How adaptation shapes spike rate oscillations in recurrent neuronal networks

Directory of Open Access Journals (Sweden)

Moritz eAugustin

2013-02-01

Full Text Available Neural mass signals from in-vivo recordings often show oscillations with frequencies ranging from <1 Hz to 100 Hz. Fast rhythmic activity in the beta and gamma range can be generated by network based mechanisms such as recurrent synaptic excitation-inhibition loops. Slower oscillations might instead depend on neuronal adaptation currents whose timescales range from tens of milliseconds to seconds. Here we investigate how the dynamics of such adaptation currents contribute to spike rate oscillations and resonance properties in recurrent networks of excitatory and inhibitory neurons. Based on a network of sparsely coupled spiking model neurons with two types of adaptation current and conductance based synapses with heterogeneous strengths and delays we use a mean-field approach to analyze oscillatory network activity. For constant external input, we find that spike-triggered adaptation currents provide a mechanism to generate slow oscillations over a wide range of adaptation timescales as long as recurrent synaptic excitation is sufficiently strong. Faster rhythms occur when recurrent inhibition is slower than excitation and oscillation frequency increases with the strength of inhibition. Adaptation facilitates such network based oscillations for fast synaptic inhibition and leads to decreased frequencies. For oscillatory external input, adaptation currents amplify a narrow band of frequencies and cause phase advances for low frequencies in addition to phase delays at higher frequencies. Our results therefore identify the different key roles of neuronal adaptation dynamics for rhythmogenesis and selective signal propagation in recurrent networks.

The Brain on Art: Intense Aesthetic Experience Activates the Default Mode Network

Directory of Open Access Journals (Sweden)

Edward A Vessel

2012-04-01

Full Text Available Aesthetic responses to visual art comprise multiple types of experiences, from sensation and perception to emotion and self-reflection. Moreover, aesthetic experience is highly individual, with observers varying significantly in their responses to the same artwork. Combining fMRI and behavioral analysis of individual differences in aesthetic response, we identify two distinct patterns of neural activity exhibited by different subnetworks. Activity increased linearly with observers’ ratings (4-level scale in sensory (occipito-temporal regions. Activity in the striatum also varied linearly with ratings, with below-baseline activations for low-rated artworks. In contrast, a network of frontal regions showed a step-like increase only for the most moving artworks (4 ratings and non-differential activity for all others. This included several regions belonging to the default mode network previously associated with self-referential mentation. Our results suggest that aesthetic experience involves the integration of sensory and emotional reactions in a manner linked with their personal relevance.

Active Versus Passive Academic Networking

DEFF Research Database (Denmark)

Goel, Rajeev K.; Grimpe, Christoph

2013-01-01

This paper examines determinants of networking by academics. Using information from a unique large survey of German researchers, the key contribution focuses on the active versus passive networking distinction. Is active networking by researchers a substitute or a complement to passive networking......? Other contributions include examining the role of geographic factors in networking and whether research bottlenecks affect a researcher's propensity to network. Are the determinants of European conference participation by German researchers different from conferences in rest of the world? Results show...... that some types of passive academic networking are complementary to active networking, while others are substitute. Further, we find differences in factors promoting participation in European conferences versus conferences in rest of the world. Finally, publishing bottlenecks as a group generally do...

Networking Technologies and the Rate of Technological Change

Directory of Open Access Journals (Sweden)

Charles Mitchell

2005-12-01

Full Text Available Network technology is changing rapidly and those adept at ICT analysis need resolve rate of change issues. Developments in networking now are in the direction of heuristic intelligence. Since about 1980, networking techniques have encouraged combining bits of information with imagination cognitively to improve ideas about reality. ICT enterprise projects utilize networking to sustain requisite imagination. Assumptions and misassuptions of project builders are rationally comprehended as networking sustains creative processes. The monopolization of valuable network techniques influences in the direction of esoteric networking. Data presents that substantial knowledge and networking is now occurring globally. As a netaphor, networking

Shapley ratings in brain networks

Directory of Open Access Journals (Sweden)

Rolf Kötter

2007-11-01

Full Text Available Recent applications of network theory to brain networks as well as the expanding empirical databases of brain architecture spawn an interest in novel techniques for analyzing connectivity patterns in the brain. Treating individual brain structures as nodes in a directed graph model permits the application of graph theoretical concepts to the analysis of these structures within their large-scale connectivity networks. In this paper, we explore the application of concepts from graph and game theory toward this end. Specifically, we utilize the Shapley value principle, which assigns a rank to players in a coalition based upon their individual contributions to the collective profit of that coalition, to assess the contributions of individual brain structures to the graph derived from the global connectivity network. We report Shapley values for variations of a prefrontal network, as well as for a visual cortical network, which had both been extensively investigated previously. This analysis highlights particular nodes as strong or weak contributors to global connectivity. To understand the nature of their contribution, we compare the Shapley values obtained from these networks and appropriate controls to other previously described nodal measures of structural connectivity. We find a strong correlation between Shapley values and both betweenness centrality and connection density. Moreover, a stepwise multiple linear regression analysis indicates that approximately 79% of the variance in Shapley values obtained from random networks can be explained by betweenness centrality alone. Finally, we investigate the effects of local lesions on the Shapley ratings, showing that the present networks have an immense structural resistance to degradation. We discuss our results highlighting the use of such measures for characterizing the organization and functional role of brain networks.

Reflections on Active Networking

Science.gov (United States)

2005-01-01

with a Software Switch for Active Networks ”. We had initially called the project “ SoftSwitch ”, but after some concerns David Farber raised that this...Reflections on Active Networking Jonathan M. Smith CIS Department, University of Pennsylvania jms@cis.upenn.edu Abstract Interactions among...telecommunications networks , computers, and other peripheral devices have been of interest since the earliest distributed computing systems. A key

Phase-dependent effects of stimuli locked to oscillatory activity in cultured cortical networks

NARCIS (Netherlands)

Stegenga, J.; le Feber, Jakob; Marani, Enrico; Rutten, Wim

The archetypal activity pattern in cultures of dissociated neurons is spontaneous network-wide bursting. Bursts may interfere with controlled activation of synaptic plasticity, but can be suppressed by the application of stimuli at a sufficient rate. We sinusoidally modulated (4 Hz) the pulse rate

High throughput route selection in multi-rate wireless mesh networks

Institute of Scientific and Technical Information of China (English)

WEI Yi-fei; GUO Xiang-li; SONG Mei; SONG Jun-de

2008-01-01

Most existing Ad-hoc routing protocols use the shortest path algorithm with a hop count metric to select paths. It is appropriate in single-rate wireless networks, but has a tendency to select paths containing long-distance links that have low data rates and reduced reliability in multi-rate networks. This article introduces a high throughput routing algorithm utilizing the multi-rate capability and some mesh characteristics in wireless fidelity (WiFi) mesh networks. It uses the medium access control (MAC) transmission time as the routing metric, which is estimated by the information passed up from the physical layer. When the proposed algorithm is adopted, the Ad-hoc on-demand distance vector (AODV) routing can be improved as high throughput AODV (HT-AODV). Simulation results show that HT-AODV is capable of establishing a route that has high data-rate, short end-to-end delay and great network throughput.

Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks.

Directory of Open Access Journals (Sweden)

Saket Navlakha

2015-07-01

Full Text Available Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains.

Management of synchronized network activity by highly active neurons

International Nuclear Information System (INIS)

Shein, Mark; Raichman, Nadav; Ben-Jacob, Eshel; Volman, Vladislav; Hanein, Yael

2008-01-01

Increasing evidence supports the idea that spontaneous brain activity may have an important functional role. Cultured neuronal networks provide a suitable model system to search for the mechanisms by which neuronal spontaneous activity is maintained and regulated. This activity is marked by synchronized bursting events (SBEs)—short time windows (hundreds of milliseconds) of rapid neuronal firing separated by long quiescent periods (seconds). However, there exists a special subset of rapidly firing neurons whose activity also persists between SBEs. It has been proposed that these highly active (HA) neurons play an important role in the management (i.e. establishment, maintenance and regulation) of the synchronized network activity. Here, we studied the dynamical properties and the functional role of HA neurons in homogeneous and engineered networks, during early network development, upon recovery from chemical inhibition and in response to electrical stimulations. We found that their sequences of inter-spike intervals (ISI) exhibit long time correlations and a unimodal distribution. During the network's development and under intense inhibition, the observed activity follows a transition period during which mostly HA neurons are active. Studying networks with engineered geometry, we found that HA neurons are precursors (the first to fire) of the spontaneous SBEs and are more responsive to electrical stimulations

Networking activities in technology-based entrepreneurial teams

DEFF Research Database (Denmark)

Neergaard, Helle

2005-01-01

Based on social network theoy, this article investigates the distribution of networking roles and responsibilities in entrepreneurial founding teams. Its focus is on the team as a collection of individuals, thus allowing the research to address differences in networking patterns. It identifies six...... central networking activities and shows that not all founding team members are equally active 'networkers'. The analyses show that team members prioritize different networking activities and that one member in particular has extensive networking activities whereas other memebrs of the team are more...

Coordinated Voltage Control of Active Distribution Network

Directory of Open Access Journals (Sweden)

Xie Jiang

2016-01-01

Full Text Available This paper presents a centralized coordinated voltage control method for active distribution network to solve off-limit problem of voltage after incorporation of distributed generation (DG. The proposed method consists of two parts, it coordinated primal-dual interior point method-based voltage regulation schemes of DG reactive powers and capacitors with centralized on-load tap changer (OLTC controlling method which utilizes system’s maximum and minimum voltages, to improve the qualified rate of voltage and reduce the operation numbers of OLTC. The proposed coordination has considered the cost of capacitors. The method is tested using a radial edited IEEE-33 nodes distribution network which is modelled using MATLAB.

Algorithm for queueing networks with multi-rate traffic

DEFF Research Database (Denmark)

Iversen, Villy Bæk; Ko, King-Tim

2011-01-01

the nodes behave as independent nodes. For closed queueing networks with multiple servers in every node and multi-rate services we may apply multidimensional convolution algorithm to aggregate the nodes so that we end up with two nodes, the aggregated node and a single node, for which we can calculate......In this paper we present a new algorithm for evaluating queueing networks with multi-rate traffic. The detailed state space of a node is evaluated by explicit formulæ. We consider reversible nodes with multi-rate traffic and find the state probabilities by taking advantage of local balance. Theory...... of queueing networks in general, presumes that we have product form between the nodes. Otherwise, we have the state space explosion. Even so, the detailed state space of each node may become very large because there is no product form between chains inside a node. A prerequisite for product form...

Activity flow over resting-state networks shapes cognitive task activations.

Science.gov (United States)

Cole, Michael W; Ito, Takuya; Bassett, Danielle S; Schultz, Douglas H

2016-12-01

Resting-state functional connectivity (FC) has helped reveal the intrinsic network organization of the human brain, yet its relevance to cognitive task activations has been unclear. Uncertainty remains despite evidence that resting-state FC patterns are highly similar to cognitive task activation patterns. Identifying the distributed processes that shape localized cognitive task activations may help reveal why resting-state FC is so strongly related to cognitive task activations. We found that estimating task-evoked activity flow (the spread of activation amplitudes) over resting-state FC networks allowed prediction of cognitive task activations in a large-scale neural network model. Applying this insight to empirical functional MRI data, we found that cognitive task activations can be predicted in held-out brain regions (and held-out individuals) via estimated activity flow over resting-state FC networks. This suggests that task-evoked activity flow over intrinsic networks is a large-scale mechanism explaining the relevance of resting-state FC to cognitive task activations.

Algorithm for queueing networks with multi-rate traffic

DEFF Research Database (Denmark)

Iversen, Villy Bæk; King-Tim, Ko

2011-01-01

the nodes behave as independent nodes. For closed queueing networks with multiple servers in every node and multi-rate services we may apply multidimensional convolutions to aggregate the nodes so that we end up with two nodes, the aggregated node and a single node, for which we can calculate the detailed......In this paper we present a new algorithm for evaluating queueing networks with multi-rate traffic. The detailed state space of a node is evaluated by explicit formulæ. We consider reversible nodes with multi-rate traffic and find the state probabilities by taking advantage of local balance. Theory...... of queueing networks in general presumes that we have product form between the nodes. Other ways we have the state space explosion. Even so the detailed state space of each node may easily become very large because there is no product form between chains inside a node. A prerequisite for product form...

Information mining in weighted complex networks with nonlinear rating projection

Science.gov (United States)

Liao, Hao; Zeng, An; Zhou, Mingyang; Mao, Rui; Wang, Bing-Hong

2017-10-01

Weighted rating networks are commonly used by e-commerce providers nowadays. In order to generate an objective ranking of online items' quality according to users' ratings, many sophisticated algorithms have been proposed in the complex networks domain. In this paper, instead of proposing new algorithms we focus on a more fundamental problem: the nonlinear rating projection. The basic idea is that even though the rating values given by users are linearly separated, the real preference of users to items between the different given values is nonlinear. We thus design an approach to project the original ratings of users to more representative values. This approach can be regarded as a data pretreatment method. Simulation in both artificial and real networks shows that the performance of the ranking algorithms can be improved when the projected ratings are used.

Forecasting Flare Activity Using Deep Convolutional Neural Networks

Science.gov (United States)

Hernandez, T.

2017-12-01

Current operational flare forecasting relies on human morphological analysis of active regions and the persistence of solar flare activity through time (i.e. that the Sun will continue to do what it is doing right now: flaring or remaining calm). In this talk we present the results of applying deep Convolutional Neural Networks (CNNs) to the problem of solar flare forecasting. CNNs operate by training a set of tunable spatial filters that, in combination with neural layer interconnectivity, allow CNNs to automatically identify significant spatial structures predictive for classification and regression problems. We will start by discussing the applicability and success rate of the approach, the advantages it has over non-automated forecasts, and how mining our trained neural network provides a fresh look into the mechanisms behind magnetic energy storage and release.

Entertainment Capture through Heart Rate Activity in Physical Interactive Playgrounds

DEFF Research Database (Denmark)

Yannakakis, Georgios; Hallam, John; Lund, Henrik Hautop

2008-01-01

children’s physiological signals, an estimator of the degree to which games provided by the playground engage the players. For this purpose children’s heart rate (HR) signals, and their expressed preferences of how much “fun” particular game variants are, are obtained from experiments using games...... that predict reported entertainment preferences given HR features. These models are expressed as artificial neural networks and are demonstrated and evaluated on two Playware games and two control tasks requiring physical activity. The best network is able to correctly match expressed preferences in 64...

Decentralized control of transmission rates in energy-critical wireless networks

KAUST Repository

Xia, Li

2013-06-01

In this paper, we discuss the decentralized optimization of delay and energy consumption in a multi-hop wireless network. The goal is to minimize the energy consumption of energy-critical nodes and the overall packet transmission delay of the network. The transmission rates of energy-critical nodes are adjustable according to the local information of nodes, i.e., the length of packets queued. The multi-hop network is modeled as a queueing network.We prove that the system performance is monotone w.r.t. (with respect to) the transmission rate, thus the “bang-bang” control is an optimal control. We also prove that there exists a threshold type control policy which is optimal. We propose a decentralized algorithm to control transmission rates of these energy-critical nodes. Some simulation experiments are conducted to demonstrate the effectiveness of our approach.

Decentralized control of transmission rates in energy-critical wireless networks

KAUST Repository

Xia, Li; Shihada, Basem

2013-01-01

In this paper, we discuss the decentralized optimization of delay and energy consumption in a multi-hop wireless network. The goal is to minimize the energy consumption of energy-critical nodes and the overall packet transmission delay of the network. The transmission rates of energy-critical nodes are adjustable according to the local information of nodes, i.e., the length of packets queued. The multi-hop network is modeled as a queueing network.We prove that the system performance is monotone w.r.t. (with respect to) the transmission rate, thus the “bang-bang” control is an optimal control. We also prove that there exists a threshold type control policy which is optimal. We propose a decentralized algorithm to control transmission rates of these energy-critical nodes. Some simulation experiments are conducted to demonstrate the effectiveness of our approach.

Physician social networks and variation in rates of complications after radical prostatectomy.

Science.gov (United States)

Evan Pollack, Craig; Wang, Hao; Bekelman, Justin E; Weissman, Gary; Epstein, Andrew J; Liao, Kaijun; Dugoff, Eva H; Armstrong, Katrina

2014-07-01

Variation in care within and across geographic areas remains poorly understood. The goal of this article was to examine whether physician social networks-as defined by shared patients-are associated with rates of complications after radical prostatectomy. In five cities, we constructed networks of physicians on the basis of their shared patients in 2004-2005 Surveillance, Epidemiology and End Results-Medicare data. From these networks, we identified subgroups of urologists who most frequently shared patients with one another. Among men with localized prostate cancer who underwent radical prostatectomy, we used multilevel analysis with generalized linear mixed-effect models to examine whether physician network structure-along with specific characteristics of the network subgroups-was associated with rates of 30-day and late urinary complications, and long-term incontinence after accounting for patient-level sociodemographic, clinical factors, and urologist patient volume. Networks included 2677 men in five cities who underwent radical prostatectomy. The unadjusted rate of 30-day surgical complications varied across network subgroups from an 18.8 percentage-point difference in the rate of complications across network subgroups in city 1 to a 26.9 percentage-point difference in city 5. Large differences in unadjusted rates of late urinary complications and long-term incontinence across subgroups were similarly found. Network subgroup characteristics-average urologist centrality and patient racial composition-were significantly associated with rates of surgical complications. Analysis of physician networks using Surveillance, Epidemiology and End Results-Medicare data provides insight into observed variation in rates of complications for localized prostate cancer. If validated, such approaches may be used to target future quality improvement interventions. Copyright © 2014 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier

A Distributed Flow Rate Control Algorithm for Networked Agent System with Multiple Coding Rates to Optimize Multimedia Data Transmission

Directory of Open Access Journals (Sweden)

Shuai Zeng

2013-01-01

Full Text Available With the development of wireless technologies, mobile communication applies more and more extensively in the various walks of life. The social network of both fixed and mobile users can be seen as networked agent system. At present, kinds of devices and access network technology are widely used. Different users in this networked agent system may need different coding rates multimedia data due to their heterogeneous demand. This paper proposes a distributed flow rate control algorithm to optimize multimedia data transmission of the networked agent system with the coexisting various coding rates. In this proposed algorithm, transmission path and upload bandwidth of different coding rate data between source node, fixed and mobile nodes are appropriately arranged and controlled. On the one hand, this algorithm can provide user nodes with differentiated coding rate data and corresponding flow rate. On the other hand, it makes the different coding rate data and user nodes networked, which realizes the sharing of upload bandwidth of user nodes which require different coding rate data. The study conducts mathematical modeling on the proposed algorithm and compares the system that adopts the proposed algorithm with the existing system based on the simulation experiment and mathematical analysis. The results show that the system that adopts the proposed algorithm achieves higher upload bandwidth utilization of user nodes and lower upload bandwidth consumption of source node.

Theorizing Network-Centric Activity in Education

Science.gov (United States)

HaLevi, Andrew

2011-01-01

Networks and network-centric activity are increasingly prevalent in schools and school districts. In addition to ubiquitous social network tools like Facebook and Twitter, educational leaders deal with a wide variety of network organizational forms that include professional development, advocacy, informational networks and network-centric reforms.…

Resource Discovery in Activity-Based Sensor Networks

DEFF Research Database (Denmark)

Bucur, Doina; Bardram, Jakob

This paper proposes a service discovery protocol for sensor networks that is specifically tailored for use in humancentered pervasive environments. It uses the high-level concept of computational activities (as logical bundles of data and resources) to give sensors in Activity-Based Sensor Networ....... ABSN enhances the generic Extended Zone Routing Protocol with logical sensor grouping and greatly lowers network overhead during the process of discovery, while keeping discovery latency close to optimal.......This paper proposes a service discovery protocol for sensor networks that is specifically tailored for use in humancentered pervasive environments. It uses the high-level concept of computational activities (as logical bundles of data and resources) to give sensors in Activity-Based Sensor Networks...... (ABSNs) knowledge about their usage even at the network layer. ABSN redesigns classical network-level service discovery protocols to include and use this logical structuring of the network for a more practically applicable service discovery scheme. Noting that in practical settings activity-based sensor...

Study Heart Rate by Tools from Complex Networks

International Nuclear Information System (INIS)

Makowiec, D.; Wdowczyk-Szulc, J.; Zarczynska-Buchowiecka, M.; Gruchala, M.; Rynkiewicz, A.

2011-01-01

Heart rate measured as beat-to-beat time intervals varies in time. It is believed that time intervals between subsequent normal heart contractions carry information about the regulatory system of the heart. How to quantify such signals is not clear and because of that heart rate variability is still apart from the clinic routine. In the following, we propose a method for representing a heart rate signal as a directed network. Then we study the signal properties by complex network tools. The signals to study were collected from patients recovering after the heart transplantation. The aim is to classify the progress of adapting of the new heart - graft. Moreover, it is expected that the method allows for visual classification. Our investigations are preliminary, however the obtained results are promising. (authors)

Ethanol affects network activity in cultured rat hippocampus: mediation by potassium channels.

Directory of Open Access Journals (Sweden)

Eduard Korkotian

Full Text Available The effects of ethanol on neuronal network activity were studied in dissociated cultures of rat hippocampus. Exposure to low (0.25-0.5% ethanol concentrations caused an increase in synchronized network spikes, and a decrease in the duration of individual spikes. Ethanol also caused an increase in rate of miniature spontaneous excitatory postsynaptic currents. Higher concentrations of ethanol eliminated network spikes. These effects were reversible upon wash. The effects of the high, but not the low ethanol were blocked by the GABA antagonist bicuculline. The enhancing action of low ethanol was blocked by apamin, an SK potassium channel antagonist, and mimicked by 1-EBIO, an SK channel opener. It is proposed that in cultured hippocampal networks low concentration of ethanol is associated with SK channel activity, rather than the GABAergic receptor.

Resource Discovery in Activity-Based Sensor Networks

DEFF Research Database (Denmark)

Bucur, Doina; Bardram, Jakob

This paper proposes a service discovery protocol for sensor networks that is specifically tailored for use in humancentered pervasive environments. It uses the high-level concept of computational activities (as logical bundles of data and resources) to give sensors in Activity-Based Sensor Networks...... (ABSNs) knowledge about their usage even at the network layer. ABSN redesigns classical network-level service discovery protocols to include and use this logical structuring of the network for a more practically applicable service discovery scheme. Noting that in practical settings activity-based sensor...

A reanalysis of "Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons".

Science.gov (United States)

Engelken, Rainer; Farkhooi, Farzad; Hansel, David; van Vreeswijk, Carl; Wolf, Fred

2016-01-01

Neuronal activity in the central nervous system varies strongly in time and across neuronal populations. It is a longstanding proposal that such fluctuations generically arise from chaotic network dynamics. Various theoretical studies predict that the rich dynamics of rate models operating in the chaotic regime can subserve circuit computation and learning. Neurons in the brain, however, communicate via spikes and it is a theoretical challenge to obtain similar rate fluctuations in networks of spiking neuron models. A recent study investigated spiking balanced networks of leaky integrate and fire (LIF) neurons and compared their dynamics to a matched rate network with identical topology, where single unit input-output functions were chosen from isolated LIF neurons receiving Gaussian white noise input. A mathematical analogy between the chaotic instability in networks of rate units and the spiking network dynamics was proposed. Here we revisit the behavior of the spiking LIF networks and these matched rate networks. We find expected hallmarks of a chaotic instability in the rate network: For supercritical coupling strength near the transition point, the autocorrelation time diverges. For subcritical coupling strengths, we observe critical slowing down in response to small external perturbations. In the spiking network, we found in contrast that the timescale of the autocorrelations is insensitive to the coupling strength and that rate deviations resulting from small input perturbations rapidly decay. The decay speed even accelerates for increasing coupling strength. In conclusion, our reanalysis demonstrates fundamental differences between the behavior of pulse-coupled spiking LIF networks and rate networks with matched topology and input-output function. In particular there is no indication of a corresponding chaotic instability in the spiking network.

Inference of financial networks using the normalised mutual information rate

Science.gov (United States)

2018-01-01

In this paper, we study data from financial markets, using the normalised Mutual Information Rate. We show how to use it to infer the underlying network structure of interrelations in the foreign currency exchange rates and stock indices of 15 currency areas. We first present the mathematical method and discuss its computational aspects, and apply it to artificial data from chaotic dynamics and to correlated normal-variates data. We then apply the method to infer the structure of the financial system from the time-series of currency exchange rates and stock indices. In particular, we study and reveal the interrelations among the various foreign currency exchange rates and stock indices in two separate networks, of which we also study their structural properties. Our results show that both inferred networks are small-world networks, sharing similar properties and having differences in terms of assortativity. Importantly, our work shows that global economies tend to connect with other economies world-wide, rather than creating small groups of local economies. Finally, the consistent interrelations depicted among the 15 currency areas are further supported by a discussion from the viewpoint of economics. PMID:29420644

Inference of financial networks using the normalised mutual information rate.

Science.gov (United States)

Goh, Yong Kheng; Hasim, Haslifah M; Antonopoulos, Chris G

2018-01-01

In this paper, we study data from financial markets, using the normalised Mutual Information Rate. We show how to use it to infer the underlying network structure of interrelations in the foreign currency exchange rates and stock indices of 15 currency areas. We first present the mathematical method and discuss its computational aspects, and apply it to artificial data from chaotic dynamics and to correlated normal-variates data. We then apply the method to infer the structure of the financial system from the time-series of currency exchange rates and stock indices. In particular, we study and reveal the interrelations among the various foreign currency exchange rates and stock indices in two separate networks, of which we also study their structural properties. Our results show that both inferred networks are small-world networks, sharing similar properties and having differences in terms of assortativity. Importantly, our work shows that global economies tend to connect with other economies world-wide, rather than creating small groups of local economies. Finally, the consistent interrelations depicted among the 15 currency areas are further supported by a discussion from the viewpoint of economics.

Dependence of synchronized bursting activity on medium stirring and the perfusion rate in a cultured network of neurons

Science.gov (United States)

Heo, Ryoun; Kim, Hyun; Lee, Kyoung J.

2016-05-01

A cultured network of neurons coupled with a multi-electrode-array (MEA) recording system has been a useful platform for investigating various issues in neuroscience and engineering. The neural activity supported by the system can be sensitive to environmental fluctuations, for example, in the medium's nutrient composition, ph, and temperature, and to mechanical disturbances, yet this issue has not been the subject. Especially, a normal practice in maintaining neuronal cell cultures involves an intermittent sequence of medium exchanges, typically at a time interval of a few days, and one such sudden medium exchange is unavoidably accompanied by many unintended disturbances. Here, based on a quantitative time-series analysis of synchronized bursting events, we explicitly demonstrate that such a medium exchange can, indeed, bring a huge change in the existing neural activity. Subsequently, we develop a medium perfusion-stirring system and an ideal protocol that can be used in conjunction with a MEA recording system, providing long-term stability. Specifically, we systematically evaluate the effects of medium stirring and perfusion rates. Unexpectedly, even some vigorous mechanical agitations do not have any impacts on neural activity. On the other hand, too much replenishment ( e.g., 1.8 ml/day for a 1.8-ml dish) of neurobasal medium results in an excitotoxicity.

A packet-based dual-rate PID control strategy for a slow-rate sensing Networked Control System.

Science.gov (United States)

Cuenca, A; Alcaina, J; Salt, J; Casanova, V; Pizá, R

2018-05-01

This paper introduces a packet-based dual-rate control strategy to face time-varying network-induced delays, packet dropouts and packet disorder in a Networked Control System. Slow-rate sensing enables to achieve energy saving and to avoid packet disorder. Fast-rate actuation makes reaching the desired control performance possible. The dual-rate PID controller is split into two parts: a slow-rate PI controller located at the remote side (with no permanent communication to the plant) and a fast-rate PD controller located at the local side. The remote side also includes a prediction stage in order to generate the packet of future, estimated slow-rate control actions. These actions are sent to the local side and converted to fast-rate ones to be used when a packet does not arrive at this side due to the network-induced delay or due to occurring dropouts. The proposed control solution is able to approximately reach the nominal (no-delay, no-dropout) performance despite the existence of time-varying delays and packet dropouts. Control system stability is ensured in terms of probabilistic Linear Matrix Inequalities (LMIs). Via real-time control for a Cartesian robot, results clearly reveal the superiority of the control solution compared to a previous proposal by authors. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Cross-Layer Active Predictive Congestion Control Protocol for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Yinfeng Wu

2009-10-01

Full Text Available In wireless sensor networks (WSNs, there are numerous factors that may cause network congestion problems, such as the many-to-one communication modes, mutual interference of wireless links, dynamic changes of network topology and the memory-restrained characteristics of nodes. All these factors result in a network being more vulnerable to congestion. In this paper, a cross-layer active predictive congestion control scheme (CL-APCC for improving the performance of networks is proposed. Queuing theory is applied in the CL-APCC to analyze data flows of a single-node according to its memory status, combined with the analysis of the average occupied memory size of local networks. It also analyzes the current data change trends of local networks to forecast and actively adjust the sending rate of the node in the next period. In order to ensure the fairness and timeliness of the network, the IEEE 802.11 protocol is revised based on waiting time, the number of the node‟s neighbors and the original priority of data packets, which dynamically adjusts the sending priority of the node. The performance of CL-APCC, which is evaluated by extensive simulation experiments. is more efficient in solving the congestion in WSNs. Furthermore, it is clear that the proposed scheme has an outstanding advantage in terms of improving the fairness and lifetime of networks.

Cross-layer active predictive congestion control protocol for wireless sensor networks.

Science.gov (United States)

Wan, Jiangwen; Xu, Xiaofeng; Feng, Renjian; Wu, Yinfeng

2009-01-01

In wireless sensor networks (WSNs), there are numerous factors that may cause network congestion problems, such as the many-to-one communication modes, mutual interference of wireless links, dynamic changes of network topology and the memory-restrained characteristics of nodes. All these factors result in a network being more vulnerable to congestion. In this paper, a cross-layer active predictive congestion control scheme (CL-APCC) for improving the performance of networks is proposed. Queuing theory is applied in the CL-APCC to analyze data flows of a single-node according to its memory status, combined with the analysis of the average occupied memory size of local networks. It also analyzes the current data change trends of local networks to forecast and actively adjust the sending rate of the node in the next period. In order to ensure the fairness and timeliness of the network, the IEEE 802.11 protocol is revised based on waiting time, the number of the node's neighbors and the original priority of data packets, which dynamically adjusts the sending priority of the node. The performance of CL-APCC, which is evaluated by extensive simulation experiments. is more efficient in solving the congestion in WSNs. Furthermore, it is clear that the proposed scheme has an outstanding advantage in terms of improving the fairness and lifetime of networks.

Network-dependent modulation of brain activity during sleep.

Science.gov (United States)

Watanabe, Takamitsu; Kan, Shigeyuki; Koike, Takahiko; Misaki, Masaya; Konishi, Seiki; Miyauchi, Satoru; Miyahsita, Yasushi; Masuda, Naoki

2014-09-01

Brain activity dynamically changes even during sleep. A line of neuroimaging studies has reported changes in functional connectivity and regional activity across different sleep stages such as slow-wave sleep (SWS) and rapid-eye-movement (REM) sleep. However, it remains unclear whether and how the large-scale network activity of human brains changes within a given sleep stage. Here, we investigated modulation of network activity within sleep stages by applying the pairwise maximum entropy model to brain activity obtained by functional magnetic resonance imaging from sleeping healthy subjects. We found that the brain activity of individual brain regions and functional interactions between pairs of regions significantly increased in the default-mode network during SWS and decreased during REM sleep. In contrast, the network activity of the fronto-parietal and sensory-motor networks showed the opposite pattern. Furthermore, in the three networks, the amount of the activity changes throughout REM sleep was negatively correlated with that throughout SWS. The present findings suggest that the brain activity is dynamically modulated even in a sleep stage and that the pattern of modulation depends on the type of the large-scale brain networks. Copyright © 2014 Elsevier Inc. All rights reserved.

Discrete rate and variable power adaptation for underlay cognitive networks

KAUST Repository

Abdallah, Mohamed M.; Salem, Ahmed H.; Alouini, Mohamed-Slim; Qaraqe, Khalid A.

2010-01-01

We consider the problem of maximizing the average spectral efficiency of a secondary link in underlay cognitive networks. In particular, we consider the network setting whereby the secondary transmitter employs discrete rate and variable power

Prediction of ozone tropospheric degradation rate constant of organic compounds by using artificial neural networks

International Nuclear Information System (INIS)

Fatemi, M.H.

2006-01-01

Ozone tropospheric degradation of organic compound is very important in environmental chemistry. The lifetime of organic chemicals in the atmosphere can be calculated from the knowledge of the rate constant of their reaction with free radicals such as OH and NO 3 or O 3 . In the present work, the rate constant for the tropospheric degradation of 137 organic compounds by reaction with ozone, the least widely and successfully modeled degradation process, are predicted by quantitative structure activity relationships modeling based on a variety of theoretical descriptors, which screened and selected by genetic algorithm variable subset selection procedure. These descriptors which can be used as inputs for generated artificial neural networks are; HOMO-LUMO gap, number of double bonds, number of single bonds, maximum net charge on C atom, minimum (>0.1) bond order of C atom and Minimum e-e repulsion of H atom. After generation, optimization and training of artificial neural network, network was used for the prediction of log KO 3 for the validation set. The root mean square error for the neural network calculated log KO 3 for training, prediction and validation set are 0.357, 0.460 and 0.481, respectively, which are smaller than those obtained by multiple linear regressions model (1.217, 0.870 and 0.968, respectively). Results obtained reveal the reliability and good predictivity of neural network model for the prediction of ozone tropospheric degradations rate constant of organic compounds

Forecasting the mortality rates of Indonesian population by using neural network

Science.gov (United States)

Safitri, Lutfiani; Mardiyati, Sri; Rahim, Hendrisman

2018-03-01

A model that can represent a problem is required in conducting a forecasting. One of the models that has been acknowledged by the actuary community in forecasting mortality rate is the Lee-Certer model. Lee Carter model supported by Neural Network will be used to calculate mortality forecasting in Indonesia. The type of Neural Network used is feedforward neural network aligned with backpropagation algorithm in python programming language. And the final result of this study is mortality rate in forecasting Indonesia for the next few years

Exponential stability of Cohen-Grossberg neural networks with a general class of activation functions

International Nuclear Information System (INIS)

Wan Anhua; Wang Miansen; Peng Jigen; Qiao Hong

2006-01-01

In this Letter, the dynamics of Cohen-Grossberg neural networks model are investigated. The activation functions are only assumed to be Lipschitz continuous, which provide a much wider application domain for neural networks than the previous results. By means of the extended nonlinear measure approach, new and relaxed sufficient conditions for the existence, uniqueness and global exponential stability of equilibrium of the neural networks are obtained. Moreover, an estimate for the exponential convergence rate of the neural networks is precisely characterized. Our results improve those existing ones

Recovery Act: Energy Efficiency of Data Networks through Rate Adaptation (EEDNRA) - Final Technical Report

Energy Technology Data Exchange (ETDEWEB)

Matthew Andrews; Spyridon Antonakopoulos; Steve Fortune; Andrea Francini; Lisa Zhang

2011-07-12

This Concept Definition Study focused on developing a scientific understanding of methods to reduce energy consumption in data networks using rate adaptation. Rate adaptation is a collection of techniques that reduce energy consumption when traffic is light, and only require full energy when traffic is at full provisioned capacity. Rate adaptation is a very promising technique for saving energy: modern data networks are typically operated at average rates well below capacity, but network equipment has not yet been designed to incorporate rate adaptation. The Study concerns packet-switching equipment, routers and switches; such equipment forms the backbone of the modern Internet. The focus of the study is on algorithms and protocols that can be implemented in software or firmware to exploit hardware power-control mechanisms. Hardware power-control mechanisms are widely used in the computer industry, and are beginning to be available for networking equipment as well. Network equipment has different performance requirements than computer equipment because of the very fast rate of packet arrival; hence novel power-control algorithms are required for networking. This study resulted in five published papers, one internal report, and two patent applications, documented below. The specific technical accomplishments are the following: • A model for the power consumption of switching equipment used in service-provider telecommunication networks as a function of operating state, and measured power-consumption values for typical current equipment. • An algorithm for use in a router that adapts packet processing rate and hence power consumption to traffic load while maintaining performance guarantees on delay and throughput. • An algorithm that performs network-wide traffic routing with the objective of minimizing energy consumption, assuming that routers have less-than-ideal rate adaptivity. • An estimate of the potential energy savings in service-provider networks

The effect of zealots on the rate of consensus achievement in complex networks

Science.gov (United States)

Kashisaz, Hadi; Hosseini, S. Samira; Darooneh, Amir H.

2014-05-01

In this study, we investigate the role of zealots on the result of voting process on both scale-free and Watts-Strogatz networks. We observe that inflexible individuals are very effective in consensus achievement and also in the rate of ordering process in complex networks. Zealots make the magnetization of the system to vary exponentially with time. We obtain that on SF networks, increasing the zealots' population, Z, exponentially increases the rate of consensus achievement. The time needed for the system to reach a desired magnetization, shows a power-law dependence on Z. As well, we obtain that the decay time of the order parameter shows a power-law dependence on Z. We also investigate the role of zealots' degree on the rate of ordering process and finally, we analyze the effect of network's randomness on the efficiency of zealots. Moving from a regular to a random network, the re-wiring probability P increases. We show that with increasing P, the efficiency of zealots for reducing the consensus achievement time increases. The rate of consensus is compared with the rate of ordering for different re-wiring probabilities of WS networks.

Application of neural networks to seismic active control

International Nuclear Information System (INIS)

Tang, Yu.

1995-01-01

An exploratory study on seismic active control using an artificial neural network (ANN) is presented in which a singledegree-of-freedom (SDF) structural system is controlled by a trained neural network. A feed-forward neural network and the backpropagation training method are used in the study. In backpropagation training, the learning rate is determined by ensuring the decrease of the error function at each training cycle. The training patterns for the neural net are generated randomly. Then, the trained ANN is used to compute the control force according to the control algorithm. The control strategy proposed herein is to apply the control force at every time step to destroy the build-up of the system response. The ground motions considered in the simulations are the N21E and N69W components of the Lake Hughes No. 12 record that occurred in the San Fernando Valley in California on February 9, 1971. Significant reduction of the structural response by one order of magnitude is observed. Also, it is shown that the proposed control strategy has the ability to reduce the peak that occurs during the first few cycles of the time history. These promising results assert the potential of applying ANNs to active structural control under seismic loads

Topic-oriented community detection of rating-based social networks

Directory of Open Access Journals (Sweden)

Ali Reihanian

2016-07-01

Full Text Available Nowadays, real world social networks contain a vast range of information including shared objects, comments, following information, etc. Finding meaningful communities in this kind of networks is an interesting research area and has attracted the attention of many researchers. The community structure of complex networks reveals both their organization and hidden relations among their constituents. Most of the researches in the field of community detection mainly focus on the topological structure of the network without performing any content analysis. In recent years, a number of researches have proposed approaches which consider both the contents that are interchanged in networks, and the topological structures of the networks in order to find more meaningful communities. In this research, the effect of topic analysis in finding more meaningful communities in social networking sites in which the users express their feelings toward different objects (like movies by means of rating is demonstrated by performing extensive experiments.

Activating and inhibiting connections in biological network dynamics

Directory of Open Access Journals (Sweden)

Knight Rob

2008-12-01

Full Text Available Abstract Background Many studies of biochemical networks have analyzed network topology. Such work has suggested that specific types of network wiring may increase network robustness and therefore confer a selective advantage. However, knowledge of network topology does not allow one to predict network dynamical behavior – for example, whether deleting a protein from a signaling network would maintain the network's dynamical behavior, or induce oscillations or chaos. Results Here we report that the balance between activating and inhibiting connections is important in determining whether network dynamics reach steady state or oscillate. We use a simple dynamical model of a network of interacting genes or proteins. Using the model, we study random networks, networks selected for robust dynamics, and examples of biological network topologies. The fraction of activating connections influences whether the network dynamics reach steady state or oscillate. Conclusion The activating fraction may predispose a network to oscillate or reach steady state, and neutral evolution or selection of this parameter may affect the behavior of biological networks. This principle may unify the dynamics of a wide range of cellular networks. Reviewers Reviewed by Sergei Maslov, Eugene Koonin, and Yu (Brandon Xia (nominated by Mark Gerstein. For the full reviews, please go to the Reviewers' comments section.

Fixed-Point Approximations of Bandwidth-Sharing Networks with Rate Constraints

NARCIS (Netherlands)

M. Frolkova (Masha); J. Reed (Josh); A.P. Zwart (Bert)

2011-01-01

htmlabstractBandwidth-sharing networks are important flow level models of communication networks. We focus on the fact that it takes a signicant number of users to saturate a link, necessitating the inclusion of individual rate constraints. In particular we extend work of Reed & Zwart on fluid

Pushing the network harder `Dynamic Ratings`

Energy Technology Data Exchange (ETDEWEB)

Liondas, V.; Howatt, C.; Norrie, P. [Prospect Electricity, Blacktown, NSW (Australia)

1995-12-31

The demand for electricity in the area serviced by Prospect Electricity, is increasing, necessitating an increase in power transfer through the distribution system. Satisfying this demand generally requires more electrical infrastructure, but this is becoming less feasible due to economic constraints and environmental considerations. This paper discusses an approach to the dynamic (or real time) rating of different network elements. Dynamic rating is taken to mean that rating which is determined essentially in real time using known temperature constraints for the relevant elements, together with the prevailing ambient or environmental conditions. The purpose of dynamic rating is to achieve greater system utilization, thus allowing significant economic benefits, particularly from deferment of capital expenditure and greater operational flexibility. A number of technologies are being developed to do this for overhead lines, underground cables and transformers. The dynamic rating of cables has proved to be the most intractable part of the dynamic rating project. Work done to date, however, using finite element techniques together with the proposals to further develop point and distributed temperature sensing using fibre optic methods gives some confidence to the future success of this development. (author). 2 tabs., 4 figs., 4 refs.

Neural electrical activity and neural network growth.

Science.gov (United States)

Gafarov, F M

2018-05-01

The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

Spontaneous Plasticity of Multineuronal Activity Patterns in Activated Hippocampal Networks

Directory of Open Access Journals (Sweden)

Atsushi Usami

2008-01-01

Full Text Available Using functional multineuron imaging with single-cell resolution, we examined how hippocampal networks by themselves change the spatiotemporal patterns of spontaneous activity during the course of emitting spontaneous activity. When extracellular ionic concentrations were changed to those that mimicked in vivo conditions, spontaneous activity was increased in active cell number and activity frequency. When ionic compositions were restored to the control conditions, the activity level returned to baseline, but the weighted spatial dispersion of active cells, as assessed by entropy-based metrics, did not. Thus, the networks can modify themselves by altering the internal structure of their correlated activity, even though they as a whole maintained the same level of activity in space and time.

Partner network communities – a resource of universities’ activities

Directory of Open Access Journals (Sweden)

Romm Mark V.

2016-01-01

Full Text Available The network activity is not only part and parcel of the modern university, but it also demonstrates the level of its success. There appeared an urgent need for understanding the nature of universities’ network interactions and finding the most effective models of their network cooperation. The article analyzes partnership network communities with higher educational establishments (universities’ participation, which are being actively created nowadays. The conditions for successful network activities of a university in scientific, academic and professional network communities are presented.

Variable synaptic strengths controls the firing rate distribution in feedforward neural networks.

