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At least 685 records · Page 38Linked to original sources
PubMed2005

Some generalized sufficient convergence criteria for nonlinear continuous neural networks.

A reason for applying the direct method of Lyapunov to artificial neural networks (ANNs) is to design dynamical neural networks so that they exhibit global asymptotic stability. Lyapunov functions that frequently appear in the ANN literature include the quadratic function, the Persidskii function, and the Luré-Postnikov function. This contribution revisits the quadratic function and shows that via Krasovskii-like stability criteria, it is possible to have a very simple and systematic procedure to obtain not only new and generalized results but also well-known sufficient conditions for convergence established recently by non-Lyapunov methods, such as the matrix measure and nonlinear measure.

Algorithms↗
PubMed2004

Escherichia coli adenylate kinase dynamics: comparison of elastic network model modes with mode-coupling (15)N-NMR relaxation data.

The dynamics of adenylate kinase of Escherichia coli (AKeco) and its complex with the inhibitor AP(5)A, are characterized by correlating the theoretical results obtained with the Gaussian Network Model (GNM) and the anisotropic network model (ANM) with the order parameters and correlation times obtained with Slowly Relaxing Local Structure (SRLS) analysis of (15)N-NMR relaxation data. The AMPbd and LID domains of AKeco execute in solution large amplitude motions associated with the catalytic reaction Mg(+2)*ATP + AMP --> Mg(+2)*ADP + ADP. Two sets of correlation times and order parameters were determined by NMR/SRLS for AKeco, attributed to slow (nanoseconds) motions with correlation time tau( perpendicular) and low order parameters, and fast (picoseconds) motions with correlation time tau( parallel) and high order parameters. The structural connotation of these patterns is examined herein by subjecting AKeco and AKeco*AP(5)A to GNM analysis, which yields the dynamic spectrum in terms of slow and fast modes. The low/high NMR order parameters correlate with the slow/fast modes of the backbone elucidated with GNM. Likewise, tau( parallel) and tau( perpendicular) are associated with fast and slow GNM modes, respectively. Catalysis-related domain motion of AMPbd and LID in AKeco, occurring per NMR with correlation time tau( perpendicular), is associated with the first and second collective slow (global) GNM modes. The ANM-predicted deformations of the unliganded enzyme conform to the functional reconfiguration induced by ligand-binding, indicating the structural disposition (or potential) of the enzyme to bind its substrates. It is shown that NMR/SRLS and GNM/ANM analyses can be advantageously synthesized to provide insights into the molecular mechanisms that control biological function.

Adenylate Kinase↗
PubMed1984

Mathematical modelling of dynamics and control in metabolic networks. II. Simple dimeric enzymes.

The dynamics of enzyme cooperativity are examined by studying a homotropic dimeric enzyme with identical reaction sites, both of which follow irreversible Michaelis-Menten kinetics. The problem is approached via scaling and linearization of the governing mass action kinetic equations. Homotropic interaction between the two sites are found to depend on three dimensionless groups, two for the substrate binding step and one for the chemical transformation. The interaction between the two reaction sites is shown capable of producing dynamic behavior qualitatively different from that of a simple Michaelis-Menten system; when the two sites interact to increase enzymatic activity over that of two independent monomeric enzymes (positive cooperativity) damped oscillatory behavior is possible, and for negative cooperativity in the chemical transformation step a multiplicity of steady states can occur, with one state unstable and leading to runaway behavior. Linear analysis gives significant insight into system dynamics, and their parametric sensitivity, and a way to identify regions of the parameter space where the approximate quasi-stationary and quasi-equilibrium analyses are appropriate.

Allosteric Regulation↗
PubMed2006

ON the influence of parameter theta- on performance of RBF neural networks trained with the dynamic decay adjustment algorithm.

The dynamic decay adjustment (DDA) algorithm is a fast constructive algorithm for training RBF neural networks (RBFNs) and probabilistic neural networks (PNNs). The algorithm has two parameters, namely, theta(+) and theta(-). The papers which introduced DDA argued that those parameters would not heavily influence classification performance and therefore they recommended using always the default values of these parameters. In contrast, this paper shows that smaller values of parameter theta(-) can, for a considerable number of datasets, result in strong improvement in generalization performance. The experiments described here were carried out using twenty benchmark classification datasets from both Proben1 and the UCI repositories. The results show that for eleven of the datasets, the parameter theta(-) strongly influenced classification performance. The influence of theta(-) was also noticeable, although much less, on six of the datasets considered. This paper also compares the performance of RBF-DDA with theta(-) selection with both AdaBoost and Support Vector Machines (SVMs).

