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At least 991 records · Page 55Linked to original sources
PubMed2005

Passivity-based neural network adaptive output feedback control for nonlinear nonnegative dynamical systems.

The potential clinical applications of adaptive neural network control for pharmacology in general, and anesthesia and critical care unit medicine in particular, are clearly apparent. Specifically, monitoring and controlling the depth of anesthesia in surgery is of particular importance. Nonnegative and compartmental models provide a broad framework for biological and physiological systems, including clinical pharmacology, and are well suited for developing models for closed-loop control of drug administration. In this paper, we develop a neural adaptive output feedback control framework for adaptive set-point regulation of nonlinear uncertain nonnegative and compartmental systems. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals corresponding to the physical system states and the neural network weighting gains. The approach is applicable to nonlinear nonnegative systems with unmodeled dynamics of unknown dimension and guarantees that the physical system states remain in the nonnegatiye orthant of the state-space for nonnegative initial conditions. Finally, a numerical example involving the infusion of the anesthetic drug midazolam for maintaining a desired constant level of depth of anesthesia for noncardiac surgery is provided to demonstrate the efficacy of the proposed approach.

Adaptation, Physiological↗
PubMedDate not supplied

Neuronal mechanisms encoding global-to-fine information in inferior-temporal cortex.

Sugase et al. found that global information is represented at the initial transient firing of a single face-responsive neuron in inferior-temporal (IT) cortex, and that finer information is represented at the subsequent sustained firing. A feed-forward model and an attractor network are conceivable models to reproduce this dynamics. The attractor network, specifically an associative memory model, is employed to elucidate the neuronal mechanisms producing the dynamics. The results obtained by computer simulations show that a state of neuronal population initially approaches to a mean state of similar memory patterns, and that it finally converges to a memory pattern. This dynamics qualitatively coincides with that of face-responsive neurons. The dynamics of a single neuron in the model also coincides with that of a single face-responsive neuron. Furthermore, we propose two physiological experiments and predict the results from our model. Both predicted results are not explainable by the feed-forward model. Therefore, if the results obtained by actual physiological experiments coincide with our predicted results, the attractor network might be the neuronal mechanisms producing the dynamics of face-responsive neurons.

Face↗
PubMed1994

Dynamics of the CA3 pyramidal neuron autoassociative memory network in the hippocampus.

A theory for the dynamics of sparse associative memory has been applied to the CA3 pyramidal recurrent network in the hippocampus. The CA3 region is modelled as a network of pyramidal neurons randomly connected through their recurrent collaterals. Both the elliptical spread of the axonal systems and the exponential decrease in connectivity with distance are taken into account in estimating the connection probabilities. Pyramidal neurons also receive connections from inhibitory interneurons which occur in large numbers throughout the network; these in turn receive inputs from other inhibitory interneurons and from pyramidal neurons. These inhibitory neurons are modelled as rapidly acting linear devices which produce outputs proportional to their inputs; they perform an important regulatory function in the setting of the membrane potentials of the pyramidal neurons. The probability of a neuron firing in a stored memory, which determines the average number of neurons active when a memory is recalled, can be set at will. Memories are stored at the recurrent collateral synapses using a two-valued Hebbian. Allowance is made in the theory both for the spatial correlations between the learned strengths of the recurrent collateral synapses and temporal correlations between the state of the network and these synaptic strengths. The recall of a memory begins with the firing of a set of CA3 pyramidal neurons that overlap with the memory to be recalled as well as the firing of a set of pyramidal neurons not in the memory to be recalled; the firing of both sets of neurons is probably induced by synapses formed on CA3 neurons by perforant pathway axons. The firing of different sets of pyramidal neurons then evolves by discrete synchronous steps. The CA3 recurrent network is shown to retrieve memories under specific conditions of the setting of the membrane potential of the pyramidal neurons by inhibitory interneurons. The adjustable parameters in the theory have been assigned values in accord with the known physiology of the CA3 region. Certain levels of overlap between the input and the memory to be retrieved must also be satisfied for almost complete retrieval. The number of memories which can be stored and retrieved without degradation is primarily a function of the number of active neurons when a memory is recalled and the degree of connectivity in the network. The inhomogeneity in the connectivity of the pyramidal cells improves both capacity and overlap of the final state with the memory.(ABSTRACT TRUNCATED AT 400 WORDS)

Animals↗
PubMed2005

Swelling of polyelectrolyte networks.