Science.gov (United States)

Ly, Cheng; Marsat, Gary

2018-02-01

Heterogeneity of firing rate statistics is known to have severe consequences on neural coding. Recent experimental recordings in weakly electric fish indicate that the distribution-width of superficial pyramidal cell firing rates (trial- and time-averaged) in the electrosensory lateral line lobe (ELL) depends on the stimulus, and also that network inputs can mediate changes in the firing rate distribution across the population. We previously developed theoretical methods to understand how two attributes (synaptic and intrinsic heterogeneity) interact and alter the firing rate distribution in a population of integrate-and-fire neurons with random recurrent coupling. Inspired by our experimental data, we extend these theoretical results to a delayed feedforward spiking network that qualitatively capture the changes of firing rate heterogeneity observed in in-vivo recordings. We demonstrate how heterogeneous neural attributes alter firing rate heterogeneity, accounting for the effect with various sensory stimuli. The model predicts how the strength of the effective network connectivity is related to intrinsic heterogeneity in such delayed feedforward networks: the strength of the feedforward input is positively correlated with excitability (threshold value for spiking) when firing rate heterogeneity is low and is negatively correlated with excitability with high firing rate heterogeneity. We also show how our theory can be used to predict effective neural architecture. We demonstrate that neural attributes do not interact in a simple manner but rather in a complex stimulus-dependent fashion to control neural heterogeneity and discuss how it can ultimately shape population codes.

State-dependent, bidirectional modulation of neural network activity by endocannabinoids.

Science.gov (United States)

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.

Impact of window decrement rate on TCP performance in an adhoc network

Science.gov (United States)

Suherman; Hutasuhut, Arief T. W.; Badra, Khaldun; Al-Akaidi, Marwan

2017-09-01

Transmission control protocol (TCP) is a reliable transport protocol handling end to end connection in TCP/IP stack. It works well in copper or optical fibre link, but experiences increasing delay in wireless network. Further, TCP experiences multiple retransmissions due to higher collision probability within wireless network. The situation may get worsen in an ad hoc network. This paper examines the impact half window or window reduction rate to the overall TCP performances. The evaluation using NS-2 simulator shows that the smaller the window decrement rate results the smaller end to end delay. Delay is reduced to 17.05% in average when window decrement rate decreases. Average jitter also decreases 4.15%, while packet loss is not affected.

Rate Aware Instantly Decodable Network Codes

KAUST Repository

Douik, Ahmed

2016-02-26

This paper addresses the problem of reducing the delivery time of data messages to cellular users using instantly decodable network coding (IDNC) with physical-layer rate awareness. While most of the existing literature on IDNC does not consider any physical layer complications, this paper proposes a cross-layer scheme that incorporates the different channel rates of the various users in the decision process of both the transmitted message combinations and the rates with which they are transmitted. The completion time minimization problem in such scenario is first shown to be intractable. The problem is, thus, approximated by reducing, at each transmission, the increase of an anticipated version of the completion time. The paper solves the problem by formulating it as a maximum weight clique problem over a newly designed rate aware IDNC (RA-IDNC) graph. Further, the paper provides a multi-layer solution to improve the completion time approximation. Simulation results suggest that the cross-layer design largely outperforms the uncoded transmissions strategies and the classical IDNC scheme. © 2015 IEEE.

Rate Aware Instantly Decodable Network Codes

KAUST Repository

Douik, Ahmed; Sorour, Sameh; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

2016-01-01

This paper addresses the problem of reducing the delivery time of data messages to cellular users using instantly decodable network coding (IDNC) with physical-layer rate awareness. While most of the existing literature on IDNC does not consider any physical layer complications, this paper proposes a cross-layer scheme that incorporates the different channel rates of the various users in the decision process of both the transmitted message combinations and the rates with which they are transmitted. The completion time minimization problem in such scenario is first shown to be intractable. The problem is, thus, approximated by reducing, at each transmission, the increase of an anticipated version of the completion time. The paper solves the problem by formulating it as a maximum weight clique problem over a newly designed rate aware IDNC (RA-IDNC) graph. Further, the paper provides a multi-layer solution to improve the completion time approximation. Simulation results suggest that the cross-layer design largely outperforms the uncoded transmissions strategies and the classical IDNC scheme. © 2015 IEEE.

PROJECT ACTIVITY ANALYSIS WITHOUT THE NETWORK MODEL

Directory of Open Access Journals (Sweden)

S. Munapo

2012-01-01

Full Text Available

ENGLISH ABSTRACT: This paper presents a new procedure for analysing and managing activity sequences in projects. The new procedure determines critical activities, critical path, start times, free floats, crash limits, and other useful information without the use of the network model. Even though network models have been successfully used in project management so far, there are weaknesses associated with the use. A network is not easy to generate, and dummies that are usually associated with it make the network diagram complex – and dummy activities have no meaning in the original project management problem. The network model for projects can be avoided while still obtaining all the useful information that is required for project management. What are required are the activities, their accurate durations, and their predecessors.

AFRIKAANSE OPSOMMING: Die navorsing beskryf ’n nuwerwetse metode vir die ontleding en bestuur van die sekwensiële aktiwiteite van projekte. Die voorgestelde metode bepaal kritiese aktiwiteite, die kritieke pad, aanvangstye, speling, verhasing, en ander groothede sonder die gebruik van ’n netwerkmodel. Die metode funksioneer bevredigend in die praktyk, en omseil die administratiewe rompslomp van die tradisionele netwerkmodelle.

Ninety-five orthotopic transplantations in 74 women of ovarian tissue after cytotoxic treatment in a fertility preservation network: tissue activity, pregnancy and delivery rates.

Science.gov (United States)

Van der Ven, H; Liebenthron, J; Beckmann, M; Toth, B; Korell, M; Krüssel, J; Frambach, T; Kupka, M; Hohl, M K; Winkler-Crepaz, K; Seitz, S; Dogan, A; Griesinger, G; Häberlin, F; Henes, M; Schwab, R; Sütterlin, M; von Wolff, M; Dittrich, R

2016-09-01

What is the success rate in terms of ovarian activity (menstrual cycles) as well as pregnancy and delivery rates 1 year after orthotopic ovarian transplantations conducted in a three-country network? In 49 women with a follow-up >1 year after transplantation, the ovaries were active in 67% of cases and the pregnancy and delivery rates were 33 and 25%, respectively. Cryopreservation of ovarian tissue in advance of cytotoxic therapies and later transplantation of the tissue is being performed increasingly often, and the total success rates in terms of pregnancy and delivery have been described in case series. However, published case series have not allowed either a more detailed analysis of patients with premature ovarian insufficiency (POI) or calculation of success rates based on the parameter 'tissue activity'. Retrospective analysis of 95 orthotopic transplantations in 74 patients who had been treated for cancer, performed in the FertiPROTEKT network from 2008 to June 2015. Of those 95 transplantations, a first subgroup (Subgroup 1) was defined for further analysis, including 49 women with a follow-up period >1 year after transplantation. Of those 49 women, a second subgroup (Subgroup 5) was further analysed, including 40 women who were transplanted for the first time and who were diagnosed with POI before transplantation. Transplantation was performed in 16 centres and data were transferred to the FertiPROTEKT registry. The transplantations were carried out after oncological treatment had been completed and after a remission period of at least 2 years. Tissue was transplanted orthotopically, either into or onto the residual ovaries or into a pelvic peritoneal pocket. The success rates were defined as tissue activity (menstrual cycles) after 1 year (primary outcome) and as pregnancies and deliveries achieved. The average age of all transplanted 74 women was 31 ± 5.9 years at the time of cryopreservation and 35 ± 5.2 at the time of transplantation. Twenty

Linking structure and activity in nonlinear spiking networks.

Directory of Open Access Journals (Sweden)

Gabriel Koch Ocker

2017-06-01

Full Text Available Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities-including those of different cell types-combine with connectivity to shape population activity and function.

Linking structure and activity in nonlinear spiking networks.

Science.gov (United States)

Ocker, Gabriel Koch; Josić, Krešimir; Shea-Brown, Eric; Buice, Michael A

2017-06-01

Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities-including those of different cell types-combine with connectivity to shape population activity and function.

Fine-Grained Rate Shaping for Video Streaming over Wireless Networks

Directory of Open Access Journals (Sweden)

Chen Tsuhan

2004-01-01

Full Text Available Video streaming over wireless networks faces challenges of time-varying packet loss rate and fluctuating bandwidth. In this paper, we focus on streaming precoded video that is both source and channel coded. Dynamic rate shaping has been proposed to “shape” the precompressed video to adapt to the fluctuating bandwidth. In our earlier work, rate shaping was extended to shape the channel coded precompressed video, and to take into account the time-varying packet loss rate as well as the fluctuating bandwidth of the wireless networks. However, prior work on rate shaping can only adjust the rate oarsely. In this paper, we propose “fine-grained rate shaping (FGRS” to allow for bandwidth adaptation over a wide range of bandwidth and packet loss rate in fine granularities. The video is precoded with fine granularity scalability (FGS followed by channel coding. Utilizing the fine granularity property of FGS and channel coding, FGRS selectively drops part of the precoded video and still yields decodable bit-stream at the decoder. Moreover, FGRS optimizes video streaming rather than achieves heuristic objectives as conventional methods. A two-stage rate-distortion (RD optimization algorithm is proposed for FGRS. Promising results of FGRS are shown.

Meditation leads to reduced default mode network activity beyond an active task.

Science.gov (United States)

Garrison, Kathleen A; Zeffiro, Thomas A; Scheinost, Dustin; Constable, R Todd; Brewer, Judson A

2015-09-01

Meditation has been associated with relatively reduced activity in the default mode network, a brain network implicated in self-related thinking and mind wandering. However, previous imaging studies have typically compared meditation to rest, despite other studies having reported differences in brain activation patterns between meditators and controls at rest. Moreover, rest is associated with a range of brain activation patterns across individuals that has only recently begun to be better characterized. Therefore, in this study we compared meditation to another active cognitive task, both to replicate the findings that meditation is associated with relatively reduced default mode network activity and to extend these findings by testing whether default mode activity was reduced during meditation, beyond the typical reductions observed during effortful tasks. In addition, prior studies had used small groups, whereas in the present study we tested these hypotheses in a larger group. The results indicated that meditation is associated with reduced activations in the default mode network, relative to an active task, for meditators as compared to controls. Regions of the default mode network showing a Group × Task interaction included the posterior cingulate/precuneus and anterior cingulate cortex. These findings replicate and extend prior work indicating that the suppression of default mode processing may represent a central neural process in long-term meditation, and they suggest that meditation leads to relatively reduced default mode processing beyond that observed during another active cognitive task.

Estimation of exponential convergence rate and exponential stability for neural networks with time-varying delay

International Nuclear Information System (INIS)

Tu Fenghua; Liao Xiaofeng

2005-01-01

We study the problem of estimating the exponential convergence rate and exponential stability for neural networks with time-varying delay. Some criteria for exponential stability are derived by using the linear matrix inequality (LMI) approach. They are less conservative than the existing ones. Some analytical methods are employed to investigate the bounds on the interconnection matrix and activation functions so that the systems are exponentially stable

On-line validation of feedwater flow rate in nuclear power plants using neural networks

International Nuclear Information System (INIS)

Khadem, M.; Ipakchi, A.; Alexandro, F.J.; Colley, R.W.

1994-01-01

On-line calibration of feedwater flow rate measurement in nuclear power plants provides a continuous realistic value of feedwater flow rate. It also reduces the manpower required for periodic calibration needed due to the fouling and defouling of the venturi meter surface condition. This paper presents a method for on-line validation of feedwater flow rate in nuclear power plants. The method is an improvement of the previously developed method which is based on the use of a set of process variables dynamically related to the feedwater flow rate. The online measurements of this set of variables are used as inputs to a neural network to obtain an estimate of the feedwater flow rate reading. The difference between the on-line feedwater flow rate reading, and the neural network estimate establishes whether there is a need to apply a correction factor to the feedwater flow rate measurement for calculation of the actual reactor power. The method was applied to the feedwater flow meters in the two feedwater flow loops of the TMI-1 nuclear power plant. The venturi meters used for flow measurements are susceptible to frequent fouling that degrades their measurement accuracy. The fouling effects can cause an inaccuracy of up to 3% relative error in feedwater flow rate reading. A neural network, whose inputs were the readings of a set of reference instruments, was designed to predict both feedwater flow rates simultaneously. A multi-layer feedforward neural network employing the backpropagation algorithm was used. A number of neural network training tests were performed to obtain an optimum filtering technique of the input/output data of the neural networks. The result of the selection of the filtering technique was confirmed by numerous Fast Fourier Transform (FFT) tests. Training and testing were done on data from TMI-1 nuclear power plant. The results show that the neural network can predict the correct flow rates with an absolute relative error of less than 2%

Exchange rate prediction with multilayer perceptron neural network using gold price as external factor

Directory of Open Access Journals (Sweden)

Mohammad Fathian

2012-04-01

Full Text Available In this paper, the problem of predicting the exchange rate time series in the foreign exchange rate market is going to be solved using a time-delayed multilayer perceptron neural network with gold price as external factor. The input for the learning phase of the artificial neural network are the exchange rate data of the last five days plus the gold price in two different currencies of the exchange rate as the external factor for helping the artificial neural network improving its forecast accuracy. The five-day delay has been chosen because of the weekly cyclic behavior of the exchange rate time series with the consideration of two holidays in a week. The result of forecasts are then compared with using the multilayer peceptron neural network without gold price external factor by two most important evaluation techniques in the literature of exchange rate prediction. For the experimental analysis phase, the data of three important exchange rates of EUR/USD, GBP/USD, and USD/JPY are used.

Fault Activity Aware Service Delivery in Wireless Sensor Networks for Smart Cities

Directory of Open Access Journals (Sweden)

Xiaomei Zhang

2017-01-01

Full Text Available Wireless sensor networks (WSNs are increasingly used in smart cities which involve multiple city services having quality of service (QoS requirements. When misbehaving devices exist, the performance of current delivery protocols degrades significantly. Nonetheless, the majority of existing schemes either ignore the faulty behaviors’ variability and time-variance in city environments or focus on homogeneous traffic for traditional data services (simple text messages rather than city services (health care units, traffic monitors, and video surveillance. We consider the problem of fault-aware multiservice delivery, in which the network performs secure routing and rate control in terms of fault activity dynamic metric. To this end, we first design a distributed framework to estimate the fault activity information based on the effects of nondeterministic faulty behaviors and to incorporate these estimates into the service delivery. Then we present a fault activity geographic opportunistic routing (FAGOR algorithm addressing a wide range of misbehaviors. We develop a leaky-hop model and design a fault activity rate-control algorithm for heterogeneous traffic to allocate resources, while guaranteeing utility fairness among multiple city services. Finally, we demonstrate the significant performance of our scheme in routing performance, effective utility, and utility fairness in the presence of misbehaving sensors through extensive simulations.

Finding quasi-optimal network topologies for information transmission in active networks.

Science.gov (United States)

Baptista, Murilo S; de Carvalho, Josué X; Hussein, Mahir S

2008-01-01

This work clarifies the relation between network circuit (topology) and behaviour (information transmission and synchronization) in active networks, e.g. neural networks. As an application, we show how one can find network topologies that are able to transmit a large amount of information, possess a large number of communication channels, and are robust under large variations of the network coupling configuration. This theoretical approach is general and does not depend on the particular dynamic of the elements forming the network, since the network topology can be determined by finding a Laplacian matrix (the matrix that describes the connections and the coupling strengths among the elements) whose eigenvalues satisfy some special conditions. To illustrate our ideas and theoretical approaches, we use neural networks of electrically connected chaotic Hindmarsh-Rose neurons.

Finding quasi-optimal network topologies for information transmission in active networks.

Directory of Open Access Journals (Sweden)

Murilo S Baptista

Full Text Available This work clarifies the relation between network circuit (topology and behaviour (information transmission and synchronization in active networks, e.g. neural networks. As an application, we show how one can find network topologies that are able to transmit a large amount of information, possess a large number of communication channels, and are robust under large variations of the network coupling configuration. This theoretical approach is general and does not depend on the particular dynamic of the elements forming the network, since the network topology can be determined by finding a Laplacian matrix (the matrix that describes the connections and the coupling strengths among the elements whose eigenvalues satisfy some special conditions. To illustrate our ideas and theoretical approaches, we use neural networks of electrically connected chaotic Hindmarsh-Rose neurons.

Opinion dynamics in activity-driven networks

Science.gov (United States)

Li, Dandan; Han, Dun; Ma, Jing; Sun, Mei; Tian, Lixin; Khouw, Timothy; Stanley, H. Eugene

2017-10-01

Social interaction between individuals constantly affects the development of their personal opinions. Previous models such as the Deffuant model and the Hegselmann-Krause (HK) model have assumed that individuals only update their opinions after interacting with neighbors whose opinions are similar to their own. However, people are capable of communicating widely with all of their neighbors to gather their ideas and opinions, even if they encounter a number of opposing attitudes. We propose a model in which agents listen to the opinions of all their neighbors. Continuous opinion dynamics are investigated in activity-driven networks with a tolerance threshold. We study how the initial opinion distribution, tolerance threshold, opinion-updating speed, and activity rate affect the evolution of opinion. We find that when the initial fraction of positive opinion is small, all opinions become negative by the end of the simulation. As the initial fraction of positive opinions rises above a certain value —about 0.45— the final fraction of positive opinions sharply increases and eventually equals 1. Increased tolerance threshold δ is found to lead to a more varied final opinion distribution. We also find that if the negative opinion has an initial advantage, the final fraction of negative opinion increases and reaches its peak as the updating speed λ approaches 0.5. Finally we show that the lower the activity rate of individuals, the greater the fluctuation range of their opinions.

BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks.

Science.gov (United States)

Richiardi, Jonas; Altmann, Andre; Milazzo, Anna-Clare; Chang, Catie; Chakravarty, M Mallar; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Bromberg, Uli; Büchel, Christian; Conrod, Patricia; Fauth-Bühler, Mira; Flor, Herta; Frouin, Vincent; Gallinat, Jürgen; Garavan, Hugh; Gowland, Penny; Heinz, Andreas; Lemaître, Hervé; Mann, Karl F; Martinot, Jean-Luc; Nees, Frauke; Paus, Tomáš; Pausova, Zdenka; Rietschel, Marcella; Robbins, Trevor W; Smolka, Michael N; Spanagel, Rainer; Ströhle, Andreas; Schumann, Gunter; Hawrylycz, Mike; Poline, Jean-Baptiste; Greicius, Michael D

2015-06-12

During rest, brain activity is synchronized between different regions widely distributed throughout the brain, forming functional networks. However, the molecular mechanisms supporting functional connectivity remain undefined. We show that functional brain networks defined with resting-state functional magnetic resonance imaging can be recapitulated by using measures of correlated gene expression in a post mortem brain tissue data set. The set of 136 genes we identify is significantly enriched for ion channels. Polymorphisms in this set of genes significantly affect resting-state functional connectivity in a large sample of healthy adolescents. Expression levels of these genes are also significantly associated with axonal connectivity in the mouse. The results provide convergent, multimodal evidence that resting-state functional networks correlate with the orchestrated activity of dozens of genes linked to ion channel activity and synaptic function. Copyright © 2015, American Association for the Advancement of Science.

Estimating Ads’ Click through Rate with Recurrent Neural Network

Directory of Open Access Journals (Sweden)

Chen Qiao-Hong

2016-01-01

Full Text Available With the development of the Internet, online advertising spreads across every corner of the world, the ads' click through rate (CTR estimation is an important method to improve the online advertising revenue. Compared with the linear model, the nonlinear models can study much more complex relationships between a large number of nonlinear characteristics, so as to improve the accuracy of the estimation of the ads’ CTR. The recurrent neural network (RNN based on Long-Short Term Memory (LSTM is an improved model of the feedback neural network with ring structure. The model overcomes the problem of the gradient of the general RNN. Experiments show that the RNN based on LSTM exceeds the linear models, and it can effectively improve the estimation effect of the ads’ click through rate.

Thermodynamically based constraints for rate coefficients of large biochemical networks.

Science.gov (United States)

Vlad, Marcel O; Ross, John

2009-01-01

Wegscheider cyclicity conditions are relationships among the rate coefficients of a complex reaction network, which ensure the compatibility of kinetic equations with the conditions for thermodynamic equilibrium. The detailed balance at equilibrium, that is the equilibration of forward and backward rates for each elementary reaction, leads to compatibility between the conditions of kinetic and thermodynamic equilibrium. Therefore, Wegscheider cyclicity conditions can be derived by eliminating the equilibrium concentrations from the conditions of detailed balance. We develop matrix algebra tools needed to carry out this elimination, reexamine an old derivation of the general form of Wegscheider cyclicity condition, and develop new derivations which lead to more compact and easier-to-use formulas. We derive scaling laws for the nonequilibrium rates of a complex reaction network, which include Wegscheider conditions as a particular case. The scaling laws for the rates are used for clarifying the kinetic and thermodynamic meaning of Wegscheider cyclicity conditions. Finally, we discuss different ways of using Wegscheider cyclicity conditions for kinetic computations in systems biology.

Max-Min Optimality of Service Rate Control in Closed Queueing Networks

KAUST Repository

Xia, Li

2013-04-01

In this technical note, we discuss the optimality properties of service rate control in closed Jackson networks. We prove that when the cost function is linear to a particular service rate, the system performance is monotonic w.r.t. (with respect to) that service rate and the optimal value of that service rate can be either maximum or minimum (we call it Max-Min optimality); When the second-order derivative of the cost function w.r.t. a particular service rate is always positive (negative), which makes the cost function strictly convex (concave), the optimal value of such service rate for the performance maximization (minimization) problem can be either maximum or minimum. To the best of our knowledge, this is the most general result for the optimality of service rates in closed Jackson networks and all the previous works only involve the first conclusion. Moreover, our result is also valid for both the state-dependent and load-dependent service rates, under both the time-average and customer-average performance criteria.

Networking activism: implications for Greece

Directory of Open Access Journals (Sweden)

Pantelis Vatikiotis

2011-12-01

Full Text Available The outbreak of December 2008 against police brutality through a wave of demonstrations and street protests in Athens, which was strongly advocated by protest activities and practices across the world, addresses several issues in relation to the transformative potentials of mediated collective action. The paper critically evaluates different accounts of December events, probing then into thevery networking of that movement. From this perspective, it points out another aspect of the local-global interplay in protest culture along new mediating practices (beyond the creation of transnational publics, that of the implications of transnational networking for local social activism and identification, addressing relevant questions in the Greek context.

Using Active Networking to Detect and Troubleshoot Issues in Tactical Data Networks

Science.gov (United States)

2014-06-01

networking (SDN) paradigm, which has gained popularity in recent years, has its roots in the idea of programmable networks [6]. By extending the...278–289, Aug. 2011. 67 [13] M. Hicks, P. Kakkar, J. T. Moore, C. A. Gunter, and S. Nettles , “Plan: A programming language for active networks,” ACM

Rate-based congestion control in networks with smart links, revision. B.S. Thesis - May 1988

Science.gov (United States)

Heybey, Andrew Tyrrell

1990-01-01

The author uses a network simulator to explore rate-based congestion control in networks with smart links that can feed back information to tell senders to adjust their transmission rates. This method differs in a very important way from congestion control in which a congested network component just drops packets - the most commonly used method. It is clearly advantageous for the links in the network to communicate with the end users about the network capacity, rather than the users unilaterally picking a transmission rate. The components in the middle of the network, not the end users, have information about the capacity and traffic in the network. The author experiments with three different algorithms for calculating the control rate to feed back to the users. All of the algorithms exhibit problems in the form of large queues when simulated with a configuration modeling the dynamics of a packet-voice system. However, the problems are not with the algorithms themselves, but with the fact that feedback takes time. If the network steady-state utilization is low enough that it can absorb transients in the traffic through it, then the large queues disappear. If the users are modified to start sending slowly, to allow the network to adapt to a new flow without causing congestion, a greater portion of the network's bandwidth can be used.

The impact of ambient dose rate measuring network and precipitation radar system for detection of environmental radioactivity released by accident

International Nuclear Information System (INIS)

Bleher, M; Stoehlker, U.

2003-01-01

For the surveillance of environmental radioactivity, the German measuring network of BfS consists of more than 2000 stations where the ambient gamma dose rate is continuously measured. This network is a helpful tool to detect and localise enhanced environmental contamination from artificial radionuclides. The threshold for early warning is so low, that already an additional dose rate contribution of 0,07 μGy/h is detectable. However, this threshold is frequently exceeded due to precipitation events caused by washout of natural activity in air. Therefore, the precipitation radar system of the German Weather Service provides valuable information on the problem, whether the increase of the ambient dose rate is due to natural or man-made events. In case of an accidental release, the data of this radar system show small area precipitation events and potential local hot spots not detected by the measuring network. For the phase of cloud passage, the ambient dose rate measuring network provides a reliable database for the evaluation of the current situation and its further development. It is possible to compare measured data for dose rate with derived intervention levels for countermeasures like ''sheltering''. Thus, critical regions can be identified and it is possible to verify implemented countermeasures. During and after this phase of cloud passage the measured data of the monitoring network help to adapt the results of the national decision support systems PARK and RODOS. Therefore, it is necessary to derive the actual additional contribution to the ambient dose rate. Map representations of measured dose rate are rapidly available and helpful to optimise measurement strategies of mobile systems and collection strategies for samples of agricultural products. (orig.)

Studying the active deformation of distributed plate boundaries by integration of GNSS networks

Science.gov (United States)

D'Agostino, Nicola; Avallone, Antonio; Cecere, Gianpaolo; D'Anastasio, Elisabetta

2013-04-01

In the last decade GNSS networks installed for different purposes have proliferated in Italy and now provide a large amount of data available to geophysical studies. In addition to the existing regional and nation-wide scientific GNSS networks developed by ASI (http://geodaf.mt.asi.it), INGV (http://ring.gm.ingv.it) and OGS (http://crs.inogs.it/frednet), a large number (> 400) of continuously-operating GPS stations have been installed in the framework of regional and national networks, both publicly-operated and commercial, developed to provide real-time positioning capability to surveyors. Although the quality of the data and metadata associated to these stations is generally lower with respect to the "scientific" CGPS stations, the increased density and redundancy in crustal motion information, resulting in more than 500 stations with more than 2.5 years of observations, significantly increase the knowledge of the active deformation of the Italian territory and provides a unique image of the crustal deformation field. The obtained GPS velocity field is analysed and various features ranging from the definition of strain distribution and microplate kinematics within the plate boundary, to the evaluation of tectonic strain accumulation on active faults are presented in this work. Undeforming, aseismic regions (Sardinia, Southern Apulia) provide test sites to evaluate the lower bound on the accuracy achievable to measure tectonic deformation. Integration of GNSS networks significantly improves the resolution of the strain rate field in Central Italy showing that active deformation is concentrated in a narrow belt along the crest of the Apennines, consistently with the distribution of the largest historical and recent earthquakes. Products derived from dense GPS velocity and strain rate fields include map of earthquake potential developed under the assumption that the rate of seismic moment accumulation measured from geodesy distributes into earthquake sizes that

Linking Environmental Orientation to Start-ups’ Networking Activities

DEFF Research Database (Denmark)

Dickel, Petra; Ritter, Thomas

Besides for-profit start-ups, an increasing number of firms start their existence with the purpose to “do good” for society – mirrored in an increasing academic discussion of sustainable firms. Yet, there is little research on the networking activities of start-ups that do not have profit...... generation as their primary focus. Addressing this research gap, we develop hypotheses on the different networking activities of environmentally oriented start-ups arguing that their societal focus has a positive impact on the frequency of their networking and the size of their network. For empirically...... investigating such networking differences, we use data from 179 technology-based start-ups and show that start-ups with a strong external environmental orientation have significantly higher networking frequency and build larger networks. On the contrary, strong internal environmental orientation is linked...

Multipath Activity Based Routing Protocol for Mobile ‎Cognitive Radio Ad Hoc Networks

Directory of Open Access Journals (Sweden)

Shereen Omar

2017-01-01

Full Text Available Cognitive radio networks improve spectrum utilization by ‎sharing licensed spectrum with cognitive radio devices. In ‎cognitive radio ad hoc networks the routing protocol is one ‎of the most challenging tasks due to the changes in ‎frequency spectrum and the interrupted connectivity ‎caused by the primary user activity. In this paper, a multi‎path activity based routing protocol for cognitive radio ‎network (MACNRP is proposed. The protocol utilizes ‎channel availability and creates multiple node-disjoint ‎routes between the source and destination nodes. The ‎proposed protocol is compared with D2CARP and FTCRP ‎protocols. The performance evaluation is conducted ‎through mathematical analysis and using OPNET ‎simulation. The performance of the proposed protocol ‎achieves an increase in network throughput; besides it ‎decreases the probability of route failure due to node ‎mobility and primary user activity. We have found that the ‎MACNRP scheme results in 50% to 75% reduction in ‎blocking probability and 33% to 78% improvement in ‎network throughput, with a reasonable additional routing ‎overhead and average packet delay. Due to the successful ‎reduction of collision between primary users and ‎cognitive users, the MACNRP scheme results in decreasing ‎the path failure rate by 50% to 87%.‎

Cultured Neural Networks: Optimization of Patterned Network Adhesiveness and Characterization of their Neural Activity

Directory of Open Access Journals (Sweden)

W. L. C. Rutten

2006-01-01

Full Text Available One type of future, improved neural interface is the “cultured probe”. It is a hybrid type of neural information transducer or prosthesis, for stimulation and/or recording of neural activity. It would consist of a microelectrode array (MEA on a planar substrate, each electrode being covered and surrounded by a local circularly confined network (“island” of cultured neurons. The main purpose of the local networks is that they act as biofriendly intermediates for collateral sprouts from the in vivo system, thus allowing for an effective and selective neuron–electrode interface. As a secondary purpose, one may envisage future information processing applications of these intermediary networks. In this paper, first, progress is shown on how substrates can be chemically modified to confine developing networks, cultured from dissociated rat cortex cells, to “islands” surrounding an electrode site. Additional coating of neurophobic, polyimide-coated substrate by triblock-copolymer coating enhances neurophilic-neurophobic adhesion contrast. Secondly, results are given on neuronal activity in patterned, unconnected and connected, circular “island” networks. For connected islands, the larger the island diameter (50, 100 or 150 μm, the more spontaneous activity is seen. Also, activity may show a very high degree of synchronization between two islands. For unconnected islands, activity may start at 22 days in vitro (DIV, which is two weeks later than in unpatterned networks.

A distributed lumped active all-pass network configuration.

Science.gov (United States)

Huelsman, L. P.; Raghunath, S.

1972-01-01

In this correspondence a new and interesting distributed lumped active network configuration that realizes an all-pass network function is described. A design chart for determining the values of the network elements is included.

Goal-congruent default network activity facilitates cognitive control.

Science.gov (United States)

Spreng, R Nathan; DuPre, Elizabeth; Selarka, Dhawal; Garcia, Juliana; Gojkovic, Stefan; Mildner, Judith; Luh, Wen-Ming; Turner, Gary R

2014-10-15

Substantial neuroimaging evidence suggests that spontaneous engagement of the default network impairs performance on tasks requiring executive control. We investigated whether this impairment depends on the congruence between executive control demands and internal mentation. We hypothesized that activation of the default network might enhance performance on an executive control task if control processes engage long-term memory representations that are supported by the default network. Using fMRI, we scanned 36 healthy young adult humans on a novel two-back task requiring working memory for famous and anonymous faces. In this task, participants (1) matched anonymous faces interleaved with anonymous face, (2) matched anonymous faces interleaved with a famous face, or (3) matched a famous faces interleaved with an anonymous face. As predicted, we observed a facilitation effect when matching famous faces, compared with anonymous faces. We also observed greater activation of the default network during these famous face-matching trials. The results suggest that activation of the default network can contribute to task performance during an externally directed executive control task. Our findings provide evidence that successful activation of the default network in a contextually relevant manner facilitates goal-directed cognition. Copyright © 2014 the authors 0270-6474/14/3414108-07$15.00/0.

Max-Min Optimality of Service Rate Control in Closed Queueing Networks

KAUST Repository

Xia, Li; Shihada, Basem

2013-01-01

of service rates in closed Jackson networks and all the previous works only involve the first conclusion. Moreover, our result is also valid for both the state-dependent and load-dependent service rates, under both the time-average and customer

Active queue management controller design for TCP communication networks: Variable structure control approach

International Nuclear Information System (INIS)

Chen, C.-K.; Liao, T.-L.; Yan, J.-J.

2009-01-01

On the basis of variable structure control (VSC), an active queue management (AQM) controller is presented for a class of TCP communication networks. In the TCP/IP networks, the packet drop probability is limited between 0 and 1. Therefore, we modeled TCP/AQM as a rate-based non-linear system with a saturated input. The objective of the VSC-based AQM controller is to achieve the desired queue size and to guarantee the asymptotic stability of the closed-loop TCP non-linear system with saturated input. The performance and effectiveness of the proposed control law are then validated for different network scenarios through numerical simulations in both MATLAB and Network Simulator-2 (NS-2). Both sets of simulation results have confirmed that the proposed scheme outperforms other AQM schemes.

Active queue management controller design for TCP communication networks: Variable structure control approach

Energy Technology Data Exchange (ETDEWEB)

Chen, C.-K. [Department of Engineering Science, National Cheng Kung University, Tainan 701, Taiwan (China); Liao, T.-L. [Department of Engineering Science, National Cheng Kung University, Tainan 701, Taiwan (China)], E-mail: tlliao@mail.ncku.edu; Yan, J.-J. [Department of Computer and Communication, Shu-Te University, Kaohsiung 824, Taiwan (China)

2009-04-15

On the basis of variable structure control (VSC), an active queue management (AQM) controller is presented for a class of TCP communication networks. In the TCP/IP networks, the packet drop probability is limited between 0 and 1. Therefore, we modeled TCP/AQM as a rate-based non-linear system with a saturated input. The objective of the VSC-based AQM controller is to achieve the desired queue size and to guarantee the asymptotic stability of the closed-loop TCP non-linear system with saturated input. The performance and effectiveness of the proposed control law are then validated for different network scenarios through numerical simulations in both MATLAB and Network Simulator-2 (NS-2). Both sets of simulation results have confirmed that the proposed scheme outperforms other AQM schemes.

Vertically oriented graphene bridging active-layer/current-collector interface for ultrahigh rate supercapacitors.

Science.gov (United States)

Bo, Zheng; Zhu, Weiguang; Ma, Wei; Wen, Zhenhai; Shuai, Xiaorui; Chen, Junhong; Yan, Jianhua; Wang, Zhihua; Cen, Kefa; Feng, Xinliang

2013-10-25

Dense networks of graphene nanosheets standing vertically on a current collector can work as numerous electrically conductive bridges to facilitate charge transport and mitigate the constriction/spreading resistance at the interface between the active material and the current collector. The vertically oriented graphene-bridged supercapacitors present excellent rate and power capabilities. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Neural networks with discontinuous/impact activations

CERN Document Server

Akhmet, Marat

2014-01-01

This book presents as its main subject new models in mathematical neuroscience. A wide range of neural networks models with discontinuities are discussed, including impulsive differential equations, differential equations with piecewise constant arguments, and models of mixed type. These models involve discontinuities, which are natural because huge velocities and short distances are usually observed in devices modeling the networks. A discussion of the models, appropriate for the proposed applications, is also provided. This book also: Explores questions related to the biological underpinning for models of neural networks\\ Considers neural networks modeling using differential equations with impulsive and piecewise constant argument discontinuities Provides all necessary mathematical basics for application to the theory of neural networks Neural Networks with Discontinuous/Impact Activations is an ideal book for researchers and professionals in the field of engineering mathematics that have an interest in app...

Collaborative Event-Driven Coverage and Rate Allocation for Event Miss-Ratio Assurances in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Ozgur Sanli H

2010-01-01

Full Text Available Wireless sensor networks are often required to provide event miss-ratio assurance for a given event type. To meet such assurances along with minimum energy consumption, this paper shows how a node's activation and rate assignment is dependent on its distance to event sources, and proposes a practical coverage and rate allocation (CORA protocol to exploit this dependency in realistic environments. Both uniform event distribution and nonuniform event distribution are considered and the notion of ideal correlation distance around a clusterhead is introduced for on-duty node selection. In correlation distance guided CORA, rate assignment assists coverage scheduling by determining which nodes should be activated for minimizing data redundancy in transmission. Coverage scheduling assists rate assignment by controlling the amount of overlap among sensing regions of neighboring nodes, thereby providing sufficient data correlation for rate assignment. Extensive simulation results show that CORA meets the required event miss-ratios in realistic environments. CORA's joint coverage scheduling and rate allocation reduce the total energy expenditure by 85%, average battery energy consumption by 25%, and the overhead of source coding up to 90% as compared to existing rate allocation techniques.

A Cluster- Based Secure Active Network Environment

Institute of Scientific and Technical Information of China (English)

CHEN Xiao-lin; ZHOU Jing-yang; DAI Han; LU Sang-lu; CHEN Gui-hai

2005-01-01

We introduce a cluster-based secure active network environment (CSANE) which separates the processing of IP packets from that of active packets in active routers. In this environment, the active code authorized or trusted by privileged users is executed in the secure execution environment (EE) of the active router, while others are executed in the secure EE of the nodes in the distributed shared memory (DSM) cluster. With the supports of a multi-process Java virtual machine and KeyNote, untrusted active packets are controlled to securely consume resource. The DSM consistency management makes that active packets can be parallelly processed in the DSM cluster as if they were processed one by one in ANTS (Active Network Transport System). We demonstrate that CSANE has good security and scalability, but imposing little changes on traditional routers.

Effects of Increasing Neuromuscular Electrical Stimulation Current Intensity on Cortical Sensorimotor Network Activation: A Time Domain fNIRS Study.

Directory of Open Access Journals (Sweden)

Makii Muthalib

Full Text Available Neuroimaging studies have shown neuromuscular electrical stimulation (NMES-evoked movements activate regions of the cortical sensorimotor network, including the primary sensorimotor cortex (SMC, premotor cortex (PMC, supplementary motor area (SMA, and secondary somatosensory area (S2, as well as regions of the prefrontal cortex (PFC known to be involved in pain processing. The aim of this study, on nine healthy subjects, was to compare the cortical network activation profile and pain ratings during NMES of the right forearm wrist extensor muscles at increasing current intensities up to and slightly over the individual maximal tolerated intensity (MTI, and with reference to voluntary (VOL wrist extension movements. By exploiting the capability of the multi-channel time domain functional near-infrared spectroscopy technique to relate depth information to the photon time-of-flight, the cortical and superficial oxygenated (O2Hb and deoxygenated (HHb hemoglobin concentrations were estimated. The O2Hb and HHb maps obtained using the General Linear Model (NIRS-SPM analysis method, showed that the VOL and NMES-evoked movements significantly increased activation (i.e., increase in O2Hb and corresponding decrease in HHb in the cortical layer of the contralateral sensorimotor network (SMC, PMC/SMA, and S2. However, the level and area of contralateral sensorimotor network (including PFC activation was significantly greater for NMES than VOL. Furthermore, there was greater bilateral sensorimotor network activation with the high NMES current intensities which corresponded with increased pain ratings. In conclusion, our findings suggest that greater bilateral sensorimotor network activation profile with high NMES current intensities could be in part attributable to increased attentional/pain processing and to increased bilateral sensorimotor integration in these cortical regions.

Effects of Increasing Neuromuscular Electrical Stimulation Current Intensity on Cortical Sensorimotor Network Activation: A Time Domain fNIRS Study.

Science.gov (United States)

Muthalib, Makii; Re, Rebecca; Zucchelli, Lucia; Perrey, Stephane; Contini, Davide; Caffini, Matteo; Spinelli, Lorenzo; Kerr, Graham; Quaresima, Valentina; Ferrari, Marco; Torricelli, Alessandro

2015-01-01

Neuroimaging studies have shown neuromuscular electrical stimulation (NMES)-evoked movements activate regions of the cortical sensorimotor network, including the primary sensorimotor cortex (SMC), premotor cortex (PMC), supplementary motor area (SMA), and secondary somatosensory area (S2), as well as regions of the prefrontal cortex (PFC) known to be involved in pain processing. The aim of this study, on nine healthy subjects, was to compare the cortical network activation profile and pain ratings during NMES of the right forearm wrist extensor muscles at increasing current intensities up to and slightly over the individual maximal tolerated intensity (MTI), and with reference to voluntary (VOL) wrist extension movements. By exploiting the capability of the multi-channel time domain functional near-infrared spectroscopy technique to relate depth information to the photon time-of-flight, the cortical and superficial oxygenated (O2Hb) and deoxygenated (HHb) hemoglobin concentrations were estimated. The O2Hb and HHb maps obtained using the General Linear Model (NIRS-SPM) analysis method, showed that the VOL and NMES-evoked movements significantly increased activation (i.e., increase in O2Hb and corresponding decrease in HHb) in the cortical layer of the contralateral sensorimotor network (SMC, PMC/SMA, and S2). However, the level and area of contralateral sensorimotor network (including PFC) activation was significantly greater for NMES than VOL. Furthermore, there was greater bilateral sensorimotor network activation with the high NMES current intensities which corresponded with increased pain ratings. In conclusion, our findings suggest that greater bilateral sensorimotor network activation profile with high NMES current intensities could be in part attributable to increased attentional/pain processing and to increased bilateral sensorimotor integration in these cortical regions.

Evaluating Maximum Wind Energy Exploitation in Active Distribution Networks

DEFF Research Database (Denmark)

Siano, Pierluigi; Chen, Peiyuan; Chen, Zhe

2010-01-01

The increased spreading of distributed and renewable generation requires moving towards active management of distribution networks. In this paper, in order to evaluate maximum wind energy exploitation in active distribution networks, a method based on a multi-period optimal power flow (OPF......) analysis is proposed. Active network management schemes such as coordinated voltage control, energy curtailment and power factor control are integrated in the method in order to investigate their impacts on the maximization of wind energy exploitation. Some case studies, using real data from a Danish...... distribution system, confirmed the effectiveness of the proposed method in evaluating the optimal applications of active management schemes to increase wind energy harvesting without costly network reinforcement for the connection of wind generation....

Handoff Rate and Coverage Analysis in Multi-tier Heterogeneous Networks

OpenAIRE

Sadr, Sanam; Adve, Raviraj S.

2015-01-01

This paper analyzes the impact of user mobility in multi-tier heterogeneous networks. We begin by obtaining the handoff rate for a mobile user in an irregular cellular network with the access point locations modeled as a homogeneous Poisson point process. The received signal-to-interference-ratio (SIR) distribution along with a chosen SIR threshold is then used to obtain the probability of coverage. To capture potential connection failures due to mobility, we assume that a fraction of handoff...

The signal extraction of fetal heart rate based on wavelet transform and BP neural network

Science.gov (United States)

Yang, Xiao Hong; Zhang, Bang-Cheng; Fu, Hu Dai

2005-04-01

This paper briefly introduces the collection and recognition of bio-medical signals, designs the method to collect FM signals. A detailed discussion on the system hardware, structure and functions is also given. Under LabWindows/CVI,the hardware and the driver do compatible, the hardware equipment work properly actively. The paper adopts multi threading technology for real-time analysis and makes use of latency time of CPU effectively, expedites program reflect speed, improves the program to perform efficiency. One threading is collecting data; the other threading is analyzing data. Using the method, it is broaden to analyze the signal in real-time. Wavelet transform to remove the main interference in the FM and by adding time-window to recognize with BP network; Finally the results of collecting signals and BP networks are discussed. 8 pregnant women's signals of FM were collected successfully by using the sensor. The correctness rate of BP network recognition is about 83.3% by using the above measure.

Adaptive intelligent power systems: Active distribution networks

International Nuclear Information System (INIS)

McDonald, Jim

2008-01-01

Electricity networks are extensive and well established. They form a key part of the infrastructure that supports industrialised society. These networks are moving from a period of stability to a time of potentially major transition, driven by a need for old equipment to be replaced, by government policy commitments to cleaner and renewable sources of electricity generation, and by change in the power industry. This paper looks at moves towards active distribution networks. The novel transmission and distribution systems of the future will challenge today's system designs. They will cope with variable voltages and frequencies, and will offer more flexible, sustainable options. Intelligent power networks will need innovation in several key areas of information technology. Active control of flexible, large-scale electrical power systems is required. Protection and control systems will have to react to faults and unusual transient behaviour and ensure recovery after such events. Real-time network simulation and performance analysis will be needed to provide decision support for system operators, and the inputs to energy and distribution management systems. Advanced sensors and measurement will be used to achieve higher degrees of network automation and better system control, while pervasive communications will allow networks to be reconfigured by intelligent systems

Active system area networks for data intensive computations. Final report

Energy Technology Data Exchange (ETDEWEB)

None

2002-04-01

The goal of the Active System Area Networks (ASAN) project is to develop hardware and software technologies for the implementation of active system area networks (ASANs). The use of the term ''active'' refers to the ability of the network interfaces to perform application-specific as well as system level computations in addition to their traditional role of data transfer. This project adopts the view that the network infrastructure should be an active computational entity capable of supporting certain classes of computations that would otherwise be performed on the host CPUs. The result is a unique network-wide programming model where computations are dynamically placed within the host CPUs or the NIs depending upon the quality of service demands and network/CPU resource availability. The projects seeks to demonstrate that such an approach is a better match for data intensive network-based applications and that the advent of low-cost powerful embedded processors and configurable hardware makes such an approach economically viable and desirable.

