Edmilson Roque

The network architecture of the human brain has become a feature of increasing interest to the neuroscientific community, largely because of its potential to illuminate human cognition, its variation over development and aging, and its alteration in disease or injury. Traditional tools and approaches to study this architecture have largely focused on single scales -- of topology, time, and space. Expanding beyond this narrow view, we focus this review on pertinent questions and novel methodological advances for the multi-scale brain. We separate our exposition into content related to multi-scale topological structure, multi-scale temporal structure, and multi-scale spatial structure. In each case, we recount empirical evidence for such structures, survey network-based methodological approaches to reveal these structures, and outline current frontiers and open questions. Although predominantly peppered with examples from human neuroimaging, we hope that this account will offer an accessible guide to any neuroscientist aiming to measure, characterize, and understand the full richness of the brain's multiscale network structure -- irrespective of species, imaging modality, or spatial resolution.

The relation between network structure and dynamics is determinant for the behavior of complex systems in numerous domains. An important longstanding problem concerns the properties of the networks that optimize the dynamics with respect to a given performance measure. Here we show that such optimization can lead to sensitive dependence of the dynamics on the structure of the network. Specifically, we demonstrate that the stability of the dynamical state, as determined by the maximum Lyapunov exponent, can exhibit a cusp-like dependence on the number of nodes and links as well as on the size of perturbations applied to the network structure. As mechanisms underlying this sensitivity, we identify discontinuous transitions occurring in the complement of optimal networks and the prevalence of eigenvector degeneracy in these networks. These findings establish a unified characterization of networks optimized for dynamical stability in diffusively coupled systems, which we illustrate using Turing instability in activator-inhibitor systems, synchronization in power-grid networks, and several other examples. Our results suggest that the network structure of a complex system operating near an optimum can potentially be fine-tuned for a significantly enhanced stability compared to what one might expect from simple extrapolation. On the other hand, they also suggest constraints on how close to the optimum the system can be in practice. Finally, the results have potential implications for biophysical networks, which have evolved under the competing pressures of optimizing fitness while remaining robust against perturbations.

Author(s): Alfonso Allen-Perkins, Javier Galeano, and Juan Manuel Pastor

Complex networks are a recent type of framework used to study complex systems with many interacting elements, such as self-organized criticality (SOC). The network nodes' tendency to link to other nodes of similar type is characterized by assortative mixing. Real networks exhibit assortative mixing …

[Phys. Rev. E 94, 052304] Published Mon Nov 07, 2016

Dynamical systems can autonomously adapt their organization so that the required target dynamics is reproduced. In the previous Rapid Communication [Phys. Rev. E 90,030901(R) (2014)], it was shown how such systems can be designed using delayed feedbacks. Here, the proposed method is further analyzed and improved. Its extension to adaptable systems, where delays are absent and inertial feedbacks are instead employed, is suggested. Numerical tests for three different models, including networks of phase and amplitude oscillators, are performed.

Abstract

Mutualistic relationships among the different species are ubiquitous in nature. To prevent mutualism from slipping into antagonism, a host often invokes a “carrot and stick” approach towards symbionts with a stabilizing effect on their symbiosis. In open human societies, a mutualistic relationship arises when a native insider population attracts outsiders with benevolent incentives in hope that the additional labor will improve the standard of all. A lingering question, however, is the extent to which insiders are willing to tolerate outsiders before mutualism slips into antagonism. To test the assertion by Karl Popper that unlimited tolerance leads to the demise of tolerance, we model a society under a growing incursion from the outside. Guided by their traditions of maintaining the social fabric and prizing tolerance, the insiders reduce their benevolence toward the growing subpopulation of outsiders but do not invoke punishment. This reduction of benevolence intensifies as less tolerant insiders (e.g., “radicals”) openly renounce benevolence. Although more tolerant insiders maintain some level of benevolence, they may also tacitly support radicals out of fear for the future. If radicals and their tacit supporters achieve a critical majority, herd behavior ensues and the relation between the insider and outsider subpopulations turns antagonistic. To control the risk of unwanted social dynamics, we map the parameter space within which the tolerance of insiders is in balance with the assimilation of outsiders, the tolerant insiders maintain a sustainable majority, and any reduction in benevolence occurs smoothly. We also identify the circumstances that cause the relations between insiders and outsiders to collapse or that lead to the dominance of the outsiders.

