973 research outputs found
Modeling Nonequilibrium Phase Transitions and Critical Behavior in Complex Systems
We comment on some recent, yet unpublished results concerning instabilities
in complex systems and their applications. In particular, we briefly describe
main observations during extensive computer simulations of two lattice
nonequilibrium models. One exhibits robust and efficient processes of pattern
recognition under synaptic coherent activity; the second example exhibits
interesting critical behavior and simulates nucleation and spinodal
decomposition processes in driven fluids.Comment: 6 pages, 4 figure
Can intrinsic noise induce various resonant peaks?
We theoretically describe how weak signals may be efficiently transmitted
throughout more than one frequency range in noisy excitable media by kind of
stochastic multiresonance. This serves us here to reinterpret recent
experiments in neuroscience, and to suggest that many other systems in nature
might be able to exhibit several resonances. In fact, the observed behavior
happens in our (network) model as a result of competition between (1) changes
in the transmitted signals as if the units were varying their activation
threshold, and (2) adaptive noise realized in the model as rapid
activity-dependent fluctuations of the connection intensities. These two
conditions are indeed known to characterize heterogeneously networked systems
of excitable units, e.g., sets of neurons and synapses in the brain. Our
results may find application also in the design of detector devices.Comment: 10 pages, 2 figure
Critical behavior of a non-equilibrium interacting particle system driven by an oscillatory field
First- and second-order temperature driven transitions are studied, in a
lattice gas driven by an oscillatory field. The short time dynamics study
provides upper and lower bounds for the first-order transition points obtained
using standard simulations. The difference between upper and lower bounds is a
measure for the strength of the first-order transition and becomes negligible
small for densities close to one half. In addition, we give strong evidence on
the existence of multicritical points and a critical temperature gap, the
latter induced by the anisotropy introduced by the driving field.Comment: 12 pages, 4 figures; to appear in Europhys. Let
Robust short-term memory without synaptic learning
Short-term memory in the brain cannot in general be explained the way
long-term memory can -- as a gradual modification of synaptic weights -- since
it takes place too quickly. Theories based on some form of cellular
bistability, however, do not seem able to account for the fact that noisy
neurons can collectively store information in a robust manner. We show how a
sufficiently clustered network of simple model neurons can be instantly induced
into metastable states capable of retaining information for a short time (a few
seconds). The mechanism is robust to different network topologies and kinds of
neural model. This could constitute a viable means available to the brain for
sensory and/or short-term memory with no need of synaptic learning. Relevant
phenomena described by neurobiology and psychology, such as local
synchronization of synaptic inputs and power-law statistics of forgetting
avalanches, emerge naturally from this mechanism, and we suggest possible
experiments to test its viability in more biological settings.Comment: 20 pages, 9 figures. Amended to include section on spiking neurons,
with general rewrit
Fluctuation-dissipation relations outside the linear response regime in a two-dimensional driven lattice gas along the direction transverse to the driving force
We performed numerical experiments on a two-dimensional driven lattice gas,
which constitutes a simple stochastic nonequilibrium many-body model. In this
model, focusing on the behavior along the direction transverse to the external
driving force, we numerically measure transport coefficients and dynamical
fluctuations outside the linear response regime far from equilibrium. Using
these quantities, we find the validity of the Einstein relation, the Green-Kubo
relation and the fluctuation-response relation.Comment: 4 pages, 5 figure
Functional Optimization in Complex Excitable Networks
We study the effect of varying wiring in excitable random networks in which
connection weights change with activity to mold local resistance or
facilitation due to fatigue. Dynamic attractors, corresponding to patterns of
activity, are then easily destabilized according to three main modes, including
one in which the activity shows chaotic hopping among the patterns. We describe
phase transitions to this regime, and show a monotonous dependence of critical
parameters on the heterogeneity of the wiring distribution. Such correlation
between topology and functionality implies, in particular, that tasks which
require unstable behavior --such as pattern recognition, family discrimination
and categorization-- can be most efficiently performed on highly heterogeneous
networks. It also follows a possible explanation for the abundance in nature of
scale--free network topologies.Comment: 7 pages, 3 figure
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