Google matrix of the world network of economic activities

Science.gov (United States)

Kandiah, Vivek; Escaith, Hubert; Shepelyansky, Dima L.

2015-07-01

Using the new data from the OECD-WTO world network of economic activities we construct the Google matrix G of this directed network and perform its detailed analysis. The network contains 58 countries and 37 activity sectors for years 1995 and 2008. The construction of G, based on Markov chain transitions, treats all countries on equal democratic grounds while the contribution of activity sectors is proportional to their exchange monetary volume. The Google matrix analysis allows to obtain reliable ranking of countries and activity sectors and to determine the sensitivity of CheiRank-PageRank commercial balance of countries in respect to price variations and labor cost in various countries. We demonstrate that the developed approach takes into account multiplicity of network links with economy interactions between countries and activity sectors thus being more efficient compared to the usual export-import analysis. The spectrum and eigenstates of G are also analyzed being related to specific activity communities of countries.

The Political Activity in the Network Environment

Directory of Open Access Journals (Sweden)

Марианна Юрьевна Павлютенкова

2015-12-01

Full Text Available The rapid development and deep penetration into all areas of modern society of information and communication technologies significantly increase the role of network interactions. Network structures represented primarily social networks, embedded in the public policy process and became one of the key political actors. Online communities take the form of public policy, where the formation of public opinion and political decision-making plays the main role. Networking environment opens up new opportunities for the opposition and protest movements, civic participation, and control of public policy in general. The article gives an insight on the political aspects of social networking, concludes on the trend formation and network's strengthening of the political activity in a wide distribution of e-networking and e-communications.

The Impact of the Physical Activity Policy Research Network.

Science.gov (United States)

Manteiga, Alicia M; Eyler, Amy A; Valko, Cheryl; Brownson, Ross C; Evenson, Kelly R; Schmid, Thomas

2017-03-01

Lack of physical activity is one of the greatest challenges of the 21st century. The Physical Activity Policy Research Network (PAPRN) is a thematic network established in 2004 to identify determinants, implementation, and outcomes of policies that are effective in increasing physical activity. The purpose of this study is to describe the products of PAPRN and make recommendations for future research and best practices. A mixed methods approach was used to obtain both quantitative and qualitative data on the network. First, in 2014, PAPRN's dissemination products from 2004 to 2014 were extracted and reviewed, including 57 publications and 56 presentations. Next, semi-structured qualitative interviews were conducted with 25 key network participants from 17 locations around the U.S. The transcripts were transcribed and coded. The results of the interviews indicated that the research network addressed several components of its mission, including the identification of physical activity policies, determinants of these policies, and the process of policy implementation. However, research focusing on physical activity policy outcomes was limited. Best practices included collaboration between researchers and practitioners and involvement of practitioners in research design, data collection, and dissemination of results. PAPRN is an example of a productive research network and has contributed to both the process and content of physical activity policy research over the past decade. Future research should emphasize physical activity policy outcomes. Additionally, increased partnerships with practitioners for collaborative, cross-sectoral physical activity policy research should be developed. Copyright © 2016 American Journal of Preventive Medicine. All rights reserved.

Synaptic model for spontaneous activity in developing networks

DEFF Research Database (Denmark)

Lerchner, Alexander; Rinzel, J.

2005-01-01

Spontaneous rhythmic activity occurs in many developing neural networks. The activity in these hyperexcitable networks is comprised of recurring "episodes" consisting of "cycles" of high activity that alternate with "silent phases" with little or no activity. We introduce a new model of synaptic...... dynamics that takes into account that only a fraction of the vesicles stored in a synaptic terminal is readily available for release. We show that our model can reproduce spontaneous rhythmic activity with the same general features as observed in experiments, including a positive correlation between...

Adaptive Relay Activation in the Network Coding Protocols

DEFF Research Database (Denmark)

Pahlevani, Peyman; Roetter, Daniel Enrique Lucani; Fitzek, Frank

2015-01-01

State-of-the-art Network coding based routing protocols exploit the link quality information to compute the transmission rate in the intermediate nodes. However, the link quality discovery protocols are usually inaccurate, and introduce overhead in wireless mesh networks. In this paper, we presen...

Dynamic Control of Synchronous Activity in Networks of Spiking Neurons.

Directory of Open Access Journals (Sweden)

Axel Hutt

Full Text Available Oscillatory brain activity is believed to play a central role in neural coding. Accumulating evidence shows that features of these oscillations are highly dynamic: power, frequency and phase fluctuate alongside changes in behavior and task demands. The role and mechanism supporting this variability is however poorly understood. We here analyze a network of recurrently connected spiking neurons with time delay displaying stable synchronous dynamics. Using mean-field and stability analyses, we investigate the influence of dynamic inputs on the frequency of firing rate oscillations. We show that afferent noise, mimicking inputs to the neurons, causes smoothing of the system's response function, displacing equilibria and altering the stability of oscillatory states. Our analysis further shows that these noise-induced changes cause a shift of the peak frequency of synchronous oscillations that scales with input intensity, leading the network towards critical states. We lastly discuss the extension of these principles to periodic stimulation, in which externally applied driving signals can trigger analogous phenomena. Our results reveal one possible mechanism involved in shaping oscillatory activity in the brain and associated control principles.

Dynamic Control of Synchronous Activity in Networks of Spiking Neurons.

Science.gov (United States)

Hutt, Axel; Mierau, Andreas; Lefebvre, Jérémie

Oscillatory brain activity is believed to play a central role in neural coding. Accumulating evidence shows that features of these oscillations are highly dynamic: power, frequency and phase fluctuate alongside changes in behavior and task demands. The role and mechanism supporting this variability is however poorly understood. We here analyze a network of recurrently connected spiking neurons with time delay displaying stable synchronous dynamics. Using mean-field and stability analyses, we investigate the influence of dynamic inputs on the frequency of firing rate oscillations. We show that afferent noise, mimicking inputs to the neurons, causes smoothing of the system's response function, displacing equilibria and altering the stability of oscillatory states. Our analysis further shows that these noise-induced changes cause a shift of the peak frequency of synchronous oscillations that scales with input intensity, leading the network towards critical states. We lastly discuss the extension of these principles to periodic stimulation, in which externally applied driving signals can trigger analogous phenomena. Our results reveal one possible mechanism involved in shaping oscillatory activity in the brain and associated control principles.

A reanalysis of “Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons” [version 1; referees: 2 approved

Directory of Open Access Journals (Sweden)

Rainer Engelken

2016-08-01

Full Text Available Neuronal activity in the central nervous system varies strongly in time and across neuronal populations. It is a longstanding proposal that such fluctuations generically arise from chaotic network dynamics. Various theoretical studies predict that the rich dynamics of rate models operating in the chaotic regime can subserve circuit computation and learning. Neurons in the brain, however, communicate via spikes and it is a theoretical challenge to obtain similar rate fluctuations in networks of spiking neuron models. A recent study investigated spiking balanced networks of leaky integrate and fire (LIF neurons and compared their dynamics to a matched rate network with identical topology, where single unit input-output functions were chosen from isolated LIF neurons receiving Gaussian white noise input. A mathematical analogy between the chaotic instability in networks of rate units and the spiking network dynamics was proposed. Here we revisit the behavior of the spiking LIF networks and these matched rate networks. We find expected hallmarks of a chaotic instability in the rate network: For supercritical coupling strength near the transition point, the autocorrelation time diverges. For subcritical coupling strengths, we observe critical slowing down in response to small external perturbations. In the spiking network, we found in contrast that the timescale of the autocorrelations is insensitive to the coupling strength and that rate deviations resulting from small input perturbations rapidly decay. The decay speed even accelerates for increasing coupling strength. In conclusion, our reanalysis demonstrates fundamental differences between the behavior of pulse-coupled spiking LIF networks and rate networks with matched topology and input-output function. In particular there is no indication of a corresponding chaotic instability in the spiking network.

A Reaction-Diffusion-Based Coding Rate Control Mechanism for Camera Sensor Networks

Directory of Open Access Journals (Sweden)

Naoki Wakamiya

2010-08-01

Full Text Available A wireless camera sensor network is useful for surveillance and monitoring for its visibility and easy deployment. However, it suffers from the limited capacity of wireless communication and a network is easily overflown with a considerable amount of video traffic. In this paper, we propose an autonomous video coding rate control mechanism where each camera sensor node can autonomously determine its coding rate in accordance with the location and velocity of target objects. For this purpose, we adopted a biological model, i.e., reaction-diffusion model, inspired by the similarity of biological spatial patterns and the spatial distribution of video coding rate. Through simulation and practical experiments, we verify the effectiveness of our proposal.

A reaction-diffusion-based coding rate control mechanism for camera sensor networks.

Science.gov (United States)

Yamamoto, Hiroshi; Hyodo, Katsuya; Wakamiya, Naoki; Murata, Masayuki

2010-01-01

A wireless camera sensor network is useful for surveillance and monitoring for its visibility and easy deployment. However, it suffers from the limited capacity of wireless communication and a network is easily overflown with a considerable amount of video traffic. In this paper, we propose an autonomous video coding rate control mechanism where each camera sensor node can autonomously determine its coding rate in accordance with the location and velocity of target objects. For this purpose, we adopted a biological model, i.e., reaction-diffusion model, inspired by the similarity of biological spatial patterns and the spatial distribution of video coding rate. Through simulation and practical experiments, we verify the effectiveness of our proposal.

Open-source hardware and software and web application for gamma dose rate network operation

International Nuclear Information System (INIS)

Luff, R.; Zaehringer, M.; Harms, W.; Bleher, M.; Prommer, B.; Stoehlker, U.

2014-01-01

The German Federal Office for Radiation Protection operates a network of about 1800 gamma dose rate stations as a part of the national emergency preparedness plan. Each of the six network centres is capable of operating the network alone. Most of the used hardware and software have been developed in-house under open-source license. Short development cycles and close cooperation between developers and users ensure robustness, transparency and fast maintenance procedures, thus avoiding unnecessary complex solutions. This also reduces the overall costs of the network operation. An easy-to-expand web interface has been developed to make the complete system available to other interested network operators in order to increase cooperation between different countries. The interface is also regularly in use for education during scholarships of trainees supported, e.g. by the 'international Atomic Energy Agency' to operate a local area dose rate monitoring test network. (authors)

TreeMAC: Localized TDMA MAC protocol for real-time high-data-rate sensor networks

Science.gov (United States)

Song, W.-Z.; Huang, R.; Shirazi, B.; Husent, R.L.

2009-01-01

Earlier sensor network MAC protocols focus on energy conservation in low-duty cycle applications, while some recent applications involve real-time high-data-rate signals. This motivates us to design an innovative localized TDMA MAC protocol to achieve high throughput and low congestion in data collection sensor networks, besides energy conservation. TreeMAC divides a time cycle into frames and frame into slots. Parent determines children's frame assigmnent based on their relative bandwidth demand, and each node calculates its own slot assignment based on its hop-count to the sink. This innovative 2-dimensional frame-slot assignment algorithm has the following nice theory properties. Firstly, given any node, at any time slot, there is at most one active sender in its neighborhood (includ ing itself). Secondly, the packet scheduling with TreelMAC is bufferless, which therefore minimizes the probability of network congestion. Thirdly, the data throughput to gateway is at least 1/3 of the optimum assuming reliable links. Our experiments on a 24 node test bed demonstrate that TreeMAC protocol significantly improves network throughput and energy efficiency, by comparing to the TinyOS's default CSMA MAC protocol and a recent TDMA MAC protocol Funneling-MAC[8]. ?? 2009 IEEE.

Tourist activated networks: Implications for dynamic packaging systems in tourism

DEFF Research Database (Denmark)

Zach, Florian; Gretzel, Ulrike; Fesenmaier, Daniel R.

2008-01-01

This paper discusses tourist activated networks as a concept to inform technological applications supporting dynamic bundling and en-route recommendations. Empirical data was collected from travellers who visited a regional destination in the US and then analyzed with respect to its network...... structure. The results indicate that the tourist activated network for the destination is rather sparse and that there are clearly differences in core and peripheral nodes. The findings illustrate the structure of a tourist activated network and provide implications for technology design and tourism...

Network Layer Protocol Activation for Packet Data Access in UMTS WCDMA Laboratory Network

OpenAIRE

Lakkisto, Erkka

2011-01-01

The purpose of this Bachelor’s Thesis was to set up the UMTS WCDMA network in the laboratory environment of Helsinki Metropolia University of Applied Sciences and to study the network layer protocol activation for packet data access. The development of 3G technology has been very rapid and it can be considered as one of the main technologies in telecommunication. Implementing the laboratory network in Metropolia enables teaching and researching of the modern network technology. Labora...

Resource management for multimedia services in high data rate wireless networks

CERN Document Server

Zhang, Ruonan; Pan, Jianping

2017-01-01

This brief offers a valuable resource on principles of quality-of-service (QoS) provisioning and the related link-layer resource management techniques for high data-rate wireless networks. The primary emphasis is on protocol modeling and analysis. It introduces media access control (MAC) protocols, standards of wireless local area networks (WLANs), wireless personal area networks (WPANs), and wireless body area networks (WBANs), discussing their key technologies, applications, and deployment scenarios. The main analytical approaches and models for performance analysis of the fundamental resource scheduling mechanisms, including the contention-based, reservation-based, and hybrid MAC, are presented. To help readers understand and evaluate system performance, the brief contains a range of simulation results. In addition, a thorough bibliography provides an additional tool. This brief is an essential resource for engineers, researchers, students, and users of wireless networks.

Information transmission and signal permutation in active flow networks

Science.gov (United States)

Woodhouse, Francis G.; Fawcett, Joanna B.; Dunkel, Jörn

2018-03-01

Recent experiments show that both natural and artificial microswimmers in narrow channel-like geometries will self-organise to form steady, directed flows. This suggests that networks of flowing active matter could function as novel autonomous microfluidic devices. However, little is known about how information propagates through these far-from-equilibrium systems. Through a mathematical analogy with spin-ice vertex models, we investigate here the input–output characteristics of generic incompressible active flow networks (AFNs). Our analysis shows that information transport through an AFN is inherently different from conventional pressure or voltage driven networks. Active flows on hexagonal arrays preserve input information over longer distances than their passive counterparts and are highly sensitive to bulk topological defects, whose presence can be inferred from marginal input–output distributions alone. This sensitivity further allows controlled permutations on parallel inputs, revealing an unexpected link between active matter and group theory that can guide new microfluidic mixing strategies facilitated by active matter and aid the design of generic autonomous information transport networks.

Consumer Activities and Reactions to Social Network Marketing

Directory of Open Access Journals (Sweden)

Bistra Vassileva

2017-06-01

Full Text Available The purpose of this paper is to understand consumer behavioural models with respect to their reactions to social network marketing. Theoretical background is focused on online and social network usage, motivations and behaviour. The research goal is to explore consumer reactions to the exposure of social network marketing based on the following criteria: level of brand engagement, word-of-mouth (WOM referral behaviour, and purchase intentions. Consumers are investigated based on their attitudes toward social network marketing and basic socio-demographic covariates using data from a sample size of 700 Bulgarian respondents (age group 21–54 years, Internet users, urban inhabitants. Factor and cluster analyses are applied. It is found that consumers are willing to receive information about brands and companies through social networks. They like to talk in social networks about these brands and companies and to share information as well (factor 2, brand engagement. Internet users are willing to share information received through social network advertising (factor 1, wom referral behaviour but they would not buy a certain brand as a result of brand communication activities in social networks (factor 3, purchase intention. Several practical implications regarding marketing activities through social networks are drawn.

Dynamical Properties of Discrete-Time Background Neural Networks with Uniform Firing Rate

Directory of Open Access Journals (Sweden)

Min Wan

2013-01-01

Full Text Available The dynamics of a discrete-time background network with uniform firing rate and background input is investigated. The conditions for stability are firstly derived. An invariant set is then obtained so that the nondivergence of the network can be guaranteed. In the invariant set, it is proved that all trajectories of the network starting from any nonnegative value will converge to a fixed point under some conditions. In addition, bifurcation and chaos are discussed. It is shown that the network can engender bifurcation and chaos with the increase of background input. The computations of Lyapunov exponents confirm the chaotic behaviors.

The efficacy of centralized flow rate control in 802.11-based wireless mesh networks

KAUST Repository

Jamshaid, K.

2013-06-13

Commodity WiFi-based wireless mesh networks (WMNs) can be used to provide last mile Internet access. These networks exhibit extreme unfairness with backlogged traffic sources. Current solutions propose distributed source-rate control algorithms requiring link-layer or transport-layer changes on all mesh nodes. This is often infeasible in large practical deployments. In wireline networks, router-assisted rate control techniques have been proposed for use alongside end-to-end mechanisms. We wish to evaluate the feasibility of establishing similar centralized control via gateways in WMNs. In this paper, we focus on the efficacy of this control rather than the specifics of the controller design mechanism. We answer the question: Given sources that react predictably to congestion notification, can we enforce a desired rate allocation through a single centralized controller? The answer is not obvious because flows experience varying contention levels, and transmissions are scheduled by a node using imperfect local knowledge. We find that common router-assisted flow control schemes used in wired networks fail in WMNs because they assume that (1) links are independent, and (2) router queue buildups are sufficient for detecting congestion. We show that non-work-conserving, rate-based centralized scheduling can effectively enforce rate allocation. It can achieve results comparable to source rate limiting, without requiring any modifications to mesh routers or client devices. 2013 Jamshaid et al.; licensee Springer.

Detection of silent cells, synchronization and modulatory activity in developing cellular networks.

Science.gov (United States)

Hjorth, Johannes J J; Dawitz, Julia; Kroon, Tim; Pires, Johny; Dassen, Valerie J; Berkhout, Janna A; Emperador Melero, Javier; Nadadhur, Aish G; Alevra, Mihai; Toonen, Ruud F; Heine, Vivi M; Mansvelder, Huibert D; Meredith, Rhiannon M

2016-04-01

Developing networks in the immature nervous system and in cellular cultures are characterized by waves of synchronous activity in restricted clusters of cells. Synchronized activity in immature networks is proposed to regulate many different developmental processes, from neuron growth and cell migration, to the refinement of synapses, topographic maps, and the mature composition of ion channels. These emergent activity patterns are not present in all cells simultaneously within the network and more immature "silent" cells, potentially correlated with the presence of silent synapses, are prominent in different networks during early developmental periods. Many current network analyses for detection of synchronous cellular activity utilize activity-based pixel correlations to identify cellular-based regions of interest (ROIs) and coincident cell activity. However, using activity-based correlations, these methods first underestimate or ignore the inactive silent cells within the developing network and second, are difficult to apply within cell-dense regions commonly found in developing brain networks. In addition, previous methods may ignore ROIs within a network that shows transient activity patterns comprising both inactive and active periods. We developed analysis software to semi-automatically detect cells within developing neuronal networks that were imaged using calcium-sensitive reporter dyes. Using an iterative threshold, modulation of activity was tracked within individual cells across the network. The distribution pattern of both inactive and active, including synchronous cells, could be determined based on distance measures to neighboring cells and according to different anatomical layers. © 2015 Wiley Periodicals, Inc.

Dynamic neural networking as a basis for plasticity in the control of heart rate.

Science.gov (United States)

Kember, G; Armour, J A; Zamir, M

2013-01-21

A model is proposed in which the relationship between individual neurons within a neural network is dynamically changing to the effect of providing a measure of "plasticity" in the control of heart rate. The neural network on which the model is based consists of three populations of neurons residing in the central nervous system, the intrathoracic extracardiac nervous system, and the intrinsic cardiac nervous system. This hierarchy of neural centers is used to challenge the classical view that the control of heart rate, a key clinical index, resides entirely in central neuronal command (spinal cord, medulla oblongata, and higher centers). Our results indicate that dynamic networking allows for the possibility of an interplay among the three populations of neurons to the effect of altering the order of control of heart rate among them. This interplay among the three levels of control allows for different neural pathways for the control of heart rate to emerge under different blood flow demands or disease conditions and, as such, it has significant clinical implications because current understanding and treatment of heart rate anomalies are based largely on a single level of control and on neurons acting in unison as a single entity rather than individually within a (plastically) interconnected network. Copyright © 2012 Elsevier Ltd. All rights reserved.

Optimal Hierarchical Modular Topologies for Producing Limited Sustained Activation of Neural Networks

OpenAIRE

Kaiser, Marcus; Hilgetag, Claus C.

2010-01-01

An essential requirement for the representation of functional patterns in complex neural networks, such as the mammalian cerebral cortex, is the existence of stable regimes of network activation, typically arising from a limited parameter range. In this range of limited sustained activity (LSA), the activity of neural populations in the network persists between the extremes of either quickly dying out or activating the whole network. Hierarchical modular networks were previously found to show...

Low-complexity full-rate transmission scheme with full diversity for two-path relay networks

KAUST Repository

Fareed, Muhammad Mehboob; Yang, Hongchuan; Alouini, Mohamed-Slim

2015-01-01

Existing full-rate transmission schemes for two-path relay networks typically cannot achieve full diversity while demanding high decoding complexity. In this paper, we present a novel low-complexity full-rate transmission scheme for two-path relay networks to harvest maximum achievable diversity. The proposed scheme adopts block transmission with small block size of four symbols, which greatly reduces the decoding complexity at the receiver. Through the performance analysis of the resulting two-path relay network in terms of the symbol error rate (SER) and diversity order, we show the proposed scheme can achieve full diversity order of four and mimic a 2 \\times 2 multiple-input multiple-output system. Simulations results are provided to validate the mathematical formulation. © 1967-2012 IEEE.

Low-complexity full-rate transmission scheme with full diversity for two-path relay networks

KAUST Repository

Fareed, Muhammad Mehboob

2015-04-01

Existing full-rate transmission schemes for two-path relay networks typically cannot achieve full diversity while demanding high decoding complexity. In this paper, we present a novel low-complexity full-rate transmission scheme for two-path relay networks to harvest maximum achievable diversity. The proposed scheme adopts block transmission with small block size of four symbols, which greatly reduces the decoding complexity at the receiver. Through the performance analysis of the resulting two-path relay network in terms of the symbol error rate (SER) and diversity order, we show the proposed scheme can achieve full diversity order of four and mimic a 2 \\\\times 2 multiple-input multiple-output system. Simulations results are provided to validate the mathematical formulation. © 1967-2012 IEEE.

Friendship Network Characteristics Are Associated with Physical Activity and Sedentary Behavior in Early Adolescence.

Science.gov (United States)

Marks, Jennifer; de la Haye, Kayla; Barnett, Lisa M; Allender, Steven

2015-01-01

There is limited understanding of the association between peer social networks and physical activity (PA), sedentary and screen-related behaviors. This study reports on associations between personal network characteristics and these important health behaviors for early adolescents. Participants were 310 students, aged 11-13 years, from fifteen randomly selected Victorian primary schools (43% response rate). PA and sedentary behaviors were collected via accelerometer and self-report questionnaire, and anthropometric measures via trained researchers. Participants nominated up to fifteen friends, and described the frequency of interaction and perceived activity intensity of these friends. Personal network predictors were examined using regression modelling for PA and sedentary/screen behavior. Perceived activity levels of friends, and friendships with very frequent interaction were associated with outside-of-school PA and/or sedentary/screen time. Differences according to sex were also observed in the association between network characteristics and PA and sedentary time. A higher number of friends and greater proportion of same sex friends were associated with boys engaging in more moderate-to-vigorous PA outside of school hours. PA intensity during school-day breaks was positively associated with having a greater proportion of friends who played sports for girls, and a greater proportion of male friends for boys. Friendship network characteristics are associated with PA and sedentary/screen time in late childhood/early adolescence, and these associations differ by sex. The positive influence of very active peers may be a promising avenue to strengthen traditional interventions for the promotion of PA and reduction in screen time.

Topological probability and connection strength induced activity in complex neural networks

International Nuclear Information System (INIS)

Du-Qu, Wei; Bo, Zhang; Dong-Yuan, Qiu; Xiao-Shu, Luo

2010-01-01

Recent experimental evidence suggests that some brain activities can be assigned to small-world networks. In this work, we investigate how the topological probability p and connection strength C affect the activities of discrete neural networks with small-world (SW) connections. Network elements are described by two-dimensional map neurons (2DMNs) with the values of parameters at which no activity occurs. It is found that when the value of p is smaller or larger, there are no active neurons in the network, no matter what the value of connection strength is; for a given appropriate connection strength, there is an intermediate range of topological probability where the activity of 2DMN network is induced and enhanced. On the other hand, for a given intermediate topological probability level, there exists an optimal value of connection strength such that the frequency of activity reaches its maximum. The possible mechanism behind the action of topological probability and connection strength is addressed based on the bifurcation method. Furthermore, the effects of noise and transmission delay on the activity of neural network are also studied. (general)

Monitoring Malware Activity on the LAN Network

Science.gov (United States)

Skrzewski, Mirosław

Many security related organizations periodically publish current network and systems security information, with the lists of top malware programs. These lists raises the question how these threats spreads out, if the worms (the only threat with own communication abilities) are low or missing on these lists. The paper discuss the research on malware network activity, aimed to deliver the answer to the question, what is the main infection channel of modern malware, done with the usage of virtual honeypot systems on dedicated, unprotected network. Systems setup, network and systems monitoring solutions, results of over three months of network traffic and malware monitoring are presented, along with the proposed answer to our research question.

Active hippocampal networks undergo spontaneous synaptic modification.

Directory of Open Access Journals (Sweden)

Masako Tsukamoto-Yasui

Full Text Available The brain is self-writable; as the brain voluntarily adapts itself to a changing environment, the neural circuitry rearranges its functional connectivity by referring to its own activity. How the internal activity modifies synaptic weights is largely unknown, however. Here we report that spontaneous activity causes complex reorganization of synaptic connectivity without any external (or artificial stimuli. Under physiologically relevant ionic conditions, CA3 pyramidal cells in hippocampal slices displayed spontaneous spikes with bistable slow oscillations of membrane potential, alternating between the so-called UP and DOWN states. The generation of slow oscillations did not require fast synaptic transmission, but their patterns were coordinated by local circuit activity. In the course of generating spontaneous activity, individual neurons acquired bidirectional long-lasting synaptic modification. The spontaneous synaptic plasticity depended on a rise in intracellular calcium concentrations of postsynaptic cells, but not on NMDA receptor activity. The direction and amount of the plasticity varied depending on slow oscillation patterns and synapse locations, and thus, they were diverse in a network. Once this global synaptic refinement occurred, the same neurons now displayed different patterns of spontaneous activity, which in turn exhibited different levels of synaptic plasticity. Thus, active networks continuously update their internal states through ongoing synaptic plasticity. With computational simulations, we suggest that with this slow oscillation-induced plasticity, a recurrent network converges on a more specific state, compared to that with spike timing-dependent plasticity alone.

Recognizing Multi-user Activities using Body Sensor Networks

DEFF Research Database (Denmark)

Gu, Tao; Wang, Liang; Chen, Hanhua

2011-01-01

The advances of wireless networking and sensor technology open up an interesting opportunity to infer human activities in a smart home environment. Existing work in this paradigm focuses mainly on recognizing activities of a single user. In this work, we address the fundamental problem...... activity classes of data—for building activity models and design a scalable, noise-resistant, Emerging Pattern based Multi-user Activity Recognizer (epMAR) to recognize both single- and multi-user activities. We develop a multi-modal, wireless body sensor network for collecting real-world traces in a smart...... home environment, and conduct comprehensive empirical studies to evaluate our system. Results show that epMAR outperforms existing schemes in terms of accuracy, scalability and robustness....

Spatio-temporal specialization of GABAergic septo-hippocampal neurons for rhythmic network activity.

Science.gov (United States)

Unal, Gunes; Crump, Michael G; Viney, Tim J; Éltes, Tímea; Katona, Linda; Klausberger, Thomas; Somogyi, Peter

2018-03-03

Medial septal GABAergic neurons of the basal forebrain innervate the hippocampus and related cortical areas, contributing to the coordination of network activity, such as theta oscillations and sharp wave-ripple events, via a preferential innervation of GABAergic interneurons. Individual medial septal neurons display diverse activity patterns, which may be related to their termination in different cortical areas and/or to the different types of innervated interneurons. To test these hypotheses, we extracellularly recorded and juxtacellularly labeled single medial septal neurons in anesthetized rats in vivo during hippocampal theta and ripple oscillations, traced their axons to distant cortical target areas, and analyzed their postsynaptic interneurons. Medial septal GABAergic neurons exhibiting different hippocampal theta phase preferences and/or sharp wave-ripple related activity terminated in restricted hippocampal regions, and selectively targeted a limited number of interneuron types, as established on the basis of molecular markers. We demonstrate the preferential innervation of bistratified cells in CA1 and of basket cells in CA3 by individual axons. One group of septal neurons was suppressed during sharp wave-ripples, maintained their firing rate across theta and non-theta network states and mainly fired along the descending phase of CA1 theta oscillations. In contrast, neurons that were active during sharp wave-ripples increased their firing significantly during "theta" compared to "non-theta" states, with most firing during the ascending phase of theta oscillations. These results demonstrate that specialized septal GABAergic neurons contribute to the coordination of network activity through parallel, target area- and cell type-selective projections to the hippocampus.

A complex-valued firing-rate model that approximates the dynamics of spiking networks.

Directory of Open Access Journals (Sweden)

Evan S Schaffer

2013-10-01

Full Text Available Firing-rate models provide an attractive approach for studying large neural networks because they can be simulated rapidly and are amenable to mathematical analysis. Traditional firing-rate models assume a simple form in which the dynamics are governed by a single time constant. These models fail to replicate certain dynamic features of populations of spiking neurons, especially those involving synchronization. We present a complex-valued firing-rate model derived from an eigenfunction expansion of the Fokker-Planck equation and apply it to the linear, quadratic and exponential integrate-and-fire models. Despite being almost as simple as a traditional firing-rate description, this model can reproduce firing-rate dynamics due to partial synchronization of the action potentials in a spiking model, and it successfully predicts the transition to spike synchronization in networks of coupled excitatory and inhibitory neurons.

A complex-valued firing-rate model that approximates the dynamics of spiking networks.

Science.gov (United States)

Schaffer, Evan S; Ostojic, Srdjan; Abbott, L F

2013-10-01

Firing-rate models provide an attractive approach for studying large neural networks because they can be simulated rapidly and are amenable to mathematical analysis. Traditional firing-rate models assume a simple form in which the dynamics are governed by a single time constant. These models fail to replicate certain dynamic features of populations of spiking neurons, especially those involving synchronization. We present a complex-valued firing-rate model derived from an eigenfunction expansion of the Fokker-Planck equation and apply it to the linear, quadratic and exponential integrate-and-fire models. Despite being almost as simple as a traditional firing-rate description, this model can reproduce firing-rate dynamics due to partial synchronization of the action potentials in a spiking model, and it successfully predicts the transition to spike synchronization in networks of coupled excitatory and inhibitory neurons.

Input data preprocessing method for exchange rate forecasting via neural network

Directory of Open Access Journals (Sweden)

Antić Dragan S.

2014-01-01

Full Text Available The aim of this paper is to present a method for neural network input parameters selection and preprocessing. The purpose of this network is to forecast foreign exchange rates using artificial intelligence. Two data sets are formed for two different economic systems. Each system is represented by six categories with 70 economic parameters which are used in the analysis. Reduction of these parameters within each category was performed by using the principal component analysis method. Component interdependencies are established and relations between them are formed. Newly formed relations were used to create input vectors of a neural network. The multilayer feed forward neural network is formed and trained using batch training. Finally, simulation results are presented and it is concluded that input data preparation method is an effective way for preprocessing neural network data. [Projekat Ministarstva nauke Republike Srbije, br.TR 35005, br. III 43007 i br. III 44006

Neural networks based identification and compensation of rate-dependent hysteresis in piezoelectric actuators

International Nuclear Information System (INIS)

Zhang, Xinliang; Tan, Yonghong; Su, Miyong; Xie, Yangqiu

2010-01-01

This paper presents a method of the identification for the rate-dependent hysteresis in the piezoelectric actuator (PEA) by use of neural networks. In this method, a special hysteretic operator is constructed from the Prandtl-Ishlinskii (PI) model to extract the changing tendency of the static hysteresis. Then, an expanded input space is constructed by introducing the proposed hysteretic operator to transform the multi-valued mapping of the hysteresis into a one-to-one mapping. Thus, a feedforward neural network is applied to the approximation of the rate-independent hysteresis on the constructed expanded input space. Moreover, in order to describe the rate-dependent performance of the hysteresis, a special hybrid model, which is constructed by a linear auto-regressive exogenous input (ARX) sub-model preceded with the previously obtained neural network based rate-independent hysteresis sub-model, is proposed. For the compensation of the effect of the hysteresis in PEA, the PID feedback controller with a feedforward hysteresis compensator is developed for the tracking control of the PEA. Thus, a corresponding inverse model based on the proposed modeling method is developed for the feedforward hysteresis compensator. Finally, both simulations and experimental results on piezoelectric actuator are presented to verify the effectiveness of the proposed approach for the rate-dependent hysteresis.

Modeling long-term human activeness using recurrent neural networks for biometric data.

Science.gov (United States)

Kim, Zae Myung; Oh, Hyungrai; Kim, Han-Gyu; Lim, Chae-Gyun; Oh, Kyo-Joong; Choi, Ho-Jin

2017-05-18

With the invention of fitness trackers, it has been possible to continuously monitor a user's biometric data such as heart rates, number of footsteps taken, and amount of calories burned. This paper names the time series of these three types of biometric data, the user's "activeness", and investigates the feasibility in modeling and predicting the long-term activeness of the user. The dataset used in this study consisted of several months of biometric time-series data gathered by seven users independently. Four recurrent neural network (RNN) architectures-as well as a deep neural network and a simple regression model-were proposed to investigate the performance on predicting the activeness of the user under various length-related hyper-parameter settings. In addition, the learned model was tested to predict the time period when the user's activeness falls below a certain threshold. A preliminary experimental result shows that each type of activeness data exhibited a short-term autocorrelation; and among the three types of data, the consumed calories and the number of footsteps were positively correlated, while the heart rate data showed almost no correlation with neither of them. It is probably due to this characteristic of the dataset that although the RNN models produced the best results on modeling the user's activeness, the difference was marginal; and other baseline models, especially the linear regression model, performed quite admirably as well. Further experimental results show that it is feasible to predict a user's future activeness with precision, for example, a trained RNN model could predict-with the precision of 84%-when the user would be less active within the next hour given the latest 15 min of his activeness data. This paper defines and investigates the notion of a user's "activeness", and shows that forecasting the long-term activeness of the user is indeed possible. Such information can be utilized by a health-related application to proactively

76 FR 79169 - Power Network New Mexico, LLC; Supplemental Notice That Initial Market-Based Rate Filing Includes...

Science.gov (United States)

2011-12-21

... DEPARTMENT OF ENERGY Federal Energy Regulatory Commission [Docket No. ER12-605-000] Power Network New Mexico, LLC; Supplemental Notice That Initial Market-Based Rate Filing Includes Request for... Power Network New Mexico, LLC's application for market-based rate authority, with an accompanying rate...

Exploiting Rating Abstention Intervals for Addressing Concept Drift in Social Network Recommender Systems

Directory of Open Access Journals (Sweden)

Dionisis Margaris

2018-04-01

Full Text Available One of the major problems that social networks face is the continuous production of successful, user-targeted information in the form of recommendations, which are produced exploiting technology from the field of recommender systems. Recommender systems are based on information about users’ past behavior to formulate recommendations about their future actions. However, as time goes by, social network users may change preferences and likings: they may like different types of clothes, listen to different singers or even different genres of music and so on. This phenomenon has been termed as concept drift. In this paper: (1 we establish that when a social network user abstains from rating submission for a long time, it is a strong indication that concept drift has occurred and (2 we present a technique that exploits the abstention interval concept, to drop from the database ratings that do not reflect the current social network user’s interests, thus improving prediction quality.

Memory-induced mechanism for self-sustaining activity in networks

Science.gov (United States)

Allahverdyan, A. E.; Steeg, G. Ver; Galstyan, A.

2015-12-01

We study a mechanism of activity sustaining on networks inspired by a well-known model of neuronal dynamics. Our primary focus is the emergence of self-sustaining collective activity patterns, where no single node can stay active by itself, but the activity provided initially is sustained within the collective of interacting agents. In contrast to existing models of self-sustaining activity that are caused by (long) loops present in the network, here we focus on treelike structures and examine activation mechanisms that are due to temporal memory of the nodes. This approach is motivated by applications in social media, where long network loops are rare or absent. Our results suggest that under a weak behavioral noise, the nodes robustly split into several clusters, with partial synchronization of nodes within each cluster. We also study the randomly weighted version of the models where the nodes are allowed to change their connection strength (this can model attention redistribution) and show that it does facilitate the self-sustained activity.

Analysis of Blocking Rate and Bandwidth Usage of Mobile IPTV Services in Wireless Cellular Networks

Directory of Open Access Journals (Sweden)

Mingfu Li

2014-01-01

Full Text Available Mobile IPTV services over wireless cellular networks become more and more popular, owing to the significant growth in access bandwidth of wireless cellular networks such as 3G/4G and WiMAX. However, the spectrum resources of wireless cellular networks is rare. How to enhance the spectral efficiency of mobile networks becomes an important issue. Unicast, broadcast, and multicast are the most important transport schemes for offering mobile IPTV services over wireless cellular networks. Therefore, bandwidth usages and blocking rates of unicast, broadcast, and multicast IPTV services were analyzed and compared in this paper. Simulations were also conducted to validate the analytical results. Numerical results demonstrate that the presented analysis is correct, and multicast scheme achieves the best bandwidth usage and blocking rate performance, relative to the other two schemes.

Analysis of blocking rate and bandwidth usage of mobile IPTV services in wireless cellular networks.

Science.gov (United States)

Li, Mingfu

2014-01-01

Mobile IPTV services over wireless cellular networks become more and more popular, owing to the significant growth in access bandwidth of wireless cellular networks such as 3G/4G and WiMAX. However, the spectrum resources of wireless cellular networks is rare. How to enhance the spectral efficiency of mobile networks becomes an important issue. Unicast, broadcast, and multicast are the most important transport schemes for offering mobile IPTV services over wireless cellular networks. Therefore, bandwidth usages and blocking rates of unicast, broadcast, and multicast IPTV services were analyzed and compared in this paper. Simulations were also conducted to validate the analytical results. Numerical results demonstrate that the presented analysis is correct, and multicast scheme achieves the best bandwidth usage and blocking rate performance, relative to the other two schemes.

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

Science.gov (United States)

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.

Personalized Social Network Activity Feeds for Increased Interaction and Content Contribution

Directory of Open Access Journals (Sweden)

Shlomo eBerkovsky

2015-10-01

Full Text Available Online social networks were originally conceived as means of sharing information and activities with friends, and their success has been one of the primary contributors of the tremendous growth of the Web. Social network activity feeds were devised as a means to aggregate recent actions of friends into a convenient list. But the volume of actions and content generated by social network users is overwhelming, such that keeping users up-to-date with friend activities is an ongoing challenge for social network providers. Personalization has been proposed as a solution to combat social network information overload and help users to identify the nuggets of relevant information in the incoming flood of network activities. In this paper, we propose and thoroughly evaluate a personalized model for predicting the relevance of the activity feed items, which informs the ranking of the feeds and facilitates personalization. Results of a live study show that the proposed feed personalization approach successfully identifies and promotes relevant feed items and boosts the uptake of the feeds. In addition, it increases the contribution of user-generated content to the social network and spurs interaction between users.

Packetized Predictive Control for Rate-Limited Networks via Sparse Representation

DEFF Research Database (Denmark)

Nagahara, Masaaki; Quevedo, Daniel; Østergaard, Jan

2012-01-01

controller and the plant input. To achieve robustness with respect to dropouts, the controller transmits data packets containing plant input predictions, which minimize a finite horizon cost function. In our formulation, we design sparse packets for rate-limited networks, by adopting an an ℓ0 optimization...

Achievable Performance of Zero-Delay Variable-Rate Coding in Rate-Constrained Networked Control Systems with Channel Delay

DEFF Research Database (Denmark)

Barforooshan, Mohsen; Østergaard, Jan; Stavrou, Fotios

2017-01-01

This paper presents an upper bound on the minimum data rate required to achieve a prescribed closed-loop performance level in networked control systems (NCSs). The considered feedback loop includes a linear time-invariant (LTI) plant with single measurement output and single control input. Moreover......, in this NCS, a causal but otherwise unconstrained feedback system carries out zero-delay variable-rate coding, and control. Between the encoder and decoder, data is exchanged over a rate-limited noiseless digital channel with a known constant time delay. Here we propose a linear source-coding scheme...

Designing container shipping network under changing demand and freight rates

Directory of Open Access Journals (Sweden)

C. Chen

2010-03-01

Full Text Available This paper focuses on the optimization of container shipping network and its operations under changing cargo demand and freight rates. The problem is formulated as a mixed integer non-linear programming problem (MINP with an objective of maximizing the average unit ship-slot profit at three stages using analytical methodology. The issues such as empty container repositioning, ship-slot allocating, ship sizing, and container configuration are simultaneously considered based on a series of the matrices of demand for a year. To solve the model, a bi-level genetic algorithm based method is proposed. Finally, numerical experiments are provided to illustrate the validity of the proposed model and algorithms. The obtained results show that the suggested model can provide a more realistic solution to the issues on the basis of changing demand and freight rates and arrange a more effective approach to the optimization of container shipping network structures and operations than does the model based on the average demand.

Tourist activated networks: Implications for dynamic bundling and en-route recommendations

DEFF Research Database (Denmark)

Zach, Florian; Gretzel, Ulrike

2011-01-01

This article discusses tourist-activated networks as a concept to inform technological applications supporting dynamic bundling and en route recommendations. Empirical data were collected from travelers who visited a regional destination in the US and then analyzed with respect to its network...... structure. The results indicate that the tourist-activated network for the destination is rather sparse and that there are clearly differences in core and peripheral nodes. The findings illustrate the structure of a tourist-activated network and provide implications for technology design and tourism...

Automatic optimisation of gamma dose rate sensor networks: The DETECT Optimisation Tool

DEFF Research Database (Denmark)

Helle, K.B.; Müller, T.O.; Astrup, Poul

2014-01-01

of the EU FP 7 project DETECT. It evaluates the gamma dose rates that a proposed set of sensors might measure in an emergency and uses this information to optimise the sensor locations. The gamma dose rates are taken from a comprehensive library of simulations of atmospheric radioactive plumes from 64......Fast delivery of comprehensive information on the radiological situation is essential for decision-making in nuclear emergencies. Most national radiological agencies in Europe employ gamma dose rate sensor networks to monitor radioactive pollution of the atmosphere. Sensor locations were often...... source locations. These simulations cover the whole European Union, so the DOT allows evaluation and optimisation of sensor networks for all EU countries, as well as evaluation of fencing sensors around possible sources. Users can choose from seven cost functions to evaluate the capability of a given...

