scholarly journals Hysteretic behavior of spatially coupled phase-oscillators

2020 ◽  
Vol 15 ◽  
pp. 18
Author(s):  
Eszter Fehér ◽  
Balázs Havasi-Tóth ◽  
Tamás Kalmár-Nagy

Motivated by phenomena related to biological systems such as the synchronously flashing swarms of fireflies, we investigate a network of phase oscillators evolving under the generalized Kuramoto model with inertia. A distance-dependent, spatial coupling between the oscillators is considered. Zeroth and first order kernel functions with finite kernel radii were chosen to investigate the effect of local interactions. The hysteretic dynamics of the synchronization depending on the coupling parameter was analyzed for different kernel radii. Numerical investigations demonstrate that (1) locally locked clusters develop for small coupling strength values, (2) the hysteretic behavior vanishes for small kernel radii, (3) the ratio of the kernel radius and the maximal distance between the oscillators characterizes the behavior of the network.

2014 ◽  
Vol 24 (07) ◽  
pp. 1430022 ◽  
Author(s):  
Alexander P. Kuznetsov ◽  
Yuliya V. Sedova

The dynamics of a low-dimensional ensemble consisting of a network of five discrete phase oscillators is considered. A two-parameter synchronization picture, which appears instead of the Arnol'd tongues with an increase of the system dimension, is discussed. An appearance of the Arnol'd resonance web is detected on the "frequency–coupling" parameter plane. The cases of attractive and repulsive interactions are discussed.


1995 ◽  
Vol 74 (6) ◽  
pp. 2665-2684 ◽  
Author(s):  
Y. Kondoh ◽  
Y. Hasegawa ◽  
J. Okuma ◽  
F. Takahashi

1. A computational model accounting for motion detection in the fly was examined by comparing responses in motion-sensitive horizontal system (HS) and centrifugal horizontal (CH) cells in the fly's lobula plate with a computer simulation implemented on a motion detector of the correlation type, the Reichardt detector. First-order (linear) and second-order (quadratic nonlinear) Wiener kernels from intracellularly recorded responses to moving patterns were computed by cross correlating with the time-dependent position of the stimulus, and were used to characterize response to motion in those cells. 2. When the fly was stimulated with moving vertical stripes with a spatial wavelength of 5-40 degrees, the HS and CH cells showed basically a biphasic first-order kernel, having an initial depolarization that was followed by hyperpolarization. The linear model matched well with the actual response, with a mean square error of 27% at best, indicating that the linear component comprises a major part of responses in these cells. The second-order nonlinearity was insignificant. When stimulated at a spatial wavelength of 2.5 degrees, the first-order kernel showed a significant decrease in amplitude, and was initially hyperpolarized; the second-order kernel was, on the other hand, well defined, having two hyperpolarizing valleys on the diagonal with two off-diagonal peaks. 3. The blockage of inhibitory interactions in the visual system by application of 10-4 M picrotoxin, however, evoked a nonlinear response that could be decomposed into the sum of the first-order (linear) and second-order (quadratic nonlinear) terms with a mean square error of 30-50%. The first-order term, comprising 10-20% of the picrotoxin-evoked response, is characterized by a differentiating first-order kernel. It thus codes the velocity of motion. The second-order term, comprising 30-40% of the response, is defined by a second-order kernel with two depolarizing peaks on the diagonal and two off-diagonal hyperpolarizing valleys, suggesting that the nonlinear component represents the power of motion. 4. Responses in the Reichardt detector, consisting of two mirror-image subunits with spatiotemporal low-pass filters followed by a multiplication stage, were computer simulated and then analyzed by the Wiener kernel method. The simulated responses were linearly related to the pattern velocity (with a mean square error of 13% for the linear model) and matched well with the observed responses in the HS and CH cells. After the multiplication stage, the linear component comprised 15-25% and the quadratic nonlinear component comprised 60-70% of the simulated response, which was similar to the picrotoxin-induced response in the HS cells. The quadratic nonlinear components were balanced between the right and left sides, and could be eliminated completely by their contralateral counterpart via a subtraction process. On the other hand, the linear component on one side was the mirror image of that on the other side, as expected from the kernel configurations. 5. These results suggest that responses to motion in the HS and CH cells depend on the multiplication process in which both the velocity and power components of motion are computed, and that a putative subtraction process selectively eliminates the nonlinear components but amplifies the linear component. The nonlinear component is directionally insensitive because of its quadratic non-linearity. Therefore the subtraction process allows the subsequent cells integrating motion (such as the HS cells) to tune the direction of motion more sharply.


