Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Dynamic network”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,189 records · Page 66Linked to original sources
PubMed2006

Diffusive capture process on complex networks.

We study the dynamical properties of a diffusing lamb captured by a diffusing lion on the complex networks with various sizes of N. We find that the lifetime {T} of a lamb scales as {T} approximately N and the survival probability S(N-->infinity, t) becomes finite on scale-free networks with degree exponent gamma > 3. However, S(N, t) for gamma < 3 has a long-living tail on tree-structured scale-free networks and decays exponentially on looped scale-free networks. This suggests that the second moment of degree distribution {k2} is the relevant factor for the dynamical properties in the diffusive capture process. We numerically find that the normalized number of capture events at a node with degree k, n(k), decreases as n(k) approximately k(-sigma). When gamma < 3, n(k) still increases anomalously for k approximately kmax, where kmax is the maximum value of k of given networks with size N. We analytically show that n(k) satisfies the relation n(k) approximately {k2}P(k) for any degree distribution P(k) and the total number of capture events Ntot is proportional to {k2}, which causes the gamma -dependent behavior of S(N, t) and {T}.

Journal Article↗
PubMed2006

A dynamic model for the p53 stress response networks under ion radiation.

P53 controls the cell cycle arrest and cell apoptosis through interaction with the downstream genes and their signal pathways. To stimulate the investigation into the complicated responses of p53 under the circumstance of ion radiation (IR) in the cellular level, a dynamic model for the p53 stress response networks is proposed. The model can be successfully used to simulate the dynamic processes of generating the double-strand breaks (DSBs) and their repairing, ataxia telangiectasia mutated (ATM) activation, as well as the oscillations occurring in the p53-MDM2 feedback loop.

Ataxia Telangiectasia Mutated Proteins↗
PubMed2000

Fast response and temporal coherent oscillations in small-world networks.

We have investigated the role that different connectivity regimes play in the dynamics of a network of Hodgkin-Huxley neurons by computer simulations. The different connectivity topologies exhibit the following features: random topologies give rise to fast system response yet are unable to produce coherent oscillations in the average activity of the network; on the other hand, regular topologies give rise to coherent oscillations, but in a temporal scale that is not in accordance with fast signal processing. Finally, small-world topologies, which fall between random and regular ones, take advantage of the best features of both, giving rise to fast system response with coherent oscillations.

Animals↗
PubMed2006

Cluster analysis of dynamic cerebral contrast-enhanced perfusion MRI time-series.

We performed neural network clustering on dynamic contrast-enhanced perfusion magnetic resonance imaging time-series in patients with and without stroke. Minimal-free-energy vector quantization, self-organizing maps, and fuzzy c-means clustering enabled self-organized data-driven segmentation with respect to fine-grained differences of signal amplitude and dynamics, thus identifying asymmetries and local abnormalities of brain perfusion. We conclude that clustering is a useful extension to conventional perfusion parameter maps.

Algorithms↗
PubMed2004

Strain-dependent localization, microscopic deformations, and macroscopic normal tensions in model polymer networks.

We use molecular dynamics simulations to investigate the microscopic and macroscopic response of model polymer networks to uniaxial elongations. By studying networks with strand lengths ranging from N(s)=20 to 200 we cover the full crossover from cross-link to entanglement dominated behavior. Our results support a recent version of the tube model which accounts for the different strain dependence of chain localization due to chemical cross-links and entanglements.

Computer Simulation↗
PubMed2000

Geometric analysis of population rhythms in synaptically coupled neuronal networks.

We develop geometric dynamical systems methods to determine how various components contribute to a neuronal network's emergent population behaviors. The results clarify the multiple roles inhibition can play in producing different rhythms. Which rhythms arise depends on how inhibition interacts with intrinsic properties of the neurons; the nature of these interactions depends on the underlying architecture of the network. Our analysis demonstrates that fast inhibitory coupling may lead to synchronized rhythms if either the cells within the network or the architecture of the network is sufficiently complicated. This cannot occur in mutually coupled networks with basic cells; the geometric approach helps explain how additional network complexity allows for synchronized rhythms in the presence of fast inhibitory coupling. The networks and issues considered are motivated by recent models for thalamic oscillations. The analysis helps clarify the roles of various biophysical features, such as fast and slow inhibition, cortical inputs, and ionic conductances, in producing network behavior associated with the spindle sleep rhythm and with paroxysmal discharge rhythms. Transitions between these rhythms are also discussed.