Chaos Synchronization Using Adaptive Dynamic Neural Network Controller with Variable Learning Rates

Directory of Open Access Journals (Sweden)

Chih-Hong Kao

2011-01-01

Full Text Available This paper addresses the synchronization of chaotic gyros with unknown parameters and external disturbance via an adaptive dynamic neural network control (ADNNC system. The proposed ADNNC system is composed of a neural controller and a smooth compensator. The neural controller uses a dynamic RBF (DRBF network to online approximate an ideal controller. The DRBF network can create new hidden neurons online if the input data falls outside the hidden layer and prune the insignificant hidden neurons online if the hidden neuron is inappropriate. The smooth compensator is designed to compensate for the approximation error between the neural controller and the ideal controller. Moreover, the variable learning rates of the parameter adaptation laws are derived based on a discrete-type Lyapunov function to speed up the convergence rate of the tracking error. Finally, the simulation results which verified the chaotic behavior of two nonlinear identical chaotic gyros can be synchronized using the proposed ADNNC scheme.

The effect of the neural activity on topological properties of growing neural networks.

Science.gov (United States)

Gafarov, F M; Gafarova, V R

2016-09-01

The connectivity structure in cortical networks defines how information is transmitted and processed, and it is a source of the complex spatiotemporal patterns of network's development, and the process of creation and deletion of connections is continuous in the whole life of the organism. In this paper, we study how neural activity influences the growth process in neural networks. By using a two-dimensional activity-dependent growth model we demonstrated the neural network growth process from disconnected neurons to fully connected networks. For making quantitative investigation of the network's activity influence on its topological properties we compared it with the random growth network not depending on network's activity. By using the random graphs theory methods for the analysis of the network's connections structure it is shown that the growth in neural networks results in the formation of a well-known "small-world" network.

How to Identify Success Among Networks That Promote Active Living.

Science.gov (United States)

Litt, Jill; Varda, Danielle; Reed, Hannah; Retrum, Jessica; Tabak, Rachel; Gustat, Jeanette; O'Hara Tompkins, Nancy

2015-11-01

We evaluated organization- and network-level factors that influence organizations' perceived success. This is important for managing interorganizational networks, which can mobilize communities to address complex health issues such as physical activity, and for achieving change. In 2011, we used structured interview and network survey data from 22 states in the United States to estimate multilevel random-intercept models to understand organization- and network-level factors that explain perceived network success. A total of 53 of 59 "whole networks" met the criteria for inclusion in the analysis (89.8%). Coordinators identified 559 organizations, with 3 to 12 organizations from each network taking the online survey (response rate = 69.7%; range = 33%-100%). Occupying a leadership position (P Organizations' perceptions of success can influence decisions about continuing involvement and investment in networks designed to promote environment and policy change for active living. Understanding these factors can help leaders manage complex networks that involve diverse memberships, varied interests, and competing community-level priorities.

Kainate-induced network activity in the anterior cingulate cortex.

Science.gov (United States)

Shinozaki, R; Hojo, Y; Mukai, H; Hashizume, M; Murakoshi, T

2016-06-14

Anterior cingulate cortex (ACC) plays a pivotal role in higher order processing of cognition, attention and emotion. The network oscillation is considered an essential means for integration of these CNS functions. The oscillation power and coherence among related areas are often dis-regulated in several psychiatric and pathological conditions with a hemispheric asymmetric manner. Here we describe the network-based activity of field potentials recorded from the superficial layer of the mouse ACC in vitro using submerged type recordings. A short activation by kainic acid administration to the preparation induced populational activities ranging over several frequency bands including theta (3-8Hz), alpha (8-12Hz), beta (13-30Hz), low gamma (30-50Hz) and high gamma (50-80Hz). These responses were repeatable and totally abolished by tetrodotoxin, and greatly diminished by inhibitors of ionotropic and metabotropic glutamate receptors, GABAA receptor or gap-junctions. These observations suggest that the kainate-induced network activity can be a useful model of the network oscillation in the ACC circuit. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Heart Rate Estimated from Body Movements at Six Degrees of Freedom by Convolutional Neural Networks.

Science.gov (United States)

Lee, Hyunwoo; Whang, Mincheol

2018-05-01

Cardiac activity has been monitored continuously in daily life by virtue of advanced medical instruments with microelectromechanical system (MEMS) technology. Seismocardiography (SCG) has been considered to be free from the burden of measurement for cardiac activity, but it has been limited in its application in daily life. The most important issues regarding SCG are to overcome the limitations of motion artifacts due to the sensitivity of motion sensor. Although novel adaptive filters for noise cancellation have been developed, they depend on the researcher’s subjective decision. Convolutional neural networks (CNNs) can extract significant features from data automatically without a researcher’s subjective decision, so that signal processing has been recently replaced as CNNs. Thus, this study aimed to develop a novel method to enhance heart rate estimation from thoracic movement by CNNs. Thoracic movement was measured by six-axis accelerometer and gyroscope signals using a wearable sensor that can be worn by simply clipping on clothes. The dataset was collected from 30 participants (15 males, 15 females) using 12 measurement conditions according to two physical conditions (i.e., relaxed and aroused conditions), three body postures (i.e., sitting, standing, and supine), and six movement speeds (i.e., 3.2, 4.5, 5.8, 6.4, 8.5, and 10.3 km/h). The motion data (i.e., six-axis accelerometer and gyroscope) and heart rate (i.e., electrocardiogram (ECG)) were determined as the input data and labels in the dataset, respectively. The CNN model was developed based on VGG Net and optimized by testing according to network depth and data augmentation. The ensemble network of the VGG-16 without data augmentation and the VGG-19 with data augmentation was determined as optimal architecture for generalization. As a result, the proposed method showed higher accuracy than the previous SCG method using signal processing in most measurement conditions. The three main contributions

Heart Rate Estimated from Body Movements at Six Degrees of Freedom by Convolutional Neural Networks

Directory of Open Access Journals (Sweden)

Hyunwoo Lee

2018-05-01

Full Text Available Cardiac activity has been monitored continuously in daily life by virtue of advanced medical instruments with microelectromechanical system (MEMS technology. Seismocardiography (SCG has been considered to be free from the burden of measurement for cardiac activity, but it has been limited in its application in daily life. The most important issues regarding SCG are to overcome the limitations of motion artifacts due to the sensitivity of motion sensor. Although novel adaptive filters for noise cancellation have been developed, they depend on the researcher’s subjective decision. Convolutional neural networks (CNNs can extract significant features from data automatically without a researcher’s subjective decision, so that signal processing has been recently replaced as CNNs. Thus, this study aimed to develop a novel method to enhance heart rate estimation from thoracic movement by CNNs. Thoracic movement was measured by six-axis accelerometer and gyroscope signals using a wearable sensor that can be worn by simply clipping on clothes. The dataset was collected from 30 participants (15 males, 15 females using 12 measurement conditions according to two physical conditions (i.e., relaxed and aroused conditions, three body postures (i.e., sitting, standing, and supine, and six movement speeds (i.e., 3.2, 4.5, 5.8, 6.4, 8.5, and 10.3 km/h. The motion data (i.e., six-axis accelerometer and gyroscope and heart rate (i.e., electrocardiogram (ECG were determined as the input data and labels in the dataset, respectively. The CNN model was developed based on VGG Net and optimized by testing according to network depth and data augmentation. The ensemble network of the VGG-16 without data augmentation and the VGG-19 with data augmentation was determined as optimal architecture for generalization. As a result, the proposed method showed higher accuracy than the previous SCG method using signal processing in most measurement conditions. The three main

A prediction method for the wax deposition rate based on a radial basis function neural network

Directory of Open Access Journals (Sweden)

Ying Xie

2017-06-01

Full Text Available The radial basis function neural network is a popular supervised learning tool based on machinery learning technology. Its high precision having been proven, the radial basis function neural network has been applied in many areas. The accumulation of deposited materials in the pipeline may lead to the need for increased pumping power, a decreased flow rate or even to the total blockage of the line, with losses of production and capital investment, so research on predicting the wax deposition rate is significant for the safe and economical operation of an oil pipeline. This paper adopts the radial basis function neural network to predict the wax deposition rate by considering four main influencing factors, the pipe wall temperature gradient, pipe wall wax crystal solubility coefficient, pipe wall shear stress and crude oil viscosity, by the gray correlational analysis method. MATLAB software is employed to establish the RBF neural network. Compared with the previous literature, favorable consistency exists between the predicted outcomes and the experimental results, with a relative error of 1.5%. It can be concluded that the prediction method of wax deposition rate based on the RBF neural network is feasible.

Noise in attractor networks in the brain produced by graded firing rate representations.

Directory of Open Access Journals (Sweden)

Tristan J Webb

Full Text Available Representations in the cortex are often distributed with graded firing rates in the neuronal populations. The firing rate probability distribution of each neuron to a set of stimuli is often exponential or gamma. In processes in the brain, such as decision-making, that are influenced by the noise produced by the close to random spike timings of each neuron for a given mean rate, the noise with this graded type of representation may be larger than with the binary firing rate distribution that is usually investigated. In integrate-and-fire simulations of an attractor decision-making network, we show that the noise is indeed greater for a given sparseness of the representation for graded, exponential, than for binary firing rate distributions. The greater noise was measured by faster escaping times from the spontaneous firing rate state when the decision cues are applied, and this corresponds to faster decision or reaction times. The greater noise was also evident as less stability of the spontaneous firing state before the decision cues are applied. The implication is that spiking-related noise will continue to be a factor that influences processes such as decision-making, signal detection, short-term memory, and memory recall even with the quite large networks found in the cerebral cortex. In these networks there are several thousand recurrent collateral synapses onto each neuron. The greater noise with graded firing rate distributions has the advantage that it can increase the speed of operation of cortical circuitry.

Pre-stimulus BOLD-network activation modulates EEG spectral activity during working memory retention

Directory of Open Access Journals (Sweden)

Mara eKottlow

2015-05-01

Full Text Available Working memory (WM processes depend on our momentary mental state and therefore exhibit considerable fluctuations. Here, we investigate the interplay of task-preparatory and task-related brain activity as represented by pre-stimulus BOLD-fluctuations and spectral EEG from the retention periods of a visual WM task. Visual WM is used to maintain sensory information in the brain enabling the performance of cognitive operations and is associated with mental health.We tested 22 subjects simultaneously with EEG and fMRI while performing a visuo-verbal Sternberg task with two different loads, allowing for the temporal separation of preparation, encoding, retention and retrieval periods.Four temporally coherent networks - the default mode network (DMN, the dorsal attention, the right and the left WM network - were extracted from the continuous BOLD data by means of a group ICA. Subsequently, the modulatory effect of these networks’ pre-stimulus activation upon retention-related EEG activity in the theta, alpha and beta frequencies was analyzed. The obtained results are informative in the context of state-dependent information processing.We were able to replicate two well-known load-dependent effects: the frontal-midline theta increase during the task and the decrease of pre-stimulus DMN activity. As our main finding, these two measures seem to depend on each other as the significant negative correlations at frontal-midline channels suggested. Thus, suppressed pre-stimulus DMN levels facilitated later task related frontal midline theta increases. In general, based on previous findings that neuronal coupling in different frequency bands may underlie distinct functions in WM retention, our results suggest that processes reflected by spectral oscillations during retention seem not only to be online synchronized with activity in different attention-related networks but are also modulated by activity in these networks during preparation intervals.

Low Duty-Cycling MAC Protocol for Low Data-Rate Medical Wireless Body Area Networks

Directory of Open Access Journals (Sweden)

Chongqing Zhang

2017-05-01

Full Text Available Wireless body area networks (WBANs are severely energy constrained, and how to improve the energy efficiency so as to prolong the network lifetime as long as possible is one of the most important goals of WBAN research. Low data-rate WBANs are promising to cut down the energy consumption and extend the network lifetime. Considering the characteristics and demands of low data-rate WBANs, a low duty-cycling medium access control (MAC protocol is specially designed for this kind of WBAN in this paper. Longer superframes are exploited to cut down the energy consumed on the transmissions and receptions of redundant beacon frames. Insertion time slots are embedded into the inactive part of a superframe to deliver the frames and satisfy the quality of service (QoS requirements. The number of the data subsections in an insertion time slot can be adaptively adjusted so as to accommodate low data-rate WBANs with different traffic. Simulation results show that the proposed MAC protocol performs well under the condition of low data-rate monitoring traffic.

Systematic network assessment of the carcinogenic activities of cadmium

International Nuclear Information System (INIS)

Chen, Peizhan; Duan, Xiaohua; Li, Mian; Huang, Chao; Li, Jingquan; Chu, Ruiai; Ying, Hao; Song, Haiyun; Jia, Xudong; Ba, Qian; Wang, Hui

2016-01-01

Cadmium has been defined as type I carcinogen for humans, but the underlying mechanisms of its carcinogenic activity and its influence on protein-protein interactions in cells are not fully elucidated. The aim of the current study was to evaluate, systematically, the carcinogenic activity of cadmium with systems biology approaches. From a literature search of 209 studies that performed with cellular models, 208 proteins influenced by cadmium exposure were identified. All of these were assessed by Western blotting and were recognized as key nodes in network analyses. The protein-protein functional interaction networks were constructed with NetBox software and visualized with Cytoscape software. These cadmium-rewired genes were used to construct a scale-free, highly connected biological protein interaction network with 850 nodes and 8770 edges. Of the network, nine key modules were identified and 60 key signaling pathways, including the estrogen, RAS, PI3K-Akt, NF-κB, HIF-1α, Jak-STAT, and TGF-β signaling pathways, were significantly enriched. With breast cancer, colorectal and prostate cancer cellular models, we validated the key node genes in the network that had been previously reported or inferred form the network by Western blotting methods, including STAT3, JNK, p38, SMAD2/3, P65, AKT1, and HIF-1α. These results suggested the established network was robust and provided a systematic view of the carcinogenic activities of cadmium in human. - Highlights: • A cadmium-influenced network with 850 nodes and 8770 edges was established. • The cadmium-rewired gene network was scale-free and highly connected. • Nine modules were identified, and 60 key signaling pathways related to cadmium-induced carcinogenesis were found. • Key mediators in the network were validated in multiple cellular models.

Systematic network assessment of the carcinogenic activities of cadmium

Energy Technology Data Exchange (ETDEWEB)

Chen, Peizhan; Duan, Xiaohua; Li, Mian; Huang, Chao [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Li, Jingquan; Chu, Ruiai; Ying, Hao; Song, Haiyun [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Jia, Xudong [Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Ba, Qian, E-mail: qba@sibs.ac.cn [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Wang, Hui, E-mail: huiwang@sibs.ac.cn [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); School of Life Science and Technology, ShanghaiTech University, Shanghai (China)

2016-11-01

Cadmium has been defined as type I carcinogen for humans, but the underlying mechanisms of its carcinogenic activity and its influence on protein-protein interactions in cells are not fully elucidated. The aim of the current study was to evaluate, systematically, the carcinogenic activity of cadmium with systems biology approaches. From a literature search of 209 studies that performed with cellular models, 208 proteins influenced by cadmium exposure were identified. All of these were assessed by Western blotting and were recognized as key nodes in network analyses. The protein-protein functional interaction networks were constructed with NetBox software and visualized with Cytoscape software. These cadmium-rewired genes were used to construct a scale-free, highly connected biological protein interaction network with 850 nodes and 8770 edges. Of the network, nine key modules were identified and 60 key signaling pathways, including the estrogen, RAS, PI3K-Akt, NF-κB, HIF-1α, Jak-STAT, and TGF-β signaling pathways, were significantly enriched. With breast cancer, colorectal and prostate cancer cellular models, we validated the key node genes in the network that had been previously reported or inferred form the network by Western blotting methods, including STAT3, JNK, p38, SMAD2/3, P65, AKT1, and HIF-1α. These results suggested the established network was robust and provided a systematic view of the carcinogenic activities of cadmium in human. - Highlights: • A cadmium-influenced network with 850 nodes and 8770 edges was established. • The cadmium-rewired gene network was scale-free and highly connected. • Nine modules were identified, and 60 key signaling pathways related to cadmium-induced carcinogenesis were found. • Key mediators in the network were validated in multiple cellular models.

Optimal multicasting in a multi-line-rate ethernet-over-WDM network

Science.gov (United States)

Harve, Shruthi; Batayneh, Marwan; Mukherjee, Biswanath

2009-11-01

Ethernet is the dominant transport technology for Local Area Networks. Efforts are now under way to use carrier-grade Ethernet in backbone networks of different service providers. With the advent of applications such as IPTV and Videoon- Demand, there is need for techniques to route multicast traffic over the Ethernet backbone networks. Here, we address the problem of Routing and Wavelength Assignment (RWA) of a set of multicast requests in a Multi-Line-Rate Ethernet backbone network with the objective of minimizing the cost of setting up the network, in terms of the Service Provider's Capital Expenditure (CAPEX). We present an Auxiliary Graph based heuristic algorithm that routes each multicast request on a light-tree structure, and assigns minimum cost wavelengths along the route. We compare the properties of the algorithm to the optimal solution given by a mathematical model formulated as an Integer Linear Program (ILP), and show that they compare very well. We also find that the algorithm is most cost-effective when the incoming requests are processed in descending order of their bandwidth requirements.

Higher frequency network activity flow predicts lower frequency node activity in intrinsic low-frequency BOLD fluctuations.

Science.gov (United States)

Bajaj, Sahil; Adhikari, Bhim Mani; Dhamala, Mukesh

2013-01-01

The brain remains electrically and metabolically active during resting conditions. The low-frequency oscillations (LFO) of the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI) coherent across distributed brain regions are known to exhibit features of this activity. However, these intrinsic oscillations may undergo dynamic changes in time scales of seconds to minutes during resting conditions. Here, using wavelet-transform based time-frequency analysis techniques, we investigated the dynamic nature of default-mode networks from intrinsic BOLD signals recorded from participants maintaining visual fixation during resting conditions. We focused on the default-mode network consisting of the posterior cingulate cortex (PCC), the medial prefrontal cortex (mPFC), left middle temporal cortex (LMTC) and left angular gyrus (LAG). The analysis of the spectral power and causal flow patterns revealed that the intrinsic LFO undergo significant dynamic changes over time. Dividing the frequency interval 0 to 0.25 Hz of LFO into four intervals slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), slow-3 (0.073-0.198 Hz) and slow-2 (0.198-0.25 Hz), we further observed significant positive linear relationships of slow-4 in-out flow of network activity with slow-5 node activity, and slow-3 in-out flow of network activity with slow-4 node activity. The network activity associated with respiratory related frequency (slow-2) was found to have no relationship with the node activity in any of the frequency intervals. We found that the net causal flow towards a node in slow-3 band was correlated with the number of fibers, obtained from diffusion tensor imaging (DTI) data, from the other nodes connecting to that node. These findings imply that so-called resting state is not 'entirely' at rest, the higher frequency network activity flow can predict the lower frequency node activity, and the network activity flow can reflect underlying structural

Network robustness assessed within a dual connectivity framework: joint dynamics of the Active and Idle Networks.

Science.gov (United States)

Tejedor, Alejandro; Longjas, Anthony; Zaliapin, Ilya; Ambroj, Samuel; Foufoula-Georgiou, Efi

2017-08-17

Network robustness against attacks has been widely studied in fields as diverse as the Internet, power grids and human societies. But current definition of robustness is only accounting for half of the story: the connectivity of the nodes unaffected by the attack. Here we propose a new framework to assess network robustness, wherein the connectivity of the affected nodes is also taken into consideration, acknowledging that it plays a crucial role in properly evaluating the overall network robustness in terms of its future recovery from the attack. Specifically, we propose a dual perspective approach wherein at any instant in the network evolution under attack, two distinct networks are defined: (i) the Active Network (AN) composed of the unaffected nodes and (ii) the Idle Network (IN) composed of the affected nodes. The proposed robustness metric considers both the efficiency of destroying the AN and that of building-up the IN. We show, via analysis of well-known prototype networks and real world data, that trade-offs between the efficiency of Active and Idle Network dynamics give rise to surprising robustness crossovers and re-rankings, which can have significant implications for decision making.

Evaluating failure rate of fault-tolerant multistage interconnection networks using Weibull life distribution

International Nuclear Information System (INIS)

Bistouni, Fathollah; Jahanshahi, Mohsen

2015-01-01

Fault-tolerant multistage interconnection networks (MINs) play a vital role in the performance of multiprocessor systems where reliability evaluation becomes one of the main concerns in analyzing these networks properly. In many cases, the primary objective in system reliability analysis is to compute a failure distribution of the entire system according to that of its components. However, since the problem is known to be NP-hard, in none of the previous efforts, the precise evaluation of the system failure rate has been performed. Therefore, our goal is to investigate this parameter for different fault-tolerant MINs using Weibull life distribution that is one of the most commonly used distributions in reliability. In this paper, four important groups of fault-tolerant MINs will be examined to find the best fault-tolerance techniques in terms of failure rate; (1) Extra-stage MINs, (2) Parallel MINs, (3) Rearrangeable non-blocking MINs, and (4) Replicated MINs. This paper comprehensively analyzes all perspectives of the reliability (terminal, broadcast, and network reliability). Moreover, in this study, all reliability equations are calculated for different network sizes. - Highlights: • The failure rate of different MINs is analyzed by using Weibull life distribution. • This article tries to find the best fault-tolerance technique in the field of MINs. • Complex series-parallel RBDs are used to determine the reliability of the MINs. • All aspects of the reliability (i.e. terminal, broadcast, and network) are analyzed. • All reliability equations will be calculated for different size N×N.

Circumpolar Active Layer Monitoring (CALM) Program Network, Version 1

Data.gov (United States)

National Aeronautics and Space Administration — The CALM network includes 168 active sites in both hemispheres with 15 participating countries. This network represents the only coordinated and standardized program...

Impairment of GABA transporter GAT-1 terminates cortical recurrent network activity via enhanced phasic inhibition

Directory of Open Access Journals (Sweden)

Daniel Simon Razik

2013-09-01

Full Text Available In the central nervous system, GABA transporters (GATs very efficiently clear synaptically released GABA from the extracellular space, and thus exert a tight control on GABAergic inhibition. In neocortex, GABAergic inhibition is heavily recruited during recurrent phases of spontaneous action potential activity which alternate with neuronally quiet periods. Therefore, such activity should be quite sensitive to minute alterations of GAT function. Here, we explored the effects of a gradual impairment of GAT-1 and GAT-2/3 on spontaneous recurrent network activity – termed network bursts and silent periods – in organotypic slice cultures of rat neocortex. The GAT-1 specific antagonist NO-711 depressed activity already at nanomolar concentrations (IC50 for depression of spontaneous multiunit firing rate of 42 nM, reaching a level of 80% at 500-1000 nM. By contrast, the GAT-2/3 preferring antagonist SNAP-5114 had weaker and less consistent effects. Several lines of evidence pointed towards an enhancement of phasic GABAergic inhibition as the dominant activity-depressing mechanism: network bursts were drastically shortened, phasic GABAergic currents decayed slower, and neuronal excitability during ongoing activity was diminished. In silent periods, NO-711 had little effect on neuronal excitability or membrane resistance, quite in contrast to the effects of muscimol, a GABA mimetic which activates GABAA receptors tonically. Our results suggest that an enhancement of phasic GABAergic inhibition efficiently curtails cortical recurrent activity and may mediate antiepileptic effects of therapeutically relevant concentrations of GAT-1 antagonists.

Distinguishing the rates of gene activation from phenotypic variations.

Science.gov (United States)

Chen, Ye; Lv, Cheng; Li, Fangting; Li, Tiejun

2015-06-18

Stochastic genetic switching driven by intrinsic noise is an important process in gene expression. When the rates of gene activation/inactivation are relatively slow, fast, or medium compared with the synthesis/degradation rates of mRNAs and proteins, the variability of protein and mRNA levels may exhibit very different dynamical patterns. It is desirable to provide a systematic approach to identify their key dynamical features in different regimes, aiming at distinguishing which regime a considered gene regulatory network is in from their phenotypic variations. We studied a gene expression model with positive feedbacks when genetic switching rates vary over a wide range. With the goal of providing a method to distinguish the regime of the switching rates, we first focus on understanding the essential dynamics of gene expression system in different cases. In the regime of slow switching rates, we found that the effective dynamics can be reduced to independent evolutions on two separate layers corresponding to gene activation and inactivation states, and the transitions between two layers are rare events, after which the system goes mainly along deterministic ODE trajectories on a particular layer to reach new steady states. The energy landscape in this regime can be well approximated by using Gaussian mixture model. In the regime of intermediate switching rates, we analyzed the mean switching time to investigate the stability of the system in different parameter ranges. We also discussed the case of fast switching rates from the viewpoint of transition state theory. Based on the obtained results, we made a proposal to distinguish these three regimes in a simulation experiment. We identified the intermediate regime from the fact that the strength of cellular memory is lower than the other two cases, and the fast and slow regimes can be distinguished by their different perturbation-response behavior with respect to the switching rates perturbations. We proposed a

Distributed state estimation for multi-agent based active distribution networks

NARCIS (Netherlands)

Nguyen, H.P.; Kling, W.L.

2010-01-01

Along with the large-scale implementation of distributed generators, the current distribution networks have changed gradually from passive to active operation. State estimation plays a vital role to facilitate this transition. In this paper, a suitable state estimation method for the active network

Active self-testing noise measurement sensors for large-scale environmental sensor networks.

Science.gov (United States)

Domínguez, Federico; Cuong, Nguyen The; Reinoso, Felipe; Touhafi, Abdellah; Steenhaut, Kris

2013-12-13

Large-scale noise pollution sensor networks consist of hundreds of spatially distributed microphones that measure environmental noise. These networks provide historical and real-time environmental data to citizens and decision makers and are therefore a key technology to steer environmental policy. However, the high cost of certified environmental microphone sensors render large-scale environmental networks prohibitively expensive. Several environmental network projects have started using off-the-shelf low-cost microphone sensors to reduce their costs, but these sensors have higher failure rates and produce lower quality data. To offset this disadvantage, we developed a low-cost noise sensor that actively checks its condition and indirectly the integrity of the data it produces. The main design concept is to embed a 13 mm speaker in the noise sensor casing and, by regularly scheduling a frequency sweep, estimate the evolution of the microphone's frequency response over time. This paper presents our noise sensor's hardware and software design together with the results of a test deployment in a large-scale environmental network in Belgium. Our middle-range-value sensor (around €50) effectively detected all experienced malfunctions, in laboratory tests and outdoor deployments, with a few false positives. Future improvements could further lower the cost of our sensor below €10.

Inferring network structure from cascades

Science.gov (United States)

Ghonge, Sushrut; Vural, Dervis Can

2017-07-01

Many physical, biological, and social phenomena can be described by cascades taking place on a network. Often, the activity can be empirically observed, but not the underlying network of interactions. In this paper we offer three topological methods to infer the structure of any directed network given a set of cascade arrival times. Our formulas hold for a very general class of models where the activation probability of a node is a generic function of its degree and the number of its active neighbors. We report high success rates for synthetic and real networks, for several different cascade models.

Fast neutron spectra determination by threshold activation detectors using neural networks

International Nuclear Information System (INIS)

Kardan, M.R.; Koohi-Fayegh, R.; Setayeshi, S.; Ghiassi-Nejad, M.

2004-01-01

Neural network method was used for fast neutron spectra unfolding in spectrometry by threshold activation detectors. The input layer of the neural networks consisted of 11 neurons for the specific activities of neutron-induced nuclear reaction products, while the output layers were fast neutron spectra which had been subdivided into 6, 8, 10, 12, 15 and 20 energy bins. Neural network training was performed by 437 fast neutron spectra and corresponding threshold activation detector readings. The trained neural network have been applied for unfolding 50 spectra, which were not in training sets and the results were compared with real spectra and unfolded spectra by SANDII. The best results belong to 10 energy bin spectra. The neural network was also trained by detector readings with 5% uncertainty and the response of the trained neural network to detector readings with 5%, 10%, 15%, 20%, 25% and 50% uncertainty was compared with real spectra. Neural network algorithm, in comparison with other unfolding methods, is very fast and needless to detector response matrix and any prior information about spectra and also the outputs have low sensitivity to uncertainty in the activity measurements. The results show that the neural network algorithm is useful when a fast response is required with reasonable accuracy

Gender differences in brain activity and the relationship between brain activity and differences in prevalence rates between male and female major depressive disorder patients: a resting-state fMRI study.

Science.gov (United States)

Yao, Zhijian; Yan, Rui; Wei, Maobin; Tang, Hao; Qin, Jiaolong; Lu, Qing

2014-11-01

We examined the gender-difference effect on abnormal spontaneous neuronal activity of male and female major depressive disorder (MDD) patients using the amplitude of low-frequency fluctuation (ALFF) and the further clarified the relationship between the abnormal ALFF and differences in MDD prevalence rates between male and female patients. Fourteen male MDD patients, 13 female MDD patients and 15 male and 15 female well matched healthy controls (HCs) completed this study. The ALFF approach was used, and Pearson correlation was conducted to observe a possible clinical relevance. There were widespread differences in ALFF values between female and male MDD patients, including some important parts of the frontoparietal network, auditory network, attention network and cerebellum network. In female MDD patients, there was a positive correlation between average ALFF values of the left postcentral gyrus and the severity of weight loss symptom. The gender-difference effect leading to abnormal brain activity is an important underlying pathomechanism for different somatic symptoms in MDD patients of different genders and is likely suggestive of higher MDD prevalence rates in females. The abnormal ALFF resulting from the gender-difference effect might improve our understanding of the differences in prevalence rates between male and female MDD patients from another perspective. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Sustained Activity in Hierarchical Modular Neural Networks: Self-Organized Criticality and Oscillations

Science.gov (United States)

Wang, Sheng-Jun; Hilgetag, Claus C.; Zhou, Changsong

2010-01-01

Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. In particular, they are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality (SOC). We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. Previously, it was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We found that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and SOC, which are not present in the respective random networks. The mechanism underlying the sustained activity is that each dense module cannot sustain activity on its own, but displays SOC in the presence of weak perturbations. Therefore, the hierarchical modular networks provide the coupling among subsystems with SOC. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivity of critical states and the predictability and timing of oscillations for efficient information

Microgrids in Active Network Management-Part I

DEFF Research Database (Denmark)

Palizban, Omid; Kauhaniemia, Kimmo; Guerrero, Josep M.

2014-01-01

The microgrid concept has been closely investigated and implemented by numerous experts worldwide. The first part of this paper describes the principles of microgrid design, considering the operational concepts and requirements arising from participation in active network management. Over the las......, energy storage systems, and market participation in both island and grid-connection operation. Finally, control techniques and the principles of energy-storage systems are summarized in a comprehensive flowchart.......The microgrid concept has been closely investigated and implemented by numerous experts worldwide. The first part of this paper describes the principles of microgrid design, considering the operational concepts and requirements arising from participation in active network management. Over the last...

Bi-directional astrocytic regulation of neuronal activity within a network

Directory of Open Access Journals (Sweden)

Susan Yu Gordleeva

2012-11-01

Full Text Available The concept of a tripartite synapse holds that astrocytes can affect both the pre- and postsynaptic compartments through the Ca2+-dependent release of gliotransmitters. Because astrocytic Ca2+ transients usually last for a few seconds, we assumed that astrocytic regulation of synaptic transmission may also occur on the scale of seconds. Here, we considered the basic physiological functions of tripartite synapses and investigated astrocytic regulation at the level of neural network activity. The firing dynamics of individual neurons in a spontaneous firing network was described by the Hodgkin-Huxley model. The neurons received excitatory synaptic input driven by the Poisson spike train with variable frequency. The mean field concentration of the released neurotransmitter was used to describe the presynaptic dynamics. The amplitudes of the excitatory postsynaptic currents (PSCs obeyed the gamma distribution law. In our model, astrocytes depressed the presynaptic release and enhanced the postsynaptic currents. As a result, low frequency synaptic input was suppressed while high frequency input was amplified. The analysis of the neuron spiking frequency as an indicator of network activity revealed that tripartite synaptic transmission dramatically changed the local network operation compared to bipartite synapses. Specifically, the astrocytes supported homeostatic regulation of the network activity by increasing or decreasing firing of the neurons. Thus, the astrocyte activation may modulate a transition of neural network into bistable regime of activity with two stable firing levels and spontaneous transitions between them.

The pairwise phase consistency in cortical network and its relationship with neuronal activation

Directory of Open Access Journals (Sweden)

Wang Daming

2017-01-01

Full Text Available Gamma-band neuronal oscillation and synchronization with the range of 30-90 Hz are ubiquitous phenomenon across numerous brain areas and various species, and correlated with plenty of cognitive functions. The phase of the oscillation, as one aspect of CTC (Communication through Coherence hypothesis, underlies various functions for feature coding, memory processing and behaviour performing. The PPC (Pairwise Phase Consistency, an improved coherence measure, statistically quantifies the strength of phase synchronization. In order to evaluate the PPC and its relationships with input stimulus, neuronal activation and firing rate, a simplified spiking neuronal network is constructed to simulate orientation columns in primary visual cortex. If the input orientation stimulus is preferred for a certain orientation column, neurons within this corresponding column will obtain higher firing rate and stronger neuronal activation, which consequently engender higher PPC values, with higher PPC corresponding to higher firing rate. In addition, we investigate the PPC in time resolved analysis with a sliding window.

Application of neural networks to validation of feedwater flow rate in a nuclear power plant

International Nuclear Information System (INIS)

Khadem, M.; Ipakchi, A.; Alexandro, F.J.; Colley, R.W.

1993-01-01

Feedwater flow rate measurement in nuclear power plants requires periodic calibration. This is due to the fact that the venturi surface condition of the feedwater flow rate sensor changes because of a chemical reaction between the surface coating material and the feedwater. Fouling of the venturi surface, due to this chemical reaction and the deposits of foreign materials, has been observed shortly after a clean venturi is put in operation. A fouled venturi causes an incorrect measurement of feedwater flow rate, which in turn results in an inaccurate calculation of the generated power. This paper presents two methods for verifying incipient and continuing fouling of the venturi of the feedwater flow rate sensors. Both methods are based on the use of a set of dissimilar process variables dynamically related to the feedwater flow rate variable. The first method uses a neural network to generate estimates of the feedwater flow rate readings. Agreement, within a given tolerance, of the feedwater flow rate instrument reading, and the corresponding neural network output establishes that the feedwater flow rate instrument is operating properly. The second method is similar to the first method except that the neural network predicts the core power which is calculated from measurements on the primary loop, rather than the feedwater flow rates. This core power is referred to the primary core power in this paper. A comparison of the power calculated from the feedwater flow measurements in the secondary loop, with the calculated and neural network predicted primary core power provides information from which it can be determined whether fouling is beginning to occur. The two methods were tested using data from the feedwater flow meters in the two feedwater flow loops of the TMI-1 nuclear power plant

Real-time Human Activity Recognition using a Body Sensor Network

DEFF Research Database (Denmark)

Wang, Liang; Gu, Tao; Chen, Hanhua

2010-01-01

Real-time activity recognition using body sensor networks is an important and challenging task and it has many potential applications. In this paper, we propose a realtime, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this mo...

Radar meteor rates and solar activity

International Nuclear Information System (INIS)

Prikryl, P.

1983-01-01

The short-term variation of diurnal radar meteor rates with solar activity represented by solar microwave flux Fsub(10.7), and sunspots relative number Rsub(z), is investigated. Applying the superposed-epoch analysis to the observational material of radar meteor rates from Christchurch (1960-61 and 1963-65), a decrease in the recorded radar rates is found during days of enhanced solar activity. No effect of geomagnetic activity similar to the one reported for the Swedish and Canadian radar meteor data was found by the author in the Christchurch data. A possible explanation of the absence of the geomagnetic effect on radar meteor rates from New Zealand due to a lower echo ceiling height of the Christchurch radar is suggested. The variation of the atmospheric parameters as a possible cause of the observed variation in radar meteor rates is also discussed. (author)

Simulating activation propagation in social networks using the graph theory

Directory of Open Access Journals (Sweden)

František Dařena

2010-01-01

Full Text Available The social-network formation and analysis is nowadays one of objects that are in a focus of intensive research. The objective of the paper is to suggest the perspective of representing social networks as graphs, with the application of the graph theory to problems connected with studying the network-like structures and to study spreading activation algorithm for reasons of analyzing these structures. The paper presents the process of modeling multidimensional networks by means of directed graphs with several characteristics. The paper also demonstrates using Spreading Activation algorithm as a good method for analyzing multidimensional network with the main focus on recommender systems. The experiments showed that the choice of parameters of the algorithm is crucial, that some kind of constraint should be included and that the algorithm is able to provide a stable environment for simulations with networks.

Reconstructing Causal Biological Networks through Active Learning.

Directory of Open Access Journals (Sweden)

Hyunghoon Cho

Full Text Available Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs, which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments.

Will available bit rate (ABR) services give us the capability to offer virtual LANs over wide-area ATM networks?

Science.gov (United States)

Ferrandiz, Ana; Scallan, Gavin

1995-10-01

The available bit rate (ABR) service allows connections to exceed their negotiated data rates during the life of the connections when excess capacity is available in the network. These connections are subject to flow control from the network in the event of network congestion. The ability to dynamically adjust the data rate of the connection can provide improved utilization of the network and be a valuable service to end users. ABR type service is therefore appropriate for the transmission of bursty LAN traffic over a wide area network in a manner that is more efficient and cost effective than allocating bandwdith at the peak cell rate. This paper describes the ABR service and discusses if it is realistic to operate a LAN like service over a wide area using ABR.

Improving quantitative structure-activity relationship models using Artificial Neural Networks trained with dropout.

Science.gov (United States)

Mendenhall, Jeffrey; Meiler, Jens

2016-02-01

Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets. Use of dropout improved both enrichment false positive rate and log-scaled area under the receiver-operating characteristic curve (logAUC) by 22-46 % over conventional ANN implementations. Optimal dropout rates are found to be a function of the signal-to-noise ratio of the descriptor set, and relatively independent of the dataset. Dropout ANNs with 2D and 3D autocorrelation descriptors outperform conventional ANNs as well as optimized fingerprint similarity search methods.

Sustained activity in hierarchical modular neural networks: self-organized criticality and oscillations

Directory of Open Access Journals (Sweden)

Sheng-Jun Wang

2011-06-01

Full Text Available Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. They are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality. We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. It was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We find that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and self-organized criticality, which are not present in the respective random networks. The underlying mechanism is that each dense module cannot sustain activity on its own, but displays self-organized criticality in the presence of weak perturbations. The hierarchical modular networks provide the coupling among subsystems with self-organized criticality. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivityof critical state and predictability and timing of oscillations for efficient

Flexibility and Balancing in Active Distribution Networks

DEFF Research Database (Denmark)

Kordheili, Reza Ahmadi

. Chapter 4 presents the details of the analysis, as well as the details of the MV network. To generalize the analysis, a standard MV network has been used for the studies. The MV network is also an active network, i.e. it involves MV wind turbines and decentralized combined heat and power (DCHP). DCHP...... units play an important role in Danish power system, and they contribute to electricity production as well. Modeling of wind turbines is done considering real data of a Vestas wind turbine. For wind speed, a modified wind speed model has been used for wind turbines, considering the available wind...... measurement. Also, a detailed model of DCHP units has been used in this thesis. Details of wind turbine model, as well as details of DCHP are presented in the thesis. The third objective of the research is to include the LV and MV networks in frequency response of the power system. Considering the increasing...

The European ALARA network. Development, functioning and main activities

International Nuclear Information System (INIS)

Schmitt-Hannig, A.

2009-01-01

The new ICRP recommendations (ICRP 103), and in particular the detailed treatment of optimisation in the ICRP Publication 101, define optimisation of protection as a source-related process aimed at keeping the likelihood of incurred exposures, the number of people exposed and the magnitude of their individual doses as low as reasonably achievable, also below constraints, taking into account economic and societal factors. Practical implementation and further development of the ALARA principle has been achieved for many years now by the successful cooperation of experts from different European organisations; first as pioneers by establishing the European ALARA Network and then by enthusiastically supporting the activities of the network itself. This contribution presents the evolution, operation and key activities of the European ALARA Network (EAN) in the last years; the successful cooperation of experts from different professional backgrounds, advocating the ALARA principle in a range of radiation protection areas, and contributing to its further development by trading experience and networking. The interaction between the EAN and international organisations, which support the ALARA principle by including relevant activities in their work programmes, is described. (orig.)

Feed-Forward Propagation of Temporal and Rate Information between Cortical Populations during Coherent Activation in Engineered In Vitro Networks.

Science.gov (United States)

DeMarse, Thomas B; Pan, Liangbin; Alagapan, Sankaraleengam; Brewer, Gregory J; Wheeler, Bruce C

2016-01-01

Transient propagation of information across neuronal assembles is thought to underlie many cognitive processes. However, the nature of the neural code that is embedded within these transmissions remains uncertain. Much of our understanding of how information is transmitted among these assemblies has been derived from computational models. While these models have been instrumental in understanding these processes they often make simplifying assumptions about the biophysical properties of neurons that may influence the nature and properties expressed. To address this issue we created an in vitro analog of a feed-forward network composed of two small populations (also referred to as assemblies or layers) of living dissociated rat cortical neurons. The populations were separated by, and communicated through, a microelectromechanical systems (MEMS) device containing a strip of microscale tunnels. Delayed culturing of one population in the first layer followed by the second a few days later induced the unidirectional growth of axons through the microtunnels resulting in a primarily feed-forward communication between these two small neural populations. In this study we systematically manipulated the number of tunnels that connected each layer and hence, the number of axons providing communication between those populations. We then assess the effect of reducing the number of tunnels has upon the properties of between-layer communication capacity and fidelity of neural transmission among spike trains transmitted across and within layers. We show evidence based on Victor-Purpura's and van Rossum's spike train similarity metrics supporting the presence of both rate and temporal information embedded within these transmissions whose fidelity increased during communication both between and within layers when the number of tunnels are increased. We also provide evidence reinforcing the role of synchronized activity upon transmission fidelity during the spontaneous synchronized

Social Network resources and self-rated health in a deprived Danish neighborhood

DEFF Research Database (Denmark)

Tanggaard Andersen, Pernille; Holst Algren, Maria; Fromsejer Heiberg, Regina

2017-01-01

Research has demonstrated that living in a deprived neighborhood contributes to the occurrence and development of poor health. Furthermore evidence shows that social networks are fundamental resources in preventing poor mental health. Neighborhood relationships and networks are vital for sustaining...... and improving quality of life. However, to determine potentials for public health action, the health impact of various types of network resources need to be explored and the association between socioeconomic position and self-rated health needs to be analysed to determine whether it is partially explained...... by social network resources. This is the main aim of this article. Cross-sectional data from one deprived neighborhood located in Denmark were collected in 2008 and 2013 using a postal health survey. The target group was defined as adults older than 16 years. In 2008, 408 residents participated...

ON ONE OF THE APPROACHES TO ENHANCING THE MOTIVATION OF STUDENTS TO EDUCATIONAL AND EXTRACURRICULAR ACTIVITIES THROUGH THE RATING SYSTEM

OpenAIRE

Soghoyan S. S.; Dyachenko R. A.; Belchenko I. V.

2016-01-01

The problems of increasing the motivation of students of organizations of secondary vocational education and higher education to educational and training activities is due to the use of score-rating approach to recording achievements. The article examines factors that have a negative impact on training and outside training activities, such as TV entertainment, online games, social networks. We consider the activities that have a positive impact on the educational and training activities outsi...

The study of RMB exchange rate complex networks based on fluctuation mode

Science.gov (United States)

Yao, Can-Zhong; Lin, Ji-Nan; Zheng, Xu-Zhou; Liu, Xiao-Feng

2015-10-01

In the paper, we research on the characteristics of RMB exchange rate time series fluctuation with methods of symbolization and coarse gaining. First, based on fluctuation features of RMB exchange rate, we define the first type of fluctuation mode as one specific foreign currency against RMB in four days' fluctuating situations, and the second type as four different foreign currencies against RMB in one day's fluctuating situation. With the transforming method, we construct the unique-currency and multi-currency complex networks. Further, through analyzing the topological features including out-degree, betweenness centrality and clustering coefficient of fluctuation-mode complex networks, we find that the out-degree distribution of both types of fluctuation mode basically follows power-law distributions with exponents between 1 and 2. The further analysis reveals that the out-degree and the clustering coefficient generally obey the approximated negative correlation. With this result, we confirm previous observations showing that the RMB exchange rate exhibits a characteristic of long-range memory. Finally, we analyze the most probable transmission route of fluctuation modes, and provide probability prediction matrix. The transmission route for RMB exchange rate fluctuation modes exhibits the characteristics of partially closed loop, repeat and reversibility, which lays a solid foundation for predicting RMB exchange rate fluctuation patterns with large volume of data.

