Network neuroscience

Nature Neuroscience, Journal Year: 2017, Volume and Issue: 20(3), P. 353 - 364

Published: Feb. 23, 2017

Language: Английский

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Networks beyond pairwise interactions: Structure and dynamics

Physics Reports, Journal Year: 2020, Volume and Issue: 874, P. 1 - 92

Published: June 13, 2020

The complexity of many biological, social and technological systems stems from the richness interactions among their units. Over past decades, a great variety complex has been successfully described as networks whose interacting pairs nodes are connected by links. Yet, in face-to-face human communication, chemical reactions ecological systems, can occur groups three or more cannot be simply just terms simple dyads. Until recently, little attention devoted to higher-order architecture real systems. However, mounting body evidence is showing that taking structure these into account greatly enhance our modeling capacities help us understand predict emerging dynamical behaviors. Here, we present complete overview field beyond pairwise interactions. We first discuss methods represent give unified presentation different frameworks used describe highlighting links between existing concepts representations. review measures designed characterize models proposed literature generate synthetic structures, such random growing simplicial complexes, bipartite graphs hypergraphs. introduce rapidly research on topology. focus novel emergent phenomena characterizing landmark processes, diffusion, spreading, synchronization games, when extended elucidate relations topology properties, conclude with summary empirical applications, providing an outlook current conceptual frontiers.

Language: Английский

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Graph theory methods: applications in brain networks

Dialogues in Clinical Neuroscience, Journal Year: 2018, Volume and Issue: 20(2), P. 111 - 121

Published: June 30, 2018

Network neuroscience is a thriving and rapidly expanding field. Empirical data on brain networks, from molecular to behavioral scales, are ever increasing in size complexity. These developments lead strong demand for appropriate tools methods that model analyze network data, such as those provided by graph theory. This brief review surveys some of the most commonly used neurobiologically insightful measures techniques. Among these, detection communities or modules, identification central elements facilitate communication signal transfer, particularly salient. A number emerging trends growing use generative models, dynamic (time-varying) multilayer well application algebraic topology. Overall, theory centrally important understanding architecture, development, evolution networks.La neurociencia de la red es un campo próspero y rápida expansión. Los datos empíricos sobre las redes cerebrales, desde niveles moleculares hasta conductuales, son cada vez más grandes en tamaño complejidad. Estos desarrollos llevan una fuerte demanda herramientas métodos apropiados que modelen analicen los cerebral, como proporcionados por teoría grafos. Esta breve revisión examina algunas medidas técnicas gráficas comúnmente empleadas neurobiológicamente discriminadoras. Entre estas, particularmente importantes detección módulos o comunidades redes, identificación elementos centrales facilitan comunicación transferencia señales. Algunas tendencias emergentes el empleo creciente modelos generativos, dinámicas (de tiempo variable) multicapa, así aplicación topología algebraica. En general, grafos especialmente para comprender arquitectura, desarrollo evolución cerebrales.La des réseaux est domaine florissant qui s'étend rapidement. Les données empiriques sur les cérébraux, l'échelle moléculaire à comportementale, ne cessent d'augmenter volume et complexité. Ces développements génèrent une demande forte d'outils méthodes appropriés pour modéliser analyser comme celles fournies par théorie graphes. Dans cette rapide analyse, nous examinons certaines techniques mesures graphes plus couramment utilisées signifiantes neurobiologiquement. Parmi elles, détection modules ou communautés l'identification éléments réseau facilite le transfert du signal, sont particulièrement marquantes. tendances émergentes, note l'utilisation croissante modèles génératifs, dynamiques (variables avec temps) multi-couches, ainsi l'application topologie algébrique. Globalement, essentielles comprendre l'architecture, développement l'évolution cérébraux.

