5,334 research outputs found

    Unstable Dynamics, Nonequilibrium Phases and Criticality in Networked Excitable Media

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    Here we numerically study a model of excitable media, namely, a network with occasionally quiet nodes and connection weights that vary with activity on a short-time scale. Even in the absence of stimuli, this exhibits unstable dynamics, nonequilibrium phases -including one in which the global activity wanders irregularly among attractors- and 1/f noise while the system falls into the most irregular behavior. A net result is resilience which results in an efficient search in the model attractors space that can explain the origin of certain phenomenology in neural, genetic and ill-condensed matter systems. By extensive computer simulation we also address a relation previously conjectured between observed power-law distributions and the occurrence of a "critical state" during functionality of (e.g.) cortical networks, and describe the precise nature of such criticality in the model.Comment: 18 pages, 9 figure

    Deep Unsupervised Learning using Nonequilibrium Thermodynamics

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    A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm

    Dynamical aspects of Kinouchi-Copelli model: emergence of avalanches at criticality

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    We analyze the behavior of bursts of neural activity in the Kinouchi-Copelli model, originally conceived to explain information processing issues in sensory systems. We show that, at a critical condition, power-law behavior emerges for the size and duration of the bursts (avalanches), with exponents experimentally observed in real biological systems.Comment: Paper accepted for Brazilian Conference on Dynamics, Control and Applications (oral presentation and poster). 4 pages, 5 figure

    Shear induced ordering in systems with competing interactions: A machine learning study

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    When short-range attractions are combined with long-range repulsions in colloidal particle systems, complex microphases can emerge. Here, we study a system of isotropic particles which can form lamellar structures or a disordered fluid phase when temperature is varied. We show that at equilibrium the lamellar structure crystallizes, while out of equilibrium the system forms a variety of structures at different shear rates and temperatures above melting. The shear-induced ordering is analyzed by means of principal component analysis and artificial neural networks, which are applied to data of reduced dimensionality. Our results reveal the possibility of inducing ordering by shear, potentially providing a feasible route to the fabrication of ordered lamellar structures from isotropic particles.Comment: The following article has been accepted by the Journal of Chemical Physics AIP. After it is published, it will be found at https://aip.scitation.org/journal/jc

    Optimization of Trading Physics Models of Markets

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    We describe an end-to-end real-time S&P futures trading system. Inner-shell stochastic nonlinear dynamic models are developed, and Canonical Momenta Indicators (CMI) are derived from a fitted Lagrangian used by outer-shell trading models dependent on these indicators. Recursive and adaptive optimization using Adaptive Simulated Annealing (ASA) is used for fitting parameters shared across these shells of dynamic and trading models

    Critical and resonance phenomena in neural networks

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    Brain rhythms contribute to every aspect of brain function. Here, we study critical and resonance phenomena that precede the emergence of brain rhythms. Using an analytical approach and simulations of a cortical circuit model of neural networks with stochastic neurons in the presence of noise, we show that spontaneous appearance of network oscillations occurs as a dynamical (non-equilibrium) phase transition at a critical point determined by the noise level, network structure, the balance between excitatory and inhibitory neurons, and other parameters. We find that the relaxation time of neural activity to a steady state, response to periodic stimuli at the frequency of the oscillations, amplitude of damped oscillations, and stochastic fluctuations of neural activity are dramatically increased when approaching the critical point of the transition.Comment: 8 pages, Proceedings of 12th Granada Seminar, September 17-21, 201

    Spin-glass model with partially annealed asymmetric bonds

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    We have considered the two-spin interaction spherical spin-glass model with asymmetric bonds (coupling constants). Besides the usual interactions between spins and bonds and between the spins and a thermostat with temperature TσT_{\sigma} there is also an additional factor: the bonds are not assumed random {\it a priori} but interact with some other thermostat at the temperature TJT_{J}. We show that when the bonds are frozen with respect to the spins a first order phase transition to a spin-glass phase occurs, and the temperature of this transition tends to zero if TJT_J is large. Our analytical results show that a spin-glass phase can exist in mean-field models with nonrelaxational dynamics.Comment: 10 pages, late
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