Truth in Reporting: How Data Capture Methods Obfuscate Actual Surgical Site Infection Rates within a Health Care Network System.

Science.gov (United States)

Bordeianou, Liliana; Cauley, Christy E; Antonelli, Donna; Bird, Sarah; Rattner, David; Hutter, Matthew; Mahmood, Sadiqa; Schnipper, Deborah; Rubin, Marc; Bleday, Ronald; Kenney, Pardon; Berger, David

2017-01-01

Two systems measure surgical site infection rates following colorectal surgeries: the American College of Surgeons National Surgical Quality Improvement Program and the Centers for Disease Control and Prevention National Healthcare Safety Network. The Centers for Medicare & Medicaid Services pay-for-performance initiatives use National Healthcare Safety Network data for hospital comparisons. This study aimed to compare database concordance. This is a multi-institution cohort study of systemwide Colorectal Surgery Collaborative. The National Surgical Quality Improvement Program requires rigorous, standardized data capture techniques; National Healthcare Safety Network allows 5 data capture techniques. Standardized surgical site infection rates were compared between databases. The Cohen κ-coefficient was calculated. This study was conducted at Boston-area hospitals. National Healthcare Safety Network or National Surgical Quality Improvement Program patients undergoing colorectal surgery were included. Standardized surgical site infection rates were the primary outcomes of interest. Thirty-day surgical site infection rates of 3547 (National Surgical Quality Improvement Program) vs 5179 (National Healthcare Safety Network) colorectal procedures (2012-2014). Discrepancies appeared: National Surgical Quality Improvement Program database of hospital 1 (N = 1480 patients) routinely found surgical site infection rates of approximately 10%, routinely deemed rate "exemplary" or "as expected" (100%). National Healthcare Safety Network data from the same hospital and time period (N = 1881) revealed a similar overall surgical site infection rate (10%), but standardized rates were deemed "worse than national average" 80% of the time. Overall, hospitals using less rigorous capture methods had improved surgical site infection rates for National Healthcare Safety Network compared with standardized National Surgical Quality Improvement Program reports. The correlation coefficient

Bounds on Rates of Variable-Basis and Neural-Network Approximation

Czech Academy of Sciences Publication Activity Database

Kůrková, Věra; Sanguineti, M.

2001-01-01

Roč. 47, č. 6 (2001), s. 2659-2665 ISSN 0018-9448 R&D Projects: GA ČR GA201/00/1482 Institutional research plan: AV0Z1030915 Keywords : approximation by variable-basis functions * bounds on rates of approximation * complexity of neural networks * high-dimensional optimal decision problems Subject RIV: BA - General Mathematics Impact factor: 2.077, year: 2001

Perceived influence and college students' diet and physical activity behaviors: an examination of ego-centric social networks.

Science.gov (United States)

Harmon, Brook E; Forthofer, Melinda; Bantum, Erin O; Nigg, Claudio R

2016-06-06

Obesity is partially a social phenomenon, with college students particularly vulnerable to changes in social networks and obesity-related behaviors. Currently, little is known about the structure of social networks among college students and their potential influence on diet and physical activity behaviors. The purpose of the study was to examine social influences impacting college students' diet and physical activity behaviors, including sources of influence, comparisons between sources' and students' behaviors, and associations with meeting diet and physical activity recommendations. Data was collected from 40 students attending college in Hawaii. Participants completed diet and physical activity questionnaires and a name generator. Participants rated nominees' influence on their diet and physical activity behaviors as well as compared nominees' behaviors to their own. Descriptive statistics were used to look at perceptions of influence across network groups. Logistic regression models were used to examine associations between network variables and odds of meeting recommendations. A total of 325 nominations were made and included: family (n = 116), college friends (n = 104), high school friends (n = 87), and significant others (n = 18). Nearly half of participants were not from Hawaii. Significant others of non-Hawaii students were perceived to be the most influential (M(SD) = 9(1.07)) and high school friends the least influential (M(SD) = 1.31(.42)) network. Overall, perceived influence was highest for diet compared to physical activity, but varied based on comparisons with nominees' behaviors. Significant others were most often perceived has having similar (44 %) or worse (39 %) eating behaviors than participants, and those with similar eating behaviors were perceived as most influential (M(SD) = 9.25(1.04)). Few associations were seen between network variables and odds of meeting recommendations. Among the groups nominated, high

Perceived influence and college students’ diet and physical activity behaviors: an examination of ego-centric social networks

Directory of Open Access Journals (Sweden)

Brook E. Harmon

2016-06-01

Full Text Available Abstract Background Obesity is partially a social phenomenon, with college students particularly vulnerable to changes in social networks and obesity-related behaviors. Currently, little is known about the structure of social networks among college students and their potential influence on diet and physical activity behaviors. The purpose of the study was to examine social influences impacting college students’ diet and physical activity behaviors, including sources of influence, comparisons between sources’ and students’ behaviors, and associations with meeting diet and physical activity recommendations. Methods Data was collected from 40 students attending college in Hawaii. Participants completed diet and physical activity questionnaires and a name generator. Participants rated nominees’ influence on their diet and physical activity behaviors as well as compared nominees' behaviors to their own. Descriptive statistics were used to look at perceptions of influence across network groups. Logistic regression models were used to examine associations between network variables and odds of meeting recommendations. Results A total of 325 nominations were made and included: family (n = 116, college friends (n = 104, high school friends (n = 87, and significant others (n = 18. Nearly half of participants were not from Hawaii. Significant others of non-Hawaii students were perceived to be the most influential (M(SD = 9(1.07 and high school friends the least influential (M(SD = 1.31(.42 network. Overall, perceived influence was highest for diet compared to physical activity, but varied based on comparisons with nominees’ behaviors. Significant others were most often perceived has having similar (44 % or worse (39 % eating behaviors than participants, and those with similar eating behaviors were perceived as most influential (M(SD = 9.25(1.04. Few associations were seen between network variables and odds of meeting

A new chaotic Hopfield network with piecewise linear activation function

International Nuclear Information System (INIS)

Peng-Sheng, Zheng; Wan-Sheng, Tang; Jian-Xiong, Zhang

2010-01-01

This paper presents a new chaotic Hopfield network with a piecewise linear activation function. The dynamic of the network is studied by virtue of the bifurcation diagram, Lyapunov exponents spectrum and power spectrum. Numerical simulations show that the network displays chaotic behaviours for some well selected parameters

Using Hierarchical Temporal Memory for Detecting Anomalous Network Activity

Science.gov (United States)

2008-03-01

warfare, computer network operations, psychological operations, military deception, and operations security, in concert with specified supporting and...you up short—you were subconsciously predicting something else and were surprised by the mismatch” [3]. Notable neurobiologist Horace Barlow of the...malicious network activity is flagged as abnormal . That is, test data should present the N-HTM network with spatial-temporal patterns that do not match 46

Backtracking and Mixing Rate of Diffusion on Uncorrelated Temporal Networks

Directory of Open Access Journals (Sweden)

Martin Gueuning

2017-10-01

Full Text Available We consider the problem of diffusion on temporal networks, where the dynamics of each edge is modelled by an independent renewal process. Despite the apparent simplicity of the model, the trajectories of a random walker exhibit non-trivial properties. Here, we quantify the walker’s tendency to backtrack at each step (return where he/she comes from, as well as the resulting effect on the mixing rate of the process. As we show through empirical data, non-Poisson dynamics may significantly slow down diffusion due to backtracking, by a mechanism intrinsically different from the standard bus paradox and related temporal mechanisms. We conclude by discussing the implications of our work for the interpretation of results generated by null models of temporal networks.

The association between social networks and self-rated risk of HIV ...

African Journals Online (AJOL)

This study describes the social networks of secondary school students in Moshi Municipality, and their association with self-rated risk of human immunodeficiency virus (HIV) infection. A cross-sectional analytical study was conducted among 300 students aged 15–24 years in 5 secondary schools in Moshi, Tanzania.

Impact of visual repetition rate on intrinsic properties of low frequency fluctuations in the visual network.

Directory of Open Access Journals (Sweden)

Yi-Chia Li

Full Text Available BACKGROUND: Visual processing network is one of the functional networks which have been reliably identified to consistently exist in human resting brains. In our work, we focused on this network and investigated the intrinsic properties of low frequency (0.01-0.08 Hz fluctuations (LFFs during changes of visual stimuli. There were two main questions to be discussed in this study: intrinsic properties of LFFs regarding (1 interactions between visual stimuli and resting-state; (2 impact of repetition rate of visual stimuli. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed scanning sessions that contained rest and visual stimuli in various repetition rates with a novel method. The method included three numerical approaches involving ICA (Independent Component Analyses, fALFF (fractional Amplitude of Low Frequency Fluctuation, and Coherence, to respectively investigate the modulations of visual network pattern, low frequency fluctuation power, and interregional functional connectivity during changes of visual stimuli. We discovered when resting-state was replaced by visual stimuli, more areas were involved in visual processing, and both stronger low frequency fluctuations and higher interregional functional connectivity occurred in visual network. With changes of visual repetition rate, the number of areas which were involved in visual processing, low frequency fluctuation power, and interregional functional connectivity in this network were also modulated. CONCLUSIONS/SIGNIFICANCE: To combine the results of prior literatures and our discoveries, intrinsic properties of LFFs in visual network are altered not only by modulations of endogenous factors (eye-open or eye-closed condition; alcohol administration and disordered behaviors (early blind, but also exogenous sensory stimuli (visual stimuli with various repetition rates. It demonstrates that the intrinsic properties of LFFs are valuable to represent physiological states of human brains.

Harvesting full-duplex rate gains in cellular networks with half-duplex user terminals

KAUST Repository

AlAmmouri, Ahmad

2016-07-26

Full-Duplex (FD) transceivers may be expensive in terms of complexity, power consumption, and price to be implemented in all user terminals. Therefore, techniques to exploit in-band full-duplex communication with FD base stations (BSs) and half-duplex (HD) users\\' equipment (UEs) are required. In this context, 3-node topology (3NT) has been recently proposed for FD BSs to reuse the uplink (UL) and downlink (DL) channels with HD terminals within the same cell. In this paper, we present a tractable mathematical framework, based on stochastic geometry, for 3NT in cellular networks. To this end, we propose a design paradigm via pulse-shaping and partial overlap between UL and DL channels to maximize the harvested rate gains in 3NT. The results show that 3NT achieves a close performance to networks with FD BSs and FD UEs, denoted by 2-node topology (2NT) networks. A maximum of 5% rate loss is reported when 3NT is compared to 2NT with efficient self-interference cancellation (SIC). If the SIC in 2NT is not efficient, 3NT highly outperforms 2NT. Consequently, we conclude that, irrespective to the UE duplexing scheme, it is sufficient to have FD BSs to harvest FD rate gains.

The association between network social capital and self-rated health: pouring old wine in new bottles?

Science.gov (United States)

Verhaeghe, Pieter-Paul; Pattyn, Elise; Bracke, Piet; Verhaeghe, Mieke; Van De Putte, Bart

2012-03-01

This study examines whether there is an association between network social capital and self-rated health after controlling for social support. Moreover, we distinguish between network social capital that emerges from strong ties and weak ties. We used a cross-sectional representative sample of 815 adults from the Belgian population. Social capital is measured with the position generator and perceived social support with the MOS Social Support-scale. Results suggest that network social capital is associated with self-rated health after adjustment for social support. Because different social classes have access to different sets of resources, resources of friends and family from the intermediate and higher service classes are beneficial for self-rated health, whereas resources of friends and family from the working class appear to be rather detrimental for self-rated health. From a health-promoting perspective, these findings indicate that policy makers should deal with the root causes of socioeconomic disadvantages in society. Copyright © 2011 Elsevier Ltd. All rights reserved.

Overexpression of cypin alters dendrite morphology, single neuron activity, and network properties via distinct mechanisms

Science.gov (United States)

Rodríguez, Ana R.; O'Neill, Kate M.; Swiatkowski, Przemyslaw; Patel, Mihir V.; Firestein, Bonnie L.

2018-02-01

Objective. This study investigates the effect that overexpression of cytosolic PSD-95 interactor (cypin), a regulator of synaptic PSD-95 protein localization and a core regulator of dendrite branching, exerts on the electrical activity of rat hippocampal neurons and networks. Approach. We cultured rat hippocampal neurons and used lipid-mediated transfection and lentiviral gene transfer to achieve high levels of cypin or cypin mutant (cypinΔPDZ PSD-95 non-binding) expression cellularly and network-wide, respectively. Main results. Our analysis revealed that although overexpression of cypin and cypinΔPDZ increase dendrite numbers and decrease spine density, cypin and cypinΔPDZ distinctly regulate neuronal activity. At the single cell level, cypin promotes decreases in bursting activity while cypinΔPDZ reduces sEPSC frequency and further decreases bursting compared to cypin. At the network level, by using the Fano factor as a measure of spike count variability, cypin overexpression results in an increase in variability of spike count, and this effect is abolished when cypin cannot bind PSD-95. This variability is also dependent on baseline activity levels and on mean spike rate over time. Finally, our spike sorting data show that overexpression of cypin results in a more complex distribution of spike waveforms and that binding to PSD-95 is essential for this complexity. Significance. Our data suggest that dendrite morphology does not play a major role in cypin action on electrical activity.

Rate Control for Network-Coded Multipath Relaying with Time-Varying Connectivity

Science.gov (United States)

2010-12-10

Armen Babikyan, Nathaniel M. Jones, Thomas H. Shake, and Andrew P. Worthen MIT Lincoln Laboratory 244 Wood Street Lexington, MA 02420 DDRE, 1777...delay U U U U SAR 11 Zach Sweet 781-981-5997 1 Rate Control for Network-Coded Multipath Relaying with Time-Varying Connectivity Brooke Shrader, Armen

Engineering Online and In-person Social Networks for Physical Activity: A Randomized Trial

Science.gov (United States)

Rovniak, Liza S.; Kong, Lan; Hovell, Melbourne F.; Ding, Ding; Sallis, James F.; Ray, Chester A.; Kraschnewski, Jennifer L.; Matthews, Stephen A.; Kiser, Elizabeth; Chinchilli, Vernon M.; George, Daniel R.; Sciamanna, Christopher N.

2016-01-01

Background Social networks can influence physical activity, but little is known about how best to engineer online and in-person social networks to increase activity. Purpose To conduct a randomized trial based on the Social Networks for Activity Promotion model to assess the incremental contributions of different procedures for building social networks on objectively-measured outcomes. Methods Physically inactive adults (n = 308, age, 50.3 (SD = 8.3) years, 38.3% male, 83.4% overweight/obese) were randomized to 1 of 3 groups. The Promotion group evaluated the effects of weekly emailed tips emphasizing social network interactions for walking (e.g., encouragement, informational support); the Activity group evaluated the incremental effect of adding an evidence-based online fitness walking intervention to the weekly tips; and the Social Networks group evaluated the additional incremental effect of providing access to an online networking site for walking, and prompting walking/activity across diverse settings. The primary outcome was mean change in accelerometer-measured moderate-to-vigorous physical activity (MVPA), assessed at 3 and 9 months from baseline. Results Participants increased their MVPA by 21.0 mins/week, 95% CI [5.9, 36.1], p = .005, at 3 months, and this change was sustained at 9 months, with no between-group differences. Conclusions Although the structure of procedures for targeting social networks varied across intervention groups, the functional effect of these procedures on physical activity was similar. Future research should evaluate if more powerful reinforcers improve the effects of social network interventions. Trial Registration Number NCT01142804 PMID:27405724

Photonic Network R&D Activities in Japan-Current Activities and Future Perspectives

Science.gov (United States)

Kitayama, Ken-Ichi; Miki, Tetsuya; Morioka, Toshio; Tsushima, Hideaki; Koga, Masafumi; Mori, Kazuyuki; Araki, Soichiro; Sato, Ken-Ichi; Onaka, Hiroshi; Namiki, Shu; Aoyama, Tomonori

2005-10-01

R&D activities on photonic networks in Japan are presented. First, milestones in current ongoing R&D programs supported by Japanese government agencies are introduced, including long-distance and wavelength division multiplexing (WDM) fiber transmission, wavelength routing, optical burst switching (OBS), and control-plane technology for IP backbone networks. Their goal was set to evolve a legacy telecommunications network to IP-over-WDM networks by introducing technologies for WDM and wavelength routing. We then discuss the perspectives of so-called PHASE II R&D programs for photonic networks over the next 5 years until 2010, by focusing on the report that has been recently issued by the Photonic Internet Forum (PIF), a consortium that has major carriers, telecom vendors, and Japanese academics as members. The PHASE II R&D programs should serve to establish a photonic platform to provide abundant bandwidth on demand, at any time on a real-time basis, through the customer's initiative to promote bandwidth-rich applications, such as grid computing, real-time digital-cinema streaming, medical and educational applications, and network storage in e-commerce.

Knowledge diffusion in complex networks by considering time-varying information channels

Science.gov (United States)

Zhu, He; Ma, Jing

2018-03-01

In this article, based on a model of epidemic spreading, we explore the knowledge diffusion process with an innovative mechanism for complex networks by considering time-varying information channels. To cover the knowledge diffusion process in homogeneous and heterogeneous networks, two types of networks (the BA network and the ER network) are investigated. The mean-field theory is used to theoretically draw the knowledge diffusion threshold. Numerical simulation demonstrates that the knowledge diffusion threshold is almost linearly correlated with the mean of the activity rate. In addition, under the influence of the activity rate and distinct from the classic Susceptible-Infected-Susceptible (SIS) model, the density of knowers almost linearly grows with the spreading rate. Finally, in consideration of the ubiquitous mechanism of innovation, we further study the evolution of knowledge in our proposed model. The results suggest that compared with the effect of the spreading rate, the average knowledge version of the population is affected more by the innovation parameter and the mean of the activity rate. Furthermore, in the BA network, the average knowledge version of individuals with higher degree is always newer than those with lower degree.

The association between social networks and self-rated risk of HIV infection among secondary school students in Moshi Municipality, Tanzania

DEFF Research Database (Denmark)

Lyimo, Elizabeth; Todd, Jim; Richey, Lisa Ann

2013-01-01

This study describes the social networks of secondary school students in Moshi Municipality, and their association with self-rated risk of human immunodeficiency virus (HIV) infection. A cross-sectional analytical study was conducted among 300 students aged 15–24 years in 5 secondary schools...... participation in bonding and bridging social networks and self-rated HIV risk behavior. More participants participated in bonding networks (72%) than in bridging networks (29%). Participation in bridging networks was greater among females (25%) than males (12%, p 

Engineering Online and In-Person Social Networks for Physical Activity: A Randomized Trial.

Science.gov (United States)

Rovniak, Liza S; Kong, Lan; Hovell, Melbourne F; Ding, Ding; Sallis, James F; Ray, Chester A; Kraschnewski, Jennifer L; Matthews, Stephen A; Kiser, Elizabeth; Chinchilli, Vernon M; George, Daniel R; Sciamanna, Christopher N

2016-12-01

Social networks can influence physical activity, but little is known about how best to engineer online and in-person social networks to increase activity. The purpose of this study was to conduct a randomized trial based on the Social Networks for Activity Promotion model to assess the incremental contributions of different procedures for building social networks on objectively measured outcomes. Physically inactive adults (n = 308, age, 50.3 (SD = 8.3) years, 38.3 % male, 83.4 % overweight/obese) were randomized to one of three groups. The Promotion group evaluated the effects of weekly emailed tips emphasizing social network interactions for walking (e.g., encouragement, informational support); the Activity group evaluated the incremental effect of adding an evidence-based online fitness walking intervention to the weekly tips; and the Social Networks group evaluated the additional incremental effect of providing access to an online networking site for walking as well as prompting walking/activity across diverse settings. The primary outcome was mean change in accelerometer-measured moderate-to-vigorous physical activity (MVPA), assessed at 3 and 9 months from baseline. Participants increased their MVPA by 21.0 min/week, 95 % CI [5.9, 36.1], p = .005, at 3 months, and this change was sustained at 9 months, with no between-group differences. Although the structure of procedures for targeting social networks varied across intervention groups, the functional effect of these procedures on physical activity was similar. Future research should evaluate if more powerful reinforcers improve the effects of social network interventions. The trial was registered with the ClinicalTrials.gov (NCT01142804).

Active Engine Mounting Control Algorithm Using Neural Network

Directory of Open Access Journals (Sweden)

Fadly Jashi Darsivan

2009-01-01

Full Text Available This paper proposes the application of neural network as a controller to isolate engine vibration in an active engine mounting system. It has been shown that the NARMA-L2 neurocontroller has the ability to reject disturbances from a plant. The disturbance is assumed to be both impulse and sinusoidal disturbances that are induced by the engine. The performance of the neural network controller is compared with conventional PD and PID controllers tuned using Ziegler-Nichols. From the result simulated the neural network controller has shown better ability to isolate the engine vibration than the conventional controllers.

Statistical inference, the bootstrap, and neural-network modeling with application to foreign exchange rates.

Science.gov (United States)

White, H; Racine, J

2001-01-01

We propose tests for individual and joint irrelevance of network inputs. Such tests can be used to determine whether an input or group of inputs "belong" in a particular model, thus permitting valid statistical inference based on estimated feedforward neural-network models. The approaches employ well-known statistical resampling techniques. We conduct a small Monte Carlo experiment showing that our tests have reasonable level and power behavior, and we apply our methods to examine whether there are predictable regularities in foreign exchange rates. We find that exchange rates do appear to contain information that is exploitable for enhanced point prediction, but the nature of the predictive relations evolves through time.

Infraslow Electroencephalographic and Dynamic Resting State Network Activity.

Science.gov (United States)

Grooms, Joshua K; Thompson, Garth J; Pan, Wen-Ju; Billings, Jacob; Schumacher, Eric H; Epstein, Charles M; Keilholz, Shella D

2017-06-01

A number of studies have linked the blood oxygenation level dependent (BOLD) signal to electroencephalographic (EEG) signals in traditional frequency bands (δ, θ, α, β, and γ), but the relationship between BOLD and its direct frequency correlates in the infraslow band (resting state magnetic resonance imaging data were acquired simultaneously. The DC EEG signals were correlated with the BOLD signal in patterns that resembled resting state networks. Subsequent dynamic analysis showed that the correlation between DC EEG and the BOLD signal varied substantially over time, even within individual subjects. The variation in DC EEG appears to reflect the time-varying contribution of different resting state networks. Furthermore, some of the patterns of DC EEG and BOLD correlation are consistent with previous work demonstrating quasiperiodic spatiotemporal patterns of large-scale network activity in resting state. These findings demonstrate that infraslow electrical activity is linked to BOLD fluctuations in humans and that it may provide a basis for large-scale organization comparable to that observed in animal studies.

Ads' click-through rates predicting based on gated recurrent unit neural networks

Science.gov (United States)

Chen, Qiaohong; Guo, Zixuan; Dong, Wen; Jin, Lingzi

2018-05-01

In order to improve the effect of online advertising and to increase the revenue of advertising, the gated recurrent unit neural networks(GRU) model is used as the ads' click through rates(CTR) predicting. Combined with the characteristics of gated unit structure and the unique of time sequence in data, using BPTT algorithm to train the model. Furthermore, by optimizing the step length algorithm of the gated unit recurrent neural networks, making the model reach optimal point better and faster in less iterative rounds. The experiment results show that the model based on the gated recurrent unit neural networks and its optimization of step length algorithm has the better effect on the ads' CTR predicting, which helps advertisers, media and audience achieve a win-win and mutually beneficial situation in Three-Side Game.

An estimation of the domain of attraction and convergence rate for Hopfield continuous feedback neural networks

International Nuclear Information System (INIS)

Cao Jinde

2004-01-01

In this Letter, the domain of attraction of memory patterns and exponential convergence rate of the network trajectories to memory patterns for Hopfield continuous associative memory are estimated by means of matrix measure and comparison principle. A new estimation is given for the domain of attraction of memory patterns and exponential convergence rate. These results can be used for the evaluation of fault-tolerance capability and the synthesis procedures for Hopfield continuous feedback associative memory neural networks

Coupling Strength and System Size Induce Firing Activity of Globally Coupled Neural Network

International Nuclear Information System (INIS)

Wei Duqu; Luo Xiaoshu; Zou Yanli

2008-01-01

We investigate how firing activity of globally coupled neural network depends on the coupling strength C and system size N. Network elements are described by space-clamped FitzHugh-Nagumo (SCFHN) neurons with the values of parameters at which no firing activity occurs. It is found that for a given appropriate coupling strength, there is an intermediate range of system size where the firing activity of globally coupled SCFHN neural network is induced and enhanced. On the other hand, for a given intermediate system size level, there exists an optimal value of coupling strength such that the intensity of firing activity reaches its maximum. These phenomena imply that the coupling strength and system size play a vital role in firing activity of neural network

Discrete rate and variable power adaptation for underlay cognitive networks

KAUST Repository

Abdallah, Mohamed M.

2010-01-01

We consider the problem of maximizing the average spectral efficiency of a secondary link in underlay cognitive networks. In particular, we consider the network setting whereby the secondary transmitter employs discrete rate and variable power adaptation under the constraints of maximum average transmit power and maximum average interference power allowed at the primary receiver due to the existence of an interference link between the secondary transmitter and the primary receiver. We first find the optimal discrete rates assuming a predetermined partitioning of the signal-to-noise ratio (SNR) of both the secondary and interference links. We then present an iterative algorithm for finding a suboptimal partitioning of the SNR of the interference link assuming a fixed partitioning of the SNR of secondary link selected for the case where no interference link exists. Our numerical results show that the average spectral efficiency attained by using the iterative algorithm is close to that achieved by the computationally extensive exhaustive search method for the case of Rayleigh fading channels. In addition, our simulations show that selecting the optimal partitioning of the SNR of the secondary link assuming no interference link exists still achieves the maximum average spectral efficiency for the case where the average interference constraint is considered. © 2010 IEEE.

Photonic network R and D activities in Japan

Science.gov (United States)

Kitayama, Ken-ichi; Miki, Tetsuya; Morioka, Toshio; Tsushima, Hideaki; Koga, Masafumi; Mori, Kazuyuki; Araki, Soichiro; Sato, Ken-ichi; Onaka, Hiroshi; Namiki, Shu; Aovama, Tomonori

2005-11-01

R and D activities on photonic networks in Japan are presented. First, milestones in current, ongoing R and D programs supported by Japanese government agencies are introduced, including long-distance and WDM fiber transmission, wavelength routing, optical burst switching, and control plane technology for IP backbone networks. Their goal was set to evolve a legacy telecommunications network to IP over WDM networks by introducing technologies for WDM and wavelength routing. We then discuss the perspectives of so-called PHASE II R and D programs for photonic networks over the next five years until 2010, by focusing on the report which has been recently issued by the Photonic Internet Forum (PIF), a consortium that has major carriers, telecom vendors, and Japanese academics as members. The PHASE II R and D programs should serve to establish a photonic platform to provide abundant bandwidth on demand, at any time on a real-time basis through the customer's initiative, to promote bandwidth-rich applications, such as grid computing, real-time digital-cinema streaming, medical and educational applications, and network storage in e-commerce.

Structural and functional social network attributes moderate the association of self-rated health with mental health in midlife and older adults.

Science.gov (United States)

Windsor, Tim D; Rioseco, Pilar; Fiori, Katherine L; Curtis, Rachel G; Booth, Heather

2016-01-01

Social relationships are multifaceted, and different social network components can operate via different processes to influence well-being. This study examined associations of social network structure and relationship quality (positive and negative social exchanges) with mental health in midlife and older adults. The focus was on both direct associations of network structure and relationship quality with mental health, and whether these social network attributes moderated the association of self-rated health (SRH) with mental health. Analyses were based on survey data provided by 2001 (Mean age = 65, SD = 8.07) midlife and older adults. We used Latent Class Analysis (LCA) to classify participants into network types based on network structure (partner status, network size, contact frequency, and activity engagement), and used continuous measures of positive and negative social exchanges to operationalize relationship quality. Regression analysis was used to test moderation. LCA revealed network types generally consistent with those reported in previous studies. Participants in more diverse networks reported better mental health than those categorized into a restricted network type after adjustment for age, sex, education, and employment status. Analysis of moderation indicated that those with poorer SRH were less likely to report poorer mental health if they were classified into more diverse networks. A similar moderation effect was also evident for positive exchanges. The findings suggest that both quantity and quality of social relationships can play a role in buffering against the negative implications of physical health decline for mental health.

Tansig activation function (of MLP network) for cardiac abnormality detection

Science.gov (United States)

Adnan, Ja'afar; Daud, Nik Ghazali Nik; Ishak, Mohd Taufiq; Rizman, Zairi Ismael; Rahman, Muhammad Izzuddin Abd

2018-02-01

Heart abnormality often occurs regardless of gender, age and races. This problem sometimes does not show any symptoms and it can cause a sudden death to the patient. In general, heart abnormality is the irregular electrical activity of the heart. This paper attempts to develop a program that can detect heart abnormality activity through implementation of Multilayer Perceptron (MLP) network. A certain amount of data of the heartbeat signals from the electrocardiogram (ECG) will be used in this project to train the MLP network by using several training algorithms with Tansig activation function.

A triboelectric motion sensor in wearable body sensor network for human activity recognition.

Science.gov (United States)

Hui Huang; Xian Li; Ye Sun

2016-08-01

The goal of this study is to design a novel triboelectric motion sensor in wearable body sensor network for human activity recognition. Physical activity recognition is widely used in well-being management, medical diagnosis and rehabilitation. Other than traditional accelerometers, we design a novel wearable sensor system based on triboelectrification. The triboelectric motion sensor can be easily attached to human body and collect motion signals caused by physical activities. The experiments are conducted to collect five common activity data: sitting and standing, walking, climbing upstairs, downstairs, and running. The k-Nearest Neighbor (kNN) clustering algorithm is adopted to recognize these activities and validate the feasibility of this new approach. The results show that our system can perform physical activity recognition with a successful rate over 80% for walking, sitting and standing. The triboelectric structure can also be used as an energy harvester for motion harvesting due to its high output voltage in random low-frequency motion.

On attracting sets in artificial networks: cross activation

Directory of Open Access Journals (Sweden)

Sadyrbaev Felix

2018-01-01

Full Text Available Mathematical models of artificial networks can be formulated in terms of dynamical systems describing the behaviour of a network over time. The interrelation between nodes (elements of a network is encoded in the regulatory matrix. We consider a system of ordinary differential equations that describes in particular also genomic regulatory networks (GRN and contains a sigmoidal function. The results are presented on attractors of such systems for a particular case of cross activation. The regulatory matrix is then of particular form consisting of unit entries everywhere except the main diagonal. We show that such a system can have not more than three critical points. At least n–1 eigenvalues corresponding to any of the critical points are negative. An example for a particular choice of sigmoidal function is considered.

A Rate-Adaptive MAC Protocol Based on TCP throughput for Ad Hoc Networks in Fading Channels

Directory of Open Access Journals (Sweden)

Shoko Uchida

2008-10-01

Full Text Available Wireless technology is becoming a leading option for future Internet access. Transmission Control Protocol (TCP is one of the protocols designed on the basis of the transmission characteristics in wired networks. It is known that the TCP performance deteriorates drastically under a wireless communication environment. On the other hand, many wireless networking standards such as IEEE 802.11a, 802.11b, and 802.11g have multirate capability. Therefore, adaptive rate control methods have been proposed for ad hoc networks. However, almost methods require the modification of the request to send (RTS and clear to send (CTS packets. Therefore, the conventional methods are not compatible with the standardized system. In this paper, we propose adaptive rate control mechanisms for ad hoc networks. Our mechanisms are based on the RTS/CTS mechanisms. However, no modifications to the RTS and CTS packets are required in the proposed method. Therefore, our proposed method can attempt to satisfy the conventional IEEE 802.11 standards. Moreover, an adequate transmission rate is selected based on an estimated TCP throughput performance. From simulation results, it is observed that the proposed method can improve the throughput performance without any modification of packet structures.

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks.

Science.gov (United States)

Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

2016-10-25

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches.

Reduced salience and default mode network activity in women with anorexia nervosa

Science.gov (United States)

McFadden, Kristina L.; Tregellas, Jason R.; Shott, Megan E.; Frank, Guido K.W.

2014-01-01

Background The neurobiology of anorexia nervosa is poorly understood. Neuronal networks contributing to action selection, self-regulation and interoception could contribute to pathologic eating and body perception in people with anorexia nervosa. We tested the hypothesis that the salience network (SN) and default mode network (DMN) would show decreased intrinsic activity in women with anorexia nervosa and those who had recovered from the disease compared to controls. The basal ganglia (BGN) and sensorimotor networks (SMN) were also investigated. Methods Between January 2008 and January 2012, women with restricting-type anorexia nervosa, women who recovered from the disease and healthy control women completed functional magnetic resonance imaging during a conditioned stimulus task. Network activity was studied using independent component analysis. Results We studied 20 women with anorexia nervosa, 24 recovered women and 24 controls. Salience network activity in the anterior cingulate cortex was reduced in women with anorexia nervosa (p = 0.030; all results false-discovery rate–corrected) and recovered women (p = 0.039) compared to controls. Default mode network activity in the precuneus was reduced in women with anorexia compared to controls (p = 0.023). Sensorimotor network activity in the supplementary motor area (SMA; p = 0.008), and the left (p = 0.028) and right (p = 0.002) postcentral gyrus was reduced in women with anorexia compared to controls; SMN activity in the SMA (p = 0.019) and the right postcentral gyrus (p = 0.008) was reduced in women with anorexia compared to recovered women. There were no group differences in the BGN. Limitations Differences between patient and control populations (e.g., depression, anxiety, medication) are potential confounds, but were included as covariates. Conclusion Reduced SN activity in women with anorexia nervosa and recovered women could be a trait-related biomarker or illness remnant, altering the drive to approach

A NEW NETWORK FOR HIGHER-TEMPERATURE GAS-PHASE CHEMISTRY. I. A PRELIMINARY STUDY OF ACCRETION DISKS IN ACTIVE GALACTIC NUCLEI

International Nuclear Information System (INIS)

Harada, Nanase; Herbst, Eric; Wakelam, Valentine

2010-01-01

We present a new interstellar chemical gas-phase reaction network for time-dependent kinetics that can be used for modeling high-temperature sources up to ∼800 K. This network contains an extended set of reactions based on the Ohio State University (OSU) gas-phase chemical network. The additional reactions include processes with significant activation energies, reverse reactions, proton exchange reactions, charge exchange reactions, and collisional dissociation. Rate coefficients already in the OSU network are modified for H 2 formation on grains, ion-neutral dipole reactions, and some radiative association reactions. The abundance of H 2 O is enhanced at high temperature by hydrogenation of atomic O. Much of the elemental oxygen is in the form of water at T ≥ 300 K, leading to effective carbon-rich conditions, which can efficiently produce carbon-chain species such as C 2 H 2 . At higher temperatures, HCN and NH 3 are also produced much more efficiently. We have applied the extended network to a simplified model of the accretion disk of an active galactic nucleus.

Modulation of neuronal network activity with ghrelin

NARCIS (Netherlands)

Stoyanova, Irina; Rutten, Wim; le Feber, Jakob

2012-01-01

Ghrelin is a neuropeptide regulating multiple physiological processes, including high brain functions such as learning and memory formation. However, the effect of ghrelin on network activity patterns and developments has not been studied yet. Therefore, we used dissociated cortical neurons plated

Detection of silent cells, synchronization and modulatory activity in developing cellular networks.

NARCIS (Netherlands)

Hjorth, J.J.J.; Dawitz, J.; Kroon, T.; da Silva Dias Pires, J.H.; Dassen, V.J.; Berkhout, J.A.; Emperador Melero, J.; Nadadhur, A.G.; Alevra, M.; Toonen, R.F.G.; Heine, V.M.; Mansvelder, H.D.; Meredith, R.M.

2016-01-01

Developing networks in the immature nervous system and in cellular cultures are characterized by waves of synchronous activity in restricted clusters of cells. Synchronized activity in immature networks is proposed to regulate many different developmental processes, from neuron growth and cell

Model Integrating Fuzzy Argument with Neural Network Enhancing the Performance of Active Queue Management

Directory of Open Access Journals (Sweden)

Nguyen Kim Quoc

2015-08-01

Full Text Available The bottleneck control by active queue management mechanisms at network nodes is essential. In recent years, some researchers have used fuzzy argument to improve the active queue management mechanisms to enhance the network performance. However, the projects using the fuzzy controller depend heavily on professionals and their parameters cannot be updated according to changes in the network, so the effectiveness of this mechanism is not high. Therefore, we propose a model combining the fuzzy controller with neural network (FNN to overcome the limitations above. Results of the training of the neural networks will find the optimal parameters for the adaptive fuzzy controller well to changes of the network. This improves the operational efficiency of the active queue management mechanisms at network nodes.

A Bootstrap Neural Network Based Heterogeneous Panel Unit Root Test: Application to Exchange Rates

OpenAIRE

Christian de Peretti; Carole Siani; Mario Cerrato

2010-01-01

This paper proposes a bootstrap artificial neural network based panel unit root test in a dynamic heterogeneous panel context. An application to a panel of bilateral real exchange rate series with the US Dollar from the 20 major OECD countries is provided to investigate the Purchase Power Parity (PPP). The combination of neural network and bootstrapping significantly changes the findings of the economic study in favour of PPP.

Information content of neural networks with self-control and variable activity

International Nuclear Information System (INIS)

Bolle, D.; Amari, S.I.; Dominguez Carreta, D.R.C.; Massolo, G.

2001-01-01

A self-control mechanism for the dynamics of neural networks with variable activity is discussed using a recursive scheme for the time evolution of the local field. It is based upon the introduction of a self-adapting time-dependent threshold as a function of both the neural and pattern activity in the network. This mechanism leads to an improvement of the information content of the network as well as an increase of the storage capacity and the basins of attraction. Different architectures are considered and the results are compared with numerical simulations

Modeling and Model Predictive Power and Rate Control of Wireless Communication Networks

Directory of Open Access Journals (Sweden)

Cunwu Han

2014-01-01

Full Text Available A novel power and rate control system model for wireless communication networks is presented, which includes uncertainties, input constraints, and time-varying delays in both state and control input. A robust delay-dependent model predictive power and rate control method is proposed, and the state feedback control law is obtained by solving an optimization problem that is derived by using linear matrix inequality (LMI techniques. Simulation results are given to illustrate the effectiveness of the proposed method.

Associations within school-based same-sex friendship networks of children's physical activity and sedentary behaviours: a cross-sectional social network analysis.

Science.gov (United States)

Salway, Ruth E; Sebire, Simon J; Solomon-Moore, Emma; Thompson, Janice L; Jago, Russell

2018-02-21

Physical activity in children is associated with better physical and mental health but many children do not meet physical activity guidelines. Friendship groups are potentially an important influence on children's physical activity and sedentary time. This paper examines the association between children of physical activity and sedentary time in school-based same-sex friendship networks, for both moderate-to-vigorous intensity physical activity (MVPA) and sedentary time. Moreover, considering the methodological challenges of conducting and interpreting these analyses, we provide examples of how to analyse these data and interpret results to encourage further work in the area. Accelerometer data for 1223 children, aged 8-9 years, were collected in 2015-2016 and analysed in 2017. Mean accelerometer minutes of MVPA and sedentary time were calculated. Children named up to four school friends and same-sex school-based friendship networks were constructed. Network models, which include correlation between friends, were fitted by sex. Both MVPA and sedentary time were found to be associated via the friendship networks, for both boys and girls. The network autocorrelation was 0.21 (95% CI: 0.15 to 0.26) for boys' MVPA, and 0.14 (95% CI: 0.07 to 0.21) for sedentary time. Network autocorrelation between girls was weaker, with 0.13 (95% CI: 0.06 to 0.19) for MVPA and 0.11 (95% CI: 0.05 to 0.17) for sedentary time. Physical activity and sedentary time of boys and girls are associated with the physical activity and sedentary time respectively of others within same-sex friendship networks, and these associations are comparable to other known factors. In this study, the correlation between friends was stronger for boys than girls, and stronger for MVPA than for sedentary time. These findings suggest that friendship networks play a part in understanding children's physical activity and sedentary time and could play a valuable role in developing effective interventions.

Joint sensor placement and power rating selection in energy harvesting wireless sensor networks

KAUST Repository

Bushnaq, Osama M.; Al-Naffouri, Tareq Y.; Chepuri, Sundeep Prabhakar; Leus, Geert

2017-01-01

In this paper, the focus is on optimal sensor placement and power rating selection for parameter estimation in wireless sensor networks (WSNs). We take into account the amount of energy harvested by the sensing nodes, communication link quality

Proposed Network Intrusion Detection System ‎In Cloud Environment Based on Back ‎Propagation Neural Network

Directory of Open Access Journals (Sweden)

Shawq Malik Mehibs

2017-12-01

Full Text Available Cloud computing is distributed architecture, providing computing facilities and storage resource as a service over the internet. This low-cost service fulfills the basic requirements of users. Because of the open nature and services introduced by cloud computing intruders impersonate legitimate users and misuse cloud resource and services. To detect intruders and suspicious activities in and around the cloud computing environment, intrusion detection system used to discover the illegitimate users and suspicious action by monitors different user activities on the network .this work proposed based back propagation artificial neural network to construct t network intrusion detection in the cloud environment. The proposed module evaluated with kdd99 dataset the experimental results shows promising approach to detect attack with high detection rate and low false alarm rate

Neural Network for Determining Risk Rate of Post-Heart Stroke Patients

Directory of Open Access Journals (Sweden)

Oldřich Trenz

2014-01-01

Full Text Available The ischemic heart disease presents an important health problem that affects a great part of the population and is the cause of one third of all deaths in the Czech Republic. The availability of data describing the patients’ prognosis enables their further analysis, with the aim of lowering the patients’ risk, by proposing optimum treatment. The main reason for creating the neural network model is not only to automate the process of establishing the risk rate of patients suffering from ischemic heart disease, but also to adapt it for practical use in clinical conditions. Our aim is to identify especially the specific group of risk-rate patients whose well-timed preventive care can improve the quality and prolong the length of their lives.The aim of the paper is to propose a patient-parameter structure, using which we could create a suitable model based on a self-taught neural network. The emphasis is placed on identifying key descriptive parameters (in the form of a reduction of the available descriptive parameters that are crucial for identifying the required patients, and simultaneously to achieve a portability of the model among individual clinical workplaces (availability of parameters.

