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PubMed2005

Self-organization of an acentrosomal microtubule network at the basal cortex of polarized epithelial cells.

Mechanisms underlying the organization of centrosome-derived microtubule arrays are well understood, but less is known about how acentrosomal microtubule networks are formed. The basal cortex of polarized epithelial cells contains a microtubule network of mixed polarity. We examined how this network is organized by imaging microtubule dynamics in acentrosomal basal cytoplasts derived from these cells. We show that the steady-state microtubule network appears to form by a combination of microtubule-microtubule and microtubule-cortex interactions, both of which increase microtubule stability. We used computational modeling to determine whether these microtubule parameters are sufficient to generate a steady-state acentrosomal microtubule network. Microtubules undergoing dynamic instability without any stabilization points continuously remodel their organization without reaching a steady-state network. However, the addition of increased microtubule stabilization at microtubule-microtubule and microtubule-cortex interactions results in the rapid assembly of a steady-state microtubule network in silico that is remarkably similar to networks formed in situ. These results define minimal parameters for the self-organization of an acentrosomal microtubule network.

Animals↗

PubMedDate not supplied

Asymmetrical dynamics of voltage spread in retinal horizontal cell networks.

Lateral voltage spread in electrically coupled retinal horizontal cell networks is the substrate of center-surround antagonism in bipolar and ganglion cells. We studied its spatial and temporal properties in more detail in turtle L1 horizontal cells by using a contrast border as light stimulus. Experimental data were contrasted with expectations from a linear continuum model to specify the impact of nonlinearities. The assumptions for the diffusion term of the continuum model were justified by neurobiotin labeling. Measured voltage spread revealed two different length constants lambda+ and lambda0, under illuminated and nonilluminated regions of the retina, respectively, as predicted by the linear model. Length constants in the illuminated region showed strong temporal dynamics. For the initial phase of the horizontal cell responses lambda+ was larger than lambda0. This was also in accordance with the model. Right at the peak of the response, however, lambda+ dropped below lambda0 and did not change any more. It is this temporal reversal of asymmetry in voltage spread and not the decrease of lambda+ itself that is lacked by the linear model. The observed independence of the mean ratio lambda+/lambda0 from light intensity in both the peak and the plateau phases of horizontal cell responses contradicts the linear assumption, too. These two effects have to be addressed to local nonlinearities in the horizontal cell network like a negative feedback loop from photoreceptors and/or voltage-dependent conductances. Due to the failure of the linear model, firm conclusions about the membrane resistance and the coupling resistance of the horizontal cell network cannot be drawn from length constant measurements.

Animals↗

PubMed2002

Relationship of nonlinear analysis, MRI and SPECT in the lateralization of temporal lobe epilepsy.

OBJECTIVES: The purpose of this study was to investigate the correlation of lateralization by nonlinear analysis, magnetic resonance imaging (MRI) and interictal single-photon emission computed tomography (SPECT) in patients with temporal lobe epilepsy. METHODS: Twenty-three patients (7 males, 16 females) were examined by MRI, interictal SPECT and EEG. Nonlinear dynamic properties of neuronal networks were estimated by calculating correlation dimensions on interictal EEG signals and corresponding surrogate data. Lateralization was detected based on the criteria introduced in this study. Concordance rates of the results among the three methods were compared. RESULTS: Epileptogenic foci were shown in the temporal areas in 21 patients using the nonlinear method (8 left, 2 right, 11 both), while 20 patients showed abnormalities in temporal lobes on MR images (13 left, 5 right, 2 both). Low cerebral blood flows of the temporal lobes were detected in all patients (11 left, 8 right, 4 both). Completely concordant lateralization was observed in 8 patients (35%) for the nonlinear method and MRI, in 9 patients (39%) for the nonlinear method and SPECT, and in 10 patients (43%) for MRI and SPECT. There were no significant differences among the concordance rates for these different methods. CONCLUSIONS: Our results revealed that correlation dimension is useful for differentiating dynamic properties of neuronal networks in the interictal state, and can provide informative data for localizing epileptogenic foci in epileptic patients. Therefore, the present nonlinear method is recommended for use with patients during presurgical evaluation.

