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At least 325 records · Page 18Linked to original sources
PubMed2025

Brain dynamics reflecting an intra-network brain state is associated with increased posttraumatic stress symptoms in the early aftermath of trauma.

Post-traumatic stress (PTS) encompasses a range of psychological responses following trauma, which may lead to more severe outcomes such as post-traumatic stress disorder (PTSD). Identifying early neuroimaging biomarkers that link brain function to PTS outcomes is critical for understanding PTSD risk. This longitudinal study examines the association between brain dynamic functional network connectivity (dFNC) and current/future PTS symptom severity, and the impact of sex on this relationship. By analyzing 275 participants' dFNC data obtained ~2 weeks after trauma exposure, we noted that brain dynamics of an inter-network brain state link negatively with current (r=-0.197, p corrected = 0.0079) and future (r=-0.176, p corrected = 0.0176) PTS symptom severity. Also, dynamics of an intra-network brain state correlated with future symptom intensity (r = 0.205, p corrected = 0.0079). We additionally observed that the association between the network dynamics of the inter-network and intra-network brain state with symptom severity is more pronounced in female group. Our findings highlight a potential link between brain network dynamics in the aftermath of trauma with current and future PTSD outcomes, with a stronger effect in female group, underscoring the importance of sex differences.

Journal Article↗
PubMed1991

Molecular dynamics simulation on a network of workstations using a machine-independent parallel programming language.

Molecular dynamics simulations investigate local and global motion in molecules. Several parallel computing approaches have been taken to attack the most computationally expensive phase of molecular simulations, the evaluation of long range interactions. This paper develops a straightforward but effective algorithm for molecular dynamics simulations using the machine-independent parallel programming language, Linda. The algorithm was run both on a shared memory parallel computer and on a network of high performance Unix workstations. Performance benchmarks were performed on both systems using two proteins. This algorithm offers a portable cost-effective alternative for molecular dynamics simulations. In view of the increasing numbers of networked workstations, this approach could help make molecular dynamics simulations more easily accessible to the research community.

Algorithms↗
PubMed1993

The dynamics of sparse random networks.

Recurrent neural networks with full symmetric connectivity have been extensively studied as associative memories and pattern recognition devices. However, there is considerable evidence that sparse, asymmetrically connected, mainly excitatory networks with broadly directed inhibition are more consistent with biological reality. In this paper, we use the technique of return maps to study the dynamics of random networks with sparse, asymmetric connectivity and nonspecific inhibition. These networks show three qualitatively different kinds of behavior: fixed points, cycles of low period, and extremely long cycles verging on aperiodicity. Using statistical arguments, we relate these behaviors to network parameters and present empirical evidence for the accuracy of this statistical model. The model, in turn, leads to methods for controlling the level of activity in networks. Studying random, untrained networks provides an understanding of the intrinsic dynamics of these systems. Such dynamics could provide a substrate for the much more complex behavior shown when synaptic modification is allowed.

Animals↗
PubMed1992

Molecular dynamics simulation on a network of workstations using a machine-independent parallel programming language.

Molecular dynamics simulations investigate local and global motion in molecules. Several parallel computing approaches have been taken to attack the most computationally expensive phase of molecular simulations, the evaluation of long range interactions. This paper reviews these approaches and develops a straightforward but effective algorithm using the machine-independent parallel programming language, Linda. The algorithm was run both on a shared memory parallel computer and on a network of high performance Unix workstations. Performance benchmarks were performed on both systems using two proteins. This algorithm offers a portable cost-effective alternative for molecular dynamics simulations. In view of the increasing numbers of networked workstations, this approach could help make molecular dynamics simulations more easily accessible to the research community.

Algorithms↗
PubMed2002

A versatile ODE approximation to a network model for the spread of sexually transmitted diseases.

We develop a moment closure approximation (MCA) to a network model of sexually transmitted disease (STD) spread through a steady/casual partnership network. MCA has been used previously to approximate static, regular lattices, whereas application to dynamic, irregular networks is a new endeavour, and application to sociologically-motivated network models has not been attempted. Our goals are 1). to investigate issues relating to the application of moment closure approximations to dynamic and irregular networks, and 2). to understand the impact of concurrent casual partnerships on STD transmission through a population of predominantly steady monogamous partnerships. We are able to derive a moment closure approximation for a dynamic irregular network representing sexual partnership dynamics, however, we are forced to use a triple approximation due to the large error of the standard pair approximation. This example underscores the importance of doing error analysis for moment closure approximations. We also find that a small number of casual partnerships drastically increases the prevalence and rate of spread of the epidemic. Finally, although the approximation is derived for a specific network model, we can recover approximations to a broad range of network models simply by varying model parameters which control the structure of the dynamic network. Thus our moment closure approximation is very flexible in the kinds of network models it can approximate.

Computer Simulation↗
PubMed1997

Radar image segmentation using self-adapting recurrent networks.

This paper presents a novel approach to the segmentation and integration of (radar) images using a second-order recurrent artificial neural network architecture consisting of two sub-networks: a function network that classifies radar measurements into four different categories of objects in sea environments (water, oil spills, land and boats), and a context network that dynamically computes the function network's input weights. It is shown that in experiments (using simulated radar images) this mechanism outperforms conventional artificial neural networks since it allows the network to learn to solve the task through a dynamic adaptation of its classification function based on its internal state closely reflecting the current context.

