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 667 records · Page 37Linked to original sources
PubMed2005

A network of electronic neural oscillators reproduces the dynamics of the periodically forced pyloric pacemaker group.

Low-dimensional oscillators are a valuable model for the neuronal activity of isolated neurons. When coupled, the self-sustained oscillations of individual free oscillators are replaced by a collective network dynamics. Here, dynamical features of such a network, consisting of three electronic implementations of the Hindmarsh-Rose mathematical model of bursting neurons, are compared to those of a biological neural motor system, specifically the pyloric CPG of the crustacean stomatogastric nervous system. We demonstrate that the network of electronic neurons exhibits realistic synchronized bursting behavior comparable to the biological system. Dynamical properties were analyzed by injecting sinusoidal currents into one of the oscillators. The temporal bursting structure of the electronic neurons in response to periodic stimulation is shown to bear a remarkable resemblance to that observed in the corresponding biological network. These findings provide strong evidence that coupled nonlinear oscillators realistically reproduce the network dynamics experimentally observed in assemblies of several neurons.

Action Potentials↗
PubMed1995

Tomographic electroencephalography/magnetoencephalography. Dynamics of human neural network switching.

New developments in multimodal registration of electroencephalography (EEG), magnetic resonance imaging (MRI), and positron emission tomography (PET) are presented as a method to create a tomographic EEG. Three-dimensional information about the x,y,z location of the sources of event-related potentials is corroborated through the use of experimental design and coregistration with MRI and PET. Once the three-dimensional location of event-related potential dipole sources is identified and corroborated, pseudoinverse procedures are used to derive a new EEG voltage sequence from each of the dipoles. Each derived EEG dipole time series is analogous to recording EEG from a deeply implanted electrode and constitutes a four-dimensional tomographic EEG (i.e., three-dimensional space plus time). EEG coherence and phase analyses are then performed on the dipole-derived time series to study the temporal and spatial dynamics of neural network switching during voluntary finger movements. The purpose of this article is to demonstrate a new method to exploit the time domain dynamics of neural network switching in behaving human subjects.

Brain↗
PubMed2006

Inferring gene expression networks via static and dynamic data integration.

This paper presents a novel approach for the extraction of gene regulatory networks from DNA microarray data. The approach is characterized by the integration of data coming from static and dynamic experiments, exploiting also prior knowledge on the biological process under analysis. A starting network topology is built by analyzing gene expression data measured during knockout experiments. The analysis of time series expression profiles allows to derive the complete network structure and to learn a model of the gene expression dynamics: to this aim a genetic algorithm search coupled with a regression model of the gene interactions is exploited. The method has been applied to the reconstruction of a network of genes involved into the Saccharomyces Cerevisiae cell cycle. The proposed approach was able to reconstruct known relationships among genes and to provide meaningful biological results.

Artificial Intelligence↗
PubMed1995

Computing with dynamic attractors in neural networks.

In this paper we report on some new architectures for neural computation, motivated in part by biological considerations. One of our goals is to demonstrate that it is just as easy for a neural net to compute with arbitrary attractors--oscillatory or chaotic--as with the more usual asymptotically stable fixed points. The advantages (if any) of such architectures are currently being investigated; but it seems reasonable that the much richer dynamics of recurrent networks, so obvious in recordings of brain activity, must be useful for something. On the other hand, the constraints of computing with biological wet-ware may make chaotic dynamics unavoidable in complex nervous systems. We hypothesize also that the as yet unrivaled capabilities of the human brain derive from an ability to integrate both analog intuitive pattern recognition operations, and digital symbolic logical operations at the ground level of its hardware. To investigate these possibilities, we have constructed a parallel distributed processing architecture inspired by the structure and dynamics of cerebral cortex. The construction assumes that cortex is a set of coupled associative memories with dynamic attractors. It is guided also by a particular concept of the physical structure required of macroscopic computational systems in general for reliable computation in the presence of noise. Our challenge is to accomplish real tasks that brains can do, using ordinary differential equations, in networks that are as faithful as possible to the known anatomy and dynamics of cortex.

Algorithms↗
PubMed2002

Maneuvering in the complex path from genotype to phenotype.

Human disease phenotypes are controlled not only by genes but by lawful self-organizing networks that display system-wide dynamics. These networks range from metabolic pathways to signaling pathways that regulate hormone action. When perturbed, networks alter their output of matter and energy which, depending on the environmental context, can produce either a pathological or a normal phenotype. Study of the dynamics of these networks by approaches such as metabolic control analysis may provide new insights into the pathogenesis and treatment of complex diseases.

