Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting

PubMed Central

Ghazali, Rozaida; Herawan, Tutut

2016-01-01

Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network. PMID:27959927

Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting.

PubMed

Waheeb, Waddah; Ghazali, Rozaida; Herawan, Tutut

2016-01-01

Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.

Feedback Regulation and Its Efficiency in Biochemical Networks

NASA Astrophysics Data System (ADS)

Kobayashi, Tetsuya J.; Yokota, Ryo; Aihara, Kazuyuki

2016-03-01

Intracellular biochemical networks fluctuate dynamically due to various internal and external sources of fluctuation. Dissecting the fluctuation into biologically relevant components is important for understanding how a cell controls and harnesses noise and how information is transferred over apparently noisy intracellular networks. While substantial theoretical and experimental advancement on the decomposition of fluctuation was achieved for feedforward networks without any loop, we still lack a theoretical basis that can consistently extend such advancement to feedback networks. The main obstacle that hampers is the circulative propagation of fluctuation by feedback loops. In order to define the relevant quantity for the impact of feedback loops for fluctuation, disentanglement of the causally interlocked influences between the components is required. In addition, we also lack an approach that enables us to infer non-perturbatively the influence of the feedback to fluctuation in the same way as the dual reporter system does in the feedforward networks. In this work, we address these problems by extending the work on the fluctuation decomposition and the dual reporter system. For a single-loop feedback network with two components, we define feedback loop gain as the feedback efficiency that is consistent with the fluctuation decomposition for feedforward networks. Then, we clarify the relation of the feedback efficiency with the fluctuation propagation in an open-looped FF network. Finally, by extending the dual reporter system, we propose a conjugate feedback and feedforward system for estimating the feedback efficiency non-perturbatively only from the statistics of the system.

Realizing actual feedback control of complex network

NASA Astrophysics Data System (ADS)

Tu, Chengyi; Cheng, Yuhua

2014-06-01

In this paper, we present the concept of feedbackability and how to identify the Minimum Feedbackability Set of an arbitrary complex directed network. Furthermore, we design an estimator and a feedback controller accessing one MFS to realize actual feedback control, i.e. control the system to our desired state according to the estimated system internal state from the output of estimator. Last but not least, we perform numerical simulations of a small linear time-invariant dynamics network and a real simple food network to verify the theoretical results. The framework presented here could make an arbitrary complex directed network realize actual feedback control and deepen our understanding of complex systems.

State feedback controller design for the synchronization of Boolean networks with time delays

NASA Astrophysics Data System (ADS)

Li, Fangfei; Li, Jianning; Shen, Lijuan

2018-01-01

State feedback control design to make the response Boolean network synchronize with the drive Boolean network is far from being solved in the literature. Motivated by this, this paper studies the feedback control design for the complete synchronization of two coupled Boolean networks with time delays. A necessary condition for the existence of a state feedback controller is derived first. Then the feedback control design procedure for the complete synchronization of two coupled Boolean networks is provided based on the necessary condition. Finally, an example is given to illustrate the proposed design procedure.

Stabilization of model-based networked control systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miranda, Francisco; Instituto Politécnico de Viana do Castelo, Viana do Castelo; Abreu, Carlos

2016-06-08

A class of networked control systems called Model-Based Networked Control Systems (MB-NCSs) is considered. Stabilization of MB-NCSs is studied using feedback controls and simulation of stabilization for different feedbacks is made with the purpose to reduce the network trafic. The feedback control input is applied in a compensated model of the plant that approximates the plant dynamics and stabilizes the plant even under slow network conditions. Conditions for global exponential stabilizability and for the choosing of a feedback control input for a given constant time between the information moments of the network are derived. An optimal control problem to obtainmore » an optimal feedback control is also presented.« less

A feedback model of figure-ground assignment.

PubMed

Domijan, Drazen; Setić, Mia

2008-05-30

A computational model is proposed in order to explain how bottom-up and top-down signals are combined into a unified perception of figure and background. The model is based on the interaction between the ventral and the dorsal stream. The dorsal stream computes saliency based on boundary signals provided by the simple and the complex cortical cells. Output from the dorsal stream is projected to the surface network which serves as a blackboard on which the surface representation is formed. The surface network is a recurrent network which segregates different surfaces by assigning different firing rates to them. The figure is labeled by the maximal firing rate. Computer simulations showed that the model correctly assigns figural status to the surface with a smaller size, a greater contrast, convexity, surroundedness, horizontal-vertical orientation and a higher spatial frequency content. The simple gradient of activity in the dorsal stream enables the simulation of the new principles of the lower region and the top-bottom polarity. The model also explains how the exogenous attention and the endogenous attention may reverse the figural assignment. Due to the local excitation in the surface network, neural activity at the cued region will spread over the whole surface representation. Therefore, the model implements the object-based attentional selection.

The Stratonovich formulation of quantum feedback network rules

NASA Astrophysics Data System (ADS)

Gough, John E.

2016-12-01

We express the rules for forming quantum feedback networks using the Stratonovich form of quantum stochastic calculus rather than the Itō or SLH (J. E. Gough and M. R. James, "Quantum feedback networks: Hamiltonian formulation," Commun. Math. Phys. 287, 1109 (2009), J. E. Gough and M. R. James, "The Series product and its application to quantum feedforward and feedback networks," IEEE Trans. Autom. Control 54, 2530 (2009)) form. Remarkably the feedback reduction rule implies that we obtain the Schur complement of the matrix of Stratonovich coupling operators where we short out the internal input/output coefficients.

Decorrelation of Neural-Network Activity by Inhibitory Feedback

PubMed Central

Einevoll, Gaute T.; Diesmann, Markus

2012-01-01

Correlations in spike-train ensembles can seriously impair the encoding of information by their spatio-temporal structure. An inevitable source of correlation in finite neural networks is common presynaptic input to pairs of neurons. Recent studies demonstrate that spike correlations in recurrent neural networks are considerably smaller than expected based on the amount of shared presynaptic input. Here, we explain this observation by means of a linear network model and simulations of networks of leaky integrate-and-fire neurons. We show that inhibitory feedback efficiently suppresses pairwise correlations and, hence, population-rate fluctuations, thereby assigning inhibitory neurons the new role of active decorrelation. We quantify this decorrelation by comparing the responses of the intact recurrent network (feedback system) and systems where the statistics of the feedback channel is perturbed (feedforward system). Manipulations of the feedback statistics can lead to a significant increase in the power and coherence of the population response. In particular, neglecting correlations within the ensemble of feedback channels or between the external stimulus and the feedback amplifies population-rate fluctuations by orders of magnitude. The fluctuation suppression in homogeneous inhibitory networks is explained by a negative feedback loop in the one-dimensional dynamics of the compound activity. Similarly, a change of coordinates exposes an effective negative feedback loop in the compound dynamics of stable excitatory-inhibitory networks. The suppression of input correlations in finite networks is explained by the population averaged correlations in the linear network model: In purely inhibitory networks, shared-input correlations are canceled by negative spike-train correlations. In excitatory-inhibitory networks, spike-train correlations are typically positive. Here, the suppression of input correlations is not a result of the mere existence of correlations between excitatory (E) and inhibitory (I) neurons, but a consequence of a particular structure of correlations among the three possible pairings (EE, EI, II). PMID:23133368

Leverage Between the Buffering Effect and the Bystander Effect in Social Networking.

PubMed

Chiu, Yu-Ping; Chang, Shu-Chen

2015-08-01

This study examined encouraged and inhibited social feedback behaviors based on the theories of the buffering effect and the bystander effect. A system program was used to collect personal data and social feedback from a Facebook data set to test the research model. The results revealed that the buffering effect induced a positive relationship between social network size and feedback gained from friends when people's social network size was under a certain cognitive constraint. For people with a social network size that exceeds this cognitive constraint, the bystander effect may occur, in which having more friends may inhibit social feedback. In this study, two social psychological theories were applied to explain social feedback behavior on Facebook, and it was determined that social network size and social feedback exhibited no consistent linear relationship.

CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Information Feedback Strategies in a Signal Controlled Network with Overlapped Routes

NASA Astrophysics Data System (ADS)

Tian, Li-Jun; Huang, Hai-Jun; Liu, Tian-Liang

2009-07-01

We investigate the effects of four different information feedback strategies on the dynamics of traffic, travelers' route choice and the resultant system performance in a signal controlled network with overlapped routes. Simulation results given by the cellular automaton model show that the system purpose-based mean velocity feedback strategy and the congestion coefficient feedback strategy have more advantages in improving network utilization efficiency and reducing travelers' travel times. The travel time feedback strategy and the individual purposed-based mean velocity feedback strategy behave slightly better to ensure user equity.

Synthetic Biology Platform for Sensing and Integrating Endogenous Transcriptional Inputs in Mammalian Cells.

PubMed

Angelici, Bartolomeo; Mailand, Erik; Haefliger, Benjamin; Benenson, Yaakov

2016-08-30

One of the goals of synthetic biology is to develop programmable artificial gene networks that can transduce multiple endogenous molecular cues to precisely control cell behavior. Realizing this vision requires interfacing natural molecular inputs with synthetic components that generate functional molecular outputs. Interfacing synthetic circuits with endogenous mammalian transcription factors has been particularly difficult. Here, we describe a systematic approach that enables integration and transduction of multiple mammalian transcription factor inputs by a synthetic network. The approach is facilitated by a proportional amplifier sensor based on synergistic positive autoregulation. The circuits efficiently transduce endogenous transcription factor levels into RNAi, transcriptional transactivation, and site-specific recombination. They also enable AND logic between pairs of arbitrary transcription factors. The results establish a framework for developing synthetic gene networks that interface with cellular processes through transcriptional regulators. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Interference Alignment With Partial CSI Feedback in MIMO Cellular Networks

NASA Astrophysics Data System (ADS)

Rao, Xiongbin; Lau, Vincent K. N.

2014-04-01

Interference alignment (IA) is a linear precoding strategy that can achieve optimal capacity scaling at high SNR in interference networks. However, most existing IA designs require full channel state information (CSI) at the transmitters, which would lead to significant CSI signaling overhead. There are two techniques, namely CSI quantization and CSI feedback filtering, to reduce the CSI feedback overhead. In this paper, we consider IA processing with CSI feedback filtering in MIMO cellular networks. We introduce a novel metric, namely the feedback dimension, to quantify the first order CSI feedback cost associated with the CSI feedback filtering. The CSI feedback filtering poses several important challenges in IA processing. First, there is a hidden partial CSI knowledge constraint in IA precoder design which cannot be handled using conventional IA design methodology. Furthermore, existing results on the feasibility conditions of IA cannot be applied due to the partial CSI knowledge. Finally, it is very challenging to find out how much CSI feedback is actually needed to support IA processing. We shall address the above challenges and propose a new IA feasibility condition under partial CSIT knowledge in MIMO cellular networks. Based on this, we consider the CSI feedback profile design subject to the degrees of freedom requirements, and we derive closed-form trade-off results between the CSI feedback cost and IA performance in MIMO cellular networks.

Relaxation rates of gene expression kinetics reveal the feedback signs of autoregulatory gene networks

NASA Astrophysics Data System (ADS)

Jia, Chen; Qian, Hong; Chen, Min; Zhang, Michael Q.

2018-03-01

The transient response to a stimulus and subsequent recovery to a steady state are the fundamental characteristics of a living organism. Here we study the relaxation kinetics of autoregulatory gene networks based on the chemical master equation model of single-cell stochastic gene expression with nonlinear feedback regulation. We report a novel relation between the rate of relaxation, characterized by the spectral gap of the Markov model, and the feedback sign of the underlying gene circuit. When a network has no feedback, the relaxation rate is exactly the decaying rate of the protein. We further show that positive feedback always slows down the relaxation kinetics while negative feedback always speeds it up. Numerical simulations demonstrate that this relation provides a possible method to infer the feedback topology of autoregulatory gene networks by using time-series data of gene expression.

Effect of inhibitory feedback on correlated firing of spiking neural network.

PubMed

Xie, Jinli; Wang, Zhijie

2013-08-01

Understanding the properties and mechanisms that generate different forms of correlation is critical for determining their role in cortical processing. Researches on retina, visual cortex, sensory cortex, and computational model have suggested that fast correlation with high temporal precision appears consistent with common input, and correlation on a slow time scale likely involves feedback. Based on feedback spiking neural network model, we investigate the role of inhibitory feedback in shaping correlations on a time scale of 100 ms. Notably, the relationship between the correlation coefficient and inhibitory feedback strength is non-monotonic. Further, computational simulations show how firing rate and oscillatory activity form the basis of the mechanisms underlying this relationship. When the mean firing rate holds unvaried, the correlation coefficient increases monotonically with inhibitory feedback, but the correlation coefficient keeps decreasing when the network has no oscillatory activity. Our findings reveal that two opposing effects of the inhibitory feedback on the firing activity of the network contribute to the non-monotonic relationship between the correlation coefficient and the strength of the inhibitory feedback. The inhibitory feedback affects the correlated firing activity by modulating the intensity and regularity of the spike trains. Finally, the non-monotonic relationship is replicated with varying transmission delay and different spatial network structure, demonstrating the universality of the results.

Students' Informal Peer Feedback Networks

ERIC Educational Resources Information Center

Headington, Rita

2018-01-01

The nature and significance of students' informal peer feedback networks is an under-explored area. This paper offers the findings of a longitudinal investigation of the informal peer feedback networks of a cohort of student teachers [n = 105] across the three years of a UK primary education degree programme. It tracked the dynamic nature of these…

Delivering Faster Congestion Feedback with the Mark-Front Strategy

NASA Technical Reports Server (NTRS)

Liu, Chunlei; Jain, Raj

2001-01-01

Computer networks use congestion feedback from the routers and destinations to control the transmission load. Delivering timely congestion feedback is essential to the performance of networks. Reaction to the congestion can be more effective if faster feedback is provided. Current TCP/IP networks use timeout, duplicate Acknowledgement Packets (ACKs) and explicit congestion notification (ECN) to deliver the congestion feedback, each provides a faster feedback than the previous method. In this paper, we propose a markfront strategy that delivers an even faster congestion feedback. With analytical and simulation results, we show that mark-front strategy reduces buffer size requirement, improves link efficiency and provides better fairness among users. Keywords: Explicit Congestion Notification, mark-front, congestion control, buffer size requirement, fairness.

Robust permanence for ecological equations with internal and external feedbacks.

PubMed

Patel, Swati; Schreiber, Sebastian J

2018-07-01

Species experience both internal feedbacks with endogenous factors such as trait evolution and external feedbacks with exogenous factors such as weather. These feedbacks can play an important role in determining whether populations persist or communities of species coexist. To provide a general mathematical framework for studying these effects, we develop a theorem for coexistence for ecological models accounting for internal and external feedbacks. Specifically, we use average Lyapunov functions and Morse decompositions to develop sufficient and necessary conditions for robust permanence, a form of coexistence robust to large perturbations of the population densities and small structural perturbations of the models. We illustrate how our results can be applied to verify permanence in non-autonomous models, structured population models, including those with frequency-dependent feedbacks, and models of eco-evolutionary dynamics. In these applications, we discuss how our results relate to previous results for models with particular types of feedbacks.

A study on haptic collaborative game in shared virtual environment

NASA Astrophysics Data System (ADS)

Lu, Keke; Liu, Guanyang; Liu, Lingzhi

2013-03-01

A study on collaborative game in shared virtual environment with haptic feedback over computer networks is introduced in this paper. A collaborative task was used where the players located at remote sites and played the game together. The player can feel visual and haptic feedback in virtual environment compared to traditional networked multiplayer games. The experiment was desired in two conditions: visual feedback only and visual-haptic feedback. The goal of the experiment is to assess the impact of force feedback on collaborative task performance. Results indicate that haptic feedback is beneficial for performance enhancement for collaborative game in shared virtual environment. The outcomes of this research can have a powerful impact on the networked computer games.

How time delay and network design shape response patterns in biochemical negative feedback systems.

PubMed

Börsch, Anastasiya; Schaber, Jörg

2016-08-24

Negative feedback in combination with time delay can bring about both sustained oscillations and adaptive behaviour in cellular networks. Here, we study which design features of systems with delayed negative feedback shape characteristic response patterns with special emphasis on the role of time delay. To this end, we analyse generic two-dimensional delay differential equations describing the dynamics of biochemical signal-response networks. We investigate the influence of several design features on the stability of the model equilibrium, i.e., presence of auto-inhibition and/or mass conservation and the kind and/or strength of the delayed negative feedback. We show that auto-inhibition and mass conservation have a stabilizing effect, whereas increasing abruptness and decreasing feedback threshold have a de-stabilizing effect on the model equilibrium. Moreover, applying our theoretical analysis to the mammalian p53 system we show that an auto-inhibitory feedback can decouple period and amplitude of an oscillatory response, whereas the delayed feedback can not. Our theoretical framework provides insight into how time delay and design features of biochemical networks act together to elicit specific characteristic response patterns. Such insight is useful for constructing synthetic networks and controlling their behaviour in response to external stimulation.

Method for neural network control of motion using real-time environmental feedback

NASA Technical Reports Server (NTRS)

Buckley, Theresa M. (Inventor)

1997-01-01

A method of motion control for robotics and other automatically controlled machinery using a neural network controller with real-time environmental feedback. The method is illustrated with a two-finger robotic hand having proximity sensors and force sensors that provide environmental feedback signals. The neural network controller is taught to control the robotic hand through training sets using back- propagation methods. The training sets are created by recording the control signals and the feedback signal as the robotic hand or a simulation of the robotic hand is moved through a representative grasping motion. The data recorded is divided into discrete increments of time and the feedback data is shifted out of phase with the control signal data so that the feedback signal data lag one time increment behind the control signal data. The modified data is presented to the neural network controller as a training set. The time lag introduced into the data allows the neural network controller to account for the temporal component of the robotic motion. Thus trained, the neural network controlled robotic hand is able to grasp a wide variety of different objects by generalizing from the training sets.

Peer Feedback on Facebook: The Use of Social Networking Websites to Develop Writing Ability of Undergraduate Students

ERIC Educational Resources Information Center

Wichadee, Saovapa

2013-01-01

The current study explores how integrating a social networking website called Facebook with peer feedback in groups supports student learning, investigates the nature of feedback students received on their writing, and examines their attitudes towards the use of Facebook for peer feedback. The study involves 30 undergraduate students who…

Highly sensitive vacuum ion pump current measurement system

DOEpatents

Hansknecht, John Christopher [Williamsburg, VA

2006-02-21

A vacuum system comprising: 1) an ion pump; 2) power supply; 3) a high voltage DC--DC converter drawing power from the power supply and powering the vacuum pump; 4) a feedback network comprising an ammeter circuit including an operational amplifier and a series of relay controlled scaling resistors of different resistance for detecting circuit feedback; 5) an optional power block section intermediate the power supply and the high voltage DC--DC converter; and 6) a microprocessor receiving feedback information from the feedback network, controlling which of the scaling resistors should be in the circuit and manipulating data from the feedback network to provide accurate vacuum measurement to an operator.

Endogenous-cue prospective memory involving incremental updating of working memory: an fMRI study.

PubMed

Halahalli, Harsha N; John, John P; Lukose, Ammu; Jain, Sanjeev; Kutty, Bindu M

2015-11-01

Prospective memory paradigms are conventionally classified on the basis of event-, time-, or activity-based intention retrieval. In the vast majority of such paradigms, intention retrieval is provoked by some kind of external event. However, prospective memory retrieval cues that prompt intention retrieval in everyday life are commonly endogenous, i.e., linked to a specific imagined retrieval context. We describe herein a novel prospective memory paradigm wherein the endogenous cue is generated by incremental updating of working memory, and investigated the hemodynamic correlates of this task. Eighteen healthy adult volunteers underwent functional magnetic resonance imaging while they performed a prospective memory task where the delayed intention was triggered by an endogenous cue generated by incremental updating of working memory. Working memory and ongoing task control conditions were also administered. The 'endogenous-cue prospective memory condition' with incremental working memory updating was associated with maximum activations in the right rostral prefrontal cortex, and additional activations in the brain regions that constitute the bilateral fronto-parietal network, central and dorsal salience networks as well as cerebellum. In the working memory control condition, maximal activations were noted in the left dorsal anterior insula. Activation of the bilateral dorsal anterior insula, a component of the central salience network, was found to be unique to this 'endogenous-cue prospective memory task' in comparison to previously reported exogenous- and endogenous-cue prospective memory tasks without incremental working memory updating. Thus, the findings of the present study highlight the important role played by the dorsal anterior insula in incremental working memory updating that is integral to our endogenous-cue prospective memory task.

Push-Pull and Feedback Mechanisms Can Align Signaling System Outputs with Inputs.

PubMed

Andrews, Steven S; Peria, William J; Yu, Richard C; Colman-Lerner, Alejandro; Brent, Roger

2016-11-23

Many cell signaling systems, including the yeast pheromone response system, exhibit "dose-response alignment" (DoRA), in which output of one or more downstream steps closely matches the fraction of occupied receptors. DoRA can improve the fidelity of transmitted dose information. Here, we searched systematically for biochemical network topologies that produced DoRA. Most networks, including many containing feedback and feedforward loops, could not produce DoRA. However, networks including "push-pull" mechanisms, in which the active form of a signaling species stimulates downstream activity and the nominally inactive form reduces downstream activity, enabled perfect DoRA. Networks containing feedbacks enabled DoRA, but only if they also compared feedback to input and adjusted output to match. Our results establish push-pull as a non-feedback mechanism to align output with variable input and maximize information transfer in signaling systems. They also suggest genetic approaches to determine whether particular signaling systems use feedback or push-pull control. Copyright © 2016 Elsevier Inc. All rights reserved.

High frequency inductive lamp and power oscillator

DOEpatents

Kirkpatrick, Douglas A.; Gitsevich, Aleksandr

2005-09-27

An oscillator includes an amplifier having an input and an output, a feedback network connected between the input of the amplifier and the output of the amplifier, the feedback network being configured to provide suitable positive feedback from the output of the amplifier to the input of the amplifier to initiate and sustain an oscillating condition, and a tuning circuit connected to the input of the amplifier, wherein the tuning circuit is continuously variable and consists of solid state electrical components with no mechanically adjustable devices including a pair of diodes connected to each other at their respective cathodes with a control voltage connected at the junction of the diodes. Another oscillator includes an amplifier having an input and an output, a feedback network connected between the input of the amplifier and the output of the amplifier, the feedback network being configured to provide suitable positive feedback from the output of the amplifier to the input of the amplifier to initiate and sustain an oscillating condition, and transmission lines connected to the input of the amplifier with an input pad and a perpendicular transmission line extending from the input pad and forming a leg of a resonant "T", and wherein the feedback network is coupled to the leg of the resonant "T".

Multi-voxel Patterns Reveal Functionally Differentiated Networks Underlying Auditory Feedback Processing of Speech

PubMed Central

Zheng, Zane Z.; Vicente-Grabovetsky, Alejandro; MacDonald, Ewen N.; Munhall, Kevin G.; Cusack, Rhodri; Johnsrude, Ingrid S.

2013-01-01

The everyday act of speaking involves the complex processes of speech motor control. An important component of control is monitoring, detection and processing of errors when auditory feedback does not correspond to the intended motor gesture. Here we show, using fMRI and converging operations within a multi-voxel pattern analysis framework, that this sensorimotor process is supported by functionally differentiated brain networks. During scanning, a real-time speech-tracking system was employed to deliver two acoustically different types of distorted auditory feedback or unaltered feedback while human participants were vocalizing monosyllabic words, and to present the same auditory stimuli while participants were passively listening. Whole-brain analysis of neural-pattern similarity revealed three functional networks that were differentially sensitive to distorted auditory feedback during vocalization, compared to during passive listening. One network of regions appears to encode an ‘error signal’ irrespective of acoustic features of the error: this network, including right angular gyrus, right supplementary motor area, and bilateral cerebellum, yielded consistent neural patterns across acoustically different, distorted feedback types, only during articulation (not during passive listening). In contrast, a fronto-temporal network appears sensitive to the speech features of auditory stimuli during passive listening; this preference for speech features was diminished when the same stimuli were presented as auditory concomitants of vocalization. A third network, showing a distinct functional pattern from the other two, appears to capture aspects of both neural response profiles. Taken together, our findings suggest that auditory feedback processing during speech motor control may rely on multiple, interactive, functionally differentiated neural systems. PMID:23467350

Exogenous vs. endogenous attention: Shifting the balance of fronto-parietal activity.

PubMed

Meyer, Kristin N; Du, Feng; Parks, Emily; Hopfinger, Joseph B

2018-03-01

Despite behavioral and electrophysiological evidence for dissociations between endogenous (voluntary) and exogenous (reflexive) attention, fMRI results have yet to consistently and clearly differentiate neural activation patterns between these two types of attention. This study specifically aimed to determine whether activity in the dorsal fronto-parietal network differed between endogenous and exogenous conditions. Participants performed a visual discrimination task in endogenous and exogenous attention conditions while undergoing fMRI scanning. Analyses revealed robust and bilateral activation throughout the dorsal fronto-parietal network for each condition, in line with many previous results. In order to investigate possible differences in the balance of neural activity within this network with greater sensitivity, a priori regions of interest (ROIs) were selected for analysis, centered on the frontal eye fields (FEF) and intraparietal sulcus (IPS) regions identified in previous studies. The results revealed a significant interaction between region, condition, and hemisphere. Specifically, in the left hemisphere, frontal areas were more active than parietal areas, but only during endogenous attention. Activity in the right hemisphere, in contrast, remained relatively consistent for these regions across conditions. Analysis of this activity over time indicates that this left-hemispheric regional imbalance is present within the FEF early, at 3-6.5 s post-stimulus presentation, whereas a regional imbalance in the exogenous condition is not evident until 6.5-8 s post-stimulus presentation. Overall, our results provide new evidence that although the dorsal fronto-parietal network is indeed associated with both types of attentional orienting, regions of the network are differentially engaged over time and across hemispheres depending on the type of attention. Copyright © 2018 Elsevier Ltd. All rights reserved.

The role of endogenous and exogenous mechanisms in the formation of R&D networks

NASA Astrophysics Data System (ADS)

Tomasello, Mario V.; Perra, Nicola; Tessone, Claudio J.; Karsai, Márton; Schweitzer, Frank

2014-07-01

We develop an agent-based model of strategic link formation in Research and Development (R&D) networks. Empirical evidence has shown that the growth of these networks is driven by mechanisms which are both endogenous to the system (that is, depending on existing alliances patterns) and exogenous (that is, driven by an exploratory search for newcomer firms). Extant research to date has not investigated both mechanisms simultaneously in a comparative manner. To overcome this limitation, we develop a general modeling framework to shed light on the relative importance of these two mechanisms. We test our model against a comprehensive dataset, listing cross-country and cross-sectoral R&D alliances from 1984 to 2009. Our results show that by fitting only three macroscopic properties of the network topology, this framework is able to reproduce a number of micro-level measures, including the distributions of degree, local clustering, path length and component size, and the emergence of network clusters. Furthermore, by estimating the link probabilities towards newcomers and established firms from the data, we find that endogenous mechanisms are predominant over the exogenous ones in the network formation, thus quantifying the importance of existing structures in selecting partner firms.

Increasing Resilience to Traumatic Stress: Understanding the Protective Role of Well-Being.

PubMed

Tory Toole, J; Rice, Mark A; Cargill, Jordan; Craddock, Travis J A; Nierenberg, Barry; Klimas, Nancy G; Fletcher, Mary Ann; Morris, Mariana; Zysman, Joel; Broderick, Gordon

2018-01-01

The brain maintains homeostasis in part through a network of feedback and feed-forward mechanisms, where neurochemicals and immune markers act as mediators. Using a previously constructed model of biobehavioral feedback, we found that in addition to healthy equilibrium another stable regulatory program supported chronic depression and anxiety. Exploring mechanisms that might underlie the contributions of subjective well-being to improved therapeutic outcomes in depression, we iteratively screened 288 candidate feedback patterns linking well-being to molecular signaling networks for those that maintained the original homeostatic regimes. Simulating stressful trigger events on each candidate network while maintaining high levels of subjective well-being isolated a specific feedback network where well-being was promoted by dopamine and acetylcholine, and itself promoted norepinephrine while inhibiting cortisol expression. This biobehavioral feedback mechanism was especially effective in reproducing well-being's clinically documented ability to promote resilience and protect against onset of depression and anxiety.

Systemic risk on different interbank network topologies

NASA Astrophysics Data System (ADS)

Lenzu, Simone; Tedeschi, Gabriele

2012-09-01

In this paper we develop an interbank market with heterogeneous financial institutions that enter into lending agreements on different network structures. Credit relationships (links) evolve endogenously via a fitness mechanism based on agents' performance. By changing the agent's trust on its neighbor's performance, interbank linkages self-organize themselves into very different network architectures, ranging from random to scale-free topologies. We study which network architecture can make the financial system more resilient to random attacks and how systemic risk spreads over the network. To perturb the system, we generate a random attack via a liquidity shock. The hit bank is not automatically eliminated, but its failure is endogenously driven by its incapacity to raise liquidity in the interbank network. Our analysis shows that a random financial network can be more resilient than a scale free one in case of agents' heterogeneity.

Effect of Heterogeneity on Decorrelation Mechanisms in Spiking Neural Networks: A Neuromorphic-Hardware Study

NASA Astrophysics Data System (ADS)

Pfeil, Thomas; Jordan, Jakob; Tetzlaff, Tom; Grübl, Andreas; Schemmel, Johannes; Diesmann, Markus; Meier, Karlheinz

2016-04-01

High-level brain function, such as memory, classification, or reasoning, can be realized by means of recurrent networks of simplified model neurons. Analog neuromorphic hardware constitutes a fast and energy-efficient substrate for the implementation of such neural computing architectures in technical applications and neuroscientific research. The functional performance of neural networks is often critically dependent on the level of correlations in the neural activity. In finite networks, correlations are typically inevitable due to shared presynaptic input. Recent theoretical studies have shown that inhibitory feedback, abundant in biological neural networks, can actively suppress these shared-input correlations and thereby enable neurons to fire nearly independently. For networks of spiking neurons, the decorrelating effect of inhibitory feedback has so far been explicitly demonstrated only for homogeneous networks of neurons with linear subthreshold dynamics. Theory, however, suggests that the effect is a general phenomenon, present in any system with sufficient inhibitory feedback, irrespective of the details of the network structure or the neuronal and synaptic properties. Here, we investigate the effect of network heterogeneity on correlations in sparse, random networks of inhibitory neurons with nonlinear, conductance-based synapses. Emulations of these networks on the analog neuromorphic-hardware system Spikey allow us to test the efficiency of decorrelation by inhibitory feedback in the presence of hardware-specific heterogeneities. The configurability of the hardware substrate enables us to modulate the extent of heterogeneity in a systematic manner. We selectively study the effects of shared input and recurrent connections on correlations in membrane potentials and spike trains. Our results confirm that shared-input correlations are actively suppressed by inhibitory feedback also in highly heterogeneous networks exhibiting broad, heavy-tailed firing-rate distributions. In line with former studies, cell heterogeneities reduce shared-input correlations. Overall, however, correlations in the recurrent system can increase with the level of heterogeneity as a consequence of diminished effective negative feedback.

fMRI characterisation of widespread brain networks relevant for behavioural variability in fine hand motor control with and without visual feedback.

PubMed

Mayhew, Stephen D; Porcaro, Camillo; Tecchio, Franca; Bagshaw, Andrew P

2017-03-01

A bilateral visuo-parietal-motor network is responsible for fine control of hand movements. However, the sub-regions which are devoted to maintenance of contraction stability and how these processes fluctuate with trial-quality of task execution and in the presence/absence of visual feedback remains unclear. We addressed this by integrating behavioural and fMRI measurements during right-hand isometric compression of a compliant rubber bulb, at 10% and 30% of maximum voluntary contraction, both with and without visual feedback of the applied force. We quantified single-trial behavioural performance during 1) the whole task period and 2) stable contraction maintenance, and regressed these metrics against the fMRI data to identify the brain activity most relevant to trial-by-trial fluctuations in performance during specific task phases. fMRI-behaviour correlations in a bilateral network of visual, premotor, primary motor, parietal and inferior frontal cortical regions emerged during performance of the entire feedback task, but only in premotor, parietal cortex and thalamus during the stable contraction period. The trials with the best task performance showed increased bilaterality and amplitude of fMRI responses. With feedback, stronger BOLD-behaviour coupling was found during 10% compared to 30% contractions. Only a small subset of regions in this network were weakly correlated with behaviour without feedback, despite wider network activated during this task than in the presence of feedback. These findings reflect a more focused network strongly coupled to behavioural fluctuations when providing visual feedback, whereas without it the task recruited widespread brain activity almost uncoupled from behavioural performance. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Modulation of dynamic modes by interplay between positive and negative feedback loops in gene regulatory networks

NASA Astrophysics Data System (ADS)

Wang, Liu-Suo; Li, Ning-Xi; Chen, Jing-Jia; Zhang, Xiao-Peng; Liu, Feng; Wang, Wei

2018-04-01

A positive and a negative feedback loop can induce bistability and oscillation, respectively, in biological networks. Nevertheless, they are frequently interlinked to perform more elaborate functions in many gene regulatory networks. Coupled positive and negative feedback loops may exhibit either oscillation or bistability depending on the intensity of the stimulus in some particular networks. It is less understood how the transition between the two dynamic modes is modulated by the positive and negative feedback loops. We developed an abstract model of such systems, largely based on the core p53 pathway, to explore the mechanism for the transformation of dynamic behaviors. Our results show that enhancing the positive feedback may promote or suppress oscillations depending on the strength of both feedback loops. We found that the system oscillates with low amplitudes in response to a moderate stimulus and switches to the on state upon a strong stimulus. When the positive feedback is activated much later than the negative one in response to a strong stimulus, the system exhibits long-term oscillations before switching to the on state. We explain this intriguing phenomenon using quasistatic approximation. Moreover, early switching to the on state may occur when the system starts from a steady state in the absence of stimuli. The interplay between the positive and negative feedback plays a key role in the transitions between oscillation and bistability. Of note, our conclusions should be applicable only to some specific gene regulatory networks, especially the p53 network, in which both oscillation and bistability exist in response to a certain type of stimulus. Our work also underscores the significance of transient dynamics in determining cellular outcome.

Closed-loop control of a fragile network: application to seizure-like dynamics of an epilepsy model

PubMed Central

Ehrens, Daniel; Sritharan, Duluxan; Sarma, Sridevi V.

2015-01-01

It has recently been proposed that the epileptic cortex is fragile in the sense that seizures manifest through small perturbations in the synaptic connections that render the entire cortical network unstable. Closed-loop therapy could therefore entail detecting when the network goes unstable, and then stimulating with an exogenous current to stabilize the network. In this study, a non-linear stochastic model of a neuronal network was used to simulate both seizure and non-seizure activity. In particular, synaptic weights between neurons were chosen such that the network's fixed point is stable during non-seizure periods, and a subset of these connections (the most fragile) were perturbed to make the same fixed point unstable to model seizure events; and, the model randomly transitions between these two modes. The goal of this study was to measure spike train observations from this epileptic network and then apply a feedback controller that (i) detects when the network goes unstable, and then (ii) applies a state-feedback gain control input to the network to stabilize it. The stability detector is based on a 2-state (stable, unstable) hidden Markov model (HMM) of the network, and detects the transition from the stable mode to the unstable mode from using the firing rate of the most fragile node in the network (which is the output of the HMM). When the unstable mode is detected, a state-feedback gain is applied to generate a control input to the fragile node bringing the network back to the stable mode. Finally, when the network is detected as stable again, the feedback control input is switched off. High performance was achieved for the stability detector, and feedback control suppressed seizures within 2 s after onset. PMID:25784851

Hierarchical Feedback Modules and Reaction Hubs in Cell Signaling Networks

PubMed Central

Xu, Jianfeng; Lan, Yueheng

2015-01-01

Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks. PMID:25951347

Feedback Theory through the Lens of Social Networking

ERIC Educational Resources Information Center

Kio, Su Iong

2015-01-01

Feedback has long been utilised as an effective tool to enhance learning. Between students and teachers; students and students; students and schools, feedback provides a channel of communication between all parties within an education system. With the emergence and development of social networking sites (SNSs) over the past decade, it has become…

Cancer Theory from Systems Biology Point of View

NASA Astrophysics Data System (ADS)

Wang, Gaowei; Tang, Ying; Yuan, Ruoshi; Ao, Ping

In our previous work, we have proposed a novel cancer theory, endogenous network theory, to understand mechanism underlying cancer genesis and development. Recently, we apply this theory to hepatocellular carcinoma (HCC). A core endogenous network of hepatocyte was established by integrating the current understanding of hepatocyte at molecular level. Quantitative description of the endogenous network consisted of a set of stochastic differential equations which could generate many local attractors with obvious or non-obvious biological functions. By comparing with clinical observation and experimental data, the results showed that two robust attractors from the model reproduced the main known features of normal hepatocyte and cancerous hepatocyte respectively at both modular and molecular level. In light of our theory, the genesis and progression of cancer is viewed as transition from normal attractor to HCC attractor. A set of new insights on understanding cancer genesis and progression, and on strategies for cancer prevention, cure, and care were provided.

Effect of endogenous angiotensin II on the frequency response of the renal vasculature.

PubMed

Dibona, Gerald F; Sawin, Linda L

2004-12-01

The renal vasculature functions as an efficient low-pass filter of the multiple frequencies contained within renal sympathetic nerve activity. This study examined the effect of angiotensin II on the frequency response of the renal vasculature. Physiological changes in the activity of the endogenous renin-angiotensin system were produced by alterations in dietary sodium intake. The frequency response of the renal vasculature was evaluated using pseudorandom binary sequence renal nerve stimulation, and the role of angiotensin II was evaluated by the administration of the angiotensin II AT(1)-receptor antagonist losartan. In low-sodium-diet rats with increased renin-angiotensin system activity, losartan steepened the renal vascular frequency response (i.e., greater attenuation); this was not seen in normal- or high-sodium-diet rats with normal or decreased renin-angiotensin system activity. Analysis of the transfer function from arterial pressure to renal blood flow, i.e., dynamic autoregulation, showed that the tubuloglomerular feedback but not the myogenic component was enhanced in low- and normal- but not in high-sodium-diet rats and that this was reversed by losartan administration. Thus physiological increases in endogenous renin-angiotensin activity inhibit the renal vascular frequency response to renal nerve stimulation while selectively enhancing the tubuloglomerular feedback component of dynamic autoregulation of renal blood flow.

Synchronization of Lienard-Type Oscillators in Uniform Electrical Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sinha, Mohit; Dorfler, Florian; Johnson, Brian B.

2016-08-01

This paper presents a condition for global asymptotic synchronization of Lienard-type nonlinear oscillators in uniform LTI electrical networks with series R-L circuits modeling interconnections. By uniform electrical networks, we mean that the per-unit-length impedances are identical for the interconnecting lines. We derive conditions for global asymptotic synchronization for a particular feedback architecture where the derivative of the oscillator output current supplements the innate current feedback induced by simply interconnecting the oscillator to the network. Our proof leverages a coordinate transformation to a set of differential coordinates that emphasizes signal differences and the particular form of feedback permits the formulation ofmore » a quadratic Lyapunov function for this class of networks. This approach is particularly interesting since synchronization conditions are difficult to obtain by means of quadratic Lyapunov functions when only current feedback is used and for networks composed of series R-L circuits. Our synchronization condition depends on the algebraic connectivity of the underlying network, and reiterates the conventional wisdom from Lyapunov- and passivity-based arguments that strong coupling is required to ensure synchronization.« less

A low noise single-transistor transimpedance preamplifier for Fourier-transform mass spectrometry using a T feedback network

PubMed Central

Lin, Tzu-Yung; Green, Roger J.; O’Connor, Peter B.

2012-01-01

A novel single-transistor transimpedance preamplifier has been introduced for improving performance in Fourier-transform ion cyclotron resonance (FT-ICR) mass spectrometry. A low noise junction field-effect transistor (JFET), BF862, is used as the main amplification stage of this trans-impedance preamplifier, and a T-shaped feedback network is introduced as both the feedback and the gate biasing solutions. The T feedback network has been studied using an operational amplifier (Op Amp), AD8099. Such a feedback system allows ∼100-fold less feedback resistance at a given transimpedance, hence preserving bandwidth, which is beneficial to applications demanding high gain. The single-transistor preamplifier yields a tested transimpedance of ∼104 Ω (80 dBΩ) in the frequency range between 1 kHz and 1 MHz (mass-to-charge ratio, m/z, of around 180-180k for a 12-T FT-ICR system), with a low power consumption of ∼6 mW, which implies that this preamplifier is well suited to a 12-T FT-ICR mass spectrometer. In trading noise performance for higher trans-impedance, an alternative preamplifier design, an AD8099 preamplifier with the T feedback network, has also been studied with a capability of ∼106 Ω (120 dBΩ) transimpedance in the same frequency range. The resistive components in the T feedback network reported here can be replaced by complex impedances, which allows adaptation of this feedback system to other frequency, transimpedance, and noise characteristics for applications not only in other mass spectrometers, such as Orbitrap, time-of-flight (TOF), and ion trap systems, but also in other charge/current detecting systems such as spectroscopy systems, microscopy systems, optical communication systems, or charge-coupled devices (CCDs). PMID:23020394

A low noise single-transistor transimpedance preamplifier for Fourier-transform mass spectrometry using a T feedback network.

PubMed

Lin, Tzu-Yung; Green, Roger J; O'Connor, Peter B

2012-09-01

A novel single-transistor transimpedance preamplifier has been introduced for improving performance in Fourier-transform ion cyclotron resonance (FT-ICR) mass spectrometry. A low noise junction field-effect transistor (JFET), BF862, is used as the main amplification stage of this trans-impedance preamplifier, and a T-shaped feedback network is introduced as both the feedback and the gate biasing solutions. The T feedback network has been studied using an operational amplifier (Op Amp), AD8099. Such a feedback system allows ~100-fold less feedback resistance at a given transimpedance, hence preserving bandwidth, which is beneficial to applications demanding high gain. The single-transistor preamplifier yields a tested transimpedance of ~10(4) Ω (80 dBΩ) in the frequency range between 1 kHz and 1 MHz (mass-to-charge ratio, m/z, of around 180-180k for a 12-T FT-ICR system), with a low power consumption of ~6 mW, which implies that this preamplifier is well suited to a 12-T FT-ICR mass spectrometer. In trading noise performance for higher trans-impedance, an alternative preamplifier design, an AD8099 preamplifier with the T feedback network, has also been studied with a capability of ~10(6) Ω (120 dBΩ) transimpedance in the same frequency range. The resistive components in the T feedback network reported here can be replaced by complex impedances, which allows adaptation of this feedback system to other frequency, transimpedance, and noise characteristics for applications not only in other mass spectrometers, such as Orbitrap, time-of-flight (TOF), and ion trap systems, but also in other charge/current detecting systems such as spectroscopy systems, microscopy systems, optical communication systems, or charge-coupled devices (CCDs).

Shaping Social Activity by Incentivizing Users

PubMed Central

Farajtabar, Mehrdad; Du, Nan; Rodriguez, Manuel Gomez; Valera, Isabel; Zha, Hongyuan; Song, Le

2015-01-01

Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network. How much external drive should be provided to each user, such that the network activity can be steered towards a target state? In this paper, we model social events using multivariate Hawkes processes, which can capture both endogenous and exogenous event intensities, and derive a time dependent linear relation between the intensity of exogenous events and the overall network activity. Exploiting this connection, we develop a convex optimization framework for determining the required level of external drive in order for the network to reach a desired activity level. We experimented with event data gathered from Twitter, and show that our method can steer the activity of the network more accurately than alternatives. PMID:26005312

Adaptive limited feedback for interference alignment in MIMO interference channels.

PubMed

Zhang, Yang; Zhao, Chenglin; Meng, Juan; Li, Shibao; Li, Li

2016-01-01

It is very important that the radar sensor network has autonomous capabilities such as self-managing, etc. Quite often, MIMO interference channels are applied to radar sensor networks, and for self-managing purpose, interference management in MIMO interference channels is critical. Interference alignment (IA) has the potential to dramatically improve system throughput by effectively mitigating interference in multi-user networks at high signal-to-noise (SNR). However, the implementation of IA predominantly relays on perfect and global channel state information (CSI) at all transceivers. A large amount of CSI has to be fed back to all transmitters, resulting in a proliferation of feedback bits. Thus, IA with limited feedback has been introduced to reduce the sum feedback overhead. In this paper, by exploiting the advantage of heterogeneous path loss, we first investigate the throughput of IA with limited feedback in interference channels while each user transmits multi-streams simultaneously, then we get the upper bound of sum rate in terms of the transmit power and feedback bits. Moreover, we propose a dynamic feedback scheme via bit allocation to reduce the throughput loss due to limited feedback. Simulation results demonstrate that the dynamic feedback scheme achieves better performance in terms of sum rate.

Understanding Informal Feedback Seeking in the Workplace: The Impact of the Position in the Organizational Hierarchy

ERIC Educational Resources Information Center

van der Rijt, Janine; Van den Bossche, Piet; Segers, Mien S. R.

2013-01-01

Purpose: The purpose of this study is to investigate whether the position of employees in the organizational hierarchy is important in explaining their feedback seeking behaviour. Design/methodology/approach: This study takes a social network perspective by using an ego-centric network survey to investigate employees' feedback seeking behaviour…

Externalizing psychopathology and gain-loss feedback in a simulated gambling task: dissociable components of brain response revealed by time-frequency analysis.

PubMed

Bernat, Edward M; Nelson, Lindsay D; Steele, Vaughn R; Gehring, William J; Patrick, Christopher J

2011-05-01

Externalizing is a broad construct that reflects propensity toward a variety of impulse control problems, including antisocial personality disorder and substance use disorders. Two event-related potential responses known to be reduced among individuals high in externalizing proneness are the P300, which reflects postperceptual processing of a stimulus, and the error-related negativity (ERN), which indexes performance monitoring based on endogenous representations. In the current study, the authors used a simulated gambling task to examine the relation between externalizing proneness and the feedback-related negativity (FRN), a brain response that indexes performance monitoring related to exogenous cues, which is thought to be highly related to the ERN. Time-frequency (TF) analysis was used to disentangle the FRN from the accompanying P300 response to feedback cues by parsing the overall feedback-locked potential into distinctive theta (4-7 Hz) and delta (<3 Hz) TF components. Whereas delta-P300 amplitude was reduced among individuals high in externalizing proneness, theta-FRN response was unrelated to externalizing. These findings suggest that in contrast with previously reported deficits in endogenously based performance monitoring (as indexed by the ERN), individuals prone to externalizing problems show intact monitoring of exogenous cues (as indexed by the FRN). The results also contribute to a growing body of evidence indicating that the P300 is attenuated across a broad range of task conditions in high-externalizing individuals.

Functional Connectivity with Distinct Neural Networks Tracks Fluctuations in Gain/Loss Framing Susceptibility

PubMed Central

Smith, David V.; Sip, Kamila E.; Delgado, Mauricio R.

2016-01-01

Multiple large-scale neural networks orchestrate a wide range of cognitive processes. For example, interoceptive processes related to self-referential thinking have been linked to the default-mode network (DMN); whereas exteroceptive processes related to cognitive control have been linked to the executive-control network (ECN). Although the DMN and ECN have been postulated to exert opposing effects on cognition, it remains unclear how connectivity with these spatially overlapping networks contribute to fluctuations in behavior. While previous work has suggested the medial prefrontal cortex (MPFC) is involved in behavioral change following feedback, these observations could be linked to interoceptive processes tied to DMN or exteroceptive processes tied to ECN because MPFC is positioned in both networks. To address this problem, we employed independent component analysis combined with dual-regression functional connectivity analysis. Participants made a series of financial decisions framed as monetary gains or losses. In some sessions, participants received feedback from a peer observing their choices; in other sessions, feedback was not provided. Following feedback, framing susceptibility—indexed as the increase in gambling behavior in loss frames compared to gain frames—was heightened in some participants and diminished in others. We examined whether these individual differences were linked to differences in connectivity by contrasting sessions containing feedback against those that did not contain feedback. We found two key results. As framing susceptibility increased, the MPFC increased connectivity with DMN; in contrast, temporal-parietal junction decreased connectivity with the ECN. Our results highlight how functional connectivity patterns with distinct neural networks contribute to idiosyncratic behavioral changes. PMID:25858445

Functional connectivity with distinct neural networks tracks fluctuations in gain/loss framing susceptibility.

PubMed

Smith, David V; Sip, Kamila E; Delgado, Mauricio R

2015-07-01

Multiple large-scale neural networks orchestrate a wide range of cognitive processes. For example, interoceptive processes related to self-referential thinking have been linked to the default-mode network (DMN); whereas exteroceptive processes related to cognitive control have been linked to the executive-control network (ECN). Although the DMN and ECN have been postulated to exert opposing effects on cognition, it remains unclear how connectivity with these spatially overlapping networks contribute to fluctuations in behavior. While previous work has suggested the medial-prefrontal cortex (MPFC) is involved in behavioral change following feedback, these observations could be linked to interoceptive processes tied to DMN or exteroceptive processes tied to ECN because MPFC is positioned in both networks. To address this problem, we employed independent component analysis combined with dual-regression functional connectivity analysis. Participants made a series of financial decisions framed as monetary gains or losses. In some sessions, participants received feedback from a peer observing their choices; in other sessions, feedback was not provided. Following feedback, framing susceptibility-indexed as the increase in gambling behavior in loss frames compared to gain frames-was heightened in some participants and diminished in others. We examined whether these individual differences were linked to differences in connectivity by contrasting sessions containing feedback against those that did not contain feedback. We found two key results. As framing susceptibility increased, the MPFC increased connectivity with DMN; in contrast, temporal-parietal junction decreased connectivity with the ECN. Our results highlight how functional connectivity patterns with distinct neural networks contribute to idiosyncratic behavioral changes. © 2015 Wiley Periodicals, Inc.

A comparative assessment of endogenous water institutional change

NASA Astrophysics Data System (ADS)

Pande, Saket; Ersten, Maurits

2013-04-01

This paper builds the theory of endogenous institutional change, first proposed by Greif and Laitin (2004), for water scarce regions in context of water institutions. The current emphasis on environmental change, including hydrological change, largely ignores the adaptation of human societies to change. Humans have mostly been considered as boundary conditions or parameters of the dynamics of hydrological change and are not considered as conduits of feedbacks. Nonetheless, the dynamical representation of hydrological change with feedbacks between various components of a system is assuring since it is reminiscent of processual ecological anthropology(Orlove, 1980), except that individual decision making is absent. This paper proposes to consider selected dryland basins of the world, to conceptualize proxies of water relevant socio-economic organisation, such as spatial scales of upstream-downstream cooperation in water use, synthesized over time and then proposes a comparative assessment to test regularities predicted by an extension of river game theory (Ambec and Ehlers, 2008; van der Brink et al, 2012) to endogenous institutional change. References: Orlove, B. S. (1980). Ecological Anthropology. Annual Review of Anthropology, Vol. 9 (1980), pp. 235-273. Greif. A. and D. D. Laitin (2004). A Theory of Endogenous Institutional Change. American Political Science Review, Vol. 98, No. 4 November 2004. Ambec, S. and L. Ehlers (2008). Sharing a river amongst satiable agents. Games and Economic Behavior, 64, 35-50. Van der Brink, G. van der Laan and N. Moes (2012). Fair agreements for sharing international rivers with multiple springs and externalities. Journal of Environmental Economics and Management, 63, 388-403.

Social Laughter Triggers Endogenous Opioid Release in Humans.

PubMed

Manninen, Sandra; Tuominen, Lauri; Dunbar, Robin I; Karjalainen, Tomi; Hirvonen, Jussi; Arponen, Eveliina; Hari, Riitta; Jääskeläinen, Iiro P; Sams, Mikko; Nummenmaa, Lauri

2017-06-21

The size of human social networks significantly exceeds the network that can be maintained by social grooming or touching in other primates. It has been proposed that endogenous opioid release after social laughter would provide a neurochemical pathway supporting long-term relationships in humans (Dunbar, 2012), yet this hypothesis currently lacks direct neurophysiological support. We used PET and the μ-opioid-receptor (MOR)-specific ligand [ 11 C]carfentanil to quantify laughter-induced endogenous opioid release in 12 healthy males. Before the social laughter scan, the subjects watched laughter-inducing comedy clips with their close friends for 30 min. Before the baseline scan, subjects spent 30 min alone in the testing room. Social laughter increased pleasurable sensations and triggered endogenous opioid release in thalamus, caudate nucleus, and anterior insula. In addition, baseline MOR availability in the cingulate and orbitofrontal cortices was associated with the rate of social laughter. In a behavioral control experiment, pain threshold-a proxy of endogenous opioidergic activation-was elevated significantly more in both male and female volunteers after watching laughter-inducing comedy versus non-laughter-inducing drama in groups. Modulation of the opioidergic activity by social laughter may be an important neurochemical pathway that supports the formation, reinforcement, and maintenance of human social bonds. SIGNIFICANCE STATEMENT Social contacts are vital to humans. The size of human social networks significantly exceeds the network that can be maintained by social grooming in other primates. Here, we used PET to show that endogenous opioid release after social laughter may provide a neurochemical mechanism supporting long-term relationships in humans. Participants were scanned twice: after a 30 min social laughter session and after spending 30 min alone in the testing room (baseline). Endogenous opioid release was stronger after laughter versus the baseline scan. Opioid receptor density in the frontal cortex predicted social laughter rates. Modulation of the opioidergic activity by social laughter may be an important neurochemical mechanism reinforcing and maintaining social bonds between humans. Copyright © 2017 the authors 0270-6474/17/376125-07$15.00/0.

Speech-induced striatal dopamine release is left lateralized and coupled to functional striatal circuits in healthy humans: A combined PET, fMRI and DTI study

PubMed Central

Simonyan, Kristina; Herscovitch, Peter; Horwitz, Barry

2013-01-01

Considerable progress has been recently made in understanding the brain mechanisms underlying speech and language control. However, the neurochemical underpinnings of normal speech production remain largely unknown. We investigated the extent of striatal endogenous dopamine release and its influences on the organization of functional striatal speech networks during production of meaningful English sentences using a combination of positron emission tomography (PET) with the dopamine D2/D3 receptor radioligand [11C]raclopride and functional MRI (fMRI). In addition, we used diffusion tensor tractography (DTI) to examine the extent of dopaminergic modulatory influences on striatal structural network organization. We found that, during sentence production, endogenous dopamine was released in the ventromedial portion of the dorsal striatum, in its both associative and sensorimotor functional divisions. In the associative striatum, speech-induced dopamine release established a significant relationship with neural activity and influenced the left-hemispheric lateralization of striatal functional networks. In contrast, there were no significant effects of endogenous dopamine release on the lateralization of striatal structural networks. Our data provide the first evidence for endogenous dopamine release in the dorsal striatum during normal speaking and point to the possible mechanisms behind the modulatory influences of dopamine on the organization of functional brain circuits controlling normal human speech. PMID:23277111

Friend networking sites and their relationship to adolescents' well-being and social self-esteem.

PubMed

Valkenburg, Patti M; Peter, Jochen; Schouten, Alexander P

2006-10-01

The aim of this study was to investigate the consequences of friend networking sites (e.g., Friendster, MySpace) for adolescents' self-esteem and well-being. We conducted a survey among 881 adolescents (10-19-year-olds) who had an online profile on a Dutch friend networking site. Using structural equation modeling, we found that the frequency with which adolescents used the site had an indirect effect on their social self-esteem and well-being. The use of the friend networking site stimulated the number of relationships formed on the site, the frequency with which adolescents received feedback on their profiles, and the tone (i.e., positive vs. negative) of this feedback. Positive feedback on the profiles enhanced adolescents' social self-esteem and well-being, whereas negative feedback decreased their self-esteem and well-being.

Bioelectronic neural pixel: Chemical stimulation and electrical sensing at the same site

PubMed Central

Jonsson, Amanda; Inal, Sahika; Uguz, Ilke; Williamson, Adam J.; Kergoat, Loïg; Rivnay, Jonathan; Khodagholy, Dion; Berggren, Magnus; Bernard, Christophe; Malliaras, George G.

2016-01-01

Local control of neuronal activity is central to many therapeutic strategies aiming to treat neurological disorders. Arguably, the best solution would make use of endogenous highly localized and specialized regulatory mechanisms of neuronal activity, and an ideal therapeutic technology should sense activity and deliver endogenous molecules at the same site for the most efficient feedback regulation. Here, we address this challenge with an organic electronic multifunctional device that is capable of chemical stimulation and electrical sensing at the same site, at the single-cell scale. Conducting polymer electrodes recorded epileptiform discharges induced in mouse hippocampal preparation. The inhibitory neurotransmitter, γ-aminobutyric acid (GABA), was then actively delivered through the recording electrodes via organic electronic ion pump technology. GABA delivery stopped epileptiform activity, recorded simultaneously and colocally. This multifunctional “neural pixel” creates a range of opportunities, including implantable therapeutic devices with automated feedback, where locally recorded signals regulate local release of specific therapeutic agents. PMID:27506784

Feedback topology and XOR-dynamics in Boolean networks with varying input structure

NASA Astrophysics Data System (ADS)

Ciandrini, L.; Maffi, C.; Motta, A.; Bassetti, B.; Cosentino Lagomarsino, M.

2009-08-01

We analyze a model of fixed in-degree random Boolean networks in which the fraction of input-receiving nodes is controlled by the parameter γ . We investigate analytically and numerically the dynamics of graphs under a parallel XOR updating scheme. This scheme is interesting because it is accessible analytically and its phenomenology is at the same time under control and as rich as the one of general Boolean networks. We give analytical formulas for the dynamics on general graphs, showing that with a XOR-type evolution rule, dynamic features are direct consequences of the topological feedback structure, in analogy with the role of relevant components in Kauffman networks. Considering graphs with fixed in-degree, we characterize analytically and numerically the feedback regions using graph decimation algorithms (Leaf Removal). With varying γ , this graph ensemble shows a phase transition that separates a treelike graph region from one in which feedback components emerge. Networks near the transition point have feedback components made of disjoint loops, in which each node has exactly one incoming and one outgoing link. Using this fact, we provide analytical estimates of the maximum period starting from topological considerations.

Feedback topology and XOR-dynamics in Boolean networks with varying input structure.

PubMed

Ciandrini, L; Maffi, C; Motta, A; Bassetti, B; Cosentino Lagomarsino, M

2009-08-01

We analyze a model of fixed in-degree random Boolean networks in which the fraction of input-receiving nodes is controlled by the parameter gamma. We investigate analytically and numerically the dynamics of graphs under a parallel XOR updating scheme. This scheme is interesting because it is accessible analytically and its phenomenology is at the same time under control and as rich as the one of general Boolean networks. We give analytical formulas for the dynamics on general graphs, showing that with a XOR-type evolution rule, dynamic features are direct consequences of the topological feedback structure, in analogy with the role of relevant components in Kauffman networks. Considering graphs with fixed in-degree, we characterize analytically and numerically the feedback regions using graph decimation algorithms (Leaf Removal). With varying gamma , this graph ensemble shows a phase transition that separates a treelike graph region from one in which feedback components emerge. Networks near the transition point have feedback components made of disjoint loops, in which each node has exactly one incoming and one outgoing link. Using this fact, we provide analytical estimates of the maximum period starting from topological considerations.

A Social Learning Management System Supporting Feedback for Incorrect Answers Based on Social Network Services

ERIC Educational Resources Information Center

Son, Jiseong; Kim, Jeong-Dong; Na, Hong-Seok; Baik, Doo-Kwon

2016-01-01

In this research, we propose a Social Learning Management System (SLMS) enabling real-time and reliable feedback for incorrect answers by learners using a social network service (SNS). The proposed system increases the accuracy of learners' assessment results by using a confidence scale and a variety of social feedback that is created and shared…

Count Your Calories and Share Them: Health Benefits of Sharing mHealth Information on Social Networking Sites.

PubMed

Oeldorf-Hirsch, Anne; High, Andrew C; Christensen, John L

2018-04-23

This study investigates the relationship between sharing tracked mobile health (mHealth) information online, supportive communication, feedback, and health behavior. Based on the Integrated Theory of mHealth, our model asserts that sharing tracked health information on social networking sites benefits users' perceptions of their health because of the supportive communication they gain from members of their online social networks and that the amount of feedback people receive moderates these associations. Users of mHealth apps (N = 511) completed an online survey, and results revealed that both sharing tracked health information and receiving feedback from an online social network were positively associated with supportive communication. Network support both corresponded with improved health behavior and mediated the association between sharing health information and users' health behavior. As users received greater amounts of feedback from their online social networks, however, the association between sharing tracked health information and health behavior decreased. Theoretical implications for sharing tracked health information and practical implications for using mHealth apps are discussed.

Superconducting Microwave Multivibrator Produced by Coherent Feedback

NASA Astrophysics Data System (ADS)

Kerckhoff, Joseph; Lehnert, K. W.

2012-10-01

We investigate a nonlinear coherent feedback circuit constructed from preexisting superconducting microwave devices. The network exhibits emergent bistable and astable states, and we demonstrate its operation as a latch and the frequency locking of its oscillations. While the network is tedious to model by hand, our observations agree quite well with the semiclassical dynamical model produced by a new software package (N. Tezak , arXiv:1111.3081v1 [Phil. Trans. R. Soc. A (to be published)]) that systematically interpreted an idealized schematic of the system as a quantum optic feedback network.

Evolving autonomous learning in cognitive networks.

PubMed

Sheneman, Leigh; Hintze, Arend

2017-12-01

There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. These methods have been previously combined, particularly in artificial neural networks using an external objective feedback mechanism. We adapt this approach to Markov Brains, which are evolvable networks of probabilistic and deterministic logic gates. Prior to this work MB could only adapt from one generation to the other, so we introduce feedback gates which augment their ability to learn during their lifetime. We show that Markov Brains can incorporate these feedback gates in such a way that they do not rely on an external objective feedback signal, but instead can generate internal feedback that is then used to learn. This results in a more biologically accurate model of the evolution of learning, which will enable us to study the interplay between evolution and learning and could be another step towards autonomously learning machines.

Anatomically constrained neural network models for the categorization of facial expression

NASA Astrophysics Data System (ADS)

McMenamin, Brenton W.; Assadi, Amir H.

2004-12-01

The ability to recognize facial expression in humans is performed with the amygdala which uses parallel processing streams to identify the expressions quickly and accurately. Additionally, it is possible that a feedback mechanism may play a role in this process as well. Implementing a model with similar parallel structure and feedback mechanisms could be used to improve current facial recognition algorithms for which varied expressions are a source for error. An anatomically constrained artificial neural-network model was created that uses this parallel processing architecture and feedback to categorize facial expressions. The presence of a feedback mechanism was not found to significantly improve performance for models with parallel architecture. However the use of parallel processing streams significantly improved accuracy over a similar network that did not have parallel architecture. Further investigation is necessary to determine the benefits of using parallel streams and feedback mechanisms in more advanced object recognition tasks.

Anatomically constrained neural network models for the categorization of facial expression

NASA Astrophysics Data System (ADS)

McMenamin, Brenton W.; Assadi, Amir H.

2005-01-01

The ability to recognize facial expression in humans is performed with the amygdala which uses parallel processing streams to identify the expressions quickly and accurately. Additionally, it is possible that a feedback mechanism may play a role in this process as well. Implementing a model with similar parallel structure and feedback mechanisms could be used to improve current facial recognition algorithms for which varied expressions are a source for error. An anatomically constrained artificial neural-network model was created that uses this parallel processing architecture and feedback to categorize facial expressions. The presence of a feedback mechanism was not found to significantly improve performance for models with parallel architecture. However the use of parallel processing streams significantly improved accuracy over a similar network that did not have parallel architecture. Further investigation is necessary to determine the benefits of using parallel streams and feedback mechanisms in more advanced object recognition tasks.

A novel positive transcriptional feedback loop in midbrain-hindbrain boundary development is revealed through analysis of the zebrafish pax2.1 promoter in transgenic lines.

PubMed

Picker, Alexander; Scholpp, Steffen; Böhli, Heike; Takeda, Hiroyuki; Brand, Michael

2002-07-01

The pax2.1 gene encodes a paired-box transcription factor that is one of the earliest genes to be specifically activated in development of the midbrain and midbrain-hindbrain boundary (MHB), and is required for the development and organizer activity of this territory. To understand how this spatially restricted transcriptional activity of pax2.1 is achieved, we have isolated and characterized the pax2.1-promoter using a lacZ and a GFP reporter gene in transient injection assays and transgenic lines. Stable transgenic expression of this reporter gene shows that a 5.3-kb fragment of the 5' region contains most, but not all, elements required for driving pax2.1 expression. The expressing tissues include the MHB, hindbrain, spinal cord, ear and pronephros. Transgene activation in the pronephros and developing ear suggests that these pax2.1-expressing tissues are composed of independently regulated subdomains. In addition, ectopic but spatially restricted activation of the reporter genes in rhombomeres 3 and 5 and in the forebrain, which do not normally express endogenous pax2.1, demonstrates the importance of negative regulation of pax2.1. Comparison of transgene expression in wild-type and homozygous pax2.1 mutant no isthmus (noi) embryos reveals that the transgene contains control element(s) for a novel, positive transcriptional feedback loop in MHB development. Transcription of endogenous pax2.1 at the MHB is known to be initially Pax2.1 independent, during activation in late gastrulation. In contrast, transgene expression requires the endogenous Pax2.1 function. Transplantations, mRNA injections and morpholino knock-down experiments show that this feedback regulation of pax2.1 transcription occurs cell-autonomously, and that it requires eng2 and eng3 as known targets for Pax2.1 regulation. We suggest that this novel feedback loop may allow continuation of pax2.1 expression, and hence development of the MHB organizer, to become independent of the patterning machinery of the gastrula embryo.

