WorldWideScience

Sample records for network memory engine

  1. Using Long-Short-Term-Memory Recurrent Neural Networks to Predict Aviation Engine Vibrations

    Science.gov (United States)

    ElSaid, AbdElRahman Ahmed

    This thesis examines building viable Recurrent Neural Networks (RNN) using Long Short Term Memory (LSTM) neurons to predict aircraft engine vibrations. The different networks are trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. RNNs can provide a more generalizable and robust method for prediction over analytical calculations of engine vibration, as analytical calculations must be solved iteratively based on specific empirical engine parameters, and this database contains multiple types of engines. Further, LSTM RNNs provide a "memory" of the contribution of previous time series data which can further improve predictions of future vibration values. LSTM RNNs were used over traditional RNNs, as those suffer from vanishing/exploding gradients when trained with back propagation. The study managed to predict vibration values for 1, 5, 10, and 20 seconds in the future, with 2.84% 3.3%, 5.51% and 10.19% mean absolute error, respectively. These neural networks provide a promising means for the future development of warning systems so that suitable actions can be taken before the occurrence of excess vibration to avoid unfavorable situations during flight.

  2. NASA Engineering Network (NEN)

    Science.gov (United States)

    Topousis, Daria; Trevarthen, Ellie; Yew, Manson

    2008-01-01

    This slide presentation reviews the NASA Engineering Network (NEN). NEN is designed to search documents over multiple repositories, submit and browse NASA Lessons Learned, collaborate and share ideas with other engineers via communities of practice, access resources from one portal, and find subject matter experts via the People, Organizations, Projects, Skills (POPS) locator.

  3. 78 FR 775 - Goodman Networks, Inc. Core Network Engineering (Deployment Engineering) Division Alpharetta, GA...

    Science.gov (United States)

    2013-01-04

    ... Employment and Training Administration Goodman Networks, Inc. Core Network Engineering (Deployment Engineering) Division Alpharetta, GA; Goodman Networks, Inc. Core Network Engineering (Deployment Engineering) Division Hunt Valley, MD; Goodman Networks, Inc. Core Network Engineering (Deployment Engineering) Division...

  4. 78 FR 12359 - Goodman Networks, Inc., Core Network Engineering (Deployment Engineering) Division Including...

    Science.gov (United States)

    2013-02-22

    ... Employment and Training Administration Goodman Networks, Inc., Core Network Engineering (Deployment Engineering) Division Including Workers in the Core Network Engineering (Deployment Engineering) Division in... of Goodman Networks, Inc., Core Network Engineering (Deployment Engineering) Division, including...

  5. Software and Network Engineering

    CERN Document Server

    2012-01-01

    The series "Studies in Computational Intelligence" (SCI) publishes new developments and advances in the various areas of computational intelligence – quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life science, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Critical to both contributors and readers are the short publication time and world-wide distribution - this permits a rapid and broad dissemination of research results.   The purpose of the first ACIS International Symposium on Software and Network Engineering held on Decembe...

  6. Network resiliency through memory health monitoring and proactive management

    Science.gov (United States)

    Andrade Costa, Carlos H.; Cher, Chen-Yong; Park, Yoonho; Rosenburg, Bryan S.; Ryu, Kyung D.

    2017-11-21

    A method for managing a network queue memory includes receiving sensor information about the network queue memory, predicting a memory failure in the network queue memory based on the sensor information, and outputting a notification through a plurality of nodes forming a network and using the network queue memory, the notification configuring communications between the nodes.

  7. Associative memory in phasing neuron networks

    Energy Technology Data Exchange (ETDEWEB)

    Nair, Niketh S [ORNL; Bochove, Erik J. [United States Air Force Research Laboratory, Kirtland Air Force Base; Braiman, Yehuda [ORNL

    2014-01-01

    We studied pattern formation in a network of coupled Hindmarsh-Rose model neurons and introduced a new model for associative memory retrieval using networks of Kuramoto oscillators. Hindmarsh-Rose Neural Networks can exhibit a rich set of collective dynamics that can be controlled by their connectivity. Specifically, we showed an instance of Hebb's rule where spiking was correlated with network topology. Based on this, we presented a simple model of associative memory in coupled phase oscillators.

  8. Industrial Engineering: creating a network!

    OpenAIRE

    Prado-Prado, José Carlos

    2016-01-01

    [EN] This paper presents a brief history of the Industrial Engineering Conference (CIO), and specially reinforces the role of the CIOs as a forum for building a network and creating log-term relationships Prado-Prado, JC. (2016). Industrial Engineering: creating a network!. International Journal of Production Management and Engineering. 4(2):41-42. doi:10.4995/ijpme.2016.5964. 41 42 4 2

  9. Engineering technology for networks

    Science.gov (United States)

    Paul, Arthur S.; Benjamin, Norman

    1991-01-01

    Space Network (SN) modeling and evaluation are presented. The following tasks are included: Network Modeling (developing measures and metrics for SN, modeling of the Network Control Center (NCC), using knowledge acquired from the NCC to model the SNC, and modeling the SN); and Space Network Resource scheduling.

  10. Electronic device aspects of neural network memories

    Science.gov (United States)

    Lambe, J.; Moopenn, A.; Thakoor, A. P.

    1985-01-01

    The basic issues related to the electronic implementation of the neural network model (NNM) for content addressable memories are examined. A brief introduction to the principles of the NNM is followed by an analysis of the information storage of the neural network in the form of a binary connection matrix and the recall capability of such matrix memories based on a hardware simulation study. In addition, materials and device architecture issues involved in the future realization of such networks in VLSI-compatible ultrahigh-density memories are considered. A possible space application of such devices would be in the area of large-scale information storage without mechanical devices.

  11. Properties of a memory network in psychology

    Science.gov (United States)

    Wedemann, Roseli S.; Donangelo, Raul; de Carvalho, Luís A. V.

    2007-12-01

    We have previously described neurotic psychopathology and psychoanalytic working-through by an associative memory mechanism, based on a neural network model, where memory was modelled by a Boltzmann machine (BM). Since brain neural topology is selectively structured, we simulated known microscopic mechanisms that control synaptic properties, showing that the network self-organizes to a hierarchical, clustered structure. Here, we show some statistical mechanical properties of the complex networks which result from this self-organization. They indicate that a generalization of the BM may be necessary to model memory.

  12. Systems engineering technology for networks

    Science.gov (United States)

    1994-01-01

    The report summarizes research pursued within the Systems Engineering Design Laboratory at Virginia Polytechnic Institute and State University between May 16, 1993 and January 31, 1994. The project was proposed in cooperation with the Computational Science and Engineering Research Center at Howard University. Its purpose was to investigate emerging systems engineering tools and their applicability in analyzing the NASA Network Control Center (NCC) on the basis of metrics and measures.

  13. Neural Network Model of memory retrieval

    Directory of Open Access Journals (Sweden)

    Stefano eRecanatesi

    2015-12-01

    Full Text Available Human memory can store large amount of information. Nevertheless, recalling is often achallenging task. In a classical free recall paradigm, where participants are asked to repeat abriefly presented list of words, people make mistakes for lists as short as 5 words. We present amodel for memory retrieval based on a Hopfield neural network where transition between itemsare determined by similarities in their long-term memory representations. Meanfield analysis ofthe model reveals stable states of the network corresponding (1 to single memory representationsand (2 intersection between memory representations. We show that oscillating feedback inhibitionin the presence of noise induces transitions between these states triggering the retrieval ofdifferent memories. The network dynamics qualitatively predicts the distribution of time intervalsrequired to recall new memory items observed in experiments. It shows that items having largernumber of neurons in their representation are statistically easier to recall and reveals possiblebottlenecks in our ability of retrieving memories. Overall, we propose a neural network model ofinformation retrieval broadly compatible with experimental observations and is consistent with ourrecent graphical model (Romani et al., 2013.

  14. Launch Control Network Engineer

    Science.gov (United States)

    Medeiros, Samantha

    2017-01-01

    The Spaceport Command and Control System (SCCS) is being built at the Kennedy Space Center in order to successfully launch NASA’s revolutionary vehicle that allows humans to explore further into space than ever before. During my internship, I worked with the Network, Firewall, and Hardware teams that are all contributing to the huge SCCS network project effort. I learned the SCCS network design and the several concepts that are running in the background. I also updated and designed documentation for physical networks that are part of SCCS. This includes being able to assist and build physical installations as well as configurations. I worked with the network design for vehicle telemetry interfaces to the Launch Control System (LCS); this allows the interface to interact with other systems at other NASA locations. This network design includes the Space Launch System (SLS), Interim Cryogenic Propulsion Stage (ICPS), and the Orion Multipurpose Crew Vehicle (MPCV). I worked on the network design and implementation in the Customer Avionics Interface Development and Analysis (CAIDA) lab.

  15. Episodic memory in aspects of large-scale brain networks

    Directory of Open Access Journals (Sweden)

    Woorim eJeong

    2015-08-01

    Full Text Available Understanding human episodic memory in aspects of large-scale brain networks has become one of the central themes in neuroscience over the last decade. Traditionally, episodic memory was regarded as mostly relying on medial temporal lobe (MTL structures. However, recent studies have suggested involvement of more widely distributed cortical network and the importance of its interactive roles in the memory process. Both direct and indirect neuro-modulations of the memory network have been tried in experimental treatments of memory disorders. In this review, we focus on the functional organization of the MTL and other neocortical areas in episodic memory. Task-related neuroimaging studies together with lesion studies suggested that specific sub-regions of the MTL are responsible for specific components of memory. However, recent studies have emphasized that connectivity within MTL structures and even their network dynamics with other cortical areas are essential in the memory process. Resting-state functional network studies also have revealed that memory function is subserved by not only the MTL system but also a distributed network, particularly the default-mode network. Furthermore, researchers have begun to investigate memory networks throughout the entire brain not restricted to the specific resting-state network. Altered patterns of functional connectivity among distributed brain regions were observed in patients with memory impairments. Recently, studies have shown that brain stimulation may impact memory through modulating functional networks, carrying future implications of a novel interventional therapy for memory impairment.

  16. Episodic memory in aspects of large-scale brain networks

    Science.gov (United States)

    Jeong, Woorim; Chung, Chun Kee; Kim, June Sic

    2015-01-01

    Understanding human episodic memory in aspects of large-scale brain networks has become one of the central themes in neuroscience over the last decade. Traditionally, episodic memory was regarded as mostly relying on medial temporal lobe (MTL) structures. However, recent studies have suggested involvement of more widely distributed cortical network and the importance of its interactive roles in the memory process. Both direct and indirect neuro-modulations of the memory network have been tried in experimental treatments of memory disorders. In this review, we focus on the functional organization of the MTL and other neocortical areas in episodic memory. Task-related neuroimaging studies together with lesion studies suggested that specific sub-regions of the MTL are responsible for specific components of memory. However, recent studies have emphasized that connectivity within MTL structures and even their network dynamics with other cortical areas are essential in the memory process. Resting-state functional network studies also have revealed that memory function is subserved by not only the MTL system but also a distributed network, particularly the default-mode network (DMN). Furthermore, researchers have begun to investigate memory networks throughout the entire brain not restricted to the specific resting-state network (RSN). Altered patterns of functional connectivity (FC) among distributed brain regions were observed in patients with memory impairments. Recently, studies have shown that brain stimulation may impact memory through modulating functional networks, carrying future implications of a novel interventional therapy for memory impairment. PMID:26321939

  17. Memory in cultured cortical networks

    NARCIS (Netherlands)

    le Feber, Jakob; Witteveen, T.; Stoyanova, Irina; Rutten, Wim

    2012-01-01

    Tetanic stimulation was applied to affect network connectivity, as assessed by conditional firing probabilities. We showed that the first period(s) of titanic stimulation at a certain electrode significantly alters functional connectivity, but subsequent, identical stimuli do not. These findings

  18. Memory-optimal neural network approximation

    Science.gov (United States)

    Bölcskei, Helmut; Grohs, Philipp; Kutyniok, Gitta; Petersen, Philipp

    2017-08-01

    We summarize the main results of a recent theory-developed by the authors-establishing fundamental lower bounds on the connectivity and memory requirements of deep neural networks as a function of the complexity of the function class to be approximated by the network. These bounds are shown to be achievable. Specifically, all function classes that are optimally approximated by a general class of representation systems-so-called affine systems-can be approximated by deep neural networks with minimal connectivity and memory requirements. Affine systems encompass a wealth of representation systems from applied harmonic analysis such as wavelets, shearlets, ridgelets, α-shearlets, and more generally α-molecules. This result elucidates a remarkable universality property of deep neural networks and shows that they achieve the optimum approximation properties of all affine systems combined. Finally, we present numerical experiments demonstrating that the standard stochastic gradient descent algorithm generates deep neural networks which provide close-to-optimal approximation rates at minimal connectivity. Moreover, stochastic gradient descent is found to actually learn approximations that are sparse in the representation system optimally sparsifying the function class the network is trained on.

  19. Artificial neural networks as quantum associative memory

    Science.gov (United States)

    Hamilton, Kathleen; Schrock, Jonathan; Imam, Neena; Humble, Travis

    We present results related to the recall accuracy and capacity of Hopfield networks implemented on commercially available quantum annealers. The use of Hopfield networks and artificial neural networks as content-addressable memories offer robust storage and retrieval of classical information, however, implementation of these models using currently available quantum annealers faces several challenges: the limits of precision when setting synaptic weights, the effects of spurious spin-glass states and minor embedding of densely connected graphs into fixed-connectivity hardware. We consider neural networks which are less than fully-connected, and also consider neural networks which contain multiple sparsely connected clusters. We discuss the effect of weak edge dilution on the accuracy of memory recall, and discuss how the multiple clique structure affects the storage capacity. Our work focuses on storage of patterns which can be embedded into physical hardware containing n States Department of Defense and used resources of the Computational Research and Development Programs as Oak Ridge National Laboratory under Contract No. DE-AC0500OR22725 with the U. S. Department of Energy.

  20. The hippocampus: hub of brain network communication for memory.

    NARCIS (Netherlands)

    Battaglia, F.P.; Benchenane, K.; Sirota, A.; Pennartz, C.M.A.; Wiener, S.I.

    2011-01-01

    A complex brain network, centered on the hippocampus, supports episodic memories throughout their lifetimes. Classically, upon memory encoding during active behavior, hippocampal activity is dominated by theta oscillations (6-10Hz). During inactivity, hippocampal neurons burst synchronously,

  1. Distributed Shared Memory for the Cell Broadband Engine (DSMCBE)

    DEFF Research Database (Denmark)

    Larsen, Morten Nørgaard; Skovhede, Kenneth; Vinter, Brian

    2009-01-01

    in and out of non-coherent local storage blocks for each special processor element. In this paper we present a software library, namely the Distributed Shared Memory for the Cell Broadband Engine (DSMCBE). By using techniques known from distributed shared memory DSMCBE allows programmers to program the CELL...

  2. Encoding Mechano-Memories in Actin Networks

    Science.gov (United States)

    Foucard, Louis; Majumdar, Sayantan; Levine, Alex; Gardel, Margaret

    The ability of cells to sense and adapt to external mechanical stimuli is vital to many of its biological functions. A critical question is therefore to understand how mechanosensory mechanisms arise in living matter, with implications in both cell biology and smart materials design. Experimental work has demonstrated that the mechanical properties of semiflexible actin networks in Eukaryotic cells can be modulated (either transiently or irreversibly) via the application of external forces. Previous work has also shown with a combination of numerical simulations and analytic calculations shows that the broken rotational symmetry of the filament orientational distribution in semiflexible networks leads to dramatic changes in the mechanical response. Here we demonstrate with a combination of numerical and analytic calculations that the observed long-lived mechano-memory in the actin networks arise from changes in the nematic order of the constituent filaments. These stress-induced changes in network topology relax slowly under zero stress and can be observed through changes in the nonlinear mechanics. Our results provide a strategy for designing a novel class of materials and demonstrate a new putative mechanism of mechanical sensing in eukaryotic cells.

  3. A Time-predictable Memory Network-on-Chip

    DEFF Research Database (Denmark)

    Schoeberl, Martin; Chong, David VH; Puffitsch, Wolfgang

    2014-01-01

    arbitration and access for chip-multiprocessors. The memory network-on-chip is organized as a tree with time-division multiplexing (TDM) of accesses to the shared memory. The TDM based arbitration completely decouples processor cores and allows WCET analysis of the memory accesses on individual cores without...

  4. Mnemonic Training Reshapes Brain Networks to Support Superior Memory

    NARCIS (Netherlands)

    Dresler, M.; Shirer, W.R.; Konrad, B.N.; Muller, N.C.J.; Wagner, I.; Fernandez, G.S.E.; Czisch, M.; Greicius, M.D.

    2017-01-01

    Memory skills strongly differ across the general population; however, little is known about the brain characteristics supporting superior memory performance. Here we assess functional brain network organization of 23 of the world's most successful memory athletes and matched controls with fMRI

  5. Dopamine D1 signaling organizes network dynamics underlying working memory.

    Science.gov (United States)

    Roffman, Joshua L; Tanner, Alexandra S; Eryilmaz, Hamdi; Rodriguez-Thompson, Anais; Silverstein, Noah J; Ho, New Fei; Nitenson, Adam Z; Chonde, Daniel B; Greve, Douglas N; Abi-Dargham, Anissa; Buckner, Randy L; Manoach, Dara S; Rosen, Bruce R; Hooker, Jacob M; Catana, Ciprian

    2016-06-01

    Local prefrontal dopamine signaling supports working memory by tuning pyramidal neurons to task-relevant stimuli. Enabled by simultaneous positron emission tomography-magnetic resonance imaging (PET-MRI), we determined whether neuromodulatory effects of dopamine scale to the level of cortical networks and coordinate their interplay during working memory. Among network territories, mean cortical D1 receptor densities differed substantially but were strongly interrelated, suggesting cross-network regulation. Indeed, mean cortical D1 density predicted working memory-emergent decoupling of the frontoparietal and default networks, which respectively manage task-related and internal stimuli. In contrast, striatal D1 predicted opposing effects within these two networks but no between-network effects. These findings specifically link cortical dopamine signaling to network crosstalk that redirects cognitive resources to working memory, echoing neuromodulatory effects of D1 signaling on the level of cortical microcircuits.

  6. Teletraffic engineering and network planning

    DEFF Research Database (Denmark)

    Iversen, Villy Bæk

    This book covers the basic theory of teletrac engineering. The mathematical backgroundrequired is elementary probability theory. The purpose of the book is to enable engineers tounderstand ITU{T recommendations on trac engineering, evaluate tools and methods, andkeep up-to-date with new practices...

  7. Application of neural networks in coastal engineering

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.

    methods. That is why it is becoming popular in various fields including coastal engineering. Waves and tides will play important roles in coastal erosion or accretion. This paper briefly describes the back-propagation neural networks and its application...

  8. Memory dynamics in attractor networks with saliency weights.

    Science.gov (United States)

    Tang, Huajin; Li, Haizhou; Yan, Rui

    2010-07-01

    Memory is a fundamental part of computational systems like the human brain. Theoretical models identify memories as attractors of neural network activity patterns based on the theory that attractor (recurrent) neural networks are able to capture some crucial characteristics of memory, such as encoding, storage, retrieval, and long-term and working memory. In such networks, long-term storage of the memory patterns is enabled by synaptic strengths that are adjusted according to some activity-dependent plasticity mechanisms (of which the most widely recognized is the Hebbian rule) such that the attractors of the network dynamics represent the stored memories. Most of previous studies on associative memory are focused on Hopfield-like binary networks, and the learned patterns are often assumed to be uncorrelated in a way that minimal interactions between memories are facilitated. In this letter, we restrict our attention to a more biological plausible attractor network model and study the neuronal representations of correlated patterns. We have examined the role of saliency weights in memory dynamics. Our results demonstrate that the retrieval process of the memorized patterns is characterized by the saliency distribution, which affects the landscape of the attractors. We have established the conditions that the network state converges to unique memory and multiple memories. The analytical result also holds for other cases for variable coding levels and nonbinary levels, indicating a general property emerging from correlated memories. Our results confirmed the advantage of computing with graded-response neurons over binary neurons (i.e., reducing of spurious states). It was also found that the nonuniform saliency distribution can contribute to disappearance of spurious states when they exit.

  9. Design and implementation of a random neural network routing engine.

    Science.gov (United States)

    Kocak, T; Seeber, J; Terzioglu, H

    2003-01-01

    Random neural network (RNN) is an analytically tractable spiked neural network model that has been implemented in software for a wide range of applications for over a decade. This paper presents the hardware implementation of the RNN model. Recently, cognitive packet networks (CPN) is proposed as an alternative packet network architecture where there is no routing table, instead the RNN based reinforcement learning is used to route packets. Particularly, we describe implementation details for the RNN based routing engine of a CPN network processor chip: the smart packet processor (SPP). The SPP is a dual port device that stores, modifies, and interprets the defining characteristics of multiple RNN models. In addition to hardware design improvements over the software implementation such as the dual access memory, output calculation step, and reduced output calculation module, this paper introduces a major modification to the reinforcement learning algorithm used in the original CPN specification such that the number of weight terms are reduced from 2n/sup 2/ to 2n. This not only yields significant memory savings, but it also simplifies the calculations for the steady state probabilities (neuron outputs in RNN). Simulations have been conducted to confirm the proper functionality for the isolated SPP design as well as for the multiple SPP's in a networked environment.

  10. Generalized memory associativity in a network model for the neuroses

    Science.gov (United States)

    Wedemann, Roseli S.; Donangelo, Raul; de Carvalho, Luís A. V.

    2009-03-01

    We review concepts introduced in earlier work, where a neural network mechanism describes some mental processes in neurotic pathology and psychoanalytic working-through, as associative memory functioning, according to the findings of Freud. We developed a complex network model, where modules corresponding to sensorial and symbolic memories interact, representing unconscious and conscious mental processes. The model illustrates Freud's idea that consciousness is related to symbolic and linguistic memory activity in the brain. We have introduced a generalization of the Boltzmann machine to model memory associativity. Model behavior is illustrated with simulations and some of its properties are analyzed with methods from statistical mechanics.

  11. Information Flows in Networked Engineering Design Projects

    DEFF Research Database (Denmark)

    Parraguez, Pedro; Maier, Anja

    networks at the project level or in studying the social networks that deliver the “actual information flow”. In this paper we propose and empirically test a model and method that integrates both social and activity networks into one compact representation, allowing to compare actual and required......Complex engineering design projects need to manage simultaneously multiple information flows across design activities associated with different areas of the design process. Previous research on this area has mostly focused on either analysing the “required information flows” through activity...... information flows between design spaces, and to assess the influence that these misalignments could have on the performance of engineering design projects....

  12. Digital associative memory neural network with optical learning capability

    Science.gov (United States)

    Watanabe, Minoru; Ohtsubo, Junji

    1994-12-01

    A digital associative memory neural network system with optical learning and recalling capabilities is proposed by using liquid crystal television spatial light modulators and an Optic RAM detector. In spite of the drawback of the limited memory capacity compared with optical analogue associative memory neural network, the proposed optical digital neural network has the advantage of all optical learning and recalling capabilities, thus an all optics network system is easily realized. Some experimental results of the learning and the recalling for character recognitions are presented. This new optical architecture offers compactness of the system and the fast learning and recalling properties. Based on the results, the practical system for the implementation of a faster optical digital associative memory neural network system with ferro-electric liquid crystal SLMs is also proposed.

  13. A parietal memory network revealed by multiple MRI methods.

    Science.gov (United States)

    Gilmore, Adrian W; Nelson, Steven M; McDermott, Kathleen B

    2015-09-01

    The manner by which the human brain learns and recognizes stimuli is a matter of ongoing investigation. Through examination of meta-analyses of task-based functional MRI and resting state functional connectivity MRI, we identified a novel network strongly related to learning and memory. Activity within this network at encoding predicts subsequent item memory, and at retrieval differs for recognized and unrecognized items. The direction of activity flips as a function of recent history: from deactivation for novel stimuli to activation for stimuli that are familiar due to recent exposure. We term this network the 'parietal memory network' (PMN) to reflect its broad involvement in human memory processing. We provide a preliminary framework for understanding the key functional properties of the network. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. Increased functional connectivity within memory networks following memory rehabilitation in multiple sclerosis.

    Science.gov (United States)

    Leavitt, Victoria M; Wylie, Glenn R; Girgis, Peter A; DeLuca, John; Chiaravalloti, Nancy D

    2014-09-01

    Identifying effective behavioral treatments to improve memory in persons with learning and memory impairment is a primary goal for neurorehabilitation researchers. Memory deficits are the most common cognitive symptom in multiple sclerosis (MS), and hold negative professional and personal consequences for people who are often in the prime of their lives when diagnosed. A 10-session behavioral treatment, the modified Story Memory Technique (mSMT), was studied in a randomized, placebo-controlled clinical trial. Behavioral improvements and increased fMRI activation were shown after treatment. Here, connectivity within the neural networks underlying memory function was examined with resting-state functional connectivity (RSFC) in a subset of participants from the clinical trial. We hypothesized that the treatment would result in increased integrity of connections within two primary memory networks of the brain, the hippocampal memory network, and the default network (DN). Seeds were placed in left and right hippocampus, and the posterior cingulate cortex. Increased connectivity was found between left hippocampus and cortical regions specifically involved in memory for visual imagery, as well as among critical hubs of the DN. These results represent the first evidence for efficacy of a behavioral intervention to impact the integrity of neural networks subserving memory functions in persons with MS.

  15. Material Engineering for Phase Change Memory

    Science.gov (United States)

    Cabrera, David M.

    As semiconductor devices continue to scale downward, and portable consumer electronics become more prevalent there is a need to develop memory technology that will scale with devices and use less energy, while maintaining performance. One of the leading prototypical memories that is being investigated is phase change memory. Phase change memory (PCM) is a non-volatile memory composed of 1 transistor and 1 resistor. The resistive structure includes a memory material alloy which can change between amorphous and crystalline states repeatedly using current/voltage pulses of different lengths and magnitudes. The most widely studied PCM materials are chalcogenides - Germanium-Antimony-Tellerium (GST) with Ge2Sb2Te3 and Germanium-Tellerium (GeTe) being some of the most popular stochiometries. As these cells are scaled downward, the current/voltage needed to switch these materials becomes comparable to the voltage needed to sense the cell's state. The International Roadmap for Semiconductors aims to raise the threshold field of these devices from 66.6 V/mum to be at least 375 V/mum for the year 2024. These cells are also prone to resistance drift between states, leading to bit corruption and memory loss. Phase change material properties are known to influence PCM device performance such as crystallization temperature having an effect on data retention and litetime, while resistivity values in the amorphous and crystalline phases have an effect on the current/voltage needed to write/erase the cell. Addition of dopants is also known to modify the phase change material parameters. The materials G2S2T5, GeTe, with dopants - nitrogen, silicon, titanium, and aluminum oxide and undoped Gallium-Antimonide (GaSb) are studied for these desired characteristics. Thin films of these compositions are deposited via physical vapor deposition at IBM Watson Research Center. Crystallization temperatures are investigated using time resolved x-ray diffraction at Brookhaven National Laboratory

  16. Mnemonic Training Reshapes Brain Networks to Support Superior Memory.

    Science.gov (United States)

    Dresler, Martin; Shirer, William R; Konrad, Boris N; Müller, Nils C J; Wagner, Isabella C; Fernández, Guillén; Czisch, Michael; Greicius, Michael D

    2017-03-08

    Memory skills strongly differ across the general population; however, little is known about the brain characteristics supporting superior memory performance. Here we assess functional brain network organization of 23 of the world's most successful memory athletes and matched controls with fMRI during both task-free resting state baseline and active memory encoding. We demonstrate that, in a group of naive controls, functional connectivity changes induced by 6 weeks of mnemonic training were correlated with the network organization that distinguishes athletes from controls. During rest, this effect was mainly driven by connections between rather than within the visual, medial temporal lobe and default mode networks, whereas during task it was driven by connectivity within these networks. Similarity with memory athlete connectivity patterns predicted memory improvements up to 4 months after training. In conclusion, mnemonic training drives distributed rather than regional changes, reorganizing the brain's functional network organization to enable superior memory performance. Copyright © 2017 Elsevier Inc. All rights reserved.

  17. Canonical correlation between LFP network and spike network during working memory task in rat.

    Science.gov (United States)

    Yi, Hu; Zhang, Xiaofan; Bai, Wenwen; Liu, Tiaotiao; Tian, Xin

    2015-08-01

    Working memory refers to a system to temporary holding and manipulation of information. Previous studies suggested that local field potentials (LFPs) and spikes as well as their coordination provide potential mechanism of working memory. Popular methods for LFP-spike coordination only focus on the two modality signals, isolating each channel from multi-channel data, ignoring the entirety of the networked brain. Therefore, we investigated the coordination between the LFP network and spike network to achieve a better understanding of working memory. Multi-channel LFPs and spikes were simultaneously recorded in rat prefrontal cortex via microelectrode array during a Y-maze working memory task. Functional connectivity in the LFP network and spike network was respectively estimated by the directed transfer function (DTF) and maximum likelihood estimation (MLE). Then the coordination between the two networks was quantified via canonical correlation analysis (CCA). The results show that the canonical correlation (CC) varied during the working memory task. The CC-curve peaked before the choice point, describing the coordination between LFP network and spike network enhanced greatly. The CC value in working memory showed a significant higher level than inter-trial interval. Our results indicate that the enhanced canonical correlation between the LFP network and spike network may provide a potential network integration mechanism for working memory. Copyright © 2015 Elsevier B.V. All rights reserved.

  18. Functional alterations in memory networks in early Alzheimer's disease.

    Science.gov (United States)

    Sperling, Reisa A; Dickerson, Bradford C; Pihlajamaki, Maija; Vannini, Patrizia; LaViolette, Peter S; Vitolo, Ottavio V; Hedden, Trey; Becker, J Alex; Rentz, Dorene M; Selkoe, Dennis J; Johnson, Keith A

    2010-03-01

    The hallmark clinical symptom of early Alzheimer's disease (AD) is episodic memory impairment. Recent functional imaging studies suggest that memory function is subserved by a set of distributed networks, which include both the medial temporal lobe (MTL) system and the set of cortical regions collectively referred to as the default network. Specific regions of the default network, in particular, the posteromedial cortices, including the precuneus and posterior cingulate, are selectively vulnerable to early amyloid deposition in AD. These regions are also thought to play a key role in both memory encoding and retrieval, and are strongly functionally connected to the MTL. Multiple functional magnetic resonance imaging (fMRI) studies during memory tasks have revealed alterations in these networks in patients with clinical AD. Similar functional abnormalities have been detected in subjects at-risk for AD, including those with genetic risk and older individuals with mild cognitive impairment. Recently, we and other groups have found evidence of functional alterations in these memory networks even among cognitively intact older individuals with occult amyloid pathology, detected by PET amyloid imaging. Taken together, these findings suggest that the pathophysiological process of AD exerts specific deleterious effects on these distributed memory circuits, even prior to clinical manifestations of significant memory impairment. Interestingly, some of the functional alterations seen in prodromal AD subjects have taken the form of increases in activity relative to baseline, rather than a loss of activity. It remains unclear whether these increases in fMRI activity may be compensatory to maintain memory performance in the setting of early AD pathology or instead, represent evidence of excitotoxicity and impending neuronal failure. Recent studies have also revealed disruption of the intrinsic connectivity of these networks observable even during the resting state in early AD

  19. Neural network based feed-forward high density associative memory

    Science.gov (United States)

    Daud, T.; Moopenn, A.; Lamb, J. L.; Ramesham, R.; Thakoor, A. P.

    1987-01-01

    A novel thin film approach to neural-network-based high-density associative memory is described. The information is stored locally in a memory matrix of passive, nonvolatile, binary connection elements with a potential to achieve a storage density of 10 to the 9th bits/sq cm. Microswitches based on memory switching in thin film hydrogenated amorphous silicon, and alternatively in manganese oxide, have been used as programmable read-only memory elements. Low-energy switching has been ascertained in both these materials. Fabrication and testing of memory matrix is described. High-speed associative recall approaching 10 to the 7th bits/sec and high storage capacity in such a connection matrix memory system is also described.

  20. Quantum state transfer and network engineering

    Energy Technology Data Exchange (ETDEWEB)

    Nikolopoulos, Georgios M. [Institute of Electronic Structure and Laser Foundation for Research and Technology, Hellas (Greece); Jex, Igor (ed.) [Czech Technical Univ., Prague (Czech Republic). Faculty of Nuclear Sciences and Physical Engineering

    2014-03-01

    Presents the basics of large-scale quantum information processing and networking. Covers most aspects of the problems of state transfer and quantum network engineering. Reflects the interdisciplinary nature of the field. Presents various theoretical approaches as well as possible implementations and related experiments. Faithful communication is a necessary precondition for large-scale quantum information processing and networking, irrespective of the physical platform. Thus, the problems of quantum-state transfer and quantum-network engineering have attracted enormous interest over the last years, and constitute one of the most active areas of research in quantum information processing. The present volume introduces the reader to fundamental concepts and various aspects of this exciting research area, including links to other related areas and problems. The implementation of state-transfer schemes and the engineering of quantum networks are discussed in the framework of various quantum optical and condensed matter systems, emphasizing the interdisciplinary character of the research area. Each chapter is a review of theoretical or experimental achievements on a particular topic, written by leading scientists in the field. The volume aims at both newcomers as well as experienced researchers.

  1. Memory Compression Techniques for Network Address Management in MPI

    Energy Technology Data Exchange (ETDEWEB)

    Guo, Yanfei; Archer, Charles J.; Blocksome, Michael; Parker, Scott; Bland, Wesley; Raffenetti, Ken; Balaji, Pavan

    2017-05-29

    MPI allows applications to treat processes as a logical collection of integer ranks for each MPI communicator, while internally translating these logical ranks into actual network addresses. In current MPI implementations the management and lookup of such network addresses use memory sizes that are proportional to the number of processes in each communicator. In this paper, we propose a new mechanism, called AV-Rankmap, for managing such translation. AV-Rankmap takes advantage of logical patterns in rank-address mapping that most applications naturally tend to have, and it exploits the fact that some parts of network address structures are naturally more performance critical than others. It uses this information to compress the memory used for network address management. We demonstrate that AV-Rankmap can achieve performance similar to or better than that of other MPI implementations while using significantly less memory.

  2. Crew exploration vehicle (CEV) attitude control using a neural-immunology/memory network

    Science.gov (United States)

    Weng, Liguo; Xia, Min; Wang, Wei; Liu, Qingshan

    2015-01-01

    This paper addresses the problem of the crew exploration vehicle (CEV) attitude control. CEVs are NASA's next-generation human spaceflight vehicles, and they use reaction control system (RCS) jet engines for attitude adjustment, which calls for control algorithms for firing the small propulsion engines mounted on vehicles. In this work, the resultant CEV dynamics combines both actuation and attitude dynamics. Therefore, it is highly nonlinear and even coupled with significant uncertainties. To cope with this situation, a neural-immunology/memory network is proposed. It is inspired by the human memory and immune systems. The control network does not rely on precise system dynamics information. Furthermore, the overall control scheme has a simple structure and demands much less computation as compared with most existing methods, making it attractive for real-time implementation. The effectiveness of this approach is also verified via simulation.

  3. Network Reverse Engineering Approach in Synthetic Biology

    Science.gov (United States)

    Zhang, Haoqian; Liu, Ao; Lu, Yuheng; Sheng, Ying; Wu, Qianzhu; Yin, Zhenzhen; Chen, Yiwei; Liu, Zairan; Pan, Heng; Ouyang, Qi

    2013-12-01

    Synthetic biology is a new branch of interdisciplinary science that has been developed in recent years. The main purpose of synthetic biology is to apply successful principles that have been developed in electronic and chemical engineering to develop basic biological functional modules, and through rational design, develop man-made biological systems that have predicted useful functions. Here, we discuss an important principle in rational design of functional biological circuits: the reverse engineering design. We will use a research project that was conducted at Peking University for the International Genetic Engineering Machine Competition (iGEM) to illustrate the principle: synthesis a cell which has a semi-log dose-response to the environment. Through this work we try to demonstrate the potential application of network engineering in synthetic biology.

  4. Operability engineering in the Deep Space Network

    Science.gov (United States)

    Wilkinson, Belinda

    1993-01-01

    Many operability problems exist at the three Deep Space Communications Complexes (DSCC's) of the Deep Space Network (DSN). Four years ago, the position of DSN Operability Engineer was created to provide the opportunity for someone to take a system-level approach to solving these problems. Since that time, a process has been developed for personnel and development engineers and for enforcing user interface standards in software designed for the DSCC's. Plans are for the participation of operations personnel in the product life-cycle to expand in the future.

  5. Quantum state transfer and network engineering

    CERN Document Server

    Nikolopoulos, Georgios M

    2013-01-01

    Faithful communication is a necessary precondition for large-scale quantum information processing and networking, irrespective of the physical platform. Thus, the problems of quantum-state transfer and quantum-network engineering have attracted enormous interest over the last years, and constitute one of the most active areas of research in quantum information processing. The present volume introduces the reader to fundamental concepts and various aspects of this exciting research area, including links to other related areas and problems. The implementation of state-transfer schemes and the en

  6. Meeting the memory challenges of brain-scale network simulation

    Directory of Open Access Journals (Sweden)

    Susanne eKunkel

    2012-01-01

    Full Text Available The development of high-performance simulation software is crucial for studying the brain connectome. Using connectome data to generate neurocomputational models requires software capable of coping with models on a variety of scales: from the microscale, investigating plasticity and dynamics of circuits in local networks, to the macroscale, investigating the interactions between distinct brain regions. Prior to any serious dynamical investigation, the first task of network simulations is to check the consistency of data integrated in the connectome and constrain ranges for yet unknown parameters. Thanks to distributed computing techniques, it is possible today to routinely simulate local cortical networks of around 10^5 neurons with up to 10^9 synapses on clusters and multi-processor shared-memory machines. However, brain-scale networks are one or two orders of magnitude larger than such local networks, in terms of numbers of neurons and synapses as well as in terms of computational load. Such networks have been studied in individual studies, but the underlying simulation technologies have neither been described in sufficient detail to be reproducible nor made publicly available. Here, we discover that as the network model sizes approach the regime of meso- and macroscale simulations, memory consumption on individual compute nodes becomes a critical bottleneck. This is especially relevant on modern supercomputers such as the Bluegene/P architecture where the available working memory per CPU core is rather limited. We develop a simple linear model to analyze the memory consumption of the constituent components of a neuronal simulator as a function of network size and the number of cores used. This approach has multiple benefits. The model enables identification of key contributing components to memory saturation and prediction of the effects of potential improvements to code before any implementation takes place.

  7. Memory processes and prefrontal network dysfunction in cryptogenic epilepsy.

    Science.gov (United States)

    Vlooswijk, Marielle C G; Jansen, Jacobus F A; Jeukens, Cécile R L P N; Majoie, H J Marian; Hofman, Paul A M; de Krom, Marc C T F M; Aldenkamp, Albert P; Backes, Walter H

    2011-08-01

    Impaired memory performance is the most frequently reported cognitive problem in patients with chronic epilepsy. To examine memory deficits many studies have focused on the role of the mesiotemporal lobe, mostly with hippocampal abnormalities. However, the role of the prefrontal brain remains unresolved. To investigate the neuronal correlates of working memory dysfunction in patients without structural lesions, a combined study of neurocognitive assessment, hippocampal and cerebral volumetry, and functional magnetic resonance imaging of temporal and frontal memory networks was performed. Thirty-six patients with cryptogenic localization-related epilepsy and 21 healthy controls underwent neuropsychological assessment of intelligence (IQ) and memory. On T(1) -weighted images obtained by 3-Tesla magnetic resonance imaging (MRI), volumetry of the hippocampi and the cerebrum was performed. Functional MRI (fMRI) was performed with a novel picture encoding and Sternberg paradigm that activated different memory-mediating brain regions. Functional connectivity analysis comprised cross-correlation of signal time-series of the most strongly activated regions involved in working memory function. Patients with epilepsy displayed lower IQ values; impaired transient aspects of information processing, as indicated by lower scores on the digit-symbol substitution test (DSST); and decreased short-term memory performance relative to healthy controls, as measured with the Wechsler Adult Intelligence Scale subtests for working memory, and word and figure recognition. This could not be related to any hippocampal volume changes. No group differences were found regarding volumetry or fMRI-derived functional activation. In the Sternberg paradigm, a network involving the anterior cingulate and the middle and inferior frontal gyrus was activated. A reduced strength of four connections in this prefrontal network was associated with the DSST and word recognition performance in the patient

  8. Musical and verbal semantic memory: two distinct neural networks?

    Science.gov (United States)

    Groussard, M; Viader, F; Hubert, V; Landeau, B; Abbas, A; Desgranges, B; Eustache, F; Platel, H

    2010-02-01

    Semantic memory has been investigated in numerous neuroimaging and clinical studies, most of which have used verbal or visual, but only very seldom, musical material. Clinical studies have suggested that there is a relative neural independence between verbal and musical semantic memory. In the present study, "musical semantic memory" is defined as memory for "well-known" melodies without any knowledge of the spatial or temporal circumstances of learning, while "verbal semantic memory" corresponds to general knowledge about concepts, again without any knowledge of the spatial or temporal circumstances of learning. Our aim was to compare the neural substrates of musical and verbal semantic memory by administering the same type of task in each modality. We used high-resolution PET H(2)O(15) to observe 11 young subjects performing two main tasks: (1) a musical semantic memory task, where the subjects heard the first part of familiar melodies and had to decide whether the second part they heard matched the first, and (2) a verbal semantic memory task with the same design, but where the material consisted of well-known expressions or proverbs. The musical semantic memory condition activated the superior temporal area and inferior and middle frontal areas in the left hemisphere and the inferior frontal area in the right hemisphere. The verbal semantic memory condition activated the middle temporal region in the left hemisphere and the cerebellum in the right hemisphere. We found that the verbal and musical semantic processes activated a common network extending throughout the left temporal neocortex. In addition, there was a material-dependent topographical preference within this network, with predominantly anterior activation during musical tasks and predominantly posterior activation during semantic verbal tasks. Copyright (c) 2009 Elsevier Inc. All rights reserved.

  9. Search engine competition with network externalities

    OpenAIRE

    Argenton, C.; Prüfer, J.

    2012-01-01

    The market for Internet search is not only economically and socially important, it is also highly concentrated. Is this a problem? We study the question of whether “competition is only a free click away.” We argue that the market for Internet search is characterized by indirect network externalities and construct a simple model of search engine competition, which produces a market share development that fits well the empirically observed developments since 2003. We find that there is a strong...

  10. NITRD LSN Workshop Report on Complex Engineered Networks

    Data.gov (United States)

    Networking and Information Technology Research and Development, Executive Office of the President — Complex engineered networks are everywhere: power grids, Internet, transportation networks, and more. They are being used more than ever before, and yet our...

  11. Hydrogels for Engineering of Perfusable Vascular Networks

    Science.gov (United States)

    Liu, Juan; Zheng, Huaiyuan; Poh, Patrina S. P.; Machens, Hans-Günther; Schilling, Arndt F.

    2015-01-01

    Hydrogels are commonly used biomaterials for tissue engineering. With their high-water content, good biocompatibility and biodegradability they resemble the natural extracellular environment and have been widely used as scaffolds for 3D cell culture and studies of cell biology. The possible size of such hydrogel constructs with embedded cells is limited by the cellular demand for oxygen and nutrients. For the fabrication of large and complex tissue constructs, vascular structures become necessary within the hydrogels to supply the encapsulated cells. In this review, we discuss the types of hydrogels that are currently used for the fabrication of constructs with embedded vascular networks, the key properties of hydrogels needed for this purpose and current techniques to engineer perfusable vascular structures into these hydrogels. We then discuss directions for future research aimed at engineering of vascularized tissue for implantation. PMID:26184185

  12. Hydrogels for Engineering of Perfusable Vascular Networks

    Directory of Open Access Journals (Sweden)

    Juan Liu

    2015-07-01

    Full Text Available Hydrogels are commonly used biomaterials for tissue engineering. With their high-water content, good biocompatibility and biodegradability they resemble the natural extracellular environment and have been widely used as scaffolds for 3D cell culture and studies of cell biology. The possible size of such hydrogel constructs with embedded cells is limited by the cellular demand for oxygen and nutrients. For the fabrication of large and complex tissue constructs, vascular structures become necessary within the hydrogels to supply the encapsulated cells. In this review, we discuss the types of hydrogels that are currently used for the fabrication of constructs with embedded vascular networks, the key properties of hydrogels needed for this purpose and current techniques to engineer perfusable vascular structures into these hydrogels. We then discuss directions for future research aimed at engineering of vascularized tissue for implantation.

  13. Services supporting collaborative alignment of engineering networks

    Science.gov (United States)

    Jansson, Kim; Uoti, Mikko; Karvonen, Iris

    2015-08-01

    Large-scale facilities such as power plants, process factories, ships and communication infrastructures are often engineered and delivered through geographically distributed operations. The competencies required are usually distributed across several contributing organisations. In these complicated projects, it is of key importance that all partners work coherently towards a common goal. VTT and a number of industrial organisations in the marine sector have participated in a national collaborative research programme addressing these needs. The main output of this programme was development of the Innovation and Engineering Maturity Model for Marine-Industry Networks. The recently completed European Union Framework Programme 7 project COIN developed innovative solutions and software services for enterprise collaboration and enterprise interoperability. One area of focus in that work was services for collaborative project management. This article first addresses a number of central underlying research themes and previous research results that have influenced the development work mentioned above. This article presents two approaches for the development of services that support distributed engineering work. Experience from use of the services is analysed, and potential for development is identified. This article concludes with a proposal for consolidation of the two above-mentioned methodologies. This article outlines the characteristics and requirements of future services supporting collaborative alignment of engineering networks.

  14. Social networks: Evolving graphs with memory dependent edges

    Science.gov (United States)

    Grindrod, Peter; Parsons, Mark

    2011-10-01

    The plethora of digital communication technologies, and their mass take up, has resulted in a wealth of interest in social network data collection and analysis in recent years. Within many such networks the interactions are transient: thus those networks evolve over time. In this paper we introduce a class of models for such networks using evolving graphs with memory dependent edges, which may appear and disappear according to their recent history. We consider time discrete and time continuous variants of the model. We consider the long term asymptotic behaviour as a function of parameters controlling the memory dependence. In particular we show that such networks may continue evolving forever, or else may quench and become static (containing immortal and/or extinct edges). This depends on the existence or otherwise of certain infinite products and series involving age dependent model parameters. We show how to differentiate between the alternatives based on a finite set of observations. To test these ideas we show how model parameters may be calibrated based on limited samples of time dependent data, and we apply these concepts to three real networks: summary data on mobile phone use from a developing region; online social-business network data from China; and disaggregated mobile phone communications data from a reality mining experiment in the US. In each case we show that there is evidence for memory dependent dynamics, such as that embodied within the class of models proposed here.

  15. Mitochondrial Networking in T Cell Memory.

    Science.gov (United States)

    Liesa, Marc; Shirihai, Orian S

    2016-06-30

    T-lymphocytes show large changes in ATP demand and nutrient utilization, imposed by their different roles as T memory and T effector cells. Therefore, T cell remodeling represents a bioenergetic challenge to mitochondria. New work from Buck et al. links changes in mitochondrial shape to T cell fate choice. Copyright © 2016. Published by Elsevier Inc.

  16. Delay-independent stability in bidirectional associative memory networks.

    Science.gov (United States)

    Gopalsamy, K; He, X Z

    1994-01-01

    It is shown that if the neuronal gains are small compared with the synaptic connection weights, then a bidirectional associative memory network with axonal signal transmission delays converges to the equilibria associated with exogenous inputs to the network. Both discrete and continuously distributed delays are considered; the asymptotic stability is global in the state space of neuronal activations and also is independent of the delays.

  17. Mechanisms of memory storage in a model perirhinal network.

    Science.gov (United States)

    Samarth, Pranit; Ball, John M; Unal, Gunes; Paré, Denis; Nair, Satish S

    2017-01-01

    The perirhinal cortex supports recognition and associative memory. Prior unit recording studies revealed that recognition memory involves a reduced responsiveness of perirhinal cells to familiar stimuli whereas associative memory formation is linked to increasing perirhinal responses to paired stimuli. Both effects are thought to depend on perirhinal plasticity but it is unclear how the same network could support these opposite forms of plasticity. However, a recent study showed that when neocortical inputs are repeatedly activated, depression or potentiation could develop, depending on the extent to which the stimulated neocortical activity recruited intrinsic longitudinal connections. We developed a biophysically realistic perirhinal model that reproduced these phenomena and used it to investigate perirhinal mechanisms of associative memory. These analyzes revealed that associative plasticity is critically dependent on a specific subset of neurons, termed conjunctive cells (CCs). When the model network was trained with spatially distributed but coincident neocortical inputs, CCs acquired excitatory responses to the paired inputs and conveyed them to distributed perirhinal sites via longitudinal projections. CC ablation during recall abolished expression of the associative memory. However, CC ablation during training did not prevent memory formation because new CCs emerged, revealing that competitive synaptic interactions governs the formation of CC assemblies.

  18. Neural Networks for Time Perception and Working Memory.

    Science.gov (United States)

    Üstün, Sertaç; Kale, Emre H; Çiçek, Metehan

    2017-01-01

    Time is an important concept which determines most human behaviors, however questions remain about how time is perceived and which areas of the brain are responsible for time perception. The aim of this study was to evaluate the relationship between time perception and working memory in healthy adults. Functional magnetic resonance imaging (fMRI) was used during the application of a visual paradigm. In all of the conditions, the participants were presented with a moving black rectangle on a gray screen. The rectangle was obstructed by a black bar for a time period and then reappeared again. During different conditions, participants ( n = 15, eight male) responded according to the instructions they were given, including details about time and the working memory or dual task requirements. The results showed activations in right dorsolateral prefrontal and right intraparietal cortical networks, together with the anterior cingulate cortex (ACC), anterior insula and basal ganglia (BG) during time perception. On the other hand, working memory engaged the left prefrontal cortex, ACC, left superior parietal cortex, BG and cerebellum activity. Both time perception and working memory were related to a strong peristriate cortical activity. On the other hand, the interaction of time and memory showed activity in the intraparietal sulcus (IPS) and posterior cingulate cortex (PCC). These results support a distributed neural network based model for time perception and that the intraparietal and posterior cingulate areas might play a role in the interface of memory and timing.

  19. 2013 International Conference on Computer Engineering and Network

    CERN Document Server

    Zhu, Tingshao

    2014-01-01

    This book aims to examine innovation in the fields of computer engineering and networking. The book covers important emerging topics in computer engineering and networking, and it will help researchers and engineers improve their knowledge of state-of-art in related areas. The book presents papers from The Proceedings of the 2013 International Conference on Computer Engineering and Network (CENet2013) which was held on July 20-21, in Shanghai, China.

  20. Progressive changes in a recognition memory network in Parkinson's disease.

    Science.gov (United States)

    Segura, Bàrbara; Ibarretxe-Bilbao, Naroa; Sala-Llonch, Roser; Baggio, Hugo Cesar; Martí, María Jose; Valldeoriola, Francesc; Vendrell, Pere; Bargalló, Nuria; Tolosa, Eduard; Junqué, Carme

    2013-04-01

    In a previous functional MRI (fMRI) study, we found that patients with Parkinson's disease (PD) presented with dysfunctions in the recruitment of recognition memory networks. We aimed to investigate the changes in these networks over time. We studied 17 PD patients and 13 age and sex matched healthy subjects. In both groups fMRI (recognition memory paradigm) and neuropsychological assessments were obtained at baseline and at follow-up. To analyse changes over time in functional networks, model free (independent component analysis) analyses of the fMRI data were carried out. Then, a cross correlation approach was used to assess the changes in the strength of functional connectivity. At follow-up, patients showed reduced recruitment of one network, including decreased activation in the orbitofrontal cortices, middle frontal gyri, frontal poles, anterior paracingulate cortex, superior parietal lobes and left middle temporal gyrus, as well as decreased deactivation in the anterior paracingulate gyrus and precuneus. Cross correlation analyses over time showed a decrease in the strength of functional connectivity between the middle frontal gyrus and the superior parietal lobe in PD patients. Model free fMRI and cross correlation connectivity analyses were able to detect progressive changes in functional networks involved in recognition memory in PD patients at early disease stages and without overt clinical deterioration. Functional connectivity analyses could be useful to monitor changes in brain networks underlying neuropsychological deficits in PD.

  1. ARTIFICIAL NEURAL NETWORK OPTIMIZATION MODELING ON ENGINE PERFORMANCE OF DIESEL ENGINE USING BIODIESEL FUEL

    National Research Council Canada - National Science Library

    M R Shukri; M M Rahman; D Ramasamy; K Kadirgama

    2015-01-01

      This paper presents a study of engine performance using a mixture of palm oil methyl ester blends with diesel oil as biodiesel in a diesel engine, and optimizes the engine performance using artificial neural network (ANN) modeling...

  2. Social Network Theory in Engineering Education

    Science.gov (United States)

    Simon, Peter A.

    Collaborative groups are important both in the learning environment of engineering education and, in the real world, the business of engineering design. Selecting appropriate individuals to form an effective group and monitoring a group's progress are important aspects of successful task performance. This exploratory study looked at using the concepts of cognitive social structures, structural balance, and centrality from social network analysis as well as the measures of emotional intelligence. The concepts were used to analyze potential team members to examine if an individual's ability to perceive emotion in others and the self and to use, understand, and manage those emotions are a factor in a group's performance. The students from a capstone design course in computer engineering were used as volunteer subjects. They were formed into groups and assigned a design exercise to determine whether and which of the above-mentioned tools would be effective in both selecting teams and predicting the quality of the resultant design. The results were inconclusive with the exception of an individual's ability to accurately perceive emotions. The instruments that were successful were the Self-Monitoring scale and the accuracy scores derived from cognitive social structures and Level IV of network levels of analysis.

  3. Spin Orbit Interaction Engineering for beyond Spin Transfer Torque memory

    Science.gov (United States)

    Wang, Kang L.

    Spin transfer torque memory uses electron current to transfer the spin torque of electrons to switch a magnetic free layer. This talk will address an alternative approach to energy efficient non-volatile spintronics through engineering of spin orbit interaction (SOC) and the use of spin orbit torque (SOT) by the use of electric field to improve further the energy efficiency of switching. I will first discuss the engineering of interface SOC, which results in the electric field control of magnetic moment or magneto-electric (ME) effect. Magnetic memory bits based on this ME effect, referred to as magnetoelectric RAM (MeRAM), is shown to have orders of magnitude lower energy dissipation compared with spin transfer torque memory (STTRAM). Likewise, interests in spin Hall as a result of SOC have led to many advances. Recent demonstrations of magnetization switching induced by in-plane current in heavy metal/ferromagnetic heterostructures have been shown to arise from the large SOC. The large SOC is also shown to give rise to the large SOT. Due to the presence of an intrinsic extraordinarily strong SOC and spin-momentum lock, topological insulators (TIs) are expected to be promising candidates for exploring spin-orbit torque (SOT)-related physics. In particular, we will show the magnetization switching in a chromium-doped magnetic TI bilayer heterostructure by charge current. A giant SOT of more than three orders of magnitude larger than those reported in heavy metals is also obtained. This large SOT is shown to come from the spin-momentum locked surface states of TI, which may further lead to innovative low power applications. I will also describe other related physics of SOC at the interface of anti-ferromagnetism/ferromagnetic structure and show the control exchange bias by electric field for high speed memory switching. The work was in part supported by ERFC-SHINES, NSF, ARO, TANMS, and FAME.

  4. Pioneers--The "Engineering byDesign[TM]" Network

    Science.gov (United States)

    Burke, Barry N.

    2006-01-01

    This article discusses the standards-based instruction model, Engineering byDesign[TM] (EbD), and a network of teachers called the Engineering byDesign[TM] Network. Engineering byDesign[TM] is the only standards-based national model for Grades K-12 that delivers technological literacy which was developed by the International Technology Education…

  5. Materials, Processes, and Environmental Engineering Network

    Science.gov (United States)

    White, Margo M.

    1993-01-01

    Attention is given to the Materials, Processes, and Environmental Engineering Network (MPEEN), which was developed as a central holding facility for materials testing information generated by the Materials and Processes Laboratory of NASA-Marshall. It contains information from other NASA centers and outside agencies, and also includes the NASA Environmental Information System (NEIS) and Failure Analysis Information System (FAIS) data. The data base is NEIS, which is accessible through MPEEN. Environmental concerns are addressed regarding materials identified by the NASA Operational Environment Team (NOET) to be hazardous to the environment. The data base also contains the usage and performance characteristics of these materials.

  6. Preparing for a Career as a Network Engineer

    Science.gov (United States)

    Morris, Gerard; Fustos, Janos; Haga, Wayne

    2012-01-01

    A network engineer is an Information Technology (IT) professional who designs, implements, maintains, and troubleshoots computer networks. While the United States is still experiencing relatively high unemployment, demand for network engineers remains strong. To determine what skills employers are looking for, data was collected and analyzed from…

  7. Working memory impairment in fibromyalgia patients associated with altered frontoparietal memory network.

    Directory of Open Access Journals (Sweden)

    Jeehye Seo

    Full Text Available BACKGROUND: Fibromyalgia (FM is a disorder characterized by chronic widespread pain and frequently associated with other symptoms. Patients with FM commonly report cognitive complaints, including memory problem. The objective of this study was to investigate the differences in neural correlates of working memory between FM patients and healthy subjects, using functional magnetic resonance imaging (MRI. METHODOLOGY/PRINCIPAL FINDINGS: Nineteen FM patients and 22 healthy subjects performed an n-back memory task during MRI scan. Functional MRI data were analyzed using within- and between-group analysis. Both activated and deactivated brain regions during n-back task were evaluated. In addition, to investigate the possible effect of depression and anxiety, group analysis was also performed with depression and anxiety level in terms of Beck depression inventory (BDI and Beck anxiety inventory (BAI as a covariate. Between-group analyses, after controlling for depression and anxiety level, revealed that within the working memory network, inferior parietal cortex was strongly associated with the mild (r = 0.309, P = 0.049 and moderate (r = 0.331, P = 0.034 pain ratings. In addition, between-group comparison revealed that within the working memory network, the left DLPFC, right VLPFC, and right inferior parietal cortex were associated with the rating of depression and anxiety? CONCLUSIONS/SIGNIFICANCE: Our results suggest that the working memory deficit found in FM patients may be attributable to differences in neural activation of the frontoparietal memory network and may result from both pain itself and depression and anxiety associated with pain.

  8. Computational implementation of a tunable multicellular memory circuit for engineered eukaryotic consortia

    Directory of Open Access Journals (Sweden)

    Josep eSardanyés

    2015-10-01

    Full Text Available Cells are complex machines capable of processing information by means of an entangled network ofmolecular interactions. A crucial component of these decision-making systems is the presence of memoryand this is also a specially relevant target of engineered synthetic systems. A classic example of memorydevices is a 1-bit memory element known as the flip-flop. Such system can be in principle designed usinga single-cell implementation, but a direct mapping between standard circuit design and a living circuitcan be cumbersome. Here we present a novel computational implementation of a 1-bit memory deviceusing a reliable multicellular design able to behave as a set-reset flip-flop that could be implemented inyeast cells. The dynamics of the proposed synthetic circuit is investigated with a mathematical modelusing biologically-meaningful parameters. The circuit is shown to behave as a flip-flop in a wide range ofparameter values. The repression strength for the NOT logics is shown to be crucial to obtain a goodflip-flop signal. Our model also shows that the circuit can be externally tuned to achieve different memorystates and dynamics, such as persistent and transient memory. We have characterised the parameterdomains for robust memory storage and retrieval as well as the corresponding time response dynamics.

  9. Manganese oxide microswitch for electronic memory based on neural networks

    Science.gov (United States)

    Ramesham, R.; Daud, T.; Moopenn, A.; Thakoor, A. P.; Khanna, S. K.

    1989-01-01

    A solid-state, resistance tailorable, programmable-once, binary, nonvolatile memory switch based on manganese oxide thin films is reported. MnO(x) exhibits irreversible memory switching from conducting (on) to insulating (off) state, with the off and on resistance ratio of greater than 10,000. The switching mechanism is current-triggered chemical transformation of a conductive MnO(2-Delta) to an insulating Mn2O3 state. The energy required for switching is of the order of 4-20 nJ/sq micron. The low switching energy, stability of the on and off states, and tailorability of the on state resistance make these microswitches well suited as programmable binary synapses in electronic associative memories based on neural network models.

