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At least 631 records · Page 35Linked to original sources
PubMed2000

Intrinsic dynamics in neuronal networks. II. experiment.

Neurons in many regions of the mammalian CNS remain active in the absence of stimuli. This activity falls into two main patterns: steady firing at low rates and rhythmic bursting. How these firing patterns are maintained in the presence of powerful recurrent excitation, and how networks switch between them, is not well understood. In the previous paper, we addressed these issues theoretically; in this paper we address them experimentally. We found in both studies that a key parameter in controlling firing patterns is the fraction of endogenously active cells. The theoretical analysis indicated that steady firing rates are possible only when the fraction of endogenously active cells is above some threshold, that there is a transition to bursting when it falls below that threshold, and that networks becomes silent when the fraction drops to zero. Experimentally, we found that all steadily firing cultures contain endogenously active cells, and that reducing the fraction of such cells in steadily firing cultures causes a transition to bursting. The latter finding implies indirectly that the elimination of endogenously active cells would cause a permanent drop to zero firing rate. The experiments described here thus corroborate the theoretical analysis.

Action Potentials↗
PubMed2001

Dynamics of motor network overactivation after striatocapsular stroke: a longitudinal PET study using a fixed-performance paradigm.

BACKGROUND AND PURPOSE: Although excessive brain activation during affected hand motion after stroke is well documented, its time course has been rarely studied, and when studied, this has either been with passive movement or with active but cognitively complex task and uncontrolled performance over time, complicating interpretation. METHODS: According to a prospective and longitudinal design, we studied 5 right-handed patients with right-sided hemiparesis due to first-ever left striatocapsular infarction. Three-dimensional PET H(2)O(15) studies were performed twice ( approximately 7 and approximately 31 weeks after stroke [PET1 and PET2, respectively]) during right thumb-to-index tapping executed at the same rate in both studies (1.26 Hz, auditory cued). With SPM96 software, significant group and individual overactivations (P<0.05, corrected for multiple comparisons) were computed by comparison with a group of 7 healthy age-matched right-handed control subjects performing the same task. RESULTS: Motor recovery was significant from PET1 to PET2. Both the group and individual analyses revealed striking overactivations at PET1, affecting notably the cortical hand area and the whole motor network bilaterally. These overactivations were less prominent at PET2 over both hemispheres, not only in terms of Z score but also in terms of spatial extent (almost reaching statistical significance in the affected hemisphere for the latter, P=0.09). However, new overactivations were found at PET2 in the left prefrontal areas, the putamen, and the premotor cortex. CONCLUSIONS: This study is the first to document that to perform the same simple movement of the paretic fingers, the brain with subcortical infarction shows less overactivations at the late than at the early timepoint, especially on the affected side, suggesting reduced recruitment of affected-hemisphere motor networks. However, unaffected-hemisphere prefrontal, premotor, and putaminal overactivations, observed at PET2 only, may suggest late-appearing compensatory reorganization.

Aged↗
PubMedDate not supplied

The competitive dynamics of network-based businesses.

Telecommunications carriers, transportation companies, and banks are among the many network-based businesses--companies that move people, goods, or information from various points to various other points. Managers have long assumed that customers valued all links in these networks equally. It was thought that banking customers, for example, sought access to all of the branches throughout the network or that shipping customers wanted to be able to send packages everywhere. Intuitively, managers thought that many of their customers' needs were, in reality, narrower, but they had no way of knowing which links were most important. New computing power and robust mapping software now make it possible to understand network customers better. In applying this technology, the authors, both consultants from McKinsey & Company, have uncovered three distinct usage patterns: one in which all links are, indeed, valued equally; another in which customers concentrate their use in particular zones; and a third in which customers value only individual links. Each of these patterns requires a different strategy to direct executives in making the decisions fundamental to managing any network-based business: whether to open or close outlets, whether to connect their network to others, and how to organize business units so that they reflect the network's structure. Those who don't spot the patterns or understand their strategic implications will find themselves on the losing end of the network battle.

Capital Financing↗
PubMed2003

Scale-free networks from a Hamiltonian dynamics.

Contrary to many recent models of growing networks, we present a model with fixed number of nodes and links, where a dynamics favoring the formation of links between nodes with degree of connectivity as different as possible is introduced. By applying a local rewiring move, the network reaches equilibrium states assuming broad degree distributions, which have a power-law form in an intermediate range of the parameters used. Interestingly, in the same range we find nontrivial hierarchical clustering.

Journal Article↗
PubMed1995

Mean-field equations, bifurcation map and chaos in discrete time, continuous state, random neural networks.

The dynamical behaviour of a very general model of neural networks with random asymmetric synaptic weights is investigated in the presence of random thresholds. Using mean-field equations, the bifurcations of the fixed points and the change of regime when varying control parameters are established. Different areas with various regimes are defined in the parameter space. Chaos arises generically by a quasi-periodicity route.

Models, Neurological↗
PubMed2004

Dynamics of gene regulatory networks with cell division cycle.

This paper focuses on modeling and analyzing the nonlinear dynamics of gene regulatory networks with the consideration of a cell division cycle with duplication process of DNA, in particular for switches and oscillators of synthetic networks. We derive two models that may correspond to the eukaryotic and prokaryotic cells, respectively. A biologically plausible three-gene model ( lac, tetR, and cI ) and a repressilator as switch and oscillator examples are used to illustrate our theoretical results. We show that the cell cycle may play a significant role in gene regulation due to the nonlinear dynamics of a gene regulatory network although gene expressions are usually tightly controlled by transcriptional factors.