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On the nature and use of models in network neuroscience

Nature reviews. Neuroscience, Journal Year: 2018, Volume and Issue: 19(9), P. 566 - 578

Published: July 12, 2018

Language: Английский

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The physics of brain network structure, function and control

Nature Reviews Physics, Journal Year: 2019, Volume and Issue: 1(5), P. 318 - 332

Published: March 27, 2019

Language: Английский

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Simplicial models of social contagion

Nature Communications, Journal Year: 2019, Volume and Issue: 10(1)

Published: June 6, 2019

Complex networks have been successfully used to describe the spread of diseases in populations interacting individuals. Conversely, pairwise interactions are often not enough characterize social contagion processes such as opinion formation or adoption novelties, where complex mechanisms influence and reinforcement at work. Here we introduce a higher-order model which system is represented by simplicial can occur through groups different sizes. Numerical simulations on both empirical synthetic complexes highlight emergence novel phenomena discontinuous transition induced interactions. We show analytically that bistable region appears healthy endemic states co-exist. Our results help explain why critical masses required initiate changes contribute understanding systems.

Language: Английский

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Abrupt Desynchronization and Extensive Multistability in Globally Coupled Oscillator Simplexes

Physical Review Letters, Journal Year: 2019, Volume and Issue: 122(24)

Published: June 19, 2019

Collective behavior in large ensembles of dynamical units with nonpairwise interactions may play an important role several systems ranging from brain function to social networks. Despite recent work pointing simplicial structure, i.e., higher-order between three or more at a time, their characteristics remain poorly understood. Here we present analysis the collective dynamics such system, namely coupled phase oscillators three-way interactions. The structure gives rise number novel phenomena, most notably continuum abrupt desynchronization transitions no synchronization transition counterpart, as well extensive multistability whereby infinitely many stable partially synchronized states exist. Our sheds light on complexity that can arise physical like human and storing information.

Language: Английский

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Higher order interactions in complex networks of phase oscillators promote abrupt synchronization switching

Communications Physics, Journal Year: 2020, Volume and Issue: 3(1)

Published: Nov. 30, 2020

Synchronization processes play critical roles in the functionality of a wide range both natural and man-made systems. Recent work physics neuroscience highlights importance higher-order interactions between dynamical units, i.e., three- four-way addition to pairwise interactions, their role shaping collective behavior. Here we show that coupled phase oscillators, encoded microscopically simplicial complex, give rise added nonlinearity macroscopic system dynamics induces abrupt synchronization transitions via hysteresis bistability synchronized incoherent states. Moreover, these can stabilize strongly states even when coupling is repulsive. These findings reveal self-organized phenomenon may be responsible for rapid switching many biological other systems exhibit without need particular correlation mechanisms oscillators topological structure.

Language: Английский

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Stability of synchronization in simplicial complexes

Nature Communications, Journal Year: 2021, Volume and Issue: 12(1)

Published: Feb. 23, 2021

Abstract Various systems in physics, biology, social sciences and engineering have been successfully modeled as networks of coupled dynamical systems, where the links describe pairwise interactions. This is, however, too strong a limitation, recent studies revealed that higher-order many-body interactions are present groups, ecosystems human brain, they actually affect emergent dynamics all these systems. Here, we introduce general framework to study accounting for precise microscopic structure their at any possible order. We show complete synchronization exists an invariant solution, give necessary condition it be observed stable state. Moreover, some relevant instances, such takes form Master Stability Function. generalizes existing results valid case complex with most architecture.

Language: Английский

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The Why, How, and When of Representations for Complex Systems

SIAM Review, Journal Year: 2021, Volume and Issue: 63(3), P. 435 - 485

Published: Jan. 1, 2021

Complex systems, composed at the most basic level of units and their interactions, describe phenomena in a wide variety domains, from neuroscience to computer science economics. The applications has resulted two key challenges: generation many domain-specific strategies for complex systems analyses that are seldom revisited, compartmentalization representation analysis ideas within domain due inconsistency language. In this work we propose basic, domain-agnostic language order advance toward more cohesive vocabulary. We use evaluate each step pipeline, beginning with system under study data collected, then moving through different mathematical frameworks encoding observed (i.e., graphs, simplicial complexes, hypergraphs), relevant computational methods framework. At consider types dependencies; these properties how existence an interaction among set may affect possibility another relation. discuss dependencies arise they alter interpretation results or entirety pipeline. close real-world examples using coauthorship email communications illustrate study, therein, research question, choice influence results. hope can serve as opportunity reflection experienced scientists, well introductory resource new researchers.

Language: Английский

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