  10. Demand Engineering: IP Network Optimisation Through Intelligent Demand Placement

    OpenAIRE

    Evans, John; Afrakteh, Arash; Xiu, Ruoyang

    2016-01-01

    Traffic engineering has been used in IP and MPLS networks for a number of years as a tool for making more efficient use of capacity by explicitly routing traffic demands where there is available network capacity that would otherwise be unused. Deployment of traffic engineering imposes an additional layer of complexity to network design and operations, however, which has constrained its adoption for capacity optimisation. The rise of Software Defined Networks has renewed interest in the use of...

  11. Generalized networking engineering: optimal pricing and routing in multiservice networks

    Science.gov (United States)

    Mitra, Debasis; Wang, Qiong

    2002-07-01

    One of the functions of network engineering is to allocate resources optimally to forecasted demand. We generalize the mechanism by incorporating price-demand relationships into the problem formulation, and optimizing pricing and routing jointly to maximize total revenue. We consider a network, with fixed topology and link bandwidths, that offers multiple services, such as voice and data, each having characteristic price elasticity of demand, and quality of service and policy requirements on routing. Prices, which depend on service type and origin-destination, determine demands, that are routed, subject to their constraints, so as to maximize revenue. We study the basic properties of the optimal solution and prove that link shadow costs provide the basis for both optimal prices and optimal routing policies. We investigate the impact of input parameters, such as link capacities and price elasticities, on prices, demand growth, and routing policies. Asymptotic analyses, in which network bandwidth is scaled to grow, give results that are noteworthy for their qualitative insights. Several numerical examples illustrate the analyses.

  12. 4th International Conference on Computer Engineering and Networks

    CERN Document Server

    2015-01-01

    This book aims to examine innovation in the fields of computer engineering and networking. The book covers important emerging topics in computer engineering and networking, and it will help researchers and engineers improve their knowledge of state-of-art in related areas. The book presents papers from the 4th International Conference on Computer Engineering and Networks (CENet2014) held July 19-20, 2014 in Shanghai, China.  ·       Covers emerging topics for computer engineering and networking ·       Discusses how to improve productivity by using the latest advanced technologies ·       Examines innovation in the fields of computer engineering and networking  

  13. VMware vSphere performance designing CPU, memory, storage, and networking for performance-intensive workloads

    CERN Document Server

    Liebowitz, Matt; Spies, Rynardt

    2014-01-01

    Covering the latest VMware vSphere software, an essential book aimed at solving vSphere performance problems before they happen VMware vSphere is the industry's most widely deployed virtualization solution. However, if you improperly deploy vSphere, performance problems occur. Aimed at VMware administrators and engineers and written by a team of VMware experts, this resource provides guidance on common CPU, memory, storage, and network-related problems. Plus, step-by-step instructions walk you through techniques for solving problems and shed light on possible causes behind the problems. Divu

  14. Materials, processes, and environmental engineering network

    Science.gov (United States)

    White, Margo M.

    1993-01-01

    The Materials, Processes, and Environmental Engineering Network (MPEEN) was developed as a central holding facility for materials testing information generated by the Materials and Processes Laboratory. It contains information from other NASA centers and outside agencies, and also includes the NASA Environmental Information System (NEIS) and Failure Analysis Information System (FAIS) data. Environmental replacement materials information is a newly developed focus of MPEEN. This database is the NASA Environmental Information System, NEIS, which is accessible through MPEEN. Environmental concerns are addressed regarding materials identified by the NASA Operational Environment Team, NOET, to be hazardous to the environment. An environmental replacement technology database is contained within NEIS. Environmental concerns about materials are identified by NOET, and control or replacement strategies are formed. This database also contains the usage and performance characteristics of these hazardous materials. In addition to addressing environmental concerns, MPEEN contains one of the largest materials databases in the world. Over 600 users access this network on a daily basis. There is information available on failure analysis, metals and nonmetals testing, materials properties, standard and commercial parts, foreign alloy cross-reference, Long Duration Exposure Facility (LDEF) data, and Materials and Processes Selection List data.

  15. 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.

  16. Fundamentals of reliability engineering applications in multistage interconnection networks

    CERN Document Server

    Gunawan, Indra

    2014-01-01

    This book presents fundamentals of reliability engineering with its applications in evaluating reliability of multistage interconnection networks. In the first part of the book, it introduces the concept of reliability engineering, elements of probability theory, probability distributions, availability and data analysis.  The second part of the book provides an overview of parallel/distributed computing, network design considerations, and more.  The book covers a comprehensive reliability engineering methods and its practical aspects in the interconnection network systems. Students, engineers, researchers, managers will find this book as a valuable reference source.

  17. Vascularization and Angiogenesis in Tissue Engineering: Beyond Creating Static Networks

    NARCIS (Netherlands)

    Rouwkema, Jeroen; Khademhosseini, A.

    2016-01-01

    Engineered tissues need a vascular network to supply cells with nutrients and oxygen after implantation. A network that can connect to the vasculature of the patient after implantation can be included during in vitro culture. For optimal integration, this network needs to be highly organized,

  18. Shape memory activation can affect cell seeding of shape memory polymer scaffolds designed for tissue engineering and regenerative medicine.

    Science.gov (United States)

    Wang, Jing; Brasch, Megan E; Baker, Richard M; Tseng, Ling-Fang; Peña, Alexis N; Henderson, James H

    2017-08-31

    The ability of a three-dimensional scaffold to support cell seeding prior to implantation is a critical criterion for many scaffold-based tissue engineering and regenerative medicine strategies. Shape memory polymer functionality may present important new opportunities and challenges in cell seeding, but the extent to which shape memory activation can positively or negatively affect cell seeding has yet to be reported. The goal of this study was to determine whether shape memory activation can affect cell seeding. The hypothesis was that shape memory activation of porous scaffolds during cell seeding can affect both the number of cells seeded in a scaffold and the distribution (in terms of average infiltration distance) of cells following seeding. Here, we used a porous shape memory foam scaffold programmed to expand when triggered to study cell number and average cell infiltration distance following shape memory activation. We found that shape memory activation can affect both the number of cells and the average cell infiltration distance. The effect was found to be a function of rate of shape change and scaffold pore interconnectivity. Magnitude of shape change had no effect. Only reductions in cell number and infiltration distance (relative to control and benchmark) were observed. The findings suggest that strategies for tissue engineering and regenerative medicine that involve shape memory activation in the presence of a cell-containing medium in vitro or in vivo should consider how recovery rate and scaffold pore interconnectivity may ultimately impact cell seeding.

  19. Is functional integration of resting state brain networks an unspecific biomarker for working memory performance?

    Science.gov (United States)

    Alavash, Mohsen; Doebler, Philipp; Holling, Heinz; Thiel, Christiane M; Gießing, Carsten

    2015-03-01

    Is there one optimal topology of functional brain networks at rest from which our cognitive performance would profit? Previous studies suggest that functional integration of resting state brain networks is an important biomarker for cognitive performance. However, it is still unknown whether higher network integration is an unspecific predictor for good cognitive performance or, alternatively, whether specific network organization during rest predicts only specific cognitive abilities. Here, we investigated the relationship between network integration at rest and cognitive performance using two tasks that measured different aspects of working memory; one task assessed visual-spatial and the other numerical working memory. Network clustering, modularity and efficiency were computed to capture network integration on different levels of network organization, and to statistically compare their correlations with the performance in each working memory test. The results revealed that each working memory aspect profits from a different resting state topology, and the tests showed significantly different correlations with each of the measures of network integration. While higher global network integration and modularity predicted significantly better performance in visual-spatial working memory, both measures showed no significant correlation with numerical working memory performance. In contrast, numerical working memory was superior in subjects with highly clustered brain networks, predominantly in the intraparietal sulcus, a core brain region of the working memory network. Our findings suggest that a specific balance between local and global functional integration of resting state brain networks facilitates special aspects of cognitive performance. In the context of working memory, while visual-spatial performance is facilitated by globally integrated functional resting state brain networks, numerical working memory profits from increased capacities for local processing

  20. Coactivation of the Default Mode Network regions and Working Memory Network regions during task preparation.

    Science.gov (United States)

    Koshino, Hideya; Minamoto, Takehiro; Yaoi, Ken; Osaka, Mariko; Osaka, Naoyuki

    2014-08-05

    The Default Mode Network (DMN) regions exhibit deactivation during a wide variety of resource demanding tasks. However, recent brain imaging studies reported that they also show activation during various cognitive activities. In addition, studies have found a negative correlation between the DMN and the working memory network (WMN). Here, we investigated activity in the DMN and WMN regions during preparation and execution phases of a verbal working memory task. Results showed that the core DMN regions, including the medial prefrontal cortex and posterior cingulate cortex, and WMN regions were activated during preparation. During execution, however, the WMN regions were activated but the DMN regions were deactivated. The results suggest that activation of these network regions is affected by allocation of attentional resources to the task relevant regions due to task demands. This study extends our previous results by showing that the core DMN regions exhibit activation during task preparation and deactivation during task execution.

  1. Preventing Out-of-Sequence for Multicast Input-Queued Space-Memory-Memory Clos-Network

    DEFF Research Database (Denmark)

    Yu, Hao; Ruepp, Sarah Renée; Berger, Michael Stübert

    2011-01-01

    This paper proposes an out-of-sequence (OOS) preventative cell dispatching algorithm, the multicast flow-based round robin (MFRR), for multicast input-queued space-memory-memory (IQ-SMM) Clos-network architecture. Independently treating each incoming cell, such as the desynchronized static round...

  2. Memory and betweenness preference in temporal networks induced from time series

    Science.gov (United States)

    Weng, Tongfeng; Zhang, Jie; Small, Michael; Zheng, Rui; Hui, Pan

    2017-02-01

    We construct temporal networks from time series via unfolding the temporal information into an additional topological dimension of the networks. Thus, we are able to introduce memory entropy analysis to unravel the memory effect within the considered signal. We find distinct patterns in the entropy growth rate of the aggregate network at different memory scales for time series with different dynamics ranging from white noise, 1/f noise, autoregressive process, periodic to chaotic dynamics. Interestingly, for a chaotic time series, an exponential scaling emerges in the memory entropy analysis. We demonstrate that the memory exponent can successfully characterize bifurcation phenomenon, and differentiate the human cardiac system in healthy and pathological states. Moreover, we show that the betweenness preference analysis of these temporal networks can further characterize dynamical systems and separate distinct electrocardiogram recordings. Our work explores the memory effect and betweenness preference in temporal networks constructed from time series data, providing a new perspective to understand the underlying dynamical systems.

  3. Intensive Working Memory Training Produces Functional Changes in Large-scale Frontoparietal Networks.

    Science.gov (United States)

    Thompson, Todd W; Waskom, Michael L; Gabrieli, John D E

    2016-04-01

    Working memory is central to human cognition, and intensive cognitive training has been shown to expand working memory capacity in a given domain. It remains unknown, however, how the neural systems that support working memory are altered through intensive training to enable the expansion of working memory capacity. We used fMRI to measure plasticity in activations associated with complex working memory before and after 20 days of training. Healthy young adults were randomly assigned to train on either a dual n-back working memory task or a demanding visuospatial attention task. Training resulted in substantial and task-specific expansion of dual n-back abilities accompanied by changes in the relationship between working memory load and activation. Training differentially affected activations in two large-scale frontoparietal networks thought to underlie working memory: the executive control network and the dorsal attention network. Activations in both networks linearly scaled with working memory load before training, but training dissociated the role of the two networks and eliminated this relationship in the executive control network. Load-dependent functional connectivity both within and between these two networks increased following training, and the magnitudes of increased connectivity were positively correlated with improvements in task performance. These results provide insight into the adaptive neural systems that underlie large gains in working memory capacity through training.

  4. Modular structure of functional networks in olfactory memory.

    Science.gov (United States)

    Meunier, David; Fonlupt, Pierre; Saive, Anne-Lise; Plailly, Jane; Ravel, Nadine; Royet, Jean-Pierre

    2014-07-15

    Graph theory enables the study of systems by describing those systems as a set of nodes and edges. Graph theory has been widely applied to characterize the overall structure of data sets in the social, technological, and biological sciences, including neuroscience. Modular structure decomposition enables the definition of sub-networks whose components are gathered in the same module and work together closely, while working weakly with components from other modules. This processing is of interest for studying memory, a cognitive process that is widely distributed. We propose a new method to identify modular structure in task-related functional magnetic resonance imaging (fMRI) networks. The modular structure was obtained directly from correlation coefficients and thus retained information about both signs and weights. The method was applied to functional data acquired during a yes-no odor recognition memory task performed by young and elderly adults. Four response categories were explored: correct (Hit) and incorrect (False alarm, FA) recognition and correct and incorrect rejection. We extracted time series data for 36 areas as a function of response categories and age groups and calculated condition-based weighted correlation matrices. Overall, condition-based modular partitions were more homogeneous in young than elderly subjects. Using partition similarity-based statistics and a posteriori statistical analyses, we demonstrated that several areas, including the hippocampus, caudate nucleus, and anterior cingulate gyrus, belonged to the same module more frequently during Hit than during all other conditions. Modularity values were negatively correlated with memory scores in the Hit condition and positively correlated with bias scores (liberal/conservative attitude) in the Hit and FA conditions. We further demonstrated that the proportion of positive and negative links between areas of different modules (i.e., the proportion of correlated and anti-correlated areas

  5. Shape memory polymers based on uniform aliphatic urethane networks

    Energy Technology Data Exchange (ETDEWEB)

    Wilson, T S; Bearinger, J P; Herberg, J L; Marion III, J E; Wright, W J; Evans, C L; Maitland, D J

    2007-01-19

    Aliphatic urethane polymers have been synthesized and characterized, using monomers with high molecular symmetry, in order to form amorphous networks with very uniform supermolecular structures which can be used as photo-thermally actuable shape memory polymers (SMPs). The monomers used include hexamethylene diisocyanate (HDI), trimethylhexamethylenediamine (TMHDI), N,N,N{prime},N{prime}-tetrakis(hydroxypropyl)ethylenediamine (HPED), triethanolamine (TEA), and 1,3-butanediol (BD). The new polymers were characterized by solvent extraction, NMR, XPS, UV/VIS, DSC, DMTA, and tensile testing. The resulting polymers were found to be single phase amorphous networks with very high gel fraction, excellent optical clarity, and extremely sharp single glass transitions in the range of 34 to 153 C. Thermomechanical testing of these materials confirms their excellent shape memory behavior, high recovery force, and low mechanical hysteresis (especially on multiple cycles), effectively behaving as ideal elastomers above T{sub g}. We believe these materials represent a new and potentially important class of SMPs, and should be especially useful in applications such as biomedical microdevices.

  6. Evidence for grid cells in a human memory network.

    Science.gov (United States)

    Doeller, Christian F; Barry, Caswell; Burgess, Neil

    2010-02-04

    Grid cells in the entorhinal cortex of freely moving rats provide a strikingly periodic representation of self-location which is indicative of very specific computational mechanisms. However, the existence of grid cells in humans and their distribution throughout the brain are unknown. Here we show that the preferred firing directions of directionally modulated grid cells in rat entorhinal cortex are aligned with the grids, and that the spatial organization of grid-cell firing is more strongly apparent at faster than slower running speeds. Because the grids are also aligned with each other, we predicted a macroscopic signal visible to functional magnetic resonance imaging (fMRI) in humans. We then looked for this signal as participants explored a virtual reality environment, mimicking the rats' foraging task: fMRI activation and adaptation showing a speed-modulated six-fold rotational symmetry in running direction. The signal was found in a network of entorhinal/subicular, posterior and medial parietal, lateral temporal and medial prefrontal areas. The effect was strongest in right entorhinal cortex, and the coherence of the directional signal across entorhinal cortex correlated with spatial memory performance. Our study illustrates the potential power of combining single-unit electrophysiology with fMRI in systems neuroscience. Our results provide evidence for grid-cell-like representations in humans, and implicate a specific type of neural representation in a network of regions which supports spatial cognition and also autobiographical memory.

  7. Reverse Engineering of Gene Regulatory Networks: A Comparative Study

    Directory of Open Access Journals (Sweden)

    Hache Hendrik

    2009-01-01

    Full Text Available Reverse engineering of gene regulatory networks has been an intensively studied topic in bioinformatics since it constitutes an intermediate step from explorative to causative gene expression analysis. Many methods have been proposed through recent years leading to a wide range of mathematical approaches. In practice, different mathematical approaches will generate different resulting network structures, thus, it is very important for users to assess the performance of these algorithms. We have conducted a comparative study with six different reverse engineering methods, including relevance networks, neural networks, and Bayesian networks. Our approach consists of the generation of defined benchmark data, the analysis of these data with the different methods, and the assessment of algorithmic performances by statistical analyses. Performance was judged by network size and noise levels. The results of the comparative study highlight the neural network approach as best performing method among those under study.

  8. Application of neural networks in coastal engineering - An overview

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Patil, S.G.; Manjunatha, Y.R.; Hegde, A.V.

    . International conference on COPEDEC VII, Dubai (UAE), paper no- 27, 1-11. Mandal, S. 2001. Tides prediction using back propagation neural networks, Proc. International Conference in Ocean Engineering, ICOE, IIT Madras, 499-504. Mandal, S. and Prabhaharan, N...

  9. WDM Systems and Networks Modeling, Simulation, Design and Engineering

    CERN Document Server

    Ellinas, Georgios; Roudas, Ioannis

    2012-01-01

    WDM Systems and Networks: Modeling, Simulation, Design and Engineering provides readers with the basic skills, concepts, and design techniques used to begin design and engineering of optical communication systems and networks at various layers. The latest semi-analytical system simulation techniques are applied to optical WDM systems and networks, and a review of the various current areas of optical communications is presented. Simulation is mixed with experimental verification and engineering to present the industry as well as state-of-the-art research. This contributed volume is divided into three parts, accommodating different readers interested in various types of networks and applications. The first part of the book presents modeling approaches and simulation tools mainly for the physical layer including transmission effects, devices, subsystems, and systems), whereas the second part features more engineering/design issues for various types of optical systems including ULH, access, and in-building system...

  10. Collaborative Trust Networks in Engineering Design Adaptation

    DEFF Research Database (Denmark)

    Atkinson, Simon Reay; Maier, Anja; Caldwell, Nicholas

    2011-01-01

    ); applying the Change Prediction Method (CPM) tool. It posits the idea of the ‘Networks-in-Being’ with varying individual and collective characteristics. [Social] networks are considered to facilitate information exchange between actors. At the same time, networks failing to provide trusted-information can...... collaboration and decision-making by using the change prediction method as a way of scoping information propagation between actors within a network....... hinder effective communication and collaboration. Different combinations of trust may therefore improve or impair the likelihood of information flow, transfer and subsequent action (cause and effect). This paper investigates how analysing different types of network-structures-in-being can support...

  11. FMT (Flight Software Memory Tracker) For Cassini Spacecraft-Software Engineering Using JAVA

    Science.gov (United States)

    Kan, Edwin P.; Uffelman, Hal; Wax, Allan H.

    1997-01-01

    The software engineering design of the Flight Software Memory Tracker (FMT) Tool is discussed in this paper. FMT is a ground analysis software set, consisting of utilities and procedures, designed to track the flight software, i.e., images of memory load and updatable parameters of the computers on-board Cassini spacecraft. FMT is implemented in Java.

  12. Network Sampling with Memory: A proposal for more efficient sampling from social networks

    Science.gov (United States)

    Mouw, Ted; Verdery, Ashton M.

    2013-01-01

    Techniques for sampling from networks have grown into an important area of research across several fields. For sociologists, the possibility of sampling from a network is appealing for two reasons: (1) A network sample can yield substantively interesting data about network structures and social interactions, and (2) it is useful in situations where study populations are difficult or impossible to survey with traditional sampling approaches because of the lack of a sampling frame. Despite its appeal, methodological concerns about the precision and accuracy of network-based sampling methods remain. In particular, recent research has shown that sampling from a network using a random walk based approach such as Respondent Driven Sampling (RDS) can result in high design effects (DE)—the ratio of the sampling variance to the sampling variance of simple random sampling (SRS). A high design effect means that more cases must be collected to achieve the same level of precision as SRS. In this paper we propose an alternative strategy, Network Sampling with Memory (NSM), which collects network data from respondents in order to reduce design effects and, correspondingly, the number of interviews needed to achieve a given level of statistical power. NSM combines a “List” mode, where all individuals on the revealed network list are sampled with the same cumulative probability, with a “Search” mode, which gives priority to bridge nodes connecting the current sample to unexplored parts of the network. We test the relative efficiency of NSM compared to RDS and SRS on 162 school and university networks from Add Health and Facebook that range in size from 110 to 16,278 nodes. The results show that the average design effect for NSM on these 162 networks is 1.16, which is very close to the efficiency of a simple random sample (DE=1), and 98.5% lower than the average DE we observed for RDS. PMID:24159246

  13. Network Sampling with Memory: A proposal for more efficient sampling from social networks.

    Science.gov (United States)

    Mouw, Ted; Verdery, Ashton M

    2012-08-01

    Techniques for sampling from networks have grown into an important area of research across several fields. For sociologists, the possibility of sampling from a network is appealing for two reasons: (1) A network sample can yield substantively interesting data about network structures and social interactions, and (2) it is useful in situations where study populations are difficult or impossible to survey with traditional sampling approaches because of the lack of a sampling frame. Despite its appeal, methodological concerns about the precision and accuracy of network-based sampling methods remain. In particular, recent research has shown that sampling from a network using a random walk based approach such as Respondent Driven Sampling (RDS) can result in high design effects (DE)-the ratio of the sampling variance to the sampling variance of simple random sampling (SRS). A high design effect means that more cases must be collected to achieve the same level of precision as SRS. In this paper we propose an alternative strategy, Network Sampling with Memory (NSM), which collects network data from respondents in order to reduce design effects and, correspondingly, the number of interviews needed to achieve a given level of statistical power. NSM combines a "List" mode, where all individuals on the revealed network list are sampled with the same cumulative probability, with a "Search" mode, which gives priority to bridge nodes connecting the current sample to unexplored parts of the network. We test the relative efficiency of NSM compared to RDS and SRS on 162 school and university networks from Add Health and Facebook that range in size from 110 to 16,278 nodes. The results show that the average design effect for NSM on these 162 networks is 1.16, which is very close to the efficiency of a simple random sample (DE=1), and 98.5% lower than the average DE we observed for RDS.

  14. An intercausal cancellation model for Bayesian-network engineering

    NARCIS (Netherlands)

    Woudenberg, Steven P D; Van Der Gaag, Linda C.; Rademaker, Carin M A

    2015-01-01

    When constructing Bayesian networks with domain experts, network engineers often use the noisy-OR model, and causal interaction models more generally, to alleviate the burden of probability elicitation: the use of such a model serves to reduce the number of probabilities to be elicited on the one

  15. Learning about memory from (very) large scale hippocampal networks

    Science.gov (United States)

    Meshulam, Leenoy; Gauthier, Jeffrey; Brody, Carlos; Tank, David; Bialek, William

    Recent technological progress has dramatically increased our access to the neural activity underlying memory-related tasks. These complex high-dimensional data call for theories that allow us to identify signatures of collective activity in the networks that are crucial for the emergence of cognitive functions. As an example, we study the neural activity in dorsal hippocampus as a mouse runs along a virtual linear track. One of the dominant features of this data is the activity of place cells, which fire when the animal visits particular locations. During the first stage of our work we used a maximum entropy framework to characterize the probability distribution of the joint activity patterns observed across ensembles of up to 100 cells. These models, which are equivalent to Ising models with competing interactions, make surprisingly accurate predictions for the activity of individual neurons given the state of the rest of the network, and this is true both for place cells and for non-place cells. Additionally, the model captures the high-order structure in the data, which cannot be explained by place-related activity alone. For the second stage of our work we study networks of 2000 neurons. To address this much larger system, we are exploring different methods of coarse graining, in the spirit of the renormalization group, searching for simplified models.

  16. Online experimentation and interactive learning resources for teaching network engineering

    OpenAIRE

    Mikroyannidis, Alexander; Gomez-Goiri, Aitor; Smith, Andrew; Domingue, John

    2017-01-01

    This paper presents a case study on teaching network engineering in conjunction with interactive learning resources. This case study has been developed in collaboration with the Cisco Networking Academy in the context of the FORGE project, which promotes online learning and experimentation by offering access to virtual and remote labs. The main goal of this work is allowing learners and educators to perform network simulations within a web browser or an interactive eBook by using any type of ...

  17. A Novel Chaotic Neural Network Using Memristive Synapse with Applications in Associative Memory

    Directory of Open Access Journals (Sweden)

    Xiaofang Hu

    2012-01-01

    Full Text Available Chaotic Neural Network, also denoted by the acronym CNN, has rich dynamical behaviors that can be harnessed in promising engineering applications. However, due to its complex synapse learning rules and network structure, it is difficult to update its synaptic weights quickly and implement its large scale physical circuit. This paper addresses an implementation scheme of a novel CNN with memristive neural synapses that may provide a feasible solution for further development of CNN. Memristor, widely known as the fourth fundamental circuit element, was theoretically predicted by Chua in 1971 and has been developed in 2008 by the researchers in Hewlett-Packard Laboratory. Memristor based hybrid nanoscale CMOS technology is expected to revolutionize the digital and neuromorphic computation. The proposed memristive CNN has four significant features: (1 nanoscale memristors can simplify the synaptic circuit greatly and enable the synaptic weights update easily; (2 it can separate stored patterns from superimposed input; (3 it can deal with one-to-many associative memory; (4 it can deal with many-to-many associative memory. Simulation results are provided to illustrate the effectiveness of the proposed scheme.

  18. Chaotic neural network for learnable associative memory recall

    Science.gov (United States)

    Hsu, Charles C.; Szu, Harold H.

    2003-04-01

    We show that the Fuzzy Membership Function (FMF) is learnable with underlying chaotic neural networks for the open set probability. A sigmoid N-shaped function is used to generate chaotic signals. We postulate that such a chaotic set of innumerable realization forms a FMF exemplified by fuzzy feature maps of eyes, nose, etc., for the invariant face classification. The CNN with FMF plays an important role for fast pattern recognition capability in examples of both habituation and novelty detections. In order to reduce the computation complexity, the nearest-neighborhood weight connection is proposed. In addition, a novel timing-sequence weight-learning algorithm is introduced to increase the capacity and recall of the associative memory. For simplicity, a piece-wise-linear (PWL) N-shaped function was designed and implemented and fabricated in a CMOS chip.

  19. Applying Model Based Systems Engineering to NASA's Space Communications Networks

    Science.gov (United States)

    Bhasin, Kul; Barnes, Patrick; Reinert, Jessica; Golden, Bert

    2013-01-01

    System engineering practices for complex systems and networks now require that requirement, architecture, and concept of operations product development teams, simultaneously harmonize their activities to provide timely, useful and cost-effective products. When dealing with complex systems of systems, traditional systems engineering methodology quickly falls short of achieving project objectives. This approach is encumbered by the use of a number of disparate hardware and software tools, spreadsheets and documents to grasp the concept of the network design and operation. In case of NASA's space communication networks, since the networks are geographically distributed, and so are its subject matter experts, the team is challenged to create a common language and tools to produce its products. Using Model Based Systems Engineering methods and tools allows for a unified representation of the system in a model that enables a highly related level of detail. To date, Program System Engineering (PSE) team has been able to model each network from their top-level operational activities and system functions down to the atomic level through relational modeling decomposition. These models allow for a better understanding of the relationships between NASA's stakeholders, internal organizations, and impacts to all related entities due to integration and sustainment of existing systems. Understanding the existing systems is essential to accurate and detailed study of integration options being considered. In this paper, we identify the challenges the PSE team faced in its quest to unify complex legacy space communications networks and their operational processes. We describe the initial approaches undertaken and the evolution toward model based system engineering applied to produce Space Communication and Navigation (SCaN) PSE products. We will demonstrate the practice of Model Based System Engineering applied to integrating space communication networks and the summary of its

  20. Engineering Issues for an Adaptive Defense Network

    National Research Council Canada - National Science Library

    Piszcz, Alan; Orlans, Nicholas; Eyler-Walker, Zachary; Moore, David

    2001-01-01

    .... The primary issue was the capability to detect and defend against DDoS. Experimentation was performed with a packet filtering firewall, a network Quality of Service manager, multiple DDoS tools, and traffic generation tools...

  1. Working memory cells' behavior may be explained by cross-regional networks with synaptic facilitation.

    Directory of Open Access Journals (Sweden)

    Sergio Verduzco-Flores

    2009-08-01

    Full Text Available Neurons in the cortex exhibit a number of patterns that correlate with working memory. Specifically, averaged across trials of working memory tasks, neurons exhibit different firing rate patterns during the delay of those tasks. These patterns include: 1 persistent fixed-frequency elevated rates above baseline, 2 elevated rates that decay throughout the tasks memory period, 3 rates that accelerate throughout the delay, and 4 patterns of inhibited firing (below baseline analogous to each of the preceding excitatory patterns. Persistent elevated rate patterns are believed to be the neural correlate of working memory retention and preparation for execution of behavioral/motor responses as required in working memory tasks. Models have proposed that such activity corresponds to stable attractors in cortical neural networks with fixed synaptic weights. However, the variability in patterned behavior and the firing statistics of real neurons across the entire range of those behaviors across and within trials of working memory tasks are typical not reproduced. Here we examine the effect of dynamic synapses and network architectures with multiple cortical areas on the states and dynamics of working memory networks. The analysis indicates that the multiple pattern types exhibited by cells in working memory networks are inherent in networks with dynamic synapses, and that the variability and firing statistics in such networks with distributed architectures agree with that observed in the cortex.

  2. Ship Benchmark Shaft and Engine Gain FDI Using Neural Network

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Izadi-Zamanabadi, Roozbeh

    2002-01-01

    This paper concerns fault detection and isolation based on neural network modeling. A neural network is trained to recognize the input-output behavior of a nonlinear plant, and faults are detected if the output estimated by the network differs from the measured plant output by more than a specified...... threshold value. In the paper a method for determining this threshold based on the neural network model is proposed, which can be used for a design strategy to handle residual sensitivity to input variations. The proposed method is used for successful FDI of a diesel engine gain fault in a ship propulsion...

  3. A system for simulating shared memory in heterogeneous distributed-memory networks with specialization for robotics applications

    Energy Technology Data Exchange (ETDEWEB)

    Jones, J.P.; Bangs, A.L.; Butler, P.L.

    1991-01-01

    Hetero Helix is a programming environment which simulates shared memory on a heterogeneous network of distributed-memory computers. The machines in the network may vary with respect to their native operating systems and internal representation of numbers. Hetero Helix presents a simple programming model to developers, and also considers the needs of designers, system integrators, and maintainers. The key software technology underlying Hetero Helix is the use of a compiler'' which analyzes the data structures in shared memory and automatically generates code which translates data representations from the format native to each machine into a common format, and vice versa. The design of Hetero Helix was motivated in particular by the requirements of robotics applications. Hetero Helix has been used successfully in an integration effort involving 27 CPUs in a heterogeneous network and a body of software totaling roughly 100,00 lines of code. 25 refs., 6 figs.

  4. Using model-based functional MRI to locate working memory updates and declarative memory retrievals in the fronto-parietal network

    NARCIS (Netherlands)

    Borst, Jelmer P.; Anderson, John R.

    2013-01-01

    In this study, we used model-based functional MRI (fMRI) to locate two functions of the fronto-parietal network: declarative memory retrievals and updating of working memory. Because regions in the fronto-parietal network are by definition coherently active, locating functions within this network is

  5. Functional connectivity pattern during rest within the episodic memory network in association with episodic memory performance in bipolar disorder.

    Science.gov (United States)

    Oertel-Knöchel, Viola; Reinke, Britta; Matura, Silke; Prvulovic, David; Linden, David E J; van de Ven, Vincent

    2015-02-28

    In this study, we sought to examine the intrinsic functional organization of the episodic memory network during rest in bipolar disorder (BD). The previous work suggests that deficits in intrinsic functional connectivity may account for impaired memory performance. We hypothesized that regions involved in episodic memory processing would reveal aberrant functional connectivity in patients with bipolar disorder. We examined 21 patients with BD and 21 healthy matched controls who underwent functional magnetic resonance imaging (fMRI) during a resting condition. We did a seed-based functional connectivity analysis (SBA), using the regions of the episodic memory network that showed a significantly different activation pattern during task-related fMRI as seeds. The functional connectivity scores (FC) were further correlated with episodic memory task performance. Our results revealed decreased FC scores within frontal areas and between frontal and temporal/hippocampal/limbic regions in BD patients in comparison with controls. We observed higher FC in BD patients compared with controls between frontal and limbic regions. The decrease in fronto-frontal functional connectivity in BD patients showed a significant positive association with episodic memory performance. The association between task-independent dysfunctional frontal-limbic FC and episodic memory performance may be relevant for current pathophysiological models of the disease. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  6. Viewing engineering offshoring in a network perspective

    DEFF Research Database (Denmark)

    Hansen, Zaza Nadja Lee; Zhang, Yufeng; Ahmed-Kristensen, Saeema

    2013-01-01

    of large multinational corporations in Denmark were carried out. Data gathering was mainly documentary studies and interviews. The main data analysis approaches were coding (Strauss and Corbin) and pattern-matching (Yin). The dataset was analysed using the GEN framework suggested by Zhang et al. and Zhang...... the associated risks of engineering offshoring will be a key area of the investigation. Design/methodology/approach – The research approach is based on the engineering design research methodology developed by Blessing and Chakrabarti, including a descriptive phase and a prescriptive phase. Four case studies...

  7. Functional Brain Network Modularity Captures Inter- and Intra-Individual Variation in Working Memory Capacity

    Science.gov (United States)

    Stevens, Alexander A.; Tappon, Sarah C.; Garg, Arun; Fair, Damien A.

    2012-01-01

    Background Cognitive abilities, such as working memory, differ among people; however, individuals also vary in their own day-to-day cognitive performance. One potential source of cognitive variability may be fluctuations in the functional organization of neural systems. The degree to which the organization of these functional networks is optimized may relate to the effective cognitive functioning of the individual. Here we specifically examine how changes in the organization of large-scale networks measured via resting state functional connectivity MRI and graph theory track changes in working memory capacity. Methodology/Principal Findings Twenty-two participants performed a test of working memory capacity and then underwent resting-state fMRI. Seventeen subjects repeated the protocol three weeks later. We applied graph theoretic techniques to measure network organization on 34 brain regions of interest (ROI). Network modularity, which measures the level of integration and segregation across sub-networks, and small-worldness, which measures global network connection efficiency, both predicted individual differences in memory capacity; however, only modularity predicted intra-individual variation across the two sessions. Partial correlations controlling for the component of working memory that was stable across sessions revealed that modularity was almost entirely associated with the variability of working memory at each session. Analyses of specific sub-networks and individual circuits were unable to consistently account for working memory capacity variability. Conclusions/Significance The results suggest that the intrinsic functional organization of an a priori defined cognitive control network measured at rest provides substantial information about actual cognitive performance. The association of network modularity to the variability in an individual's working memory capacity suggests that the organization of this network into high connectivity within modules

  8. Functional brain network modularity captures inter- and intra-individual variation in working memory capacity.

    Directory of Open Access Journals (Sweden)

    Alexander A Stevens

    Full Text Available Cognitive abilities, such as working memory, differ among people; however, individuals also vary in their own day-to-day cognitive performance. One potential source of cognitive variability may be fluctuations in the functional organization of neural systems. The degree to which the organization of these functional networks is optimized may relate to the effective cognitive functioning of the individual. Here we specifically examine how changes in the organization of large-scale networks measured via resting state functional connectivity MRI and graph theory track changes in working memory capacity.Twenty-two participants performed a test of working memory capacity and then underwent resting-state fMRI. Seventeen subjects repeated the protocol three weeks later. We applied graph theoretic techniques to measure network organization on 34 brain regions of interest (ROI. Network modularity, which measures the level of integration and segregation across sub-networks, and small-worldness, which measures global network connection efficiency, both predicted individual differences in memory capacity; however, only modularity predicted intra-individual variation across the two sessions. Partial correlations controlling for the component of working memory that was stable across sessions revealed that modularity was almost entirely associated with the variability of working memory at each session. Analyses of specific sub-networks and individual circuits were unable to consistently account for working memory capacity variability.The results suggest that the intrinsic functional organization of an a priori defined cognitive control network measured at rest provides substantial information about actual cognitive performance. The association of network modularity to the variability in an individual's working memory capacity suggests that the organization of this network into high connectivity within modules and sparse connections between modules may reflect

  9. Default network connectivity is linked to memory status in multiple sclerosis.

    Science.gov (United States)

    Leavitt, Victoria M; Paxton, Jessica; Sumowski, James F

    2014-10-01

    Memory impairment affects 50% of multiple sclerosis (MS) patients. Altered resting-state functional connectivity (FC) has been observed in the default network (DN) of MS patients. No study to date has examined the association of DN FC to its behavioral concomitant, memory. The approach of the present study represents a methodological shift allowing straightforward interpretation of FC alterations in MS, as it presupposes specificity of a network to its paired cognitive function. We examined FC from fMRI collected during rest in the DN of 43 MS patients with and without memory-impairment. Memory-intact patients showed increased DN FC relative to memory-impaired patients. There were no regions of higher FC in memory-impaired patients. DN FC was positively correlated with memory function, such that higher FC was associated with better memory performance. Results were unchanged after controlling for cognitive efficiency, supporting specificity of the DN to memory and not cognitive status more generally. These findings support DN FC as a marker of memory function in MS patients that can be targeted by future treatment interventions. Pairing a functional network with its behavioral concomitant represents a straightforward method for interpreting FC alterations in patients with MS.

  10. Changes in Brain Network Efficiency and Working Memory Performance in Aging

    Science.gov (United States)

    Stanley, Matthew L.; Simpson, Sean L.; Dagenbach, Dale; Lyday, Robert G.; Burdette, Jonathan H.; Laurienti, Paul J.

    2015-01-01

    Working memory is a complex psychological construct referring to the temporary storage and active processing of information. We used functional connectivity brain network metrics quantifying local and global efficiency of information transfer for predicting individual variability in working memory performance on an n-back task in both young (n = 14) and older (n = 15) adults. Individual differences in both local and global efficiency during the working memory task were significant predictors of working memory performance in addition to age (and an interaction between age and global efficiency). Decreases in local efficiency during the working memory task were associated with better working memory performance in both age cohorts. In contrast, increases in global efficiency were associated with much better working performance for young participants; however, increases in global efficiency were associated with a slight decrease in working memory performance for older participants. Individual differences in local and global efficiency during resting-state sessions were not significant predictors of working memory performance. Significant group whole-brain functional network decreases in local efficiency also were observed during the working memory task compared to rest, whereas no significant differences were observed in network global efficiency. These results are discussed in relation to recently developed models of age-related differences in working memory. PMID:25875001

  11. Search Engine Competition with Network Externalities

    NARCIS (Netherlands)

    Argenton, C.; Prüfer, J.

    2011-01-01

    The market for Internet search is not only economically and socially important, it is also highly concentrated. Is this a problem? We study the question whether "competition is only a free click away". We argue that the market for Internet search is characterized by indirect network externalities

  12. Large-scale networks in engineering and life sciences

    CERN Document Server

    Findeisen, Rolf; Flockerzi, Dietrich; Reichl, Udo; Sundmacher, Kai

    2014-01-01

    This edited volume provides insights into and tools for the modeling, analysis, optimization, and control of large-scale networks in the life sciences and in engineering. Large-scale systems are often the result of networked interactions between a large number of subsystems, and their analysis and control are becoming increasingly important. The chapters of this book present the basic concepts and theoretical foundations of network theory and discuss its applications in different scientific areas such as biochemical reactions, chemical production processes, systems biology, electrical circuits, and mobile agents. The aim is to identify common concepts, to understand the underlying mathematical ideas, and to inspire discussions across the borders of the various disciplines.  The book originates from the interdisciplinary summer school “Large Scale Networks in Engineering and Life Sciences” hosted by the International Max Planck Research School Magdeburg, September 26-30, 2011, and will therefore be of int...

  13. Re-engineering Nascom's network management architecture

    Science.gov (United States)

    Drake, Brian C.; Messent, David

    1994-01-01

    The development of Nascom systems for ground communications began in 1958 with Project Vanguard. The low-speed systems (rates less than 9.6 Kbs) were developed following existing standards; but, there were no comparable standards for high-speed systems. As a result, these systems were developed using custom protocols and custom hardware. Technology has made enormous strides since the ground support systems were implemented. Standards for computer equipment, software, and high-speed communications exist and the performance of current workstations exceeds that of the mainframes used in the development of the ground systems. Nascom is in the process of upgrading its ground support systems and providing additional services. The Message Switching System (MSS), Communications Address Processor (CAP), and Multiplexer/Demultiplexer (MDM) Automated Control System (MACS) are all examples of Nascom systems developed using standards such as, X-windows, Motif, and Simple Network Management Protocol (SNMP). Also, the Earth Observing System (EOS) Communications (Ecom) project is stressing standards as an integral part of its network. The move towards standards has produced a reduction in development, maintenance, and interoperability costs, while providing operational quality improvement. The Facility and Resource Manager (FARM) project has been established to integrate the Nascom networks and systems into a common network management architecture. The maximization of standards and implementation of computer automation in the architecture will lead to continued cost reductions and increased operational efficiency. The first step has been to derive overall Nascom requirements and identify the functionality common to all the current management systems. The identification of these common functions will enable the reuse of processes in the management architecture and promote increased use of automation throughout the Nascom network. The MSS, CAP, MACS, and Ecom projects have indicated

  14. Engineering Algorithms for Route Planning in Multimodal Transportation Networks

    OpenAIRE

    Dibbelt, Julian Matthias

    2016-01-01

    Practical algorithms for route planning in transportation networks are a showpiece of successful Algorithm Engineering. This has produced many speedup techniques, varying in preprocessing time, space, query performance, simplicity, and ease of implementation. This thesis explores solutions to more realistic scenarios, taking into account, e.g., traffic, user preferences, public transit schedules, and the options offered by the many modalities of modern transportation networks.

  15. Network-wide BGP route prediction for traffic engineering

    Science.gov (United States)

    Feamster, Nick; Rexford, Jennifer

    2002-07-01

    The Internet consists of about 13,000 Autonomous Systems (AS's) that exchange routing information using the Border Gateway Protocol (BGP). The operators of each AS must have control over the flow of traffic through their network and between neighboring AS's. However, BGP is a complicated, policy-based protocol that does not include any direct support for traffic engineering. In previous work, we have demonstrated that network operators can adapt the flow of traffic in an efficient and predictable fashion through careful adjustments to the BGP policies running on their edge routers. Nevertheless, many details of the BGP protocol and decision process make predicting the effects of these policy changes difficult. In this paper, we describe a tool that predicts traffic flow at network exit points based on the network topology, the import policy associated with each BGP session, and the routing advertisements received from neighboring AS's. We present a linear-time algorithm that computes a network-wide view of the best BGP routes for each destination prefix given a static snapshot of the network state, without simulating the complex details of BGP message passing. We describe how to construct this snapshot using the BGP routing tables and router configuration files available from operational routers. We verify the accuracy of our algorithm by applying our tool to routing and configuration data from AT&T's commercial IP network. Our route prediction techniques help support the operation of large IP backbone networks, where interdomain routing is an important aspect of traffic engineering.

  16. Control of 12-Cylinder Camless Engine with Neural Networks

    Directory of Open Access Journals (Sweden)

    Ashhab Moh’d Sami

    2017-01-01

    Full Text Available The 12-cyliner camless engine breathing process is modeled with artificial neural networks (ANN’s. The inputs to the net are the intake valve lift (IVL and intake valve closing timing (IVC whereas the output of the net is the cylinder air charge (CAC. The ANN is trained with data collected from an engine simulation model which is based on thermodynamics principles and calibrated against real engine data. A method for adapting single-output feed-forward neural networks is proposed and applied to the camless engine ANN model. As a consequence the overall 12-cyliner camless engine feedback controller is upgraded and the necessary changes are implemented in order to contain the adaptive neural network with the objective of tracking the cylinder air charge (driver’s torque demand while minimizing the pumping losses (increasing engine efficiency. All the needed measurements are extracted only from the two conventional and inexpensive sensors, namely, the mass air flow through the throttle body (MAF and the intake manifold absolute pressure (MAP sensors. The feedback controller’s capability is demonstrated through computer simulation.

  17. Shape memory alloys: New materials for future engineering

    Science.gov (United States)

    Hornbogen, E.

    1988-01-01

    Shape memory is a new material property. An alloy which experiences relative severe plastic deformation resumes its original shape again after heating by 10 to 100 C. Besides simple shape memory, in similar alloys there is the second effect where the change in shape is caused exclusively by little temperature change. In pseudo-elasticity, the alloy exhibits a rubber-like behavior, i.e., large, reversible deformation at little change in tension. Beta Cu and beta NiTi alloys have been used in practice. The probability is that soon alloys based on Fe will become available. Recently increasing applications for this alloy were found in various areas of technology, even medical technology. A review with 24 references is given, including properties, production, applications and fundamental principles of the shape memory effect.

  18. Engineering stability in gene networks by autoregulation

    Science.gov (United States)

    Becskei, Attila; Serrano, Luis

    2000-06-01

    The genetic and biochemical networks which underlie such things as homeostasis in metabolism and the developmental programs of living cells, must withstand considerable variations and random perturbations of biochemical parameters. These occur as transient changes in, for example, transcription, translation, and RNA and protein degradation. The intensity and duration of these perturbations differ between cells in a population. The unique state of cells, and thus the diversity in a population, is owing to the different environmental stimuli the individual cells experience and the inherent stochastic nature of biochemical processes (for example, refs 5 and 6). It has been proposed, but not demonstrated, that autoregulatory, negative feedback loops in gene circuits provide stability, thereby limiting the range over which the concentrations of network components fluctuate. Here we have designed and constructed simple gene circuits consisting of a regulator and transcriptional repressor modules in Escherichia coli and we show the gain of stability produced by negative feedback.

  19. Beta and gamma oscillatory activities associated with olfactory memory tasks: different rhythms for different functional networks?

    National Research Council Canada - National Science Library

    Martin, Claire; Ravel, Nadine

    2014-01-01

    .... The anatomy of the olfactory network (olfactory bulb, piriform, and entorhinal cortices) and its unique direct access to the limbic system makes it particularly attractive to study how sensory processing could be modulated by learning and memory...

  20. Audiovisual classification of vocal outbursts in human conversation using long-short-term memory networks

    NARCIS (Netherlands)

    Eyben, Florian; Petridis, Stavros; Schuller, Björn; Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja

    We investigate classification of non-linguistic vocalisations with a novel audiovisual approach and Long Short-Term Memory (LSTM) Recurrent Neural Networks as highly successful dynamic sequence classifiers. As database of evaluation serves this year's Paralinguistic Challenge's Audiovisual Interest

  1. Memory networks in tinnitus: a functional brain image study.

    Science.gov (United States)

    Laureano, Maura Regina; Onishi, Ektor Tsuneo; Bressan, Rodrigo Affonseca; Castiglioni, Mario Luiz Vieira; Batista, Ilza Rosa; Reis, Marilia Alves; Garcia, Michele Vargas; de Andrade, Adriana Neves; de Almeida, Roberta Ribeiro; Garrido, Griselda J; Jackowski, Andrea Parolin

    2014-01-01

    Tinnitus is characterized by the perception of sound in the absence of an external auditory stimulus. The network connectivity of auditory and non-auditory brain structures associated with emotion, memory and attention are functionally altered in debilitating tinnitus. Current studies suggest that tinnitus results from neuroplastic changes in the frontal and limbic temporal regions. The objective of this study was to use Single-Photon Emission Computed Tomography (SPECT) to evaluate changes in the cerebral blood flow in tinnitus patients with normal hearing compared with healthy controls. Twenty tinnitus patients with normal hearing and 17 healthy controls, matched for sex, age and years of education, were subjected to Single Photon Emission Computed Tomography using the radiotracer ethylenedicysteine diethyl ester, labeled with Technetium 99 m (99 mTc-ECD SPECT). The severity of tinnitus was assessed using the "Tinnitus Handicap Inventory" (THI). The images were processed and analyzed using "Statistical Parametric Mapping" (SPM8). A significant increase in cerebral perfusion in the left parahippocampal gyrus (pFWE <0.05) was observed in patients with tinnitus compared with healthy controls. The average total THI score was 50.8+18.24, classified as moderate tinnitus. It was possible to identify significant changes in the limbic system of the brain perfusion in tinnitus patients with normal hearing, suggesting that central mechanisms, not specific to the auditory pathway, are involved in the pathophysiology of symptoms, even in the absence of clinically diagnosed peripheral changes.

  2. Memory networks in tinnitus: a functional brain image study.

    Directory of Open Access Journals (Sweden)

    Maura Regina Laureano

    Full Text Available Tinnitus is characterized by the perception of sound in the absence of an external auditory stimulus. The network connectivity of auditory and non-auditory brain structures associated with emotion, memory and attention are functionally altered in debilitating tinnitus. Current studies suggest that tinnitus results from neuroplastic changes in the frontal and limbic temporal regions. The objective of this study was to use Single-Photon Emission Computed Tomography (SPECT to evaluate changes in the cerebral blood flow in tinnitus patients with normal hearing compared with healthy controls.Twenty tinnitus patients with normal hearing and 17 healthy controls, matched for sex, age and years of education, were subjected to Single Photon Emission Computed Tomography using the radiotracer ethylenedicysteine diethyl ester, labeled with Technetium 99 m (99 mTc-ECD SPECT. The severity of tinnitus was assessed using the "Tinnitus Handicap Inventory" (THI. The images were processed and analyzed using "Statistical Parametric Mapping" (SPM8.A significant increase in cerebral perfusion in the left parahippocampal gyrus (pFWE <0.05 was observed in patients with tinnitus compared with healthy controls. The average total THI score was 50.8+18.24, classified as moderate tinnitus.It was possible to identify significant changes in the limbic system of the brain perfusion in tinnitus patients with normal hearing, suggesting that central mechanisms, not specific to the auditory pathway, are involved in the pathophysiology of symptoms, even in the absence of clinically diagnosed peripheral changes.

  3. Short-term memory in olfactory network dynamics

    Science.gov (United States)

    Stopfer, Mark; Laurent, Gilles

    1999-12-01

    Neural assemblies in a number of animal species display self-organized, synchronized oscillations in response to sensory stimuli in a variety of brain areas.. In the olfactory system of insects, odour-evoked oscillatory synchronization of antennal lobe projection neurons (PNs) is superimposed on slower and stimulus-specific temporal activity patterns. Hence, each odour activates a specific and dynamic projection neuron assembly whose evolution during a stimulus is locked to the oscillation clock. Here we examine, using locusts, the changes in population dynamics of projection-neuron assemblies over repeated odour stimulations, as would occur when an animal first encounters and then repeatedly samples an odour for identification or localization. We find that the responses of these assemblies rapidly decrease in intensity, while they show a marked increase in spike time precision and inter-neuronal oscillatory coherence. Once established, this enhanced precision in the representation endures for several minutes. This change is stimulus-specific, and depends on events within the antennal lobe circuits, independent of olfactory receptor adaptation: it may thus constitute a form of sensory memory. Our results suggest that this progressive change in olfactory network dynamics serves to converge, over repeated odour samplings, on a more precise and readily classifiable odour representation, using relational information contained across neural assemblies.

  4. Altered intrinsic hippocmapus declarative memory network and its association with impulsivity in abstinent heroin dependent subjects.

    Science.gov (United States)

    Zhai, Tian-Ye; Shao, Yong-Cong; Xie, Chun-Ming; Ye, En-Mao; Zou, Feng; Fu, Li-Ping; Li, Wen-Jun; Chen, Gang; Chen, Guang-Yu; Zhang, Zheng-Guo; Li, Shi-Jiang; Yang, Zheng

    2014-10-01

    Converging evidence suggests that addiction can be considered a disease of aberrant learning and memory with impulsive decision-making. In the past decades, numerous studies have demonstrated that drug addiction is involved in multiple memory systems such as classical conditioned drug memory, instrumental learning memory and the habitual learning memory. However, most of these studies have focused on the contributions of non-declarative memory, and declarative memory has largely been neglected in the research of addiction. Based on a recent finding that hippocampus, as a core functioning region of declarative memory, was proved biased the decision-making process based on past experiences by spreading associated reward values throughout memory. Our present study focused on the hippocampus. By utilizing seed-based network analysis on the resting-state functional MRI datasets with the seed hippocampus we tested how the intrinsic hippocampal memory network altered toward drug addiction, and examined how the functional connectivity strength within the altered hippocampal network correlated with behavioral index 'impulsivity'. Our results demonstrated that HD group showed enhanced coherence between hippocampus which represents declarative memory system and non-declarative reward-guided learning memory system, and also showed attenuated intrinsic functional link between hippocampus and top-down control system, compared to the CN group. This alteration was furthered found to have behavioral significance over the behavioral index 'impulsivity' measured with Barratt Impulsiveness Scale (BIS). These results provide insights into the mechanism of declarative memory underlying the impulsive behavior in drug addiction. Copyright © 2014 Elsevier B.V. All rights reserved.

  5. Repeated Stimulation of Cultured Networks of Rat Cortical Neurons Induces Parallel Memory Traces

    Science.gov (United States)

    le Feber, Joost; Witteveen, Tim; van Veenendaal, Tamar M.; Dijkstra, Jelle

    2015-01-01

    During systems consolidation, memories are spontaneously replayed favoring information transfer from hippocampus to neocortex. However, at present no empirically supported mechanism to accomplish a transfer of memory from hippocampal to extra-hippocampal sites has been offered. We used cultured neuronal networks on multielectrode arrays and…

  6. Improving a Computer Networks Course Using the Partov Simulation Engine

    Science.gov (United States)

    Momeni, B.; Kharrazi, M.

    2012-01-01

    Computer networks courses are hard to teach as there are many details in the protocols and techniques involved that are difficult to grasp. Employing programming assignments as part of the course helps students to obtain a better understanding and gain further insight into the theoretical lectures. In this paper, the Partov simulation engine and…

  7. Successful neural network projects at the Idaho National Engineering Laboratory

    Energy Technology Data Exchange (ETDEWEB)

    Cordes, G.A.

    1991-01-01

    This paper presents recent and current projects at the Idaho National Engineering Laboratory (INEL) that research and apply neural network technology. The projects are summarized in the paper and their direct application to space reactor power and propulsion systems activities is discussed. 9 refs., 10 figs., 3 tabs.

  8. The structural connectivity pattern of the default mode network and its association with memory and anxiety

    Directory of Open Access Journals (Sweden)

    Yan eTao

    2015-11-01

    Full Text Available The default mode network (DMN is one of the most widely studied resting state functional networks. The structural basis for the DMN is of particular interest and has been studied by several researchers using diffusion tensor imaging (DTI. Most of these previous studies focused on a few regions or white matter tracts of the DMN so that the global structural connectivity pattern and network properties of the DMN remain unclear. Moreover, evidences indicate that the DMN is involved in both memory and emotion, but how the DMN regulates memory and anxiety from the perspective of the whole DMN structural network remains unknown. We used multimodal neuroimaging methods to investigate the structural connectivity pattern of the DMN and the association of its network properties with memory and anxiety in 205 young healthy subjects. Using a probabilistic fiber tractography technique based on DTI data and graph theory methods, we constructed the global structural connectivity pattern of the DMN and found that memory quotient (MQ score was significantly positively correlated with the global and local efficiency of the DMN whereas anxiety was found to be negatively correlated with the efficiency. The strong structural connectivity between multiple brain regions within DMN may reflect that the DMN has certain structural basis. Meanwhile, we found the network efficiency of the DMN were related to memory and anxiety measures, which indicated that the DMN may play a role in the memory and anxiety.

  9. Reconstruction of an engine combustion process with a neural network

    Energy Technology Data Exchange (ETDEWEB)

    Jacob, P.J.; Gu, F.; Ball, A.D. [School of Engineering, University of Manchester, Manchester (United Kingdom)

    1997-12-31

    The cylinder pressure waveform in an internal combustion engine is one of the most important parameters in describing the engine combustion process. It is used for a range of diagnostic tasks such as identification of ignition faults or mechanical wear in the cylinders. However, it is very difficult to measure this parameter directly. Never-the-less, the cylinder pressure may be inferred from other more readily obtainable parameters. In this presentation it is shown how a Radial Basis Function network, which may be regarded as a form of neural network, may be used to model the cylinder pressure as a function of the instantaneous crankshaft velocity, recorded with a simple magnetic sensor. The application of the model is demonstrated on a four cylinder DI diesel engine with data from a wide range of speed and load settings. The prediction capabilities of the model once trained are validated against measured data. (orig.) 4 refs.

  10. Advanced systems engineering and network planning support

    Science.gov (United States)

    Walters, David H.; Barrett, Larry K.; Boyd, Ronald; Bazaj, Suresh; Mitchell, Lionel; Brosi, Fred

    1990-01-01

    The objective of this task was to take a fresh look at the NASA Space Network Control (SNC) element for the Advanced Tracking and Data Relay Satellite System (ATDRSS) such that it can be made more efficient and responsive to the user by introducing new concepts and technologies appropriate for the 1997 timeframe. In particular, it was desired to investigate the technologies and concepts employed in similar systems that may be applicable to the SNC. The recommendations resulting from this study include resource partitioning, on-line access to subsets of the SN schedule, fluid scheduling, increased use of demand access on the MA service, automating Inter-System Control functions using monitor by exception, increase automation for distributed data management and distributed work management, viewing SN operational control in terms of the OSI Management framework, and the introduction of automated interface management.

  11. Neural networks supporting autobiographical memory retrieval in post-traumatic stress disorder

    Science.gov (United States)

    Jacques, Peggy L.; Kragel, Philip A.; Rubin, David C.

    2013-01-01

    Post-traumatic stress disorder (PTSD) affects the functional recruitment and connectivity between neural regions during autobiographical memory (AM) retrieval that overlap with default and control networks. Whether such univariate changes relate to potential differences in the contribution of large-scale neural networks supporting cognition in PTSD is unknown. In the current functional MRI (fMRI) study we employ independent component analysis to examine the influence the engagement of neural networks during the recall of personal memories in PTSD (15 participants) compared to non-trauma exposed, healthy controls (14 participants). We found that the PTSD group recruited similar neural networks when compared to controls during AM recall, including default network subsystems and control networks, but there were group differences in the spatial and temporal characteristics of these networks. First, there were spatial differences in the contribution of the anterior and posterior midline across the networks, and with the amygdala in particular for the medial temporal subsystem of the default network. Second, there were temporal differences in the relationship of the medial prefrontal subsystem of the default network, with less temporal coupling of this network during AM retrieval in PTSD relative to controls. These findings suggest that spatial and temporal characteristics of the default and control networks potentially differ in PTSD versus healthy controls, and contribute to altered recall of personal memory. PMID:23483523

  12. Effective visual working memory capacity: an emergent effect from the neural dynamics in an attractor network.

    Directory of Open Access Journals (Sweden)

    Laura Dempere-Marco

    Full Text Available The study of working memory capacity is of outmost importance in cognitive psychology as working memory is at the basis of general cognitive function. Although the working memory capacity limit has been thoroughly studied, its origin still remains a matter of strong debate. Only recently has the role of visual saliency in modulating working memory storage capacity been assessed experimentally and proved to provide valuable insights into working memory function. In the computational arena, attractor networks have successfully accounted for psychophysical and neurophysiological data in numerous working memory tasks given their ability to produce a sustained elevated firing rate during a delay period. Here we investigate the mechanisms underlying working memory capacity by means of a biophysically-realistic attractor network with spiking neurons while accounting for two recent experimental observations: 1 the presence of a visually salient item reduces the number of items that can be held in working memory, and 2 visually salient items are commonly kept in memory at the cost of not keeping as many non-salient items. Our model suggests that working memory capacity is determined by two fundamental processes: encoding of visual items into working memory and maintenance of the encoded items upon their removal from the visual display. While maintenance critically depends on the constraints that lateral inhibition imposes to the mnemonic activity, encoding is limited by the ability of the stimulated neural assemblies to reach a sufficiently high level of excitation, a process governed by the dynamics of competition and cooperation among neuronal pools. Encoding is therefore contingent upon the visual working memory task and has led us to introduce the concept of effective working memory capacity (eWMC in contrast to the maximal upper capacity limit only reached under ideal conditions.