Network interactions underlying mirror feedback in stroke: A dynamic causal modeling study.

PubMed

Saleh, Soha; Yarossi, Mathew; Manuweera, Thushini; Adamovich, Sergei; Tunik, Eugene

2017-01-01

Mirror visual feedback (MVF) is potentially a powerful tool to facilitate recovery of disordered movement and stimulate activation of under-active brain areas due to stroke. The neural mechanisms underlying MVF have therefore been a focus of recent inquiry. Although it is known that sensorimotor areas can be activated via mirror feedback, the network interactions driving this effect remain unknown. The aim of the current study was to fill this gap by using dynamic causal modeling to test the interactions between regions in the frontal and parietal lobes that may be important for modulating the activation of the ipsilesional motor cortex during mirror visual feedback of unaffected hand movement in stroke patients. Our intent was to distinguish between two theoretical neural mechanisms that might mediate ipsilateral activation in response to mirror-feedback: transfer of information between bilateral motor cortices versus recruitment of regions comprising an action observation network which in turn modulate the motor cortex. In an event-related fMRI design, fourteen chronic stroke subjects performed goal-directed finger flexion movements with their unaffected hand while observing real-time visual feedback of the corresponding (veridical) or opposite (mirror) hand in virtual reality. Among 30 plausible network models that were tested, the winning model revealed significant mirror feedback-based modulation of the ipsilesional motor cortex arising from the contralesional parietal cortex, in a region along the rostral extent of the intraparietal sulcus. No winning model was identified for the veridical feedback condition. We discuss our findings in the context of supporting the latter hypothesis, that mirror feedback-based activation of motor cortex may be attributed to engagement of a contralateral (contralesional) action observation network. These findings may have important implications for identifying putative cortical areas, which may be targeted with non-invasive brain stimulation as a means of potentiating the effects of mirror training.

Dynamic Motivational Processing of Antimarijuana Messages: Coactivation Begets Attention

ERIC Educational Resources Information Center

Wang, Zheng; Solloway, Tyler; Tchernev, John M.; Barker, Bethany

2012-01-01

In the theoretical framework of dynamic motivational activation, this study reveals the dynamics of antimarijuana public service announcement (PSA) processing, especially the processing of co-occurring positive and negative content. It specifies the important role of endogenous feedback dynamics of the information processing system and teases them…

Confinement and diffusion modulate bistability and stochastic switching in a reaction network with positive feedback

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mlynarczyk, Paul J.; Pullen, Robert H.; Abel, Steven M., E-mail: abel@utk.edu

2016-01-07

Positive feedback is a common feature in signal transduction networks and can lead to phenomena such as bistability and signal propagation by domain growth. Physical features of the cellular environment, such as spatial confinement and the mobility of proteins, play important but inadequately understood roles in shaping the behavior of signaling networks. Here, we use stochastic, spatially resolved kinetic Monte Carlo simulations to explore a positive feedback network as a function of system size, system shape, and mobility of molecules. We show that these physical properties can markedly alter characteristics of bistability and stochastic switching when compared with well-mixed simulations.more » Notably, systems of equal volume but different shapes can exhibit qualitatively different behaviors under otherwise identical conditions. We show that stochastic switching to a state maintained by positive feedback occurs by cluster formation and growth. Additionally, the frequency at which switching occurs depends nontrivially on the diffusion coefficient, which can promote or suppress switching relative to the well-mixed limit. Taken together, the results provide a framework for understanding how confinement and protein mobility influence emergent features of the positive feedback network by modulating molecular concentrations, diffusion-influenced rate parameters, and spatiotemporal correlations between molecules.« less

Observer-Based Adaptive Neural Network Control for Nonlinear Systems in Nonstrict-Feedback Form.

PubMed

Chen, Bing; Zhang, Huaguang; Lin, Chong

2016-01-01

This paper focuses on the problem of adaptive neural network (NN) control for a class of nonlinear nonstrict-feedback systems via output feedback. A novel adaptive NN backstepping output-feedback control approach is first proposed for nonlinear nonstrict-feedback systems. The monotonicity of system bounding functions and the structure character of radial basis function (RBF) NNs are used to overcome the difficulties that arise from nonstrict-feedback structure. A state observer is constructed to estimate the immeasurable state variables. By combining adaptive backstepping technique with approximation capability of radial basis function NNs, an output-feedback adaptive NN controller is designed through backstepping approach. It is shown that the proposed controller guarantees semiglobal boundedness of all the signals in the closed-loop systems. Two examples are used to illustrate the effectiveness of the proposed approach.

Transition to parenthood: the role of social interaction and endogenous networks.

PubMed

Diaz, Belinda Aparicio; Fent, Thomas; Prskawetz, Alexia; Bernardi, Laura

2011-05-01

Empirical studies indicate that the transition to parenthood is influenced by an individual's peer group. To study the mechanisms creating interdependencies across individuals' transition to parenthood and its timing, we apply an agent-based simulation model. We build a one-sex model and provide agents with three different characteristics: age, intended education, and parity. Agents endogenously form their network based on social closeness. Network members may then influence the agents' transition to higher parity levels. Our numerical simulations indicate that accounting for social interactions can explain the shift of first-birth probabilities in Austria during the period 1984 to 2004. Moreover, we apply our model to forecast age-specific fertility rates up to 2016.

A bilateral cortical network responds to pitch perturbations in speech feedback

PubMed Central

Kort, Naomi S.; Nagarajan, Srikantan S.; Houde, John F.

2014-01-01

Auditory feedback is used to monitor and correct for errors in speech production, and one of the clearest demonstrations of this is the pitch perturbation reflex. During ongoing phonation, speakers respond rapidly to shifts of the pitch of their auditory feedback, altering their pitch production to oppose the direction of the applied pitch shift. In this study, we examine the timing of activity within a network of brain regions thought to be involved in mediating this behavior. To isolate auditory feedback processing relevant for motor control of speech, we used magnetoencephalography (MEG) to compare neural responses to speech onset and to transient (400ms) pitch feedback perturbations during speaking with responses to identical acoustic stimuli during passive listening. We found overlapping, but distinct bilateral cortical networks involved in monitoring speech onset and feedback alterations in ongoing speech. Responses to speech onset during speaking were suppressed in bilateral auditory and left ventral supramarginal gyrus/posterior superior temporal sulcus (vSMG/pSTS). In contrast, during pitch perturbations, activity was enhanced in bilateral vSMG/pSTS, bilateral premotor cortex, right primary auditory cortex, and left higher order auditory cortex. We also found speaking-induced delays in responses to both unaltered and altered speech in bilateral primary and secondary auditory regions, the left vSMG/pSTS and right premotor cortex. The network dynamics reveal the cortical processing involved in both detecting the speech error and updating the motor plan to create the new pitch output. These results implicate vSMG/pSTS as critical in both monitoring auditory feedback and initiating rapid compensation to feedback errors. PMID:24076223

Identifying Functional Mechanisms of Gene and Protein Regulatory Networks in Response to a Broader Range of Environmental Stresses

PubMed Central

Li, Cheng-Wei; Chen, Bor-Sen

2010-01-01

Cellular responses to sudden environmental stresses or physiological changes provide living organisms with the opportunity for final survival and further development. Therefore, it is an important topic to understand protective mechanisms against environmental stresses from the viewpoint of gene and protein networks. We propose two coupled nonlinear stochastic dynamic models to reconstruct stress-activated gene and protein regulatory networks via microarray data in response to environmental stresses. According to the reconstructed gene/protein networks, some possible mutual interactions, feedforward and feedback loops are found for accelerating response and filtering noises in these signaling pathways. A bow-tie core network is also identified to coordinate mutual interactions and feedforward loops, feedback inhibitions, feedback activations, and cross talks to cope efficiently with a broader range of environmental stresses with limited proteins and pathways. PMID:20454442

Endocannabinoids in brain plasticity: Cortical maturation, HPA axis function and behavior.

PubMed

Dow-Edwards, Diana; Silva, Lindsay

2017-01-01

Marijuana use during adolescence has reached virtually every strata of society. The general population has the perception that marijuana use is safe for mature people and therefore is also safe for developing adolescents. However, both clinical and preclinical research shows that marijuana use, particularly prior to age 16, could have long-term effects on cognition, anxiety and stress-related behaviors, mood disorders and substance abuse. These effects derive from the role of the endocannabinoid system, the endogenous cannabinoid system, in the development of cortex, amygdala, hippocampus and hypothalamus during adolescence. Endocannabinoids are necessary for normal neuronal excitation and inhibition through actions at glutamate and GABA terminals. Synaptic pruning at excitatory synapses and sparing of inhibitory synapses likely results in changes in the balance of excitation/inhibition in individual neurons and within networks; processes which are necessary for normal cortical development. The interaction between prefrontal cortex (PFC), amygdala and hippocampus is responsible for emotional memory, anxiety-related behaviors and drug abuse and all utilize the endogenous cannabinoid system to maintain homeostasis. Also, endocannabinoids are required for fast and slow feedback in the normal stress response, processes which mature during adolescence. Therefore, exogenous cannabinoids, such as marijuana, have the potential to alter the course of development of each of these major systems (limbic, hypothalamic-pituitary-adrenal (HPA) axis and neocortex) if used during the critical period of brain development, adolescence. This article is part of a Special Issue entitled SI: Adolescent plasticity. Copyright © 2016 Elsevier B.V. All rights reserved.

Expected Number of Fixed Points in Boolean Networks with Arbitrary Topology.

PubMed

Mori, Fumito; Mochizuki, Atsushi

2017-07-14

Boolean network models describe genetic, neural, and social dynamics in complex networks, where the dynamics depend generally on network topology. Fixed points in a genetic regulatory network are typically considered to correspond to cell types in an organism. We prove that the expected number of fixed points in a Boolean network, with Boolean functions drawn from probability distributions that are not required to be uniform or identical, is one, and is independent of network topology if only a feedback arc set satisfies a stochastic neutrality condition. We also demonstrate that the expected number is increased by the predominance of positive feedback in a cycle.

A Framework for Engineering Stress Resilient Plants Using Genetic Feedback Control and Regulatory Network Rewiring.

PubMed

Foo, Mathias; Gherman, Iulia; Zhang, Peijun; Bates, Declan G; Denby, Katherine J

2018-05-23

Crop disease leads to significant waste worldwide, both pre- and postharvest, with subsequent economic and sustainability consequences. Disease outcome is determined both by the plants' response to the pathogen and by the ability of the pathogen to suppress defense responses and manipulate the plant to enhance colonization. The defense response of a plant is characterized by significant transcriptional reprogramming mediated by underlying gene regulatory networks, and components of these networks are often targeted by attacking pathogens. Here, using gene expression data from Botrytis cinerea-infected Arabidopsis plants, we develop a systematic approach for mitigating the effects of pathogen-induced network perturbations, using the tools of synthetic biology. We employ network inference and system identification techniques to build an accurate model of an Arabidopsis defense subnetwork that contains key genes determining susceptibility of the plant to the pathogen attack. Once validated against time-series data, we use this model to design and test perturbation mitigation strategies based on the use of genetic feedback control. We show how a synthetic feedback controller can be designed to attenuate the effect of external perturbations on the transcription factor CHE in our subnetwork. We investigate and compare two approaches for implementing such a controller biologically-direct implementation of the genetic feedback controller, and rewiring the regulatory regions of multiple genes-to achieve the network motif required to implement the controller. Our results highlight the potential of combining feedback control theory with synthetic biology for engineering plants with enhanced resilience to environmental stress.

Neural dynamic programming and its application to control systems

NASA Astrophysics Data System (ADS)

Seong, Chang-Yun

There are few general practical feedback control methods for nonlinear MIMO (multi-input-multi-output) systems, although such methods exist for their linear counterparts. Neural Dynamic Programming (NDP) is proposed as a practical design method of optimal feedback controllers for nonlinear MIMO systems. NDP is an offspring of both neural networks and optimal control theory. In optimal control theory, the optimal solution to any nonlinear MIMO control problem may be obtained from the Hamilton-Jacobi-Bellman equation (HJB) or the Euler-Lagrange equations (EL). The two sets of equations provide the same solution in different forms: EL leads to a sequence of optimal control vectors, called Feedforward Optimal Control (FOC); HJB yields a nonlinear optimal feedback controller, called Dynamic Programming (DP). DP produces an optimal solution that can reject disturbances and uncertainties as a result of feedback. Unfortunately, computation and storage requirements associated with DP solutions can be problematic, especially for high-order nonlinear systems. This dissertation presents an approximate technique for solving the DP problem based on neural network techniques that provides many of the performance benefits (e.g., optimality and feedback) of DP and benefits from the numerical properties of neural networks. We formulate neural networks to approximate optimal feedback solutions whose existence DP justifies. We show the conditions under which NDP closely approximates the optimal solution. Finally, we introduce the learning operator characterizing the learning process of the neural network in searching the optimal solution. The analysis of the learning operator provides not only a fundamental understanding of the learning process in neural networks but also useful guidelines for selecting the number of weights of the neural network. As a result, NDP finds---with a reasonable amount of computation and storage---the optimal feedback solutions to nonlinear MIMO control problems that would be very difficult to solve with DP. NDP was demonstrated on several applications such as the lateral autopilot logic for a Boeing 747, the minimum fuel control of a double-integrator plant with bounded control, the backward steering of a two-trailer truck, and the set-point control of a two-link robot arm.

Isolation, characterization, and expression analyses of plant elicitor peptides (pep) genes in maize

USDA-ARS?s Scientific Manuscript database

PROPEP1, PROPEP 2, and PROPEP3 genes appear to have roles in a feedback loop that amplifies defense signaling pathways initiated by pathogens. We present evidence to support the role of peptides derived from PROPEP genes as endogenous elicitors that are generated in response to pathogens. The preval...

Enhancing synchronization stability in a multi-area power grid

PubMed Central

Wang, Bing; Suzuki, Hideyuki; Aihara, Kazuyuki

2016-01-01

Maintaining a synchronous state of generators is of central importance to the normal operation of power grids, in which many networks are generally interconnected. In order to understand the condition under which the stability can be optimized, it is important to relate network stability with feedback control strategies as well as network structure. Here, we present a stability analysis on a multi-area power grid by relating it with several control strategies and topological design of network structure. We clarify the minimal feedback gain in the self-feedback control, and build the optimal communication network for the local and global control strategies. Finally, we consider relationship between the interconnection pattern and the synchronization stability; by optimizing the network interlinks, the obtained network shows better synchronization stability than the original network does, in particular, at a high power demand. Our analysis shows that interlinks between spatially distant nodes will improve the synchronization stability. The results seem unfeasible to be implemented in real systems but provide a potential guide for the design of stable power systems. PMID:27225708

CSI Feedback Reduction for MIMO Interference Alignment

NASA Astrophysics Data System (ADS)

Rao, Xiongbin; Ruan, Liangzhong; Lau, Vincent K. N.

2013-09-01

Interference alignment (IA) is a linear precoding strategy that can achieve optimal capacity scaling at high SNR in interference networks. Most of the existing IA designs require full channel state information (CSI) at the transmitters, which induces a huge CSI signaling cost. Hence it is desirable to improve the feedback efficiency for IA and in this paper, we propose a novel IA scheme with a significantly reduced CSI feedback. To quantify the CSI feedback cost, we introduce a novel metric, namely the feedback dimension. This metric serves as a first-order measurement of CSI feedback overhead. Due to the partial CSI feedback constraint, conventional IA schemes can not be applied and hence, we develop a novel IA precoder / decorrelator design and establish new IA feasibility conditions. Via dynamic feedback profile design, the proposed IA scheme can also achieve a flexible tradeoff between the degree of freedom (DoF) requirements for data streams, the antenna resources and the CSI feedback cost. We show by analysis and simulations that the proposed scheme achieves substantial reductions of CSI feedback overhead under the same DoF requirement in MIMO interference networks.

Endogenous Versus Exogenous Shocks in Complex Networks: An Empirical Test Using Book Sale Rankings

NASA Astrophysics Data System (ADS)

Sornette, D.; Deschâtres, F.; Gilbert, T.; Ageon, Y.

2004-11-01

We study the precursory and recovery signatures accompanying shocks in complex networks, that we test on a unique database of the Amazon.com ranking of book sales. We find clear distinguishing signatures classifying two types of sales peaks. Exogenous peaks occur abruptly and are followed by a power law relaxation, while endogenous peaks occur after a progressively accelerating power law growth followed by an approximately symmetrical power law relaxation which is slower than for exogenous peaks. These results are rationalized quantitatively by a simple model of epidemic propagation of interactions with long memory within a network of acquaintances. The observed relaxation of sales implies that the sales dynamics is dominated by cascades rather than by the direct effects of news or advertisements, indicating that the social network is close to critical.

Endogenous versus exogenous shocks in complex networks: an empirical test using book sale rankings.

PubMed

Sornette, D; Deschâtres, F; Gilbert, T; Ageon, Y

2004-11-26

We study the precursory and recovery signatures accompanying shocks in complex networks, that we test on a unique database of the Amazon.com ranking of book sales. We find clear distinguishing signatures classifying two types of sales peaks. Exogenous peaks occur abruptly and are followed by a power law relaxation, while endogenous peaks occur after a progressively accelerating power law growth followed by an approximately symmetrical power law relaxation which is slower than for exogenous peaks. These results are rationalized quantitatively by a simple model of epidemic propagation of interactions with long memory within a network of acquaintances. The observed relaxation of sales implies that the sales dynamics is dominated by cascades rather than by the direct effects of news or advertisements, indicating that the social network is close to critical.

Endogenous cholinergic tone modulates spontaneous network level neuronal activity in primary cortical cultures grown on multi-electrode arrays.

PubMed

Hammond, Mark W; Xydas, Dimitris; Downes, Julia H; Bucci, Giovanna; Becerra, Victor; Warwick, Kevin; Constanti, Andrew; Nasuto, Slawomir J; Whalley, Benjamin J

2013-03-26

Cortical cultures grown long-term on multi-electrode arrays (MEAs) are frequently and extensively used as models of cortical networks in studies of neuronal firing activity, neuropharmacology, toxicology and mechanisms underlying synaptic plasticity. However, in contrast to the predominantly asynchronous neuronal firing activity exhibited by intact cortex, electrophysiological activity of mature cortical cultures is dominated by spontaneous epileptiform-like global burst events which hinders their effective use in network-level studies, particularly for neurally-controlled animat ('artificial animal') applications. Thus, the identification of culture features that can be exploited to produce neuronal activity more representative of that seen in vivo could increase the utility and relevance of studies that employ these preparations. Acetylcholine has a recognised neuromodulatory role affecting excitability, rhythmicity, plasticity and information flow in vivo although its endogenous production by cortical cultures and subsequent functional influence upon neuronal excitability remains unknown. Consequently, using MEA electrophysiological recording supported by immunohistochemical and RT-qPCR methods, we demonstrate for the first time, the presence of intrinsic cholinergic neurons and significant, endogenous cholinergic tone in cortical cultures with a characterisation of the muscarinic and nicotinic components that underlie modulation of spontaneous neuronal activity. We found that tonic muscarinic ACh receptor (mAChR) activation affects global excitability and burst event regularity in a culture age-dependent manner whilst, in contrast, tonic nicotinic ACh receptor (nAChR) activation can modulate burst duration and the proportion of spikes occurring within bursts in a spatio-temporal fashion. We suggest that the presence of significant endogenous cholinergic tone in cortical cultures and the comparability of its modulatory effects to those seen in intact brain tissues support emerging, exploitable commonalities between in vivo and in vitro preparations. We conclude that experimental manipulation of endogenous cholinergic tone could offer a novel opportunity to improve the use of cortical cultures for studies of network-level mechanisms in a manner that remains largely consistent with its functional role.

Consensus Algorithms for Networks of Systems with Second- and Higher-Order Dynamics

NASA Astrophysics Data System (ADS)

Fruhnert, Michael

This thesis considers homogeneous networks of linear systems. We consider linear feedback controllers and require that the directed graph associated with the network contains a spanning tree and systems are stabilizable. We show that, in continuous-time, consensus with a guaranteed rate of convergence can always be achieved using linear state feedback. For networks of continuous-time second-order systems, we provide a new and simple derivation of the conditions for a second-order polynomials with complex coefficients to be Hurwitz. We apply this result to obtain necessary and sufficient conditions to achieve consensus with networks whose graph Laplacian matrix may have complex eigenvalues. Based on the conditions found, methods to compute feedback gains are proposed. We show that gains can be chosen such that consensus is achieved robustly over a variety of communication structures and system dynamics. We also consider the use of static output feedback. For networks of discrete-time second-order systems, we provide a new and simple derivation of the conditions for a second-order polynomials with complex coefficients to be Schur. We apply this result to obtain necessary and sufficient conditions to achieve consensus with networks whose graph Laplacian matrix may have complex eigenvalues. We show that consensus can always be achieved for marginally stable systems and discretized systems. Simple conditions for consensus achieving controllers are obtained when the Laplacian eigenvalues are all real. For networks of continuous-time time-variant higher-order systems, we show that uniform consensus can always be achieved if systems are quadratically stabilizable. In this case, we provide a simple condition to obtain a linear feedback control. For networks of discrete-time higher-order systems, we show that constant gains can be chosen such that consensus is achieved for a variety of network topologies. First, we develop simple results for networks of time-invariant systems and networks of time-variant systems that are given in controllable canonical form. Second, we formulate the problem in terms of Linear Matrix Inequalities (LMIs). The condition found simplifies the design process and avoids the parallel solution of multiple LMIs. The result yields a modified Algebraic Riccati Equation (ARE) for which we present an equivalent LMI condition.

The Emerging Role of Epigenetics in Stroke

PubMed Central

Qureshi, Irfan A.; Mehler, Mark F.

2013-01-01

The transplantation of exogenous stem cells and the activation of endogenous neural stem and progenitor cells (NSPCs) are promising treatments for stroke. These cells can modulate intrinsic responses to ischemic injury and may even integrate directly into damaged neural networks. However, the neuroprotective and neural regenerative effects that can be mediated by these cells are limited and may even be deleterious. Epigenetic reprogramming represents a novel strategy for enhancing the intrinsic potential of the brain to protect and repair itself by modulating pathologic neural gene expression and promoting the recapitulation of seminal neural developmental processes. In fact, recent evidence suggests that emerging epigenetic mechanisms are critical for orchestrating nearly every aspect of neural development and homeostasis, including brain patterning, neural stem cell maintenance, neurogenesis and gliogenesis, neural subtype specification, and synaptic and neural network connectivity and plasticity. In this review, we survey the therapeutic potential of exogenous stem cells and endogenous NSPCs and highlight innovative technological approaches for designing, developing, and delivering epigenetic therapies for targeted reprogramming of endogenous pools of NSPCs, neural cells at risk, and dysfunctional neural networks to rescue and restore neurologic function in the ischemic brain. PMID:21403016

Resumption of dynamism in damaged networks of coupled oscillators

NASA Astrophysics Data System (ADS)

Kundu, Srilena; Majhi, Soumen; Ghosh, Dibakar

2018-05-01

Deterioration in dynamical activities may come up naturally or due to environmental influences in a massive portion of biological and physical systems. Such dynamical degradation may have outright effect on the substantive network performance. This requires us to provide some proper prescriptions to overcome undesired circumstances. In this paper, we present a scheme based on external feedback that can efficiently revive dynamism in damaged networks of active and inactive oscillators and thus enhance the network survivability. Both numerical and analytical investigations are performed in order to verify our claim. We also provide a comparative study on the effectiveness of this mechanism for feedbacks to the inactive group or to the active group only. Most importantly, resurrection of dynamical activity is realized even in time-delayed damaged networks, which are considered to be less persistent against deterioration in the form of inactivity in the oscillators. Furthermore, prominence in our approach is substantiated by providing evidence of enhanced network persistence in complex network topologies taking small-world and scale-free architectures, which makes the proposed remedy quite general. Besides the study in the network of Stuart-Landau oscillators, affirmative influence of external feedback has been justified in the network of chaotic Rössler systems as well.

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

PubMed

Zenke, Friedemann; Ganguli, Surya

2018-06-01

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

Autoshaping and automaintenance: a neural-network approach.

PubMed

Burgos, José E

2007-07-01

This article presents an interpretation of autoshaping, and positive and negative automaintenance, based on a neural-network model. The model makes no distinction between operant and respondent learning mechanisms, and takes into account knowledge of hippocampal and dopaminergic systems. Four simulations were run, each one using an A-B-A design and four instances of feedfoward architectures. In A, networks received a positive contingency between inputs that simulated a conditioned stimulus (CS) and an input that simulated an unconditioned stimulus (US). Responding was simulated as an output activation that was neither elicited by nor required for the US. B was an omission-training procedure. Response directedness was defined as sensory feedback from responding, simulated as a dependence of other inputs on responding. In Simulation 1, the phenomena were simulated with a fully connected architecture and maximally intense response feedback. The other simulations used a partially connected architecture without competition between CS and response feedback. In Simulation 2, a maximally intense feedback resulted in substantial autoshaping and automaintenance. In Simulation 3, eliminating response feedback interfered substantially with autoshaping and automaintenance. In Simulation 4, intermediate autoshaping and automaintenance resulted from an intermediate response feedback. Implications for the operant-respondent distinction and the behavior-neuroscience relation are discussed.

Autoshaping and Automaintenance: A Neural-Network Approach

PubMed Central

Burgos, José E

2007-01-01

This article presents an interpretation of autoshaping, and positive and negative automaintenance, based on a neural-network model. The model makes no distinction between operant and respondent learning mechanisms, and takes into account knowledge of hippocampal and dopaminergic systems. Four simulations were run, each one using an A-B-A design and four instances of feedfoward architectures. In A, networks received a positive contingency between inputs that simulated a conditioned stimulus (CS) and an input that simulated an unconditioned stimulus (US). Responding was simulated as an output activation that was neither elicited by nor required for the US. B was an omission-training procedure. Response directedness was defined as sensory feedback from responding, simulated as a dependence of other inputs on responding. In Simulation 1, the phenomena were simulated with a fully connected architecture and maximally intense response feedback. The other simulations used a partially connected architecture without competition between CS and response feedback. In Simulation 2, a maximally intense feedback resulted in substantial autoshaping and automaintenance. In Simulation 3, eliminating response feedback interfered substantially with autoshaping and automaintenance. In Simulation 4, intermediate autoshaping and automaintenance resulted from an intermediate response feedback. Implications for the operant–respondent distinction and the behavior–neuroscience relation are discussed. PMID:17725055

When Feedback Harms and Collaboration Helps in Computer Simulation Environments: An Expertise Reversal Effect

ERIC Educational Resources Information Center

Nihalani, Priya K.; Mayrath, Michael; Robinson, Daniel H.

2011-01-01

We investigated the effects of feedback and collaboration on undergraduates' transfer performance when using a computer networking training simulation. In Experiment 1, 65 computer science "novices" worked through an instructional protocol individually (control), individually with feedback, or collaboratively with feedback. Unexpectedly,…

Effect of Temperature on Synthetic Positive and Negative Feedback Gene Networks

NASA Astrophysics Data System (ADS)

Charlebois, Daniel A.; Marshall, Sylvia; Balazsi, Gabor

Synthetic biological systems are built and tested under well controlled laboratory conditions. How altering the environment, such as the ambient temperature affects their function is not well understood. To address this question for synthetic gene networks with positive and negative feedback, we used mathematical modeling coupled with experiments in the budding yeast Saccharomyces cerevisiae. We found that cellular growth rates and gene expression dose responses change significantly at temperatures above and below the physiological optimum for yeast. Gene expression distributions for the negative feedback-based circuit changed from unimodal to bimodal at high temperature, while the bifurcation point of the positive feedback circuit shifted up with temperature. These results demonstrate that synthetic gene network function is context-dependent. Temperature effects should thus be tested and incorporated into their design and validation for real-world applications. NSERC Postdoctoral Fellowship (Grant No. PDF-453977-2014).

Robust consensus control with guaranteed rate of convergence using second-order Hurwitz polynomials

NASA Astrophysics Data System (ADS)

Fruhnert, Michael; Corless, Martin

2017-10-01

This paper considers homogeneous networks of general, linear time-invariant, second-order systems. We consider linear feedback controllers and require that the directed graph associated with the network contains a spanning tree and systems are stabilisable. We show that consensus with a guaranteed rate of convergence can always be achieved using linear state feedback. To achieve this, we provide a new and simple derivation of the conditions for a second-order polynomial with complex coefficients to be Hurwitz. We apply this result to obtain necessary and sufficient conditions to achieve consensus with networks whose graph Laplacian matrix may have complex eigenvalues. Based on the conditions found, methods to compute feedback gains are proposed. We show that gains can be chosen such that consensus is achieved robustly over a variety of communication structures and system dynamics. We also consider the use of static output feedback.

Competing endogenous RNA network crosstalk reveals novel molecular markers in colorectal cancer.

PubMed

Samir, Nehal; Matboli, Marwa; El-Tayeb, Hanaa; El-Tawdi, Ahmed; Hassan, Mohmed K; Waly, Amr; El-Akkad, Hesham A E; Ramadan, Mohamed G; Al-Belkini, Tarek N; El-Khamisy, Sherif; El-Asmar, Farid

2018-05-08

The competing endogenous RNA networks play a pivotal role in cancer diagnosis and progression. Novel properstrategies for early detection of colorectal cancer (CRC) are strongly needed. We investigated a novel CRC-specific RNA-based integrated competing endogenous network composed of lethal3 malignant brain tumor like1 (L3MBTL1) gene, long non-coding intergenic RNA- (lncRNA RP11-909B2.1) and homo sapiens microRNA-595 (hsa-miRNA-595) using in silico data analysis. RT-qPCR-based validation of the network was achieved in serum of 70 patients with CRC, 40 patients with benign colorectal neoplasm, and 20 healthy controls. Moreover, in cancer tissues of 20 of the 70 CRC cases were involved in the study. The expression of RNA-based biomarker network in both CRC and adjacent non-tumor tissues and their correlation with the serum levels of this network members was investigated. Lastly, the expression levels of the chosen ceRNA was verified in CRC cell line. Our results revealed that the three RNAs-based biomarker network (long non-coding intergenic RNA-[lncRNA RP11-909B2.1], Homo sapiens microRNA-595 [hsa-miRNA-595], and L3MBTL1 mRNA), had high sensitivity and specificity for discriminating CRC from healthy controls and also from benign colorectal neoplasm. The data suggest that among these three RNAs, serum lncRNA RP11-909B2.1 could be a promising independent prognostic factors in CRC. The circulatory RNA based biomarker panel can act as potential biomarker for CRC diagnosis and prognosis. © 2018 Wiley Periodicals, Inc.

The Role of Retinal Determination Gene Network (RDGN) in Hormone Signaling Transduction and Prostate Tumorigenes

DTIC Science & Technology

2014-10-01

McCue P, Lisanti MP, Wang C, Davis RJ, Mardon G, Pestell RG. The Endogenous Cell-Fate Factor Dachshund Restrains Prostate Epithelial Cell Migration via...Loro E, Pestell RG. “Inhibition of Breast Tumor Stem Cells Expansion by the Endogenous Cell Fate Determination Factor Dachshund.” Chapter in Volume

Analysis of Network Topologies Underlying Ethylene Growth Response Kinetics

PubMed Central

Prescott, Aaron M.; McCollough, Forest W.; Eldreth, Bryan L.; Binder, Brad M.; Abel, Steven M.

2016-01-01

Most models for ethylene signaling involve a linear pathway. However, measurements of seedling growth kinetics when ethylene is applied and removed have resulted in more complex network models that include coherent feedforward, negative feedback, and positive feedback motifs. The dynamical responses of the proposed networks have not been explored in a quantitative manner. Here, we explore (i) whether any of the proposed models are capable of producing growth-response behaviors consistent with experimental observations and (ii) what mechanistic roles various parts of the network topologies play in ethylene signaling. To address this, we used computational methods to explore two general network topologies: The first contains a coherent feedforward loop that inhibits growth and a negative feedback from growth onto itself (CFF/NFB). In the second, ethylene promotes the cleavage of EIN2, with the product of the cleavage inhibiting growth and promoting the production of EIN2 through a positive feedback loop (PFB). Since few network parameters for ethylene signaling are known in detail, we used an evolutionary algorithm to explore sets of parameters that produce behaviors similar to experimental growth response kinetics of both wildtype and mutant seedlings. We generated a library of parameter sets by independently running the evolutionary algorithm many times. Both network topologies produce behavior consistent with experimental observations, and analysis of the parameter sets allows us to identify important network interactions and parameter constraints. We additionally screened these parameter sets for growth recovery in the presence of sub-saturating ethylene doses, which is an experimentally-observed property that emerges in some of the evolved parameter sets. Finally, we probed simplified networks maintaining key features of the CFF/NFB and PFB topologies. From this, we verified observations drawn from the larger networks about mechanisms underlying ethylene signaling. Analysis of each network topology results in predictions about changes that occur in network components that can be experimentally tested to give insights into which, if either, network underlies ethylene responses. PMID:27625669

Neural node network and model, and method of teaching same

DOEpatents

Parlos, A.G.; Atiya, A.F.; Fernandez, B.; Tsai, W.K.; Chong, K.T.

1995-12-26

The present invention is a fully connected feed forward network that includes at least one hidden layer. The hidden layer includes nodes in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device occurring in the feedback path (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit from all the other nodes within the same layer. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing. 21 figs.

Neural node network and model, and method of teaching same

DOEpatents

Parlos, Alexander G.; Atiya, Amir F.; Fernandez, Benito; Tsai, Wei K.; Chong, Kil T.

1995-01-01

The present invention is a fully connected feed forward network that includes at least one hidden layer 16. The hidden layer 16 includes nodes 20 in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device 24 occurring in the feedback path 22 (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit 36 from all the other nodes within the same layer 16. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing.

Cushing's syndrome: from physiological principles to diagnosis and clinical care

PubMed Central

Raff, Hershel; Carroll, Ty

2015-01-01

The physiological control of cortisol synthesis in the adrenal cortex involves stimulation of adrenocorticotrophic hormone (ACTH) by hypothalamic corticotrophin-releasing hormone (CRH) and then stimulation of the adrenal by ACTH. The control loop of the hypothalamic–pituitary–adrenal (HPA) axis is closed by negative feedback of cortisol on the hypothalamus and pituitary. Understanding this system is required to master the diagnosis, differential diagnosis and treatment of endogenous hypercortisolism – Cushing's syndrome. Endogenous Cushing's syndrome is caused either by excess ACTH secretion or by autonomous cortisol release from the adrenal cortex. Diagnosis of cortisol excess exploits three physiological principles: failure to achieve the normal nadir in the cortisol diurnal rhythm, loss of sensitivity of ACTH-secreting tumours to cortisol negative feedback, and increased excretion of free cortisol in the urine. Differentiating a pituitary source of excess ACTH (Cushing's disease) from an ectopic source is accomplished by imaging the pituitary and sampling for ACTH in the venous drainage of the pituitary. With surgical removal of ACTH or cortisol-secreting tumours, secondary adrenal insufficiency ensues because of the prior suppression of the HPA axis by glucocorticoid negative feedback. Medical therapy is targeted to the anatomical location of the dysregulated component of the HPA axis. Future research will focus on new diagnostics and treatments of Cushing's syndrome. These are elegant examples of translational research: understanding basic physiology informs the development of new approaches to diagnosis and treatment. Appreciating pathophysiology generates new areas for inquiry of basic physiological and biochemical mechanisms. PMID:25480800

Mean field analysis of a spatial stochastic model of a gene regulatory network.

PubMed

Sturrock, M; Murray, P J; Matzavinos, A; Chaplain, M A J

2015-10-01

A gene regulatory network may be defined as a collection of DNA segments which interact with each other indirectly through their RNA and protein products. Such a network is said to contain a negative feedback loop if its products inhibit gene transcription, and a positive feedback loop if a gene product promotes its own production. Negative feedback loops can create oscillations in mRNA and protein levels while positive feedback loops are primarily responsible for signal amplification. It is often the case in real biological systems that both negative and positive feedback loops operate in parameter regimes that result in low copy numbers of gene products. In this paper we investigate the spatio-temporal dynamics of a single feedback loop in a eukaryotic cell. We first develop a simplified spatial stochastic model of a canonical feedback system (either positive or negative). Using a Gillespie's algorithm, we compute sample trajectories and analyse their corresponding statistics. We then derive a system of equations that describe the spatio-temporal evolution of the stochastic means. Subsequently, we examine the spatially homogeneous case and compare the results of numerical simulations with the spatially explicit case. Finally, using a combination of steady-state analysis and data clustering techniques, we explore model behaviour across a subregion of the parameter space that is difficult to access experimentally and compare the parameter landscape of our spatio-temporal and spatially-homogeneous models.

Optimizing Dynamical Network Structure for Pinning Control

NASA Astrophysics Data System (ADS)

Orouskhani, Yasin; Jalili, Mahdi; Yu, Xinghuo

2016-04-01

Controlling dynamics of a network from any initial state to a final desired state has many applications in different disciplines from engineering to biology and social sciences. In this work, we optimize the network structure for pinning control. The problem is formulated as four optimization tasks: i) optimizing the locations of driver nodes, ii) optimizing the feedback gains, iii) optimizing simultaneously the locations of driver nodes and feedback gains, and iv) optimizing the connection weights. A newly developed population-based optimization technique (cat swarm optimization) is used as the optimization method. In order to verify the methods, we use both real-world networks, and model scale-free and small-world networks. Extensive simulation results show that the optimal placement of driver nodes significantly outperforms heuristic methods including placing drivers based on various centrality measures (degree, betweenness, closeness and clustering coefficient). The pinning controllability is further improved by optimizing the feedback gains. We also show that one can significantly improve the controllability by optimizing the connection weights.

Adolescents' Social Network Site Use, Peer Appearance-Related Feedback, and Body Dissatisfaction: Testing a Mediation Model.

PubMed

de Vries, Dian A; Peter, Jochen; de Graaf, Hanneke; Nikken, Peter

2016-01-01

Previous correlational research indicates that adolescent girls who use social network sites more frequently are more dissatisfied with their bodies. However, we know little about the causal direction of this relationship, the mechanisms underlying this relationship, and whether this relationship also occurs among boys to the same extent. The present two-wave panel study (18 month time lag) among 604 Dutch adolescents (aged 11-18; 50.7% female; 97.7% native Dutch) aimed to fill these gaps in knowledge. Structural equation modeling showed that social network site use predicted increased body dissatisfaction and increased peer influence on body image in the form of receiving peer appearance-related feedback. Peer appearance-related feedback did not predict body dissatisfaction and thus did not mediate the effect of social network site use on body dissatisfaction. Gender did not moderate the findings. Hence, social network sites can play an adverse role in the body image of both adolescent boys and girls.

Squeezed light in an optical parametric oscillator network with coherent feedback quantum control.

PubMed

Crisafulli, Orion; Tezak, Nikolas; Soh, Daniel B S; Armen, Michael A; Mabuchi, Hideo

2013-07-29

We present squeezing and anti-squeezing spectra of the output from a degenerate optical parametric oscillator (OPO) network arranged in different coherent quantum feedback configurations. One OPO serves as a quantum plant, the other as a quantum controller. The addition of coherent feedback enables shaping of the output squeezing spectrum of the plant, and is found to be capable of pushing the frequency of maximum squeezing away from the optical driving frequency and broadening the spectrum over a wider frequency band. The experimental results are in excellent agreement with the developed theory, and illustrate the use of coherent quantum feedback to engineer the quantum-optical properties of the plant OPO output.

The behaviour of basic autocatalytic signalling modules in isolation and embedded in networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Krishnan, J.; Mois, Kristina; Suwanmajo, Thapanar

2014-11-07

In this paper, we examine the behaviour of basic autocatalytic feedback modules involving a species catalyzing its own production, either directly or indirectly. We first perform a systematic study of the autocatalytic feedback module in isolation, examining the effect of different factors, showing how this module is capable of exhibiting monostable threshold and bistable switch-like behaviour. We then study the behaviour of this module embedded in different kinds of basic networks including (essentially) irreversible cycles, open and closed reversible chains, and networks with additional feedback. We study the behaviour of the networks deterministically and also stochastically, using simulations, analytical work,more » and bifurcation analysis. We find that (i) there are significant differences between the behaviour of this module in isolation and in a network: thresholds may be altered or destroyed and bistability may be destroyed or even induced, even when the ambient network is simple. The global characteristics and topology of this network and the position of the module in the ambient network can play important and unexpected roles. (ii) There can be important differences between the deterministic and stochastic dynamics of the module embedded in networks, which may be accentuated by the ambient network. This provides new insights into the functioning of such enzymatic modules individually and as part of networks, with relevance to other enzymatic signalling modules as well.« less

The behaviour of basic autocatalytic signalling modules in isolation and embedded in networks

NASA Astrophysics Data System (ADS)

Krishnan, J.; Mois, Kristina; Suwanmajo, Thapanar

2014-11-01

In this paper, we examine the behaviour of basic autocatalytic feedback modules involving a species catalyzing its own production, either directly or indirectly. We first perform a systematic study of the autocatalytic feedback module in isolation, examining the effect of different factors, showing how this module is capable of exhibiting monostable threshold and bistable switch-like behaviour. We then study the behaviour of this module embedded in different kinds of basic networks including (essentially) irreversible cycles, open and closed reversible chains, and networks with additional feedback. We study the behaviour of the networks deterministically and also stochastically, using simulations, analytical work, and bifurcation analysis. We find that (i) there are significant differences between the behaviour of this module in isolation and in a network: thresholds may be altered or destroyed and bistability may be destroyed or even induced, even when the ambient network is simple. The global characteristics and topology of this network and the position of the module in the ambient network can play important and unexpected roles. (ii) There can be important differences between the deterministic and stochastic dynamics of the module embedded in networks, which may be accentuated by the ambient network. This provides new insights into the functioning of such enzymatic modules individually and as part of networks, with relevance to other enzymatic signalling modules as well.

Improved methods in neural network-based adaptive output feedback control, with applications to flight control

NASA Astrophysics Data System (ADS)

Kim, Nakwan

Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.

Students' Feedback of mDPBL Approach and the Learning Impact towards Computer Networks Teaching and Learning

ERIC Educational Resources Information Center

Winarno, Sri; Muthu, Kalaiarasi Sonai; Ling, Lew Sook

2018-01-01

This study presents students' feedback and learning impact on design and development of a multimedia learning in Direct Problem-Based Learning approach (mDPBL) for Computer Networks in Dian Nuswantoro University, Indonesia. This study examined the usefulness, contents and navigation of the multimedia learning as well as learning impacts towards…

Dual Logic and Cerebral Coordinates for Reciprocal Interaction in Eye Contact

PubMed Central

Lee, Ray F.

2015-01-01

In order to scientifically study the human brain’s response to face-to-face social interaction, the scientific method itself needs to be reconsidered so that both quantitative observation and symbolic reasoning can be adapted to the situation where the observer is also observed. In light of the recent development of dyadic fMRI which can directly observe dyadic brain interacting in one MRI scanner, this paper aims to establish a new form of logic, dual logic, which provides a theoretical platform for deductive reasoning in a complementary dual system with emergence mechanism. Applying the dual logic in the dfMRI experimental design and data analysis, the exogenous and endogenous dual systems in the BOLD responses can be identified; the non-reciprocal responses in the dual system can be suppressed; a cerebral coordinate for reciprocal interaction can be generated. Elucidated by dual logic deductions, the cerebral coordinate for reciprocal interaction suggests: the exogenous and endogenous systems consist of the empathy network and the mentalization network respectively; the default-mode network emerges from the resting state to activation in the endogenous system during reciprocal interaction; the cingulate plays an essential role in the emergence from the exogenous system to the endogenous system. Overall, the dual logic deductions are supported by the dfMRI experimental results and are consistent with current literature. Both the theoretical framework and experimental method set the stage to formally apply the scientific method in studying complex social interaction. PMID:25885446

Dynamic effective connectivity in cortically embedded systems of recurrently coupled synfire chains.

PubMed

Trengove, Chris; Diesmann, Markus; van Leeuwen, Cees

2016-02-01

As a candidate mechanism of neural representation, large numbers of synfire chains can efficiently be embedded in a balanced recurrent cortical network model. Here we study a model in which multiple synfire chains of variable strength are randomly coupled together to form a recurrent system. The system can be implemented both as a large-scale network of integrate-and-fire neurons and as a reduced model. The latter has binary-state pools as basic units but is otherwise isomorphic to the large-scale model, and provides an efficient tool for studying its behavior. Both the large-scale system and its reduced counterpart are able to sustain ongoing endogenous activity in the form of synfire waves, the proliferation of which is regulated by negative feedback caused by collateral noise. Within this equilibrium, diverse repertoires of ongoing activity are observed, including meta-stability and multiple steady states. These states arise in concert with an effective connectivity structure (ECS). The ECS admits a family of effective connectivity graphs (ECGs), parametrized by the mean global activity level. Of these graphs, the strongly connected components and their associated out-components account to a large extent for the observed steady states of the system. These results imply a notion of dynamic effective connectivity as governing neural computation with synfire chains, and related forms of cortical circuitry with complex topologies.

Understanding the role of speech production in reading: Evidence for a print-to-speech neural network using graphical analysis.

PubMed

Cummine, Jacqueline; Cribben, Ivor; Luu, Connie; Kim, Esther; Bahktiari, Reyhaneh; Georgiou, George; Boliek, Carol A

2016-05-01

The neural circuitry associated with language processing is complex and dynamic. Graphical models are useful for studying complex neural networks as this method provides information about unique connectivity between regions within the context of the entire network of interest. Here, the authors explored the neural networks during covert reading to determine the role of feedforward and feedback loops in covert speech production. Brain activity of skilled adult readers was assessed in real word and pseudoword reading tasks with functional MRI (fMRI). The authors provide evidence for activity coherence in the feedforward system (inferior frontal gyrus-supplementary motor area) during real word reading and in the feedback system (supramarginal gyrus-precentral gyrus) during pseudoword reading. Graphical models provided evidence of an extensive, highly connected, neural network when individuals read real words that relied on coordination of the feedforward system. In contrast, when individuals read pseudowords the authors found a limited/restricted network that relied on coordination of the feedback system. Together, these results underscore the importance of considering multiple pathways and articulatory loops during language tasks and provide evidence for a print-to-speech neural network. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Missing piece of the puzzle in the science of consciousness: Resting state and endogenous correlates of consciousness.

PubMed

Havlík, Marek

2017-03-01

Consciousness still stands as one of the most interesting and the most elusive problems of neuroscience. Finding its correlates is the first step toward its satisfactory explanation. Several theories have proposed its correlates but none of them seem to be generally accepted even though most of them share some very similar elements. These elements are the activity of the thalamus, which is considered by some as the central region for consciousness, and gamma synchronization, which should be the general principal for the emergence of conscious experience. However, all of these proposed theories share one characteristic and that is that they do not take into consideration the recently discovered endogenous activity of the brain, which is generally associated with the default mode network. Although the activity of this large scale brain network is in correlation with various levels of consciousness it is still missing in discussions of consciousness. This review recognizes the importance of endogenous activity and points out the important discoveries of endogenous activity that could be an important step toward a satisfactory explanation of consciousness. Copyright © 2017 The Author. Published by Elsevier Inc. All rights reserved.

The neural component-process architecture of endogenously generated emotion

PubMed Central

Kanske, Philipp; Singer, Tania

2017-01-01

Abstract Despite the ubiquity of endogenous emotions and their role in both resilience and pathology, the processes supporting their generation are largely unknown. We propose a neural component process model of endogenous generation of emotion (EGE) and test it in two functional magnetic resonance imaging (fMRI) experiments (N = 32/293) where participants generated and regulated positive and negative emotions based on internal representations, usin self-chosen generation methods. EGE activated nodes of salience (SN), default mode (DMN) and frontoparietal control (FPCN) networks. Component processes implemented by these networks were established by investigating their functional associations, activation dynamics and integration. SN activation correlated with subjective affect, with midbrain nodes exclusively distinguishing between positive and negative affect intensity, showing dynamics consistent generation of core affect. Dorsomedial DMN, together with ventral anterior insula, formed a pathway supporting multiple generation methods, with activation dynamics suggesting it is involved in the generation of elaborated experiential representations. SN and DMN both coupled to left frontal FPCN which in turn was associated with both subjective affect and representation formation, consistent with FPCN supporting the executive coordination of the generation process. These results provide a foundation for research into endogenous emotion in normal, pathological and optimal function. PMID:27522089

Modelling Feedback Excitation, Pacemaker Properties and Sensory Switching of Electrically Coupled Brainstem Neurons Controlling Rhythmic Activity

PubMed Central

Hull, Michael J.; Soffe, Stephen R.; Willshaw, David J.; Roberts, Alan

2016-01-01

What cellular and network properties allow reliable neuronal rhythm generation or firing that can be started and stopped by brief synaptic inputs? We investigate rhythmic activity in an electrically-coupled population of brainstem neurons driving swimming locomotion in young frog tadpoles, and how activity is switched on and off by brief sensory stimulation. We build a computational model of 30 electrically-coupled conditional pacemaker neurons on one side of the tadpole hindbrain and spinal cord. Based on experimental estimates for neuron properties, population sizes, synapse strengths and connections, we show that: long-lasting, mutual, glutamatergic excitation between the neurons allows the network to sustain rhythmic pacemaker firing at swimming frequencies following brief synaptic excitation; activity persists but rhythm breaks down without electrical coupling; NMDA voltage-dependency doubles the range of synaptic feedback strengths generating sustained rhythm. The network can be switched on and off at short latency by brief synaptic excitation and inhibition. We demonstrate that a population of generic Hodgkin-Huxley type neurons coupled by glutamatergic excitatory feedback can generate sustained asynchronous firing switched on and off synaptically. We conclude that networks of neurons with NMDAR mediated feedback excitation can generate self-sustained activity following brief synaptic excitation. The frequency of activity is limited by the kinetics of the neuron membrane channels and can be stopped by brief inhibitory input. Network activity can be rhythmic at lower frequencies if the neurons are electrically coupled. Our key finding is that excitatory synaptic feedback within a population of neurons can produce switchable, stable, sustained firing without synaptic inhibition. PMID:26824331

Short-term depression and transient memory in sensory cortex.

PubMed

Gillary, Grant; Heydt, Rüdiger von der; Niebur, Ernst

2017-12-01

Persistent neuronal activity is usually studied in the context of short-term memory localized in central cortical areas. Recent studies show that early sensory areas also can have persistent representations of stimuli which emerge quickly (over tens of milliseconds) and decay slowly (over seconds). Traditional positive feedback models cannot explain sensory persistence for at least two reasons: (i) They show attractor dynamics, with transient perturbations resulting in a quasi-permanent change of system state, whereas sensory systems return to the original state after a transient. (ii) As we show, those positive feedback models which decay to baseline lose their persistence when their recurrent connections are subject to short-term depression, a common property of excitatory connections in early sensory areas. Dual time constant network behavior has also been implemented by nonlinear afferents producing a large transient input followed by much smaller steady state input. We show that such networks require unphysiologically large onset transients to produce the rise and decay observed in sensory areas. Our study explores how memory and persistence can be implemented in another model class, derivative feedback networks. We show that these networks can operate with two vastly different time courses, changing their state quickly when new information is coming in but retaining it for a long time, and that these capabilities are robust to short-term depression. Specifically, derivative feedback networks with short-term depression that acts differentially on positive and negative feedback projections are capable of dynamically changing their time constant, thus allowing fast onset and slow decay of responses without requiring unrealistically large input transients.

State feedback control design for Boolean networks.

PubMed

Liu, Rongjie; Qian, Chunjiang; Liu, Shuqian; Jin, Yu-Fang

2016-08-26

Driving Boolean networks to desired states is of paramount significance toward our ultimate goal of controlling the progression of biological pathways and regulatory networks. Despite recent computational development of controllability of general complex networks and structural controllability of Boolean networks, there is still a lack of bridging the mathematical condition on controllability to real boolean operations in a network. Further, no realtime control strategy has been proposed to drive a Boolean network. In this study, we applied semi-tensor product to represent boolean functions in a network and explored controllability of a boolean network based on the transition matrix and time transition diagram. We determined the necessary and sufficient condition for a controllable Boolean network and mapped this requirement in transition matrix to real boolean functions and structure property of a network. An efficient tool is offered to assess controllability of an arbitrary Boolean network and to determine all reachable and non-reachable states. We found six simplest forms of controllable 2-node Boolean networks and explored the consistency of transition matrices while extending these six forms to controllable networks with more nodes. Importantly, we proposed the first state feedback control strategy to drive the network based on the status of all nodes in the network. Finally, we applied our reachability condition to the major switch of P53 pathway to predict the progression of the pathway and validate the prediction with published experimental results. This control strategy allowed us to apply realtime control to drive Boolean networks, which could not be achieved by the current control strategy for Boolean networks. Our results enabled a more comprehensive understanding of the evolution of Boolean networks and might be extended to output feedback control design.

Quantifying reflexivity in financial markets: Toward a prediction of flash crashes

NASA Astrophysics Data System (ADS)

Filimonov, Vladimir; Sornette, Didier

2012-05-01

We introduce a measure of activity of financial markets that provides a direct access to their level of endogeneity. This measure quantifies how much of price changes is due to endogenous feedback processes, as opposed to exogenous news. For this, we calibrate the self-excited conditional Poisson Hawkes model, which combines in a natural and parsimonious way exogenous influences with self-excited dynamics, to the E-mini S&P 500 futures contracts traded in the Chicago Mercantile Exchange from 1998 to 2010. We find that the level of endogeneity has increased significantly from 1998 to 2010, with only 70% in 1998 to less than 30% since 2007 of the price changes resulting from some revealed exogenous information. Analogous to nuclear plant safety measures concerned with avoiding “criticality,” our measure provides a direct quantification of the distance of the financial market from a critical state defined precisely as the limit of diverging trading activity in the absence of any external driving.

Construction and modelling of an inducible positive feedback loop stably integrated in a mammalian cell-line.

PubMed

Siciliano, Velia; Menolascina, Filippo; Marucci, Lucia; Fracassi, Chiara; Garzilli, Immacolata; Moretti, Maria Nicoletta; di Bernardo, Diego

2011-06-01

Understanding the relationship between topology and dynamics of transcriptional regulatory networks in mammalian cells is essential to elucidate the biology of complex regulatory and signaling pathways. Here, we characterised, via a synthetic biology approach, a transcriptional positive feedback loop (PFL) by generating a clonal population of mammalian cells (CHO) carrying a stable integration of the construct. The PFL network consists of the Tetracycline-controlled transactivator (tTA), whose expression is regulated by a tTA responsive promoter (CMV-TET), thus giving rise to a positive feedback. The same CMV-TET promoter drives also the expression of a destabilised yellow fluorescent protein (d2EYFP), thus the dynamic behaviour can be followed by time-lapse microscopy. The PFL network was compared to an engineered version of the network lacking the positive feedback loop (NOPFL), by expressing the tTA mRNA from a constitutive promoter. Doxycycline was used to repress tTA activation (switch off), and the resulting changes in fluorescence intensity for both the PFL and NOPFL networks were followed for up to 43 h. We observed a striking difference in the dynamics of the PFL and NOPFL networks. Using non-linear dynamical models, able to recapitulate experimental observations, we demonstrated a link between network topology and network dynamics. Namely, transcriptional positive autoregulation can significantly slow down the "switch off" times, as compared to the non-autoregulated system. Doxycycline concentration can modulate the response times of the PFL, whereas the NOPFL always switches off with the same dynamics. Moreover, the PFL can exhibit bistability for a range of Doxycycline concentrations. Since the PFL motif is often found in naturally occurring transcriptional and signaling pathways, we believe our work can be instrumental to characterise their behaviour.

Reinforced communication and social navigation: Remember your friends and remember yourself

NASA Astrophysics Data System (ADS)

Mirshahvalad, A.; Rosvall, M.

2011-09-01

In social systems, people communicate with each other and form groups based on their interests. The pattern of interactions, the network, and the ideas that flow on the network naturally evolve together. Researchers use simple models to capture the feedback between changing network patterns and ideas on the network, but little is understood about the role of past events in the feedback process. Here, we introduce a simple agent-based model to study the coupling between peoples’ ideas and social networks, and better understand the role of history in dynamic social networks. We measure how information about ideas can be recovered from information about network structure and, the other way around, how information about network structure can be recovered from information about ideas. We find that it is, in general, easier to recover ideas from the network structure than vice versa.

Experiments with arbitrary networks in time-multiplexed delay systems

NASA Astrophysics Data System (ADS)

Hart, Joseph D.; Schmadel, Don C.; Murphy, Thomas E.; Roy, Rajarshi

2017-12-01

We report a new experimental approach using an optoelectronic feedback loop to investigate the dynamics of oscillators coupled on large complex networks with arbitrary topology. Our implementation is based on a single optoelectronic feedback loop with time delays. We use the space-time interpretation of systems with time delay to create large networks of coupled maps. Others have performed similar experiments using high-pass filters to implement the coupling; this restricts the network topology to the coupling of only a few nearest neighbors. In our experiment, the time delays and coupling are implemented on a field-programmable gate array, allowing the creation of networks with arbitrary coupling topology. This system has many advantages: the network nodes are truly identical, the network is easily reconfigurable, and the network dynamics occur at high speeds. We use this system to study cluster synchronization and chimera states in both small and large networks of different topologies.

Matching and selection of a specific subjective experience: conjugate matching and experience.

PubMed

Vimal, Ram Lakhan Pandey

2010-06-01

We incorporate the dual-mode concept in our dual-aspect PE-SE (proto-experience-subjective experience) framework. The two modes are: (1) the non-tilde mode that is the physical (material) and mental aspect of cognition (memory and attention) related feedback signals in a neural-network, which refers to the cognitive nearest past approaching towards present; and (2) the tilde mode that is the material and mental aspect of the feed-forward signals due to external environmental input and internal endogenous input, which pertains to the nearest future approaching towards present and is a entropy-reversed representation of non-tilde mode. Furthermore, one could argue that there are at least five sub-pathways in the stimulus-dependent feed-forward pathway and cognitive feedback pathway for information transfer in the brain dynamics: (i) classical axonal-dendritic neural sub-pathway including electromagnetic information field sub-pathway; (ii) quantum dendritic-dendritic microtubule (MT) (dendritic webs) sub-pathway; (iii) Ca(++)-related astroglial-neural sub-pathway; (iv) (a) the sub-pathway related to extrasynaptic signal transmission between fine distal dendrites of cortical neurons for the local subtle modulation due to voltages created by intradendritic dual-aspect charged surface effects within the Debye layer around endogenous structures such as microtubules (MT) and endoplasmic reticulum (ER) in dendrites, and (b) the sub-pathway related to extracellular volume transmission as fields of neural activity for the global modulation in axonal-dendritic neural sub-pathway; and (v) the sub-pathway related to information transmission via soliton propagation. We propose that: (i) the quantum conjugate matching between experiences in the mental aspect of the tilde mode and that of the non-tilde mode is related more to the mental aspect of the quantum microtubule-dendritic-web and less to that of the non-quantum sub-pathways; and (ii) the classical matching between experiences in the mental aspect of the tilde mode and that of the non-tilde mode is related to the mental aspect of the non-quantum sub-pathways (such as classical axonal-dendritic neural sub-pathway). In both cases, a specific SE is selected when the tilde mode interacts with the non-tilde mode to match for a specific SE, and when the necessary ingredients of SEs (such as the formation of neural networks, wakefulness, re-entry, attention, working memory, and so on) are satisfied. When the conjugate match is made between the two modes, the world-presence (Now) is disclosed. The material aspects in the tilde mode and that in the non-tilde mode are matched to link structure with function, whereas the mental aspects in the tilde mode and that in the non-tilde mode are matched to link experience with structure and function.

Origin of Pareto-like spatial distributions in ecosystems.

PubMed

Manor, Alon; Shnerb, Nadav M

2008-12-31

Recent studies of cluster distribution in various ecosystems revealed Pareto statistics for the size of spatial colonies. These results were supported by cellular automata simulations that yield robust criticality for endogenous pattern formation based on positive feedback. We show that this patch statistics is a manifestation of the law of proportionate effect. Mapping the stochastic model to a Markov birth-death process, the transition rates are shown to scale linearly with cluster size. This mapping provides a connection between patch statistics and the dynamics of the ecosystem; the "first passage time" for different colonies emerges as a powerful tool that discriminates between endogenous and exogenous clustering mechanisms. Imminent catastrophic shifts (such as desertification) manifest themselves in a drastic change of the stability properties of spatial colonies.

Climate-society feedbacks and the avoidance of dangerous climate change

NASA Astrophysics Data System (ADS)

Jarvis, A. J.; Leedal, D. T.; Hewitt, C. N.

2012-09-01

The growth in anthropogenic CO2 emissions experienced since the onset of the Industrial Revolution is the most important disturbance operating on the Earth's climate system. To avoid dangerous climate change, future greenhouse-gas emissions will have to deviate from business-as-usual trajectories. This implies that feedback links need to exist between climate change and societal actions. Here, we show that, consciously or otherwise, these feedbacks can be represented by linking global mean temperature change to the growth dynamics of CO2 emissions. We show that the global growth of new renewable sources of energy post-1990 represents a climate-society feedback of ~0.25%yr-1 per degree increase in global mean temperature. We also show that to fulfil the outcomes negotiated in Durban in 2011, society will have to become ~ 50 times more responsive to global mean temperature change than it has been since 1990. If global energy use continues to grow as it has done historically then this would result in amplification of the long-term endogenous rate of decarbonization from -0.6%yr-1 to ~-13%yr-1. It is apparent that modest levels of feedback sensitivity pay large dividends in avoiding climate change but that the marginal return on this effort diminishes rapidly as the required feedback strength increases.

A wearable biofeedback control system based body area network for freestyle swimming.

PubMed

Rui Li; Zibo Cai; WeeSit Lee; Lai, Daniel T H

2016-08-01

Wearable posture measurement units are capable of enabling real-time performance evaluation and providing feedback to end users. This paper presents a wearable feedback prototype designed for freestyle swimming with focus on trunk rotation measurement. The system consists of a nine-degree-of-freedom inertial sensor, which is built in a central data collection and processing unit, and two vibration motors for delivering real-time feedback. Theses devices form a fundamental body area network (BAN). In the experiment setup, four recreational swimmers were asked to do two sets of 4 x 25m freestyle swimming without and with feedback provided respectively. Results showed that real-time biofeedback mechanism improves swimmers kinematic performance by an average of 4.5% reduction in session time. Swimmers can gradually adapt to feedback signals, and the biofeedback control system can be employed in swimmers daily training for fitness maintenance.

Implications of behavioral architecture for the evolution of self-organized division of labor.

PubMed

Duarte, A; Scholtens, E; Weissing, F J

2012-01-01

Division of labor has been studied separately from a proximate self-organization and an ultimate evolutionary perspective. We aim to bring together these two perspectives. So far this has been done by choosing a behavioral mechanism a priori and considering the evolution of the properties of this mechanism. Here we use artificial neural networks to allow for a more open architecture. We study whether emergent division of labor can evolve in two different network architectures; a simple feedforward network, and a more complex network that includes the possibility of self-feedback from previous experiences. We focus on two aspects of division of labor; worker specialization and the ratio of work performed for each task. Colony fitness is maximized by both reducing idleness and achieving a predefined optimal work ratio. Our results indicate that architectural constraints play an important role for the outcome of evolution. With the simplest network, only genetically determined specialization is possible. This imposes several limitations on worker specialization. Moreover, in order to minimize idleness, networks evolve a biased work ratio, even when an unbiased work ratio would be optimal. By adding self-feedback to the network we increase the network's flexibility and worker specialization evolves under a wider parameter range. Optimal work ratios are more easily achieved with the self-feedback network, but still provide a challenge when combined with worker specialization.

Implications of Behavioral Architecture for the Evolution of Self-Organized Division of Labor

PubMed Central

Duarte, A.; Scholtens, E.; Weissing, F. J.

2012-01-01

Division of labor has been studied separately from a proximate self-organization and an ultimate evolutionary perspective. We aim to bring together these two perspectives. So far this has been done by choosing a behavioral mechanism a priori and considering the evolution of the properties of this mechanism. Here we use artificial neural networks to allow for a more open architecture. We study whether emergent division of labor can evolve in two different network architectures; a simple feedforward network, and a more complex network that includes the possibility of self-feedback from previous experiences. We focus on two aspects of division of labor; worker specialization and the ratio of work performed for each task. Colony fitness is maximized by both reducing idleness and achieving a predefined optimal work ratio. Our results indicate that architectural constraints play an important role for the outcome of evolution. With the simplest network, only genetically determined specialization is possible. This imposes several limitations on worker specialization. Moreover, in order to minimize idleness, networks evolve a biased work ratio, even when an unbiased work ratio would be optimal. By adding self-feedback to the network we increase the network's flexibility and worker specialization evolves under a wider parameter range. Optimal work ratios are more easily achieved with the self-feedback network, but still provide a challenge when combined with worker specialization. PMID:22457609

Disseminating educational innovations in health care practice: training versus social networks.