Employing molecular dynamics simulations of a coarse-grained model, the equilibrium swelling behavior of a cross-linked polyelectrolyte gel has been investigated with a bead-spring defect-free network with diamond-like topology under good solvent conditions and close to the theta-point, the counterions were treated explicitly. To examine the validity range of simple scaling arguments we varied the amount of charges in the system, the length of the network chains, the strength of the electrostatic interactions for both solvent qualities over a wide range of parameters. We find that classical theories only apply in the limit of weak electrostatics and suggest corrections based on the microscopic interplay of the systems' components accessible in a simulation, which model successfully our measured data.

Journal Article↗
PubMed2017

Novel Influences of IL-10 on CNS Inflammation Revealed by Integrated Analyses of Cytokine Networks and Microglial Morphology.

Coordinated interactions between cytokine signaling and morphological dynamics of microglial cells regulate neuroinflammation in CNS injury and disease. We found that pro-inflammatory cytokine gene expression in vivo showed a pronounced recovery following systemic LPS. We performed a novel multivariate analysis of microglial morphology and identified changes in specific morphological properties of microglia that matched the expression dynamics of pro-inflammatory cytokine TNFα. The adaptive recovery kinetics of TNFα expression and microglial soma size showed comparable profiles and dependence on anti-inflammatory cytokine IL-10 expression. The recovery of cytokine variations and microglial morphology responses to inflammation were negatively regulated by IL-10. Our novel morphological analysis of microglia is able to detect subtle changes and can be used widely. We implemented in silico simulations of cytokine network dynamics which showed-counter-intuitively, but in line with our experimental observations-that negative feedback from IL-10 was sufficient to impede the adaptive recovery of TNFα-mediated inflammation. Our integrative approach is a powerful tool to study changes in specific components of microglial morphology for insights into their functional states, in relation to cytokine network dynamics, during CNS injury and disease.

CNS inflammation↗
PubMed2005

Estimating phase synchronization in dynamical systems using cellular nonlinear networks.

We propose a method for estimating phase synchronization between time series using the parallel computing architecture of cellular nonlinear networks (CNN's). Applying this method to time series of coupled nonlinear model systems and to electroencephalographic time series from epilepsy patients, we show that an accurate approximation of the mean phase coherence R--a bivariate measure for phase synchronization--can be achieved with CNN's using polynomial-type templates.

Action Potentials↗
PubMed2005

Mutation-based genetic neural network.

Evolving gradient-learning artificial neural networks (ANNs) using an evolutionary algorithm (EA) is a popular approach to address the local optima and design problems of ANN. The typical approach is to combine the strength of backpropagation (BP) in weight learning and EA's capability of searching the architecture space. However, the BP's "gradient descent" approach requires a highly computer-intensive operation that relatively restricts the search coverage of EA by compelling it to use a small population size. To address this problem, we utilized mutation-based genetic neural network (MGNN) to replace BP by using the mutation strategy of local adaptation of evolutionary programming (EP) to effect weight learning. The MGNN's mutation enables the network to dynamically evolve its structure and adapt its weights at the same time. Moreover, MGNN's EP-based encoding scheme allows for a flexible and less restricted formulation of the fitness function and makes fitness computation fast and efficient. This makes it feasible to use larger population sizes and allows MGNN to have a relatively wide search coverage of the architecture space. MGNN implements a stopping criterion where overfitness occurrences are monitored through "sliding-windows" to avoid premature learning and overlearning. Statistical analysis of its performance to some well-known classification problems demonstrate its good generalization capability. It also reveals that locally adapting or scheduling the strategy parameters embedded in each individual network may provide a proper balance between the local and global searching capabilities of MGNN.

Algorithms↗
PubMed2005

A Bayesian approach to modeling dynamic effective connectivity with fMRI data.