  13. Effective Visual Working Memory Capacity: An Emergent Effect from the Neural Dynamics in an Attractor Network

    Science.gov (United States)

    Dempere-Marco, Laura; Melcher, David P.; Deco, Gustavo

    2012-01-01

    The study of working memory capacity is of outmost importance in cognitive psychology as working memory is at the basis of general cognitive function. Although the working memory capacity limit has been thoroughly studied, its origin still remains a matter of strong debate. Only recently has the role of visual saliency in modulating working memory storage capacity been assessed experimentally and proved to provide valuable insights into working memory function. In the computational arena, attractor networks have successfully accounted for psychophysical and neurophysiological data in numerous working memory tasks given their ability to produce a sustained elevated firing rate during a delay period. Here we investigate the mechanisms underlying working memory capacity by means of a biophysically-realistic attractor network with spiking neurons while accounting for two recent experimental observations: 1) the presence of a visually salient item reduces the number of items that can be held in working memory, and 2) visually salient items are commonly kept in memory at the cost of not keeping as many non-salient items. Our model suggests that working memory capacity is determined by two fundamental processes: encoding of visual items into working memory and maintenance of the encoded items upon their removal from the visual display. While maintenance critically depends on the constraints that lateral inhibition imposes to the mnemonic activity, encoding is limited by the ability of the stimulated neural assemblies to reach a sufficiently high level of excitation, a process governed by the dynamics of competition and cooperation among neuronal pools. Encoding is therefore contingent upon the visual working memory task and has led us to introduce the concept of effective working memory capacity (eWMC) in contrast to the maximal upper capacity limit only reached under ideal conditions. PMID:22952608

  14. Effective visual working memory capacity: an emergent effect from the neural dynamics in an attractor network.

    Science.gov (United States)

    Dempere-Marco, Laura; Melcher, David P; Deco, Gustavo

    2012-01-01

    The study of working memory capacity is of outmost importance in cognitive psychology as working memory is at the basis of general cognitive function. Although the working memory capacity limit has been thoroughly studied, its origin still remains a matter of strong debate. Only recently has the role of visual saliency in modulating working memory storage capacity been assessed experimentally and proved to provide valuable insights into working memory function. In the computational arena, attractor networks have successfully accounted for psychophysical and neurophysiological data in numerous working memory tasks given their ability to produce a sustained elevated firing rate during a delay period. Here we investigate the mechanisms underlying working memory capacity by means of a biophysically-realistic attractor network with spiking neurons while accounting for two recent experimental observations: 1) the presence of a visually salient item reduces the number of items that can be held in working memory, and 2) visually salient items are commonly kept in memory at the cost of not keeping as many non-salient items. Our model suggests that working memory capacity is determined by two fundamental processes: encoding of visual items into working memory and maintenance of the encoded items upon their removal from the visual display. While maintenance critically depends on the constraints that lateral inhibition imposes to the mnemonic activity, encoding is limited by the ability of the stimulated neural assemblies to reach a sufficiently high level of excitation, a process governed by the dynamics of competition and cooperation among neuronal pools. Encoding is therefore contingent upon the visual working memory task and has led us to introduce the concept of effective working memory capacity (eWMC) in contrast to the maximal upper capacity limit only reached under ideal conditions.

  15. Applied Ontology Engineering in Cloud Services, Networks and Management Systems

    CERN Document Server

    Serrano Orozco, J Martín

    2012-01-01

    Metadata standards in today’s ICT sector are proliferating at unprecedented levels, while automated information management systems collect and process exponentially increasing quantities of data. With interoperability and knowledge exchange identified as a core challenge in the sector, this book examines the role ontology engineering can play in providing solutions to the problems of information interoperability and linked data. At the same time as introducing basic concepts of ontology engineering, the book discusses methodological approaches to formal representation of data and information models, thus facilitating information interoperability between heterogeneous, complex and distributed communication systems. In doing so, the text advocates the advantages of using ontology engineering in telecommunications systems. In addition, it offers a wealth of guidance and best-practice techniques for instances in which ontology engineering is applied in cloud services, computer networks and management systems. �...

  16. Basal functional connectivity within the anterior temporal network is associated with performance on declarative memory tasks.

    Science.gov (United States)

    Gour, Natalina; Ranjeva, Jean-Philippe; Ceccaldi, Mathieu; Confort-Gouny, Sylviane; Barbeau, Emmanuel; Soulier, Elisabeth; Guye, Maxime; Didic, Mira; Felician, Olivier

    2011-09-15

    Spontaneous fluctuations in the blood oxygenation level-dependent (BOLD) signal, as measured by functional magnetic resonance imaging (fMRI) at rest, exhibit a temporally coherent activity thought to reflect functionally relevant networks. Antero-mesial temporal structures are the site of early pathological changes in Alzheimer's disease and have been shown to be critical for declarative memory. Our study aimed at exploring the functional impact of basal connectivity of an anterior temporal network (ATN) on declarative memory. A heterogeneous group of subjects with varying performance on tasks assessing memory was therefore selected, including healthy subjects and patients with isolated memory complaint, amnestic Mild Cognitive Impairment (aMCI) and mild Alzheimer's disease (AD). Using Independent Component Analysis on resting-state fMRI, we extracted a relevant anterior temporal network (ATN) composed of the perirhinal and entorhinal cortex, the hippocampal head, the amygdala and the lateral temporal cortex extending to the temporal pole. A default mode network and an executive-control network were also selected to serve as control networks. We first compared basal functional connectivity of the ATN between patients and control subjects. Relative to controls, patients exhibited significantly increased functional connectivity in the ATN during rest. Specifically, voxel-based analysis revealed an increase within the inferior and superior temporal gyrus and the uncus. In the patient group, positive correlations between averaged connectivity values of ATN and performance on anterograde and retrograde object-based memory tasks were observed, while no correlation was found with other evaluated cognitive measures. These correlations were specific to the ATN, as no correlation between performance on memory tasks and the other selected networks was found. Taken together, these findings provide evidence that basal connectivity inside the ATN network has a functional role in

  17. Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks.

    Science.gov (United States)

    Zenke, Friedemann; Agnes, Everton J; Gerstner, Wulfram

    2015-04-21

    Synaptic plasticity, the putative basis of learning and memory formation, manifests in various forms and across different timescales. Here we show that the interaction of Hebbian homosynaptic plasticity with rapid non-Hebbian heterosynaptic plasticity is, when complemented with slower homeostatic changes and consolidation, sufficient for assembly formation and memory recall in a spiking recurrent network model of excitatory and inhibitory neurons. In the model, assemblies were formed during repeated sensory stimulation and characterized by strong recurrent excitatory connections. Even days after formation, and despite ongoing network activity and synaptic plasticity, memories could be recalled through selective delay activity following the brief stimulation of a subset of assembly neurons. Blocking any component of plasticity prevented stable functioning as a memory network. Our modelling results suggest that the diversity of plasticity phenomena in the brain is orchestrated towards achieving common functional goals.

  18. Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks

    Science.gov (United States)

    Zenke, Friedemann; Agnes, Everton J.; Gerstner, Wulfram

    2015-01-01

    Synaptic plasticity, the putative basis of learning and memory formation, manifests in various forms and across different timescales. Here we show that the interaction of Hebbian homosynaptic plasticity with rapid non-Hebbian heterosynaptic plasticity is, when complemented with slower homeostatic changes and consolidation, sufficient for assembly formation and memory recall in a spiking recurrent network model of excitatory and inhibitory neurons. In the model, assemblies were formed during repeated sensory stimulation and characterized by strong recurrent excitatory connections. Even days after formation, and despite ongoing network activity and synaptic plasticity, memories could be recalled through selective delay activity following the brief stimulation of a subset of assembly neurons. Blocking any component of plasticity prevented stable functioning as a memory network. Our modelling results suggest that the diversity of plasticity phenomena in the brain is orchestrated towards achieving common functional goals. PMID:25897632

  19. Out-of-Sequence Preventative Cell Dispatching for Multicast Input-Queued Space-Memory-Memory Clos-Network

    DEFF Research Database (Denmark)

    Yu, Hao; Ruepp, Sarah Renée; Berger, Michael Stübert

    2011-01-01

    This paper proposes two out-of-sequence (OOS) preventative cell dispatching algorithms for the multicast input-queued space-memory-memory (IQ-SMM) Clos-network switch architecture, i.e. the multicast flow-based DSRR (MF-DSRR) and the multicast flow-based round-robin (MFRR). Treating each cell...... independently, the desynchronized static round-robin (DSRR) cell dispatching scheme can evenly distribute cells to the central switching modules, however, its frequent change of the input switching module connection pattern causes a serious OOS problem to the IQ-SMM architecture. Therefore large reassembly...

  20. Designing Green Networks and Network Operations Saving Run-the-Engine Costs

    CERN Document Server

    Minoli, Daniel

    2011-01-01

    In recent years the confluence of socio-political trends toward environmental responsibility and the pressing need to reduce Run-the-Engine (RTE) costs has given birth to a nascent discipline of Green IT. A clear and concise introduction to green networks and green network operations, this book examines analytical measures and discusses virtualization, network computing, and web services as approaches for green data centers and networks. It identifies some strategies for green appliance and end devices and examines the methodical steps that can be taken over time to achieve a seamless migratio

  1. Exploring Neural Network Models with Hierarchical Memories and Their Use in Modeling Biological Systems

    Science.gov (United States)

    Pusuluri, Sai Teja

    Energy landscapes are often used as metaphors for phenomena in biology, social sciences and finance. Different methods have been implemented in the past for the construction of energy landscapes. Neural network models based on spin glass physics provide an excellent mathematical framework for the construction of energy landscapes. This framework uses a minimal number of parameters and constructs the landscape using data from the actual phenomena. In the past neural network models were used to mimic the storage and retrieval process of memories (patterns) in the brain. With advances in the field now, these models are being used in machine learning, deep learning and modeling of complex phenomena. Most of the past literature focuses on increasing the storage capacity and stability of stored patterns in the network but does not study these models from a modeling perspective or an energy landscape perspective. This dissertation focuses on neural network models both from a modeling perspective and from an energy landscape perspective. I firstly show how the cellular interconversion phenomenon can be modeled as a transition between attractor states on an epigenetic landscape constructed using neural network models. The model allows the identification of a reaction coordinate of cellular interconversion by analyzing experimental and simulation time course data. Monte Carlo simulations of the model show that the initial phase of cellular interconversion is a Poisson process and the later phase of cellular interconversion is a deterministic process. Secondly, I explore the static features of landscapes generated using neural network models, such as sizes of basins of attraction and densities of metastable states. The simulation results show that the static landscape features are strongly dependent on the correlation strength and correlation structure between patterns. Using different hierarchical structures of the correlation between patterns affects the landscape features

  2. Factors affecting reorganisation of memory encoding networks in temporal lobe epilepsy.

    Science.gov (United States)

    Sidhu, M K; Stretton, J; Winston, G P; Symms, M; Thompson, P J; Koepp, M J; Duncan, J S

    2015-02-01

    In temporal lobe epilepsy (TLE) due to hippocampal sclerosis reorganisation in the memory encoding network has been consistently described. Distinct areas of reorganisation have been shown to be efficient when associated with successful subsequent memory formation or inefficient when not associated with successful subsequent memory. We investigated the effect of clinical parameters that modulate memory functions: age at onset of epilepsy, epilepsy duration and seizure frequency in a large cohort of patients. We studied 53 patients with unilateral TLE and hippocampal sclerosis (29 left). All participants performed a functional magnetic resonance imaging memory encoding paradigm of faces and words. A continuous regression analysis was used to investigate the effects of age at onset of epilepsy, epilepsy duration and seizure frequency on the activation patterns in the memory encoding network. Earlier age at onset of epilepsy was associated with left posterior hippocampus activations that were involved in successful subsequent memory formation in left hippocampal sclerosis patients. No association of age at onset of epilepsy was seen with face encoding in right hippocampal sclerosis patients. In both left hippocampal sclerosis patients during word encoding and right hippocampal sclerosis patients during face encoding, shorter duration of epilepsy and lower seizure frequency were associated with medial temporal lobe activations that were involved in successful memory formation. Longer epilepsy duration and higher seizure frequency were associated with contralateral extra-temporal activations that were not associated with successful memory formation. Age at onset of epilepsy influenced verbal memory encoding in patients with TLE due to hippocampal sclerosis in the speech-dominant hemisphere. Shorter duration of epilepsy and lower seizure frequency were associated with less disruption of the efficient memory encoding network whilst longer duration and higher seizure

  3. Minimum network constraint on reverse engineering to develop biological regulatory networks.

    Science.gov (United States)

    Shao, Bin; Wu, Jiayi; Tian, Binghui; Ouyang, Qi

    2015-09-07

    Reconstructing the topological structure of biological regulatory networks from microarray expression data or data of protein expression profiles is one of major tasks in systems biology. In recent years, various mathematical methods have been developed to meet this task. Here, based on our previously reported reverse engineering method, we propose a new constraint, i.e., the minimum network constraint, to facilitate the reconstruction of biological networks. Three well studied regulatory networks (the budding yeast cell cycle network, the fission yeast cell cycle network, and the SOS network of Escherichia coli) were used as the test sets to verify the performance of this method. Numerical results show that the biological networks prefer to use the minimal networks to fulfill their functional tasks, making it possible to apply minimal network criteria in the network reconstruction process. Two scenarios were considered in the reconstruction process: generating data using different initial conditions; and generating data from knock out and over-expression experiments. In both cases, network structures are revealed faithfully in a few steps using our approach. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. CellNet: network biology applied to stem cell engineering.

    Science.gov (United States)

    Cahan, Patrick; Li, Hu; Morris, Samantha A; Lummertz da Rocha, Edroaldo; Daley, George Q; Collins, James J

    2014-08-14

    Somatic cell reprogramming, directed differentiation of pluripotent stem cells, and direct conversions between differentiated cell lineages represent powerful approaches to engineer cells for research and regenerative medicine. We have developed CellNet, a network biology platform that more accurately assesses the fidelity of cellular engineering than existing methodologies and generates hypotheses for improving cell derivations. Analyzing expression data from 56 published reports, we found that cells derived via directed differentiation more closely resemble their in vivo counterparts than products of direct conversion, as reflected by the establishment of target cell-type gene regulatory networks (GRNs). Furthermore, we discovered that directly converted cells fail to adequately silence expression programs of the starting population and that the establishment of unintended GRNs is common to virtually every cellular engineering paradigm. CellNet provides a platform for quantifying how closely engineered cell populations resemble their target cell type and a rational strategy to guide enhanced cellular engineering. Copyright © 2014 Elsevier Inc. All rights reserved.

  5. Computer, Network, Software, and Hardware Engineering with Applications

    CERN Document Server

    Schneidewind, Norman F

    2012-01-01

    There are many books on computers, networks, and software engineering but none that integrate the three with applications. Integration is important because, increasingly, software dominates the performance, reliability, maintainability, and availability of complex computer and systems. Books on software engineering typically portray software as if it exists in a vacuum with no relationship to the wider system. This is wrong because a system is more than software. It is comprised of people, organizations, processes, hardware, and software. All of these components must be considered in an integr

  6. Dynamic Connectivity between Brain Networks Supports Working Memory: Relationships to Dopamine Release and Schizophrenia

    Science.gov (United States)

    Van Snellenberg, Jared X.; Benavides, Caridad; Slifstein, Mark; Wang, Zhishun; Moore, Holly; Abi-Dargham, Anissa

    2016-01-01

    Connectivity between brain networks may adapt flexibly to cognitive demand, a process that could underlie adaptive behaviors and cognitive deficits, such as those observed in neuropsychiatric conditions like schizophrenia. Dopamine signaling is critical for working memory but its influence on internetwork connectivity is relatively unknown. We addressed these questions in healthy humans using functional magnetic resonance imaging (during an n-back working-memory task) and positron emission tomography using the radiotracer [11C]FLB457 before and after amphetamine to measure the capacity for dopamine release in extrastriatal brain regions. Brain networks were defined by spatial independent component analysis (ICA) and working-memory-load-dependent connectivity between task-relevant pairs of networks was determined via a modified psychophysiological interaction analysis. For most pairs of task-relevant networks, connectivity significantly changed as a function of working-memory load. Moreover, load-dependent changes in connectivity between left and right frontoparietal networks (Δ connectivity lFPN-rFPN) predicted interindividual differences in task performance more accurately than other fMRI and PET imaging measures. Δ Connectivity lFPN-rFPN was not related to cortical dopamine release capacity. A second study in unmedicated patients with schizophrenia showed no abnormalities in load-dependent connectivity but showed a weaker relationship between Δ connectivity lFPN-rFPN and working memory performance in patients compared with matched healthy individuals. Poor working memory performance in patients was, in contrast, related to deficient cortical dopamine release. Our findings indicate that interactions between brain networks dynamically adapt to fluctuating environmental demands. These dynamic adaptations underlie successful working memory performance in healthy individuals and are not well predicted by amphetamine-induced dopamine release capacity. SIGNIFICANCE

  7. Engineering education research: Impacts of an international network of female engineers on the persistence of Liberian undergraduate women studying engineering

    Science.gov (United States)

    Rimer, Sara; Reddivari, Sahithya; Cotel, Aline

    2015-11-01

    As international efforts to educate and empower women continue to rise, engineering educators are in a unique position to be a part of these efforts by encouraging and supporting women across the world at the university level through STEM education and outreach. For the past two years, the University of Michigan has been a part of a grassroots effort to encourage and support the persistence of engineering female students at University of Liberia. This effort has led to the implementation of a leadership camp this past August for Liberian engineering undergraduate women, meant to: (i) to empower engineering students with the skills, support, and inspiration necessary to become successful and well-rounded engineering professionals in a global engineering market; and (ii) to strengthen the community of Liberian female engineers by building cross-cultural partnerships among students resulting in a international network of women engineers. This session will present qualitative research findings on the impact of this grassroots effort on Liberian female students? persistence in engineering, and the future directions of this work.

  8. Distributed memory in a heterogeneous network, as used in the CERN-PS complex timing system

    CERN Document Server

    Kovaltsov, V I

    1995-01-01

    The Distributed Table Manager (DTM) is a fast and efficient utility for distributing named binary data structures called Tables, of arbitrary size and structure, around a heterogeneous network of computers to a set of registered clients. The Tables are transmitted over a UDP network between DTM servers in network format, where the servers perform the conversions to and from host format for local clients. The servers provide clients with synchronization mechanisms, a choice of network data flows, and table options such as keeping table disc copies, shared memory or heap memory table allocation, table read/write permissions, and table subnet broadcasting. DTM has been designed to be easily maintainable, and to automatically recover from the type of errors typically encountered in a large control system network. The DTM system is based on a three level server daemon hierarchy, in which an inter daemon protocol handles network failures, and incorporates recovery procedures which will guarantee table consistency w...

  9. Differences in short-term memory span of social sciences, science and engineering, and business majors

    Science.gov (United States)

    Khan, Naeem Ullah

    This study investigated the difference in the short-term memory span of students of three major groups, namely Social Sciences, Science and Engineering, and Business. This study was designed to answer the following two questions: (1) Is there a difference between short-term memory span, measured by digit span, among the students in or intended for Social Sciences, Science and Engineering, and Business majors? (2) Is there a difference of short-term memory span, measured by word span, among students in or intended for Social Sciences, Science and Engineering, and Business majors? For answering these two questions, inferential and descriptive statistics were used. Analysis of Variance (ANOVA) was used to compare the means of the scores of digit span and word span among the three major groups. The means of digit span and word span among the three groups were compared to find out if a statistically significant difference existed among them or not. The observations were recorded at the level of significance at alpha = .05, and highly significant at alpha = .01. The answer to the first question is yes. The results of this study showed a statistically significant difference in the means of the digit span of the three major groups of students in or intended for Social Sciences, Science and Engineering, and Business. The mean scaled score for digit span was 12.88 for Social Sciences, 14.27 for Science and Engineering, and 15.33 for Business majors, respectively. The means of the free recalls word span of the three groups was 7.23 for Social Sciences, 7.89 for Science and Engineering, and 7.12 for Business majors, respectively. No significant difference was observed in the means of the word span of the three groups. In general observations, it is noted that students want to stay in the subjects or majors in which they can perform well or feel comfortable. In addition to this, students are screened in the school system due to levels of performance or selection pressure

  10. Altered Effective Connectivity of Hippocampus-Dependent Episodic Memory Network in mTBI Survivors

    Directory of Open Access Journals (Sweden)

    Hao Yan

    2016-01-01

    Full Text Available Traumatic brain injuries (TBIs are generally recognized to affect episodic memory. However, less is known regarding how external force altered the way functionally connected brain structures of the episodic memory system interact. To address this issue, we adopted an effective connectivity based analysis, namely, multivariate Granger causality approach, to explore causal interactions within the brain network of interest. Results presented that TBI induced increased bilateral and decreased ipsilateral effective connectivity in the episodic memory network in comparison with that of normal controls. Moreover, the left anterior superior temporal gyrus (aSTG, the concept forming hub, left hippocampus (the personal experience binding hub, and left parahippocampal gyrus (the contextual association hub were no longer network hubs in TBI survivors, who compensated for hippocampal deficits by relying more on the right hippocampus (underlying perceptual memory and the right medial frontal gyrus (MeFG in the anterior prefrontal cortex (PFC. We postulated that the overrecruitment of the right anterior PFC caused dysfunction of the strategic component of episodic memory, which caused deteriorating episodic memory in mTBI survivors. Our findings also suggested that the pattern of brain network changes in TBI survivors presented similar functional consequences to normal aging.

  11. A reverse engineering algorithm for neural networks, applied to the subthalamopallidal network of basal ganglia.

    Science.gov (United States)

    Floares, Alexandru George

    2008-01-01

    Modeling neural networks with ordinary differential equations systems is a sensible approach, but also very difficult. This paper describes a new algorithm based on linear genetic programming which can be used to reverse engineer neural networks. The RODES algorithm automatically discovers the structure of the network, including neural connections, their signs and strengths, estimates its parameters, and can even be used to identify the biophysical mechanisms involved. The algorithm is tested on simulated time series data, generated using a realistic model of the subthalamopallidal network of basal ganglia. The resulting ODE system is highly accurate, and results are obtained in a matter of minutes. This is because the problem of reverse engineering a system of coupled differential equations is reduced to one of reverse engineering individual algebraic equations. The algorithm allows the incorporation of common domain knowledge to restrict the solution space. To our knowledge, this is the first time a realistic reverse engineering algorithm based on linear genetic programming has been applied to neural networks.

  12. Complex network structure influences processing in long-term and short-term memory

    OpenAIRE

    Vitevitch, Michael S.; Chan, Kit Ying; Roodenrys, Steven

    2012-01-01

    Complex networks describe how entities in systems interact; the structure of such networks is argued to influence processing. One measure of network structure, clustering coefficient, C, measures the extent to which neighbors of a node are also neighbors of each other. Previous psycholinguistic experiments found that the C of phonological word-forms influenced retrieval from the mental lexicon (that portion of long-term memory dedicated to language) during the on-line recognition and producti...

  13. Adaptive Multiclient Network-on-Chip Memory Core: Hardware Architecture, Software Abstraction Layer, and Application Exploration

    Directory of Open Access Journals (Sweden)

    Diana Göhringer

    2012-01-01

    Full Text Available This paper presents the hardware architecture and the software abstraction layer of an adaptive multiclient Network-on-Chip (NoC memory core. The memory core supports the flexibility of a heterogeneous FPGA-based runtime adaptive multiprocessor system called RAMPSoC. The processing elements, also called clients, can access the memory core via the Network-on-Chip (NoC. The memory core supports a dynamic mapping of an address space for the different clients as well as different data transfer modes, such as variable burst sizes. Therefore, two main limitations of FPGA-based multiprocessor systems, the restricted on-chip memory resources and that usually only one physical channel to an off-chip memory exists, are leveraged. Furthermore, a software abstraction layer is introduced, which hides the complexity of the memory core architecture and which provides an easy to use interface for the application programmer. Finally, the advantages of the novel memory core in terms of performance, flexibility, and user friendliness are shown using a real-world image processing application.

  14. Analysis of naming game over networks in the presence of memory loss

    Science.gov (United States)

    Fu, Guiyuan; Cai, Yunze; Zhang, Weidong

    2017-08-01

    In this paper, we study the dynamics of naming game where individuals are under the influence of memory loss. An extended naming game incorporating memory loss is proposed. Different from the existing naming game models, the individual in the proposed model would forget some words with a probability in his memory during interaction and keep his conveyed word unchanged until he reaches a local agreement. We analyze the dynamics of the proposed model through extensive and comprehensive simulations, where four typical networks with different configuration are employed. The influence of memory loss as well as the population size on the performance of the proposed model is investigated. The simulation results show that (i) the stronger memory loss, the larger convergence time; (ii) as the strength of memory loss becomes stronger, maximum number of total words will decrease, while the maximum number of different words among the population remains almost unchanged; (iii) the maximum number of different words increases linearly with the increase of the population size and coincides with each other under different strength of memory loss. The findings in the proposed model may give an insight to understand better the influence of memory loss on the transient dynamics of language evolution and opinion formation over networks.

  15. Hydrogel Bioprinted Microchannel Networks for Vascularization of Tissue Engineering Constructs

    Science.gov (United States)

    Bertassoni, Luiz E.; Cecconi, Martina; Manoharan, Vijayan; Nikkhah, Mehdi; Hjortnaes, Jesper; Cristino, Ana Luiza; Barabaschi, Giada; Demarchi, Danilo; Dokmeci, Mehmet R.; Yang, Yunzhi; Khademhosseini, Ali

    2014-01-01

    Vascularization remains a critical challenge in tissue engineering. The development of vascular networks within densely populated and metabolically functional tissues facilitate transport of nutrients and removal of waste products, thus preserving cellular viability over a long period of time. Despite tremendous progress in fabricating complex tissue constructs in the past few years, approaches for controlled vascularization within hydrogel based engineered tissue constructs have remained limited. Here, we report a three dimensional (3D) micromolding technique utilizing bioprinted agarose template fibers to fabricate microchannel networks with various architectural features within photo cross linkable hydrogel constructs. Using the proposed approach, we were able to successfully embed functional and perfusable microchannels inside methacrylated gelatin (GelMA), star poly (ethylene glycol-co-lactide) acrylate (SPELA), poly (ethylene glycol) dimethacrylate (PEGDMA) and poly (ethylene glycol) diacrylate (PEGDA) hydrogels at different concentrations. In particular, GelMA hydrogels were used as a model to demonstrate the functionality of the fabricated vascular networks in improving mass transport, cellular viability and differentiation within the cell-laden tissue constructs. In addition, successful formation of endothelial monolayers within the fabricated channels was confirmed. Overall, our proposed strategy represents an effective technique for vascularization of hydrogel constructs with useful applications in tissue engineering and organs on a chip. PMID:24860845

  16. The Impact of Auditory Working Memory Training on the Fronto-Parietal Working Memory Network

    Directory of Open Access Journals (Sweden)

    Julia eSchneiders

    2012-06-01

    Full Text Available Working memory training has been widely used to investigate working memory processes. We have shown previously that visual working memory benefits only from intra-modal visual but not from across-modal auditory working memory training. In the present functional magnetic resonance imaging study we examined whether auditory working memory processes can also be trained specifically and which training-induced activation changes accompany theses effects. It was investigated whether working memory training with strongly distinct auditory materials transfers exclusively to an auditory (intra-modal working memory task or whether it generalizes to an (across-modal visual working memory task. We used an adaptive n-back training with tonal sequences and a passive control condition. The memory training led to a reliable training gain. Transfer effects were found for the (intra-modal auditory but not for the (across-modal visual 2-back task. Training-induced activation changes in the auditory 2-back task were found in two regions in the right inferior frontal gyrus. These effects confirm our previous findings in the visual modality and extends intra-modal effects to the auditory modality. These results might reflect increased neural efficiency in auditory working memory processes as in the right inferior frontal gyrus is frequently found in maintaining modality-specific auditory information. By this, these effects are analogical to the activation decreases in the right middle frontal gyrus for the visual modality in our previous study. Furthermore, task-unspecific (across-modal activation decreases in the visual and auditory 2-back task were found in the right inferior parietal lobule and the superior portion of the right middle frontal gyrus reflecting less demands on general attentional control processes. These data are in good agreement with across-modal activation decreases within the same brain regions on a visual 2-back task reported previously.

  17. Evolutionary swarm neural network game engine for Capture Go.

    Science.gov (United States)

    Cai, Xindi; Venayagamoorthy, Ganesh K; Wunsch, Donald C

    2010-03-01

    Evaluation of the current board position is critical in computer game engines. In sufficiently complex games, such a task is too difficult for a traditional brute force search to accomplish, even when combined with expert knowledge bases. This motivates the investigation of alternatives. This paper investigates the combination of neural networks, particle swarm optimization (PSO), and evolutionary algorithms (EAs) to train a board evaluator from zero knowledge. By enhancing the survivors of an EA with PSO, the hybrid algorithm successfully trains the high-dimensional neural networks to provide an evaluation of the game board through self-play. Experimental results, on the benchmark game of Capture Go, demonstrate that the hybrid algorithm can be more powerful than its individual parts, with the system playing against EA and PSO trained game engines. Also, the winning results of tournaments against a Hill-Climbing trained game engine confirm that the improvement comes from the hybrid algorithm itself. The hybrid game engine is also demonstrated against a hand-coded defensive player and a web player. Copyright 2009 Elsevier Ltd. All rights reserved.

  18. Reverse Engineering Cellular Networks with Information Theoretic Methods

    Directory of Open Access Journals (Sweden)

    Julio R. Banga

    2013-05-01

    Full Text Available Building mathematical models of cellular networks lies at the core of systems biology. It involves, among other tasks, the reconstruction of the structure of interactions between molecular components, which is known as network inference or reverse engineering. Information theory can help in the goal of extracting as much information as possible from the available data. A large number of methods founded on these concepts have been proposed in the literature, not only in biology journals, but in a wide range of areas. Their critical comparison is difficult due to the different focuses and the adoption of different terminologies. Here we attempt to review some of the existing information theoretic methodologies for network inference, and clarify their differences. While some of these methods have achieved notable success, many challenges remain, among which we can mention dealing with incomplete measurements, noisy data, counterintuitive behaviour emerging from nonlinear relations or feedback loops, and computational burden of dealing with large data sets.

  19. Recognizing partially occluded objects by a bidirectional associative memory neural network

    Science.gov (United States)

    Ansari, Nirwan; Liu, Xianjun

    1993-07-01

    We develop a bidirectional associative memory (BAM)-based neural network to achieve high- speed partial shape recognition. To recognize objects that are partially occluded, we represent each object by a set of landmarks. The landmarks of an object are points of interest relative to the object that have important shape attributes. To achieve recognition, feature values (landmark values) of each model object are trained and stored in the network. Each memory cell is trained to store landmark values of a model object for all possible positions. Given a scene that may consist of several objects, landmarks in the scene are first extracted, and their corresponding landmark values are computed. Scene landmark values are entered to each trained memory cell. The memory cell is shown to be able to recall the position of the model object in the scene. A heuristic measure is then computed to validate the recognition.

  20. Synaptic Plasticity, Engrams, and Network Oscillations in Amygdala Circuits for Storage and Retrieval of Emotional Memories.

    Science.gov (United States)

    Bocchio, Marco; Nabavi, Sadegh; Capogna, Marco

    2017-05-17

    The neuronal circuits of the basolateral amygdala (BLA) are crucial for acquisition, consolidation, retrieval, and extinction of associative emotional memories. Synaptic plasticity in BLA neurons is essential for associative emotional learning and is a candidate mechanism through which subsets of BLA neurons (commonly termed "engram") are recruited during learning and reactivated during memory retrieval. In parallel, synchronous oscillations in the theta and gamma bands between the BLA and interconnected structures have been shown to occur during consolidation and retrieval of emotional memories. Understanding how these cellular and network phenomena interact is vital to decipher the roles of emotional memory formation and storage in the healthy and pathological brain. Here, we review data on synaptic plasticity, engrams, and network oscillations in the rodent BLA. We explore mechanisms through which synaptic plasticity, engrams, and long-range synchrony might be interconnected. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. Tailored (meth)acrylate shape-memory polymer networks for ophthalmic applications.

    Science.gov (United States)

    Song, Li; Hu, Wang; Wang, Guojie; Niu, Guoguang; Zhang, Hongbin; Cao, Hui; Wang, Kaijie; Yang, Huai; Zhu, Siquan

    2010-10-08

    The unique features of shape-memory polymers enables their use in minimally invasive surgical procedures with a compact starting material switching over to a voluminous structure in vivo. In this work, a series of transparent, thermoset (meth)acrylate shape-memory polymer networks with tailored thermomechanics have been synthesized and evaluated. Fundamental trends were established for the effect of the crosslinker content and crosslinker molecular weight on glass transition temperature, rubbery modulus and shape-recovery behavior, and the results are intended to help with future shape-memory device design. The prepared (meth)acrylate networks with high transparency and favorable biocompatibility are presented as a promising shape-memory ophthalmic biomaterial.

  2. A functional magnetic resonance imaging study mapping the episodic memory encoding network in temporal lobe epilepsy

    Science.gov (United States)

    Sidhu, Meneka K.; Stretton, Jason; Winston, Gavin P.; Bonelli, Silvia; Centeno, Maria; Vollmar, Christian; Symms, Mark; Thompson, Pamela J.; Koepp, Matthias J.

    2013-01-01

    Functional magnetic resonance imaging has demonstrated reorganization of memory encoding networks within the temporal lobe in temporal lobe epilepsy, but little is known of the extra-temporal networks in these patients. We investigated the temporal and extra-temporal reorganization of memory encoding networks in refractory temporal lobe epilepsy and the neural correlates of successful subsequent memory formation. We studied 44 patients with unilateral temporal lobe epilepsy and hippocampal sclerosis (24 left) and 26 healthy control subjects. All participants performed a functional magnetic resonance imaging memory encoding paradigm of faces and words with subsequent out-of-scanner recognition assessments. A blocked analysis was used to investigate activations during encoding and neural correlates of subsequent memory were investigated using an event-related analysis. Event-related activations were then correlated with out-of-scanner verbal and visual memory scores. During word encoding, control subjects activated the left prefrontal cortex and left hippocampus whereas patients with left hippocampal sclerosis showed significant additional right temporal and extra-temporal activations. Control subjects displayed subsequent verbal memory effects within left parahippocampal gyrus, left orbitofrontal cortex and fusiform gyrus whereas patients with left hippocampal sclerosis activated only right posterior hippocampus, parahippocampus and fusiform gyrus. Correlational analysis showed that patients with left hippocampal sclerosis with better verbal memory additionally activated left orbitofrontal cortex, anterior cingulate cortex and left posterior hippocampus. During face encoding, control subjects showed right lateralized prefrontal cortex and bilateral hippocampal activations. Patients with right hippocampal sclerosis showed increased temporal activations within the superior temporal gyri bilaterally and no increased extra-temporal areas of activation compared with

  3. 1st International Conference on Engineering and Applied Sciences Optimization : Dedicated to the Memory of Professor M.G. Karlaftis

    CERN Document Server

    Papadrakakis, Manolis; OPT-i

    2015-01-01

    The chapters which appear in this volume are selected studies presented at the First International Conference on Engineering and Applied Sciences Optimization (OPT-i), Kos, Greece, 4-6 June 2014,  and works written by friends, former colleagues and students of the late Professor M. G. Karlaftis; all in the area of optimization that he loved and published so much in himself. The subject areas represented here range from structural optimization, logistics, transportation, traffic and telecommunication networks to operational research, metaheuristics, multidisciplinary and multiphysics design optimization, etc.  This volume is dedicated to the life and the memory of Professor Matthew G. Karlaftis, who passed away a few hours before he was to give the opening speech at OPT-i. All contributions reflect the warmth and genuine friendship which he enjoyed from his associates and show how much his scientific contribution has been appreciated. He will be greatly missed and it is hoped that this volume will be receive...

  4. Using chaotic artificial neural networks to model memory in the brain

    Science.gov (United States)

    Aram, Zainab; Jafari, Sajad; Ma, Jun; Sprott, Julien C.; Zendehrouh, Sareh; Pham, Viet-Thanh

    2017-03-01

    In the current study, a novel model for human memory is proposed based on the chaotic dynamics of artificial neural networks. This new model explains a biological fact about memory which is not yet explained by any other model: There are theories that the brain normally works in a chaotic mode, while during attention it shows ordered behavior. This model uses the periodic windows observed in a previously proposed model for the brain to store and then recollect the information.

  5. Salience Network and Parahippocampal Dopamine Dysfunction in Memory-Impaired Parkinson Disease

    Science.gov (United States)

    Christopher, Leigh; Duff-Canning, Sarah; Koshimori, Yuko; Segura, Barbara; Boileau, Isabelle; Chen, Robert; Lang, Anthony E.; Houle, Sylvain; Rusjan, Pablo; Strafella, Antonio P.

    2016-01-01

    Objective Patients with Parkinson disease (PD) and mild cognitive impairment (MCI) are vulnerable to dementia and frequently experience memory deficits. This could be the result of dopamine dysfunction in corticostriatal networks (salience, central executive networks, and striatum) and/or the medial temporal lobe. Our aim was to investigate whether dopamine dysfunction in these regions contributes to memory impairment in PD. Methods We used positron emission tomography imaging to compare D2 receptor availability in the cortex and striatal (limbic and associative) dopamine neuron integrity in 4 groups: memory-impaired PD (amnestic MCI; n=9), PD with nonamnestic MCI (n=10), PD without MCI (n=11), and healthy controls (n=14). Subjects were administered a full neuropsychological test battery for cognitive performance. Results Memory-impaired patients demonstrated more significant reductions in D2 receptor binding in the salience network (insular cortex and anterior cingulate cortex [ACC] and the right parahippocampal gyrus [PHG]) compared to healthy controls and patients with no MCI. They also presented reductions in the right insula and right ACC compared to nonamnestic MCI patients. D2 levels were correlated with memory performance in the right PHG and left insula of amnestic patients and with executive performance in the bilateral insula and left ACC of all MCI patients. Associative striatal dopamine denervation was significant in all PD patients. Interpretation Dopaminergic differences in the salience network and the medial temporal lobe contribute to memory impairment in PD. Furthermore, these findings indicate the vulnerability of the salience network in PD and its potential role in memory and executive dysfunction. PMID:25448687

  6. Opportunities for protein interaction network-guided cellular engineering.

    Science.gov (United States)

    Wright, Phillip C; Jaffe, Stephen; Noirel, Josselin; Zou, Xin

    2013-01-01

    As we move further into the postgenomics age where the mountain of systems biology-generated data keeps growing, as does the number of genomes that have been sequenced, we have the exciting opportunity to understand more deeply the biology of important systems, those that are amenable to genetic manipulation and metabolic engineering. This is, of course, if we can make 'head or tail' of what we have measured and use this for robust predictions. The use of modern mass spectrometry tools has greatly facilitated our understanding of which proteins are present in a particular phenotype, their relative and absolute abundances and their state of modifications. Coupled with modern bioinformatics and systems biology modelling tools, this has the opportunity of not just providing information and understanding but also to provide targets for engineering and suggest new genetic/metabolic designs. Cellular engineering, whether it be via metabolic engineering, synthetic biology or a combination of both approaches, offers exciting potential for biotechnological exploitation in fields as diverse as medicine and energy as well as fine and bulk chemicals production. At the heart of such effective designs, proteins' interactions with other proteins or with DNA will become increasingly important. In this work, we examine the work done until now in protein-protein interactions and how this network knowledge can be used to inform ambitious cellular engineering strategies. Some examples demonstrating small molecules/biofuels and biopharmaceuticals applications are presented. Copyright © 2012 International Union of Biochemistry and Molecular Biology, Inc.

  7. Biofabricated soft network composites for cartilage tissue engineering.

    Science.gov (United States)

    Bas, Onur; De-Juan-Pardo, Elena M; Meinert, Christoph; D'Angella, Davide; Baldwin, Jeremy G; Bray, Laura J; Wellard, R Mark; Kollmannsberger, Stefan; Rank, Ernst; Werner, Carsten; Klein, Travis J; Catelas, Isabelle; Hutmacher, Dietmar W

    2017-05-12

    Articular cartilage from a material science point of view is a soft network composite that plays a critical role in load-bearing joints during dynamic loading. Its composite structure, consisting of a collagen fiber network and a hydrated proteoglycan matrix, gives rise to the complex mechanical properties of the tissue including viscoelasticity and stress relaxation. Melt electrospinning writing allows the design and fabrication of medical grade polycaprolactone (mPCL) fibrous networks for the reinforcement of soft hydrogel matrices for cartilage tissue engineering. However, these fiber-reinforced constructs underperformed under dynamic and prolonged loading conditions, suggesting that more targeted design approaches and material selection are required to fully exploit the potential of fibers as reinforcing agents for cartilage tissue engineering. In the present study, we emulated the proteoglycan matrix of articular cartilage by using highly negatively charged star-shaped poly(ethylene glycol)/heparin hydrogel (sPEG/Hep) as the soft matrix. These soft hydrogels combined with mPCL melt electrospun fibrous networks exhibited mechanical anisotropy, nonlinearity, viscoelasticity and morphology analogous to those of their native counterpart, and provided a suitable microenvironment for in vitro human chondrocyte culture and neocartilage formation. In addition, a numerical model using the p-version of the finite element method (p-FEM) was developed in order to gain further insights into the deformation mechanisms of the constructs in silico, as well as to predict compressive moduli. To our knowledge, this is the first study presenting cartilage tissue-engineered constructs that capture the overall transient, equilibrium and dynamic biomechanical properties of human articular cartilage.

  8. Memory

    Science.gov (United States)

    ... it has to decide what is worth remembering. Memory is the process of storing and then remembering this information. There are different types of memory. Short-term memory stores information for a few ...

  9. Preparation and characterization of shape memory composite foams with interpenetrating polymer networks

    Science.gov (United States)

    Yao, Yongtao; Zhou, Tianyang; Yang, Cheng; Liu, Yanju; Leng, Jinsong

    2016-03-01

    The present study reports a feasible approach of fabricating shape memory composite foams with an interpenetrating polymer network (IPN) based on polyurethane (PU) and shape memory epoxy resin (SMER) via a simultaneous polymerization technique. The PU component is capable of constructing a foam structure and the SMER is grafted on the PU network to offer its shape memory property in the final IPN foams. A series of IPN foams without phase separation were produced due to good compatibility and a tight chemical interaction between PU and SMER components. The relationships of the geometry of the foam cell were investigated via varying compositions of PU and SMER. The physical property and shape memory property were also evaluated. The stimulus temperature of IPN shape memory composite foams, glass temperature (Tg), could be tunable by varying the constituents and Tg of PU and SMER. The mechanism of the shape memory effect of IPN foams has been proposed. The shape memory composite foam with IPN developed in this study has the potential to extend its application field.

  10. Brain functional network changes following Prelimbic area inactivation in a spatial memory extinction task.

    Science.gov (United States)

    Méndez-Couz, Marta; Conejo, Nélida M; Vallejo, Guillermo; Arias, Jorge L

    2015-01-01

    Several studies suggest a prefrontal cortex involvement during the acquisition and consolidation of spatial memory, suggesting an active modulating role at late stages of acquisition processes. Recently, we have reported that the prelimbic and infralimbic areas of the prefrontal cortex, among other structures, are also specifically involved in the late phases of spatial memory extinction. This study aimed to evaluate whether the inactivation of the prelimbic area of the prefrontal cortex impaired spatial memory extinction. For this purpose, male Wistar rats were implanted bilaterally with cannulae into the prelimbic region of the prefrontal cortex. Animals were trained during 5 consecutive days in a hidden platform task and tested for reference spatial memory immediately after the last training session. One day after completing the training task, bilateral infusion of the GABAA receptor agonist Muscimol was performed before the extinction protocol was carried out. Additionally, cytochrome c oxidase histochemistry was applied to map the metabolic brain activity related to the spatial memory extinction under prelimbic cortex inactivation. Results show that animals acquired the reference memory task in the water maze, and the extinction task was successfully completed without significant impairment. However, analysis of the functional brain networks involved by cytochrome oxidase activity interregional correlations showed changes in brain networks between the group treated with Muscimol as compared to the saline-treated group, supporting the involvement of the mammillary bodies at a the late stage in the memory extinction process. Copyright © 2015 Elsevier B.V. All rights reserved.

  11. A quantitative theory of the functions of the hippocampal CA3 network in memory.

    Science.gov (United States)

    Rolls, Edmund T

    2013-01-01

    A quantitative computational theory of the operation of the hippocampal CA3 system as an autoassociation or attractor network used in episodic memory system is described. In this theory, the CA3 system operates as a single attractor or autoassociation network to enable rapid, one-trial, associations between any spatial location (place in rodents, or spatial view in primates) and an object or reward, and to provide for completion of the whole memory during recall from any part. The theory is extended to associations between time and object or reward to implement temporal order memory, also important in episodic memory. The dentate gyrus (DG) performs pattern separation by competitive learning to produce sparse representations suitable for setting up new representations in CA3 during learning, producing for example neurons with place-like fields from entorhinal cortex grid cells. The dentate granule cells produce by the very small number of mossy fiber (MF) connections to CA3 a randomizing pattern separation effect important during learning but not recall that separates out the patterns represented by CA3 firing to be very different from each other, which is optimal for an unstructured episodic memory system in which each memory must be kept distinct from other memories. The direct perforant path (pp) input to CA3 is quantitatively appropriate to provide the cue for recall in CA3, but not for learning. Tests of the theory including hippocampal subregion analyses and hippocampal NMDA receptor knockouts are described, and support the theory.

  12. Elaboration versus Suppression of Cued Memories: Influence of Memory Recall Instruction and Success on Parietal Lobe, Default Network, and Hippocampal Activity

    Science.gov (United States)

    Gimbel, Sarah I.; Brewer, James B.

    2014-01-01

    Functional imaging studies of episodic memory retrieval consistently report task-evoked and memory-related activity in the medial temporal lobe, default network and parietal lobe subregions. Associated components of memory retrieval, such as attention-shifts, search, retrieval success, and post-retrieval processing also influence regional activity, but these influences remain ill-defined. To better understand how top-down control affects the neural bases of memory retrieval, we examined how regional activity responses were modulated by task goals during recall success or failure. Specifically, activity was examined during memory suppression, recall, and elaborative recall of paired-associates. Parietal lobe was subdivided into dorsal (BA 7), posterior ventral (BA 39), and anterior ventral (BA 40) regions, which were investigated separately to examine hypothesized distinctions in sub-regional functional responses related to differential attention-to-memory and memory strength. Top-down suppression of recall abolished memory strength effects in BA 39, which showed a task-negative response, and BA 40, which showed a task-positive response. The task-negative response in default network showed greater negatively-deflected signal for forgotten pairs when task goals required recall. Hippocampal activity was task-positive and was influenced by memory strength only when task goals required recall. As in previous studies, we show a memory strength effect in parietal lobe and hippocampus, but we show that this effect is top-down controlled and sensitive to whether the subject is trying to suppress or retrieve a memory. These regions are all implicated in memory recall, but their individual activity patterns show distinct memory-strength-related responses when task goals are varied. In parietal lobe, default network, and hippocampus, top-down control can override the commonly identified effects of memory strength. PMID:24586492

  13. Elaboration versus suppression of cued memories: influence of memory recall instruction and success on parietal lobe, default network, and hippocampal activity.

    Science.gov (United States)

    Gimbel, Sarah I; Brewer, James B

    2014-01-01

    Functional imaging studies of episodic memory retrieval consistently report task-evoked and memory-related activity in the medial temporal lobe, default network and parietal lobe subregions. Associated components of memory retrieval, such as attention-shifts, search, retrieval success, and post-retrieval processing also influence regional activity, but these influences remain ill-defined. To better understand how top-down control affects the neural bases of memory retrieval, we examined how regional activity responses were modulated by task goals during recall success or failure. Specifically, activity was examined during memory suppression, recall, and elaborative recall of paired-associates. Parietal lobe was subdivided into dorsal (BA 7), posterior ventral (BA 39), and anterior ventral (BA 40) regions, which were investigated separately to examine hypothesized distinctions in sub-regional functional responses related to differential attention-to-memory and memory strength. Top-down suppression of recall abolished memory strength effects in BA 39, which showed a task-negative response, and BA 40, which showed a task-positive response. The task-negative response in default network showed greater negatively-deflected signal for forgotten pairs when task goals required recall. Hippocampal activity was task-positive and was influenced by memory strength only when task goals required recall. As in previous studies, we show a memory strength effect in parietal lobe and hippocampus, but we show that this effect is top-down controlled and sensitive to whether the subject is trying to suppress or retrieve a memory. These regions are all implicated in memory recall, but their individual activity patterns show distinct memory-strength-related responses when task goals are varied. In parietal lobe, default network, and hippocampus, top-down control can override the commonly identified effects of memory strength.

  14. Induction of Innate Immune Memory by Engineered Nanoparticles: A Hypothesis That May Become True.

    Science.gov (United States)

    Italiani, Paola; Boraschi, Diana

    2017-01-01

    Innate immune memory is the capacity of cells of the innate immune system, such as monocytes and macrophages, to react differently to an inflammatory or infectious challenge if previously exposed to the same or to another agent. Innate immune memory is a protective mechanism, based on epigenetic reprogramming, that ensures effective protection while limiting side effects of tissue damage, by controlling innate/inflammatory responses to repeated stimulations. Engineered nanoparticles (NPs) are novel challenges for our innate immune system, and their ability to induce inflammatory activation, thereby posing health risks, is currently being investigated with controversial results. Besides their putative direct inflammation-inducing effects, we hypothesize that engineered NPs may induce innate memory based on their capacity to induce epigenetic modulation of gene expression. Preliminary results using non-toxic non-inflammatory gold NPs show that in fact NPs can induce memory by modulating in either positive or negative fashion the inflammatory activation of human monocytes to a subsequent bacterial challenge. The possibility of shaping innate/inflammatory reactivity with NPs could open the way to future novel approaches of preventive and therapeutic immunomodulation.

  15. Induction of Innate Immune Memory by Engineered Nanoparticles: A Hypothesis That May Become True

    Directory of Open Access Journals (Sweden)

    Paola Italiani

    2017-06-01

    Full Text Available Innate immune memory is the capacity of cells of the innate immune system, such as monocytes and macrophages, to react differently to an inflammatory or infectious challenge if previously exposed to the same or to another agent. Innate immune memory is a protective mechanism, based on epigenetic reprogramming, that ensures effective protection while limiting side effects of tissue damage, by controlling innate/inflammatory responses to repeated stimulations. Engineered nanoparticles (NPs are novel challenges for our innate immune system, and their ability to induce inflammatory activation, thereby posing health risks, is currently being investigated with controversial results. Besides their putative direct inflammation-inducing effects, we hypothesize that engineered NPs may induce innate memory based on their capacity to induce epigenetic modulation of gene expression. Preliminary results using non-toxic non-inflammatory gold NPs show that in fact NPs can induce memory by modulating in either positive or negative fashion the inflammatory activation of human monocytes to a subsequent bacterial challenge. The possibility of shaping innate/inflammatory reactivity with NPs could open the way to future novel approaches of preventive and therapeutic immunomodulation.

  16. 77 FR 58415 - Large Scale Networking (LSN); Joint Engineering Team (JET)

    Science.gov (United States)

    2012-09-20

    ... Large Scale Networking (LSN); Joint Engineering Team (JET) AGENCY: The Networking and Information... agencies and non-Federal participants with interest in high performance research networking and networking to support science applications. The JET reports to the Large Scale Networking (LSN) Coordinating...

  17. 78 FR 70076 - Large Scale Networking (LSN)-Joint Engineering Team (JET)

    Science.gov (United States)

    2013-11-22

    ... Large Scale Networking (LSN)--Joint Engineering Team (JET) AGENCY: The Networking and Information... and non-Federal participants with interest in high performance research networking and networking to support science applications. The JET reports to the Large Scale Networking (LSN) Coordinating Group (CG...

  18. 78 FR 7464 - Large Scale Networking (LSN) ; Joint Engineering Team (JET)

    Science.gov (United States)

    2013-02-01

    ... Large Scale Networking (LSN) ; Joint Engineering Team (JET) AGENCY: The Networking and Information... research networking and networking to support science applications. The JET reports to the Large Scale Networking (LSN) Coordinating Group (CG). Public Comments: The government seeks individual input; attendees...

  19. Chemogenetic Interrogation of a Brain-wide Fear Memory Network in Mice.

    Science.gov (United States)

    Vetere, Gisella; Kenney, Justin W; Tran, Lina M; Xia, Frances; Steadman, Patrick E; Parkinson, John; Josselyn, Sheena A; Frankland, Paul W

    2017-04-19

    Behavior depends on coordinated activity across multiple brain regions. Within such networks, highly connected hub regions are assumed to disproportionately influence behavioral output, although this hypothesis has not been systematically evaluated. Previously, by mapping brain-wide expression of the activity-regulated gene c-fos, we identified a network of brain regions co-activated by fear memory. To test the hypothesis that hub regions are more important for network function, here, we simulated node deletion in silico in this behaviorally defined functional network. Removal of high degree nodes produced the greatest network disruption (e.g., reduction in global efficiency). To test these predictions in vivo, we examined the impact of post-training chemogenetic silencing of different network nodes on fear memory consolidation. In a series of independent experiments encompassing 25% of network nodes (i.e., 21/84 brain regions), we found that node degree accurately predicted observed deficits in memory consolidation, with silencing of highly connected hubs producing the largest impairments. Copyright © 2017 Elsevier Inc. All rights reserved.

  20. Fragile X syndrome: Neural network models of sequencing and memory

    NARCIS (Netherlands)

    Johnson, M.C.

    2008-01-01

    A comparative framework of memory processes in males with fragile X syndrome (FXS) and typically developing (TYP) mental age-match children is presented. Results indicate a divergence in sequencing skills, such that males with FXS recall sequences similarly to TYP children around five and a half

  1. Episodic Memory Retrieval Benefits from a Less Modular Brain Network Organization.

    Science.gov (United States)

    Westphal, Andrew J; Wang, Siliang; Rissman, Jesse

    2017-03-29

    Most complex cognitive tasks require the coordinated interplay of multiple brain networks, but the act of retrieving an episodic memory may place especially heavy demands for communication between the frontoparietal control network (FPCN) and the default mode network (DMN), two networks that do not strongly interact with one another in many task contexts. We applied graph theoretical analysis to task-related fMRI functional connectivity data from 20 human participants and found that global brain modularity-a measure of network segregation-is markedly reduced during episodic memory retrieval relative to closely matched analogical reasoning and visuospatial perception tasks. Individual differences in modularity were correlated with memory task performance, such that lower modularity levels were associated with a lower false alarm rate. Moreover, the FPCN and DMN showed significantly elevated coupling with each other during the memory task, which correlated with the global reduction in brain modularity. Both networks also strengthened their functional connectivity with the hippocampus during the memory task. Together, these results provide a novel demonstration that reduced modularity is conducive to effective episodic retrieval, which requires close collaboration between goal-directed control processes supported by the FPCN and internally oriented self-referential processing supported by the DMN.SIGNIFICANCE STATEMENT Modularity, an index of the degree to which nodes of a complex system are organized into discrete communities, has emerged as an important construct in the characterization of brain connectivity dynamics. We provide novel evidence that the modularity of the human brain is reduced when individuals engage in episodic memory retrieval, relative to other cognitive tasks, and that this state of lower modularity is associated with improved memory performance. We propose a neural systems mechanism for this finding where the nodes of the frontoparietal control

  2. Innovation, Quality and Networking in Engineering Education in Europe: the Contribution of SOCRATES THEMATIC NETWORKS

    Science.gov (United States)

    Borri, Claudio; Guberti, Elisa; Maffioli, Francesco

    Accreditation of the degree programmes in Engineering is surely an argument which stimulates a great interest not only in the Italian level but above all in the European perspective. It appears strategic that Europe is equipped with a system which permits to compare the degree programmes in Engineering offered by various universities in Europe also in view of a major competition in the area of higher education in the European Union, in comparison with third countries. This appears the principal basis of different actions financed by the European Commission, which have among their own objectives also the study of an accreditation system of the degree programmes in Engineering in Europe. In this article 3 SOCRATES Thematic Networks are presented, which, one after the other, starting from 1998, have been operational in the European panorama. Among their objectives there is a recurrent motive: accreditation of the degree programmes in Engineering in Europe.

  3. Global network on engineering education research and expertise in PBL

    DEFF Research Database (Denmark)

    Enemark, Stig; Kolmos, Anette; Moesby, Egon

    2006-01-01

    . UCPBL Centre for Problem Based Learning is currently involved in a number of projects world wide focusing on institutional change toward a more student centred, project organized, and problem based approach to learning. The Centre is also establishing a UCPBL Global Network on Problem Based Learning......The UCPBL Centre for Problem Based Learning is based at Aalborg University, Denmark, known world-wide for its successful educational approach based on problem oriented project work. Due to more than 30 years of experience in utilizing PBL-learning principles in Engineering Education, an increasing...... number of universities and engineering schools throughout the world are seeking consultancy and cooperation with Aalborg University. The establishment of UCPBL is therefore a timely opportunity to merge the efforts into one organizational structure aiming to promote and support PBL interests worldwide...

  4. Green IT engineering components, networks and systems implementation

    CERN Document Server

    Kondratenko, Yuriy; Kacprzyk, Janusz

    2017-01-01

    This book presents modern approaches to improving the energy efficiency, safety and environmental performance of industrial processes and products, based on the application of advanced trends in Green Information Technologies (IT) Engineering to components, networks and complex systems (software, programmable and hardware components, communications, Cloud and IoT-based systems, as well as IT infrastructures). The book’s 16 chapters, prepared by authors from Greece, Malaysia, Russia, Slovakia, Ukraine and the United Kingdom, are grouped into four sections: (1) The Green Internet of Things, Cloud Computing and Data Mining, (2) Green Mobile and Embedded Control Systems, (3) Green Logic and FPGA Design, and (4) Green IT for Industry and Smart Grids. The book will motivate researchers and engineers from different IT domains to develop, implement and propagate green values in complex systems. Further, it will benefit all scientists and graduate students pursuing research in computer science with a focus on green ...

  5. Resistive Random Access Memory from Materials Development fnd Engineering to Novel Encryption and Neuromorphic Applications

    Science.gov (United States)

    Beckmann, Karsten

    Resistive random access memory (ReRAM or RRAM) is a novel form of non-volatile memory that is expected to play a major role in future computing and memory solutions. It has been shown that the resistance state of ReRAM devices can be precisely tuned by modulating switching voltages, by limiting peak current, and by adjusting the switching pulse properties. This enables the realization of novel applications such as memristive neuromorphic computing and neural network computing. I have developed two processes based on 100 and 300mm wafer platforms to demonstrate functional HfO2 based ReRAM devices. The first process is designed for a rapid materials engineering and device characterization, while the second is an advanced hybrid ReRAM/CMOS combination based on the IBM 65nm 10LPe process technology. The 100mm wafer efforts were used to show impacts of etch processes on ReRAM switching performance and the need for a rigorous structural evaluation of ReRAM devices before starting materials development. After an etch development, a bottom electrode comparison between the inert materials Pt, Ru and W was performed where Ru showed superior results with respect to yield and resilience against environmental impacts such as humidity over a 2-month period. A comparison of amorphous and crystalline devices showed no statistical difference in the performance with respect to random telegraph noise. This demonstrates, that the forming process fundamentally alters the crystallographic structure within and around the filament. The 300mm wafer development efforts were aimed towards implementing ReRAM in the FEOL, combined with CMOS, to yield a seamless process flow of 1 transistor 1 ReRAM structures (1T1R). This technology was customized with custom-developed tungsten metal 1 (M1) and dual tungsten/copper via 1 (V1) structures, within which the ReRAM stack is embedded. The ReRAM itself consists of an inert W bottom electrode, HfO2 based active switching layer, a Ti oxygen scavenger

  6. An integrated network visualization framework towards metabolic engineering applications.

    Science.gov (United States)

    Noronha, Alberto; Vilaça, Paulo; Rocha, Miguel

    2014-12-30

    Over the last years, several methods for the phenotype simulation of microorganisms, under specified genetic and environmental conditions have been proposed, in the context of Metabolic Engineering (ME). These methods provided insight on the functioning of microbial metabolism and played a key role in the design of genetic modifications that can lead to strains of industrial interest. On the other hand, in the context of Systems Biology research, biological network visualization has reinforced its role as a core tool in understanding biological processes. However, it has been scarcely used to foster ME related methods, in spite of the acknowledged potential. In this work, an open-source software that aims to fill the gap between ME and metabolic network visualization is proposed, in the form of a plugin to the OptFlux ME platform. The framework is based on an abstract layer, where the network is represented as a bipartite graph containing minimal information about the underlying entities and their desired relative placement. The framework provides input/output support for networks specified in standard formats, such as XGMML, SBGN or SBML, providing a connection to genome-scale metabolic models. An user-interface makes it possible to edit, manipulate and query nodes in the network, providing tools to visualize diverse effects, including visual filters and aspect changing (e.g. colors, shapes and sizes). These tools are particularly interesting for ME, since they allow overlaying phenotype simulation results or elementary flux modes over the networks. The framework and its source code are freely available, together with documentation and other resources, being illustrated with well documented case studies.