PubMed

Jippes, Erik; Achterkamp, Marjolein C; Brand, Paul L P; Kiewiet, Derk Jan; Pols, Jan; van Engelen, Jo M L

2010-05-01

Improvements and innovation in health service organization and delivery have become more and more important due to the gap between knowledge and practice, rising costs, medical errors, and the organization of health care systems. Since training and education is widely used to convey and distribute innovative initiatives, we examined the effect that following an intensive Teach-the-Teacher training had on the dissemination of a new structured competency-based feedback technique of assessing clinical competencies among medical specialists in the Netherlands. We compared this with the effect of the structure of the social network of medical specialists, specifically the network tie strength (strong ties versus weak ties). We measured dissemination of the feedback technique by using a questionnaire filled in by Obstetrics & Gynecology and Pediatrics residents (n=63). Data on network tie strength was gathered with a structured questionnaire given to medical specialists (n=81). Social network analysis was used to compose the required network coefficients. We found a strong effect for network tie strength and no effect for the Teach-the-Teacher training course on the dissemination of the new structured feedback technique. This paper shows the potential that social networks have for disseminating innovations in health service delivery and organization. Further research is needed into the role and structure of social networks on the diffusion of innovations between departments and the various types of innovations involved. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Effects of endogenous small molecular compounds on the rheological properties, texture and microstructure of soymilk coagulum: Removal of phytate using ultrafiltration.

PubMed

Wang, Ruican; Guo, Shuntang

2016-11-15

This study aims to clarify the roles played by endogenous small molecular components in soymilk coagulation process and the properties of gels. Soymilk samples with decreasing levels of small molecules were prepared by ultrafiltration, to reduce the amount of phytate and salts. CaSO4-induced coagulation process was analyzed using rheological methods. Results showed that removal of free small molecules decreased the activation energy of protein coagulation, resulting in accelerated reaction and increased gel strength. However, too fast a reaction led to the drop in storage modulus (G'). Microscopic observation suggested that accelerated coagulation generated a coarse and non-uniform gel network with large pores. This network could not hold much water, leading to serious syneresis. Endogenous small molecules in soymilk were vital in the fine gel structure. Coagulation rate could be controlled by adjusting the amount of small molecules to obtain tofu products with the optimal texture. Copyright © 2016 Elsevier Ltd. All rights reserved.

NT3-chitosan elicits robust endogenous neurogenesis to enable functional recovery after spinal cord injury

PubMed Central

Yang, Zhaoyang; Zhang, Aifeng; Duan, Hongmei; Zhang, Sa; Hao, Peng; Ye, Keqiang; Sun, Yi E.; Li, Xiaoguang

2015-01-01

Neural stem cells (NSCs) in the adult mammalian central nervous system (CNS) hold the key to neural regeneration through proper activation, differentiation, and maturation, to establish nascent neural networks, which can be integrated into damaged neural circuits to repair function. However, the CNS injury microenvironment is often inhibitory and inflammatory, limiting the ability of activated NSCs to differentiate into neurons and form nascent circuits. Here we report that neurotrophin-3 (NT3)-coupled chitosan biomaterial, when inserted into a 5-mm gap of completely transected and excised rat thoracic spinal cord, elicited robust activation of endogenous NSCs in the injured spinal cord. Through slow release of NT3, the biomaterial attracted NSCs to migrate into the lesion area, differentiate into neurons, and form functional neural networks, which interconnected severed ascending and descending axons, resulting in sensory and motor behavioral recovery. Our study suggests that enhancing endogenous neurogenesis could be a novel strategy for treatment of spinal cord injury. PMID:26460015

The Role of Regional Conferences in Research Resident Career Development: The California Psychiatry Research Resident Retreat.

PubMed

Besterman, Aaron D; Williams, Jody K; Reus, Victor I; Pato, Michele T; Voglmaier, Susan M; Mathews, Carol A

2017-04-01

For psychiatry research resident career development, there is a recognized need for improved cross-institutional mentoring and networking opportunities. One method to address this need is via regional conferences, open to current and recently graduated research residents and their mentors. With this in mind, we developed the biennial California Psychiatry Research Resident Retreat (CPRRR) and collected feedback from participants to 1) Assess resident satisfaction, 2) Determine the utility of the retreat as a networking and mentorship tool, and 3) Identify areas for improvement. We gathered survey data from resident attendees at the two first CPRRRs. We analyzed the data to look for trends in satisfaction as well as areas that need improvement. Thirty-two residents from five California training programs attended the CPRRR in 2013 while 33 attended from six programs in 2015. The residents were from all years of training, but concentrated in their second and third years. Approximately 41% and 49% of the attendees were female and 53% and 39% had an MD/PhD in 2013 and 2015, respectively. Twenty-four and 32 residents provided anonymous feedback in 2013 and 2015, respectively. Mean feedback scores were very high (> 4/5) for overall satisfaction, peer- and faculty-networking, the keynote speaker and the flash talks for both years. Mean feedback scores for the ethics debates and mentoring sessions were somewhat lower (≤ 4/5), however, both showed significant improvement from 2013 to 2015. The CPRRRs appear to be an effective mechanism for providing psychiatry research residents with a meaningful cross-institutional opportunity for networking and mentorship. Feedback-driven changes to the CPRRRs improved participant satisfaction for several components of the conference. Future efforts will be aimed at broadening mentorship and networking opportunities, optimizing teaching approaches for research ethics, and considering different feedback-gathering approaches to allow for improved longitudinal follow-up and subgroup analysis.

Engineering microbial phenotypes through rewiring of genetic networks

PubMed Central

Rodrigues, Rui T.L.; Lee, Sangjin; Haines, Matthew

2017-01-01

Abstract The ability to program cellular behaviour is a major goal of synthetic biology, with applications in health, agriculture and chemicals production. Despite efforts to build ‘orthogonal’ systems, interactions between engineered genetic circuits and the endogenous regulatory network of a host cell can have a significant impact on desired functionality. We have developed a strategy to rewire the endogenous cellular regulatory network of yeast to enhance compatibility with synthetic protein and metabolite production. We found that introducing novel connections in the cellular regulatory network enabled us to increase the production of heterologous proteins and metabolites. This strategy is demonstrated in yeast strains that show significantly enhanced heterologous protein expression and higher titers of terpenoid production. Specifically, we found that the addition of transcriptional regulation between free radical induced signalling and nitrogen regulation provided robust improvement of protein production. Assessment of rewired networks revealed the importance of key topological features such as high betweenness centrality. The generation of rewired transcriptional networks, selection for specific phenotypes, and analysis of resulting library members is a powerful tool for engineering cellular behavior and may enable improved integration of heterologous protein and metabolite pathways. PMID:28369627

An Investigation of the Application of Artificial Neural Networks to Adaptive Optics Imaging Systems

DTIC Science & Technology

1991-12-01

neural network and the feedforward neural network studied is the single layer perceptron artificial neural network . The recurrent artificial neural network input...features are the wavefront sensor slope outputs and neighboring actuator feedback commands. The feedforward artificial neural network input

Feedback (F) Fueling Adaptation (A) Network Growth (N) and Self-Organization (S): A Complex Systems Design and Evaluation Approach to Professional Development

ERIC Educational Resources Information Center

Yoon, Susan A.; Klopfer, Eric

2006-01-01

This paper reports on the efficacy of a professional development framework premised on four complex systems design principles: Feedback, Adaptation, Network Growth and Self-organization (FANS). The framework is applied to the design and delivery of the first 2 years of a 3-year study aimed at improving teacher and student understanding of…

Feedback Controller Design for the Synchronization of Boolean Control Networks.

PubMed

Liu, Yang; Sun, Liangjie; Lu, Jianquan; Liang, Jinling

2016-09-01

This brief investigates the partial and complete synchronization of two Boolean control networks (BCNs). Necessary and sufficient conditions for partial and complete synchronization are established by the algebraic representations of logical dynamics. An algorithm is obtained to construct the feedback controller that guarantees the synchronization of master and slave BCNs. Two biological examples are provided to illustrate the effectiveness of the obtained results.

A network of networks model to study phase synchronization using structural connection matrix of human brain

NASA Astrophysics Data System (ADS)

Ferrari, F. A. S.; Viana, R. L.; Reis, A. S.; Iarosz, K. C.; Caldas, I. L.; Batista, A. M.

2018-04-01

The cerebral cortex plays a key role in complex cortical functions. It can be divided into areas according to their function (motor, sensory and association areas). In this paper, the cerebral cortex is described as a network of networks (cortex network), we consider that each cortical area is composed of a network with small-world property (cortical network). The neurons are assumed to have bursting properties with the dynamics described by the Rulkov model. We study the phase synchronization of the cortex network and the cortical networks. In our simulations, we verify that synchronization in cortex network is not homogeneous. Besides, we focus on the suppression of neural phase synchronization. Synchronization can be related to undesired and pathological abnormal rhythms in the brain. For this reason, we consider the delayed feedback control to suppress the synchronization. We show that delayed feedback control is efficient to suppress synchronous behavior in our network model when an appropriate signal intensity and time delay are defined.

Ramifying feedback networks, cross-scale interactions, and emergent quasi individuals in Conway's game of Life.

PubMed

Gotts, Nicholas M

2009-01-01

Small patterns of state 1 cells on an infinite, otherwise empty array of Conway's game of Life can produce sets of growing structures resembling in significant ways a population of spatially situated individuals in a nonuniform, highly structured environment. Ramifying feedback networks and cross-scale interactions play a central role in the emergence and subsequent dynamics of the quasi population. The implications are discussed: It is proposed that analogous networks and interactions may have been precursors to natural selection in the real world.

Involvement of anteroventral periventricular metastin/kisspeptin neurons in estrogen positive feedback action on luteinizing hormone release in female rats.

PubMed

Adachi, Sachika; Yamada, Shunji; Takatsu, Yoshihiro; Matsui, Hisanori; Kinoshita, Mika; Takase, Kenji; Sugiura, Hitomi; Ohtaki, Tetsuya; Matsumoto, Hirokazu; Uenoyama, Yoshihisa; Tsukamura, Hiroko; Inoue, Kinji; Maeda, Kei-Ichiro

2007-04-01

Metastin/kisspeptin, the KiSS-1 gene product, has been identified as an endogenous ligand of GPR54 that reportedly regulates GnRH/LH surges and estrous cyclicity in female rats. The aim of the present study was to determine if metastin/kisspeptin neurons are a target of estrogen positive feedback to induce GnRH/LH surges. We demonstrated that preoptic area (POA) infusion of the anti-rat metastin/kisspeptin monoclonal antibody blocked the estrogen-induced LH surge, indicating that endogenous metastin/kisspeptin released around the POA mediates the estrogen positive feedback effect on GnRH/LH release. Metastin/kisspeptin neurons in the anteroventral periventricular nucleus (AVPV) may be responsible for mediating the feedback effect because the percentage of c-Fos-expressing KiSS-1 mRNA-positive cells to total KiSS-1 mRNA-positive cells was significantly higher in the afternoon than in the morning in the anteroventral periventricular nucleus (AVPV) of high estradiol (E(2))-treated females. The percentage of c-Fos-expressing metastin/kisspeptin neurons was not different between the afternoon and morning in the arcuate nucleus (ARC). Most of the KiSS-1 mRNA expressing cells contain ERalpha immunoreactivity in the AVPV and ARC. In addition, AVPV KiSS-1 mRNA expressions were highest in the proestrous afternoon and lowest in the diestrus 1 in females and were increased by estrogen treatment in ovariectomized animals. On the other hand, the ARC KiSS-1 mRNA expressions were highest at diestrus 2 and lowest at proestrous afternoon and were increased by ovariectomy and decreased by high estrogen treatment. Males lacking the surge mode of GnRH/LH release showed no obvious cluster of metastin/kisspeptin-immunoreactive neurons in the AVPV when compared with high E(2)-treated females, which showed a much greater density of these neurons. Taken together, the present study demonstrates that the AVPV metastin/kisspeptin neurons are a target of estrogen positive feedback to induce GnRH/LH surges in female rats.

Cushing's syndrome: from physiological principles to diagnosis and clinical care.

PubMed

Raff, Hershel; Carroll, Ty

2015-02-01

The physiological control of cortisol synthesis in the adrenal cortex involves stimulation of adrenocorticotrophic hormone (ACTH) by hypothalamic corticotrophin-releasing hormone (CRH) and then stimulation of the adrenal by ACTH. The control loop of the hypothalamic-pituitary-adrenal (HPA) axis is closed by negative feedback of cortisol on the hypothalamus and pituitary. Understanding this system is required to master the diagnosis, differential diagnosis and treatment of endogenous hypercortisolism--Cushing's syndrome. Endogenous Cushing's syndrome is caused either by excess ACTH secretion or by autonomous cortisol release from the adrenal cortex. Diagnosis of cortisol excess exploits three physiological principles: failure to achieve the normal nadir in the cortisol diurnal rhythm, loss of sensitivity of ACTH-secreting tumours to cortisol negative feedback, and increased excretion of free cortisol in the urine. Differentiating a pituitary source of excess ACTH (Cushing's disease) from an ectopic source is accomplished by imaging the pituitary and sampling for ACTH in the venous drainage of the pituitary. With surgical removal of ACTH or cortisol-secreting tumours, secondary adrenal insufficiency ensues because of the prior suppression of the HPA axis by glucocorticoid negative feedback. Medical therapy is targeted to the anatomical location of the dysregulated component of the HPA axis. Future research will focus on new diagnostics and treatments of Cushing's syndrome. These are elegant examples of translational research: understanding basic physiology informs the development of new approaches to diagnosis and treatment. Appreciating pathophysiology generates new areas for inquiry of basic physiological and biochemical mechanisms. © 2014 The Authors. The Journal of Physiology © 2014 The Physiological Society.

Video-based peer feedback through social networking for robotic surgery simulation: a multicenter randomized controlled trial.

PubMed

Carter, Stacey C; Chiang, Alexander; Shah, Galaxy; Kwan, Lorna; Montgomery, Jeffrey S; Karam, Amer; Tarnay, Christopher; Guru, Khurshid A; Hu, Jim C

2015-05-01

To examine the feasibility and outcomes of video-based peer feedback through social networking to facilitate robotic surgical skill acquisition. The acquisition of surgical skills may be challenging for novel techniques and/or those with prolonged learning curves. Randomized controlled trial involving 41 resident physicians performing the Tubes (Da Vinci Intuitive Surgical, Sunnyvale, CA) simulator exercise with versus without peer feedback of video-recorded performance through a social networking Web page. Data collected included simulator exercise score, time to completion, and comfort and satisfaction with robotic surgery simulation. There were no baseline differences between the intervention group (n = 20) and controls (n = 21). The intervention group showed improvement in mean scores from session 1 to sessions 2 and 3 (60.7 vs 75.5, P < 0.001, and 60.7 vs 80.1, P < 0.001, respectively). The intervention group scored significantly higher than controls at sessions 2 and 3 (75.5 vs 59.6, P = 0.009, and 80.1 vs 65.9, P = 0.019, respectively). The mean time (seconds) to complete the task was shorter for the intervention group than for controls during sessions 2 and 3 (217.4 vs 279.0, P = 0.004, and 201.4 vs 261.9, P = 0.006, respectively). At the study conclusion, feedback subjects were more comfortable with robotic surgery than controls (90% vs 62%, P = 0.021) and expressed greater satisfaction with the learning experience (100% vs 67%, P = 0.014). Of the intervention subjects, 85% found that peer feedback was useful and 100% found it effective. Video-based peer feedback through social networking appears to be an effective paradigm for surgical education and accelerates the robotic surgery learning curve during simulation.

Modelling and analysis of gene regulatory network using feedback control theory

NASA Astrophysics Data System (ADS)

El-Samad, H.; Khammash, M.

2010-01-01

Molecular pathways are a part of a remarkable hierarchy of regulatory networks that operate at all levels of organisation. These regulatory networks are responsible for much of the biological complexity within the cell. The dynamic character of these pathways and the prevalence of feedback regulation strategies in their operation make them amenable to systematic mathematical analysis using the same tools that have been used with success in analysing and designing engineering control systems. In this article, we aim at establishing this strong connection through various examples where the behaviour exhibited by gene networks is explained in terms of their underlying control strategies. We complement our analysis by a survey of mathematical techniques commonly used to model gene regulatory networks and analyse their dynamic behaviour.

Cooperative learning neural network output feedback control of uncertain nonlinear multi-agent systems under directed topologies

NASA Astrophysics Data System (ADS)

Wang, W.; Wang, D.; Peng, Z. H.

2017-09-01

Without assuming that the communication topologies among the neural network (NN) weights are to be undirected and the states of each agent are measurable, the cooperative learning NN output feedback control is addressed for uncertain nonlinear multi-agent systems with identical structures in strict-feedback form. By establishing directed communication topologies among NN weights to share their learned knowledge, NNs with cooperative learning laws are employed to identify the uncertainties. By designing NN-based κ-filter observers to estimate the unmeasurable states, a new cooperative learning output feedback control scheme is proposed to guarantee that the system outputs can track nonidentical reference signals with bounded tracking errors. A simulation example is given to demonstrate the effectiveness of the theoretical results.

Robust output feedback H∞ control for networked control systems based on the occurrence probabilities of time delays

NASA Astrophysics Data System (ADS)

Guo, Chenyu; Zhang, Weidong; Bao, Jie

2012-02-01

This article is concerned with the problem of robust H ∞ output feedback control for a kind of networked control systems with time-varying network-induced delays. Instead of using boundaries of time delays to represent all time delays, the occurrence probability of each time delay is considered in H∞ stability analysis and stabilisation. The problem addressed is the design of an output feedback controller such that, for all admissible uncertainties, the resulting closed-loop system is stochastically stable for the zero disturbance input and also simultaneously achieves a prescribed H∞ performance level. It is shown that less conservativeness is obtained. A set of linear matrix inequalities is given to solve the corresponding controller design problem. An example is provided to show the effectiveness and applicability of the proposed method.

Event-triggered output feedback control for distributed networked systems.

PubMed

Mahmoud, Magdi S; Sabih, Muhammad; Elshafei, Moustafa

2016-01-01

This paper addresses the problem of output-feedback communication and control with event-triggered framework in the context of distributed networked control systems. The design problem of the event-triggered output-feedback control is proposed as a linear matrix inequality (LMI) feasibility problem. The scheme is developed for the distributed system where only partial states are available. In this scheme, a subsystem uses local observers and share its information to its neighbors only when the subsystem's local error exceeds a specified threshold. The developed method is illustrated by using a coupled cart example from the literature. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A positive feedback at the cellular level promotes robustness and modulation at the circuit level

PubMed Central

Dethier, Julie; Drion, Guillaume; Franci, Alessio

2015-01-01

This article highlights the role of a positive feedback gating mechanism at the cellular level in the robustness and modulation properties of rhythmic activities at the circuit level. The results are presented in the context of half-center oscillators, which are simple rhythmic circuits composed of two reciprocally connected inhibitory neuronal populations. Specifically, we focus on rhythms that rely on a particular excitability property, the postinhibitory rebound, an intrinsic cellular property that elicits transient membrane depolarization when released from hyperpolarization. Two distinct ionic currents can evoke this transient depolarization: a hyperpolarization-activated cation current and a low-threshold T-type calcium current. The presence of a slow activation is specific to the T-type calcium current and provides a slow positive feedback at the cellular level that is absent in the cation current. We show that this slow positive feedback is required to endow the network rhythm with physiological modulation and robustness properties. This study thereby identifies an essential cellular property to be retained at the network level in modeling network robustness and modulation. PMID:26311181

Adaptive neural network output feedback control for stochastic nonlinear systems with unknown dead-zone and unmodeled dynamics.

PubMed

Tong, Shaocheng; Wang, Tong; Li, Yongming; Zhang, Huaguang

2014-06-01

This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

Negative feedback in ants: crowding results in less trail pheromone deposition

PubMed Central

Czaczkes, Tomer J.; Grüter, Christoph; Ratnieks, Francis L. W.

2013-01-01

Crowding in human transport networks reduces efficiency. Efficiency can be increased by appropriate control mechanisms, which are often imposed externally. Ant colonies also have distribution networks to feeding sites outside the nest and can experience crowding. However, ants do not have external controllers or leaders. Here, we report a self-organized negative feedback mechanism, based on local information, which downregulates the production of recruitment signals in crowded parts of a network by Lasius niger ants. We controlled crowding by manipulating trail width and the number of ants on a trail, and observed a 5.6-fold reduction in the number of ants depositing trail pheromone from least to most crowded conditions. We also simulated crowding by placing glass beads covered in nest-mate cuticular hydrocarbons on the trail. After 10 bead encounters over 20 cm, forager ants were 45 per cent less likely to deposit pheromone. The mechanism of negative feedback reported here is unusual in that it acts by downregulating the production of a positive feedback signal, rather than by direct inhibition or the production of an inhibitory signal. PMID:23365196

TP53 regulates miRNA association with AGO2 to remodel the miRNA-mRNA interaction network.

PubMed

Krell, Jonathan; Stebbing, Justin; Carissimi, Claudia; Dabrowska, Aleksandra F; de Giorgio, Alexander; Frampton, Adam E; Harding, Victoria; Fulci, Valerio; Macino, Giuseppe; Colombo, Teresa; Castellano, Leandro

2016-03-01

DNA damage activates TP53-regulated surveillance mechanisms that are crucial in suppressing tumorigenesis. TP53 orchestrates these responses directly by transcriptionally modulating genes, including microRNAs (miRNAs), and by regulating miRNA biogenesis through interacting with the DROSHA complex. However, whether the association between miRNAs and AGO2 is regulated following DNA damage is not yet known. Here, we show that, following DNA damage, TP53 interacts with AGO2 to induce or reduce AGO2's association of a subset of miRNAs, including multiple let-7 family members. Furthermore, we show that specific mutations in TP53 decrease rather than increase the association of let-7 family miRNAs, reducing their activity without preventing TP53 from interacting with AGO2. This is consistent with the oncogenic properties of these mutants. Using AGO2 RIP-seq and PAR-CLIP-seq, we show that the DNA damage-induced increase in binding of let-7 family members to the RISC complex is functional. We unambiguously determine the global miRNA-mRNA interaction networks involved in the DNA damage response, validating them through the identification of miRNA-target chimeras formed by endogenous ligation reactions. We find that the target complementary region of the let-7 seed tends to have highly fixed positions and more variable ones. Additionally, we observe that miRNAs, whose cellular abundance or differential association with AGO2 is regulated by TP53, are involved in an intricate network of regulatory feedback and feedforward circuits. TP53-mediated regulation of AGO2-miRNA interaction represents a new mechanism of miRNA regulation in carcinogenesis. © 2016 Krell et al.; Published by Cold Spring Harbor Laboratory Press.

Quaternion-based adaptive output feedback attitude control of spacecraft using Chebyshev neural networks.

PubMed

Zou, An-Min; Dev Kumar, Krishna; Hou, Zeng-Guang

2010-09-01

This paper investigates the problem of output feedback attitude control of an uncertain spacecraft. Two robust adaptive output feedback controllers based on Chebyshev neural networks (CNN) termed adaptive neural networks (NN) controller-I and adaptive NN controller-II are proposed for the attitude tracking control of spacecraft. The four-parameter representations (quaternion) are employed to describe the spacecraft attitude for global representation without singularities. The nonlinear reduced-order observer is used to estimate the derivative of the spacecraft output, and the CNN is introduced to further improve the control performance through approximating the spacecraft attitude motion. The implementation of the basis functions of the CNN used in the proposed controllers depends only on the desired signals, and the smooth robust compensator using the hyperbolic tangent function is employed to counteract the CNN approximation errors and external disturbances. The adaptive NN controller-II can efficiently avoid the over-estimation problem (i.e., the bound of the CNNs output is much larger than that of the approximated unknown function, and hence, the control input may be very large) existing in the adaptive NN controller-I. Both adaptive output feedback controllers using CNN can guarantee that all signals in the resulting closed-loop system are uniformly ultimately bounded. For performance comparisons, the standard adaptive controller using the linear parameterization of spacecraft attitude motion is also developed. Simulation studies are presented to show the advantages of the proposed CNN-based output feedback approach over the standard adaptive output feedback approach.

Optimal occlusion uniformly partitions red blood cells fluxes within a microvascular network

PubMed Central

Tu, Shenyinying; Liu, Yu-Hsiu; Savage, Van M.; Hsiai, Tzung K.; Roper, Marcus

2017-01-01

In animals, gas exchange between blood and tissues occurs in narrow vessels, whose diameter is comparable to that of a red blood cell. Red blood cells must deform to squeeze through these narrow vessels, transiently blocking or occluding the vessels they pass through. Although the dynamics of vessel occlusion have been studied extensively, it remains an open question why microvessels need to be so narrow. We study occlusive dynamics within a model microvascular network: the embryonic zebrafish trunk. We show that pressure feedbacks created when red blood cells enter the finest vessels of the trunk act together to uniformly partition red blood cells through the microvasculature. Using mathematical models as well as direct observation, we show that these occlusive feedbacks are tuned throughout the trunk network to prevent the vessels closest to the heart from short-circuiting the network. Thus occlusion is linked with another open question of microvascular function: how are red blood cells delivered at the same rate to each micro-vessel? Our analysis shows that tuning of occlusive feedbacks increase the total dissipation within the network by a factor of 11, showing that uniformity of flows rather than minimization of transport costs may be prioritized by the microvascular network. PMID:29244812

A Continuum Model of Actin Waves in Dictyostelium discoideum

PubMed Central

Khamviwath, Varunyu; Hu, Jifeng; Othmer, Hans G.

2013-01-01

Actin waves are complex dynamical patterns of the dendritic network of filamentous actin in eukaryotes. We developed a model of actin waves in PTEN-deficient Dictyostelium discoideum by deriving an approximation of the dynamics of discrete actin filaments and combining it with a signaling pathway that controls filament branching. This signaling pathway, together with the actin network, contains a positive feedback loop that drives the actin waves. Our model predicts the structure, composition, and dynamics of waves that are consistent with existing experimental evidence, as well as the biochemical dependence on various protein partners. Simulation suggests that actin waves are initiated when local actin network activity, caused by an independent process, exceeds a certain threshold. Moreover, diffusion of proteins that form a positive feedback loop with the actin network alone is sufficient for propagation of actin waves at the observed speed of . Decay of the wave back can be caused by scarcity of network components, and the shape of actin waves is highly dependent on the filament disassembly rate. The model allows retraction of actin waves and captures formation of new wave fronts in broken waves. Our results demonstrate that a delicate balance between a positive feedback, filament disassembly, and local availability of network components is essential for the complex dynamics of actin waves. PMID:23741312

Prosody production networks are modulated by sensory cues and social context.

PubMed

Klasen, Martin; von Marschall, Clara; Isman, Güldehen; Zvyagintsev, Mikhail; Gur, Ruben C; Mathiak, Klaus

2018-03-05

The neurobiology of emotional prosody production is not well investigated. In particular, the effects of cues and social context are not known. The present study sought to differentiate cued from free emotion generation and the effect of social feedback from a human listener. Online speech filtering enabled fMRI during prosodic communication in 30 participants. Emotional vocalizations were a) free, b) auditorily cued, c) visually cued, or d) with interactive feedback. In addition to distributed language networks, cued emotions increased activity in auditory and - in case of visual stimuli - visual cortex. Responses were larger in pSTG at the right hemisphere and the ventral striatum when participants were listened to and received feedback from the experimenter. Sensory, language, and reward networks contributed to prosody production and were modulated by cues and social context. The right pSTG is a central hub for communication in social interactions - in particular for interpersonal evaluation of vocal emotions.

Perturbed cooperative-state feedback strategy for model predictive networked control of interconnected systems.

PubMed

Tran, Tri; Ha, Q P

2018-01-01

A perturbed cooperative-state feedback (PSF) strategy is presented for the control of interconnected systems in this paper. The subsystems of an interconnected system can exchange data via the communication network that has multiple connection topologies. The PSF strategy can resolve both issues, the sensor data losses and the communication network breaks, thanks to the two components of the control including a cooperative-state feedback and a perturbation variable, e.g., u i =K ij x j +w i . The PSF is implemented in a decentralized model predictive control scheme with a stability constraint and a non-monotonic storage function (ΔV(x(k))≥0), derived from the dissipative systems theory. Numerical simulation for the automatic generation control problem in power systems is studied to illustrate the effectiveness of the presented PSF strategy. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Developing a theory of the societal lifecycle of cigarette smoking : explaining and anticipating trends using information feedback.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brodsky, Nancy S.; Glass, Robert John, Jr.; Zagonel, Aldo A.

Cigarette smoking presented the most significant public health challenge in the United States in the 20th Century and remains the single most preventable cause of morbidity and mortality in this country. A number of System Dynamics models exist that inform tobacco control policies. We reviewed them and discuss their contributions. We developed a theory of the societal lifecycle of smoking, using a parsimonious set of feedback loops to capture historical trends and explore future scenarios. Previous work did not explain the long-term historical patterns of smoking behaviors. Much of it used stock-and-flow to represent the decline in prevalence in themore » recent past. With noted exceptions, information feedbacks were not embedded in these models. We present and discuss our feedback-rich conceptual model and illustrate the results of a series of simulations. A formal analysis shows phenomena composed of different phases of behavior with specific dominant feedbacks associated with each phase. We discuss the implications of our society's current phase, and conclude with simulations of what-if scenarios. Because System Dynamics models must contain information feedback to be able to anticipate tipping points and to help identify policies that exploit leverage in a complex system, we expanded this body of work to provide an endogenous representation of the century-long societal lifecycle of smoking.« less

Testing a model of facilitated reflection on network feedback: a mixed method study on integration of rural mental healthcare services for older people.

PubMed

Fuller, Jeffrey; Oster, Candice; Muir Cochrane, Eimear; Dawson, Suzanne; Lawn, Sharon; Henderson, Julie; O'Kane, Deb; Gerace, Adam; McPhail, Ruth; Sparkes, Deb; Fuller, Michelle; Reed, Richard L

2015-11-11

To test a management model of facilitated reflection on network feedback as a means to engage services in problem solving the delivery of integrated primary mental healthcare to older people. Participatory mixed methods case study evaluating the impact of a network management model using organisational network feedback (through social network analysis, key informant interviews and policy review). A model of facilitated network reflection using network theory and methods. A rural community in South Australia. 32 staff from 24 services and 12 senior service managers from mental health, primary care and social care services. Health and social care organisations identified that they operated in clustered self-managed networks within sectors, with no overarching purposive older people's mental healthcare network. The model of facilitated reflection revealed service goal and role conflicts. These discussions helped local services to identify as a network, and begin the problem-solving communication and referral links. A Governance Group assisted this process. Barriers to integrated servicing through a network included service funding tied to performance of direct care tasks and the lack of a clear lead network administration organisation. A model of facilitated reflection helped organisations to identify as a network, but revealed sensitivity about organisational roles and goals, which demonstrated that conflict should be expected. Networked servicing needed a neutral network administration organisation with cross-sectoral credibility, a mandate and the resources to monitor the network, to deal with conflict, negotiate commitment among the service managers, and provide opportunities for different sectors to meet and problem solve. This requires consistency and sustained intersectoral policies that include strategies and funding to facilitate and maintain health and social care networks in rural communities. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

A feedback-based secure path approach for wireless sensor network data collection.

PubMed

Mao, Yuxin; Wei, Guiyi

2010-01-01

The unattended nature of wireless sensor networks makes them very vulnerable to malicious attacks. Therefore, how to preserve secure data collection is an important issue to wireless sensor networks. In this paper, we propose a novel approach of secure data collection for wireless sensor networks. We explore secret sharing and multipath routing to achieve secure data collection in wireless sensor network with compromised nodes. We present a novel tracing-feedback mechanism, which makes full use of the routing functionality of wireless sensor networks, to improve the quality of data collection. The major advantage of the approach is that the secure paths are constructed as a by-product of data collection. The process of secure routing causes little overhead to the sensor nodes in the network. Compared with existing works, the algorithms of the proposed approach are easy to implement and execute in resource-constrained wireless sensor networks. According to the result of a simulation experiment, the performance of the approach is better than the recent approaches with a similar purpose.

Dynamic Data-Driven UAV Network for Plume Characterization

DTIC Science & Technology

2016-05-23

data collection where simulations and measurements become a symbiotic feedback control system where simulations inform measurement locations and the...and measurements become a symbiotic feedback control system where simulations inform measurement locations and the measured data augments simulations...data analysis techniques with mobile sensor data collection where simulations and measurements become a symbiotic feedback control system where

Genetic influences on functional connectivity associated with feedback processing and prediction error: Phase coupling of theta-band oscillations in twins.

PubMed

Demiral, Şükrü Barış; Golosheykin, Simon; Anokhin, Andrey P

2017-05-01

Detection and evaluation of the mismatch between the intended and actually obtained result of an action (reward prediction error) is an integral component of adaptive self-regulation of behavior. Extensive human and animal research has shown that evaluation of action outcome is supported by a distributed network of brain regions in which the anterior cingulate cortex (ACC) plays a central role, and the integration of distant brain regions into a unified feedback-processing network is enabled by long-range phase synchronization of cortical oscillations in the theta band. Neural correlates of feedback processing are associated with individual differences in normal and abnormal behavior, however, little is known about the role of genetic factors in the cerebral mechanisms of feedback processing. Here we examined genetic influences on functional cortical connectivity related to prediction error in young adult twins (age 18, n=399) using event-related EEG phase coherence analysis in a monetary gambling task. To identify prediction error-specific connectivity pattern, we compared responses to loss and gain feedback. Monetary loss produced a significant increase of theta-band synchronization between the frontal midline region and widespread areas of the scalp, particularly parietal areas, whereas gain resulted in increased synchrony primarily within the posterior regions. Genetic analyses showed significant heritability of frontoparietal theta phase synchronization (24 to 46%), suggesting that individual differences in large-scale network dynamics are under substantial genetic control. We conclude that theta-band synchronization of brain oscillations related to negative feedback reflects genetically transmitted differences in the neural mechanisms of feedback processing. To our knowledge, this is the first evidence for genetic influences on task-related functional brain connectivity assessed using direct real-time measures of neuronal synchronization. Copyright © 2016 Elsevier B.V. All rights reserved.

Competing endogenous RNA and interactome bioinformatic analyses on human telomerase.

PubMed

Arancio, Walter; Pizzolanti, Giuseppe; Genovese, Swonild Ilenia; Baiamonte, Concetta; Giordano, Carla

2014-04-01

We present a classic interactome bioinformatic analysis and a study on competing endogenous (ce) RNAs for hTERT. The hTERT gene codes for the catalytic subunit and limiting component of the human telomerase complex. Human telomerase reverse transcriptase (hTERT) is essential for the integrity of telomeres. Telomere dysfunctions have been widely reported to be involved in aging, cancer, and cellular senescence. The hTERT gene network has been analyzed using the BioGRID interaction database (http://thebiogrid.org/) and related analysis tools such as Osprey (http://biodata.mshri.on.ca/osprey/servlet/Index) and GeneMANIA (http://genemania.org/). The network of interaction of hTERT transcripts has been further analyzed following the competing endogenous (ce) RNA hypotheses (messenger [m] RNAs cross-talk via micro [mi] RNAs) using the miRWalk database and tools (www.ma.uni-heidelberg.de/apps/zmf/mirwalk/). These analyses suggest a role for Akt, nuclear factor-κB (NF-κB), heat shock protein 90 (HSP90), p70/p80 autoantigen, 14-3-3 proteins, and dynein in telomere functions. Roles for histone acetylation/deacetylation and proteoglycan metabolism are also proposed.

Endogenous miRNA and Target Concentrations Determine Susceptibility to Potential ceRNA Competition

PubMed Central

Bosson, Andrew D.; Zamudio, Jesse R.; Sharp, Phillip A.

2016-01-01

SUMMARY Target competition (ceRNA crosstalk) within miRNA-regulated gene networks has been proposed to influence biological systems. To assess target competition, we characterize and quantitate miRNA networks in two cell types. Argonaute iCLIP reveals that hierarchical binding of high- to low-affinity miRNA targets is a key characteristic of in vivo activity. Quantification of cellular miRNA and mRNA/ncRNA target pool levels indicates that miRNA:target pool ratios and an affinity partitioned target pool accurately predict in vivo Ago binding profiles and miRNA susceptibility to target competition. Using single-cell reporters, we directly test predictions and estimate that ~3,000 additional high-affinity target sites can affect active miRNA families with low endogenous miRNA:target ratios, such as miR-92/25. In contrast, the highly expressed miR-294 and let-7 families are not susceptible to increases of nearly 10,000 sites. These results show differential susceptibility based on endogenous miRNA:target pool ratios and provide a physiological context for ceRNA competition in vivo. PMID:25449132

Feedback-Equivalence of Nonlinear Systems with Applications to Power System Equations.

NASA Astrophysics Data System (ADS)

Marino, Riccardo

The key concept of the dissertation is feedback equivalence among systems affine in control. Feedback equivalence to linear systems in Brunovsky canonical form and the construction of the corresponding feedback transformation are used to: (i) design a nonlinear regulator for a detailed nonlinear model of a synchronous generator connected to an infinite bus; (ii) establish which power system network structures enjoy the feedback linearizability property and design a stabilizing control law for these networks with a constraint on the control space which comes from the use of d.c. lines. It is also shown that the feedback linearizability property allows the use of state feedback to contruct a linear controllable system with a positive definite linear Hamiltonian structure for the uncontrolled part if the state space is even; a stabilizing control law is derived for such systems. Feedback linearizability property is characterized by the involutivity of certain nested distributions for strongly accessible analytic systems; if the system is defined on a manifold M diffeomorphic to the Euclidean space, it is established that the set where the property holds is a submanifold open and dense in M. If an analytic output map is defined, a set of nested involutive distributions can be always defined and that allows the introduction of an observability property which is the dual concept, in some sense, to feedback linearizability: the goal is to investigate when a nonlinear system affine in control with an analytic output map is feedback equivalent to a linear controllable and observable system. Finally a nested involutive structure of distributions is shown to guarantee the existence of a state feedback that takes a nonlinear system affine in control to a single input one, both feedback equivalent to linear controllable systems, preserving one controlled vector field.

Mittag-Leffler synchronization of delayed fractional-order bidirectional associative memory neural networks with discontinuous activations: state feedback control and impulsive control schemes.

PubMed

Ding, Xiaoshuai; Cao, Jinde; Zhao, Xuan; Alsaadi, Fuad E

2017-08-01

This paper is concerned with the drive-response synchronization for a class of fractional-order bidirectional associative memory neural networks with time delays, as well as in the presence of discontinuous activation functions. The global existence of solution under the framework of Filippov for such networks is firstly obtained based on the fixed-point theorem for condensing map. Then the state feedback and impulsive controllers are, respectively, designed to ensure the Mittag-Leffler synchronization of these neural networks and two new synchronization criteria are obtained, which are expressed in terms of a fractional comparison principle and Razumikhin techniques. Numerical simulations are presented to validate the proposed methodologies.

An expanding universe of circadian networks in higher plants.

PubMed

Pruneda-Paz, Jose L; Kay, Steve A

2010-05-01

Extensive circadian clock networks regulate almost every biological process in plants. Clock-controlled physiological responses are coupled with daily oscillations in environmental conditions resulting in enhanced fitness and growth vigor. Identification of core clock components and their associated molecular interactions has established the basic network architecture of plant clocks, which consists of multiple interlocked feedback loops. A hierarchical structure of transcriptional feedback overlaid with regulated protein turnover sets the pace of the clock and ultimately drives all clock-controlled processes. Although originally described as linear entities, increasing evidence suggests that many signaling pathways can act as both inputs and outputs within the overall network. Future studies will determine the molecular mechanisms involved in these complex regulatory loops. 2010 Elsevier Ltd. All rights reserved.

Coding and non-coding gene regulatory networks underlie the immune response in liver cirrhosis.

PubMed

Gao, Bo; Zhang, Xueming; Huang, Yongming; Yang, Zhengpeng; Zhang, Yuguo; Zhang, Weihui; Gao, Zu-Hua; Xue, Dongbo

2017-01-01

Liver cirrhosis is recognized as being the consequence of immune-mediated hepatocyte damage and repair processes. However, the regulation of these immune responses underlying liver cirrhosis has not been elucidated. In this study, we used GEO datasets and bioinformatics methods to established coding and non-coding gene regulatory networks including transcription factor-/lncRNA-microRNA-mRNA, and competing endogenous RNA interaction networks. Our results identified 2224 mRNAs, 70 lncRNAs and 46 microRNAs were differentially expressed in liver cirrhosis. The transcription factor -/lncRNA- microRNA-mRNA network we uncovered that results in immune-mediated liver cirrhosis is comprised of 5 core microRNAs (e.g., miR-203; miR-219-5p), 3 transcription factors (i.e., FOXP3, ETS1 and FOS) and 7 lncRNAs (e.g., ENTS00000671336, ENST00000575137). The competing endogenous RNA interaction network we identified includes a complex immune response regulatory subnetwork that controls the entire liver cirrhosis network. Additionally, we found 10 overlapping GO terms shared by both liver cirrhosis and hepatocellular carcinoma including "immune response" as well. Interestingly, the overlapping differentially expressed genes in liver cirrhosis and hepatocellular carcinoma were enriched in immune response-related functional terms. In summary, a complex gene regulatory network underlying immune response processes may play an important role in the development and progression of liver cirrhosis, and its development into hepatocellular carcinoma.

76 FR 17657 - Medical Device Epidemiology Network 2011: Second Annual Public Workshop

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-30

...] Medical Device Epidemiology Network 2011: Second Annual Public Workshop AGENCY: Food and Drug... public workshop entitled ``Medical Device Epidemiology Network (MDEpiNet) 2011: Second Annual Public... and solicit feedback on the establishment of a network that works with FDA experts to determine the...

Competition between Local Collisions and Collective Hydrodynamic Feedback Controls Traffic Flows in Microfluidic Networks

NASA Astrophysics Data System (ADS)

Belloul, M.; Engl, W.; Colin, A.; Panizza, P.; Ajdari, A.

2009-05-01

By studying the repartition of monodisperse droplets at a simple T junction, we show that the traffic of discrete fluid systems in microfluidic networks results from two competing mechanisms, whose significance is driven by confinement. Traffic is dominated by collisions occurring at the junction for small droplets and by collective hydrodynamic feedback for large ones. For each mechanism, we present simple models in terms of the pertinent dimensionless parameters of the problem.

Neural Network Classifies Teleoperation Data

NASA Technical Reports Server (NTRS)

Fiorini, Paolo; Giancaspro, Antonio; Losito, Sergio; Pasquariello, Guido

1994-01-01

Prototype artificial neural network, implemented in software, identifies phases of telemanipulator tasks in real time by analyzing feedback signals from force sensors on manipulator hand. Prototype is early, subsystem-level product of continuing effort to develop automated system that assists in training and supervising human control operator: provides symbolic feedback (e.g., warnings of impending collisions or evaluations of performance) to operator in real time during successive executions of same task. Also simplifies transition between teleoperation and autonomous modes of telerobotic system.

Neural cryptography with feedback.

PubMed

Ruttor, Andreas; Kinzel, Wolfgang; Shacham, Lanir; Kanter, Ido

2004-04-01

Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.

Cross-entropy optimization for neuromodulation.

PubMed

Brar, Harleen K; Yunpeng Pan; Mahmoudi, Babak; Theodorou, Evangelos A

2016-08-01

This study presents a reinforcement learning approach for the optimization of the proportional-integral gains of the feedback controller represented in a computational model of epilepsy. The chaotic oscillator model provides a feedback control systems view of the dynamics of an epileptic brain with an internal feedback controller representative of the natural seizure suppression mechanism within the brain circuitry. Normal and pathological brain activity is simulated in this model by adjusting the feedback gain values of the internal controller. With insufficient gains, the internal controller cannot provide enough feedback to the brain dynamics causing an increase in correlation between different brain sites. This increase in synchronization results in the destabilization of the brain dynamics, which is representative of an epileptic seizure. To provide compensation for an insufficient internal controller an external controller is designed using proportional-integral feedback control strategy. A cross-entropy optimization algorithm is applied to the chaotic oscillator network model to learn the optimal feedback gains for the external controller instead of hand-tuning the gains to provide sufficient control to the pathological brain and prevent seizure generation. The correlation between the dynamics of neural activity within different brain sites is calculated for experimental data to show similar dynamics of epileptic neural activity as simulated by the network of chaotic oscillators.

Adaptive feedback synchronisation of complex dynamical network with discrete-time communications and delayed nodes

NASA Astrophysics Data System (ADS)

Wang, Tong; Ding, Yongsheng; Zhang, Lei; Hao, Kuangrong

2016-08-01

This paper considered the synchronisation of continuous complex dynamical networks with discrete-time communications and delayed nodes. The nodes in the dynamical networks act in the continuous manner, while the communications between nodes are discrete-time; that is, they communicate with others only at discrete time instants. The communication intervals in communication period can be uncertain and variable. By using a piecewise Lyapunov-Krasovskii function to govern the characteristics of the discrete communication instants, we investigate the adaptive feedback synchronisation and a criterion is derived to guarantee the existence of the desired controllers. The globally exponential synchronisation can be achieved by the controllers under the updating laws. Finally, two numerical examples including globally coupled network and nearest-neighbour coupled networks are presented to demonstrate the validity and effectiveness of the proposed control scheme.

Theory of feedback controlled brain stimulations for Parkinson's disease

NASA Astrophysics Data System (ADS)

Sanzeni, A.; Celani, A.; Tiana, G.; Vergassola, M.

2016-01-01

Limb tremor and other debilitating symptoms caused by the neurodegenerative Parkinson's disease are currently treated by administering drugs and by fixed-frequency deep brain stimulation. The latter interferes directly with the brain dynamics by delivering electrical impulses to neurons in the subthalamic nucleus. While deep brain stimulation has shown therapeutic benefits in many instances, its mechanism is still unclear. Since its understanding could lead to improved protocols of stimulation and feedback control, we have studied a mathematical model of the many-body neural network dynamics controlling the dynamics of the basal ganglia. On the basis of the results obtained from the model, we propose a new procedure of active stimulation, that depends on the feedback of the network and that respects the constraints imposed by existing technology. We show by numerical simulations that the new protocol outperforms the standard ones for deep brain stimulation and we suggest future experiments that could further improve the feedback procedure.

Network Architecture Predisposes an Enzyme to Either Pharmacologic or Genetic Targeting.

PubMed

Jensen, Karin J; Moyer, Christian B; Janes, Kevin A

2016-02-24

Chemical inhibition and genetic knockdown of enzymes are not equivalent in cells, but network-level mechanisms that cause discrepancies between knockdown and inhibitor perturbations are not understood. Here we report that enzymes regulated by negative feedback are robust to knockdown but susceptible to inhibition. Using the Raf-MEK-ERK kinase cascade as a model system, we find that ERK activation is resistant to genetic knockdown of MEK but susceptible to a comparable degree of chemical MEK inhibition. We demonstrate that negative feedback from ERK to Raf causes this knockdown-versus-inhibitor discrepancy in vivo. Exhaustive mathematical modeling of three-tiered enzyme cascades suggests that this result is general: negative autoregulation or feedback favors inhibitor potency, whereas positive autoregulation or feedback favors knockdown potency. Our findings provide a rationale for selecting pharmacologic versus genetic perturbations in vivo and point out the dangers of using knockdown approaches in search of drug targets.

Adaptive NN control for discrete-time pure-feedback systems with unknown control direction under amplitude and rate actuator constraints.

PubMed

Chen, Weisheng

2009-07-01

This paper focuses on the problem of adaptive neural network tracking control for a class of discrete-time pure-feedback systems with unknown control direction under amplitude and rate actuator constraints. Two novel state-feedback and output-feedback dynamic control laws are established where the function tanh(.) is employed to solve the saturation constraint problem. Implicit function theorem and mean value theorem are exploited to deal with non-affine variables that are used as actual control. Radial basis function neural networks are used to approximate the desired input function. Discrete Nussbaum gain is used to estimate the unknown sign of control gain. The uniform boundedness of all closed-loop signals is guaranteed. The tracking error is proved to converge to a small residual set around the origin. A simulation example is provided to illustrate the effectiveness of control schemes proposed in this paper.

Regular network model for the sea ice-albedo feedback in the Arctic.

PubMed

Müller-Stoffels, Marc; Wackerbauer, Renate

2011-03-01

The Arctic Ocean and sea ice form a feedback system that plays an important role in the global climate. The complexity of highly parameterized global circulation (climate) models makes it very difficult to assess feedback processes in climate without the concurrent use of simple models where the physics is understood. We introduce a two-dimensional energy-based regular network model to investigate feedback processes in an Arctic ice-ocean layer. The model includes the nonlinear aspect of the ice-water phase transition, a nonlinear diffusive energy transport within a heterogeneous ice-ocean lattice, and spatiotemporal atmospheric and oceanic forcing at the surfaces. First results for a horizontally homogeneous ice-ocean layer show bistability and related hysteresis between perennial ice and perennial open water for varying atmospheric heat influx. Seasonal ice cover exists as a transient phenomenon. We also find that ocean heat fluxes are more efficient than atmospheric heat fluxes to melt Arctic sea ice.

Endogenous fibrinolysis facilitates clot retraction in vivo.

PubMed

Samson, Andre L; Alwis, Imala; Maclean, Jessica A A; Priyananda, Pramith; Hawkett, Brian; Schoenwaelder, Simone M; Jackson, Shaun P

2017-12-07

Clot retraction refers to the process whereby activated platelets transduce contractile forces onto the fibrin network of a thrombus, which over time increases clot density and decreases clot size. This process is considered important for promoting clot stability and maintaining blood vessel patency. Insights into the mechanisms regulating clot retraction at sites of vascular injury have been hampered by a paucity of in vivo experimental models. By pairing localized vascular injury with thrombin microinjection in the mesenteric circulation of mice, we have demonstrated that the fibrin network of thrombi progressively compacts over a 2-hour period. This was a genuine retraction process, as treating thrombi with blebbistatin to inhibit myosin IIa-mediated platelet contractility prevented shrinkage of the fibrin network. Real-time confocal analysis of fibrinolysis after recombinant tissue-type plasminogen activator (tPA) administration revealed that incomplete proteolysis of fibrin polymers markedly facilitated clot retraction. Similarly, inhibiting endogenous fibrinolysis with tranexamic acid reduced retraction of fibrin polymers in vivo. In vitro clot retraction experiments indicated that subthreshold doses of tPA facilitated clot retraction through a plasmin-dependent mechanism. These effects correlated with changes in the elastic modulus of fibrin clots. These findings define the endogenous fibrinolytic system as an important regulator of clot retraction, and show that promoting clot retraction is a novel and complementary means by which fibrinolytic enzymes can reduce thrombus size. © 2017 by The American Society of Hematology.

On the origins of the universal dynamics of endogenous granules in mammalian cells.

PubMed

Vanapalli, Siva A; Li, Yixuan; Mugele, Frieder; Duits, Michel H G

2009-12-01

Endogenous granules (EGs) that consist of lipid droplets and mitochondria have been commonly used to assess intracellular mechanical properties via multiple particle tracking microrheology (MPTM). Despite their widespread use, the nature of interaction of EGs with the cytoskeletal network and the type of forces driving their dynamics--both of which are crucial for the interpretation of the results from MPTM technique--are yet to be resolved. In this report, we study the dynamics of endogenous granules in mammalian cells using particle tracking methods. We find that the ensemble dynamics of EGs is diffusive in three types of mammalian cells (endothelial cells, smooth muscle cells and fibroblasts), thereby suggesting an apparent universality in their dynamical behavior. Moreover, in a given cell, the amplitude of the mean-squared displacement for EGs is an order of magnitude larger than that of injected particles. This observation along with results from ATP depletion and temperature intervention studies suggests that cytoskeletal active forces drive the dynamics of EGs. To elucidate the dynamical origin of the diffusive-like nonthermal motion, we consider three active force generation mechanisms--molecular motor transport, actomyosin contractility and microtubule polymerization forces. We test these mechanisms using pharmacological interventions. Experimental evidence and model calculations suggest that EGs are intimately linked to microtubules and that microtubule polymerization forces drive their dynamics. Thus, endogenous granules could serve as non-invasive probes for microtubule network dynamics in mammalian cells.

Influence of reciprocal edges on degree distribution and degree correlations

NASA Astrophysics Data System (ADS)

Zlatić, Vinko; Štefančić, Hrvoje

2009-07-01

Reciprocal edges represent the lowest-order cycle possible to find in directed graphs without self-loops. Representing also a measure of feedback between vertices, it is interesting to understand how reciprocal edges influence other properties of complex networks. In this paper, we focus on the influence of reciprocal edges on vertex degree distribution and degree correlations. We show that there is a fundamental difference between properties observed on the static network compared to the properties of networks, which are obtained by simple evolution mechanism driven by reciprocity. We also present a way to statistically infer the portion of reciprocal edges, which can be explained as a consequence of feedback process on the static network. In the rest of the paper, the influence of reciprocal edges on a model of growing network is also presented. It is shown that our model of growing network nicely interpolates between Barabási-Albert (BA) model for undirected and the BA model for directed networks.

Impact of real-time fMRI working memory feedback training on the interactions between three core brain networks.

PubMed

Zhang, Qiushi; Zhang, Gaoyan; Yao, Li; Zhao, Xiaojie

2015-01-01

Working memory (WM) refers to the temporary holding and manipulation of information during the performance of a range of cognitive tasks, and WM training is a promising method for improving an individual's cognitive functions. Our previous work demonstrated that WM performance can be improved through self-regulation of dorsal lateral prefrontal cortex (PFC) activation using real-time functional magnetic resonance imaging (rtfMRI), which enables individuals to control local brain activities volitionally according to the neurofeedback. Furthermore, research concerning large-scale brain networks has demonstrated that WM training requires the engagement of several networks, including the central executive network (CEN), the default mode network (DMN) and the salience network (SN), and functional connectivity within the CEN and DMN can be changed by WM training. Although a switching role of the SN between the CEN and DMN has been demonstrated, it remains unclear whether WM training can affect the interactions between the three networks and whether a similar mechanism also exists during the training process. In this study, we investigated the dynamic functional connectivity between the three networks during the rtfMRI feedback training using independent component analysis (ICA) and correlation analysis. The results indicated that functional connectivity within and between the three networks were significantly enhanced by feedback training, and most of the changes were associated with the insula and correlated with behavioral improvements. These findings suggest that the insula plays a critical role in the reorganization of functional connectivity among the three networks induced by rtfMRI training and in WM performance, thus providing new insights into the mechanisms of high-level functions and the clinical treatment of related functional impairments.

Application of Neural Networks for classification of Patau, Edwards, Down, Turner and Klinefelter Syndrome based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics.

PubMed

Catic, Aida; Gurbeta, Lejla; Kurtovic-Kozaric, Amina; Mehmedbasic, Senad; Badnjevic, Almir

2018-02-13

The usage of Artificial Neural Networks (ANNs) for genome-enabled classifications and establishing genome-phenotype correlations have been investigated more extensively over the past few years. The reason for this is that ANNs are good approximates of complex functions, so classification can be performed without the need for explicitly defined input-output model. This engineering tool can be applied for optimization of existing methods for disease/syndrome classification. Cytogenetic and molecular analyses are the most frequent tests used in prenatal diagnostic for the early detection of Turner, Klinefelter, Patau, Edwards and Down syndrome. These procedures can be lengthy, repetitive; and often employ invasive techniques so a robust automated method for classifying and reporting prenatal diagnostics would greatly help the clinicians with their routine work. The database consisted of data collected from 2500 pregnant woman that came to the Institute of Gynecology, Infertility and Perinatology "Mehmedbasic" for routine antenatal care between January 2000 and December 2016. During first trimester all women were subject to screening test where values of maternal serum pregnancy-associated plasma protein A (PAPP-A) and free beta human chorionic gonadotropin (β-hCG) were measured. Also, fetal nuchal translucency thickness and the presence or absence of the nasal bone was observed using ultrasound. The architectures of linear feedforward and feedback neural networks were investigated for various training data distributions and number of neurons in hidden layer. Feedback neural network architecture out performed feedforward neural network architecture in predictive ability for all five aneuploidy prenatal syndrome classes. Feedforward neural network with 15 neurons in hidden layer achieved classification sensitivity of 92.00%. Classification sensitivity of feedback (Elman's) neural network was 99.00%. Average accuracy of feedforward neural network was 89.6% and for feedback was 98.8%. The results presented in this paper prove that an expert diagnostic system based on neural networks can be efficiently used for classification of five aneuploidy syndromes, covered with this study, based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics. Developed Expert System proved to be simple, robust, and powerful in properly classifying prenatal aneuploidy syndromes.

75 FR 55392 - Employment Network Report Card

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-10

... SOCIAL SECURITY ADMINISTRATION [Docket No. SSA-2010-0046] Employment Network Report Card AGENCY... quality assurance, including a ticket consumer Employment Network Report Card. SUMMARY: We are soliciting... this goal by combining a user-friendly EN Report Card, which contains customer satisfaction feedback...

Influence of the feedback loops in the trp operon of B. subtilis on the system dynamic response and noise amplitude.

PubMed

Zamora-Chimal, Criseida; Santillán, Moisés; Rodríguez-González, Jesús

2012-10-07

In this paper we introduce a mathematical model for the tryptophan operon regulatory pathway in Bacillus subtilis. This model considers the transcription-attenuation, and the enzyme-inhibition regulatory mechanisms. Special attention is paid to the estimation of all the model parameters from reported experimental data. With the aid of this model we investigate, from a mathematical-modeling point of view, whether the existing multiplicity of regulatory feedback loops is advantageous in some sense, regarding the dynamic response and the biochemical noise in the system. The tryptophan operon dynamic behavior is studied by means of deterministic numeric simulations, while the biochemical noise is analyzed with the aid of stochastic simulations. The model feasibility is tested comparing its stochastic and deterministic results with experimental reports. Our results for the wildtype and for a couple of mutant bacterial strains suggest that the enzyme-inhibition feedback loop, dynamically accelerates the operon response, and plays a major role in the reduction of biochemical noise. Also, the transcription-attenuation feedback loop makes the trp operon sensitive to changes in the endogenous tryptophan level, and increases the amplitude of the biochemical noise. Copyright © 2012 Elsevier Ltd. All rights reserved.

From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

NASA Technical Reports Server (NTRS)

Ross, Muriel D.

1991-01-01

The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks.

PubMed

Briat, Corentin; Gupta, Ankit; Khammash, Mustafa

2018-06-01

The ability of a cell to regulate and adapt its internal state in response to unpredictable environmental changes is called homeostasis and this ability is crucial for the cell's survival and proper functioning. Understanding how cells can achieve homeostasis, despite the intrinsic noise or randomness in their dynamics, is fundamentally important for both systems and synthetic biology. In this context, a significant development is the proposed antithetic integral feedback (AIF) motif, which is found in natural systems, and is known to ensure robust perfect adaptation for the mean dynamics of a given molecular species involved in a complex stochastic biomolecular reaction network. From the standpoint of applications, one drawback of this motif is that it often leads to an increased cell-to-cell heterogeneity or variance when compared to a constitutive (i.e. open-loop) control strategy. Our goal in this paper is to show that this performance deterioration can be countered by combining the AIF motif and a negative feedback strategy. Using a tailored moment closure method, we derive approximate expressions for the stationary variance for the controlled network that demonstrate that increasing the strength of the negative feedback can indeed decrease the variance, sometimes even below its constitutive level. Numerical results verify the accuracy of these results and we illustrate them by considering three biomolecular networks with two types of negative feedback strategies. Our computational analysis indicates that there is a trade-off between the speed of the settling-time of the mean trajectories and the stationary variance of the controlled species; i.e. smaller variance is associated with larger settling-time. © 2018 The Author(s).

Time-delayed feedback control of coherence resonance chimeras

NASA Astrophysics Data System (ADS)

Zakharova, Anna; Semenova, Nadezhda; Anishchenko, Vadim; Schöll, Eckehard

2017-11-01

Using the model of a FitzHugh-Nagumo system in the excitable regime, we investigate the influence of time-delayed feedback on noise-induced chimera states in a network with nonlocal coupling, i.e., coherence resonance chimeras. It is shown that time-delayed feedback allows for the control of the range of parameter values where these chimera states occur. Moreover, for the feedback delay close to the intrinsic period of the system, we find a novel regime which we call period-two coherence resonance chimera.

Distributed Bandpass Filtering and Signal Demodulation in Cortical Network Models

NASA Astrophysics Data System (ADS)

McDonnell, Mark D.

Experimental recordings of cortical activity often exhibit narrowband oscillations, at various center frequencies ranging in the order of 1-200 Hz. Many neuronal mechanisms are known to give rise to oscillations, but here we focus on a population effect known as sparsely synchronised oscillations. In this effect, individual neurons in a cortical network fire irregularly at slow average spike rates (1-10 Hz), but the population spike rate oscillates at gamma frequencies (greater than 40 Hz) in response to spike bombardment from the thalamus. These cortical networks form recurrent (feedback) synapses. Here we describe a model of sparsely synchronized population oscillations using the language of feedback control engineering, where we treat spiking as noisy feedback. We show, using a biologically realistic model of synaptic current that includes a delayed response to inputs, that the collective behavior of the neurons in the network is like a distributed bandpass filter acting on the network inputs. Consequently, the population response has the character of narrowband random noise, and therefore has an envelope and instantaneous frequency with lowpass characteristics. Given that there exist biologically plausible neuronal mechanisms for demodulating the envelope and instantaneous frequency, we suggest there is potential for similar effects to be exploited in nanoscale electronics implementations of engineered communications receivers.

Percolation of a general network of networks.

PubMed

Gao, Jianxi; Buldyrev, Sergey V; Stanley, H Eugene; Xu, Xiaoming; Havlin, Shlomo

2013-12-01

Percolation theory is an approach to study the vulnerability of a system. We develop an analytical framework and analyze the percolation properties of a network composed of interdependent networks (NetONet). Typically, percolation of a single network shows that the damage in the network due to a failure is a continuous function of the size of the failure, i.e., the fraction of failed nodes. In sharp contrast, in NetONet, due to the cascading failures, the percolation transition may be discontinuous and even a single node failure may lead to an abrupt collapse of the system. We demonstrate our general framework for a NetONet composed of n classic Erdős-Rényi (ER) networks, where each network depends on the same number m of other networks, i.e., for a random regular network (RR) formed of interdependent ER networks. The dependency between nodes of different networks is taken as one-to-one correspondence, i.e., a node in one network can depend only on one node in the other network (no-feedback condition). In contrast to a treelike NetONet in which the size of the largest connected cluster (mutual component) depends on n, the loops in the RR NetONet cause the largest connected cluster to depend only on m and the topology of each network but not on n. We also analyzed the extremely vulnerable feedback condition of coupling, where the coupling between nodes of different networks is not one-to-one correspondence. In the case of NetONet formed of ER networks, percolation only exhibits two phases, a second order phase transition and collapse, and no first order percolation transition regime is found in the case of the no-feedback condition. In the case of NetONet composed of RR networks, there exists a first order phase transition when the coupling strength q (fraction of interdependency links) is large and a second order phase transition when q is small. Our insight on the resilience of coupled networks might help in designing robust interdependent systems.

Modeling gene regulatory network motifs using statecharts

PubMed Central

2012-01-01

Background Gene regulatory networks are widely used by biologists to describe the interactions among genes, proteins and other components at the intra-cellular level. Recently, a great effort has been devoted to give gene regulatory networks a formal semantics based on existing computational frameworks. For this purpose, we consider Statecharts, which are a modular, hierarchical and executable formal model widely used to represent software systems. We use Statecharts for modeling small and recurring patterns of interactions in gene regulatory networks, called motifs. Results We present an improved method for modeling gene regulatory network motifs using Statecharts and we describe the successful modeling of several motifs, including those which could not be modeled or whose models could not be distinguished using the method of a previous proposal. We model motifs in an easy and intuitive way by taking advantage of the visual features of Statecharts. Our modeling approach is able to simulate some interesting temporal properties of gene regulatory network motifs: the delay in the activation and the deactivation of the "output" gene in the coherent type-1 feedforward loop, the pulse in the incoherent type-1 feedforward loop, the bistability nature of double positive and double negative feedback loops, the oscillatory behavior of the negative feedback loop, and the "lock-in" effect of positive autoregulation. Conclusions We present a Statecharts-based approach for the modeling of gene regulatory network motifs in biological systems. The basic motifs used to build more complex networks (that is, simple regulation, reciprocal regulation, feedback loop, feedforward loop, and autoregulation) can be faithfully described and their temporal dynamics can be analyzed. PMID:22536967

A theory of circular organization and negative feedback: defining life in a cybernetic context.

PubMed

Tsokolov, Sergey

2010-12-01

All life today incorporates a variety of systems controlled by negative feedback loops and sometimes amplified by positive feedback loops. The first forms of life necessarily also required primitive versions of feedback, yet surprisingly little emphasis has been given to the question of how feedback emerged out of primarily chemical systems. One chemical system has been established that spontaneously develops autocatalytic feedback, the Belousov-Zhabotinsky (BZ) reaction. In this essay, I discuss the BZ reaction as a possible model for similar reactions that could have occurred under prebiotic Earth conditions. The main point is that the metabolism of contemporary life evolved from primitive homeostatic networks regulated by negative feedback. Because life could not exist in their absence, feedback loops should be included in definitions of life.

