Philipp Hövel

Clustered chimera states in systems of type-I excitability

The chimera state is a fascinating phenomenon of coexisting synchronized and desynchronized behaviour that was discovered in networks of nonlocally coupled identical phase oscillators over ten years ago. Since then, chimeras have been found in numerous theoretical and experimental studies and more recently in models of neuronal dynamics as well. In this work, we consider a generic model for a saddle-node bifurcation on a limit cycle representative of neural excitability type I. We obtain chimera states with multiple coherent regions (clustered chimeras/multi-chimeras) depending on the distance from the excitability threshold, the range of nonlocal coupling and the coupling strength. A detailed stability diagram for these chimera states and other interesting coexisting patterns (like traveling waves) is presented.

On the Robustness of In- and Out-Components in a Temporal Network

Background Many networks exhibit time-dependent topologies, where an edge only exists during a certain period of time. The first measurements of such networks are very recent so that a profound theoretical understanding is still lacking. In this work, we focus on the propagation properties of infectious diseases in time-dependent networks. In particular, we analyze a dataset containing livestock trade movements. The corresponding networks are known to be a major route for the spread of animal diseases. In this context chronology is crucial. A disease can only spread if the temporal sequence of trade contacts forms a chain of causality. Therefore, the identification of relevant nodes under time-varying network topologies is of great interest for the implementation of counteractions. Methodology/Findings We find that a time-aggregated approach might fail to identify epidemiologically relevant nodes. Hence, we explore the adaptability of the concept of centrality of nodes to temporal networks using a data-driven approach on the example of animal trade. We utilize the size of the in- and out-component of nodes as centrality measures. Both measures are refined to gain full awareness of the time-dependent topology and finite infectious periods. We show that the size of the components exhibit strong temporal heterogeneities. In particular, we find that the size of the components is overestimated in time-aggregated networks. For disease control, however, a risk assessment independent of time and specific disease properties is usually favored. We therefore explore the disease parameter range, in which a time-independent identification of central nodes remains possible. Conclusions We find a ranking of nodes according to their component sizes reasonably stable for a wide range of infectious periods. Samples based on this ranking are robust enough against varying disease parameters and hence are promising tools for disease control.

Nonlinearity of local dynamics promotes multi-chimeras.

Chimera states are complex spatio-temporal patterns in which domains of synchronous and asynchronous dynamics coexist in coupled systems of oscillators. We examine how the character of the individual elements influences chimera states by studying networks of nonlocally coupled Van der Pol oscillators. Varying the bifurcation parameter of the Van der Pol system, we can interpolate between regular sinusoidal and strongly nonlinear relaxation oscillations and demonstrate that more pronounced nonlinearity induces multi-chimera states with multiple incoherent domains. We show that the stability regimes for multi-chimera states and the mean phase velocity profiles of the oscillators change significantly as the nonlinearity becomes stronger. Furthermore, we reveal the influence of time delay on chimera patterns.

Delay stabilization of periodic orbits in coupled oscillator systems.

We study diffusively coupled oscillators in Hopf normal form. By introducing a non-invasive delay coupling, we are able to stabilize the inherently unstable anti-phase orbits. For the super- and subcritical cases, we state a condition on the oscillator’s nonlinearity that is necessary and sufficient to find coupling parameters for successful stabilization. We prove these conditions and review previous results on the stabilization of odd-number orbits by time-delayed feedback. Finally, we illustrate the results with numerical simulations.

Functional connectivity of distant cortical regions: role of remote synchronization and symmetry in interactions.

Functional MRI (fMRI) of ongoing brain activity at rest i.e. without any overt-directed behavior has revealed patterns of coherent activity, so called resting-state functional networks. The dynamical organization of nodes into these functional networks is closely related to the underlying structural connections. However, functional correlations have also been observed between cortical regions without apparent neural links, and mechanisms generating functional connectivity between distant cortical regions are largely unknown. It has been suggested that indirect connections and collective effects governed by the network properties of the cortex play a significant role. We use numerical simulations to investigate these mechanisms with reference to remote synchronization and network symmetry. Neural activity and the inferred hemodynamic response of the network nodes are modeled as sets of self-sustained oscillators, which are embedded in topologies of complex functional brain interactions. The coupling topology is based on connectivity maps derived from fMRI and DTI experiments. Consequently, our network model includes important information on whether direct or indirect neural connections exist between functionally associated regions. In the simulated functional networks, remote synchrony between pairs of nodes clearly arises from symmetry in the interactions, which are quantified by the number of shared neighbors. A larger joint neighborhood positively correlates with a higher level of synchrony. Therefore, our results indicate that a large overlapping neighborhood in complex networks of brain interactions gives rise to functional similarity between distant cortical regions.

