xue Li,Tian Qiu,Stefano Boccaletti,Irene Sendiña-Nadal,Irene Sendiña-Nadal,Zonghua Liu,Shuguang Guan +6 more
Posted Content
Functional brain networks: great expectations, hard times, and the big leap forward
TL;DR: It is argued that complex network theory has not yet been taken full advantage of, as many of its important aspects are yet to make their appearance in the neuroscience literature, and it is proposed that, rather than simply borrowing from an existing theory, functional neural networks can inspire a fundamental reformulation ofcomplex network theory.