  7. Avalanches and generalized memory associativity in a network model for conscious and unconscious mental functioning

    Science.gov (United States)

    Siddiqui, Maheen; Wedemann, Roseli S.; Jensen, Henrik Jeldtoft

    2018-01-01

    We explore statistical characteristics of avalanches associated with the dynamics of a complex-network model, where two modules corresponding to sensorial and symbolic memories interact, representing unconscious and conscious mental processes. The model illustrates Freud's ideas regarding the neuroses and that consciousness is related with symbolic and linguistic memory activity in the brain. It incorporates the Stariolo-Tsallis generalization of the Boltzmann Machine in order to model memory retrieval and associativity. In the present work, we define and measure avalanche size distributions during memory retrieval, in order to gain insight regarding basic aspects of the functioning of these complex networks. The avalanche sizes defined for our model should be related to the time consumed and also to the size of the neuronal region which is activated, during memory retrieval. This allows the qualitative comparison of the behaviour of the distribution of cluster sizes, obtained during fMRI measurements of the propagation of signals in the brain, with the distribution of avalanche sizes obtained in our simulation experiments. This comparison corroborates the indication that the Nonextensive Statistical Mechanics formalism may indeed be more well suited to model the complex networks which constitute brain and mental structure.

  8. Evaluation of a degradable shape-memory polymer network as matrix for controlled drug release.

    Science.gov (United States)

    Wischke, Christian; Neffe, Axel T; Steuer, Susi; Lendlein, Andreas

    2009-09-15

    Degradable shape-memory polymers are multifunctional materials with broad applicability for medical devices. They are designed to acquire their therapeutically relevant shape and mechanical properties after implantation. In this study, the potential of a completely amorphous shape-memory polymer matrix for controlled drug release was comprehensively characterized according to a four step general strategy which provides concepts for validating multifunctional materials for pharmaceutical applications. Independent functionalities are thereby crucial for fully exploiting the potential of the materials. The copolyester urethane network was synthesized by crosslinking star-shaped tetrahydroxy telechelics of oligo[(rac-lactide)-co-glycolide] with an aliphatic diisocyanate. In step 1 of the four step characterization procedure, this material showed the thermal and mechanical properties, which are required for the shape-memory effect under physiological conditions. Shape recovery could be realized by a one-step or a multi-step methodology. In step 2, feasibility of drug loading of pre-formed shape-memory networks has been demonstrated with drugs of different hydrophobicities. The presence of drugs did not disturb the material's functionalities directly after loading (step 3) and under release conditions (step 4). A predictable release of about 90% of the payload in 80 days was observed. Overall, the synthesized amorphous polymer network showed three independent functionalities, i.e., a shape-memory effect combined with biodegradability and controlled drug release.

  9. Memory networks supporting retrieval effort and retrieval success under conditions of full and divided attention.

    Science.gov (United States)

    Skinner, Erin I; Fernandes, Myra A; Grady, Cheryl L

    2009-01-01

    We used a multivariate analysis technique, partial least squares (PLS), to identify distributed patterns of brain activity associated with retrieval effort and retrieval success. Participants performed a recognition memory task under full attention (FA) or two different divided attention (DA) conditions during retrieval. Behaviorally, recognition was disrupted when a word, but not digit-based distracting task, was performed concurrently with retrieval. PLS was used to identify patterns of brain activation that together covaried with the three memory conditions and which were functionally connected with activity in the right hippocampus to produce successful memory performance. Results indicate that activity in the right dorsolateral frontal cortex increases during conditions of DA at retrieval, and that successful memory performance in the DA-digit condition is associated with activation of the same network of brain regions functionally connected to the right hippocampus, as under FA, which increases with increasing memory performance. Finally, DA conditions that disrupt successful memory performance (DA-word) interfere with recruitment of both retrieval-effort and retrieval-success networks.

  10. Order recall in verbal short-term memory: The role of semantic networks.

    Science.gov (United States)

    Poirier, Marie; Saint-Aubin, Jean; Mair, Ali; Tehan, Gerry; Tolan, Anne

    2015-04-01

    In their recent article, Acheson, MacDonald, and Postle (Journal of Experimental Psychology: Learning, Memory, and Cognition 37:44-59, 2011) made an important but controversial suggestion: They hypothesized that (a) semantic information has an effect on order information in short-term memory (STM) and (b) order recall in STM is based on the level of activation of items within the relevant lexico-semantic long-term memory (LTM) network. However, verbal STM research has typically led to the conclusion that factors such as semantic category have a large effect on the number of correctly recalled items, but little or no impact on order recall (Poirier & Saint-Aubin, Quarterly Journal of Experimental Psychology 48A:384-404, 1995; Saint-Aubin, Ouellette, & Poirier, Psychonomic Bulletin & Review 12:171-177, 2005; Tse, Memory 17:874-891, 2009). Moreover, most formal models of short-term order memory currently suggest a separate mechanism for order coding-that is, one that is separate from item representation and not associated with LTM lexico-semantic networks. Both of the experiments reported here tested the predictions that we derived from Acheson et al. The findings show that, as predicted, manipulations aiming to affect the activation of item representations significantly impacted order memory.

  11. The role of autobiographical memory networks in the experience of negative emotions: how our remembered past elicits our current feelings.

    Science.gov (United States)

    Philippe, Frederick L; Koestner, Richard; Lecours, Serge; Beaulieu-Pelletier, Genevieve; Bois, Katy

    2011-12-01

    The present research examined the role of autobiographical memory networks on negative emotional experiences. Results from 2 studies found support for an active but also discriminant role of autobiographical memories and their related networked memories on negative emotions. In addition, in line with self-determination theory, thwarting of the psychological needs for competence, autonomy, and relatedness was found to be the critical component of autobiographical memory affecting negative emotional experiences. Study 1 revealed that need thwarting in a specific autobiographical memory network related to the theme of loss was positively associated with depressive negative emotions, but not with other negative emotions. Study 2 showed within a prospective design a differential predictive validity between 2 autobiographical memory networks (an anger-related vs. a guilt-related memory) on situational anger reactivity with respect to unfair treatment. All of these results held after controlling for neuroticism (Studies 1 and 2), self-control (Study 2), and for the valence (Study 1) and emotions (Study 2) found in the measured autobiographical memory network. These findings highlight the ongoing emotional significance of representations of need thwarting in autobiographical memory networks. (c) 2011 APA, all rights reserved.

  12. Central Engine Memory of Gamma-Ray Bursts and Soft Gamma-Ray Repeaters

    Science.gov (United States)

    Zhang, Bin-Bin; Zhang, Bing; Castro-Tirado, Alberto J.

    2016-04-01

    Gamma-ray bursts (GRBs) are bursts of γ-rays generated from relativistic jets launched from catastrophic events such as massive star core collapse or binary compact star coalescence. Previous studies suggested that GRB emission is erratic, with no noticeable memory in the central engine. Here we report a discovery that similar light curve patterns exist within individual bursts for at least some GRBs. Applying the Dynamic Time Warping method, we show that similarity of light curve patterns between pulses of a single burst or between the light curves of a GRB and its X-ray flare can be identified. This suggests that the central engine of at least some GRBs carries “memory” of its activities. We also show that the same technique can identify memory-like emission episodes in the flaring emission in soft gamma-ray repeaters (SGRs), which are believed to be Galactic, highly magnetized neutron stars named magnetars. Such a phenomenon challenges the standard black hole central engine models for GRBs, and suggest a common physical mechanism behind GRBs and SGRs, which points toward a magnetar central engine of GRBs.

  13. CENTRAL ENGINE MEMORY OF GAMMA-RAY BURSTS AND SOFT GAMMA-RAY REPEATERS

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Bin-Bin; Castro-Tirado, Alberto J. [Instituto de Astrofísica de Andalucá (IAA-CSIC), P.O. Box 03004, E-18080 Granada (Spain); Zhang, Bing, E-mail: zhang.grb@gmail.com [Department of Physics and Astronomy, University of Nevada, Las Vegas, NV 89154 (United States)

    2016-04-01

    Gamma-ray bursts (GRBs) are bursts of γ-rays generated from relativistic jets launched from catastrophic events such as massive star core collapse or binary compact star coalescence. Previous studies suggested that GRB emission is erratic, with no noticeable memory in the central engine. Here we report a discovery that similar light curve patterns exist within individual bursts for at least some GRBs. Applying the Dynamic Time Warping method, we show that similarity of light curve patterns between pulses of a single burst or between the light curves of a GRB and its X-ray flare can be identified. This suggests that the central engine of at least some GRBs carries “memory” of its activities. We also show that the same technique can identify memory-like emission episodes in the flaring emission in soft gamma-ray repeaters (SGRs), which are believed to be Galactic, highly magnetized neutron stars named magnetars. Such a phenomenon challenges the standard black hole central engine models for GRBs, and suggest a common physical mechanism behind GRBs and SGRs, which points toward a magnetar central engine of GRBs.

  14. A Neural Network Model of the Visual Short-Term Memory

    DEFF Research Database (Denmark)

    Petersen, Anders; Kyllingsbæk, Søren; Hansen, Lars Kai

    2009-01-01

    In this paper a neural network model of Visual Short-Term Memory (VSTM) is presented. The model links closely with Bundesen’s (1990) well-established mathematical theory of visual attention. We evaluate the model’s ability to fit experimental data from a classical whole and partial report study...

  15. Episodic memory and the role of the brain's default-mode network

    NARCIS (Netherlands)

    Huijbers, W.

    2010-01-01

    This thesis provides a number of new insights into episodic memory and the role of the default-mode network. First, it provides the first direct evidence for the contrasting role of DMN during encoding and retrieval. Secondly, the experimental findings eliminate several possible explanations for the

  16. Working Memory Modulation of Frontoparietal Network Connectivity in First-Episode Schizophrenia

    DEFF Research Database (Denmark)

    Nielsen, Jesper Duemose; Madsen, Kristoffer Hougaard; Wang, Zheng

    2017-01-01

    Working memory (WM) impairment is regarded as a core aspect of schizophrenia. However, the neural mechanisms behind this cognitive deficit remain unclear. The connectivity of a frontoparietal network is known to be important for subserving WM. Using functional magnetic resonance imaging...

  17. Episodic memory and the role of the brain’s default-mode network

    NARCIS (Netherlands)

    Huijbers, W.

    2010-01-01

    This thesis provides a number of new insights into episodic memory and the role of the default-mode network. First, it provides the first direct evidence for the contrasting role of DMN during encoding and retrieval. Secondly, the experimental findings eliminate several possible explanations for the

  18. The CA3 Network as a Memory Store for Spatial Representations

    Science.gov (United States)

    Papp, Gergely; Witter, Menno P.; Treves, Alessandro

    2007-01-01

    Comparative neuroanatomy suggests that the CA3 region of the mammalian hippocampus is directly homologous with the medio-dorsal pallium in birds and reptiles, with which it largely shares the basic organization of primitive cortex. Autoassociative memory models, which are generically applicable to cortical networks, then help assess how well CA3…

  19. Parvalbumin-expressing interneurons coordinate hippocampal network dynamics required for memory consolidation

    Science.gov (United States)

    Ognjanovski, Nicolette; Schaeffer, Samantha; Wu, Jiaxing; Mofakham, Sima; Maruyama, Daniel; Zochowski, Michal; Aton, Sara J.

    2017-04-01

    Activity in hippocampal area CA1 is essential for consolidating episodic memories, but it is unclear how CA1 activity patterns drive memory formation. We find that in the hours following single-trial contextual fear conditioning (CFC), fast-spiking interneurons (which typically express parvalbumin (PV)) show greater firing coherence with CA1 network oscillations. Post-CFC inhibition of PV+ interneurons blocks fear memory consolidation. This effect is associated with loss of two network changes associated with normal consolidation: (1) augmented sleep-associated delta (0.5-4 Hz), theta (4-12 Hz) and ripple (150-250 Hz) oscillations; and (2) stabilization of CA1 neurons' functional connectivity patterns. Rhythmic activation of PV+ interneurons increases CA1 network coherence and leads to a sustained increase in the strength and stability of functional connections between neurons. Our results suggest that immediately following learning, PV+ interneurons drive CA1 oscillations and reactivation of CA1 ensembles, which directly promotes network plasticity and long-term memory formation.

  20. Folk music style modelling by recurrent neural networks with long short term memory units

    OpenAIRE

    Sturm, Bob,; Santos, João Felipe; Korshunova, Iryna

    2015-01-01

    We demonstrate two generative models created by training a recurrent neural network (RNN) with three hidden layers of long short-term memory (LSTM) units. This extends past work in numerous directions, including training deeper models with nearly 24,000 high-level transcriptions of folk tunes. We discuss our on-going work.

  1. Bidirectional Long Short-Term Memory Network with a Conditional Random Field Layer for Uyghur Part-Of-Speech Tagging

    Directory of Open Access Journals (Sweden)

    Maihemuti Maimaiti

    2017-11-01

    Full Text Available Uyghur is an agglutinative and a morphologically rich language; natural language processing tasks in Uyghur can be a challenge. Word morphology is important in Uyghur part-of-speech (POS tagging. However, POS tagging performance suffers from error propagation of morphological analyzers. To address this problem, we propose a few models for POS tagging: conditional random fields (CRF, long short-term memory (LSTM, bidirectional LSTM networks (BI-LSTM, LSTM networks with a CRF layer, and BI-LSTM networks with a CRF layer. These models do not depend on stemming and word disambiguation for Uyghur and combine hand-crafted features with neural network models. State-of-the-art performance on Uyghur POS tagging is achieved on test data sets using the proposed approach: 98.41% accuracy on 15 labels and 95.74% accuracy on 64 labels, which are 2.71% and 4% improvements, respectively, over the CRF model results. Using engineered features, our model achieves further improvements of 0.2% (15 labels and 0.48% (64 labels. The results indicate that the proposed method could be an effective approach for POS tagging in other morphologically rich languages.

  2. Preliminary systems engineering evaluations for the National Ecological Observatory Network.

    Energy Technology Data Exchange (ETDEWEB)

    Robertson, Perry J.; Kottenstette, Richard Joseph; Crouch, Shannon M.; Brocato, Robert Wesley; Zak, Bernard Daniel; Osborn, Thor D.; Ivey, Mark D.; Gass, Karl Leslie; Heller, Edwin J.; Dishman, James Larry; Schubert, William Kent; Zirzow, Jeffrey A.

    2008-11-01

    The National Ecological Observatory Network (NEON) is an ambitious National Science Foundation sponsored project intended to accumulate and disseminate ecologically informative sensor data from sites among 20 distinct biomes found within the United States and Puerto Rico over a period of at least 30 years. These data are expected to provide valuable insights into the ecological impacts of climate change, land-use change, and invasive species in these various biomes, and thereby provide a scientific foundation for the decisions of future national, regional, and local policy makers. NEON's objectives are of substantial national and international importance, yet they must be achieved with limited resources. Sandia National Laboratories was therefore contracted to examine four areas of significant systems engineering concern; specifically, alternatives to commercial electrical utility power for remote operations, approaches to data acquisition and local data handling, protocols for secure long-distance data transmission, and processes and procedures for the introduction of new instruments and continuous improvement of the sensor network. The results of these preliminary systems engineering evaluations are presented, with a series of recommendations intended to optimize the efficiency and probability of long-term success for the NEON enterprise.

  3. Intrinsic Default Mode Network Connectivity Predicts Spontaneous Verbal Descriptions of Autobiographical Memories during Social Processing.

    Science.gov (United States)

    Yang, Xiao-Fei; Bossmann, Julia; Schiffhauer, Birte; Jordan, Matthew; Immordino-Yang, Mary Helen

    2012-01-01

    Neural systems activated in a coordinated way during rest, known as the default mode network (DMN), also support autobiographical memory (AM) retrieval and social processing/mentalizing. However, little is known about how individual variability in reliance on personal memories during social processing relates to individual differences in DMN functioning during rest (intrinsic functional connectivity). Here we examined 18 participants' spontaneous descriptions of autobiographical memories during a 2 h, private, open-ended interview in which they reacted to a series of true stories about real people's social situations and responded to the prompt, "how does this person's story make you feel?" We classified these descriptions as either containing factual information ("semantic" AMs) or more elaborate descriptions of emotionally meaningful events ("episodic" AMs). We also collected resting state fMRI scans from the participants and related individual differences in frequency of described AMs to participants' intrinsic functional connectivity within regions of the DMN. We found that producing more descriptions of either memory type correlated with stronger intrinsic connectivity in the parahippocampal and middle temporal gyri. Additionally, episodic AM descriptions correlated with connectivity in the bilateral hippocampi and medial prefrontal cortex, and semantic memory descriptions correlated with connectivity in right inferior lateral parietal cortex. These findings suggest that in individuals who naturally invoke more memories during social processing, brain regions involved in memory retrieval and self/social processing are more strongly coupled to the DMN during rest.

  4. Applying long short-term memory recurrent neural networks to intrusion detection

    Directory of Open Access Journals (Sweden)

    Ralf C. Staudemeyer

    2015-07-01

    Full Text Available We claim that modelling network traffic as a time series with a supervised learning approach, using known genuine and malicious behaviour, improves intrusion detection. To substantiate this, we trained long short-term memory (LSTM recurrent neural networks with the training data provided by the DARPA / KDD Cup ’99 challenge. To identify suitable LSTM-RNN network parameters and structure we experimented with various network topologies. We found networks with four memory blocks containing two cells each offer a good compromise between computational cost and detection performance. We applied forget gates and shortcut connections respectively. A learning rate of 0.1 and up to 1,000 epochs showed good results. We tested the performance on all features and on extracted minimal feature sets respectively. We evaluated different feature sets for the detection of all attacks within one network and also to train networks specialised on individual attack classes. Our results show that the LSTM classifier provides superior performance in comparison to results previously published results of strong static classifiers. With 93.82% accuracy and 22.13 cost, LSTM outperforms the winning entries of the KDD Cup ’99 challenge by far. This is due to the fact that LSTM learns to look back in time and correlate consecutive connection records. For the first time ever, we have demonstrated the usefulness of LSTM networks to intrusion detection.

  5. Non-classical state engineering for quantum networks

    Energy Technology Data Exchange (ETDEWEB)

    Vollmer, Christina E.

    2014-01-24

    The wide field of quantum information processing and quantum networks has developed very fast in the last two decades. Besides the regime of discrete variables, which was developed first, the regime of continuous variables represents an alternative approach to realize many quantum applications. Non-classical states of light, like squeezed or entangled states, are a fundamental resource for quantum applications like quantum repeaters, quantum memories, quantum key distribution, quantum spectroscopy, and quantum metrology. These states can be generated successfully in the infrared wavelength regime. However, for some tasks other wavelengths, especially in the visible wavelength regime, are desirable. To generate non-classical states of light in this wavelength regime frequency up-conversion can be used, since all quantum properties are maintained in this process. The first part of this thesis deals with the experimental frequency up-conversion of quantum states. Squeezed vacuum states of light at 1550 nm were up-converted to 532 nm and a noise reduction of -1.5 dB at 532 nm was achieved. These states can be used for increasing the sensitivity of gravitational wave detectors or spectroscopic measurements. Furthermore, one part of an entangled state at 1550 nm was up-converted to 532 nm and, thus, entanglement between these two wavelengths was generated and characterized to -1.4 dB following Duan et al. With such a quantum link it is possible to establish a quantum network, which takes advantage of the low optical loss at 1550 nm for information transmission and of atomic transitions around 532 nm for a quantum memory in a quantum repeater. For quantum networks the distribution of entanglement and especially of a quantum key is essential. In the second part of this thesis the experimental distribution of entanglement by separable states is demonstrated. The underlying protocol requires a special three-mode state, which is separable in two of the three splittings. With

  6. Analysing the Correlation between Social Network Analysis Measures and Performance of Students in Social Network-Based Engineering Education

    Science.gov (United States)

    Putnik, Goran; Costa, Eric; Alves, Cátia; Castro, Hélio; Varela, Leonilde; Shah, Vaibhav

    2016-01-01

    Social network-based engineering education (SNEE) is designed and implemented as a model of Education 3.0 paradigm. SNEE represents a new learning methodology, which is based on the concept of social networks and represents an extended model of project-led education. The concept of social networks was applied in the real-life experiment,…

  7. Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory

    DEFF Research Database (Denmark)

    Lüders, Benno; Schläger, Mikkel; Korach, Aleksandra

    2017-01-01

    Training neural networks to quickly learn new skills without forgetting previously learned skills is an important open challenge in machine learning. A common problem for adaptive networks that can learn during their lifetime is that the weights encoding a particular task are often overridden when...... a new task is learned. This paper takes a step in overcoming this limitation by building on the recently proposed Evolving Neural Turing Machine (ENTM) approach. In the ENTM, neural networks are augmented with an external memory component that they can write to and read from, which allows them to store...

  8. Convolutional neural network-based data page classification for holographic memory.

    Science.gov (United States)

    Shimobaba, Tomoyoshi; Kuwata, Naoki; Homma, Mizuha; Takahashi, Takayuki; Nagahama, Yuki; Sano, Marie; Hasegawa, Satoki; Hirayama, Ryuji; Kakue, Takashi; Shiraki, Atsushi; Takada, Naoki; Ito, Tomoyoshi

    2017-09-10

    We propose a deep-learning-based classification of data pages used in holographic memory. We numerically investigated the classification performance of a conventional multilayer perceptron (MLP) and a deep neural network, under the condition that reconstructed page data are contaminated by some noise and are randomly laterally shifted. When data pages are randomly laterally shifted, the MLP was found to have a classification accuracy of 93.02%, whereas the deep neural network was able to classify data pages at an accuracy of 99.98%. The accuracy of the deep neural network is 2 orders of magnitude better than the MLP.

  9. IDI diesel engine performance and exhaust emission analysis using biodiesel with an artificial neural network (ANN)

    National Research Council Canada - National Science Library

    Prasada Rao, K; Victor Babu, T; Anuradha, G; Appa Rao, B.V

    ...) engine fueled with Rice Bran Methyl Ester (RBME) with Isopropanol additive. The investigation is done through a combination of experimental data analysis and artificial neural network (ANN) modeling...

  10. The Dorsal Attention Network Reflects Both Encoding Load and Top-down Control during Working Memory.

    Science.gov (United States)

    Majerus, Steve; Péters, Frédéric; Bouffier, Marion; Cowan, Nelson; Phillips, Christophe

    2018-02-01

    The dorsal attention network is consistently involved in verbal and visual working memory (WM) tasks and has been associated with task-related, top-down control of attention. At the same time, WM capacity has been shown to depend on the amount of information that can be encoded in the focus of attention independently of top-down strategic control. We examined the role of the dorsal attention network in encoding load and top-down memory control during WM by manipulating encoding load and memory control requirements during a short-term probe recognition task for sequences of auditory (digits, letters) or visual (lines, unfamiliar faces) stimuli. Encoding load was manipulated by presenting sequences with small or large sets of memoranda while maintaining the amount of sensory stimuli constant. Top-down control was manipulated by instructing participants to passively maintain all stimuli or to selectively maintain stimuli from a predefined category. By using ROI and searchlight multivariate analysis strategies, we observed that the dorsal attention network encoded information for both load and control conditions in verbal and visuospatial modalities. Decoding of load conditions was in addition observed in modality-specific sensory cortices. These results highlight the complexity of the role of the dorsal attention network in WM by showing that this network supports both quantitative and qualitative aspects of attention during WM encoding, and this is in a partially modality-specific manner.

  11. Spatiotemporal memory is an intrinsic property of networks of dissociated cortical neurons.

    Science.gov (United States)

    Ju, Han; Dranias, Mark R; Banumurthy, Gokulakrishna; VanDongen, Antonius M J

    2015-03-04

    The ability to process complex spatiotemporal information is a fundamental process underlying the behavior of all higher organisms. However, how the brain processes information in the temporal domain remains incompletely understood. We have explored the spatiotemporal information-processing capability of networks formed from dissociated rat E18 cortical neurons growing in culture. By combining optogenetics with microelectrode array recording, we show that these randomly organized cortical microcircuits are able to process complex spatiotemporal information, allowing the identification of a large number of temporal sequences and classification of musical styles. These experiments uncovered spatiotemporal memory processes lasting several seconds. Neural network simulations indicated that both short-term synaptic plasticity and recurrent connections are required for the emergence of this capability. Interestingly, NMDA receptor function is not a requisite for these short-term spatiotemporal memory processes. Indeed, blocking the NMDA receptor with the antagonist APV significantly improved the temporal processing ability of the networks, by reducing spontaneously occurring network bursts. These highly synchronized events have disastrous effects on spatiotemporal information processing, by transiently erasing short-term memory. These results show that the ability to process and integrate complex spatiotemporal information is an intrinsic property of generic cortical networks that does not require specifically designed circuits. Copyright © 2015 the authors 0270-6474/15/354040-12$15.00/0.

  12. Using model-based functional MRI to locate working memory updates and declarative memory retrievals in the fronto-parietal network.

    Science.gov (United States)

    Borst, Jelmer P; Anderson, John R

    2013-01-29

    In this study, we used model-based functional MRI (fMRI) to locate two functions of the fronto-parietal network: declarative memory retrievals and updating of working memory. Because regions in the fronto-parietal network are by definition coherently active, locating functions within this network is difficult. To overcome this problem, we applied model-based fMRI, an analysis method that uses predictions of a computational model to inform the analysis. We applied model-based fMRI to five previously published datasets with associated computational cognitive models, and subsequently integrated the results in a meta-analysis. The meta-analysis showed that declarative memory retrievals correlated with activity in the inferior frontal gyrus and the anterior cingulate, whereas updating of working memory corresponded to activation in the inferior parietal lobule, as well as to activation around the inferior frontal gyrus and the anterior cingulate.

  13. Silicon Carbide Defect Qubits/Quantum Memory with Field-Tuning: OSD Quantum Science and Engineering Program (QSEP)

    Science.gov (United States)

    2017-08-01

    TECHNICAL REPORT 3073 August 2017 Silicon Carbide Defect Qubits/Quantum Memory with Field-tuning: OSD Quantum Science and Engineering Program ...Higa SSC Pacific Lance Lerum Hector Romero Naval Research Enterprise Internship Program Mohammed Fahem San Diego State University Research...Quantum Science and Engineering Program ) by the Advanced Concepts and Applied Research Branch (Code 71730), the Energy and Environmental Sustainability

  14. Reverse engineering and analysis of genome-wide gene regulatory networks from gene expression profiles using high-performance computing.

    Science.gov (United States)

    Belcastro, Vincenzo; Gregoretti, Francesco; Siciliano, Velia; Santoro, Michele; D'Angelo, Giovanni; Oliva, Gennaro; di Bernardo, Diego

    2012-01-01

    Regulation of gene expression is a carefully regulated phenomenon in the cell. “Reverse-engineering” algorithms try to reconstruct the regulatory interactions among genes from genome-scale measurements of gene expression profiles (microarrays). Mammalian cells express tens of thousands of genes; hence, hundreds of gene expression profiles are necessary in order to have acceptable statistical evidence of interactions between genes. As the number of profiles to be analyzed increases, so do computational costs and memory requirements. In this work, we designed and developed a parallel computing algorithm to reverse-engineer genome-scale gene regulatory networks from thousands of gene expression profiles. The algorithm is based on computing pairwise Mutual Information between each gene-pair. We successfully tested it to reverse engineer the Mus Musculus (mouse) gene regulatory network in liver from gene expression profiles collected from a public repository. A parallel hierarchical clustering algorithm was implemented to discover “communities” within the gene network. Network communities are enriched for genes involved in the same biological functions. The inferred network was used to identify two mitochondrial proteins.

  15. System of Systems Engineering and Integration Process for Network Transport Assessment

    Science.gov (United States)

    2016-09-01

    through the process to ensure oversight of design and tradeoff decisions for network throughput analyses. 14. SUBJECT TERMS network transport , SoS... Distribution is unlimited. SYSTEM OF SYSTEMS ENGINEERING AND INTEGRATION PROCESS FOR NETWORK TRANSPORT ASSESSMENT Matthew B. Rambo Civilian...engineering processes to utilize to address network transport design and testing? 2. How can SoS data throughput requirements be identified and

  16. Is Cooperative Memory Special? The Role of Costly Errors, Context, and Social Network Size When Remembering Cooperative Actions

    Directory of Open Access Journals (Sweden)

    Tim Winke

    2017-10-01

    Full Text Available Theoretical studies of cooperative behavior have focused on decision strategies, such as tit-for-tat, that depend on remembering a partner’s last choices. Yet, an empirical study by Stevens et al. (2011 demonstrated that human memory may not meet the requirements that needed to use these strategies. When asked to recall the previous behavior of simulated partners in a cooperative memory task, participants performed poorly, making errors in 10–24% of the trials. However, we do not know the extent to which this task taps specialized cognition for cooperation. It may be possible to engage participants in more cooperative, strategic thinking, which may improve memory. On the other hand, compared with other situations, a cooperative context may already engage improved memory via cheater detection mechanisms. This study investigated the specificity of memory in cooperative contexts by varying (1 the costs of errors in memory by making forgetting defection more costly and (2 whether the recall situation is framed as a cooperative or neutral context. Also, we investigated whether variation in participants’ social network size could account for individual differences observed in memory accuracy. We found that neither including differential costs for misremembering defection nor removing the cooperative context influenced memory accuracy for cooperation. Combined, these results suggest that memory accuracy is robust to differences in the cooperative context: Adding more strategic components does not help accuracy, and removing cooperative components does not hurt accuracy. Social network size, however, did correlate with memory accuracy: People with larger networks remembered the events better. These findings suggest that cooperative memory does not seem to be special compared with other forms of memory, which aligns with previous work demonstrating the domain generality of memory. However, the demands of interacting in a large social network may

  17. The effect of arousal on the emotional memory network depends on valence.

    Science.gov (United States)

    Mickley Steinmetz, Katherine R; Addis, Donna Rose; Kensinger, Elizabeth A

    2010-10-15

    Some suggest that arousal is the essential element needed to engage the amygdala. However, the role of arousal in the larger emotional memory network may differ depending on the valence (positive, negative) of the to-be-remembered information. The goal of the current study was to determine the influence of arousal-based changes in amygdalar connectivity for positive and negative items. Participants were shown emotional and neutral pictures while they underwent a functional magnetic resonance imaging (fMRI) scan. The emotional pictures varied by valence (positive or negative) and arousal (high or low). Approximately 90minutes later, outside of the scanner, participants took a surprise recognition test. Effective connectivity analysis examined how arousal affected successful encoding activity. For negative information, arousal increased the strength of amygdala connections to the inferior frontal gyrus and the middle occipital gyrus, while for positive information arousal decreased the strength of these amygdala efferents. Further, while the effect of arousal on memory for positive information was restricted to amygdalar efferents, arousal had a more widespread effect for negative items, enhancing connectivity between other nodes of the emotional memory network. These findings emphasize that the effect of arousal on the connectivity within the emotional memory network depends on item valence. Copyright 2010 Elsevier Inc. All rights reserved.

  18. A New Type of Photo-Thermo Staged-Responsive Shape-Memory Polyurethanes Network

    Directory of Open Access Journals (Sweden)

    Jinghao Yang

    2017-07-01

    Full Text Available In this paper, we developed a photo-thermo staged-responsive shape-memory polymer network which has a unique ability of being spontaneously photo-responsive deformable and thermo-responsive shape recovery. This new type of shape-memory polyurethane network (A-SMPUs was successfully synthesized with 4,4-azodibenzoic acid (Azoa, hexamethylenediisocyanate (HDI and polycaprolactone (PCL, followed by chemical cross-linking with glycerol (Gl. The structures, morphology, and shape-memory properties of A-SMPUs have been carefully investigated. The results demonstrate that the A-SMPUs form micro-phase separation structures consisting of a semi-crystallized PCL soft phase and an Azoa amorphous hard phase that could influence the crystallinity of PCL soft phases. The chemical cross-linking provided a stable network and good thermal stability to the A-SMPUs. All A-SMPUs exhibited good triple-shape-memory properties with higher than 97% shape fixity ratio and 95% shape recovery ratio. Additionally, the A-SMPUs with higher Azoa content exhibited interesting photo-thermo two-staged responsiveness. A pre-processed film with orientated Azoa structure exhibited spontaneous curling deformation upon exposing to ultraviolet (UV light, and curling deformation is constant even under Vis light. Finally, the curling deformation can spontaneously recover to the original shape by applying a thermal stimulus. This work demonstrates new synergistically multi-responsive SMPUs that will have many applications in smart science and technology.

  19. Modulation of effective connectivity in the default mode network at rest and during a memory task.

    Science.gov (United States)

    Li, Xingfeng; Kehoe, Elizabeth G; McGinnity, Thomas Martin; Coyle, Damien; Bokde, Arun L W

    2015-02-01

    It is known that the default mode network (DMN) may be modulated by a cognitive task and by performance level. Changes in the DMN have been examined by investigating resting-state activation levels, but there have been very few studies examining the modulation of effective connectivity of the DMN during a task in healthy older subjects. In this study, the authors examined how effective connectivity changed in the DMN between rest and during a memory task. The authors also investigated whether there was any relationship between effective connectivity modulation in the DMN and memory performance, to establish whether variations in cognitive performance are related to neural network effective connectivity, either at rest or during task performance. Twenty-eight healthy older participants underwent a resting-state functional magnetic resonance imaging scan and an emotional face-name encoding task. Effective connectivity analyses were performed on the DMN to examine the effective connectivity modulation in these two different conditions. During the resting state, there was strong self-influence in the regions of the DMN, while the main regions with statistically significant cross-regional effective connectivity were the posterior cingulate cortex (PCC) and the hippocampus (HP). During the memory task, the self-influence effective connectivities remained statistically significant across the DMN, and there were statistically significant effective connectivities from the PCC, HP, amygdala (AM), and parahippocampal region to other DMN regions. The authors found that effective connectivities from PCC, HP, and AM (in both resting state and during task) were linearly correlated to memory performance. The results suggest that superior memory ability in this older cohort was associated with effective connectivity both at rest and during the memory task of three DMN regions, which are also known to be important for memory function.

  20. CA1 hippocampal network activity changes during sleep-dependent memory consolidation

    Directory of Open Access Journals (Sweden)

    Nicolette N Ognjanovski

    2014-04-01

    Full Text Available A period of sleep over the first few hours following single-trial contextual fear conditioning (CFC is essential for hippocampally-mediated memory consolidation. Recent studies have uncovered intracellular mechanisms required for memory formation that are affected by post-conditioning sleep and sleep deprivation. However, almost nothing is known about the circuit-level activity changes during sleep that underlie activation of these intracellular pathways. Here we continuously record neuronal activity from the CA1 region of freely-behaving mice to characterize neuronal and network activity changes occurring during active memory consolidation. C57BL/6J mice were implanted with custom stereotrode recording arrays to monitor activity of individual CA1 neurons, local field potentials (LFPs, and electromyographic activity. Sleep architecture and state-specific CA1 activity patterns were assessed during a 24 h baseline recording period, and for 24 h following either single-trial CFC or Sham conditioning. We find that consolidation of CFC is not associated with significant sleep architecture changes, but is accompanied by long-lasting increases in CA1 neuronal firing, as well as increases in delta, theta, and gamma-frequency CA1 LFP activity. These changes occurred in both sleep and wakefulness, and may drive synaptic plasticity within the hippocampus during memory formation. We also find that functional connectivity within the CA1 network, assessed through functional clustering analysis (FCA of spike timing relationships among recorded neurons, becomes more stable during consolidation of CFC. This increase in network stability was not present following Sham conditioning, was most evident during post-CFC slow wave sleep, and was negligible during post-CFC wakefulness. Thus in the interval between encoding and recall, slow wave sleep may stabilize the hippocampal contextual fear memory trace by promoting CA1 network stability.

  1. Youthful Brains in Older Adults: Preserved Neuroanatomy in the Default Mode and Salience Networks Contributes to Youthful Memory in Superaging.

    Science.gov (United States)

    Sun, Felicia W; Stepanovic, Michael R; Andreano, Joseph; Barrett, Lisa Feldman; Touroutoglou, Alexandra; Dickerson, Bradford C

    2016-09-14

    Decline in cognitive skills, especially in memory, is often viewed as part of "normal" aging. Yet some individuals "age better" than others. Building on prior research showing that cortical thickness in one brain region, the anterior midcingulate cortex, is preserved in older adults with memory performance abilities equal to or better than those of people 20-30 years younger (i.e., "superagers"), we examined the structural integrity of two large-scale intrinsic brain networks in superaging: the default mode network, typically engaged during memory encoding and retrieval tasks, and the salience network, typically engaged during attention, motivation, and executive function tasks. We predicted that superagers would have preserved cortical thickness in critical nodes in these networks. We defined superagers (60-80 years old) based on their performance compared to young adults (18-32 years old) on the California Verbal Learning Test Long Delay Free Recall test. We found regions within the networks of interest where the cerebral cortex of superagers was thicker than that of typical older adults, and where superagers were anatomically indistinguishable from young adults; hippocampal volume was also preserved in superagers. Within the full group of older adults, thickness of a number of regions, including the anterior temporal cortex, rostral medial prefrontal cortex, and anterior midcingulate cortex, correlated with memory performance, as did the volume of the hippocampus. These results indicate older adults with youthful memory abilities have youthful brain regions in key paralimbic and limbic nodes of the default mode and salience networks that support attentional, executive, and mnemonic processes subserving memory function. Memory performance typically declines with age, as does cortical structural integrity, yet some older adults maintain youthful memory. We tested the hypothesis that superagers (older individuals with youthful memory performance) would exhibit

  2. Reactivation in Working Memory: An Attractor Network Model of Free Recall

    Science.gov (United States)

    Lansner, Anders; Marklund, Petter; Sikström, Sverker; Nilsson, Lars-Göran

    2013-01-01

    The dynamic nature of human working memory, the general-purpose system for processing continuous input, while keeping no longer externally available information active in the background, is well captured in immediate free recall of supraspan word-lists. Free recall tasks produce several benchmark memory phenomena, like the U-shaped serial position curve, reflecting enhanced memory for early and late list items. To account for empirical data, including primacy and recency as well as contiguity effects, we propose here a neurobiologically based neural network model that unifies short- and long-term forms of memory and challenges both the standard view of working memory as persistent activity and dual-store accounts of free recall. Rapidly expressed and volatile synaptic plasticity, modulated intrinsic excitability, and spike-frequency adaptation are suggested as key cellular mechanisms underlying working memory encoding, reactivation and recall. Recent findings on the synaptic and molecular mechanisms behind early LTP and on spiking activity during delayed-match-to-sample tasks support this view. PMID:24023690

  3. Training Working Memory in Childhood Enhances Coupling between Frontoparietal Control Network and Task-Related Regions.

    Science.gov (United States)

    Barnes, Jessica J; Nobre, Anna Christina; Woolrich, Mark W; Baker, Kate; Astle, Duncan E

    2016-08-24

    Working memory is a capacity upon which many everyday tasks depend and which constrains a child's educational progress. We show that a child's working memory can be significantly enhanced by intensive computer-based training, relative to a placebo control intervention, in terms of both standardized assessments of working memory and performance on a working memory task performed in a magnetoencephalography scanner. Neurophysiologically, we identified significantly increased cross-frequency phase amplitude coupling in children who completed training. Following training, the coupling between the upper alpha rhythm (at 16 Hz), recorded in superior frontal and parietal cortex, became significantly coupled with high gamma activity (at ∼90 Hz) in inferior temporal cortex. This altered neural network activity associated with cognitive skill enhancement is consistent with a framework in which slower cortical rhythms enable the dynamic regulation of higher-frequency oscillatory activity related to task-related cognitive processes. Whether we can enhance cognitive abilities through intensive training is one of the most controversial topics of cognitive psychology in recent years. This is particularly controversial in childhood, where aspects of cognition, such as working memory, are closely related to school success and are implicated in numerous developmental disorders. We provide the first neurophysiological account of how working memory training may enhance ability in childhood, using a brain recording technique called magnetoencephalography. We borrowed an analysis approach previously used with intracranial recordings in adults, or more typically in other animal models, called "phase amplitude coupling." Copyright © 2016 Barnes et al.

  4. Reactivation in working memory: an attractor network model of free recall.

    Science.gov (United States)

    Lansner, Anders; Marklund, Petter; Sikström, Sverker; Nilsson, Lars-Göran

    2013-01-01

    The dynamic nature of human working memory, the general-purpose system for processing continuous input, while keeping no longer externally available information active in the background, is well captured in immediate free recall of supraspan word-lists. Free recall tasks produce several benchmark memory phenomena, like the U-shaped serial position curve, reflecting enhanced memory for early and late list items. To account for empirical data, including primacy and recency as well as contiguity effects, we propose here a neurobiologically based neural network model that unifies short- and long-term forms of memory and challenges both the standard view of working memory as persistent activity and dual-store accounts of free recall. Rapidly expressed and volatile synaptic plasticity, modulated intrinsic excitability, and spike-frequency adaptation are suggested as key cellular mechanisms underlying working memory encoding, reactivation and recall. Recent findings on the synaptic and molecular mechanisms behind early LTP and on spiking activity during delayed-match-to-sample tasks support this view.

  5. The influence of age and mild cognitive impairment on associative memory performance and underlying brain networks.

    Science.gov (United States)

    Oedekoven, Christiane S H; Jansen, Andreas; Keidel, James L; Kircher, Tilo; Leube, Dirk

    2015-12-01

    Associative memory is essential to everyday activities, such as the binding of faces and corresponding names to form single bits of information. However, this ability often becomes impaired with increasing age. The most important neural substrate of associative memory is the hippocampus, a structure crucially implicated in the pathogenesis of Alzheimer's disease (AD). The main aim of this study was to compare neural correlates of associative memory in healthy aging and mild cognitive impairment (MCI), an at-risk state for AD. We used fMRI to investigate differences in brain activation and connectivity between young controls (n = 20), elderly controls (n = 32) and MCI patients (n = 21) during associative memory retrieval. We observed lower hippocampal activation in MCI patients than control groups during a face-name recognition task, and the magnitude of this decrement was correlated with lower associative memory performance. Further, increased activation in precentral regions in all older adults indicated a stronger involvement of the task positive network (TPN) with age. Finally, functional connectivity analysis revealed a stronger link of hippocampal and striatal components in older adults in comparison to young controls, regardless of memory impairment. In elderly controls, this went hand-in-hand with a stronger activation of striatal areas. Increased TPN activation may be linked to greater reliance on cognitive control in both older groups, while increased functional connectivity between the hippocampus and the striatum may suggest dedifferentiation, especially in elderly controls.

  6. Using spatial multiple regression to identify intrinsic connectivity networks involved in working memory performance.

    Science.gov (United States)

    Gordon, Evan M; Stollstorff, Melanie; Vaidya, Chandan J

    2012-07-01

    Many researchers have noted that the functional architecture of the human brain is relatively invariant during task performance and the resting state. Indeed, intrinsic connectivity networks (ICNs) revealed by resting-state functional connectivity analyses are spatially similar to regions activated during cognitive tasks. This suggests that patterns of task-related activation in individual subjects may result from the engagement of one or more of these ICNs; however, this has not been tested. We used a novel analysis, spatial multiple regression, to test whether the patterns of activation during an N-back working memory task could be well described by a linear combination of ICNs delineated using Independent Components Analysis at rest. We found that across subjects, the cingulo-opercular Set Maintenance ICN, as well as right and left Frontoparietal Control ICNs, were reliably activated during working memory, while Default Mode and Visual ICNs were reliably deactivated. Further, involvement of Set Maintenance, Frontoparietal Control, and Dorsal Attention ICNs was sensitive to varying working memory load. Finally, the degree of left Frontoparietal Control network activation predicted response speed, while activation in both left Frontoparietal Control and Dorsal Attention networks predicted task accuracy. These results suggest that a close relationship between resting-state networks and task-evoked activation is functionally relevant for behavior, and that spatial multiple regression analysis is a suitable method for revealing that relationship. Copyright © 2011 Wiley-Liss, Inc.

  7. Synaptic potentiation facilitates memory-like attractor dynamics in cultured in vitro hippocampal networks.

    Directory of Open Access Journals (Sweden)

    Mark Niedringhaus

    Full Text Available Collective rhythmic dynamics from neurons is vital for cognitive functions such as memory formation but how neurons self-organize to produce such activity is not well understood. Attractor-based computational models have been successfully implemented as a theoretical framework for memory storage in networks of neurons. Additionally, activity-dependent modification of synaptic transmission is thought to be the physiological basis of learning and memory. The goal of this study is to demonstrate that using a pharmacological treatment that has been shown to increase synaptic strength within in vitro networks of hippocampal neurons follows the dynamical postulates theorized by attractor models. We use a grid of extracellular electrodes to study changes in network activity after this perturbation and show that there is a persistent increase in overall spiking and bursting activity after treatment. This increase in activity appears to recruit more "errant" spikes into bursts. Phase plots indicate a conserved activity pattern suggesting that a synaptic potentiation perturbation to the attractor leaves it unchanged. Lastly, we construct a computational model to demonstrate that these synaptic perturbations can account for the dynamical changes seen within the network.

  8. Recurrent Neural Network For Forecasting Time Series With Long Memory Pattern

    Science.gov (United States)

    Walid; Alamsyah

    2017-04-01

    Recurrent Neural Network as one of the hybrid models are often used to predict and estimate the issues related to electricity, can be used to describe the cause of the swelling of electrical load which experienced by PLN. In this research will be developed RNN forecasting procedures at the time series with long memory patterns. Considering the application is the national electrical load which of course has a different trend with the condition of the electrical load in any country. This research produces the algorithm of time series forecasting which has long memory pattern using E-RNN after this referred to the algorithm of integrated fractional recurrent neural networks (FIRNN).The prediction results of long memory time series using models Fractional Integrated Recurrent Neural Network (FIRNN) showed that the model with the selection of data difference in the range of [-1,1] and the model of Fractional Integrated Recurrent Neural Network (FIRNN) (24,6,1) provides the smallest MSE value, which is 0.00149684.

  9. Using Spatial Multiple Regression to Identify Intrinsic Connectivity Networks Involved in Working Memory Performance

    Science.gov (United States)

    Gordon, Evan M.; Stollstorff, Melanie; Vaidya, Chandan J.

    2012-01-01

    Many researchers have noted that the functional architecture of the human brain is relatively invariant during task performance and the resting state. Indeed, intrinsic connectivity networks (ICNs) revealed by resting-state functional connectivity analyses are spatially similar to regions activated during cognitive tasks. This suggests that patterns of task-related activation in individual subjects may result from the engagement of one or more of these ICNs; however, this has not been tested. We used a novel analysis, spatial multiple regression, to test whether the patterns of activation during an N-back working memory task could be well described by a linear combination of ICNs delineated using Independent Components Analysis at rest. We found that across subjects, the cingulo-opercular Set Maintenance ICN, as well as right and left Frontoparietal Control ICNs, were reliably activated during working memory, while Default Mode and Visual ICNs were reliably deactivated. Further, involvement of Set Maintenance, Frontoparietal Control, and Dorsal Attention ICNs was sensitive to varying working memory load. Finally, the degree of left Frontoparietal Control network activation predicted response speed, while activation in both left Frontoparietal Control and Dorsal Attention networks predicted task accuracy. These results suggest that a close relationship between resting-state networks and task-evoked activation is functionally relevant for behavior, and that spatial multiple regression analysis is a suitable method for revealing that relationship. PMID:21761505

  10. Gender differences in working memory networks: a BrainMap meta-analysis.

    Science.gov (United States)

    Hill, Ashley C; Laird, Angela R; Robinson, Jennifer L

    2014-10-01

    Gender differences in psychological processes have been of great interest in a variety of fields. While the majority of research in this area has focused on specific differences in relation to test performance, this study sought to determine the underlying neurofunctional differences observed during working memory, a pivotal cognitive process shown to be predictive of academic achievement and intelligence. Using the BrainMap database, we performed a meta-analysis and applied activation likelihood estimation to our search set. Our results demonstrate consistent working memory networks across genders, but also provide evidence for gender-specific networks whereby females consistently activate more limbic (e.g., amygdala and hippocampus) and prefrontal structures (e.g., right inferior frontal gyrus), and males activate a distributed network inclusive of more parietal regions. These data provide a framework for future investigations using functional or effective connectivity methods to elucidate the underpinnings of gender differences in neural network recruitment during working memory tasks. Copyright © 2014 Elsevier B.V. All rights reserved.

  11. Effects of Stress and Task Difficulty on Working Memory and Cortical Networking.

    Science.gov (United States)

    Kim, Yujin; Woo, Jihwan; Woo, Minjung

    2017-12-01

    This study investigated interactive effects of stress and task difficulty on working memory and cortico-cortical communication during memory encoding. Thirty-eight adolescent participants (mean age of 15.7 ± 1.5 years) completed easy and hard working memory tasks under low- and high-stress conditions. We analyzed the accuracy and reaction time (RT) of working memory performance and inter- and intrahemispheric electroencephalogram coherences during memory encoding. Working memory accuracy was higher, and RT shorter, in the easy versus the hard task. RT was shorter under the high-stress (TENS) versus low-stress (no-TENS) condition, while there was no difference in memory accuracy between the two stress conditions. For electroencephalogram coherence, we found higher interhemispheric coherence in all bands but only at frontal electrode sites in the easy versus the hard task. On the other hand, intrahemispheric coherence was higher in the left hemisphere in the easy (versus hard task) and higher in the right hemisphere (with one exception) in the hard (versus easy task). Inter- and intracoherences were higher in the low- versus high-stress condition. Significant interactions between task difficulty and stress condition were observed in coherences of the beta frequency band. The difference in coherence between low- and high-stress conditions was greater in the hard compared with the easy task, with lower coherence under the high-stress condition relative to the low-stress condition. Stress seemed to cause a decrease in cortical network communications between memory-relevant cortical areas as task difficulty increased.

  12. Memory consolidation from seconds to weeks: A three-stage neural network model with autonomous reinstatement dynamics

    Directory of Open Access Journals (Sweden)

    Florian eFiebig

    2014-07-01

    Full Text Available Declarative long-term memories are not created at an instant. Gradual stabilization and temporally shifting dependence of acquired declarative memories on different brain regions - called systems consolidation - can be tracked in time by lesion experiments. The observation of temporally graded retrograde amnesia following hippocampal lesions, points to a gradual transfer of memory from hippocampus to neocortical long-term memory. Spontaneous reactivations of hippocampal memories, as observed in place cell reactivations during slow-wave-sleep, are supposed to drive neocortical reinstatements and facilitate this process.We propose a functional neural network implementation of these ideas and furthermore suggest an extended three-stage framework that also includes the prefrontal cortex and bridges the temporal chasm between working memory percepts on the scale of seconds and consolidated long-term memory on the scale of weeks or months.We show that our three-stage model can autonomously produce the necessary stochastic reactivation dynamics for successful episodic memory consolidation. The resulting learning system is shown to exhibit classical memory effects seen in experimental studies, such as retrograde and anterograde amnesia after simulated hippocampal lesioning; furthermore the model reproduces peculiar biological findings on memory modulation, such as retrograde facilitation of memory after suppressed acquisition of new long-term memories - similar to the effects of benzodiazepines on memory.

  13. Principles of Biomimetic Vascular Network Design Applied to a Tissue-Engineered Liver Scaffold

    Science.gov (United States)

    Hoganson, David M.; Pryor, Howard I.; Spool, Ira D.; Burns, Owen H.; Gilmore, J. Randall

    2010-01-01

    Branched vascular networks are a central component of scaffold architecture for solid organ tissue engineering. In this work, seven biomimetic principles were established as the major guiding technical design considerations of a branched vascular network for a tissue-engineered scaffold. These biomimetic design principles were applied to a branched radial architecture to develop a liver-specific vascular network. Iterative design changes and computational fluid dynamic analysis were used to optimize the network before mold manufacturing. The vascular network mold was created using a new mold technique that achieves a 1:1 aspect ratio for all channels. In vitro blood flow testing confirmed the physiologic hemodynamics of the network as predicted by computational fluid dynamic analysis. These results indicate that this biomimetic liver vascular network design will provide a foundation for developing complex vascular networks for solid organ tissue engineering that achieve physiologic blood flow. PMID:20001254

  14. 76 FR 46359 - Announcing the Nineteenth Public Meeting of the Crash Injury Research and Engineering Network...

    Science.gov (United States)

    2011-08-02

    ... National Highway Traffic Safety Administration Announcing the Nineteenth Public Meeting of the Crash Injury Research and Engineering Network (CIREN) AGENCY: National Highway Traffic Safety Administration (NHTSA... members of the Crash Injury Research and Engineering Network. CIREN is a collaborative effort to conduct...

  15. How the amygdala affects emotional memory by altering brain network properties.

    Science.gov (United States)

    Hermans, Erno J; Battaglia, Francesco P; Atsak, Piray; de Voogd, Lycia D; Fernández, Guillén; Roozendaal, Benno

    2014-07-01

    The amygdala has long been known to play a key role in supporting memory for emotionally arousing experiences. For example, classical fear conditioning depends on neural plasticity within this anterior medial temporal lobe region. Beneficial effects of emotional arousal on memory, however, are not restricted to simple associative learning. Our recollection of emotional experiences often includes rich representations of, e.g., spatiotemporal context, visceral states, and stimulus-response associations. Critically, such memory features are known to bear heavily on regions elsewhere in the brain. These observations led to the modulation account of amygdala function, which postulates that amygdala activation enhances memory consolidation by facilitating neural plasticity and information storage processes in its target regions. Rodent work in past decades has identified the most important brain regions and neurochemical processes involved in these modulatory actions, and neuropsychological and neuroimaging work in humans has produced a large body of convergent data. Importantly, recent methodological developments make it increasingly realistic to monitor neural interactions underlying such modulatory effects as they unfold. For instance, functional connectivity network modeling in humans has demonstrated how information exchanges between the amygdala and specific target regions occur within the context of large-scale neural network interactions. Furthermore, electrophysiological and optogenetic techniques in rodents are beginning to make it possible to quantify and even manipulate such interactions with millisecond precision. In this paper we will discuss that these developments will likely lead to an updated view of the amygdala as a critical nexus within large-scale networks supporting different aspects of memory processing for emotionally arousing experiences. Copyright © 2014 Elsevier Inc. All rights reserved.

  16. Sleep, plasticity and memory from molecules to whole-brain networks.

    Science.gov (United States)

    Abel, Ted; Havekes, Robbert; Saletin, Jared M; Walker, Matthew P

    2013-09-09

    Despite the ubiquity of sleep across phylogeny, its function remains elusive. In this review, we consider one compelling candidate: brain plasticity associated with memory processing. Focusing largely on hippocampus-dependent memory in rodents and humans, we describe molecular, cellular, network, whole-brain and behavioral evidence establishing a role for sleep both in preparation for initial memory encoding, and in the subsequent offline consolidation of memory. Sleep and sleep deprivation bidirectionally alter molecular signaling pathways that regulate synaptic strength and control plasticity-related gene transcription and protein translation. At the cellular level, sleep deprivation impairs cellular excitability necessary for inducing synaptic potentiation and accelerates the decay of long-lasting forms of synaptic plasticity. In contrast, rapid eye movement (REM) and non-rapid eye movement (NREM) sleep enhance previously induced synaptic potentiation, although synaptic de-potentiation during sleep has also been observed. Beyond single cell dynamics, large-scale cell ensembles express coordinated replay of prior learning-related firing patterns during subsequent NREM sleep. At the whole-brain level, somewhat analogous learning-associated hippocampal (re)activation during NREM sleep has been reported in humans. Moreover, the same cortical NREM oscillations associated with replay in rodents also promote human hippocampal memory consolidation, and this process can be manipulated using exogenous reactivation cues during sleep. Mirroring molecular findings in rodents, specific NREM sleep oscillations before encoding refresh human hippocampal learning capacity, while deprivation of sleep conversely impairs subsequent hippocampal activity and associated encoding. Together, these cross-descriptive level findings demonstrate that the unique neurobiology of sleep exerts powerful effects on molecular, cellular and network mechanisms of plasticity that govern both initial

  17. Network Sampling with Memory: A proposal for more efficient sampling from social networks

    OpenAIRE

    Mouw, Ted; Verdery, Ashton M.

    2012-01-01

    Techniques for sampling from networks have grown into an important area of research across several fields. For sociologists, the possibility of sampling from a network is appealing for two reasons: (1) A network sample can yield substantively interesting data about network structures and social interactions, and (2) it is useful in situations where study populations are difficult or impossible to survey with traditional sampling approaches because of the lack of a sampling frame. Despite its ...

  18. A One-Pass Real-Time Decoder Using Memory-Efficient State Network

    Science.gov (United States)

    Shao, Jian; Li, Ta; Zhang, Qingqing; Zhao, Qingwei; Yan, Yonghong

    This paper presents our developed decoder which adopts the idea of statically optimizing part of the knowledge sources while handling the others dynamically. The lexicon, phonetic contexts and acoustic model are statically integrated to form a memory-efficient state network, while the language model (LM) is dynamically incorporated on the fly by means of extended tokens. The novelties of our approach for constructing the state network are (1) introducing two layers of dummy nodes to cluster the cross-word (CW) context dependent fan-in and fan-out triphones, (2) introducing a so-called “WI layer” to store the word identities and putting the nodes of this layer in the non-shared mid-part of the network, (3) optimizing the network at state level by a sufficient forward and backward node-merge process. The state network is organized as a multi-layer structure for distinct token propagation at each layer. By exploiting the characteristics of the state network, several techniques including LM look-ahead, LM cache and beam pruning are specially designed for search efficiency. Especially in beam pruning, a layer-dependent pruning method is proposed to further reduce the search space. The layer-dependent pruning takes account of the neck-like characteristics of WI layer and the reduced variety of word endings, which enables tighter beam without introducing much search errors. In addition, other techniques including LM compression, lattice-based bookkeeping and lattice garbage collection are also employed to reduce the memory requirements. Experiments are carried out on a Mandarin spontaneous speech recognition task where the decoder involves a trigram LM and CW triphone models. A comparison with HDecode of HTK toolkits shows that, within 1% performance deviation, our decoder can run 5 times faster with half of the memory footprint.

  19. Finite-Time Stability for Fractional-Order Bidirectional Associative Memory Neural Networks with Time Delays

    Science.gov (United States)

    Xu, Chang-Jin; Li, Pei-Luan; Pang, Yi-Cheng

    2017-02-01

    This paper is concerned with fractional-order bidirectional associative memory (BAM) neural networks with time delays. Applying Laplace transform, the generalized Gronwall inequality and estimates of Mittag-Leffler functions, some sufficient conditions which ensure the finite-time stability of fractional-order bidirectional associative memory neural networks with time delays are obtained. Two examples with their simulations are given to illustrate the theoretical findings. Our results are new and complement previously known results. Supported by National Natural Science Foundation of China under Grant Nos.~61673008, 11261010, 11101126, Project of High-Level Innovative Talents of Guizhou Province ([2016]5651), Natural Science and Technology Foundation of Guizhou Province (J[2015]2025 and J[2015]2026), 125 Special Major Science and Technology of Department of Education of Guizhou Province ([2012]011) and Natural Science Foundation of the Education Department of Guizhou Province (KY[2015]482)

  20. Entanglement distillation for quantum communication network with atomic-ensemble memories.

    Science.gov (United States)

    Li, Tao; Yang, Guo-Jian; Deng, Fu-Guo

    2014-10-06

    Atomic ensembles are effective memory nodes for quantum communication network due to the long coherence time and the collective enhancement effect for the nonlinear interaction between an ensemble and a photon. Here we investigate the possibility of achieving the entanglement distillation for nonlocal atomic ensembles by the input-output process of a single photon as a result of cavity quantum electrodynamics. We give an optimal entanglement concentration protocol (ECP) for two-atomic-ensemble systems in a partially entangled pure state with known parameters and an efficient ECP for the systems in an unknown partially entangled pure state with a nondestructive parity-check detector (PCD). For the systems in a mixed entangled state, we introduce an entanglement purification protocol with PCDs. These entanglement distillation protocols have high fidelity and efficiency with current experimental techniques, and they are useful for quantum communication network with atomic-ensemble memories.

  1. Fabrication and characterization of shape memory polyurethane porous scaffold for bone tissue engineering.

    Science.gov (United States)

    Yu, Juhong; Xia, Hong; Teramoto, Akira; Ni, Qing-Qing

    2017-04-01

    Tissue engineering is a promising alternative for treating bone defects. However, improvements in scaffold design are needed to precisely match the irregular boundaries of bone defects as well as facilitate clinical application. In this study, a shape memory polyurethane scaffold was fabricated using a salt-leaching-phase inverse technique. Different sizes of salts were used to obtain scaffolds with different pore sizes. Scanning electron microscope, X-ray photoelectron spectroscopy, and X-ray micro-computed tomography analysis confirmed that three-dimensional porous polyurethane scaffolds were obtained. The mechanical properties and biocompatibility of the scaffolds were analyzed by compression testing, thermal mechanical analysis, and cell experiments with osteosarcoma MG-63 cells. The results revealed that the scaffolds had good mechanical properties and shape memory properties for bone repair, and also had the ability to promote cell proliferation. Thus, this scaffold design has good prospects for application to bone tissue engineering. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 1132-1137, 2017. © 2017 Wiley Periodicals, Inc.

