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Register a new userStatistical Complexity and Nontrivial Collective Behavior in Electroencephalografic Signals
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We calculate a measure of statistical complexity from the global dynamics of electroencephalographic (EEG) signals from healthy subjects and epileptic patients, and are able to stablish a criterion to characterize the collective behavior in both groups of individuals. It is found that the collective dynamics of EEG signals possess relative higher values of complexity for healthy subjects in comparison to that for epileptic patients. To interpret these results, we propose a model of a network of coupled chaotic maps where we calculate the complexity as a function of a parameter and relate this measure with the emergence of nontrivial collective behavior in the system. Our results show that the presence of nontrivial collective behavior is associated to high values of complexity; thus suggesting that similar dynamical collective process may take place in the human brain. Our findings also suggest that epilepsy is a degenerative illness related to the loss of complexity in the brain.
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This article considers a model for alternative processes for securities prices and compares this model with actual return data of several securities. The distributions of returns that appear in the model can be Gaussian as well as non-Gaussian; in particular they may have two peaks. We consider a discrete Markov chain model. This model in some aspects is similar to well-known Ising model describing ferromagnetics. Namely we consider a set of N investors, each of whom has either bullish or bearish opinion, denoted by plus or minus respectively. At every time step each of N investors can change his/her sign. The probability of a plus becoming a minus and the probability of a minus becoming a plus depends only on the bullish sentiment described as the number of bullish investors among the total of N investors. The number of bullish investors then forms a Markov chain whose transition matrix is calculated explicitly. The transition matrix of that chain is ergodic and any initial distribution of bullish investors converges to stationary. Stationary distributions of bullish investors in this Markov chain model are similar to continuous distributions of the theory of social imitation of Callen and Shapero. Distributions obtained this way can represent 3 types of market behavior: one-peaked distribution that is close to Gaussian, transition market (flattening of the top), and two-peaked distribution.
Proponents of Complexity Science believe that the huge variety of emergent phenomena observed throughout nature, are generated by relatively few microscopic mechanisms. Skeptics however point to the lack of concrete examples in which a single mechanistic model manages to capture relevant macroscopic and microscopic properties for two or more distinct systems operating across radically different length and time scales. Here we show how a single complexity model built around cluster coalescence and fragmentation, can cross the fundamental divide between many-body quantum physics and social science. It simultaneously (i) explains a mysterious recent finding of Fratini et al. concerning quantum many-body effects in cuprate superconductors (i.e. scale of 10^{-9} - 10^{-4} meters and 10^{-12} - 10^{-6} seconds), (ii) explains the apparent universality of the casualty distributions in distinct human insurgencies and terrorism (i.e. scale of 10^3 - 10^6 meters and 10^4 - 10^8 seconds), (iii) shows consistency with various established empirical facts for financial markets, neurons and human gangs and (iv) makes microscopic sense for each application. Our findings also suggest that a potentially productive shift can be made in Complexity research toward the identification of equivalent many-body dynamics in both classical and quantum regimes.
Many neuronal systems and models display a certain class of mixed mode oscillations (MMOs) consisting of periods of small amplitude oscillations interspersed with spikes. Various models with different underlying mechanisms have been proposed to generate this type of behavior. Stochast
In this paper, we present the empirical investigation results on the neuroendocrine system by bipartite graphs. This neuroendocrine network model can describe the structural characteristic of neuroendocrine system. The act degree distribution and cumulate act degree distribution show so-called shifted power law-SPL function forms. In neuroendocrine network, the act degree stands for the number of the cells that secretes a single mediator, in which bFGF(basic fibroblast growth factor) is the largest node act degree. It is an important mitogenic cytokine, followed by TGF-beta, IL-6, IL1-beta, VEGF, IGF-1and so on. They are critical in neuroendocrine system to maintain bodily healthiness, emotional stabilization and endocrine harmony. The average act degree of neuroendocrine network is h = 3.01, It means each mediator is secreted by three cells on an average . The similarity that stand for the average probability of secreting the same mediators by all the neuroendocrine cells is s = 0.14. Our results may be used in the research of the medical treatment of neuroendocrine diseases.
The controllability of synchronization is an intriguing question in complex systems, in which hiearchically-organized heterogeneous elements have asymmetric and activity-dependent couplings. In this study, we introduce a simple and effective way to control synchronization in such a complex system by changing the complexity of subsystems. We consider three Stuart-Landau oscillators as a minimal subsystem for generating various complexity, and hiearchically connect the subsystems through a mean field of their activities. Depending on the coupling signs between three oscillators, subsystems can generate ample dynamics, in which the number of attractors specify their complexity. The degree of synchronization between subsystems is then controllable by changing the complexity of subsystems. This controllable synchronization can be applied to understand the synchronization behavior of complex biological networks.
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