We point out the existence of a transition from partial to global generalized synchronization (GS) in symmetrically coupled structurally different time-delay systems of different orders using the auxiliary system approach and the mutual false nearest neighbor method. The present authors have recently reported that there exists a common GS manifold even in an ensemble of structurally nonidentical scalar time-delay systems with different fractal dimensions and shown that GS occurs simultaneously with phase synchronization (PS). In this paper we confirm that the above result is not confined just to scalar one-dimensional time-delay systems alone but there exists a similar type of transition even in the case of time-delay systems with different orders. We calculate the maximal transverse Lyapunov exponent to evaluate the asymptotic stability of the complete synchronization manifold of each of the main and the corresponding auxiliary systems, which in turn ensures the stability of the GS manifold between the main systems. Further we estimate the correlation coefficient and the correlation of probability of recurrence to establish the relation between GS and PS. We also calculate the mutual false nearest neighbor parameter which doubly confirms the occurrence of the global GS manifold.

The relation between network structure and dynamics is determinant for the behavior of complex systems in numerous domains. An important long-standing problem concerns the properties of the networks that optimize the dynamics with respect to a given performance measure. Here we show that such optimization can lead to sensitive dependence of the dynamics on the structure of the network. Specifically, using diffusively coupled systems as examples, we demonstrate that the stability of a dynamical state can exhibit sensitivity to unweighted structural perturbations (i.e., link removals and node additions) for undirected optimal networks and to weighted perturbations (i.e., small changes in link weights) for directed optimal networks. As mechanisms underlying this sensitivity, we identify discontinuous transitions occurring in the complement of undirected optimal networks and the prevalence of eigenvector degeneracy in directed optimal networks. These findings establish a unified characterization of networks optimized for dynamical stability, which we illustrate using Turing instability in activator-inhibitor systems, synchronization in power-grid networks, network diffusion, and several other network processes. Our results suggest that the network structure of a complex system operating near an optimum can potentially be fine-tuned for a significantly enhanced stability compared to what one might expect from simple extrapolation. On the other hand, they also suggest constraints on how close to the optimum the system can be in practice. Finally, the results have potential implications for biophysical networks, which have evolved under the competing pressures of optimizing fitness while remaining robust against perturbations.

Abstract

In this review, we highlight the physics of synchronization in collections of beating cilia and flagella. We survey the nonlinear dynamics of synchronization in collections of noisy oscillators. This framework is applied to flagellar synchronization by hydrodynamic interactions. The time-reversibility of hydrodynamics at low Reynolds numbers requires swimming strokes that break time-reversal symmetry to facilitate hydrodynamic synchronization. We discuss different physical mechanisms for flagellar synchronization, which break this symmetry in different ways.

The average individual is typically a mediocre singer, with a rather restricted capacity to sing a melody in tune. Yet when many singers are assembled to perform collectively, the resulting melody of the crowd is suddenly perceived by an external listener as perfectly tuned —as if it was actually a choral performance— even if each individual singer is out of tune. This collective phenomenon is an example of a wisdom of crowds effect that can be routinely observed in music concerts or other social events, when a group of people spontaneously sings at unison. In this paper we rely on the psychoacoustic properties of pitch and provide a simple mechanistic explanation for the onset of this emergent behavior.