  2. Sensor Fault Diagnosis for Aero Engine Based on Online Sequential Extreme Learning Machine with Memory Principle

    Directory of Open Access Journals (Sweden)

    Junjie Lu

    2017-01-01

    Full Text Available The on-board sensor fault detection and isolation (FDI system is essential to guarantee the reliability and safety of an aero engine. In this paper, a novel online sequential extreme learning machine with memory principle (MOS-ELM is proposed for detecting, isolating, and reconstructing the fault sensor signal of aero engines. In many practical online applications, the sequentially coming data chunk usually possesses a characteristic of timeliness, and the overdue training data may mislead the subsequent learning process. The proposed MOS-ELM can improve the training process by introducing the concept of memory principle into the online sequential extreme learning machine (OS-ELM to tackle the timeliness of the data chunk. Simulations on some time series problems and some benchmark databases show that MOS-ELM performs better in generalization performance, stability, and prediction accuracy than OS-ELM. The experiment results of the MOS-ELM-based sensor fault diagnosis system also verify the excellent generalization performance of MOS-ELM and indicate the effectiveness and feasibility of the developed diagnosis system.

  3. Synchronization and long-time memory in neural networks with inhibitory hubs and synaptic plasticity

    Science.gov (United States)

    Bertolotti, Elena; Burioni, Raffaella; di Volo, Matteo; Vezzani, Alessandro

    2017-01-01

    We investigate the dynamical role of inhibitory and highly connected nodes (hub) in synchronization and input processing of leaky-integrate-and-fire neural networks with short term synaptic plasticity. We take advantage of a heterogeneous mean-field approximation to encode the role of network structure and we tune the fraction of inhibitory neurons fI and their connectivity level to investigate the cooperation between hub features and inhibition. We show that, depending on fI, highly connected inhibitory nodes strongly drive the synchronization properties of the overall network through dynamical transitions from synchronous to asynchronous regimes. Furthermore, a metastable regime with long memory of external inputs emerges for a specific fraction of hub inhibitory neurons, underlining the role of inhibition and connectivity also for input processing in neural networks.

  4. Inferring long memory processes in the climate network via ordinal pattern analysis

    CERN Document Server

    Barreiro, Marcelo; Masoller, Cristina

    2010-01-01

    We use ordinal patterns and symbolic analysis to construct global climate networks and uncover long and short term memory processes. The data analyzed is the monthly averaged surface air temperature (SAT field) and the results suggest that the time variability of the SAT field is determined by patterns of oscillatory behavior that repeat from time to time, with a periodicity related to intraseasonal oscillations and to El Ni\\~{n}o on seasonal-to-interannual time scales.

  5. The Effect of Arousal on the Emotional Memory Network Depends on Valence

    OpenAIRE

    Mickley Steinmetz, Katherine R.; Addis, Donna Rose; Kensinger, Elizabeth A.

    2010-01-01

    Some suggest that arousal is the essential element in order to engage the amygdala. However, the role of arousal in the larger emotional memory network may differ depending on the valence (positive, negative) of the to-be-remembered information. The goal of the current study was to determine the influence of arousal-based changes in amygdalar connectivity for positive and negative items. Participants were shown emotional and neutral pictures while they underwent a functional magnetic resonanc...

  6. Formation and Stability of a Memory State in the Immune Network

    Science.gov (United States)

    Sonoda, Takashi

    1992-04-01

    The immune system is investigated as a complex adaptive network. A nonlinear dynamical model is proposed to study roles of lymphocyte and antibody in the regulation of the immune response. Three kinds of lymphocytes; B cell, TH cell, and TS cell, interact and compose a functional unit. Furthermore this unit interacts with other units through antibodies. These two types of interactions cooperatively work and regulate the immune response. The model can explain how the memory state is formed and stabilized in the immune network. Behaviors of the model are verified by the computer simulations.

  7. Global Exponential Stability Criteria for Bidirectional Associative Memory Neural Networks with Time-Varying Delays

    Directory of Open Access Journals (Sweden)

    J. Thipcha

    2013-01-01

    Full Text Available The global exponential stability for bidirectional associative memory neural networks with time-varying delays is studied. In our study, the lower and upper bounds of the activation functions are allowed to be either positive, negative, or zero. By constructing new and improved Lyapunov-Krasovskii functional and introducing free-weighting matrices, a new and improved delay-dependent exponential stability for BAM neural networks with time-varying delays is derived in the form of linear matrix inequality (LMI. Numerical examples are given to demonstrate that the derived condition is less conservative than some existing results given in the literature.

  8. Statistical downscaling of precipitation using long short-term memory recurrent neural networks

    Science.gov (United States)

    Misra, Saptarshi; Sarkar, Sudeshna; Mitra, Pabitra

    2017-11-01

    Hydrological impacts of global climate change on regional scale are generally assessed by downscaling large-scale climatic variables, simulated by General Circulation Models (GCMs), to regional, small-scale hydrometeorological variables like precipitation, temperature, etc. In this study, we propose a new statistical downscaling model based on Recurrent Neural Network with Long Short-Term Memory which captures the spatio-temporal dependencies in local rainfall. The previous studies have used several other methods such as linear regression, quantile regression, kernel regression, beta regression, and artificial neural networks. Deep neural networks and recurrent neural networks have been shown to be highly promising in modeling complex and highly non-linear relationships between input and output variables in different domains and hence we investigated their performance in the task of statistical downscaling. We have tested this model on two datasets—one on precipitation in Mahanadi basin in India and the second on precipitation in Campbell River basin in Canada. Our autoencoder coupled long short-term memory recurrent neural network model performs the best compared to other existing methods on both the datasets with respect to temporal cross-correlation, mean squared error, and capturing the extremes.

  9. Collaborative Networked Organizations as System of Systems: A Model-Based Engineering Approach

    OpenAIRE

    Bilal, Mustapha; Daclin, Nicolas; Chapurlat, Vincent

    2014-01-01

    Part 6: Engineering and Implementation of Collaborative Networks; International audience; It is admitted that there is parallel between a System of Systems (SoS) and Collaborative Networked Organizations (CNOs). SoS Engineering (SoSE) carefully focuses on choosing, assembling and interfacing existing systems to build the so-called SoS. In this context, and as demonstrated by the literature and the System Engineering domain, interoperability takes on its full meaning and has to be fully consid...

  10. Correlations of striatal dopamine synthesis with default network deactivations during working memory in younger adults.

    Science.gov (United States)

    Braskie, Meredith N; Landau, Susan M; Wilcox, Claire E; Taylor, Stephanie D; O'Neil, James P; Baker, Suzanne L; Madison, Cindee M; Jagust, William J

    2011-06-01

    Age-related deficits have been demonstrated in working memory performance and in the dopamine system thought to support it. We performed positron emission tomography (PET) scans on 12 younger (mean 22.7 years) and 19 older (mean 65.8 years) adults using the radiotracer 6-[(18)F]-fluoro-L-m-tyrosine (FMT), which measures dopamine synthesis capacity. Subjects also underwent functional magnetic resonance imaging (fMRI) while performing a delayed recognition working memory task. We evaluated age-related fMRI activity differences and examined how they related to FMT signal variations in dorsal caudate within each age group. In posterior cingulate cortex and precuneus (PCC/Pc), older adults showed diminished fMRI deactivations during memory recognition compared with younger adults. Greater task-induced deactivation (in younger adults only) was associated both with higher FMT signal and with worse memory performance. Our results suggest that dopamine synthesis helps modulate default network activity in younger adults and that alterations to the dopamine system may contribute to age-related changes in working memory function. Copyright © 2010 Wiley-Liss, Inc.

  11. Implications of synaptic biophysics for recurrent network dynamics and active memory.

    Science.gov (United States)

    Durstewitz, Daniel

    2009-10-01

    In cortical networks, synaptic excitation is mediated by AMPA- and NMDA-type receptors. NMDA differ from AMPA synaptic potentials with regard to peak current, time course, and a strong voltage-dependent nonlinearity. Here we illustrate based on empirical and computational findings that these specific biophysical properties may have profound implications for the dynamics of cortical networks, and via dynamics on cognitive functions like active memory. The discussion will be led along a minimal set of neural equations introduced to capture the essential dynamics of the various phenomena described. NMDA currents could establish cortical bistability and may provide the relatively constant synaptic drive needed to robustly maintain enhanced levels of activity during working memory epochs, freeing fast AMPA currents for other computational purposes. Perhaps more importantly, variations in NMDA synaptic input-due to their biophysical particularities-control the dynamical regime within which single neurons and networks reside. By provoking bursting, chaotic irregularity, and coherent oscillations their major effect may be on the temporal pattern of spiking activity, rather than on average firing rate. During active memory, neurons may thus be pushed into a spiking regime that harbors complex temporal structure, potentially optimal for the encoding and processing of temporal sequence information. These observations provide a qualitatively different view on the role of synaptic excitation in neocortical dynamics than entailed by many more abstract models. In this sense, this article is a plead for taking the specific biophysics of real neurons and synapses seriously when trying to account for the neurobiology of cognition.

  12. Network profiles of the dorsal anterior cingulate and dorsal prefrontal cortex in schizophrenia during hippocampal-based associative memory

    Directory of Open Access Journals (Sweden)

    Eric eWoodcock

    2016-04-01

    Full Text Available Schizophrenia is a disorder characterized by brain network dysfunction, particularly during behavioral tasks that depend on frontal and hippocampal mechanisms. Here, we investigated network profiles of the regions of the frontal cortex during memory encoding and retrieval, phases of processing essential to associative memory. Schizophrenia patients (n=12 and healthy control subjects (n=10 participated in an established object-location associative memory paradigm that drives frontal-hippocampal interactions. Network profiles were modeled of both the dorsal prefrontal (dPFC and the dorsal anterior cingulate cortex (dACC as seeds using psychophysiological interaction analyses, a robust framework for investigating seed-based connectivity in specific task contexts. The choice of seeds was motivated by previous evidence of involvement of these regions during associative memory. Differences between patients and controls were evaluated using second-level analyses of variance with seed (dPFC vs. dACC, group (patients vs. controls, and memory process (encoding vs. retrieval as factors. Patients showed a pattern of exaggerated modulation by each of the dACC and the dPFC during memory encoding and retrieval. Furthermore, group by memory process interactions were observed within regions of the hippocampus. In schizophrenia patients, relatively diminished modulation during encoding was associated with increased modulation during retrieval. These results suggest a pattern of complex dysfunctional network signatures of critical forebrain regions in schizophrenia. Evidence of dysfunctional frontal-medial temporal lobe network signatures in schizophrenia is consistent with the illness’ characterization as a disconnection syndrome.

  13. Network Profiles of the Dorsal Anterior Cingulate and Dorsal Prefrontal Cortex in Schizophrenia During Hippocampal-Based Associative Memory.

    Science.gov (United States)

    Woodcock, Eric A; Wadehra, Sunali; Diwadkar, Vaibhav A

    2016-01-01

    Schizophrenia is a disorder characterized by brain network dysfunction, particularly during behavioral tasks that depend on frontal and hippocampal mechanisms. Here, we investigated network profiles of the regions of the frontal cortex during memory encoding and retrieval, phases of processing essential to associative memory. Schizophrenia patients (n = 12) and healthy control (HC) subjects (n = 10) participated in an established object-location associative memory paradigm that drives frontal-hippocampal interactions. Network profiles were modeled of both the dorsal prefrontal (dPFC) and the dorsal anterior cingulate cortex (dACC) as seeds using psychophysiological interaction analyses, a robust framework for investigating seed-based connectivity in specific task contexts. The choice of seeds was motivated by previous evidence of involvement of these regions during associative memory. Differences between patients and controls were evaluated using second-level analyses of variance (ANOVA) with seed (dPFC vs. dACC), group (patients vs. controls), and memory process (encoding and retrieval) as factors. Patients showed a pattern of exaggerated modulation by each of the dACC and the dPFC during memory encoding and retrieval. Furthermore, group by memory process interactions were observed within regions of the hippocampus. In schizophrenia patients, relatively diminished modulation during encoding was associated with increased modulation during retrieval. These results suggest a pattern of complex dysfunctional network signatures of critical forebrain regions in schizophrenia. Evidence of dysfunctional frontal-medial temporal lobe network signatures in schizophrenia is consistent with the illness' characterization as a disconnection syndrome.

  14. PIYAS-Proceeding to Intelligent Service Oriented Memory Allocation for Flash Based Data Centric Sensor Devices in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Sanam Shahla Rizvi

    2009-12-01

    Full Text Available Flash memory has become a more widespread storage medium for modern wireless devices because of its effective characteristics like non-volatility, small size, light weight, fast access speed, shock resistance, high reliability and low power consumption. Sensor nodes are highly resource constrained in terms of limited processing speed, runtime memory, persistent storage, communication bandwidth and finite energy. Therefore, for wireless sensor networks supporting sense, store, merge and send schemes, an efficient and reliable file system is highly required with consideration of sensor node constraints. In this paper, we propose a novel log structured external NAND flash memory based file system, called Proceeding to Intelligent service oriented memorY Allocation for flash based data centric Sensor devices in wireless sensor networks (PIYAS. This is the extended version of our previously proposed PIYA [1]. The main goals of the PIYAS scheme are to achieve instant mounting and reduced SRAM space by keeping memory mapping information to a very low size of and to provide high query response throughput by allocation of memory to the sensor data by network business rules. The scheme intelligently samples and stores the raw data and provides high in-network data availability by keeping the aggregate data for a longer period of time than any other scheme has done before. We propose effective garbage collection and wear-leveling schemes as well. The experimental results show that PIYAS is an optimized memory management scheme allowing high performance for wireless sensor networks.

  15. Engineering Design Tools for Shape Memory Alloy Actuators: CASMART Collaborative Best Practices and Case Studies

    Science.gov (United States)

    Wheeler, Robert W.; Benafan, Othmane; Gao, Xiujie; Calkins, Frederick T; Ghanbari, Zahra; Hommer, Garrison; Lagoudas, Dimitris; Petersen, Andrew; Pless, Jennifer M.; Stebner, Aaron P.; hide

    2016-01-01

    The primary goal of the Consortium for the Advancement of Shape Memory Alloy Research and Technology (CASMART) is to enable the design of revolutionary applications based on shape memory alloy (SMA) technology. In order to help realize this goal and reduce the development time and required experience for the fabrication of SMA actuation systems, several modeling tools have been developed for common actuator types and are discussed herein along with case studies, which highlight the capabilities and limitations of these tools. Due to their ability to sustain high stresses and recover large deformations, SMAs have many potential applications as reliable, lightweight, solid-state actuators. Their advantage over classical actuators can also be further improved when the actuator geometry is modified to fit the specific application. In this paper, three common actuator designs are studied: wires, which are lightweight, low-profile, and easily implemented; springs, which offer actuation strokes upwards of 200 at reduced mechanical loads; and torque tubes, which can provide large actuation forces in small volumes and develop a repeatable zero-load actuation response (known as the two-way shape memory effect). The modeling frameworks, which have been implemented in the design tools, are developed for each of these frequently used SMA actuator types. In order to demonstrate the versatility and flexibility of the presented design tools, as well as validate their modeling framework, several design challenges were completed. These case studies include the design and development of an active hinge for the deployment of a solar array or foldable space structure, an adaptive solar array deployment and positioning system, a passive air temperature controller for regulation flow temperatures inside of a jet engine, and a redesign of the Corvette active hatch, which allows for pressure equalization of the car interior. For each of the presented case studies, a prototype or proof

  16. Information model for management and preservation of scientific digital memory of the Institute of Nuclear Engineering, Brazil

    Energy Technology Data Exchange (ETDEWEB)

    Sales, Luana Farias, E-mail: lsales@ien.gov.br [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil); Sayao, Luis Fernando, E-mail: isayao@cnen.gov.br [Centro de Informacoes Nucleares (CIN/CNEN-RJ), Rio de Janeiro, RJ (Brazil)

    2013-07-01

    In the context of the data-oriented science (eScience), a considerable part of the results of research activities has been created in digital formats. This means that the memory of the scientific institutions involved in this new scientific paradigm may be at risk of being lost by rapid technological obsolescence, the known fragility of digital media and also by the fragmentation of information and knowledge scattered across multiples repositories. Thus, management of research data in a digital networked and distributed environment becomes an increasing challenge for the research world and the whole area of information: information science, librarianship, knowledge management, archival science and information technology; moreover, in the dynamic environment featuring eScience, there is a need for novel concepts of documents establishing a linkage between traditional documents - printed or digital - stored in repositories, with the data sets stored in data repositories. In this new research environment, an important issue is how to preserve these new complex documents so that they maintain their structure, meaning and authenticity and also its ability to be retrieved, accessed and reused through time and space. In this sense, this paper proposes an information model focused on the curation of scientific memory of the Institute of Nuclear Engineering of the Brazilian Commission of Nuclear Energy (CNEN/IEN). The model considers the traditional scientific documents (theses, articles, books, etc.) in digital formats and all other relevant data and information related to them, such as: scientific data, software, simulations, photos, videos, historical facts, news, etc., compounding an enhanced publication type oriented to the nuclear area. (author)

  17. Fuel economy and torque tracking in camless engines through optimization of neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ashhab, Moh' d Sami S. [Department of Mechanical Engineering, The Hashemite University, Zarqa 13115 (Jordan)

    2008-02-15

    The feed forward controller of a camless internal combustion engine is modeled by inverting a multi-input multi-output feed forward artificial neural network (ANN) model of the engine. The engine outputs, pumping loss and cylinder air charge, are related to the inputs, intake valve lift and closing timing, by the artificial neural network model, which is trained with historical input-output data. The controller selects the intake valve lift and closing timing that will mimimize the pumping loss and achieve engine torque tracking. Lower pumping loss means better fuel economy, whereas engine torque tracking guarantees the driver's torque demand. The inversion of the ANN is performed with the complex method constrained optimization. How the camless engine inverse controller can be augmented with adaptive techniques to maintain accuracy even when the engine parts degrade is discussed. The simulation results demonstrate the effectiveness of the developed camless engine controller. (author)

  18. Exploring Complex Engineering Learning over Time with Epistemic Network Analysis

    Science.gov (United States)

    Svarovsky, Gina Navoa

    2011-01-01

    Recently, K-12 engineering education has received increased attention as a pathway to building stronger foundations in math and science and introducing young people to the profession. However, the National Academy of Engineering found that many K-12 engineering programs focus heavily on engineering design and science and math learning while…

  19. Sleep, Plasticity and Memory from Molecules to Whole-Brain Networks

    Science.gov (United States)

    Abel, Ted; Havekes, Robbert; Saletin, Jared M.; Walker, Matthew P.

    2014-01-01

    Despite the ubiquity of sleep across phylogeny, its function remains elusive. In this review, we consider one compelling candidate: brain plasticity associated with memory processing. Focusing largely on hippocampus-dependent memory in rodents and humans, we describe molecular, cellular, network, whole-brain and behavioral evidence establishing a role for sleep both in preparation for initial memory encoding, and in the subsequent offline consolidation ofmemory. Sleep and sleep deprivation bidirectionally alter molecular signaling pathways that regulate synaptic strength and control plasticity-related gene transcription and protein translation. At the cellular level, sleep deprivation impairs cellular excitability necessary for inducing synaptic potentiation and accelerates the decay of long-lasting forms of synaptic plasticity. In contrast, NREM and REM sleep enhance previously induced synaptic potentiation, although synaptic de-potentiation during sleep has also been observed. Beyond single cell dynamics, large-scale cell ensembles express coordinated replay of prior learning-related firing patterns during subsequent sleep. This occurs in the hippocampus, in the cortex, and between the hippocampus and cortex, commonly in association with specific NREM sleep oscillations. At the whole-brain level, somewhat analogous learning-associated hippocampal (re)activation during NREM sleep has been reported in humans. Moreover, the same cortical NREM oscillations associated with replay in rodents also promote human hippocampal memory consolidation, and this process can be manipulated using exogenous reactivation cues during sleep. Mirroring molecular findings in rodents, specific NREM sleep oscillations before encoding refresh human hippocampal learning capacity, while deprivation of sleep conversely impairs subsequent hippocampal activity and associated encoding. Together, these cross-descriptive level findings demonstrate that the unique neurobiology of sleep exert

  20. A dual-subsystem model of the brain's default network: self-referential processing, memory retrieval processes, and autobiographical memory retrieval.

    Science.gov (United States)

    Kim, Hongkeun

    2012-07-16

    Most internally oriented mental activities are known to strongly activate the default network, which includes remembering the past, future thinking and social cognition, and are heavily self-referential, and demanding of memory retrieval processes. Based on these observations and building on related findings from the literature, the present article proposed a simple, dual-subsystem model of the default network. The ability of the model to estimate brain activity during autobiographical memory (AM) retrieval and related reference conditions was then tested by performing a quantitative meta-analysis of relevant literature. The model divided the default network into two subsystems. The first, called the 'cortical midline subsystem (CMS)', was comprised of the anteromedial prefrontal cortex and posterior cingulate cortex, and primarily mediates self-referential processing. The other, termed the 'parieto-temporal subsystem (PTS)', included the inferior parietal lobule, medial temporal lobe and lateral temporal cortex, and mainly supports memory retrieval processes. The meta-analysis of AM retrieval contrasts yielded a double dissociation that was consistent with this model. First, CMS regions associated more with an AM>laboratory-based memory (LM) contrast than with an AM>rest contrast, confirming that these regions play more critical roles in self-referential processing than memory retrieval processes. Second, all three PTS regions showed a greater association with an AM>rest contrast than with an AM>LM contrast, confirming that their role in memory retrieval processes is greater than in self-referential processing. Although the present model is limited in scope, both in terms of anatomical and functional specifications, it integrates diverse processes such as self-referential processing, episodic and semantic memory and subsystem interface, and provides useful heuristics that can guide further research on fractionation of the default network. Copyright © 2012

  1. Cortical networks for working memory and executive functions sustain the conscious resting state in man.

    Science.gov (United States)

    Mazoyer, B; Zago, L; Mellet, E; Bricogne, S; Etard, O; Houdé, O; Crivello, F; Joliot, M; Petit, L; Tzourio-Mazoyer, N

    2001-02-01

    The cortical anatomy of the conscious resting state (REST) was investigated using a meta-analysis of nine positron emission tomography (PET) activation protocols that dealt with different cognitive tasks but shared REST as a common control state. During REST, subjects were in darkness and silence, and were instructed to relax, refrain from moving, and avoid systematic thoughts. Each protocol contrasted REST to a different cognitive task consisting either of language, mental imagery, mental calculation, reasoning, finger movement, or spatial working memory, using either auditory, visual or no stimulus delivery, and requiring either vocal, motor or no output. A total of 63 subjects and 370 spatially normalized PET scans were entered in the meta-analysis. Conjunction analysis revealed a network of brain areas jointly activated during conscious REST as compared to the nine cognitive tasks, including the bilateral angular gyrus, the left anterior precuneus and posterior cingulate cortex, the left medial frontal and anterior cingulate cortex, the left superior and medial frontal sulcus, and the left inferior frontal cortex. These results suggest that brain activity during conscious REST is sustained by a large scale network of heteromodal associative parietal and frontal cortical areas, that can be further hierarchically organized in an episodic working memory parieto-frontal network, driven in part by emotions, working under the supervision of an executive left prefrontal network.

  2. Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

    Science.gov (United States)

    Kobayashi, Takahisa; Simon, Donald L.

    2002-01-01

    As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

  3. A genome-wide regulatory network identifies key transcription factors for memory CD8⁺ T-cell development

    National Research Council Canada - National Science Library

    Hu, Guangan; Chen, Jianzhu

    2013-01-01

    .... To identify transcription factors and their interactions in memory CD8⁺ T-cell development, we construct a genome-wide regulatory network and apply it to identify key transcription factors that regulate memory signature genes...

  4. The dorsal prefrontal and dorsal anterior cingulate cortices exert complementary network signatures during encoding and retrieval in associative memory.

    Science.gov (United States)

    Woodcock, Eric A; White, Richard; Diwadkar, Vaibhav A

    2015-09-01

    Cognitive control includes processes that facilitate execution of effortful cognitive tasks, including associative memory. Regions implicated in cognitive control during associative memory include the dorsal prefrontal (dPFC) and dorsal anterior cingulate cortex (dACC). Here we investigated the relative degrees of network-related interactions originating in the dPFC and dACC during oscillating phases of associative memory: encoding and cued retrieval. Volunteers completed an established object-location associative memory paradigm during fMRI. Psychophysiological interactions modeled modulatory network interactions from the dPFC and dACC during memory encoding and retrieval. Results were evaluated in second level analyses of variance with seed region and memory process as factors. Each seed exerted differentiable modulatory effects during encoding and retrieval. The dACC exhibited greater modulation (than the dPFC) on the fusiform and parahippocampal gyrus during encoding, while the dPFC exhibited greater modulation (than the dACC) on the fusiform, hippocampus, dPFC and basal ganglia. During retrieval, the dPFC exhibited greater modulation (than the dACC) on the parahippocampal gyrus, hippocampus, superior parietal lobule, and dPFC. The most notable finding was a seed by process interaction indicating that the dACC and the dPFC exerted complementary modulatory control on the hippocampus during each of the associative memory processes. These results provide evidence for differentiable, yet complementary, control-related modulation by the dACC and dPFC, while establishing the primacy of dPFC in exerting network control during both associative memory phases. Our approach and findings are relevant for understanding basic processes in human memory and psychiatric disorders that impact associative memory-related networks. Copyright © 2015. Published by Elsevier B.V.

  5. Energy-aware Traffic Engineering in Hybrid SDN/IP Backbone Networks

    OpenAIRE

    Wei, Yunkai; Zhang, XiaoNing; Xie, Lei; Leng, Supeng

    2016-01-01

    Software Defined Networking (SDN) can effectively improve the performance of traffic engineering and has promising application foreground in backbone networks. Therefore, new energy saving schemes must take SDN into account, which is extremely important considering the rapidly increasing energy consumption from Telecom and ISP networks. At the same time, the introduction of SDN in a current network must be incremental in most cases, for both technical and economic reasons. During this period,...

  6. CLASSIFICATION OF NEURAL NETWORK FOR TECHNICAL CONDITION OF TURBOFAN ENGINES BASED ON HYBRID ALGORITHM

    Directory of Open Access Journals (Sweden)

    Valentin Potapov

    2016-12-01

    Full Text Available Purpose: This work presents a method of diagnosing the technical condition of turbofan engines using hybrid neural network algorithm based on software developed for the analysis of data obtained in the aircraft life. Methods: allows the engine diagnostics with deep recognition to the structural assembly in the presence of single structural damage components of the engine running and the multifaceted damage. Results: of the optimization of neural network structure to solve the problems of evaluating technical state of the bypass turbofan engine, when used with genetic algorithms.

  7. Ultralow-power switching via defect engineering in germanium telluride phase-change memory devices

    Science.gov (United States)

    Nukala, Pavan; Lin, Chia-Chun; Composto, Russell; Agarwal, Ritesh

    2016-01-01

    Crystal-amorphous transformation achieved via the melt-quench pathway in phase-change memory involves fundamentally inefficient energy conversion events; and this translates to large switching current densities, responsible for chemical segregation and device degradation. Alternatively, introducing defects in the crystalline phase can engineer carrier localization effects enhancing carrier-lattice coupling; and this can efficiently extract work required to introduce bond distortions necessary for amorphization from input electrical energy. Here, by pre-inducing extended defects and thus carrier localization effects in crystalline GeTe via high-energy ion irradiation, we show tremendous improvement in amorphization current densities (0.13-0.6 MA cm-2) compared with the melt-quench strategy (~50 MA cm-2). We show scaling behaviour and good reversibility on these devices, and explore several intermediate resistance states that are accessible during both amorphization and recrystallization pathways. Existence of multiple resistance states, along with ultralow-power switching and scaling capabilities, makes this approach promising in context of low-power memory and neuromorphic computation.

  8. Engineering

    National Research Council Canada - National Science Library

    Includes papers in the following fields: Aerospace Engineering, Agricultural Engineering, Chemical Engineering, Civil Engineering, Electrical Engineering, Environmental Engineering, Industrial Engineering, Materials Engineering, Mechanical...

  9. Realization of Associative Memory in an Enzymatic Process: Toward Biomolecular Networks with Learning and Unlearning Functionalities.

    Science.gov (United States)

    Bocharova, Vera; MacVittie, Kevin; Chinnapareddy, Soujanya; Halámek, Jan; Privman, Vladimir; Katz, Evgeny

    2012-05-17

    We report a realization of an associative memory signal/information processing system based on simple enzyme-catalyzed biochemical reactions. Optically detected chemical output is always obtained in response to the triggering input, but the system can also "learn" by association, to later respond to the second input if it is initially applied in combination with the triggering input as the "training" step. This second chemical input is not self-reinforcing in the present system, which therefore can later "unlearn" to react to the second input if it is applied several times on its own. Such processing steps realized with (bio)chemical kinetics promise applications of bioinspired/memory-involving components in "networked" (concatenated) biomolecular processes for multisignal sensing and complex information processing.

  10. A generalized voter model with time-decaying memory on a multilayer network

    Science.gov (United States)

    Zhong, Li-Xin; Xu, Wen-Juan; Chen, Rong-Da; Zhong, Chen-Yang; Qiu, Tian; Shi, Yong-Dong; Wang, Li-Liang

    2016-09-01

    By incorporating a multilayer network and time-decaying memory into the original voter model, we investigate the coupled effects of spatial and temporal accumulation of peer pressure on the consensus. Heterogeneity in peer pressure and the time-decaying mechanism are both shown to be detrimental to the consensus. We find the transition points below which a consensus can always be reached and above which two opposed opinions are more likely to coexist. Our mean-field analysis indicates that the phase transitions in the present model are governed by the cumulative influence of peer pressure and the updating threshold. We find a functional relation between the consensus threshold and the decay rate of the influence of peer is found. As to the pressure. The time required to reach a consensus is governed by the coupling of the memory length and the decay rate. An intermediate decay rate may greatly reduce the time required to reach a consensus.

  11. Interaction of multiple networks modulated by the working memory training based on real-time fMRI

    Science.gov (United States)

    Shen, Jiahui; Zhang, Gaoyan; Zhu, Chaozhe; Yao, Li; Zhao, Xiaojie

    2015-03-01

    Neuroimaging studies of working memory training have identified the alteration of brain activity as well as the regional interactions within the functional networks such as central executive network (CEN) and default mode network (DMN). However, how the interaction within and between these multiple networks is modulated by the training remains unclear. In this paper, we examined the interaction of three training-induced brain networks during working memory training based on real-time functional magnetic resonance imaging (rtfMRI). Thirty subjects assigned to the experimental and control group respectively participated in two times training separated by seven days. Three networks including silence network (SN), CEN and DMN were identified by the training data with the calculated function connections within each network. Structural equation modeling (SEM) approach was used to construct the directional connectivity patterns. The results showed that the causal influences from the percent signal changes of target ROI to the SN were positively changed in both two groups, as well as the causal influence from the SN to CEN was positively changed in experimental group but negatively changed in control group from the SN to DMN. Further correlation analysis of the changes in each network with the behavioral improvements showed that the changes in SN were stronger positively correlated with the behavioral improvement of letter memory task. These findings indicated that the SN was not only a switch between the target ROI and the other networks in the feedback training but also an essential factor to the behavioral improvement.

  12. Simulated apoptosis/neurogenesis regulates learning and memory capabilities of adaptive neural networks.

    Science.gov (United States)

    Chambers, R Andrew; Potenza, Marc N; Hoffman, Ralph E; Miranker, Willard

    2004-04-01

    Characterization of neuronal death and neurogenesis in the adult brain of birds, humans, and other mammals raises the possibility that neuronal turnover represents a special form of neuroplasticity associated with stress responses, cognition, and the pathophysiology and treatment of psychiatric disorders. Multilayer neural network models capable of learning alphabetic character representations via incremental synaptic connection strength changes were used to assess additional learning and memory effects incurred by simulation of coordinated apoptotic and neurogenic events in the middle layer. Using a consistent incremental learning capability across all neurons and experimental conditions, increasing the number of middle layer neurons undergoing turnover increased network learning capacity for new information, and increased forgetting of old information. Simulations also showed that specific patterns of neural turnover based on individual neuronal connection characteristics, or the temporal-spatial pattern of neurons chosen for turnover during new learning impacts new learning performance. These simulations predict that apoptotic and neurogenic events could act together to produce specific learning and memory effects beyond those provided by ongoing mechanisms of connection plasticity in neuronal populations. Regulation of rates as well as patterns of neuronal turnover may serve an important function in tuning the informatic properties of plastic networks according to novel informational demands. Analogous regulation in the hippocampus may provide for adaptive cognitive and emotional responses to novel and stressful contexts, or operate suboptimally as a basis for psychiatric disorders. The implications of these elementary simulations for future biological and neural modeling research on apoptosis and neurogenesis are discussed.

  13. Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

    Science.gov (United States)

    Wen, Shameng; Meng, Qingkun; Feng, Chao; Tang, Chaojing

    2017-01-01

    Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

  14. Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

    Directory of Open Access Journals (Sweden)

    Shameng Wen

    Full Text Available Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

  15. Reverse engineering biological networks :applications in immune responses to bio-toxins.

    Energy Technology Data Exchange (ETDEWEB)

    Martino, Anthony A.; Sinclair, Michael B.; Davidson, George S.; Haaland, David Michael; Timlin, Jerilyn Ann; Thomas, Edward Victor; Slepoy, Alexander; Zhang, Zhaoduo; May, Elebeoba Eni; Martin, Shawn Bryan; Faulon, Jean-Loup Michel

    2005-12-01

    Our aim is to determine the network of events, or the regulatory network, that defines an immune response to a bio-toxin. As a model system, we are studying T cell regulatory network triggered through tyrosine kinase receptor activation using a combination of pathway stimulation and time-series microarray experiments. Our approach is composed of five steps (1) microarray experiments and data error analysis, (2) data clustering, (3) data smoothing and discretization, (4) network reverse engineering, and (5) network dynamics analysis and fingerprint identification. The technological outcome of this study is a suite of experimental protocols and computational tools that reverse engineer regulatory networks provided gene expression data. The practical biological outcome of this work is an immune response fingerprint in terms of gene expression levels. Inferring regulatory networks from microarray data is a new field of investigation that is no more than five years old. To the best of our knowledge, this work is the first attempt that integrates experiments, error analyses, data clustering, inference, and network analysis to solve a practical problem. Our systematic approach of counting, enumeration, and sampling networks matching experimental data is new to the field of network reverse engineering. The resulting mathematical analyses and computational tools lead to new results on their own and should be useful to others who analyze and infer networks.

  16. Patricia Hyer receives Women in Engineering Program Advocates Network University Change Agent Award

    OpenAIRE

    Owczarski, Mark

    2009-01-01

    Virginia Tech's Patricia Hyer, associate provost for academic administration, was awarded the University Change Agent Award at the Women in Engineering Program Advocates Network (WEPAN) national conference in Austin, Texas.

  17. A Mercury Frequency Standard Engineering Prototype for the NASA Deep Space Network

    Science.gov (United States)

    Tjoelker, R. L.; Bricker, C.; Diener, W.; Hamell, R. L.; Kirk, A.; Kuhnle, P.; Maleki, L.; Prestage, J. D.; Santiago, D.; Seidel, D.; hide

    1996-01-01

    An engineering prototype linear ion trap frequency standar (LITS-4) using (sup 199)Hg+ is operational and currently under test for NASA's Deep Space Network (DSN). The DSN requires high stability and reliability with continuous operation.

  18. Anosognosia in Alzheimer disease: Disconnection between memory and self-related brain networks.

    Science.gov (United States)

    Perrotin, Audrey; Desgranges, Béatrice; Landeau, Brigitte; Mézenge, Florence; La Joie, Renaud; Egret, Stéphanie; Pélerin, Alice; de la Sayette, Vincent; Eustache, Francis; Chételat, Gaël

    2015-09-01

    Impaired awareness is a common symptom in many mental disorders including Alzheimer disease (AD). This study aims at improving our understanding of the neural mechanisms underlying anosognosia of memory deficits in AD by combining measures of regional brain metabolism (resting state fluorodeoxyglucose positron emission tomography [FDG-PET]) and intrinsic connectivity (resting state functional magnetic resonance imaging [fMRI]). Twenty-three patients diagnosed with probable AD based on clinical and biomarker data and 30 matched healthy control subjects were recruited in this study. An anosognosia index (difference between subjective and objective memory scores) was obtained in each participant. Resting state FDG-PET for glucose metabolism measurement and resting state fMRI for intrinsic connectivity measurement were also performed. AD and control groups were compared on behavioral data, and voxelwise correlations between anosognosia and neuroimaging data were conducted within the AD group. AD patients underestimated their memory deficits. Anosognosia in AD patients correlated with hypometabolism in orbitofrontal (OFC) and posterior cingulate (PCC) cortices. Using OFC and PCC as seed regions, intrinsic connectivity analyses in AD revealed a significant association between anosognosia and reduced intrinsic connectivity between these regions as well as with the medial temporal lobe. Anosognosia in AD is due not only to functional changes within cortical midline structures involved in self-referential processes (OFC, PCC), but also to disconnection between these regions as well as with the medial temporal lobe. These findings suggest that the lack of awareness of memory deficits in AD results from a disruption of the communication within, but also between, the self-related and the memory-related brain networks. © 2015 American Neurological Association.

  19. A New Local Bipolar Autoassociative Memory Based on External Inputs of Discrete Recurrent Neural Networks With Time Delay.

    Science.gov (United States)

    Zhou, Caigen; Zeng, Xiaoqin; Luo, Chaomin; Zhang, Huaguang

    In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation function. The weight parameters of neural networks are acquired by a set of inequalities without the learning procedure. The global exponential stability criteria are established to ensure the accuracy of the restored patterns by considering time delays and external inputs. The proposed methodology is capable of effectively overcoming spurious memory patterns and achieving memory capacity. The effectiveness, robustness, and fault-tolerant capability are validated by simulated experiments.In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation function. The weight parameters of neural networks are acquired by a set of inequalities without the learning procedure. The global exponential stability criteria are established to ensure the accuracy of the restored patterns by considering time delays and external inputs. The proposed methodology is capable of effectively overcoming spurious memory patterns and achieving memory capacity. The effectiveness, robustness, and fault-tolerant capability are validated by simulated experiments.

  20. THE BLENDED LEARNING ACCOMPLISHMENT OF COMPUTER AND NETWORK ENGINEERING EXPERTISE PROGRAM IN VOCATIONAL SCHOOLS

    OpenAIRE

    Aries Alfian Prasetyo; Setiadi Cahyono Putro; I Made Wirawan

    2016-01-01

    This study aims to (1) describe supporting and inhibiting factors in blended learning implementation for the students of computer and network engineering expertise program and (2) describe the accomplishment level of the implementation. This study is designed as a descriptive study with quantitative approach. The research object is the blended learning implementation in computer and network engineering expertise program in SMK N 1 Baureno Bojonegoro. The research subjects consist of teachers,...

  1. Do the right thing: neural network mechanisms of memory formation, expression and update in Drosophila.

    Science.gov (United States)

    Cognigni, Paola; Felsenberg, Johannes; Waddell, Scott

    2017-12-16

    When animals learn, plasticity in brain networks that respond to specific cues results in a change in the behavior that these cues elicit. Individual network components in the mushroom bodies of the fruit fly Drosophila melanogaster represent cues, learning signals and behavioral outcomes of learned experience. Recent findings have highlighted the importance of dopamine-driven plasticity and activity in feedback and feedforward connections, between various elements of the mushroom body neural network. These computational motifs have been shown to be crucial for long term olfactory memory consolidation, integration of internal states, re-evaluation and updating of learned information. The often recurrent circuit anatomy and a prolonged requirement for activity in parts of these underlying networks, suggest that self-sustained and precisely timed activity is a fundamental feature of network computations in the insect brain. Together these processes allow flies to continuously adjust the content of their learned knowledge and direct their behavior in a way that best represents learned expectations and serves their most pressing current needs. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  2. Artificial neural network applications in the calibration of spark-ignition engines: An overview

    Directory of Open Access Journals (Sweden)

    Richard Fiifi Turkson

    2016-09-01

    Full Text Available Emission legislation has become progressively tighter, making the development of new internal combustion engines very challenging. New engine technologies for complying with these regulations introduce an exponential dependency between the number of test combinations required for obtaining optimum results and the time and cost outlays. This makes the calibration task very expensive and virtually impossible to carry out. The potential use of trained neural networks in combination with Design of Experiments (DoE methods for engine calibration has been a subject of research activities in recent times. This is because artificial neural networks, compared with other data-driven modeling techniques, perform better in satisfying a majority of the modeling requirements for engine calibration including the curse of dimensionality; the use of DoE for obtaining few measurements as practicable, with the aim of reducing engine calibration costs; the required flexibility that allows model parameters to be optimized to avoid overfitting; and the facilitation of automated online optimization during the engine calibration process that eliminates the need for user intervention. The purpose of this review is to give an overview of the various applications of neural networks in the calibration of spark-ignition engines. The identified and discussed applications include system identification for rapid prototyping, virtual sensing, use of neural networks as look-up table surrogates, emerging control strategies and On-Board Diagnostic (OBD applications. The demerits of neural networks, future possibilities and alternatives were also discussed.

  3. Innovations and advances in computing, informatics, systems sciences, networking and engineering

    CERN Document Server

    Elleithy, Khaled

    2015-01-01

    Innovations and Advances in Computing, Informatics, Systems Sciences, Networking and Engineering  This book includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art research projects in the areas of Computer Science, Informatics, and Systems Sciences, and Engineering. It includes selected papers from the conference proceedings of the Eighth and some selected papers of the Ninth International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 2012 & CISSE 2013). Coverage includes topics in: Industrial Electronics, Technology & Automation, Telecommunications and Networking, Systems, Computing Sciences and Software Engineering, Engineering Education, Instructional Technology, Assessment, and E-learning.  ·       Provides the latest in a series of books growing out of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering; ·       Includes chapters in the most a...

  4. Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks

    Directory of Open Access Journals (Sweden)

    M. Bazazzadeh

    2011-01-01

    Full Text Available This paper presents a successful approach in designing a Fuzzy Logic Controller (FLC for a specific Jet Engine. At first, a suitable mathematical model for the jet engine is presented by the aid of SIMULINK. Then by applying different reasonable fuel flow functions via the engine model, some important engine-transient operation parameters (such as thrust, compressor surge margin, turbine inlet temperature, etc. are obtained. These parameters provide a precious database, which train a neural network. At the second step, by designing and training a feedforward multilayer perceptron neural network according to this available database; a number of different reasonable fuel flow functions for various engine acceleration operations are determined. These functions are used to define the desired fuzzy fuel functions. Indeed, the neural networks are used as an effective method to define the optimum fuzzy fuel functions. At the next step, we propose a FLC by using the engine simulation model and the neural network results. The proposed control scheme is proved by computer simulation using the designed engine model. The simulation results of engine model with FLC illustrate that the proposed controller achieves the desired performance and stability.

  5. Interdisciplinary Approach to the Mental Lexicon: Neural Network and Text Extraction From Long-term Memory

    Directory of Open Access Journals (Sweden)

    Vardan G. Arutyunyan

    2013-01-01

    Full Text Available The paper touches upon the principles of mental lexicon organization in the light of recent research in psycho- and neurolinguistics. As a focal point of discussion two main approaches to mental lexicon functioning are considered: modular or dual-system approach, developed within generativism and opposite single-system approach, representatives of which are the connectionists and supporters of network models. The paper is an endeavor towards advocating the viewpoint that mental lexicon is complex psychological organization based upon specific composition of neural network. In this regard, the paper further elaborates on the matter of storing text in human mental space and introduces a model of text extraction from long-term memory. Based upon data available, the author develops a methodology of modeling structures of knowledge representation in the systems of artificial intelligence.

  6. Memories.

    Science.gov (United States)

    Brand, Judith, Ed.

    1998-01-01

    This theme issue of the journal "Exploring" covers the topic of "memories" and describes an exhibition at San Francisco's Exploratorium that ran from May 22, 1998 through January 1999 and that contained over 40 hands-on exhibits, demonstrations, artworks, images, sounds, smells, and tastes that demonstrated and depicted the biological,…

  7. Crash Injury Research and Engineering Network (CIREN) - CIREN data files

    Data.gov (United States)

    Department of Transportation — The CIREN process combines prospective data collection with professional multidisciplinary analysis of medical and engineering evidence to determine injury causation...

  8. Artificial neural network approach to predicting engine-out emissions and performance parameters of a turbo charged diesel engine

    Directory of Open Access Journals (Sweden)

    Özener Orkun

    2013-01-01

    Full Text Available This study details the artificial neural network (ANN modelling of a diesel engine to predict the torque, power, brake-specific fuel consumption and pollutant emissions, including carbon dioxide, carbon monoxide, nitrogen oxides, total hydrocarbons and filter smoke number. To collect data for training and testing the neural network, experiments were performed on a four cylinder, four stroke compression ignition engine. A total of 108 test points were run on a dynamometer. For the first part of this work, a parameter packet was used as the inputs for the neural network, and satisfactory regression was found with the outputs (over ~95%, excluding total hydrocarbons. The second stage of this work addressed developing new networks with additional inputs for predicting the total hydrocarbons, and the regression was raised from 75 % to 90 %. This study shows that the ANN approach can be used for accurately predicting characteristic values of an internal combustion engine and that the neural network performance can be increased using additional related input data.

  9. Serotonergic modulation of spatial working memory: predictions from a computational network model

    Directory of Open Access Journals (Sweden)

    Maria eCano-Colino

    2013-09-01

    Full Text Available Serotonin (5-HT receptors of types 1A and 2A are massively expressed in prefrontal cortex (PFC neurons, an area associated with cognitive function. Hence, 5-HT could be effective in modulating prefrontal-dependent cognitive functions, such as spatial working memory (SWM. However, a direct association between 5-HT and SWM has proved elusive in psycho-pharmacological studies. Recently, a computational network model of the PFC microcircuit was used to explore the relationship between 5‑HT and SWM (Cano-Colino et al. 2013. This study found that both excessive and insufficient 5-HT levels lead to impaired SWM performance in the network, and it concluded that analyzing behavioral responses based on confidence reports could facilitate the experimental identification of SWM behavioral effects of 5‑HT neuromodulation. Such analyses may have confounds based on our limited understanding of metacognitive processes. Here, we extend these results by deriving three additional predictions from the model that do not rely on confidence reports. Firstly, only excessive levels of 5-HT should result in SWM deficits that increase with delay duration. Secondly, excessive 5-HT baseline concentration makes the network vulnerable to distractors at distances that were robust to distraction in control conditions, while the network still ignores distractors efficiently for low 5‑HT levels that impair SWM. Finally, 5-HT modulates neuronal memory fields in neurophysiological experiments: Neurons should be better tuned to the cued stimulus than to the behavioral report for excessive 5-HT levels, while the reverse should happen for low 5-HT concentrations. In all our simulations agonists of 5-HT1A receptors and antagonists of 5-HT2A receptors produced behavioral and physiological effects in line with global 5-HT level increases. Our model makes specific predictions to be tested experimentally and advance our understanding of the neural basis of SWM and its neuromodulation

  10. Decreased efficiency of task-positive and task-negative networks during working memory in schizophrenia.

    Science.gov (United States)

    Metzak, Paul D; Riley, Jennifer D; Wang, Liang; Whitman, Jennifer C; Ngan, Elton T C; Woodward, Todd S

    2012-06-01

    Working memory (WM) is one of the most impaired cognitive processes in schizophrenia. Functional magnetic resonance imaging (fMRI) studies in this area have typically found a reduction in information processing efficiency but have focused on the dorsolateral prefrontal cortex. In the current study using the Sternberg Item Recognition Test, we consider networks of regions supporting WM and measure the activation of functionally connected neural networks over different WM load conditions. We used constrained principal component analysis with a finite impulse response basis set to compare the estimated hemodynamic response associated with different WM load condition for 15 healthy control subjects and 15 schizophrenia patients. Three components emerged, reflecting activated (task-positive) and deactivated (task-negative or default-mode) neural networks. Two of the components (with both task-positive and task-negative aspects) were load dependent, were involved in encoding and delay phases (one exclusively encoding and the other both encoding and delay), and both showed evidence for decreased efficiency in patients. The results suggest that WM capacity is reached sooner for schizophrenia patients as the overt levels of WM load increase, to the point that further increases in overt memory load do not increase fMRI activation, and lead to performance impairments. These results are consistent with an account holding that patients show reduced efficiency in task-positive and task-negative networks during WM and also partially support the shifted inverted-U-shaped curve theory of the relationship between WM load and fMRI activation in schizophrenia.

  11. Inefficient preparatory fMRI-BOLD network activations predict working memory dysfunctions in patients with schizophrenia

    Directory of Open Access Journals (Sweden)

    Anja eBaenninger

    2016-03-01

    Full Text Available Patients with schizophrenia show abnormal dynamics and structure of temporally coherent networks (TCNs assessed using fMRI, which undergo adaptive shifts in preparation for a cognitively demanding task. During working memory (WM tasks, patients with schizophrenia show persistent deficits in TCNs as well as EEG indices of WM. Studying their temporal relationship during WM tasks might provide novel insights into WM performance deficits seen in schizophrenia.Simultaneous EEG-fMRI data were acquired during the performance of a verbal Sternberg WM task with two load levels (load 2 & load 5 in 17 patients with schizophrenia and 17 matched healthy controls. Using covariance mapping, we investigated the relationship of the activity in the TCNs before the memoranda were encoded and EEG spectral power during the retention interval. We assessed four TCNs – default mode network (DMN, dorsal attention network (dAN, left and right working memory networks (WMNs – and three EEG bands – theta, alpha, and beta.In healthy controls, there was a load dependent inverse relation between DMN and frontal-midline theta power and an anti-correlation between DMN and dAN. Both effects were not significantly detectable in patients. In addition, healthy controls showed a left-lateralized load-dependent recruitment of the WMNs. Activation of the WMNs was bilateral in patients, suggesting more resources were recruited for successful performance on the WM task.Our findings support the notion of schizophrenia patients showing deviations in their neurophysiological responses before the retention of relevant information in a verbal WM task. Thus, treatment strategies as neurofeedback targeting pre-states could be beneficial as task performance relies on the preparatory state of the brain.

  12. A 'reverse network engineering' framework to develop tourism using a lifestyle approach

    NARCIS (Netherlands)

    Kamann, DJF; Strijker, D; Sijtsma, FJ

    1998-01-01

    This paper uses the network approach in the design of policies for regional development focusing on tourism in rural and peripheral areas. The methodology applied - 'reverse network engineering' - is a combination of a top-down and a bottom-up approach, The top-down approach starts with the demand

  13. Vibration Based Damage Assessment of a Civil Engineering Structures using a Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Rytter, A.

    In this paper the possibility of using a Multilayer Perceptron (MLP) network trained with the Backpropagation Algorith as a non-destructive damage assessment technique to locate and quantify a damage in Civil Engineering structures is investigated. Since artificial neural networks are proving...

  14. A mismatch-based model for memory reconsolidation and extinction in attractor networks.

    Directory of Open Access Journals (Sweden)

    Remus Osan

    Full Text Available The processes of memory reconsolidation and extinction have received increasing attention in recent experimental research, as their potential clinical applications begin to be uncovered. A number of studies suggest that amnestic drugs injected after reexposure to a learning context can disrupt either of the two processes, depending on the behavioral protocol employed. Hypothesizing that reconsolidation represents updating of a memory trace in the hippocampus, while extinction represents formation of a new trace, we have built a neural network model in which either simple retrieval, reconsolidation or extinction of a stored attractor can occur upon contextual reexposure, depending on the similarity between the representations of the original learning and reexposure sessions. This is achieved by assuming that independent mechanisms mediate Hebbian-like synaptic strengthening and mismatch-driven labilization of synaptic changes, with protein synthesis inhibition preferentially affecting the former. Our framework provides a unified mechanistic explanation for experimental data showing (a the effect of reexposure duration on the occurrence of reconsolidation or extinction and (b the requirement of memory updating during reexposure to drive reconsolidation.

  15. Changes in whole-brain functional networks and memory performance in aging.

    Science.gov (United States)

    Sala-Llonch, Roser; Junqué, Carme; Arenaza-Urquijo, Eider M; Vidal-Piñeiro, Dídac; Valls-Pedret, Cinta; Palacios, Eva M; Domènech, Sara; Salvà, Antoni; Bargalló, Nuria; Bartrés-Faz, David

    2014-10-01

    We used resting-functional magnetic resonance imaging data from 98 healthy older adults to analyze how local and global measures of functional brain connectivity are affected by age, and whether they are related to differences in memory performance. Whole-brain networks were created individually by parcellating the brain into 90 cerebral regions and obtaining pairwise connectivity. First, we studied age-associations in interregional connectivity and their relationship with the length of the connections. Aging was associated with less connectivity in the long-range connections of fronto-parietal and fronto-occipital systems and with higher connectivity of the short-range connections within frontal, parietal, and occipital lobes. We also used the graph theory to measure functional integration and segregation. The pattern of the overall age-related correlations presented positive correlations of average minimum path length (r = 0.380, p = 0.008) and of global clustering coefficients (r = 0.454, p memory functions. In conclusion, we found that older participants showed lower connectivity of long-range connections together with higher functional segregation of these same connections, which appeared to indicate a more local clustering of information processing. Higher local clustering in older participants was negatively related to memory performance. Copyright © 2014 Elsevier Inc. All rights reserved.

  16. Modulation of steady state functional connectivity in the default mode and working memory networks by cognitive load.

    Science.gov (United States)

    Newton, Allen T; Morgan, Victoria L; Rogers, Baxter P; Gore, John C

    2011-10-01

    Interregional correlations between blood oxygen level dependent (BOLD) magnetic resonance imaging (fMRI) signals in the resting state have been interpreted as measures of connectivity across the brain. Here we investigate whether such connectivity in the working memory and default mode networks is modulated by changes in cognitive load. Functional connectivity was measured in a steady-state verbal identity N-back task for three different conditions (N = 1, 2, and 3) as well as in the resting state. We found that as cognitive load increases, the functional connectivity within both the working memory the default mode network increases. To test whether functional connectivity between the working memory and the default mode networks changed, we constructed maps of functional connectivity to the working memory network as a whole and found that increasingly negative correlations emerged in a dorsal region of the posterior cingulate cortex. These results provide further evidence that low frequency fluctuations in BOLD signals reflect variations in neural activity and suggests interaction between the default mode network and other cognitive networks. Copyright © 2010 Wiley-Liss, Inc.

  17. Creating Global Networks through an Online Engineering Graduate Programme

    Science.gov (United States)

    Murray, M. H.

    2011-01-01

    Internationally, the railway industry is facing a severe shortage of engineers with high-level, relevant, professional and technical knowledge and abilities, in particular amongst engineers involved in the design, construction and maintenance of railway infrastructure. A unique graduate level programme has been created to meet that global need via…

  18. Continuous Timescale Long-Short Term Memory Neural Network for Human Intent Understanding

    Directory of Open Access Journals (Sweden)

    Zhibin Yu

    2017-08-01

    Full Text Available Understanding of human intention by observing a series of human actions has been a challenging task. In order to do so, we need to analyze longer sequences of human actions related with intentions and extract the context from the dynamic features. The multiple timescales recurrent neural network (MTRNN model, which is believed to be a kind of solution, is a useful tool for recording and regenerating a continuous signal for dynamic tasks. However, the conventional MTRNN suffers from the vanishing gradient problem which renders it impossible to be used for longer sequence understanding. To address this problem, we propose a new model named Continuous Timescale Long-Short Term Memory (CTLSTM in which we inherit the multiple timescales concept into the Long-Short Term Memory (LSTM recurrent neural network (RNN that addresses the vanishing gradient problem. We design an additional recurrent connection in the LSTM cell outputs to produce a time-delay in order to capture the slow context. Our experiments show that the proposed model exhibits better context modeling ability and captures the dynamic features on multiple large dataset classification tasks. The results illustrate that the multiple timescales concept enhances the ability of our model to handle longer sequences related with human intentions and hence proving to be more suitable for complex tasks, such as intention recognition.

  19. Same task, different strategies: how brain networks can be influenced by memory strategy.

    Science.gov (United States)

    Sanfratello, Lori; Caprihan, Arvind; Stephen, Julia M; Knoefel, Janice E; Adair, John C; Qualls, Clifford; Lundy, S Laura; Aine, Cheryl J

    2014-10-01

    Previous functional neuroimaging studies demonstrated that different neural networks underlie different types of cognitive processing by engaging participants in particular tasks, such as verbal or spatial working memory (WM) tasks. However, we report here that even when a WM task is defined as verbal or spatial, different types of memory strategies may be used to complete it, with concomitant variations in brain activity. We developed a questionnaire to characterize the type of strategy used by individual members in a group of 28 young healthy participants (18-25 years) during a spatial WM task. A cluster analysis was performed to differentiate groups. We acquired functional magnetoencephalography and structural diffusion tensor imaging measures to characterize the brain networks associated with the use of different strategies. We found two types of strategies were used during the spatial WM task, a visuospatial and a verbal strategy, and brain regions and time courses of activation differed between participants who used each. Task performance also varied by type of strategy used with verbal strategies showing an advantage. In addition, performance on neuropsychological tests (indices from Wechsler Adult Intelligence Scale-IV, Rey Complex Figure Test) correlated significantly with fractional anisotropy measures for the visuospatial strategy group in white matter tracts implicated in other WM and attention studies. We conclude that differences in memory strategy can have a pronounced effect on the locations and timing of brain activation and that these differences need further investigation as a possible confounding factor for studies using group averaging as a means for summarizing results. Copyright © 2014 Wiley Periodicals, Inc.

  20. Robust spatial memory maps in flickering neuronal networks: a topological model

    Science.gov (United States)

    Dabaghian, Yuri; Babichev, Andrey; Memoli, Facundo; Chowdhury, Samir; Rice University Collaboration; Ohio State University Collaboration

    It is widely accepted that the hippocampal place cells provide a substrate of the neuronal representation of the environment--the ``cognitive map''. However, hippocampal network, as any other network in the brain is transient: thousands of hippocampal neurons die every day and the connections formed by these cells constantly change due to various forms of synaptic plasticity. What then explains the remarkable reliability of our spatial memories? We propose a computational approach to answering this question based on a couple of insights. First, we propose that the hippocampal cognitive map is fundamentally topological, and hence it is amenable to analysis by topological methods. We then apply several novel methods from homology theory, to understand how dynamic connections between cells influences the speed and reliability of spatial learning. We simulate the rat's exploratory movements through different environments and study how topological invariants of these environments arise in a network of simulated neurons with ``flickering'' connectivity. We find that despite transient connectivity the network of place cells produces a stable representation of the topology of the environment.

  1. Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks.

    Science.gov (United States)

    Hanson, Jack; Yang, Yuedong; Paliwal, Kuldip; Zhou, Yaoqi

    2017-03-01

    Capturing long-range interactions between structural but not sequence neighbors of proteins is a long-standing challenging problem in bioinformatics. Recently, long short-term memory (LSTM) networks have significantly improved the accuracy of speech and image classification problems by remembering useful past information in long sequential events. Here, we have implemented deep bidirectional LSTM recurrent neural networks in the problem of protein intrinsic disorder prediction. The new method, named SPOT-Disorder, has steadily improved over a similar method using a traditional, window-based neural network (SPINE-D) in all datasets tested without separate training on short and long disordered regions. Independent tests on four other datasets including the datasets from critical assessment of structure prediction (CASP) techniques and >10 000 annotated proteins from MobiDB, confirmed SPOT-Disorder as one of the best methods in disorder prediction. Moreover, initial studies indicate that the method is more accurate in predicting functional sites in disordered regions. These results highlight the usefulness combining LSTM with deep bidirectional recurrent neural networks in capturing non-local, long-range interactions for bioinformatics applications. SPOT-disorder is available as a web server and as a standalone program at: http://sparks-lab.org/server/SPOT-disorder/index.php . j.hanson@griffith.edu.au or yuedong.yang@griffith.edu.au or yaoqi.zhou@griffith.edu.au. Supplementary data is available at Bioinformatics online.

  2. Multi-Temporal Land Cover Classification with Long Short-Term Memory Neural Networks

    Science.gov (United States)

    Rußwurm, M.; Körner, M.

