367 research outputs found
Poissonian bursts in e-mail correspondence
Recent work has shown that the distribution of inter-event times for e-mail
communication exhibits a heavy tail which is statistically consistent with a
cascading Poisson process. In this work we extend the analysis to higher-order
statistics, using the Fano and Allan factors to quantify the extent to which
the empirical data depart from the known correlations of Poissonian statistics.
The analysis shows that the higher-order statistics from the empirical data is
indistinguishable from that of randomly reordered time series, thus
demonstrating that e-mail correspondence is no more bursty or correlated than a
Poisson process. Furthermore synthetic data sets generated by a cascading
Poisson process replicate the burstiness and correlations observed in the
empirical data. Finally, a simple rescaling analysis using the best-estimate
rate of activity, confirms that the empirically observed correlations arise
from a non-homogeneus Poisson process
A simple conceptual model of abrupt glacial climate events
Here we use a very simple conceptual model in an attempt to reduce essential
parts of the complex nonlinearity of abrupt glacial climate changes (the
so-called Dansgaard-Oeschger events) to a few simple principles, namely (i) a
threshold process, (ii) an overshooting in the stability of the system and
(iii) a millennial-scale relaxation. By comparison with a so-called Earth
system model of intermediate complexity (CLIMBER-2), in which the events
represent oscillations between two climate states corresponding to two
fundamentally different modes of deep-water formation in the North Atlantic, we
demonstrate that the conceptual model captures fundamental aspects of the
nonlinearity of the events in that model. We use the conceptual model in order
to reproduce and reanalyse nonlinear resonance mechanisms that were already
suggested in order to explain the characteristic time scale of
Dansgaard-Oeschger events. In doing so we identify a new form of stochastic
resonance (i.e. an overshooting stochastic resonance) and provide the first
explicitly reported manifestation of ghost resonance in a geosystem, i.e. of a
mechanism which could be relevant for other systems with thresholds and with
multiple states of operation. Our work enables us to explicitly simulate
realistic probability measures of Dansgaard-Oeschger events (e.g. waiting time
distributions, which are a prerequisite for statistical analyses on the
regularity of the events by means of Monte-Carlo simulations). We thus think
that our study is an important advance in order to develop more adequate
methods to test the statistical significance and the origin of the proposed
glacial 1470-year climate cycle
Self-similar correlation function in brain resting-state fMRI
Adaptive behavior, cognition and emotion are the result of a bewildering
variety of brain spatiotemporal activity patterns. An important problem in
neuroscience is to understand the mechanism by which the human brain's 100
billion neurons and 100 trillion synapses manage to produce this large
repertoire of cortical configurations in a flexible manner. In addition, it is
recognized that temporal correlations across such configurations cannot be
arbitrary, but they need to meet two conflicting demands: while diverse
cortical areas should remain functionally segregated from each other, they must
still perform as a collective, i.e., they are functionally integrated. Here, we
investigate these large-scale dynamical properties by inspecting the character
of the spatiotemporal correlations of brain resting-state activity. In physical
systems, these correlations in space and time are captured by measuring the
correlation coefficient between a signal recorded at two different points in
space at two different times. We show that this two-point correlation function
extracted from resting-state fMRI data exhibits self-similarity in space and
time. In space, self-similarity is revealed by considering three successive
spatial coarse-graining steps while in time it is revealed by the 1/f frequency
behavior of the power spectrum. The uncovered dynamical self-similarity implies
that the brain is spontaneously at a continuously changing (in space and time)
intermediate state between two extremes, one of excessive cortical integration
and the other of complete segregation. This dynamical property may be seen as
an important marker of brain well-being both in health and disease.Comment: 14 pages 13 figures; published online before print September 2
Signal integration enhances the dynamic range in neuronal systems
The dynamic range measures the capacity of a system to discriminate the
intensity of an external stimulus. Such an ability is fundamental for living
beings to survive: to leverage resources and to avoid danger. Consequently, the
larger is the dynamic range, the greater is the probability of survival. We
investigate how the integration of different input signals affects the dynamic
range, and in general the collective behavior of a network of excitable units.
By means of numerical simulations and a mean-field approach, we explore the
nonequilibrium phase transition in the presence of integration. We show that
the firing rate in random and scale-free networks undergoes a discontinuous
phase transition depending on both the integration time and the density of
integrator units. Moreover, in the presence of external stimuli, we find that a
system of excitable integrator units operating in a bistable regime largely
enhances its dynamic range.Comment: 5 pages, 4 figure
Intelligent systems in the context of surrounding environment
We investigate the behavioral patterns of a population of agents, each controlled by a simple biologically motivated neural network model, when they are set in competition against each other in the Minority Model of Challet and Zhang. We explore the effects of changing agent characteristics, demonstrating that crowding behavior takes place among agents of similar memory, and show how this allows unique `rogue' agents with higher memory values to take advantage of a majority population. We also show that agents' analytic capability is largely determined by the size of the intermediary layer of neurons.
In the context of these results, we discuss the general nature of natural and artificial intelligence systems, and suggest intelligence only exists in the context of the surrounding environment (embodiment).
Source code for the programs used can be found at http://neuro.webdrake.net/
Brownian rectifiers in the presence of temporally asymmetric unbiased forces
The efficiency of energy transduction in a temporally asymmetric rocked
ratchet is studied. Time asymmetry favours current in one direction and
suppresses it in the opposite direction due to which large efficiency ~ 50% is
readily obtained. The spatial asymmetry in the potential together with system
inhomogeneity may help in further enhancing the efficiency. Fine tuning of
system parameters considered leads to multiple current reversals even in the
adiabatic regime
Probing the mechanical properties of graphene using a corrugated elastic substrate
The exceptional mechanical properties of graphene have made it attractive for
nano-mechanical devices and functional composite materials. Two key aspects of
graphene's mechanical behavior are its elastic and adhesive properties. These
are generally determined in separate experiments, and it is moreover typically
difficult to extract parameters for adhesion. In addition, the mechanical
interplay between graphene and other elastic materials has not been well
studied. Here, we demonstrate a technique for studying both the elastic and
adhesive properties of few-layer graphene (FLG) by placing it on deformable,
micro-corrugated substrates. By measuring deformations of the composite
graphene-substrate structures, and developing a related linear elasticity
theory, we are able to extract information about graphene's bending rigidity,
adhesion, critical stress for interlayer sliding, and sample-dependent tension.
The results are relevant to graphene-based mechanical and electronic devices,
and to the use of graphene in composite, flexible, and strain-engineered
materials.Comment: 5 pages, 4 figure
Rate-dependent propagation of cardiac action potentials in a one-dimensional fiber
Action potential duration (APD) restitution, which relates APD to the
preceding diastolic interval (DI), is a useful tool for predicting the onset of
abnormal cardiac rhythms. However, it is known that different pacing protocols
lead to different APD restitution curves (RCs). This phenomenon, known as APD
rate-dependence, is a consequence of memory in the tissue. In addition to APD
restitution, conduction velocity restitution also plays an important role in
the spatiotemporal dynamics of cardiac tissue. We present new results
concerning rate-dependent restitution in the velocity of propagating action
potentials in a one-dimensional fiber. Our numerical simulations show that,
independent of the amount of memory in the tissue, waveback velocity exhibits
pronounced rate-dependence and the wavefront velocity does not. Moreover, the
discrepancy between waveback velocity RCs is most significant for small DI. We
provide an analytical explanation of these results, using a system of coupled
maps to relate the wavefront and waveback velocities. Our calculations show
that waveback velocity rate-dependence is due to APD restitution, not memory.Comment: 17 pages, 7 figure
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