Chimera-like states are manifested through the coexistence of synchronous and asynchronous dynamics and have been observed in various systems. To analyze the role of network topology in giving rise to chimera-like states we study a heterogeneous network model comprising two group of nodes, of high and low degrees of connectivity. The architecture facilitates the analysis of the system, which separates into a densely-connected coherent group of nodes, perturbed by their sparsely-connected drifting neighbors. It describes a synchronous behavior of the densely-connected group and scaling properties of the induced perturbations.

The analysis of the dynamics of delays propagation is one of the major topics inside Air Transport Management research. Delays are generated by the elements of the system, but their propagation is a global process fostered by relationships inside the network. If the topology of such propagation process has been extensively studied in the literature, little attention has been devoted to the fact that such topology may have a dynamical nature. Here we differentiate between two phases of the system by applying two causality metrics, respectively describing the standard phase (i.e. propagation of normal delays) and a disrupted one (corresponding to abnormal and unexpected delays). We identify the critical point triggering the change of the topology of the system, in terms of delays magnitude, using a historical data set of flights crossing Europe in 2011. We anticipate that the proposed results will open new doors towards the understanding of the delay propagation dynamics and the mitigation of extreme events.

Nature Physics 12, 1076 (2016). doi:10.1038/nphys3812

Authors: Kaj-Kolja Kleineberg, Marián Boguñá, M. Ángeles Serrano & Fragkiskos Papadopoulos

Nature Physics 12, 995 (2016). doi:10.1038/nphys3939

Author: Thilo Gross

Intuition informs a widespread policy of epidemic response, replacing infected workers in classrooms or hospitals with healthy substitutes. But modelling now suggests that this mechanism may be a key factor in the accelerated spread of an epidemic.

Author(s): Joydeep Singha and Neelima Gupte

We study the existence and stability of splay states in the coupled sine circle map lattice system using analytic and numerical techniques. The splay states are observed for very low values of the nonlinearity parameter, i.e., for maps which deviate very slightly from the shift map case. We also obs…

[Phys. Rev. E 94, 052204] Published Thu Nov 03, 2016

The chimera state with co-existing coherent and incoherent dynamics has recently attracted a lot of attention due to its wide applicability. We investigate non-locally coupled identical chaotic maps with delayed interactions in the multiplex network framework and find that an interplay of delay and multiplexing brings about an enhanced or suppressed appearance of the chimera state depending on the distribution as well as the parity of delay values in the layers. Additionally, we report a layer chimera state with the existence of one layer exhibiting coherent and another layer incoherent dynamical evolution. The rich variety of dynamical behavior demonstrated here can be used to gain further insight into the real-world networks which inherently possess such multi-layer architecture with delayed interactions.

Abstract

We address the issue of the proximity of interacting diffusion models on large graphs with a uniform degree property and a corresponding mean field model, i.e., a model on the complete graph with a suitably renormalized interaction parameter. Examples include Erdős–Rényi graphs with edge probability \(p_n\) , n is the number of vertices, such that \(\lim _{n \rightarrow \infty }p_n n= \infty \) . The purpose of this note is twofold: (1) to establish this proximity on finite time horizon, by exploiting the fact that both systems are accurately described by a Fokker–Planck PDE (or, equivalently, by a nonlinear diffusion process) in the \(n=\infty \) limit; (2) to remark that in reality this result is unsatisfactory when it comes to applying it to systems with n large but finite, for example the values of n that can be reached in simulations or that correspond to the typical number of interacting units in a biological system.

The solution of today's complex problems requires the grouping of task forces whose members are usually connected remotely over long physical distances and different time zones. Hence, understanding the effects of imposed communication patterns (i.e., who can communicate with whom) on group performance is important. Here, we use an agent-based model to explore the influence of the betweenness centrality of the nodes on the time the group requires to find the global maxima of NK-fitness landscapes. The agents cooperate by broadcasting messages, informing on their fitness to their neighbors, and use this information to copy the more successful agents in their neighborhood. We find that for easy tasks (smooth landscapes), the topology of the communication network has no effect on the performance of the group, and that the more central nodes are the most likely to find the global maximum first. For difficult tasks (rugged landscapes), however, we find a positive correlation between the variance of the betweenness among the network nodes and the group performance. For these tasks, the performances of individual nodes are strongly influenced by the agents dispositions to cooperate and by the particular realizations of the rugged landscapes.

