Renormalized phase dynamics in oscillatory media

Based on the complex Ginzburg-Landau equation (CGLE), a new mapping model of oscillatory media is proposed. The present dynamics is fully determined by an effective phase field renormalized by amplitude. The model exhibits phase turbulence, amplitude turbulence, and a frozen state reported in the CGLE. In addition, we find a state in which the phase and amplitude have spiral structures with opposite rotational directions. This state is found to be observed also in the CGLE. Thus, one concludes that the behaviors observed in the CGLE can be described by only the phase dynamics appropriately constructed.     

Vortex dynamics in oscillatory chemical systems

Vortex core dynamics is studied in the Brusselator both near to and far from the Hopf bifurcation line for random and pair initial conditions. Extensive simulations are carried out for a pair of counter-rotating vortices close to the Hopf bifurcation line. Provided the vortices are not so far apart that wave-front annihilation produces strong gradients between their centers, the simulation results compare favorably with theories based on the complex Ginzburg-Landau equation. Far from the Hopf line the vortex core dynamics changes character and phenomena such as periodic motion of the vortex centers arise.     

Spiking dynamics of interacting oscillatory neurons

Spiking sequences emerging from dynamical interaction in a pair of oscillatory neurons are investigated theoretically and experimentally. The model comprises two unidirectionally coupled FitzHugh-Nagumo units with modified excitability (MFHN). The first (master) unit exhibits a periodic spike sequence with a certain frequency. The second (slave) unit is in its excitable mode and responds on the input signal with a complex (chaotic) spike trains. We analyze the dynamic mechanisms underlying different response behavior depending on interaction strength. Spiking phase maps describing the response dynamics are obtained. Complex phase locking and chaotic sequences are investigated. We show how the response spike trains can be effectively controlled by the interaction parameter and discuss the problem of neuronal information encoding.     

How effective delays shape oscillatory dynamics in neuronal networks

Synaptic, dendritic and single-cell kinetics generate significant time delays that shape the dynamics of large networks of spiking neurons. Previous work has shown that such effective delays can be taken into account with a rate model through the addition of an explicit, fixed delay (Roxin et al. (2005,2006) [29] and [30]). Here we extend this work to account for arbitrary symmetric patterns of synaptic connectivity and generic nonlinear transfer functions. Specifically, we conduct a weakly nonlinear analysis of the dynamical states arising via primary instabilities of the asynchronous state. In this way we determine analytically how the nature and stability of these states depend on the choice of transfer function and connectivity. We arrive at two general observations of physiological relevance that could not be explained in previous work. These are: 1 — fast oscillations are always supercritical for realistic transfer functions and 2 — traveling waves are preferred over standing waves given plausible patterns of local connectivity. We finally demonstrate that these results show good agreement with those obtained performing numerical simulations of a network of Hodgkin-Huxley neurons.     

Global organization of dynamics in oscillatory heterogeneous excitable media

An oscillatory heterogeneous excitable medium undergoes a transition from periodic target patterns to a bursting rhythm driven by the spontaneous initiation and termination of spiral waves as coupling or density is reduced. We illustrate these phenomena in monolayers of chick embryonic heart cells using calcium-sensitive fluorescent dyes. These results are modeled in a heterogeneous cellular automaton in which the neighborhood of interaction and cell density is modified. Parameters that give rise to bursting rhythms are organized in distinct zones in parameter space, leading to a global organization that should be applicable to the dynamics in a large class of excitable media.     

Cluster synchronization and spatio-temporal dynamics in networks of oscillatory and excitable Luo-Rudy cells

We study collective phenomena in nonhomogeneous cardiac cell culture models, including one- and two-dimensional lattices of oscillatory cells and mixtures of oscillatory and excitable cells. Individual cell dynamics is described by a modified Luo-Rudy model with depolarizing current. We focus on the transition from incoherent behavior to global synchronization via cluster synchronization regimes as coupling strength is increased. These regimes are characterized qualitatively by space-time plots and quantitatively by profiles of local frequencies and distributions of cluster sizes in dependence upon coupling strength. We describe spatio-temporal patterns arising during this transition, including pacemakers, spiral waves, and complicated irregular activity.     

Effects of oscillatory shear flow on chain-like cluster dynamics in ferrofluids without magnetic fields

Brownian dynamics simulations of interacting magnetic particles in a quasi-two-dimensional ferrofluid system are performed at zero temperature, under the influence of oscillatory shear flow in the absence of external magnetic fields. Starting from chain-like clusters of the particles, we study the time-dependent behavior of both magnetization and microstructures of the ferrofluid by changing values of two parameters, the shear rate strength and frequency of oscillatory shear flow. Simulation results show that there are three different dynamical regimes for the chain clusters dynamics, depending on these two parameters. Scaling behavior of the asymptotic magnetization is also observed for a certain range of parameters.     

Stochastic qualifiers of epileptic brain dynamics

We evaluate the capability of reconstructing Fokker-Planck equations for an improved characterization of electroencephalographic (EEG) recordings from epilepsy patients. We derive stochastic qualifiers of brain dynamics that are based on specific characteristics of the Kramers-Moyal coefficients estimated from the EEG. Analyzing long-lasting multichannel EEG recordings from eight patients suffering from focal epilepsies we show that particularly the stochastic part of the dynamics can yield valuable information for diagnostic purposes.     