A state-space modeling approach for examining dynamic relationship between multiple brain regions was proposed in Ho, Ombao and Shumway (Ho, M.R., Ombao, H., Shumway, R., 2005. A State-Space Approach to Modelling Brain Dynamics to Appear in Statistica Sinica). Their approach assumed that the quantity representing the influence of one neuronal system over another, or effective connectivity, is time-invariant. However, more and more empirical evidence suggests that the connectivity between brain areas may be dynamic which calls for temporal modeling of effective connectivity. A Bayesian approach is proposed to solve this problem in this paper. Our approach first decomposes the observed time series into measurement error and the BOLD (blood oxygenation level-dependent) signals. To capture the complexities of the dynamic processes in the brain, region-specific activations are subsequently modeled, as a linear function of the BOLD signals history at other brain regions. The coefficients in these linear functions represent effective connectivity between the regions under consideration. They are further assumed to follow a random walk process so to characterize the dynamic nature of brain connectivity. We also consider the temporal dependence that may be present in the measurement errors. ML-II method (Berger, J.O., 1985. Statistical Decision Theory and Bayesian Analysis (2nd ed.). Springer, New York) was employed to estimate the hyperparameters in the model and Bayes factor was used to compare among competing models. Statistical inference of the effective connectivity coefficients was based on their posterior distributions and the corresponding Bayesian credible regions (Carlin, B.P., Louis, T.A., 2000. Bayes and Empirical Bayes Methods for Data Analysis (2nd ed.). Chapman and Hall, Boca Raton). The proposed method was applied to a functional magnetic resonance imaging data set and results support the theory of attentional control network and demonstrate that this network is dynamic in nature.

Attention↗
PubMed2005

Dynamic transitions in small world networks: approach to equilibrium limit.

We study the transition to phase synchronization in a model for the spread of infection defined in a small world network. It was shown [Phys. Rev. Lett. 86, 2909 (2001)] that the transition occurs at a finite degree of disorder p, unlike equilibrium models where systems behave as random networks even at infinitesimal p in the infinite-size limit. We examine this system under variation of a parameter determining the driving rate and show that the transition point decreases as we drive the system more slowly. Thus it appears that the transition moves to p=0 in the very slow driving limit, just as in the equilibrium case.

Animals↗
PubMed2000

Medial frontal cortex in action monitoring.

Effective behavior requires continuous action monitoring. Electrophysiological studies in both monkeys and humans have shown activity in the medial frontal cortex that reflects dynamic control and monitoring of behavioral acts. In humans, the centromedial frontal cortex shows an electrical response within 100 msec of an error, the error-related negativity (ERN). The ERN occurs only when subjects are aware of making an error, suggesting that a critical factor may be self-monitoring of the action process. In the present study, we examined late responses in a deadline reaction time task, in which the subject becomes increasingly aware of making an error as the response becomes increasingly late. We found evidence of response conflict before errors defined by late responses but not before errors defined by incorrect responses. The results also show a linear increase in the amplitude of the ERN with increasingly late responses. These data suggest that frontal networks provide dynamic representations that monitor and evaluate the unfolding action plan.

Adolescent↗
PubMed2007

The relation between spike-timing dependent plasticity and Ca2+ dynamics in the hippocampal CA1 network.

In our previous study, spike timing dependent synaptic plasticity (STDP) was investigated in the CA1 area of rat hippocampal slices using optical imaging. It was revealed that the profiles of STDP could be classified into two types depending upon layer specific location along the dendrite. The first was characterized by a symmetric time window observed in the proximal region of the stratum radiatum (SR), and the second by an asymmetric time window in the distal region of the SR. Our methods involved the bath-application of bicuculline (GABA(A) receptor antagonist) to hippocampal slices, which revealed that GABAergic interneuron projections were responsible for the symmetry of a time window. In this study, the intracellular Ca2+ increase of hippocampal CA1 neurons, induced by the protocol of timing between pre- and post-synaptic excitation (i.e. STDP protocol), was measured spatially by using optical imaging to investigate how the triggering of STDP is dependent on intracellular calcium concentration. We found that the magnitude of STDP was closely related to the rate of Ca2+ increase ("velocity") of calcium transient during application of induction stimuli. Location dependency was also analyzed in terms of Ca2+ influx. Furthermore, it was shown that decay time constant of Ca2+ dynamics during the application of STDP-inducing stimuli was also significantly correlated with STDP.

Action Potentials↗
PubMed2005

Coalescence of membrane tethers: experiments, theory, and applications.

Tethers are nanocylinders of lipid bilayer membrane, arising in situations ranging from micromanipulation experiments on synthetic vesicles to the formation of dynamic tubular networks in the Golgi apparatus. Relying on the extensive theoretical and experimental works aimed to understand the physics of individual tethers formation, we addressed the problem of the interaction between two nanotubes. By using a combination of micropipette manipulation and optical tweezers, we quantitatively studied the process of coalescence that occurred when the separation distance between both vesicle-tether junctions became smaller than a threshold length. Our experiments, which were supported by an original theoretical analysis, demonstrated that the measurements of the tether force and angle between tethers at coalescence directly yield the bending rigidity, kappa, and the membrane tension, sigma, of the vesicles. Contrary to other methods used to probe the bending rigidity of vesicles, the proposed approach permits a direct measurement of kappa without requiring any control of the membrane tension. Finally, after validation of the method and proposal of possible applications, we experimentally investigated the dynamics of the coalescence process.