Adolescent↗

PubMed1997

A simple neural network exhibiting selective activation of neuronal ensembles: from winner-take-all to winners-share-all.

A neuroecological equation of the Lotka-Volterra type for mean firing rate is derived from the conventional membrane dynamics of a neural network with lateral inhibition and self-inhibition. Neural selection mechanisms employed by the competitive neural network receiving external inputs are studied with analytic and numerical calculations. A remarkable findings is that the strength of lateral inhibition relative to that of self-inhibition is crucial for determining the steady states of the network among three qualitatively different types of behavior. Equal strength of both types of inhibitory connections leads the network to the well-known winner-take-all behavior. If, however, the lateral inhibition is weaker than the self-inhibition, a certain number of neurons are activated in the steady states or the number of winners is in general more than one (the winners-share-all behavior). On the other hand, if the self-inhibition is weaker than the lateral one, only one neuron is activated, but the winner is not necessarily the neuron receiving the largest input. It is suggested that our simple network model provides a mathematical basis for understanding neural selection mechanisms.

Algorithms↗

PubMed1992

A neural network model of adaptively timed reinforcement learning and hippocampal dynamics.

A neural model is described of how adaptively timed reinforcement learning occurs. The adaptive timing circuit is suggested to exist in the hippocampus, and to involve convergence of dentate granule cells on CA3 pyramidal cells, and N-methyl-D-aspartate (NMDA) receptors. This circuit forms part of a model neural system for the coordinated control of recognition learning, reinforcement learning, and motor learning, whose properties clarify how an animal can learn to acquire a delayed reward. Behavioral and neural data are summarized in support of each processing stage of the system. The relevant anatomical sites are in thalamus, neocortex, hippocampus, hypothalamus, amygdala and cerebellum. Cerebellar influences on motor learning are distinguished from hippocampal influences on adaptive timing of reinforcement learning. The model simulates how damage to the hippocampal formation disrupts adaptive timing, eliminates attentional blocking and causes symptoms of medial temporal amnesia. Properties of learned expectations, attentional focussing, memory search and orienting reactions to novel events are used to analyze the blocking and amnesia data. The model also suggests how normal acquisition of subcortical emotional conditioning can occur after cortical ablation, even though extinction of emotional conditioning is retarded by cortical ablation. The model simulates how increasing the duration of an unconditioned stimulus increases the amplitude of emotional conditioning, but does not change adaptive timing; and how an increase in the intensity of a conditioned stimulus 'speeds up the clock', but an increase in the intensity of an unconditioned stimulus does not. Computer simulations of the model fit parametric conditioning data, including a Weber law property and an inverted U property. Both primary and secondary adaptively timed conditionings are simulated, as are data concerning conditioning using multiple interstimulus intervals (ISIs), gradually or abruptly changing ISIs, partial reinforcement and multiple stimuli that lead to time-averaging of responses. Neurobiologically testable predictions are made to facilitate further tests of the model.

Animals↗

PubMed2005

Reconstructing the regulatory network controlling commitment and sporulation in Physarum polycephalum based on hierarchical Petri Net modelling and simulation.

We reconstruct the regulatory network controlling commitment and sporulation of Physarum polycephalum from experimental results using a hierarchical Petri Net-based modelling and simulation framework. The stochastic Petri Net consistently describes the structure and simulates the dynamics of the molecular network as analysed by genetic, biochemical and physiological experiments within a single coherent model. The Petri Net then is extended to simulate time-resolved somatic complementation experiments performed by mixing the cytoplasms of mutants altered in the sporulation response, to systematically explore the network structure and to probe its dynamics. This reverse engineering approach presumably can be employed to explore other molecular or genetic signalling systems where the activity of genes or their products can be experimentally controlled in a time-resolved manner.

Animals↗

PubMed1990

Dynamics of F-actin and F-actin/filamin networks as studied by photon correlation spectroscopy.