    2017-05-01

    Land cover classification (LCC) is a central and wide field of research in earth observation and has already put forth a variety of classification techniques. Many approaches are based on classification techniques considering observation at certain points in time. However, some land cover classes, such as crops, change their spectral characteristics due to environmental influences and can thus not be monitored effectively with classical mono-temporal approaches. Nevertheless, these temporal observations should be utilized to benefit the classification process. After extensive research has been conducted on modeling temporal dynamics by spectro-temporal profiles using vegetation indices, we propose a deep learning approach to utilize these temporal characteristics for classification tasks. In this work, we show how long short-term memory (LSTM) neural networks can be employed for crop identification purposes with SENTINEL 2A observations from large study areas and label information provided by local authorities. We compare these temporal neural network models, i.e., LSTM and recurrent neural network (RNN), with a classical non-temporal convolutional neural network (CNN) model and an additional support vector machine (SVM) baseline. With our rather straightforward LSTM variant, we exceeded state-of-the-art classification performance, thus opening promising potential for further research.

  3. MULTI-TEMPORAL LAND COVER CLASSIFICATION WITH LONG SHORT-TERM MEMORY NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    M. Rußwurm

    2017-05-01

    Full Text Available Land cover classification (LCC is a central and wide field of research in earth observation and has already put forth a variety of classification techniques. Many approaches are based on classification techniques considering observation at certain points in time. However, some land cover classes, such as crops, change their spectral characteristics due to environmental influences and can thus not be monitored effectively with classical mono-temporal approaches. Nevertheless, these temporal observations should be utilized to benefit the classification process. After extensive research has been conducted on modeling temporal dynamics by spectro-temporal profiles using vegetation indices, we propose a deep learning approach to utilize these temporal characteristics for classification tasks. In this work, we show how long short-term memory (LSTM neural networks can be employed for crop identification purposes with SENTINEL 2A observations from large study areas and label information provided by local authorities. We compare these temporal neural network models, i.e., LSTM and recurrent neural network (RNN, with a classical non-temporal convolutional neural network (CNN model and an additional support vector machine (SVM baseline. With our rather straightforward LSTM variant, we exceeded state-of-the-art classification performance, thus opening promising potential for further research.

  4. Using elements of game engine architecture to simulate sensor networks for eldercare.

    Science.gov (United States)

    Godsey, Chad; Skubic, Marjorie

    2009-01-01

    When dealing with a real time sensor network, building test data with a known ground truth is a tedious and cumbersome task. In order to quickly build test data for such a network, a simulation solution is a viable option. Simulation environments have a close relationship with computer game environments, and therefore there is much to be learned from game engine design. In this paper, we present our vision for a simulated in-home sensor network and describe ongoing work on using elements of game engines for building the simulator. Validation results are included to show agreement on motion sensor simulation with the physical environment.

  5. VoIP attacks detection engine based on neural network

    Science.gov (United States)

    Safarik, Jakub; Slachta, Jiri

    2015-05-01

    The security is crucial for any system nowadays, especially communications. One of the most successful protocols in the field of communication over IP networks is Session Initiation Protocol. It is an open-source project used by different kinds of applications, both open-source and proprietary. High penetration and text-based principle made SIP number one target in IP telephony infrastructure, so security of SIP server is essential. To keep up with hackers and to detect potential malicious attacks, security administrator needs to monitor and evaluate SIP traffic in the network. But monitoring and following evaluation could easily overwhelm the security administrator in networks, typically in networks with a number of SIP servers, users and logically or geographically separated networks. The proposed solution lies in automatic attack detection systems. The article covers detection of VoIP attacks through a distributed network of nodes. Then the gathered data analyze aggregation server with artificial neural network. Artificial neural network means multilayer perceptron network trained with a set of collected attacks. Attack data could also be preprocessed and verified with a self-organizing map. The source data is detected by distributed network of detection nodes. Each node contains a honeypot application and traffic monitoring mechanism. Aggregation of data from each node creates an input for neural networks. The automatic classification on a centralized server with low false positive detection reduce the cost of attack detection resources. The detection system uses modular design for easy deployment in final infrastructure. The centralized server collects and process detected traffic. It also maintains all detection nodes.

  6. Reverse engineering Boolean networks: from Bernoulli mixture models to rule based systems.

    Science.gov (United States)

    Saeed, Mehreen; Ijaz, Maliha; Javed, Kashif; Babri, Haroon Atique

    2012-01-01

    A Boolean network is a graphical model for representing and analyzing the behavior of gene regulatory networks (GRN). In this context, the accurate and efficient reconstruction of a Boolean network is essential for understanding the gene regulation mechanism and the complex relations that exist therein. In this paper we introduce an elegant and efficient algorithm for the reverse engineering of Boolean networks from a time series of multivariate binary data corresponding to gene expression data. We call our method ReBMM, i.e., reverse engineering based on Bernoulli mixture models. The time complexity of most of the existing reverse engineering techniques is quite high and depends upon the indegree of a node in the network. Due to the high complexity of these methods, they can only be applied to sparsely connected networks of small sizes. ReBMM has a time complexity factor, which is independent of the indegree of a node and is quadratic in the number of nodes in the network, a big improvement over other techniques and yet there is little or no compromise in accuracy. We have tested ReBMM on a number of artificial datasets along with simulated data derived from a plant signaling network. We also used this method to reconstruct a network from real experimental observations of microarray data of the yeast cell cycle. Our method provides a natural framework for generating rules from a probabilistic model. It is simple, intuitive and illustrates excellent empirical results.

  7. New tools for non-invasive exploration of collagen network in cartilaginous tissue-engineered substitute.

    Science.gov (United States)

    Henrionnet, Christel; Dumas, Dominique; Hupont, Sébastien; Stoltz, Jean François; Mainard, Didier; Gillet, Pierre; Pinzano, Astrid

    2017-01-01

    In tissue engineering approaches, the quality of substitutes is a key element to determine its ability to treat cartilage defects. However, in clinical practice, the evaluation of tissue-engineered cartilage substitute quality is not possible due to the invasiveness of the standard procedure, which is to date histology. The aim of this work was to validate a new innovative system performed from two-photon excitation laser adapted to an optical macroscope to evaluate at macroscopic scale the collagen network in cartilage tissue-engineered substitutes in confrontation with gold standard histologic techniques or immunohistochemistry to visualize type II collagen. This system permitted to differentiate the quality of collagen network between ITS and TGF-β1 treatments. Multiscale large field imaging combined to multimodality approaches (SHG-TCSPC) at macroscopical scale represent an innovative and non-invasive technique to monitor the quality of collagen network in cartilage tissue-engineered substitutes before in vivo implantation.

  8. Molecular Engineering of Robustness and Resilience in Enzymatic Reaction Networks.

    Science.gov (United States)

    Wong, Albert S Y; Pogodaev, Aleksandr A; Vialshin, Ilia N; Helwig, Britta; Huck, Wilhelm T S

    2017-06-21

    Living systems rely on complex networks of chemical reactions to control the concentrations of molecules in space and time. Despite the enormous complexity in biological networks, it is possible to identify network motifs that lead to functional outputs such as bistability or oscillations. One of the greatest challenges in chemistry is the creation of such functionality from chemical reactions. A key limitation is our lack of understanding of how molecular structure impacts on the dynamics of chemical reaction networks, preventing the design of networks that are robust (i.e., function in a large parameter space) and resilient (i.e., reach their out-of-equilibrium function rapidly). Here we demonstrate that reaction rates of individual reactions in the network can control the dynamics by which the system reaches limit cycle oscillations, thereby gaining information on the key parameters that govern the dynamics of these networks. We envision that these principles will be incorporated into the design of network motifs, enabling chemists to develop "molecular software" to create functional behavior in chemical systems.

  9. How Will Online Affiliate Marketing Networks Impact Search Engine Rankings?

    NARCIS (Netherlands)

    D. Janssen (David); H.W.G.M. van Heck (Eric)

    2007-01-01

    textabstractIn online affiliate marketing networks advertising web sites offer their affiliates revenues based on provided web site traffic and associated leads and sales. Advertising web sites can have a network of thousands of affiliates providing them with web site traffic through hyperlinks on

  10. 77 FR 46154 - Announcing the Twentieth Public Meeting of the Crash Injury Research and Engineering Network (CIREN)

    Science.gov (United States)

    2012-08-02

    ... Announcing the Twentieth Public Meeting of the Crash Injury Research and Engineering Network (CIREN) AGENCY..., and then click on Crash Injury Research and Engineering Network (CIREN) in the box on the left. The... notice announces the Twentieth Public Meeting of members of the Crash Injury Research and Engineering...

  11. H∞ Networked Cascade Control System Design for Turboshaft Engines with Random Packet Dropouts

    Directory of Open Access Journals (Sweden)

    Xiaofeng Liu

    2017-01-01

    Full Text Available The distributed control architecture becomes more and more important in future gas turbine engine control systems, in which the sensors and actuators will be connected to the controllers via a network. Therefore, the control problem of network-enabled high-performance distributed engine control (DEC has come to play an important role in modern gas turbine control systems, while, due to the properties of the network, the packet dropouts must be considered. This study introduces a distributed control system architecture based on a networked cascade control system (NCCS. Typical turboshaft engine distributed controllers are designed based on the NCCS framework with H∞ state feedback under random packet dropouts. The sufficient robust stable conditions are derived via the Lyapunov stability theory and linear matrix inequality approach. Simulations illustrate the effectiveness of the presented method.

  12. Engineering reaction-diffusion networks with properties of neural tissue.

    Science.gov (United States)

    Litschel, Thomas; Norton, Michael M; Tserunyan, Vardges; Fraden, Seth

    2018-01-03

    We present an experimental system of networks of coupled non-linear chemical reactors, which we theoretically model within a reaction-diffusion framework. The networks consist of patterned arrays of diffusively coupled nanoliter-scale reactors containing the Belousov-Zhabotinsky (BZ) reaction. Microfluidic fabrication techniques are developed that provide the ability to vary the network topology and the reactor coupling strength and offer the freedom to choose whether an arbitrary reactor is inhibitory or excitatory coupled to its neighbor. This versatile experimental and theoretical framework can be used to create a wide variety of chemical networks. Here we design, construct and characterize chemical networks that achieve the complexity of central pattern generators (CPGs), which are found in the autonomic nervous system of a variety of organisms.

  13. 78 FR 52605 - Announcing the Twenty First Public Meeting of the Crash Injury Research and Engineering Network...

    Science.gov (United States)

    2013-08-23

    ... Injury Research and Engineering Network (CIREN) AGENCY: National Highway Traffic Safety Administration... Meeting of members of the Crash Injury Research and Engineering Network. CIREN is a collaborative effort... linked by a computer network. The current CIREN model utilizes two types of centers, medical and...

  14. Cascade Optimization for Aircraft Engines With Regression and Neural Network Analysis - Approximators

    Science.gov (United States)

    Patnaik, Surya N.; Guptill, James D.; Hopkins, Dale A.; Lavelle, Thomas M.

    2000-01-01

    The NASA Engine Performance Program (NEPP) can configure and analyze almost any type of gas turbine engine that can be generated through the interconnection of a set of standard physical components. In addition, the code can optimize engine performance by changing adjustable variables under a set of constraints. However, for engine cycle problems at certain operating points, the NEPP code can encounter difficulties: nonconvergence in the currently implemented Powell's optimization algorithm and deficiencies in the Newton-Raphson solver during engine balancing. A project was undertaken to correct these deficiencies. Nonconvergence was avoided through a cascade optimization strategy, and deficiencies associated with engine balancing were eliminated through neural network and linear regression methods. An approximation-interspersed cascade strategy was used to optimize the engine's operation over its flight envelope. Replacement of Powell's algorithm by the cascade strategy improved the optimization segment of the NEPP code. The performance of the linear regression and neural network methods as alternative engine analyzers was found to be satisfactory. This report considers two examples-a supersonic mixed-flow turbofan engine and a subsonic waverotor-topped engine-to illustrate the results, and it discusses insights gained from the improved version of the NEPP code.

  15. Online Learning in Engineering Courses Using Wireless Sensor and Actuator Networks

    Directory of Open Access Journals (Sweden)

    Alberto Cardoso

    2013-02-01

    Full Text Available The Information and Communication Technologies have been increasingly playing an essential role to support teaching and learning, especially in engineering courses, by taking advantage of online resources and online experiments. In particular, Wireless Sensor and Actuator Networks are simultaneously a subject of interest for several engineering courses and a way to enable remote access to laboratory experimental setups. The Remote and Virtual Laboratory of the Department of Informatics Engineering of the University of Coimbra (Portugal is part of the flock.uc.pt web-based platform under development to allow users to perform remote and virtual experiments in several engineering domains. This paper aims to describe its potential use mainly in engineering courses and lifelong learning programs. The main characteristics of some remote experiments and the potential of Wireless Sensor and Actuator Networks to interact with laboratory systems are described.

  16. Interaction of language, auditory and memory brain networks in auditory verbal hallucinations.

    Science.gov (United States)

    Ćurčić-Blake, Branislava; Ford, Judith M; Hubl, Daniela; Orlov, Natasza D; Sommer, Iris E; Waters, Flavie; Allen, Paul; Jardri, Renaud; Woodruff, Peter W; David, Olivier; Mulert, Christoph; Woodward, Todd S; Aleman, André

    2017-01-01

    Auditory verbal hallucinations (AVH) occur in psychotic disorders, but also as a symptom of other conditions and even in healthy people. Several current theories on the origin of AVH converge, with neuroimaging studies suggesting that the language, auditory and memory/limbic networks are of particular relevance. However, reconciliation of these theories with experimental evidence is missing. We review 50 studies investigating functional (EEG and fMRI) and anatomic (diffusion tensor imaging) connectivity in these networks, and explore the evidence supporting abnormal connectivity in these networks associated with AVH. We distinguish between functional connectivity during an actual hallucination experience (symptom capture) and functional connectivity during either the resting state or a task comparing individuals who hallucinate with those who do not (symptom association studies). Symptom capture studies clearly reveal a pattern of increased coupling among the auditory, language and striatal regions. Anatomical and symptom association functional studies suggest that the interhemispheric connectivity between posterior auditory regions may depend on the phase of illness, with increases in non-psychotic individuals and first episode patients and decreases in chronic patients. Leading hypotheses involving concepts as unstable memories, source monitoring, top-down attention, and hybrid models of hallucinations are supported in part by the published connectivity data, although several caveats and inconsistencies remain. Specifically, possible changes in fronto-temporal connectivity are still under debate. Precise hypotheses concerning the directionality of connections deduced from current theoretical approaches should be tested using experimental approaches that allow for discrimination of competing hypotheses. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  17. Fate of Zinc and Silver Engineered Nanoparticles in Sewerage Networks

    Science.gov (United States)

    Engineered zinc oxide (ZnO) and silver (Ag) nanoparticles (NPs) used in consumer products are largely released into the environment through the wastewater stream. Limited information is available regarding the transformations they undergo during their transit through sewerage sy...

  18. Out-of-Sequence Prevention for Multicast Input-Queuing Space-Memory-Memory Clos-Network

    DEFF Research Database (Denmark)

    Yu, Hao; Ruepp, Sarah; Berger, Michael Stübert

    2011-01-01

    This paper proposes two cell dispatching algorithms for the input-queuing space-memory-memory (IQ-SMM) Closnetwork to reduce out-of-sequence (OOS) for multicast traffic. The frequent connection pattern change of DSRR results in a severe OOS problem. Based on the principle of DSRR, MFDSRR is able...

  19. Temporal entrainment of cognitive functions: musical mnemonics induce brain plasticity and oscillatory synchrony in neural networks underlying memory.

    Science.gov (United States)

    Thaut, Michael H; Peterson, David A; McIntosh, Gerald C

    2005-12-01

    In a series of experiments, we have begun to investigate the effect of music as a mnemonic device on learning and memory and the underlying plasticity of oscillatory neural networks. We used verbal learning and memory tests (standardized word lists, AVLT) in conjunction with electroencephalographic analysis to determine differences between verbal learning in either a spoken or musical (verbal materials as song lyrics) modality. In healthy adults, learning in both the spoken and music condition was associated with significant increases in oscillatory synchrony across all frequency bands. A significant difference between the spoken and music condition emerged in the cortical topography of the learning-related synchronization. When using EEG measures as predictors during learning for subsequent successful memory recall, significantly increased coherence (phase-locked synchronization) within and between oscillatory brain networks emerged for music in alpha and gamma bands. In a similar study with multiple sclerosis patients, superior learning and memory was shown in the music condition when controlled for word order recall, and subjects were instructed to sing back the word lists. Also, the music condition was associated with a significant power increase in the low-alpha band in bilateral frontal networks, indicating increased neuronal synchronization. Musical learning may access compensatory pathways for memory functions during compromised PFC functions associated with learning and recall. Music learning may also confer a neurophysiological advantage through the stronger synchronization of the neuronal cell assemblies underlying verbal learning and memory. Collectively our data provide evidence that melodic-rhythmic templates as temporal structures in music may drive internal rhythm formation in recurrent cortical networks involved in learning and memory.

  20. Characterizing Design Process Interfaces as Organization Networks: Insights for Engineering Systems Management

    DEFF Research Database (Denmark)

    Ruiz, Pedro Parraguez; Eppinger, Steven; Maier, Anja

    2016-01-01

    The engineering design literature has provided guidance on how to identify and analyze design activities and their information dependencies. However, a systematic characterization of process interfaces between engineering design activities is missing, and the impact of structural and compositional...... and interpret the effect of those characteristics on interface problems. As a result, we show how structural and compositional aspects of the organization networks between information-dependent activities provide valuable insights to better manage complex engineering design processes. The proposed approach...... and organization architectures, the systematic identification of key performance metrics associated with interface problems, and improved support for engineering managers by means of a better overview of information flows between activities....

  1. Shared Leadership and Team Creativity: A Social Network Analysis in Engineering Design Teams

    Directory of Open Access Journals (Sweden)

    Qiong Wu

    2016-06-01

    Full Text Available This research explores the relationship between shared leadership and creativity in engineering design teams. To do this, a social network perspective was adopted using four measures to assess key elements of shared leadership networks. These are (a network density, (b centralization, (c efficiency and (d strength. Data was collected from a sample of 22 engineering design teams who adopt a shared leadership approach. Our results support previous findings that the density of a shared leadership network is positively related to team creativity. In contrast, we learned that centralization exerts a negative influence on it. Moreover, while we found that there is no evidence to support a positive correlation between efficiency and team creativity, we demonstrate an inverted U-shaped relationship between strength and team creativity in a shared leadership network. These findings are important because they add to the academic debate in the shared leadership area and provide valuable insights for managers.

  2. Visually Augmented Analysis of Socio-Technical Networks in Engineering Systems Design Research

    DEFF Research Database (Denmark)

    Storga, M.; Stankovic, T.; Cash, Philip

    2013-01-01

    , but only fairly recently has the study of networks in general become a major topic of research in complex engineering systems. The research reported in this paper is discussing how the visually augmented analysis of complex socio-networks (networks of people and technology engaged in a product/service-system...... captured during experiments and observations that are more and more used as a main research method. Case studies that are presented illustrate also the significance of the network based research approach in providing insight into ways of improving the design process for complex engineering systems.......In characterizing systems behaviour, complex-systems scientists use tools from a variety of disciplines, including nonlinear dynamics, information theory, computation theory, evolutionary biology and social network analysis, among others. All of these topics have been studied for some time...

  3. UX of Social Network Edmodo in Undergraduate Engineering Students

    National Research Council Canada - National Science Library

    Angélica Gómez; Angel Alberto Magreñán; Lara Orcos

    2015-01-01

    ... (social network service) and detail the relationship between socio-demographic and academic factors associated with the use of EDMODO and the perception of the contribution to the acquisition of skills for the future career...

  4. Indeterminacy of reverse engineering of Gene Regulatory Networks: the curse of gene elasticity.

    Directory of Open Access Journals (Sweden)

    Arun Krishnan

    Full Text Available BACKGROUND: Gene Regulatory Networks (GRNs have become a major focus of interest in recent years. A number of reverse engineering approaches have been developed to help uncover the regulatory networks giving rise to the observed gene expression profiles. However, this is an overspecified problem due to the fact that more than one genotype (network wiring can give rise to the same phenotype. We refer to this phenomenon as "gene elasticity." In this work, we study the effect of this particular problem on the pure, data-driven inference of gene regulatory networks. METHODOLOGY: We simulated a four-gene network in order to produce "data" (protein levels that we use in lieu of real experimental data. We then optimized the network connections between the four genes with a view to obtain the original network that gave rise to the data. We did this for two different cases: one in which only the network connections were optimized and the other in which both the network connections as well as the kinetic parameters (given as reaction probabilities in our case were estimated. We observed that multiple genotypes gave rise to very similar protein levels. Statistical experimentation indicates that it is impossible to differentiate between the different networks on the basis of both equilibrium as well as dynamic data. CONCLUSIONS: We show explicitly that reverse engineering of GRNs from pure expression data is an indeterminate problem. Our results suggest the unsuitability of an inferential, purely data-driven approach for the reverse engineering transcriptional networks in the case of gene regulatory networks displaying a certain level of complexity.

  5. Best Practices Handbook: Traffic Engineering in Range Networks

    Science.gov (United States)

    2016-03-01

    accommodate an offered load or if traffic streams are inefficiently mapped onto available resources, causing subsets of network resources to become over...access equipment and video encoding equipment. 3. A response system, consisting of protocols and access mechanisms that allow the flow of traffic ...Multiprotocol Label Switching (MPLS) that enable the predictable traffic flow across the MRTFBs. A detailed description of network elements relevant to the

  6. For showing to U.S. and Brazilian people: a memory of bilateral relations through the social network Flickr

    Directory of Open Access Journals (Sweden)

    João Gilberto Neves Saraiva

    2014-09-01

    Full Text Available This paper investigates the memory of bilateral relations produced by the U.S. Embassy in Brazil on the social network Flickr in the early months of the administration of the Democrat President Barack Obama. The set of images and texts in 19 posts made by the Embassy’s profile on the network in the first half of 2009 is analyzed here. It establishes connections between the production of memory, the U.S. political conjuncture, and U.S. political myths, such as Abraham Lincoln and John Kennedy. It also discusses ways of remembering and forgetting several events in bilateral relations throughout the 20th and 21st centuries, besides the positions published on the social network regarding Brazilian politics and history. Keywords: Social Networks; Flickr; President – United States.

  7. 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.

  8. Modeling and adaptive control of a camless engine using neural networks and estimation techniques

    Energy Technology Data Exchange (ETDEWEB)

    Ashhab, S. [Hashemite Univ., Zarqa (Jordan). Dept. of Mechanical Engineering

    2007-08-09

    A system to control the cylinder air charge (CAC) in a camless internal combustion (IC) engine was recently developed. The performance of an IC engine connected to an adaptive artificial neural network (ANN) based feedback controller was then investigated. A control oriented model for the engine intake process was created based on thermodynamics laws and was validated against engine experimental data. Input-output data at a speed of 1500 RPM was generated and used to train an ANN model for the engine. The inputs were the intake valve lift (IVL) and closing timing (IVC). The output was the CAC. The controller consisted of a feedforward controller, CAC estimator, and on-line ANN parameter estimator. The feedforward controller provided IVL and IVC that satisfied the driver's torque demand and was the inverse of the engine ANN model. The on-line ANN used the error between the CAC measurement from the CAC estimator and its predicted value from the ANN to update the network's parameters. The feedforward controller was therefore adapted since its operation depended on the ANN model. The adaptation scheme improved the ANN prediction accuracy when the engine parts degraded, the speed changed or when modeling errors occurred. The engine controller exhibited good CAC tracking performance. Computer simulation demonstrated the capability of the camless engine controller. 17 refs., 5 figs.

  9. Reverse engineering highlights potential principles of large gene regulatory network design and learning.

    Science.gov (United States)

    Carré, Clément; Mas, André; Krouk, Gabriel

    2017-01-01

    Inferring transcriptional gene regulatory networks from transcriptomic datasets is a key challenge of systems biology, with potential impacts ranging from medicine to agronomy. There are several techniques used presently to experimentally assay transcription factors to target relationships, defining important information about real gene regulatory networks connections. These techniques include classical ChIP-seq, yeast one-hybrid, or more recently, DAP-seq or target technologies. These techniques are usually used to validate algorithm predictions. Here, we developed a reverse engineering approach based on mathematical and computer simulation to evaluate the impact that this prior knowledge on gene regulatory networks may have on training machine learning algorithms. First, we developed a gene regulatory networks-simulating engine called FRANK (Fast Randomizing Algorithm for Network Knowledge) that is able to simulate large gene regulatory networks (containing 10(4) genes) with characteristics of gene regulatory networks observed in vivo. FRANK also generates stable or oscillatory gene expression directly produced by the simulated gene regulatory networks. The development of FRANK leads to important general conclusions concerning the design of large and stable gene regulatory networks harboring scale free properties (built ex nihilo). In combination with supervised (accepting prior knowledge) support vector machine algorithm we (i) address biologically oriented questions concerning our capacity to accurately reconstruct gene regulatory networks and in particular we demonstrate that prior-knowledge structure is crucial for accurate learning, and (ii) draw conclusions to inform experimental design to performed learning able to solve gene regulatory networks in the future. By demonstrating that our predictions concerning the influence of the prior-knowledge structure on support vector machine learning capacity holds true on real data (Escherichia coli K14 network

  10. Walking Across Wikipedia: A Scale-Free Network Model of Semantic Memory Retrieval

    Directory of Open Access Journals (Sweden)

    Graham William Thompson

    2014-02-01

    Full Text Available Semantic knowledge has been investigated using both online and offline methods. One common online method is category recall, in which members of a semantic category like animals are retrieved in a given period of time. The order, timing, and number of retrievals are used as assays of semantic memory processes. One common offline method is corpus analysis, in which the structure of semantic knowledge is extracted from texts using co-occurrence or encyclopedic methods. Online measures of semantic processing, as well as offline measures of semantic structure, have yielded data resembling inverse power law distributions. The aim of the present study is to investigate whether these patterns in data might be related. A semantic network model of animal knowledge is formulated on the basis of Wikipedia pages and their overlap in word probability distributions. The network is scale-free, in that node degree is related to node frequency as an inverse power law. A random walk over this network is shown to simulate a number of results from a category recall experiment, including power law-like distributions of inter-response intervals. Results are discussed in terms of theories of semantic structure and processing.

  11. Fronto-parietal network oscillations reveal relationship between working memory capacity and cognitive control

    Directory of Open Access Journals (Sweden)

    Rasa eGulbinaite

    2014-09-01

    Full Text Available Executive-attention theory proposes a close relationship between working memory capacity (WMC and cognitive control abilities. However, conflicting results are documented in the literature, with some studies reporting that individual variations in WMC predict differences in cognitive control and trial-to-trial control adjustments (operationalized as the size of the congruency effect and congruency sequence effects, respectively, while others report no WMC-related differences. We hypothesized that brain network dynamics might be a more sensitive measure of WMC-related differences in cognitive control abilities. Thus, in the present study, we measured human EEG during the Simon task to characterize WMC-related differences in the neural dynamics of conflict processing and adaptation to conflict. Although high- and low-WMC individuals did not differ behaviorally, there were substantial WMC-related differences in theta (4-8 Hz and delta (1-3 Hz connectivity in fronto-parietal networks. Group differences in local theta and delta power were relatively less pronounced. These results suggest that the relationship between WMC and cognitive control abilities is more strongly reflected in large-scale oscillatory network dynamics than in spatially localized activity or in behavioral task performance.

  12. Fronto-parietal network oscillations reveal relationship between working memory capacity and cognitive control.

    Science.gov (United States)

    Gulbinaite, Rasa; van Rijn, Hedderik; Cohen, Michael X

    2014-01-01

    Executive-attention theory proposes a close relationship between working memory capacity (WMC) and cognitive control abilities. However, conflicting results are documented in the literature, with some studies reporting that individual variations in WMC predict differences in cognitive control and trial-to-trial control adjustments (operationalized as the size of the congruency effect and congruency sequence effects, respectively), while others report no WMC-related differences. We hypothesized that brain network dynamics might be a more sensitive measure of WMC-related differences in cognitive control abilities. Thus, in the present study, we measured human EEG during the Simon task to characterize WMC-related differences in the neural dynamics of conflict processing and adaptation to conflict. Although high- and low-WMC individuals did not differ behaviorally, there were substantial WMC-related differences in theta (4-8 Hz) and delta (1-3 Hz) connectivity in fronto-parietal networks. Group differences in local theta and delta power were relatively less pronounced. These results suggest that the relationship between WMC and cognitive control abilities is more strongly reflected in large-scale oscillatory network dynamics than in spatially localized activity or in behavioral task performance.

  13. Robust working memory in an asynchronously spiking neural network realized in neuromorphic VLSI

    Directory of Open Access Journals (Sweden)

    Massimiliano eGiulioni

    2012-02-01

    Full Text Available We demonstrate bistable attractor dynamics in a spiking neural network implemented with neuromorphic VLSI hardware. The on-chip network consists of three interacting populations (two excitatory, one inhibitory of integrate-and-fire (LIF neurons. One excitatory population is distinguished by strong synaptic self-excitation, which sustains meta-stable states of ‘high’ and ‘low’-firing activity. Depending on the overall excitability, transitions to the ‘high’ state may be evoked by external stimulation, or may occur spontaneously due to random activity fluctuations. In the former case, the ‘high’ state retains a working memory of a stimulus until well after its release. In the latter case, ‘high’ states remain stable for seconds, three orders of magnitude longer than the largest time-scale implemented in the circuitry. Evoked and spontaneous transitions form a continuum and may exhibit a wide range of latencies, depending on the strength of external stimulation and of recurrent synaptic excitation. In addition, we investigated corrupted ‘high’ states comprising neurons of both excitatory populations. Within a basin of attraction, the network dynamics corrects such states and re-establishes the prototypical ‘high’ state. We conclude that, with effective theoretical guidance, full-fledged attractor dynamics can be realized with comparatively small populations of neuromorphic hardware neurons.

  14. 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.

  15. Emotion perception and executive control interact in the salience network during emotionally charged working memory processing.

    Science.gov (United States)

    Luo, Yu; Qin, Shaozheng; Fernández, Guillén; Zhang, Yu; Klumpers, Floris; Li, Hong

    2014-11-01

    Processing of emotional stimuli can either hinder or facilitate ongoing working memory (WM); however, the neural basis of these effects remains largely unknown. Here we examined the neural mechanisms of these paradoxical effects by implementing a novel emotional WM task in an fMRI study. Twenty-five young healthy participants performed an N-back task with fearful and neutral faces as stimuli. Participants made more errors when performing 0-back task with fearful versus neutral faces, whereas they made fewer errors when performing 2-back task with fearful versus neutral faces. These emotional impairment and enhancement on behavioral performance paralleled significant interactions in distributed regions in the salience network including anterior insula (AI) and dorsal cingulate cortex (dACC), as well as in emotion perception network including amygdala and temporal-occipital association cortex (TOC). The dorsal AI (dAI) and dACC were more activated when comparing fearful with neutral faces in 0-back task. Contrarily, dAI showed reduced activation, while TOC and amygdala showed stronger responses to fearful as compared to neutral faces in the 2-back task. These findings provide direct neural evidence to the emerging dual competition model suggesting that the salience network plays a critical role in mediating interaction between emotion perception and executive control when facing ever-changing behavioral demands. Copyright © 2014 Wiley Periodicals, Inc.

  16. The parietal memory network activates similarly for true and associative false recognition elicited via the DRM procedure.

    Science.gov (United States)

    McDermott, Kathleen B; Gilmore, Adrian W; Nelson, Steven M; Watson, Jason M; Ojemann, Jeffrey G

    2017-02-01

    Neuroimaging investigations of human memory encoding and retrieval have revealed that multiple regions of parietal cortex contribute to memory. Recently, a sparse network of regions within parietal cortex has been identified using resting state functional connectivity (MRI techniques). The regions within this network exhibit consistent task-related responses during memory formation and retrieval, leading to its being called the parietal memory network (PMN). Among its signature patterns are: deactivation during initial experience with an item (e.g., encoding); activation during subsequent repetitions (e.g., at retrieval); greater activation for successfully retrieved familiar words than novel words (e.g., hits relative to correctly-rejected lures). The question of interest here is whether novel words that are subjectively experienced as having been recently studied would elicit PMN activation similar to that of hits. That is, we compared old items correctly recognized to two types of novel items on a recognition test: those correctly identified as new and those incorrectly labeled as old due to their strong associative relation to the studied words (in the DRM false memory protocol). Subjective oldness plays a strong role in driving activation, as hits and false alarms activated similarly (and greater than correctly-rejected lures). Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. Systematic reverse engineering of network topologies: a case study of resettable bistable cellular responses.

    Science.gov (United States)

    Mondal, Debasish; Dougherty, Edward; Mukhopadhyay, Abhishek; Carbo, Adria; Yao, Guang; Xing, Jianhua

    2014-01-01

    A focused theme in systems biology is to uncover design principles of biological networks, that is, how specific network structures yield specific systems properties. For this purpose, we have previously developed a reverse engineering procedure to identify network topologies with high likelihood in generating desired systems properties. Our method searches the continuous parameter space of an assembly of network topologies, without enumerating individual network topologies separately as traditionally done in other reverse engineering procedures. Here we tested this CPSS (continuous parameter space search) method on a previously studied problem: the resettable bistability of an Rb-E2F gene network in regulating the quiescence-to-proliferation transition of mammalian cells. From a simplified Rb-E2F gene network, we identified network topologies responsible for generating resettable bistability. The CPSS-identified topologies are consistent with those reported in the previous study based on individual topology search (ITS), demonstrating the effectiveness of the CPSS approach. Since the CPSS and ITS searches are based on different mathematical formulations and different algorithms, the consistency of the results also helps cross-validate both approaches. A unique advantage of the CPSS approach lies in its applicability to biological networks with large numbers of nodes. To aid the application of the CPSS approach to the study of other biological systems, we have developed a computer package that is available in Information S1.

  18. New trends in networking, computing, e-learning, systems sciences, and engineering

    CERN Document Server

    Sobh, Tarek

    2015-01-01

    This book includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art research projects in the areas of Computer Science, Informatics, and Systems Sciences, and Engineering. It includes selected papers form the conference proceedings of the Ninth International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 2013). Coverage includes topics in: Industrial Electronics, Technology & Automation, Telecommunications and Networking, Systems, Computing Sciences and Software Engineering, Engineering Education, Instructional Technology, Assessment, and E-learning.  • Provides the latest in a series of books growing out of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering; • Includes chapters in the most advanced areas of Computing, Informatics, Systems Sciences, and Engineering; • Accessible to a wide range of readership, including professors, researchers, practitioners and...

  19. Deficits in episodic memory retrieval reveal impaired default mode network connectivity in amnestic mild cognitive impairment

    Directory of Open Access Journals (Sweden)

    Cameron J. Dunn

    2014-01-01

    Full Text Available Amnestic mild cognitive impairment (aMCI is believed to represent a transitional stage between normal healthy ageing and the development of dementia. In particular, aMCI patients have been shown to have higher annual transition rates to Alzheimer's Disease (AD than individuals without cognitive impairment. Despite intensifying interest investigating the neuroanatomical basis of this transition, there remain a number of questions regarding the pathophysiological process underlying aMCI itself. A number of recent studies in aMCI have shown specific impairments in connectivity within the default mode network (DMN, which is a group of regions strongly related to episodic memory capacities. However to date, no study has investigated the integrity of the DMN between patients with aMCI and those with a non-amnestic pattern of MCI (naMCI, who have cognitive impairment, but intact memory storage systems. In this study, we contrasted the DMN connectivity in 24 aMCI and 33 naMCI patients using seed-based resting state fMRI. The two groups showed no statistical difference in their DMN intra-connectivity. However when connectivity was analysed according to performance on measures of episodic memory retrieval, the two groups were separable, with aMCI patients demonstrating impaired functional connectivity between the hippocampal formation and the posterior cingulate cortex. We provide evidence that this lack of connectivity is driven by impaired communication from the posterior cingulate hub and does not simply represent hippocampal atrophy, suggesting that posterior cingulate degeneration is the driving force behind impaired DMN connectivity in aMCI.

  20. Graph-Theoretic Properties of Networks Based on Word Association Norms: Implications for Models of Lexical Semantic Memory

    Science.gov (United States)

    Gruenenfelder, Thomas M.; Recchia, Gabriel; Rubin, Tim; Jones, Michael N.

    2016-01-01

    We compared the ability of three different contextual models of lexical semantic memory (BEAGLE, Latent Semantic Analysis, and the Topic model) and of a simple associative model (POC) to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network…

  1. Effects of Network Structure, Competition and Memory Time on Social Spreading Phenomena

    Science.gov (United States)

    Gleeson, James P.; O'Sullivan, Kevin P.; Baños, Raquel A.; Moreno, Yamir

    2016-04-01

    Online social media has greatly affected the way in which we communicate with each other. However, little is known about what fundamental mechanisms drive dynamical information flow in online social systems. Here, we introduce a generative model for online sharing behavior that is analytically tractable and that can reproduce several characteristics of empirical micro-blogging data on hashtag usage, such as (time-dependent) heavy-tailed distributions of meme popularity. The presented framework constitutes a null model for social spreading phenomena that, in contrast to purely empirical studies or simulation-based models, clearly distinguishes the roles of two distinct factors affecting meme popularity: the memory time of users and the connectivity structure of the social network.

  2. Effects of Network Structure, Competition and Memory Time on Social Spreading Phenomena

    Directory of Open Access Journals (Sweden)

    James P. Gleeson

    2016-05-01

    Full Text Available Online social media has greatly affected the way in which we communicate with each other. However, little is known about what fundamental mechanisms drive dynamical information flow in online social systems. Here, we introduce a generative model for online sharing behavior that is analytically tractable and that can reproduce several characteristics of empirical micro-blogging data on hashtag usage, such as (time-dependent heavy-tailed distributions of meme popularity. The presented framework constitutes a null model for social spreading phenomena that, in contrast to purely empirical studies or simulation-based models, clearly distinguishes the roles of two distinct factors affecting meme popularity: the memory time of users and the connectivity structure of the social network.

  3. Automatic temporal segment detection via bilateral long short-term memory recurrent neural networks

    Science.gov (United States)

    Sun, Bo; Cao, Siming; He, Jun; Yu, Lejun; Li, Liandong

    2017-03-01

    Constrained by the physiology, the temporal factors associated with human behavior, irrespective of facial movement or body gesture, are described by four phases: neutral, onset, apex, and offset. Although they may benefit related recognition tasks, it is not easy to accurately detect such temporal segments. An automatic temporal segment detection framework using bilateral long short-term memory recurrent neural networks (BLSTM-RNN) to learn high-level temporal-spatial features, which synthesizes the local and global temporal-spatial information more efficiently, is presented. The framework is evaluated in detail over the face and body database (FABO). The comparison shows that the proposed framework outperforms state-of-the-art methods for solving the problem of temporal segment detection.

  4. Use of group 3-level memory telefacsimiles for enhanced interlibrary loan, part II: Network application.

    Science.gov (United States)

    Bennett, V M; Dell, E Y

    1993-07-01

    The Interlibrary Loan, Document Delivery, and Union List Task Force of the Health Sciences Libraries Consortium (HSLC) accepted the charge of maximizing use of the advanced features of group 3-level memory telefacsimiles. A pilot project was initiated to address the task force's recommendation to the HSLC Board that all nonrush interlibrary loan (ILL) documents be transmitted to participants within forty-eight hours of request receipt. This paper describes the project, which tests a network application for unattended, overnight transmission of documents. In determining whether this technology could be used as the primary medium for ILL of photocopies, the following criteria were used: the percentage of ILL requests filled by telefacsimile; the speed, quality, and reliability of service; and the impact of telefacsimile on document delivery costs. The article discusses the project history, optimal use of equipment features for library applications, full-scale implementation, and operational issues that affect ILL policy.

  5. Animal Hairs as Water-stimulated Shape Memory Materials: Mechanism and Structural Networks in Molecular Assemblies

    Science.gov (United States)

    Xiao, Xueliang; Hu, Jinlian

    2016-05-01

    Animal hairs consisting of α-keratin biopolymers existing broadly in nature may be responsive to water for recovery to the innate shape from their fixed deformation, thus possess smart behavior, namely shape memory effect (SME). In this article, three typical animal hair fibers were first time investigated for their water-stimulated SME, and therefrom to identify the corresponding net-points and switches in their molecular and morphological structures. Experimentally, the SME manifested a good stability of high shape fixation ratio and reasonable recovery rate after many cycles of deformation programming under water stimulation. The effects of hydration on hair lateral size, recovery kinetics, dynamic mechanical behaviors and structural components (crystal, disulfide and hydrogen bonds) were then systematically studied. SME mechanisms were explored based on the variations of structural components in molecular assemblies of such smart fibers. A hybrid structural network model with single-switch and twin-net-points was thereafter proposed to interpret the water-stimulated shape memory mechanism of animal hairs. This original work is expected to provide inspiration for exploring other natural materials to reveal their smart functions and natural laws in animals including human as well as making more remarkable synthetic smart materials.

  6. Reversible mechano-memory in sheared cross-linked actin networks

    Science.gov (United States)

    Majumdar, Sayantan; Gardel, Margaret L.

    2015-03-01

    Is it possible to control the shear modulus of a material mechanically? We reconstitute a network of actin filaments cross-linked with Filamin A and show that the system has remarkable property to respond under shear in a deformation history dependent manner. When a large shear stress pulse is applied to the system, the system remembers the direction of deformation long after the stress pulse is removed. For the next loading cycle, shear response of the system becomes anisotropic; if the applied pulse direction is same as the previous one, the system behaves like a viscoelastic solid but a transient liquefaction is observed if the pulse direction is reversed. Imaging and normal force measurements under shear suggest that this anisotropic response comes from stretching and bending dominated deformation directions induced by the large shear deformation giving rise to a direction dependent mechano-memory. The long time scale over which the memory effect persists has relevance in various deformations in cellular and multicellular systems. S.M. acknowledges support from a Kadanoff-Rice Post Doctoral fellowship from MRSEC, University of Chicago.

  7. Multichannel activity propagation across an engineered axon network

    Science.gov (United States)

    Chen, H. Isaac; Wolf, John A.; Smith, Douglas H.

    2017-04-01

    Objective. Although substantial progress has been made in mapping the connections of the brain, less is known about how this organization translates into brain function. In particular, the massive interconnectivity of the brain has made it difficult to specifically examine data transmission between two nodes of the connectome, a central component of the ‘neural code.’ Here, we investigated the propagation of multiple streams of asynchronous neuronal activity across an isolated in vitro ‘connectome unit.’ Approach. We used the novel technique of axon stretch growth to create a model of a long-range cortico-cortical network, a modular system consisting of paired nodes of cortical neurons connected by axon tracts. Using optical stimulation and multi-electrode array recording techniques, we explored how input patterns are represented by cortical networks, how these representations shift as they are transmitted between cortical nodes and perturbed by external conditions, and how well the downstream node distinguishes different patterns. Main results. Stimulus representations included direct, synaptic, and multiplexed responses that grew in complexity as the distance between the stimulation source and recorded neuron increased. These representations collapsed into patterns with lower information content at higher stimulation frequencies. With internodal activity propagation, a hierarchy of network pathways, including latent circuits, was revealed using glutamatergic blockade. As stimulus channels were added, divergent, non-linear effects were observed in local versus distant network layers. Pairwise difference analysis of neuronal responses suggested that neuronal ensembles generally outperformed individual cells in discriminating input patterns. Significance. Our data illuminate the complexity of spiking activity propagation in cortical networks in vitro, which is characterized by the transformation of an input into myriad outputs over several network layers

  8. Into new territory: improved microbial synthesis through engineering of the essential metabolic network.

    Science.gov (United States)

    Lynch, Michael D

    2016-04-01

    Advances in synthetic biology and metabolic engineering offer the promise of next generation bioprocesses to produce numerous products including specialty and bulk chemicals and even biofuels sustainably from renewable feedstocks. A primary challenge is the optimization of product flux, within a much larger and complex metabolic network. While simple gene deletion methods can be used in the case of non-essential byproduct pathways, more sophisticated approaches are required when competitive fluxes are essential to host cellular functions. Engineering essential metabolic networks has been traditionally off-limits to metabolic engineers. Newer approaches to be reviewed include the rebalancing or rewiring of the metabolic network by tuning the levels of essential enzymes and the use of dynamic metabolic control strategies to conditionally reduce essential competitive fluxes. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. Integrated Business and Engineering Framework for Synthesis and Design of Enterprise-Wide Processing Networks

    DEFF Research Database (Denmark)

    Quaglia, Alberto; Sarup, Bent; Sin, Gürkan

    2012-01-01

    The synthesis and design of processing networks is a complex and multidisciplinary problem, which involves many strategic and tactical decisions at business (considering financial criteria, market competition, supply chain network, etc) and engineering levels (considering synthesis, design...... and optimisation of production technology, R&D, etc), all of which have a deep impact on the profitability of processing industries. In this study, an integrated business and engineering framework for synthesis and design of processing networks is presented. The framework employs a systematic approach to manage...... the complexity while solving simultaneously both the business and the engineering aspects of problems, allowing at the same time, comparison of a large number of alternatives at their optimal points. The results identify the optimal raw material, the product portfolio and select the process technology...

  10. A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

    Science.gov (United States)

    Kobayashi, Takahisa; Simon, Donald L.

    2001-01-01

    In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

  11. Multilayer Network Modeling of Change Propagation for Engineering Change Management

    Science.gov (United States)

    2010-06-01

    ation 411 PNC C ac 2 C PC Not Predicted & Propagated wI Comunication ENot Predicted & Not Propagated w ConPnCcation 04 PPC 5CPredicted & Propagated w...documentation, and product requirements. Formal change impact analysis allows an engineering firm to keep tabs on their products’ satisfaction of

  12. Elements of microwave networks: basics of microwave engineering

    National Research Council Canada - National Science Library

    Vittoria, C

    1998-01-01

    ... of students majoring in science and engineering. Using criteria (1) as a basis I have purposely left out all formal derivations of Maxwell's equations and subsequent material related to it. I introduced Maxwell's equations as a given fact only to make some connections in later chapters. From criteria (2) the following topics were chosen: (a) lossy med...

  13. Reverse engineering large-scale genetic networks: synthetic versus ...

    Indian Academy of Sciences (India)

    School of Computer Engineering and Science, Shanghai University, 149 Yanchang Road, Zhabei District, Shanghai 200072, People's Republic of China; Institute of Systems Biology, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai 200072, People's Republic of China; Academy of Mathematics and ...

  14. Reverse engineering large-scale genetic networks: synthetic versus ...

    Indian Academy of Sciences (India)

    2010-04-19

    Apr 19, 2010 ... 1School of Computer Engineering and Science, Shanghai University, 149 Yanchang Road, Zhabei District,. Shanghai 200072 ... plex biological system by efficient computer modelling tools. Such tools are gaining ..... combining both assessment approaches, we are likely to ob- tain a more reliable picture ...

  15. Global adaptation in networks of selfish components: emergent associative memory at the system scale.

    Science.gov (United States)

    Watson, Richard A; Mills, Rob; Buckley, C L

    2011-01-01

    In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organize into structures that enhance global adaptation, efficiency, or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology, and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalization, and optimization are well understood. Such global functions within a single agent or organism are not wholly surprising, since the mechanisms (e.g., Hebbian learning) that create these neural organizations may be selected for this purpose; but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviors when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g., when they can influence which other agents they interact with), then, in adapting these inter-agent relationships to maximize their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviors as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalize by idealizing stored patterns and/or creating new combinations of subpatterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviors in the same sense, and by the same mechanism, as with the organizational

  16. Consciousness and the prefrontal parietal network: Insights from attention, working memory and chunking

    Directory of Open Access Journals (Sweden)

    Daniel eBor

    2012-03-01

    Full Text Available Consciousness has of late become a hot topic in neuroscience. Empirical work has centred on identifying potential neural correlates of consciousness (NCCs, with a converging view that the prefrontal parietal network (PPN is closely associated with this process. Theoretical work has primarily sought to explain how informational properties of this cortical network could account for phenomenal properties of consciousness. However, both empirical and theoretical research has given less focus to the psychological features that may account for the NCCs. The PPN has also been heavily linked with cognitive processes, such as attention. We describe how this literature is under-appreciated in consciousness science, in part due to the increasingly entrenched assumption of a strong dissociation between attention and consciousness. We argue instead that there is more common ground between attention and consciousness than is usually emphasized: although objects can under certain circumstances be attended to in the absence of conscious access, attention as a content selection and boosting mechanism is an important and necessary aspect of consciousness. Like attention, working memory and executive control involve the interlinking of multiple mental objects and have also been closely associated with the PPN. We propose that this set of cognitive functions, in concert with attention, make up the core psychological components of consciousness. One related process, chunking, has been shown to activate PPN particularly robustly, even compared with other cognitively demanding tasks, such as working memory or mental arithmetic. It is therefore possible that chunking, as a tool to detect useful patterns within an integrated set of intensely processed (attended information, has a central role to play in consciousness. Following on from this, we suggest that the main evolutionary purpose of consciousness may be to provide innovative solutions to complex or novel problems.

  17. Consciousness and the prefrontal parietal network: insights from attention, working memory, and chunking.

    Science.gov (United States)

    Bor, Daniel; Seth, Anil K

    2012-01-01

    Consciousness has of late become a "hot topic" in neuroscience. Empirical work has centered on identifying potential neural correlates of consciousness (NCCs), with a converging view that the prefrontal parietal network (PPN) is closely associated with this process. Theoretical work has primarily sought to explain how informational properties of this cortical network could account for phenomenal properties of consciousness. However, both empirical and theoretical research has given less focus to the psychological features that may account for the NCCs. The PPN has also been heavily linked with cognitive processes, such as attention. We describe how this literature is under-appreciated in consciousness science, in part due to the increasingly entrenched assumption of a strong dissociation between attention and consciousness. We argue instead that there is more common ground between attention and consciousness than is usually emphasized: although objects can under certain circumstances be attended to in the absence of conscious access, attention as a content selection and boosting mechanism is an important and necessary aspect of consciousness. Like attention, working memory and executive control involve the interlinking of multiple mental objects and have also been closely associated with the PPN. We propose that this set of cognitive functions, in concert with attention, make up the core psychological components of consciousness. One related process, chunking, exploits logical or mnemonic redundancies in a dataset so that it can be recoded and a given task optimized. Chunking has been shown to activate PPN particularly robustly, even compared with other cognitively demanding tasks, such as working memory or mental arithmetic. It is therefore possible that chunking, as a tool to detect useful patterns within an integrated set of intensely processed (attended) information, has a central role to play in consciousness. Following on from this, we suggest that a key

  18. From distributed resources to limited slots in multiple-item working memory: a spiking network model with normalization.

    Science.gov (United States)

    Wei, Ziqiang; Wang, Xiao-Jing; Wang, Da-Hui

    2012-08-15

    Recent behavioral studies have given rise to two contrasting models for limited working memory capacity: a "discrete-slot" model in which memory items are stored in a limited number of slots, and a "shared-resource" model in which the neural representation of items is distributed across a limited pool of resources. To elucidate the underlying neural processes, we investigated a continuous network model for working memory of an analog feature. Our model network fundamentally operates with a shared resource mechanism, and stimuli in cue arrays are encoded by a distributed neural population. On the other hand, the network dynamics and performance are also consistent with the discrete-slot model, because multiple objects are maintained by distinct localized population persistent activity patterns (bump attractors). We identified two phenomena of recurrent circuit dynamics that give rise to limited working memory capacity. As the working memory load increases, a localized persistent activity bump may either fade out (so the memory of the corresponding item is lost) or merge with another nearby bump (hence the resolution of mnemonic representation for the merged items becomes blurred). We identified specific dependences of these two phenomena on the strength and tuning of recurrent synaptic excitation, as well as network normalization: the overall population activity is invariant to set size and delay duration; therefore, a constant neural resource is shared by and dynamically allocated to the memorized items. We demonstrate that the model reproduces salient observations predicted by both discrete-slot and shared-resource models, and propose testable predictions of the merging phenomenon.

  19. Reverse engineering cellular decisions for hybrid reconfigurable network modeling

    Science.gov (United States)

    Blair, Howard A.; Saranak, Jureepan; Foster, Kenneth W.

    2011-06-01

    Cells as microorganisms and within multicellular organisms make robust decisions. Knowing how these complex cells make decisions is essential to explain, predict or mimic their behavior. The discovery of multi-layer multiple feedback loops in the signaling pathways of these modular hybrid systems suggests their decision making is sophisticated. Hybrid systems coordinate and integrate signals of various kinds: discrete on/off signals, continuous sensory signals, and stochastic and continuous fluctuations to regulate chemical concentrations. Such signaling networks can form reconfigurable networks of attractors and repellors giving them an extra level of organization that has resilient decision making built in. Work on generic attractor and repellor networks and on the already identified feedback networks and dynamic reconfigurable regulatory topologies in biological cells suggests that biological systems probably exploit such dynamic capabilities. We present a simple behavior of the swimming unicellular alga Chlamydomonas that involves interdependent discrete and continuous signals in feedback loops. We show how to rigorously verify a hybrid dynamical model of a biological system with respect to a declarative description of a cell's behavior. The hybrid dynamical systems we use are based on a unification of discrete structures and continuous topologies developed in prior work on convergence spaces. They involve variables of discrete and continuous types, in the sense of type theory in mathematical logic. A unification such as afforded by convergence spaces is necessary if one wants to take account of the affect of the structural relationships within each type on the dynamics of the system.

  20. Discrete Event Modeling and Simulation-Driven Engineering for the ATLAS Data Acquisition Network

    CERN Document Server

    Bonaventura, Matias Alejandro; The ATLAS collaboration; Castro, Rodrigo Daniel

    2016-01-01

    We present an iterative and incremental development methodology for simulation models in network engineering projects. Driven by the DEVS (Discrete Event Systems Specification) formal framework for modeling and simulation we assist network design, test, analysis and optimization processes. A practical application of the methodology is presented for a case study in the ATLAS particle physics detector, the largest scientific experiment built by man where scientists around the globe search for answers about the origins of the universe. The ATLAS data network convey real-time information produced by physics detectors as beams of particles collide. The produced sub-atomic evidences must be filtered and recorded for further offline scrutiny. Due to the criticality of the transported data, networks and applications undergo careful engineering processes with stringent quality of service requirements. A tight project schedule imposes time pressure on design decisions, while rapid technology evolution widens the palett...

  1. Information Flow Through Stages of Complex Engineering Design Projects: A Dynamic Network Analysis Approach

    DEFF Research Database (Denmark)

    Parraguez, Pedro; Eppinger, Steven D.; Maier, Anja

    2015-01-01

    as those activities are implemented through the network of people executing the project. To address this gap, we develop a dynamic modeling method that integrates both the network of people and the network of activities in the project. We then employ a large dataset collected from an industrial setting...... design process and thus support theory-building toward the evolution of information flows through systems engineering stages. Implications include guidance on how to analyze and predict information flows as well as better planning of information flows in engineering design projects according......, consisting of project-related e-mails and activity records from the design and development of a renewable energy plant over the course of more than three years. Using network metrics for centrality and clustering, we make three important contributions: 1)We demonstrate a novel method for analyzing...

  2. IDI diesel engine performance and exhaust emission analysis using biodiesel with an artificial neural network (ANN)

    OpenAIRE

    K. Prasada Rao; T. Victor Babu; Anuradha, G.; B.V. Appa Rao

    2016-01-01

    Biodiesel is receiving increasing attention each passing day because of its fuel properties and compatibility. This study investigates the performance and emission characteristics of single cylinder four stroke indirect diesel injection (IDI) engine fueled with Rice Bran Methyl Ester (RBME) with Isopropanol additive. The investigation is done through a combination of experimental data analysis and artificial neural network (ANN) modeling. The study used IDI engine experimental data to evaluat...

  3. Networked Memory Project: A Policy Thought Experiment for the Archiving of Social Networks by the Library of Congress of the United States

    Directory of Open Access Journals (Sweden)

    Chloé S. Georas

    2014-07-01

    Full Text Available This article explores the challenges posed by an archival interest in the broad palimpsest of daily life left on social networks that are controlled by private corporations. It addresses whether social networks should be archived for the benefit of future generations and proposes a policy thought experiment to help grapple with these questions, namely, the proposal for the formation of the public interest-oriented Networked Memory Project by the Library of Congress for the archiving of social networks. My discussion of the challenges posed by this thought experiment will focus on the U.S. legal framework within which the Library of Congress operates and take Facebook. To the extent that social networks have user-generated contents that range from the highly “private” to “public” as opposed to other networked platforms that contain materials that are considered “public”, the bar for the historical archival of social networks is much higher. Almost every archival effort must contend with the legal hurdle of copyright, but the archiving of social networks must also address how to handle the potentially sensitive nature of materials that are considered “private” from the perspective of the social and legal constructions of privacy. My theoretical exercise of proposing the formation of the Networked Memory Project by the Library of Congress responds to the need to consider the benefits of a public interest-oriented archive of social networks that can counter the drawbacks of the incidental corporate archiving taking place on social networks.

  4. A New Delay Connection for Long Short-Term Memory Networks.

    Science.gov (United States)

    Wang, Jianyong; Zhang, Lei; Chen, Yuanyuan; Yi, Zhang

    2017-12-17

    Connections play a crucial role in neural network (NN) learning because they determine how information flows in NNs. Suitable connection mechanisms may extensively enlarge the learning capability and reduce the negative effect of gradient problems. In this paper, a new delay connection is proposed for Long Short-Term Memory (LSTM) unit to develop a more sophisticated recurrent unit, called Delay Connected LSTM (DCLSTM). The proposed delay connection brings two main merits to DCLSTM with introducing no extra parameters. First, it allows the output of the DCLSTM unit to maintain LSTM, which is absent in the LSTM unit. Second, the proposed delay connection helps to bridge the error signals to previous time steps and allows it to be back-propagated across several layers without vanishing too quickly. To evaluate the performance of the proposed delay connections, the DCLSTM model with and without peephole connections was compared with four state-of-the-art recurrent model on two sequence classification tasks. DCLSTM model outperformed the other models with higher accuracy and F1[Formula: see text]score. Furthermore, the networks with multiple stacked DCLSTM layers and the standard LSTM layer were evaluated on Penn Treebank (PTB) language modeling. The DCLSTM model achieved lower perplexity (PPL)/bit-per-character (BPC) than the standard LSTM model. The experiments demonstrate that the learning of the DCLSTM models is more stable and efficient.

  5. Cross-frequency synchronization connects networks of fast and slow oscillations during visual working memory maintenance

    Science.gov (United States)

    Siebenhühner, Felix; Wang, Sheng H; Palva, J Matias; Palva, Satu

    2016-01-01

    Neuronal activity in sensory and fronto-parietal (FP) areas underlies the representation and attentional control, respectively, of sensory information maintained in visual working memory (VWM). Within these regions, beta/gamma phase-synchronization supports the integration of sensory functions, while synchronization in theta/alpha bands supports the regulation of attentional functions. A key challenge is to understand which mechanisms integrate neuronal processing across these distinct frequencies and thereby the sensory and attentional functions. We investigated whether such integration could be achieved by cross-frequency phase synchrony (CFS). Using concurrent magneto- and electroencephalography, we found that CFS was load-dependently enhanced between theta and alpha–gamma and between alpha and beta-gamma oscillations during VWM maintenance among visual, FP, and dorsal attention (DA) systems. CFS also connected the hubs of within-frequency-synchronized networks and its strength predicted individual VWM capacity. We propose that CFS integrates processing among synchronized neuronal networks from theta to gamma frequencies to link sensory and attentional functions. DOI: http://dx.doi.org/10.7554/eLife.13451.001 PMID:27669146

  6. Reverse engineering GTPase programming languages with reconstituted signaling networks.

    Science.gov (United States)

    Coyle, Scott M

    2016-07-02

    The Ras superfamily GTPases represent one of the most prolific signaling currencies used in Eukaryotes. With these remarkable molecules, evolution has built GTPase networks that control diverse cellular processes such as growth, morphology, motility and trafficking. (1-4) Our knowledge of the individual players that underlie the function of these networks is deep; decades of biochemical and structural data has provided a mechanistic understanding of the molecules that turn GTPases ON and OFF, as well as how those GTPase states signal by controlling the assembly of downstream effectors. However, we know less about how these different activities work together as a system to specify complex dynamic signaling outcomes. Decoding this molecular "programming language" would help us understand how different species and cell types have used the same GTPase machinery in different ways to accomplish different tasks, and would also provide new insights as to how mutations to these networks can cause disease. We recently developed a bead-based microscopy assay to watch reconstituted H-Ras signaling systems at work under arbitrary configurations of regulators and effectors. (5) Here we highlight key observations and insights from this study and propose extensions to our method to further study this and other GTPase signaling systems.