Animals↗
PubMed2006

Emergent criticality from coevolution in random Boolean networks.

The coevolution of network topology and dynamics is studied in an evolutionary Boolean network model that is a simple model of a gene regulatory network. We find that a critical state emerges spontaneously resulting from the interplay between topology and dynamics during the evolution. The final evolved state is shown to be independent of initial conditions. The network appears to be driven to a random Boolean network with uniform in-degree of 2 in the large-network limit. However, for biologically realized network sizes, significant finite-size effects are observed including a broad in-degree distribution and an average in-degree connections between 2 and 3. These results may be important for explaining the properties of gene regulatory networks.

Cell Physiological Phenomena↗
PubMed1997

Root cytoskeleton: its role in perception of and response to gravity.

We have critically evaluated the possible functions of the plant cytoskeleton in root gravisensing and graviresponse and discussed the evidence that microtubules (MTs) and actin microfilaments (MFs) do not control differential cell growth during bending of roots. On the other hand, MF and MT networks are envisaged to participate in gravisensing because of the mechanical properties of the cytoskeletal structures that interconnect plant cell organelles with the plasma membrane. In restrained gravisensing, forces are suggested to be transmitted to membranes because large-scale gravity-dependent repositioning of organelles is effectively prevented due to the cytoskeleton-mediated anchorage of their envelopes at the plasma membrane. From the cytoskeletal point of view, we can also envisage an unrestrained gravity sensing when cytoskeletal tethers are not strong enough to preserve the tight control over distribution of organelles and the latter, if heavy enough, are allowed to sediment towards the physical bottom of cells. This situation obviously occurs in root cap statocytes because these uniquely organized cells are depleted of prominent actin MF bundles, endoplasmic MT arrays, and ER elements in their internal cytoplasm. Nevertheless, indirect evidence clearly indicates that sedimented root cap statoliths are enmeshed within fine but dynamic MF networks and that their behaviour is obviously under, at least partial, cytoskeletal control. The actomyosin-enriched domain among and around amyloplasts is proposed to increase the perception of gravity due to the grouping effect of sedimenting statoliths. Cytoskeletal links between myosin-rich statoliths, and cell peripheries well equipped with dense cortical MTs, membrane-associated cytoskeleton, as well as with ER elements, would allow efficient restrained gravisensing only at the statocyte cell cortex. As a consequence of cytoskeletal depletion in the internal statocyte cytoplasm and bulk sedimentation of large amyloplasts, restrained gravisensing is spatially restricted to the bottom of the statocyte irrespective of whether roots are vertical or horizontal. This spatial aspect allows for efficient gravisensing via amplification of gravity-induced impacts on the cellular architecture, a phenomenon which is unique to root cap statocytes.

Actin Cytoskeleton↗
PubMed2005

Adaptive diagnosis in distributed systems.

Real-time problem diagnosis in large distributed computer systems and networks is a challenging task that requires fast and accurate inferences from potentially huge data volumes. In this paper, we propose a cost-efficient, adaptive diagnostic technique called active probing. Probes are end-to-end test transactions that collect information about the performance of a distributed system. Active probing uses probabilistic reasoning techniques combined with information-theoretic approach, and allows a fast online inference about the current system state via active selection of only a small number of most-informative tests. We demonstrate empirically that the active probing scheme greatly reduces both the number of probes (from 60% to 75% in most of our real-life applications), and the time needed for localizing the problem when compared with nonadaptive (preplanned) probing schemes. We also provide some theoretical results on the complexity of probe selection, and the effect of "noisy" probes on the accuracy of diagnosis. Finally, we discuss how to model the system's dynamics using dynamic Bayesian networks (DBNs), and an efficient approximate approach called sequential multifault; empirical results demonstrate clear advantage of such approaches over "static" techniques that do not handle system's changes.

Algorithms↗
PubMed2006

The dynamics of injection drug users' personal networks and HIV risk behaviors.