A Theory of Circular Organization and Negative Feedback: Defining Life in a Cybernetic Context

NASA Astrophysics Data System (ADS)

Tsokolov, Sergey

2010-12-01

All life today incorporates a variety of systems controlled by negative feedback loops and sometimes amplified by positive feedback loops. The first forms of life necessarily also required primitive versions of feedback, yet surprisingly little emphasis has been given to the question of how feedback emerged out of primarily chemical systems. One chemical system has been established that spontaneously develops autocatalytic feedback, the Belousov-Zhabotinsky (BZ) reaction. In this essay, I discuss the BZ reaction as a possible model for similar reactions that could have occurred under prebiotic Earth conditions. The main point is that the metabolism of contemporary life evolved from primitive homeostatic networks regulated by negative feedback. Because life could not exist in their absence, feedback loops should be included in definitions of life.

Oxytocin attenuates trust as a subset of more general reinforcement learning, with altered reward circuit functional connectivity in males.

PubMed

Ide, Jaime S; Nedic, Sanja; Wong, Kin F; Strey, Shmuel L; Lawson, Elizabeth A; Dickerson, Bradford C; Wald, Lawrence L; La Camera, Giancarlo; Mujica-Parodi, Lilianne R

2018-07-01

Oxytocin (OT) is an endogenous neuropeptide that, while originally thought to promote trust, has more recently been found to be context-dependent. Here we extend experimental paradigms previously restricted to de novo decision-to-trust, to a more realistic environment in which social relationships evolve in response to iterative feedback over twenty interactions. In a randomized, double blind, placebo-controlled within-subject/crossover experiment of human adult males, we investigated the effects of a single dose of intranasal OT (40 IU) on Bayesian expectation updating and reinforcement learning within a social context, with associated brain circuit dynamics. Subjects participated in a neuroeconomic task (Iterative Trust Game) designed to probe iterative social learning while their brains were scanned using ultra-high field (7T) fMRI. We modeled each subject's behavior using Bayesian updating of belief-states ("willingness to trust") as well as canonical measures of reinforcement learning (learning rate, inverse temperature). Behavioral trajectories were then used as regressors within fMRI activation and connectivity analyses to identify corresponding brain network functionality affected by OT. Behaviorally, OT reduced feedback learning, without bias with respect to positive versus negative reward. Neurobiologically, reduced learning under OT was associated with muted communication between three key nodes within the reward circuit: the orbitofrontal cortex, amygdala, and lateral (limbic) habenula. Our data suggest that OT, rather than inspiring feelings of generosity, instead attenuates the brain's encoding of prediction error and therefore its ability to modulate pre-existing beliefs. This effect may underlie OT's putative role in promoting what has typically been reported as 'unjustified trust' in the face of information that suggests likely betrayal, while also resolving apparent contradictions with regard to OT's context-dependent behavioral effects. Copyright © 2018 Elsevier Inc. All rights reserved.

Do the Images of Neuronal Pathways in the Human Central Nervous System Show Feed-back? A Comparative Study in Fifteen Countries.

ERIC Educational Resources Information Center

Clement, Pierre; Mouelhi, Lassaad; Kochkar, Momahed; Valanides, Nicos; Nisiforou, Olia; Thiaw, Seyni Mame; Ndiaye, Valdiodio; Jeanbart, Paula; Horvath, Daniel; Ferreira, Claudia; Carvalho, Graca S.

2010-01-01

In the human brain, the neuronal pathways are networks which support our learning, memory and thought, and which work with permanent feedback. However, only 19% of illustrations of these neuronal pathways, in the 55 analysed school textbooks coming from 15 countries, were showing feedbacks. The neuronal pathways related to movements were generally…

LOGISTIC NETWORK REGRESSION FOR SCALABLE ANALYSIS OF NETWORKS WITH JOINT EDGE/VERTEX DYNAMICS

PubMed Central

Almquist, Zack W.; Butts, Carter T.

2015-01-01

Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach. PMID:26120218

LOGISTIC NETWORK REGRESSION FOR SCALABLE ANALYSIS OF NETWORKS WITH JOINT EDGE/VERTEX DYNAMICS.

PubMed

Almquist, Zack W; Butts, Carter T

2014-08-01

Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach.

Research in Network Management Techniques for Tactical Data Communications Network.

DTIC Science & Technology

1982-09-01

the control period. Research areas include Packet Network modelling, adaptive network routing, network design algorithms, network design techniques...contro!lers are designed to perform their limited tasks optimally. For the dynamic routing problem considered here, the local controllers are node...feedback to finding in optimum stead-o-state routing (static strategies) under non - control which can be easily implemented in real time. congested

Generalized Adaptive Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Tawel, Raoul

1993-01-01

Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.

Modeling neural circuits in Parkinson's disease.

PubMed

Psiha, Maria; Vlamos, Panayiotis

2015-01-01

Parkinson's disease (PD) is caused by abnormal neural activity of the basal ganglia which are connected to the cerebral cortex in the brain surface through complex neural circuits. For a better understanding of the pathophysiological mechanisms of PD, it is important to identify the underlying PD neural circuits, and to pinpoint the precise nature of the crucial aberrations in these circuits. In this paper, the general architecture of a hybrid Multilayer Perceptron (MLP) network for modeling the neural circuits in PD is presented. The main idea of the proposed approach is to divide the parkinsonian neural circuitry system into three discrete subsystems: the external stimuli subsystem, the life-threatening events subsystem, and the basal ganglia subsystem. The proposed model, which includes the key roles of brain neural circuit in PD, is based on both feed-back and feed-forward neural networks. Specifically, a three-layer MLP neural network with feedback in the second layer was designed. The feedback in the second layer of this model simulates the dopamine modulatory effect of compacta on striatum.

Reducing a cortical network to a Potts model yields storage capacity estimates

NASA Astrophysics Data System (ADS)

Naim, Michelangelo; Boboeva, Vezha; Kang, Chol Jun; Treves, Alessandro

2018-04-01

An autoassociative network of Potts units, coupled via tensor connections, has been proposed and analysed as an effective model of an extensive cortical network with distinct short- and long-range synaptic connections, but it has not been clarified in what sense it can be regarded as an effective model. We draw here the correspondence between the two, which indicates the need to introduce a local feedback term in the reduced model, i.e. in the Potts network. An effective model allows the study of phase transitions. As an example, we study the storage capacity of the Potts network with this additional term, the local feedback w, which contributes to drive the activity of the network towards one of the stored patterns. The storage capacity calculation, performed using replica tools, is limited to fully connected networks, for which a Hamiltonian can be defined. To extend the results to the case of intermediate partial connectivity, we also derive the self-consistent signal-to-noise analysis for the Potts network; and finally we discuss the implications for semantic memory in humans.

A Feedback-Based Secure Path Approach for Wireless Sensor Network Data Collection

PubMed Central

Mao, Yuxin; Wei, Guiyi

2010-01-01

The unattended nature of wireless sensor networks makes them very vulnerable to malicious attacks. Therefore, how to preserve secure data collection is an important issue to wireless sensor networks. In this paper, we propose a novel approach of secure data collection for wireless sensor networks. We explore secret sharing and multipath routing to achieve secure data collection in wireless sensor network with compromised nodes. We present a novel tracing-feedback mechanism, which makes full use of the routing functionality of wireless sensor networks, to improve the quality of data collection. The major advantage of the approach is that the secure paths are constructed as a by-product of data collection. The process of secure routing causes little overhead to the sensor nodes in the network. Compared with existing works, the algorithms of the proposed approach are easy to implement and execute in resource-constrained wireless sensor networks. According to the result of a simulation experiment, the performance of the approach is better than the recent approaches with a similar purpose. PMID:22163424

A minimal mathematical model combining several regulatory cycles from the budding yeast cell cycle.

PubMed

Sriram, K; Bernot, G; Képès, F

2007-11-01

A novel topology of regulatory networks abstracted from the budding yeast cell cycle is studied by constructing a simple nonlinear model. A ternary positive feedback loop with only positive regulations is constructed with elements that activates the subsequent element in a clockwise fashion. A ternary negative feedback loop with only negative regulations is constructed with the elements that inhibit the subsequent element in an anticlockwise fashion. Positive feedback loop exhibits bistability, whereas the negative feedback loop exhibits limit cycle oscillations. The novelty of the topology is that the corresponding elements in these two homogeneous feedback loops are linked by the binary positive feedback loops with only positive regulations. This results in the emergence of mixed feedback loops in the network that displays complex behaviour like the coexistence of multiple steady states, relaxation oscillations and chaos. Importantly, the arrangement of the feedback loops brings in the notion of checkpoint in the model. The model also exhibits domino-like behaviour, where the limit cycle oscillations take place in a stepwise fashion. As the aforementioned topology is abstracted from the budding yeast cell cycle, the events that govern the cell cycle are considered for the present study. In budding yeast, the sequential activation of the transcription factors, cyclins and their inhibitors form mixed feedback loops. The transcription factors that involve in the positive regulation in a clockwise orientation generates ternary positive feedback loop, while the cyclins and their inhibitors that involve in the negative regulation in an anticlockwise orientation generates ternary negative feedback loop. The mutual regulation between the corresponding elements in the transcription factors and the cyclins and their inhibitors generates binary positive feedback loops. The bifurcation diagram constructed for the whole system can be related to the different events of the cell cycle in terms of dynamical system theory. The checkpoint mechanism that plays an important role in different phases of the cell cycle are accounted for by silencing appropriate feedback loops in the model.

Feedback power control strategies in wireless sensor networks with joint channel decoding.

PubMed

Abrardo, Andrea; Ferrari, Gianluigi; Martalò, Marco; Perna, Fabio

2009-01-01

In this paper, we derive feedback power control strategies for block-faded multiple access schemes with correlated sources and joint channel decoding (JCD). In particular, upon the derivation of the feasible signal-to-noise ratio (SNR) region for the considered multiple access schemes, i.e., the multidimensional SNR region where error-free communications are, in principle, possible, two feedback power control strategies are proposed: (i) a classical feedback power control strategy, which aims at equalizing all link SNRs at the access point (AP), and (ii) an innovative optimized feedback power control strategy, which tries to make the network operational point fall in the feasible SNR region at the lowest overall transmit energy consumption. These strategies will be referred to as "balanced SNR" and "unbalanced SNR," respectively. While they require, in principle, an unlimited power control range at the sources, we also propose practical versions with a limited power control range. We preliminary consider a scenario with orthogonal links and ideal feedback. Then, we analyze the robustness of the proposed power control strategies to possible non-idealities, in terms of residual multiple access interference and noisy feedback channels. Finally, we successfully apply the proposed feedback power control strategies to a limiting case of the class of considered multiple access schemes, namely a central estimating officer (CEO) scenario, where the sensors observe noisy versions of a common binary information sequence and the AP's goal is to estimate this sequence by properly fusing the soft-output information output by the JCD algorithm.

Evidence for Dynamic Network Regulation of Drosophila Photoreceptor Function from Mutants Lacking the Neurotransmitter Histamine

PubMed Central

Dau, An; Friederich, Uwe; Dongre, Sidhartha; Li, Xiaofeng; Bollepalli, Murali K.; Hardie, Roger C.; Juusola, Mikko

2016-01-01

Synaptic feedback from interneurons to photoreceptors can help to optimize visual information flow by balancing its allocation on retinal pathways under changing light conditions. But little is known about how this critical network operation is regulated dynamically. Here, we investigate this question by comparing signaling properties and performance of wild-type Drosophila R1–R6 photoreceptors to those of the hdcJK910 mutant, which lacks the neurotransmitter histamine and therefore cannot transmit information to interneurons. Recordings show that hdcJK910 photoreceptors sample similar amounts of information from naturalistic stimulation to wild-type photoreceptors, but this information is packaged in smaller responses, especially under bright illumination. Analyses reveal how these altered dynamics primarily resulted from network overload that affected hdcJK910 photoreceptors in two ways. First, the missing inhibitory histamine input to interneurons almost certainly depolarized them irrevocably, which in turn increased their excitatory feedback to hdcJK910 R1–R6s. This tonic excitation depolarized the photoreceptors to artificially high potentials, reducing their operational range. Second, rescuing histamine input to interneurons in hdcJK910 mutant also restored their normal phasic feedback modulation to R1–R6s, causing photoreceptor output to accentuate dynamic intensity differences at bright illumination, similar to the wild-type. These results provide mechanistic explanations of how synaptic feedback connections optimize information packaging in photoreceptor output and novel insight into the operation and design of dynamic network regulation of sensory neurons. PMID:27047343

Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control.

PubMed

Wan, Ying; Cao, Jinde; Wen, Guanghui

In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.

Globally fixed-time synchronization of coupled neutral-type neural network with mixed time-varying delays.

PubMed

Zheng, Mingwen; Li, Lixiang; Peng, Haipeng; Xiao, Jinghua; Yang, Yixian; Zhang, Yanping; Zhao, Hui

2018-01-01

This paper mainly studies the globally fixed-time synchronization of a class of coupled neutral-type neural networks with mixed time-varying delays via discontinuous feedback controllers. Compared with the traditional neutral-type neural network model, the model in this paper is more general. A class of general discontinuous feedback controllers are designed. With the help of the definition of fixed-time synchronization, the upper right-hand derivative and a defined simple Lyapunov function, some easily verifiable and extensible synchronization criteria are derived to guarantee the fixed-time synchronization between the drive and response systems. Finally, two numerical simulations are given to verify the correctness of the results.

Globally fixed-time synchronization of coupled neutral-type neural network with mixed time-varying delays

PubMed Central

2018-01-01

This paper mainly studies the globally fixed-time synchronization of a class of coupled neutral-type neural networks with mixed time-varying delays via discontinuous feedback controllers. Compared with the traditional neutral-type neural network model, the model in this paper is more general. A class of general discontinuous feedback controllers are designed. With the help of the definition of fixed-time synchronization, the upper right-hand derivative and a defined simple Lyapunov function, some easily verifiable and extensible synchronization criteria are derived to guarantee the fixed-time synchronization between the drive and response systems. Finally, two numerical simulations are given to verify the correctness of the results. PMID:29370248

Loop Mirror Laser Neural Network with a Fast Liquid-Crystal Display

NASA Astrophysics Data System (ADS)

Mos, Evert C.; Schleipen, Jean J. H. B.; de Waardt, Huug; Khoe, Djan G. D.

1999-07-01

In our laser neural network (LNN) all-optical threshold action is obtained by application of controlled optical feedback to a laser diode. Here an extended experimental LNN is presented with as many as 32 neurons and 12 inputs. In the setup we use a fast liquid-crystal display to implement an optical matrix vector multiplier. This display, based on ferroelectric liquid-crystal material, enables us to present 125 training examples s to the LNN. To maximize the optical feedback efficiency of the setup, a loop mirror is introduced. We use a -rule learning algorithm to train the network to perform a number of functions toward the application area of telecommunication data switching.

Force feedback controls motor activity and mechanical properties of self-assembling branched actin networks

PubMed Central

Bieling, Peter; Li, Tai-De; Weichsel, Julian; McGorty, Ryan; Jreij, Pamela; Huang, Bo; Fletcher, Daniel A.; Mullins, R. Dyche

2016-01-01

Branched actin networks–created by the Arp2/3 complex, capping protein, and a nucleation promoting factor– generate and transmit forces required for many cellular processes, but their response to force is poorly understood. To address this, we assembled branched actin networks in vitro from purified components and used simultaneous fluorescence and atomic force microscopy to quantify their molecular composition and material properties under various forces. Remarkably, mechanical loading of these self-assembling materials increases their density, power, and efficiency. Microscopically, increased density reflects increased filament number and altered geometry, but no change in average length. Macroscopically, increased density enhances network stiffness and resistance to mechanical failure beyond those of isotropic actin networks. These effects endow branched actin networks with memory of their mechanical history that shapes their material properties and motor activity. This work reveals intrinsic force feedback mechanisms by which mechanical resistance makes self-assembling actin networks stiffer, stronger, and more powerful. PMID:26771487

Output-feedback control of combined sewer networks through receding horizon control with moving horizon estimation

NASA Astrophysics Data System (ADS)

Joseph-Duran, Bernat; Ocampo-Martinez, Carlos; Cembrano, Gabriela

2015-10-01

An output-feedback control strategy for pollution mitigation in combined sewer networks is presented. The proposed strategy provides means to apply model-based predictive control to large-scale sewer networks, in-spite of the lack of measurements at most of the network sewers. In previous works, the authors presented a hybrid linear control-oriented model for sewer networks together with the formulation of Optimal Control Problems (OCP) and State Estimation Problems (SEP). By iteratively solving these problems, preliminary Receding Horizon Control with Moving Horizon Estimation (RHC/MHE) results, based on flow measurements, were also obtained. In this work, the RHC/MHE algorithm has been extended to take into account both flow and water level measurements and the resulting control loop has been extensively simulated to assess the system performance according different measurement availability scenarios and rain events. All simulations have been carried out using a detailed physically based model of a real case-study network as virtual reality.

Coding and non-coding gene regulatory networks underlie the immune response in liver cirrhosis

PubMed Central

Zhang, Xueming; Huang, Yongming; Yang, Zhengpeng; Zhang, Yuguo; Zhang, Weihui; Gao, Zu-hua; Xue, Dongbo

2017-01-01

Liver cirrhosis is recognized as being the consequence of immune-mediated hepatocyte damage and repair processes. However, the regulation of these immune responses underlying liver cirrhosis has not been elucidated. In this study, we used GEO datasets and bioinformatics methods to established coding and non-coding gene regulatory networks including transcription factor-/lncRNA-microRNA-mRNA, and competing endogenous RNA interaction networks. Our results identified 2224 mRNAs, 70 lncRNAs and 46 microRNAs were differentially expressed in liver cirrhosis. The transcription factor -/lncRNA- microRNA-mRNA network we uncovered that results in immune-mediated liver cirrhosis is comprised of 5 core microRNAs (e.g., miR-203; miR-219-5p), 3 transcription factors (i.e., FOXP3, ETS1 and FOS) and 7 lncRNAs (e.g., ENTS00000671336, ENST00000575137). The competing endogenous RNA interaction network we identified includes a complex immune response regulatory subnetwork that controls the entire liver cirrhosis network. Additionally, we found 10 overlapping GO terms shared by both liver cirrhosis and hepatocellular carcinoma including “immune response” as well. Interestingly, the overlapping differentially expressed genes in liver cirrhosis and hepatocellular carcinoma were enriched in immune response-related functional terms. In summary, a complex gene regulatory network underlying immune response processes may play an important role in the development and progression of liver cirrhosis, and its development into hepatocellular carcinoma. PMID:28355233

Comprehensive Analysis of Interaction Networks of Telomerase Reverse Transcriptase with Multiple Bioinformatic Approaches: Deep Mining the Potential Functions of Telomere and Telomerase.

PubMed

Hou, Chunyu; Wang, Fei; Liu, Xuewen; Chang, Guangming; Wang, Feng; Geng, Xin

2017-08-01

Telomerase reverse transcriptase (TERT) is the protein component of telomerase complex. Evidence has accumulated showing that the nontelomeric functions of TERT are independent of telomere elongation. However, the mechanisms governing the interaction between TERT and its target genes are not clearly revealed. The biological functions of TERT are not fully elucidated and have thus far been underestimated. To further explore these functions, we investigated TERT interaction networks using multiple bioinformatic databases, including BioGRID, STRING, DAVID, GeneCards, GeneMANIA, PANTHER, miRWalk, mirTarBase, miRNet, miRDB, and TargetScan. In addition, network diagrams were built using Cytoscape software. As competing endogenous RNAs (ceRNAs) are endogenous transcripts that compete for the binding of microRNAs (miRNAs) by using shared miRNA recognition elements, they are involved in creating widespread regulatory networks. Therefore, the ceRNA regulatory networks of TERT were also investigated in this study. Interestingly, we found that the three genes PABPC1, SLC7A11, and TP53 were present in both TERT interaction networks and ceRNAs target genes. It was predicted that TERT might play nontelomeric roles in the generation or development of some rare diseases, such as Rift Valley fever and dyscalculia. Thus, our data will help to decipher the interaction networks of TERT and reveal the unknown functions of telomerase in cancer and aging-related diseases.

A network approach for modulating memory processes via direct and indirect brain stimulation: Toward a causal approach for the neural basis of memory.

PubMed

Kim, Kamin; Ekstrom, Arne D; Tandon, Nitin

2016-10-01

Electrical stimulation of the brain is a unique tool to perturb endogenous neural signals, allowing us to evaluate the necessity of given neural processes to cognitive processing. An important issue, gaining increasing interest in the literature, is whether and how stimulation can be employed to selectively improve or disrupt declarative memory processes. Here, we provide a comprehensive review of both invasive and non-invasive stimulation studies aimed at modulating memory performance. The majority of past studies suggest that invasive stimulation of the hippocampus impairs memory performance; similarly, most non-invasive studies show that disrupting frontal or parietal regions also impairs memory performance, suggesting that these regions also play necessary roles in declarative memory. On the other hand, a handful of both invasive and non-invasive studies have also suggested modest improvements in memory performance following stimulation. These studies typically target brain regions connected to the hippocampus or other memory "hubs," which may affect endogenous activity in connected areas like the hippocampus, suggesting that to augment declarative memory, altering the broader endogenous memory network activity is critical. Together, studies reporting memory improvements/impairments are consistent with the idea that a network of distinct brain "hubs" may be crucial for successful memory encoding and retrieval rather than a single primary hub such as the hippocampus. Thus, it is important to consider neurostimulation from the network perspective, rather than from a purely localizationalist viewpoint. We conclude by proposing a novel approach to neurostimulation for declarative memory modulation that aims to facilitate interactions between multiple brain "nodes" underlying memory rather than considering individual brain regions in isolation. Copyright © 2016. Published by Elsevier Inc.

Hippocampal Sharp-Wave Ripples Influence Selective Activation of the Default Mode Network

PubMed Central

Kaplan, Raphael; Adhikari, Mohit H.; Hindriks, Rikkert; Mantini, Dante; Murayama, Yusuke; Logothetis, Nikos K.; Deco, Gustavo

2016-01-01

Summary The default mode network (DMN) is a commonly observed resting-state network (RSN) that includes medial temporal, parietal, and prefrontal regions involved in episodic memory [1, 2, 3]. The behavioral relevance of endogenous DMN activity remains elusive, despite an emerging literature correlating resting fMRI fluctuations with memory performance [4, 5]—particularly in DMN regions [6, 7, 8]. Mechanistic support for the DMN’s role in memory consolidation might come from investigation of large deflections (sharp-waves) in the hippocampal local field potential that co-occur with high-frequency (>80 Hz) oscillations called ripples—both during sleep [9, 10] and awake deliberative periods [11, 12, 13]. Ripples are ideally suited for memory consolidation [14, 15], since the reactivation of hippocampal place cell ensembles occurs during ripples [16, 17, 18, 19]. Moreover, the number of ripples after learning predicts subsequent memory performance in rodents [20, 21, 22] and humans [23], whereas electrical stimulation of the hippocampus after learning interferes with memory consolidation [24, 25, 26]. A recent study in macaques showed diffuse fMRI neocortical activation and subcortical deactivation specifically after ripples [27]. Yet it is unclear whether ripples and other hippocampal neural events influence endogenous fluctuations in specific RSNs—like the DMN—unitarily. Here, we examine fMRI datasets from anesthetized monkeys with simultaneous hippocampal electrophysiology recordings, where we observe a dramatic increase in the DMN fMRI signal following ripples, but not following other hippocampal electrophysiological events. Crucially, we find increases in ongoing DMN activity after ripples, but not in other RSNs. Our results relate endogenous DMN fluctuations to hippocampal ripples, thereby linking network-level resting fMRI fluctuations with behaviorally relevant circuit-level neural dynamics. PMID:26898464

Soft Cysteine Signaling Network: The Functional Significance of Cysteine in Protein Function and the Soft Acids/Bases Thiol Chemistry That Facilitates Cysteine Modification.

PubMed

Wible, Ryan S; Sutter, Thomas R

2017-03-20

The unique biophysical and electronic properties of cysteine make this molecule one of the most biologically critical amino acids in the proteome. The defining sulfur atom in cysteine is much larger than the oxygen and nitrogen atoms more commonly found in the other amino acids. As a result of its size, the valence electrons of sulfur are highly polarizable. Unique protein microenvironments favor the polarization of sulfur, thus increasing the overt reactivity of cysteine. Here, we provide a brief overview of the endogenous generation of reactive oxygen and electrophilic species and specific examples of enzymes and transcription factors in which the oxidation or covalent modification of cysteine in those proteins modulates their function. The perspective concludes with a discussion of cysteine chemistry and biophysics, the hard and soft acids and bases model, and the proposal of the Soft Cysteine Signaling Network: a hypothesis proposing the existence of a complex signaling network governed by layered chemical reactivity and cross-talk in which the chemical modification of reactive cysteine in biological networks triggers the reorganization of intracellular biochemistry to mitigate spikes in endogenous or exogenous oxidative or electrophilic stress.

A complex network for studying the transmission mechanisms in stock market

NASA Astrophysics Data System (ADS)

Long, Wen; Guan, Lijing; Shen, Jiangjian; Song, Linqiu; Cui, Lingxiao

2017-10-01

This paper introduces a new complex network to describe the volatility transmission mechanisms in stock market. The network can not only endogenize stock market's volatility but also figure out the direction of volatility spillover. In this model, we first use BEKK-GARCH to estimate the volatility spillover effects among Chinese 18 industry sectors. Then, based on the ARCH coefficients and GARCH coefficients, the directional shock networks and variance networks in different stages are constructed separately. We find that the spillover effects and network structures changes in different stages. The results of the topological stability test demonstrate that the connectivity of networks becomes more fragile to selective attacks than stochastic attacks.

Balanced Cortical Microcircuitry for Spatial Working Memory Based on Corrective Feedback Control

PubMed Central

2014-01-01

A hallmark of working memory is the ability to maintain graded representations of both the spatial location and amplitude of a memorized stimulus. Previous work has identified a neural correlate of spatial working memory in the persistent maintenance of spatially specific patterns of neural activity. How such activity is maintained by neocortical circuits remains unknown. Traditional models of working memory maintain analog representations of either the spatial location or the amplitude of a stimulus, but not both. Furthermore, although most previous models require local excitation and lateral inhibition to maintain spatially localized persistent activity stably, the substrate for lateral inhibitory feedback pathways is unclear. Here, we suggest an alternative model for spatial working memory that is capable of maintaining analog representations of both the spatial location and amplitude of a stimulus, and that does not rely on long-range feedback inhibition. The model consists of a functionally columnar network of recurrently connected excitatory and inhibitory neural populations. When excitation and inhibition are balanced in strength but offset in time, drifts in activity trigger spatially specific negative feedback that corrects memory decay. The resulting networks can temporally integrate inputs at any spatial location, are robust against many commonly considered perturbations in network parameters, and, when implemented in a spiking model, generate irregular neural firing characteristic of that observed experimentally during persistent activity. This work suggests balanced excitatory–inhibitory memory circuits implementing corrective negative feedback as a substrate for spatial working memory. PMID:24828633

Mathematical Model of a Telomerase Transcriptional Regulatory Network Developed by Cell-Based Screening: Analysis of Inhibitor Effects and Telomerase Expression Mechanisms

PubMed Central

Bilsland, Alan E.; Stevenson, Katrina; Liu, Yu; Hoare, Stacey; Cairney, Claire J.; Roffey, Jon; Keith, W. Nicol

2014-01-01

Cancer cells depend on transcription of telomerase reverse transcriptase (TERT). Many transcription factors affect TERT, though regulation occurs in context of a broader network. Network effects on telomerase regulation have not been investigated, though deeper understanding of TERT transcription requires a systems view. However, control over individual interactions in complex networks is not easily achievable. Mathematical modelling provides an attractive approach for analysis of complex systems and some models may prove useful in systems pharmacology approaches to drug discovery. In this report, we used transfection screening to test interactions among 14 TERT regulatory transcription factors and their respective promoters in ovarian cancer cells. The results were used to generate a network model of TERT transcription and to implement a dynamic Boolean model whose steady states were analysed. Modelled effects of signal transduction inhibitors successfully predicted TERT repression by Src-family inhibitor SU6656 and lack of repression by ERK inhibitor FR180204, results confirmed by RT-QPCR analysis of endogenous TERT expression in treated cells. Modelled effects of GSK3 inhibitor 6-bromoindirubin-3′-oxime (BIO) predicted unstable TERT repression dependent on noise and expression of JUN, corresponding with observations from a previous study. MYC expression is critical in TERT activation in the model, consistent with its well known function in endogenous TERT regulation. Loss of MYC caused complete TERT suppression in our model, substantially rescued only by co-suppression of AR. Interestingly expression was easily rescued under modelled Ets-factor gain of function, as occurs in TERT promoter mutation. RNAi targeting AR, JUN, MXD1, SP3, or TP53, showed that AR suppression does rescue endogenous TERT expression following MYC knockdown in these cells and SP3 or TP53 siRNA also cause partial recovery. The model therefore successfully predicted several aspects of TERT regulation including previously unknown mechanisms. An extrapolation suggests that a dominant stimulatory system may programme TERT for transcriptional stability. PMID:24550717

Impaired Feedforward Control and Enhanced Feedback Control of Speech in Patients with Cerebellar Degeneration

PubMed Central

Agnew, Zarinah; Nagarajan, Srikantan; Houde, John; Ivry, Richard B.

2017-01-01

The cerebellum has been hypothesized to form a crucial part of the speech motor control network. Evidence for this comes from patients with cerebellar damage, who exhibit a variety of speech deficits, as well as imaging studies showing cerebellar activation during speech production in healthy individuals. To date, the precise role of the cerebellum in speech motor control remains unclear, as it has been implicated in both anticipatory (feedforward) and reactive (feedback) control. Here, we assess both anticipatory and reactive aspects of speech motor control, comparing the performance of patients with cerebellar degeneration and matched controls. Experiment 1 tested feedforward control by examining speech adaptation across trials in response to a consistent perturbation of auditory feedback. Experiment 2 tested feedback control, examining online corrections in response to inconsistent perturbations of auditory feedback. Both male and female patients and controls were tested. The patients were impaired in adapting their feedforward control system relative to controls, exhibiting an attenuated anticipatory response to the perturbation. In contrast, the patients produced even larger compensatory responses than controls, suggesting an increased reliance on sensory feedback to guide speech articulation in this population. Together, these results suggest that the cerebellum is crucial for maintaining accurate feedforward control of speech, but relatively uninvolved in feedback control. SIGNIFICANCE STATEMENT Speech motor control is a complex activity that is thought to rely on both predictive, feedforward control as well as reactive, feedback control. While the cerebellum has been shown to be part of the speech motor control network, its functional contribution to feedback and feedforward control remains controversial. Here, we use real-time auditory perturbations of speech to show that patients with cerebellar degeneration are impaired in adapting feedforward control of speech but retain the ability to make online feedback corrections; indeed, the patients show an increased sensitivity to feedback. These results indicate that the cerebellum forms a crucial part of the feedforward control system for speech but is not essential for online, feedback control. PMID:28842410

Loads Bias Genetic and Signaling Switches in Synthetic and Natural Systems

PubMed Central

Medford, June; Prasad, Ashok

2014-01-01

Biological protein interactions networks such as signal transduction or gene transcription networks are often treated as modular, allowing motifs to be analyzed in isolation from the rest of the network. Modularity is also a key assumption in synthetic biology, where it is similarly expected that when network motifs are combined together, they do not lose their essential characteristics. However, the interactions that a network module has with downstream elements change the dynamical equations describing the upstream module and thus may change the dynamic and static properties of the upstream circuit even without explicit feedback. In this work we analyze the behavior of a ubiquitous motif in gene transcription and signal transduction circuits: the switch. We show that adding an additional downstream component to the simple genetic toggle switch changes its dynamical properties by changing the underlying potential energy landscape, and skewing it in favor of the unloaded side, and in some situations adding loads to the genetic switch can also abrogate bistable behavior. We find that an additional positive feedback motif found in naturally occurring toggle switches could tune the potential energy landscape in a desirable manner. We also analyze autocatalytic signal transduction switches and show that a ubiquitous positive feedback switch can lose its switch-like properties when connected to a downstream load. Our analysis underscores the necessity of incorporating the effects of downstream components when understanding the physics of biochemical network motifs, and raises the question as to how these effects are managed in real biological systems. This analysis is particularly important when scaling synthetic networks to more complex organisms. PMID:24676102

Experimental validation of a predicted feedback loop in the multi-oscillator clock of Arabidopsis thaliana

PubMed Central

Locke, James C W; Kozma-Bognár, László; Gould, Peter D; Fehér, Balázs; Kevei, Éva; Nagy, Ferenc; Turner, Matthew S; Hall, Anthony; Millar, Andrew J

2006-01-01

Our computational model of the circadian clock comprised the feedback loop between LATE ELONGATED HYPOCOTYL (LHY), CIRCADIAN CLOCK ASSOCIATED 1 (CCA1) and TIMING OF CAB EXPRESSION 1 (TOC1), and a predicted, interlocking feedback loop involving TOC1 and a hypothetical component Y. Experiments based on model predictions suggested GIGANTEA (GI) as a candidate for Y. We now extend the model to include a recently demonstrated feedback loop between the TOC1 homologues PSEUDO-RESPONSE REGULATOR 7 (PRR7), PRR9 and LHY and CCA1. This three-loop network explains the rhythmic phenotype of toc1 mutant alleles. Model predictions fit closely to new data on the gi;lhy;cca1 mutant, which confirm that GI is a major contributor to Y function. Analysis of the three-loop network suggests that the plant clock consists of morning and evening oscillators, coupled intracellularly, which may be analogous to coupled, morning and evening clock cells in Drosophila and the mouse. PMID:17102804

Ask and you shall receive: desire and receipt of feedback via Facebook predicts disordered eating concerns.

PubMed

Hummel, Alexandra C; Smith, April R

2015-05-01

The current study examined whether certain types of Facebook content (i.e., status updates, comments) relate to eating concerns and attitudes. We examined the effects of seeking and receiving negative feedback via Facebook on disordered eating concerns in a sample of 185 undergraduate students followed for approximately 4 weeks. Results indicated that individuals with a negative feedback seeking style who received a high number of comments on Facebook were more likely to report disordered eating attitudes four weeks later. Additionally, individuals who received extremely negative comments in response to their personally revealing status updates were more likely to report disordered eating concerns four weeks later. Results of the current study provide preliminary evidence that seeking and receiving negative feedback via social networking sites can increase risk for disordered eating attitudes, and suggest that reducing maladaptive social networking usage may be an important target for prevention and intervention efforts aimed at reducing disordered eating attitudes. © 2014 Wiley Periodicals, Inc.

Chaos synchronization in networks of semiconductor superlattices

NASA Astrophysics Data System (ADS)

Li, Wen; Aviad, Yaara; Reidler, Igor; Song, Helun; Huang, Yuyang; Biermann, Klaus; Rosenbluh, Michael; Zhang, Yaohui; Grahn, Holger T.; Kanter, Ido

2015-11-01

Chaos synchronization has been demonstrated as a useful building block for various tasks in secure communications, including a source of all-electronic ultrafast physical random number generators based on room temperature spontaneous chaotic oscillations in a DC-biased weakly coupled GaAs/Al0.45Ga0.55As semiconductor superlattice (SSL). Here, we experimentally demonstrate the emergence of several types of chaos synchronization, e.g. leader-laggard, face-to-face and zero-lag synchronization in network motifs of coupled SSLs consisting of unidirectional and mutual coupling as well as self-feedback coupling. Each type of synchronization clearly reflects the symmetry of the topology of its network motif. The emergence of a chaotic SSL without external feedback and synchronization among different structured SSLs open up the possibility for advanced secure multi-user communication methods based on large networks of coupled SSLs.

Expression of ESR1 in Glutamatergic and GABAergic Neurons Is Essential for Normal Puberty Onset, Estrogen Feedback, and Fertility in Female Mice.

PubMed

Cheong, Rachel Y; Czieselsky, Katja; Porteous, Robert; Herbison, Allan E

2015-10-28

Circulating estradiol exerts a profound influence on the activity of the gonadotropin-releasing hormone (GnRH) neuronal network controlling fertility. Using genetic strategies enabling neuron-specific deletion of estrogen receptor α (Esr1), we examine here whether estradiol-modulated GABA and glutamate transmission are critical for the functioning of the GnRH neuron network in the female mouse. Using Vgat- and Vglut2-ires-Cre knock-in mice and ESR1 immunohistochemistry, we demonstrate that subpopulations of GABA and glutamate neurons throughout the limbic forebrain express ESR1, with ESR1-GABAergic neurons being more widespread and numerous than ESR1-glutamatergic neurons. We crossed Vgat- and Vglut2-ires-Cre mice with an Esr1(lox/lox) line to generate animals with GABA-neuron-specific or glutamate-neuron-specific deletion of Esr1. Vgat-ires-Cre;Esr1(lox/lox) mice were infertile, with abnormal estrous cycles, and exhibited a complete failure of the estrogen positive feedback mechanism responsible for the preovulatory GnRH surge. However, puberty onset and estrogen negative feedback were normal. Vglut2-ires-Cre;Esr1(lox/lox) mice were also infertile but displayed a wider range of deficits, including advanced puberty onset, abnormal negative feedback, and abolished positive feedback. Whereas <25% of preoptic kisspeptin neurons expressed Cre in Vgat- and Vglut2-ires-Cre lines, ∼70% of arcuate kisspeptin neurons were targeted in Vglut2-ires-Cre;Esr1(lox/lox) mice, possibly contributing to their advanced puberty phenotype. These observations show that, unexpectedly, ESR1-GABA neurons are only essential for the positive feedback mechanism. In contrast, we reveal the key importance of ESR1 in glutamatergic neurons for multiple estrogen feedback loops within the GnRH neuronal network required for fertility in the female mouse. Copyright © 2015 the authors 0270-6474/15/3514533-11$15.00/0.

Adaptive Neural Networks Decentralized FTC Design for Nonstrict-Feedback Nonlinear Interconnected Large-Scale Systems Against Actuator Faults.

PubMed

Li, Yongming; Tong, Shaocheng

The problem of active fault-tolerant control (FTC) is investigated for the large-scale nonlinear systems in nonstrict-feedback form. The nonstrict-feedback nonlinear systems considered in this paper consist of unstructured uncertainties, unmeasured states, unknown interconnected terms, and actuator faults (e.g., bias fault and gain fault). A state observer is designed to solve the unmeasurable state problem. Neural networks (NNs) are used to identify the unknown lumped nonlinear functions so that the problems of unstructured uncertainties and unknown interconnected terms can be solved. By combining the adaptive backstepping design principle with the combination Nussbaum gain function property, a novel NN adaptive output-feedback FTC approach is developed. The proposed FTC controller can guarantee that all signals in all subsystems are bounded, and the tracking errors for each subsystem converge to a small neighborhood of zero. Finally, numerical results of practical examples are presented to further demonstrate the effectiveness of the proposed control strategy.The problem of active fault-tolerant control (FTC) is investigated for the large-scale nonlinear systems in nonstrict-feedback form. The nonstrict-feedback nonlinear systems considered in this paper consist of unstructured uncertainties, unmeasured states, unknown interconnected terms, and actuator faults (e.g., bias fault and gain fault). A state observer is designed to solve the unmeasurable state problem. Neural networks (NNs) are used to identify the unknown lumped nonlinear functions so that the problems of unstructured uncertainties and unknown interconnected terms can be solved. By combining the adaptive backstepping design principle with the combination Nussbaum gain function property, a novel NN adaptive output-feedback FTC approach is developed. The proposed FTC controller can guarantee that all signals in all subsystems are bounded, and the tracking errors for each subsystem converge to a small neighborhood of zero. Finally, numerical results of practical examples are presented to further demonstrate the effectiveness of the proposed control strategy.

Disturbed cortisol secretion in man: contrasting Cushing's disease and endogenous depression.

PubMed

Voigt, K H; Bossert, S; Bretschneider, S; Bliestle, A; Fehm, H L

1985-08-01

A disturbed regulation of cortisol secretion is the principal pathology of Cushing's disease and is also the most widely reported neuroendocrine dysfunction in endogenous depression. Because additional clinical signs in both diseases indicated a hypothetical common pathway, we examined 17 patients suffering from Cushing's disease, following a protocol identical to that used in depressed patients (e.g., Hamilton Rating Scale for Depression, self-rating scales, and a clinical interview). Affective disorders, frequently observed in patients with Cushing's disease, were undetectable after surgical treatment (adrenalectomy or microadenomectomy of hypercortisolism). This was an unexpected result, since we found that recovered patients were still characterized by a disturbance of glucocorticoid feedback regulation, probably acting at the hypothalamic level. Our results, as well as numerous reports from others, failed to support the hypothesis that an impaired regulation of cortisol is directly linked to depressive illness.

Modeling beta-adrenergic control of cardiac myocyte contractility in silico.

PubMed

Saucerman, Jeffrey J; Brunton, Laurence L; Michailova, Anushka P; McCulloch, Andrew D

2003-11-28

The beta-adrenergic signaling pathway regulates cardiac myocyte contractility through a combination of feedforward and feedback mechanisms. We used systems analysis to investigate how the components and topology of this signaling network permit neurohormonal control of excitation-contraction coupling in the rat ventricular myocyte. A kinetic model integrating beta-adrenergic signaling with excitation-contraction coupling was formulated, and each subsystem was validated with independent biochemical and physiological measurements. Model analysis was used to investigate quantitatively the effects of specific molecular perturbations. 3-Fold overexpression of adenylyl cyclase in the model allowed an 85% higher rate of cyclic AMP synthesis than an equivalent overexpression of beta 1-adrenergic receptor, and manipulating the affinity of Gs alpha for adenylyl cyclase was a more potent regulator of cyclic AMP production. The model predicted that less than 40% of adenylyl cyclase molecules may be stimulated under maximal receptor activation, and an experimental protocol is suggested for validating this prediction. The model also predicted that the endogenous heat-stable protein kinase inhibitor may enhance basal cyclic AMP buffering by 68% and increasing the apparent Hill coefficient of protein kinase A activation from 1.0 to 2.0. Finally, phosphorylation of the L-type calcium channel and phospholamban were found sufficient to predict the dominant changes in myocyte contractility, including a 2.6x increase in systolic calcium (inotropy) and a 28% decrease in calcium half-relaxation time (lusitropy). By performing systems analysis, the consequences of molecular perturbations in the beta-adrenergic signaling network may be understood within the context of integrative cellular physiology.

Endogenously generated gamma-band oscillations in early visual cortex: A neurofeedback study.

PubMed

Merkel, Nina; Wibral, Michael; Bland, Gareth; Singer, Wolf

2018-04-26

Human subjects were trained with neurofeedback (NFB) to enhance the power of narrow-band gamma oscillations in circumscribed regions of early visual cortex. To select the region and the oscillation frequency for NFB training, gamma oscillations were induced with locally presented drifting gratings. The source and frequency of these induced oscillations were determined using beamforming methods. During NFB training the power of narrow band gamma oscillations was continuously extracted from this source with online beamforming and converted into the pitch of a tone signal. We found that seven out of ten subjects were able to selectively increase the amplitude of gamma oscillations in the absence of visual stimulation. One subject however failed completely and two subjects succeeded to manipulate the feedback signal by contraction of muscles. In all subjects the attempts to enhance visual gamma oscillations were associated with an increase of beta oscillations over precentral/frontal regions. Only successful subjects exhibited an additional marked increase of theta oscillations over precentral/prefrontal and temporal regions whereas unsuccessful subjects showed an increase of alpha band oscillations over occipital regions. We argue that spatially confined networks in early visual cortex can be entrained to engage in narrow band gamma oscillations not only by visual stimuli but also by top down signals. We interpret the concomitant increase in beta oscillations as indication for an engagement of the fronto-parietal attention network and the increase of theta oscillations as a correlate of imagery. Our finding support the application of NFB in disease conditions associated with impaired gamma synchronization. © 2018 Wiley Periodicals, Inc.

Modeling beta-adrenergic control of cardiac myocyte contractility in silico

NASA Technical Reports Server (NTRS)

Saucerman, Jeffrey J.; Brunton, Laurence L.; Michailova, Anushka P.; McCulloch, Andrew D.; McCullough, A. D. (Principal Investigator)

2003-01-01

The beta-adrenergic signaling pathway regulates cardiac myocyte contractility through a combination of feedforward and feedback mechanisms. We used systems analysis to investigate how the components and topology of this signaling network permit neurohormonal control of excitation-contraction coupling in the rat ventricular myocyte. A kinetic model integrating beta-adrenergic signaling with excitation-contraction coupling was formulated, and each subsystem was validated with independent biochemical and physiological measurements. Model analysis was used to investigate quantitatively the effects of specific molecular perturbations. 3-Fold overexpression of adenylyl cyclase in the model allowed an 85% higher rate of cyclic AMP synthesis than an equivalent overexpression of beta 1-adrenergic receptor, and manipulating the affinity of Gs alpha for adenylyl cyclase was a more potent regulator of cyclic AMP production. The model predicted that less than 40% of adenylyl cyclase molecules may be stimulated under maximal receptor activation, and an experimental protocol is suggested for validating this prediction. The model also predicted that the endogenous heat-stable protein kinase inhibitor may enhance basal cyclic AMP buffering by 68% and increasing the apparent Hill coefficient of protein kinase A activation from 1.0 to 2.0. Finally, phosphorylation of the L-type calcium channel and phospholamban were found sufficient to predict the dominant changes in myocyte contractility, including a 2.6x increase in systolic calcium (inotropy) and a 28% decrease in calcium half-relaxation time (lusitropy). By performing systems analysis, the consequences of molecular perturbations in the beta-adrenergic signaling network may be understood within the context of integrative cellular physiology.

Application of system dynamics for developing financially self-sustaining management policies for water and wastewater systems.

PubMed

Rehan, R; Knight, M A; Haas, C T; Unger, A J A

2011-10-15

Recently enacted regulations in Canada and elsewhere require water utilities to be financially self-sustaining over the long-term. This implies full cost recovery for providing water and wastewater services to users. This study proposes a new approach to help water utilities plan to meet the requirements of the new regulations. A causal loop diagram is developed for a financially self-sustaining water utility which frames water and wastewater network management as a complex system with multiple interconnections and feedback loops. The novel System Dynamics approach is used to develop a demonstration model for water and wastewater network management. This is the first known application of System Dynamics to water and wastewater network management. The network simulated is that of a typical Canadian water utility that has under invested in maintenance. Model results show that with no proactive rehabilitation strategy the utility will need to substantially increase its user fees to achieve financial sustainability. This increase is further exacerbated when price elasticity of water demand is considered. When the utility pursues proactive rehabilitation, financial sustainability is achieved with lower user fees. Having demonstrated the significance of feedback loops for financial management of water and wastewater networks, the paper makes the case for a more complete utility model that considers the complexity of the system by incorporating all feedback loops. Crown Copyright © 2011. Published by Elsevier Ltd. All rights reserved.

Positive feedback regulation of a Lycium chinense-derived VDE gene by drought-induced endogenous ABA, and over-expression of this VDE gene improve drought-induced photo-damage in Arabidopsis.

PubMed

Guan, Chunfeng; Ji, Jing; Zhang, Xuqiang; Li, Xiaozhou; Jin, Chao; Guan, Wenzhu; Wang, Gang

2015-03-01

Violaxanthin de-epoxidase (VDE) plays an important role in protecting the photosynthetic apparatus from photo-damage by dissipating excessively absorbed light energy as heat, via the conversion of violaxanthin (V) to intermediate product antheraxanthin (A) and final product zeaxanthin (Z) under light stress. We have cloned a VDE gene (LcVDE) from Lycium chinense, a deciduous woody perennial halophyte, which can grow in a large variety of soil types. The amino acid sequence of LcVDE has high homology with VDEs in other plants. Under drought stress, relative expression of LcVDE and the de-epoxidation ratio (Z+0.5A)/(V+A+Z) increased rapidly, and non-photochemical quenching (NPQ) also rose. Interestingly, these elevations induced by drought stress were reduced by the topical administration of abamine SG, a potent ABA inhibitor via inhibition of NCED in the ABA synthesis pathway. Until now, little has been done to explore the relationship between endogenous ABA and the expression of VDE genes. Since V serves as a common precursor for ABA, these data support the possible involvement of endogenous ABA in the positive feedback regulation of LcVDE gene expression in L. chinense under drought stress. Moreover, the LcVDE may be involved in modulating the level of photosynthesis damage caused by drought stress. Furthermore, the ratio of (Z+0.5A)/(V+A+Z) and NPQ increased more in transgenic Arabidopsis over-expressing LcVDE gene than the wild types under drought stress. The maximum quantum yield of primary photochemistry of PSII (Fv/Fm) in transgenic Arabidopsis decreased more slowly during the stressed period than that in wild types under the same conditions. Furthermore, transgenic Arabidopsis over-expressing LcVDE showed increased tolerance to drought stress. Copyright © 2014 Elsevier GmbH. All rights reserved.

MQW Optical Feedback Modulators And Phase Shifters

NASA Technical Reports Server (NTRS)

Jackson, Deborah J.

1995-01-01

Laser diodes equipped with proposed multiple-quantum-well (MQW) optical feedback modulators prove useful in variety of analog and digital optical-communication applications, including fiber-optic signal-distribution networks and high-speed, low-crosstalk interconnections among super computers or very-high-speed integrated circuits. Development exploits accompanying electro-optical aspect of QCSE - variation in index of refraction with applied electric field. Also exploits sensitivity of laser diodes to optical feedback. Approach is reverse of prior approach.

ENHANCED 5-HT1A RECEPTOR-DEPENDENT FEEDBACK CONTROL OVER DORSAL RAPHE SEROTONIN NEURONS IN THE SERT KNOCKOUT MOUSE

PubMed Central

Soiza-Reilly, Mariano; Goodfellow, Nathalie M.; Lambe, Evelyn K.; Commons, Kathryn G.

2014-01-01

5-HT1A receptors are widely expressed in the brain and play a critical role in feedback inhibition of serotonin (5-HT) neurons through multiple mechanisms. Yet, it remains poorly understood how these feedback mechanisms, particularly those involving long-range projections, adapt in mood disorders. Here, we examined several aspects of 5-HT1A receptor function in the 5-HT transporter knockout mouse (SERT-KO), a model of vulnerability to stress and mood disorders. We found that in comparison to wild-type (WT) mice, SERT-KO mice had more passive coping in response to acute swim stress and this was accompanied by hypo-activation of medial prefrontal cortex (mPFC) Fos expression. Both of these effects were reversed by systemically blocking 5-HT1A receptors. Ex-vivo electrophysiological experiments showed that 5-HT exerted greater 5-HT1A-mediated inhibitory effects in the mPFC of SERT-KO mice compared to WT. Since 5-HT1A receptors in the mPFC provide a key feedback regulation of the dorsal raphe nucleus (DRN), we used a disinhibition strategy to examined endogenous feedback control of 5-HT neurons. Blocking 5-HT1A receptors disinhibited several fold more 5-HT neurons in the DRN of SERT-KO than in WT mice, revealing the presence of enhanced feedback inhibition of 5-HT neurons in the SERT-KO. Taken together our results indicate that increased stress sensitivity in the SERT-KO is associated with the enhanced capacity of 5-HT1A receptors to inhibit neurons in the mPFC as well as to exert feedback inhibition of DRN 5-HT neurons. PMID:25261781

Feedback Power Control Strategies in Wireless Sensor Networks with Joint Channel Decoding

PubMed Central

Abrardo, Andrea; Ferrari, Gianluigi; Martalò, Marco; Perna, Fabio

2009-01-01

In this paper, we derive feedback power control strategies for block-faded multiple access schemes with correlated sources and joint channel decoding (JCD). In particular, upon the derivation of the feasible signal-to-noise ratio (SNR) region for the considered multiple access schemes, i.e., the multidimensional SNR region where error-free communications are, in principle, possible, two feedback power control strategies are proposed: (i) a classical feedback power control strategy, which aims at equalizing all link SNRs at the access point (AP), and (ii) an innovative optimized feedback power control strategy, which tries to make the network operational point fall in the feasible SNR region at the lowest overall transmit energy consumption. These strategies will be referred to as “balanced SNR” and “unbalanced SNR,” respectively. While they require, in principle, an unlimited power control range at the sources, we also propose practical versions with a limited power control range. We preliminary consider a scenario with orthogonal links and ideal feedback. Then, we analyze the robustness of the proposed power control strategies to possible non-idealities, in terms of residual multiple access interference and noisy feedback channels. Finally, we successfully apply the proposed feedback power control strategies to a limiting case of the class of considered multiple access schemes, namely a central estimating officer (CEO) scenario, where the sensors observe noisy versions of a common binary information sequence and the AP's goal is to estimate this sequence by properly fusing the soft-output information output by the JCD algorithm. PMID:22291536

Power oscillator

DOEpatents

Gitsevich, Aleksandr

2001-01-01

An oscillator includes an amplifier having an input and an output, and an impedance transformation network connected between the input of the amplifier and the output of the amplifier, wherein the impedance transformation network is configured to provide suitable positive feedback from the output of the amplifier to the input of the amplifier to initiate and sustain an oscillating condition, and wherein the impedance transformation network is configured to protect the input of the amplifier from a destructive feedback signal. One example of the oscillator is a single active element device capable of providing over 70 watts of power at over 70% efficiency. Various control circuits may be employed to match the driving frequency of the oscillator to a plurality of tuning states of the lamp.

A 16-Channel Distributed-Feedback Laser Array with a Monolithic Integrated Arrayed Waveguide Grating Multiplexer for a Wavelength Division Multiplex-Passive Optical Network System Network

NASA Astrophysics Data System (ADS)

Zhao, Jian-Yi; Chen, Xin; Zhou, Ning; Huang, Xiao-Dong; Cao, Ming-De; Liu, Wen

2014-07-01

A 16-channel distributed-feedback (DFB) laser array with a monolithic integrated arrayed waveguide grating multiplexer for a wavelength division multiplex-passive optical network system is fabricated by using the butt-joint metal organic chemical vapor deposition technology and nanoimpirnt technology. The results show that the threshold current is about 20-30 mA at 25°C. The DFB laser side output power is about 16 mW with a 150 mA injection current. The lasing wavelength is from 1550 nm to 1575 nm covering a more than 25 nm range with 200 GHz channel space. A more than 55 dB sidemode suppression ratio is obtained.

Feedback Enhances Feedforward Figure-Ground Segmentation by Changing Firing Mode

PubMed Central

Supèr, Hans; Romeo, August

2011-01-01

In the visual cortex, feedback projections are conjectured to be crucial in figure-ground segregation. However, the precise function of feedback herein is unclear. Here we tested a hypothetical model of reentrant feedback. We used a previous developed 2-layered feedforwardspiking network that is able to segregate figure from ground and included feedback connections. Our computer model data show that without feedback, neurons respond with regular low-frequency (∼9 Hz) bursting to a figure-ground stimulus. After including feedback the firing pattern changed into a regular (tonic) spiking pattern. In this state, we found an extra enhancement of figure responses and a further suppression of background responses resulting in a stronger figure-ground signal. Such push-pull effect was confirmed by comparing the figure-ground responses withthe responses to a homogenous texture. We propose that feedback controlsfigure-ground segregation by influencing the neural firing patterns of feedforward projecting neurons. PMID:21738747

Feedback enhances feedforward figure-ground segmentation by changing firing mode.

PubMed

Supèr, Hans; Romeo, August

2011-01-01

In the visual cortex, feedback projections are conjectured to be crucial in figure-ground segregation. However, the precise function of feedback herein is unclear. Here we tested a hypothetical model of reentrant feedback. We used a previous developed 2-layered feedforward spiking network that is able to segregate figure from ground and included feedback connections. Our computer model data show that without feedback, neurons respond with regular low-frequency (∼9 Hz) bursting to a figure-ground stimulus. After including feedback the firing pattern changed into a regular (tonic) spiking pattern. In this state, we found an extra enhancement of figure responses and a further suppression of background responses resulting in a stronger figure-ground signal. Such push-pull effect was confirmed by comparing the figure-ground responses with the responses to a homogenous texture. We propose that feedback controls figure-ground segregation by influencing the neural firing patterns of feedforward projecting neurons.

Fitting ERGMs on big networks.

PubMed

An, Weihua

2016-09-01

The exponential random graph model (ERGM) has become a valuable tool for modeling social networks. In particular, ERGM provides great flexibility to account for both covariates effects on tie formations and endogenous network formation processes. However, there are both conceptual and computational issues for fitting ERGMs on big networks. This paper describes a framework and a series of methods (based on existent algorithms) to address these issues. It also outlines the advantages and disadvantages of the methods and the conditions to which they are most applicable. Selected methods are illustrated through examples. Copyright © 2016 Elsevier Inc. All rights reserved.

Sensory-Motor Networks Involved in Speech Production and Motor Control: An fMRI Study

PubMed Central

Behroozmand, Roozbeh; Shebek, Rachel; Hansen, Daniel R.; Oya, Hiroyuki; Robin, Donald A.; Howard, Matthew A.; Greenlee, Jeremy D.W.

2015-01-01

Speaking is one of the most complex motor behaviors developed to facilitate human communication. The underlying neural mechanisms of speech involve sensory-motor interactions that incorporate feedback information for online monitoring and control of produced speech sounds. In the present study, we adopted an auditory feedback pitch perturbation paradigm and combined it with functional magnetic resonance imaging (fMRI) recordings in order to identify brain areas involved in speech production and motor control. Subjects underwent fMRI scanning while they produced a steady vowel sound /a/ (speaking) or listened to the playback of their own vowel production (playback). During each condition, the auditory feedback from vowel production was either normal (no perturbation) or perturbed by an upward (+600 cents) pitch shift stimulus randomly. Analysis of BOLD responses during speaking (with and without shift) vs. rest revealed activation of a complex network including bilateral superior temporal gyrus (STG), Heschl's gyrus, precentral gyrus, supplementary motor area (SMA), Rolandic operculum, postcentral gyrus and right inferior frontal gyrus (IFG). Performance correlation analysis showed that the subjects produced compensatory vocal responses that significantly correlated with BOLD response increases in bilateral STG and left precentral gyrus. However, during playback, the activation network was limited to cortical auditory areas including bilateral STG and Heschl's gyrus. Moreover, the contrast between speaking vs. playback highlighted a distinct functional network that included bilateral precentral gyrus, SMA, IFG, postcentral gyrus and insula. These findings suggest that speech motor control involves feedback error detection in sensory (e.g. auditory) cortices that subsequently activate motor-related areas for the adjustment of speech parameters during speaking. PMID:25623499

CoGAPS matrix factorization algorithm identifies transcriptional changes in AP-2alpha target genes in feedback from therapeutic inhibition of the EGFR network

PubMed Central

Thakar, Manjusha; Howard, Jason D.; Kagohara, Luciane T.; Krigsfeld, Gabriel; Ranaweera, Ruchira S.; Hughes, Robert M.; Perez, Jimena; Jones, Siân; Favorov, Alexander V.; Carey, Jacob; Stein-O'Brien, Genevieve; Gaykalova, Daria A.; Ochs, Michael F.; Chung, Christine H.

2016-01-01

Patients with oncogene driven tumors are treated with targeted therapeutics including EGFR inhibitors. Genomic data from The Cancer Genome Atlas (TCGA) demonstrates molecular alterations to EGFR, MAPK, and PI3K pathways in previously untreated tumors. Therefore, this study uses bioinformatics algorithms to delineate interactions resulting from EGFR inhibitor use in cancer cells with these genetic alterations. We modify the HaCaT keratinocyte cell line model to simulate cancer cells with constitutive activation of EGFR, HRAS, and PI3K in a controlled genetic background. We then measure gene expression after treating modified HaCaT cells with gefitinib, afatinib, and cetuximab. The CoGAPS algorithm distinguishes a gene expression signature associated with the anticipated silencing of the EGFR network. It also infers a feedback signature with EGFR gene expression itself increasing in cells that are responsive to EGFR inhibitors. This feedback signature has increased expression of several growth factor receptors regulated by the AP-2 family of transcription factors. The gene expression signatures for AP-2alpha are further correlated with sensitivity to cetuximab treatment in HNSCC cell lines and changes in EGFR expression in HNSCC tumors with low CDKN2A gene expression. In addition, the AP-2alpha gene expression signatures are also associated with inhibition of MEK, PI3K, and mTOR pathways in the Library of Integrated Network-Based Cellular Signatures (LINCS) data. These results suggest that AP-2 transcription factors are activated as feedback from EGFR network inhibition and may mediate EGFR inhibitor resistance. PMID:27650546

Adaptive Sliding Mode Control of Dynamic Systems Using Double Loop Recurrent Neural Network Structure.

PubMed

Fei, Juntao; Lu, Cheng

2018-04-01

In this paper, an adaptive sliding mode control system using a double loop recurrent neural network (DLRNN) structure is proposed for a class of nonlinear dynamic systems. A new three-layer RNN is proposed to approximate unknown dynamics with two different kinds of feedback loops where the firing weights and output signal calculated in the last step are stored and used as the feedback signals in each feedback loop. Since the new structure has combined the advantages of internal feedback NN and external feedback NN, it can acquire the internal state information while the output signal is also captured, thus the new designed DLRNN can achieve better approximation performance compared with the regular NNs without feedback loops or the regular RNNs with a single feedback loop. The new proposed DLRNN structure is employed in an equivalent controller to approximate the unknown nonlinear system dynamics, and the parameters of the DLRNN are updated online by adaptive laws to get favorable approximation performance. To investigate the effectiveness of the proposed controller, the designed adaptive sliding mode controller with the DLRNN is applied to a -axis microelectromechanical system gyroscope to control the vibrating dynamics of the proof mass. Simulation results demonstrate that the proposed methodology can achieve good tracking property, and the comparisons of the approximation performance between radial basis function NN, RNN, and DLRNN show that the DLRNN can accurately estimate the unknown dynamics with a fast speed while the internal states of DLRNN are more stable.

Balanced cortical microcircuitry for spatial working memory based on corrective feedback control.

PubMed

Lim, Sukbin; Goldman, Mark S

2014-05-14

A hallmark of working memory is the ability to maintain graded representations of both the spatial location and amplitude of a memorized stimulus. Previous work has identified a neural correlate of spatial working memory in the persistent maintenance of spatially specific patterns of neural activity. How such activity is maintained by neocortical circuits remains unknown. Traditional models of working memory maintain analog representations of either the spatial location or the amplitude of a stimulus, but not both. Furthermore, although most previous models require local excitation and lateral inhibition to maintain spatially localized persistent activity stably, the substrate for lateral inhibitory feedback pathways is unclear. Here, we suggest an alternative model for spatial working memory that is capable of maintaining analog representations of both the spatial location and amplitude of a stimulus, and that does not rely on long-range feedback inhibition. The model consists of a functionally columnar network of recurrently connected excitatory and inhibitory neural populations. When excitation and inhibition are balanced in strength but offset in time, drifts in activity trigger spatially specific negative feedback that corrects memory decay. The resulting networks can temporally integrate inputs at any spatial location, are robust against many commonly considered perturbations in network parameters, and, when implemented in a spiking model, generate irregular neural firing characteristic of that observed experimentally during persistent activity. This work suggests balanced excitatory-inhibitory memory circuits implementing corrective negative feedback as a substrate for spatial working memory. Copyright © 2014 the authors 0270-6474/14/346790-17$15.00/0.

Evaluating the performance of vehicular platoon control under different network topologies of initial states

NASA Astrophysics Data System (ADS)

Li, Yongfu; Li, Kezhi; Zheng, Taixiong; Hu, Xiangdong; Feng, Huizong; Li, Yinguo

2016-05-01

This study proposes a feedback-based platoon control protocol for connected autonomous vehicles (CAVs) under different network topologies of initial states. In particularly, algebraic graph theory is used to describe the network topology. Then, the leader-follower approach is used to model the interactions between CAVs. In addition, feedback-based protocol is designed to control the platoon considering the longitudinal and lateral gaps simultaneously as well as different network topologies. The stability and consensus of the vehicular platoon is analyzed using the Lyapunov technique. Effects of different network topologies of initial states on convergence time and robustness of platoon control are investigated. Results from numerical experiments demonstrate the effectiveness of the proposed protocol with respect to the position and velocity consensus in terms of the convergence time and robustness. Also, the findings of this study illustrate the convergence time of the control protocol is associated with the initial states, while the robustness is not affected by the initial states significantly.

Complex Networks/Foundations of Information Systems

DTIC Science & Technology

2013-03-06

the benefit of feedback or dynamic correlations in coding and protocol. Using Renyi correlation analysis and entropy to model this wider class of...dynamic heterogeneous conditions. Lizhong Zheng, MIT Renyi Channel Correlation Analysis (connected to geometric curvature) Network Channel

Adaptive artificial neural network for autonomous robot control

NASA Technical Reports Server (NTRS)

Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

1992-01-01

The topics are presented in viewgraph form and include: neural network controller for robot arm positioning with visual feedback; initial training of the arm; automatic recovery from cumulative fault scenarios; and error reduction by iterative fine movements.

Suppression of phase synchronisation in network based on cat's brain.

PubMed

Lameu, Ewandson L; Borges, Fernando S; Borges, Rafael R; Iarosz, Kelly C; Caldas, Iberê L; Batista, Antonio M; Viana, Ricardo L; Kurths, Jürgen

2016-04-01

We have studied the effects of perturbations on the cat's cerebral cortex. According to the literature, this cortex structure can be described by a clustered network. This way, we construct a clustered network with the same number of areas as in the cat matrix, where each area is described as a sub-network with a small-world property. We focus on the suppression of neuronal phase synchronisation considering different kinds of perturbations. Among the various controlling interventions, we choose three methods: delayed feedback control, external time-periodic driving, and activation of selected neurons. We simulate these interventions to provide a procedure to suppress undesired and pathological abnormal rhythms that can be associated with many forms of synchronisation. In our simulations, we have verified that the efficiency of synchronisation suppression by delayed feedback control is higher than external time-periodic driving and activation of selected neurons of the cat's cerebral cortex with the same coupling strengths.