1990 ◽  
Vol 63 (1) ◽  
pp. 120-130 ◽  
Author(s):  
H. M. Sakai ◽  
K. I. Naka

1. Simultaneous intracellular recordings were made from two neighboring N amacrine cells, one an ON amacrine (NA) cell and the other an OFF amacrine (NB) cell. Extrinsic current was injected into one amacrine cell, and the resulting intracellular responses were recorded from the other amacrine cell. Test signals included 1) a single-frequency sinusoid, 2) a depolarizing or hyperpolarizing pulse, or 3) a white-noise modulated current. In some cell pairs, membrane noise was measured in the dark as well as under a steady background illumination. 2. Current pulses injected into a NA cell evoked a damped oscillation from a NB cell. The first-order kernel derived by cross-correlating the white-noise current injected into a NA cell against the evoked response from a NB cell was a large depolarization followed by a damped oscillation. The frequency of oscillations varied slightly from pair to pair but averaged 35 Hz. 3. Current pulses injected into a NB cell evoked a sign-inverting response (hyperpolarization) of very small amplitude from a NA cell. Similarly, the first-order kernel was a hyperpolarization of very small amplitude. 4. The power spectrum of the membrane noise recorded from NA and NB cells in the dark or during steady illumination often showed a peak at 35 Hz. Such membrane noise synchronizes synergistically among NA cells and among NB cells in the dark. In addition, the membrane fluctuations seen in NA and NB cells in the dark were out of phase. 5. Transmission between NA and NB cells was largely accounted for by a linear component; however, a very small but significant second- and third-order nonlinearity was also generated. 6. These results show that the interactions occurring between amacrine cells of opposite response polarity are much more complex than those between cells of the same response polarity and that the neural circuitry in the inner retina actively controls interactions between ON and OFF channels in the dark as well as in the presence of light stimuli.


2020 ◽  
Author(s):  
Torstein Fjeldstad ◽  
Henning Omre

<p>A Bayesian model for prediction and uncertainty quantification of subsurface lithology/fluid classes, petrophysical properties and elastic material properties conditional on seismic amplitude-versus-offset measurements is defined. We demonstrate the proposed methodology  on a real Norwegian Sea gas discovery in 3D in a seismic inversion framework.</p><p>The likelihood model is assumed to be Gaussian, and it is constructed in two steps. First, the reflectivity coefficients of the elastic material properties are computed based on the linear Aki Richards approximation valid for weak contrasts. The reflectivity coefficients are then convolved in depth with a wavelet.  We assume a Markov random field prior model for the lithology/fluid classes with a first order neighborhood system to ensure spatial coupling. Conditional on the lithology/fluid classes we define a Gauss-linear petrophysical and rock physics model. The marginal prior spatial model for the petrophysical properties and elastic attributes is then a multivariate Gaussian mixture random field.</p><p>The convolution kernel in the likelihood model restricts analytic assessment of the posterior model since the neighborhood system of the lithology/fluid classes is no longer a simple first order neighborhood. We propose a recursive algorithm that translates the Gibbs formulation into a set of vertical Markov chains. The vertical posterior model is approximated by a higher order Markov chain, which is computationally tractable. Finally, the approximate posterior model is used as a proposal model in a Markov chain Monte Carlo algorithm. It can be verified that the Gaussian mixture model is a conjugate prior with respect to the Gauss-linear likelihood model; thus, the posterior density for petrophysical properties and elastic attributes is also a Gaussian mixture random field.</p><p>We compare the proposed spatially coupled 3D model to a set of independent vertical 1D inversions. We obtain an increase of the average acceptance rate of 13.6 percentage points in the Markov chain Monte Carlo algorithm compared to a simpler model without lateral spatial coupling. At a blind well location we obtain a reduction of at most 60 % in mean absolute error and root mean square error for the proposed spatially coupled 3D model.</p>


2006 ◽  
Vol 326-328 ◽  
pp. 1459-1462
Author(s):  
Young Min Han ◽  
Quoc Hung Nguyen ◽  
Seung Bok Choi ◽  
Kyung Su Kim

This paper experimentally investigates the hysteretic behaviors of yield stress in electrorheological (ER) and magnetorheological (MR) materials which are known as smart materials. As a first step, the PMA-based ER material is prepared by dispersing the chemically synthesized polymethylaniline (PMA) particles into non-conducting oil. For the MR material, commercially available one (Lord MRF-132LD) is chosen for the test. Using the rheometer, the torque resulting from the shear stress of the ER/MR materials is measured, and then the yield stress is calculated from the measured torque. In order to describe the hysteretic behavior of the fielddependent yield stress, a nonlinear hysteresis model of the ER/MR materials is formulated between input (field) and output (yield stress). Subsequently, the Preisach model is identified using experimental first order descending (FOD) curves of yield stress in discrete manner. The effectiveness of the identified hysteresis model is verified in time domain by comparing the predicted field-dependent yield stress with the measured one.