Algorithms↗
PubMed2003

Information exchange and the robustness of organizational networks.

The dynamics of information exchange is an important but understudied aspect of collective communication, coordination, and problem solving in a wide range of distributed systems, both physical (e.g., the Internet) and social (e.g., business firms). In this paper, we introduce a model of organizational networks according to which links are added incrementally to a hierarchical backbone and test the resulting networks under variable conditions of information exchange. Our main result is the identification of a class of multiscale networks that reduce, over a wide range of environments, the likelihood that individual nodes will suffer congestion-related failure and that the network as a whole will disintegrate when failures do occur. We call this dual robustness property of multiscale networks "ultrarobustness." Furthermore, we find that multiscale networks attain most of their robustness with surprisingly few link additions, suggesting that ultrarobust organizational networks can be generated in an efficient and scalable manner. Our results are directly relevant to the relief of congestion in communication networks and also more broadly to activities, like distributed problem solving, that require individuals to exchange information in an unpredictable manner.

Journal Article↗
PubMed1998

Self-organization and dynamics reduction in recurrent networks: stimulus presentation and learning.

Freeman's investigations on the olfactory bulb of the rabbit showed that its signal dynamics was chaotic, and that recognition of a learned stimulus is linked to a dimension reduction of the dynamics attractor. In this paper we address the question whether this behavior is specific of this particular architecture, or if it is a general property. We study the dynamics of a non-convergent recurrent model-the random recurrent neural networks. In that model a mean-field theory can be used to analyze the autonomous dynamics. We extend this approach with various observations on significant changes in the dynamical regime when sending static random stimuli. Then we propose a Hebb-like learning rule, viewed as a self-organization dynamical process inducing specific reactivity to one random stimulus. We numerically show the dynamics reduction during learning and recognition processes and analyze it in terms of dynamical repartition of local neural activity.

Journal Article↗
PubMed1995

Information processing in neural networks by means of controlled dynamic regimes.

This paper is concerned with the modeling of neural systems regarded as information processing entities. I investigate the various dynamic regimes that are accessible in neural networks considered as nonlinear adaptive dynamic systems. The possibilities of obtaining steady, oscillatory or chaotic regimes are illustrated with different neural network models. Some aspects of the dependence of the dynamic regimes upon the synaptic couplings are examined. I emphasize the role that the various regimes may play to support information processing abilities. I present an example where controlled transient evolutions in a neural network, are used to model the regulation of motor activities by the cerebellar cortex.

Cerebellum↗
PubMed2004

Chaotic itinerancy as a mechanism of irregular changes between synchronization and desynchronization in a neural network.

We investigate the dynamic character of a network of electrotonically coupled cells consisting of class I point neurons, in terms of a finite dimensional dynamical system. We classify a subclass of class I point neurons, called class I* point neurons. Based on this classification, we use a reduced Hindmarsh-Rose (H-R) model, which consists of two dynamical variables, to construct a network model consisting of electrotonically coupled H-R neurons. Although biologically simple, the system is sufficient to extract the essence of the complex dynamics, which the system may yield under certain physiological conditions. The network model produces a transitory behavior as well as a periodic motion and spatio-temporal chaos. The transitory dynamics that the network model exhibits is shown numerically to be chaotic itinerancy. The transitions appear between various metachronal waves and all-synchronization states. The network model shows that this transitory dynamics can be viewed as a chaotic switch between synchronized and desynchronized states. Despite the use of spatially discrete point neurons as basic elements of the network, the overall dynamics exhibits scale-free activity including various scales of spatio-temporal patterns.

Action Potentials↗
PubMed1996

Permanence of sparse catalytic networks.

Some global dynamical properties of catalytic networks, in particular permanence, are closely related with a directed graph representing the differential equation. It can be shown that for every directed graph with a Hamiltonian circuit there is a choice of rate constants such that the system is permanent. On the other hand, one can find properties of the graphs, for example, reducibility or the presence of endpoints, that are incompatible with permanence.

Biological Evolution↗
PubMedDate not supplied

Software tools and databases for bacterial systematics and their dissemination via global networks.