Systematical analysis of lncRNA-mRNA competing endogenous RNA network in breast cancer subtypes.

PubMed

Zhou, Shunheng; Wang, Lihong; Yang, Qian; Liu, Haizhou; Meng, Qianqian; Jiang, Leiming; Wang, Shuyuan; Jiang, Wei

2018-06-01

Breast cancer is one of the most common solid tumors in women involving multiple subtypes. However, the mechanism for subtypes of breast cancer is still complicated and unclear. Recently, several studies indicated that long non-coding RNAs (lncRNAs) could act as sponges to compete miRNAs with mRNAs, participating in various biological processes. We concentrated on the competing interactions between lncRNAs and mRNAs in four subtypes of breast cancer (basal-like, HER2+, luminal A and luminal B), and analyzed the impacts of competing endogenous RNAs (ceRNAs) on each subtype systematically. We constructed four breast cancer subtype-related lncRNA-mRNA ceRNA networks by integrating the miRNA target information and the expression data of lncRNAs, miRNAs and mRNAs. We constructed the ceRNA network for each breast cancer subtype. Functional analysis revealed that the subtype-related ceRNA networks were enriched in cancer-related pathways in KEGG, such as pathways in cancer, miRNAs in cancer, and PI3k-Akt signaling pathway. In addition, we found three common lncRNAs across the four subtype-related ceRNA networks, NEAT1, OPI5-AS1 and AC008124.1, which played specific roles in each subtype through competing with diverse mRNAs. Finally, the potential drugs for treatment of basal-like subtype could be predicted through reversing the differentially expressed lncRNA in the ceRNA network. This study provided a novel perspective of lncRNA-involved ceRNA network to dissect the molecular mechanism for breast cancer.

Observer-based output feedback control of networked control systems with non-uniform sampling and time-varying delay

NASA Astrophysics Data System (ADS)

Meng, Su; Chen, Jie; Sun, Jian

2017-10-01

This paper investigates the problem of observer-based output feedback control for networked control systems with non-uniform sampling and time-varying transmission delay. The sampling intervals are assumed to vary within a given interval. The transmission delay belongs to a known interval. A discrete-time model is first established, which contains time-varying delay and norm-bounded uncertainties coming from non-uniform sampling intervals. It is then converted to an interconnection of two subsystems in which the forward channel is delay-free. The scaled small gain theorem is used to derive the stability condition for the closed-loop system. Moreover, the observer-based output feedback controller design method is proposed by utilising a modified cone complementary linearisation algorithm. Finally, numerical examples illustrate the validity and superiority of the proposed method.

Responses to auxin signals: an operating principle for dynamical sensitivity yet high resilience

PubMed Central

Bravi, B.; Martin, O. C.

2018-01-01

Plants depend on the signalling of the phytohormone auxin for their development and for responding to environmental perturbations. The associated biomolecular signalling network involves a negative feedback on Aux/IAA proteins which mediate the influence of auxin (the signal) on the auxin response factor (ARF) transcription factors (the drivers of the response). To probe the role of this feedback, we consider alternative in silico signalling networks implementing different operating principles. By a comparative analysis, we find that the presence of a negative feedback allows the system to have a far larger sensitivity in its dynamical response to auxin and that this sensitivity does not prevent the system from being highly resilient. Given this insight, we build a new biomolecular signalling model for quantitatively describing such Aux/IAA and ARF responses. PMID:29410878

Stationary average consensus protocol for a class of heterogeneous high-order multi-agent systems with application for aircraft

NASA Astrophysics Data System (ADS)

Rezaei, Mohammad Hadi; Menhaj, Mohammad Bagher

2018-01-01

This paper investigates the stationary average consensus problem for a class of heterogeneous-order multi-agent systems. The goal is to bring the positions of agents to the average of their initial positions while letting the other states converge to zero. To this end, three different consensus protocols are proposed. First, based on the auxiliary variables information among the agents under switching directed networks and state-feedback control, a protocol is proposed whereby all the agents achieve stationary average consensus. In the second and third protocols, by resorting to only measurements of relative positions of neighbouring agents under fixed balanced directed networks, two control frameworks are presented with two strategies based on state-feedback and output-feedback control. Finally, simulation results are given to illustrate the effectiveness of the proposed protocols.

In-vivo detection of binary PKA network interactions upon activation of endogenous GPCRs

PubMed Central

Röck, Ruth; Bachmann, Verena; Bhang, Hyo-eun C; Malleshaiah, Mohan; Raffeiner, Philipp; Mayrhofer, Johanna E; Tschaikner, Philipp M; Bister, Klaus; Aanstad, Pia; Pomper, Martin G; Michnick, Stephen W; Stefan, Eduard

2015-01-01

Membrane receptor-sensed input signals affect and modulate intracellular protein-protein interactions (PPIs). Consequent changes occur to the compositions of protein complexes, protein localization and intermolecular binding affinities. Alterations of compartmentalized PPIs emanating from certain deregulated kinases are implicated in the manifestation of diseases such as cancer. Here we describe the application of a genetically encoded Protein-fragment Complementation Assay (PCA) based on the Renilla Luciferase (Rluc) enzyme to compare binary PPIs of the spatially and temporally controlled protein kinase A (PKA) network in diverse eukaryotic model systems. The simplicity and sensitivity of this cell-based reporter allows for real-time recordings of mutually exclusive PPIs of PKA upon activation of selected endogenous G protein-coupled receptors (GPCRs) in cancer cells, xenografts of mice, budding yeast, and zebrafish embryos. This extends the application spectrum of Rluc PCA for the quantification of PPI-based receptor-effector relationships in physiological and pathological model systems. PMID:26099953

Floral pathway integrator gene expression mediates gradual transmission of environmental and endogenous cues to flowering time.

PubMed

van Dijk, Aalt D J; Molenaar, Jaap

2017-01-01

The appropriate timing of flowering is crucial for the reproductive success of plants. Hence, intricate genetic networks integrate various environmental and endogenous cues such as temperature or hormonal statues. These signals integrate into a network of floral pathway integrator genes. At a quantitative level, it is currently unclear how the impact of genetic variation in signaling pathways on flowering time is mediated by floral pathway integrator genes. Here, using datasets available from literature, we connect Arabidopsis thaliana flowering time in genetic backgrounds varying in upstream signalling components with the expression levels of floral pathway integrator genes in these genetic backgrounds. Our modelling results indicate that flowering time depends in a quite linear way on expression levels of floral pathway integrator genes. This gradual, proportional response of flowering time to upstream changes enables a gradual adaptation to changing environmental factors such as temperature and light.

Method and Apparatus for Reducing the Vulnerability of Latches to Single Event Upsets

NASA Technical Reports Server (NTRS)

Shuler, Robert L., Jr. (Inventor)

2002-01-01

A delay circuit includes a first network having an input and an output node, a second network having an input and an output, the input of the second network being coupled to the output node of the first network. The first network and the second network are configured such that: a glitch at the input to the first network having a length of approximately one-half of a standard glitch time or less does not cause the voltage at the output of the second network to cross a threshold, a glitch at the input to the first network having a length of between approximately one-half and two standard glitch times causes the voltage at the output of the second network to cross the threshold for less than the length of the glitch, and a glitch at the input to the first network having a length of greater than approximately two standard glitch times causes the voltage at the output of the second network to cross the threshold for approximately the time of the glitch. The method reduces the vulnerability of a latch to single event upsets. The latch includes a gate having an input and an output and a feedback path from the output to the input of the gate. The method includes inserting a delay into the feedback path and providing a delay in the gate.

Method and Apparatus for Reducing the Vulnerability of Latches to Single Event Upsets

NASA Technical Reports Server (NTRS)

Shuler, Robert L., Jr. (Inventor)

2002-01-01

A delay circuit includes a first network having an input and an output node, a second network having an input and an output, the input of the second network being coupled to the output node of the first network. The first network and the second network are configured such that: a glitch at the input to the first network having a length of approximately one-half of a standard glitch time or less does not cause tile voltage at the output of the second network to cross a threshold, a glitch at the input to the first network having a length of between approximately one-half and two standard glitch times causes the voltage at the output of the second network to cross the threshold for less than the length of the glitch, and a glitch at the input to the first network having a length of greater than approximately two standard glitch times causes the voltage at the output of the second network to cross the threshold for approximately the time of the glitch. A method reduces the vulnerability of a latch to single event upsets. The latch includes a gate having an input and an output and a feedback path from the output to the input of the gate. The method includes inserting a delay into the feedback path and providing a delay in the gate.

Communication analysis for feedback control of civil infrastructure using cochlea-inspired sensing nodes

NASA Astrophysics Data System (ADS)

Peckens, Courtney A.; Cook, Ireana; Lynch, Jerome P.

2016-04-01

Wireless sensor networks (WSNs) have emerged as a reliable, low-cost alternative to the traditional wired sensing paradigm. While such networks have made significant progress in the field of structural monitoring, significantly less development has occurred for feedback control applications. Previous work in WSNs for feedback control has highlighted many of the challenges of using this technology including latency in the wireless communication channel and computational inundation at the individual sensing nodes. This work seeks to overcome some of those challenges by drawing inspiration from the real-time sensing and control techniques employed by the biological central nervous system and in particular the mammalian cochlea. A novel bio-inspired wireless sensor node was developed that employs analog filtering techniques to perform time-frequency decomposition of a sensor signal, thus encompassing the functionality of the cochlea. The node then utilizes asynchronous sampling of the filtered signal to compress the signal prior to communication. This bio-inspired sensing architecture is extended to a feedback control application in order to overcome the traditional challenges currently faced by wireless control. In doing this, however, the network experiences high bandwidths of low-significance information exchange between nodes, resulting in some lost data. This study considers the impact of this lost data on the control capabilities of the bio-inspired control architecture and finds that it does not significantly impact the effectiveness of control.

Longitudinal development of frontoparietal activity during feedback learning: Contributions of age, performance, working memory and cortical thickness.

PubMed

Peters, Sabine; Van Duijvenvoorde, Anna C K; Koolschijn, P Cédric M P; Crone, Eveline A

2016-06-01

Feedback learning is a crucial skill for cognitive flexibility that continues to develop into adolescence, and is linked to neural activity within a frontoparietal network. Although it is well conceptualized that activity in the frontoparietal network changes during development, there is surprisingly little consensus about the direction of change. Using a longitudinal design (N=208, 8-27 years, two measurements in two years), we investigated developmental trajectories in frontoparietal activity during feedback learning. Our first aim was to test for linear and nonlinear developmental trajectories in dorsolateral prefrontal cortex (DLPFC), superior parietal cortex (SPC), supplementary motor area (SMA) and anterior cingulate cortex (ACC). Second, we tested which factors (task performance, working memory, cortical thickness) explained additional variance in time-related changes in activity besides age. Developmental patterns for activity in DLPFC and SPC were best characterized by a quadratic age function leveling off/peaking in late adolescence. There was a linear increase in SMA and a linear decrease with age in ACC activity. In addition to age, task performance explained variance in DLPFC and SPC activity, whereas cortical thickness explained variance in SMA activity. Together, these findings provide a novel perspective of linear and nonlinear developmental changes in the frontoparietal network during feedback learning. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

A feedback control model for network flow with multiple pure time delays

NASA Technical Reports Server (NTRS)

Press, J.

1972-01-01

A control model describing a network flow hindered by multiple pure time (or transport) delays is formulated. Feedbacks connect each desired output with a single control sector situated at the origin. The dynamic formulation invokes the use of differential difference equations. This causes the characteristic equation of the model to consist of transcendental functions instead of a common algebraic polynomial. A general graphical criterion is developed to evaluate the stability of such a problem. A digital computer simulation confirms the validity of such criterion. An optimal decision making process with multiple delays is presented.

Hypertonic stress induces rapid and widespread protein damage in C. elegans

PubMed Central

Burkewitz, Kris; Choe, Keith

2011-01-01

Proteostasis is defined as the homeostatic mechanisms that maintain the function of all cytoplasmic proteins. We recently demonstrated that the capacity of the proteostasis network is a critical factor that defines the limits of cellular and organismal survival in hypertonic environments. The current studies were performed to determine the extent of protein damage induced by cellular water loss. Using worm strains expressing fluorescently tagged foreign and endogenous proteins and proteins with temperature-sensitive point mutations, we demonstrate that hypertonic stress causes aggregation and misfolding of diverse proteins in multiple cell types. Protein damage is rapid. Aggregation of a polyglutamine yellow fluorescent protein reporter is observable with <1 h of hypertonic stress, and aggregate volume doubles approximately every 10 min. Aggregate formation is irreversible and occurs after as little as 10 min of exposure to hypertonic conditions. To determine whether endogenous proteins are aggregated by hypertonic stress, we quantified the relative amount of total cellular protein present in detergent-insoluble extracts. Exposure for 4 h to 400 mM or 500 mM NaCl induced a 55–120% increase in endogenous protein aggregation. Inhibition of insulin signaling or acclimation to mild hypertonic stress increased survival under extreme hypertonic conditions and prevented aggregation of endogenous proteins. Our results demonstrate that hypertonic stress causes widespread and dramatic protein damage and that cells have a significant capacity to remodel the network of proteins that function to maintain proteostasis. These findings have important implications for understanding how cells cope with hypertonic stress and other protein-damaging stressors. PMID:21613604

Roles of glucocorticoids in human parturition: a controversial fact?

PubMed

Li, X Q; Zhu, P; Myatt, L; Sun, K

2014-05-01

The pivotal role of glucocorticoids in the initiation of parturition has been very well documented in several domestic mammalian animal species. However the role of glucocorticoids in human parturition remains controversial mainly because of the absence of effect of synthetic glucocorticoids, given to promote fetal organ maturation in pregnant women with threatened preterm delivery, on the length of gestation. This article will review studies of glucocorticoids in human parturition and provide evidence for an important role of glucocorticoids in human parturition as well but a simultaneous high concentration of estrogen within the intrauterine tissues may be necessary for GCs to initiate parturition. The synthetic GCs dexamethasone and betamethasone pass through the placenta intact resulting in potent negative feedback on the fetal HPA axis and diminished production of DHEA from fetal adrenal glands for estrogen synthesis by the placenta. This may negate the effect of systemic administration of GCs on the induction of labor, especially in cases where the myometrium is not yet fully primed by estrogen. Endogenous glucocorticoids are inactivated by the placental 11β-HSD2 thus limiting the negative feedback of maternal cortisol on the fetal HPA axis and allowing the simultaneous rise of cortisol and estrogen levels towards the end of gestation. Therefore, endogenous glucocorticoids, particularly glucocorticoids produced locally in the intrauterine tissues may play an important role in parturition in humans by enhancing prostaglandin production in the fetal membranes and stimulating estrogen and CRH production in the placenta. Copyright © 2014 Elsevier Ltd. All rights reserved.

Fast temporal neural learning using teacher forcing

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad (Inventor); Bahren, Jacob (Inventor)

1992-01-01

A neural network is trained to output a time dependent target vector defined over a predetermined time interval in response to a time dependent input vector defined over the same time interval by applying corresponding elements of the error vector, or difference between the target vector and the actual neuron output vector, to the inputs of corresponding output neurons of the network as corrective feedback. This feedback decreases the error and quickens the learning process, so that a much smaller number of training cycles are required to complete the learning process. A conventional gradient descent algorithm is employed to update the neural network parameters at the end of the predetermined time interval. The foregoing process is repeated in repetitive cycles until the actual output vector corresponds to the target vector. In the preferred embodiment, as the overall error of the neural network output decreasing during successive training cycles, the portion of the error fed back to the output neurons is decreased accordingly, allowing the network to learn with greater freedom from teacher forcing as the network parameters converge to their optimum values. The invention may also be used to train a neural network with stationary training and target vectors.

Fast temporal neural learning using teacher forcing

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad (Inventor); Bahren, Jacob (Inventor)

1995-01-01

A neural network is trained to output a time dependent target vector defined over a predetermined time interval in response to a time dependent input vector defined over the same time interval by applying corresponding elements of the error vector, or difference between the target vector and the actual neuron output vector, to the inputs of corresponding output neurons of the network as corrective feedback. This feedback decreases the error and quickens the learning process, so that a much smaller number of training cycles are required to complete the learning process. A conventional gradient descent algorithm is employed to update the neural network parameters at the end of the predetermined time interval. The foregoing process is repeated in repetitive cycles until the actual output vector corresponds to the target vector. In the preferred embodiment, as the overall error of the neural network output decreasing during successive training cycles, the portion of the error fed back to the output neurons is decreased accordingly, allowing the network to learn with greater freedom from teacher forcing as the network parameters converge to their optimum values. The invention may also be used to train a neural network with stationary training and target vectors.

REAC technology and hyaluron synthase 2, an interesting network to slow down stem cell senescence.

PubMed

Maioli, Margherita; Rinaldi, Salvatore; Pigliaru, Gianfranco; Santaniello, Sara; Basoli, Valentina; Castagna, Alessandro; Fontani, Vania; Ventura, Carlo

2016-06-24

Hyaluronic acid (HA) plays a fundamental role in cell polarity and hydrodynamic processes, affording significant modulation of proliferation, migration, morphogenesis and senescence, with deep implication in the ability of stem cells to execute their differentiating plans. The Radio Electric Asymmetric Conveyer (REAC) technology is aimed to optimize the ions fluxes at the molecular level in order to optimize the molecular mechanisms driving cellular asymmetry and polarization. Here, we show that treatment with 4-methylumbelliferone (4-MU), a potent repressor of type 2 HA synthase and endogenous HA synthesis, dramatically antagonized the ability of REAC to recover the gene and protein expression of Bmi1, Oct4, Sox2, and Nanog in ADhMSCs that had been made senescent by prolonged culture up to the 30(th) passage. In senescent ADhMSCs, 4-MU also counteracted the REAC ability to rescue the gene expression of TERT, and the associated resumption of telomerase activity. Hence, the anti-senescence action of REAC is largely dependent upon the availability of endogenous HA synthesis. Endogenous HA and HA-binding proteins with REAC technology create an interesting network that acts on the modulation of cell polarity and intracellular environment. This suggests that REAC technology is effective on an intracellular niche level of stem cell regulation.

Route guidance strategies revisited: Comparison and evaluation in an asymmetric two-route traffic network

NASA Astrophysics Data System (ADS)

He, Zhengbing; Chen, Bokui; Jia, Ning; Guan, Wei; Lin, Benchuan; Wang, Binghong

2014-12-01

To alleviate traffic congestion, a variety of route guidance strategies have been proposed for intelligent transportation systems. A number of strategies are introduced and investigated on a symmetric two-route traffic network over the past decade. To evaluate the strategies in a more general scenario, this paper conducts eight prevalent strategies on an asymmetric two-route traffic network with different slowdown behaviors on alternative routes. The results show that only mean velocity feedback strategy (MVFS) is able to equalize travel time, i.e. approximate user optimality (UO); while the others fail due to incapability of establishing relations between the feedback parameters and travel time. The paper helps better understand these strategies, and suggests MVFS if the authority intends to achieve user optimality.

Developing Automated Feedback Materials for a Training Simulator: An Interaction between Users and Researchers.

ERIC Educational Resources Information Center

Shlechter, Theodore M.; And Others

This paper focuses upon the research and development (R&D) process associated with developing automated feedback materials for the SIMulation NETworking (SIMNET) training system. This R&D process involved a partnership among instructional developers, practitioners, and researchers. Users' input has been utilized to help: (1) design the…

Effects of Response-Driven Feedback in Computer Science Learning

ERIC Educational Resources Information Center

Fernandez Aleman, J. L.; Palmer-Brown, D.; Jayne, C.

2011-01-01

This paper presents the results of a project on generating diagnostic feedback for guided learning in a first-year course on programming and a Master's course on software quality. An online multiple-choice questions (MCQs) system is integrated with neural network-based data analysis. Findings about how students use the system suggest that the…

Why don't you like me? Midfrontal theta power in response to unexpected peer rejection feedback.

PubMed

van der Molen, M J W; Dekkers, L M S; Westenberg, P M; van der Veen, F M; van der Molen, M W

2017-02-01

Social connectedness theory posits that the brain processes social rejection as a threat to survival. Recent electrophysiological evidence suggests that midfrontal theta (4-8Hz) oscillations in the EEG provide a window on the processing of social rejection. Here we examined midfrontal theta dynamics (power and inter-trial phase synchrony) during the processing of social evaluative feedback. We employed the Social Judgment paradigm in which 56 undergraduate women (mean age=19.67 years) were asked to communicate their expectancies about being liked vs. disliked by unknown peers. Expectancies were followed by feedback indicating social acceptance vs. rejection. Results revealed a significant increase in EEG theta power to unexpected social rejection feedback. This EEG theta response could be source-localized to brain regions typically reported during activation of the saliency network (i.e., dorsal anterior cingulate cortex, insula, inferior frontal gyrus, frontal pole, and the supplementary motor area). Theta phase dynamics mimicked the behavior of the time-domain averaged feedback-related negativity (FRN) by showing stronger phase synchrony for feedback that was unexpected vs. expected. Theta phase, however, differed from the FRN by also displaying stronger phase synchrony in response to rejection vs. acceptance feedback. Together, this study highlights distinct roles for midfrontal theta power and phase synchrony in response to social evaluative feedback. Our findings contribute to the literature by showing that midfrontal theta oscillatory power is sensitive to social rejection but only when peer rejection is unexpected, and this theta response is governed by a widely distributed neural network implicated in saliency detection and conflict monitoring. Copyright © 2016 Elsevier Inc. All rights reserved.

Adaptive neural network decentralized backstepping output-feedback control for nonlinear large-scale systems with time delays.

PubMed

Tong, Shao Cheng; Li, Yong Ming; Zhang, Hua-Guang

2011-07-01

In this paper, two adaptive neural network (NN) decentralized output feedback control approaches are proposed for a class of uncertain nonlinear large-scale systems with immeasurable states and unknown time delays. Using NNs to approximate the unknown nonlinear functions, an NN state observer is designed to estimate the immeasurable states. By combining the adaptive backstepping technique with decentralized control design principle, an adaptive NN decentralized output feedback control approach is developed. In order to overcome the problem of "explosion of complexity" inherent in the proposed control approach, the dynamic surface control (DSC) technique is introduced into the first adaptive NN decentralized control scheme, and a simplified adaptive NN decentralized output feedback DSC approach is developed. It is proved that the two proposed control approaches can guarantee that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded, and the observer errors and the tracking errors converge to a small neighborhood of the origin. Simulation results are provided to show the effectiveness of the proposed approaches.

Assessing attentional systems in children with Attention Deficit Hyperactivity Disorder.

PubMed

Casagrande, Maria; Martella, Diana; Ruggiero, Maria Cleonice; Maccari, Lisa; Paloscia, Claudio; Rosa, Caterina; Pasini, Augusto

2012-01-01

The aim of this study was to evaluate the efficiency and interactions of attentional systems in children with Attention Deficit Hyperactivity Disorder (ADHD) by considering the effects of reinforcement and auditory warning on each component of attention. Thirty-six drug-naïve children (18 children with ADHD/18 typically developing children) performed two revised versions of the Attentional Network Test, which assess the efficiency of alerting, orienting, and executive systems. In feedback trials, children received feedback about their accuracy, whereas in the no-feedback trials, feedback was not given. In both conditions, children with ADHD performed more slowly than did typically developing children. They also showed impairments in the ability to disengage attention and in executive functioning, which improved when alertness was increased by administering the auditory warning. The performance of the attentional networks appeared to be modulated by the absence or the presence of reinforcement. We suggest that the observed executive system deficit in children with ADHD could depend on their low level of arousal rather than being an independent disorder. © The Author 2011. Published by Oxford University Press. All rights reserved.

Stochastic resonance in feedforward acupuncture networks

NASA Astrophysics Data System (ADS)

Qin, Ying-Mei; Wang, Jiang; Men, Cong; Deng, Bin; Wei, Xi-Le; Yu, Hai-Tao; Chan, Wai-Lok

2014-10-01

Effects of noises and some other network properties on the weak signal propagation are studied systematically in feedforward acupuncture networks (FFN) based on FitzHugh-Nagumo neuron model. It is found that noises with medium intensity can enhance signal propagation and this effect can be further increased by the feedforward network structure. Resonant properties in the noisy network can also be altered by several network parameters, such as heterogeneity, synapse features, and feedback connections. These results may also provide a novel potential explanation for the propagation of acupuncture signal.

Impaired Feedforward Control and Enhanced Feedback Control of Speech in Patients with Cerebellar Degeneration.

PubMed

Parrell, Benjamin; Agnew, Zarinah; Nagarajan, Srikantan; Houde, John; Ivry, Richard B

2017-09-20

The cerebellum has been hypothesized to form a crucial part of the speech motor control network. Evidence for this comes from patients with cerebellar damage, who exhibit a variety of speech deficits, as well as imaging studies showing cerebellar activation during speech production in healthy individuals. To date, the precise role of the cerebellum in speech motor control remains unclear, as it has been implicated in both anticipatory (feedforward) and reactive (feedback) control. Here, we assess both anticipatory and reactive aspects of speech motor control, comparing the performance of patients with cerebellar degeneration and matched controls. Experiment 1 tested feedforward control by examining speech adaptation across trials in response to a consistent perturbation of auditory feedback. Experiment 2 tested feedback control, examining online corrections in response to inconsistent perturbations of auditory feedback. Both male and female patients and controls were tested. The patients were impaired in adapting their feedforward control system relative to controls, exhibiting an attenuated anticipatory response to the perturbation. In contrast, the patients produced even larger compensatory responses than controls, suggesting an increased reliance on sensory feedback to guide speech articulation in this population. Together, these results suggest that the cerebellum is crucial for maintaining accurate feedforward control of speech, but relatively uninvolved in feedback control. SIGNIFICANCE STATEMENT Speech motor control is a complex activity that is thought to rely on both predictive, feedforward control as well as reactive, feedback control. While the cerebellum has been shown to be part of the speech motor control network, its functional contribution to feedback and feedforward control remains controversial. Here, we use real-time auditory perturbations of speech to show that patients with cerebellar degeneration are impaired in adapting feedforward control of speech but retain the ability to make online feedback corrections; indeed, the patients show an increased sensitivity to feedback. These results indicate that the cerebellum forms a crucial part of the feedforward control system for speech but is not essential for online, feedback control. Copyright © 2017 the authors 0270-6474/17/379249-10$15.00/0.

A role of right middle frontal gyrus in reorienting of attention: a case study

PubMed Central

Japee, Shruti; Holiday, Kelsey; Satyshur, Maureen D.; Mukai, Ikuko; Ungerleider, Leslie G.

2015-01-01

The right middle fontal gyrus (MFG) has been proposed to be a site of convergence of the dorsal and ventral attention networks, by serving as a circuit-breaker to interrupt ongoing endogenous attentional processes in the dorsal network and reorient attention to an exogenous stimulus. Here, we probed the contribution of the right MFG to both endogenous and exogenous attention by comparing performance on an orientation discrimination task of a patient with a right MFG resection and a group of healthy controls. On endogenously cued trials, participants were shown a central cue that predicted with 90% accuracy the location of a subsequent peri-threshold Gabor patch stimulus. On exogenously cued trials, a cue appeared briefly at one of two peripheral locations, followed by a variable inter-stimulus interval (ISI; range 0–700 ms) and a Gabor patch in the same or opposite location as the cue. Behavioral data showed that for endogenous, and short ISI exogenous trials, valid cues facilitated responses compared to invalid cues, for both the patient and controls. However, at long ISIs, the patient exhibited difficulty in reverting to top-down attentional control, once the facilitatory effect of the exogenous cue had dissipated. When explicitly cued during long ISIs to attend to both stimulus locations, the patient was able to engage successfully in top-down control. This result indicates that the right MFG may play an important role in reorienting attention from exogenous to endogenous attentional control. Resting state fMRI data revealed that the right superior parietal lobule and right orbitofrontal cortex, showed significantly higher correlations with a left MFG seed region (a region tightly coupled with the right MFG in controls) in the patient relative to controls. We hypothesize that this paradoxical increase in cortical coupling represents a compensatory mechanism in the patient to offset the loss of function of the resected tissue in right prefrontal cortex. PMID:25784862

Adaptive nonlinear polynomial neural networks for control of boundary layer/structural interaction

NASA Technical Reports Server (NTRS)

Parker, B. Eugene, Jr.; Cellucci, Richard L.; Abbott, Dean W.; Barron, Roger L.; Jordan, Paul R., III; Poor, H. Vincent

1993-01-01

The acoustic pressures developed in a boundary layer can interact with an aircraft panel to induce significant vibration in the panel. Such vibration is undesirable due to the aerodynamic drag and structure-borne cabin noises that result. The overall objective of this work is to develop effective and practical feedback control strategies for actively reducing this flow-induced structural vibration. This report describes the results of initial evaluations using polynomial, neural network-based, feedback control to reduce flow induced vibration in aircraft panels due to turbulent boundary layer/structural interaction. Computer simulations are used to develop and analyze feedback control strategies to reduce vibration in a beam as a first step. The key differences between this work and that going on elsewhere are as follows: that turbulent and transitional boundary layers represent broadband excitation and thus present a more complex stochastic control scenario than that of narrow band (e.g., laminar boundary layer) excitation; and secondly, that the proposed controller structures are adaptive nonlinear infinite impulse response (IIR) polynomial neural network, as opposed to the traditional adaptive linear finite impulse response (FIR) filters used in most studies to date. The controllers implemented in this study achieved vibration attenuation of 27 to 60 dB depending on the type of boundary layer established by laminar, turbulent, and intermittent laminar-to-turbulent transitional flows. Application of multi-input, multi-output, adaptive, nonlinear feedback control of vibration in aircraft panels based on polynomial neural networks appears to be feasible today. Plans are outlined for Phase 2 of this study, which will include extending the theoretical investigation conducted in Phase 2 and verifying the results in a series of laboratory experiments involving both bum and plate models.

Sensory-motor networks involved in speech production and motor control: an fMRI study.

PubMed

Behroozmand, Roozbeh; Shebek, Rachel; Hansen, Daniel R; Oya, Hiroyuki; Robin, Donald A; Howard, Matthew A; Greenlee, Jeremy D W

2015-04-01

Speaking is one of the most complex motor behaviors developed to facilitate human communication. The underlying neural mechanisms of speech involve sensory-motor interactions that incorporate feedback information for online monitoring and control of produced speech sounds. In the present study, we adopted an auditory feedback pitch perturbation paradigm and combined it with functional magnetic resonance imaging (fMRI) recordings in order to identify brain areas involved in speech production and motor control. Subjects underwent fMRI scanning while they produced a steady vowel sound /a/ (speaking) or listened to the playback of their own vowel production (playback). During each condition, the auditory feedback from vowel production was either normal (no perturbation) or perturbed by an upward (+600 cents) pitch-shift stimulus randomly. Analysis of BOLD responses during speaking (with and without shift) vs. rest revealed activation of a complex network including bilateral superior temporal gyrus (STG), Heschl's gyrus, precentral gyrus, supplementary motor area (SMA), Rolandic operculum, postcentral gyrus and right inferior frontal gyrus (IFG). Performance correlation analysis showed that the subjects produced compensatory vocal responses that significantly correlated with BOLD response increases in bilateral STG and left precentral gyrus. However, during playback, the activation network was limited to cortical auditory areas including bilateral STG and Heschl's gyrus. Moreover, the contrast between speaking vs. playback highlighted a distinct functional network that included bilateral precentral gyrus, SMA, IFG, postcentral gyrus and insula. These findings suggest that speech motor control involves feedback error detection in sensory (e.g. auditory) cortices that subsequently activate motor-related areas for the adjustment of speech parameters during speaking. Copyright © 2015 Elsevier Inc. All rights reserved.

Implementing Nonlinear Feedback Controllers Using DNA Strand Displacement Reactions.

PubMed

Sawlekar, Rucha; Montefusco, Francesco; Kulkarni, Vishwesh V; Bates, Declan G

2016-07-01

We show how an important class of nonlinear feedback controllers can be designed using idealized abstract chemical reactions and implemented via DNA strand displacement (DSD) reactions. Exploiting chemical reaction networks (CRNs) as a programming language for the design of complex circuits and networks, we show how a set of unimolecular and bimolecular reactions can be used to realize input-output dynamics that produce a nonlinear quasi sliding mode (QSM) feedback controller. The kinetics of the required chemical reactions can then be implemented as enzyme-free, enthalpy/entropy driven DNA reactions using a toehold mediated strand displacement mechanism via Watson-Crick base pairing and branch migration. We demonstrate that the closed loop response of the nonlinear QSM controller outperforms a traditional linear controller by facilitating much faster tracking response dynamics without introducing overshoots in the transient response. The resulting controller is highly modular and is less affected by retroactivity effects than standard linear designs.

Neural network-based optimal adaptive output feedback control of a helicopter UAV.

PubMed

Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani

2013-07-01

Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.

Design of an embedded inverse-feedforward biomolecular tracking controller for enzymatic reaction processes.

PubMed

Foo, Mathias; Kim, Jongrae; Sawlekar, Rucha; Bates, Declan G

2017-04-06

Feedback control is widely used in chemical engineering to improve the performance and robustness of chemical processes. Feedback controllers require a 'subtractor' that is able to compute the error between the process output and the reference signal. In the case of embedded biomolecular control circuits, subtractors designed using standard chemical reaction network theory can only realise one-sided subtraction, rendering standard controller design approaches inadequate. Here, we show how a biomolecular controller that allows tracking of required changes in the outputs of enzymatic reaction processes can be designed and implemented within the framework of chemical reaction network theory. The controller architecture employs an inversion-based feedforward controller that compensates for the limitations of the one-sided subtractor that generates the error signals for a feedback controller. The proposed approach requires significantly fewer chemical reactions to implement than alternative designs, and should have wide applicability throughout the fields of synthetic biology and biological engineering.

Enhanced vaccine control of epidemics in adaptive networks

NASA Astrophysics Data System (ADS)

Shaw, Leah B.; Schwartz, Ira B.

2010-04-01

We study vaccine control for disease spread on an adaptive network modeling disease avoidance behavior. Control is implemented by adding Poisson-distributed vaccination of susceptibles. We show that vaccine control is much more effective in adaptive networks than in static networks due to feedback interaction between the adaptive network rewiring and the vaccine application. When compared to extinction rates in static social networks, we find that the amount of vaccine resources required to sustain similar rates of extinction are as much as two orders of magnitude lower in adaptive networks.

Enhanced vaccine control of epidemics in adaptive networks.

PubMed

Shaw, Leah B; Schwartz, Ira B

2010-04-01

We study vaccine control for disease spread on an adaptive network modeling disease avoidance behavior. Control is implemented by adding Poisson-distributed vaccination of susceptibles. We show that vaccine control is much more effective in adaptive networks than in static networks due to feedback interaction between the adaptive network rewiring and the vaccine application. When compared to extinction rates in static social networks, we find that the amount of vaccine resources required to sustain similar rates of extinction are as much as two orders of magnitude lower in adaptive networks.

Positive social behaviours are induced and retained after oxytocin manipulations mimicking endogenous concentrations in a wild mammal

PubMed Central

Twiss, Sean D.; Hazon, Neil; Moss, Simon; Pomeroy, Patrick P.

2017-01-01

The neuropeptide hormone oxytocin modulates numerous social and parental behaviours across a wide range of species, including humans. We conducted manipulation experiments on wild grey seals (Halichoerus grypus) to determine whether oxytocin increases proximity-seeking behaviour, which has previously been correlated with endogenous oxytocin concentrations in wild seal populations. Pairs of seals that had never met previously were given intravenous injections of 0.41 µg kg−1 oxytocin or saline and were observed for 1 h post-manipulation. The dose was designed to mimic endogenous oxytocin concentrations during the observation period, and is one of the lowest doses used to manipulate behaviour to date. Seals given oxytocin spent significantly more time in close proximity to each other, confirming that oxytocin causes conspecifics to seek others out and remain close to one another. Aggressive and investigative behaviours also significantly fell after oxytocin manipulations. Despite using a minimal oxytocin dose, pro-social behavioural changes unexpectedly persisted for 2 days despite rapid dose clearance from circulation post-injection. This study verifies that oxytocin promotes individuals staying together, demonstrating how the hormone can form positive feedback loops of oxytocin release following conspecific stimuli, increased motivation to remain in close proximity and additional oxytocin release from stimuli received while in close proximity. PMID:28539519

Positive social behaviours are induced and retained after oxytocin manipulations mimicking endogenous concentrations in a wild mammal.

PubMed

Robinson, Kelly J; Twiss, Sean D; Hazon, Neil; Moss, Simon; Pomeroy, Patrick P

2017-05-31

The neuropeptide hormone oxytocin modulates numerous social and parental behaviours across a wide range of species, including humans. We conducted manipulation experiments on wild grey seals ( Halichoerus grypus ) to determine whether oxytocin increases proximity-seeking behaviour, which has previously been correlated with endogenous oxytocin concentrations in wild seal populations. Pairs of seals that had never met previously were given intravenous injections of 0.41 µg kg -1 oxytocin or saline and were observed for 1 h post-manipulation. The dose was designed to mimic endogenous oxytocin concentrations during the observation period, and is one of the lowest doses used to manipulate behaviour to date. Seals given oxytocin spent significantly more time in close proximity to each other, confirming that oxytocin causes conspecifics to seek others out and remain close to one another. Aggressive and investigative behaviours also significantly fell after oxytocin manipulations. Despite using a minimal oxytocin dose, pro-social behavioural changes unexpectedly persisted for 2 days despite rapid dose clearance from circulation post-injection. This study verifies that oxytocin promotes individuals staying together, demonstrating how the hormone can form positive feedback loops of oxytocin release following conspecific stimuli, increased motivation to remain in close proximity and additional oxytocin release from stimuli received while in close proximity. © 2017 The Authors.

ADP Compartmentation Analysis Reveals Coupling between Pyruvate Kinase and ATPases in Heart Muscle

PubMed Central

Sepp, Mervi; Vendelin, Marko; Vija, Heiki; Birkedal, Rikke

2010-01-01

Abstract Cardiomyocytes have intracellular diffusion restrictions, which spatially compartmentalize ADP and ATP. However, the models that predict diffusion restrictions have used data sets generated in rat heart permeabilized fibers, where diffusion distances may be heterogeneous. This is avoided by using isolated, permeabilized cardiomyocytes. The aim of this work was to analyze the intracellular diffusion of ATP and ADP in rat permeabilized cardiomyocytes. To do this, we measured respiration rate, ATPase rate, and ADP concentration in the surrounding solution. The data were analyzed using mathematical models that reflect different levels of cell compartmentalization. In agreement with previous studies, we found significant diffusion restriction by the mitochondrial outer membrane and confirmed a functional coupling between mitochondria and a fraction of ATPases in the cell. In addition, our experimental data show that considerable activity of endogenous pyruvate kinase (PK) remains in the cardiomyocytes after permeabilization. A fraction of ATPases were inactive without ATP feedback by this endogenous PK. When analyzing the data, we were able to reproduce the measurements only with the mathematical models that include a tight coupling between the fraction of endogenous PK and ATPases. To our knowledge, this is the first time such a strong coupling of PK to ATPases has been demonstrated in permeabilized cardiomyocytes. PMID:20550890

Mentoring, Networking and Supervision: Parallelogram, Vortex, or Merging Point?

ERIC Educational Resources Information Center

Hernandez, Mary N.

1994-01-01

Discussion of recruiting and hiring minorities in academic libraries focuses on steps needed for the retention of these minorities. Highlights include literature search strategies; mentoring systems; networking within and outside the organization; and supervision, including immediate feedback. (Contains 20 references.) (LRW)

An integrated pavement data management and feedback system (PAMS) : final report.

DOT National Transportation Integrated Search

1987-04-01

This report discusses the implementation of a pavement condition rating (PCR) procedure to sample sections of the road network system. The resources needed are identified for such implementation. The uses of PCR data at the network and project level ...

Fetal programming: excess prenatal testosterone reduces postnatal luteinizing hormone, but not follicle-stimulating hormone responsiveness, to estradiol negative feedback in the female.

PubMed

Sarma, Hirendra N; Manikkam, Mohan; Herkimer, Carol; Dell'Orco, James; Welch, Kathleen B; Foster, Douglas L; Padmanabhan, Vasantha

2005-10-01

Exposure of female sheep fetuses to excess testosterone (T) during early to midgestation produces postnatal hypergonadotropism manifest as a selective increase in LH. This hypergonadotropism may result from reduced sensitivity to estradiol (E2) negative feedback and/or increased pituitary sensitivity to GnRH. We tested the hypothesis that excess T before birth reduces responsiveness of LH and FSH to E2 negative feedback after birth. Pregnant ewes were treated with T propionate (100 mg/kg in cotton seed oil) or vehicle twice weekly from d 30-90 gestation. Responsiveness to E2 negative feedback was assessed at 12 and 24 wk of age in the ovary-intact female offspring. Our experimental strategy was first to arrest follicular growth and reduce endogenous E2 by administering the GnRH antagonist (GnRH-A), Nal-Glu (50 microg/kg sc every 12 h for 72 h), and then provide a fixed amount of exogenous E2 via an implant. Blood samples were obtained every 20 min at 12 wk and every 10 min at 24 wk before treatment, during and after GnRH-A treatment both before and after E2 implant. GnRH-A ablated LH pulsatility, reduced FSH by approximately 25%, and E2 production diminished to near detection limit of assay at both ages in both groups. Prenatal T treatment produced a precocious and selective reduction in responsiveness of LH but not FSH to E2 negative feedback, which was manifest mainly at the level of LH/GnRH pulse frequency. Collectively, these findings support the hypothesis that prenatal exposure to excess T decreases postnatal responsiveness to E2 inhibitory feedback of LH/GnRH secretion to contribute to the development of hypergonadotropism.

A Stochastic Super-Exponential Growth Model for Population Dynamics

NASA Astrophysics Data System (ADS)

Avila, P.; Rekker, A.

2010-11-01

A super-exponential growth model with environmental noise has been studied analytically. Super-exponential growth rate is a property of dynamical systems exhibiting endogenous nonlinear positive feedback, i.e., of self-reinforcing systems. Environmental noise acts on the growth rate multiplicatively and is assumed to be Gaussian white noise in the Stratonovich interpretation. An analysis of the stochastic super-exponential growth model with derivations of exact analytical formulae for the conditional probability density and the mean value of the population abundance are presented. Interpretations and various applications of the results are discussed.

What Is Going on Inside the Arrows? Discovering the Hidden Springs in Causal Models

PubMed Central

Murray-Watters, Alexander; Glymour, Clark

2016-01-01

Using Gebharter's (2014) representation, we consider aspects of the problem of discovering the structure of unmeasured sub-mechanisms when the variables in those sub-mechanisms have not been measured. Exploiting an early insight of Sober's (1998), we provide a correct algorithm for identifying latent, endogenous structure—sub-mechanisms—for a restricted class of structures. The algorithm can be merged with other methods for discovering causal relations among unmeasured variables, and feedback relations between measured variables and unobserved causes can sometimes be learned. PMID:27313331

Data-driven model reference control of MIMO vertical tank systems with model-free VRFT and Q-Learning.

PubMed

Radac, Mircea-Bogdan; Precup, Radu-Emil; Roman, Raul-Cristian

2018-02-01

This paper proposes a combined Virtual Reference Feedback Tuning-Q-learning model-free control approach, which tunes nonlinear static state feedback controllers to achieve output model reference tracking in an optimal control framework. The novel iterative Batch Fitted Q-learning strategy uses two neural networks to represent the value function (critic) and the controller (actor), and it is referred to as a mixed Virtual Reference Feedback Tuning-Batch Fitted Q-learning approach. Learning convergence of the Q-learning schemes generally depends, among other settings, on the efficient exploration of the state-action space. Handcrafting test signals for efficient exploration is difficult even for input-output stable unknown processes. Virtual Reference Feedback Tuning can ensure an initial stabilizing controller to be learned from few input-output data and it can be next used to collect substantially more input-state data in a controlled mode, in a constrained environment, by compensating the process dynamics. This data is used to learn significantly superior nonlinear state feedback neural networks controllers for model reference tracking, using the proposed Batch Fitted Q-learning iterative tuning strategy, motivating the original combination of the two techniques. The mixed Virtual Reference Feedback Tuning-Batch Fitted Q-learning approach is experimentally validated for water level control of a multi input-multi output nonlinear constrained coupled two-tank system. Discussions on the observed control behavior are offered. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

From Central Pattern Generator to Sensory Template in the Evolution of Birdsong

ERIC Educational Resources Information Center

Konishi, Masakazu

2010-01-01

Central nervous networks, be they a part of the human brain or a group of neurons in a snail, may be designed to produce distinct patterns of movement. Central pattern generators can account for the development and production of normal vocal signals without auditory feedback in non-songbirds. Songbirds need auditory feedback to develop and…

Emergence of localized patterns in globally coupled networks of relaxation oscillators with heterogeneous connectivity

NASA Astrophysics Data System (ADS)

Leiser, Randolph J.; Rotstein, Horacio G.

2017-08-01

Oscillations in far-from-equilibrium systems (e.g., chemical, biochemical, biological) are generated by the nonlinear interplay of positive and negative feedback effects operating at different time scales. Relaxation oscillations emerge when the time scales between the activators and the inhibitors are well separated. In addition to the large-amplitude oscillations (LAOs) or relaxation type, these systems exhibit small-amplitude oscillations (SAOs) as well as abrupt transitions between them (canard phenomenon). Localized cluster patterns in networks of relaxation oscillators consist of one cluster oscillating in the LAO regime or exhibiting mixed-mode oscillations (LAOs interspersed with SAOs), while the other oscillates in the SAO regime. Because the individual oscillators are monostable, localized patterns are a network phenomenon that involves the interplay of the connectivity and the intrinsic dynamic properties of the individual nodes. Motivated by experimental and theoretical results on the Belousov-Zhabotinsky reaction, we investigate the mechanisms underlying the generation of localized patterns in globally coupled networks of piecewise-linear relaxation oscillators where the global feedback term affects the rate of change of the activator (fast variable) and depends on the weighted sum of the inhibitor (slow variable) at any given time. We also investigate whether these patterns are affected by the presence of a diffusive type of coupling whose synchronizing effects compete with the symmetry-breaking global feedback effects.

Electron beam energy stabilization using a neural network hybrid controller at the Australian Synchrotron Linac.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meier, E.; Morgan, M. J.; Biedron, S. G.

2009-01-01

This paper describes the implementation of a neural network hybrid controller for energy stabilization at the Australian Synchrotron Linac. The structure of the controller consists of a neural network (NNET) feed forward control, augmented by a conventional Proportional-Integral (PI) feedback controller to ensure stability of the system. The system is provided with past states of the machine in order to predict its future state, and therefore apply appropriate feed forward control. The NNET is able to cancel multiple frequency jitter in real-time. When it is not performing optimally due to jitter changes, the system can successfully be augmented by themore » PI controller to attenuate the remaining perturbations. With a view to control the energy and bunch length at the FERMI{at}Elettra Free Electron Laser (FEL), the present study considers a neural network hybrid feed forward-feedback type of control to rectify limitations related to feedback systems, such as poor response for high jitter frequencies or limited bandwidth, while ensuring robustness of control. The Australian Synchrotron Linac is equipped with a beam position monitor (BPM), that was provided by Sincrotrone Trieste from a former transport line thus allowing energy measurements and energy control experiments. The present study will consequently focus on correcting energy jitter induced by variations in klystron phase and voltage.« less

The Sander parallelogram illusion dissociates action and perception despite control for the litany of past confounds.

PubMed

Whitwell, Robert L; Goodale, Melvyn A; Merritt, Kate E; Enns, James T

2018-01-01

The two visual systems hypothesis proposes that human vision is supported by an occipito-temporal network for the conscious visual perception of the world and a fronto-parietal network for visually-guided, object-directed actions. Two specific claims about the fronto-parietal network's role in sensorimotor control have generated much data and controversy: (1) the network relies primarily on the absolute metrics of target objects, which it rapidly transforms into effector-specific frames of reference to guide the fingers, hands, and limbs, and (2) the network is largely unaffected by scene-based information extracted by the occipito-temporal network for those same targets. These two claims lead to the counter-intuitive prediction that in-flight anticipatory configuration of the fingers during object-directed grasping will resist the influence of pictorial illusions. The research confirming this prediction has been criticized for confounding the difference between grasping and explicit estimates of object size with differences in attention, sensory feedback, obstacle avoidance, metric sensitivity, and priming. Here, we address and eliminate each of these confounds. We asked participants to reach out and pick up 3D target bars resting on a picture of the Sander Parallelogram illusion and to make explicit estimates of the length of those bars. Participants performed their grasps without visual feedback, and were permitted to grasp the targets after making their size-estimates to afford them an opportunity to reduce illusory error with haptic feedback. The results show unequivocally that the effect of the illusion is stronger on perceptual judgments than on grasping. Our findings from the normally-sighted population provide strong support for the proposal that human vision is comprised of functionally and anatomically dissociable systems. Copyright © 2017 Elsevier Ltd. All rights reserved.

Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons.

PubMed

Burbank, Kendra S

2015-12-01

The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field's Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks.

Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons

PubMed Central

Burbank, Kendra S.

2015-01-01

The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field’s Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks. PMID:26633645

Functional architecture and global properties of the Corynebacterium glutamicum regulatory network: Novel insights from a dataset with a high genomic coverage.

PubMed

Freyre-González, Julio A; Tauch, Andreas

2017-09-10

Corynebacterium glutamicum is a Gram-positive, anaerobic, rod-shaped soil bacterium able to grow on a diversity of carbon sources like sugars and organic acids. It is a biotechnological relevant organism because of its highly efficient ability to biosynthesize amino acids, such as l-glutamic acid and l-lysine. Here, we reconstructed the most complete C. glutamicum regulatory network to date and comprehensively analyzed its global organizational properties, systems-level features and functional architecture. Our analyses show the tremendous power of Abasy Atlas to study the functional organization of regulatory networks. We created two models of the C. glutamicum regulatory network: all-evidences (containing both weak and strong supported interactions, genomic coverage=73%) and strongly-supported (only accounting for strongly supported evidences, genomic coverage=71%). Using state-of-the-art methodologies, we prove that power-law behaviors truly govern the connectivity and clustering coefficient distributions. We found a non-previously reported circuit motif that we named complex feed-forward motif. We highlighted the importance of feedback loops for the functional architecture, beyond whether they are statistically over-represented or not in the network. We show that the previously reported top-down approach is inadequate to infer the hierarchy governing a regulatory network because feedback bridges different hierarchical layers, and the top-down approach disregards the presence of intermodular genes shaping the integration layer. Our findings all together further support a diamond-shaped, three-layered hierarchy exhibiting some feedback between processing and coordination layers, which is shaped by four classes of systems-level elements: global regulators, locally autonomous modules, basal machinery and intermodular genes. Copyright © 2016 Elsevier B.V. All rights reserved.

Processing speed in recurrent visual networks correlates with general intelligence.

PubMed

Jolij, Jacob; Huisman, Danielle; Scholte, Steven; Hamel, Ronald; Kemner, Chantal; Lamme, Victor A F

2007-01-08

Studies on the neural basis of general fluid intelligence strongly suggest that a smarter brain processes information faster. Different brain areas, however, are interconnected by both feedforward and feedback projections. Whether both types of connections or only one of the two types are faster in smarter brains remains unclear. Here we show, by measuring visual evoked potentials during a texture discrimination task, that general fluid intelligence shows a strong correlation with processing speed in recurrent visual networks, while there is no correlation with speed of feedforward connections. The hypothesis that a smarter brain runs faster may need to be refined: a smarter brain's feedback connections run faster.

Clusters of poverty and disease emerge from feedbacks on an epidemiological network.

PubMed

Pluciński, Mateusz M; Ngonghala, Calistus N; Getz, Wayne M; Bonds, Matthew H

2013-03-06

The distribution of health conditions is characterized by extreme inequality. These disparities have been alternately attributed to disease ecology and the economics of poverty. Here, we provide a novel framework that integrates epidemiological and economic growth theory on an individual-based hierarchically structured network. Our model indicates that, under certain parameter regimes, feedbacks between disease ecology and economics create clusters of low income and high disease that can stably persist in populations that become otherwise predominantly rich and free of disease. Surprisingly, unlike traditional poverty trap models, these localized disease-driven poverty traps can arise despite homogeneity of parameters and evenly distributed initial economic conditions.

Sparsity-aware multiple relay selection in large multi-hop decode-and-forward relay networks

NASA Astrophysics Data System (ADS)

Gouissem, A.; Hamila, R.; Al-Dhahir, N.; Foufou, S.

2016-12-01

In this paper, we propose and investigate two novel techniques to perform multiple relay selection in large multi-hop decode-and-forward relay networks. The two proposed techniques exploit sparse signal recovery theory to select multiple relays using the orthogonal matching pursuit algorithm and outperform state-of-the-art techniques in terms of outage probability and computation complexity. To reduce the amount of collected channel state information (CSI), we propose a limited-feedback scheme where only a limited number of relays feedback their CSI. Furthermore, a detailed performance-complexity tradeoff investigation is conducted for the different studied techniques and verified by Monte Carlo simulations.

A cost-effective measurement-device-independent quantum key distribution system for quantum networks

NASA Astrophysics Data System (ADS)

Valivarthi, Raju; Zhou, Qiang; John, Caleb; Marsili, Francesco; Verma, Varun B.; Shaw, Matthew D.; Nam, Sae Woo; Oblak, Daniel; Tittel, Wolfgang

2017-12-01

We experimentally realize a measurement-device-independent quantum key distribution (MDI-QKD) system. It is based on cost-effective and commercially available hardware such as distributed feedback lasers and field-programmable gate arrays that enable time-bin qubit preparation and time-tagging, and active feedback systems that allow for compensation of time-varying properties of photons after transmission through deployed fiber. We examine the performance of our system, and conclude that its design does not compromise performance. Our demonstration paves the way for MDI-QKD-based quantum networks in star-type topology that extend over more than 100 km distance.

Bifurcation analysis of a delay reaction-diffusion malware propagation model with feedback control

NASA Astrophysics Data System (ADS)

Zhu, Linhe; Zhao, Hongyong; Wang, Xiaoming

2015-05-01

With the rapid development of network information technology, information networks security has become a very critical issue in our work and daily life. This paper attempts to develop a delay reaction-diffusion model with a state feedback controller to describe the process of malware propagation in mobile wireless sensor networks (MWSNs). By analyzing the stability and Hopf bifurcation, we show that the state feedback method can successfully be used to control unstable steady states or periodic oscillations. Moreover, formulas for determining the properties of the bifurcating periodic oscillations are derived by applying the normal form method and center manifold theorem. Finally, we conduct extensive simulations on large-scale MWSNs to evaluate the proposed model. Numerical evidences show that the linear term of the controller is enough to delay the onset of the Hopf bifurcation and the properties of the bifurcation can be regulated to achieve some desirable behaviors by choosing the appropriate higher terms of the controller. Furthermore, we obtain that the spatial-temporal dynamic characteristics of malware propagation are closely related to the rate constant for nodes leaving the infective class for recovered class and the mobile behavior of nodes.

Motorized CPM/CAM physiotherapy device with sliding-mode Fuzzy Neural Network control loop.

PubMed

Ho, Hung-Jung; Chen, Tien-Chi

2009-11-01

Continuous passive motion (CPM) and controllable active motion (CAM) physiotherapy devices promote rehabilitation of damaged joints. This paper presents a computerized CPM/CAM system that obviates the need for mechanical resistance devices such as springs. The system is controlled by a computer which performs sliding-mode Fuzzy Neural Network (FNN) calculations online. CAM-type resistance force is generated by the active performance of an electric motor which is controlled so as to oppose the motion of the patient's leg. A force sensor under the patient's foot on the device pedal provides data for feedback in a sliding-mode FNN control loop built around the motor. Via an active impedance control feedback system, the controller drives the motor to behave similarly to a damped spring by generating and controlling the amplitude and direction of the pedal force in relation to the patient's leg. Experiments demonstrate the high sensitivity and speed of the device. The PC-based feedback nature of the control loop means that sophisticated auto-adaptable CPM/CAM custom-designed physiotherapy becomes possible. The computer base also allows extensive data recording, data analysis and network-connected remote patient monitoring.

Tweeting in the Classroom: Instant feedback and assessment using a mobile web app

NASA Astrophysics Data System (ADS)

Saravanan, R.

2011-12-01

Cell phones with texting capabilities are ubiquitous in the college classroom, and smart phones are becoming increasingly common. These phones are used primarily for personal activities, including social networking, and are expected to remain switched off during instruction. The powerful communication capability of these devices, which could potentially facilitate novel forms of "instructional networking", remains untapped. Instead, special-purpose devices ("clickers") are used when instant feedback is desired in the classroom. A number of technical and behavioral challenges need to be overcome before mobile phones can be used routinely to assist in classroom instruction. This presentation will describe the experience of developing and deploying a mobile web app that enables students to provide instant feedback in the classroom using their mobile phones. The web app leverages existing social networking infrastructure, e.g., using the Twitter microblogging service to aggregate text messages sent by students, to promote classroom interaction. The web app was deployed both in a regular lecture hall and in a computer lab. Topics to be discussed include the technical challenges of deploying a mobile web app in a classroom setting, such as internet accessibility and latency, as well as non-technical issues relating to privacy, student reactions, etc.

Network efficient power control for wireless communication systems.

PubMed

Campos-Delgado, Daniel U; Luna-Rivera, Jose Martin; Martinez-Sánchez, C J; Gutierrez, Carlos A; Tecpanecatl-Xihuitl, J L

2014-01-01

We introduce a two-loop power control that allows an efficient use of the overall power resources for commercial wireless networks based on cross-layer optimization. This approach maximizes the network's utility in the outer-loop as a function of the averaged signal to interference-plus-noise ratio (SINR) by considering adaptively the changes in the network characteristics. For this purpose, the concavity property of the utility function was verified with respect to the SINR, and an iterative search was proposed with guaranteed convergence. In addition, the outer-loop is in charge of selecting the detector that minimizes the overall power consumption (transmission and detection). Next the inner-loop implements a feedback power control in order to achieve the optimal SINR in the transmissions despite channel variations and roundtrip delays. In our proposal, the utility maximization process and detector selection and feedback power control are decoupled problems, and as a result, these strategies are implemented at two different time scales in the two-loop framework. Simulation results show that substantial utility gains may be achieved by improving the power management in the wireless network.

Finite-horizon control-constrained nonlinear optimal control using single network adaptive critics.

PubMed

Heydari, Ali; Balakrishnan, Sivasubramanya N

2013-01-01

To synthesize fixed-final-time control-constrained optimal controllers for discrete-time nonlinear control-affine systems, a single neural network (NN)-based controller called the Finite-horizon Single Network Adaptive Critic is developed in this paper. Inputs to the NN are the current system states and the time-to-go, and the network outputs are the costates that are used to compute optimal feedback control. Control constraints are handled through a nonquadratic cost function. Convergence proofs of: 1) the reinforcement learning-based training method to the optimal solution; 2) the training error; and 3) the network weights are provided. The resulting controller is shown to solve the associated time-varying Hamilton-Jacobi-Bellman equation and provide the fixed-final-time optimal solution. Performance of the new synthesis technique is demonstrated through different examples including an attitude control problem wherein a rigid spacecraft performs a finite-time attitude maneuver subject to control bounds. The new formulation has great potential for implementation since it consists of only one NN with single set of weights and it provides comprehensive feedback solutions online, though it is trained offline.

Network Efficient Power Control for Wireless Communication Systems

PubMed Central

Campos-Delgado, Daniel U.; Luna-Rivera, Jose Martin; Martinez-Sánchez, C. J.; Gutierrez, Carlos A.; Tecpanecatl-Xihuitl, J. L.

2014-01-01

We introduce a two-loop power control that allows an efficient use of the overall power resources for commercial wireless networks based on cross-layer optimization. This approach maximizes the network's utility in the outer-loop as a function of the averaged signal to interference-plus-noise ratio (SINR) by considering adaptively the changes in the network characteristics. For this purpose, the concavity property of the utility function was verified with respect to the SINR, and an iterative search was proposed with guaranteed convergence. In addition, the outer-loop is in charge of selecting the detector that minimizes the overall power consumption (transmission and detection). Next the inner-loop implements a feedback power control in order to achieve the optimal SINR in the transmissions despite channel variations and roundtrip delays. In our proposal, the utility maximization process and detector selection and feedback power control are decoupled problems, and as a result, these strategies are implemented at two different time scales in the two-loop framework. Simulation results show that substantial utility gains may be achieved by improving the power management in the wireless network. PMID:24683350

Multistability in the lactose utilization network of Escherichia coli

NASA Astrophysics Data System (ADS)

Ozbudak, Ertugrul M.; Thattai, Mukund; Lim, Han N.; Shraiman, Boris I.; van Oudenaarden, Alexander

2004-02-01

Multistability, the capacity to achieve multiple internal states in response to a single set of external inputs, is the defining characteristic of a switch. Biological switches are essential for the determination of cell fate in multicellular organisms, the regulation of cell-cycle oscillations during mitosis and the maintenance of epigenetic traits in microbes. The multistability of several natural and synthetic systems has been attributed to positive feedback loops in their regulatory networks. However, feedback alone does not guarantee multistability. The phase diagram of a multistable system, a concise description of internal states as key parameters are varied, reveals the conditions required to produce a functional switch. Here we present the phase diagram of the bistable lactose utilization network of Escherichia coli. We use this phase diagram, coupled with a mathematical model of the network, to quantitatively investigate processes such as sugar uptake and transcriptional regulation in vivo. We then show how the hysteretic response of the wild-type system can be converted to an ultrasensitive graded response. The phase diagram thus serves as a sensitive probe of molecular interactions and as a powerful tool for rational network design.

Cognitive task analysis of network analysts and managers for network situational awareness

NASA Astrophysics Data System (ADS)

Erbacher, Robert F.; Frincke, Deborah A.; Wong, Pak Chung; Moody, Sarah; Fink, Glenn

2010-01-01

The goal of our project is to create a set of next-generation cyber situational-awareness capabilities with applications to other domains in the long term. The situational-awareness capabilities being developed focus on novel visualization techniques as well as data analysis techniques designed to improve the comprehensibility of the visualizations. The objective is to improve the decision-making process to enable decision makers to choose better actions. To this end, we put extensive effort into ensuring we had feedback from network analysts and managers and understanding what their needs truly are. This paper discusses the cognitive task analysis methodology we followed to acquire feedback from the analysts. This paper also provides the details we acquired from the analysts on their processes, goals, concerns, etc. A final result we describe is the generation of a task-flow diagram.

SigFlux: a novel network feature to evaluate the importance of proteins in signal transduction networks.

PubMed

Liu, Wei; Li, Dong; Zhang, Jiyang; Zhu, Yunping; He, Fuchu

2006-11-27

Measuring each protein's importance in signaling networks helps to identify the crucial proteins in a cellular process, find the fragile portion of the biology system and further assist for disease therapy. However, there are relatively few methods to evaluate the importance of proteins in signaling networks. We developed a novel network feature to evaluate the importance of proteins in signal transduction networks, that we call SigFlux, based on the concept of minimal path sets (MPSs). An MPS is a minimal set of nodes that can perform the signal propagation from ligands to target genes or feedback loops. We define SigFlux as the number of MPSs in which each protein is involved. We applied this network feature to the large signal transduction network in the hippocampal CA1 neuron of mice. Significant correlations were simultaneously observed between SigFlux and both the essentiality and evolutionary rate of genes. Compared with another commonly used network feature, connectivity, SigFlux has similar or better ability as connectivity to reflect a protein's essentiality. Further classification according to protein function demonstrates that high SigFlux, low connectivity proteins are abundant in receptors and transcriptional factors, indicating that SigFlux candescribe the importance of proteins within the context of the entire network. SigFlux is a useful network feature in signal transduction networks that allows the prediction of the essentiality and conservation of proteins. With this novel network feature, proteins that participate in more pathways or feedback loops within a signaling network are proved far more likely to be essential and conserved during evolution than their counterparts.

Systemic Adenosine Triphosphate Impairs Neutrophil Chemotaxis and Host Defense in Sepsis.