Doppler Effect↗
PubMed1993

The dynamic clamp: artificial conductances in biological neurons.

The dynamic clamp is a novel method that uses computer simulation to introduce conductances into biological neurons. This method can be used to study the role of various conductances in shaping the activity of single neurons, or neurons within networks. The dynamic clamp can also be used to form circuits from previously unconnected neurons. This approach makes computer simulation an interactive experimental tool, and will be useful in many applications where the role of synaptic strengths and intrinsic properties in neuronal and network dynamics is of interest.

Animals↗
PubMed2001

Water and the cytoskeleton.

The diffusion of intracellular fluid and solutes is mainly limited by the density and the geometry of crossbridges between cytoskeletal polymers mediating the formation of an integrated cytoplasmic scaffold. Evidence for specific relationships between water and cytoskeletal polymers arises from the effect of heavy water on their polymerization process in vitro and on the cytoskeleton of living cells. The hydration of cytoskeletal subunits is modified through polymerization, a mechanism which may be involved in the direct contribution of the cytoskeleton to the osmotic properties of cells together with changes of hydration of polymers within networks. The dynamic properties of the hydration layer of cytoskeletal polymers may reflect the repetitive distribution of the surface charges of subunits within the polymer lattice, thus inducing a local and long range ordering of the diffusion flows of water and solutes inside polymer networks. The interactions between subunits in protofilaments and between protofilaments determine the specific viscoelastic properties of each type of polymer, regulated by associated proteins, and the mechanical properties of the cell through the formation of bundles and gels. Individual polymers are interconnected into dynamic networks through crossbridging by structural associated proteins and molecular motors, the activity of which involves cooperative interactions with the polymer lattice and likely the occurence of coordinated modifications of the hydration layer of the polymer surface. The cytoskeletal polymers are polyelectrolytes which constitute a large intracellular surface of condensed anionic charges and form a buffering structure for the sequestration of cations involved in the regulation of intracellular events. This property allows also the association of cytoplasmic enzymes and multimolecular complexes with the cytoskeleton, facilitating metabolic channelling and the localization of these complexes in specific subdomains of the cytoplasm. The consequences of interactions between membranes and the cytoskeleton in all cellular compartments range from the local immobilization and clustering of lipids and membrane proteins to the regulation of water and ion flows by the association of cytoskeletal subunits or polymers with transmembrane channels. The possibility that the polyelectrolyte properties of the cytoskeletal polymers contribute to the modulation of membrane potentials supports the hypothesis of a direct involvement of the cytoskeleton in intercellular communications.

Actins↗
PubMed2000

Dynamic properties of hydrogen-bonded networks in supercritical water

Dynamic properties of supercritical water at temperatures between 573 and 773 K and densities between 0.49 and 0.83 g/cm(3) have been investigated by molecular dynamics simulation and compared to states located on the vapor-liquid coexistence curve. A flexible simple point charge potential has been assumed for interactions in the subcritical states, whereas a reparameterization of that model has been performed to model the supercritical states. The hydrogen bonding structure and the diffusion coefficients in an ensemble of simulated states were previously analyzed and a good agreement with available experimental data was found. Dynamic properties of hydrogen bonding like the bond lifetimes and the influence of hydrogen bonds in the vibrational spectra are discussed along a range of simulation conditions. A nonlinear behavior of the hydrogen-bond lifetime as a function of temperature is observed in subcritical water whereas a linear dependence is found in supercritical water. Special attention is paid to the intermolecular vibrational spectrum (10-400 cm(-1)). It has been observed that the mode centered at 200 cm(-1), attributed to the intermolecular O-O stretching vibration in the ambient state remains active in the supercritical states.

Journal Article↗
PubMed2002

Pattern storage and processing in attractor networks with short-time synaptic dynamics.

Neurophysiological experiments show that the strength of synaptic connections can undergo substantial changes on a short time scale. These changes depend on the history of the presynaptic input. Using mean-field techniques, we study how short-time dynamics of synaptic connections influence the performance of attractor neural networks in terms of their memory capacity and capability to process external signals. For binary discrete-time as well as for firing rate continuous-time neural networks, the fixed points of the network dynamics are shown to be unaffected by synaptic dynamics. However, the stability of patterns changes considerably. Synaptic depression turns out to reduce the storage capacity. On the other hand, synaptic depression is shown to be advantageous for processing of pattern sequences. The analytical results on stability, size of the basins of attraction and on the switching between patterns are complemented by numerical simulations.

Algorithms↗
PubMed1994

Optimization dynamics for partitioned neural networks.

Given a relaxation-based neural network and a desired partition of the neurons in the network into modules with relatively slow communication between modules, we investigate relaxation dynamics for the resulting partitioned neural network. In particular, we show how the slow inter-module communication channels can be modeled by means of certain transformations of the original objective function which introduce new state variables for the inter-module communication links. We report on a parallel implementation of the resulting relaxation dynamics, for a two-dimensional image segmentation network, using a network of workstations. Experiments demonstrate a functional and efficient parallelization of this neural network algorithm. We also discuss implications for analog hardware implementations of relaxation networks.

Kinetics↗
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↗
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↗
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↗
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↗