Cerebral Cortex↗
PubMed1997

Role of Itinerancy Among Attractors as Dynamical Map in Distributed Coding Scheme.

A basic frame, based on which certain features of sensory stimuli can be extracted systematically in a distributed coding scheme, has not been clarified yet. This paper proposes that the basic frame can be a dynamical map represented by itinerancy among attractors. Features of entities are encoded by attractors of neural networks. Relations between the features in each modality are mapped on dynamical links between the attractors in each network relevant to each modality. The itinerant states, in which the network dynamic state itinerates chaotically or cyclically among the attractors, can work as dynamical maps. The recognition of features is carried out by a phase transition from an itinerant state to a constituent attractor. The phase transition is induced by a short-term synaptic change based on the Hebbian rule under application of a relevant stimulation. A theta-like global oscillation is necessary for self-organized formation of the chaotically itinerant state.

Journal Article↗
PubMed2004

Long-term characterization of firing dynamics of spontaneous bursts in cultured neural networks.

Extracellular action potentials were recorded from developing dissociated rat neocortical networks continuously for up to 49 days in vitro using planar multielectrode arrays. Spontaneous neuronal activity emerged toward the end of the first week in vitro and from then on exhibited periods of elevated firing rates, lasting for a few days up to weeks, which were largely uncorrelated among different recording sites. On a time scale of seconds to minutes, network activity typically displayed an ongoing repetition of distinctive firing patterns, including short episodes of synchronous firing at many sites (network bursts). Network bursts were highly variable in their individual spatio-temporal firing patterns but showed a remarkably stable underlying probabilistic structure (obtained by summing consecutive bursts) on a time scale of hours. On still longer time scales, network bursts evolved gradually, with a significant broadening (to about 2 s) in the third week in vitro, followed by a drastic shortening after about one month in vitro. Bursts at this age were characterized by highly synchronized onsets reaching peak firing levels within less than ca. 60 ms. This pattern persisted for the rest of the culture period. Throughout the recording period, active sites showed highly persistent temporal relationships within network bursts. These longitudinal recordings of network firing have, thus, brought to light a reproducible pattern of complex changes in spontaneous firing dynamics of bursts during the development of isolated cortical neurons into synaptically interconnected networks.

Action Potentials↗
PubMed2004

Two-state membrane potential fluctuations driven by weak pairwise correlations.

Physiological experiments demonstrate the existence of weak pairwise correlations of neuronal activity in mammalian cortex (Singer, 1993). The functional implications of this correlated activity are hotly debated (Roskies et al., 1999). Nevertheless, it is generally considered a widespread feature of cortical dynamics. In recent years, another line of research has attracted great interest: the observation of a bimodal distribution of the membrane potential defining up states and down states at the single cell level (Wilson & Kawaguchi, 1996; Steriade, Contreras, & Amzica, 1994; Contreras & Steriade, 1995; Steriade, 2001). Here we use a theoretical approach to demonstrate that the latter phenomenon is a natural consequence of the former. In particular, we show that weak pairwise correlations of the inputs to a compartmental model of a layer V pyramidal cell can induce bimodality in its membrane potential. We show how this relationship can account for the observed increase of the power in the gamma-frequency band during up states, as well as the increase in the standard deviation and fraction of time spent in the depolarized state (Anderson, Lampl, Reichova, Carandini, & Ferster, 2000). In order to quantify the relationship between the correlation properties of a cortical network and the bistable dynamics of single neurons, we introduce a number of new indices. Subsequently, we demonstrate that a quantitative agreement with the experimental data can be achieved, introducing voltage-dependent mechanisms in our neuronal model such as Ca(2+)- and Ca(2+)-dependent K(+) channels. In addition, we show that the up states and down states of the membrane potential are dependent on the dendritic morphology of cortical neurons. Furthermore, bringing together network and single cell dynamics under a unified view allows the direct transfer of results obtained in one context to the other and suggests a new experimental paradigm: the use of specific intracellular analysis as a powerful tool to reveal the properties of the correlation structure present in the network dynamics.