PubMed

Li, Xiaoou; Kondo, Yutaka; Bao, Yi; Staudenmaier, Laura; Lee, Albert; Zhang, Jingping; Ledderose, Carola; Junger, Wolfgang G

2017-01-01

Sepsis remains an unresolved clinical problem. Therapeutic strategies focusing on inhibition of neutrophils (polymorphonuclear neutrophils) have failed, which indicates that a more detailed understanding of the underlying pathophysiology of sepsis is required. Polymorphonuclear neutrophil activation and chemotaxis require cellular adenosine triphosphate release via pannexin-1 channels that fuel autocrine feedback via purinergic receptors. In the current study, we examined the roles of endogenous and systemic adenosine triphosphate on polymorphonuclear neutrophil activation and host defense in sepsis. Prospective randomized animal investigation and in vitro studies. Preclinical academic research laboratory. Wild-type C57BL/6 mice, pannexin-1 knockout mice, and healthy human subjects used to obtain polymorphonuclear neutrophils for in vitro studies. Wild-type and pannexin-1 knockout mice were treated with suramin or apyrase to block the endogenous or systemic effects of adenosine triphosphate. Mice were subjected to cecal ligation and puncture and polymorphonuclear neutrophil activation (CD11b integrin expression), organ (liver) injury (plasma aspartate aminotransferase), bacterial spread, and survival were monitored. Human polymorphonuclear neutrophils were used to study the effect of systemic adenosine triphosphate and apyrase on chemotaxis. Inhibiting endogenous adenosine triphosphate reduced polymorphonuclear neutrophil activation and organ injury, but increased the spread of bacteria and mortality in sepsis. By contrast, removal of systemic adenosine triphosphate improved bacterial clearance and survival in sepsis by improving polymorphonuclear neutrophil chemotaxis. Systemic adenosine triphosphate impairs polymorphonuclear neutrophil functions by disrupting the endogenous purinergic signaling mechanisms that regulate cell activation and chemotaxis. Removal of systemic adenosine triphosphate improves polymorphonuclear neutrophil function and host defenses, making this a promising new treatment strategy for sepsis.

The self-medication hypothesis: Evidence from terrorism and cigarette accessibility.

PubMed

Pesko, Michael F; Baum, Christopher F

2016-09-01

We use single equation and system instrumental variable models to explore if individuals smoke during times of stress (the motivation effect) and if they are successful in self-medicating short-term stress (the self-medication effect). Short-term stress is a powerful motivator of smoking, and the decision to smoke could trigger biological feedback that immediately reduces short-term stress. We use data on self-reported smoking and stress from 240,388 current and former smokers. We instrument short-term stress with temporal distance from September 11, 2001 (using date of interview). We instrument smoking with cigarette accessibility measures of cigarette price changes and distance to state borders. In the absence of accounting for endogeneity, we find that smoking is associated with increases in short-term stress. However, when we account for endogeneity we find no evidence of smoking affecting short-term stress. We do find a consistent positive effect of short-term stress on smoking. Copyright © 2016 Elsevier B.V. All rights reserved.

Hybrid function projective synchronization in complex dynamical networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wei, Qiang; Wang, Xing-yuan, E-mail: wangxy@dlut.edu.cn; Hu, Xiao-peng

2014-02-15

This paper investigates hybrid function projective synchronization in complex dynamical networks. When the complex dynamical networks could be synchronized up to an equilibrium or periodic orbit, a hybrid feedback controller is designed to realize the different component of vector of node could be synchronized up to different desired scaling function in complex dynamical networks with time delay. Hybrid function projective synchronization (HFPS) in complex dynamical networks with constant delay and HFPS in complex dynamical networks with time-varying coupling delay are researched, respectively. Finally, the numerical simulations show the effectiveness of theoretical analysis.

Discovery of multiple interacting partners of gankyrin, a proteasomal chaperone and an oncoprotein--evidence for a common hot spot site at the interface and its functional relevance.

PubMed

Nanaware, Padma P; Ramteke, Manoj P; Somavarapu, Arun K; Venkatraman, Prasanna

2014-07-01

Gankyrin, a non-ATPase component of the proteasome and a chaperone of proteasome assembly, is also an oncoprotein. Gankyrin regulates a variety of oncogenic signaling pathways in cancer cells and accelerates degradation of tumor suppressor proteins p53 and Rb. Therefore gankyrin may be a unique hub integrating signaling networks with the degradation pathway. To identify new interactions that may be crucial in consolidating its role as an oncogenic hub, crystal structure of gankyrin-proteasome ATPase complex was used to predict novel interacting partners. EEVD, a four amino acid linear sequence seems a hot spot site at this interface. By searching for EEVD in exposed regions of human proteins in PDB database, we predicted 34 novel interactions. Eight proteins were tested and seven of them were found to interact with gankyrin. Affinity of four interactions is high enough for endogenous detection. Others require gankyrin overexpression in HEK 293 cells or occur endogenously in breast cancer cell line- MDA-MB-435, reflecting lower affinity or presence of a deregulated network. Mutagenesis and peptide inhibition confirm that EEVD is the common hot spot site at these interfaces and therefore a potential polypharmacological drug target. In MDA-MB-231 cells in which the endogenous CLIC1 is silenced, trans-expression of Wt protein (CLIC1_EEVD) and not the hot spot site mutant (CLIC1_AAVA) resulted in significant rescue of the migratory potential. Our approach can be extended to identify novel functionally relevant protein-protein interactions, in expansion of oncogenic networks and in identifying potential therapeutic targets. © 2013 Wiley Periodicals, Inc.

Audit feedback on reading performance of screening mammograms: An international comparison.

PubMed

Hofvind, S; Bennett, R L; Brisson, J; Lee, W; Pelletier, E; Flugelman, A; Geller, B

2016-09-01

Providing feedback to mammography radiologists and facilities may improve interpretive performance. We conducted a web-based survey to investigate how and why such feedback is undertaken and used in mammographic screening programmes. The survey was sent to representatives in 30 International Cancer Screening Network member countries where mammographic screening is offered. Seventeen programmes in 14 countries responded to the survey. Audit feedback was aimed at readers in 14 programmes, and facilities in 12 programmes. Monitoring quality assurance was the most common purpose of audit feedback. Screening volume, recall rate, and rate of screen-detected cancers were typically reported performance measures. Audit reports were commonly provided annually, but more frequently when target guidelines were not reached. The purpose, target audience, performance measures included, form and frequency of the audit feedback varied amongst mammographic screening programmes. These variations may provide a basis for those developing and improving such programmes. © The Author(s) 2016.

Router Agent Technology for Policy-Based Network Management

NASA Technical Reports Server (NTRS)

Chow, Edward T.; Sudhir, Gurusham; Chang, Hsin-Ping; James, Mark; Liu, Yih-Chiao J.; Chiang, Winston

2011-01-01

This innovation can be run as a standalone network application on any computer in a networked environment. This design can be configured to control one or more routers (one instance per router), and can also be configured to listen to a policy server over the network to receive new policies based on the policy- based network management technology. The Router Agent Technology transforms the received policies into suitable Access Control List syntax for the routers it is configured to control. It commits the newly generated access control lists to the routers and provides feedback regarding any errors that were faced. The innovation also automatically generates a time-stamped log file regarding all updates to the router it is configured to control. This technology, once installed on a local network computer and started, is autonomous because it has the capability to keep listening to new policies from the policy server, transforming those policies to router-compliant access lists, and committing those access lists to a specified interface on the specified router on the network with any error feedback regarding commitment process. The stand-alone application is named RouterAgent and is currently realized as a fully functional (version 1) implementation for the Windows operating system and for CISCO routers.

Social networks and expertise development for Australian breast radiologists.

PubMed

Taba, Seyedamir Tavakoli; Hossain, Liaquat; Willis, Karen; Lewis, Sarah

2017-02-11

In this study, we explore the nexus between social networks and expertise development of Australian breast radiologists. Background literature has shown that a lack of appropriate social networks and interaction among certain professional group(s) may be an obstacle for knowledge acquisition, information flow and expertise sharing. To date there have not been any systematic studies investigating how social networks and expertise development are interconnected and whether this leads to improved performance for breast radiologists. This study explores the value of social networks in building expertise alongside with other constructs of performance for the Australian radiology workforce using semi-structured in-depth interviews with 17 breast radiologists. The findings from this study emphasise the influences of knowledge transfer and learning through social networks and interactions as well as knowledge acquisition and development through experience and feedback. The results also show that accessibility to learning resources and a variety of timely feedback on performance through the information and communication technologies (ICT) is likely to facilitate improved performance and build social support. We argue that radiologists' and, in particular, breast radiologists' work performance, needs to be explored not only through individual numerical characteristics but also by analysing the social context and peer support networks in which they operate and we identify multidisciplinary care as a core entity of social learning.

Universal photonic quantum computation via time-delayed feedback

PubMed Central

Pichler, Hannes; Choi, Soonwon; Zoller, Peter; Lukin, Mikhail D.

2017-01-01

We propose and analyze a deterministic protocol to generate two-dimensional photonic cluster states using a single quantum emitter via time-delayed quantum feedback. As a physical implementation, we consider a single atom or atom-like system coupled to a 1D waveguide with a distant mirror, where guided photons represent the qubits, while the mirror allows the implementation of feedback. We identify the class of many-body quantum states that can be produced using this approach and characterize them in terms of 2D tensor network states. PMID:29073057

Monetary Reward and Punishment to Response Inhibition Modulate Activation and Synchronization Within the Inhibitory Brain Network.

PubMed

Chikara, Rupesh K; Chang, Erik C; Lu, Yi-Chen; Lin, Dar-Shong; Lin, Chin-Teng; Ko, Li-Wei

2018-01-01

A reward or punishment can modulate motivation and emotions, which in turn affect cognitive processing. The present simultaneous functional magnetic resonance imaging-electroencephalography study examines neural mechanisms of response inhibition under the influence of a monetary reward or punishment by implementing a modified stop-signal task in a virtual battlefield scenario. The participants were instructed to play as snipers who open fire at a terrorist target but withhold shooting in the presence of a hostage. The participants performed the task under three different feedback conditions in counterbalanced order: a reward condition where each successfully withheld response added a bonus (i.e., positive feedback) to the startup credit, a punishment condition where each failure in stopping deduced a penalty (i.e., negative feedback), and a no-feedback condition where response outcome had no consequences and served as a control setting. Behaviorally both reward and punishment conditions led to significantly down-regulated inhibitory function in terms of the critical stop-signal delay. As for the neuroimaging results, increased activities were found for the no-feedback condition in regions previously reported to be associated with response inhibition, including the right inferior frontal gyrus and the pre-supplementary motor area. Moreover, higher activation of the lingual gyrus, posterior cingulate gyrus (PCG) and inferior parietal lobule were found in the reward condition, while stronger activation of the precuneus gyrus was found in the punishment condition. The positive feedback was also associated with stronger changes of delta, theta, and alpha synchronization in the PCG than were the negative or no-feedback conditions. These findings depicted the intertwining relationship between response inhibition and motivation networks.

Slip speed feedback for grip force control.

PubMed

Damian, D D; Arita, A H; Martinez, H; Pfeifer, R

2012-08-01

Grasp stability in the human hand has been resolved by means of an intricate network of mechanoreceptors integrating numerous cues about mechanical events, through an ontogenetic grasp practice. An engineered prosthetic interface introduces considerable perturbation risks in grasping, calling for feedback modalities that address the underlying slip phenomenon. In this study, we propose an enhanced slip feedback modality, with potential for myoelectric-based prosthetic applications, that relays information regarding slip events, particularly slip occurrence and slip speed. The proposed feedback modality, implemented using electrotactile stimulation, was evaluated in psychophysical studies of slip control in a simplified setup. The obtained results were compared with vision and a binary slip feedback that transmits on-off information about slip detection. The slip control efficiency of the slip speed display is comparable to that obtained with vision feedback, and it clearly outperforms the efficiency of the on-off slip modality in such tasks. These results suggest that the proposed tactile feedback is a promising sensory method for the restoration of stable grasp in prosthetic applications.

Transfer of Training: The Role of Feedback in Supportive Social Networks

ERIC Educational Resources Information Center

Van den Bossche, Piet; Segers, Mien; Jansen, Niekie

2010-01-01

The transfer of training to the workplace often fails to occur. The authors argue that feedback generated within the work environment about the application of newly learned skills in the workplace helps to close the gap between the current performance and the desired goal of full application of what is learned during training. This study takes a…

A feedback model of visual attention.

PubMed

Spratling, M W; Johnson, M H

2004-03-01

Feedback connections are a prominent feature of cortical anatomy and are likely to have a significant functional role in neural information processing. We present a neural network model of cortical feedback that successfully simulates neurophysiological data associated with attention. In this domain, our model can be considered a more detailed, and biologically plausible, implementation of the biased competition model of attention. However, our model is more general as it can also explain a variety of other top-down processes in vision, such as figure/ground segmentation and contextual cueing. This model thus suggests that a common mechanism, involving cortical feedback pathways, is responsible for a range of phenomena and provides a unified account of currently disparate areas of research.

Towards a Quantitative Endogenous Network Theory of Cancer Genesis and Progression: beyond ``cancer as diseases of genome''

NASA Astrophysics Data System (ADS)

Ao, Ping

2011-03-01

There has been a tremendous progress in cancer research. However, it appears the current dominant cancer research framework of regarding cancer as diseases of genome leads impasse. Naturally questions have been asked that whether it is possible to develop alternative frameworks such that they can connect both to mutations and other genetic/genomic effects and to environmental factors. Furthermore, such framework can be made quantitative and with predictions experimentally testable. In this talk, I will present a positive answer to this calling. I will explain on our construction of endogenous network theory based on molecular-cellular agencies as dynamical variable. Such cancer theory explicitly demonstrates a profound connection to many fundamental concepts in physics, as such stochastic non-equilibrium processes, ``energy'' landscape, metastability, etc. It suggests that neneath cancer's daunting complexity may lie a simplicity that gives grounds for hope. The rationales behind such theory, its predictions, and its initial experimental verifications will be presented. Supported by USA NIH and China NSF.

Endogenous Bioelectric Signaling Networks: Exploiting Voltage Gradients for Control of Growth and Form.

PubMed

Levin, Michael; Pezzulo, Giovanni; Finkelstein, Joshua M

2017-06-21

Living systems exhibit remarkable abilities to self-assemble, regenerate, and remodel complex shapes. How cellular networks construct and repair specific anatomical outcomes is an open question at the heart of the next-generation science of bioengineering. Developmental bioelectricity is an exciting emerging discipline that exploits endogenous bioelectric signaling among many cell types to regulate pattern formation. We provide a brief overview of this field, review recent data in which bioelectricity is used to control patterning in a range of model systems, and describe the molecular tools being used to probe the role of bioelectrics in the dynamic control of complex anatomy. We suggest that quantitative strategies recently developed to infer semantic content and information processing from ionic activity in the brain might provide important clues to cracking the bioelectric code. Gaining control of the mechanisms by which large-scale shape is regulated in vivo will drive transformative advances in bioengineering, regenerative medicine, and synthetic morphology, and could be used to therapeutically address birth defects, traumatic injury, and cancer.

Neural-network-based state feedback control of a nonlinear discrete-time system in nonstrict feedback form.

PubMed

Jagannathan, Sarangapani; He, Pingan

2008-12-01

In this paper, a suite of adaptive neural network (NN) controllers is designed to deliver a desired tracking performance for the control of an unknown, second-order, nonlinear discrete-time system expressed in nonstrict feedback form. In the first approach, two feedforward NNs are employed in the controller with tracking error as the feedback variable whereas in the adaptive critic NN architecture, three feedforward NNs are used. In the adaptive critic architecture, two action NNs produce virtual and actual control inputs, respectively, whereas the third critic NN approximates certain strategic utility function and its output is employed for tuning action NN weights in order to attain the near-optimal control action. Both the NN control methods present a well-defined controller design and the noncausal problem in discrete-time backstepping design is avoided via NN approximation. A comparison between the controller methodologies is highlighted. The stability analysis of the closed-loop control schemes is demonstrated. The NN controller schemes do not require an offline learning phase and the NN weights can be initialized at zero or random. Results show that the performance of the proposed controller schemes is highly satisfactory while meeting the closed-loop stability.

Feedback modulation of neural network synchrony and seizure susceptibility by Mdm2-p53-Nedd4-2 signaling.

PubMed

Jewett, Kathryn A; Christian, Catherine A; Bacos, Jonathan T; Lee, Kwan Young; Zhu, Jiuhe; Tsai, Nien-Pei

2016-03-22

Neural network synchrony is a critical factor in regulating information transmission through the nervous system. Improperly regulated neural network synchrony is implicated in pathophysiological conditions such as epilepsy. Despite the awareness of its importance, the molecular signaling underlying the regulation of neural network synchrony, especially after stimulation, remains largely unknown. In this study, we show that elevation of neuronal activity by the GABA(A) receptor antagonist, Picrotoxin, increases neural network synchrony in primary mouse cortical neuron cultures. The elevation of neuronal activity triggers Mdm2-dependent degradation of the tumor suppressor p53. We show here that blocking the degradation of p53 further enhances Picrotoxin-induced neural network synchrony, while promoting the inhibition of p53 with a p53 inhibitor reduces Picrotoxin-induced neural network synchrony. These data suggest that Mdm2-p53 signaling mediates a feedback mechanism to fine-tune neural network synchrony after activity stimulation. Furthermore, genetically reducing the expression of a direct target gene of p53, Nedd4-2, elevates neural network synchrony basally and occludes the effect of Picrotoxin. Finally, using a kainic acid-induced seizure model in mice, we show that alterations of Mdm2-p53-Nedd4-2 signaling affect seizure susceptibility. Together, our findings elucidate a critical role of Mdm2-p53-Nedd4-2 signaling underlying the regulation of neural network synchrony and seizure susceptibility and reveal potential therapeutic targets for hyperexcitability-associated neurological disorders.

Predicting workload profiles of brain-robot interface and electromygraphic neurofeedback with cortical resting-state networks: personal trait or task-specific challenge?

NASA Astrophysics Data System (ADS)

Fels, Meike; Bauer, Robert; Gharabaghi, Alireza

2015-08-01

Objective. Novel rehabilitation strategies apply robot-assisted exercises and neurofeedback tasks to facilitate intensive motor training. We aimed to disentangle task-specific and subject-related contributions to the perceived workload of these interventions and the related cortical activation patterns. Approach. We assessed the perceived workload with the NASA Task Load Index in twenty-one subjects who were exposed to two different feedback tasks in a cross-over design: (i) brain-robot interface (BRI) with haptic/proprioceptive feedback of sensorimotor oscillations related to motor imagery, and (ii) control of neuromuscular activity with feedback of the electromyography (EMG) of the same hand. We also used electroencephalography to examine the cortical activation patterns beforehand in resting state and during the training session of each task. Main results. The workload profile of BRI feedback differed from EMG feedback and was particularly characterized by the experience of frustration. The frustration level was highly correlated across tasks, suggesting subject-related relevance of this workload component. Those subjects who were specifically challenged by the respective tasks could be detected by an interhemispheric alpha-band network in resting state before the training and by their sensorimotor theta-band activation pattern during the exercise. Significance. Neurophysiological profiles in resting state and during the exercise may provide task-independent workload markers for monitoring and matching participants’ ability and task difficulty of neurofeedback interventions.

Impaired coupling of local and global functional feedbacks underlies abnormal synchronization and negative symptoms of schizophrenia.

PubMed

Noh, Kyungchul; Shin, Kyung Soon; Shin, Dongkwan; Hwang, Jae Yeon; Kim, June Sic; Jang, Joon Hwan; Chung, Chun Kee; Kwon, Jun Soo; Cho, Kwang-Hyun

2013-04-10

Abnormal synchronization of brain oscillations is found to be associated with various core symptoms of schizophrenia. However, the underlying mechanism of this association remains yet to be elucidated. In this study, we found that coupled local and global feedback (CLGF) circuits in the cortical functional network are related to the abnormal synchronization and also correlated to the negative symptom of schizophrenia. Analysis of the magnetoencephalography data obtained from patients with chronic schizophrenia during rest revealed an increase in beta band synchronization and a reduction in gamma band power compared to healthy controls. Using a feedback identification method based on non-causal impulse responses, we constructed functional feedback networks and found that CLGF circuits were significantly reduced in schizophrenia. From computational analysis on the basis of the Wilson-Cowan model, we unraveled that the CLGF circuits are critically involved in the abnormal synchronization and the dynamical switching between beta and gamma bands power in schizophrenia. Moreover, we found that the abundance of CLGF circuits was negatively correlated with the development of negative symptoms of schizophrenia, suggesting that the negative symptom is closely related to the impairment of this circuit. Our study implicates that patients with schizophrenia might have the impaired coupling of inter- and intra-regional functional feedbacks and that the CLGF circuit might serve as a critical bridge between abnormal synchronization and the negative symptoms of schizophrenia.

The Steroid Metabolome in the Isolated Ovarian Follicle and Its Response to Androgen Exposure and Antagonism

PubMed Central

Lebbe, Marie; Taylor, Angela E.; Visser, Jenny A.; Kirkman-Brown, Jackson C.; Woodruff, Teresa K.

2017-01-01

The ovarian follicle is a major site of steroidogenesis, crucially required for normal ovarian function and female reproduction. Our understanding of androgen synthesis and metabolism in the developing follicle has been limited by the sensitivity and specificity issues of previously used assays. Here we used liquid chromatography–tandem mass spectrometry to map the stage-dependent endogenous steroid metabolome in an encapsulated in vitro follicle growth system, from murine secondary through antral follicles. Furthermore, follicles were cultured in the presence of androgen precursors, nonaromatizable active androgen, and androgen receptor (AR) antagonists to assess effects on steroidogenesis and follicle development. Cultured follicles showed a stage-dependent increase in endogenous androgen, estrogen, and progesterone production, and incubations with the sex steroid precursor dehydroepiandrosterone revealed the follicle as capable of active androgen synthesis at early developmental stages. Androgen exposure and antagonism demonstrated AR–mediated effects on follicle growth and antrum formation that followed a biphasic pattern, with low levels of androgens inducing more rapid follicle maturation and high doses inhibiting oocyte maturation and follicle growth. Crucially, our study provides evidence for an intrafollicular feedback circuit regulating steroidogenesis, with decreased follicle androgen synthesis after exogenous androgen exposure and increased androgen output after additional AR antagonist treatment. We propose that this feedback circuit helps maintain an equilibrium of androgen exposure in the developing follicle. The observed biphasic response of follicle growth and function in increasing androgen supplementations has implications for our understanding of polycystic ovary syndrome pathophysiology and the dose-dependent utility of androgens in in vitro fertilization settings. PMID:28323936

Using Reputation Systems and Non-Deterministic Routing to Secure Wireless Sensor Networks

PubMed Central

Moya, José M.; Vallejo, Juan Carlos; Fraga, David; Araujo, Álvaro; Villanueva, Daniel; de Goyeneche, Juan-Mariano

2009-01-01

Security in wireless sensor networks is difficult to achieve because of the resource limitations of the sensor nodes. We propose a trust-based decision framework for wireless sensor networks coupled with a non-deterministic routing protocol. Both provide a mechanism to effectively detect and confine common attacks, and, unlike previous approaches, allow bad reputation feedback to the network. This approach has been extensively simulated, obtaining good results, even for unrealistically complex attack scenarios. PMID:22412345

Design Issues for Traffic Management for the ATM UBR + Service for TCP Over Satellite Networks

NASA Technical Reports Server (NTRS)

Jain, Raj

1999-01-01

This project was a comprehensive research program for developing techniques for improving the performance of Internet protocols over Asynchronous Transfer Mode (ATM) based satellite networks. Among the service categories provided by ATM networks, the most commonly used category for data traffic is the unspecified bit rate (UBR) service. UBR allows sources to send data into the network without any feedback control. The project resulted in the numerous ATM Forum contributions and papers.

Comprehensive joint feedback control for standing by functional neuromuscular stimulation-a simulation study.

PubMed

Nataraj, Raviraj; Audu, Musa L; Kirsch, Robert F; Triolo, Ronald J

2010-12-01

Previous investigations of feedback control of standing after spinal cord injury (SCI) using functional neuromuscular stimulation (FNS) have primarily targeted individual joints. This study assesses the potential efficacy of comprehensive (trunk, hips, knees, and ankles) joint feedback control against postural disturbances using a bipedal, 3-D computer model of SCI stance. Proportional-derivative feedback drove an artificial neural network trained to produce muscle excitation patterns consistent with maximal joint stiffness values achievable about neutral stance given typical SCI muscle properties. Feedback gains were optimized to minimize upper extremity (UE) loading required to stabilize against disturbances. Compared to the baseline case of maximum constant muscle excitations used clinically, the controller reduced UE loading by 55% in resisting external force perturbations and by 84% during simulated one-arm functional tasks. Performance was most sensitive to inaccurate measurements of ankle plantar/dorsiflexion position and hip ab/adduction velocity feedback. In conclusion, comprehensive joint feedback demonstrates potential to markedly improve FNS standing function. However, alternative control structures capable of effective performance with fewer sensor-based feedback parameters may better facilitate clinical usage.

Comprehensive Joint Feedback Control for Standing by Functional Neuromuscular Stimulation – a Simulation Study

PubMed Central

Nataraj, Raviraj; Audu, Musa L.; Kirsch, Robert F.; Triolo, Ronald J.

2013-01-01

Previous investigations of feedback control of standing after spinal cord injury (SCI) using functional neuromuscular stimulation (FNS) have primarily targeted individual joints. This study assesses the potential efficacy of comprehensive (trunk, hips, knees, and ankles) joint-feedback control against postural disturbances using a bipedal, three-dimensional computer model of SCI stance. Proportional-derivative feedback drove an artificial neural network trained to produce muscle excitation patterns consistent with maximal joint stiffness values achievable about neutral stance given typical SCI muscle properties. Feedback gains were optimized to minimize upper extremity (UE) loading required to stabilize against disturbances. Compared to the baseline case of maximum constant muscle excitations used clinically, the controller reduced UE loading by 55% in resisting external force perturbations and by 84% during simulated one-arm functional tasks. Performance was most sensitive to inaccurate measurements of ankle plantar/dorsiflexion position and hip ab/adduction velocity feedback. In conclusion, comprehensive joint-feedback demonstrates potential to markedly improve FNS standing function. However, alternative control structures capable of effective performance with fewer sensor-based feedback parameters may better facilitate clinical usage. PMID:20923741

Graduate students navigating social-ecological research: insights from the Long-Term Ecological Research Network

Treesearch

Sydne Record; Paige F. B. Ferguson; Elise Benveniste; Rose A. Graves; Vera W. Pfeiffer; Michele Romolini; Christie E. Yorke; Ben Beardmore

2016-01-01

Interdisciplinary, collaborative research capable of capturing the feedbacks between biophysical and social systems can improve the capacity for sustainable environmental decision making. Networks of researchers provide unique opportunities to foster social-ecological inquiry. Although insights into interdisciplinary research have been discussed elsewhere,...

Cognitive Task Analysis of Network Analysts and Managers for Network Situational Awareness

DOE Office of Scientific and Technical Information (OSTI.GOV)

Erbacher, Robert; Frincke, Deborah A.; Wong, Pak C.

The goal of the project was to create a set of next generation cyber situational awareness capabilities with applications to other domains in the long term. The goal is to improve the decision making process such that decision makers can choose better actions. To this end, we put extensive effort into ensuring we had feedback from network analysts and managers and understood what their needs truly were. Consequently, this is the focus of this portion of the research. This paper discusses the methodology we followed to acquire this feedback from the analysts, namely a cognitive task analysis. Additionally, this papermore » provides the details we acquired from the analysts. This essentially provides details on their processes, goals, concerns, the data and meta-data they analyze, etc. A final result we describe is the generation of a task-flow diagram.« less

Reliable Adaptive Video Streaming Driven by Perceptual Semantics for Situational Awareness

PubMed Central

Pimentel-Niño, M. A.; Saxena, Paresh; Vazquez-Castro, M. A.

2015-01-01

A novel cross-layer optimized video adaptation driven by perceptual semantics is presented. The design target is streamed live video to enhance situational awareness in challenging communications conditions. Conventional solutions for recreational applications are inadequate and novel quality of experience (QoE) framework is proposed which allows fully controlled adaptation and enables perceptual semantic feedback. The framework relies on temporal/spatial abstraction for video applications serving beyond recreational purposes. An underlying cross-layer optimization technique takes into account feedback on network congestion (time) and erasures (space) to best distribute available (scarce) bandwidth. Systematic random linear network coding (SRNC) adds reliability while preserving perceptual semantics. Objective metrics of the perceptual features in QoE show homogeneous high performance when using the proposed scheme. Finally, the proposed scheme is in line with content-aware trends, by complying with information-centric-networking philosophy and architecture. PMID:26247057

Framework and Method for Controlling a Robotic System Using a Distributed Computer Network

NASA Technical Reports Server (NTRS)

Sanders, Adam M. (Inventor); Strawser, Philip A. (Inventor); Barajas, Leandro G. (Inventor); Permenter, Frank Noble (Inventor)

2015-01-01

A robotic system for performing an autonomous task includes a humanoid robot having a plurality of compliant robotic joints, actuators, and other integrated system devices that are controllable in response to control data from various control points, and having sensors for measuring feedback data at the control points. The system includes a multi-level distributed control framework (DCF) for controlling the integrated system components over multiple high-speed communication networks. The DCF has a plurality of first controllers each embedded in a respective one of the integrated system components, e.g., the robotic joints, a second controller coordinating the components via the first controllers, and a third controller for transmitting a signal commanding performance of the autonomous task to the second controller. The DCF virtually centralizes all of the control data and the feedback data in a single location to facilitate control of the robot across the multiple communication networks.

Multiple types of synchronization analysis for discontinuous Cohen-Grossberg neural networks with time-varying delays.

PubMed

Li, Jiarong; Jiang, Haijun; Hu, Cheng; Yu, Zhiyong

2018-03-01

This paper is devoted to the exponential synchronization, finite time synchronization, and fixed-time synchronization of Cohen-Grossberg neural networks (CGNNs) with discontinuous activations and time-varying delays. Discontinuous feedback controller and Novel adaptive feedback controller are designed to realize global exponential synchronization, finite time synchronization and fixed-time synchronization by adjusting the values of the parameters ω in the controller. Furthermore, the settling time of the fixed-time synchronization derived in this paper is less conservative and more accurate. Finally, some numerical examples are provided to show the effectiveness and flexibility of the results derived in this paper. Copyright © 2018 Elsevier Ltd. All rights reserved.

Biophysical network modeling of the dLGN circuit: Effects of cortical feedback on spatial response properties of relay cells

PubMed Central

Martínez-Cañada, Pablo; Halnes, Geir; Fyhn, Marianne

2018-01-01

Despite half-a-century of research since the seminal work of Hubel and Wiesel, the role of the dorsal lateral geniculate nucleus (dLGN) in shaping the visual signals is not properly understood. Placed on route from retina to primary visual cortex in the early visual pathway, a striking feature of the dLGN circuit is that both the relay cells (RCs) and interneurons (INs) not only receive feedforward input from retinal ganglion cells, but also a prominent feedback from cells in layer 6 of visual cortex. This feedback has been proposed to affect synchronicity and other temporal properties of the RC firing. It has also been seen to affect spatial properties such as the center-surround antagonism of thalamic receptive fields, i.e., the suppression of the response to very large stimuli compared to smaller, more optimal stimuli. Here we explore the spatial effects of cortical feedback on the RC response by means of a a comprehensive network model with biophysically detailed, single-compartment and multicompartment neuron models of RCs, INs and a population of orientation-selective layer 6 simple cells, consisting of pyramidal cells (PY). We have considered two different arrangements of synaptic feedback from the ON and OFF zones in the visual cortex to the dLGN: phase-reversed (‘push-pull’) and phase-matched (‘push-push’), as well as different spatial extents of the corticothalamic projection pattern. Our simulation results support that a phase-reversed arrangement provides a more effective way for cortical feedback to provide the increased center-surround antagonism seen in experiments both for flashing spots and, even more prominently, for patch gratings. This implies that ON-center RCs receive direct excitation from OFF-dominated cortical cells and indirect inhibitory feedback from ON-dominated cortical cells. The increased center-surround antagonism in the model is accompanied by spatial focusing, i.e., the maximum RC response occurs for smaller stimuli when feedback is present. PMID:29377888

SPANNER: A Self-Repairing Spiking Neural Network Hardware Architecture.

PubMed

Liu, Junxiu; Harkin, Jim; Maguire, Liam P; McDaid, Liam J; Wade, John J

2018-04-01

Recent research has shown that a glial cell of astrocyte underpins a self-repair mechanism in the human brain, where spiking neurons provide direct and indirect feedbacks to presynaptic terminals. These feedbacks modulate the synaptic transmission probability of release (PR). When synaptic faults occur, the neuron becomes silent or near silent due to the low PR of synapses; whereby the PRs of remaining healthy synapses are then increased by the indirect feedback from the astrocyte cell. In this paper, a novel hardware architecture of Self-rePAiring spiking Neural NEtwoRk (SPANNER) is proposed, which mimics this self-repairing capability in the human brain. This paper demonstrates that the hardware can self-detect and self-repair synaptic faults without the conventional components for the fault detection and fault repairing. Experimental results show that SPANNER can maintain the system performance with fault densities of up to 40%, and more importantly SPANNER has only a 20% performance degradation when the self-repairing architecture is significantly damaged at a fault density of 80%.

Attributions of the "causes" of group performance as an alternative explanation of the relationship between organizational citizenship behavior and organizational performance.

PubMed

Bachrach, D G; Bendoly, E; Podsakoff, P M

2001-12-01

The purpose of this study was to examine the possibility that feedback regarding team performance may influence team members' reports of organizational citizenship behaviors. Ninety-five teams of business students (N = 412) participated in a labor-scheduling simulation over a local area network. Teams were provided with false negative, false positive, or neutral feedback regarding their performance. Results support the hypothesis that the perception of 2 forms of organizational citizenship behavior (helping behavior and civic virtue) in work groups may, in part. be a function of the nature of the performance feedback that group members receive. However, negative feedback appears to play a more critical role than positive feedback in this attributional process. Possible reasons for these findings, as well as their implications, are discussed.

Synchronization in node of complex networks consist of complex chaotic system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wei, Qiang, E-mail: qiangweibeihua@163.com; Digital Images Processing Institute of Beihua University, BeiHua University, Jilin, 132011, Jilin; Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024

2014-07-15

A new synchronization method is investigated for node of complex networks consists of complex chaotic system. When complex networks realize synchronization, different component of complex state variable synchronize up to different scaling complex function by a designed complex feedback controller. This paper change synchronization scaling function from real field to complex field for synchronization in node of complex networks with complex chaotic system. Synchronization in constant delay and time-varying coupling delay complex networks are investigated, respectively. Numerical simulations are provided to show the effectiveness of the proposed method.

Electronic neural networks for global optimization

NASA Technical Reports Server (NTRS)

Thakoor, A. P.; Moopenn, A. W.; Eberhardt, S.

1990-01-01

An electronic neural network with feedback architecture, implemented in analog custom VLSI is described. Its application to problems of global optimization for dynamic assignment is discussed. The convergence properties of the neural network hardware are compared with computer simulation results. The neural network's ability to provide optimal or near optimal solutions within only a few neuron time constants, a speed enhancement of several orders of magnitude over conventional search methods, is demonstrated. The effect of noise on the circuit dynamics and the convergence behavior of the neural network hardware is also examined.

A fast, robust and tunable synthetic gene oscillator.

PubMed

Stricker, Jesse; Cookson, Scott; Bennett, Matthew R; Mather, William H; Tsimring, Lev S; Hasty, Jeff

2008-11-27

One defining goal of synthetic biology is the development of engineering-based approaches that enable the construction of gene-regulatory networks according to 'design specifications' generated from computational modelling. This approach provides a systematic framework for exploring how a given regulatory network generates a particular phenotypic behaviour. Several fundamental gene circuits have been developed using this approach, including toggle switches and oscillators, and these have been applied in new contexts such as triggered biofilm development and cellular population control. Here we describe an engineered genetic oscillator in Escherichia coli that is fast, robust and persistent, with tunable oscillatory periods as fast as 13 min. The oscillator was designed using a previously modelled network architecture comprising linked positive and negative feedback loops. Using a microfluidic platform tailored for single-cell microscopy, we precisely control environmental conditions and monitor oscillations in individual cells through multiple cycles. Experiments reveal remarkable robustness and persistence of oscillations in the designed circuit; almost every cell exhibited large-amplitude fluorescence oscillations throughout observation runs. The oscillatory period can be tuned by altering inducer levels, temperature and the media source. Computational modelling demonstrates that the key design principle for constructing a robust oscillator is a time delay in the negative feedback loop, which can mechanistically arise from the cascade of cellular processes involved in forming a functional transcription factor. The positive feedback loop increases the robustness of the oscillations and allows for greater tunability. Examination of our refined model suggested the existence of a simplified oscillator design without positive feedback, and we construct an oscillator strain confirming this computational prediction.

Evolving the NCSA CyberCollaboratory for Distributed Environmental Observatory Networks

NASA Astrophysics Data System (ADS)

Myers, J.; Liu, Y.; Minsker, B.; Futrelle, J.; Downey, S.; Kim, I.; Rantanen, E.

2007-12-01

Since 2004, NCSA's Cybercollaboratory, which is built on top of the open source Liferay portal framework, has been evolving as part of NCSA's efforts to build national cyberinfrastructure to support collaborative research in environmental engineering and hydrological sciences and allow users to efficiently share contents (sensors, data, model, documents, etc.) in a context-sensitive way (e.g., providing different tools/data based on group affiliation and geospatial contexts). During this period, we provided the CyberCollaboratory to users in CLEANER (Collaborative Large-scale Engineering Analysis Network for Environmental Research, now WATer and Environmental Research Systems (WATERS) network) Project Office and several CLEANER /WATERS testbed projects. Preliminary statistics shows that one in four users (among over 400 registered users) provided contents with many other reading/accessing those contents (such as messages, documents, wikis). During the course of this use, and in evaluation by others including representatives from the CUAHSI (Consortium of Universities for the Advancement of Hydrologic Science) community, we have received significant feedback on issues of usability and suitability to various communities involved in environmental observatories. Much of this feedback applies to collaborative portals in general and some reflect a comparison of portals with newer Web 2.0 style social -networking sites. For example, users working in multiple groups found it difficult to get an overview of all of their activities and found differences in group layouts to be confusing. Users also found the standard account creation and group management processes cumbersome compared to inviting people to be friends on social sites and wanted a better sense of presence and social networks within the portal. The fragmentation of group documents between local stores, the portal document repository and email, and issues of "lost updates" was another significant concern. This poster reviews the usability feedback, identifies key issues that hinder traditional portal-based collaboration environments, and presents design changes made to the Cybercollaboratory to address them. Feedback on the effectiveness of the new design from hydrologists and environmental researchers and preliminary results from a formal usability study will also be presented.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kerns, Q.A.; Jackson, G.; Kerns, C.R.

This paper describes the damper design for 6 proton on 6 pbar bunches in the Tevatron collider. Signal pickup, transient phase detection, derivative networks, and phase correction via the high-level rf are covered. Each rf station is controlled by a slow feedback loop. In addition, global feedback loops control each set of four cavities, one set for protons and one set for antiprotons. Operational experience with these systems is discussed. 7 refs., 9 figs.

What Affective Neuroscience Means for Science Of Consciousness

PubMed Central

Almada, Leonardo Ferreira; Pereira, Alfredo; Carrara-Augustenborg, Claudia

2013-01-01

The field of affective neuroscience has emerged from the efforts of Jaak Panksepp in the 1990s and reinforced by the work of, among others, Joseph LeDoux in the 2000s. It is based on the ideas that affective processes are supported by brain structures that appeared earlier in the phylogenetic scale (as the periaqueductal gray area), they run in parallel with cognitive processes, and can influence behaviour independently of cognitive judgements. This kind of approach contrasts with the hegemonic concept of conscious processing in cognitive neurosciences, which is based on the identification of brain circuits responsible for the processing of (cognitive) representations. Within cognitive neurosciences, the frontal lobes are assigned the role of coordinators in maintaining affective states and their emotional expressions under cognitive control. An intermediary view is the Damasio-Bechara Somatic Marker model, which puts cognition under partial somatic-affective control. We present here our efforts to make a synthesis of these views, by proposing the existence of two interacting brain circuits; the first one in charge of cognitive processes and the second mediating feelings about cognitive contents. The coupling of the two circuits promotes an endogenous feedback that supports conscious processes. Within this framework, we present the defence that detailed study of both affective and cognitive processes, their interactions, as well of their respective brain networks, is necessary for a science of consciousness. PMID:23678246

Enhancement of GABA release through endogenous activation of axonal GABA(A) receptors in juvenile cerebellum.

PubMed

Trigo, Federico F; Chat, Mireille; Marty, Alain

2007-11-14

Recent evidence indicates the presence of presynaptic GABA(A) receptors (GABA(A)Rs) in the axon domain of several classes of central neurons, including cerebellar basket and stellate cells. Here, we investigate the possibility that these receptors could be activated in the absence of electrical or chemical stimulation. We find that low concentrations of GABA increase the frequency of miniature GABAergic synaptic currents. Submaximal concentrations of a GABA(A)R blocker, gabazine, decrease both the miniature current frequency and the probability of evoked GABA release. Zolpidem, an agonist of the benzodiazepine binding site, and NO-711 (1-[2-[[(diphenylmethylene)imino]oxy]ethyl]-1,2,5,6-tetrahydro-3-pyridinecarboxylic acid hydrochloride), a blocker of GABA uptake, both increase the frequency of miniature currents. These effects occur up to postnatal day 14, but not later. Immunohistochemistry indicates the presence of alpha1-containing GABA(A)Rs in interneuron presynaptic terminals with a similar age dependence. We conclude that, under resting conditions, axonal GABA(A)Rs are significantly activated, that this activation results in enhanced GABA release, and that it can be augmented by increasing the affinity of GABA(A)Rs or reducing GABA uptake. Our findings suggest the existence of a positive-feedback mechanism involving presynaptic GABA(A)Rs that maintains a high release rate and a high local GABA concentration in the immature cerebellar network.

Adaptive Robust Output Feedback Control for a Marine Dynamic Positioning System Based on a High-Gain Observer.

PubMed

Du, Jialu; Hu, Xin; Liu, Hongbo; Chen, C L Philip

2015-11-01

This paper develops an adaptive robust output feedback control scheme for dynamically positioned ships with unavailable velocities and unknown dynamic parameters in an unknown time-variant disturbance environment. The controller is designed by incorporating the high-gain observer and radial basis function (RBF) neural networks in vectorial backstepping method. The high-gain observer provides the estimations of the ship position and heading as well as velocities. The RBF neural networks are employed to compensate for the uncertainties of ship dynamics. The adaptive laws incorporating a leakage term are designed to estimate the weights of RBF neural networks and the bounds of unknown time-variant environmental disturbances. In contrast to the existing results of dynamic positioning (DP) controllers, the proposed control scheme relies only on the ship position and heading measurements and does not require a priori knowledge of the ship dynamics and external disturbances. By means of Lyapunov functions, it is theoretically proved that our output feedback controller can control a ship's position and heading to the arbitrarily small neighborhood of the desired target values while guaranteeing that all signals in the closed-loop DP control system are uniformly ultimately bounded. Finally, simulations involving two ships are carried out, and simulation results demonstrate the effectiveness of the proposed control scheme.

Some Problems of Queues with Feedback.

DTIC Science & Technology

1978-11-01

occur in comr,uter networks , production networks, Street traffic networks , neural networks , and the like . In spite of these potential applications...55— - - - ~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~ 29 b where h — ~ j ~ . Now, caking the Lap lace —Stieltjes transform of the above k

Attention and predictions: control of spatial attention beyond the endogenous-exogenous dichotomy

PubMed Central

Macaluso, Emiliano; Doricchi, Fabrizio

2013-01-01

The mechanisms of attention control have been extensively studied with a variety of methodologies in animals and in humans. Human studies using non-invasive imaging techniques highlighted a remarkable difference between the pattern of responses in dorsal fronto-parietal regions vs. ventral fronto-parietal (vFP) regions, primarily lateralized to the right hemisphere. Initially, this distinction at the neuro-physiological level has been related to the distinction between cognitive processes associated with strategic/endogenous vs. stimulus-driven/exogenous of attention control. Nonetheless, quite soon it has become evident that, in almost any situation, attention control entails a complex combination of factors related to both the current sensory input and endogenous aspects associated with the experimental context. Here, we review several of these aspects first discussing the joint contribution of endogenous and stimulus-driven factors during spatial orienting in complex environments and, then, turning to the role of expectations and predictions in spatial re-orienting. We emphasize that strategic factors play a pivotal role for the activation of the ventral system during stimulus-driven control, and that the dorsal system makes use of stimulus-driven signals for top-down control. We conclude that both the dorsal and the vFP networks integrate endogenous and exogenous signals during spatial attention control and that future investigations should manipulate both these factors concurrently, so as to reveal to full extent of these interactions. PMID:24155707

Frontostriatal anatomical connections predict age- and difficulty-related differences in reinforcement learning.

PubMed

van de Vijver, Irene; Ridderinkhof, K Richard; Harsay, Helga; Reneman, Liesbeth; Cavanagh, James F; Buitenweg, Jessika I V; Cohen, Michael X

2016-10-01

Reinforcement learning (RL) is supported by a network of striatal and frontal cortical structures that are connected through white-matter fiber bundles. With age, the integrity of these white-matter connections declines. The role of structural frontostriatal connectivity in individual and age-related differences in RL is unclear, although local white-matter density and diffusivity have been linked to individual differences in RL. Here we show that frontostriatal tract counts in young human adults (aged 18-28), as assessed noninvasively with diffusion-weighted magnetic resonance imaging and probabilistic tractography, positively predicted individual differences in RL when learning was difficult (70% valid feedback). In older adults (aged 63-87), in contrast, learning under both easy (90% valid feedback) and difficult conditions was predicted by tract counts in the same frontostriatal network. Furthermore, network-level analyses showed a double dissociation between the task-relevant networks in young and older adults, suggesting that older adults relied on different frontostriatal networks than young adults to obtain the same task performance. These results highlight the importance of successful information integration across striatal and frontal regions during RL, especially with variable outcomes. Copyright © 2016 Elsevier Inc. All rights reserved.

Feedback control stabilization of critical dynamics via resource transport on multilayer networks: How glia enable learning dynamics in the brain

NASA Astrophysics Data System (ADS)

Virkar, Yogesh S.; Shew, Woodrow L.; Restrepo, Juan G.; Ott, Edward

2016-10-01

Learning and memory are acquired through long-lasting changes in synapses. In the simplest models, such synaptic potentiation typically leads to runaway excitation, but in reality there must exist processes that robustly preserve overall stability of the neural system dynamics. How is this accomplished? Various approaches to this basic question have been considered. Here we propose a particularly compelling and natural mechanism for preserving stability of learning neural systems. This mechanism is based on the global processes by which metabolic resources are distributed to the neurons by glial cells. Specifically, we introduce and study a model composed of two interacting networks: a model neural network interconnected by synapses that undergo spike-timing-dependent plasticity; and a model glial network interconnected by gap junctions that diffusively transport metabolic resources among the glia and, ultimately, to neural synapses where they are consumed. Our main result is that the biophysical constraints imposed by diffusive transport of metabolic resources through the glial network can prevent runaway growth of synaptic strength, both during ongoing activity and during learning. Our findings suggest a previously unappreciated role for glial transport of metabolites in the feedback control stabilization of neural network dynamics during learning.

Querying graphs in protein-protein interactions networks using feedback vertex set.

PubMed

Blin, Guillaume; Sikora, Florian; Vialette, Stéphane

2010-01-01

Recent techniques increase rapidly the amount of our knowledge on interactions between proteins. The interpretation of these new information depends on our ability to retrieve known substructures in the data, the Protein-Protein Interactions (PPIs) networks. In an algorithmic point of view, it is an hard task since it often leads to NP-hard problems. To overcome this difficulty, many authors have provided tools for querying patterns with a restricted topology, i.e., paths or trees in PPI networks. Such restriction leads to the development of fixed parameter tractable (FPT) algorithms, which can be practicable for restricted sizes of queries. Unfortunately, Graph Homomorphism is a W[1]-hard problem, and hence, no FPT algorithm can be found when patterns are in the shape of general graphs. However, Dost et al. gave an algorithm (which is not implemented) to query graphs with a bounded treewidth in PPI networks (the treewidth of the query being involved in the time complexity). In this paper, we propose another algorithm for querying pattern in the shape of graphs, also based on dynamic programming and the color-coding technique. To transform graphs queries into trees without loss of informations, we use feedback vertex set coupled to a node duplication mechanism. Hence, our algorithm is FPT for querying graphs with a bounded size of their feedback vertex set. It gives an alternative to the treewidth parameter, which can be better or worst for a given query. We provide a python implementation which allows us to validate our implementation on real data. Especially, we retrieve some human queries in the shape of graphs into the fly PPI network.

Rooting Theories of Plant Community Ecology in Microbial Interactions

PubMed Central

Bever, James D.; Dickie, Ian A.; Facelli, Evelina; Facelli, Jose M.; Klironomos, John; Moora, Mari; Rillig, Matthias C.; Stock, William D.; Tibbett, Mark; Zobel, Martin

2010-01-01

Predominant frameworks for understanding plant ecology have an aboveground bias that neglects soil micro-organisms. This is inconsistent with recent work illustrating the importance of soil microbes in terrestrial ecology. Microbial effects have been incorporated into plant community dynamics using ideas of niche modification and plant-soil community feedbacks. Here, we expand and integrate qualitative conceptual models of plant niche and feedback to explore implications of microbial interactions for understanding plant community ecology. At the same time we review the empirical evidence for these processes. We also consider common mycorrhizal networks, and suggest these are best interpreted within the feedback framework. Finally, we apply our integrated model of niche and feedback to understanding plant coexistence, monodominance, and invasion ecology. PMID:20557974

Multi-function robots with speech interaction and emotion feedback

NASA Astrophysics Data System (ADS)

Wang, Hongyu; Lou, Guanting; Ma, Mengchao

2018-03-01

Nowadays, the service robots have been applied in many public circumstances; however, most of them still don’t have the function of speech interaction, especially the function of speech-emotion interaction feedback. To make the robot more humanoid, Arduino microcontroller was used in this study for the speech recognition module and servo motor control module to achieve the functions of the robot’s speech interaction and emotion feedback. In addition, W5100 was adopted for network connection to achieve information transmission via Internet, providing broad application prospects for the robot in the area of Internet of Things (IoT).

Homophily through Nonreciprocity: Results of an Experiment

ERIC Educational Resources Information Center

Schaefer, David R.

2012-01-01

This study outlines a new explanation for homophily in social networks that is neither intended nor imposed by constraints on partner choices. Rather, homophily is an endogenous product of the emergent exchange process, in which actors seek high-value partners who reciprocate their gestures. Whereas all actors initially direct exchange toward…

Delayed excitatory and inhibitory feedback shape neural information transmission

NASA Astrophysics Data System (ADS)

Chacron, Maurice J.; Longtin, André; Maler, Leonard

2005-11-01

Feedback circuitry with conduction and synaptic delays is ubiquitous in the nervous system. Yet the effects of delayed feedback on sensory processing of natural signals are poorly understood. This study explores the consequences of delayed excitatory and inhibitory feedback inputs on the processing of sensory information. We show, through numerical simulations and theory, that excitatory and inhibitory feedback can alter the firing frequency response of stochastic neurons in opposite ways by creating dynamical resonances, which in turn lead to information resonances (i.e., increased information transfer for specific ranges of input frequencies). The resonances are created at the expense of decreased information transfer in other frequency ranges. Using linear response theory for stochastically firing neurons, we explain how feedback signals shape the neural transfer function for a single neuron as a function of network size. We also find that balanced excitatory and inhibitory feedback can further enhance information tuning while maintaining a constant mean firing rate. Finally, we apply this theory to in vivo experimental data from weakly electric fish in which the feedback loop can be opened. We show that it qualitatively predicts the observed effects of inhibitory feedback. Our study of feedback excitation and inhibition reveals a possible mechanism by which optimal processing may be achieved over selected frequency ranges.

The correlated network of acupuncture effect: a functional connectivity study.

PubMed

Qin, Wei; Tian, Jie; Pan, Xiaohong; Yang, Lin; Zhen, Zonglei

2006-01-01

A functional connectivity, which are temporally correlated in functionally related brain regions, before and after acupuncture manipulation was measured by MRI. Amygdala, as the control system of endogenetic analgesia, was selected for "seed" point. We found that compelling similarity existed in the network of resting state before and after acupuncture manipulation. A paired student t-test was implemented to investigate under the different conditions. The main difference was found in the limbic system, brainstem and cerebellum. We conclude that the default endogenous analgesia functional network exists in human brain at a low level, and it could be increased to a higher level by acupuncture modulation.

Optimal Signal Processing in Small Stochastic Biochemical Networks

PubMed Central

Ziv, Etay; Nemenman, Ilya; Wiggins, Chris H.

2007-01-01

We quantify the influence of the topology of a transcriptional regulatory network on its ability to process environmental signals. By posing the problem in terms of information theory, we do this without specifying the function performed by the network. Specifically, we study the maximum mutual information between the input (chemical) signal and the output (genetic) response attainable by the network in the context of an analytic model of particle number fluctuations. We perform this analysis for all biochemical circuits, including various feedback loops, that can be built out of 3 chemical species, each under the control of one regulator. We find that a generic network, constrained to low molecule numbers and reasonable response times, can transduce more information than a simple binary switch and, in fact, manages to achieve close to the optimal information transmission fidelity. These high-information solutions are robust to tenfold changes in most of the networks' biochemical parameters; moreover they are easier to achieve in networks containing cycles with an odd number of negative regulators (overall negative feedback) due to their decreased molecular noise (a result which we derive analytically). Finally, we demonstrate that a single circuit can support multiple high-information solutions. These findings suggest a potential resolution of the “cross-talk” phenomenon as well as the previously unexplained observation that transcription factors that undergo proteolysis are more likely to be auto-repressive. PMID:17957259

Big words, halved brains and small worlds: complex brain networks of figurative language comprehension.

PubMed

Arzouan, Yossi; Solomon, Sorin; Faust, Miriam; Goldstein, Abraham

2011-04-27

Language comprehension is a complex task that involves a wide network of brain regions. We used topological measures to qualify and quantify the functional connectivity of the networks used under various comprehension conditions. To that aim we developed a technique to represent functional networks based on EEG recordings, taking advantage of their excellent time resolution in order to capture the fast processes that occur during language comprehension. Networks were created by searching for a specific causal relation between areas, the negative feedback loop, which is ubiquitous in many systems. This method is a simple way to construct directed graphs using event-related activity, which can then be analyzed topologically. Brain activity was recorded while subjects read expressions of various types and indicated whether they found them meaningful. Slightly different functional networks were obtained for event-related activity evoked by each expression type. The differences reflect the special contribution of specific regions in each condition and the balance of hemispheric activity involved in comprehending different types of expressions and are consistent with the literature in the field. Our results indicate that representing event-related brain activity as a network using a simple temporal relation, such as the negative feedback loop, to indicate directional connectivity is a viable option for investigation which also derives new information about aspects not reflected in the classical methods for investigating brain activity.

An Adaptive Data Gathering Scheme for Multi-Hop Wireless Sensor Networks Based on Compressed Sensing and Network Coding.

PubMed

Yin, Jun; Yang, Yuwang; Wang, Lei

2016-04-01

Joint design of compressed sensing (CS) and network coding (NC) has been demonstrated to provide a new data gathering paradigm for multi-hop wireless sensor networks (WSNs). By exploiting the correlation of the network sensed data, a variety of data gathering schemes based on NC and CS (Compressed Data Gathering--CDG) have been proposed. However, these schemes assume that the sparsity of the network sensed data is constant and the value of the sparsity is known before starting each data gathering epoch, thus they ignore the variation of the data observed by the WSNs which are deployed in practical circumstances. In this paper, we present a complete design of the feedback CDG scheme where the sink node adaptively queries those interested nodes to acquire an appropriate number of measurements. The adaptive measurement-formation procedure and its termination rules are proposed and analyzed in detail. Moreover, in order to minimize the number of overall transmissions in the formation procedure of each measurement, we have developed a NP-complete model (Maximum Leaf Nodes Minimum Steiner Nodes--MLMS) and realized a scalable greedy algorithm to solve the problem. Experimental results show that the proposed measurement-formation method outperforms previous schemes, and experiments on both datasets from ocean temperature and practical network deployment also prove the effectiveness of our proposed feedback CDG scheme.

Resonant spatiotemporal learning in large random recurrent networks.

PubMed

Daucé, Emmanuel; Quoy, Mathias; Doyon, Bernard

2002-09-01

Taking a global analogy with the structure of perceptual biological systems, we present a system composed of two layers of real-valued sigmoidal neurons. The primary layer receives stimulating spatiotemporal signals, and the secondary layer is a fully connected random recurrent network. This secondary layer spontaneously displays complex chaotic dynamics. All connections have a constant time delay. We use for our experiments a Hebbian (covariance) learning rule. This rule slowly modifies the weights under the influence of a periodic stimulus. The effect of learning is twofold: (i) it simplifies the secondary-layer dynamics, which eventually stabilizes to a periodic orbit; and (ii) it connects the secondary layer to the primary layer, and realizes a feedback from the secondary to the primary layer. This feedback signal is added to the incoming signal, and matches it (i.e., the secondary layer performs a one-step prediction of the forthcoming stimulus). After learning, a resonant behavior can be observed: the system resonates with familiar stimuli, which activates a feedback signal. In particular, this resonance allows the recognition and retrieval of partial signals, and dynamic maintenance of the memory of past stimuli. This resonance is highly sensitive to the temporal relationships and to the periodicity of the presented stimuli. When we present stimuli which do not match in time or space, the feedback remains silent. The number of different stimuli for which resonant behavior can be learned is analyzed. As with Hopfield networks, the capacity is proportional to the size of the second, recurrent layer. Moreover, the high capacity displayed allows the implementation of our model on real-time systems interacting with their environment. Such an implementation is reported in the case of a simple behavior-based recognition task on a mobile robot. Finally, we present some functional analogies with biological systems in terms of autonomy and dynamic binding, and present some hypotheses on the computational role of feedback connections.

The rice YABBY1 gene is involved in the feedback regulation of gibberellin metabolism.

PubMed

Dai, Mingqiu; Zhao, Yu; Ma, Qian; Hu, Yongfeng; Hedden, Peter; Zhang, Qifa; Zhou, Dao-Xiu

2007-05-01

Gibberellin (GA) biosynthesis is regulated by feedback control providing a mechanism for GA homeostasis in plants. However, regulatory elements involved in the feedback control are not known. In this report, we show that a rice (Oryza sativa) YABBY1 (YAB1) gene had a similar expression pattern as key rice GA biosynthetic genes GA3ox2 and GA20ox2. Overexpression of YAB1 in transgenic rice resulted in a semidwarf phenotype that could be fully rescued by applied GA. Quantification of the endogenous GA content revealed increases of GA(20) and decreases of GA(1) levels in the overexpression plants, in which the transcripts of the biosynthetic gene GA3ox2 were decreased. Cosuppression of YAB1 in transgenic plants induced expression of GA3ox2. The repression of GA3ox2 could be obtained upon treatment by dexamethasone of transgenic plants expressing a YAB1-glucocorticoid receptor fusion. Importantly, we show that YAB1 bound to a GA-responsive element within the GA3ox2 promoter. In addition, the expression of YAB1 was deregulated in GA biosynthesis and signaling mutants and could be either transiently induced by GA or repressed by a GA inhibitor. Finally, either overexpression or cosuppression of YAB1 impaired GA-mediated repression of GA3ox2. These data together suggest that YAB1 is involved in the feedback regulation of GA biosynthesis in rice.

Social Dynamics within Electronic Networks of Practice

ERIC Educational Resources Information Center

Mattson, Thomas A., Jr.

2013-01-01

Electronic networks of practice (eNoP) are special types of electronic social structures focused on discussing domain-specific problems related to a skill-based craft or profession in question and answer style forums. eNoP have implemented peer-to-peer feedback systems in order to motivate future contributions and to distinguish contribution…

Systemic risk in multiplex networks with asymmetric coupling and threshold feedback

NASA Astrophysics Data System (ADS)

Burkholz, Rebekka; Leduc, Matt V.; Garas, Antonios; Schweitzer, Frank

2016-06-01

We study cascades on a two-layer multiplex network, with asymmetric feedback that depends on the coupling strength between the layers. Based on an analytical branching process approximation, we calculate the systemic risk measured by the final fraction of failed nodes on a reference layer. The results are compared with the case of a single layer network that is an aggregated representation of the two layers. We find that systemic risk in the two-layer network is smaller than in the aggregated one only if the coupling strength between the two layers is small. Above a critical coupling strength, systemic risk is increased because of the mutual amplification of cascades in the two layers. We even observe sharp phase transitions in the cascade size that are less pronounced on the aggregated layer. Our insights can be applied to a scenario where firms decide whether they want to split their business into a less risky core business and a more risky subsidiary business. In most cases, this may lead to a drastic increase of systemic risk, which is underestimated in an aggregated approach.

The Persuasive Effect of Social Network Feedback on Mediated Communication: A Case Study in a Real Organization.

PubMed

Varotto, Alessandra; Gamberini, Luciano; Spagnolli, Anna; Martino, Francesco; Giovannardi, Isabella

2016-03-01

This study focuses on social feedback, namely on information on the outcome of users' online activity indirectly generated by other users, and investigates in a real setting whether it can affect subsequent activity and, if so, whether participants are aware of that. SkyPas, an application that calculates, transmits, and displays social feedback, was embedded in a common instant messaging service (Skype(™)) and used during a 7-week trial by 24 office workers at a large business organization. The trial followed an ABA scheme in which the B phase was the feedback provision phase. Results show that social feedback affects users' communication activity (participation, inward communication, outward communication, and reciprocity), sometimes even after the feedback provision phase. At the same time, users were poorly aware of this effect, showing a discrepancy between self-reported and observational measures. These results are then discussed in terms of design transparency and task compatibility.

Metabolomic homeostasis shifts after callus formation and shoot regeneration in tomato

PubMed Central

Kumari, Alka; Ray, Kamalika; Sadhna, Sadhna; Pandey, Arun Kumar; Sreelakshmi, Yellamaraju; Sharma, Rameshwar

2017-01-01

Plants can regenerate from a variety of tissues on culturing in appropriate media. However, the metabolic shifts involved in callus formation and shoot regeneration are largely unknown. The metabolic profiles of callus generated from tomato (Solanum lycopersicum) cotyledons and that of shoot regenerated from callus were compared with the pct1-2 mutant that exhibits enhanced polar auxin transport and the shr mutant that exhibits elevated nitric oxide levels. The transformation from cotyledon to callus involved a major shift in metabolite profiles with denser metabolic networks in the callus. In contrast, the transformation from callus to shoot involved minor changes in the networks. The metabolic networks in pct1-2 and shr mutants were distinct from wild type and were rewired with shifts in endogenous hormones and metabolite interactions. The callus formation was accompanied by a reduction in the levels of metabolites involved in cell wall lignification and cellular immunity. On the contrary, the levels of monoamines were upregulated in the callus and regenerated shoot. The callus formation and shoot regeneration were accompanied by an increase in salicylic acid in wild type and mutants. The transformation to the callus and also to the shoot downregulated LST8 and upregulated TOR transcript levels indicating a putative linkage between metabolic shift and TOR signalling pathway. The network analysis indicates that shift in metabolite profiles during callus formation and shoot regeneration is governed by a complex interaction between metabolites and endogenous hormones. PMID:28481937

Analyzing the interactions of mRNAs, miRNAs, lncRNAs and circRNAs to predict competing endogenous RNA networks in glioblastoma.

PubMed

Yuan, Yang; Jiaoming, Li; Xiang, Wang; Yanhui, Liu; Shu, Jiang; Maling, Gou; Qing, Mao

2018-05-01

Cross-talk between competitive endogenous RNAs (ceRNAs) may play a critical role in revealing potential mechanisms of tumor development and physiology. Glioblastoma is the most common type of malignant primary brain tumor, and the mechanisms of tumor genesis and development in glioblastoma are unclear. Here, to investigate the role of non-coding RNAs and the ceRNA network in glioblastoma, we performed paired-end RNA sequencing and microarray analyses to obtain the expression profiles of mRNAs, lncRNAs, circRNAs and miRNAs. We identified that the expression of 501 lncRNAs, 1999 mRNAs, 2038 circRNAs and 143 miRNAs were often altered between glioblastoma and matched normal brain tissue. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed on these differentially expressed mRNAs and miRNA-mediated target genes of lncRNAs and circRNAs. Furthermore, we used a multi-step computational framework and several bioinformatics methods to construct a ceRNA network combining mRNAs, miRNAs, lncRNAs and circRNA, based on co-expression analysis between the differentially expressed RNAs. We identified that plenty of lncRNAs, CircRNAs and their downstream target genes in the ceRNA network are related to glutamatergic synapse, suggesting that glutamate metabolism is involved in glioma biological functions. Our results will accelerate the understanding of tumorigenesis, cancer progression and even therapeutic targeting in glioblastoma.

A mathematical model of the mevalonate cholesterol biosynthesis pathway.

PubMed

Pool, Frances; Currie, Richard; Sweby, Peter K; Salazar, José Domingo; Tindall, Marcus J

2018-04-14

We formulate, parameterise and analyse a mathematical model of the mevalonate pathway, a key pathway in the synthesis of cholesterol. Of high clinical importance, the pathway incorporates rate limiting enzymatic reactions with multiple negative feedbacks. In this work we investigate the pathway dynamics and demonstrate that rate limiting steps and negative feedbacks within it act in concert to tightly regulate intracellular cholesterol levels. Formulated using the theory of nonlinear ordinary differential equations and parameterised in the context of a hepatocyte, the governing equations are analysed numerically and analytically. Sensitivity and mathematical analysis demonstrate the importance of the two rate limiting enzymes 3-hydroxy-3-methylglutaryl-CoA reductase and squalene synthase in controlling the concentration of substrates within the pathway as well as that of cholesterol. The role of individual feedbacks, both global (between that of cholesterol and sterol regulatory element-binding protein 2; SREBP-2) and local internal (between substrates in the pathway) are investigated. We find that whilst the cholesterol SREBP-2 feedback regulates the overall system dynamics, local feedbacks activate within the pathway to tightly regulate the overall cellular cholesterol concentration. The network stability is analysed by constructing a reduced model of the full pathway and is shown to exhibit one real, stable steady-state. We close by addressing the biological question as to how farnesyl-PP levels are affected by CYP51 inhibition, and demonstrate that the regulatory mechanisms within the network work in unison to ensure they remain bounded. Copyright © 2018 Elsevier Ltd. All rights reserved.

