Quasi-periodic events on structured earthquake models

There has been much interest in studying quasi-periodic events on earthquake models.Here we investigate quasiperiodic events in the avalanche time series on structured earthquake models by the analysis of the autocorrelation function and the fast Fourier transform.For random spatial earthquake models, quasi-periodic events are robust and we obtain a simple rule for a period that is proportional to the choice of unit time and the dissipation of the system.Moreover, computer simulations validate this rule for two-dimensional lattice models and cycle graphs, but our simulation results also show that small-world models, scale-free models, and random rule graphs do not have periodic phenomena.Although the periodicity of avalanche does not depend on the criticality of the system or the average degree of the system or the size of the system,there is evidence that it depends on the time series of the average force of the system.

Effects of refractory period on dynamical range in excitable networks

Effects of refractory period on the dynamical range in excitable networks are studied by computer simulations and theoretical analysis. The first effect is that the maximum or peak of the dynamical range appears when the largest eigenvalue of adjacent matrix is larger than one. We present a modification of the theory of the critical point by considering the correlation between excited nodes and their neighbors, which is brought by the refractory period. Our analysis provides the interpretation for the shift of the peak of the dynamical range. The effect is negligible when the average degree of the network is large. The second effect is that the dynamical range increases as the length of refractory period increases, and it is independent of the average degree. We present the mechanism of the second effect. As the refractory period increases,the saturated response decreases. This makes the bottom boundary of the dynamical range smaller and the dynamical range extend.

Power-law statistics of synchronous transition in inhibitory neuronal networks

We investigate the relationship between the synchronous transition and the power law behavior in spiking networks which are composed of inhibitory neurons and balanced by dc current. In the region of the synchronous transition, the avalanche size and duration distribution obey a power law distribution. We demonstrate the robustness of the power law for event sizes at different parameters and multiple time scales. Importantly, the exponent of the event size and duration distribution can satisfy the critical scaling relation. By changing the network structure parameters in the parameter region of transition, quasicriticality is observed, that is, critical exponents depart away from the criticality while still hold approximately to a dynamical scaling relation. The results suggest that power law statistics can emerge in networks composed of inhibitory neurons when the networks are balanced by external driving signal.

Phase order in one-dimensional piecewise linear discontinuous map

The phase order in a one-dimensional（1 D） piecewise linear discontinuous map is investigated. The striking feature is that the phase order may be ordered or disordered in multi-band chaotic regimes, in contrast to the ordered phase in continuous systems. We carried out an analysis to illuminate the underlying mechanism for the emergence of the disordered phase in multi-band chaotic regimes, and proved that the phase order is sensitive to the density distribution of the trajectories of the attractors. The scaling behavior of the net direction phase at a transition point is observed. The analytical proof of this scaling relation is obtained. Both the numerical and analytical results show that the exponent is 1, which is controlled by the feature of the map independent on whether the system is continuous or discontinuous. It extends the universality of the scaling behavior to systems with discontinuity. The result in this work is important to understanding the property of chaotic motion in discontinuous systems.

Effects of Inhibitory Signal on Criticality in Excitatory-Inhibitory Networks

We study the criticality in excitatory-inhibitory networks consisting of excitable elements. We investigate the effects of the inhibitory strength using both numerical simulations and theoretical analysis. We show that the inhibitory strength cannot affect the critical point. The dynamic range is decreased as the inhibitory strength increases.To simulate of decreasing the efficacy of excitation and inhibition which was studied in experiments, we remove excitatory or inhibitory nodes, delete excitatory or inhibitory links, and weaken excitatory or inhibitory coupling strength in critical excitatory-inhibitory network. Decreasing the excitation, the change of the dynamic range is most dramatic as the same as previous experimental results. However, decreasing inhibition has no effect on the criticality in excitatory-inhibitory network.

Power law decay of stored pattern stability in sparse Hopfield neural networks

Hopfield neural networks on scale-free networks display the power law relation between the stability of patterns and the number of patterns.The stability is measured by the overlap between the output state and the stored pattern which is presented to a neural network.In simulations the overlap declines to a constant by a power law decay.Here we provide the explanation for the power law behavior through the signal-to-noise ratio analysis.We show that on sparse networks storing a plenty of patterns the stability of stored patterns can be approached by a power law function with the exponent-0.5.There is a difference between analytic and simulation results that the analytic results of overlap decay to 0.The difference exists because the signal and noise term of nodes diverge from the mean-field approach in the sparse finite size networks.

Self-Organized Criticality in an Anisotropic Earthquake Model

We have made an extensive numerical study of a modified model proposed by Olami,Feder,and Christensen to describe earthquake behavior.Two situations were considered in this paper.One situation is that the energy of the unstable site is redistributed to its nearest neighbors randomly not averagely and keeps itself to zero.The other situation is that the energy of the unstable site is redistributed to its nearest neighbors randomly and keeps some energy for itself instead of reset to zero.Different boundary conditions were considered as well.By analyzing the distribution of earthquake sizes,we found that self-organized criticality can be excited only in the conservative case or the approximate conservative case in the above situations.Some evidence indicated that the critical exponent of both above situations and the original OFC model tend to the same result in the conservative case.The only difference is that the avalanche size in the original model is bigger.This result may be closer to the real world,after all,every crust plate size is different.