Connectivity, excitability and activity patterns in neuronal networks

International Nuclear Information System (INIS)

Le Feber, Joost; Stoyanova, Irina I; Chiappalone, Michela

2014-01-01

Extremely synchronized firing patterns such as those observed in brain diseases like epilepsy may result from excessive network excitability. Although network excitability is closely related to (excitatory) connectivity, a direct measure for network excitability remains unavailable. Several methods currently exist for estimating network connectivity, most of which are related to cross-correlation. An example is the conditional firing probability (CFP) analysis which calculates the pairwise probability (CFP i,j ) that electrode j records an action potential at time t = τ, given that electrode i recorded a spike at t = 0. However, electrode i often records multiple spikes within the analysis interval, and CFP values are biased by the on-going dynamic state of the network. Here we show that in a linear approximation this bias may be removed by deconvoluting CFP i,j with the autocorrelation of i (i.e. CFP i,i ), to obtain the single pulse response (SPR i,j )—the average response at electrode j to a single spike at electrode i. Thus, in a linear system SPRs would be independent of the dynamic network state. Nonlinear components of synaptic transmission, such as facilitation and short term depression, will however still affect SPRs. Therefore SPRs provide a clean measure of network excitability. We used carbachol and ghrelin to moderately activate cultured cortical networks to affect their dynamic state. Both neuromodulators transformed the bursting firing patterns of the isolated networks into more dispersed firing. We show that the influence of the dynamic state on SPRs is much smaller than the effect on CFPs, but not zero. The remaining difference reflects the alteration in network excitability. We conclude that SPRs are less contaminated by the dynamic network state and that mild excitation may decrease network excitability, possibly through short term synaptic depression. (papers)

Frequency Count Attribute Oriented Induction of Corporate Network Data for Mapping Business Activity

Directory of Open Access Journals (Sweden)

Tanutama Lukas

2014-03-01

Full Text Available Companies increasingly rely on Internet for effective and efficient business communication. As Information Technology infrastructure backbone for business activities, corporate network connects the company to Internet and enables its activities globally. It carries data packets generated by the activities of the users performing their business tasks. Traditionally, infrastructure operations mainly maintain data carrying capacity and network devices performance. It would be advantageous if a company knows what activities are running in its network. The research provides a simple method of mapping the business activity reflected by the network data. To map corporate users’ activities, a slightly modified Attribute Oriented Induction (AOI approach to mine the network data was applied. The frequency of each protocol invoked were counted to show what the user intended to do. The collected data was samples taken within a certain sampling period. Samples were taken due to the enormous data packets generated. Protocols of interest are only Internet related while intranet protocols are ignored. It can be concluded that the method could provide the management a general overview of the usage of its infrastructure and lead to efficient, effective and secure ICT infrastructure.

Frequency Count Attribute Oriented Induction of Corporate Network Data for Mapping Business Activity

Science.gov (United States)

Tanutama, Lukas

2014-03-01

Companies increasingly rely on Internet for effective and efficient business communication. As Information Technology infrastructure backbone for business activities, corporate network connects the company to Internet and enables its activities globally. It carries data packets generated by the activities of the users performing their business tasks. Traditionally, infrastructure operations mainly maintain data carrying capacity and network devices performance. It would be advantageous if a company knows what activities are running in its network. The research provides a simple method of mapping the business activity reflected by the network data. To map corporate users' activities, a slightly modified Attribute Oriented Induction (AOI) approach to mine the network data was applied. The frequency of each protocol invoked were counted to show what the user intended to do. The collected data was samples taken within a certain sampling period. Samples were taken due to the enormous data packets generated. Protocols of interest are only Internet related while intranet protocols are ignored. It can be concluded that the method could provide the management a general overview of the usage of its infrastructure and lead to efficient, effective and secure ICT infrastructure.

Ripples Make Waves: Binding Structured Activity and Plasticity in Hippocampal Networks

Directory of Open Access Journals (Sweden)

Josef H. L. P. Sadowski

2011-01-01

Full Text Available Establishing novel episodic memories and stable spatial representations depends on an exquisitely choreographed, multistage process involving the online encoding and offline consolidation of sensory information, a process that is largely dependent on the hippocampus. Each step is influenced by distinct neural network states that influence the pattern of activation across cellular assemblies. In recent years, the occurrence of hippocampal sharp wave ripple (SWR oscillations has emerged as a potentially vital network phenomenon mediating the steps between encoding and consolidation, both at a cellular and network level by promoting the rapid replay and reactivation of recent activity patterns. Such events facilitate memory formation by optimising the conditions for synaptic plasticity to occur between contingent neural elements. In this paper, we explore the ways in which SWRs and other network events can bridge the gap between spatiomnemonic processing at cellular/synaptic and network levels in the hippocampus.

Nash Equilibrium of an Energy Saving Strategy with Dual Rate Transmission in Wireless Regional Area Network

Directory of Open Access Journals (Sweden)

Zhanqiang Huo

2017-01-01

Full Text Available Wireless regional area network (WRAN adopts centralized network architecture and is currently one of the most typical cognitive radio networks. In order to reduce the energy consumption of the communication networks with the constraint of spectrum resource utilization, a working sleep mechanism is introduced into the base station (BS, and a novel energy saving strategy with dual rate transmission is proposed. Combining the multiple-vacation queue and priority queue, using the quasi-birth-death process and the matrix-geometric solution method, we assess the average latency and the forced termination probability of secondary user packets, as well as the energy saving ratio and the channel utilization of system. Based on the revenue-expenditure structure, a profit function is built, and then the Nash equilibrium behavior and the socially optimal behavior are investigated. With the help of the particle swarm optimization, an intelligent optimization algorithm to search the socially optimal arrival rate of secondary user packets is presented. In order to unify the arrival rates of secondary user packets with Nash equilibrium and social optimization, a reasonable pricing policy is formulated. In addition, system experiments are carried out to verify the effectiveness of the energy saving strategy and the rationality of the pricing policy.

Engineering online and in-person social networks to sustain physical activity: application of a conceptual model.

Science.gov (United States)

Rovniak, Liza S; Sallis, James F; Kraschnewski, Jennifer L; Sciamanna, Christopher N; Kiser, Elizabeth J; Ray, Chester A; Chinchilli, Vernon M; Ding, Ding; Matthews, Stephen A; Bopp, Melissa; George, Daniel R; Hovell, Melbourne F

2013-08-14

High rates of physical inactivity compromise the health status of populations globally. Social networks have been shown to influence physical activity (PA), but little is known about how best to engineer social networks to sustain PA. To improve procedures for building networks that shape PA as a normative behavior, there is a need for more specific hypotheses about how social variables influence PA. There is also a need to integrate concepts from network science with ecological concepts that often guide the design of in-person and electronically-mediated interventions. Therefore, this paper: (1) proposes a conceptual model that integrates principles from network science and ecology across in-person and electronically-mediated intervention modes; and (2) illustrates the application of this model to the design and evaluation of a social network intervention for PA. A conceptual model for engineering social networks was developed based on a scoping literature review of modifiable social influences on PA. The model guided the design of a cluster randomized controlled trial in which 308 sedentary adults were randomly assigned to three groups: WalkLink+: prompted and provided feedback on participants' online and in-person social-network interactions to expand networks for PA, plus provided evidence-based online walking program and weekly walking tips; WalkLink: evidence-based online walking program and weekly tips only; Minimal Treatment Control: weekly tips only. The effects of these treatment conditions were assessed at baseline, post-program, and 6-month follow-up. The primary outcome was accelerometer-measured PA. Secondary outcomes included objectively-measured aerobic fitness, body mass index, waist circumference, blood pressure, and neighborhood walkability; and self-reported measures of the physical environment, social network environment, and social network interactions. The differential effects of the three treatment conditions on primary and secondary

Task-dependent changes in cross-level coupling between single neurons and oscillatory activity in multiscale networks.

Directory of Open Access Journals (Sweden)

Ryan T Canolty

Full Text Available Understanding the principles governing the dynamic coordination of functional brain networks remains an important unmet goal within neuroscience. How do distributed ensembles of neurons transiently coordinate their activity across a variety of spatial and temporal scales? While a complete mechanistic account of this process remains elusive, evidence suggests that neuronal oscillations may play a key role in this process, with different rhythms influencing both local computation and long-range communication. To investigate this question, we recorded multiple single unit and local field potential (LFP activity from microelectrode arrays implanted bilaterally in macaque motor areas. Monkeys performed a delayed center-out reach task either manually using their natural arm (Manual Control, MC or under direct neural control through a brain-machine interface (Brain Control, BC. In accord with prior work, we found that the spiking activity of individual neurons is coupled to multiple aspects of the ongoing motor beta rhythm (10-45 Hz during both MC and BC, with neurons exhibiting a diversity of coupling preferences. However, here we show that for identified single neurons, this beta-to-rate mapping can change in a reversible and task-dependent way. For example, as beta power increases, a given neuron may increase spiking during MC but decrease spiking during BC, or exhibit a reversible shift in the preferred phase of firing. The within-task stability of coupling, combined with the reversible cross-task changes in coupling, suggest that task-dependent changes in the beta-to-rate mapping play a role in the transient functional reorganization of neural ensembles. We characterize the range of task-dependent changes in the mapping from beta amplitude, phase, and inter-hemispheric phase differences to the spike rates of an ensemble of simultaneously-recorded neurons, and discuss the potential implications that dynamic remapping from oscillatory activity to

Social network activation: the role of health discussion partners in recovery from mental illness.

Science.gov (United States)

Perry, Brea L; Pescosolido, Bernice A

2015-01-01

In response to health problems, individuals may strategically activate their social network ties to help manage crisis and uncertainty. While it is well-established that social relationships provide a crucial safety net, little is known about who is chosen to help during an episode of illness. Guided by the Network Episode Model, two aspects of consulting others in the face of mental illness are considered. First, we ask who activates ties, and what kinds of ties and networks they attempt to leverage for discussing health matters. Second, we ask about the utility of activating health-focused network ties. Specifically, we examine the consequences of network activation at time of entry into treatment for individuals' quality of life, social satisfaction, ability to perform social roles, and mental health functioning nearly one year later. Using interview data from the longitudinal Indianapolis Network Mental Health Study (INMHS, N = 171), we focus on a sample of new patients with serious mental illness and a group with less severe disorders who are experiencing their first contact with the mental health treatment system. Three findings stand out. First, our results reveal the nature of agency in illness response. Whether under a rational choice or habitus logic, individuals appear to evaluate support needs, identifying the best possible matches among a larger group of potential health discussants. These include members of the core network and those with prior mental health experiences. Second, selective activation processes have implications for recovery. Those who secure adequate network resources report better outcomes than those who injudiciously activate network ties. Individuals who activate weaker relationships and those who are unsupportive of medical care experience poorer functioning, limited success in fulfilling social roles, and lower social satisfaction and quality of life later on. Third, the evidence suggests that social networks matter above and

Fragility in dynamic networks: application to neural networks in the epileptic cortex.

Science.gov (United States)

Sritharan, Duluxan; Sarma, Sridevi V

2014-10-01

Epilepsy is a network phenomenon characterized by atypical activity at the neuronal and population levels during seizures, including tonic spiking, increased heterogeneity in spiking rates, and synchronization. The etiology of epilepsy is unclear, but a common theme among proposed mechanisms is that structural connectivity between neurons is altered. It is hypothesized that epilepsy arises not from random changes in connectivity, but from specific structural changes to the most fragile nodes or neurons in the network. In this letter, the minimum energy perturbation on functional connectivity required to destabilize linear networks is derived. Perturbation results are then applied to a probabilistic nonlinear neural network model that operates at a stable fixed point. That is, if a small stimulus is applied to the network, the activation probabilities of each neuron respond transiently but eventually recover to their baseline values. When the perturbed network is destabilized, the activation probabilities shift to larger or smaller values or oscillate when a small stimulus is applied. Finally, the structural modifications to the neural network that achieve the functional perturbation are derived. Simulations of the unperturbed and perturbed networks qualitatively reflect neuronal activity observed in epilepsy patients, suggesting that the changes in network dynamics due to destabilizing perturbations, including the emergence of an unstable manifold or a stable limit cycle, may be indicative of neuronal or population dynamics during seizure. That is, the epileptic cortex is always on the brink of instability and minute changes in the synaptic weights associated with the most fragile node can suddenly destabilize the network to cause seizures. Finally, the theory developed here and its interpretation of epileptic networks enables the design of a straightforward feedback controller that first detects when the network has destabilized and then applies linear state

Multi-level Control Framework for Enhanced Flexibility of Active Distribution Network

DEFF Research Database (Denmark)

Nainar, Karthikeyan; Pokhrel, Basanta Raj; Pillai, Jayakrishnan Radhakrishna

2017-01-01

In this paper, the control objectives of future active distribution networks with high penetration of renewables and flexible loads are analyzed and reviewed. From a state of the art review, the important control objectives seen from the perspective of a distribution system operator are identifie......-ordination and management of the network assets at different voltage levels and geographical locations. The paper finally shows the applicability of the multi-level control architecture to some of the key challenges in the distribution system operation by relevant scenarios....... to be hosting capacity improvement, high reliable operation and cost effective network management. Based on this review and a state of the art review concerning future distribution network control methods, a multi-level control architecture is constructed for an active distribution network, which satisfies...... the selected control objectives and provides enhanced flexibility. The control architecture is supported by generation/load forecasting and distribution state estimation techniques to improve the controllability of the network. The multi-level control architecture consists of three levels of hierarchical...

A Hierarchical Approach to Real-time Activity Recognition in Body Sensor Networks

DEFF Research Database (Denmark)

Wang, Liang; Gu, Tao; Tao, Xianping

2012-01-01

Real-time activity recognition in body sensor networks is an important and challenging task. In this paper, we propose a real-time, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this model, we rst use a fast and lightweight al...

Visualization of heart rate variability of long-term heart transplant patient by transition networks: a case report

Directory of Open Access Journals (Sweden)

Joanna eWdowczyk

2016-03-01

Full Text Available We present a heart transplant patient at his 17th year of uncomplicated follow-up. Within a frame of routine check out several tests were performed. With such a long and uneventful follow-up some degree of graft reinnervation could be anticipated. However, the patient's electrocardiogram and exercise parameters seemed largely inconclusive in this regard. The exercise heart rate dynamics were suggestive of only mild, if any parasympathetic reinnervation of the graft with persisting sympathetic activation. On the other hand, traditional heart rate variability (HRV indices were inadequately high, due to erratic rhythm resulting from interference of the persisting recipient sinus node or nonconducted atrial parasystole. New tools, originated from network representation of time series, by visualization short-term dynamical patterns, provided a method to discern HRV increase due to reinnervation from other reasons.

Altered brain activation and functional connectivity in working memory related networks in patients with type 2 diabetes: An ICA-based analysis

Science.gov (United States)

Zhang, Yang; Lu, Shan; Liu, Chunlei; Zhang, Huimei; Zhou, Xuanhe; Ni, Changlin; Qin, Wen; Zhang, Quan

2016-01-01

Type 2 diabetes mellitus (T2DM) can cause multidimensional cognitive deficits, among which working memory (WM) is usually involved at an early stage. However, the neural substrates underlying impaired WM in T2DM patients are still unclear. To clarify this issue, we utilized functional magnetic resonance imaging (fMRI) and independent component analysis to evaluate T2DM patients for alterations in brain activation and functional connectivity (FC) in WM networks and to determine their associations with cognitive and clinical variables. Twenty complication-free T2DM patients and 19 matched healthy controls (HCs) were enrolled, and fMRI data were acquired during a block-designed 1-back WM task. The WM metrics of the T2DM patients showed no differences compared with those of the HCs, except for a slightly lower accuracy rate in the T2DM patients. Compared with the HCs, the T2DM patients demonstrated increased activation within their WM fronto-parietal networks, and activation strength was significantly correlated with WM performance. The T2DM patients also showed decreased FC within and between their WM networks. Our results indicate that the functional integration of WM sub-networks was disrupted in the complication-free T2DM patients and that strengthened regional activity in fronto-parietal networks may compensate for the WM impairment caused by T2DM. PMID:27021340

Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations

OpenAIRE

Harradon, Michael; Druce, Jeff; Ruttenberg, Brian

2018-01-01

Deep neural networks are complex and opaque. As they enter application in a variety of important and safety critical domains, users seek methods to explain their output predictions. We develop an approach to explaining deep neural networks by constructing causal models on salient concepts contained in a CNN. We develop methods to extract salient concepts throughout a target network by using autoencoders trained to extract human-understandable representations of network activations. We then bu...

A Spike Neural Controller for Traffic Load Parameter with Priority-Based Rate in Wireless Multimedia Sensor Networks

Directory of Open Access Journals (Sweden)

Nadia Adnan Shiltagh

2015-11-01

Full Text Available Wireless Multimedia Sensor Networks (WMSNs are a type of sensor network that contains sensor nodes equipped with cameras, microphones; therefore the WMSNS are able to produce multimedia data such as video and audio streams, still images, and scalar data from the surrounding environment. Most multimedia applications typically produce huge volumes of data, this leads to congestion. To address this challenge, This paper proposes Modify Spike Neural Network control for Traffic Load Parameter with Exponential Weight of Priority Based Rate Control algorithm (MSNTLP with EWBPRC. The Modify Spike Neural Network controller (MSNC can calculate the appropriate traffic load parameter μ for each parent node and then use in the EWPBRC algorithm to estimate the transmission rate of parent nodes and then assign a suitable transmission rate for each child node. A comparative study between (MSNTLP with EWBPRC and fuzzy logic controller for traffic load parameter with Exponential Weight of Priority Based Rate Control algorithm (FTLP with EWBPRC algorithm shows that the (MSNTLP with EWBPRC is more efficient than (FTLP with EWBPRC algorithm in terms of packet loss, queue delay and throughput. Another comparative study between (MSNTLP with EWBPRC and EWBPRC with fixed traffic load parameter (µ shows that the MSNTLP with EWBPRC is more efficient than EWBPRC with fixed traffic load parameter (µ in terms of packet loss ratio and queue delay. A simulation process is developed and tested using the network simulator _2 (NS2 in a computer having the following properties: windows 7 (64-bit, core i7, RAM 8GB, hard 1TB.

Residue Geometry Networks: A Rigidity-Based Approach to the Amino Acid Network and Evolutionary Rate Analysis

Science.gov (United States)

Fokas, Alexander S.; Cole, Daniel J.; Ahnert, Sebastian E.; Chin, Alex W.

2016-01-01

Amino acid networks (AANs) abstract the protein structure by recording the amino acid contacts and can provide insight into protein function. Herein, we describe a novel AAN construction technique that employs the rigidity analysis tool, FIRST, to build the AAN, which we refer to as the residue geometry network (RGN). We show that this new construction can be combined with network theory methods to include the effects of allowed conformal motions and local chemical environments. Importantly, this is done without costly molecular dynamics simulations required by other AAN-related methods, which allows us to analyse large proteins and/or data sets. We have calculated the centrality of the residues belonging to 795 proteins. The results display a strong, negative correlation between residue centrality and the evolutionary rate. Furthermore, among residues with high closeness, those with low degree were particularly strongly conserved. Random walk simulations using the RGN were also successful in identifying allosteric residues in proteins involved in GPCR signalling. The dynamic function of these residues largely remain hidden in the traditional distance-cutoff construction technique. Despite being constructed from only the crystal structure, the results in this paper suggests that the RGN can identify residues that fulfil a dynamical function. PMID:27623708

Restructuring the crystalline cellulose hydrogen bond network enhances its depolymerization rate

Science.gov (United States)

Shishir P.S. Chundawat; Giovanni Bellesia; Nirmal Uppugundla; Leonardo da Costa Sousa; Dahai Gao; Albert M. Cheh; Umesh P. Agarwal; Christopher M. Bianchetti; George N. Phillips; Paul Langan; Venkatesh Balan; S. Gnanakaran; Bruce E. Dale

2011-01-01

Conversion of lignocellulose to biofuels is partly inefficient due to the deleterious impact of cellulose crystallinity on enzymatic saccharification. We demonstrate how the synergistic activity of cellulases was enhanced by altering the hydrogen bond network within crystalline cellulose fibrils. We provide a molecular-scale explanation of these phenomena through...

On the Development and Application of High Data Rate Architecture (HiDRA) in Future Space Networks

Science.gov (United States)

Hylton, Alan; Raible, Daniel; Clark, Gilbert

2017-01-01

Historically, space missions have been severely constrained by their ability to downlink the data they have collected. These constraints are a result of relatively low link rates on the spacecraft as well as limitations on the time during which data can be sent. As part of a coherent strategy to address existing limitations and get more data to the ground more quickly, the Space Communications and Navigation (SCaN) program has been developing an architecture for a future solar system Internet. The High Data Rate Architecture (HiDRA) project is designed to fit into such a future SCaN network. HiDRA's goal is to describe a general packet-based networking capability which can be used to provide assets with efficient networking capabilities while simultaneously reducing the capital costs and operational costs of developing and flying future space systems.Along these lines, this paper begins by reviewing various characteristics of modern satellite design as well as relevant characteristics of emerging technologies (such as free-space optical links capable of working at 100+ Gbps). Next, the paper describes HiDRA's design, and how the system is able to both integrate and support the operation of not only today's high-rate systems, but also the high-rate systems likely to be found in the future. This section also explores both existing and future networking technologies, such as Delay Tolerant Networking (DTN) protocol (RFC4838 citeRFC:1, RFC5050citeRFC:2), and explains how HiDRA supports them. Additionally, this section explores how HiDRA is used for scheduling data movement through both proactive and reactive link management. After this, the paper moves on to explore a reference implementation of HiDRA. This implementation is currently being realized based on a Field Programmable Gate Array (FPGA) memory and interface controller that is itself controlled by a local computer running DTN software. Next, this paper explores HiDRA's natural evolution, which includes an

Associations between Aspects of Friendship Networks, Physical Activity, and Sedentary Behaviour among Adolescents

Science.gov (United States)

McCormack, Gavin R.; Nettel-Aguirre, Alberto; Blackstaffe, Anita; Perry, Rosemary; Hawe, Penelope

2014-01-01

Background. Adolescent friendships have been linked to physical activity levels; however, network characteristics have not been broadly examined. Method. In a cross-sectional analysis of 1061 adolescents (11–15 years), achieving 60 minutes/day of moderate-to-vigorous physical activity (MVPA) and participating in over 2 hours/day of sedentary behaviour were determined based on friendship network characteristics (density; proportion of active/sedentary friends; betweenness centrality; popularity; clique membership) and perceived social support. Results. Adolescents with no friendship nominations participated in less MVPA. For boys and girls, a ten percent point increase in active friends was positively associated with achievement of 60 minutes/day of MVPA (OR 1.11; 95% CI 1.02–1.21, OR 1.14; 95% CI 1.02–1.27, resp.). For boys, higher social support from friends was negatively associated with achieving 60 minutes/day of MVPA (OR 0.63; 95% CI 0.42–0.96). Compared with low density networks, boys in higher density networks were more likely to participate in over 2 hours/day of sedentary behaviour (OR 2.93; 95% CI 1.32–6.49). Social support from friends also modified associations between network characteristics and MVPA and sedentary behaviour. Conclusion. Different network characteristics appeared to have different consequences. The proportion of active close friends was associated with MVPA, while network density was associated with sedentary behaviour. This poses challenges for intervention design. PMID:25328690

Associations between Aspects of Friendship Networks, Physical Activity, and Sedentary Behaviour among Adolescents

Directory of Open Access Journals (Sweden)

Keri Jo Sawka

2014-01-01

Full Text Available Background. Adolescent friendships have been linked to physical activity levels; however, network characteristics have not been broadly examined. Method. In a cross-sectional analysis of 1061 adolescents (11–15 years, achieving 60 minutes/day of moderate-to-vigorous physical activity (MVPA and participating in over 2 hours/day of sedentary behaviour were determined based on friendship network characteristics (density; proportion of active/sedentary friends; betweenness centrality; popularity; clique membership and perceived social support. Results. Adolescents with no friendship nominations participated in less MVPA. For boys and girls, a ten percent point increase in active friends was positively associated with achievement of 60 minutes/day of MVPA (OR 1.11; 95% CI 1.02–1.21, OR 1.14; 95% CI 1.02–1.27, resp.. For boys, higher social support from friends was negatively associated with achieving 60 minutes/day of MVPA (OR 0.63; 95% CI 0.42–0.96. Compared with low density networks, boys in higher density networks were more likely to participate in over 2 hours/day of sedentary behaviour (OR 2.93; 95% CI 1.32–6.49. Social support from friends also modified associations between network characteristics and MVPA and sedentary behaviour. Conclusion. Different network characteristics appeared to have different consequences. The proportion of active close friends was associated with MVPA, while network density was associated with sedentary behaviour. This poses challenges for intervention design.

Networking in Sport Management: Ideas and Activities to Enhance Student Engagement and Career Development

Directory of Open Access Journals (Sweden)

Alan S. Kornspan

2013-01-01

Full Text Available The primary purpose of this paper is to present information regarding the development of networking skills to enhance the career development of sport management students. Specifically, literature is reviewed which supports the importance of networking in the attainment of employment and career advancement in the sport industry. This is followed by an overview of emerging networking activities that allow opportunities for sport management students to expand their network. Sport industry career fairs and career conferences that students can attend are discussed. Additionally, sport industry professional associations that students can become involved with are presented. This is then followed with information related to the development of sport management clubs and various events that can be promoted to enhance the networking process. Specifically, activities provided by university faculty to enhance the educational experience of sport management students are detailed. Finally, a sample schedule of semester activities focused on student engagement and networking activities is provided.

Alumni Activities : International Alumni Network for TUAS

OpenAIRE

Saarinen, Riikka-Maria

2013-01-01

Turku University of Applied Sciences is currently planning on creating an International Alumni Network for the former exchange students who had their exchange period at TUAS. In this thesis, alumni functions are divided into three sections, i.e. the purpose of the alumni, the activities of the alumni and the management of the communication of the alumni. The research of the alumni functions was conducted by introduction of alumni activities in general and introducing three examples of Amer...

Predicting forest insect flight activity: A Bayesian network approach.

Directory of Open Access Journals (Sweden)

Stephen M Pawson

Full Text Available Daily flight activity patterns of forest insects are influenced by temporal and meteorological conditions. Temperature and time of day are frequently cited as key drivers of activity; however, complex interactions between multiple contributing factors have also been proposed. Here, we report individual Bayesian network models to assess the probability of flight activity of three exotic insects, Hylurgus ligniperda, Hylastes ater, and Arhopalus ferus in a managed plantation forest context. Models were built from 7,144 individual hours of insect sampling, temperature, wind speed, relative humidity, photon flux density, and temporal data. Discretized meteorological and temporal variables were used to build naïve Bayes tree augmented networks. Calibration results suggested that the H. ater and A. ferus Bayesian network models had the best fit for low Type I and overall errors, and H. ligniperda had the best fit for low Type II errors. Maximum hourly temperature and time since sunrise had the largest influence on H. ligniperda flight activity predictions, whereas time of day and year had the greatest influence on H. ater and A. ferus activity. Type II model errors for the prediction of no flight activity is improved by increasing the model's predictive threshold. Improvements in model performance can be made by further sampling, increasing the sensitivity of the flight intercept traps, and replicating sampling in other regions. Predicting insect flight informs an assessment of the potential phytosanitary risks of wood exports. Quantifying this risk allows mitigation treatments to be targeted to prevent the spread of invasive species via international trade pathways.

User Participation and Honesty in Online Rating Systems: What a Social Network Can Do

OpenAIRE

Davoust, Alan; Esfandiari, Babak

2016-01-01

An important problem with online communities in general, and online rating systems in particular, is uncooperative behavior: lack of user participation, dishonest contributions. This may be due to an incentive structure akin to a Prisoners' Dilemma (PD). We show that introducing an explicit social network to PD games fosters cooperative behavior, and use this insight to design a new aggregation technique for online rating systems. Using a dataset of ratings from Yelp, we show that our aggrega...

Evolution on neutral networks accelerates the ticking rate of the molecular clock.

Science.gov (United States)

Manrubia, Susanna; Cuesta, José A

2015-01-06

Large sets of genotypes give rise to the same phenotype, because phenotypic expression is highly redundant. Accordingly, a population can accept mutations without altering its phenotype, as long as the genotype mutates into another one on the same set. By linking every pair of genotypes that are mutually accessible through mutation, genotypes organize themselves into neutral networks (NNs). These networks are known to be heterogeneous and assortative, and these properties affect the evolutionary dynamics of the population. By studying the dynamics of populations on NNs with arbitrary topology, we analyse the effect of assortativity, of NN (phenotype) fitness and of network size. We find that the probability that the population leaves the network is smaller the longer the time spent on it. This progressive 'phenotypic entrapment' entails a systematic increase in the overdispersion of the process with time and an acceleration in the fixation rate of neutral mutations. We also quantify the variation of these effects with the size of the phenotype and with its fitness relative to that of neighbouring alternatives. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Topological quantum computing with a very noisy network and local error rates approaching one percent.

Science.gov (United States)

Nickerson, Naomi H; Li, Ying; Benjamin, Simon C

2013-01-01

A scalable quantum computer could be built by networking together many simple processor cells, thus avoiding the need to create a single complex structure. The difficulty is that realistic quantum links are very error prone. A solution is for cells to repeatedly communicate with each other and so purify any imperfections; however prior studies suggest that the cells themselves must then have prohibitively low internal error rates. Here we describe a method by which even error-prone cells can perform purification: groups of cells generate shared resource states, which then enable stabilization of topologically encoded data. Given a realistically noisy network (≥10% error rate) we find that our protocol can succeed provided that intra-cell error rates for initialisation, state manipulation and measurement are below 0.82%. This level of fidelity is already achievable in several laboratory systems.

Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks.

Science.gov (United States)

Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S

2017-08-03

Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages.

Plasticity of Neuron-Glial Transmission: Equipping Glia for Long-Term Integration of Network Activity

Directory of Open Access Journals (Sweden)

Wayne Croft

2015-01-01

Full Text Available The capacity of synaptic networks to express activity-dependent changes in strength and connectivity is essential for learning and memory processes. In recent years, glial cells (most notably astrocytes have been recognized as active participants in the modulation of synaptic transmission and synaptic plasticity, implicating these electrically nonexcitable cells in information processing in the brain. While the concept of bidirectional communication between neurons and glia and the mechanisms by which gliotransmission can modulate neuronal function are well established, less attention has been focussed on the computational potential of neuron-glial transmission itself. In particular, whether neuron-glial transmission is itself subject to activity-dependent plasticity and what the computational properties of such plasticity might be has not been explored in detail. In this review, we summarize current examples of plasticity in neuron-glial transmission, in many brain regions and neurotransmitter pathways. We argue that induction of glial plasticity typically requires repetitive neuronal firing over long time periods (minutes-hours rather than the short-lived, stereotyped trigger typical of canonical long-term potentiation. We speculate that this equips glia with a mechanism for monitoring average firing rates in the synaptic network, which is suited to the longer term roles proposed for astrocytes in neurophysiology.

Assembling the puzzle for promoting physical activity in Brazil: a social network analysis.

Science.gov (United States)

Brownson, Ross C; Parra, Diana C; Dauti, Marsela; Harris, Jenine K; Hallal, Pedro C; Hoehner, Christine; Malta, Deborah Carvalho; Reis, Rodrigo S; Ramos, Luiz Roberto; Ribeiro, Isabela C; Soares, Jesus; Pratt, Michael

2010-07-01

Physical inactivity is a significant public health problem in Brazil that may be addressed by partnerships and networks. In conjunction with Project GUIA (Guide for Useful Interventions for Physical Activity in Brazil and Latin America), the aim of this study was to conduct a social network analysis of physical activity in Brazil. An online survey was completed by 28 of 35 organizations contacted from December 2008 through March 2009. Network analytic methods examined measures of collaboration, importance, leadership, and attributes of the respondent and organization. Leadership nominations for organizations studied ranged from 0 to 23. Positive predictors of collaboration included: south region, GUIA membership, years working in physical activity, and research, education, and promotion/practice areas of physical activity. The most frequently reported barrier to collaboration was bureaucracy. Social network analysis identified factors that are likely to improve collaboration among organizations in Brazil.

Active node determination for correlated data gathering in wireless sensor networks

OpenAIRE

Karasabun, Efe

2009-01-01

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009. Thesis (Master's) -- Bilkent University, 2009. Includes bibliographical references leaves 53-55. In wireless sensor network applications where data gathered by different sensor nodes is correlated, not all sensor nodes need to be active for the wireless sensor network to be functional. However, the sensor nodes that are selected as active should form a co...

On a multistable competitive network model in the case of an inhomogeneous growth rate spectrum: With an application to priming

International Nuclear Information System (INIS)

Frank, T.D.

2009-01-01

A stability analysis of a network model proposed by Haken is carried out for the case of an inhomogeneous spectrum of growth rates. The degree of multistability as a function of the coupling strength between network units is determined. An application to priming shows that the network can reconstruct the fundamental phenomenon that primed items have shorter recall latencies than non-primed items when assuming that learning affects the inhomogeneity of the growth rate spectrum.

Innovation diffusion on time-varying activity driven networks

Science.gov (United States)

Rizzo, Alessandro; Porfiri, Maurizio

2016-01-01

Since its introduction in the 1960s, the theory of innovation diffusion has contributed to the advancement of several research fields, such as marketing management and consumer behavior. The 1969 seminal paper by Bass [F.M. Bass, Manag. Sci. 15, 215 (1969)] introduced a model of product growth for consumer durables, which has been extensively used to predict innovation diffusion across a range of applications. Here, we propose a novel approach to study innovation diffusion, where interactions among individuals are mediated by the dynamics of a time-varying network. Our approach is based on the Bass' model, and overcomes key limitations of previous studies, which assumed timescale separation between the individual dynamics and the evolution of the connectivity patterns. Thus, we do not hypothesize homogeneous mixing among individuals or the existence of a fixed interaction network. We formulate our approach in the framework of activity driven networks to enable the analysis of the concurrent evolution of the interaction and individual dynamics. Numerical simulations offer a systematic analysis of the model behavior and highlight the role of individual activity on market penetration when targeted advertisement campaigns are designed, or a competition between two different products takes place.

A network-based rating system and its resistance to bribery

OpenAIRE

Turrini, P; Grandi, U

2016-01-01

We study a rating system in which a set of individ- uals (e.g., the customers of a restaurant) evaluate a given service (e.g, the restaurant), with their ag- gregated opinion determining the probability of all individuals to use the service and thus its generated revenue. We explicitly model the influence relation by a social network, with individuals being influ- enced by the evaluation of their trusted peers. On top of that we allow a malicious service provider (e.g., the restaurant owne...

Formation of Valley Networks in a Cold and Icy Early Mars Climate: Predictions for Erosion Rates and Channel Morphology

Science.gov (United States)

Cassanelli, J.

2017-12-01

Mars is host to a diverse array of valley networks, systems of linear-to-sinuous depressions which are widely distributed across the surface and which exhibit branching patterns similar to the dendritic drainage patterns of terrestrial fluvial systems. Characteristics of the valley networks are indicative of an origin by fluvial activity, providing among the most compelling evidence for the past presence of flowing liquid water on the surface of Mars. Stratigraphic and crater age dating techniques suggest that the formation of the valley networks occurred predominantly during the early geologic history of Mars ( 3.7 Ga). However, whether the valley networks formed predominantly by rainfall in a relatively warm and wet early Mars climate, or by snowmelt and episodic rainfall in an ambient cold and icy climate, remains disputed. Understanding the formative environment of the valley networks will help distinguish between these warm and cold end-member early Mars climate models. Here we test a conceptual model for channel incision and evolution under cold and icy conditions with a substrate characterized by the presence of an ice-free dry active layer and subjacent ice-cemented regolith, similar to that found in the Antarctic McMurdo Dry Valleys. We implement numerical thermal models, quantitative erosion and transport estimates, and morphometric analyses in order to outline predictions for (1) the precise nature and structure of the substrate, (2) fluvial erosion/incision rates, and (3) channel morphology. Model predictions are compared against morphologic and morphometric observational data to evaluate consistency with the assumed cold climate scenario. In the cold climate scenario, the substrate is predicted to be characterized by a kilometers-thick globally-continuous cryosphere below a 50-100 meter thick desiccated ice-free zone. Initial results suggest that, with the predicted substrate structure, fluvial channel erosion and morphology in a cold early Mars

Apriori-based network intrusion detection system

International Nuclear Information System (INIS)

Wang Wenjin; Liu Junrong; Liu Baoxu

2012-01-01

With the development of network communication technology, more and more social activities run by Internet. In the meantime, the network information security is getting increasingly serious. Intrusion Detection System (IDS) has greatly improved the general security level of whole network. But there are still many problem exists in current IDS, e.g. high leak rate detection/false alarm rates and feature library need frequently upgrade. This paper presents an association-rule based IDS. This system can detect unknown attack by generate rules from training data. Experiment in last chapter proved the system has great accuracy on unknown attack detection. (authors)

Activity in the fronto-parietal network indicates numerical inductive reasoning beyond calculation: An fMRI study combined with a cognitive model.

Science.gov (United States)

Liang, Peipeng; Jia, Xiuqin; Taatgen, Niels A; Borst, Jelmer P; Li, Kuncheng

2016-05-19

Numerical inductive reasoning refers to the process of identifying and extrapolating the rule involved in numeric materials. It is associated with calculation, and shares the common activation of the fronto-parietal regions with calculation, which suggests that numerical inductive reasoning may correspond to a general calculation process. However, compared with calculation, rule identification is critical and unique to reasoning. Previous studies have established the central role of the fronto-parietal network for relational integration during rule identification in numerical inductive reasoning. The current question of interest is whether numerical inductive reasoning exclusively corresponds to calculation or operates beyond calculation, and whether it is possible to distinguish between them based on the activity pattern in the fronto-parietal network. To directly address this issue, three types of problems were created: numerical inductive reasoning, calculation, and perceptual judgment. Our results showed that the fronto-parietal network was more active in numerical inductive reasoning which requires more exchanges between intermediate representations and long-term declarative knowledge during rule identification. These results survived even after controlling for the covariates of response time and error rate. A computational cognitive model was developed using the cognitive architecture ACT-R to account for the behavioral results and brain activity in the fronto-parietal network.

Three-dimensional neural cultures produce networks that mimic native brain activity.

Science.gov (United States)

Bourke, Justin L; Quigley, Anita F; Duchi, Serena; O'Connell, Cathal D; Crook, Jeremy M; Wallace, Gordon G; Cook, Mark J; Kapsa, Robert M I

2018-02-01

Development of brain function is critically dependent on neuronal networks organized through three dimensions. Culture of central nervous system neurons has traditionally been limited to two dimensions, restricting growth patterns and network formation to a single plane. Here, with the use of multichannel extracellular microelectrode arrays, we demonstrate that neurons cultured in a true three-dimensional environment recapitulate native neuronal network formation and produce functional outcomes more akin to in vivo neuronal network activity. Copyright © 2017 John Wiley & Sons, Ltd.

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.

Science.gov (United States)

Falat, Lukas; Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

Memory effects induce structure in social networks with activity-driven agents

International Nuclear Information System (INIS)

Medus, A D; Dorso, C O

2014-01-01

Activity-driven modelling has recently been proposed as an alternative growth mechanism for time varying networks,displaying power-law degree distribution in time-aggregated representation. This approach assumes memoryless agents developing random connections with total disregard of their previous contacts. Thus, such an assumption leads to time-aggregated random networks that do not reproduce the positive degree-degree correlation and high clustering coefficient widely observed in real social networks. In this paper, we aim to study the incidence of the agents' long-term memory on the emergence of new social ties. To this end, we propose a dynamical network model assuming heterogeneous activity for agents, together with a triadic-closure step as main connectivity mechanism. We show that this simple mechanism provides some of the fundamental topological features expected for real social networks in their time-aggregated picture. We derive analytical results and perform extensive numerical simulations in regimes with and without population growth. Finally, we present an illustrative comparison with two case studies, one comprising face-to-face encounters in a closed gathering, while the other one corresponding to social friendship ties from an online social network. (paper)

Choosing optimum noise figure and data rate in wireless sensor network radio transceivers

NARCIS (Netherlands)

Dutta, R.; van der Zee, Ronan A.R.; Bentum, Marinus Jan; Kokkeler, Andre B.J.

2011-01-01

To reduce the energy consumption in wireless sensor network transceivers, we propose an approach which combines two tradeoffs. The first tradeoff is between the receiver sensitivity and transmitter output power. The second one is the duty cycle and data rate of the transceiver. The combined approach

Estimating Memory Deterioration Rates Following Neurodegeneration and Traumatic Brain Injuries in a Hopfield Network Model

Directory of Open Access Journals (Sweden)

Melanie Weber

2017-11-01

Full Text Available Neurodegenerative diseases and traumatic brain injuries (TBI are among the main causes of cognitive dysfunction in humans. At a neuronal network level, they both extensively exhibit focal axonal swellings (FAS, which in turn, compromise the information encoded in spike trains and lead to potentially severe functional deficits. There are currently no satisfactory quantitative predictors of decline in memory-encoding neuronal networks based on the impact and statistics of FAS. Some of the challenges of this translational approach include our inability to access small scale injuries with non-invasive methods, the overall complexity of neuronal pathologies, and our limited knowledge of how networks process biological signals. The purpose of this computational study is three-fold: (i to extend Hopfield's model for associative memory to account for the effects of FAS, (ii to calibrate FAS parameters from biophysical observations of their statistical distribution and size, and (iii to systematically evaluate deterioration rates for different memory-recall tasks as a function of FAS injury. We calculate deterioration rates for a face-recognition task to account for highly correlated memories and also for a discrimination task of random, uncorrelated memories with a size at the capacity limit of the Hopfield network. While it is expected that the performance of any injured network should decrease with injury, our results link, for the first time, the memory recall ability to observed FAS statistics. This allows for plausible estimates of cognitive decline for different stages of brain disorders within neuronal networks, bridging experimental observations following neurodegeneration and TBI with compromised memory recall. The work lends new insights to help close the gap between theory and experiment on how biological signals are processed in damaged, high-dimensional functional networks, and towards positing new diagnostic tools to measure cognitive

Estimating Memory Deterioration Rates Following Neurodegeneration and Traumatic Brain Injuries in a Hopfield Network Model

Science.gov (United States)

Weber, Melanie; Maia, Pedro D.; Kutz, J. Nathan

2017-01-01

Neurodegenerative diseases and traumatic brain injuries (TBI) are among the main causes of cognitive dysfunction in humans. At a neuronal network level, they both extensively exhibit focal axonal swellings (FAS), which in turn, compromise the information encoded in spike trains and lead to potentially severe functional deficits. There are currently no satisfactory quantitative predictors of decline in memory-encoding neuronal networks based on the impact and statistics of FAS. Some of the challenges of this translational approach include our inability to access small scale injuries with non-invasive methods, the overall complexity of neuronal pathologies, and our limited knowledge of how networks process biological signals. The purpose of this computational study is three-fold: (i) to extend Hopfield's model for associative memory to account for the effects of FAS, (ii) to calibrate FAS parameters from biophysical observations of their statistical distribution and size, and (iii) to systematically evaluate deterioration rates for different memory-recall tasks as a function of FAS injury. We calculate deterioration rates for a face-recognition task to account for highly correlated memories and also for a discrimination task of random, uncorrelated memories with a size at the capacity limit of the Hopfield network. While it is expected that the performance of any injured network should decrease with injury, our results link, for the first time, the memory recall ability to observed FAS statistics. This allows for plausible estimates of cognitive decline for different stages of brain disorders within neuronal networks, bridging experimental observations following neurodegeneration and TBI with compromised memory recall. The work lends new insights to help close the gap between theory and experiment on how biological signals are processed in damaged, high-dimensional functional networks, and towards positing new diagnostic tools to measure cognitive deficits. PMID

Investigating solvability and complexity of linear active networks by means of matroids

DEFF Research Database (Denmark)

Petersen, Bjørn

1979-01-01

The solvability and complexity problems of finear active network are approached from a purely combinatorial point of view, using the concepts of matroid theory. Since the method is purely combinatorial, we take into account the network topology alone. Under this assumption necessary and sufficient...... conditions are given for the unique solvablity of linear active networks. The complexity and the number of dc-eigenfrequencies are also given. The method enables.you to decide if degeneracies are due to the topology alone, or if they are caused by special relations among network parameter values....... If the network parameter values are taken into account, the complexity and number of dc-eigenfrequencies given by the method, are only upper and lower bounds, respectively. The above conditions are fairly easily checked, and the complexity and number of dc-elgenfrequencies are found, using polynomially bounded...

What Motivates Young Adults to Talk About Physical Activity on Social Network Sites?