Song decrystallization in adult zebra finches does not require the song nucleus NIf.

PubMed

Roy, Arani; Mooney, Richard

2009-08-01

In adult male zebra finches, transecting the vocal nerve causes previously stable (i.e., crystallized) song to slowly degrade, presumably because of the resulting distortion in auditory feedback. How and where distorted feedback interacts with song motor networks to induce this process of song decrystallization remains unknown. The song premotor nucleus HVC is a potential site where auditory feedback signals could interact with song motor commands. Although the forebrain nucleus interface of the nidopallium (NIf) appears to be the primary auditory input to HVC, NIf lesions made in adult zebra finches do not trigger song decrystallization. One possibility is that NIf lesions do not interfere with song maintenance, but do compromise the adult zebra finch's ability to express renewed vocal plasticity in response to feedback perturbations. To test this idea, we bilaterally lesioned NIf and then transected the vocal nerve in adult male zebra finches. We found that bilateral NIf lesions did not prevent nerve section-induced song decrystallization. To test the extent to which the NIf lesions disrupted auditory processing in the song system, we made in vivo extracellular recordings in HVC and a downstream anterior forebrain pathway (AFP) in NIf-lesioned birds. We found strong and selective auditory responses to the playback of the birds' own song persisted in HVC and the AFP following NIf lesions. These findings suggest that auditory inputs to the song system other than NIf, such as the caudal mesopallium, could act as a source of auditory feedback signals to the song motor network.

Feedforward-Feedback Hybrid Control for Magnetic Shape Memory Alloy Actuators Based on the Krasnosel'skii-Pokrovskii Model

PubMed Central

Zhou, Miaolei; Zhang, Qi; Wang, Jingyuan

2014-01-01

As a new type of smart material, magnetic shape memory alloy has the advantages of a fast response frequency and outstanding strain capability in the field of microdrive and microposition actuators. The hysteresis nonlinearity in magnetic shape memory alloy actuators, however, limits system performance and further application. Here we propose a feedforward-feedback hybrid control method to improve control precision and mitigate the effects of the hysteresis nonlinearity of magnetic shape memory alloy actuators. First, hysteresis nonlinearity compensation for the magnetic shape memory alloy actuator is implemented by establishing a feedforward controller which is an inverse hysteresis model based on Krasnosel'skii-Pokrovskii operator. Secondly, the paper employs the classical Proportion Integration Differentiation feedback control with feedforward control to comprise the hybrid control system, and for further enhancing the adaptive performance of the system and improving the control accuracy, the Radial Basis Function neural network self-tuning Proportion Integration Differentiation feedback control replaces the classical Proportion Integration Differentiation feedback control. Utilizing self-learning ability of the Radial Basis Function neural network obtains Jacobian information of magnetic shape memory alloy actuator for the on-line adjustment of parameters in Proportion Integration Differentiation controller. Finally, simulation results show that the hybrid control method proposed in this paper can greatly improve the control precision of magnetic shape memory alloy actuator and the maximum tracking error is reduced from 1.1% in the open-loop system to 0.43% in the hybrid control system. PMID:24828010

Feedforward-feedback hybrid control for magnetic shape memory alloy actuators based on the Krasnosel'skii-Pokrovskii model.

PubMed

Zhou, Miaolei; Zhang, Qi; Wang, Jingyuan

2014-01-01

As a new type of smart material, magnetic shape memory alloy has the advantages of a fast response frequency and outstanding strain capability in the field of microdrive and microposition actuators. The hysteresis nonlinearity in magnetic shape memory alloy actuators, however, limits system performance and further application. Here we propose a feedforward-feedback hybrid control method to improve control precision and mitigate the effects of the hysteresis nonlinearity of magnetic shape memory alloy actuators. First, hysteresis nonlinearity compensation for the magnetic shape memory alloy actuator is implemented by establishing a feedforward controller which is an inverse hysteresis model based on Krasnosel'skii-Pokrovskii operator. Secondly, the paper employs the classical Proportion Integration Differentiation feedback control with feedforward control to comprise the hybrid control system, and for further enhancing the adaptive performance of the system and improving the control accuracy, the Radial Basis Function neural network self-tuning Proportion Integration Differentiation feedback control replaces the classical Proportion Integration Differentiation feedback control. Utilizing self-learning ability of the Radial Basis Function neural network obtains Jacobian information of magnetic shape memory alloy actuator for the on-line adjustment of parameters in Proportion Integration Differentiation controller. Finally, simulation results show that the hybrid control method proposed in this paper can greatly improve the control precision of magnetic shape memory alloy actuator and the maximum tracking error is reduced from 1.1% in the open-loop system to 0.43% in the hybrid control system.

Physical constraints on biological integral control design for homeostasis and sensory adaptation.

PubMed

Ang, Jordan; McMillen, David R

2013-01-22

Synthetic biology includes an effort to use design-based approaches to create novel controllers, biological systems aimed at regulating the output of other biological processes. The design of such controllers can be guided by results from control theory, including the strategy of integral feedback control, which is central to regulation, sensory adaptation, and long-term robustness. Realization of integral control in a synthetic network is an attractive prospect, but the nature of biochemical networks can make the implementation of even basic control structures challenging. Here we present a study of the general challenges and important constraints that will arise in efforts to engineer biological integral feedback controllers or to analyze existing natural systems. Constraints arise from the need to identify target output values that the combined process-plus-controller system can reach, and to ensure that the controller implements a good approximation of integral feedback control. These constraints depend on mild assumptions about the shape of input-output relationships in the biological components, and thus will apply to a variety of biochemical systems. We summarize our results as a set of variable constraints intended to provide guidance for the design or analysis of a working biological integral feedback controller. Copyright © 2013 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Structures of the Bacillus subtilis Glutamine Synthetase Dodecamer Reveal Large Intersubunit Catalytic Conformational Changes Linked to a Unique Feedback Inhibition Mechanism*

PubMed Central

Murray, David S.; Chinnam, Nagababu; Tonthat, Nam Ky; Whitfill, Travis; Wray, Lewis V.; Fisher, Susan H.; Schumacher, Maria A.

2013-01-01

Glutamine synthetase (GS), which catalyzes the production of glutamine, plays essential roles in nitrogen metabolism. There are two main bacterial GS isoenzymes, GSI-α and GSI-β. GSI-α enzymes, which have not been structurally characterized, are uniquely feedback-inhibited by Gln. To gain insight into GSI-α function, we performed biochemical and cellular studies and obtained structures for all GSI-α catalytic and regulatory states. GSI-α forms a massive 600-kDa dodecameric machine. Unlike other characterized GS, the Bacillus subtilis enzyme undergoes dramatic intersubunit conformational alterations during formation of the transition state. Remarkably, these changes are required for active site construction. Feedback inhibition arises from a hydrogen bond network between Gln, the catalytic glutamate, and the GSI-α-specific residue, Arg62, from an adjacent subunit. Notably, Arg62 must be ejected for proper active site reorganization. Consistent with these findings, an R62A mutation abrogates Gln feedback inhibition but does not affect catalysis. Thus, these data reveal a heretofore unseen restructuring of an enzyme active site that is coupled with an isoenzyme-specific regulatory mechanism. This GSI-α-specific regulatory network could be exploited for inhibitor design against Gram-positive pathogens. PMID:24158439

Regulation of the ErbB network by the MIG6 feedback loop in physiology, tumor suppression and responses to oncogene-targeted therapeutics.

PubMed

Anastasi, Sergio; Lamberti, Dante; Alemà, Stefano; Segatto, Oreste

2016-02-01

The ErbB signaling network instructs the execution of key cellular programs, such as cell survival, proliferation and motility, through the generation of robust signals of defined strength and duration. In contrast, unabated ErbB signaling disrupts tissue homeostasis and leads to cell transformation. Cells oppose the threat inherent in excessive ErbB activity through several mechanisms of negative feedback regulation. Inducible feedback inhibitors (IFIs) are expressed in the context of transcriptional responses triggered by ErbB signaling, thus being uniquely suited to regulate ErbB activity during the execution of complex cellular programs. This review focuses on MIG6, an IFI that restrains ErbB signaling by mediating ErbB kinase suppression and receptor down-regulation. We will review key issues in MIG6 function, regulation and tumor suppressor activity. Subsequently, the role for MIG6 loss in the pathogenesis of tumors driven by ErbB oncogenes as well as in the generation of cellular addiction to ErbB signaling will be discussed. We will conclude by analyzing feedback inhibition by MIG6 in the context of therapies directed against ErbB and non-ErbB oncogenes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Tactile Feedback Display with Spatial and Temporal Resolutions

PubMed Central

Vishniakou, Siarhei; Lewis, Brian W.; Niu, Xiaofan; Kargar, Alireza; Sun, Ke; Kalajian, Michael; Park, Namseok; Yang, Muchuan; Jing, Yi; Brochu, Paul; Sun, Zhelin; Li, Chun; Nguyen, Truong; Pei, Qibing; Wang, Deli

2013-01-01

We report the electronic recording of the touch contact and pressure using an active matrix pressure sensor array made of transparent zinc oxide thin-film transistors and tactile feedback display using an array of diaphragm actuators made of an interpenetrating polymer elastomer network. Digital replay, editing and manipulation of the recorded touch events were demonstrated with both spatial and temporal resolutions. Analog reproduction of the force is also shown possible using the polymer actuators, despite of the high driving voltage. The ability to record, store, edit, and replay touch information adds an additional dimension to digital technologies and extends the capabilities of modern information exchange with the potential to revolutionize physical learning, social networking, e-commerce, robotics, gaming, medical and military applications. PMID:23982053

Tactile feedback display with spatial and temporal resolutions.

PubMed

Vishniakou, Siarhei; Lewis, Brian W; Niu, Xiaofan; Kargar, Alireza; Sun, Ke; Kalajian, Michael; Park, Namseok; Yang, Muchuan; Jing, Yi; Brochu, Paul; Sun, Zhelin; Li, Chun; Nguyen, Truong; Pei, Qibing; Wang, Deli

2013-01-01

We report the electronic recording of the touch contact and pressure using an active matrix pressure sensor array made of transparent zinc oxide thin-film transistors and tactile feedback display using an array of diaphragm actuators made of an interpenetrating polymer elastomer network. Digital replay, editing and manipulation of the recorded touch events were demonstrated with both spatial and temporal resolutions. Analog reproduction of the force is also shown possible using the polymer actuators, despite of the high driving voltage. The ability to record, store, edit, and replay touch information adds an additional dimension to digital technologies and extends the capabilities of modern information exchange with the potential to revolutionize physical learning, social networking, e-commerce, robotics, gaming, medical and military applications.

Tactile Feedback Display with Spatial and Temporal Resolutions

NASA Astrophysics Data System (ADS)

Vishniakou, Siarhei; Lewis, Brian W.; Niu, Xiaofan; Kargar, Alireza; Sun, Ke; Kalajian, Michael; Park, Namseok; Yang, Muchuan; Jing, Yi; Brochu, Paul; Sun, Zhelin; Li, Chun; Nguyen, Truong; Pei, Qibing; Wang, Deli

2013-08-01

We report the electronic recording of the touch contact and pressure using an active matrix pressure sensor array made of transparent zinc oxide thin-film transistors and tactile feedback display using an array of diaphragm actuators made of an interpenetrating polymer elastomer network. Digital replay, editing and manipulation of the recorded touch events were demonstrated with both spatial and temporal resolutions. Analog reproduction of the force is also shown possible using the polymer actuators, despite of the high driving voltage. The ability to record, store, edit, and replay touch information adds an additional dimension to digital technologies and extends the capabilities of modern information exchange with the potential to revolutionize physical learning, social networking, e-commerce, robotics, gaming, medical and military applications.

[Effect of vinegar-processed Curcumae Rhizoma on bile metabolism in rats].

PubMed

Gu, Wei; Lu, Tu-Lin; Li, Jin-Ci; Wang, Qiao-Han; Pan, Zi-Hao; Ji, De; Li, Lin; Zhang, Ji; Mao, Chun-Qin

2016-04-01

To explore the effect of vinegar-processed Curcumae Rhizoma on endogenous metabolites in bile by investigating the endogenous metabolites difference in bile before and after Curcumae Rhizoma was processed with vinegar. Alcohol extracts of crude and vinegar-processed Curcumae Rhizoma, as well as normal saline were prepared respectively, which were then given to the rats by intragastric administration for 0.5 h. Then common bile duct intubation drainage was conducted to collect 12 h bile of the rats. UPLC-TOF-MS analysis of bile samples was applied after 1∶3 acetonitrile protein precipitation; unidimensional statistics were combined with multivariate statistics and PeakView software was compared with network database to identify the potential biomarkers. Vinegar-processed Curcumae Rhizoma extracts had significant effects on metabolites spectrum in bile of the rats. With the boundaries of P<0.05, 13 metabolites with significant differences were found in bile of crude and vinegar-processed Curcumae Rhizoma groups, and 8 of them were identified when considering the network database. T-test unidimensional statistical analysis was applied between administration groups and blank group to obtain 7 metabolites with significant differences and identify them as potential biomarkers. 6 of the potential biomarkers were up-regulated in vinegar-processed group, which were related to the metabolism regulation of phospholipid metabolism, fat metabolism, bile acid metabolism, and N-acylethanolamine hydrolysis reaction balance, indicating the mechanism of vinegar-processed Curcumae Rhizoma on endogenous metabolites in bile of the rats. Copyright© by the Chinese Pharmaceutical Association.

Modulators of Nucleoside Metabolism in the Therapy of Brain Diseases

PubMed Central

Boison, Detlev

2010-01-01

Nucleoside receptors are known to be important targets for a variety of brain diseases. However, the therapeutic modulation of their endogenous agonists by inhibitors of nucleoside metabolism represents an alternative therapeutic strategy that has gained increasing attention in recent years. Deficiency in endogenous nucleosides, in particular of adenosine, may causally be linked to a variety of neurological diseases and neuropsychiatric conditions ranging from epilepsy and chronic pain to schizophrenia. Consequently, augmentation of nucleoside function by inhibiting their metabolism appears to be a rational therapeutic strategy with distinct advantages: (i) in contrast to specific receptor modulation, the increase (or decrease) of the amount of a nucleoside will affect several signal transduction pathways simultaneously and therefore have the unique potential to modify complex neurochemical networks; (ii) by acting on the network level, inhibitors of nucleoside metabolism are highly suited to fine-tune, restore, or amplify physiological functions of nucleosides; (iii) therefore inhibitors of nucleoside metabolism have promise for the “soft and smart” therapy of neurological diseases with the added advantage of reduced systemic side effects. This review will first highlight the role of nucleoside function and dysfunction in physiological and pathophysiological situations with a particular emphasis on the anticonvulsant, neuroprotective, and antinociceptive roles of adenosine. The second part of this review will cover pharmacological approaches to use inhibitors of nucleoside metabolism, with a special emphasis on adenosine kinase, the key regulator of endogenous adenosine. Finally, novel gene-based therapeutic strategies to inhibit nucleoside metabolism and focal treatment approaches will be discussed. PMID:21401494

Selective attention modulates high-frequency activity in the face-processing network.

PubMed

Müsch, Kathrin; Hamamé, Carlos M; Perrone-Bertolotti, Marcela; Minotti, Lorella; Kahane, Philippe; Engel, Andreas K; Lachaux, Jean-Philippe; Schneider, Till R

2014-11-01

Face processing depends on the orchestrated activity of a large-scale neuronal network. Its activity can be modulated by attention as a function of task demands. However, it remains largely unknown whether voluntary, endogenous attention and reflexive, exogenous attention to facial expressions equally affect all regions of the face-processing network, and whether such effects primarily modify the strength of the neuronal response, the latency, the duration, or the spectral characteristics. We exploited the good temporal and spatial resolution of intracranial electroencephalography (iEEG) and recorded from depth electrodes to uncover the fast dynamics of emotional face processing. We investigated frequency-specific responses and event-related potentials (ERP) in the ventral occipito-temporal cortex (VOTC), ventral temporal cortex (VTC), anterior insula, orbitofrontal cortex (OFC), and amygdala when facial expressions were task-relevant or task-irrelevant. All investigated regions of interest (ROI) were clearly modulated by task demands and exhibited stronger changes in stimulus-induced gamma band activity (50-150 Hz) when facial expressions were task-relevant. Observed latencies demonstrate that the activation is temporally coordinated across the network, rather than serially proceeding along a processing hierarchy. Early and sustained responses to task-relevant faces in VOTC and VTC corroborate their role for the core system of face processing, but they also occurred in the anterior insula. Strong attentional modulation in the OFC and amygdala (300 msec) suggests that the extended system of the face-processing network is only recruited if the task demands active face processing. Contrary to our expectation, we rarely observed differences between fearful and neutral faces. Our results demonstrate that activity in the face-processing network is susceptible to the deployment of selective attention. Moreover, we show that endogenous attention operates along the whole face-processing network, and that these effects are reflected in frequency-specific changes in the gamma band. Copyright © 2014 Elsevier Ltd. All rights reserved.

Native and enzymatically modified wheat (Triticum aestivum L.) endogenous lipids in bread making: a focus on gas cell stabilization mechanisms.

PubMed

Gerits, Lien R; Pareyt, Bram; Masure, Hanne G; Delcour, Jan A

2015-04-01

Lipopan F and Lecitase Ultra lipases were used in straight dough bread making to study how wheat lipids affect bread loaf volume (LV) and crumb structure setting. Lipase effects on LV were dose and dough piece weight dependent. The bread quality improving mechanisms exerted by endogenous lipids were studied in terms of gluten network strengthening, which indirectly stabilizes gas cells, and in terms of direct interfacial gas cell stabilization. Unlike diacetyl tartaric esters of mono- and diacylglycerols (DATEM, used as control), lipase use did not impact dough extensibility. The effect on dough extensibility was therefore related to its lipid composition at the start of mixing. Both lipases and DATEM strongly increase the levels of polar lipids in dough liquor and their availability for and potential accumulation at gas cell interfaces. Lipases form lysolipids that emulsify other lipids. We speculate that DATEM competes with (endogenous) polar lipids for interacting with gluten proteins. Copyright © 2014 Elsevier Ltd. All rights reserved.

Ventral tegmental area orexin 1 receptors promote palatable food intake and oppose postingestive negative feedback.

PubMed

Terrill, Sarah J; Hyde, Kellie M; Kay, Kristen E; Greene, Hayden E; Maske, Calyn B; Knierim, Amanda E; Davis, Jon F; Williams, Diana L

2016-09-01

Hypothalamic orexin neurons project to numerous brain areas, including the ventral tegmental area (VTA), which is involved in motivation and food-seeking behavior. Here we address how exogenously administered orexin-A and endogenous orexin 1 receptor (OX1R) activation in the VTA affects feeding behavior. We hypothesized that orexin-A and OX1R antagonist SB334867 delivered to the VTA, at doses that were subthreshold for effect when injected into the ventricle, would affect intake of palatable foods in multiple test situations. We first used a hedonic feeding model in which satiated rats selectively consume a high-fat diet (HFD). Intra-VTA orexin-A stimulated additional consumption of chow and increased HFD intake in this model. In ad libitum-fed rats given daily 30-min test sessions, intra-VTA orexin-A also increased intake of HFD and 0.1 M sucrose. Further analysis of licking patterns revealed that that VTA orexin-A increased meal size and licking burst size only toward the end of the meal. Consistent with this finding, a subthreshold dose of VTA orexin-A prevented intake suppression induced by gastrointestinal nutrient infusion. Surprisingly, intra-VTA orexin-A had no effect on operant responding for sucrose pellets on a progressive ratio schedule of reinforcement. A role for endogenous VTA OX1R stimulation is supported by our finding that bilateral VTA injection of the selective OX1R antagonist SB334867 suppressed 0.1 M sucrose intake. Together, our data suggest that OX1R activity in the VTA facilitates food intake, potentially by counteracting postingestive negative feedback that would normally suppress feeding later in a meal. Copyright © 2016 the American Physiological Society.

Proteinase-activated receptor-4 evoked colorectal analgesia in mice: an endogenously activated feed-back loop in visceral inflammatory pain.

PubMed

Annaházi, A; Dabek, M; Gecse, K; Salvador-Cartier, C; Polizzi, A; Rosztóczy, A; Róka, R; Theodorou, V; Wittmann, T; Bueno, L; Eutamene, H

2012-01-01

Activation of proteinase-activated receptor-4 (PAR-4) from the colonic lumen has an antinociceptive effect to colorectal distension (CRD) in mice in basal conditions. We aimed to determine the functional localization of the responsible receptors and to test their role in two different hyperalgesia models. Mice received PAR-4 activating peptide (PAR-4-AP, AYPGKF-NH(2)) or vehicle intraperitoneally (IP), and abdominal EMG response to CRD was measured. The next group received PAR-4-AP intracolonically (IC) with or without 2,4,6-triaminopyrimidine, a chemical tight junction blocker, before CRD. The SCID mice were used to test the role of lymphocytes in the antihyperalgesic effect. The effects of PAR-4-AP and PAR-4-antagonist (P4pal-10) were evaluated in water avoidance stress (WAS) model and low grade 2,4,6-trinitrobenzene sulfonic acid (TNBS) colitis. Spinal Fos protein expression was visualized by immunohistochemistry. The antinociceptive effect of PAR-4-AP disappeared when was administrered IP, or with the blockade of colonic epithelial tight junctions, suggesting that PAR-4-AP needs to reach directly the nerve terminals in the colon. The CRD-induced spinal Fos overexpression was reduced by 43% by PAR-4-AP. The PAR-4-AP was antihyperalgesic in both hyperalgesia models and in mice with impaired lymphocytes. The PAR-4-antagonist significantly increased the TNBS, but not the WAS-induced colonic hyperalgesia. The antinociceptive effect of PAR-4-AP depends on its penetration to the colonic mucosa. The PAR-4 activation is endogenously involved as a feedback loop to attenuate inflammatory colonic hyperalgesia to CRD. © 2011 Blackwell Publishing Ltd.

On the improvement of neural cryptography using erroneous transmitted information with error prediction.

PubMed

Allam, Ahmed M; Abbas, Hazem M

2010-12-01

Neural cryptography deals with the problem of "key exchange" between two neural networks using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between the two communicating parties is eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an attacker is capable of synchronizing with any of the two parties during the training process. Therefore, diminishing the probability of such a threat improves the reliability of exchanging the output bits through a public channel. The synchronization with feedback algorithm is one of the existing algorithms that enhances the security of neural cryptography. This paper proposes three new algorithms to enhance the mutual learning process. They mainly depend on disrupting the attacker confidence in the exchanged outputs and input patterns during training. The first algorithm is called "Do not Trust My Partner" (DTMP), which relies on one party sending erroneous output bits, with the other party being capable of predicting and correcting this error. The second algorithm is called "Synchronization with Common Secret Feedback" (SCSFB), where inputs are kept partially secret and the attacker has to train its network on input patterns that are different from the training sets used by the communicating parties. The third algorithm is a hybrid technique combining the features of the DTMP and SCSFB. The proposed approaches are shown to outperform the synchronization with feedback algorithm in the time needed for the parties to synchronize.

Endocannabinoids and stress.

PubMed

Riebe, Caitlin J; Wotjak, Carsten T

2011-07-01

Endogenous cannabinoids play an important role in the physiology and behavioral expression of stress responses. Activation of the hypothalamic-pituitary-adrenal (HPA) axis, including the release of glucocorticoids, is the fundamental hormonal response to stress. Endocannabinoid (eCB) signaling serves to maintain HPA-axis homeostasis, by buffering basal activity as well as by mediating glucocorticoid fast feedback mechanisms. Following chronic stressor exposure, eCBs are also involved in physiological and behavioral habituation processes. Behavioral consequences of stress include fear and stress-induced anxiety as well as memory formation in the context of stress, involving contextual fear conditioning and inhibitory avoidance learning. Chronic stress can also lead to depression-like symptoms. Prominent in these behavioral stress responses is the interaction between eCBs and the HPA-axis. Future directions may differentiate among eCB signaling within various brain structures/neuronal subpopulations as well as between the distinct roles of the endogenous cannabinoid ligands. Investigation into the role of the eCB system in allostatic states and recovery processes may give insight into possible therapeutic manipulations of the system in treating chronic stress-related conditions in humans.

Studies on the mechanism of endogenous pyrogen production. II. Role of cell products in the regulation of pyrogen release from blood leukocytes.

PubMed

Bodel, P

1974-09-01

Some characteristics of the process by which endogenous pyrogen (EP), the mediator of fever, is released from cells were examined by using human blood leukocytes incubated in vitro. Studies were designed to examine a possible role for leukocyte products, including EP, in the induction, augmentation, or suppression of pyrogen release by blood leukocytes. Products of stimulated leukocytes, including a partially purified preparation of EP, did not induce significant activation of nonstimulated cells. Also, no evidence was obtained that stimulated cell products either augment or inhibit pyrogen production by other stimulated cells. A feedback control of EP production was thus not observed. A crude preparation of EP, containing other products of activated cells, maintained its pyrogenicity when incubated at pH 7.4 but not at pH 5.0. These studies thus provide no support for hypothesized control mechanisms regulating production of EP by blood leukocytes. By contrast, local inactivation of EP at inflammatory sites may modify the amount of EP entering the blood, and hence fever.

Studies on the Mechanism of Endogenous Pyrogen Production II. Role of Cell Products in the Regulation of Pyrogen Release from Blood Leukocytes

PubMed Central

Bodel, Phyllis

1974-01-01

Some characteristics of the process by which endogenous pyrogen (EP), the mediator of fever, is released from cells were examined by using human blood leukocytes incubated in vitro. Studies were designed to examine a possible role for leukocyte products, including EP, in the induction, augmentation, or suppression of pyrogen release by blood leukocytes. Products of stimulated leukocytes, including a partially purified preparation of EP, did not induce significant activation of nonstimulated cells. Also, no evidence was obtained that stimulated cell products either augment or inhibit pyrogen production by other stimulated cells. A feedback control of EP production was thus not observed. A crude preparation of EP, containing other products of activated cells, maintained its pyrogenicity when incubated at pH 7.4 but not at pH 5.0. These studies thus provide no support for hypothesized control mechanisms regulating production of EP by blood leukocytes. By contrast, local inactivation of EP at inflammatory sites may modify the amount of EP entering the blood, and hence fever. PMID:4426696

Positive And Negative Feedback Loops Coupled By Common Transcription Activator And Repressor

NASA Astrophysics Data System (ADS)

Sielewiesiuk, Jan; Łopaciuk, Agata

2015-03-01

Dynamical systems consisting of two interlocked loops with negative and positive feedback have been studied using the linear analysis of stability and numerical solutions. Conditions for saddle-node bifurcation were formulated in a general form. Conditions for Hopf bifurcations were found in a few symmetrical cases. Auto-oscillations, when they exist, are generated by the negative feedback repressive loop. This loop determines the frequency and amplitude of oscillations. The positive feedback loop of activation slightly modifies the oscillations. Oscillations are possible when the difference between Hilll's coefficients of the repression and activation is sufficiently high. The highly cooperative activation loop with a fast turnover slows down or even makes the oscillations impossible. The system under consideration can constitute a component of epigenetic or enzymatic regulation network.

Networks to Strengthen Health Systems for Chronic Disease Prevention

PubMed Central

Riley, Barbara L.; Herbert, Carol P.; Best, Allan

2013-01-01

Interorganizational networks that harness the priorities, capacities, and skills of various agencies and individuals have emerged as useful approaches for strengthening preventive services in public health systems. We use examples from the Canadian Heart Health Initiative and Alberta’s Primary Care Networks to illustrate characteristics of networks, describe the limitations of existing frameworks for assessing the performance of prevention-oriented networks, and propose a research agenda for guiding future efforts to improve the performance of these initiatives. Prevention-specific assessment strategies that capture relevant aspects of network performance need to be identified, and feedback mechanisms are needed that make better use of these data to drive change in network activities. PMID:24028225

Development of an Integrated Team Training Design and Assessment Architecture to Support Adaptability in Healthcare Teams

DTIC Science & Technology

2016-10-01

and implementation of embedded, adaptive feedback and performance assessment. The investigators also initiated work designing a Bayesian Belief ...training; Teamwork; Adaptive performance; Leadership; Simulation; Modeling; Bayesian belief networks (BBN) 16. SECURITY CLASSIFICATION OF: 17. LIMITATION...Trauma teams Team training Teamwork Adaptability Adaptive performance Leadership Simulation Modeling Bayesian belief networks (BBN) 6

Supporting More Inclusive Learning with Social Networking: A Case Study of Blended Socialised Design Education

ERIC Educational Resources Information Center

Rodrigo, Russell; Nguyen, Tam

2013-01-01

This paper presents a qualitative case study of socialised blended learning, using a social network platform to investigate the level of literacies and interactions of students in a blended learning environment of traditional face-to-face design studio and online participatory teaching. Using student and staff feedback, the paper examines the use…

Ethylene contributes to mir1-mediated maize defense against the phloem-sap sucking insect Rhopalosiphum maidis.

USDA-ARS?s Scientific Manuscript database

Signaling networks among multiple phytohormones fine-tune plant defense responses to insect herbivore attack. Previously, it was reported that the synergistic combination of ethylene (ET) and jasmonic acid (JA) was required for providing maize insect resistance1 (mir1), a key endogenous defense sign...

Water Diplomacy: A Synthesis of Explicit and Tacit Water Information to Create Actionable Knowledge

NASA Astrophysics Data System (ADS)

Islam, S.; Moomaw, W.; Portney, K.; Reed, M.; Vogel, R. M.; Water Diplomacy

2011-12-01

Water issues are complex because they cross multiple boundaries and involve various stakeholders with competing needs. The origin of many water issues is a dynamic consequence of competition and feedback among variables in the natural, societal and political domains. Together, these interactions generate what we call water networks. As population growth, economic development and climate change impose pressures on finite water resources, management of these water networks becomes crucial. Science alone is not sufficient; nor can policy-making that does not take science into account yield sustainable management solutions. Rather, sustainable solutions may only be found through a diplomatic or negotiated approach that simultaneously takes science, policy, and politics into account. Water issues need to be understood as the product of competition, interconnection, and feedback among variables in the Natural and Societal Domains (NSDs). Within the natural domain: water quantity (Q), water quality (P), and ecosystem (E) constrain and define network dynamics. While in the societal domain, interactions among culture and values (V), assets (C), and governance and institutions (G) create complex contextual differences in the network. These six NSD variables constitute the nodes of a water network while interactions and feedback among natural, societal and political forces define the complexity of a network. The knowledge needed to resolve water conflicts and to manage water networks effectively must extend beyond scientific assessment that ignore societal variables (C, G, and V) or treat them as exogenous, and beyond policy analysis that does not consider the impact of natural variables (E, P, and Q) and the couplings among them. Many water conflicts arise when NSD variables, and the networks they define, are mismanaged. These networks are open-ended systems that cross boundaries (physical, disciplinary, and jurisdictional ) and change continuously; thus, efforts to manage them assuming that they have fixed boundaries , or can be optimized with scientific objectivity without properly accounting for contextual differences, are likely to fail. Once water conflicts are framed properly, the tools of joint fact-finding and collaborative problem-solving can be used to negotiate solutions that are both adaptive and enforceable. We will use AquaPedia - a growing knowledge base of water issues from across the world - to demonstrate the utility of this synthesis of explicit and tacit knowledge in addressing water problems and creating actionable knowledge.

New results for global exponential synchronization in neural networks via functional differential inclusions.

PubMed

Wang, Dongshu; Huang, Lihong; Tang, Longkun

2015-08-01

This paper is concerned with the synchronization dynamical behaviors for a class of delayed neural networks with discontinuous neuron activations. Continuous and discontinuous state feedback controller are designed such that the neural networks model can realize exponential complete synchronization in view of functional differential inclusions theory, Lyapunov functional method and inequality technique. The new proposed results here are very easy to verify and also applicable to neural networks with continuous activations. Finally, some numerical examples show the applicability and effectiveness of our main results.

Finite-time synchronization of uncertain coupled switched neural networks under asynchronous switching.

PubMed

Wu, Yuanyuan; Cao, Jinde; Li, Qingbo; Alsaedi, Ahmed; Alsaadi, Fuad E

2017-01-01

This paper deals with the finite-time synchronization problem for a class of uncertain coupled switched neural networks under asynchronous switching. By constructing appropriate Lyapunov-like functionals and using the average dwell time technique, some sufficient criteria are derived to guarantee the finite-time synchronization of considered uncertain coupled switched neural networks. Meanwhile, the asynchronous switching feedback controller is designed to finite-time synchronize the concerned networks. Finally, two numerical examples are introduced to show the validity of the main results. Copyright © 2016 Elsevier Ltd. All rights reserved.

Dynamics in microbial communities: Unraveling mechanisms to identify principles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Konopka, Allan; Lindemann, Stephen R.; Fredrickson, Jim K.

2015-07-01

Diversity begets higher order properties such as functional stability and robustness in microbial communities, but principles that inform conceptual (and eventually predictive) models of community dynamics are lacking. Recent work has shown that selection as well as dispersal and drift shape communities, but the mechanistic bases for assembly of communities and the forces that maintain their function in the face of environmental perturbation are not well understood. Conceptually, some interactions among community members could generate endogenous dynamics in composition, even in the absence of environmental changes. These endogenous dynamics are further perturbed by exogenous forcing factors to produce a richermore » network of community interactions, and it is this “system” that is the basis for higher order community properties. Elucidation of principles that follow from this conceptual model requires identifying the mechanisms that (a) optimize diversity within a community and (b) impart community stability. The network of interactions between organisms can be an important element by providing a buffer against disturbance beyond the effect of functional redundancy, as alternative pathways with different combinations of microbes can be recruited to fulfill specific functions.« less

Identification of Potential Prostate Cancer-Related Pseudogenes Based on Competitive Endogenous RNA Network Hypothesis.

PubMed

Jiang, Tao; Guo, Junjie; Hu, Zhongchun; Zhao, Ming; Gu, Zhenggang; Miao, Shu

2018-06-20

BACKGROUND Long noncoding RNAs (lncRNAs) have been revealed to function as competing endogenous RNAs (ceRNAs), which can seclude the common microRNAs (miRNAs) and hence prevent the miRNAs from binding to their ancestral gene. Nonetheless, the role of lncRNA-mediated ceRNAs in prostate cancer has not yet been elucidated. MATERIAL AND METHODS Using The Cancer Genome Atlas (TCGA) database, lncRNA, miRNA, and mRNA profiles from 499 prostate cancer tissues and 52 normal prostate tissues were analyzed with the R package "DESeq" to identify the differentially expressed RNAs. GO and KEGG pathway analyses were performed using "DAVID6.8" and R packages "Clusterprofile." The ceRNA network in prostate cancer was constructed using miRDB, miRTarBase, and TargetScan databases. Survival analysis was performed with Kaplan-Meier analysis. RESULTS A total of 376 lncRNAs, 33 miRNAs, and 687 mRNAs were identified as significant factors in tumorigenesis. Based on the hypothesis that the ceRNA network (lncRNA-miRNA-mRNA regulatory axis) is involved in prostate cancer and forms competitive interrelations between miRNA and mRNA or lncRNA, we constructed a ceRNA network that included 23 lncRNAs, 6 miRNAs, and 2 mRNAs that were differentially expressed in prostate cancer. Only 3 lncRNAs (LINC00308, LINC00355, and OSTN-AS1) had a significant association with survival (P<0.05). The 3 prostate cancer-specific lncRNA were validated in prostate cancer cell lines PC3 and DU145 using qRT-PCR. CONCLUSIONS We demonstrated the differential lncRNA expression profiles in prostate cancer, which provides new insights for future studies of the ceRNA network and its regulatory mechanisms in prostate cancer.

Posttranscriptional control of neuronal development by microRNA networks.

PubMed

Gao, Fen-Biao

2008-01-01

The proper development of the nervous system requires precise spatial and temporal control of gene expression at both the transcriptional and translational levels. In different experimental model systems, microRNAs (miRNAs) - a class of small, endogenous, noncoding RNAs that control the translation and stability of many mRNAs - are emerging as important regulators of various aspects of neuronal development. Further dissection of the in vivo physiological functions of individual miRNAs promises to offer novel mechanistic insights into the gene regulatory networks that ensure the precise assembly of a functional nervous system.

Acute Stress Modulates Feedback Processing in Men and Women: Differential Effects on the Feedback-Related Negativity and Theta and Beta Power

PubMed Central

Banis, Stella; Geerligs, Linda; Lorist, Monicque M.

2014-01-01

Sex-specific prevalence rates in mental and physical disorders may be partly explained by sex differences in physiological stress responses. Neural networks that might be involved are those underlying feedback processing. Aim of the present EEG study was to investigate whether acute stress alters feedback processing, and whether stress effects differ between men and women. Male and female participants performed a gambling task, in a control and a stress condition. Stress was induced by exposing participants to a noise stressor. Brain activity was analyzed using both event-related potential and time-frequency analyses, measuring the feedback-related negativity (FRN) and feedback-related changes in theta and beta oscillatory power, respectively. While the FRN and feedback-related theta power were similarly affected by stress induction in both sexes, feedback-related beta power depended on the combination of stress induction condition and sex. FRN amplitude and theta power increases were smaller in the stress relative to the control condition in both sexes, demonstrating that acute noise stress impairs performance monitoring irrespective of sex. However, in the stress but not in the control condition, early lower beta-band power increases were larger for men than women, indicating that stress effects on feedback processing are partly sex-dependent. Our findings suggest that sex-specific effects on feedback processing may comprise a factor underlying sex-specific stress responses. PMID:24755943

Feedback associated with expectation for larger-reward improves visuospatial working memory performances in children with ADHD.

PubMed

Hammer, Rubi; Tennekoon, Michael; Cooke, Gillian E; Gayda, Jessica; Stein, Mark A; Booth, James R

2015-08-01

We tested the interactive effect of feedback and reward on visuospatial working memory in children with ADHD. Seventeen boys with ADHD and 17 Normal Control (NC) boys underwent functional magnetic resonance imaging (fMRI) while performing four visuospatial 2-back tasks that required monitoring the spatial location of letters presented on a display. Tasks varied in reward size (large; small) and feedback availability (no-feedback; feedback). While the performance of NC boys was high in all conditions, boys with ADHD exhibited higher performance (similar to those of NC boys) only when they received feedback associated with large-reward. Performance pattern in both groups was mirrored by neural activity in an executive function neural network comprised of few distinct frontal brain regions. Specifically, neural activity in the left and right middle frontal gyri of boys with ADHD became normal-like only when feedback was available, mainly when feedback was associated with large-reward. When feedback was associated with small-reward, or when large-reward was expected but feedback was not available, boys with ADHD exhibited altered neural activity in the medial orbitofrontal cortex and anterior insula. This suggests that contextual support normalizes activity in executive brain regions in children with ADHD, which results in improved working memory. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Dynamic Mesoscale Land-Atmosphere Feedbacks in Fragmented Forests in Amazonia

NASA Astrophysics Data System (ADS)

Rastogi, D.; Baidya Roy, S.

2011-12-01

This paper investigates land-atmosphere feedbacks in disturbed rainforests of Amazonia. Deforestation along the rapidly expanding highways and road network has created the unique fishbone land cover pattern in Rondonia, a state in southwestern Amazonia. Numerical experiments and observations show that sharp gradients in land cover due to the fishbone heterogeneity triggers mesoscale circulations. These circulations significantly change the spatial pattern of local hydrometeorology, especially convection, clouds and precipitation. The primary research question now is can these changes in local hydrometeorology affect vegetation growth in the clearings. If so, that would be a clear indication that land-atmosphere feedbacks can affect vegetation recovery in fragmented forests. A computationally-efficient modeling tool consisting of a mesoscale atmospheric model dynamically coupled with a plant growth model has been specifically developed to identify the atmospheric feedback pathways. Preliminary experiments focus on the seasonal-scale feedbacks during the dry season. Results show that temperature, incoming shortwave and precipitation are the three primary drivers through which the feedbacks operate. Increasing temperature increases respiratory losses generating a positive feedback. Increased cloud cover reduces incoming PAR and photosynthesis, resulting in a positive feedback. Increased precipitation reduces water stress and promotes growth resulting in a negative feedback. The net effect is a combination of these 3 feedback loops. These findings can significantly improve our understanding of ecosystem resiliency in disturbed tropical forests.

Fiber distributed feedback laser

NASA Technical Reports Server (NTRS)

Elachi, C.; Evans, G. A.; Yeh, C. (Inventor)

1976-01-01

Utilizing round optical fibers as communication channels in optical communication networks presents the problem of obtaining a high efficiency coupling between the optical fiber and the laser. A laser is made an integral part of the optical fiber channel by either diffusing active material into the optical fiber or surrounding the optical fiber with the active material. Oscillation within the active medium to produce lasing action is established by grating the optical fiber so that distributed feedback occurs.

Nonlinear filter based decision feedback equalizer for optical communication systems.

PubMed

Han, Xiaoqi; Cheng, Chi-Hao

2014-04-07

Nonlinear impairments in optical communication system have become a major concern of optical engineers. In this paper, we demonstrate that utilizing a nonlinear filter based Decision Feedback Equalizer (DFE) with error detection capability can deliver a better performance compared with the conventional linear filter based DFE. The proposed algorithms are tested in simulation using a coherent 100 Gb/sec 16-QAM optical communication system in a legacy optical network setting.

Algorithm for Training a Recurrent Multilayer Perceptron

NASA Technical Reports Server (NTRS)

Parlos, Alexander G.; Rais, Omar T.; Menon, Sunil K.; Atiya, Amir F.

2004-01-01

An improved algorithm has been devised for training a recurrent multilayer perceptron (RMLP) for optimal performance in predicting the behavior of a complex, dynamic, and noisy system multiple time steps into the future. [An RMLP is a computational neural network with self-feedback and cross-talk (both delayed by one time step) among neurons in hidden layers]. Like other neural-network-training algorithms, this algorithm adjusts network biases and synaptic-connection weights according to a gradient-descent rule. The distinguishing feature of this algorithm is a combination of global feedback (the use of predictions as well as the current output value in computing the gradient at each time step) and recursiveness. The recursive aspect of the algorithm lies in the inclusion of the gradient of predictions at each time step with respect to the predictions at the preceding time step; this recursion enables the RMLP to learn the dynamics. It has been conjectured that carrying the recursion to even earlier time steps would enable the RMLP to represent a noisier, more complex system.

Regulation of Dynamical Systems to Optimal Solutions of Semidefinite Programs: Algorithms and Applications to AC Optimal Power Flow

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall'Anese, Emiliano; Dhople, Sairaj V.; Giannakis, Georgios B.

2015-07-01

This paper considers a collection of networked nonlinear dynamical systems, and addresses the synthesis of feedback controllers that seek optimal operating points corresponding to the solution of pertinent network-wide optimization problems. Particular emphasis is placed on the solution of semidefinite programs (SDPs). The design of the feedback controller is grounded on a dual e-subgradient approach, with the dual iterates utilized to dynamically update the dynamical-system reference signals. Global convergence is guaranteed for diminishing stepsize rules, even when the reference inputs are updated at a faster rate than the dynamical-system settling time. The application of the proposed framework to the controlmore » of power-electronic inverters in AC distribution systems is discussed. The objective is to bridge the time-scale separation between real-time inverter control and network-wide optimization. Optimization objectives assume the form of SDP relaxations of prototypical AC optimal power flow problems.« less

Emergence and robustness of target waves in a neuronal network

NASA Astrophysics Data System (ADS)

Xu, Ying; Jin, Wuyin; Ma, Jun

2015-08-01

Target waves in excitable media such as neuronal network can regulate the spatial distribution and orderliness as a continuous pacemaker. Three different schemes are used to develop stable target wave in the network, and the potential mechanism for emergence of target waves in the excitable media is investigated. For example, a local pacing driven by external periodical forcing can generate stable target wave in the excitable media, furthermore, heterogeneity and local feedback under self-feedback coupling are also effective to generate continuous target wave as well. To discern the difference of these target waves, a statistical synchronization factor is defined by using mean field theory and artificial defects are introduced into the network to block the target wave, thus the robustness of these target waves could be detected. However, these target waves developed from the above mentioned schemes show different robustness to the blocking from artificial defects. A regular network of Hindmarsh-Rose neurons is designed in a two-dimensional square array, target waves are induced by using three different ways, and then some artificial defects, which are associated with anatomical defects, are set in the network to detect the effect of defects blocking on the travelling waves. It confirms that the robustness of target waves to defects blocking depends on the intrinsic properties (ways to generate target wave) of target waves.

Chaos and Robustness in a Single Family of Genetic Oscillatory Networks

PubMed Central

Fu, Daniel; Tan, Patrick; Kuznetsov, Alexey; Molkov, Yaroslav I.

2014-01-01

Genetic oscillatory networks can be mathematically modeled with delay differential equations (DDEs). Interpreting genetic networks with DDEs gives a more intuitive understanding from a biological standpoint. However, it presents a problem mathematically, for DDEs are by construction infinitely-dimensional and thus cannot be analyzed using methods common for systems of ordinary differential equations (ODEs). In our study, we address this problem by developing a method for reducing infinitely-dimensional DDEs to two- and three-dimensional systems of ODEs. We find that the three-dimensional reductions provide qualitative improvements over the two-dimensional reductions. We find that the reducibility of a DDE corresponds to its robustness. For non-robust DDEs that exhibit high-dimensional dynamics, we calculate analytic dimension lines to predict the dependence of the DDEs’ correlation dimension on parameters. From these lines, we deduce that the correlation dimension of non-robust DDEs grows linearly with the delay. On the other hand, for robust DDEs, we find that the period of oscillation grows linearly with delay. We find that DDEs with exclusively negative feedback are robust, whereas DDEs with feedback that changes its sign are not robust. We find that non-saturable degradation damps oscillations and narrows the range of parameter values for which oscillations exist. Finally, we deduce that natural genetic oscillators with highly-regular periods likely have solely negative feedback. PMID:24667178

A Simplified Algorithm for Statistical Investigation of Damage Spreading

NASA Astrophysics Data System (ADS)

Gecow, Andrzej

2009-04-01

On the way to simulating adaptive evolution of complex system describing a living object or human developed project, a fitness should be defined on node states or network external outputs. Feedbacks lead to circular attractors of these states or outputs which make it difficult to define a fitness. The main statistical effects of adaptive condition are the result of small change tendency and to appear, they only need a statistically correct size of damage initiated by evolutionary change of system. This observation allows to cut loops of feedbacks and in effect to obtain a particular statistically correct state instead of a long circular attractor which in the quenched model is expected for chaotic network with feedback. Defining fitness on such states is simple. We calculate only damaged nodes and only once. Such an algorithm is optimal for investigation of damage spreading i.e. statistical connections of structural parameters of initial change with the size of effected damage. It is a reversed-annealed method—function and states (signals) may be randomly substituted but connections are important and are preserved. The small damages important for adaptive evolution are correctly depicted in comparison to Derrida annealed approximation which expects equilibrium levels for large networks. The algorithm indicates these levels correctly. The relevant program in Pascal, which executes the algorithm for a wide range of parameters, can be obtained from the author.

Pluripotency gene network dynamics: System views from parametric analysis.

PubMed

Akberdin, Ilya R; Omelyanchuk, Nadezda A; Fadeev, Stanislav I; Leskova, Natalya E; Oschepkova, Evgeniya A; Kazantsev, Fedor V; Matushkin, Yury G; Afonnikov, Dmitry A; Kolchanov, Nikolay A

2018-01-01

Multiple experimental data demonstrated that the core gene network orchestrating self-renewal and differentiation of mouse embryonic stem cells involves activity of Oct4, Sox2 and Nanog genes by means of a number of positive feedback loops among them. However, recent studies indicated that the architecture of the core gene network should also incorporate negative Nanog autoregulation and might not include positive feedbacks from Nanog to Oct4 and Sox2. Thorough parametric analysis of the mathematical model based on this revisited core regulatory circuit identified that there are substantial changes in model dynamics occurred depending on the strength of Oct4 and Sox2 activation and molecular complexity of Nanog autorepression. The analysis showed the existence of four dynamical domains with different numbers of stable and unstable steady states. We hypothesize that these domains can constitute the checkpoints in a developmental progression from naïve to primed pluripotency and vice versa. During this transition, parametric conditions exist, which generate an oscillatory behavior of the system explaining heterogeneity in expression of pluripotent and differentiation factors in serum ESC cultures. Eventually, simulations showed that addition of positive feedbacks from Nanog to Oct4 and Sox2 leads mainly to increase of the parametric space for the naïve ESC state, in which pluripotency factors are strongly expressed while differentiation ones are repressed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gecow, Andrzej

On the way to simulating adaptive evolution of complex system describing a living object or human developed project, a fitness should be defined on node states or network external outputs. Feedbacks lead to circular attractors of these states or outputs which make it difficult to define a fitness. The main statistical effects of adaptive condition are the result of small change tendency and to appear, they only need a statistically correct size of damage initiated by evolutionary change of system. This observation allows to cut loops of feedbacks and in effect to obtain a particular statistically correct state instead ofmore » a long circular attractor which in the quenched model is expected for chaotic network with feedback. Defining fitness on such states is simple. We calculate only damaged nodes and only once. Such an algorithm is optimal for investigation of damage spreading i.e. statistical connections of structural parameters of initial change with the size of effected damage. It is a reversed-annealed method--function and states (signals) may be randomly substituted but connections are important and are preserved. The small damages important for adaptive evolution are correctly depicted in comparison to Derrida annealed approximation which expects equilibrium levels for large networks. The algorithm indicates these levels correctly. The relevant program in Pascal, which executes the algorithm for a wide range of parameters, can be obtained from the author.« less

Distributed force feedback in the spinal cord and the regulation of limb mechanics.

PubMed

Nichols, T Richard

2018-03-01

This review is an update on the role of force feedback from Golgi tendon organs in the regulation of limb mechanics during voluntary movement. Current ideas about the role of force feedback are based on modular circuits linking idealized systems of agonists, synergists, and antagonistic muscles. In contrast, force feedback is widely distributed across the muscles of a limb and cannot be understood based on these circuit motifs. Similarly, muscle architecture cannot be understood in terms of idealized systems, since muscles cross multiple joints and axes of rotation and further influence remote joints through inertial coupling. It is hypothesized that distributed force feedback better represents the complex mechanical interactions of muscles, including the stresses in the musculoskeletal network born by muscle articulations, myofascial force transmission, and inertial coupling. Together with the strains of muscle fascicles measured by length feedback from muscle spindle receptors, this integrated proprioceptive feedback represents the mechanical state of the musculoskeletal system. Within the spinal cord, force feedback has excitatory and inhibitory components that coexist in various combinations based on motor task and integrated with length feedback at the premotoneuronal and motoneuronal levels. It is concluded that, in agreement with other investigators, autogenic, excitatory force feedback contributes to propulsion and weight support. It is further concluded that coexistent inhibitory force feedback, together with length feedback, functions to manage interjoint coordination and the mechanical properties of the limb in the face of destabilizing inertial forces and positive force feedback, as required by the accelerations and changing directions of both predator and prey.

Interaction in Spoken Word Recognition Models: Feedback Helps.

PubMed

Magnuson, James S; Mirman, Daniel; Luthra, Sahil; Strauss, Ted; Harris, Harlan D

2018-01-01

Human perception, cognition, and action requires fast integration of bottom-up signals with top-down knowledge and context. A key theoretical perspective in cognitive science is the interactive activation hypothesis: forward and backward flow in bidirectionally connected neural networks allows humans and other biological systems to approximate optimal integration of bottom-up and top-down information under real-world constraints. An alternative view is that online feedback is neither necessary nor helpful; purely feed forward alternatives can be constructed for any feedback system, and online feedback could not improve processing and would preclude veridical perception. In the domain of spoken word recognition, the latter view was apparently supported by simulations using the interactive activation model, TRACE, with and without feedback: as many words were recognized more quickly without feedback as were recognized faster with feedback, However, these simulations used only a small set of words and did not address a primary motivation for interaction: making a model robust in noise. We conducted simulations using hundreds of words, and found that the majority were recognized more quickly with feedback than without. More importantly, as we added noise to inputs, accuracy and recognition times were better with feedback than without. We follow these simulations with a critical review of recent arguments that online feedback in interactive activation models like TRACE is distinct from other potentially helpful forms of feedback. We conclude that in addition to providing the benefits demonstrated in our simulations, online feedback provides a plausible means of implementing putatively distinct forms of feedback, supporting the interactive activation hypothesis.

Interaction in Spoken Word Recognition Models: Feedback Helps

PubMed Central

Magnuson, James S.; Mirman, Daniel; Luthra, Sahil; Strauss, Ted; Harris, Harlan D.

2018-01-01

Human perception, cognition, and action requires fast integration of bottom-up signals with top-down knowledge and context. A key theoretical perspective in cognitive science is the interactive activation hypothesis: forward and backward flow in bidirectionally connected neural networks allows humans and other biological systems to approximate optimal integration of bottom-up and top-down information under real-world constraints. An alternative view is that online feedback is neither necessary nor helpful; purely feed forward alternatives can be constructed for any feedback system, and online feedback could not improve processing and would preclude veridical perception. In the domain of spoken word recognition, the latter view was apparently supported by simulations using the interactive activation model, TRACE, with and without feedback: as many words were recognized more quickly without feedback as were recognized faster with feedback, However, these simulations used only a small set of words and did not address a primary motivation for interaction: making a model robust in noise. We conducted simulations using hundreds of words, and found that the majority were recognized more quickly with feedback than without. More importantly, as we added noise to inputs, accuracy and recognition times were better with feedback than without. We follow these simulations with a critical review of recent arguments that online feedback in interactive activation models like TRACE is distinct from other potentially helpful forms of feedback. We conclude that in addition to providing the benefits demonstrated in our simulations, online feedback provides a plausible means of implementing putatively distinct forms of feedback, supporting the interactive activation hypothesis. PMID:29666593

Intelligent robust tracking control for a class of uncertain strict-feedback nonlinear systems.

PubMed

Chang, Yeong-Chan

2009-02-01

This paper addresses the problem of designing robust tracking controls for a large class of strict-feedback nonlinear systems involving plant uncertainties and external disturbances. The input and virtual input weighting matrices are perturbed by bounded time-varying uncertainties. An adaptive fuzzy-based (or neural-network-based) dynamic feedback tracking controller will be developed such that all the states and signals of the closed-loop system are bounded and the trajectory tracking error should be as small as possible. First, the adaptive approximators with linearly parameterized models are designed, and a partitioned procedure with respect to the developed adaptive approximators is proposed such that the implementation of the fuzzy (or neural network) basis functions depends only on the state variables but does not depend on the tuning approximation parameters. Furthermore, we extend to design the nonlinearly parameterized adaptive approximators. Consequently, the intelligent robust tracking control schemes developed in this paper possess the properties of computational simplicity and easy implementation. Finally, simulation examples are presented to demonstrate the effectiveness of the proposed control algorithms.

Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.

PubMed

Pan, Yongping; Yu, Haoyong

2017-06-01

This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference trajectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.

Effects of emotional preferences on value-based decision making are mediated by mentalizing not reward networks

PubMed Central

Evans, Simon; Fleming, Stephen M.; Dolan, Raymond J.; Averbeck, Bruno B.

2012-01-01

Real-world decision-making often involves social considerations. Consequently, the social value of stimuli can induce preferences in choice behavior. However, it is unknown how financial and social values are integrated in the brain. Here, we investigated how smiling and angry face stimuli interacted with financial reward feedback in a stochastically-rewarded decision-making task. Subjects reliably preferred the smiling faces despite equivalent reward feedback, demonstrating a socially driven bias. We fit a Bayesian reinforcement learning model to factor the effects of financial rewards and emotion preferences in individual subjects, and regressed model predictions on the trial-by-trial fMRI signal. Activity in the sub-callosal cingulate and the ventral striatum, both involved in reward learning, correlated with financial reward feedback, whereas the differential contribution of social value activated dorsal temporo-parietal junction and dorsal anterior cingulate cortex, previously proposed as components of a mentalizing network. We conclude that the impact of social stimuli on value-based decision processes is mediated by effects in brain regions partially separable from classical reward circuitry. PMID:20946058

Noise in genetic and neural networks

NASA Astrophysics Data System (ADS)

Swain, Peter S.; Longtin, André

2006-06-01

Both neural and genetic networks are significantly noisy, and stochastic effects in both cases ultimately arise from molecular events. Nevertheless, a gulf exists between the two fields, with researchers in one often being unaware of similar work in the other. In this Special Issue, we focus on bridging this gap and present a collection of papers from both fields together. For each field, the networks studied range from just a single gene or neuron to endogenous networks. In this introductory article, we describe the sources of noise in both genetic and neural systems. We discuss the modeling techniques in each area and point out similarities. We hope that, by reading both sets of papers, ideas developed in one field will give insight to scientists from the other and that a common language and methodology will develop.

NETWORK STRUCTURE, MULTIPLEXITY, AND EVOLUTION AS INFLUENCES ON COMMUNITY-BASED PARTICIPATORY INTERVENTIONS.

PubMed

Wang, Rong; Tanjasiri, Sora Park; Palmer, Paula; Valente, Thomas W

2016-08-01

This study applies an ecological perspective to the context of community-based participatory research (CBPR). Specifically, it examines how endogenous and exogenous factors influence the dynamics of CBPR partnerships, including the tendency toward reciprocity and transitivity, the organizational type, the level of resource sufficiency, the level of organizational influence, and the perceived CBPR effect on organizations. The results demonstrate that network structure is related to the selection and retention of interorganizational networks over time, and organizations of the same type are more likely to form partnerships with each other. It shows that the dynamics of the CBPR initiative presented in this article were driven by the structure of the interorganizational networks rather than their individual organizational attributes. Implications for sustaining CBPR partnerships are drawn from the findings.

NETWORK STRUCTURE, MULTIPLEXITY, AND EVOLUTION AS INFLUENCES ON COMMUNITY-BASED PARTICIPATORY INTERVENTIONS

PubMed Central

Wang, Rong; Tanjasiri, Sora Park; Palmer, Paula; Valente, Thomas W.

2017-01-01

This study applies an ecological perspective to the context of community-based participatory research (CBPR). Specifically, it examines how endogenous and exogenous factors influence the dynamics of CBPR partnerships, including the tendency toward reciprocity and transitivity, the organizational type, the level of resource sufficiency, the level of organizational influence, and the perceived CBPR effect on organizations. The results demonstrate that network structure is related to the selection and retention of interorganizational networks over time, and organizations of the same type are more likely to form partnerships with each other. It shows that the dynamics of the CBPR initiative presented in this article were driven by the structure of the interorganizational networks rather than their individual organizational attributes. Implications for sustaining CBPR partnerships are drawn from the findings. PMID:29430067

Network adaptation improves temporal representation of naturalistic stimuli in Drosophila eye: II mechanisms.

PubMed

Nikolaev, Anton; Zheng, Lei; Wardill, Trevor J; O'Kane, Cahir J; de Polavieja, Gonzalo G; Juusola, Mikko

2009-01-01

Retinal networks must adapt constantly to best present the ever changing visual world to the brain. Here we test the hypothesis that adaptation is a result of different mechanisms at several synaptic connections within the network. In a companion paper (Part I), we showed that adaptation in the photoreceptors (R1-R6) and large monopolar cells (LMC) of the Drosophila eye improves sensitivity to under-represented signals in seconds by enhancing both the amplitude and frequency distribution of LMCs' voltage responses to repeated naturalistic contrast series. In this paper, we show that such adaptation needs both the light-mediated conductance and feedback-mediated synaptic conductance. A faulty feedforward pathway in histamine receptor mutant flies speeds up the LMC output, mimicking extreme light adaptation. A faulty feedback pathway from L2 LMCs to photoreceptors slows down the LMC output, mimicking dark adaptation. These results underline the importance of network adaptation for efficient coding, and as a mechanism for selectively regulating the size and speed of signals in neurons. We suggest that concert action of many different mechanisms and neural connections are responsible for adaptation to visual stimuli. Further, our results demonstrate the need for detailed circuit reconstructions like that of the Drosophila lamina, to understand how networks process information.

A scalable strategy for high-throughput GFP tagging of endogenous human proteins.

PubMed

Leonetti, Manuel D; Sekine, Sayaka; Kamiyama, Daichi; Weissman, Jonathan S; Huang, Bo

2016-06-21

A central challenge of the postgenomic era is to comprehensively characterize the cellular role of the ∼20,000 proteins encoded in the human genome. To systematically study protein function in a native cellular background, libraries of human cell lines expressing proteins tagged with a functional sequence at their endogenous loci would be very valuable. Here, using electroporation of Cas9 nuclease/single-guide RNA ribonucleoproteins and taking advantage of a split-GFP system, we describe a scalable method for the robust, scarless, and specific tagging of endogenous human genes with GFP. Our approach requires no molecular cloning and allows a large number of cell lines to be processed in parallel. We demonstrate the scalability of our method by targeting 48 human genes and show that the resulting GFP fluorescence correlates with protein expression levels. We next present how our protocols can be easily adapted for the tagging of a given target with GFP repeats, critically enabling the study of low-abundance proteins. Finally, we show that our GFP tagging approach allows the biochemical isolation of native protein complexes for proteomic studies. Taken together, our results pave the way for the large-scale generation of endogenously tagged human cell lines for the proteome-wide analysis of protein localization and interaction networks in a native cellular context.

Undermining and Strengthening Social Networks through Network Modification

PubMed Central

Mellon, Jonathan; Yoder, Jordan; Evans, Daniel

2016-01-01

Social networks have well documented effects at the individual and aggregate level. Consequently it is often useful to understand how an attempt to influence a network will change its structure and consequently achieve other goals. We develop a framework for network modification that allows for arbitrary objective functions, types of modification (e.g. edge weight addition, edge weight removal, node removal, and covariate value change), and recovery mechanisms (i.e. how a network responds to interventions). The framework outlined in this paper helps both to situate the existing work on network interventions but also opens up many new possibilities for intervening in networks. In particular use two case studies to highlight the potential impact of empirically calibrating the objective function and network recovery mechanisms as well as showing how interventions beyond node removal can be optimised. First, we simulate an optimal removal of nodes from the Noordin terrorist network in order to reduce the expected number of attacks (based on empirically predicting the terrorist collaboration network from multiple types of network ties). Second, we simulate optimally strengthening ties within entrepreneurial ecosystems in six developing countries. In both cases we estimate ERGM models to simulate how a network will endogenously evolve after intervention. PMID:27703198

Undermining and Strengthening Social Networks through Network Modification.

PubMed

Mellon, Jonathan; Yoder, Jordan; Evans, Daniel

2016-10-05

Social networks have well documented effects at the individual and aggregate level. Consequently it is often useful to understand how an attempt to influence a network will change its structure and consequently achieve other goals. We develop a framework for network modification that allows for arbitrary objective functions, types of modification (e.g. edge weight addition, edge weight removal, node removal, and covariate value change), and recovery mechanisms (i.e. how a network responds to interventions). The framework outlined in this paper helps both to situate the existing work on network interventions but also opens up many new possibilities for intervening in networks. In particular use two case studies to highlight the potential impact of empirically calibrating the objective function and network recovery mechanisms as well as showing how interventions beyond node removal can be optimised. First, we simulate an optimal removal of nodes from the Noordin terrorist network in order to reduce the expected number of attacks (based on empirically predicting the terrorist collaboration network from multiple types of network ties). Second, we simulate optimally strengthening ties within entrepreneurial ecosystems in six developing countries. In both cases we estimate ERGM models to simulate how a network will endogenously evolve after intervention.

Undermining and Strengthening Social Networks through Network Modification

NASA Astrophysics Data System (ADS)

Mellon, Jonathan; Yoder, Jordan; Evans, Daniel

2016-10-01

Social networks have well documented effects at the individual and aggregate level. Consequently it is often useful to understand how an attempt to influence a network will change its structure and consequently achieve other goals. We develop a framework for network modification that allows for arbitrary objective functions, types of modification (e.g. edge weight addition, edge weight removal, node removal, and covariate value change), and recovery mechanisms (i.e. how a network responds to interventions). The framework outlined in this paper helps both to situate the existing work on network interventions but also opens up many new possibilities for intervening in networks. In particular use two case studies to highlight the potential impact of empirically calibrating the objective function and network recovery mechanisms as well as showing how interventions beyond node removal can be optimised. First, we simulate an optimal removal of nodes from the Noordin terrorist network in order to reduce the expected number of attacks (based on empirically predicting the terrorist collaboration network from multiple types of network ties). Second, we simulate optimally strengthening ties within entrepreneurial ecosystems in six developing countries. In both cases we estimate ERGM models to simulate how a network will endogenously evolve after intervention.

Synchronization of a Class of Switched Neural Networks with Time-Varying Delays via Nonlinear Feedback Control.