2019 ◽  
Author(s):  
Sheila Crewther ◽  
Jacqueline Rutkowski ◽  
David Crewther

AbstractThe neural basis of dyslexia remains unresolved, despite many theories relating dyslexia to dysfunction in visual magnocellular and auditory temporal processing, cerebellar dysfunction, attentional deficits, as well as excessive neural noise. Recent research identifies perceptual speed as a common factor, integrating several of these systems. Optimal perceptual speed invokes transient attention as a necessary component, and change detection in gap paradigm tasks is impaired in those with dyslexia. This research has also identified an overall better change detection for targets presented in the upper compared with lower visual fields. Despite the magnocellular visual pathway being implicated in the aetiology of dyslexia over 30 years ago, objective physiological measures have been lacking. Thus, we employed nonlinear visual evoked potential (VEP) techniques which generate second order kernel terms specific for magno and parvocellular processing as a means to assessing the physiological status of poor readers (PR, n=12) compared with good readers (GR, n=16) selected from children with a mean age of 10yr. The first and second order Wiener kernels using multifocal VEP were recorded from a 4° foveal stimulus patch as well as for upper and lower visual field peripheral arcs. Foveal responses showed little difference between GR and PR for low contrast stimulation, except for the second slice of the second order kernel where lower peak amplitudes were recorded for PR vs GR. At high contrast, there was a trend to smaller first order kernel amplitudes for short latency peaks of the PR vs GR. In addition, there were significant latency differences for the first negativity in the first two slices of the second order kernel. In terms of peripheral stimulation, lower visual field response amplitudes were larger compared with upper visual field responses, for both PR and GR. A trend to larger second/first order ratio for magnocellularly driven responses suggests the possibility of lesser neural efficiency in the periphery for the PR compared with the GR. Stronger lower field peripheral response may relate to better upper visual field change detection performance when target visibility is controlled through flicking masks. In conclusion, early cortical magnocellular processing at low contrast was normal in those with dyslexia, while cortical activity related to parvocellular afferents was reduced. In addition, the study demonstrated a physiological basis for upper versus lower visual field differences related to magnocellular function.


Author(s):  
Hui Wu ◽  
Dongwook Kim

The synchronization in large populations of interacting oscillators has been observed abundantly in nature, emergining in fields such as physical, biological and chemical system. For this reason, many scientists are seeking to understand the underlying mechansim of the generation of synchronous patterns in oscillatory system. The synchronization is analyzed in one of the most representative models of coupled phase oscillators, the Kuramoto model. The Kuramoto model can be used to understand the emergence of synchronization in nextworks of coupled, nonlinear oscillators. In particular, this model presents a phase transition from incoherence to synchronization. In this paper, we investigated the distribution of order parameter γ which describes the strength of synchrony of these oscillators. The larger the order parameter γ is, the more extent the oscillators are synchronized together. This order parameter γ is a critical parameter in the Kuramoto model. Kuramoto gave a initial estimate equation for the value of the order parameter by giving the value of the coupling constant. But our numerical results show that the distribution of the order parameter is slightly different from Kuramoto’s estimation. We gave an estimation for the distribution of order parameter for different values of initial conditions. We discussed how the numerical result will be distributed around Kuramoto’s analytical equation.


2017 ◽  
Vol 118 (6) ◽  
pp. 60005 ◽  
Author(s):  
Yu Xiao ◽  
Wenjing Jia ◽  
Can Xu ◽  
Huaping Lü ◽  
Zhigang Zheng

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Huihui Song ◽  
Xuewei Zhang ◽  
Jinjie Wu ◽  
Yanbin Qu

AbstractThis work considers a second-order Kuramoto oscillator network periodically driven at one node to model low-frequency forced oscillations in power grids. The phase fluctuation magnitude at each node and the disturbance propagation in the network are numerically analyzed. The coupling strengths in this work are sufficiently large to ensure the stability of equilibria in the unforced system. It is found that the phase fluctuation is primarily determined by the network structural properties and forcing parameters, not the parameters specific to individual nodes such as power and damping. A new “resonance” phenomenon is observed in which the phase fluctuation magnitudes peak at certain critical coupling strength in the forced system. In the cases of long chain and ring-shaped networks, the Kuramoto model yields an important but somehow counter-intuitive result that the fluctuation magnitude distribution does not necessarily follow a simple attenuating trend along the propagation path and the fluctuation at nodes far from the disturbance source could be stronger than that at the source. These findings are relevant to low-frequency forced oscillations in power grids and will help advance the understanding of their dynamics and mechanisms and improve the detection and mitigation techniques.


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