Science.gov (United States)

Zhang, Ni; Campo, Shelly; Yang, Jingzhen; Eckler, Petya; Snetselaar, Linda; Janz, Kathleen; Leary, Emily

2017-06-22

Electronic word-of-mouth on social network sites has been used successfully in marketing. In social marketing, electronic word-of-mouth about products as health behaviors has the potential to be more effective and reach more young adults than health education through traditional mass media. However, little is known about what motivates people to actively initiate electronic word-of-mouth about health behaviors on their personal pages or profiles on social network sites, thus potentially reaching all their contacts on those sites. This study filled the gap by applying a marketing theoretical model to explore the factors associated with electronic word-of-mouth on social network sites about leisure-time physical activity. A Web survey link was sent to undergraduate students at one of the Midwestern universities and 439 of them completed the survey. The average age of the 439 participants was 19 years (SD=1 year, range: 18-24). Results suggested that emotional engagement with leisure-time physical activity (ie, affective involvement in leisure-time physical activity) predicted providing relevant opinions or information on social network sites. Social network site users who perceived stronger ties with all their contacts were more likely to provide and seek leisure-time physical activity opinions and information. People who provided leisure-time physical activity opinions and information were more likely to seek opinions and information, and people who forwarded information about leisure-time physical activity were more likely to chat about it. This study shed light on the application of the electronic word-of-mouth theoretical framework in promoting health behaviors. The findings can also guide the development of future social marketing interventions using social network sites to promote leisure-time physical activity. ©Ni Zhang, Shelly Campo, Jingzhen Yang, Petya Eckler, Linda Snetselaar, Kathleen Janz, Emily Leary. Originally published in the Journal of Medical

Modeling and Visualization of Human Activities for Multicamera Networks

Directory of Open Access Journals (Sweden)

Aswin C. Sankaranarayanan

2009-01-01

Full Text Available Multicamera networks are becoming complex involving larger sensing areas in order to capture activities and behavior that evolve over long spatial and temporal windows. This necessitates novel methods to process the information sensed by the network and visualize it for an end user. In this paper, we describe a system for modeling and on-demand visualization of activities of groups of humans. Using the prior knowledge of the 3D structure of the scene as well as camera calibration, the system localizes humans as they navigate the scene. Activities of interest are detected by matching models of these activities learnt a priori against the multiview observations. The trajectories and the activity index for each individual summarize the dynamic content of the scene. These are used to render the scene with virtual 3D human models that mimic the observed activities of real humans. In particular, the rendering framework is designed to handle large displays with a cluster of GPUs as well as reduce the cognitive dissonance by rendering realistic weather effects and illumination. We envision use of this system for immersive visualization as well as summarization of videos that capture group behavior.

Time-variant coherence between heart rate variability and EEG activity in epileptic patients: an advanced coupling analysis between physiological networks

International Nuclear Information System (INIS)

Piper, D; Schiecke, K; Pester, B; Witte, H; Benninger, F; Feucht, M

2014-01-01

Time-variant coherence analysis between the heart rate variability (HRV) and the channel-related envelopes of adaptively selected EEG components was used as an indicator for the occurrence of (correlative) couplings between the central autonomic network (CAN) and the epileptic network before, during and after epileptic seizures. Two groups of patients were investigated, a group with left and a group with right hemispheric temporal lobe epilepsy. The individual EEG components were extracted by a signal-adaptive approach, the multivariate empirical mode decomposition, and the envelopes of each resulting intrinsic mode function (IMF) were computed by using Hilbert transform. Two IMFs, whose envelopes were strongly correlated with the HRV’s low-frequency oscillation (HRV-LF; ≈0.1 Hz) before and after the seizure were identified. The frequency ranges of these IMFs correspond to the EEG delta-band. The time-variant coherence was statistically quantified and tensor decomposition of the time-frequency coherence maps was applied to explore the topography-time-frequency characteristics of the coherence analysis. Results allow the hypothesis that couplings between the CAN, which controls the cardiovascular-cardiorespiratory system, and the ‘epileptic neural network’ exist. Additionally, our results confirm the hypothesis of a right hemispheric lateralization of sympathetic cardiac control of the HRV-LF. (paper)

Italian retail gasoline activities: inadequate distribution network

International Nuclear Information System (INIS)

Verde, Stefano

2005-01-01

It is common belief that competition in the Italian retail gasoline activities is hindered by oil companies' collusive behaviour. However, when developing a broader analysis of the sector, low efficiency and scarce competition could results as the consequences coming from an inadequate distribution network and from the recognition of international markets and focal point [it

Characterization of Early Cortical Neural Network ...

Science.gov (United States)

We examined the development of neural network activity using microelectrode array (MEA) recordings made in multi-well MEA plates (mwMEAs) over the first 12 days in vitro (DIV). In primary cortical cultures made from postnatal rats, action potential spiking activity was essentially absent on DIV 2 and developed rapidly between DIV 5 and 12. Spiking activity was primarily sporadic and unorganized at early DIV, and became progressively more organized with time in culture, with bursting parameters, synchrony and network bursting increasing between DIV 5 and 12. We selected 12 features to describe network activity and principal components analysis using these features demonstrated a general segregation of data by age at both the well and plate levels. Using a combination of random forest classifiers and Support Vector Machines, we demonstrated that 4 features (CV of within burst ISI, CV of IBI, network spike rate and burst rate) were sufficient to predict the age (either DIV 5, 7, 9 or 12) of each well recording with >65% accuracy. When restricting the classification problem to a binary decision, we found that classification improved dramatically, e.g. 95% accuracy for discriminating DIV 5 vs DIV 12 wells. Further, we present a novel resampling approach to determine the number of wells that might be needed for conducting comparisons of different treatments using mwMEA plates. Overall, these results demonstrate that network development on mwMEA plates is similar to

Time-Efficient High-Rate Data Flooding in One-Dimensional Acoustic Underwater Sensor Networks

Directory of Open Access Journals (Sweden)

Jae Kyun Kwon

2015-10-01

Full Text Available Because underwater communication environments have poor characteristics, such as severe attenuation, large propagation delays and narrow bandwidths, data is normally transmitted at low rates through acoustic waves. On the other hand, as high traffic has recently been required in diverse areas, high rate transmission has become necessary. In this paper, transmission/reception timing schemes that maximize the time axis use efficiency to improve the resource efficiency for high rate transmission are proposed. The excellence of the proposed scheme is identified by examining the power distributions by node, rate bounds, power levels depending on the rates and number of nodes, and network split gains through mathematical analysis and numerical results. In addition, the simulation results show that the proposed scheme outperforms the existing packet train method.

Friendship networks and physical activity and sedentary behavior among youth: a systematized review.

Science.gov (United States)

Sawka, Keri Jo; McCormack, Gavin R; Nettel-Aguirre, Alberto; Hawe, Penelope; Doyle-Baker, Patricia K

2013-12-01

Low levels of physical activity and increased participation in sedentary leisure-time activities are two important obesity-risk behaviors that impact the health of today's youth. Friend's health behaviors have been shown to influence individual health behaviors; however, current evidence on the specific role of friendship networks in relation to levels of physical activity and sedentary behavior is limited. The purpose of this review was to summarize evidence on friendship networks and both physical activity and sedentary behavior among children and adolescents. After a search of seven scientific databases and reference scans, a total of thirteen articles were eligible for inclusion. All assessed the association between friendship networks and physical activity, while three also assessed sedentary behavior. Overall, higher levels of physical activity among friends are associated with higher levels of physical activity of the individual. Longitudinal studies reveal that an individual's level of physical activity changes to reflect his/her friends' higher level of physical activity. Boys tend to be influenced by their friendship network to a greater extent than girls. There is mixed evidence surrounding a friend's sedentary behavior and individual sedentary behavior. Friends' physical activity level appears to have a significant influence on individual's physical activity level. Evidence surrounding sedentary behavior is limited and mixed. Results from this review could inform effective public health interventions that harness the influence of friends to increase physical activity levels among children and adolescents.

Synaptic network activity induces neuronal differentiation of adult hippocampal precursor cells through BDNF signaling

Directory of Open Access Journals (Sweden)

Harish Babu

2009-09-01

Full Text Available Adult hippocampal neurogenesis is regulated by activity. But how do neural precursor cells in the hippocampus respond to surrounding network activity and translate increased neural activity into a developmental program? Here we show that long-term potential (LTP-like synaptic activity within a cellular network of mature hippocampal neurons promotes neuronal differentiation of newly generated cells. In co-cultures of precursor cells with primary hippocampal neurons, LTP-like synaptic plasticity induced by addition of glycine in Mg2+-free media for 5 min, produced synchronous network activity and subsequently increased synaptic strength between neurons. Furthermore, this synchronous network activity led to a significant increase in neuronal differentiation from the co-cultured neural precursor cells. When applied directly to precursor cells, glycine and Mg2+-free solution did not induce neuronal differentiation. Synaptic plasticity-induced neuronal differentiation of precursor cells was observed in the presence of GABAergic neurotransmission blockers but was dependent on NMDA-mediated Ca2+ influx. Most importantly, neuronal differentiation required the release of brain-derived neurotrophic factor (BDNF from the underlying substrate hippocampal neurons as well as TrkB receptor phosphorylation in precursor cells. This suggests that activity-dependent stem cell differentiation within the hippocampal network is mediated via synaptically evoked BDNF signaling.

Default-mode-like network activation in awake rodents.

Directory of Open Access Journals (Sweden)

Jaymin Upadhyay

Full Text Available During wakefulness and in absence of performing tasks or sensory processing, the default-mode network (DMN, an intrinsic central nervous system (CNS network, is in an active state. Non-human primate and human CNS imaging studies have identified the DMN in these two species. Clinical imaging studies have shown that the pattern of activity within the DMN is often modulated in various disease states (e.g., Alzheimer's, schizophrenia or chronic pain. However, whether the DMN exists in awake rodents has not been characterized. The current data provides evidence that awake rodents also possess 'DMN-like' functional connectivity, but only subsequent to habituation to what is initially a novel magnetic resonance imaging (MRI environment as well as physical restraint. Specifically, the habituation process spanned across four separate scanning sessions (Day 2, 4, 6 and 8. At Day 8, significant (p<0.05 functional connectivity was observed amongst structures such as the anterior cingulate (seed region, retrosplenial, parietal, and hippocampal cortices. Prior to habituation (Day 2, functional connectivity was only detected (p<0.05 amongst CNS structures known to mediate anxiety (i.e., anterior cingulate (seed region, posterior hypothalamic area, amygdala and parabracial nucleus. In relating functional connectivity between cingulate-default-mode and cingulate-anxiety structures across Days 2-8, a significant inverse relationship (r = -0.65, p = 0.0004 was observed between these two functional interactions such that increased cingulate-DMN connectivity corresponded to decreased cingulate anxiety network connectivity. This investigation demonstrates that the cingulate is an important component of both the rodent DMN-like and anxiety networks.

Deep Recurrent Neural Networks for Human Activity Recognition

Directory of Open Access Journals (Sweden)

Abdulmajid Murad

2017-11-01

Full Text Available Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM and k-nearest neighbors (KNN. Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs and CNNs.

Deep Recurrent Neural Networks for Human Activity Recognition.

Science.gov (United States)

Murad, Abdulmajid; Pyun, Jae-Young

2017-11-06

Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.

Resource allocation via sum-rate maximization in the uplink of multi-cell OFDMA networks

KAUST Repository

Tabassum, Hina; Alouini, Mohamed-Slim; Dawy, Zaher

2012-01-01

In this paper, we consider maximizing the sum rate in the uplink of a multi-cell orthogonal frequency-division multiple access network. The problem has a non-convex combinatorial structure and is known to be NP-hard. Because of the inherent

Tectonic implications of seismic activity recorded by the northern Ontario seismograph network

International Nuclear Information System (INIS)

Wetmiller, R.J.; Cajka, M.G.

1989-01-01

The northern Ontario seismograph network, which has operated under the Canadian Nuclear Fuel Waste Management Program since 1982, has provided valuable data to supplement those recorded by the Canadian national networks on earthquake activity, rockburst activity, the distribution of regional seismic velocities, and the contemporary stress field in northern Ontario. The combined networks recorded the largest earthquake known in northwestern Ontario, M 3.9 near Sioux Lookout on February 11, 1984, and many smaller earthquakes in northeastern Ontario. Focal mechanism solutions of these and older events showed high horizontal stress and thrust faulting to be dominant features of the contemporary tectonics of northern Ontario. The zone of more intense earthquake activity in western Quebec appeared to extend northwestward into the Kapuskasing area of northeastern Ontario, where an area of persistent microearthquake activity had been identified by a seismograph station near Kapuskasing. Controlled explosions of the 1984 Kapuskasing Uplift seismic profile experiment recorded on the northern Ontario seismograph network showed the presence of anomalously high LG velocities in northeastern Ontario (3.65 km/s) that when properly taken into account reduced the mislocation errors of well-recorded seismic events by 50% on average

Nonlinearly Activated Neural Network for Solving Time-Varying Complex Sylvester Equation.

Science.gov (United States)

Li, Shuai; Li, Yangming

2013-10-28

The Sylvester equation is often encountered in mathematics and control theory. For the general time-invariant Sylvester equation problem, which is defined in the domain of complex numbers, the Bartels-Stewart algorithm and its extensions are effective and widely used with an O(n³) time complexity. When applied to solving the time-varying Sylvester equation, the computation burden increases intensively with the decrease of sampling period and cannot satisfy continuous realtime calculation requirements. For the special case of the general Sylvester equation problem defined in the domain of real numbers, gradient-based recurrent neural networks are able to solve the time-varying Sylvester equation in real time, but there always exists an estimation error while a recently proposed recurrent neural network by Zhang et al [this type of neural network is called Zhang neural network (ZNN)] converges to the solution ideally. The advancements in complex-valued neural networks cast light to extend the existing real-valued ZNN for solving the time-varying real-valued Sylvester equation to its counterpart in the domain of complex numbers. In this paper, a complex-valued ZNN for solving the complex-valued Sylvester equation problem is investigated and the global convergence of the neural network is proven with the proposed nonlinear complex-valued activation functions. Moreover, a special type of activation function with a core function, called sign-bi-power function, is proven to enable the ZNN to converge in finite time, which further enhances its advantage in online processing. In this case, the upper bound of the convergence time is also derived analytically. Simulations are performed to evaluate and compare the performance of the neural network with different parameters and activation functions. Both theoretical analysis and numerical simulations validate the effectiveness of the proposed method.

Noise influence on spike activation in a Hindmarsh–Rose small-world neural network

International Nuclear Information System (INIS)

Zhe, Sun; Micheletto, Ruggero

2016-01-01

We studied the role of noise in neural networks, especially focusing on its relation to the propagation of spike activity in a small sized system. We set up a source of information using a single neuron that is constantly spiking. This element called initiator x o feeds spikes to the rest of the network that is initially quiescent and subsequently reacts with vigorous spiking after a transitional period of time. We found that noise quickly suppresses the initiator’s influence and favors spontaneous spike activity and, using a decibel representation of noise intensity, we established a linear relationship between noise amplitude and the interval from the initiator’s first spike and the rest of the network activation. We studied the same process with networks of different sizes (number of neurons) and found that the initiator x o has a measurable influence on small networks, but as the network grows in size, spontaneous spiking emerges disrupting its effects on networks of more than about N = 100 neurons. This suggests that the mechanism of internal noise generation allows information transmission within a small neural neighborhood, but decays for bigger network domains. We also analyzed the Fourier spectrum of the whole network membrane potential and verified that noise provokes the reduction of main θ and α peaks before transitioning into chaotic spiking. However, network size does not reproduce a similar phenomena; instead we recorded a reduction in peaks’ amplitude, a better sharpness and definition of Fourier peaks, but not the evident degeneration to chaos observed with increasing external noise. This work aims to contribute to the understanding of the fundamental mechanisms of propagation of spontaneous spiking in neural networks and gives a quantitative assessment of how noise can be used to control and modulate this phenomenon in Hindmarsh−Rose (H−R) neural networks. (paper)

Noise influence on spike activation in a Hindmarsh-Rose small-world neural network

Science.gov (United States)

Zhe, Sun; Micheletto, Ruggero

2016-07-01

We studied the role of noise in neural networks, especially focusing on its relation to the propagation of spike activity in a small sized system. We set up a source of information using a single neuron that is constantly spiking. This element called initiator x o feeds spikes to the rest of the network that is initially quiescent and subsequently reacts with vigorous spiking after a transitional period of time. We found that noise quickly suppresses the initiator’s influence and favors spontaneous spike activity and, using a decibel representation of noise intensity, we established a linear relationship between noise amplitude and the interval from the initiator’s first spike and the rest of the network activation. We studied the same process with networks of different sizes (number of neurons) and found that the initiator x o has a measurable influence on small networks, but as the network grows in size, spontaneous spiking emerges disrupting its effects on networks of more than about N = 100 neurons. This suggests that the mechanism of internal noise generation allows information transmission within a small neural neighborhood, but decays for bigger network domains. We also analyzed the Fourier spectrum of the whole network membrane potential and verified that noise provokes the reduction of main θ and α peaks before transitioning into chaotic spiking. However, network size does not reproduce a similar phenomena; instead we recorded a reduction in peaks’ amplitude, a better sharpness and definition of Fourier peaks, but not the evident degeneration to chaos observed with increasing external noise. This work aims to contribute to the understanding of the fundamental mechanisms of propagation of spontaneous spiking in neural networks and gives a quantitative assessment of how noise can be used to control and modulate this phenomenon in Hindmarsh-Rose (H-R) neural networks.

PersonA: Persuasive social network for physical Activity.

Science.gov (United States)

Ayubi, Soleh U; Parmanto, Bambang

2012-01-01

Advances in physical activity (PA) monitoring devices provide ample opportunities for innovations in the way the information produced by these devices is used to encourage people to have more active lifestyles. One such innovation is expanding the current use of the information from self-management to social support. We developed a Persuasive social network for physical Activity (PersonA) that combines automatic input of physical activity data, a smartphone, and a social networking system (SNS). This paper describes the motivation for and overarching design of the PersonA and its functional and non-functional features. PersonA is designed to intelligently and automatically receive raw PA data from the sensors in the smartphone, calculate the data into meaningful PA information, store the information on a secure server, and show the information to the users as persuasive and real-time feedbacks or publish the information to the SNS to generate social support. The implementation of self-monitoring, social support, and persuasive concepts using currently available technologies has the potential for promoting healthy lifestyle, greater community participation, and higher quality of life. We also expect that PersonA will enable health professionals to collect in situ data related to physical activity. The platform is currently being used and tested to improve PA level of three groups of users in Pittsburgh, PA, USA.

RelEx: Visualization for Actively Changing Overlay Network Specifications.

Science.gov (United States)

Sedlmair, M; Frank, A; Munzner, T; Butz, A

2012-12-01

We present a network visualization design study focused on supporting automotive engineers who need to specify and optimize traffic patterns for in-car communication networks. The task and data abstractions that we derived support actively making changes to an overlay network, where logical communication specifications must be mapped to an underlying physical network. These abstractions are very different from the dominant use case in visual network analysis, namely identifying clusters and central nodes, that stems from the domain of social network analysis. Our visualization tool RelEx was created and iteratively refined through a full user-centered design process that included a full problem characterization phase before tool design began, paper prototyping, iterative refinement in close collaboration with expert users for formative evaluation, deployment in the field with real analysts using their own data, usability testing with non-expert users, and summative evaluation at the end of the deployment. In the summative post-deployment study, which entailed domain experts using the tool over several weeks in their daily practice, we documented many examples where the use of RelEx simplified or sped up their work compared to previous practices.

Resolution enhancement in neural networks with dynamical synapses

Directory of Open Access Journals (Sweden)

C. C. Alan Fung

2013-06-01

Full Text Available Conventionally, information is represented by spike rates in the neural system. Here, we consider the ability of temporally modulated activities in neuronal networks to carry information extra to spike rates. These temporal modulations, commonly known as population spikes, are due to the presence of synaptic depression in a neuronal network model. We discuss its relevance to an experiment on transparent motions in macaque monkeys by Treue et al. in 2000. They found that if the moving directions of objects are too close, the firing rate profile will be very similar to that with one direction. As the difference in the moving directions of objects is large enough, the neuronal system would respond in such a way that the network enhances the resolution in the moving directions of the objects. In this paper, we propose that this behavior can be reproduced by neural networks with dynamical synapses when there are multiple external inputs. We will demonstrate how resolution enhancement can be achieved, and discuss the conditions under which temporally modulated activities are able to enhance information processing performances in general.

From Structure to Activity: Using Centrality Measures to Predict Neuronal Activity.

Science.gov (United States)

Fletcher, Jack McKay; Wennekers, Thomas

2018-03-01

It is clear that the topological structure of a neural network somehow determines the activity of the neurons within it. In the present work, we ask to what extent it is possible to examine the structural features of a network and learn something about its activity? Specifically, we consider how the centrality (the importance of a node in a network) of a neuron correlates with its firing rate. To investigate, we apply an array of centrality measures, including In-Degree, Closeness, Betweenness, Eigenvector, Katz, PageRank, Hyperlink-Induced Topic Search (HITS) and NeuronRank to Leaky-Integrate and Fire neural networks with different connectivity schemes. We find that Katz centrality is the best predictor of firing rate given the network structure, with almost perfect correlation in all cases studied, which include purely excitatory and excitatory-inhibitory networks, with either homogeneous connections or a small-world structure. We identify the properties of a network which will cause this correlation to hold. We argue that the reason Katz centrality correlates so highly with neuronal activity compared to other centrality measures is because it nicely captures disinhibition in neural networks. In addition, we argue that these theoretical findings are applicable to neuroscientists who apply centrality measures to functional brain networks, as well as offer a neurophysiological justification to high level cognitive models which use certain centrality measures.

Effect of acute stretch injury on action potential and network activity of rat neocortical neurons in culture.

Science.gov (United States)

Magou, George C; Pfister, Bryan J; Berlin, Joshua R

2015-10-22

The basis for acute seizures following traumatic brain injury (TBI) remains unclear. Animal models of TBI have revealed acute hyperexcitablility in cortical neurons that could underlie seizure activity, but studying initiating events causing hyperexcitability is difficult in these models. In vitro models of stretch injury with cultured cortical neurons, a surrogate for TBI, allow facile investigation of cellular changes after injury but they have only demonstrated post-injury hypoexcitability. The goal of this study was to determine if neuronal hyperexcitability could be triggered by in vitro stretch injury. Controlled uniaxial stretch injury was delivered to a spatially delimited region of a spontaneously active network of cultured rat cortical neurons, yielding a region of stretch-injured neurons and adjacent regions of non-stretched neurons that did not directly experience stretch injury. Spontaneous electrical activity was measured in non-stretched and stretch-injured neurons, and in control neuronal networks not subjected to stretch injury. Non-stretched neurons in stretch-injured cultures displayed a three-fold increase in action potential firing rate and bursting activity 30-60 min post-injury. Stretch-injured neurons, however, displayed dramatically lower rates of action potential firing and bursting. These results demonstrate that acute hyperexcitability can be observed in non-stretched neurons located in regions adjacent to the site of stretch injury, consistent with reports that seizure activity can arise from regions surrounding the site of localized brain injury. Thus, this in vitro procedure for localized neuronal stretch injury may provide a model to study the earliest cellular changes in neuronal function associated with acute post-traumatic seizures. Copyright © 2015. Published by Elsevier B.V.

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

Directory of Open Access Journals (Sweden)

Lukas Falat

2016-01-01

Full Text Available This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

Science.gov (United States)

Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process. PMID:26977450

Effects of Vertex Activity and Self-organized Criticality Behavior on a Weighted Evolving Network

International Nuclear Information System (INIS)

Zhang Guiqing; Yang Qiuying; Chen Tianlun

2008-01-01

Effects of vertex activity have been analyzed on a weighted evolving network. The network is characterized by the probability distribution of vertex strength, each edge weight and evolution of the strength of vertices with different vertex activities. The model exhibits self-organized criticality behavior. The probability distribution of avalanche size for different network sizes is also shown. In addition, there is a power law relation between the size and the duration of an avalanche and the average of avalanche size has been studied for different vertex activities

Passive and Active Monitoring on a High Performance Research Network

International Nuclear Information System (INIS)

Matthews, Warren

2001-01-01

The bold network challenges described in ''Internet End-to-end Performance Monitoring for the High Energy and Nuclear Physics Community'' presented at PAM 2000 have been tackled by the intrepid administrators and engineers providing the network services. After less than a year, the BaBar collaboration has collected almost 100 million particle collision events in a database approaching 165TB (Tera=10 12 ). Around 20TB has been exported via the Internet to the BaBar regional center at IN2P3 in Lyon, France, for processing and around 40 TB of simulated events have been imported to SLAC from Lawrence Livermore National Laboratory (LLNL). An unforseen challenge has arisen due to recent events and highlighted security concerns at DoE funded labs. New rules and regulations suggest it is only a matter of time before many active performance measurements may not be possible between many sites. Yet, at the same time, the importance of understanding every aspect of the network and eradicating packet loss for high throughput data transfers has become apparent. Work at SLAC to employ passive monitoring using netflow and OC3MON is underway and techniques to supplement and possibly replace the active measurements are being considered. This paper will detail the special needs and traffic characterization of a remarkable research project, and how the networking hurdles have been resolved (or not) to achieve the required high data throughput. Results from active and passive measurements will be compared, and methods for achieving high throughput and the effect on the network will be assessed along with tools that directly measure throughput and applications used to actually transfer data

Passive and Active Monitoring on a High Performance Research Network.

Energy Technology Data Exchange (ETDEWEB)

Matthews, Warren

2001-05-01

The bold network challenges described in ''Internet End-to-end Performance Monitoring for the High Energy and Nuclear Physics Community'' presented at PAM 2000 have been tackled by the intrepid administrators and engineers providing the network services. After less than a year, the BaBar collaboration has collected almost 100 million particle collision events in a database approaching 165TB (Tera=10{sup 12}). Around 20TB has been exported via the Internet to the BaBar regional center at IN2P3 in Lyon, France, for processing and around 40 TB of simulated events have been imported to SLAC from Lawrence Livermore National Laboratory (LLNL). An unforseen challenge has arisen due to recent events and highlighted security concerns at DoE funded labs. New rules and regulations suggest it is only a matter of time before many active performance measurements may not be possible between many sites. Yet, at the same time, the importance of understanding every aspect of the network and eradicating packet loss for high throughput data transfers has become apparent. Work at SLAC to employ passive monitoring using netflow and OC3MON is underway and techniques to supplement and possibly replace the active measurements are being considered. This paper will detail the special needs and traffic characterization of a remarkable research project, and how the networking hurdles have been resolved (or not!) to achieve the required high data throughput. Results from active and passive measurements will be compared, and methods for achieving high throughput and the effect on the network will be assessed along with tools that directly measure throughput and applications used to actually transfer data.

Green corona, geomagnetic activity and radar meteor rates

International Nuclear Information System (INIS)

Prikryl, P.

1979-01-01

The short-term dependence of radar meteor rates on geomagnetic activity and/or central meridian passage (CMP) of bright or faint green corona regions is studied. A superimposed-epoch analysis was applied to radar meteor observations from the Ottawa patrol radar (Springhill, Ont.) and Ksub(p)-indices of geomagnetic activity for the period 1963 to 1967. During the minimum of solar activity (1963 to 1965) the CMP of bright coronal regions was followed by the maximum in the daily rates of persistent meteor echoes (>=4s), and the minimum in the daily sums of Ksub(p)-indices whereas the minimum or the maximum, respectively, occurs after the CMP of faint coronal regions. The time delay between the CMP of coronal structures and the corresponding maxima or minima is found to be 3 to 4 days. However, for the period immediately after the minimum of solar activity (1966 to 1967) the above correlation with the green corona is void both for the geomagnetic activity and radar meteor rates. An inverse correlation was found between the radar meteor rates and the geomagnetic activity irrespective of the solar activity. The observed effect can be ascribed to the solar-wind-induced ''geomagnetic'' heating of the upper atmosphere and to the subsequent change in the density gradient in the meteor zone. (author)

Neuronal Population Activity in Spinal Motor Circuits

DEFF Research Database (Denmark)

Berg, Rune W.

2017-01-01

The core elements of stereotypical movements such as locomotion, scratching and breathing are generated by networks in the lower brainstem and the spinal cord. Ensemble activities in spinal motor networks had until recently been merely a black box, but with the emergence of ultra-thin Silicon multi......-electrode technology it was possible to reveal the spiking activity of larger parts of the network. A series of experiments revealed unexpected features of spinal networks, such as multiple spiking regimes and lognormal firing rate distributions. The lognormality renders the widespread idea of a typical firing rate...

Active Queue Management in TCP Networks Based on Fuzzy-Pid Controller

Directory of Open Access Journals (Sweden)

Hossein ASHTIANI

2012-01-01

Full Text Available We introduce a novel and robust active queue management (AQM scheme based on a fuzzy controller, called hybrid fuzzy-PID controller. In the TCP network, AQM is important to regulate the queue length by passing or dropping the packets at the intermediate routers. RED, PI, and PID algorithms have been used for AQM. But these algorithms show weaknesses in the detection and control of congestion under dynamically changing network situations. In this paper a novel Fuzzy-based proportional-integral derivative (PID controller, which acts as an active queue manager (AQM for Internet routers, is proposed. These controllers are used to reduce packet loss and improve network utilization in TCP/IP networks. A new hybrid controller is proposed and compared with traditional RED based controller. Simulations are carried out to demonstrate the effectiveness of the proposed method and show that, the new hybrid fuzzy PID controller provides better performance than random early detection (RED and PID controllers

Recurrently connected and localized neuronal communities initiate coordinated spontaneous activity in neuronal networks

Science.gov (United States)

Amin, Hayder; Maccione, Alessandro; Nieus, Thierry

2017-01-01

Developing neuronal systems intrinsically generate coordinated spontaneous activity that propagates by involving a large number of synchronously firing neurons. In vivo, waves of spikes transiently characterize the activity of developing brain circuits and are fundamental for activity-dependent circuit formation. In vitro, coordinated spontaneous spiking activity, or network bursts (NBs), interleaved within periods of asynchronous spikes emerge during the development of 2D and 3D neuronal cultures. Several studies have investigated this type of activity and its dynamics, but how a neuronal system generates these coordinated events remains unclear. Here, we investigate at a cellular level the generation of network bursts in spontaneously active neuronal cultures by exploiting high-resolution multielectrode array recordings and computational network modelling. Our analysis reveals that NBs are generated in specialized regions of the network (functional neuronal communities) that feature neuronal links with high cross-correlation peak values, sub-millisecond lags and that share very similar structural connectivity motifs providing recurrent interactions. We show that the particular properties of these local structures enable locally amplifying spontaneous asynchronous spikes and that this mechanism can lead to the initiation of NBs. Through the analysis of simulated and experimental data, we also show that AMPA currents drive the coordinated activity, while NMDA and GABA currents are only involved in shaping the dynamics of NBs. Overall, our results suggest that the presence of functional neuronal communities with recurrent local connections allows a neuronal system to generate spontaneous coordinated spiking activity events. As suggested by the rules used for implementing our computational model, such functional communities might naturally emerge during network development by following simple constraints on distance-based connectivity. PMID:28749937

Recurrently connected and localized neuronal communities initiate coordinated spontaneous activity in neuronal networks.

Directory of Open Access Journals (Sweden)

Davide Lonardoni

2017-07-01

Full Text Available Developing neuronal systems intrinsically generate coordinated spontaneous activity that propagates by involving a large number of synchronously firing neurons. In vivo, waves of spikes transiently characterize the activity of developing brain circuits and are fundamental for activity-dependent circuit formation. In vitro, coordinated spontaneous spiking activity, or network bursts (NBs, interleaved within periods of asynchronous spikes emerge during the development of 2D and 3D neuronal cultures. Several studies have investigated this type of activity and its dynamics, but how a neuronal system generates these coordinated events remains unclear. Here, we investigate at a cellular level the generation of network bursts in spontaneously active neuronal cultures by exploiting high-resolution multielectrode array recordings and computational network modelling. Our analysis reveals that NBs are generated in specialized regions of the network (functional neuronal communities that feature neuronal links with high cross-correlation peak values, sub-millisecond lags and that share very similar structural connectivity motifs providing recurrent interactions. We show that the particular properties of these local structures enable locally amplifying spontaneous asynchronous spikes and that this mechanism can lead to the initiation of NBs. Through the analysis of simulated and experimental data, we also show that AMPA currents drive the coordinated activity, while NMDA and GABA currents are only involved in shaping the dynamics of NBs. Overall, our results suggest that the presence of functional neuronal communities with recurrent local connections allows a neuronal system to generate spontaneous coordinated spiking activity events. As suggested by the rules used for implementing our computational model, such functional communities might naturally emerge during network development by following simple constraints on distance-based connectivity.

Spatial interpolation and radiological mapping of ambient gamma dose rate by using artificial neural networks and fuzzy logic methods.

Science.gov (United States)

Yeşilkanat, Cafer Mert; Kobya, Yaşar; Taşkın, Halim; Çevik, Uğur

2017-09-01

The aim of this study was to determine spatial risk dispersion of ambient gamma dose rate (AGDR) by using both artificial neural network (ANN) and fuzzy logic (FL) methods, compare the performances of methods, make dose estimations for intermediate stations with no previous measurements and create dose rate risk maps of the study area. In order to determine the dose distribution by using artificial neural networks, two main networks and five different network structures were used; feed forward ANN; Multi-layer perceptron (MLP), Radial basis functional neural network (RBFNN), Quantile regression neural network (QRNN) and recurrent ANN; Jordan networks (JN), Elman networks (EN). In the evaluation of estimation performance obtained for the test data, all models appear to give similar results. According to the cross-validation results obtained for explaining AGDR distribution, Pearson's r coefficients were calculated as 0.94, 0.91, 0.89, 0.91, 0.91 and 0.92 and RMSE values were calculated as 34.78, 43.28, 63.92, 44.86, 46.77 and 37.92 for MLP, RBFNN, QRNN, JN, EN and FL, respectively. In addition, spatial risk maps showing distributions of AGDR of the study area were created by all models and results were compared with geological, topological and soil structure. Copyright © 2017 Elsevier Ltd. All rights reserved.

Maturation of Cerebellar Purkinje Cell Population Activity during Postnatal Refinement of Climbing Fiber Network

Directory of Open Access Journals (Sweden)

Jean-Marc Good

2017-11-01

Full Text Available Neural circuits undergo massive refinements during postnatal development. In the developing cerebellum, the climbing fiber (CF to Purkinje cell (PC network is drastically reshaped by eliminating early-formed redundant CF to PC synapses. To investigate the impact of CF network refinement on PC population activity during postnatal development, we monitored spontaneous CF responses in neighboring PCs and the activity of populations of nearby CF terminals using in vivo two-photon calcium imaging. Population activity is highly synchronized in newborn mice, and the degree of synchrony gradually declines during the first postnatal week in PCs and, to a lesser extent, in CF terminals. Knockout mice lacking P/Q-type voltage-gated calcium channel or glutamate receptor δ2, in which CF network refinement is severely impaired, exhibit an abnormally high level of synchrony in PC population activity. These results suggest that CF network refinement is a structural basis for developmental desynchronization and maturation of PC population activity.

3D Filament Network Segmentation with Multiple Active Contours

Science.gov (United States)

Xu, Ting; Vavylonis, Dimitrios; Huang, Xiaolei

2014-03-01

Fluorescence microscopy is frequently used to study two and three dimensional network structures formed by cytoskeletal polymer fibers such as actin filaments and microtubules. While these cytoskeletal structures are often dilute enough to allow imaging of individual filaments or bundles of them, quantitative analysis of these images is challenging. To facilitate quantitative, reproducible and objective analysis of the image data, we developed a semi-automated method to extract actin networks and retrieve their topology in 3D. Our method uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then evolve along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments. The proposed approach is generally applicable to images of curvilinear networks with low SNR. We demonstrate its potential by extracting the centerlines of synthetic meshwork images, actin networks in 2D TIRF Microscopy images, and 3D actin cable meshworks of live fission yeast cells imaged by spinning disk confocal microscopy.

Nutrients interaction investigation to improve Monascus purpureus FTC5391 growth rate using Response Surface Methodology and Artificial Neural Network

Directory of Open Access Journals (Sweden)

Mohamad, R.

2013-01-01

Full Text Available Aims: Two vital factors, certain environmental conditions and nutrients as a source of energy are entailed for successful growth and reproduction of microorganisms. Manipulation of nutritional requirement is the simplest and most effectual strategy to stimulate and enhance the activity of microorganisms. Methodology and Results: In this study, response surface methodology (RSM and artificial neural network (ANN were employed to optimize the carbon and nitrogen sources in order to improve growth rate of Monascus purpureus FTC5391,a new local isolate. The best models for optimization of growth rate were a multilayer full feed-forward incremental back propagation network, and a modified response surface model using backward elimination. The optimum condition for cell mass production was: sucrose 2.5%, yeast extract 0.045%, casamino acid 0.275%, sodium nitrate 0.48%, potato starch 0.045%, dextrose 1%, potassium nitrate 0.57%. The experimental cell mass production using this optimal condition was 21 mg/plate/12days, which was 2.2-fold higher than the standard condition (sucrose 5%, yeast extract 0.15%, casamino acid 0.25%, sodium nitrate 0.3%, potato starch 0.2%, dextrose 1%, potassium nitrate 0.3%. Conclusion, significance and impact of study: The results of RSM and ANN showed that all carbon and nitrogen sources tested had significant effect on growth rate (P-value < 0.05. In addition the use of RSM and ANN alongside each other provided a proper growth prediction model.

Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network

Energy Technology Data Exchange (ETDEWEB)

Du, Zhimin; Jin, Xinqiao; Yang, Yunyu [School of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai (China)

2009-09-15

Wavelet neural network, the integration of wavelet analysis and neural network, is presented to diagnose the faults of sensors including temperature, flow rate and pressure in variable air volume (VAV) systems to ensure well capacity of energy conservation. Wavelet analysis is used to process the original data collected from the building automation first. With three-level wavelet decomposition, the series of characteristic information representing various operation conditions of the system are obtained. In addition, neural network is developed to diagnose the source of the fault. To improve the diagnosis efficiency, three data groups based on several physical models or balances are classified and constructed. Using the data decomposed by three-level wavelet, the neural network can be well trained and series of convergent networks are obtained. Finally, the new measurements to diagnose are similarly processed by wavelet. And the well-trained convergent neural networks are used to identify the operation condition and isolate the source of the fault. (author)

Dynamics on networks: the role of local dynamics and global networks on the emergence of hypersynchronous neural activity.

Directory of Open Access Journals (Sweden)

Helmut Schmidt

2014-11-01

Full Text Available Graph theory has evolved into a useful tool for studying complex brain networks inferred from a variety of measures of neural activity, including fMRI, DTI, MEG and EEG. In the study of neurological disorders, recent work has discovered differences in the structure of graphs inferred from patient and control cohorts. However, most of these studies pursue a purely observational approach; identifying correlations between properties of graphs and the cohort which they describe, without consideration of the underlying mechanisms. To move beyond this necessitates the development of computational modeling approaches to appropriately interpret network interactions and the alterations in brain dynamics they permit, which in the field of complexity sciences is known as dynamics on networks. In this study we describe the development and application of this framework using modular networks of Kuramoto oscillators. We use this framework to understand functional networks inferred from resting state EEG recordings of a cohort of 35 adults with heterogeneous idiopathic generalized epilepsies and 40 healthy adult controls. Taking emergent synchrony across the global network as a proxy for seizures, our study finds that the critical strength of coupling required to synchronize the global network is significantly decreased for the epilepsy cohort for functional networks inferred from both theta (3-6 Hz and low-alpha (6-9 Hz bands. We further identify left frontal regions as a potential driver of seizure activity within these networks. We also explore the ability of our method to identify individuals with epilepsy, observing up to 80% predictive power through use of receiver operating characteristic analysis. Collectively these findings demonstrate that a computer model based analysis of routine clinical EEG provides significant additional information beyond standard clinical interpretation, which should ultimately enable a more appropriate mechanistic

Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors

Directory of Open Access Journals (Sweden)

Fernando Moya Rueda

2018-05-01

Full Text Available Human activity recognition (HAR is a classification task for recognizing human movements. Methods of HAR are of great interest as they have become tools for measuring occurrences and durations of human actions, which are the basis of smart assistive technologies and manual processes analysis. Recently, deep neural networks have been deployed for HAR in the context of activities of daily living using multichannel time-series. These time-series are acquired from body-worn devices, which are composed of different types of sensors. The deep architectures process these measurements for finding basic and complex features in human corporal movements, and for classifying them into a set of human actions. As the devices are worn at different parts of the human body, we propose a novel deep neural network for HAR. This network handles sequence measurements from different body-worn devices separately. An evaluation of the architecture is performed on three datasets, the Oportunity, Pamap2, and an industrial dataset, outperforming the state-of-the-art. In addition, different network configurations will also be evaluated. We find that applying convolutions per sensor channel and per body-worn device improves the capabilities of convolutional neural network (CNNs.

Who Do Hospital Physicians and Nurses Go to for Advice About Medications? A Social Network Analysis and Examination of Prescribing Error Rates.

Science.gov (United States)

Creswick, Nerida; Westbrook, Johanna Irene

2015-09-01

To measure the weekly medication advice-seeking networks of hospital staff, to compare patterns across professional groups, and to examine these in the context of prescribing error rates. A social network analysis was conducted. All 101 staff in 2 wards in a large, academic teaching hospital in Sydney, Australia, were surveyed (response rate, 90%) using a detailed social network questionnaire. The extent of weekly medication advice seeking was measured by density of connections, proportion of reciprocal relationships by reciprocity, number of colleagues to whom each person provided advice by in-degree, and perceptions of amount and impact of advice seeking between physicians and nurses. Data on prescribing error rates from the 2 wards were compared. Weekly medication advice-seeking networks were sparse (density: 7% ward A and 12% ward B). Information sharing across professional groups was modest, and rates of reciprocation of advice were low (9% ward A, 14% ward B). Pharmacists provided advice to most people, and junior physicians also played central roles. Senior physicians provided medication advice to few people. Many staff perceived that physicians rarely sought advice from nurses when prescribing, but almost all believed that an increase in communication between physicians and nurses about medications would improve patient safety. The medication networks in ward B had higher measures for density, reciprocation, and fewer senior physicians who were isolates. Ward B had a significantly lower rate of both procedural and clinical prescribing errors than ward A (0.63 clinical prescribing errors per admission [95%CI, 0.47-0.79] versus 1.81/ admission [95%CI, 1.49-2.13]). Medication advice-seeking networks among staff on hospital wards are limited. Hubs of advice provision include pharmacists, junior physicians, and senior nurses. Senior physicians are poorly integrated into medication advice networks. Strategies to improve the advice-giving networks between senior

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

OpenAIRE

Francisco Javier Ordóñez; Daniel Roggen

2016-01-01

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we pro...

Recursive Estimation for Dynamical Systems with Different Delay Rates Sensor Network and Autocorrelated Process Noises

Directory of Open Access Journals (Sweden)

Jianxin Feng

2014-01-01

Full Text Available The recursive estimation problem is studied for a class of uncertain dynamical systems with different delay rates sensor network and autocorrelated process noises. The process noises are assumed to be autocorrelated across time and the autocorrelation property is described by the covariances between different time instants. The system model under consideration is subject to multiplicative noises or stochastic uncertainties. The sensor delay phenomenon occurs in a random way and each sensor in the sensor network has an individual delay rate which is characterized by a binary switching sequence obeying a conditional probability distribution. By using the orthogonal projection theorem and an innovation analysis approach, the desired recursive robust estimators including recursive robust filter, predictor, and smoother are obtained. Simulation results are provided to demonstrate the effectiveness of the proposed approaches.

Impact of dynamic rate coding aspects of mobile phone networks on forensic voice comparison.

Science.gov (United States)

Alzqhoul, Esam A S; Nair, Balamurali B T; Guillemin, Bernard J

2015-09-01

Previous studies have shown that landline and mobile phone networks are different in their ways of handling the speech signal, and therefore in their impact on it. But the same is also true of the different networks within the mobile phone arena. There are two major mobile phone technologies currently in use today, namely the global system for mobile communications (GSM) and code division multiple access (CDMA) and these are fundamentally different in their design. For example, the quality of the coded speech in the GSM network is a function of channel quality, whereas in the CDMA network it is determined by channel capacity (i.e., the number of users sharing a cell site). This paper examines the impact on the speech signal of a key feature of these networks, namely dynamic rate coding, and its subsequent impact on the task of likelihood-ratio-based forensic voice comparison (FVC). Surprisingly, both FVC accuracy and precision are found to be better for both GSM- and CDMA-coded speech than for uncoded. Intuitively one expects FVC accuracy to increase with increasing coded speech quality. This trend is shown to occur for the CDMA network, but, surprisingly, not for the GSM network. Further, in respect to comparisons between these two networks, FVC accuracy for CDMA-coded speech is shown to be slightly better than for GSM-coded speech, particularly when the coded-speech quality is high, but in terms of FVC precision the two networks are shown to be very similar. Copyright © 2015 The Chartered Society of Forensic Sciences. Published by Elsevier Ireland Ltd. All rights reserved.