The dynamic expansion of the taxonomic knowledge base is fundamental to further developments in biotechnology and sustainable conservation strategies. The vast array of software tools for numerical taxonomy and probabilistic identification, in conjunction with automated systems for data generation are allowing the construction of large computerised strain databases. New techniques available for the generation of chemical and molecular data, associated with new software tools for data analysis, are leading to a quantum leap in bacterial systematics. The easy exchange of data through an interactive and highly distributed global computer network, such as the Internet, is facilitating the dissemination of taxonomic data. Relevant information for comparative sequence analysis, ribotyping, protein and DNA electrophoretic pattern analysis is available on-line through computerised networks. Several software packages are available for the analysis of molecular data. Nomenclatural and taxonomic 'Authority Files' are available from different sources together with strain specific information. The increasing availability of public domain software, is leading to the establishment and integration of public domain databases all over the world, and promoting co-operative research projects on a scale never seen before.

Amino Acid Sequence↗
PubMed2006

Effects of substrate network topologies on competition dynamics.

We study a competition dynamics, based on the minority game, endowed with various substrate network structures. We observe the effects of the network topologies by investigating the volatility of the system and the structure of follower networks. The topology of substrate structures significantly influences the system efficiency represented by the volatility and such substrate networks are shown to amplify the herding effect and cause inefficiency in most cases. The follower networks emerging from the leadership structure show a power-law incoming degree distribution. This study shows the emergence of scale-free structures of leadership in the minority game and the effects of the interaction among players on the networked version of the game.

Journal Article↗
PubMed2005

Estimating time-dependent gene networks from time series microarray data by dynamic linear models with Markov switching.

In gene network estimation from time series microarray data, dynamic models such as differential equations and dynamic Bayesian networks assume that the network structure is stable through all time points, while the real network might changes its structure depending on time, affection of some shocks and so on. If the true network structure underlying the data changes at certain points, the fitting of the usual dynamic linear models fails to estimate the structure of gene network and we cannot obtain efficient information from data. To solve this problem, we propose a dynamic linear model with Markov switching for estimating time-dependent gene network structure from time series gene expression data. Using our proposed method, the network structure between genes and its change points are automatically estimated. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle time series data.

Algorithms↗
PubMed1992

Dynamics for communications data.

To create a complex dynamical model for a complex system, it is normally necessary to have a directed graph of the network, a dynamical model for each node, and a coupling function for each directed edge. But in many applications, the only observable data consists of communications from one node to another. In this situation, the modeler may infer a complex dynamical model for the network without any explicit knowledge of the independent dynamical behavior of the component systems (nodes). Here we present one procedure for this type of modeling problem, inspired by the attractor reconstruction procedure of chaos theory. Part of this proposal consists of a strategy for computer graphic presentation of the interactive dynamics of the complex system (or social network of dynamical schemes) called a netscope. We can imagine applications to diverse situations, such as decision groups, management, forecasting, international relations, classroom monitoring, therapy (personal, family, group, etc.) and distributed processors, to name a few.

Communication↗
PubMed2004

Structure and evolution of protein interaction networks: a statistical model for link dynamics and gene duplications.

BACKGROUND: The structure of molecular networks derives from dynamical processes on evolutionary time scales. For protein interaction networks, global statistical features of their structure can now be inferred consistently from several large-throughput datasets. Understanding the underlying evolutionary dynamics is crucial for discerning random parts of the network from biologically important properties shaped by natural selection. RESULTS: We present a detailed statistical analysis of the protein interactions in Saccharomyces cerevisiae based on several large-throughput datasets. Protein pairs resulting from gene duplications are used as tracers into the evolutionary past of the network. From this analysis, we infer rate estimates for two key evolutionary processes shaping the network: (i) gene duplications and (ii) gain and loss of interactions through mutations in existing proteins, which are referred to as link dynamics. Importantly, the link dynamics is asymmetric, i.e., the evolutionary steps are mutations in just one of the binding parters. The link turnover is shown to be much faster than gene duplications. Both processes are assembled into an empirically grounded, quantitative model for the evolution of protein interaction networks. CONCLUSIONS: According to this model, the link dynamics is the dominant evolutionary force shaping the statistical structure of the network, while the slower gene duplication dynamics mainly affects its size. Specifically, the model predicts (i) a broad distribution of the connectivities (i.e., the number of binding partners of a protein) and (ii) correlations between the connectivities of interacting proteins, a specific consequence of the asymmetry of the link dynamics. Both features have been observed in the protein interaction network of S. cerevisiae.

Biological Evolution↗
PubMed2005

Coupling the dynamics of two actin networks--new views on the mechanics of cell protrusion.