Photon correlation spectroscopy was used to study both F-actin and F-actin/filamin networks in solution. The measured autocorrelation functions were analyzed with the inverse Laplace transform CONTIN. The resulting frequency distributions consist of maximal five relatively narrow peaks. This rather unexpected finding disagrees with the frequency spectra calculated for an entangled rigid rod model. For this model, we expect spectra consisting of a single broad peak. Factors like flexibility, deviation from an experimental length distribution, interference with chemical reactions, and translation-rotation coupling that would influence the profile of the frequency distribution could be excluded by comparison of the data with model calculations or qualitative estimates. We conclude that our data for F-actin are consistent with the dynamics of an infinite network stable on the time scale of the observed modes. Further support for this interpretation is provided by a comparison with frequency spectra of actin/filamin networks. These appear to be very similar in shape. The main peak of these spectra that corresponds to the slow motions shifts to lower frequencies with increasing cross-link density. It appears that higher frequency modes of the gel are more efficiently damped with progressive cross-link density, resulting in a predominance of the slow motions in the spectra. This behavior has been already found in other systems and seems to be a general feature of cross-linked systems.

Actins↗

PubMed2003

Dynamical diseases of brain systems: different routes to epileptic seizures.

In this overview, we consider epilepsies as dynamical diseases of brain systems since they are manifestations of the property of neuronal networks to display multistable dynamics. To illustrate this concept we may assume that at least two states of the epileptic brain are possible: the interictal state characterized by a normal, apparently random, steady-state electroencephalography (EEG) ongoing activity, and the ictal state, that is characterized by paroxysmal occurrence of synchronous oscillations and is generally called, in neurology, a seizure. The transition between these two states can either occur: 1) as a continuous sequence of phases, like in some cases of mesial temporal lobe epilepsy (MTLE); or 2) as a sudden leap, like in most cases of absence seizures. In the mathematical terminology of nonlinear systems, we can say that in the first case the system's attractor gradually deforms from an interictal to an ictal attractor. The causes for such a deformation can be either endogenous or external. In this type of ictal transition, the seizure possibly may be anticipated in its early, preclinical phases. In the second case, where a sharp critical transition takes place, we can assume that the system has at least two simultaneous interictal and ictal attractors all the time. To which attractor the trajectories converge, depends on the initial conditions and the system's parameters. An essential question in this scenario is how the transition between the normal ongoing and the seizure activity takes place. Such a transition can occur either due to the influence of external or endogenous factors or due to a random perturbation and, thus, it will be unpredictable. These dynamical changes may not be detectable from the analysis of the ongoing EEG, but they may be observable only by measuring the system's response to externally administered stimuli. In the special cases of reflex epilepsy, the leap between the normal ongoing attractor and the ictal attractor is caused by a well-defined external perturbation. Examples from these different scenarios are presented and discussed.

Brain↗

PubMed1996

Analysis of nonlinear properties of immune network reactions.

On the basis of biochemical reaction dynamics, the temporal behavior of the immune network system was analyzed theoretically to promote the analysis of quantitative changes in the reactions of immune disorders and organ substitution. The idiotype immune network reaction system was expressed by 64 nonlinear differential equations that comprised four kinds of antibodies and B-cell subpopulations. All four kinds of antibodies decreased rapidly. With the progress of the reactions, they have increased gradually. The single and double bound antibodies increased rapidly from the onset of the reaction. The single-bound antibodies did not show a definite increase after the rapid increasing phase. The antibody-antibody complex increased parallel with the double bound antibodies. The effects of rate constant expand to all the immune complexes in the network system. The double bound antibodies and antibody-antibody complexes were oscillatory functions of a given antibody. Therefore, the idiotypic immune network system must be a chaotic one. The present theoretical method is available to evaluate the total ability of immune reaction system that operates as a network system.

Antibodies↗

PubMed1995

Has there been a failure to prepare and support parish-based clergy in their role as frontline community mental health workers: a review.

Addresses the issue that parish-based clergy, functioning as frontline community mental health workers, often do so with inadequate training and limited support from the mental health community. Claims that although a clergyperson is as likely to have a severely mentally distressed person seek her or his assistance as is a mental health specialist, there is inadequate research on the function of clergy in the mental health network or the psychological dynamics of religion. Suggests that clergy can serve most effectively in the mental health network as skilled facilitators, identifying the needs of persons, and connecting them to a larger circle of specialized helpers. Argues that the mental health and religious communities share many common values and goals and need to work together more effectively for the best interest of those they are called to serve.

