84 research outputs found
Comprehensive study of phase transitions in relaxational systems with field-dependent coefficients
We present a comprehensive study of phase transitions in single-field systems
that relax to a non-equilibrium global steady state. The mechanism we focus on
is not the so-called Stratonovich drift combined with collective effects, but
is instead similar to the one associated with noise-induced transitions a la
Horsthemke-Lefever in zero-dimensional systems. As a consequence, the noise
interpretation (e.g., Ito vs Stratonvich) merely shifts the phase boundaries.
With the help of a mean-field approximation, we present a broad qualitative
picture of the various phase diagrams that can be found in these systems. To
complement the theoretical analysis we present numerical simulations that
confirm the findings of the mean-field theory
Fluctuation Dissipation Relation for a Langevin Model with Multiplicative Noise
A random multiplicative process with additive noise is described by a
Langevin equation. We show that the fluctuation-dissipation relation is
satisfied in the Langevin model, if the noise strength is not so strong.Comment: 11 pages, 6 figures, other comment
An associative network with spatially organized connectivity
We investigate the properties of an autoassociative network of
threshold-linear units whose synaptic connectivity is spatially structured and
asymmetric. Since the methods of equilibrium statistical mechanics cannot be
applied to such a network due to the lack of a Hamiltonian, we approach the
problem through a signal-to-noise analysis, that we adapt to spatially
organized networks. The conditions are analyzed for the appearance of stable,
spatially non-uniform profiles of activity with large overlaps with one of the
stored patterns. It is also shown, with simulations and analytic results, that
the storage capacity does not decrease much when the connectivity of the
network becomes short range. In addition, the method used here enables us to
calculate exactly the storage capacity of a randomly connected network with
arbitrary degree of dilution.Comment: 27 pages, 6 figures; Accepted for publication in JSTA
Associative memory storing an extensive number of patterns based on a network of oscillators with distributed natural frequencies in the presence of external white noise
We study associative memory based on temporal coding in which successful
retrieval is realized as an entrainment in a network of simple phase
oscillators with distributed natural frequencies under the influence of white
noise. The memory patterns are assumed to be given by uniformly distributed
random numbers on so that the patterns encode the phase differences
of the oscillators. To derive the macroscopic order parameter equations for the
network with an extensive number of stored patterns, we introduce the effective
transfer function by assuming the fixed-point equation of the form of the TAP
equation, which describes the time-averaged output as a function of the
effective time-averaged local field. Properties of the networks associated with
synchronization phenomena for a discrete symmetric natural frequency
distribution with three frequency components are studied based on the order
parameter equations, and are shown to be in good agreement with the results of
numerical simulations. Two types of retrieval states are found to occur with
respect to the degree of synchronization, when the size of the width of the
natural frequency distribution is changed.Comment: published in Phys. Rev.
Spectroscopic signatures of a bandwidth-controlled Mott transition at the surface of 1T-TaSe
High-resolution angle-resolved photoemission (ARPES) data show that a
metal-insulator Mott transition occurs at the surface of the quasi-two
dimensional compound TaSe. The transition is driven by the narrowing of the
Ta band induced by a temperature-dependent modulation of the atomic
positions. A dynamical mean-field theory calculation of the spectral function
of the half-filled Hubbard model captures the main qualitative feature of the
data, namely the rapid transfer of spectral weight from the observed
quasiparticle peak at the Fermi surface to the Hubbard bands, as the
correlation gap opens up.Comment: 4 pages, 4 figures; one modified figure, added referenc
Linear stability analysis of retrieval state in associative memory neural networks of spiking neurons
We study associative memory neural networks of the Hodgkin-Huxley type of
spiking neurons in which multiple periodic spatio-temporal patterns of spike
timing are memorized as limit-cycle-type attractors. In encoding the
spatio-temporal patterns, we assume the spike-timing-dependent synaptic
plasticity with the asymmetric time window. Analysis for periodic solution of
retrieval state reveals that if the area of the negative part of the time
window is equivalent to the positive part, then crosstalk among encoded
patterns vanishes. Phase transition due to the loss of the stability of
periodic solution is observed when we assume fast alpha-function for direct
interaction among neurons. In order to evaluate the critical point of this
phase transition, we employ Floquet theory in which the stability problem of
the infinite number of spiking neurons interacting with alpha-function is
reduced into the eigenvalue problem with the finite size of matrix. Numerical
integration of the single-body dynamics yields the explicit value of the
matrix, which enables us to determine the critical point of the phase
transition with a high degree of precision.Comment: Accepted for publication in Phys. Rev.
Analysis of Oscillator Neural Networks for Sparsely Coded Phase Patterns
We study a simple extended model of oscillator neural networks capable of
storing sparsely coded phase patterns, in which information is encoded both in
the mean firing rate and in the timing of spikes. Applying the methods of
statistical neurodynamics to our model, we theoretically investigate the
model's associative memory capability by evaluating its maximum storage
capacities and deriving its basins of attraction. It is shown that, as in the
Hopfield model, the storage capacity diverges as the activity level decreases.
We consider various practically and theoretically important cases. For example,
it is revealed that a dynamically adjusted threshold mechanism enhances the
retrieval ability of the associative memory. It is also found that, under
suitable conditions, the network can recall patterns even in the case that
patterns with different activity levels are stored at the same time. In
addition, we examine the robustness with respect to damage of the synaptic
connections. The validity of these theoretical results is confirmed by
reasonable agreement with numerical simulations.Comment: 23 pages, 11 figure
The spike-timing-dependent learning rule to encode spatiotemporal patterns in a network of spiking neurons
We study associative memory neural networks based on the Hodgkin-Huxley type
of spiking neurons. We introduce the spike-timing-dependent learning rule, in
which the time window with the negative part as well as the positive part is
used to describe the biologically plausible synaptic plasticity. The learning
rule is applied to encode a number of periodical spatiotemporal patterns, which
are successfully reproduced in the periodical firing pattern of spiking neurons
in the process of memory retrieval. The global inhibition is incorporated into
the model so as to induce the gamma oscillation. The occurrence of gamma
oscillation turns out to give appropriate spike timings for memory retrieval of
discrete type of spatiotemporal pattern. The theoretical analysis to elucidate
the stationary properties of perfect retrieval state is conducted in the limit
of an infinite number of neurons and shows the good agreement with the result
of numerical simulations. The result of this analysis indicates that the
presence of the negative and positive parts in the form of the time window
contributes to reduce the size of crosstalk term, implying that the time window
with the negative and positive parts is suitable to encode a number of
spatiotemporal patterns. We draw some phase diagrams, in which we find various
types of phase transitions with change of the intensity of global inhibition.Comment: Accepted for publication in Physical Review
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