We study how mechanical forces integrate spatially and temporally with regulatory signals at the leading edge of migrating cells. To probe the dynamics of this system, we developed quantitative fluorescent speckle microscopy, which maps out actin cytoskeleton transport, assembly and disassembly with high spatial resolution. Statistical processing of single speckle properties revealed two kinetically, kinematically and molecularly distinct, yet spatially overlapping, actin arrays at the leading edge of migrating epithelial cells. The first network, referred to as the lamellipodium, polymerizes and depolymerizes 1-2 microm from the edge in an Arp2/3 (actin-related protein 2/3)- and cofilin-dependent fashion. The second network, referred to as the lamella, exhibits Arp2/3-independent polymerization. To elucidate the dynamic relationship between the two networks, we have begun to examine how assembly and flow are temporally modulated with respect to a protrusion event. In control cells we found bursts of protrusion preceding bursts of F-actin assembly. The time lag disappears in cells where Arp2/3-function is impaired. This and other results allowed us to propose a model in which tropomyosin protects lamella filaments from branching and severing, and to conjecture that Arp2/3-mediated lamellipodium assembly is a natural consequence of lamella expansion, but not the initiator of cell protrusion.

Actins↗
PubMed2006

Nonequilibrium dynamics of language games on complex networks.

The naming game is a model of nonequilibrium dynamics for the self-organized emergence of a linguistic convention or a communication system in a population of agents with pairwise local interactions. We present an extensive study of its dynamics on complex networks, that can be considered as the most natural topological embedding for agents involved in language games and opinion dynamics. Except for some community structured networks on which metastable phases can be observed, agents playing the naming game always manage to reach a global consensus. This convergence is obtained after a time generically scaling with the population's size N as t(conv) approximately N(1.4+/-0.1), i.e., much faster than for agents embedded on regular lattices. Moreover, the memory capacity required by the system scales only linearly with its size. Particular attention is given to heterogeneous networks, in which the dynamical activity pattern of a node depends on its degree. High-degree nodes have a fundamental role, but require larger memory capacity. They govern the dynamics acting as spreaders of (linguistic) conventions. The effects of other properties, such as the average degree and the clustering, are also discussed.

Journal Article↗
PubMed2006

A mutation in the S-switch region of the Runt domain alters the dynamics of an allosteric network responsible for CBFbeta regulation.

The Runt domain is the DNA binding domain of the core binding factor (CBF) Runx subunits. The CBFs are transcription factors that play critical roles in hematopoiesis, bone, and neuron development in mammals. A common non-DNA binding CBFbeta subunit heterodimerizes with the Runt domain of the Runx proteins and allosterically regulates its affinity for DNA. Previous NMR dynamics studies suggested a model whereby CBFbeta allosterically regulates DNA binding by quenching conformational exchange in the Runt domain, particularly in the S-switch region and the betaE'-F loop. We sought to test this model, and to this end introduced all possible single amino acid substitutions into the S-switch region and the betaE'-F loop, and screened for mutations that enhanced DNA-binding. We demonstrate that one Runt domain mutant, R164N, binds both DNA and CBFbeta with higher affinity, but it is less sensitive to allosteric regulation by CBFbeta. Analysis of NMR relaxation data shows that the chemical exchange exhibited by the wild-type Runt domain is largely quenched by the R164N substitution. These data support a model in which the dynamic behavior of a network of residues connecting the CBFbeta and DNA binding sites on the Runt domain plays a critical role in the mechanism of allosteric regulation. This study provides an important functional link between dynamic behavior and protein allosteric function, consistent with results on other allosterically regulated proteins.

Allosteric Regulation↗
PubMed2003

Reduction of intracellular calcium removal rate can explain changes in seizure dynamics: studies in neuronal network models.

Complex partial seizures originating from mesial temporal structures are characterized by relatively short durations of organized rhythmic activity (ORA) of 5-8 Hz, typically lasting less than 60s. Previous investigations into seizure dynamics have revealed that this ORA undergoes a monotonic decline prior to seizure evolution into intermittent bursting and subsequent seizure termination. Large neural network models of simplified single-compartment neurons were employed to address the hypothesis that changes in the free intracellular calcium ([Ca2+]i) removal rate during network bursting can result in the alterations of rhythmic seizure activity similar to that observed in recordings from humans. Both exponential and linear models of decreasing calcium removal rates resulted in changes in the predominant frequency of network bursting very similar in frequency and time course to those seen in human intracranial recordings. This supports the concept that changes in [Ca2+]i removal can explain this important network behavior, while not excluding alternative hypotheses. Identifying potential mechanisms underlying the dynamic changes seen in epileptogenic activity in large neural networks can provide important insights into seizure evolution and termination. Model neural network ensembles are attractive systems to address these questions that are difficult to investigate in biological preparations.

Calcium↗