We consider a population of interconnected individuals that, with respect to a piece of information, at each time instant can be subdivided into three (time-dependent) categories: agnostics, influenced, and evangelists. A dynamical process of information diffusion evolves among the individuals of the population according to the following rules. Initially, all individuals are agnostic. Then, a set of people is chosen from the outside and convinced to start evangelizing, i.e., to start spreading the information. When a number of evangelists, greater than a given threshold, communicate with a node v, the node v becomes influenced, whereas, as soon as the individual v is contacted by a sufficiently much larger number of evangelists, it is itself converted into an evangelist and consequently it starts spreading the information. The question is: How to choose a bounded cardinality initial set of evangelists so as to maximize the final number of influenced individuals? We prove that the problem is hard to solve, even in an approximate sense. On the positive side, we present exact polynomial time algorithms for trees and complete graphs. For general graphs, we derive exact parameterized algorithms. We also investigate the problem when the objective is to select a minimum number of evangelists capable of influencing the whole network. Our motivations to study these problems come from the areas of Viral Marketing and the analysis of quantitative models of spreading of influence in social networks.

The effects of nonlocal and reflecting connectivity are investigated in coupled Leaky Integrate-and-Fire (LIF) elements, which assimilate the exchange of electrical signals between neurons. Earlier investigations have demonstrated that non-local and hierarchical network connectivity often induces complex synchronization patterns and chimera states in systems of coupled oscillators. In the LIF system we show that if the elements are non-locally linked with positive diffusive coupling in a ring architecture the system splits into a number of alternating domains. Half of these domains contain elements, whose potential stays near the threshold, while they are interrupted by active domains, where the elements perform regular LIF oscillations. The active domains move around the ring with constant velocity, depending on the system parameters. The idea of introducing reflecting non-local coupling in LIF networks originates from signal exchange between neurons residing in the two hemispheres in the brain. We show evidence that this connectivity induces novel complex spatial and temporal structures: for relatively extensive ranges of parameter values the system splits in two coexisting domains, one domain where all elements stay near-threshold and one where incoherent states develop with multileveled mean phase velocity distribution.

Author(s): Sima Mofakham, Christian G. Fink, Victoria Booth, and Michal R. Zochowski

While the interplay between neuronal excitability properties and global properties of network topology is known to affect network propensity for synchronization, it is not clear how detailed characteristics of these properties affect spatiotemporal pattern formation. Here we study mixed networks, co…

[Phys. Rev. E 94, 042427] Published Mon Oct 31, 2016

The average individual is typically a mediocre singer, with a rather restricted capacity to sing a melody in tune. Yet when many singers are assembled to perform collectively, the resulting melody of the crowd is suddenly perceived by an external listener as perfectly tuned -as if it was actually a choral performance- even if each individual singer is out of tune. This collective phenomenon is an example of a wisdom of crowds effect that can be routinely observed in music concerts or other social events, when a group of people spontaneously sings at unison. In this paper we rely on the psychoacoustic properties of pitch and provide a simple mechanistic explanation for the onset of this emergent behavior.

We present an exact mathematical framework able to describe site-percolation transitions in real multiplex networks. Specifically, we consider the average percolation diagram valid over an infinite number of random configurations where nodes are present in the system with given probability. The approach relies on the locally treelike ansatz, so that it is expected to accurately reproduce the true percolation diagram of sparse multiplex networks with negligible number of short loops. The performance of our theory is tested in social, biological, and transportation multiplex graphs. When compared against previously introduced methods, we observe improvements in the prediction of the percolation diagrams in all networks analyzed. Results from our method confirm previous claims about the robustness of real multiplex networks, in the sense that the average connectedness of the system does not exhibit any significant abrupt change as its individual components are randomly destroyed.

