38,626 research outputs found
How memory generates heterogeneous dynamics in temporal networks
Empirical temporal networks display strong heterogeneities in their dynamics,
which profoundly affect processes taking place on these networks, such as rumor
and epidemic spreading. Despite the recent wealth of data on temporal networks,
little work has been devoted to the understanding of how such heterogeneities
can emerge from microscopic mechanisms at the level of nodes and links. Here we
show that long-term memory effects are present in the creation and
disappearance of links in empirical networks. We thus consider a simple
generative modeling framework for temporal networks able to incorporate these
memory mechanisms. This allows us to study separately the role of each of these
mechanisms in the emergence of heterogeneous network dynamics. In particular,
we show analytically and numerically how heterogeneous distributions of contact
durations, of inter-contact durations and of numbers of contacts per link
emerge. We also study the individual effect of heterogeneities on dynamical
processes, such as the paradigmatic Susceptible-Infected epidemic spreading
model. Our results confirm in particular the crucial role of the distributions
of inter-contact durations and of the numbers of contacts per link
Structural network heterogeneities and network dynamics: a possible dynamical mechanism for hippocampal memory reactivation
The hippocampus has the capacity for reactivating recently acquired memories
[1-3] and it is hypothesized that one of the functions of sleep reactivation is
the facilitation of consolidation of novel memory traces [4-11]. The dynamic
and network processes underlying such a reactivation remain, however, unknown.
We show that such a reactivation characterized by local, self-sustained
activity of a network region may be an inherent property of the recurrent
excitatory-inhibitory network with a heterogeneous structure. The entry into
the reactivation phase is mediated through a physiologically feasible
regulation of global excitability and external input sources, while the
reactivated component of the network is formed through induced network
heterogeneities during learning. We show that structural changes needed for
robust reactivation of a given network region are well within known
physiological parameters [12,13].Comment: 16 pages, 5 figure
Impact of spatially constrained sampling of temporal contact networks on the evaluation of the epidemic risk
The ability to directly record human face-to-face interactions increasingly
enables the development of detailed data-driven models for the spread of
directly transmitted infectious diseases at the scale of individuals. Complete
coverage of the contacts occurring in a population is however generally
unattainable, due for instance to limited participation rates or experimental
constraints in spatial coverage. Here, we study the impact of spatially
constrained sampling on our ability to estimate the epidemic risk in a
population using such detailed data-driven models. The epidemic risk is
quantified by the epidemic threshold of the
susceptible-infectious-recovered-susceptible model for the propagation of
communicable diseases, i.e. the critical value of disease transmissibility
above which the disease turns endemic. We verify for both synthetic and
empirical data of human interactions that the use of incomplete data sets due
to spatial sampling leads to the underestimation of the epidemic risk. The bias
is however smaller than the one obtained by uniformly sampling the same
fraction of contacts: it depends nonlinearly on the fraction of contacts that
are recorded and becomes negligible if this fraction is large enough. Moreover,
it depends on the interplay between the timescales of population and spreading
dynamics.Comment: 21 pages, 7 figure
The effect of heterogeneity on decorrelation mechanisms in spiking neural networks: a neuromorphic-hardware study
High-level brain function such as memory, classification or reasoning can be
realized by means of recurrent networks of simplified model neurons. Analog
neuromorphic hardware constitutes a fast and energy efficient substrate for the
implementation of such neural computing architectures in technical applications
and neuroscientific research. The functional performance of neural networks is
often critically dependent on the level of correlations in the neural activity.
In finite networks, correlations are typically inevitable due to shared
presynaptic input. Recent theoretical studies have shown that inhibitory
feedback, abundant in biological neural networks, can actively suppress these
shared-input correlations and thereby enable neurons to fire nearly
independently. For networks of spiking neurons, the decorrelating effect of
inhibitory feedback has so far been explicitly demonstrated only for
homogeneous networks of neurons with linear sub-threshold dynamics. Theory,
however, suggests that the effect is a general phenomenon, present in any
system with sufficient inhibitory feedback, irrespective of the details of the
network structure or the neuronal and synaptic properties. Here, we investigate
the effect of network heterogeneity on correlations in sparse, random networks
of inhibitory neurons with non-linear, conductance-based synapses. Emulations
of these networks on the analog neuromorphic hardware system Spikey allow us to
test the efficiency of decorrelation by inhibitory feedback in the presence of
hardware-specific heterogeneities. The configurability of the hardware
substrate enables us to modulate the extent of heterogeneity in a systematic
manner. We selectively study the effects of shared input and recurrent
connections on correlations in membrane potentials and spike trains. Our
results confirm ...Comment: 20 pages, 10 figures, supplement
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