A network-based multi-target computational estimation scheme for anticoagulant activities of compounds.

Directory of Open Access Journals (Sweden)

Qian Li

Full Text Available BACKGROUND: Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. METHODOLOGY: We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671 between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. CONCLUSIONS: This article proposes a network-based multi-target computational estimation

A network-based multi-target computational estimation scheme for anticoagulant activities of compounds.

Science.gov (United States)

Li, Qian; Li, Xudong; Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

2011-03-22

Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by

Patterns recognition of electric brain activity using artificial neural networks

Science.gov (United States)

Musatov, V. Yu.; Pchelintseva, S. V.; Runnova, A. E.; Hramov, A. E.

2017-04-01

An approach for the recognition of various cognitive processes in the brain activity in the perception of ambiguous images. On the basis of developed theoretical background and the experimental data, we propose a new classification of oscillating patterns in the human EEG by using an artificial neural network approach. After learning of the artificial neural network reliably identified cube recognition processes, for example, left-handed or right-oriented Necker cube with different intensity of their edges, construct an artificial neural network based on Perceptron architecture and demonstrate its effectiveness in the pattern recognition of the EEG in the experimental.

Too-connected versus too-big-to-fail: banks’ network centrality and overnight interest rates.

OpenAIRE

Gabrieli, S.

2012-01-01

What influences banks’ borrowing costs in the unsecured money market? The objective of this paper is to test whether measures of centrality, quantifying network effects due to interactions among banks in the market, can help explain heterogeneous patterns in the interest rates paid to borrow unsecured funds once bank size and other bank and market factors that affect the overnight segment are controlled for. Preliminary evidence shows that large banks borrow on average at better rates compare...

Physical and social activities mediate the associations between social network types and ventilatory function in Chinese older adults.

Science.gov (United States)

Cheng, Sheung-Tak; Leung, Edward M F; Chan, Trista Wai Sze

2014-06-01

This study examined the associations between social network types and peak expiratory flow (PEF), and whether these associations were mediated by social and physical activities and mood. Nine hundred twenty-four community-dwelling Chinese older adults, who were classified into five network types (diverse, friend-focused, family-focused, distant family, and restricted), provided data on demographics, social and physical activities, mood, smoking, chronic diseases, and instrumental activities of daily living. PEF and biological covariates, including blood lipids and glucose, blood pressure, and height and weight, were assessed. Two measures of PEF were analyzed: the raw reading in L/min and the reading expressed as percentage of predicted normal value on the basis of age, sex, and height. Diverse, friend-focused, and distant family networks were hypothesized to have better PEF values compared with restricted networks, through higher physical and/or social activities. No relative advantage was predicted for family-focused networks because such networks tend to be associated with lower physical activity. Older adults with diverse, friend-focused, and distant family networks had significantly better PEF measures than those with restricted networks. The associations between diverse network and PEF measures were partially mediated by physical exercise and socializing activity. The associations between friend-focused network and PEF measures were partially mediated by socializing activity. No significant PEF differences between family-focused and restricted networks were found. Findings suggest that social network types are associated with PEF in older adults, and that network-type differences in physical and socializing activity is partly responsible for this relationship. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Contextual Multi-Scale Region Convolutional 3D Network for Activity Detection

KAUST Repository

Bai, Yancheng

2018-01-28

Activity detection is a fundamental problem in computer vision. Detecting activities of different temporal scales is particularly challenging. In this paper, we propose the contextual multi-scale region convolutional 3D network (CMS-RC3D) for activity detection. To deal with the inherent temporal scale variability of activity instances, the temporal feature pyramid is used to represent activities of different temporal scales. On each level of the temporal feature pyramid, an activity proposal detector and an activity classifier are learned to detect activities of specific temporal scales. Temporal contextual information is fused into activity classifiers for better recognition. More importantly, the entire model at all levels can be trained end-to-end. Our CMS-RC3D detector can deal with activities at all temporal scale ranges with only a single pass through the backbone network. We test our detector on two public activity detection benchmarks, THUMOS14 and ActivityNet. Extensive experiments show that the proposed CMS-RC3D detector outperforms state-of-the-art methods on THUMOS14 by a substantial margin and achieves comparable results on ActivityNet despite using a shallow feature extractor.

Contextual Multi-Scale Region Convolutional 3D Network for Activity Detection

KAUST Repository

Bai, Yancheng; Xu, Huijuan; Saenko, Kate; Ghanem, Bernard

2018-01-01

Activity detection is a fundamental problem in computer vision. Detecting activities of different temporal scales is particularly challenging. In this paper, we propose the contextual multi-scale region convolutional 3D network (CMS-RC3D) for activity detection. To deal with the inherent temporal scale variability of activity instances, the temporal feature pyramid is used to represent activities of different temporal scales. On each level of the temporal feature pyramid, an activity proposal detector and an activity classifier are learned to detect activities of specific temporal scales. Temporal contextual information is fused into activity classifiers for better recognition. More importantly, the entire model at all levels can be trained end-to-end. Our CMS-RC3D detector can deal with activities at all temporal scale ranges with only a single pass through the backbone network. We test our detector on two public activity detection benchmarks, THUMOS14 and ActivityNet. Extensive experiments show that the proposed CMS-RC3D detector outperforms state-of-the-art methods on THUMOS14 by a substantial margin and achieves comparable results on ActivityNet despite using a shallow feature extractor.

Design of a heart rate controller for treadmill exercise using a recurrent fuzzy neural network.

Science.gov (United States)

Lu, Chun-Hao; Wang, Wei-Cheng; Tai, Cheng-Chi; Chen, Tien-Chi

2016-05-01

In this study, we developed a computer controlled treadmill system using a recurrent fuzzy neural network heart rate controller (RFNNHRC). Treadmill speeds and inclines were controlled by corresponding control servo motors. The RFNNHRC was used to generate the control signals to automatically control treadmill speed and incline to minimize the user heart rate deviations from a preset profile. The RFNNHRC combines a fuzzy reasoning capability to accommodate uncertain information and an artificial recurrent neural network learning process that corrects for treadmill system nonlinearities and uncertainties. Treadmill speeds and inclines are controlled by the RFNNHRC to achieve minimal heart rate deviation from a pre-set profile using adjustable parameters and an on-line learning algorithm that provides robust performance against parameter variations. The on-line learning algorithm of RFNNHRC was developed and implemented using a dsPIC 30F4011 DSP. Application of the proposed control scheme to heart rate responses of runners resulted in smaller fluctuations than those produced by using proportional integra control, and treadmill speeds and inclines were smoother. The present experiments demonstrate improved heart rate tracking performance with the proposed control scheme. The RFNNHRC scheme with adjustable parameters and an on-line learning algorithm was applied to a computer controlled treadmill system with heart rate control during treadmill exercise. Novel RFNNHRC structure and controller stability analyses were introduced. The RFNNHRC were tuned using a Lyapunov function to ensure system stability. The superior heart rate control with the proposed RFNNHRC scheme was demonstrated with various pre-set heart rates. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A decaying factor accounts for contained activity in neuronal networks with no need of hierarchical or modular organization

International Nuclear Information System (INIS)

Amancio, Diego R; Oliveira Jr, Osvaldo N; Costa, Luciano da F

2012-01-01

The mechanisms responsible for containing activity in systems represented by networks are crucial in various phenomena, for example, in diseases such as epilepsy that affect the neuronal networks and for information dissemination in social networks. The first models to account for contained activity included triggering and inhibition processes, but they cannot be applied to social networks where inhibition is clearly absent. A recent model showed that contained activity can be achieved with no need of inhibition processes provided that the network is subdivided into modules (communities). In this paper, we introduce a new concept inspired in the Hebbian theory, through which containment of activity is achieved by incorporating a dynamics based on a decaying activity in a random walk mechanism preferential to the node activity. Upon selecting the decay coefficient within a proper range, we observed sustained activity in all the networks tested, namely, random, Barabási–Albert and geographical networks. The generality of this finding was confirmed by showing that modularity is no longer needed if the dynamics based on the integrate-and-fire dynamics incorporated the decay factor. Taken together, these results provide a proof of principle that persistent, restrained network activation might occur in the absence of any particular topological structure. This may be the reason why neuronal activity does not spread out to the entire neuronal network, even when no special topological organization exists. (paper)

Adaptive sampling rate control for networked systems based on statistical characteristics of packet disordering.

Science.gov (United States)

Li, Jin-Na; Er, Meng-Joo; Tan, Yen-Kheng; Yu, Hai-Bin; Zeng, Peng

2015-09-01

This paper investigates an adaptive sampling rate control scheme for networked control systems (NCSs) subject to packet disordering. The main objectives of the proposed scheme are (a) to avoid heavy packet disordering existing in communication networks and (b) to stabilize NCSs with packet disordering, transmission delay and packet loss. First, a novel sampling rate control algorithm based on statistical characteristics of disordering entropy is proposed; secondly, an augmented closed-loop NCS that consists of a plant, a sampler and a state-feedback controller is transformed into an uncertain and stochastic system, which facilitates the controller design. Then, a sufficient condition for stochastic stability in terms of Linear Matrix Inequalities (LMIs) is given. Moreover, an adaptive tracking controller is designed such that the sampling period tracks a desired sampling period, which represents a significant contribution. Finally, experimental results are given to illustrate the effectiveness and advantages of the proposed scheme. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

AMETH laboratories network activities; Activites du reseau de Laboratoires AMETH

Energy Technology Data Exchange (ETDEWEB)

Marimbordes, T.; Ould El Moctar, A.; Peerhossaini, H. [Nantes Univ., Ecole Polytechnique, UMR CNRS 6607, Lab. de Thermocinetique, 44 (France)] [and others

2000-07-01

The AMETH laboratories are a network for the improvement of thermal exchanges for one or two phases. This meeting of the 15 november 2000, dealt with the activities of this network of laboratories in the following topics: thermal-hydrodynamic instabilities and control of the limit layer; transfers with change in the liquid-vapor phase; transfers with change in the solid-liquid phase. Ten papers were presented. (A.L.B.)

The importance of delineating networks by activity type in bottlenose dolphins (Tursiops truncatus) in Cedar Key, Florida.

Science.gov (United States)

Gazda, Stefanie; Iyer, Swami; Killingback, Timothy; Connor, Richard; Brault, Solange

2015-03-01

Network analysis has proved to be a valuable tool for studying the behavioural patterns of complex social animals. Often such studies either do not distinguish between different behavioural states of the organisms or simply focus attention on a single behavioural state to the exclusion of all others. In either of these approaches it is impossible to ascertain how the behavioural patterns of individuals depend on the type of activity they are engaged in. Here we report on a network-based analysis of the behavioural associations in a population of bottlenose dolphins (Tursiops truncatus) in Cedar Key, Florida. We consider three distinct behavioural states-socializing, travelling and foraging-and analyse the association networks corresponding to each activity. Moreover, in constructing the different activity networks we do not simply record a spatial association between two individuals as being either present or absent, but rather quantify the degree of any association, thus allowing us to construct weighted networks describing each activity. The results of these weighted activity networks indicate that networks can reveal detailed patterns of bottlenose dolphins at the population level; dolphins socialize in large groups with preferential associations; travel in small groups with preferential associates; and spread out to forage in very small, weakly connected groups. There is some overlap in the socialize and travel networks but little overlap between the forage and other networks. This indicates that the social bonds maintained in other activities are less important as they forage on dispersed, solitary prey. The overall network, not sorted by activity, does not accurately represent any of these patterns.

Contagion processes on the static and activity-driven coupling networks

Science.gov (United States)

Lei, Yanjun; Jiang, Xin; Guo, Quantong; Ma, Yifang; Li, Meng; Zheng, Zhiming

2016-03-01

The evolution of network structure and the spreading of epidemic are common coexistent dynamical processes. In most cases, network structure is treated as either static or time-varying, supposing the whole network is observed in the same time window. In this paper, we consider the epidemics spreading on a network which has both static and time-varying structures. Meanwhile, the time-varying part and the epidemic spreading are supposed to be of the same time scale. We introduce a static and activity-driven coupling (SADC) network model to characterize the coupling between the static ("strong") structure and the dynamic ("weak") structure. Epidemic thresholds of the SIS and SIR models are studied using the SADC model both analytically and numerically under various coupling strategies, where the strong structure is of homogeneous or heterogeneous degree distribution. Theoretical thresholds obtained from the SADC model can both recover and generalize the classical results in static and time-varying networks. It is demonstrated that a weak structure might make the epidemic threshold low in homogeneous networks but high in heterogeneous cases. Furthermore, we show that the weak structure has a substantive effect on the outbreak of the epidemics. This result might be useful in designing some efficient control strategies for epidemics spreading in networks.

Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification.

Science.gov (United States)

Rueckauer, Bodo; Lungu, Iulia-Alexandra; Hu, Yuhuang; Pfeiffer, Michael; Liu, Shih-Chii

2017-01-01

Spiking neural networks (SNNs) can potentially offer an efficient way of doing inference because the neurons in the networks are sparsely activated and computations are event-driven. Previous work showed that simple continuous-valued deep Convolutional Neural Networks (CNNs) can be converted into accurate spiking equivalents. These networks did not include certain common operations such as max-pooling, softmax, batch-normalization and Inception-modules. This paper presents spiking equivalents of these operations therefore allowing conversion of nearly arbitrary CNN architectures. We show conversion of popular CNN architectures, including VGG-16 and Inception-v3, into SNNs that produce the best results reported to date on MNIST, CIFAR-10 and the challenging ImageNet dataset. SNNs can trade off classification error rate against the number of available operations whereas deep continuous-valued neural networks require a fixed number of operations to achieve their classification error rate. From the examples of LeNet for MNIST and BinaryNet for CIFAR-10, we show that with an increase in error rate of a few percentage points, the SNNs can achieve more than 2x reductions in operations compared to the original CNNs. This highlights the potential of SNNs in particular when deployed on power-efficient neuromorphic spiking neuron chips, for use in embedded applications.

Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification

Directory of Open Access Journals (Sweden)

Bodo Rueckauer

2017-12-01

Full Text Available Spiking neural networks (SNNs can potentially offer an efficient way of doing inference because the neurons in the networks are sparsely activated and computations are event-driven. Previous work showed that simple continuous-valued deep Convolutional Neural Networks (CNNs can be converted into accurate spiking equivalents. These networks did not include certain common operations such as max-pooling, softmax, batch-normalization and Inception-modules. This paper presents spiking equivalents of these operations therefore allowing conversion of nearly arbitrary CNN architectures. We show conversion of popular CNN architectures, including VGG-16 and Inception-v3, into SNNs that produce the best results reported to date on MNIST, CIFAR-10 and the challenging ImageNet dataset. SNNs can trade off classification error rate against the number of available operations whereas deep continuous-valued neural networks require a fixed number of operations to achieve their classification error rate. From the examples of LeNet for MNIST and BinaryNet for CIFAR-10, we show that with an increase in error rate of a few percentage points, the SNNs can achieve more than 2x reductions in operations compared to the original CNNs. This highlights the potential of SNNs in particular when deployed on power-efficient neuromorphic spiking neuron chips, for use in embedded applications.

Active influence in dynamical models of structural balance in social networks

Science.gov (United States)

Summers, Tyler H.; Shames, Iman

2013-07-01

We consider a nonlinear dynamical system on a signed graph, which can be interpreted as a mathematical model of social networks in which the links can have both positive and negative connotations. In accordance with a concept from social psychology called structural balance, the negative links play a key role in both the structure and dynamics of the network. Recent research has shown that in a nonlinear dynamical system modeling the time evolution of “friendliness levels” in the network, two opposing factions emerge from almost any initial condition. Here we study active external influence in this dynamical model and show that any agent in the network can achieve any desired structurally balanced state from any initial condition by perturbing its own local friendliness levels. Based on this result, we also introduce a new network centrality measure for signed networks. The results are illustrated in an international-relations network using United Nations voting record data from 1946 to 2008 to estimate friendliness levels amongst various countries.

Activity-dependent switch of GABAergic inhibition into glutamatergic excitation in astrocyte-neuron networks.

Science.gov (United States)

Perea, Gertrudis; Gómez, Ricardo; Mederos, Sara; Covelo, Ana; Ballesteros, Jesús J; Schlosser, Laura; Hernández-Vivanco, Alicia; Martín-Fernández, Mario; Quintana, Ruth; Rayan, Abdelrahman; Díez, Adolfo; Fuenzalida, Marco; Agarwal, Amit; Bergles, Dwight E; Bettler, Bernhard; Manahan-Vaughan, Denise; Martín, Eduardo D; Kirchhoff, Frank; Araque, Alfonso

2016-12-24

Interneurons are critical for proper neural network function and can activate Ca 2+ signaling in astrocytes. However, the impact of the interneuron-astrocyte signaling into neuronal network operation remains unknown. Using the simplest hippocampal Astrocyte-Neuron network, i.e., GABAergic interneuron, pyramidal neuron, single CA3-CA1 glutamatergic synapse, and astrocytes, we found that interneuron-astrocyte signaling dynamically affected excitatory neurotransmission in an activity- and time-dependent manner, and determined the sign (inhibition vs potentiation) of the GABA-mediated effects. While synaptic inhibition was mediated by GABA A receptors, potentiation involved astrocyte GABA B receptors, astrocytic glutamate release, and presynaptic metabotropic glutamate receptors. Using conditional astrocyte-specific GABA B receptor ( Gabbr1 ) knockout mice, we confirmed the glial source of the interneuron-induced potentiation, and demonstrated the involvement of astrocytes in hippocampal theta and gamma oscillations in vivo. Therefore, astrocytes decode interneuron activity and transform inhibitory into excitatory signals, contributing to the emergence of novel network properties resulting from the interneuron-astrocyte interplay.

Advances in the GRADE approach to rate the certainty in estimates from a network meta-analysis.

Science.gov (United States)

Brignardello-Petersen, Romina; Bonner, Ashley; Alexander, Paul E; Siemieniuk, Reed A; Furukawa, Toshi A; Rochwerg, Bram; Hazlewood, Glen S; Alhazzani, Waleed; Mustafa, Reem A; Murad, M Hassan; Puhan, Milo A; Schünemann, Holger J; Guyatt, Gordon H

2018-01-01

This article describes conceptual advances of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) working group guidance to evaluate the certainty of evidence (confidence in evidence, quality of evidence) from network meta-analysis (NMA). Application of the original GRADE guidance, published in 2014, in a number of NMAs has resulted in advances that strengthen its conceptual basis and make the process more efficient. This guidance will be useful for systematic review authors who aim to assess the certainty of all pairwise comparisons from an NMA and who are familiar with the basic concepts of NMA and the traditional GRADE approach for pairwise meta-analysis. Two principles of the original GRADE NMA guidance are that we need to rate the certainty of the evidence for each pairwise comparison within a network separately and that in doing so we need to consider both the direct and indirect evidence. We present, discuss, and illustrate four conceptual advances: (1) consideration of imprecision is not necessary when rating the direct and indirect estimates to inform the rating of NMA estimates, (2) there is no need to rate the indirect evidence when the certainty of the direct evidence is high and the contribution of the direct evidence to the network estimate is at least as great as that of the indirect evidence, (3) we should not trust a statistical test of global incoherence of the network to assess incoherence at the pairwise comparison level, and (4) in the presence of incoherence between direct and indirect evidence, the certainty of the evidence of each estimate can help decide which estimate to believe. Copyright © 2017 Elsevier Inc. All rights reserved.

Network governance of active employment policy

DEFF Research Database (Denmark)

Damgaard, Bodil; Torfing, Jacob

2010-01-01

The recent reform of the Danish governance system in the field of active employment policy has been subject to fierce criticism, as many commentators fear that it is the beginning of the end of the Danish Model of active stakeholder involvement. Drawing on both quantitative and qualitative data, ......, the tight metagovernance of the LECs does not seem to straightjacket the LECs as there is a considerable scope for local policy making which makes it worthwhile for the social partners to participate in the local networks.......The recent reform of the Danish governance system in the field of active employment policy has been subject to fierce criticism, as many commentators fear that it is the beginning of the end of the Danish Model of active stakeholder involvement. Drawing on both quantitative and qualitative data......, this study aims to analyse the impact of the governance reform by assessing the initial experiences with the Local Employment Councils (LECs). The analysis shows that the LECs are relatively well-functioning and contribute to an effective and democratic governance of local employment policy. Furthermore...

External pallidal stimulation improves parkinsonian motor signs and modulates neuronal activity throughout the basal ganglia thalamic network.

Science.gov (United States)

Vitek, Jerrold L; Zhang, Jianyu; Hashimoto, Takao; Russo, Gary S; Baker, Kenneth B

2012-01-01

Deep brain stimulation (DBS) of the internal segment of the globus pallidus (GPi) and the subthalamic nucleus (STN) are effective for the treatment of advanced Parkinson's disease (PD). We have shown previously that DBS of the external segment of the globus pallidus (GPe) is associated with improvements in parkinsonian motor signs; however, the mechanism of this effect is not known. In this study, we extend our findings on the effect of STN and GPi DBS on neuronal activity in the basal ganglia thalamic network to include GPe DBS using the 1-methyl-4-phenyl-1.2.3.6-tetrahydropyridine (MPTP) monkey model. Stimulation parameters that improved bradykinesia were associated with changes in the pattern and mean discharge rate of neuronal activity in the GPi, STN, and the pallidal [ventralis lateralis pars oralis (VLo) and ventralis anterior (VA)] and cerebellar [ventralis lateralis posterior pars oralis (VPLo)] receiving areas of the motor thalamus. Population post-stimulation time histograms revealed a complex pattern of stimulation-related inhibition and excitation for the GPi and VA/VLo, with a more consistent pattern of inhibition in STN and excitation in VPLo. Mean discharge rate was reduced in the GPi and STN and increased in the VPLo. Effective GPe DBS also reduced bursting in the STN and GPi. These data support the hypothesis that therapeutic DBS activates output from the stimulated structure and changes the temporal pattern of neuronal activity throughout the basal ganglia thalamic network and provide further support for GPe as a potential therapeutic target for DBS in the treatment of PD. Copyright © 2011 Elsevier Inc. All rights reserved.

Memory and pattern storage in neural networks with activity dependent synapses

Science.gov (United States)

Mejias, J. F.; Torres, J. J.

2009-01-01

We present recently obtained results on the influence of the interplay between several activity dependent synaptic mechanisms, such as short-term depression and facilitation, on the maximum memory storage capacity in an attractor neural network [1]. In contrast with the case of synaptic depression, which drastically reduces the capacity of the network to store and retrieve activity patterns [2], synaptic facilitation is able to enhance the memory capacity in different situations. In particular, we find that a convenient balance between depression and facilitation can enhance the memory capacity, reaching maximal values similar to those obtained with static synapses, that is, without activity-dependent processes. We also argue, employing simple arguments, that this level of balance is compatible with experimental data recorded from some cortical areas, where depression and facilitation may play an important role for both memory-oriented tasks and information processing. We conclude that depressing synapses with a certain level of facilitation allow to recover the good retrieval properties of networks with static synapses while maintaining the nonlinear properties of dynamic synapses, convenient for information processing and coding.

Phase diagram of spiking neural networks.

Science.gov (United States)

Seyed-Allaei, Hamed

2015-01-01

In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probability of 2%, 20% of neurons are inhibitory and 80% are excitatory. These common values are based on experiments, observations, and trials and errors, but here, I take a different perspective, inspired by evolution, I systematically simulate many networks, each with a different set of parameters, and then I try to figure out what makes the common values desirable. I stimulate networks with pulses and then measure their: dynamic range, dominant frequency of population activities, total duration of activities, maximum rate of population and the occurrence time of maximum rate. The results are organized in phase diagram. This phase diagram gives an insight into the space of parameters - excitatory to inhibitory ratio, sparseness of connections and synaptic weights. This phase diagram can be used to decide the parameters of a model. The phase diagrams show that networks which are configured according to the common values, have a good dynamic range in response to an impulse and their dynamic range is robust in respect to synaptic weights, and for some synaptic weights they oscillates in α or β frequencies, independent of external stimuli.

[Establishment of regional active neonatal transport network].

Science.gov (United States)

Kong, Xiang-yong; Gao, Xin; Yin, Xiao-juan; Hong, Xiao-yang; Fang, Huan-sheng; Wang, Zi-zhen; Li, Ai-hua; Luo, Fen-ping; Feng, Zhi-chun

2010-01-01

To evaluate the clinical function and significance of establishing a regional active neonatal transport network (ANTN) in Beijing. The authors retrospectively studied intensive care and the role of ANTN system in management of critically ill neonates and compared the outcome of newborn infants transported to our NICU before and after we established standardized NICU and ANTN system (phase 1: July 2004 to June 2006 vs phase 2: July 2006 to May 2008). The number of neonatal transport significantly increased from 587 during phase 1 to 2797 during phase 2. Success rate of transport and the total cure rate in phase 2 were 97.85% and 91.99% respectively, which were significantly higher than those in phase 1 (94.36% and 88.69%, respectively, P capacity of our NICU was enlarged following the development of ANTN. There are 200 beds for level 3 infants in phase 2, but there were only 20 beds in phase 1. Significantly less patients in the phase 2 had hypothermia, acidosis and the blood glucose instability than those in phase 1 (P transported to our NICU were higher in phase 2 compared with that in phase 1, especially infants whose gestational age was below 32 weeks. The proportions of asphyxia and respiratory distress syndrome were lower in phase 2 than that in phase 1, but the total cure rates of these two diseases had no significant changes between the two phases. The most important finding was that the improvement of outcome of premature infants and those with asphyxia and aspiration syndrome was noted following the development of ANTN. Establishing regional ANTN for a tertiary hospital is very important to elevate the total level in management of critically ill newborn infants. It plays a very important role in reducing mortality and improving total outcomes of newborn infants. There are still some problems remained to solve after four years practice in order to optimize the ANTN to meet needs of the development of neonatology.

Study of active crossover network | Tyona | Nigerian Journal of Physics

African Journals Online (AJOL)

An active crossover network system has been realized using an active component LF356 with a JFET input. The net work has two drives, the low frequency drive (Bass) and the high frequency drive (Treble). It employs high level crossover technique. The circuit performance was adequately verified and the frequency ...

Adolescents' social environment and depression: social networks, extracurricular activity, and family relationship influences.

Science.gov (United States)

Mason, Michael J; Schmidt, Christopher; Abraham, Anisha; Walker, Leslie; Tercyak, Kenneth

2009-12-01

The present study examined components of adolescents' social environment (social network, extracurricular activities, and family relationships) in association with depression. A total of 332 adolescents presenting for a routine medical check-up were self-assessed for social network risk (i.e., smoking habits of best male and female friends), extracurricular activity level (i.e., participation in organized sports teams, clubs, etc.), family relationship quality (i.e., cohesion and conflict), and symptoms of depression (i.e., minimal, mild, moderate/severe). Results of a forward linear regression modeling indicate that social environment components were associated with a significant proportion of the variance in adolescent depression (Adjusted R (2) = .177, p social network (beta = .107, p depression symptoms. Conversely, adolescents who engaged in more extracurricular activities (beta = -.118, p depressive symptoms. These findings highlight the important role that the social environment plays in adolescent depression, as well as yields new insights into socially-based intervention targets that may ameliorate adolescent depression. These intervention targets may be gender-specific, include positive social network skills training, increase adolescents' engagement in organized activities, and attend to the quality of their family relationships.

Modelling temporal networks of human face-to-face contacts with public activity and individual reachability

Science.gov (United States)

Zhang, Yi-Qing; Cui, Jing; Zhang, Shu-Min; Zhang, Qi; Li, Xiang

2016-02-01

Modelling temporal networks of human face-to-face contacts is vital both for understanding the spread of airborne pathogens and word-of-mouth spreading of information. Although many efforts have been devoted to model these temporal networks, there are still two important social features, public activity and individual reachability, have been ignored in these models. Here we present a simple model that captures these two features and other typical properties of empirical face-to-face contact networks. The model describes agents which are characterized by an attractiveness to slow down the motion of nearby people, have event-triggered active probability and perform an activity-dependent biased random walk in a square box with periodic boundary. The model quantitatively reproduces two empirical temporal networks of human face-to-face contacts which are testified by their network properties and the epidemic spread dynamics on them.

Wireless Powered Relaying Networks Under Imperfect Channel State Information: System Performance and Optimal Policy for Instantaneous Rate

Directory of Open Access Journals (Sweden)

D. T. Do

2017-09-01

Full Text Available In this investigation, we consider wireless powered relaying systems, where energy is scavenged by a relay via radio frequency (RF signals. We explore hybrid time switching-based and power splitting-based relaying protocol (HTPSR and compare performance of Amplify-and-Forward (AF with Decode-and-Forward (DF scheme under imperfect channel state information (CSI. Most importantly, the instantaneous rate, achievable bit error rate (BER are determined in the closed-form expressions under the impact of imperfect CSI. Through numerical analysis, we evaluate system insights via different parameters such as power splitting (PS and time switching (TS ratio of the considered HTPSR which affect outage performance and BER. It is noted that DF relaying networks outperform AF relaying networks. Besides that, the numerical results are given to prove the optimization problems of PS and TS ratio to obtain optimal instantaneous rate.

Cost-related model for transit rates in electric power distribution networks

International Nuclear Information System (INIS)

Collstrand, F.

1994-02-01

The planned deregulation of the swedish electrical power market will require a new structure of the electrical energy rates. In this report different models of transit rates are studied. The report includes studies of literature and a proposal to a rate structure and is made specifically for Malmoe Energi AB. The differences between various methods of calculating the transfer cost are illustrated. Further, the build-up of the tariff structure and its base elements are discussed. The costs are divided on different categories of costumers and shows the cost for each customer. The new regulations should apply simultaneously to all networks, independent of the voltage level. The transit cost should be based on a number of basic elements: capital cost, operation and maintenance, losses, measuring and administration. Capital cost and operation and maintenance should be charged as power fees, the loss cost as an energy fee and the measuring and administration cost as a fixed fee. The customer bill should be split into two parts, one for the transit cost and one for the energy usage. 15 refs., 37 tabs., 6 figs

Activation of professional and personal network relations when experiencing a symptom: a population-based cross-sectional study

DEFF Research Database (Denmark)

Elnegaard, Sandra; Andersen, Rikke Sand; Pedersen, Anette Fischer

2017-01-01

Objective To describe patterns of disclosure of symptoms experienced among people in the general population to persons in their personal and/or professional network. Design A population-based cross-sectional study. Data were collected from a web-based survey. Setting The general population...... in Denmark. Participants 100 000 individuals randomly selected, representative of the adult Danish population aged ≥20 years were invited. Approximately 5% were not eligible for inclusion. 49 706 (men=23 240; women=26 466) of 95 253 eligible individuals completed the questionnaire; yielding a response rate...... of 52.2%. Individuals completing all questions regarding social network relations form the study base (n=44 313). Primary and secondary outcome measures Activation of personal and/or professional relations when experiencing a symptom. Results The 44 313 individuals reported in total 260 079 symptom...

Simulating ensembles of nonlinear continuous time dynamical systems via active ultra wideband wireless network

Energy Technology Data Exchange (ETDEWEB)

Dmitriev, Alexander S.; Yemelyanov, Ruslan Yu. [V.A. Kotelnikov Institute of Radio Engineering and Electronics of the RAS Mokhovaya 11-7, Moscow, 125009 (Russian Federation); Moscow Institute of Physics and Technology (State University) 9 Institutskiy per., Dolgoprudny, Moscow, 141700 (Russian Federation); Gerasimov, Mark Yu. [V.A. Kotelnikov Institute of Radio Engineering and Electronics of the RAS Mokhovaya 11-7, Moscow, 125009 (Russian Federation); Itskov, Vadim V. [Moscow Institute of Physics and Technology (State University) 9 Institutskiy per., Dolgoprudny, Moscow, 141700 (Russian Federation)

2016-06-08

The paper deals with a new multi-element processor platform assigned for modelling the behaviour of interacting dynamical systems, i.e., active wireless network. Experimentally, this ensemble is implemented in an active network, the active nodes of which include direct chaotic transceivers and special actuator boards containing microcontrollers for modelling the dynamical systems and an information display unit (colored LEDs). The modelling technique and experimental results are described and analyzed.

Exponential convergence rate estimation for uncertain delayed neural networks of neutral type

International Nuclear Information System (INIS)

Lien, C.-H.; Yu, K.-W.; Lin, Y.-F.; Chung, Y.-J.; Chung, L.-Y.

2009-01-01

The global exponential stability for a class of uncertain delayed neural networks (DNNs) of neutral type is investigated in this paper. Delay-dependent and delay-independent criteria are proposed to guarantee the robust stability of DNNs via LMI and Razumikhin-like approaches. For a given delay, the maximal allowable exponential convergence rate will be estimated. Some numerical examples are given to illustrate the effectiveness of our results. The simulation results reveal significant improvement over the recent results.

Exchange rate variability, market activity and heterogeneity

OpenAIRE

Rime, Dagfinn; Sucarrat, Genaro

2007-01-01

We study the role played by geographic and bank-size heterogeneity in the relation between exchange rate variability and market activity. We find some support for the hypothesis that increases in short-term global interbank market activity, which can be interpreted as due to variation in information arrival, increase variability. However, our results do not suggest that local short-term activity increases variability. With respect to long-term market activity, which can be interpreted as a me...

Network analysis of inter-organizational relationships and policy use among active living organizations in Alberta, Canada.

Science.gov (United States)

Loitz, Christina C; Stearns, Jodie A; Fraser, Shawn N; Storey, Kate; Spence, John C

2017-08-09

Coordinated partnerships and collaborations can optimize the efficiency and effectiveness of service and program delivery in organizational networks. However, the extent to which organizations are working together to promote physical activity, and use physical activity policies in Canada, is unknown. This project sought to provide a snapshot of the funding, coordination and partnership relationships among provincial active living organizations (ALOs) in Alberta, Canada. Additionally, the awareness, and use of the provincial policy and national strategy by the organizations was examined. Provincial ALOs (N = 27) answered questions regarding their funding, coordination and partnership connections with other ALOs in the network. Social network analysis was employed to examine network structure and position of each ALO. Discriminant function analysis determined the extent to which degree centrality was associated with the use of the Active Alberta (AA) policy and Active Canada 20/20 (AC 20/20) strategy. The funding network had a low density level (density = .20) and was centralized around Alberta Tourism Parks and Recreation (ATPR; degree centralization = 48.77%, betweenness centralization = 32.43%). The coordination network had a moderate density level (density = .31), and was low-to-moderately centralized around a few organizations (degree centralization = 45.37%, betweenness centrality = 19.92%). The partnership network had a low density level (density = .15), and was moderate-to-highly centralized around ATPR. Most organizations were aware of AA (89%) and AC 20/20 (78%), however more were using AA (67%) compared to AC 20/20 (33%). Central ALOs in the funding network were more likely to use AA and AC 20/20. Central ALOs in the coordination network were more likely to use AC 20/20, but not AA. Increasing formal and informal relationships between organizations and integrating disconnected or peripheral organizations could increase the capacity of the

Tunable deformation modes shape contractility in active biopolymer networks

Science.gov (United States)

Stam, Samantha; Banerjee, Shiladitya; Weirich, Kim; Freedman, Simon; Dinner, Aaron; Gardel, Margaret

Biological polymer-based materials remodel under active, molecular motor-driven forces to perform diverse physiological roles, such as force transmission and spatial self-organization. Critical to understanding these biomaterials is elucidating the role of microscopic polymer deformations, such as stretching, bending, buckling, and relative sliding, on material remodeling. Here, we report that the shape of motor-driven deformations can be used to identify microscopic deformation modes and determine how they propagate to longer length scales. In cross-linked actin networks with sufficiently low densities of the motor protein myosin II, microscopic network deformations are predominantly uniaxial, or dominated by sliding. However, longer-wavelength modes are mostly biaxial, or dominated by bending and buckling, indicating that deformations with uniaxial shapes do not propagate across length scales significantly larger than that of individual polymers. As the density of myosin II is increased, biaxial modes dominate on all length scales we examine due to buildup of sufficient stress to produce smaller-wavelength buckling. In contrast, when we construct networks from unipolar, rigid actin bundles, we observe uniaxial, sliding-based contractions on 1 to 100 μm length scales. Our results demonstrate the biopolymer mechanics can be used to tune deformation modes which, in turn, control shape changes in active materials.

Fitness, but not physical activity, is related to functional integrity of brain networks associated with aging.

Science.gov (United States)

Voss, Michelle W; Weng, Timothy B; Burzynska, Agnieszka Z; Wong, Chelsea N; Cooke, Gillian E; Clark, Rachel; Fanning, Jason; Awick, Elizabeth; Gothe, Neha P; Olson, Erin A; McAuley, Edward; Kramer, Arthur F

2016-05-01

Greater physical activity and cardiorespiratory fitness are associated with reduced age-related cognitive decline and lower risk for dementia. However, significant gaps remain in the understanding of how physical activity and fitness protect the brain from adverse effects of brain aging. The primary goal of the current study was to empirically evaluate the independent relationships between physical activity and fitness with functional brain health among healthy older adults, as measured by the functional connectivity of cognitively and clinically relevant resting state networks. To build context for fitness and physical activity associations in older adults, we first demonstrate that young adults have greater within-network functional connectivity across a broad range of cortical association networks. Based on these results and previous research, we predicted that individual differences in fitness and physical activity would be most strongly associated with functional integrity of the networks most sensitive to aging. Consistent with this prediction, and extending on previous research, we showed that cardiorespiratory fitness has a positive relationship with functional connectivity of several cortical networks associated with age-related decline, and effects were strongest in the default mode network (DMN). Furthermore, our results suggest that the positive association of fitness with brain function can occur independent of habitual physical activity. Overall, our findings provide further support that cardiorespiratory fitness is an important factor in moderating the adverse effects of aging on cognitively and clinically relevant functional brain networks. Copyright © 2015 Elsevier Inc. All rights reserved.

Fitness, but not physical activity, is related to functional integrity of brain networks associated with aging

Science.gov (United States)

Voss, Michelle W.; Weng, Timothy B.; Burzynska, Agnieszka Z.; Wong, Chelsea N.; Cooke, Gillian E.; Clark, Rachel; Fanning, Jason; Awick, Elizabeth; Gothe, Neha P.; Olson, Erin A.; McAuley, Edward; Kramer, Arthur F.

2015-01-01

Greater physical activity and cardiorespiratory fitness are associated with reduced age-related cognitive decline and lower risk for dementia. However, significant gaps remain in the understanding of how physical activity and fitness protect the brain from adverse effects of brain aging. The primary goal of the current study was to empirically evaluate the independent relationships between physical activity and fitness with functional brain health among healthy older adults, as measured by the functional connectivity of cognitively and clinically relevant resting state networks. To build context for fitness and physical activity associations in older adults, we first demonstrate that young adults have greater within-network functional connectivity across a broad range of cortical association networks. Based on these results and previous research, we predicted that individual differences in fitness and physical activity would be most strongly associated with functional integrity of the networks most sensitive to aging. Consistent with this prediction, and extending on previous research, we showed that cardiorespiratory fitness has a positive relationship with functional connectivity of several cortical networks associated with age-related decline, and effects were strongest in the Default Mode Network (DMN). Furthermore, our results suggest that the positive association of fitness with brain function can occur independent of habitual physical activity. Overall, our findings provide further support that cardiorespiratory fitness is an important factor in moderating the adverse effects of aging on cognitively and clinically relevant functional brain networks. PMID:26493108

Combined effect of chemical and electrical synapses in Hindmarsh-Rose neural networks on synchronization and the rate of information.

Science.gov (United States)

Baptista, M S; Moukam Kakmeni, F M; Grebogi, C

2010-09-01

In this work we studied the combined action of chemical and electrical synapses in small networks of Hindmarsh-Rose (HR) neurons on the synchronous behavior and on the rate of information produced (per time unit) by the networks. We show that if the chemical synapse is excitatory, the larger the chemical synapse strength used the smaller the electrical synapse strength needed to achieve complete synchronization, and for moderate synaptic strengths one should expect to find desynchronous behavior. Otherwise, if the chemical synapse is inhibitory, the larger the chemical synapse strength used the larger the electrical synapse strength needed to achieve complete synchronization, and for moderate synaptic strengths one should expect to find synchronous behaviors. Finally, we show how to calculate semianalytically an upper bound for the rate of information produced per time unit (Kolmogorov-Sinai entropy) in larger networks. As an application, we show that this upper bound is linearly proportional to the number of neurons in a network whose neurons are highly connected.

Spontaneous brain network activity: Analysis of its temporal complexity

Directory of Open Access Journals (Sweden)

Mangor Pedersen

2017-06-01

Full Text Available The brain operates in a complex way. The temporal complexity underlying macroscopic and spontaneous brain network activity is still to be understood. In this study, we explored the brain’s complexity by combining functional connectivity, graph theory, and entropy analyses in 25 healthy people using task-free functional magnetic resonance imaging. We calculated the pairwise instantaneous phase synchrony between 8,192 brain nodes for a total of 200 time points. This resulted in graphs for which time series of clustering coefficients (the “cliquiness” of a node and participation coefficients (the between-module connectivity of a node were estimated. For these two network metrics, sample entropy was calculated. The procedure produced a number of results: (1 Entropy is higher for the participation coefficient than for the clustering coefficient. (2 The average clustering coefficient is negatively related to its associated entropy, whereas the average participation coefficient is positively related to its associated entropy. (3 The level of entropy is network-specific to the participation coefficient, but not to the clustering coefficient. High entropy for the participation coefficient was observed in the default-mode, visual, and motor networks. These results were further validated using an independent replication dataset. Our work confirms that brain networks are temporally complex. Entropy is a good candidate metric to explore temporal network alterations in diseases with paroxysmal brain disruptions, including schizophrenia and epilepsy. In recent years, connectomics has provided significant insights into the topological complexity of brain networks. However, the temporal complexity of brain networks still remains somewhat poorly understood. In this study we used entropy analysis to demonstrate that the properties of network segregation (the clustering coefficient and integration (the participation coefficient are temporally complex

Taurine activates GABAergic networks in the neocortex of immature mice

Directory of Open Access Journals (Sweden)

Bogdan Aurel Sava

2014-02-01

Full Text Available Although it has been suggested that taurine is the main endogenous neurotransmitter acting on glycine receptors, the implications of glycine receptor-mediated taurine actions on immature neocortical networks have not been addressed yet. To investigate the influence of taurine on the excitability of neuronal networks in the immature neocortex, we performed whole-cell patch-clamp recordings from visually identified pyramidal neurons and interneurons in coronal slices from C57Bl/6 and GAD67-GFP transgenic mice (postnatal days 2-4. In 46 % of the pyramidal neurons bath-application of taurine at concentrations ≥ 300 mM significantly enhanced the frequency of postsynaptic currents (PSCs by 744.3 ± 93.8 % (n = 120 cells. This taurine-induced increase of PSC frequency was abolished by 0.2 mM tetrodotoxine, 1 mM strychnine or 3 mM gabazine, but was unaffected by the glutamatergic antagonists 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX and (± R(--3-(2-carboxypiperazine-4-yl-propyl-1-phosphonic acid (CPP, suggesting that taurine specifically activates GABAergic network activity projecting to pyramidal neurons. Cell-attached recordings revealed that taurine enhanced the frequency of action potentials in pyramidal neurons, indicating an excitatory action of the GABAergic PSCs. In order to identify the presynaptic targets of taurine we demonstrate that bath application of taurine induced in GAD67-GFP labeled interneurons an inward current that is mainly mediated by glycine receptors and can generate action potentials in these cells. We conclude from these results that taurine can enhance network excitability in the immature neocortex by selectively activating GABAergic interneurons via interactions with glycine receptors.