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We comment on some recent, yet unpublished results concerning instabilities in complex systems and their applications.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

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

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

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

The role of diffusion in branching and annihilation random walk models

Different branching and annihilating random walk models are investigated by cluster mean-field method and simulations in one and two dimensions. In case of the A -> 2A, 2A -> 0 model the cluster mean-field approximations show diffusion dependence in the phase diagram as was found recently by non-perturbative renormalization group method (L. Canet et al., cond-mat/0403423). The same type of survey for the A -> 2A, 4A -> 0 model results in a reentrant phase diagram, similar to that of 2A -> 3A, 4A -> 0 model (G. \'Odor, PRE {\bf 69}, 036112 (2004)). Simulations of the A -> 2A, 4A -> 0 model in one and two dimensions confirm the presence of both the directed percolation transitions at finite branching rates and the mean-field transition at zero branching rate. In two dimensions the directed percolation transition disappears for strong diffusion rates. These results disagree with the predictions of the perturbative renormalization group method.Comment: 4 pages, 4 figures, 1 table include

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