Aged↗

PubMed2005

Augmented breath phase volume and timing relationships in the anesthetized rat.

Augmented breaths (ABs), or sighs, are airway protective reflexes and part of the normal repertoire of respiratory behaviors. ABs consist of two phases, where phase I volume and timing resembles preceding eupnic breaths, and phase II is an augmenting motor pattern and occurs at the end of phase I. Recent evidence suggest multiple respiratory motor patterns can occur following dynamic functional reconfiguration of one respiratory neural network. It follows that the response of the respiratory network to modulatory inputs also may undergo dynamic reconfiguration. We hypothesized that lung-volume related feedback during ABs would alter AB timing differentially during phase I and II. We measured phase I and II volumes and durations in urethane anesthetized rats with decreased lung volume secondary to three models of varying phrenic motor impairment (spinal injury alone, unilateral phrenicotomy, and combined injuries). AB phase I and II inspired volume were decreased after phrenic motor impairment (p<0.05). In contrast, only phase I duration following injury was altered compared to controls. Phase II duration remaining unchanged despite the greatest effect of injury on volume occurring during phase II. Thus, sigh volume-timing relationships differ between phases of an augmented breath suggesting that the response of the respiratory network to modulatory inputs has changed. These data support the hypothesis that multiple respiratory behaviors occur following dynamic reconfiguration of the respiratory neural network.

Anesthesia, General↗

PubMed2004

Learning to generate combinatorial action sequences utilizing the initial sensitivity of deterministic dynamical systems.

This study shows how sensory-action sequences of imitating finite state machines (FSMs) can be learned by utilizing the deterministic dynamics of recurrent neural networks (RNNs). Our experiments indicated that each possible combinatorial sequence can be recalled by specifying its respective initial state value and also that fractal structures appear in this initial state mapping after the learning converges. We also observed that the sequences of mimicking FSMs are encoded utilizing the transient regions rather than the invariant sets of the evolved dynamical systems of the RNNs.

Computer Simulation↗

PubMed2001

Exploring complex networks.

The study of networks pervades all of science, from neurobiology to statistical physics. The most basic issues are structural: how does one characterize the wiring diagram of a food web or the Internet or the metabolic network of the bacterium Escherichia coli? Are there any unifying principles underlying their topology? From the perspective of nonlinear dynamics, we would also like to understand how an enormous network of interacting dynamical systems-be they neurons, power stations or lasers-will behave collectively, given their individual dynamics and coupling architecture. Researchers are only now beginning to unravel the structure and dynamics of complex networks.

Models, Theoretical↗

PubMed2005

Complete condensation in a zero range process on scale-free networks.

We study a zero range process on scale-free networks in order to investigate how network structure influences particle dynamics. The zero range process is defined with the rate p(n) = n(delta) at which particles hop out of nodes with n particles. We show analytically that a complete condensation occurs when delta < or = delta(c) triple bond 1/(gamma-1) where gamma is the degree distribution exponent of the underlying networks. In the complete condensation, those nodes whose degree is higher than a threshold are occupied by macroscopic numbers of particles, while the other nodes are occupied by negligible numbers of particles. We also show numerically that the relaxation time follows a power-law scaling tau approximately L(z) with the network size L and a dynamic exponent z in the condensed phase.

Journal Article↗

PubMed2004

Stable adaptive neurocontrol for nonlinear discrete-time systems.

This paper presents a novel approach in designing neural network based adaptive controllers for a class of nonlinear discrete-time systems. This type of controllers has its simplicity in parallelism to linear generalized minimum variance (GMV) controller design and efficiency to deal with complex nonlinear dynamics. A recurrent neural network is introduced as a bridge to compensation simplify controller design procedure and efficiently to deal with nonlinearity. The network weight adaptation law is derived from Lyapunov stability analysis and the connection between convergence of the network weight and the reconstruction error of the network is established. A theorem is presented for the conditions of the stability of the closed-loop systems. Two simulation examples are provided to demonstrate the efficiency of the approach.