Alzheimer Disease↗
PubMed2004

Locality in quantum and Markov dynamics on lattices and networks.

We consider gapped systems governed by either quantum or Markov dynamics, with the low-lying states below the gap being approximately degenerate. For a broad class of dynamics, we prove that ground or stationary state correlation functions can be written as a piece decaying exponentially in space plus a term set by matrix elements between the low-lying states. The key to the proof is a local approximation to the negative energy, or annihilation, part of an operator in a gapped system. Applications to numerical simulation of quantum systems and to networks are discussed.

Journal Article↗
PubMed2006

A thermodynamic view of networks.

Networks can be described by the frequency distribution of the number of links associated with each node (the degree of the node). Of particular interest are the power law distributions, which give rise to the so-called scale-free networks, and the distributions of the form of the simplified canonical law (SCL) introduced by Mandelbrot, which give what we shall call the Mandelbrot networks. Many dynamical methods have been obtained for the construction of scale-free networks, but no dynamical construction of Mandelbrot networks has been demonstrated. Here we develop a systematic technique to obtain networks with any given distribution of the degrees of the nodes. This is done using a thermodynamic approach in which we maximise the entropy associated with degree distribution of the nodes of the network subject to certain constraints. These constraints can be chosen systematically to produce the desired network architecture. For large networks we therefore replace a dynamical approach to the stationary state by a thermodynamical viewpoint. We use the method to generate scale-free and Mandelbrot networks with arbitrarily chosen parameters. We emphasise that this approach opens the possibility of insights into a thermodynamics of networks by suggesting thermodynamic relations between macroscopic variables for networks.

Computer Simulation↗
PubMed2006

Synchronized state of coupled dynamics on time-varying networks.

We consider synchronization properties of coupled dynamics on time-varying networks and the corresponding time-average network. We find that if the different Laplacians corresponding to the time-varying networks commute with each other then the stability of the synchronized state for both the time-varying and the time-average topologies are approximately the same. On the other hand for noncommuting Laplacians the stability of the synchronized state for the time-varying topology is in general better than the time-average topology.

Action Potentials↗
PubMedDate not supplied

[A discrete dynamic model of a neuronal network with space-time organization].

A dynamical model of the neuronal network based on the principles of spatial-temporal organization of information processing was constructed. The additivity law for the formation of input connection factors in this model provides its versatility for a variety of logical functions. The model takes into account real properties of the systemic organization of brain neurones.

Neural Networks, Computer↗
PubMed2005

Prediction of molecular-dynamics simulation results using feedforward neural networks: reaction of a C2 dimer with an activated diamond (100) surface.

A new approach involving neural networks combined with molecular dynamics has been used for the determination of reaction probabilities as a function of various input parameters for the reactions associated with the chemical-vapor deposition of carbon dimers on a diamond (100) surface. The data generated by the simulations have been used to train and test neural networks. The probabilities of chemisorption, scattering, and desorption as a function of input parameters, such as rotational energy, translational energy, and direction of the incident velocity vector of the carbon dimer, have been considered. The very good agreement obtained between the predictions of neural networks and those provided by molecular dynamics and the fact that, after training the network, the determination of the interpolated probabilities as a function of various input parameters involves only the evaluation of simple analytical expressions rather than computationally intensive algorithms show that neural networks are extremely powerful tools for interpolating the probabilities and rates of chemical reactions. We also find that a neural network fits the underlying trends in the data rather than the statistical variations present in the molecular-dynamics results. Consequently, neural networks can also provide a computationally convenient means of averaging the statistical variations inherent in molecular-dynamics calculations. In the present case the application of this method is found to reduce the statistical uncertainty in the molecular-dynamics results by about a factor of 3.5.

Journal Article↗
PubMed2006

Cell-signalling dynamics in time and space.

The specificity of cellular responses to receptor stimulation is encoded by the spatial and temporal dynamics of downstream signalling networks. Temporal dynamics are coupled to spatial gradients of signalling activities, which guide pivotal intracellular processes and tightly regulate signal propagation across a cell. Computational models provide insights into the complex relationships between the stimuli and the cellular responses, and reveal the mechanisms that are responsible for signal amplification, noise reduction and generation of discontinuous bistable dynamics or oscillations.