Effects of nonlinear degradation of protein on the oscillatory dynamics in a simple gene regulatory network

Cooperative stability of protein is here defined as the tendency for the oligomers to be more stable than their monomeric components and to perform their physiological functions. In this paper, we incorporate nonlinear degradation of protein induced by cooperative stability into a simple model which has been previously presented in the biological literature. Linear analysis gives a critical time delay beyond which a periodic solution is born in a Hopf bifurcation. Lindstedt’s method is applied to the nonlinear system, resulting in closed form approximate expressions for the amplitude and frequency of oscillation. Our findings indicate that cooperative stability can cause periodic dynamics through reducing the critical time delay. In addition, we show that cooperative stability may increase the amplitude of oscillation. Our study contributes to the theoretical demonstration of the effect of cooperative stability in the simple gene regulatory network.     

The oscillatory instability of a spatially homogeneous state in large aspect ratio systems of fluid dynamics

We derive the generalized Ginzburg-Landau equation for the case of an oscillatory instability of a spatially homogeneous state in systems whose geometry is characterized by two entirely different length scales. This evolution equation is applied to describe the spatio-temporal behaviour of the onset of convection in binary fluid mixtures in large aspect ratio systems. We obtain time periodic traveling wave motions, quasiperiodic fluid motions with two and more frequencies modulating the intensities of the traveling waves as well as chaotic temporal behaviour.     

Nonlinear brain dynamics as macroscopic manifestation of underlying many-body field dynamics

Neural activity patterns related to behavior occur at many scales in time and space from the atomic and molecular to the whole brain. Patterns form through interactions in both directions, so that the impact of transmitter molecule release can be analyzed to larger scales through synapses, dendrites, neurons, populations and brain systems to behavior, and control of that release can be described step-wise through transforms to smaller scales. Here we explore the feasibility of interpreting neurophysiological data in the context of many-body physics by using tools that physicists have devised to analyze comparable hierarchies in other fields of science. We focus on a mesoscopic level that offers a multi-step pathway between the microscopic functions of neurons and the macroscopic functions of brain systems revealed by hemodynamic imaging. We use electroencephalographic (EEG) records collected from high-density electrode arrays fixed on the epidural surfaces of primary sensory and limbic areas in rabbits and cats trained to discriminate conditioned stimuli (CS) in the various modalities. High temporal resolution of EEG signals with the Hilbert transform gives evidence for diverse intermittent spatial patterns of amplitude (AM) and phase modulations (PM) of carrier waves that repeatedly re-synchronize in the beta and gamma ranges in very short time lags over very long distances. The dominant mechanism for neural interactions by axodendritic synaptic transmission should impose distance-dependent delays on the EEG oscillations owing to finite propagation velocities and sequential synaptic delays. It does not. EEGs show evidence for anomalous dispersion: neural populations have a low velocity range of information and energy transfers, and a high velocity range of the spread of phase transitions. This distinction labels the phenomenon but does not explain it. In this report we analyze these phenomena using concepts of energy dissipation, the maintenance by cortex of multiple ground states corresponding to AM patterns, and the exclusive selection by spontaneous breakdown of symmetry (SBS) of single states in sequential phase transitions.     

Red blood cells and other nonspherical capsules in shear flow: oscillatory dynamics and the tank-treading-to-tumbling transition

We consider the motion of red blood cells and other nonspherical microcapsules dilutely suspended in a simple shear flow. Our analysis indicates that depending on the viscosity, membrane elasticity, geometry, and shear rate, the particle exhibits either tumbling, tank-treading of the membrane about the viscous interior with periodic oscillations of the orientation angle, or intermittent behavior in which the two modes occur alternately. For red blood cells, we compute the complete phase diagram and identify a novel tank-treading-to-tumbling transition as the shear rate decreases. Observations of such motions coupled with our theoretical framework may provide a sensitive means of assessing capsule properties.     

Ripening of surface phases coupled with oscillatory dynamics and self-induced spatial chaos through surface roughening

Some pattern formation processes on single-crystal catalytic surfaces involve transitions between alternative surface phases coupled with oscillatory reaction dynamics. We describe a two-tier symmetry-breaking model of this process, based on nanoscale boundary dynamics interacting with oscillations of adsorbate coverage on microscale. The surface phase distribution oscillates together with adsorbate coverage, and, in addition, undergoes a slow coarsening process due to the curvature dependence of the drift velocity of interphase boundaries. The coarsening is studied both statistically, assuming a circular shape of islands of the minority phase, and through detailed Lagrangian modeling of boundary dynamics. Direct simulation of boundary dynamics allows us to take into account processes of surface reconstruction, leading to self-induced surface roughening. As a result, the surface becomes inhomogeneous, and the coarsening process is arrested way before the thermodynamic limit is reached, leaving a chaotic distribution of surface phases. (c) 1999 American Institute of Physics.     

Symbiotic relationship between brain structure and dynamics

Background  

Brain structure and dynamics are interdependent through processes such as activity-dependent neuroplasticity. In this study, we aim to theoretically examine this interdependence in a model of spontaneous cortical activity. To this end, we simulate spontaneous brain dynamics on structural connectivity networks, using coupled nonlinear maps. On slow time scales structural connectivity is gradually adjusted towards the resulting functional patterns via an unsupervised, activity-dependent rewiring rule. The present model has been previously shown to generate cortical-like, modular small-world structural topology from initially random connectivity. We provide further biophysical justification for this model and quantitatively characterize the relationship between structure, function and dynamics that accompanies the ensuing self-organization.     

Aftereffect in an Artificially Disturbed Ionospheric Plasma

By sounding an artificially disturbed ionosphere with radio pulses of the ordinary polarization, we detected a scattered signal which appeared after switching off the disturbing transmitter. We consider different types of time dependence of this signal as well as its altitude and frequency characteristics.