PubMed

Wang, Leimin; Shen, Yi; Zhang, Guodong

2016-10-01

This paper is concerned with the synchronization problem for a class of switched neural networks (SNNs) with time-varying delays. First, a new crucial lemma which includes and extends the classical exponential stability theorem is constructed. Then by using the lemma, new algebraic criteria of ψ -type synchronization (synchronization with general decay rate) for SNNs are established via the designed nonlinear feedback control. The ψ -type synchronization which is in a general framework is obtained by introducing a ψ -type function. It contains exponential synchronization, polynomial synchronization, and other synchronization as its special cases. The results of this paper are general, and they also complement and extend some previous results. Finally, numerical simulations are carried out to demonstrate the effectiveness of the obtained results.

A multi-phase network situational awareness cognitive task analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Erbacher, Robert; Frincke, Deborah A.; Wong, Pak C.

Abstract The goal of our project is to create a set of next-generation cyber situational-awareness capabilities with applications to other domains in the long term. The objective is to improve the decision-making process to enable decision makers to choose better actions. To this end, we put extensive effort into making certain that we had feedback from network analysts and managers and understand what their genuine needs are. This article discusses the cognitive task-analysis methodology that we followed to acquire feedback from the analysts. This article also provides the details we acquired from the analysts on their processes, goals, concerns, themore » data and metadata that they analyze. Finally, we describe the generation of a novel task-flow diagram representing the activities of the target user base.« less

An Ultra-low-power Medium Access Control Protocol for Body Sensor Network.

PubMed

Li, Huaming; Tan, Jindong

2005-01-01

In this paper, a medium access control (MAC) protocol designed for Body Sensor Network (BSN-MAC) is proposed. BSN-MAC is an adaptive, feedback-based and IEEE 802.15.4-compatible MAC protocol. Due to the traffic coupling and sensor diversity characteristics of BSNs, common MAC protocols can not satisfy the unique requirements of the biomedical sensors in BSN. BSN-MAC exploits the feedback information from the deployed sensors to form a closed-loop control of the MAC parameters. A control algorithm is proposed to enable the BSN coordinator to adjust parameters of the IEEE 802.15.4 superframe to achieve both energy efficiency and low latency on energy critical nodes. We evaluate the performance of BSN-MAC using energy efficiency as the primary metric.

Song Decrystallization in Adult Zebra Finches Does Not Require the Song Nucleus NIf

PubMed Central

Roy, Arani; Mooney, Richard

2009-01-01

In adult male zebra finches, transecting the vocal nerve causes previously stable (i.e., crystallized) song to slowly degrade, presumably because of the resulting distortion in auditory feedback. How and where distorted feedback interacts with song motor networks to induce this process of song decrystallization remains unknown. The song premotor nucleus HVC is a potential site where auditory feedback signals could interact with song motor commands. Although the forebrain nucleus interface of the nidopallium (NIf) appears to be the primary auditory input to HVC, NIf lesions made in adult zebra finches do not trigger song decrystallization. One possibility is that NIf lesions do not interfere with song maintenance, but do compromise the adult zebra finch's ability to express renewed vocal plasticity in response to feedback perturbations. To test this idea, we bilaterally lesioned NIf and then transected the vocal nerve in adult male zebra finches. We found that bilateral NIf lesions did not prevent nerve section–induced song decrystallization. To test the extent to which the NIf lesions disrupted auditory processing in the song system, we made in vivo extracellular recordings in HVC and a downstream anterior forebrain pathway (AFP) in NIf-lesioned birds. We found strong and selective auditory responses to the playback of the birds' own song persisted in HVC and the AFP following NIf lesions. These findings suggest that auditory inputs to the song system other than NIf, such as the caudal mesopallium, could act as a source of auditory feedback signals to the song motor network. PMID:19515953

Dynamic reconfiguration of human brain functional networks through neurofeedback.

PubMed

Haller, Sven; Kopel, Rotem; Jhooti, Permi; Haas, Tanja; Scharnowski, Frank; Lovblad, Karl-Olof; Scheffler, Klaus; Van De Ville, Dimitri

2013-11-01

Recent fMRI studies demonstrated that functional connectivity is altered following cognitive tasks (e.g., learning) or due to various neurological disorders. We tested whether real-time fMRI-based neurofeedback can be a tool to voluntarily reconfigure brain network interactions. To disentangle learning-related from regulation-related effects, we first trained participants to voluntarily regulate activity in the auditory cortex (training phase) and subsequently asked participants to exert learned voluntary self-regulation in the absence of feedback (transfer phase without learning). Using independent component analysis (ICA), we found network reconfigurations (increases in functional network connectivity) during the neurofeedback training phase between the auditory target region and (1) the auditory pathway; (2) visual regions related to visual feedback processing; (3) insula related to introspection and self-regulation and (4) working memory and high-level visual attention areas related to cognitive effort. Interestingly, the auditory target region was identified as the hub of the reconfigured functional networks without a-priori assumptions. During the transfer phase, we again found specific functional connectivity reconfiguration between auditory and attention network confirming the specific effect of self-regulation on functional connectivity. Functional connectivity to working memory related networks was no longer altered consistent with the absent demand on working memory. We demonstrate that neurofeedback learning is mediated by widespread changes in functional connectivity. In contrast, applying learned self-regulation involves more limited and specific network changes in an auditory setup intended as a model for tinnitus. Hence, neurofeedback training might be used to promote recovery from neurological disorders that are linked to abnormal patterns of brain connectivity. Copyright © 2013 Elsevier Inc. All rights reserved.

Virtual Proprioception for eccentric training.

PubMed

LeMoyne, Robert; Mastroianni, Timothy

2017-07-01

Wireless inertial sensors enable quantified feedback, which can be applied to evaluate the efficacy of therapy and rehabilitation. In particular eccentric training promotes a beneficial rehabilitation and strength training strategy. Virtual Proprioception for eccentric training applies real-time feedback from a wireless gyroscope platform enabled through a software application for a smartphone. Virtual Proprioception for eccentric training is applied to the eccentric phase of a biceps brachii strength training and contrasted to a biceps brachii strength training scenario without feedback. During the operation of Virtual Proprioception for eccentric training the intent is to not exceed a prescribed gyroscope signal threshold based on the real-time presentation of the gyroscope signal, in order to promote the eccentric aspect of the strength training endeavor. The experimental trial data is transmitted wireless through connectivity to the Internet as an email attachment for remote post-processing. A feature set is derived from the gyroscope signal for machine learning classification of the two scenarios of Virtual Proprioception real-time feedback for eccentric training and eccentric training without feedback. Considerable classification accuracy is achieved through the application of a multilayer perceptron neural network for distinguishing between the Virtual Proprioception real-time feedback for eccentric training and eccentric training without feedback.

Experience with Quality Assurance in Two Store-and-Forward Telemedicine Networks.

PubMed

Wootton, Richard; Liu, Joanne; Bonnardot, Laurent; Venugopal, Raghu; Oakley, Amanda

2015-01-01

Despite the increasing use of telemedicine around the world, little has been done to incorporate quality assurance (QA) into these operations. The purpose of the present study was to examine the feasibility of QA in store-and-forward teleconsulting using a previously published framework. During a 2-year study period, we examined the feasibility of using QA tools in two mature telemedicine networks [Médecins Sans Frontières (MSF) and New Zealand Teledermatology (NZT)]. The tools included performance reporting to assess trends, automated follow-up of patients to obtain outcomes data, automated surveying of referrers to obtain user feedback, and retrospective assessment of randomly selected cases to assess quality. In addition, the senior case coordinators in each network were responsible for identifying potential adverse events from email reports received from users. During the study period, there were 149 responses to the patient follow-up questions relating to the 1241 MSF cases (i.e., 12% of cases), and there were 271 responses to the follow-up questions relating to the 639 NZT cases (i.e., 42% of cases). The collection of user feedback reports was combined with the collection of patient follow-up data, thus producing the same response rates. The outcomes data suggested that the telemedicine advice proved useful for the referring doctor in the majority of cases and was likely to benefit the patient. The user feedback was overwhelmingly positive, over 90% of referrers in the two networks finding the advice received to be of educational benefit. The feedback also suggested that the teleconsultation had provided cost savings in about 20% of cases, either to the patient/family, or to the hospital/clinic treating the patient. Various problems were detected by regular monitoring, and certain adverse events were identified from email reports by the users. A single aberrant quality reading was detected by using a process control chart. The present study demonstrates that a QA program is feasible in store-and-forward telemedicine, and shows that it was useful in two different networks, because certain problems were detected (and then solved) that would not have been identified until much later. It seems likely that QA could be used much more widely in telemedicine generally to benefit patient care.

Predicting neural network firing pattern from phase resetting curve

NASA Astrophysics Data System (ADS)

Oprisan, Sorinel; Oprisan, Ana

2007-04-01

Autonomous neural networks called central pattern generators (CPG) are composed of endogenously bursting neurons and produce rhythmic activities, such as flying, swimming, walking, chewing, etc. Simplified CPGs for quadrupedal locomotion and swimming are modeled by a ring of neural oscillators such that the output of one oscillator constitutes the input for the subsequent neural oscillator. The phase response curve (PRC) theory discards the detailed conductance-based description of the component neurons of a network and reduces them to ``black boxes'' characterized by a transfer function, which tabulates the transient change in the intrinsic period of a neural oscillator subject to external stimuli. Based on open-loop PRC, we were able to successfully predict the phase-locked period and relative phase between neurons in a half-center network. We derived existence and stability criteria for heterogeneous ring neural networks that are in good agreement with experimental data.

State-dependent, bidirectional modulation of neural network activity by endocannabinoids.

PubMed

Piet, Richard; Garenne, André; Farrugia, Fanny; Le Masson, Gwendal; Marsicano, Giovanni; Chavis, Pascale; Manzoni, Olivier J

2011-11-16

The endocannabinoid (eCB) system and the cannabinoid CB1 receptor (CB1R) play key roles in the modulation of brain functions. Although actions of eCBs and CB1Rs are well described at the synaptic level, little is known of their modulation of neural activity at the network level. Using microelectrode arrays, we have examined the role of CB1R activation in the modulation of the electrical activity of rat and mice cortical neural networks in vitro. We find that exogenous activation of CB1Rs expressed on glutamatergic neurons decreases the spontaneous activity of cortical neural networks. Moreover, we observe that the net effect of the CB1R antagonist AM251 inversely correlates with the initial level of activity in the network: blocking CB1Rs increases network activity when basal network activity is low, whereas it depresses spontaneous activity when its initial level is high. Our results reveal a complex role of CB1Rs in shaping spontaneous network activity, and suggest that the outcome of endogenous neuromodulation on network function might be state dependent.

Lessons Learnt from and Sustainability of Adopting a Personal Learning Environment & Network (Ple&N)

ERIC Educational Resources Information Center

Tsui, Eric; Sabetzadeh, Farzad

2014-01-01

This paper describes the feedback from the configuration and deployment of a Personal Learning Environment & Network (PLE&N) tool to support peer-based social learning for university students and graduates. An extension of an earlier project in which a generic and PLE&N was deployed for all learners, the current PLE&N is a…

A neural network controller of a flotation process

DOE Office of Scientific and Technical Information (OSTI.GOV)

Durao, F.; Cortez, L.

1995-12-31

The dynamic control of a froth flotation section is simulated through a neural network feedback controller, trained in order to stabilize the concentrate metal grade and recovery by applying random step changes to the feed metal grade. The results of the application example show that this controller seems to be sufficiently robust and a good alternative to handle a non-linear process.

A Mixed-Methods Randomized Controlled Trial of Financial Incentives and Peer Networks to Promote Walking among Older Adults

ERIC Educational Resources Information Center

Kullgren, Jeffrey T.; Harkins, Kristin A.; Bellamy, Scarlett L.; Gonzales, Amy; Tao, Yuanyuan; Zhu, Jingsan; Volpp, Kevin G.; Asch, David A.; Heisler, Michele; Karlawish, Jason

2014-01-01

Background: Financial incentives and peer networks could be delivered through eHealth technologies to encourage older adults to walk more. Methods: We conducted a 24-week randomized trial in which 92 older adults with a computer and Internet access received a pedometer, daily walking goals, and weekly feedback on goal achievement. Participants…

Emergent Complexity in Conway's Game of Life

NASA Astrophysics Data System (ADS)

Gotts, Nick

It is shown that both small, finite patterns and random infinite very low density ("sparse") arrays of the Game of Life can produce emergent structures and processes of great complexity, through ramifying feedback networks and cross-scale interactions. The implications are discussed: it is proposed that analogous networks and interactions may have been precursors to natural selection in the real world.

Dynamics of book sales: endogenous versus exogenous shocks in complex networks.

PubMed

Deschâtres, F; Sornette, D

2005-07-01

We present an extensive study of the foreshock and aftershock signatures accompanying peaks of book sales. The time series of book sales are derived from the ranking system of Amazon.com. We present two independent ways of classifying peaks, one based on the acceleration pattern of sales and the other based on the exponent of the relaxation. They are found to be consistent and reveal the coexistence of two types of sales peaks: exogenous peaks occur abruptly and are followed by a power law relaxation, while endogenous sale peaks occur after a progressively accelerating power law growth followed by an approximately symmetrical power law relaxation which is slower than for exogenous peaks. We develop a simple epidemic model of buyers connected within a network of acquaintances which propagates rumors and opinions on books. The comparison between the predictions of the model and the empirical data confirms the validity of the model and suggests in addition that social networks have evolved to converge very close to criticality (here in the sense of critical branching processes of opinion spreading). We test in detail the evidence for a power law distribution of book sales and confirm a previous indirect study suggesting that the fraction of books (density distribution) P (S) of sales S is a power law P(S) approximately 1/ S(1+mu) with mu approximately equal to 2 .

Dynamics of book sales: Endogenous versus exogenous shocks in complex networks

NASA Astrophysics Data System (ADS)

Deschâtres, F.; Sornette, D.

2005-07-01

We present an extensive study of the foreshock and aftershock signatures accompanying peaks of book sales. The time series of book sales are derived from the ranking system of Amazon.com. We present two independent ways of classifying peaks, one based on the acceleration pattern of sales and the other based on the exponent of the relaxation. They are found to be consistent and reveal the coexistence of two types of sales peaks: exogenous peaks occur abruptly and are followed by a power law relaxation, while endogenous sale peaks occur after a progressively accelerating power law growth followed by an approximately symmetrical power law relaxation which is slower than for exogenous peaks. We develop a simple epidemic model of buyers connected within a network of acquaintances which propagates rumors and opinions on books. The comparison between the predictions of the model and the empirical data confirms the validity of the model and suggests in addition that social networks have evolved to converge very close to criticality (here in the sense of critical branching processes of opinion spreading). We test in detail the evidence for a power law distribution of book sales and confirm a previous indirect study suggesting that the fraction of books (density distribution) P(S) of sales S is a power law P(S)˜1/S1+μ with μ≈2 .

Reinforcement learning controller design for affine nonlinear discrete-time systems using online approximators.

PubMed

Yang, Qinmin; Jagannathan, Sarangapani

2012-04-01

In this paper, reinforcement learning state- and output-feedback-based adaptive critic controller designs are proposed by using the online approximators (OLAs) for a general multi-input and multioutput affine unknown nonlinear discretetime systems in the presence of bounded disturbances. The proposed controller design has two entities, an action network that is designed to produce optimal signal and a critic network that evaluates the performance of the action network. The critic estimates the cost-to-go function which is tuned online using recursive equations derived from heuristic dynamic programming. Here, neural networks (NNs) are used both for the action and critic whereas any OLAs, such as radial basis functions, splines, fuzzy logic, etc., can be utilized. For the output-feedback counterpart, an additional NN is designated as the observer to estimate the unavailable system states, and thus, separation principle is not required. The NN weight tuning laws for the controller schemes are also derived while ensuring uniform ultimate boundedness of the closed-loop system using Lyapunov theory. Finally, the effectiveness of the two controllers is tested in simulation on a pendulum balancing system and a two-link robotic arm system.

Stochastic Characterization of Communication Network Latency for Wide Area Grid Control Applications.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ameme, Dan Selorm Kwami; Guttromson, Ross

This report characterizes communications network latency under various network topologies and qualities of service (QoS). The characterizations are probabilistic in nature, allowing deeper analysis of stability for Internet Protocol (IP) based feedback control systems used in grid applications. The work involves the use of Raspberry Pi computers as a proxy for a controlled resource, and an ns-3 network simulator on a Linux server to create an experimental platform (testbed) that can be used to model wide-area grid control network communications in smart grid. Modbus protocol is used for information transport, and Routing Information Protocol is used for dynamic route selectionmore » within the simulated network.« less

Sensitivity of emergent sociohydrologic dynamics to internal system properties and external sociopolitical factors: Implications for water management

NASA Astrophysics Data System (ADS)

Elshafei, Y.; Tonts, M.; Sivapalan, M.; Hipsey, M. R.

2016-06-01

It is increasingly acknowledged that effective management of water resources requires a holistic understanding of the coevolving dynamics inherent in the coupled human-hydrology system. One of the fundamental information gaps concerns the sensitivity of coupled system feedbacks to various endogenous system properties and exogenous societal contexts. This paper takes a previously calibrated sociohydrology model and applies an idealized implementation, in order to: (i) explore the sensitivity of emergent dynamics resulting from bidirectional feedbacks to assumptions regarding (a) internal system properties that control the internal dynamics of the coupled system and (b) the external sociopolitical context; and (ii) interpret the results within the context of water resource management decision making. The analysis investigates feedback behavior in three ways, (a) via a global sensitivity analysis on key parameters and assessment of relevant model outputs, (b) through a comparative analysis based on hypothetical placement of the catchment along various points on the international sociopolitical gradient, and (c) by assessing the effects of various direct management intervention scenarios. Results indicate the presence of optimum windows that might offer the greatest positive impact per unit of management effort. Results further advocate management tools that encourage an adaptive learning, community-based approach with respect to water management, which are found to enhance centralized policy measures. This paper demonstrates that it is possible to use a place-based sociohydrology model to make abstractions as to the dynamics of bidirectional feedback behavior, and provide insights as to the efficacy of water management tools under different circumstances.

Portraying the unique contribution of the default mode network to internally driven mnemonic processes

PubMed Central

Shapira-Lichter, Irit; Oren, Noga; Jacob, Yael; Gruberger, Michal; Hendler, Talma

2013-01-01

Numerous neuroimaging studies have implicated default mode network (DMN) involvement in both internally driven processes and memory. Nevertheless, it is unclear whether memory operations reflect a particular case of internally driven processing or alternatively involve the DMN in a distinct manner, possibly depending on memory type. This question is critical for refining neurocognitive memory theorem in the context of other endogenic processes and elucidating the functional significance of this key network. We used functional MRI to examine DMN activity and connectivity patterns while participants overtly generated words according to nonmnemonic (phonemic) or mnemonic (semantic or episodic) cues. Overall, mnemonic word fluency was found to elicit greater DMN activity and stronger within-network functional connectivity compared with nonmnemonic fluency. Furthermore, two levels of functional organization of memory retrieval were shown. First, across both mnemonic tasks, activity was greater mainly in the posterior cingulate cortex, implying selective contribution to generic aspects of memory beyond its general involvement in endogenous processes. Second, parts of the DMN showed distinct selectivity for each of the mnemonic conditions; greater recruitment of the anterior prefrontal cortex, retroesplenial cortex, and hippocampi and elevated connectivity between anterior and posterior medial DMN nodes characterized the semantic condition, whereas increased recruitment of posterior DMN components and elevated connectivity between them characterized the episodic condition. This finding emphasizes the involvement of DMN elements in discrete aspects of memory retrieval. Altogether, our results show a specific contribution of the DMN to memory processes, corresponding to the specific type of memory retrieval. PMID:23479650

Performance evaluation of a burst-mode EDFA in an optical packet and circuit integrated network.

PubMed

Shiraiwa, Masaki; Awaji, Yoshinari; Furukawa, Hideaki; Shinada, Satoshi; Puttnam, Benjamin J; Wada, Naoya

2013-12-30

We experimentally investigate the performance of burst-mode EDFA in an optical packet and circuit integrated system. In such networks, packets and light paths can be dynamically assigned to the same fibers, resulting in gain transients in EDFAs throughout the network that can limit network performance. Here, we compare the performance of a 'burst-mode' EDFA (BM-EDFA), employing transient suppression techniques and optical feedback, with conventional EDFAs, and those using automatic gain control and previous BM-EDFA implementations. We first measure gain transients and other impairments in a simplified set-up before making frame error-rate measurements in a network demonstration.

Regulatory gene networks and the properties of the developmental process

NASA Technical Reports Server (NTRS)

Davidson, Eric H.; McClay, David R.; Hood, Leroy

2003-01-01

Genomic instructions for development are encoded in arrays of regulatory DNA. These specify large networks of interactions among genes producing transcription factors and signaling components. The architecture of such networks both explains and predicts developmental phenomenology. Although network analysis is yet in its early stages, some fundamental commonalities are already emerging. Two such are the use of multigenic feedback loops to ensure the progressivity of developmental regulatory states and the prevalence of repressive regulatory interactions in spatial control processes. Gene regulatory networks make it possible to explain the process of development in causal terms and eventually will enable the redesign of developmental regulatory circuitry to achieve different outcomes.

Analysis of a dc bus system with a nonlinear constant power load and its delayed feedback control.

PubMed

Konishi, Keiji; Sugitani, Yoshiki; Hara, Naoyuki

2014-02-01

This paper tackles a destabilizing problem of a direct-current (dc) bus system with constant power loads, which can be considered a fundamental problem of dc power grid networks. The present paper clarifies scenarios of the destabilization and applies the well-known delayed-feedback control to the stabilization of the destabilized bus system on the basis of nonlinear science. Further, we propose a systematic procedure for designing the delayed feedback controller. This controller can converge the bus voltage exactly on an unstable operating point without accurate information and can track it using tiny control energy even when a system parameter, such as the power consumption of the load, is slowly varied. These features demonstrate that delayed feedback control can be considered a strong candidate for solving the destabilizing problem.

Adaptive Neural Output Feedback Control for Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis and Unknown Control Directions.

PubMed

Yu, Zhaoxu; Li, Shugang; Yu, Zhaosheng; Li, Fangfei

2018-04-01

This paper investigates the problem of output feedback adaptive stabilization for a class of nonstrict-feedback stochastic nonlinear systems with both unknown backlashlike hysteresis and unknown control directions. A new linear state transformation is applied to the original system, and then, control design for the new system becomes feasible. By combining the neural network's (NN's) parameterization, variable separation technique, and Nussbaum gain function method, an input-driven observer-based adaptive NN control scheme, which involves only one parameter to be updated, is developed for such systems. All closed-loop signals are bounded in probability and the error signals remain semiglobally bounded in the fourth moment (or mean square). Finally, the effectiveness and the applicability of the proposed control design are verified by two simulation examples.

Quantum enhanced feedback cooling of a mechanical oscillator using nonclassical light.

PubMed

Schäfermeier, Clemens; Kerdoncuff, Hugo; Hoff, Ulrich B; Fu, Hao; Huck, Alexander; Bilek, Jan; Harris, Glen I; Bowen, Warwick P; Gehring, Tobias; Andersen, Ulrik L

2016-11-29

Laser cooling is a fundamental technique used in primary atomic frequency standards, quantum computers, quantum condensed matter physics and tests of fundamental physics, among other areas. It has been known since the early 1990s that laser cooling can, in principle, be improved by using squeezed light as an electromagnetic reservoir; while quantum feedback control using a squeezed light probe is also predicted to allow improved cooling. Here we show the implementation of quantum feedback control of a micro-mechanical oscillator using squeezed probe light. This allows quantum-enhanced feedback cooling with a measurement rate greater than it is possible with classical light, and a consequent reduction in the final oscillator temperature. Our results have significance for future applications in areas ranging from quantum information networks, to quantum-enhanced force and displacement measurements and fundamental tests of macroscopic quantum mechanics.

Analyzing Feedback Control Systems

NASA Technical Reports Server (NTRS)

Bauer, Frank H.; Downing, John P.

1987-01-01

Interactive controls analysis (INCA) program developed to provide user-friendly environment for design and analysis of linear control systems, primarily feedback control. Designed for use with both small- and large-order systems. Using interactive-graphics capability, INCA user quickly plots root locus, frequency response, or time response of either continuous-time system or sampled-data system. Configuration and parameters easily changed, allowing user to design compensation networks and perform sensitivity analyses in very convenient manner. Written in Pascal and FORTRAN.

Output feedback control of a quadrotor UAV using neural networks.

PubMed

Dierks, Travis; Jagannathan, Sarangapani

2010-01-01

In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.

Non-Markovian quantum feedback networks II: Controlled flows

NASA Astrophysics Data System (ADS)

Gough, John E.

2017-06-01

The concept of a controlled flow of a dynamical system, especially when the controlling process feeds information back about the system, is of central importance in control engineering. In this paper, we build on the ideas presented by Bouten and van Handel [Quantum Stochastics and Information: Statistics, Filtering and Control (World Scientific, 2008)] and develop a general theory of quantum feedback. We elucidate the relationship between the controlling processes, Z, and the measured processes, Y, and to this end we make a distinction between what we call the input picture and the output picture. We should note that the input-output relations for the noise fields have additional terms not present in the standard theory but that the relationship between the control processes and measured processes themselves is internally consistent—we do this for the two main cases of quadrature measurement and photon-counting measurement. The theory is general enough to include a modulating filter which post-processes the measurement readout Y before returning to the system. This opens up the prospect of applying very general engineering feedback control techniques to open quantum systems in a systematic manner, and we consider a number of specific modulating filter problems. Finally, we give a brief argument as to why most of the rules for making instantaneous feedback connections [J. Gough and M. R. James, Commun. Math. Phys. 287, 1109 (2009)] ought to apply for controlled dynamical networks as well.

Practical synchronization on complex dynamical networks via optimal pinning control

NASA Astrophysics Data System (ADS)

Li, Kezan; Sun, Weigang; Small, Michael; Fu, Xinchu

2015-07-01

We consider practical synchronization on complex dynamical networks under linear feedback control designed by optimal control theory. The control goal is to minimize global synchronization error and control strength over a given finite time interval, and synchronization error at terminal time. By utilizing the Pontryagin's minimum principle, and based on a general complex dynamical network, we obtain an optimal system to achieve the control goal. The result is verified by performing some numerical simulations on Star networks, Watts-Strogatz networks, and Barabási-Albert networks. Moreover, by combining optimal control and traditional pinning control, we propose an optimal pinning control strategy which depends on the network's topological structure. Obtained results show that optimal pinning control is very effective for synchronization control in real applications.

Effects of Oxytocin and Vasopressin on Preferential Brain Responses to Negative Social Feedback.

PubMed

Gozzi, Marta; Dashow, Erica M; Thurm, Audrey; Swedo, Susan E; Zink, Caroline F

2017-06-01

Receiving negative social feedback can be detrimental to emotional, cognitive, and physical well-being, and fear of negative social feedback is a prominent feature of mental illnesses that involve social anxiety. A large body of evidence has implicated the neuropeptides oxytocin and vasopressin in the modulation of human neural activity underlying social cognition, including negative emotion processing; however, the influence of oxytocin and vasopressin on neural activity elicited during negative social evaluation remains unknown. Here 21 healthy men underwent functional magnetic resonance imaging in a double-blind, placebo-controlled, crossover design to determine how intranasally administered oxytocin and vasopressin modulated neural activity when receiving negative feedback on task performance from a study investigator. We found that under placebo, a preferential response to negative social feedback compared with positive social feedback was evoked in brain regions putatively involved in theory of mind (temporoparietal junction), pain processing (anterior insula and supplementary motor area), and identification of emotionally important visual cues in social perception (right fusiform). These activations weakened with oxytocin and vasopressin administration such that neural responses to receiving negative social feedback were not significantly greater than positive social feedback. Our results show effects of both oxytocin and vasopressin on the brain network involved in negative social feedback, informing the possible use of a pharmacological approach targeting these regions in multiple disorders with impairments in social information processing.

Cross-Layer Adaptive Feedback Scheduling of Wireless Control Systems

PubMed Central

Xia, Feng; Ma, Longhua; Peng, Chen; Sun, Youxian; Dong, Jinxiang

2008-01-01

There is a trend towards using wireless technologies in networked control systems. However, the adverse properties of the radio channels make it difficult to design and implement control systems in wireless environments. To attack the uncertainty in available communication resources in wireless control systems closed over WLAN, a cross-layer adaptive feedback scheduling (CLAFS) scheme is developed, which takes advantage of the co-design of control and wireless communications. By exploiting cross-layer design, CLAFS adjusts the sampling periods of control systems at the application layer based on information about deadline miss ratio and transmission rate from the physical layer. Within the framework of feedback scheduling, the control performance is maximized through controlling the deadline miss ratio. Key design parameters of the feedback scheduler are adapted to dynamic changes in the channel condition. An event-driven invocation mechanism for the feedback scheduler is also developed. Simulation results show that the proposed approach is efficient in dealing with channel capacity variations and noise interference, thus providing an enabling technology for control over WLAN. PMID:27879934

Role for Dynamin in Late Endosome Dynamics and Trafficking of the Cation-independent Mannose 6-Phosphate Receptor

PubMed Central

Nicoziani, Paolo; Vilhardt, Frederik; Llorente, Alicia; Hilout, Leila; Courtoy, Pierre J.; Sandvig, Kirsten; van Deurs, Bo

2000-01-01

It is well established that dynamin is involved in clathrin-dependent endocytosis, but relatively little is known about possible intracellular functions of this GTPase. Using confocal imaging, we found that endogenous dynamin was associated with the plasma membrane, the trans-Golgi network, and a perinuclear cluster of cation-independent mannose 6-phosphate receptor (CI-MPR)–containing structures. By electron microscopy (EM), it was shown that these structures were late endosomes and that the endogenous dynamin was preferentially localized to tubulo-vesicular appendices on these late endosomes. Upon induction of the dominant-negative dynK44A mutant, confocal microscopy demonstrated a redistribution of the CI-MPR in mutant-expressing cells. Quantitative EM analysis of the ratio of CI-MPR to lysosome-associated membrane protein-1 in endosome profiles revealed a higher colocalization of the two markers in dynK44A-expressing cells than in control cells. Western blot analysis showed that dynK44A-expressing cells had an increased cellular procathepsin D content. Finally, EM revealed that in dynK44A-expressing cells, endosomal tubules containing CI-MPR were formed. These results are in contrast to recent reports that dynamin-2 is exclusively associated with endocytic structures at the plasma membrane. They suggest instead that endogenous dynamin also plays an important role in the molecular machinery behind the recycling of the CI-MPR from endosomes to the trans-Golgi network, and we propose that dynamin is required for the final scission of vesicles budding from endosome tubules. PMID:10679008

Influence of Gap-Filling to Generate Continuous Datasets on Process Network Analysis

NASA Astrophysics Data System (ADS)

Yun, J.; Kim, J.; Kim, S.; Chun, J.

2013-12-01

The interplay of environmental conditions, energy, matter, and information defines the context and constraints for the set of processes and structures that may emerge during self-organization in complex ecosystems. Following Ruddell and Kumar (2009), we have evaluated statistical measures of characterizing the organization of the information flow in ecohydrological process networks in a deciduous forest ecosystem. We used the time series data obtained in 2008 (normal year) from the KoFlux forest tower site in central Korea. The 30-minute averages of eddy fluxes of energy, water and CO2 were measured at 40m above an oak-dominated old deciduous forest along with other micrometeorological variables. In this analysis, we selected 13 variables: atmospheric pressure (Pa), net ecosystem CO2 exchange (NEE), gross primary productivity (GPP), ecosystem respiration (RE), latent heat flux (LE), precipitation (Precip), solar radiation (Rg), air temperature (T), vapor pressure deficit (VPD), sensible heat flux (H), canopy temperature (Tc), wind direction (WD), and wind speed (WS). Our results support that a process network approach can be used to formally resolve feedback, time scales, and subsystems that define the complex ecosystem's organization by considering mutual information and transfer entropy simultaneously. We also observed that the turbulent and atmospheric boundary layer subsystems are coupled through feedback loops, and form a regional self-organizing subsystem in August when the forest is in healthy environment. In particular, we noted that the observed feedback loops in the process network disappeared when the time series data were artificially gap-filled for missing data, which is a common practice in post-data processing. In this presentation, we report the influence of gap-filling on the process network analysis by artificially assigning different sizes and periods of missing data and discuss the implication of our results on validation and calibration of ecosystem models. Acknowledgment. This research was supported by the Korea Meteorological Administration Research and Development Program under Grant CATER 2013-3030.

Implementation of pulse-coupled neural networks in a CNAPS environment.

PubMed

Kinser, J M; Lindblad, T

1999-01-01

Pulse coupled neural networks (PCNN's) are biologically inspired algorithms very well suited for image/signal preprocessing. While several analog implementations are proposed we suggest a digital implementation in an existing environment, the connected network of adapted processors system (CNAPS). The reason for this is two fold. First, CNAPS is a commercially available chip which has been used for several neural-network implementations. Second, the PCNN is, in almost all applications, a very efficient component of a system requiring subsequent and additional processing. This may include gating, Fourier transforms, neural classifiers, data mining, etc, with or without feedback to the PCNN.

Space shuttle main engine fault detection using neural networks

NASA Technical Reports Server (NTRS)

Bishop, Thomas; Greenwood, Dan; Shew, Kenneth; Stevenson, Fareed

1991-01-01

A method for on-line Space Shuttle Main Engine (SSME) anomaly detection and fault typing using a feedback neural network is described. The method involves the computation of features representing time-variance of SSME sensor parameters, using historical test case data. The network is trained, using backpropagation, to recognize a set of fault cases. The network is then able to diagnose new fault cases correctly. An essential element of the training technique is the inclusion of randomly generated data along with the real data, in order to span the entire input space of potential non-nominal data.

Inferring Instantaneous, Multivariate and Nonlinear Sensitivities for the Analysis of Feedback Processes in a Dynamical System: Lorenz Model Case Study

NASA Technical Reports Server (NTRS)

Aires, Filipe; Rossow, William B.; Hansen, James E. (Technical Monitor)

2001-01-01

A new approach is presented for the analysis of feedback processes in a nonlinear dynamical system by observing its variations. The new methodology consists of statistical estimates of the sensitivities between all pairs of variables in the system based on a neural network modeling of the dynamical system. The model can then be used to estimate the instantaneous, multivariate and nonlinear sensitivities, which are shown to be essential for the analysis of the feedbacks processes involved in the dynamical system. The method is described and tested on synthetic data from the low-order Lorenz circulation model where the correct sensitivities can be evaluated analytically.

STABILIZED OSCILLATOR

DOEpatents

Jessen, P.L.; Price, H.J.

1958-03-18

This patent relates to sine-wave generators and in particular describes a generator with a novel feedback circuit resulting in improved frequency stability. The generator comprises two triodes having a common cathode circuit connected to oscillate at a frequency and amplitude at which the loop galn of the circutt ls unity, and another pair of triodes having a common cathode circuit arranged as a conventional amplifier. A signal is conducted from the osciliator through a frequency selective network to the amplifier and fed back to the osciliator. The unique feature of the feedback circuit is the amplifier operates in the nonlinear portion of its tube characteristics thereby providing a relatively constant feedback voltage to the oscillator irrespective of the amplitude of its input signal.

Gravity effects on endogenous movements

NASA Astrophysics Data System (ADS)

Johnsson, Anders; Antonsen, Frank

Gravity effects on endogenous movements A. Johnsson * and F. Antonsen *+ * Department of Physics, Norwegian University of Science and Technology,NO-7491, Trond-heim, Norway, E-mail: anders.johnsson@ntnu.no + Present address: Statoil Research Center Trondheim, NO-7005, Trondheim, Norway Circumnutations in stems/shoots exist in many plants and often consists of more or less regular helical movements around the plumb line under Earth conditions. Recent results on circumnu-tations of Arabidopsis in space (Johnsson et al. 2009) showed that minute amplitude oscilla-tions exist in weightlessness, but that centripetal acceleration (mimicking the gravity) amplified and/or created large amplitude oscillations. Fundamental mechanisms underlying these results will be discussed by modeling the plant tissue as a cylinder of cells coupled together. As a starting point we have modeled (Antonsen 1998) standing waves on a ring of biological cells, as first discussed in a classical paper (Turing 1952). If the coupled cells can change their water content, an `extension' wave could move around the ring. We have studied several, stacked rings of cells coupled into a cylinder that together represent a cylindrical plant tissue. Waves of extensions travelling around the cylinder could then represent the observable circumnutations. The coupling between cells can be due to cell-to-cell diffusion, or to transport via channels, and the coupling can be modeled to vary in both longitudinal and transversal direction of the cylinder. The results from ISS experiments indicate that this cylindrical model of coupled cells should be able to 1) show self-sustained oscillations without the impact of gravity (being en-dogenous) and 2) show how an environmental factor like gravity can amplify or generate the oscillatory movements. Gravity has been introduced in the model by a negative, time-delayed feed-back transport across the cylinder. This represents the physiological reactions to acceler-ation stimulations (gravitropism reactions). Such a negative feedback can account for gravity initiated transport, resulting in lateral water transport and overall movements. The simulation results indicate that self-sustained oscillations can occur on such a cylinder of cells. It will also be demonstrated that the introduction of feedback in the model results in longer circum-nutation periods. It will be discussed how this generic modeling approach could be applied to discuss oscillatory plant movements in general and how other environmental factors, as for instance light gradients, could couple to the self-sustained movements. The oscillations require weightlessness for proper investigations. References: Antonsen F.: Biophysical studies of plant growth movements in microgravity and under 1 g conditions. PhD thesis, Norwegian University of Science and Technology 1998. Johnsson A., Solheim BGB, Iversen T.-H.: Gravity amplifies and microgravity decreases cir-cumnutations in Arabidopsis thaliana stems: results from a space experiment.-New Phytologist 182: 621-629. 2009. Turing AM.: The chemical basis for morphogenesis.-Phil Trans. R. Soc. London Ser B237:37 -72. 1952.

Fundamental Principles of Network Formation among Preschool Children1

PubMed Central

Schaefer, David R.; Light, John M.; Fabes, Richard A.; Hanish, Laura D.; Martin, Carol Lynn

2009-01-01

The goal of this research was to investigate the origins of social networks by examining the formation of children’s peer relationships in 11 preschool classes throughout the school year. We investigated whether several fundamental processes of relationship formation were evident at this age, including reciprocity, popularity, and triadic closure effects. We expected these mechanisms to change in importance over time as the network crystallizes, allowing more complex structures to evolve from simpler ones in a process we refer to as structural cascading. We analyzed intensive longitudinal observational data of children’s interactions using the SIENA actor-based model. We found evidence that reciprocity, popularity, and triadic closure all shaped the formation of preschool children’s networks. The influence of reciprocity remained consistent, whereas popularity and triadic closure became increasingly important over the course of the school year. Interactions between age and endogenous network effects were nonsignificant, suggesting that these network formation processes were not moderated by age in this sample of young children. We discuss the implications of our longitudinal network approach and findings for the study of early network developmental processes. PMID:20161606

Physics textbooks from the viewpoint of network structures

NASA Astrophysics Data System (ADS)

Králiková, Petra; Teleki, Aba

2017-01-01

We can observe self-organized networks all around us. These networks are, in general, scale invariant networks described by the Bianconi-Barabasi model. The self-organized networks (networks formed naturally when feedback acts on the system) show certain universality. These networks, in simplified models, have scale invariant distribution (Pareto distribution type I) and parameter α has value between 2 and 5. The textbooks are extremely important in the learning process and from this reason we studied physics textbook at the level of sentences and physics terms (bipartite network). The nodes represent physics terms, sentences, and pictures, tables, connected by links (by physics terms and transitional words and transitional phrases). We suppose that learning process are more robust and goes faster and easier if the physics textbook has a structure similar to structures of self-organized networks.

Network Community Detection based on the Physarum-inspired Computational Framework.

PubMed

Gao, Chao; Liang, Mingxin; Li, Xianghua; Zhang, Zili; Wang, Zhen; Zhou, Zhili

2016-12-13

Community detection is a crucial and essential problem in the structure analytics of complex networks, which can help us understand and predict the characteristics and functions of complex networks. Many methods, ranging from the optimization-based algorithms to the heuristic-based algorithms, have been proposed for solving such a problem. Due to the inherent complexity of identifying network structure, how to design an effective algorithm with a higher accuracy and a lower computational cost still remains an open problem. Inspired by the computational capability and positive feedback mechanism in the wake of foraging process of Physarum, which is a large amoeba-like cell consisting of a dendritic network of tube-like pseudopodia, a general Physarum-based computational framework for community detection is proposed in this paper. Based on the proposed framework, the inter-community edges can be identified from the intra-community edges in a network and the positive feedback of solving process in an algorithm can be further enhanced, which are used to improve the efficiency of original optimization-based and heuristic-based community detection algorithms, respectively. Some typical algorithms (e.g., genetic algorithm, ant colony optimization algorithm, and Markov clustering algorithm) and real-world datasets have been used to estimate the efficiency of our proposed computational framework. Experiments show that the algorithms optimized by Physarum-inspired computational framework perform better than the original ones, in terms of accuracy and computational cost. Moreover, a computational complexity analysis verifies the scalability of our framework.

Scheduling of network access for feedback-based embedded systems

NASA Astrophysics Data System (ADS)

Liberatore, Vincenzo

2002-07-01

nd communication capabilities. Examples range from smart dust embedded in building materials to networks of appliances in the home. Embedded devices will be deployed in unprecedented numbers, will enable pervasive distributed computing, and will radically change the way people interact with the surrounding environment [EGH00a]. The paper targets embedded systems and their real-time (RT) communication requirements. RT requirements arise from the

Synchronization of fractional-order complex-valued neural networks with time delay.

PubMed

Bao, Haibo; Park, Ju H; Cao, Jinde

2016-09-01

This paper deals with the problem of synchronization of fractional-order complex-valued neural networks with time delays. By means of linear delay feedback control and a fractional-order inequality, sufficient conditions are obtained to guarantee the synchronization of the drive-response systems. Numerical simulations are provided to show the effectiveness of the obtained results. Copyright © 2016 Elsevier Ltd. All rights reserved.

Hurry Up and "Like" Me: Immediate Feedback on Social Networking Sites and the Impact on Adolescent Girls

ERIC Educational Resources Information Center

Jong, Stephanie T.; Drummond, Murray J. N.

2016-01-01

At an age identified as the period with the most intense focus on appearance, and where young girls are establishing their identity, it appears that social networking site (SNS) interactions are playing a pivotal role in determining what is, and what is not, socially endorsed. This paper draws on data obtained during five separate focus group…

Network-Based Methods for Identifying Key Active Proteins in the Extracellular Electron Transfer Process in Shewanella oneidensis MR-1.

PubMed

Ding, Dewu; Sun, Xiao

2018-01-16

Shewanella oneidensis MR-1 can transfer electrons from the intracellular environment to the extracellular space of the cells to reduce the extracellular insoluble electron acceptors (Extracellular Electron Transfer, EET). Benefiting from this EET capability, Shewanella has been widely used in different areas, such as energy production, wastewater treatment, and bioremediation. Genome-wide proteomics data was used to determine the active proteins involved in activating the EET process. We identified 1012 proteins with decreased expression and 811 proteins with increased expression when the EET process changed from inactivation to activation. We then networked these proteins to construct the active protein networks, and identified the top 20 key active proteins by network centralization analysis, including metabolism- and energy-related proteins, signal and transcriptional regulatory proteins, translation-related proteins, and the EET-related proteins. We also constructed the integrated protein interaction and transcriptional regulatory networks for the active proteins, then found three exclusive active network motifs involved in activating the EET process-Bi-feedforward Loop, Regulatory Cascade with a Feedback, and Feedback with a Protein-Protein Interaction (PPI)-and identified the active proteins involved in these motifs. Both enrichment analysis and comparative analysis to the whole-genome data implicated the multiheme c -type cytochromes and multiple signal processing proteins involved in the process. Furthermore, the interactions of these motif-guided active proteins and the involved functional modules were discussed. Collectively, by using network-based methods, this work reported a proteome-wide search for the key active proteins that potentially activate the EET process.

Generation of oscillating gene regulatory network motifs

NASA Astrophysics Data System (ADS)

van Dorp, M.; Lannoo, B.; Carlon, E.

2013-07-01

Using an improved version of an evolutionary algorithm originally proposed by François and Hakim [Proc. Natl. Acad. Sci. USAPNASA60027-842410.1073/pnas.0304532101 101, 580 (2004)], we generated small gene regulatory networks in which the concentration of a target protein oscillates in time. These networks may serve as candidates for oscillatory modules to be found in larger regulatory networks and protein interaction networks. The algorithm was run for 105 times to produce a large set of oscillating modules, which were systematically classified and analyzed. The robustness of the oscillations against variations of the kinetic rates was also determined, to filter out the least robust cases. Furthermore, we show that the set of evolved networks can serve as a database of models whose behavior can be compared to experimentally observed oscillations. The algorithm found three smallest (core) oscillators in which nonlinearities and number of components are minimal. Two of those are two-gene modules: the mixed feedback loop, already discussed in the literature, and an autorepressed gene coupled with a heterodimer. The third one is a single gene module which is competitively regulated by a monomer and a dimer. The evolutionary algorithm also generated larger oscillating networks, which are in part extensions of the three core modules and in part genuinely new modules. The latter includes oscillators which do not rely on feedback induced by transcription factors, but are purely of post-transcriptional type. Analysis of post-transcriptional mechanisms of oscillation may provide useful information for circadian clock research, as recent experiments showed that circadian rhythms are maintained even in the absence of transcription.

Dynamics of neuromodulatory feedback determines frequency modulation in a reduced respiratory network: a computational study.

PubMed

Toporikova, Natalia; Butera, Robert J

2013-02-01

Neuromodulators, such as amines and neuropeptides, alter the activity of neurons and neuronal networks. In this work, we investigate how neuromodulators, which activate G(q)-protein second messenger systems, can modulate the bursting frequency of neurons in a critical portion of the respiratory neural network, the pre-Bötzinger complex (preBötC). These neurons are a vital part of the ponto-medullary neuronal network, which generates a stable respiratory rhythm whose frequency is regulated by neuromodulator release from the nearby Raphe nucleus. Using a simulated 50-cell network of excitatory preBötC neurons with a heterogeneous distribution of persistent sodium conductance and Ca(2+), we determined conditions for frequency modulation in such a network by simulating interaction between Raphe and preBötC nuclei. We found that the positive feedback between the Raphe excitability and preBötC activity induces frequency modulation in the preBötC neurons. In addition, the frequency of the respiratory rhythm can be regulated via phasic release of excitatory neuromodulators from the Raphe nucleus. We predict that the application of a G(q) antagonist will eliminate this frequency modulation by the Raphe and keep the network frequency constant and low. In contrast, application of a G(q) agonist will result in a high frequency for all levels of Raphe stimulation. Our modeling results also suggest that high [K(+)] requirement in respiratory brain slice experiments may serve as a compensatory mechanism for low neuromodulatory tone. Copyright © 2012 Elsevier B.V. All rights reserved.

34 CFR 263.3 - What definitions apply to the Professional Development program?

Code of Federal Regulations, 2010 CFR

2010-07-01

... improve performance, (2) Access to research materials and information on teaching and learning, (3... collaboration, feedback, and peer networking and support. In-service training means professional activities and...

Emergence of Leadership in Communication

PubMed Central

Allahverdyan, Armen E.; Galstyan, Aram

2016-01-01

We study a neuro-inspired model that mimics a discussion (or information dissemination) process in a network of agents. During their interaction, agents redistribute activity and network weights, resulting in emergence of leader(s). The model is able to reproduce the basic scenarios of leadership known in nature and society: laissez-faire (irregular activity, weak leadership, sizable inter-follower interaction, autonomous sub-leaders); participative or democratic (strong leadership, but with feedback from followers); and autocratic (no feedback, one-way influence). Several pertinent aspects of these scenarios are found as well—e.g., hidden leadership (a hidden clique of agents driving the official autocratic leader), and successive leadership (two leaders influence followers by turns). We study how these scenarios emerge from inter-agent dynamics and how they depend on behavior rules of agents—in particular, on their inertia against state changes. PMID:27532484

Emergence of Leadership in Communication.

PubMed

Allahverdyan, Armen E; Galstyan, Aram

2016-01-01

We study a neuro-inspired model that mimics a discussion (or information dissemination) process in a network of agents. During their interaction, agents redistribute activity and network weights, resulting in emergence of leader(s). The model is able to reproduce the basic scenarios of leadership known in nature and society: laissez-faire (irregular activity, weak leadership, sizable inter-follower interaction, autonomous sub-leaders); participative or democratic (strong leadership, but with feedback from followers); and autocratic (no feedback, one-way influence). Several pertinent aspects of these scenarios are found as well-e.g., hidden leadership (a hidden clique of agents driving the official autocratic leader), and successive leadership (two leaders influence followers by turns). We study how these scenarios emerge from inter-agent dynamics and how they depend on behavior rules of agents-in particular, on their inertia against state changes.

Adaptive Time Stepping for Transient Network Flow Simulation in Rocket Propulsion Systems

NASA Technical Reports Server (NTRS)

Majumdar, Alok K.; Ravindran, S. S.

2017-01-01

Fluid and thermal transients found in rocket propulsion systems such as propellant feedline system is a complex process involving fast phases followed by slow phases. Therefore their time accurate computation requires use of short time step initially followed by the use of much larger time step. Yet there are instances that involve fast-slow-fast phases. In this paper, we present a feedback control based adaptive time stepping algorithm, and discuss its use in network flow simulation of fluid and thermal transients. The time step is automatically controlled during the simulation by monitoring changes in certain key variables and by feedback. In order to demonstrate the viability of time adaptivity for engineering problems, we applied it to simulate water hammer and cryogenic chill down in pipelines. Our comparison and validation demonstrate the accuracy and efficiency of this adaptive strategy.

Network interactions: non-geniculate input to V1.

PubMed

Muckli, Lars; Petro, Lucy S

2013-04-01

The strongest connections to V1 are fed back from neighbouring area V2 and from a network of higher cortical areas (e.g. V3, V5, LOC, IPS and A1), transmitting the results of cognitive operations such as prediction, attention and imagination. V1 is therefore at the receiving end of a complex cortical processing cascade and not only at the entrance stage of cortical processing of retinal input. One elegant strategy to investigate this information-rich feedback to V1 is to eliminate feedforward input, that is, exploit V1's retinotopic organisation to isolate subregions receiving no direct bottom-up stimulation. We highlight the diverse mechanisms of cortical feedback, ranging from gain control to predictive coding, and conclude that V1 is involved in rich internal communication processes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Modeling the hypothalamus-pituitary-adrenal axis: A review and extension.

PubMed

Hosseinichimeh, Niyousha; Rahmandad, Hazhir; Wittenborn, Andrea K

2015-10-01

Multiple models of the hypothalamus-pituitary-adrenal (HPA) axis have been developed to characterize the oscillations seen in the hormone concentrations and to examine HPA axis dysfunction. We reviewed the existing models, then replicated and compared five of them by finding their correspondence to a dataset consisting of ACTH and cortisol concentrations of 17 healthy individuals. We found that existing models use different feedback mechanisms, vary in the level of details and complexities, and offer inconsistent conclusions. None of the models fit the validation dataset well. Therefore, we re-calibrated the best performing model using partial calibration and extended the model by adding individual fixed effects and an exogenous circadian function. Our estimated parameters reduced the mean absolute percent error significantly and offer a validated reference model that can be used in diverse applications. Our analysis suggests that the circadian and ultradian cycles are not created endogenously by the HPA axis feedbacks, which is consistent with the recent literature on the circadian clock and HPA axis. Copyright © 2015 Elsevier Inc. All rights reserved.

Women recovering from social rejection: The effect of the person and the situation on a hormonal mechanism of affiliation.

PubMed

Duffy, Korrina A; Harris, Lasana T; Chartrand, Tanya L; Stanton, Steven J

2017-02-01

Rejection can motivate either affiliation or withdrawal. In order to study how personality and situational variables influence whether women will be motivated to affiliate versus withdraw, we manipulate social feedback (rejection vs. acceptance) and opportunity for face-to-face interaction (blocked vs. face-to-face) and measure the individual difference variables rejection sensitivity and social anxiety. We test how these variables affect endogenous progesterone and cortisol concentrations, which are presumed to signal motivational responses to rejection. We find that three-way interactions involving social feedback, opportunity for face-to-face interactions, and either social anxiety or rejection sensitivity significantly predict progesterone change, but not cortisol change. Both interactions are driven by sharp progesterone decreases for women high in social anxiety/rejection sensitivity who have been rejected and who have no opportunity to reaffiliate in a face-to-face interaction. This progesterone change may be a physiological marker of motivation for social avoidance following rejection for women who cannot reaffiliate and who are particularly socially anxious or sensitive to rejection. Published by Elsevier Ltd.

BDNF-induced nitric oxide signals in cultured rat hippocampal neurons: time course, mechanism of generation, and effect on neurotrophin secretion.

PubMed

Kolarow, Richard; Kuhlmann, Christoph R W; Munsch, Thomas; Zehendner, Christoph; Brigadski, Tanja; Luhmann, Heiko J; Lessmann, Volkmar

2014-01-01

BDNF and nitric oxide signaling both contribute to plasticity at glutamatergic synapses. However, the role of combined signaling of both pathways at the same synapse is largely unknown. Using NO imaging with diaminofluoresceine in cultured hippocampal neurons we analyzed the time course of neurotrophin-induced NO signals. Application of exogenous BDNF, NT-4, and NT-3 (but not NGF) induced NO signals in the soma and in proximal dendrites of hippocampal neurons that were sensitive to NO synthase activity, TrkB signaling, and intracellular calcium elevation. The effect of NO signaling on neurotrophin secretion was analyzed in BDNF-GFP, and NT-3-GFP transfected hippocampal neurons. Exogenous application of the NO donor sodium-nitroprusside markedly inhibited neurotrophin secretion. However, endogenously generated NO in response to depolarization and neurotrophin stimulation, both did not result in a negative feedback on neurotrophin secretion. These results suggest that a negative feedback of NO signaling on synaptic secretion of neurotrophins operates only at high intracellular levels of nitric oxide that are under physiological conditions not reached by depolarization or BDNF signaling.

BDNF-induced nitric oxide signals in cultured rat hippocampal neurons: time course, mechanism of generation, and effect on neurotrophin secretion

PubMed Central

Kolarow, Richard; Kuhlmann, Christoph R. W.; Munsch, Thomas; Zehendner, Christoph; Brigadski, Tanja; Luhmann, Heiko J.; Lessmann, Volkmar

2014-01-01

BDNF and nitric oxide signaling both contribute to plasticity at glutamatergic synapses. However, the role of combined signaling of both pathways at the same synapse is largely unknown. Using NO imaging with diaminofluoresceine in cultured hippocampal neurons we analyzed the time course of neurotrophin-induced NO signals. Application of exogenous BDNF, NT-4, and NT-3 (but not NGF) induced NO signals in the soma and in proximal dendrites of hippocampal neurons that were sensitive to NO synthase activity, TrkB signaling, and intracellular calcium elevation. The effect of NO signaling on neurotrophin secretion was analyzed in BDNF-GFP, and NT-3-GFP transfected hippocampal neurons. Exogenous application of the NO donor sodium-nitroprusside markedly inhibited neurotrophin secretion. However, endogenously generated NO in response to depolarization and neurotrophin stimulation, both did not result in a negative feedback on neurotrophin secretion. These results suggest that a negative feedback of NO signaling on synaptic secretion of neurotrophins operates only at high intracellular levels of nitric oxide that are under physiological conditions not reached by depolarization or BDNF signaling. PMID:25426021

Roles for gut vagal sensory signals in determining energy availability and energy expenditure.

PubMed

Schwartz, Gary J

2018-08-15

The gut sensory vagus transmits a wide range of meal-related mechanical, chemical and gut peptide signals from gastrointestinal and hepatic tissues to the central nervous system at the level of the caudal brainstem. Results from studies using neurophysiological, behavioral physiological and metabolic approaches that challenge the integrity of this gut-brain axis support an important role for these gut signals in the negative feedback control of energy availability by limiting food intake during a meal. These experimental approaches have now been applied to identify important and unanticipated contributions of the vagal sensory gut-brain axis to the control of two additional effectors of overall energy balance: the feedback control of endogenous energy availability through hepatic glucose production and metabolism, and the control of energy expenditure through brown adipose tissue thermogenesis. Taken together, these studies reveal the pleiotropic influences of gut vagal meal-related signals on energy balance, and encourage experimental efforts aimed at understanding how the brainstem represents, organizes and coordinates gut vagal sensory signals with these three determinants of energy homeostasis. Copyright © 2018 Elsevier B.V. All rights reserved.

Automatic Adaptation to Fast Input Changes in a Time-Invariant Neural Circuit

PubMed Central

Bharioke, Arjun; Chklovskii, Dmitri B.

2015-01-01

Neurons must faithfully encode signals that can vary over many orders of magnitude despite having only limited dynamic ranges. For a correlated signal, this dynamic range constraint can be relieved by subtracting away components of the signal that can be predicted from the past, a strategy known as predictive coding, that relies on learning the input statistics. However, the statistics of input natural signals can also vary over very short time scales e.g., following saccades across a visual scene. To maintain a reduced transmission cost to signals with rapidly varying statistics, neuronal circuits implementing predictive coding must also rapidly adapt their properties. Experimentally, in different sensory modalities, sensory neurons have shown such adaptations within 100 ms of an input change. Here, we show first that linear neurons connected in a feedback inhibitory circuit can implement predictive coding. We then show that adding a rectification nonlinearity to such a feedback inhibitory circuit allows it to automatically adapt and approximate the performance of an optimal linear predictive coding network, over a wide range of inputs, while keeping its underlying temporal and synaptic properties unchanged. We demonstrate that the resulting changes to the linearized temporal filters of this nonlinear network match the fast adaptations observed experimentally in different sensory modalities, in different vertebrate species. Therefore, the nonlinear feedback inhibitory network can provide automatic adaptation to fast varying signals, maintaining the dynamic range necessary for accurate neuronal transmission of natural inputs. PMID:26247884

Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs

DOE PAGES

Buitrago, Jaime; Asfour, Shihab

2017-01-01

Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input.more » Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. Finally, the New England electrical load data are used to train and validate the forecast prediction.« less

Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Buitrago, Jaime; Asfour, Shihab

Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input.more » Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. Finally, the New England electrical load data are used to train and validate the forecast prediction.« less

Goal-directed control with cortical units that are gated by both top-down feedback and oscillatory coherence.

PubMed

Kerr, Robert R; Grayden, David B; Thomas, Doreen A; Gilson, Matthieu; Burkitt, Anthony N

2014-01-01

The brain is able to flexibly select behaviors that adapt to both its environment and its present goals. This cognitive control is understood to occur within the hierarchy of the cortex and relies strongly on the prefrontal and premotor cortices, which sit at the top of this hierarchy. Pyramidal neurons, the principal neurons in the cortex, have been observed to exhibit much stronger responses when they receive inputs at their soma/basal dendrites that are coincident with inputs at their apical dendrites. This corresponds to inputs from both lower-order regions (feedforward) and higher-order regions (feedback), respectively. In addition to this, coherence between oscillations, such as gamma oscillations, in different neuronal groups has been proposed to modulate and route communication in the brain. In this paper, we develop a simple, but novel, neural mass model in which cortical units (or ensembles) exhibit gamma oscillations when they receive coherent oscillatory inputs from both feedforward and feedback connections. By forming these units into circuits that can perform logic operations, we identify the different ways in which operations can be initiated and manipulated by top-down feedback. We demonstrate that more sophisticated and flexible top-down control is possible when the gain of units is modulated by not only top-down feedback but by coherence between the activities of the oscillating units. With these types of units, it is possible to not only add units to, or remove units from, a higher-level unit's logic operation using top-down feedback, but also to modify the type of role that a unit plays in the operation. Finally, we explore how different network properties affect top-down control and processing in large networks. Based on this, we make predictions about the likely connectivities between certain brain regions that have been experimentally observed to be involved in goal-directed behavior and top-down attention.

Goal-directed control with cortical units that are gated by both top-down feedback and oscillatory coherence

PubMed Central

Kerr, Robert R.; Grayden, David B.; Thomas, Doreen A.; Gilson, Matthieu; Burkitt, Anthony N.

2014-01-01

The brain is able to flexibly select behaviors that adapt to both its environment and its present goals. This cognitive control is understood to occur within the hierarchy of the cortex and relies strongly on the prefrontal and premotor cortices, which sit at the top of this hierarchy. Pyramidal neurons, the principal neurons in the cortex, have been observed to exhibit much stronger responses when they receive inputs at their soma/basal dendrites that are coincident with inputs at their apical dendrites. This corresponds to inputs from both lower-order regions (feedforward) and higher-order regions (feedback), respectively. In addition to this, coherence between oscillations, such as gamma oscillations, in different neuronal groups has been proposed to modulate and route communication in the brain. In this paper, we develop a simple, but novel, neural mass model in which cortical units (or ensembles) exhibit gamma oscillations when they receive coherent oscillatory inputs from both feedforward and feedback connections. By forming these units into circuits that can perform logic operations, we identify the different ways in which operations can be initiated and manipulated by top-down feedback. We demonstrate that more sophisticated and flexible top-down control is possible when the gain of units is modulated by not only top-down feedback but by coherence between the activities of the oscillating units. With these types of units, it is possible to not only add units to, or remove units from, a higher-level unit's logic operation using top-down feedback, but also to modify the type of role that a unit plays in the operation. Finally, we explore how different network properties affect top-down control and processing in large networks. Based on this, we make predictions about the likely connectivities between certain brain regions that have been experimentally observed to be involved in goal-directed behavior and top-down attention. PMID:25152715

Combined feedforward and feedback control of a redundant, nonlinear, dynamic musculoskeletal system.

PubMed

Blana, Dimitra; Kirsch, Robert F; Chadwick, Edward K

2009-05-01

A functional electrical stimulation controller is presented that uses a combination of feedforward and feedback for arm control in high-level injury. The feedforward controller generates the muscle activations nominally required for desired movements, and the feedback controller corrects for errors caused by muscle fatigue and external disturbances. The feedforward controller is an artificial neural network (ANN) which approximates the inverse dynamics of the arm. The feedback loop includes a PID controller in series with a second ANN representing the nonlinear properties and biomechanical interactions of muscles and joints. The controller was designed and tested using a two-joint musculoskeletal model of the arm that includes four mono-articular and two bi-articular muscles. Its performance during goal-oriented movements of varying amplitudes and durations showed a tracking error of less than 4 degrees in ideal conditions, and less than 10 degrees even in the case of considerable fatigue and external disturbances.

Identifying partial topology of complex dynamical networks via a pinning mechanism

NASA Astrophysics Data System (ADS)

Zhu, Shuaibing; Zhou, Jin; Lu, Jun-an

2018-04-01

In this paper, we study the problem of identifying the partial topology of complex dynamical networks via a pinning mechanism. By using the network synchronization theory and the adaptive feedback controlling method, we propose a method which can greatly reduce the number of nodes and observers in the response network. Particularly, this method can also identify the whole topology of complex networks. A theorem is established rigorously, from which some corollaries are also derived in order to make our method more cost-effective. Several numerical examples are provided to verify the effectiveness of the proposed method. In the simulation, an approach is also given to avoid possible identification failure caused by inner synchronization of the drive network.

Noise in Neural Networks: Thresholds, Hysteresis, and Neuromodulation of Signal-To-Noise

NASA Astrophysics Data System (ADS)

Keeler, James D.; Pichler, Elgar E.; Ross, John

1989-03-01

We study a neural-network model including Gaussian noise, higher-order neuronal interactions, and neuromodulation. For a first-order network, there is a threshold in the noise level (phase transition) above which the network displays only disorganized behavior and critical slowing down near the noise threshold. The network can tolerate more noise if it has higher-order feedback interactions, which also lead to hysteresis and multistability in the network dynamics. The signal-to-noise ratio can be adjusted in a biological neural network by neuromodulators such as norepinephrine. Comparisons are made to experimental results and further investigations are suggested to test the effects of hysteresis and neuromodulation in pattern recognition and learning. We propose that norepinephrine may ``quench'' the neural patterns of activity to enhance the ability to learn details.

Prediction of missing links and reconstruction of complex networks

NASA Astrophysics Data System (ADS)

Zhang, Cheng-Jun; Zeng, An

2016-04-01

Predicting missing links in complex networks is of great significance from both theoretical and practical point of view, which not only helps us understand the evolution of real systems but also relates to many applications in social, biological and online systems. In this paper, we study the features of different simple link prediction methods, revealing that they may lead to the distortion of networks’ structural and dynamical properties. Moreover, we find that high prediction accuracy is not definitely corresponding to a high performance in preserving the network properties when using link prediction methods to reconstruct networks. Our work highlights the importance of considering the feedback effect of the link prediction methods on network properties when designing the algorithms.

A biologically inspired neural network for dynamic programming.

PubMed

Francelin Romero, R A; Kacpryzk, J; Gomide, F

2001-12-01

An artificial neural network with a two-layer feedback topology and generalized recurrent neurons, for solving nonlinear discrete dynamic optimization problems, is developed. A direct method to assign the weights of neural networks is presented. The method is based on Bellmann's Optimality Principle and on the interchange of information which occurs during the synaptic chemical processing among neurons. The neural network based algorithm is an advantageous approach for dynamic programming due to the inherent parallelism of the neural networks; further it reduces the severity of computational problems that can occur in methods like conventional methods. Some illustrative application examples are presented to show how this approach works out including the shortest path and fuzzy decision making problems.