  7. An introduction to network modeling and simulation for the practicing engineer

    CERN Document Server

    Burbank, Jack; Ward, Jon

    2011-01-01

    This book provides the practicing engineer with a concise listing of commercial and open-source modeling and simulation tools currently available including examples of implementing those tools for solving specific Modeling and Simulation examples. Instead of focusing on the underlying theory of Modeling and Simulation and fundamental building blocks for custom simulations, this book compares platforms used in practice, and gives rules enabling the practicing engineer to utilize available Modeling and Simulation tools. This book will contain insights regarding common pitfalls in network Modeling and Simulation and practical methods for working engineers.

  8. Task positive and default mode networks during a working memory in children with primary monosymptomatic nocturnal enuresis and healthy controls.

    Science.gov (United States)

    Zhang, Kaihua; Ma, Jun; Lei, Du; Wang, Mengxing; Zhang, Jilei; Du, Xiaoxia

    2015-10-01

    Nocturnal enuresis is a common developmental disorder in children, and primary monosymptomatic nocturnal enuresis (PMNE) is the dominant subtype. This study investigated brain functional abnormalities that are specifically related to working memory in children with PMNE using function magnetic resonance imaging (fMRI) in combination with an n-back task. Twenty children with PMNE and 20 healthy children, group-matched for age and sex, participated in this experiment. Several brain regions exhibited reduced activation during the n-back task in children with PMNE, including the right precentral gyrus and the right inferior parietal lobule extending to the postcentral gyrus. Children with PMNE exhibited decreased cerebral activation in the task-positive network, increased task-related cerebral deactivation during a working memory task, and longer response times. Patients exhibited different brain response patterns to different levels of working memory and tended to compensate by greater default mode network deactivation to sustain normal working memory function. Our results suggest that children with PMNE have potential working memory dysfunction.

  9. Avalanche atomic switching in strain engineered Sb2Te3-GeTe interfacial phase-change memory cells

    Science.gov (United States)

    Zhou, Xilin; Behera, Jitendra K.; Lv, Shilong; Wu, Liangcai; Song, Zhitang; Simpson, Robert E.

    2017-09-01

    By confining phase transitions to the nanoscale interface between two different crystals, interfacial phase change memory heterostructures represent the state of the art for energy efficient data storage. We present the effect of strain engineering on the electrical switching performance of the {{Sb}}2{{Te}}3-GeTe superlattice van der Waals devices. Multiple Ge atoms switching through a two-dimensional Te layer reduces the activation barrier for further atoms to switch; an effect that can be enhanced by biaxial strain. The out-of-plane phonon mode of the GeTe crystal remains active in the superlattice heterostructures. The large in-plane biaxial strain imposed by the {{Sb}}2{{Te}}3 layers on the GeTe layers substantially improves the switching speed, reset energy, and cyclability of the superlattice memory devices. Moreover, carefully controlling residual stress in the layers of {{Sb}}2{{Te}}3-GeTe interfacial phase change memories provides a new degree of freedom to design the properties of functional superlattice structures for memory and photonics applications.

  10. How social networks influence female students' choices to major in engineering

    Science.gov (United States)

    Weinland, Kathryn Ann

    Scope and Method of Study: This study examined how social influence plays a part in female students' choices of college major, specifically engineering instead of science, technology, and math. Social influence may show itself through peers, family members, and teachers and may encompass resources under the umbrella of social capital. The purpose of this study was to examine how female students' social networks, through the lens of social capital, influence her major choice of whether or not to study engineering. The variables of peer influence, parental influence, teacher/counselor influence, perception of engineering, and academic background were addressed in a 52 question, Likert scale survey. This survey has been modified from an instrument previously used by Reyer (2007) at Bradley University. Data collection was completed using the Dillman (2009) tailored design model. Responses were grouped into four main scales of the dependent variables of social influence, encouragement, perceptions of engineering and career motivation. A factor analysis was completed on the four factors as a whole, and individual questions were not be analyzed. Findings and Conclusions: This study addressed the differences in social network support for female freshmen majoring in engineering versus female freshmen majoring in science, technology, or math. Social network support, when working together from all angles of peers, teachers, parents, and teachers/counselors, transforms itself into a new force that is more powerful than the summation of the individual parts. Math and science preparation also contributed to female freshmen choosing to major in engineering instead of choosing to major in science, technology, or math. The STEM pipeline is still weak and ways in which to reinforce it should be examined. Social network support is crucial for female freshmen who are majoring in science, technology, engineering, and math.

  11. Effective connectivity of AKT1-mediated dopaminergic working memory networks and pharmacogenetics of anti-dopaminergic treatment.

    Science.gov (United States)

    Tan, Hao Yang; Chen, Anthony G; Kolachana, Bhaskar; Apud, Jose A; Mattay, Venkata S; Callicott, Joseph H; Chen, Qiang; Weinberger, Daniel R

    2012-05-01

    Working memory is a limited capacity system that integrates and manipulates information across brief periods of time, engaging a network of prefrontal, parietal and subcortical brain regions. Genetic control of these heritable brain processes have been suggested by functional genetic variations influencing dopamine signalling, which affect prefrontal activity during complex working memory tasks. However, less is known about genetic control over component working memory cortical-subcortical networks in humans, and the pharmacogenetic implications of dopamine-related genes on cognition in patients receiving anti-dopaminergic drugs. Here, we examined predictions from basic models of dopaminergic signalling in cortical and cortical-subcortical circuitries implicated in dissociable working memory maintenance and manipulation processes. We also examined pharmacogenetic effects on cognition in the context of anti-dopaminergic drug therapy. Using dynamic causal models of functional magnetic resonance imaging in normal subjects (n = 46), we identified differentiated effects of functional polymorphisms in COMT, DRD2 and AKT1 genes on prefrontal-parietal and prefrontal-striatal circuits engaged during maintenance and manipulation, respectively. Cortical synaptic dopamine monitored by the COMT Val158Met polymorphism influenced prefrontal control of both parietal processing in working memory maintenance and striatal processing in working memory manipulation. DRD2 and AKT1 polymorphisms implicated in DRD2 signalling influenced only the prefrontal-striatal network associated with manipulation. In the context of anti-psychotic drugs, the DRD2 and AKT1 polymorphisms altered dose-response effects of anti-psychotic drugs on cognition in schizophrenia (n = 111). Thus, we suggest that genetic modulation of DRD2-AKT1-related prefrontal-subcortical circuits could at least in part influence cognitive dysfunction in psychosis and its treatment.

  12. RESEARCH OF ENGINEERING TRAFFIC IN COMPUTER UZ NETWORK USING MPLS TE TECHNOLOGY

    Directory of Open Access Journals (Sweden)

    V. M. Pakhomovа

    2014-12-01

    Full Text Available Purpose. In railway transport of Ukraine one requires the use of computer networks of different technologies: Ethernet, Token Bus, Token Ring, FDDI and others. In combined computer networks on the railway transport it is necessary to use packet switching technology in multiprotocol networks MPLS (MultiProtocol Label Switching more effectively. They are based on the use of tags. Packet network must transmit different types of traffic with a given quality of service. The purpose of the research is development a methodology for determining the sequence of destination flows for the considered fragment of computer network of UZ. Methodology. When optimizing traffic management in MPLS networks has the important role of technology traffic engineering (Traffic Engineering, TE. The main mechanism of TE in MPLS is the use of unidirectional tunnels (MPLS TE tunnel to specify the path of the specified traffic. The mathematical model of the problem of traffic engineering in computer network of UZ technology MPLS TE was made. Computer UZ network is represented with the directed graph, their vertices are routers of computer network, and each arc simulates communication between nodes. As an optimization criterion serves the minimum value of the maximum utilization of the TE-tunnel. Findings. The six options destination flows were determined; rational sequence of flows was found, at which the maximum utilization of TE-tunnels considered a simplified fragment of a computer UZ network does not exceed 0.5. Originality. The method of solving the problem of traffic engineering in Multiprotocol network UZ technology MPLS TE was proposed; for different classes its own way is laid, depending on the bandwidth and channel loading. Practical value. Ability to determine the values of the maximum coefficient of use of TE-tunnels in computer UZ networks based on developed software model «TraffEng». The input parameters of the model: number of routers, channel capacity, the

  13. BAYESIAN NETWORK APPLICATIONS IN POWER SYSTEMS ENGINEERING. A REVIEW

    Directory of Open Access Journals (Sweden)

    CRACIUN M.

    2017-09-01

    Full Text Available This paper is areview of last10years research papersrelatedto the Bayesiannetworks (BNapplication in power systems engineering (PSE. The various uses of BN in the PSE have been classified according to: forecasting, reliability, stability, defects, steady stateestimation, other. The paper is structured in three parts followed by conclusions. The first part introduces the purpose of the paper, the second part presents the concept of BN, the third part deals with application of BN in the PSE, the paper finalizing with the conclusions.

  14. Artificial neural network modeling of jatropha oil fueled diesel engine for emission predictions

    Directory of Open Access Journals (Sweden)

    Ganapathy Thirunavukkarasu

    2009-01-01

    Full Text Available This paper deals with artificial neural network modeling of diesel engine fueled with jatropha oil to predict the unburned hydrocarbons, smoke, and NOx emissions. The experimental data from the literature have been used as the data base for the proposed neural network model development. For training the networks, the injection timing, injector opening pressure, plunger diameter, and engine load are used as the input layer. The outputs are hydrocarbons, smoke, and NOx emissions. The feed forward back propagation learning algorithms with two hidden layers are used in the networks. For each output a different network is developed with required topology. The artificial neural network models for hydrocarbons, smoke, and NOx emissions gave R2 values of 0.9976, 0.9976, and 0.9984 and mean percent errors of smaller than 2.7603, 4.9524, and 3.1136, respectively, for training data sets, while the R2 values of 0.9904, 0.9904, and 0.9942, and mean percent errors of smaller than 6.5557, 6.1072, and 4.4682, respectively, for testing data sets. The best linear fit of regression to the artificial neural network models of hydrocarbons, smoke, and NOx emissions gave the correlation coefficient values of 0.98, 0.995, and 0.997, respectively.

  15. UX of Social Network Edmodo in Undergraduate Engineering Students

    Directory of Open Access Journals (Sweden)

    Angélica Gómez

    2015-08-01

    Full Text Available The main objective of this research is to describe the use that students make of an academic SNS (social network service and detail the relationship between socio-demographic and academic factors associated with the use of EDMODO and the perception of the contribution to the acquisition of skills for the future career. In the analysis of user experience, participants positively evaluated EDMODO and found that the level of satisfaction is positively associated with the academic results obtained, and negatively with perceived usefulness in terms of the impact on their grades.

  16. Сhoosing the best type neural network jet contour diagnostics engines

    Directory of Open Access Journals (Sweden)

    О.С. Якушенко

    2006-01-01

    Full Text Available  In the paper the  choice problems of neurons  type for neural network is considered. The neurons types has to be , optimal from the point of work stability, training speed and quality of gas turbine engine  technical condition class recognition by work process parameters. Results of researches are given.

  17. Strategic plan : providing high precision search to NASA employees using the NASA engineering network

    Science.gov (United States)

    Dutra, Jayne E.; Smith, Lisa

    2006-01-01

    The goal of this plan is to briefly describe new technologies available to us in the arenas of information discovery and discuss the strategic value they have for the NASA enterprise with some considerations and suggestions for near term implementations using the NASA Engineering Network (NEN) as a delivery venue.

  18. Tools for Large-Scale Data Analytic Examination of Relational and Epistemic Networks in Engineering Education

    Science.gov (United States)

    Madhavan, Krishna; Johri, Aditya; Xian, Hanjun; Wang, G. Alan; Liu, Xiaomo

    2014-01-01

    The proliferation of digital information technologies and related infrastructure has given rise to novel ways of capturing, storing and analyzing data. In this paper, we describe the research and development of an information system called Interactive Knowledge Networks for Engineering Education Research (iKNEER). This system utilizes a framework…

  19. Industrial Engineering : Innovative Networks - 5th International Conference on Industrial Engineering and Industrial Management

    CERN Document Server

    Bogataj, Marija; Ros-McDonnell, Lorenzo

    2012-01-01

    The Spanish Conference of Industrial Engineering /Ingeniería de Organización Industrial (CIO) is an annual meeting promoted by Asociación para el Desarrollo de la Ingeniería de Organización/ Industrial Engineers Association (ADINGOR). The aim of CIO is to establish a forum for the open and free exchange of ideas, opinions and academic experiences about research, technology transfer or successful business experiences in the field of Industrial Engineering. The Scientific Committee is composed by 68 international referees and we foresee the attendance of some 200 people from more than 15 countries and following the rotation of venue and organization between various Spanish universities, the 2011 Conference will be the fifteenth National Conference and the fifth International Conference in Cartagena.   During three days the 2011 Conference will include the participation of European and other foreign countries researchers and practitioners that will presenting communications, reproduced in this volume, on ...

  20. Creating permeable fracture networks for EGS: Engineered systems versus nature

    Energy Technology Data Exchange (ETDEWEB)

    Stephen L Karner

    2005-10-01

    The United States Department of Energy has set long-term national goals for the development of geothermal energy that are significantly accelerated compared to historical development of the resource. To achieve these goals, it is crucial to evaluate the performance of previous and existing efforts to create enhanced geothermal systems (EGS). Two recently developed EGS sites are evaluated from the standpoint of geomechanics. These sites have been established in significantly different tectonic regimes: 1. compressional Cooper Basin (Australia), and 2. extensional Soultz-sous-Fôrets (France). Mohr-Coulomb analyses of the stimulation procedures employed at these sites, coupled with borehole observations, indicate that pre-existing fractures play a significant role in the generation of permeability networks. While pre-existing fabric can be exploited to produce successful results for geothermal energy development, such fracture networks may not be omnipresent. For mostly undeformed reservoirs, it may be necessary to create new fractures using processes that merge existing technologies or use concepts borrowed from natural hydrofracture examples (e.g. dyke swarms).

  1. Re-Engineering Alzheimer Clinical Trials: Global Alzheimer's Platform Network.

    Science.gov (United States)

    Cummings, J; Aisen, P; Barton, R; Bork, J; Doody, R; Dwyer, J; Egan, J C; Feldman, H; Lappin, D; Truyen, L; Salloway, S; Sperling, R; Vradenburg, G

    2016-06-01

    Alzheimer's disease (AD) drug development is costly, time-consuming, and inefficient. Trial site functions, trial design, and patient recruitment for trials all require improvement. The Global Alzheimer Platform (GAP) was initiated in response to these challenges. Four GAP work streams evolved in the US to address different trial challenges: 1) registry-to-cohort web-based recruitment; 2) clinical trial site activation and site network construction (GAP-NET); 3) adaptive proof-of-concept clinical trial design; and 4) finance and fund raising. GAP-NET proposes to establish a standardized network of continuously funded trial sites that are highly qualified to perform trials (with established clinical, biomarker, imaging capability; certified raters; sophisticated management system. GAP-NET will conduct trials for academic and biopharma industry partners using standardized instrument versions and administration. Collaboration with the Innovative Medicines Initiative (IMI) European Prevention of Alzheimer's Disease (EPAD) program, the Canadian Consortium on Neurodegeneration in Aging (CCNA) and other similar international initiatives will allow conduct of global trials. GAP-NET aims to increase trial efficiency and quality, decrease trial redundancy, accelerate cohort development and trial recruitment, and decrease trial costs. The value proposition for sites includes stable funding and uniform training and trial execution; the value to trial sponsors is decreased trial costs, reduced time to execute trials, and enhanced data quality. The value for patients and society is the more rapid availability of new treatments for AD.

  2. Caffeine impact on working memory-related network activation patterns in early stages of cognitive decline

    Energy Technology Data Exchange (ETDEWEB)

    Haller, Sven [Affidea Centre de Diagnostic Radiologique de Carouge CDRC, Geneva (Switzerland); Uppsala University, Department of Surgical Sciences, Radiology, Uppsala (Sweden); Montandon, Marie-Louise; Rodriguez, Cristelle; Moser, Dominik; Toma, Simona; Hofmeister, Jeremy; Giannakopoulos, Panteleimon [University Hospitals of Geneva, Department of Psychiatry, Faculty of Medicine, Medical Direction, Geneva (Switzerland)

    2017-04-15

    Recent evidence indicates that caffeine may have a beneficial effect on cognitive decline and dementia. The current investigation assessed the effect of acute caffeine administration on working memory during the earliest stage of cognitive decline in elderly participants. The study includes consecutive 45 elderly controls and 18 individuals with mild cognitive impairment (MCI, 71.6 ± 4.7 years, 7 females). During neuropsychological follow-up at 18 months, 24 controls remained stable (sCON, 70.0 ± 4.3 years, 11 women), while the remaining 21 showed subtle cognitive deterioration (dCON, 73.4 ± 5.9 years, 14 women). All participants underwent an established 2-back working task in a crossover design of 200 mg caffeine versus placebo. Data analysis included task-related general linear model and functional connectivity tensorial independent component analysis. Working memory behavioral performances did not differ between sCON and dCON, while MCI was slower and less accurate than both control groups (p < 0.05). The dCON group had a less pronounced effect of acute caffeine administration essentially restricted to the right hemisphere (p < 0.05 corrected) and reduced default mode network (DMN) deactivation compared to sCON (p < 0.01 corrected). dCON cases are characterized by decreased sensitivity to caffeine effects on brain activation and DMN deactivation. These complex fMRI patterns possibly reflect the instable status of these cases with intact behavioral performances despite already existing functional alterations in neocortical circuits. (orig.)

  3. Memory trace stabilization leads to large-scale changes in the retrieval network: a functional MRI study on associative memory.

    NARCIS (Netherlands)

    Takashima, A.; Nieuwenhuis, I.L.C.; Rijpkema, M.; Petersson, K.M.; Jensen, O.; Fernandez, G.

    2007-01-01

    Spaced learning with time to consolidate leads to more stabile memory traces. However, little is known about the neural correlates of trace stabilization, especially in humans. The present fMRI study contrasted retrieval activity of two well-learned sets of face-location associations, one learned in

  4. Enhanced structural connectivity within a brain sub-network supporting working memory and engagement processes after cognitive training.

    Science.gov (United States)

    Román, Francisco J; Iturria-Medina, Yasser; Martínez, Kenia; Karama, Sherif; Burgaleta, Miguel; Evans, Alan C; Jaeggi, Susanne M; Colom, Roberto

    2017-05-01

    The structural connectome provides relevant information about experience and training-related changes in the brain. Here, we used network-based statistics (NBS) and graph theoretical analyses to study structural changes in the brain as a function of cognitive training. Fifty-six young women were divided in two groups (experimental and control). We assessed their cognitive function before and after completing a working memory intervention using a comprehensive battery that included fluid and crystallized abilities, working memory and attention control, and we also obtained structural MRI images. We acquired and analyzed diffusion-weighted images to reconstruct the anatomical connectome and we computed standardized changes in connectivity as well as group differences across time using NBS. We also compared group differences relying on a variety of graph-theory indices (clustering, characteristic path length, global and local efficiency and strength) for the whole network as well as for the sub-network derived from NBS analyses. Finally, we calculated correlations between these graph indices and training performance as well as the behavioral changes in cognitive function. Our results revealed enhanced connectivity for the training group within one specific network comprised of nodes/regions supporting cognitive processes required by the training (working memory, interference resolution, inhibition, and task engagement). Significant group differences were also observed for strength and global efficiency indices in the sub-network detected by NBS. Therefore, the connectome approach is a valuable method for tracking the effects of cognitive training interventions across specific sub-networks. Moreover, this approach allowsfor the computation of graph theoretical network metricstoquantifythetopological architecture of the brain networkdetected. The observed structural brain changes support the behavioral results reported earlier (see Colom, Román, et al., 2013

  5. Knowledge engineering for temporal dependency networks as operations procedures. [in space communication

    Science.gov (United States)

    Fayyad, Kristina E.; Hill, Randall W., Jr.; Wyatt, E. J.

    1993-01-01

    This paper presents a case study of the knowledge engineering process employed to support the Link Monitor and Control Operator Assistant (LMCOA). The LMCOA is a prototype system which automates the configuration, calibration, test, and operation (referred to as precalibration) of the communications, data processing, metric data, antenna, and other equipment used to support space-ground communications with deep space spacecraft in NASA's Deep Space Network (DSN). The primary knowledge base in the LMCOA is the Temporal Dependency Network (TDN), a directed graph which provides a procedural representation of the precalibration operation. The TDN incorporates precedence, temporal, and state constraints and uses several supporting knowledge bases and data bases. The paper provides a brief background on the DSN, and describes the evolution of the TDN and supporting knowledge bases, the process used for knowledge engineering, and an analysis of the successes and problems of the knowledge engineering effort.

  6. Reorganization of functional brain networks mediates the improvement of cognitive performance following real-time neurofeedback training of working memory.

    Science.gov (United States)

    Zhang, Gaoyan; Yao, Li; Shen, Jiahui; Yang, Yihong; Zhao, Xiaojie

    2015-05-01

    Working memory (WM) is essential for individuals' cognitive functions. Neuroimaging studies indicated that WM fundamentally relied on a frontoparietal working memory network (WMN) and a cinguloparietal default mode network (DMN). Behavioral training studies demonstrated that the two networks can be modulated by WM training. Different from the behavioral training, our recent study used a real-time functional MRI (rtfMRI)-based neurofeedback method to conduct WM training, demonstrating that WM performance can be significantly improved after successfully upregulating the activity of the target region of interest (ROI) in the left dorsolateral prefrontal cortex (Zhang et al., [2013]: PloS One 8:e73735); however, the neural substrate of rtfMRI-based WM training remains unclear. In this work, we assessed the intranetwork and internetwork connectivity changes of WMN and DMN during the training, and their correlations with the change of brain activity in the target ROI as well as with the improvement of post-training behavior. Our analysis revealed an "ROI-network-behavior" correlation relationship underlying the rtfMRI training. Further mediation analysis indicated that the reorganization of functional brain networks mediated the effect of self-regulation of the target brain activity on the improvement of cognitive performance following the neurofeedback training. The results of this study enhance our understanding of the neural basis of real-time neurofeedback and suggest a new direction to improve WM performance by regulating the functional connectivity in the WM related networks. © 2014 Wiley Periodicals, Inc.

  7. A Networked Perspective on the Engineering Design Process: At the Intersection of Process and Organisation Architectures

    DEFF Research Database (Denmark)

    Parraguez, Pedro

    The design process of engineering systems frequently involves hundreds of activities and people over long periods of time and is implemented through complex networks of information exchanges. Such socio-technical complexity makes design processes hard to manage, and as a result, engineering design...... of a networked perspective also has limited the study of the relationships between process complexity and process performance. This thesis argues that to understand and improve design processes, we must look beyond the planned process and unfold the network structure and composition that actually implement...... the process. This combination of process structure—how people and activities are connected—and composition—the functional diversity of the groups participating in the process—is referred to as the actual design process architecture. This thesis reports on research undertaken to develop, apply and test...

  8. Shape Memory Polymers: A Joint Chemical and Materials Engineering Hands-On Experience

    Science.gov (United States)

    Seif, Mujan; Beck, Matthew

    2018-01-01

    Hands-on experiences are excellent tools for increasing retention of first year engineering students. They also encourage interdisciplinary collaboration, a critical skill for modern engineers. In this paper, we describe and evaluate a joint Chemical and Materials Engineering hands-on lab that explores cross-linking and glass transition in…

  9. Team performance in networked supervisory control of unmanned air vehicles: effects of automation, working memory, and communication content.

    Science.gov (United States)

    McKendrick, Ryan; Shaw, Tyler; de Visser, Ewart; Saqer, Haneen; Kidwell, Brian; Parasuraman, Raja

    2014-05-01

    Assess team performance within a net-worked supervisory control setting while manipulating automated decision aids and monitoring team communication and working memory ability. Networked systems such as multi-unmanned air vehicle (UAV) supervision have complex properties that make prediction of human-system performance difficult. Automated decision aid can provide valuable information to operators, individual abilities can limit or facilitate team performance, and team communication patterns can alter how effectively individuals work together. We hypothesized that reliable automation, higher working memory capacity, and increased communication rates of task-relevant information would offset performance decrements attributed to high task load. Two-person teams performed a simulated air defense task with two levels of task load and three levels of automated aid reliability. Teams communicated and received decision aid messages via chat window text messages. Task Load x Automation effects were significant across all performance measures. Reliable automation limited the decline in team performance with increasing task load. Average team spatial working memory was a stronger predictor than other measures of team working memory. Frequency of team rapport and enemy location communications positively related to team performance, and word count was negatively related to team performance. Reliable decision aiding mitigated team performance decline during increased task load during multi-UAV supervisory control. Team spatial working memory, communication of spatial information, and team rapport predicted team success. An automated decision aid can improve team performance under high task load. Assessment of spatial working memory and the communication of task-relevant information can help in operator and team selection in supervisory control systems.

  10. Transient synchronization of hippocampo-striato-thalamo-cortical networks during sleep spindle oscillations induces motor memory consolidation.

    Science.gov (United States)

    Boutin, Arnaud; Pinsard, Basile; Boré, Arnaud; Carrier, Julie; Fogel, Stuart M; Doyon, Julien

    2017-12-24

    Sleep benefits motor memory consolidation. This mnemonic process is thought to be mediated by thalamo-cortical spindle activity during NREM-stage2 sleep episodes as well as changes in striatal and hippocampal activity. However, direct experimental evidence supporting the contribution of such sleep-dependent physiological mechanisms to motor memory consolidation in humans is lacking. In the present study, we combined EEG and fMRI sleep recordings following practice of a motor sequence learning (MSL) task to determine whether spindle oscillations support sleep-dependent motor memory consolidation by transiently synchronizing and coordinating specialized cortical and subcortical networks. To that end, we conducted EEG source reconstruction on spindle epochs in both cortical and subcortical regions using novel deep-source localization techniques. Coherence-based metrics were adopted to estimate functional connectivity between cortical and subcortical structures over specific frequency bands. Our findings not only confirm the critical and functional role of NREM-stage2 sleep spindles in motor skill consolidation, but provide first-time evidence that spindle oscillations [11-17 Hz] may be involved in sleep-dependent motor memory consolidation by locally reactivating and functionally binding specific task-relevant cortical and subcortical regions within networks including the hippocampus, putamen, thalamus and motor-related cortical regions. Copyright © 2017. Published by Elsevier Inc.

  11. Advanced Engine Cycles Analyzed for Turbofans With Variable-Area Fan Nozzles Actuated by a Shape Memory Alloy

    Science.gov (United States)

    Berton, Jeffrey J.

    2002-01-01

    Advanced, large commercial turbofan engines using low-fan-pressure-ratio, very high bypass ratio thermodynamic cycles can offer significant fuel savings over engines currently in operation. Several technological challenges must be addressed, however, before these engines can be designed. To name a few, the high-diameter fans associated with these engines pose a significant packaging and aircraft installation challenge, and a large, heavy gearbox is often necessary to address the differences in ideal operating speeds between the fan and the low-pressure turbine. Also, the large nacelles contribute aerodynamic drag penalties and require long, heavy landing gear when mounted on conventional, low wing aircraft. Nevertheless, the reduced fuel consumption rates of these engines are a compelling economic incentive, and fans designed with low pressure ratios and low tip speeds offer attractive noise-reduction benefits. Another complication associated with low-pressure-ratio fans is their need for variable flow-path geometry. As the design fan pressure ratio is reduced below about 1.4, an operational disparity is set up in the fan between high and low flight speeds. In other words, between takeoff and cruise there is too large a swing in several key fan parameters-- such as speed, flow, and pressure--for a fan to accommodate. One solution to this problem is to make use of a variable-area fan nozzle (VAFN). However, conventional, hydraulically actuated variable nozzles have weight, cost, maintenance, and reliability issues that discourage their use with low-fan-pressure-ratio engine cycles. United Technologies Research, in cooperation with NASA, is developing a revolutionary, lightweight, and reliable shape memory alloy actuator system that can change the on-demand nozzle exit area by up to 20 percent. This "smart material" actuation technology, being studied under NASA's Ultra-Efficient Engine Technology (UEET) Program and Revolutionary Concepts in Aeronautics (Rev

  12. pth moment exponential stability of stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays.

    Science.gov (United States)

    Wang, Fen; Chen, Yuanlong; Liu, Meichun

    2018-02-01

    Stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays play an increasingly important role in the design and implementation of neural network systems. Under the framework of Filippov solutions, the issues of the pth moment exponential stability of stochastic memristor-based BAM neural networks are investigated. By using the stochastic stability theory, Itô's differential formula and Young inequality, the criteria are derived. Meanwhile, with Lyapunov approach and Cauchy-Schwarz inequality, we derive some sufficient conditions for the mean square exponential stability of the above systems. The obtained results improve and extend previous works on memristor-based or usual neural networks dynamical systems. Four numerical examples are provided to illustrate the effectiveness of the proposed results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Effect of baseline cannabis use and working-memory network function on changes in cannabis use in heavy cannabis users: a prospective fMRI study.

    Science.gov (United States)

    Cousijn, Janna; Wiers, Reinout W; Ridderinkhof, K Richard; van den Brink, Wim; Veltman, Dick J; Goudriaan, Anna E

    2014-05-01

    Theoretical models of addiction suggest that a substance use disorder represents an imbalance between hypersensitive motivational processes and deficient regulatory executive functions. Working-memory (a central executive function) may be a powerful predictor of the course of drug use and drug-related problems. Goal of the current functional magnetic resonance imaging study was to assess the predictive power of working-memory network function for future cannabis use and cannabis-related problem severity in heavy cannabis users. Tensor independent component analysis was used to investigate differences in working-memory network function between 32 heavy cannabis users and 41 nonusing controls during an N-back working-memory task. In addition, associations were examined between working-memory network function and cannabis use and problem severity at baseline and at 6-month follow-up. Behavioral performance and working-memory network function did not significantly differ between heavy cannabis users and controls. However, among heavy cannabis users, individual differences in working-memory network response had an independent effect on change in weekly cannabis use 6 months later (ΔR(2) = 0.11, P = 0.006, f(2) = 0.37) beyond baseline cannabis use (ΔR(2) = 0.41) and a behavioral measure of approach bias (ΔR(2) = 0.18): a stronger network response during the N-back task was related to an increase in weekly cannabis use. These findings imply that heavy cannabis users requiring greater effort to accurately complete an N-back working-memory task have a higher probability of escalating cannabis use. Working-memory network function may be a biomarker for the prediction of course and treatment outcome in cannabis users. Copyright © 2013 Wiley Periodicals, Inc.

  14. A real-time in-memory discovery service leveraging hierarchical packaging information in a unique identifier network to retrieve track and trace information

    CERN Document Server

    Müller, Jürgen

    2014-01-01

    This book examines how to efficiently retrieve track and trace information for an item that took a certain path through a complex network of manufacturers, wholesalers, retailers and consumers. It includes valuable tips on in-memory data management.

  15. SEMANTIC ANALYSIS OVER LESSONS LEARNED CONTAINED IN SOCIAL NETWORKS FOR GENERATING ORGANIZATIONAL MEMORY IN CENTERS R&D

    OpenAIRE

    Marco Javier Suárez Barón

    2016-01-01

    This paper shows the construction of an organizational memory metamodel focused on R&D centers. The metamodel uses lessons learned extracted from corporative social networks; the metamodel aims to promote learning and management of organizational knowledge at these types of organizations. The analysis is applied initially from lessons learned on topics of R&D in Spanish language. The metamodel use natural languages processing together with ontologies for analyze the semantic an...

  16. Impaired White Matter Connections of the Limbic System Networks Associated with Impaired Emotional Memory in Alzheimer's Disease.

    Science.gov (United States)

    Li, Xiaoshu; Wang, Haibao; Tian, Yanghua; Zhou, Shanshan; Li, Xiaohu; Wang, Kai; Yu, Yongqiang

    2016-01-01

    Background: Discrepancies persist regarding retainment of emotional enhancement of memory (EEM) in mild cognitive impairment (MCI) and early Alzheimer's disease (AD) patients.In addition, the neural mechanisms are still poorly understood, little is known about emotional memory related changes in white matter (WM). Objective: To observe whether EEM is absent in amnestic MCI (aMCI) and AD patients, and to investigate if emotional memory is associated with WM connections and gray matters (GM) of the limbic system networks. Methods: Twenty-one AD patients, 20 aMCI patients and 25 normal controls participated in emotional picture recognition tests and MRI scanning. Tract-based spatial statistics (TBSS) and voxel-based morphometry (VBM) methods were used to determine white and gray matter changes of patients. Fourteen regions of interest (ROI) of WM and 20 ROIs of GM were then selected for the correlation analyses with behavioral scores. Results: The EEM effect was lost in AD patients. Both white and gray matter of the limbic system networks were impaired in AD patients. Significant correlations or tendencies between the bilateral uncinate fasciculus, corpus callosum (genu and body), left cingulum bundle, left parahippocampal WM and the recognition sensitivity of emotional valence pictures, and significant correlations or tendencies between the splenium of corpus callosum, left cingulum bundle, left crus of fornix and stria terminalis and the recognition sensitivity of EEM were found. The volume of left amygdala, bilateral insula, medial frontal lobe, anterior and middle cingulum gyrus were positively correlated with the recognition sensitivity of emotional photos, and the right precuneus was positively correlated with the negative EEM effect. However, the affected brain areas of aMCI patients were more localized, and aMCI patients benefited only from positive stimuli. Conclusion: There are impairments of the limbic system networks of AD patients. Damaged WM connections

  17. Impaired white matter connections of the limbic system networks associated with impaired emotional memory in Alzheimer's disease

    Directory of Open Access Journals (Sweden)

    Xiaoshu Li

    2016-10-01

    Full Text Available Background: Discrepancies persist regarding retainment of emotional enhancement of memory (EEM in mild cognitive impairment (MCI and early Alzheimer’s disease (AD patients. In addition, the neural mechanisms are still poorly understood, little is known about emotional memory related changes in white matter (WM.Objective: To observe whether EEM is absent in amnestic MCI (aMCI and AD patients, and to investigate if emotional memory is associated with WM connections and gray matters (GM of the limbic system networks. Methods: Twenty-one AD patients, 20 aMCI patients and 25 normal controls participated in emotional picture recognition tests and MRI scanning. Tract-based spatial statistics (TBSS and voxel-based morphometry (VBM methods were used to determine white and gray matter changes of patients. Fourteen regions of interest (ROI of WM and 20 ROIs of GM were then selected for the correlation analyses with behavioral scores. Results: The EEM effect was lost in AD patients. Both white and gray matter of the limbic system networks were impaired in AD patients. Significant correlations or tendencies between the bilateral uncinate fasciculus, corpus callosum (genu and body, left cingulum bundle, left parahippocampal WM and the recognition sensitivity of emotional valence pictures, and significant correlations or tendencies between the splenium of corpus callosum, left cingulum bundle, left crus of fornix and stria terminalis and the recognition sensitivity of EEM were found. The volume of left amygdala, bilateral insula, medial frontal lobe, anterior and middle cingulum gyrus were positively correlated with the recognition sensitivity of emotional photos, and the right precuneus was positively correlated with the negative EEM effect. However, the affected brain areas of aMCI patients were more localized, and aMCI patients benefited only from positive stimuli. Conclusion: There are impairments of the limbic system networks of AD patients. Damaged WM

  18. NASA/DOD Aerospace Knowledge Diffusion Research Project. Report 35: The use of computer networks in aerospace engineering

    Science.gov (United States)

    Bishop, Ann P.; Pinelli, Thomas E.

    1995-01-01

    This research used survey research to explore and describe the use of computer networks by aerospace engineers. The study population included 2000 randomly selected U.S. aerospace engineers and scientists who subscribed to Aerospace Engineering. A total of 950 usable questionnaires were received by the cutoff date of July 1994. Study results contribute to existing knowledge about both computer network use and the nature of engineering work and communication. We found that 74 percent of mail survey respondents personally used computer networks. Electronic mail, file transfer, and remote login were the most widely used applications. Networks were used less often than face-to-face interactions in performing work tasks, but about equally with reading and telephone conversations, and more often than mail or fax. Network use was associated with a range of technical, organizational, and personal factors: lack of compatibility across systems, cost, inadequate access and training, and unwillingness to embrace new technologies and modes of work appear to discourage network use. The greatest positive impacts from networking appear to be increases in the amount of accurate and timely information available, better exchange of ideas across organizational boundaries, and enhanced work flexibility, efficiency, and quality. Involvement with classified or proprietary data and type of organizational structure did not distinguish network users from nonusers. The findings can be used by people involved in the design and implementation of networks in engineering communities to inform the development of more effective networking systems, services, and policies.

  19. Capturing Uncertainty Information and Categorical Characteristics for Network Payload Grouping in Protocol Reverse Engineering

    Directory of Open Access Journals (Sweden)

    Jian-Zhen Luo

    2015-01-01

    Full Text Available As a promising tool to recover the specifications of unknown protocols, protocol reverse engineering has drawn more and more attention in research over the last decade. It is a critical task of protocol reverse engineering to extract the protocol keywords from network trace. Since the messages of different types have different sets of protocol keywords, it is an effective method to improve the accuracy of protocol keyword extraction by clustering the network payload of unknown traffic into clusters and analyzing each clusters to extract the protocol keywords. Although the classic algorithms such as K-means and EM can be used for network payload clustering, the quality of resultant traffic clusters was far from satisfactory when these algorithms are applied to cluster application layer traffic with categorical attributes. In this paper, we propose a novel method to improve the accuracy of protocol reverse engineering by applying a rough set-based technique for clustering the application layer traffic. This technique analyze multidimension uncertain information in multiple categorical attributes based on rough sets theory to cluster network payload, and apply the Minimum Description Length criteria to determine the optimal number of clusters. The experiments show that our method outperforms the existing algorithms and improves the results of protocol keyword extraction.

  20. Object segmentation and reconstruction via an oscillatory neural network: interaction among learning, memory, topological organization and gamma-band synchronization.

    Science.gov (United States)

    Magosso, E; Cuppini, C; Ursino, M

    2006-01-01

    Synchronization of neuronal activity in the gamma-band has been shown to play an important role in higher cognitive functions, by grouping together the necessary information in different cortical areas to achieve a coherent perception. In the present work, we used a neural network of Wilson-Cowan oscillators to analyze the problem of binding and segmentation of high-level objects. Binding is achieved by implementing in the network the similarity and prior knowledge Gestalt rules. Similarity law is realized via topological maps within the network. Prior knowledge originates by means of a Hebbian rule of synaptic change; objects are memorized in the network with different strengths. Segmentation is realized via a global inhibitor which allows desynchronisation among multiple objects avoiding interference. Simulation results performed with a 40x40 neural grid, using three simultaneous input objects, show that the network is able to recognize and segment objects in several different conditions (different degrees of incompleteness or distortion of input patterns), exhibiting the higher reconstruction performances the higher the strength of object memory. The presented model represents an integrated approach for investigating the relationships among learning, memory, topological organization and gamma-band synchronization.

  1. Turbofan engine diagnostics neuron network size optimization method which takes into account overlaerning effect

    Directory of Open Access Journals (Sweden)

    О.С. Якушенко

    2010-01-01

    Full Text Available  The article is devoted to the problem of gas turbine engine (GTE technical state class automatic recognition with operation parameters by neuron networks. The one of main problems for creation the neuron networks is determination of their optimal structures size (amount of layers in network and count of neurons in each layer.The method of neuron network size optimization intended for classification of GTE technical state is considered in the article. Optimization is cared out with taking into account of overlearning effect possibility when a learning network loses property of generalization and begins strictly describing educational data set. To determinate a moment when overlearning effect is appeared in learning neuron network the method  of three data sets is used. The method is based on the comparison of recognition quality parameters changes which were calculated during recognition of educational and control data sets. As the moment when network overlearning effect is appeared the moment when control data set recognition quality begins deteriorating but educational data set recognition quality continues still improving is used. To determinate this moment learning process periodically is terminated and simulation of network with education and control data sets is fulfilled. The optimization of two-, three- and four-layer networks is conducted and some results of optimization are shown. Also the extended educational set is created and shown. The set describes 16 GTE technical state classes and each class is represented with 200 points (200 possible technical state class realizations instead of 20 points using in the former articles. It was done to increase representativeness of data set.In the article the algorithm of optimization is considered and some results which were obtained with it are shown. The results of experiments were analyzed to determinate most optimal neuron network structure. This structure provides most high-quality GTE

  2. An Approach for Reduction of False Predictions in Reverse Engineering of Gene Regulatory Networks.

    Science.gov (United States)

    Khan, Abhinandan; Saha, Goutam; Pal, Rajat Kumar

    2018-02-17

    A gene regulatory network discloses the regulatory interactions amongst genes, at a particular condition of the human body. The accurate reconstruction of such networks from time-series genetic expression data using computational tools offers a stiff challenge for contemporary computer scientists. This is crucial to facilitate the understanding of the proper functioning of a living organism. Unfortunately, the computational methods produce many false predictions along with the correct predictions, which is unwanted. Investigations in the domain focus on the identification of as many correct regulations as possible in the reverse engineering of gene regulatory networks to make it more reliable and biologically relevant. One way to achieve this is to reduce the number of incorrect predictions in the reconstructed networks. In the present investigation, we have proposed a novel scheme to decrease the number of false predictions by suitably combining several metaheuristic techniques. We have implemented the same using a dataset ensemble approach (i.e. combining multiple datasets) also. We have employed the proposed methodology on real-world experimental datasets of the SOS DNA Repair network of Escherichia coli and the IMRA network of Saccharomyces cerevisiae. Subsequently, we have experimented upon somewhat larger, in silico networks, namely, DREAM3 and DREAM4 Challenge networks, and 15-gene and 20-gene networks extracted from the GeneNetWeaver database. To study the effect of multiple datasets on the quality of the inferred networks, we have used four datasets in each experiment. The obtained results are encouraging enough as the proposed methodology can reduce the number of false predictions significantly, without using any supplementary prior biological information for larger gene regulatory networks. It is also observed that if a small amount of prior biological information is incorporated here, the results improve further w.r.t. the prediction of true positives

  3. Base structure consisting of an endothelialized vascular-tree network and hepatocytes for whole liver engineering.

    Science.gov (United States)

    Shirakigawa, Nana; Takei, Takayuki; Ijima, Hiroyuki

    2013-12-01

    Reconstructed liver has been desired as a liver substitute for transplantation. However, reconstruction of a whole liver has not been achieved because construction of a vascular network at an organ scale is very difficult. We focused on decellularized liver (DC-liver) as an artificial scaffold for the construction of a hierarchical vascular network. In this study, we obtained DC-liver and the tubular network structure in which both portal vein and hepatic vein systems remained intact. Furthermore, endothelialization of the tubular structure in DC-liver was achieved, which prevented blood leakage from the tubular structure. In addition, hepatocytes suspended in a collagen sol were injected from the surroundings using a syringe as a suitable procedure for liver cell inoculation. In summary, we developed a base structure consisting of an endothelialized vascular-tree network and hepatocytes for whole liver engineering. Crown Copyright © 2013. Published by Elsevier B.V. All rights reserved.

  4. Engine cylinder pressure reconstruction using crank kinematics and recurrently-trained neural networks

    Science.gov (United States)

    Bennett, C.; Dunne, J. F.; Trimby, S.; Richardson, D.

    2017-02-01

    A recurrent non-linear autoregressive with exogenous input (NARX) neural network is proposed, and a suitable fully-recurrent training methodology is adapted and tuned, for reconstructing cylinder pressure in multi-cylinder IC engines using measured crank kinematics. This type of indirect sensing is important for cost effective closed-loop combustion control and for On-Board Diagnostics. The challenge addressed is to accurately predict cylinder pressure traces within the cycle under generalisation conditions: i.e. using data not previously seen by the network during training. This involves direct construction and calibration of a suitable inverse crank dynamic model, which owing to singular behaviour at top-dead-centre (TDC), has proved difficult via physical model construction, calibration, and inversion. The NARX architecture is specialised and adapted to cylinder pressure reconstruction, using a fully-recurrent training methodology which is needed because the alternatives are too slow and unreliable for practical network training on production engines. The fully-recurrent Robust Adaptive Gradient Descent (RAGD) algorithm, is tuned initially using synthesised crank kinematics, and then tested on real engine data to assess the reconstruction capability. Real data is obtained from a 1.125 l, 3-cylinder, in-line, direct injection spark ignition (DISI) engine involving synchronised measurements of crank kinematics and cylinder pressure across a range of steady-state speed and load conditions. The paper shows that a RAGD-trained NARX network using both crank velocity and crank acceleration as input information, provides fast and robust training. By using the optimum epoch identified during RAGD training, acceptably accurate cylinder pressures, and especially accurate location-of-peak-pressure, can be reconstructed robustly under generalisation conditions, making it the most practical NARX configuration and recurrent training methodology for use on production engines.

  5. An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines.

    Science.gov (United States)

    Amozegar, M; Khorasani, K

    2016-04-01

    In this paper, a new approach for Fault Detection and Isolation (FDI) of gas turbine engines is proposed by developing an ensemble of dynamic neural network identifiers. For health monitoring of the gas turbine engine, its dynamics is first identified by constructing three separate or individual dynamic neural network architectures. Specifically, a dynamic multi-layer perceptron (MLP), a dynamic radial-basis function (RBF) neural network, and a dynamic support vector machine (SVM) are trained to individually identify and represent the gas turbine engine dynamics. Next, three ensemble-based techniques are developed to represent the gas turbine engine dynamics, namely, two heterogeneous ensemble models and one homogeneous ensemble model. It is first shown that all ensemble approaches do significantly improve the overall performance and accuracy of the developed system identification scheme when compared to each of the stand-alone solutions. The best selected stand-alone model (i.e., the dynamic RBF network) and the best selected ensemble architecture (i.e., the heterogeneous ensemble) in terms of their performances in achieving an accurate system identification are then selected for solving the FDI task. The required residual signals are generated by using both a single model-based solution and an ensemble-based solution under various gas turbine engine health conditions. Our extensive simulation studies demonstrate that the fault detection and isolation task achieved by using the residuals that are obtained from the dynamic ensemble scheme results in a significantly more accurate and reliable performance as illustrated through detailed quantitative confusion matrix analysis and comparative studies. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. Identification of a Typical CSTR Using Optimal Focused Time Lagged Recurrent Neural Network Model with Gamma Memory Filter

    Directory of Open Access Journals (Sweden)

    S. N. Naikwad

    2009-01-01

    Full Text Available A focused time lagged recurrent neural network (FTLR NN with gamma memory filter is designed to learn the subtle complex dynamics of a typical CSTR process. Continuous stirred tank reactor exhibits complex nonlinear operations where reaction is exothermic. It is noticed from literature review that process control of CSTR using neuro-fuzzy systems was attempted by many, but optimal neural network model for identification of CSTR process is not yet available. As CSTR process includes temporal relationship in the input-output mappings, time lagged recurrent neural network is particularly used for identification purpose. The standard back propagation algorithm with momentum term has been proposed in this model. The various parameters like number of processing elements, number of hidden layers, training and testing percentage, learning rule and transfer function in hidden and output layer are investigated on the basis of performance measures like MSE, NMSE, and correlation coefficient on testing data set. Finally effects of different norms are tested along with variation in gamma memory filter. It is demonstrated that dynamic NN model has a remarkable system identification capability for the problems considered in this paper. Thus FTLR NN with gamma memory filter can be used to learn underlying highly nonlinear dynamics of the system, which is a major contribution of this paper.

  7. Comparative analysis of module-based versus direct methods for reverse-engineering transcriptional regulatory networks

    Directory of Open Access Journals (Sweden)

    Joshi Anagha

    2009-05-01

    Full Text Available Abstract Background A myriad of methods to reverse-engineer transcriptional regulatory networks have been developed in recent years. Direct methods directly reconstruct a network of pairwise regulatory interactions while module-based methods predict a set of regulators for modules of coexpressed genes treated as a single unit. To date, there has been no systematic comparison of the relative strengths and weaknesses of both types of methods. Results We have compared a recently developed module-based algorithm, LeMoNe (Learning Module Networks, to a mutual information based direct algorithm, CLR (Context Likelihood of Relatedness, using benchmark expression data and databases of known transcriptional regulatory interactions for Escherichia coli and Saccharomyces cerevisiae. A global comparison using recall versus precision curves hides the topologically distinct nature of the inferred networks and is not informative about the specific subtasks for which each method is most suited. Analysis of the degree distributions and a regulator specific comparison show that CLR is 'regulator-centric', making true predictions for a higher number of regulators, while LeMoNe is 'target-centric', recovering a higher number of known targets for fewer regulators, with limited overlap in the predicted interactions between both methods. Detailed biological examples in E. coli and S. cerevisiae are used to illustrate these differences and to prove that each method is able to infer parts of the network where the other fails. Biological validation of the inferred networks cautions against over-interpreting recall and precision values computed using incomplete reference networks. Conclusion Our results indicate that module-based and direct methods retrieve largely distinct parts of the underlying transcriptional regulatory networks. The choice of algorithm should therefore be based on the particular biological problem of interest and not on global metrics which cannot be

  8. Design and Control of a Proof-of-Concept Active Jet Engine Intake Using Shape Memory Alloy Actuators

    Science.gov (United States)

    Song, Gangbing; Ma, Ning; Penney, Nicholas; Barr, Todd; Lee, Ho-Jun; Arnold, Steven M.

    2004-01-01

    The design and control of a novel proof-of-concept active jet engine intake using Nickel-Titanium (Ni-Ti or Nitinol) shape memory alloy (SMA) wire actuators is used to demonstrate the potential of an adaptive intake to improve the fuel efficiency of a jet engine. The Nitinol SMA material is selected for this research due to the material's ability to generate large strains of up to 5 percent for repeated operations, a high power-to-weight ratio, electrical resistive actuation, and easy fabrication into a variety of shapes. The proof-of-concept engine intake employs an overlapping leaf design arranged in a concentric configuration. Each leaf is mounted on a supporting bar that rotates upon actuation by SMA wires electrical resistive heating. Feedback control is enabled through the use of a laser range sensor to detect the movement of a leaf and determine the radius of the intake area. Due to the hysteresis behavior inherent in SMAs, a nonlinear robust controller is used to direct the SMA wire actuation. The controller design utilizes the sliding-mode approach to compensate for the nonlinearities associated with the SMA actuator. Feedback control experiments conducted on a fabricated proof-of-concept model have demonstrated the capability to precisely control the intake area and achieve up to a 25 percent reduction in intake area. The experiments demonstrate the feasibility of engine intake area control using the proposed design.

  9. Reliability analysis of C-130 turboprop engine components using artificial neural network

    Science.gov (United States)

    Qattan, Nizar A.

    In this study, we predict the failure rate of Lockheed C-130 Engine Turbine. More than thirty years of local operational field data were used for failure rate prediction and validation. The Weibull regression model and the Artificial Neural Network model including (feed-forward back-propagation, radial basis neural network, and multilayer perceptron neural network model); will be utilized to perform this study. For this purpose, the thesis will be divided into five major parts. First part deals with Weibull regression model to predict the turbine general failure rate, and the rate of failures that require overhaul maintenance. The second part will cover the Artificial Neural Network (ANN) model utilizing the feed-forward back-propagation algorithm as a learning rule. The MATLAB package will be used in order to build and design a code to simulate the given data, the inputs to the neural network are the independent variables, the output is the general failure rate of the turbine, and the failures which required overhaul maintenance. In the third part we predict the general failure rate of the turbine and the failures which require overhaul maintenance, using radial basis neural network model on MATLAB tool box. In the fourth part we compare the predictions of the feed-forward back-propagation model, with that of Weibull regression model, and radial basis neural network model. The results show that the failure rate predicted by the feed-forward back-propagation artificial neural network model is closer in agreement with radial basis neural network model compared with the actual field-data, than the failure rate predicted by the Weibull model. By the end of the study, we forecast the general failure rate of the Lockheed C-130 Engine Turbine, the failures which required overhaul maintenance and six categorical failures using multilayer perceptron neural network (MLP) model on DTREG commercial software. The results also give an insight into the reliability of the engine

  10. NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 39: The role of computer networks in aerospace engineering

    Science.gov (United States)

    Bishop, Ann P.; Pinelli, Thomas E.

    1994-01-01

    This paper presents selected results from an empirical investigation into the use of computer networks in aerospace engineering. Such networks allow aerospace engineers to communicate with people and access remote resources through electronic mail, file transfer, and remote log-in. The study drew its subjects from private sector, government and academic organizations in the U.S. aerospace industry. Data presented here were gathered in a mail survey, conducted in Spring 1993, that was distributed to aerospace engineers performing a wide variety of jobs. Results from the mail survey provide a snapshot of the current use of computer networks in the aerospace industry, suggest factors associated with the use of networks, and identify perceived impacts of networks on aerospace engineering work and communication.

  11. Using GMDH Neural Networks to Model the Power and Torque of a Stirling Engine

    Directory of Open Access Journals (Sweden)

    Mohammad Hossein Ahmadi

    2015-02-01

    Full Text Available Different variables affect the performance of the Stirling engine and are considered in optimization and designing activities. Among these factors, torque and power have the greatest effect on the robustness of the Stirling engine, so they need to be determined with low uncertainty and high precision. In this article, the distribution of torque and power are determined using experimental data. Specifically, a novel polynomial approach is proposed to specify torque and power, on the basis of previous experimental work. This research addresses the question of whether GMDH (group method of data handling-type neural networks can be utilized to predict the torque and power based on determined parameters.

  12. Teaching strategies applied to teaching computer networks in Engineering in Telecommunications and Electronics

    Directory of Open Access Journals (Sweden)

    Elio Manuel Castañeda-González

    2016-07-01

    Full Text Available Because of the large impact that today computer networks, their study in related fields such as Telecommunications Engineering and Electronics is presented to the student with great appeal. However, by digging in content, lacking a strong practical component, you can make this interest decreases considerably. This paper proposes the use of teaching strategies and analogies, media and interactive applications that enhance the teaching of discipline networks and encourage their study. It is part of an analysis of how the teaching of the discipline process is performed and then a description of each of these strategies is done with their respective contribution to student learning.

  13. Increasing galactose consumption by Saccharomyces cerevisiae through metabolic engineering of the GAL gene regulatory network

    DEFF Research Database (Denmark)

    Østergaard, Simon; Olsson, Lisbeth; Johnston, M.

    2000-01-01

    Increasing the flux through central carbon metabolism is difficult because of rigidity in regulatory structures, at both the genetic and the enzymatic levels. Here we describe metabolic engineering of a regulatory network to obtain a balanced increase in the activity of all the enzymes...... in the pathway, and ultimately, increasing metabolic flux through the pathway of interest, By manipulating the GAL gene regulatory network of Saccharomyces cerevisiae, which is a tightly regulated system, we produced prototroph mutant strains, which increased the flux through the galactose utilization pathway...

  14. Network structure dependence on unconstrained isothermal-recovery processes for shape-memory thiol-epoxy "click" systems

    Science.gov (United States)

    Belmonte, Alberto; Fernández-Francos, Xavier; De la Flor, Silvia; Serra, Àngels

    2017-05-01

    The shape-memory response ( SMR) of "click" thiol-epoxy polymers produced using latent catalysts, with different network structure and thermo-mechanical properties, was tested on unconstrained shape-recovery processes under isothermal conditions. Experiments at several programming temperatures (T_{prog}) and isothermal-recovery temperatures (T_{iso}) were carried out, and the shape-memory stability was analyzed through various consecutive shape-memory cycles. The temperature profile during the isothermal-recovery experiments was monitored, and it showed that the shape-recovery process takes place while the sample is becoming thermally stable and before stable isothermal temperature conditions are eventually reached. The shape-recovery process takes place in two different stages regardless of T_{iso}: a slow initial stage until the process is triggered at a temperature strongly related with the beginning of network relaxation, followed by the typical exponential decay of the relaxation processes until completion at a temperature below or very close to Tg. The shape-recovery process is slower in materials with more densely crosslinked and hindered network structures. The shape-recovery time (t_{sr}) is significantly reduced when the isothermal-recovery temperature T_{iso} increases from below to above Tg because the network relaxation dynamics accelerates. However, the temperature range from the beginning to the end of the recovery process is hardly affected by T_{iso}; at higher T_{iso} it is only slightly shifted to higher temperatures. These results suggest that the shape-recovery process can be controlled by changing the network structure and working at T_{iso} < Tg to maximize the effect of the structure and/or by increasing T_{iso} to minimize the effect but increasing the shape-recovery rate.

  15. Neural network controller development and implementation for spark ignition engines with high EGR levels.

    Science.gov (United States)

    Vance, Jonathan Blake; Singh, Atmika; Kaul, Brian C; Jagannathan, Sarangapani; Drallmeier, James A

    2007-07-01

    Past research has shown substantial reductions in the oxides of nitrogen (NOx) concentrations by using 10%-25% exhaust gas recirculation (EGR) in spark ignition (SI) engines (see Dudek and Sain, 1989). However, under high EGR levels, the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance preventing commercial engines to operate with high EGR levels. A neural network (NN)-based output feedback controller is developed to reduce cyclic variation in the heat release under high levels of EGR even when the engine dynamics are unknown by using fuel as the control input. A separate control loop was designed for controlling EGR levels. The stability analysis of the closed-loop system is given and the boundedness of the control input is demonstrated by relaxing separation principle, persistency of excitation condition, certainty equivalence principle, and linear in the unknown parameter assumptions. Online training is used for the adaptive NN and no offline training phase is needed. This online learning feature and model-free approach is used to demonstrate the applicability of the controller on a different engine with minimal effort. Simulation results demonstrate that the cyclic dispersion is reduced significantly using the proposed controller when implemented on an engine model that has been validated experimentally. For a single cylinder research engine fitted with a modern four-valve head (Ricardo engine), experimental results at 15% EGR indicate that cyclic dispersion was reduced 33% by the controller, an improvement of fuel efficiency by 2%, and a 90% drop in NOx from stoichiometric operation without EGR was observed. Moreover, unburned hydrocarbons (uHC) drop by 6% due to NN control as compared to the uncontrolled scenario due to the drop in cyclic dispersion. Similar performance was observed with the controller on a different engine.

  16. Relationship between working-memory network function and substance use: a 3-year longitudinal fMRI study in heavy cannabis users and controls.

    Science.gov (United States)

    Cousijn, Janna; Vingerhoets, Wilhelmina A M; Koenders, Laura; de Haan, Lieuwe; van den Brink, Wim; Wiers, Reinout W; Goudriaan, Anna E

    2014-03-01

    Deficient executive functions play an important role in the development of addiction. Working-memory may therefore be a powerful predictor of the course of drug use, but chronic substance use may also impair working-memory. The aim of this 3-year longitudinal neuro-imaging study was to investigate the relationship between substance use (e.g. alcohol, cannabis, nicotine, illegal psychotropic drugs) and working-memory network function over time in heavy cannabis users and controls. Forty-nine participants performed an n-back working-memory task at baseline and at 3-year follow-up. At follow-up, there were 22 current heavy cannabis users, 4 abstinent heavy cannabis users and 23 non-cannabis-using controls. Tensor-independent component analysis (Tensor-ICA) was used to investigate individual differences in working-memory network functionality over time. Within the group of cannabis users, cannabis-related problems remained stable, whereas alcohol-related problems, nicotine dependence and illegal psychotropic substance use increased over time. At both measurements, behavioral performance and network functionality during the n-back task did not differ between heavy cannabis users and controls. Although n-back accuracy improved, working-memory network function remained stable over time. Within the group of cannabis users, working-memory network functionality was not associated with substance use. These results suggest that sustained moderate to heavy levels of cannabis, nicotine, alcohol and illegal psychotropic substance use do not change working-memory network functionality. Moreover, baseline network functionality did not predict cannabis use and related problems three years later, warranting longitudinal studies in more chronic or dependent cannabis users. © 2013 Society for the Study of Addiction.

  17. Extending distributed shared memory for the cell broadband engine to a channel model

    DEFF Research Database (Denmark)

    Skovhede, Kenneth; Larsen, Morten Nørgaard; Vinter, Brian

    2010-01-01

    at the price of a quite complex programming model. In this paper we present an easy-to-use, CSP-like, communication method, which enables transfers of shared memory objects. The channel based communication method can significantly reduce the complexity of massively parallel programs. By implementing a few...

  18. Phonological memory in sign language relies on the visuomotor neural system outside the left hemisphere language network.