Author(s): Francesca Collet, Marco Formentin, and Daniele Tovazzi

Many real systems composed of a large number of interacting components, as, for instance, neural networks, may exhibit collective periodic behavior even though single components have no natural tendency to behave periodically. Macroscopic oscillations are indeed one of the most common self-organized…

[Phys. Rev. E 94, 042139] Published Fri Oct 28, 2016

Author(s): J. Barré and D. Métivier

We prove that any non zero inertia, however small, is able to change the nature of the synchronization transition in Kuramoto-like models, either from continuous to discontinuous, or from discontinuous to continuous. This result is obtained through an unstable manifold expansion in the spirit of J.D…

[Phys. Rev. Lett.] Published Tue Oct 25, 2016

Author(s): Sarika Jalan, Anil Kumar, Alexey Zaikin, and Jurgen Kurths

We study the evolution of coupled chaotic dynamics on networks and investigate the role of degree-degree correlation on the networks cluster synchronizability. We find that an increase in the disassortativity can lead to an increase or a decrease in the cluster synchronizability depending on the deg…

[Phys. Rev. E] Published Tue Oct 25, 2016

Author(s): Jinjie Zhu and Xianbin Liu

In the present, the locking phenomenon induced by distance-dependent delay in ring structured neuronal networks is investigated, wherein each neuron is modeled by a FitzHugh-Nagumo neuron. Through increasing the element time delay, the different spatiotemporal patterns are observed. By calculating t…

[Phys. Rev. E] Published Wed Oct 26, 2016

Author(s): Sebastian M. Krause, Michael M. Danziger, and Vinko Zlatić

Many networks—electronic, physical, or biological—have mutually shared vulnerabilities that render them significantly less secure and robust. Now, the conditions necessary for secure connectivity within a network characterized by vulnerabilities affecting many nodes are calculated

[Phys. Rev. X 6, 041022] Published Thu Oct 27, 2016

The average individual is typically a mediocre singer, with a rather restricted capacity to sing a melody in tune. Yet when many singers are assembled to perform collectively, the resulting melody of the crowd is suddenly perceived by an external listener as perfectly tuned -as if it was actually a choral performance- even if each individual singer is out of tune. This collective phenomenon is an example of a wisdom of crowds effect that can be routinely observed in music concerts or other social events, when a group of people spontaneously sings at unison. In this paper we rely on the psychoacoustic properties of pitch and provide a simple mechanistic explanation for the onset of this emergent behavior.

Publication date: 17 November 2016
Source:Physics Reports, Volume 660
Author(s): S. Boccaletti, J.A. Almendral, S. Guan, I. Leyva, Z. Liu, I. Sendiña-Nadal, Z. Wang, Y. Zou
Percolation and synchronization are two phase transitions that have been extensively studied since already long ago. A classic result is that, in the vast majority of cases, these transitions are of the second-order type, i.e. continuous and reversible. Recently, however, explosive phenomena have been reported in complex networks’ structure and dynamics, which rather remind first-order (discontinuous and irreversible) transitions. Explosive percolation, which was discovered in 2009, corresponds to an abrupt change in the network’s structure, and explosive synchronization (which is concerned, instead, with the abrupt emergence of a collective state in the networks’ dynamics) was studied as early as the first models of globally coupled phase oscillators were taken into consideration. The two phenomena have stimulated investigations and debates, attracting attention in many relevant fields. So far, various substantial contributions and progresses (including experimental verifications) have been made, which have provided insights on what structural and dynamical properties are needed for inducing such abrupt transformations, as well as have greatly enhanced our understanding of phase transitions in networked systems. Our intention is to offer here a monographic review on the main-stream literature, with the twofold aim of summarizing the existing results and pointing out possible directions for future research.