arXiv subjects

Lucilla de Arcangelis

Publications and source records attributed to Lucilla de Arcangelis.

33 records · Page 2Linked to original sources

arXiv2012

Activity-dependent neuronal model on complex networks

Neuronal avalanches are a novel mode of activity in neuronal networks, experimentally found in vitro and in vivo, and exhibit a robust critical behaviour: These avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems. We present a recent model inspired in self-organized criticality, which consists of an electrical network with threshold firing, refractory period and activity-dependent synaptic plasticity. The model reproduces the critical behaviour of the distribution of avalanche sizes and durations measured experimentally. Moreover, the power spectra of the electrical signal reproduce very robustly the power law behaviour found in human electroencephalogram (EEG) spectra. We implement this model on a variety of complex networks, i.e. regular, small-world and scale-free and verify the robustness of the critical behaviour.

q-bio.NC↗

arXiv2011

Modeling the topology of protein interaction networks

A major issue in biology is the understanding of the interactions between proteins. These interactions can be described by a network, where the proteins are modeled by nodes and the interactions by edges. The origin of these protein networks is not well understood yet. Here we present a two-step model, which generates clusters with the same topological properties as networks for protein-protein interactions, namely, the same degree distribution, cluster size distribution, clustering coefficient and shortest path length. The biological and model networks are not scale free but exhibit small world features. The model allows the fitting of different biological systems by tuning a single parameter.

physics.bio-ph↗

arXiv2011

Scale free networks by preferential depletion

We show that not only preferential attachment but also preferential depletion leads to scale-free networks. The resulting degree distribution exponents is typically less than two (5/3) as opposed to the case of the growth models studied before where the exponents are larger. Our approach applies in particular to biological networks where in fact we find interesting agreement with experimental measurements. We investigate the most important properties characterizing these networks, as the cluster size distribution, the average shortest path and the clustering coefficient.

physics.soc-ph↗

arXiv2010

Inter-arrival times of message propagation on directed networks

One of the challenges in fighting cybercrime is to understand the dynamics of message propagation on botnets, networks of infected computers used to send viruses, unsolicited commercial emails (SPAM) or denial of service attacks. We map this problem to the propagation of multiple random walkers on directed networks and we evaluate the inter-arrival time distribution between successive walkers arriving at a target. We show that the temporal organization of this process, which models information propagation on unstructured peer to peer networks, has the same features as SPAM arriving to a single user. We study the behavior of the message inter-arrival time distribution on three different network topologies using two different rules for sending messages. In all networks the propagation is not a pure Poisson process. It shows universal features on Poissonian networks and a more complex behavior on scale free networks. Results open the possibility to indirectly learn about the process of sending messages on networks with unknown topologies, by studying inter-arrival times at any node of the network.

cond-mat.dis-nn↗

arXiv2010

Multiple-Time Scaling and Universal Behavior of the Earthquake Interevent Time Distribution

The interevent time distribution characterizes the temporal occurrence in seismic catalogs. Universal scaling properties of this distribution have been evidenced for entire catalogs and seismic sequences. Recently, these universal features have been questioned and some criticisms have been raised. We investigate the existence of universal scaling properties by analyzing a Californian catalog and by means of numerical simulations of an epidemic-type model. We show that the interevent time distribution exhibits a universal behavior over the entire temporal range if four characteristic times are taken into account. The above analysis allows us to identify the scaling form leading to universal behavior and explains the observed deviations. Furthermore, it provides a tool to identify the dependence on the mainshock magnitude of the c parameter that fixes the onset of the power law decay in the Omori law.

physics.geo-ph↗

arXiv2010

Learning as a phenomenon occurring in a critical state

Recent physiological measurements have provided clear evidence about scale-free avalanche brain activity and EEG spectra, feeding the classical enigma of how such a chaotic system can ever learn or respond in a controlled and reproducible way. Models for learning, like neural networks or perceptrons, have traditionally avoided strong fluctuations. Conversely, we propose that brain activity having features typical of systems at a critical point, represents a crucial ingredient for learning. We present here a study which provides novel insights toward the understanding of the problem. Our model is able to reproduce quantitatively the experimentally observed critical state of the brain and, at the same time, learns and remembers logical rules including the exclusive OR (XOR), which has posed difficulties to several previous attempts. We implement the model on a network with topological properties close to the functionality network in real brains. Learning occurs via plastic adaptation of synaptic strengths and exhibits universal features. We find that the learning performance and the average time required to learn are controlled by the strength of plastic adaptation, in a way independent of the specific task assigned to the system. Even complex rules can be learned provided that the plastic adaptation is sufficiently slow.

q-bio.NC↗

arXiv2010

Time-energy correlations in solar flare occurrence

The existence of time-energy correlations in flare occurrence is still an open and much debated problem. This study addresses the question whether statistically significant correlations are present between energies of successive flares as well as energies and waiting times. We analyze the GOES catalog with a statistical approach based on the comparison of the real catalog with a reshuffled one where energies are decorrelated. This analysis reduces the effect of background activity and is able to reveal the role of obscuration. We show the existence of non-trivial correlations between waiting times and energies, as well as between energies of subsequent flares. More precisely, we find that flares close in time tend to have the second event with large energy. Moreover, after large flares the flaring rate significantly increases, together with the probability of other large flares. Results suggest that correlations between energies and waiting times are a physical property and not an effect of obscuration. These findings could give important information on the mechanisms for energy storage and release in the solar corona.