Neural Networks, Computer↗

PubMed1996

Prediction of arterial "burst" activity and transitions between chaotic attractors with a multilayer perceptron optimized by a new stopping criterion.

We have explored the potential of an artificial neural network to capture the dynamics of chaotic temporal fluctuations in arterial pressure and flow. Model generated signals that simulate this ubiquitous physiological phenomenon in both form and complexity were used to train a Multilayer Perceptron (MLP) after first locating the optimum time delay to unfold the attractor governing the dynamics. Prediction horizons were maximized with a new stopping criterion capable of continuously tracking the trajectories of the model system. Single-step predictions were consistently good throughout the study. Long-term predictions obtained by using the MLP as a signal generator were very successful when the number of hidden nodes was carefully chosen. Moreover, short- and long-term predictions could also be obtained even when the dynamics was nonstationary.

Algorithms↗

PubMed1999

Modeling and simulation of the dynamic behavior of monoliths. Effects of pore structure from pore network model analysis and comparison with columns packed with porous spherical particles.

A mathematical model is presented that could be used to describe the dynamic behavior, scale-up, and design of monoliths involving the adsorption of a solute of interest. The value of the pore diffusivity of the solute in the pores of the skeletons of the monolith is determined in an a priori manner by employing the pore network modeling theory of Meyers and Liapis [J. Chromatogr. A, 827 (1998) 197 and 852 (1999) 3]. The results clearly show that the pore diffusion coefficient, Dmp, of the solute depends on both the pore size distribution and the pore connectivity, nT, of the pores in the skeletons. It is shown that, for a given type of monolith, the film mass transfer coefficient, Kf, of the solute in the monolith could be determined from experiments based on Eq. (3) which was derived by Liapis [Math. Modelling Sci. Comput., 1 (1993) 397] from the fundamental physics. The mathematical model presented in this work is numerically solved in order to study the dynamic behavior of the adsorption of bovine serum albumin (BSA) in a monolith having skeletons of radius r(o) = 0.75x10(-6) m and through-pores having diameters of 1.5x10(-6)-1.8x10(-6) m [H. Minakuchi et al., J. Chromatogr. A, 762 (1997) 135]. The breakthrough curves of the BSA obtained from the monolith were steeper than those from columns packed with porous spherical particles whose radii ranged from 2.50x10(-6) m to 15.00x10(-6) m. Furthermore, and most importantly, the dynamic adsorptive capacity of the monolith was always greater than that of the packed beds for all values of the superficial fluid velocity, Vtp. The results of this work indicate that since in monoliths the size of through-pores could be controlled independently from the size of the skeletons, then if one could construct monolith structures having (a) relatively large through-pores with high through-pore connectivity that can provide high flow-rates at low pressure drops and (b) small-sized skeletons with mesopores having an appropriate pore size distribution (mesopores having diameters that are relatively large when compared with the diameter of the diffusing solute) and high pore connectivity, nT, the following positive results, which are necessary for obtaining efficient separations, could be realized: (i) the value of the pore diffusion coefficient, Dmp, of the solute would be large, (ii) the diffusion path length in the skeletons would be short, (iii) the diffusion velocity, vD, would be high, and (iv) the diffusional response time, t(drt), would be small. Monoliths with such pore structures could provide more efficient separations with respect to (a) dynamic adsorptive capacity and (b) required pressure drop for a given flow-rate, than columns packed with porous particles.

Adsorption↗

PubMed2002

Negative autoregulation speeds the response times of transcription networks.

Cells regulate gene expression using networks of transcription interactions; it is of interest to discover the principles that govern the dynamical behavior of such networks. An important characteristic of these systems is the rise-time: the delay from the initiation of production until half maximal product concentration is reached. Here we employ synthetic gene circuits in Escherichia coli to measure the rise-times of non-self-regulated and of negatively autoregulated transcription units. Non-self-regulated units have a rise-time of one cell-cycle. We demonstrate experimentally that negative autoregulation feedback (also termed autogenous control) reduces the rise-time to about one fifth of a cell-cycle. This agrees with an analytical solution of a mathematical model for negative autoregulation. This may help in understanding the function of negative autoregulation, which appears in over 40% of known transcription factors in E.coli.

Escherichia coli↗