286 research outputs found
Dynamic range of hypercubic stochastic excitable media
We study the response properties of d-dimensional hypercubic excitable
networks to a stochastic stimulus. Each site, modelled either by a three-state
stochastic susceptible-infected-recovered-susceptible system or by the
probabilistic Greenberg-Hastings cellular automaton, is continuously and
independently stimulated by an external Poisson rate h. The response function
(mean density of active sites rho versus h) is obtained via simulations (for
d=1, 2, 3, 4) and mean field approximations at the single-site and pair levels
(for all d). In any dimension, the dynamic range of the response function is
maximized precisely at the nonequilibrium phase transition to self-sustained
activity, in agreement with a reasoning recently proposed. Moreover, the
maximum dynamic range attained at a given dimension d is a decreasing function
of d.Comment: 7 pages, 4 figure
On the Aging Dynamics in an Immune Network Model
Recently we have used a cellular automata model which describes the dynamics
of a multi-connected network to reproduce the refractory behavior and aging
effects obtained in immunization experiments performed with mice when subjected
to multiple perturbations. In this paper we investigate the similarities
between the aging dynamics observed in this multi-connected network and the one
observed in glassy systems, by using the usual tools applied to analyze the
latter. An interesting feature we show here is that the model reproduces the
biological aspects observed in the experiments during the long transient time
it takes to reach the stationary state. Depending on the initial conditions,
and without any perturbation, the system may reach one of a family of
long-period attractors. The pertrubations may drive the system from its natural
attractor to other attractors of the same family. We discuss the different
roles played by the small random perturbations (noise) and by the large
periodic perturbations (immunizations)
Excitable Scale Free Networks
When a simple excitable system is continuously stimulated by a Poissonian
external source, the response function (mean activity versus stimulus rate)
generally shows a linear saturating shape. This is experimentally verified in
some classes of sensory neurons, which accordingly present a small dynamic
range (defined as the interval of stimulus intensity which can be appropriately
coded by the mean activity of the excitable element), usually about one or two
decades only. The brain, on the other hand, can handle a significantly broader
range of stimulus intensity, and a collective phenomenon involving the
interaction among excitable neurons has been suggested to account for the
enhancement of the dynamic range. Since the role of the pattern of such
interactions is still unclear, here we investigate the performance of a
scale-free (SF) network topology in this dynamic range problem. Specifically,
we study the transfer function of disordered SF networks of excitable
Greenberg-Hastings cellular automata. We observe that the dynamic range is
maximum when the coupling among the elements is critical, corroborating a
general reasoning recently proposed. Although the maximum dynamic range yielded
by general SF networks is slightly worse than that of random networks, for
special SF networks which lack loops the enhancement of the dynamic range can
be dramatic, reaching nearly five decades. In order to understand the role of
loops on the transfer function we propose a simple model in which the density
of loops in the network can be gradually increased, and show that this is
accompanied by a gradual decrease of dynamic range.Comment: 6 pages, 4 figure
Emergence of Hierarchy on a Network of Complementary Agents
Complementarity is one of the main features underlying the interactions in
biological and biochemical systems. Inspired by those systems we propose a
model for the dynamical evolution of a system composed by agents that interact
due to their complementary attributes rather than their similarities. Each
agent is represented by a bit-string and has an activity associated to it; the
coupling among complementary peers depends on their activity. The connectivity
of the system changes in time respecting the constraint of complementarity. We
observe the formation of a network of active agents whose stability depends on
the rate at which activity diffuses in the system. The model exhibits a
non-equilibrium phase transition between the ordered phase, where a stable
network is generated, and a disordered phase characterized by the absence of
correlation among the agents. The ordered phase exhibits multi-modal
distributions of connectivity and activity, indicating a hierarchy of
interaction among different populations characterized by different degrees of
activity. This model may be used to study the hierarchy observed in social
organizations as well as in business and other networks.Comment: 13 pages, 4 figures, submitte
Response of electrically coupled spiking neurons: a cellular automaton approach
Experimental data suggest that some classes of spiking neurons in the first
layers of sensory systems are electrically coupled via gap junctions or
ephaptic interactions. When the electrical coupling is removed, the response
function (firing rate {\it vs.} stimulus intensity) of the uncoupled neurons
typically shows a decrease in dynamic range and sensitivity. In order to assess
the effect of electrical coupling in the sensory periphery, we calculate the
response to a Poisson stimulus of a chain of excitable neurons modeled by
-state Greenberg-Hastings cellular automata in two approximation levels. The
single-site mean field approximation is shown to give poor results, failing to
predict the absorbing state of the lattice, while the results for the pair
approximation are in good agreement with computer simulations in the whole
stimulus range. In particular, the dynamic range is substantially enlarged due
to the propagation of excitable waves, which suggests a functional role for
lateral electrical coupling. For probabilistic spike propagation the Hill
exponent of the response function is , while for deterministic spike
propagation we obtain , which is close to the experimental values
of the psychophysical Stevens exponents for odor and light intensities. Our
calculations are in qualitative agreement with experimental response functions
of ganglion cells in the mammalian retina.Comment: 11 pages, 8 figures, to appear in the Phys. Rev.
- …