astro-ph.SR↗

arXiv2008

Correlations and Omori law in Spamming

The most costly and annoying characteristic of the e-mail communication system is the large number of unsolicited commercial e-mails, known as spams, that are continuously received. Via the investigation of the statistical properties of the spam delivering intertimes, we show that spams delivered to a given recipient are time correlated: if the intertime between two consecutive spams is small (large), then the next spam will most probably arrive after a small (large) intertime. Spam temporal correlations are reproduced by a numerical model based on the random superposition of spam sequences, each one described by the Omori law. This and other experimental findings suggest that statistical approaches may be used to infer how spammers operate.

physics.soc-ph↗

arXiv2007

On the influence of time and space correlations on the next earthquake magnitude

A crucial point in the debate on feasibility of earthquake prediction is the dependence of an earthquake magnitude from past seismicity. Indeed, whilst clustering in time and space is widely accepted, much more questionable is the existence of magnitude correlations. The standard approach generally assumes that magnitudes are independent and therefore in principle unpredictable. Here we show the existence of clustering in magnitude: earthquakes occur with higher probability close in time, space and magnitude to previous events. More precisely, the next earthquake tends to have a magnitude similar but smaller than the previous one. A dynamical scaling relation between magnitude, time and space distances reproduces the complex pattern of magnitude, spatial and temporal correlations observed in experimental seismic catalogs.

physics.geo-ph↗

arXiv2007

Dynamical scaling in branching models for seismicity

We propose a branching process based on a dynamical scaling hypothesis relating time and mass. In the context of earthquake occurrence, we show that experimental power laws in size and time distribution naturally originate solely from this scaling hypothesis. We present a numerical protocol able to generate a synthetic catalog with an arbitrary large number of events. The numerical data reproduce the hierarchical organization in time and magnitude of experimental inter-event time distribution.

cond-mat.stat-mech↗

arXiv2006

Self-Organized Criticality model for Brain Plasticity

Networks of living neurons exhibit an avalanche mode of activity, experimentally found in organotypic cultures. Here we present a model based on self-organized criticality and taking into account brain plasticity, which is able to reproduce the spectrum of electroencephalograms (EEG). The model consists in an electrical network with threshold firing and activity-dependent synapse strenghts. The system exhibits an avalanche activity power law distributed. The analysis of the power spectra of the electrical signal reproduces very robustly the power law behaviour with the exponent 0.8, experimentally measured in EEG spectra. The same value of the exponent is found on small-world lattices and for leaky neurons, indicating that universality holds for a wide class of brain models.

q-bio.NC↗

arXiv2005

Magnitude clustering and dynamical scaling in trigger models for earthquake forecasting

One of the main interests in seismology is the formulation of models able to describe the clustering in time occurrence of earthquakes. Analysis of the Southern California Catalog shows magnitude clustering in correspondence to temporal clustering. Here we propose a dynamical scaling hypothesis in which time is rescaled in terms of magnitude. This hypothesis is introduced in the context of a generalized trigger model and gives account for clustering in time and magnitude for earthquake occurrence. The model is able to generate a synthetic catalog reproducing magnitude and inter-even time distribution of thirty years California seismicity.

physics.geo-ph↗

arXiv2005

Memory in Self Organized Criticality

Many natural phenomena exhibit power law behaviour in the distribution of event size. This scaling is successfully reproduced by Self Organized Criticality (SOC). On the other hand, temporal occurrence in SOC models has a Poisson-like statistics, i.e. exponential behaviour in the inter-event time distribution, in contrast with experimental observations. We present a SOC model with memory: events are nucleated not only as a consequence of the instantaneous value of the local field with respect to the firing threshold, but on the basis of the whole history of the system. The model is able to reproduce the complex behaviour of inter-event time distribution, in excellent agreement with experimental seismic data.

cond-mat.stat-mech↗

arXiv2004

Activity-dependent brain model explaining EEG spectra

Most brain models focus on associative memory or calculation capability, experimentally inaccessible using physiological methods. Here we present a model explaining a basic feature of electroencephalograms (EEG). Our model is based on an electrical network with threshold firing and plasticity of synapses that reproduces very robustly the measured exponent 0.8 of the medical EEG spectra, a solid evidence for self-organized criticality. Our result are also valid on small-world lattices. We propose that an universal scaling behaviour characterizes many physiological signal spectra for brain controlled activities.

q-bio.NC↗

arXiv2002

Elastic critical behaviour in a 3d model for polymer gels

The elastic response in polymeric gels is studied by means of a percolation dynamic model. By numerical simulations the fluctuations in the gyration radius and in the center of mass motion of the percolating cluster are determined. Their scaling behaviour at the gelation threshold gives a critical exponent for the elastic modulus $f \sim 2.5 \pm 0.1$ in agreement with the prediction $f = d ν$.

cond-mat.soft↗