Algorithms↗
PubMed2005

Synchrony and clustering in heterogeneous networks with global coupling and parameter dispersion.

Networks with nonidentical nodes and global coupling may display a large variety of dynamic behaviors, such as phase clustered solutions, synchrony, and oscillator death. The network dynamics is a function of the parameter dispersion and may be captured by conventional mean field approaches if it is close to the completely synchronous state. In this Letter we introduce a novel method based on a mode decomposition in the parameter space, which provides a low-dimensional network description for more complex dynamic behaviors and captures the mean field approach as a special case. The example of globally coupled Fitzhugh-Nagumo neurons is discussed.

Action Potentials↗
PubMed1988

The microtubule-dependent formation of a tubulovesicular network with characteristics of the ER from cultured cell extracts.

The formation of a dynamic tubulovesicular membrane network that resembles the endoplasmic reticulum (ER) has been observed in extracts of cultured chick embryo fibroblasts (CEF cells) using video-enhanced differential interference contrast microscopy. Initially, membranes in the CEF extracts appeared amorphous and aggregated, but with time, membrane tubules moved out along stationary microtubules. The membrane tubules formed new branches on intersecting microtubules and fused with other branches to form a network of interconnected polygons. The tubulovesicular network was solubilized by detergent and took on a beaded morphology in a hypotonic buffer. Formation of the tubulovesicular network required ATP and microtubules. The network did not contain elements of the plasma membrane, Golgi apparatus, or mitochondria but could be labeled with ER markers. We suggest that the tubulovesicular network contains components from the ER and is formed by membrane associated motors moving upon microtubules in a process we call microtubule-dependent tethering.

Adenosine Triphosphate↗
PubMed2005

Small-molecule dynamics and mechanisms underlying the macroscopic mechanical properties of coordinatively cross-linked polymer networks.

Specific metal-ligand coordination between bis-Pd(II) and Pt(II) organometallic cross-linkers and poly(4-vinylpyridine) in DMSO defines a three-dimensional associative polymer network. Frequency-dependent dynamic mechanical moduli of a series of four different bulk materials, measured across several decades of oscillatory strain rates, are found to be quantitatively related through the pyridine exchange rates measured on model Pd(II) and Pt(II) complexes. Importantly, the mechanism of ligand exchange in the networks is found to be the same solvent-assisted pathway observed in the model complexes, and so the bulk mechanical properties are determined by relaxations that occur when the cross-links are dissociated from the polymer backbone. It is how often the cross-links dissociate, independently of how long they remain dissociated, that determines the bulk mechanical properties. The quantitative relationship between bulk materials properties and the kinetics and mechanisms observed in model compounds holds promise for the rational, molecular design of materials with tailored mechanical properties.

Computer Simulation↗
PubMed1989

Modulations of EEG activity in the entorhinal cortex and forebrain olfactory areas during odour sampling.

With the aim of determining a possible role of the entorhinal cortex (EC) in the processing of olfactory information, its electrical activity was recorded during different types of odour sampling behaviour. The EC spontaneous field potentials (EEGs) were related to those recorded simultaneously from the more rostrally lying olfactory bulb (OB) and prepiriform cortex (PPC), using coherence functions. The coherence values are measures of coupling between two neuronal populations. Auto- and coherence spectra were characterized by a peak in the beta-band (35-36 Hz) and another in the 16-20 Hz band. For the beta-peak of the PPC-EC coherence it was shown that both the maximal values and the median frequency decrease during the first 0.5-1.0 s of a trial in a two-choice odour discrimination task. These transients differed significantly for CS+ and CS- trials. However, no such difference was observed during exploratory sniffing at the same pair of odorants. It was concluded that during sniffing the degree of coupling of the EC with the PPC depends on the behavioural context but not on the quality of the odorants. As regards the 16-20 Hz components it was shown, using bispectral analysis, that these components represent the 1/2 subharmonic of the beta-components. The subharmonic components were enhanced during the initiation of sniffing. This means that the neural networks of OB, PPC and EC have non-linear dynamic properties. These networks show different modes of oscillatory behaviour, characteristic of the restful state and of the active sniffing state. Theoretical implications of these experimental results are discussed. In conclusion, it was demonstrated that modulations with sniffing behaviour are present in the beta-EEG components and their subharmonics in the olfactory brain areas including the EC. This indicates that the latter is involved in processes of the evaluation of olfactory cues in relation to the animal's behaviour.

