1,260,890 research outputs found
Computational study of resting state network dynamics
Lo scopo di questa tesi è quello di mostrare, attraverso una simulazione con il software The Virtual Brain, le più importanti proprietà della dinamica cerebrale durante il resting state, ovvero quando non si è coinvolti in nessun compito preciso e non si è sottoposti a nessuno stimolo particolare. Si comincia con lo spiegare cos’è il resting state attraverso una breve revisione storica della sua scoperta, quindi si passano in rassegna alcuni metodi sperimentali utilizzati nell’analisi dell’attività cerebrale, per poi evidenziare la differenza tra connettività strutturale e funzionale. In seguito, si riassumono brevemente i concetti dei sistemi dinamici, teoria indispensabile per capire un sistema complesso come il cervello. Nel capitolo successivo, attraverso un approccio ‘bottom-up’, si illustrano sotto il profilo biologico le principali strutture del sistema nervoso, dal neurone alla corteccia cerebrale. Tutto ciò viene spiegato anche dal punto di vista dei sistemi dinamici, illustrando il pionieristico modello di Hodgkin-Huxley e poi il concetto di dinamica di popolazione. Dopo questa prima parte preliminare si entra nel dettaglio della simulazione. Prima di tutto si danno maggiori informazioni sul software The Virtual Brain, si definisce il modello di network del resting state utilizzato nella simulazione e si descrive il ‘connettoma’ adoperato. Successivamente vengono mostrati i risultati dell’analisi svolta sui dati ricavati, dai quali si mostra come la criticità e il rumore svolgano un ruolo chiave nell'emergenza di questa attività di fondo del cervello. Questi risultati vengono poi confrontati con le più importanti e recenti ricerche in questo ambito, le quali confermano i risultati del nostro lavoro. Infine, si riportano brevemente le conseguenze che porterebbe in campo medico e clinico una piena comprensione del fenomeno del resting state e la possibilità di virtualizzare l’attività cerebrale
Dynamic Effects Increasing Network Vulnerability to Cascading Failures
We study cascading failures in networks using a dynamical flow model based on
simple conservation and distribution laws to investigate the impact of
transient dynamics caused by the rebalancing of loads after an initial network
failure (triggering event). It is found that considering the flow dynamics may
imply reduced network robustness compared to previous static overload failure
models. This is due to the transient oscillations or overshooting in the loads,
when the flow dynamics adjusts to the new (remaining) network structure. We
obtain {\em upper} and {\em lower} limits to network robustness, and it is
shown that {\it two} time scales and , defined by the network
dynamics, are important to consider prior to accurately addressing network
robustness or vulnerability. The robustness of networks showing cascading
failures is generally determined by a complex interplay between the network
topology and flow dynamics, where the ratio determines the
relative role of the two of them.Comment: 4 pages Latex, 4 figure
Robust oscillations in SIS epidemics on adaptive networks: Coarse-graining by automated moment closure
We investigate the dynamics of an epidemiological
susceptible-infected-susceptible (SIS) model on an adaptive network. This model
combines epidemic spreading (dynamics on the network) with rewiring of network
connections (topological evolution of the network). We propose and implement a
computational approach that enables us to study the dynamics of the network
directly on an emergent, coarse-grained level. The approach sidesteps the
derivation of closed low-dimensional approximations. Our investigations reveal
that global coupling, which enters through the awareness of the population to
the disease, can result in robust large-amplitude oscillations of the state and
topology of the network.Comment: revised version 6 pages, 4 figure
Impact of constrained rewiring on network structure and node dynamics
In this paper, we study an adaptive spatial network. We consider a susceptible-infected-susceptible (SIS) epidemic on the network, with a link or contact rewiring process constrained by spatial proximity. In particular, we assume that susceptible nodes break links with infected nodes independently of distance and reconnect at random to susceptible nodes available within a given radius. By systematically manipulating this radius we investigate the impact of rewiring on the structure of the network and characteristics of the epidemic.We adopt a step-by-step approach whereby we first study the impact of rewiring on the network structure in the absence of an epidemic, then with nodes assigned a disease status but without disease dynamics, and finally running network and epidemic dynamics simultaneously. In the case of no labeling and no epidemic dynamics, we provide both analytic and semianalytic formulas for the value of clustering achieved in the network. Our results also show that the rewiring radius and the network’s initial structure have a pronounced effect on the endemic equilibrium, with increasingly large rewiring radiuses yielding smaller disease prevalence
Distinct dynamical behavior in Erd\H{o}s-R\'enyi networks, regular random networks, ring lattices, and all-to-all neuronal networks
Neuronal network dynamics depends on network structure. In this paper we
study how network topology underpins the emergence of different dynamical
behaviors in neuronal networks. In particular, we consider neuronal network
dynamics on Erd\H{o}s-R\'enyi (ER) networks, regular random (RR) networks, ring
lattices, and all-to-all networks. We solve analytically a neuronal network
model with stochastic binary-state neurons in all the network topologies,
except ring lattices. Given that apart from network structure, all four models
are equivalent, this allows us to understand the role of network structure in
neuronal network dynamics. Whilst ER and RR networks are characterized by
similar phase diagrams, we find strikingly different phase diagrams in the
all-to-all network. Neuronal network dynamics is not only different within
certain parameter ranges, but it also undergoes different bifurcations (with a
richer repertoire of bifurcations in ER and RR compared to all-to-all
networks). This suggests that local heterogeneity in the ratio between
excitation and inhibition plays a crucial role on emergent dynamics.
Furthermore, we also observe one subtle discrepancy between ER and RR networks,
namely ER networks undergo a neuronal activity jump at lower noise levels
compared to RR networks, presumably due to the degree heterogeneity in ER
networks that is absent in RR networks. Finally, a comparison between network
oscillations in RR networks and ring lattices shows the importance of
small-world properties in sustaining stable network oscillations.Comment: 9 pages, 4 figure
The Bursty Dynamics of the Twitter Information Network
In online social media systems users are not only posting, consuming, and
resharing content, but also creating new and destroying existing connections in
the underlying social network. While each of these two types of dynamics has
individually been studied in the past, much less is known about the connection
between the two. How does user information posting and seeking behavior
interact with the evolution of the underlying social network structure?
Here, we study ways in which network structure reacts to users posting and
sharing content. We examine the complete dynamics of the Twitter information
network, where users post and reshare information while they also create and
destroy connections. We find that the dynamics of network structure can be
characterized by steady rates of change, interrupted by sudden bursts.
Information diffusion in the form of cascades of post re-sharing often creates
such sudden bursts of new connections, which significantly change users' local
network structure. These bursts transform users' networks of followers to
become structurally more cohesive as well as more homogenous in terms of
follower interests. We also explore the effect of the information content on
the dynamics of the network and find evidence that the appearance of new topics
and real-world events can lead to significant changes in edge creations and
deletions. Lastly, we develop a model that quantifies the dynamics of the
network and the occurrence of these bursts as a function of the information
spreading through the network. The model can successfully predict which
information diffusion events will lead to bursts in network dynamics
- …