Animals↗
PubMed1998

Structural change and development in real and artificial neural networks.

Two related but different fields are reviewed. Initially some basic facts about developing real brains are set out and then work on dynamic neural networks is described. A dynamic neural network is defined as any artificial neural network that automatically changes its structure through exposure to input stimuli. Various models are described and evaluated and the functional correlates of both regressive and progressive structural changes are discussed. The paper concludes that, if future modelling work is to be set within a more neurally-plausible framework, then it would be fruitful to examine networks in which the connectivity between extant units is progressively embellished.

Journal Article↗
PubMed2003

Synchronization and symmetry-breaking bifurcations in constructive networks of coupled chaotic oscillators.

The spatiotemporal dynamics of networks based on a ring of coupled oscillators with regular shortcuts beyond the nearest-neighbor couplings is studied by using master stability equations and numerical simulations. The generic criterion for dynamic synchronization has been extended to arbitrary network topologies with zero row-sum. The symmetry-breaking oscillation patterns that resulted from the Hopf bifurcation from synchronous states are analyzed by the symmetry group theory.

Journal Article↗
PubMed2006

Epidemic dynamics on an adaptive network.

Many real-world networks are characterized by adaptive changes in their topology depending on the state of their nodes. Here we study epidemic dynamics on an adaptive network, where the susceptibles are able to avoid contact with the infected by rewiring their network connections. This gives rise to assortative degree correlation, oscillations, hysteresis, and first order transitions. We propose a low-dimensional model to describe the system and present a full local bifurcation analysis. Our results indicate that the interplay between dynamics and topology can have important consequences for the spreading of infectious diseases and related applications.

Information Services↗
PubMed2004

Weighted evolving networks: coupling topology and weight dynamics.

We propose a model for the growth of weighted networks that couples the establishment of new edges and vertices and the weights' dynamical evolution. The model is based on a simple weight-driven dynamics and generates networks exhibiting the statistical properties observed in several real-world systems. In particular, the model yields a nontrivial time evolution of vertices' properties and scale-free behavior for the weight, strength, and degree distributions.

Ecology↗
PubMed1998

Motion deblurring in a neural network model of retino-cortical dynamics.

Simulations of a neural network model of retino-cortical dynamics (Oğmen H, Neural Netw 6 (1993) 245-273) are presented. The temporal-step response of the model to a single dot (spatial impulse) consists of three post-retinal phases: reset, feed-forward dominant and feedback dominant. In response to a single moving dot, the model predicts the perception of extensive blur. This extensive blur is proposed to be due to the relative spatial and temporal offsets between transient and sustained signals conveyed from retina to post-retinal levels. In response to a pair of horizontally separated dots moving in the horizontal direction, the model predicts extensive blur for the trailing dot irrespective of dot-to-dot separation. For the leading dot, the model predicts a decrease in perceived blur for long exposure durations when dot-to-dot separations are small. The reduction of perceived blur at long exposure durations for small dot-to-dot separations is proposed to stem from the spatio-temporal overlap between the transient activity generated by the trailing dot and the sustained activity generated by the leading dot. The model also predicts that targets moving at higher speeds generate more blur even when blur is normalized with respect to speed. The mechanism in the model generating this effect is a slow inhibition within the sustained channel. These predictions are compared with recent psychophysical data (Chen S, Bedell HE, Oğmen H, Vis Res 35 (1995) 2315-2328) and are found to be in excellent agreement. The model is used to offer a coherent explanation for several controversial findings published in the literature. This computational study shows that a model without any motion-compensation mechanism can give a good account of motion deblurring phenomenon and supplements our recent experimental study which provided evidence against motion-compensation type models in explaining the motion deblurring phenomenon (Chen S, Bedell HE, Oğmen H, Vis Res 35 (1995) 2315-2328).

Feedback↗
PubMed2006

WebCell: a web-based environment for kinetic modeling and dynamic simulation of cellular networks.

SUMMARY: WebCell is a web-based environment for managing quantitative and qualitative information on cellular networks and for interactively exploring their steady-state and dynamic behaviors in response to systemic perturbations. It is designed as a user-friendly web interface, allowing users to efficiently construct, visualize, analyze and store reaction network models, thereby facilitating kinetic modeling and in silico simulation of biological systems of interest. A collected model library is also available to provide comprehensive implications for cellular dynamics of the published models.

Cell Physiological Phenomena↗