Animals↗
PubMed2006

Disease evolution across a range of spatio-temporal scales.

Traditional explorations of infectious disease evolution have considered the competition between two cross-reactive strains within the standard framework of disease models. Such techniques predict that diseases should evolve to be highly transmissible, benign to the host and possess a long infectious period: in general, diseases do not conform to this ideal. Here we consider a more holistic approach, suggesting that evolution is a trade-off between adaptive pressures at different scales: within host, between hosts and at the population level. We present a model combining within-host pathogen dynamics and transmission between individuals governed by an explicit contact network, where transmission dynamics between hosts are a function of the interaction between the pathogen and the hosts' immune system, though ultimately constrained by the contacts each infected host possesses. Our results show how each of the scales places constraints on the evolutionary behavior, and that complex dynamics may emerge due to the feedbacks between epidemiological and evolutionary dynamics. In particular, multiple stable states can occur with switching between them stochastically driven.

Communicable Diseases↗
PubMed1996

Reversible deactivation of cerebral network components.

Reversible deactivation techniques have shown that the cerebral network: (1) is dynamic, its functions depending on contemporaneous processing elsewhere in the network; (2) is composed of single nodes that contribute to several behaviors; (3) possesses an inherent plasticity that tends to minimize lesion-induced deficits; and (4) comprises feedforward and lateral connections that contribute in different ways to network operations. The next major advances in understanding network operations will probably be made by applying a combination of behavioral, neuron-recording and deactivation techniques. The greatest near-term gains are likely to be made in understanding the contributions that feedback projections make to cerebral network function.

Animals↗
PubMed1996

Dynamical computational properties of local cortical networks for visual and motor processing: a bayesian framework.

A major unsolved question concerns the interaction between the coding of information in the cortex and the collective neural operations (such as perceptual grouping, mental rotation) that can be performed on this information. A key property of the local networks in the cerebral cortex is to combine thalamocortical or feedforward information with horizontal cortico-cortical connections. Among different types of neural networks compatible with the known functional and architectural properties of the cortex, we show that there exist interesting bayesian solutions resulting in an optimal collective decision made by the neuronal population. We suggest that thalamo-cortical and cortico-cortical synaptic plasticity can be differentially modulated to optimize this collective bayesian decision process. We take two examples of cortical dynamics, one for perceptual grouping in MT, and the other one for mental rotation in M1. We show that a neural implementation of the bayesian principle is both computationally efficient to perform these tasks and consistent with the experimental data on the related neuronal activities. A major implication is that a similar collective decision mechanism should exist in different cortical regions due to the similarity of the cortical functional architecture.

Algorithms↗
PubMed2005

Topological approach to neural complexity.

Considerable effort in modern statistical physics is devoted to the study of networked systems. One of the most important example of them is the brain, which creates and continuously develops complex networks of correlated dynamics. An important quantity which captures fundamental aspects of brain network organization is the neural complexity C(X) introduced by Tononi et al. [Proc. Natl. Acad. Sci. USA 91, 5033 (1994)]. This work addresses the dependence of this measure on the topological features of a network in the case of a Gaussian stationary process. Both analytical and numerical results show that the degree of complexity has a clear and simple meaning from a topological point of view. Moreover, the analytical result offers a straightforward and faster algorithm to compute the complexity of a graph than the standard one.

Animals↗
PubMed2006

Hierarchical synchronization in complex networks with heterogeneous degrees.

We study synchronization behavior in networks of coupled chaotic oscillators with heterogeneous connection degrees. Our focus is on regimes away from the complete synchronization state, when the coupling is not strong enough, when the oscillators are under the influence of noise or when the oscillators are nonidentical. We have found a hierarchical organization of the synchronization behavior with respect to the collective dynamics of the network. Oscillators with more connections (hubs) are synchronized more closely by the collective dynamics and constitute the dynamical core of the network. The numerical observation of this hierarchical synchronization is supported with an analysis based on a mean field approximation and the master stability function.

Journal Article↗