109,994 research outputs found
Modeling memory effects in activity-driven networks
Activity-driven networks (ADNs) have recently emerged as a powerful paradigm to study the temporal evolution of stochastic networked systems. All the information on the time-varying nature of the system is encapsulated into a constant activity parameter, which represents the propensity to generate connections. This formulation has enabled the scientific community to perform effective analytical studies on temporal networks. However, the hypothesis that the whole dynamics of the system is summarized by constant parameters might be excessively restrictive. Empirical studies suggest that activity evolves in time, intertwined with the system evolution, causing burstiness and clustering phenomena. In this paper, we propose a novel model for temporal networks, in which a self-excitement mechanism governs the temporal evolution of the activity, linking it to the evolution of the networked system. We investigate the effect of self-excitement on the epidemic inception by comparing the epidemic threshold of a Susceptible-Infected-Susceptible model in the presence and in the absence of the self-excitement mechanism. Our results suggest that the temporal nature of the activity favors the epidemic inception. Hence, neglecting self-excitement mechanisms might lead to harmful underestimation of the risk of an epidemic outbreak. Extensive numerical simulations are presented to support and extend our analysis, exploring parameter heterogeneities and noise, transient dynamics, and immunization processes. Our results constitute a first, necessary step toward a theory of ADNs that accounts for memory effects in the network evolution
Memory effects induce structure in social networks with activity-driven agents
Activity-driven modeling has been recently proposed as an alternative growth
mechanism for time varying networks, displaying power-law degree distribution
in time-aggregated representation. This approach assumes memoryless agents
developing random connections, thus leading to random networks that fail to
reproduce two-nodes degree correlations and the high clustering coefficient
widely observed in real social networks. In this work we introduce these
missing topological features by accounting for memory effects on the dynamic
evolution of time-aggregated networks. To this end, we propose an
activity-driven network growth model including a triadic-closure step as main
connectivity mechanism. We show that this mechanism provides some of the
fundamental topological features expected for social networks. We derive
analytical results and perform extensive numerical simulations in regimes with
and without population growth. Finally, we present two cases of study, one
comprising face-to-face encounters in a closed gathering, while the other one
from an online social friendship network.Comment: 19 pages, 12 figures, Major changes. Re-written wor
Effect of risk perception on epidemic spreading in temporal networks
Many progresses in the understanding of epidemic spreading models have been
obtained thanks to numerous modeling efforts and analytical and numerical
studies, considering host populations with very different structures and
properties, including complex and temporal interaction networks. Moreover, a
number of recent studies have started to go beyond the assumption of an absence
of coupling between the spread of a disease and the structure of the contacts
on which it unfolds. Models including awareness of the spread have been
proposed, to mimic possible precautionary measures taken by individuals that
decrease their risk of infection, but have mostly considered static networks.
Here, we adapt such a framework to the more realistic case of temporal networks
of interactions between individuals. We study the resulting model by analytical
and numerical means on both simple models of temporal networks and empirical
time-resolved contact data. Analytical results show that the epidemic threshold
is not affected by the awareness but that the prevalence can be significantly
decreased. Numerical studies highlight however the presence of very strong
finite-size effects, in particular for the more realistic synthetic temporal
networks, resulting in a significant shift of the effective epidemic threshold
in the presence of risk awareness. For empirical contact networks, the
awareness mechanism leads as well to a shift in the effective threshold and to
a strong reduction of the epidemic prevalence
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Therapeutic applications of computer models of brain activity for Alzheimer disease.
THERAPEUTIC IMPLICATIONS OF COMPUTER MODELS OF BRAIN ACTIVITY FOR ALZHEIMER DISEASE
A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems
In this paper we present a methodological framework that meets novel
requirements emerging from upcoming types of accelerated and highly
configurable neuromorphic hardware systems. We describe in detail a device with
45 million programmable and dynamic synapses that is currently under
development, and we sketch the conceptual challenges that arise from taking
this platform into operation. More specifically, we aim at the establishment of
this neuromorphic system as a flexible and neuroscientifically valuable
modeling tool that can be used by non-hardware-experts. We consider various
functional aspects to be crucial for this purpose, and we introduce a
consistent workflow with detailed descriptions of all involved modules that
implement the suggested steps: The integration of the hardware interface into
the simulator-independent model description language PyNN; a fully automated
translation between the PyNN domain and appropriate hardware configurations; an
executable specification of the future neuromorphic system that can be
seamlessly integrated into this biology-to-hardware mapping process as a test
bench for all software layers and possible hardware design modifications; an
evaluation scheme that deploys models from a dedicated benchmark library,
compares the results generated by virtual or prototype hardware devices with
reference software simulations and analyzes the differences. The integration of
these components into one hardware-software workflow provides an ecosystem for
ongoing preparative studies that support the hardware design process and
represents the basis for the maturity of the model-to-hardware mapping
software. The functionality and flexibility of the latter is proven with a
variety of experimental results
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