5,334 research outputs found
Unstable Dynamics, Nonequilibrium Phases and Criticality in Networked Excitable Media
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
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
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
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
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
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
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
there is also an additional factor: the bonds are not assumed
random {\it a priori} but interact with some other thermostat at the
temperature . 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 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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