AIMS: While studies of the social networks of injection drug users (IDUs) have provided insight into how the structures of interpersonal relationships among IDUs affect HIV risk behaviors, the majority of these studies have been cross-sectional. The present study examined the dynamics of IDUs' social networks and HIV risk behaviors over time. DESIGN: Using data from a longitudinal HIV-intervention study conducted in Baltimore, MD, this study assessed changes in the composition of the personal networks of 409 IDUs. We used a multi-nomial logistic regression analysis to assess the association between changes in network composition and simultaneous changes in levels of injection HIV risk behaviors. Using the regression parameters generated by the multi-nomial model, we estimated the predicted probability of being in each of four HIV risk behavior change groups. FINDINGS: Compared to the base case, individuals who reported an entirely new set of drug-using network contacts at follow-up were more than three times as likely to be in the increasing risk group. In contrast, reporting all new non-drug-using contacts at follow-up increased the likelihood of being in the stable low-risk group by almost 50% and decreased the probability of being in the consistently high-risk group by more than 70%. CONCLUSIONS: The findings from this study show that, over and above IDUs' baseline characteristics, changes in their personal networks are associated with changes in individuals' risky injection behaviors. They also suggest that interventions aimed at reducing HIV risk among IDUs might benefit from increasing IDUs' social contacts with individuals who are not drug users.

Adolescent↗
PubMed1996

Dynamics of functional connectivity in visual cortical networks: an overview.

The aim of the Royaumont Symposium was to review various dynamic aspects of adaptive changes in functional connectivity, expressed in cortical networks during development, learning, and possibly during recognition and cognitive processing. The link between the various experimental and theoretical models was the comparison of cellular and molecular mechanisms that could be involved in the up- and down-regulation of functional connectivity, over different time scales. These processes have been investigated using several approaches in parallel: 1) at the molecular/subcellular level, to identify postsynaptic receptors (NMDA, mGluR) and second messengers (calcium protein kinases and phosphatases) involved in the induction of synaptic potentiation and depression, and to characterize diffusible factors (NO), released pre- or postsynaptically, involved in the spatial generalization of local changes to neighboring synapses; 2) at the level of integrating networks, to develop electrophysiological (single and multiple recording), pharmacological and optical imaging techniques in order to compare the dynamics of adaptive processes put into play during the natural development of cortical specificity and connectivity, versus those triggered during forced regimes of temporal correlations between pre- and post synaptic activities. Both in vitro and in vivo approaches have been combined in the primary visual cortex of the developing and adult vertebrate (rat, guinea-pig, ferret, cat and monkey). The various forms of 'slow' synaptic plasticity, demonstrated during epigenesis and selective phases of learning in the adult, can be compared with 'fast' forms of functional coupling (or synchronous firing) shown to develop during the time span required for perception and cognitive processing Phenomenology of the dynamics in functional connectivity and their relative dependence on temporal correlation in neuronal activity have been analyzed in each of these situations. Experimental results have been compared at different levels of neuronal integration (synapse, column map and cell assembly) in order to gain a better understanding of functional grouping within cortical networks.

Adaptation, Physiological↗
PubMed2000

Segregation of nociceptive and non-nociceptive networks in the squirrel monkey somatosensory thalamus.

The somatosensory thalamus (here we examine neurons in the caudal cutaneous portion of ventral posterior lateral nucleus, VPL) is composed of a somatotopic arrangement of anteroposteriorly oriented rods. Each rod is a collection of neurons with homogeneous properties that relay sensory information to specific cortical columns. We developed a multi-electrode recording technique, using fixed-geometry four-tip electrodes that allow simultaneous recordings from small populations of neurons (4-11), in a approximately 150 x 150 x 150 micrometer(3) volume of brain tissue (i.e., the approximate diameter of rods) and study of their spatiotemporal interactions. Due to the fixed geometry of the four-tip electrodes, the relative locations of these neurons can be determined, and due to the simultaneity of the recordings, their spike-timing coordination can be calculated. With this method, we demonstrate the existence of two distinct functional networks: nociceptive and non-nociceptive networks. The population dynamics of these two types of networks are different: cross-correlations in each type of network were different in direction and strength, were a function of the distance between neurons, had an opponent organization for nociceptive networks and a non-opponent organization for non-nociceptive networks, and rapidly changed under different stimulus conditions independent of changes in firing rates. A simple neural network model mimicked these physiological findings, demonstrating the necessity of inhibitory interneurons and different amounts of afferent input synchronization. Based on these results, we conclude that the somatosensory thalamus is composed of two modules, nociceptive and non-nociceptive rods, and that the response dynamics differences between these modules are due to spatiotemporal differences of their afferent inputs.

Action Potentials↗