Animals↗
PubMed1998

Dynamics of the genetic regulatory network for Arabidopsis thaliana flower morphogenesis.

We present a network model and its dynamic analysis for the regulatory relationships among 11 genes that participate in Arabidopsis thaliana flower morphogenesis. The topology of the network and the relative strengths of interactions among these genes were based from published genetic and molecular data, mainly relying on mRNA expression patterns under wild type and mutant backgrounds. The network model is made of binary elements and we used a particular dynamic implementation for the network that we call semi-synchronic. Using this method the network reaches six attractors; four of them correspond to observed patterns of gene expression found in the floral organs of Arabidopsis (sepals, petals, stamens and carpels) as predicted by the ABC model of flower morphogenesis. The fifth state corresponds to cells that are not competent to flowering, and the sixth attractor predicted by the model is never found in wild-type plants, but it could be induced experimentally. We discuss the biological implications and the potential use of this network modeling approach to integrate functional data of regulatory genes of plant development.

Arabidopsis↗
PubMed1999

Dynamic monitoring and control of patient anaesthetic and dose levels: time-delay, moving-average neural networks, and principal components analysis.

The goal of this study was to examine the capabilities of neural network models for dynamic monitoring and control of patient anaesthetic and dose levels. The network models that we considered are split into two basic groups: static networks and dynamic networks. Static networks are characterised by equations that are memoryless. On the other hand, dynamic networks are systems with memory. Additionally, principal components analysis was used to introduce a further improvement to network design by reducing the dimensionality of the encoded temporal information. Principal components analysis was applied as both pre-processing and post-processing techniques. In the first instance it was used to reduce the dimensionality of the data to more manageable intrinsic information. In the second instance it was employed to understand how the hidden layers separate the data, in order to optimise the network architecture.

Anesthesia↗
PubMed2000

Shape-dependent control of cell growth, differentiation, and apoptosis: switching between attractors in cell regulatory networks.

Development of characteristic tissue patterns requires that individual cells be switched locally between different phenotypes or "fates;" while one cell may proliferate, its neighbors may differentiate or die. Recent studies have revealed that local switching between these different gene programs is controlled through interplay between soluble growth factors, insoluble extracellular matrix molecules, and mechanical forces which produce cell shape distortion. Although the precise molecular basis remains unknown, shape-dependent control of cell growth and function appears to be mediated by tension-dependent changes in the actin cytoskeleton. However, the question remains: how can a generalized physical stimulus, such as cell distortion, activate the same set of genes and signaling proteins that are triggered by molecules which bind to specific cell surface receptors. In this article, we use computer simulations based on dynamic Boolean networks to show that the different cell fates that a particular cell can exhibit may represent a preprogrammed set of common end programs or "attractors" which self-organize within the cell's regulatory networks. In this type of dynamic network model of information processing, generalized stimuli (e.g., mechanical forces) and specific molecular cues elicit signals which follow different trajectories, but eventually converge onto one of a small set of common end programs (growth, quiescence, differentiation, apoptosis, etc.). In other words, if cells use this type of information processing system, then control of cell function would involve selection of preexisting (latent) behavioral modes of the cell, rather than instruction by specific binding molecules. Importantly, the results of the computer simulation closely mimic experimental data obtained with living endothelial cells. The major implication of this finding is that current methods used for analysis of cell function that rely on characterization of linear signaling pathways or clusters of genes with common activity profiles may overlook the most critical features of cellular information processing which normally determine how signal specificity is established and maintained in living cells.

Animals↗
PubMed1992

Steady state dynamics of intermediate filament networks.

We have conducted experiments to examine the dynamic exchange between subunit and polymer of vimentin intermediate filaments (IF) at steady state through the use of xrhodamine-labeled vimentin in fluorescence recovery after photobleaching (FRAP) analysis. The xrhodamine-vimentin incorporated into the endogenous vimentin IF network after microinjection into fibroblasts and could be visualized with a cooled charge-coupled device (CCD) camera and digital imaging fluorescence microscopy. Bar shaped regions were bleached in the fluorescent IF network using a beam from an argon ion laser and the cells were monitored at various times after bleaching to assess recovery of fluorescence in the bleached zones. We determined that bleached vimentin fibers can recover their fluorescence over relatively short time periods. Vimentin fibers in living cells also can exhibit significant movements, but the recovery of fluorescence was not dependent upon movement of fibers. Fluorescence recovery within individual fibers did not exhibit any marked polarity and was most consistent with a steady state exchange of vimentin subunits along the lengths of IF.

3T3 Cells↗
PubMed2004

Generalized neural networks for spectral analysis: dynamics and Liapunov functions.

This paper analyzes local and global behavior of several dynamical systems which generalize some artificial neural network (ANN) semilinear models originally designed for principal component analysis (PCA) in the characterization of random vectors. These systems implicitly performed the spectral analysis of correlation (i.e. symmetric positive definite) matrices. Here, the proposed generalizations cover both nonsymmetric matrices as well as fully nonlinear models. Local stability analysis is performed via linearization and global behavior is analyzed by constructing several Liapunov functions.

Algorithms↗
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↗
PubMed2006

Pairwise decoherence in coupled spin qubit networks.

Experiments involving phase coherent dynamics of networks of spins, such as echo experiments, will only work if decoherence can be suppressed. We show here, by analyzing the particular example of a crystalline network of Fe8 molecules, that most decoherence typically comes from pairwise interactions (particularly dipolar interactions) between the spins, which cause "correlated errors." However, at very low T these are strongly suppressed. These results have important implications for the design of quantum information processing systems using electronic spins.

Journal Article↗