Structural controllability of temporal networks

The control of complex systems is an ongoing challenge of complexity research. Recent advances using concepts of structural control deduce a wide range of control related properties from the network representation of complex systems. Here, we examine the controllability of systems for which the timescale of the dynamics we control and the timescale of changes in the network are comparable. We provide analytical and computational tools to study controllability based on temporal network characteristics. We apply these results to investigate the controllable subnetwork using a single input. For a generic class of model networks, we witness a phase transition depending upon the density of the interactions, describing the emergence of a giant controllable subspace. We show the existence of the two phases in real-world networks. Using randomization procedures, we find that the overall activity and the degree distribution of the underlying network are the main features influencing controllability.

Synchronisation in networks of delay-coupled type-I excitable systems

We use a generic model for type-I excitability (known as the SNIPER or SNIC model) to describe the local dynamics of nodes within a network in the presence of non-zero coupling delays. Utilising the method of the Master Stability Function, we investigate the stability of the zero-lag synchronised dynamics of the network nodes and its dependence on the two coupling parameters, namely the coupling strength and delay time. Unlike in the FitzHugh-Nagumo model (a model for type-II excitability), there are parameter ranges where the stability of synchronisation depends on the coupling strength and delay time. One important implication of these results is that there exist complex networks for which the adding of inhibitory links in a small-world fashion may not only lead to a loss of stable synchronisation, but may also restabilise synchronisation or introduce multiple transitions between synchronisation and desynchronisation. To underline the scope of our results, we show using the Stuart-Landau model that such multiple transitions do not only occur in excitable systems, but also in oscillatory ones.

Disease Spread through Animal Movements: A Static and Temporal Network Analysis of Pig Trade in Germany.

Background Animal trade plays an important role for the spread of infectious diseases in livestock populations. The central question of this work is how infectious diseases can potentially spread via trade in such a livestock population. We address this question by analyzing the underlying network of animal movements. In particular, we consider pig trade in Germany, where trade actors (agricultural premises) form a complex network. Methodology The considered pig trade dataset spans several years and is analyzed with respect to its potential to spread infectious diseases. Focusing on measurements of network-topological properties, we avoid the usage of external parameters, since these properties are independent of specific pathogens. They are on the contrary of great importance for understanding any general spreading process on this particular network. We analyze the system using different network models, which include varying amounts of information: (i) static network, (ii) network as a time series of uncorrelated snapshots, (iii) temporal network, where causality is explicitly taken into account. Findings We find that a static network view captures many relevant aspects of the trade system, and premises can be classified into two clearly defined risk classes. Moreover, our results allow for an efficient allocation strategy for intervention measures using centrality measures. Data on trade volume do barely alter the results and is therefore of secondary importance. Although a static network description yields useful results, the temporal resolution of data plays an outstanding role for an in-depth understanding of spreading processes. This applies in particular for an accurate calculation of the maximum outbreak size.

Dynamic changes in network synchrony reveal resting-state functional networks

Experimental functional magnetic resonance imaging studies have shown that spontaneous brain activity, i.e., in the absence of any external input, exhibit complex spatial and temporal patterns of co-activity between segregated brain regions. These so-called large-scale resting-state functional connectivity networks represent dynamically organized neural assemblies interacting with each other in a complex way. It has been suggested that looking at the dynamical properties of complex patterns of brain functional co-activity may reveal neural mechanisms underlying the dynamic changes in functional interactions. Here, we examine how global network dynamics is shaped by different network configurations, derived from realistic brain functional interactions. We focus on two main dynamics measures: synchrony and variations in synchrony. Neural activity and the inferred hemodynamic response of the network nodes are simulated using a system of 90 FitzHugh-Nagumo neural models subject to system noise and time-delayed interactions. These models are embedded into the topology of the complex brain functional interactions, whose architecture is additionally reduced to its main structural pathways. In the simulated functional networks, patterns of correlated regional activity clearly arise from dynamical properties that maximize synchrony and variations in synchrony. Our results on the fast changes of the level of the network synchrony also show how flexible changes in the large-scale network dynamics could be.

CONTROL OF SYNCHRONIZATION IN DELAY-COUPLED NETWORKS

We consider synchronization in networks of delay-coupled oscillators. In these systems, the coupling phase has been found to be a crucial control parameter. By proper choice of this parameter one can switch between different synchronous oscillatory states of the network, e.g., in-phase oscillation, splay or various cluster states. Applying the speed-gradient method, we derive an adaptive algorithm for an automatic adjustment of the coupling phase, coupling strength, and delay time such that a desired state can be selected from an otherwise multistable regime.