    Science.gov (United States)

    Kanazawa, Yuji; Nakamura, Kimihiro; Ishii, Toru; Aso, Toshihiko; Yamazaki, Hiroshi; Omori, Koichi

    2017-01-01

    Sign language is an essential medium for everyday social interaction for deaf people and plays a critical role in verbal learning. In particular, language development in those people should heavily rely on the verbal short-term memory (STM) via sign language. Most previous studies compared neural activations during signed language processing in deaf signers and those during spoken language processing in hearing speakers. For sign language users, it thus remains unclear how visuospatial inputs are converted into the verbal STM operating in the left-hemisphere language network. Using functional magnetic resonance imaging, the present study investigated neural activation while bilinguals of spoken and signed language were engaged in a sequence memory span task. On each trial, participants viewed a nonsense syllable sequence presented either as written letters or as fingerspelling (4-7 syllables in length) and then held the syllable sequence for 12 s. Behavioral analysis revealed that participants relied on phonological memory while holding verbal information regardless of the type of input modality. At the neural level, this maintenance stage broadly activated the left-hemisphere language network, including the inferior frontal gyrus, supplementary motor area, superior temporal gyrus and inferior parietal lobule, for both letter and fingerspelling conditions. Interestingly, while most participants reported that they relied on phonological memory during maintenance, direct comparisons between letters and fingers revealed strikingly different patterns of neural activation during the same period. Namely, the effortful maintenance of fingerspelling inputs relative to letter inputs activated the left superior parietal lobule and dorsal premotor area, i.e., brain regions known to play a role in visuomotor analysis of hand/arm movements. These findings suggest that the dorsal visuomotor neural system subserves verbal learning via sign language by relaying gestural inputs to

  19. Diagnosing of car engine fuel injectors damage using DWT analysis and PNN neural networks

    Directory of Open Access Journals (Sweden)

    Piotr CZECH

    2013-01-01

    Full Text Available In many research centers all over the world nowadays works are being carried out aimed at compiling method for diagnosis machines technical condition. Special meaning have non-invasive methods including methods using vibroacoustic phenomena. In this article is proposed using DWT analysis and energy or entropy, which are a base for diagnostic system of fuel injectors damage in car combustion engine. There were conducted researches aimed at building of diagnostic system using PNN neural networks.

  20. Basic Principles of Industrial Electric Power Network Computer Aided Design and Engineering

    Directory of Open Access Journals (Sweden)

    M. I. Fursanov

    2012-01-01

    Full Text Available A conceptual model for a computer aided design and engineering system has been developed in the paper. The paper presents basic automation process principles including a graphical representation   network and calculation results, convenient user interface, automatic mode calculation, selection of transformer rated power and cross-section area of wires. The developed algorithm and program make it possible to save time and improve quality of project implementation.

  1. The hippocampus: A central node in a large-scale brain network for memory.

    Science.gov (United States)

    Huijgen, J; Samson, S

    2015-03-01

    The medial temporal lobe is a key region in the formation and consolidation of conscious or declarative memories. In this review, we will first consider the role of the hippocampus and its surrounding medial temporal lobe structures in recognition memory from a historical perspective. According to the dual process model of recognition memory, recognition judgments can be based on the recollection of details about previous presented stimuli or on the feeling of familiarity. Studies in humans, primates and rodents suggest that the hippocampus, the parahippocampal cortex and the perirhinal cortex play different roles in recollection and familiarity. Then, we will describe the role of the hippocampus and neocortex in memory consolidation: a process in which novel memories become integrated into long-term memory. After presenting possible mechanisms underlying sleep-dependent declarative memory consolidation, we will discuss the phenomenon of accelerated long-term forgetting. This type of memory deficit is often observed in epileptic patients with a hippocampal lesion, and provides a novel opportunity to investigate post-encoding and memory consolidation processes. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

  2. Dopamine modulation of GABAergic function enables network stability and input selectivity for sustaining working memory in a computational model of the prefrontal cortex.

    Science.gov (United States)

    Lew, Sergio E; Tseng, Kuei Y

    2014-12-01

    Dopamine modulation of GABAergic transmission in the prefrontal cortex (PFC) is thought to be critical for sustaining cognitive processes such as working memory and decision-making. Here, we developed a neurocomputational model of the PFC that includes physiological features of the facilitatory action of dopamine on fast-spiking interneurons to assess how a GABAergic dysregulation impacts on the prefrontal network stability and working memory. We found that a particular non-linear relationship between dopamine transmission and GABA function is required to enable input selectivity in the PFC for the formation and retention of working memory. Either degradation of the dopamine signal or the GABAergic function is sufficient to elicit hyperexcitability in pyramidal neurons and working memory impairments. The simulations also revealed an inverted U-shape relationship between working memory and dopamine, a function that is maintained even at high levels of GABA degradation. In fact, the working memory deficits resulting from reduced GABAergic transmission can be rescued by increasing dopamine tone and vice versa. We also examined the role of this dopamine-GABA interaction for the termination of working memory and found that the extent of GABAergic excitation needed to reset the PFC network begins to occur when the activity of fast-spiking interneurons surpasses 40 Hz. Together, these results indicate that the capability of the PFC to sustain working memory and network stability depends on a robust interplay of compensatory mechanisms between dopamine tone and the activity of local GABAergic interneurons.

  3. Automated and comprehensive link engineering supporting branched, ring, and mesh network topologies

    Science.gov (United States)

    Farina, J.; Khomchenko, D.; Yevseyenko, D.; Meester, J.; Richter, A.

    2016-02-01

    Link design, while relatively easy in the past, can become quite cumbersome with complex channel plans and equipment configurations. The task of designing optical transport systems and selecting equipment is often performed by an applications or sales engineer using simple tools, such as custom Excel spreadsheets. Eventually, every individual has their own version of the spreadsheet as well as their own methodology for building the network. This approach becomes unmanageable very quickly and leads to mistakes, bending of the engineering rules and installations that do not perform as expected. We demonstrate a comprehensive planning environment, which offers an efficient approach to unify, control and expedite the design process by controlling libraries of equipment and engineering methodologies, automating the process and providing the analysis tools necessary to predict system performance throughout the system and for all channels. In addition to the placement of EDFAs and DCEs, performance analysis metrics are provided at every step of the way. Metrics that can be tracked include power, CD and OSNR, SPM, XPM, FWM and SBS. Automated routine steps assist in design aspects such as equalization, padding and gain setting for EDFAs, the placement of ROADMs and transceivers, and creating regeneration points. DWDM networks consisting of a large number of nodes and repeater huts, interconnected in linear, branched, mesh and ring network topologies, can be designed much faster when compared with conventional design methods. Using flexible templates for all major optical components, our technology-agnostic planning approach supports the constant advances in optical communications.

  4. 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

  5. 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).

  6. Using Web 2.0 Techniques in NASA's Ares Engineering Operations Network (AEON) Environment - First Impressions

    Science.gov (United States)

    Scott, David W.

    2010-01-01

    The Mission Operations Laboratory (MOL) at Marshall Space Flight Center (MSFC) is responsible for Engineering Support capability for NASA s Ares rocket development and operations. In pursuit of this, MOL is building the Ares Engineering and Operations Network (AEON), a web-based portal to support and simplify two critical activities: Access and analyze Ares manufacturing, test, and flight performance data, with access to Shuttle data for comparison Establish and maintain collaborative communities within the Ares teams/subteams and with other projects, e.g., Space Shuttle, International Space Station (ISS). AEON seeks to provide a seamless interface to a) locally developed engineering applications and b) a Commercial-Off-The-Shelf (COTS) collaborative environment that includes Web 2.0 capabilities, e.g., blogging, wikis, and social networking. This paper discusses how Web 2.0 might be applied to the typically conservative engineering support arena, based on feedback from Integration, Verification, and Validation (IV&V) testing and on searching for their use in similar environments.

  7. IDI diesel engine performance and exhaust emission analysis using biodiesel with an artificial neural network (ANN

    Directory of Open Access Journals (Sweden)

    K. Prasada Rao

    2017-09-01

    Full Text Available Biodiesel is receiving increasing attention each passing day because of its fuel properties and compatibility. This study investigates the performance and emission characteristics of single cylinder four stroke indirect diesel injection (IDI engine fueled with Rice Bran Methyl Ester (RBME with Isopropanol additive. The investigation is done through a combination of experimental data analysis and artificial neural network (ANN modeling. The study used IDI engine experimental data to evaluate nine engine performance and emission parameters including Exhaust Gas Temperature (E.G.T, Brake Specific Fuel Consumption (BSFC, Brake Thermal Efficiency (B.The and various emissions like Hydrocarbons (HC, Carbon monoxide (CO, Carbon dioxide (CO2, Oxygen (O2, Nitrogen oxides (NOX and smoke. For the ANN modeling standard back propagation algorithm was found to be the optimum choice for training the model. A multi-layer perception (MLP network was used for non-linear mapping between the input and output parameters. It was found that ANN was able to predict the engine performance and exhaust emissions with a correlation coefficient of 0.995, 0.980, 0.999, 0.985, 0.999, 0.999, 0.980, 0.999, and 0.999 for E.G.T, BSFC, B.The, HC, O2, CO2, CO, NOX, smoke respectively.

  8. From kinetic-structure analysis to engineering crystalline fiber networks in soft materials.

    Science.gov (United States)

    Wang, Rong-Yao; Wang, Peng; Li, Jing-Liang; Yuan, Bing; Liu, Yu; Li, Li; Liu, Xiang-Yang

    2013-03-07

    Understanding the role of kinetics in fiber network microstructure formation is of considerable importance in engineering gel materials to achieve their optimized performances/functionalities. In this work, we present a new approach for kinetic-structure analysis for fibrous gel materials. In this method, kinetic data is acquired using a rheology technique and is analyzed in terms of an extended Dickinson model in which the scaling behaviors of dynamic rheological properties in the gelation process are taken into account. It enables us to extract the structural parameter, i.e. the fractal dimension, of a fibrous gel from the dynamic rheological measurement of the gelation process, and to establish the kinetic-structure relationship suitable for both dilute and concentrated gelling systems. In comparison to the fractal analysis method reported in a previous study, our method is advantageous due to its general validity for a wide range of fractal structures of fibrous gels, from a highly compact network of the spherulitic domains to an open fibrous network structure. With such a kinetic-structure analysis, we can gain a quantitative understanding of the role of kinetic control in engineering the microstructure of the fiber network in gel materials.

  9. Women in Engineering Program Advocates Network (WEPAN): Evaluation of the seventh annual conference

    Energy Technology Data Exchange (ETDEWEB)

    Brainard, S.G.

    1996-08-01

    The primary goals of the 1996 WEPAN Conference were to: (1) Conduct technical and programmatic seminars for institutions desiring to initiate, replicate, or expand women in engineering programs; (2) Provide assistance in fundraising and grant writing; (3) Profile women in engineering programs of excellence; (4) Sponsor inspiring, knowledgeable and motivational keynote speakers; and, (5) Offer a series of workshops focused on topics such as: establishing partnerships with industry, current research findings, retention strategies, issues affecting special populations, and early intervention techniques. In an effort to provide greater access for women to engineering careers, women in engineering program directors at Purdue University, Stevens Institute of Technology and the University of Washington joined together in 1990 to establish WEPAN, a national network of individuals interested in the recruitment, admission, retention, and graduation of women engineering students. This is the seventh year of operation. Success of this effort has been reflected in numerous ways: increased membership in the organization; increased number of women in engineering programs; increased number of women graduating in engineering; and grants from the U.S. Department of Energy, the National Science Foundation, the Alfred P. Sloan Foundation, the AT&T Foundation, and many other corporations to carry out the goals of WEPAN. The Seventh Annual Women in Engineering Conference entitled, Capitalizing on Today`s Challenges, was held in Denver, Colorado on June 1-4, 1996 at the Hyatt Regency. The conference brought together representatives from academia, government, and industry and examined current issues and initiatives for women in technology, science, and education. Building on the successes of the previous conferences, the seventh conference offered a new variety of speakers and topics.

  10. Architecture and System Engineering Development Study of Space-Based Satellite Networks for NASA Missions

    Science.gov (United States)

    Ivancic, William D.

    2003-01-01

    Traditional NASA missions, both near Earth and deep space, have been stovepipe in nature and point-to-point in architecture. Recently, NASA and others have conceptualized missions that required space-based networking. The notion of networks in space is a drastic shift in thinking and requires entirely new architectures, radio systems (antennas, modems, and media access), and possibly even new protocols. A full system engineering approach for some key mission architectures will occur that considers issues such as the science being performed, stationkeeping, antenna size, contact time, data rates, radio-link power requirements, media access techniques, and appropriate networking and transport protocols. This report highlights preliminary architecture concepts and key technologies that will be investigated.

  11. A large-scale neural network model of the influence of neuromodulatory levels on working memory and behavior.

    Science.gov (United States)

    Avery, Michael C; Dutt, Nikil; Krichmar, Jeffrey L

    2013-01-01

    The dorsolateral prefrontal cortex (dlPFC), which is regarded as the primary site for visuospatial working memory in the brain, is significantly modulated by dopamine (DA) and norepinephrine (NE). DA and NE originate in the ventral tegmental area (VTA) and locus coeruleus (LC), respectively, and have been shown to have an "inverted-U" dose-response profile in dlPFC, where the level of arousal and decision-making performance is a function of DA and NE concentrations. Moreover, there appears to be a sweet spot, in terms of the level of DA and NE activation, which allows for optimal working memory and behavioral performance. When either DA or NE is too high, input to the PFC is essentially blocked. When either DA or NE is too low, PFC network dynamics become noisy and activity levels diminish. Mechanisms for how this is occurring have been suggested, however, they have not been tested in a large-scale model with neurobiologically plausible network dynamics. Also, DA and NE levels have not been simultaneously manipulated experimentally, which is not realistic in vivo due to strong bi-directional connections between the VTA and LC. To address these issues, we built a spiking neural network model that includes D1, α2A, and α1 receptors. The model was able to match the inverted-U profiles that have been shown experimentally for differing levels of DA and NE. Furthermore, we were able to make predictions about what working memory and behavioral deficits may occur during simultaneous manipulation of DA and NE outside of their optimal levels. Specifically, when DA levels were low and NE levels were high, cues could not be held in working memory due to increased noise. On the other hand, when DA levels were high and NE levels were low, incorrect decisions were made due to weak overall network activity. We also show that lateral inhibition in working memory may play a more important role in increasing signal-to-noise ratio than increasing recurrent excitatory input.

  12. Remapping of memory encoding and retrieval networks: insights from neuroimaging in primates.

    Science.gov (United States)

    Miyamoto, Kentaro; Osada, Takahiro; Adachi, Yusuke

    2014-12-15

    Advancements in neuroimaging techniques have allowed for the investigation of the neural correlates of memory functions in the whole human brain. Thus, the involvement of various cortical regions, including the medial temporal lobe (MTL) and posterior parietal cortex (PPC), has been repeatedly reported in the human memory processes of encoding and retrieval. However, the functional roles of these sites could be more fully characterized utilizing nonhuman primate models, which afford the potential for well-controlled, finer-scale experimental procedures that are inapplicable to humans, including electrophysiology, histology, genetics, and lesion approaches. Yet, the presence and localization of the functional counterparts of these human memory-related sites in the macaque monkey MTL or PPC were previously unknown. Therefore, to bridge the inter-species gap, experiments were required in monkeys using functional magnetic resonance imaging (fMRI), the same methodology adopted in human studies. Here, we briefly review the history of experimentation on memory systems using a nonhuman primate model and our recent fMRI studies examining memory processing in monkeys performing recognition memory tasks. We will discuss the memory systems common to monkeys and humans and future directions of finer cell-level characterization of memory-related processes using electrophysiological recording and genetic manipulation approaches. Copyright © 2014 Elsevier B.V. All rights reserved.

  13. Perceptual Filtering in L2 Lexical Memory: A Neural Network Approach to Second Language Acquisition

    Science.gov (United States)

    Nelson, Robert

    2012-01-01

    A number of asymmetries in lexical memory emerge when monolinguals and early bilinguals are compared to (relatively) late second language (L2) learners. Their study promises to provide insight into the internal processes that both support and ultimately limit L2 learner achievement. Generally, theory building in L2 and bilingual lexical memory has…

  14. Spatial and Temporal Episodic Memory Retrieval Recruit Dissociable Functional Networks in the Human Brain

    Science.gov (United States)

    Ekstrom, Arne D.; Bookheimer, Susan Y.

    2007-01-01

    Imaging, electrophysiological studies, and lesion work have shown that the medial temporal lobe (MTL) is important for episodic memory; however, it is unclear whether different MTL regions support the spatial, temporal, and item elements of episodic memory. In this study we used fMRI to examine retrieval performance emphasizing different aspects…

  15. Resilient Amorphous Networks Prepared by Photo-Crosslinking High-Molecular-Weight D,L-Lactide and Trimethylene Carbonate Macromers : Mechanical Properties and Shape-Memory Behavior

    NARCIS (Netherlands)

    Sharifi, Shahriar; Grijpma, Dirk W.

    2012-01-01

    Tough networks are prepared by photo-crosslinking high-molecular-weight DLLA and TMC macromers. These amorphous networks exhibit tunable thermal and mechanical properties and have excellent shape-memory features. Variation of the monomer ratio allows adjustment of Tg between approximately -13 and

  16. Resilient Amorphous Networks Prepared by Photo-Crosslinking High-Molecular-Weight D,L-Lactide and Trimethylene Carbonate Macromers: Mechanical Properties and Shape-Memory Behavior

    NARCIS (Netherlands)

    Sharifi, Shahriar; Grijpma, Dirk W.

    2012-01-01

    Tough networks are prepared by photo-crosslinking high-molecular-weight DLLA and TMC macromers. These amorphous networks exhibit tunable thermal and mechanical properties and have excellent shape-memory features. Variation of the monomer ratio allows adjustment of Tg between approximately −13 and

  17. Pattern classification of working memory networks reveals differential effects of methylphenidate, atomoxetine, and placebo in healthy volunteers.

    Science.gov (United States)

    Marquand, Andre F; De Simoni, Sara; O'Daly, Owen G; Williams, Steven C R; Mourão-Miranda, Janaina; Mehta, Mitul A

    2011-05-01

    Stimulant and non-stimulant drugs can reduce symptoms of attention deficit/hyperactivity disorder (ADHD). The stimulant drug methylphenidate (MPH) and the non-stimulant drug atomoxetine (ATX) are both widely used for ADHD treatment, but their differential effects on human brain function remain unclear. We combined event-related fMRI with multivariate pattern recognition to characterize the effects of MPH and ATX in healthy volunteers performing a rewarded working memory (WM) task. The effects of MPH and ATX on WM were strongly dependent on their behavioral context. During non-rewarded trials, only MPH could be discriminated from placebo (PLC), with MPH producing a similar activation pattern to reward. During rewarded trials both drugs produced the opposite effect to reward, that is, attenuating WM networks and enhancing task-related deactivations (TRDs) in regions consistent with the default mode network (DMN). The drugs could be directly discriminated during the delay component of rewarded trials: MPH produced greater activity in WM networks and ATX produced greater activity in the DMN. Our data provide evidence that: (1) MPH and ATX have prominent effects during rewarded WM in task-activated and -deactivated networks; (2) during the delay component of rewarded trials, MPH and ATX have opposing effects on activated and deactivated networks: MPH enhances TRDs more than ATX, whereas ATX attenuates WM networks more than MPH; and (3) MPH mimics reward during encoding. Thus, interactions between drug effects and motivational state are crucial in defining the effects of MPH and ATX.

  18. Titanium-nickel shape memory alloy foams for bone tissue engineering.

    Science.gov (United States)

    Xiong, J Y; Li, Y C; Wang, X J; Hodgson, P D; Wen, C E

    2008-07-01

    Titanium-nickel (TiNi) shape memory alloy (SMA) foams with an open-cell porous structure were fabricated by space-holder sintering process and characterized by scanning electron microscopy (SEM) and X-ray diffraction (XRD) analysis. The mechanical properties and shape memory properties of the TiNi foam samples were investigated using compressive test. Results indicate that the plateau stresses and elastic moduli of the foams under compression decrease with the increase of their porosities. The plateau stresses and elastic moduli are measured to be from 1.9 to 38.3 MPa and from 30 to 860 MPa for the TiNi foam samples with porosities ranged from 71% to 87%, respectively. The mechanical properties of the TiNi alloy foams can be tailored to match those of bone. The TiNi alloy foams exhibit shape memory effect (SME), and it is found that the recoverable strain due to SME decreases with the increase of foam porosity.

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

    Directory of Open Access Journals (Sweden)

    Qiushi eZhang

    2015-09-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

  1. Insights gained from the reverse engineering of gene networks in keloid fibroblasts

    Directory of Open Access Journals (Sweden)

    Phan Toan

    2011-05-01

    Full Text Available Abstract Background Keloids are protrusive claw-like scars that have a propensity to recur even after surgery, and its molecular etiology remains elusive. The goal of reverse engineering is to infer gene networks from observational data, thus providing insight into the inner workings of a cell. However, most attempts at modeling biological networks have been done using simulated data. This study aims to highlight some of the issues involved in working with experimental data, and at the same time gain some insights into the transcriptional regulatory mechanism present in keloid fibroblasts. Methods Microarray data from our previous study was combined with microarray data obtained from the literature as well as new microarray data generated by our group. For the physical approach, we used the fREDUCE algorithm for correlating expression values to binding motifs. For the influence approach, we compared the Bayesian algorithm BANJO with the information theoretic method ARACNE in terms of performance in recovering known influence networks obtained from the KEGG database. In addition, we also compared the performance of different normalization methods as well as different types of gene networks. Results Using the physical approach, we found consensus sequences that were active in the keloid condition, as well as some sequences that were responsive to steroids, a commonly used treatment for keloids. From the influence approach, we found that BANJO was better at recovering the gene networks compared to ARACNE and that transcriptional networks were better suited for network recovery compared to cytokine-receptor interaction networks and intracellular signaling networks. We also found that the NFKB transcriptional network that was inferred from normal fibroblast data was more accurate compared to that inferred from keloid data, suggesting a more robust network in the keloid condition. Conclusions Consensus sequences that were found from this study are

  2. Effects of aging on functional connectivity of the amygdala for subsequent memory of negative pictures: a network analysis of functional magnetic resonance imaging data.

    Science.gov (United States)

    St Jacques, Peggy L; Dolcos, Florin; Cabeza, Roberto

    2009-01-01

    Aging is associated with preserved enhancement of emotional memory, as well as with age-related reductions in memory for negative stimuli, but the neural networks underlying such alterations are not clear. We used a subsequent-memory paradigm to identify brain activity predicting enhanced emotional memory in young and older adults. Activity in the amygdala predicted enhanced emotional memory, with subsequent-memory activity greater for negative stimuli than for neutral stimuli, across age groups, a finding consistent with an overall enhancement of emotional memory. However, older adults recruited greater activity in anterior regions and less activity in posterior regions in general for negative stimuli that were subsequently remembered. Functional connectivity of the amygdala with the rest of the brain was consistent with age-related reductions in memory for negative stimuli: Older adults showed decreased functional connectivity between the amygdala and the hippocampus, but increased functional connectivity between the amygdala and dorsolateral prefrontal cortices. These findings suggest that age-related differences in the enhancement of emotional memory might reflect decreased connectivity between the amygdala and typical subsequent-memory regions, as well as the engagement of regulatory processes that inhibit emotional responses.

  3. Development of a computational model on the neural activity patterns of a visual working memory in a hierarchical feedforward Network

    Science.gov (United States)

    An, Soyoung; Choi, Woochul; Paik, Se-Bum

    2015-11-01

    Understanding the mechanism of information processing in the human brain remains a unique challenge because the nonlinear interactions between the neurons in the network are extremely complex and because controlling every relevant parameter during an experiment is difficult. Therefore, a simulation using simplified computational models may be an effective approach. In the present study, we developed a general model of neural networks that can simulate nonlinear activity patterns in the hierarchical structure of a neural network system. To test our model, we first examined whether our simulation could match the previously-observed nonlinear features of neural activity patterns. Next, we performed a psychophysics experiment for a simple visual working memory task to evaluate whether the model could predict the performance of human subjects. Our studies show that the model is capable of reproducing the relationship between memory load and performance and may contribute, in part, to our understanding of how the structure of neural circuits can determine the nonlinear neural activity patterns in the human brain.

  4. Towards 4D Printed Scaffolds for Tissue Engineering : Exploiting 3D Shape Memory Polymers to Deliver Time-Controlled Stimulus on Cultured Cells

    NARCIS (Netherlands)

    Hendrikson, Wilhelmus J.; Rouwkema, Jeroen; Clementi, Federico; van Blitterswijk, Clemens; Farè, Silvia; Moroni, Lorenzo

    2017-01-01

    Tissue engineering needs innovative solutions to better fit the requirements of a minimally invasive approach, providing at the same time instructive cues to cells. The use of shape memory polyurethane has been investigated by producing 4D scaffolds via additive manufacturing technology. Scaffolds

  5. A Measure of Systems Engineering Effectiveness in Government Acquisition of Complex Information Systems: A Bayesian Belief Network-Based Approach

    Science.gov (United States)

    Doskey, Steven Craig

    2014-01-01

    This research presents an innovative means of gauging Systems Engineering effectiveness through a Systems Engineering Relative Effectiveness Index (SE REI) model. The SE REI model uses a Bayesian Belief Network to map causal relationships in government acquisitions of Complex Information Systems (CIS), enabling practitioners to identify and…

  6. Promoting wired links in wireless mesh networks: an efficient engineering solution.

    Science.gov (United States)

    Barekatain, Behrang; Raahemifar, Kaamran; Ariza Quintana, Alfonso; Triviño Cabrera, Alicia

    2015-01-01

    Wireless Mesh Networks (WMNs) cannot completely guarantee good performance of traffic sources such as video streaming. To improve the network performance, this study proposes an efficient engineering solution named Wireless-to-Ethernet-Mesh-Portal-Passageway (WEMPP) that allows effective use of wired communication in WMNs. WEMPP permits transmitting data through wired and stable paths even when the destination is in the same network as the source (Intra-traffic). Tested with four popular routing protocols (Optimized Link State Routing or OLSR as a proactive protocol, Dynamic MANET On-demand or DYMO as a reactive protocol, DYMO with spanning tree ability and HWMP), WEMPP considerably decreases the end-to-end delay, jitter, contentions and interferences on nodes, even when the network size or density varies. WEMPP is also cost-effective and increases the network throughput. Moreover, in contrast to solutions proposed by previous studies, WEMPP is easily implemented by modifying the firmware of the actual Ethernet hardware without altering the routing protocols and/or the functionality of the IP/MAC/Upper layers. In fact, there is no need for modifying the functionalities of other mesh components in order to work with WEMPPs. The results of this study show that WEMPP significantly increases the performance of all routing protocols, thus leading to better video quality on nodes.

  7. Promoting wired links in wireless mesh networks: an efficient engineering solution.

    Directory of Open Access Journals (Sweden)

    Behrang Barekatain

    Full Text Available Wireless Mesh Networks (WMNs cannot completely guarantee good performance of traffic sources such as video streaming. To improve the network performance, this study proposes an efficient engineering solution named Wireless-to-Ethernet-Mesh-Portal-Passageway (WEMPP that allows effective use of wired communication in WMNs. WEMPP permits transmitting data through wired and stable paths even when the destination is in the same network as the source (Intra-traffic. Tested with four popular routing protocols (Optimized Link State Routing or OLSR as a proactive protocol, Dynamic MANET On-demand or DYMO as a reactive protocol, DYMO with spanning tree ability and HWMP, WEMPP considerably decreases the end-to-end delay, jitter, contentions and interferences on nodes, even when the network size or density varies. WEMPP is also cost-effective and increases the network throughput. Moreover, in contrast to solutions proposed by previous studies, WEMPP is easily implemented by modifying the firmware of the actual Ethernet hardware without altering the routing protocols and/or the functionality of the IP/MAC/Upper layers. In fact, there is no need for modifying the functionalities of other mesh components in order to work with WEMPPs. The results of this study show that WEMPP significantly increases the performance of all routing protocols, thus leading to better video quality on nodes.

  8. Engineering the Mechanical Properties of Polymer Networks with Precise Doping of Primary Defects.

    Science.gov (United States)

    Chan, Doreen; Ding, Yichuan; Dauskardt, Reinhold H; Appel, Eric A

    2017-12-06

    Polymer networks are extensively utilized across numerous applications ranging from commodity superabsorbent polymers and coatings to high-performance microelectronics and biomaterials. For many applications, desirable properties are known; however, achieving them has been challenging. Additionally, the accurate prediction of elastic modulus has been a long-standing difficulty owing to the presence of loops. By tuning the prepolymer formulation through precise doping of monomers, specific primary network defects can be programmed into an elastomeric scaffold, without alteration of their resulting chemistry. The addition of these monomers that respond mechanically as primary defects is used both to understand their impact on the resulting mechanical properties of the materials and as a method to engineer the mechanical properties. Indeed, these materials exhibit identical bulk and surface chemistry, yet vastly different mechanical properties. Further, we have adapted the real elastic network theory (RENT) to the case of primary defects in the absence of loops, thus providing new insights into the mechanism for material strength and failure in polymer networks arising from primary network defects, and to accurately predict the elastic modulus of the polymer system. The versatility of the approach we describe and the fundamental knowledge gained from this study can lead to new advancements in the development of novel materials with precisely defined and predictable chemical, physical, and mechanical properties.

  9. Engineering a Blood Vessel Network Module for Body-on-a-Chip Applications.

    Science.gov (United States)

    Ryu, Hyunryul; Oh, Soojung; Lee, Hyun Jae; Lee, Jin Young; Lee, Hae Kwang; Jeon, Noo Li

    2015-06-01

    The blood circulatory system links all organs from one to another to support and maintain each organ's functions consistently. Therefore, blood vessels have been considered as a vital unit. Engineering perfusable functional blood vessels in vitro has been challenging due to difficulties in designing the connection between rigid macroscale tubes and fragile microscale ones. Here, we propose a generalizable method to engineer a "long" perfusable blood vessel network. To form millimeter-scale vessels, fibroblasts were co-cultured with human umbilical vein endothelial cells (HUVECs) in close proximity. In contrast to previous works, in which all cells were permanently placed within the device, we developed a novel method to culture paracrine factor secreting fibroblasts on an O-ring-shaped guide that can be transferred in and out. This approach affords flexibility in co-culture, where the effects of secreted factors can be decoupled. Using this, blood vessels with length up to 2 mm were successfully produced in a reproducible manner (>90%). Because the vessels form a perfusable network within the channel, simple links to inlets and outlets of the device allowed connections to the outside world. The robust and reproducible formation of in vitro engineered vessels can be used as a module to link various organ components as parts of future body-on-a-chip applications. © 2014 Society for Laboratory Automation and Screening.

  10. Applications of genome-scale metabolic network model in metabolic engineering.

    Science.gov (United States)

    Kim, Byoungjin; Kim, Won Jun; Kim, Dong In; Lee, Sang Yup

    2015-03-01

    Genome-scale metabolic network model (GEM) is a fundamental framework in systems metabolic engineering. GEM is built upon extensive experimental data and literature information on gene annotation and function, metabolites and enzymes so that it contains all known metabolic reactions within an organism. Constraint-based analysis of GEM enables the identification of phenotypic properties of an organism and hypothesis-driven engineering of cellular functions to achieve objectives. Along with the advances in omics, high-throughput technology and computational algorithms, the scope and applications of GEM have substantially expanded. In particular, various computational algorithms have been developed to predict beneficial gene deletion and amplification targets and used to guide the strain development process for the efficient production of industrially important chemicals. Furthermore, an Escherichia coli GEM was integrated with a pathway prediction algorithm and used to evaluate all possible routes for the production of a list of commodity chemicals in E. coli. Combined with the wealth of experimental data produced by high-throughput techniques, much effort has been exerted to add more biological contexts into GEM through the integration of omics data and regulatory network information for the mechanistic understanding and improved prediction capabilities. In this paper, we review the recent developments and applications of GEM focusing on the GEM-based computational algorithms available for microbial metabolic engineering.

  11. Designing of Simulation for Engine Room Km. Sinabung with Control Monitoring Web Server Based by Wireless Network and Power Line Communication

    OpenAIRE

    Hadi, Eko Sasmito; Adietya, Berlian Arswendo; S.P, Firdaus

    2013-01-01

    Engine room monitoring control system is monitoring and controlling main engine and auxiliary engine from long distance by powerline communication network and wireless network to ease the operator in operating the ship and save operational cost. To prevent error in programming the main engine and auxiliary engine, a simulation using instrument software is needed to know the machine characteristic. After simulation result fulfills the requirement which is approached the value of test record, i...

  12. DESIGNING OF SIMULATION FOR ENGINE ROOM KM. SINABUNG WITH CONTROL MONITORING WEB SERVER BASED BY WIRELESS NETWORK AND POWER LINE COMMUNICATION

    OpenAIRE

    Eko Sasmito Hadi; Berlian Arswendo Adietya; Firdaus S.P

    2013-01-01

    Engine room monitoring control system is monitoring and controlling main engine and auxiliary engine from long distance by powerline communication network and wireless network to ease the operator in operating the ship and save operational cost. To prevent error in programming the main engine and auxiliary engine, a simulation using instrument software is needed to know the machine characteristic. After simulation result fulfills the requirement which is approached the value of test record, i...

  13. Fronto-parietal network oscillations reveal relationship between working memory capacity and cognitive control

    NARCIS (Netherlands)

    Gulbinaite, R.; van Rijn, H.; Cohen, M.X.

    2014-01-01

    Executive-attention theory proposes a close relationship between working memory capacity (WMC) and cognitive control abilities. However, conflicting results are documented in the literature, with some studies reporting that individual variations in WMC predict differences in cognitive control and

  14. Animal Hairs as Water-stimulated Shape Memory Materials: Mechanism and Structural Networks in Molecular Assemblies

    National Research Council Canada - National Science Library

    Xiao, Xueliang; Hu, Jinlian

    2016-01-01

    Animal hairs consisting of α-keratin biopolymers existing broadly in nature may be responsive to water for recovery to the innate shape from their fixed deformation, thus possess smart behavior, namely shape memory effect (SME...

  15. An Integrated Business and Engineering Framework for Synthesis and Design of Processing Networks

    DEFF Research Database (Denmark)

    Quaglia, Alberto

    , in which all the aspects of the problem (technical, economical, regulatory, logistical, etc.) need to be considered simultaneously, in order to be able to identify the optimal design. Through the developments realized in the last decades, Process Systems Engineering has shown the potential to contribute...... for synthesis and design of processing networks in the industrial sector is still lower than what could be expected. One of the key reasons for this lack of acceptance lays in their complexity. The formulation of these problems, in fact, often results in a time-consuming activity, due to the number of data...... that need to be gathered and of equations that need to be specified. The solution of the optimization problem formulated, moreover, requires expertise in discrete optimization, which is often not part of the standard skills set of design engineers and decision-makers. This Ph.D. project, therefore, aims...

  16. Feed-forward control for magnetic shape memory alloy actuators based on the radial basis function neural network model.

    Science.gov (United States)

    Zhou, Miaolei; Wang, Yifan; Xu, Rui; Zhang, Qi; Zhu, Dong

    2017-06-16

    Hysteresis exists in magnetic shape memory alloy (MSMA) actuators, which restricts MSMA actuators' application. To describe hysteresis of the MSMA actuators, a hysteresis model based on the radial basis function neural network (RBFNN) is put forward. Then, an inverse RBFNN model is set up, and it is compared with the inverse model based on the traditional cut-and-try method. Finally, to solve hysteresis of the actuators, an inverse model for MSMA actuators is used to build feed-forward controller. Simulation results show the maximum modeling error for inverse hysteresis model designed by neural network is 0.79% and compared with traditional cut-and-try method, the maximum modeling error decreases by 1.85%. The maximum tracking error rate of feed-forward control is 0.38%. The hysteresis of MSMA actuators is reduced. By using the feed-forward controller, high precision control is achieved.

  17. Fencing network direct memory access data transfers in a parallel active messaging interface of a parallel computer

    Science.gov (United States)

    Blocksome, Michael A.; Mamidala, Amith R.

    2015-07-07

    Fencing direct memory access (`DMA`) data transfers in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI including data communications endpoints, each endpoint including specifications of a client, a context, and a task, the endpoints coupled for data communications through the PAMI and through DMA controllers operatively coupled to a deterministic data communications network through which the DMA controllers deliver data communications deterministically, including initiating execution through the PAMI of an ordered sequence of active DMA instructions for DMA data transfers between two endpoints, effecting deterministic DMA data transfers through a DMA controller and the deterministic data communications network; and executing through the PAMI, with no FENCE accounting for DMA data transfers, an active FENCE instruction, the FENCE instruction completing execution only after completion of all DMA instructions initiated prior to execution of the FENCE instruction for DMA data transfers between the two endpoints.

  18. The Engineering Strong Ground Motion Network of the National Autonomous University of Mexico

    Science.gov (United States)

    Velasco Miranda, J. M.; Ramirez-Guzman, L.; Aguilar Calderon, L. A.; Almora Mata, D.; Ayala Hernandez, M.; Castro Parra, G.; Molina Avila, I.; Mora, A.; Torres Noguez, M.; Vazquez Larquet, R.

    2014-12-01

    The coverage, design, operation and monitoring capabilities of the strong ground motion program at the Institute of Engineering (IE) of the National Autonomous University of Mexico (UNAM) is presented. Started in 1952, the seismic instrumentation intended initially to bolster earthquake engineering projects in Mexico City has evolved into the largest strong ground motion monitoring system in the region. Today, it provides information not only to engineering projects, but also to the near real-time risk mitigation systems of the country, and enhances the general understanding of the effects and causes of earthquakes in Mexico. The IE network includes more than 100 free-field stations and several buildings, covering the largest urban centers and zones of significant seismicity in Central Mexico. Of those stations, approximately one-fourth send the observed acceleration to a processing center in Mexico City continuously, and the rest require either periodic visits for the manual recovery of the data or remote interrogation, for later processing and cataloging. In this research, we document the procedures and telecommunications systems used systematically to recover information. Additionally, we analyze the spatial distribution of the free-field accelerographs, the quality of the instrumentation, and the recorded ground motions. The evaluation criteria are based on the: 1) uncertainty in the generation of ground motion parameter maps due to the spatial distribution of the stations, 2) potential of the array to provide localization and magnitude estimates for earthquakes with magnitudes greater than Mw 5, and 3) adequacy of the network for the development of Ground Motion Prediction Equations due to intra-plate and intra-slab earthquakes. We conclude that the monitoring system requires a new redistribution, additional stations, and a substantial improvement in the instrumentation and telecommunications. Finally, we present an integral plan to improve the current network

  19. Effect of synapse dilution on the memory retrieval in structured attractor neural networks

    Science.gov (United States)

    Brunel, N.

    1993-08-01

    We investigate a simple model of structured attractor neural network (ANN). In this network a module codes for the category of the stored information, while another group of neurons codes for the remaining information. The probability distribution of stabilities of the patterns and the prototypes of the categories are calculated, for two different synaptic structures. The stability of the prototypes is shown to increase when the fraction of neurons coding for the category goes down. Then the effect of synapse destruction on the retrieval is studied in two opposite situations : first analytically in sparsely connected networks, then numerically in completely connected ones. In both cases the behaviour of the structured network and that of the usual homogeneous networks are compared. When lesions increase, two transitions are shown to appear in the behaviour of the structured network when one of the patterns is presented to the network. After the first transition the network recognizes the category of the pattern but not the individual pattern. After the second transition the network recognizes nothing. These effects are similar to syndromes caused by lesions in the central visual system, namely prosopagnosia and agnosia. In both types of networks (structured or homogeneous) the stability of the prototype is greater than the stability of individual patterns, however the first transition, for completely connected networks, occurs only when the network is structured.

  20. Complex Network Structure Influences Processing in Long-Term and Short-Term Memory

    Science.gov (United States)

    Vitevitch, Michael S.; Chan, Kit Ying; Roodenrys, Steven

    2012-01-01

    Complex networks describe how entities in systems interact; the structure of such networks is argued to influence processing. One measure of network structure, clustering coefficient, C, measures the extent to which neighbors of a node are also neighbors of each other. Previous psycholinguistic experiments found that the C of phonological…

  1. Heading in the right direction: thermodynamics-based network analysis and pathway engineering.

    Science.gov (United States)

    Ataman, Meric; Hatzimanikatis, Vassily

    2015-12-01

    Thermodynamics-based network analysis through the introduction of thermodynamic constraints in metabolic models allows a deeper analysis of metabolism and guides pathway engineering. The number and the areas of applications of thermodynamics-based network analysis methods have been increasing in the last ten years. We review recent applications of these methods and we identify the areas that such analysis can contribute significantly, and the needs for future developments. We find that organisms with multiple compartments and extremophiles present challenges for modeling and thermodynamics-based flux analysis. The evolution of current and new methods must also address the issues of the multiple alternatives in flux directionalities and the uncertainties and partial information from analytical methods. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  2. Non-equilibrium physics of neural networks for leaning, memory and decision making: landscape and flux perspectives

    Science.gov (United States)

    Wang, Jin

    Cognitive behaviors are determined by underlying neural networks. Many brain functions, such as learning and memory, can be described by attractor dynamics. We developed a theoretical framework for global dynamics by quantifying the landscape associated with the steady state probability distributions and steady state curl flux, measuring the degree of non-equilibrium through detailed balance breaking. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. Both landscape and flux determine the kinetic paths and speed of decision making. The kinetics and global stability of decision making are explored by quantifying the landscape topography through the barrier heights and the mean first passage time. The theoretical predictions are in agreement with experimental observations: more errors occur under time pressure. We quantitatively explored two mechanisms of the speed-accuracy tradeoff with speed emphasis and further uncovered the tradeoffs among speed, accuracy, and energy cost. Our results show an optimal balance among speed, accuracy, and the energy cost in decision making. We uncovered possible mechanisms of changes of mind and how mind changes improve performance in decision processes. Our landscape approach can help facilitate an understanding of the underlying physical mechanisms of cognitive processes and identify the key elements in neural networks.

  3. Context-dependent encoding of fear and extinction memories in a large-scale network model of the basal amygdala.

    Directory of Open Access Journals (Sweden)

    Ioannis Vlachos

    2011-03-01

    Full Text Available The basal nucleus of the amygdala (BA is involved in the formation of context-dependent conditioned fear and extinction memories. To understand the underlying neural mechanisms we developed a large-scale neuron network model of the BA, composed of excitatory and inhibitory leaky-integrate-and-fire neurons. Excitatory BA neurons received conditioned stimulus (CS-related input from the adjacent lateral nucleus (LA and contextual input from the hippocampus or medial prefrontal cortex (mPFC. We implemented a plasticity mechanism according to which CS and contextual synapses were potentiated if CS and contextual inputs temporally coincided on the afferents of the excitatory neurons. Our simulations revealed a differential recruitment of two distinct subpopulations of BA neurons during conditioning and extinction, mimicking the activation of experimentally observed cell populations. We propose that these two subgroups encode contextual specificity of fear and extinction memories, respectively. Mutual competition between them, mediated by feedback inhibition and driven by contextual inputs, regulates the activity in the central amygdala (CEA thereby controlling amygdala output and fear behavior. The model makes multiple testable predictions that may advance our understanding of fear and extinction memories.

  4. Phase-Change Memory Materials by Design: A Strain Engineering Approach.

    Science.gov (United States)

    Zhou, Xilin; Kalikka, Janne; Ji, Xinglong; Wu, Liangcai; Song, Zhitang; Simpson, Robert E

    2016-04-20

    Van der Waals heterostructure superlattices of Sb2 Te1 and GeTe are strain-engineered to promote switchable atomic disordering, which is confined to the GeTe layer. Careful control of the strain in the structures presents a new degree of freedom to design the properties of functional superlattice structures for data storage and photonics applications. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. Reveal, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures

    Science.gov (United States)

    Liang, Shoudan; Fuhrman, Stefanie; Somogyi, Roland

    1998-01-01

    Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

  6. A network engineering perspective on probing and perturbing cognition with neurofeedback.

    Science.gov (United States)

    Bassett, Danielle S; Khambhati, Ankit N

    2017-05-01

    Network science and engineering provide a flexible and generalizable tool set to describe and manipulate complex systems characterized by heterogeneous interaction patterns among component parts. While classically applied to social systems, these tools have recently proven to be particularly useful in the study of the brain. In this review, we describe the nascent use of these tools to understand human cognition, and we discuss their utility in informing the meaningful and predictable perturbation of cognition in combination with the emerging capabilities of neurofeedback. To blend these disparate strands of research, we build on emerging conceptualizations of how the brain functions (as a complex network) and how we can develop and target interventions or modulations (as a form of network control). We close with an outline of current frontiers that bridge neurofeedback, connectomics, and network control theory to better understand human cognition. © 2017 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals Inc. on behalf of The New York Academy of Sciences.

  7. Functional connectivity change across multiple cortical networks relates to episodic memory changes in aging.

    Science.gov (United States)

    Fjell, Anders M; Sneve, Markus H; Grydeland, Håkon; Storsve, Andreas B; de Lange, Ann-Marie Glasø; Amlien, Inge K; Røgeberg, Ole J; Walhovd, Kristine B

    2015-12-01

    A major task of contemporary cognitive neuroscience of aging is to explain why episodic memory declines. Change in resting-state functional connectivity (rsFC) could be a mechanism accounting for reduced function. We addressed this through 3 studies. In study 1, 119 healthy participants (20-83 years) were followed for 3.5 years with verbal recall testing and magnetic resonance imaging. Independent of atrophy, recall change was related to change in rsFC in anatomically widespread areas. Striking age-effects were observed in that a positive relationship between rsFC and memory characterized older participants while a negative relationship was seen among the younger and middle-aged. This suggests that cognitive consequences of rsFC change are not stable across age. In study 2 and 3, the age-dependent differences in rsFC-memory relationship were replicated by use of a simulation model (study 2) and by a cross-sectional experimental recognition memory task (study 3). In conclusion, memory changes were related to altered rsFC in an age-dependent manner, and future research needs to detail the mechanisms behind age-varying relationships. Copyright © 2015 Elsevier Inc. All rights reserved.

  8. Apolipoprotein E (APOE) genotype has dissociable effects on memory and attentional–executive network function in Alzheimer’s disease

    Science.gov (United States)

    Wolk, David A.; Dickerson, Bradford C.; Weiner, Michael; Aiello, Marilyn; Aisen, Paul; Albert, Marilyn S.; Alexander, Gene; Anderson, Heather S.; Anderson, Karen; Apostolova, Liana; Arnold, Steve; Ashford, Wes; Assaly, Michele; Asthana, Sanjay; Bandy, Dan; Bartha, Rob; Bates, Vernice; Beckett, Laurel; Bell, Karen L.; Benincasa, Amanda L.; Bergman, Howard; Bernick, Charles; Bernstein, Matthew; Black, Sandra; Blank, Karen; Borrie, Michael; Brand, Connie; Brewer, James; Brown, Alice D.; Burns, Jeffrey M.; Cairns, Nigel J.; Caldwell, Curtis; Capote, Horacio; Carlsson, Cynthia M.; Carmichael, Owen; Cellar, Janet S.; Celmins, Dzintra; Chen, Kewei; Chertkow, Howard; Chowdhury, Munir; Clark, David; Connor, Donald; Correia, Stephen; Crawford, Karen; Dale, Anders; de Leon, Mony J; De Santi, Susan M; DeCarli, Charles; deToledo-Morrell, Leyla; DeVous, Michael; Diaz-Arrastia, Ramon; Dolen, Sara; Donohue, Michael; Doody, Rachelle S.; Doraiswamy, P. Murali; Duara, Ranjan; Englert, Jessica; Farlow, Martin; Feldman, Howard; Felmlee, Joel; Fleisher, Adam; Fletcher, Evan; Foroud, Tatiana M.; Foster, Norm; Fox, Nick; Frank, Richard; Gamst, Anthony; Given, Curtis A.; Graff-Radford, Neill R; Green, Robert C.; Griffith, Randall; Grossman, Hillel; Hake, Ann M.; Hardy, Peter; Harvey, Danielle; Heidebrink, Judith L.; Hendin, Barry A.; Herring, Scott; Honig, Lawrence S.; Hosein, Chris; Robin Hsiung, Ging-Yuek; Hudson, Leon; Ismail, M. Saleem; Jack, Clifford R.; Jacobson, Sandra; Jagust, William; Jayam-Trouth, Annapurni; Johnson, Kris; Johnson, Heather; Johnson, Nancy; Johnson, Kathleen; Johnson, Keith A.; Johnson, Sterling; Kachaturian, Zaven; Karlawish, Jason H.; Kataki, Maria; Kaye, Jeffrey; Kertesz, Andrew; Killiany, Ronald; Kittur, Smita; Koeppe, Robert A.; Korecka, Magdalena; Kornak, John; Kozauer, Nicholas; Lah, James J.; Laubinger, Mary M.; Lee, Virginia M.-Y.; Lee, T.-Y.; Lerner, Alan; Levey, Allan I.; Longmire, Crystal Flynn; Lopez, Oscar L.; Lord, Joanne L.; Lu, Po H.; MacAvoy, Martha G.; Malloy, Paul; Marson, Daniel; Martin-Cook, Kristen; Martinez, Walter; Marzloff, George; Mathis, Chet; Mc-Adams-Ortiz, Catherine; Mesulam, Marsel; Miller, Bruce L.; Mintun, Mark A.; Mintzer, Jacobo; Molchan, Susan; Montine, Tom; Morris, John; Mulnard, Ruth A.; Munic, Donna; Nair, Anil; Neu, Scott; Nguyen, Dana; Norbash, Alexander; Oakley, MaryAnn; Obisesan, Thomas O.; Ogrocki, Paula; Ott, Brian R.; Parfitt, Francine; Pawluczyk, Sonia; Pearlson, Godfrey; Petersen, Ronald; Petrella, Jeffrey R.; Potkin, Steven; Potter, William Z.; Preda, Adrian; Quinn, Joseph; Rainka, Michelle; Reeder, Stephanie; Reiman, Eric M.; Rentz, Dorene M.; Reynolds, Brigid; Richard, Jennifer; Roberts, Peggy; Rogers, John; Rosen, Allyson; Rosen, Howard J.; Rusinek, Henry; Sabbagh, Marwan; Sadowsky, Carl; Salloway, Stephen; Santulli, Robert B.; Saykin, Andrew J.; Scharre, Douglas W.; Schneider, Lon; Schneider, Stacy; Schuff, Norbert; Shah, Raj C.; Shaw, Les; Shen, Li; Silverman, Daniel H.S.; Simpson, Donna M.; Sink, Kaycee M.; Smith, Charles D.; Snyder, Peter J.; Spann, Bryan M.; Sperling, Reisa A.; Spicer, Kenneth; Stefanovic, Bojana; Stern, Yaakov; Stopa, Edward; Tang, Cheuk; Tariot, Pierre; Taylor-Reinwald, Lisa; Thai, Gaby; Thomas, Ronald G.; Thompson, Paul; Tinklenberg, Jared; Toga, Arthur W.; Tremont, Geoffrey; Trojanowki, J.Q.; Trost, Dick; Turner, Raymond Scott; van Dyck, Christopher H.; Vanderswag, Helen; Varon, Daniel; Villanueva-Meyer, Javier; Villena, Teresa; Walter, Sarah; Wang, Paul; Watkins, Franklin; Weiner, Michael; Williamson, Jeff D.; Wolk, David; Wu, Chuang-Kuo; Zerrate, Maria; Zimmerman., Earl A.

    2010-01-01

    The ε4 allele of the apolipoprotein E (APOE) gene is the major genetic risk factor for Alzheimer’s disease (AD), but limited work has suggested that APOE genotype may modulate disease phenotype. Carriers of the ε4 allele have been reported to have greater medial temporal lobe (MTL) pathology and poorer memory than noncarriers. Less attention has focused on whether there are domains of cognition and neuroanatomical regions more affected in noncarriers. Further, a major potential confound of prior in vivo studies is the possibility of different rates of clinical misdiagnosis for carriers vs. noncarriers. We compared phenotypic differences in cognition and topography of regional cortical atrophy of ε4 carriers (n = 67) vs. noncarriers (n = 24) with mild AD from the Alzheimer’s Disease Neuroimaging Initiative, restricted to those with a cerebrospinal fluid (CSF) molecular profile consistent with AD. Between-group comparisons were made for psychometric tests and morphometric measures of cortical thickness and hippocampal volume. Carriers displayed significantly greater impairment on measures of memory retention, whereas noncarriers were more impaired on tests of working memory, executive control, and lexical access. Consistent with this cognitive dissociation, carriers exhibited greater MTL atrophy, whereas noncarriers had greater frontoparietal atrophy. Performance deficits in particular cognitive domains were associated with disproportionate regional brain atrophy within nodes of cortical networks thought to subserve these cognitive processes. These convergent cognitive and neuroanatomic findings in individuals with a CSF molecular profile consistent with AD support the hypothesis that APOE genotype modulates the clinical phenotype of AD through influence on specific large-scale brain networks. PMID:20479234

  9. Hydrogels of collagen/chondroitin sulfate/hyaluronan interpenetrating polymer network for cartilage tissue engineering.

    Science.gov (United States)

    Guo, Yan; Yuan, Tun; Xiao, Zhanwen; Tang, Pingping; Xiao, Yumei; Fan, Yujiang; Zhang, Xingdong

    2012-09-01

    The network structure of a three-dimensional hydrogel scaffold dominates its performance such as mechanical strength, mass transport capacity, degradation rate and subsequent cellular behavior. The hydrogels scaffolds with interpenetrating polymeric network (IPN) structure have an advantage over the individual component gels and could simulate partly the structure of native extracellular matrix of cartilage tissue. In this study, to develop perfect cartilage tissue engineering scaffolds, IPN hydrogels of collagen/chondroitin sulfate/hyaluronan were prepared via two simultaneous processes of collagen self-assembly and cross linking polymerization of chondroitin sulfate-methacrylate (CSMA) and hyaluronic acid-methacrylate. The degradation rate, swelling performance and compressive modulus of IPN hydrogels could be adjusted by varying the degree of methacrylation of CSMA. The results of proliferation and fluorescence staining of rabbit articular chondrocytes in vitro culture demonstrated that the IPN hydrogels possessed good cytocompatibility. Furthermore, the IPN hydrogels could upregulate cartilage-specific gene expression and promote the chondrocytes secreting glycosaminoglycan and collagen II. These results suggested that IPN hydrogels might serve as promising hydrogel scaffolds for cartilage tissue engineering.

  10. Evaluation of Fibrin-Based Interpenetrating Polymer Networks as Potential Biomaterials for Tissue Engineering

    Directory of Open Access Journals (Sweden)

    Olfat Gsib

    2017-12-01

    Full Text Available Interpenetrating polymer networks (IPNs have gained great attention for a number of biomedical applications due to their improved properties compared to individual components alone. In this study, we investigated the capacity of newly-developed naturally-derived IPNs as potential biomaterials for tissue engineering. These IPNs combine the biologic properties of a fibrous fibrin network polymerized at the nanoscale and the mechanical stability of polyethylene oxide (PEO. First, we assessed their cytotoxicity in vitro on L929 fibroblasts. We further evaluated their biocompatibility ex vivo with a chick embryo organotypic culture model. Subcutaneous implantations of the matrices were subsequently conducted on nude mice to investigate their biocompatibility in vivo. Our preliminary data highlighted that our biomaterials were non-cytotoxic (viability above 90%. The organotypic culture showed that the IPN matrices induced higher cell adhesion (across all the explanted organ tissues and migration (skin, intestine than the control groups, suggesting the advantages of using a biomimetic, yet mechanically-reinforced IPN-based matrix. We observed no major inflammatory response up to 12 weeks post implantation. All together, these data suggest that these fibrin-based IPNs are promising biomaterials for tissue engineering.

  11. THE BLENDED LEARNING ACCOMPLISHMENT OF COMPUTER AND NETWORK ENGINEERING EXPERTISE PROGRAM IN VOCATIONAL SCHOOLS

    Directory of Open Access Journals (Sweden)

    Aries Alfian Prasetyo

    2016-10-01

    Full Text Available This study aims to (1 describe supporting and inhibiting factors in blended learning implementation for the students of computer and network engineering expertise program and (2 describe the accomplishment level of the implementation. This study is designed as a descriptive study with quantitative approach. The research object is the blended learning implementation in computer and network engineering expertise program in SMK N 1 Baureno Bojonegoro. The research subjects consist of teachers, facilities, materials and applications and students in the blended learning implementation process. The data was collected using observation, surveys and interviews. It was analyzed using percentages and classification analysis. The results reveals that the blended learning has been appropriately implemented. It is proven by the analysis result of supporting and inhibiting factors including facilities, teachers’ skill, materials and applications and blended learning accomplishment. The result is also supported by the description about blended learning activity, the use of facilities, blended learning composition and the impact of implementing blended learning. The weaknesses in the implementation process are the low quantity and quality of personal computers and inadequate internet connection. Teachers and school boards are expected to work collaboratively to solve the problems thus the implementation of blended learning can be maximized.

  12. Biomedical engineering in design and application of nitinol stents with shape memory effect

    Science.gov (United States)

    Ryklina, E. P.; Khmelevskaya, I. Y.; Morozova, Tamara V.; Prokoshkin, S. D.

    1996-04-01

    Our studies in the field of endosurgery in collaboration with the physicians of the National Research Center of Surgery of the Academy of Medical Sciences are carried out beginning in 1983. These studies laid the foundation for the new direction of X-ray surgery--X-ray Nitinol stenting of vessels and tubular structures. X-ray nitinol stents are unique self-fixing shells based on the shape memory effect and superelasticity of nickel-titanium alloys self- reconstructed under human body temperature. Applied for stenting of arteries in cases of stenosis etc., bile ducts in cases of benign and malignant stenoses, digestive tract in cases of oesophageal cancer and cervical canal uterus in cases of postsurgical atresiss and strictures of uterine. The purpose of stenting is restoration of the shape of artery or tubular structure by a cylinder frame formation. The especially elaborated original method of stenting allows to avoid the traditional surgical operation, i.e. the stenting is performed without blood, narcosis and surgical knife. The stent to be implanted is transported into the affected zone through the puncture under the X-ray control. Clinical applications of X-ray endovascular stenting has been started in March 1984. During this period nearly 400 operations on stenting have been performed on femoral, iliac, brachio-cephalic, subclavian arteries, bile ducts, tracheas, digestive tract and cervical canal uterus.

  13. A Neural Network Model of the Effects of Entrenchment and Memory Development on Grammatical Gender Learning

    Science.gov (United States)

    Monner, Derek; Vatz, Karen; Morini, Giovanna; Hwang, So-One; DeKeyser, Robert

    2013-01-01

    To investigate potential causes of L2 performance deficits that correlate with age of onset, we use a computational model to explore the individual contributions of L1 entrenchment and aspects of memory development. Since development and L1 entrenchment almost invariably coincide, studying them independently is seldom possible in humans. To avoid…

  14. Short-Term Memory for Serial Order: A Recurrent Neural Network Model

    Science.gov (United States)

    Botvinick, Matthew M.; Plaut, David C.

    2006-01-01

    Despite a century of research, the mechanisms underlying short-term or working memory for serial order remain uncertain. Recent theoretical models have converged on a particular account, based on transient associations between independent item and context representations. In the present article, the authors present an alternative model, according…

  15. Altered engagement of autobiographical memory networks in adult offspring of postnatally depressed mothers.

    Science.gov (United States)

    Macdonald, Birthe; Murray, Lynne; Moutsiana, Christina; Fearon, Pasco; Cooper, Peter J; Halligan, Sarah L; Johnstone, Tom

    2016-07-01

    Maternal depression is associated with increased risk for offspring mood and anxiety disorders. One possible impact of maternal depression during offspring development is on the emotional autobiographical memory system. We investigated the neural mechanisms of emotional autobiographical memory in adult offspring of mothers with postnatal depression (N=16) compared to controls (N=21). During fMRI, recordings of participants describing one pleasant and one unpleasant situation with their mother and with a companion, were used as prompts to re-live the situations. Compared to controls we predicted the PND offspring would show: greater activation in medial and posterior brain regions implicated in autobiographical memory and rumination; and decreased activation in lateral prefrontal cortex and decreased connectivity between lateral prefrontal and posterior regions, reflecting reduced control of autobiographical recall. For negative situations, we found no group differences. For positive situations with their mothers, PND offspring showed higher activation than controls in left lateral prefrontal cortex, right frontal pole, cingulate cortex and precuneus, and lower connectivity of right middle frontal gyrus, left middle temporal gyrus, thalamus and lingual gyrus with the posterior cingulate. Our results are consistent with adult offspring of PND mothers having less efficient prefrontal regulation of personally relevant pleasant autobiographical memories. Copyright © 2016 The Author(s). Published by Elsevier B.V. All rights reserved.

  16. Growth Factor Signaling and Memory Formation: Temporal and Spatial Integration of a Molecular Network

    Science.gov (United States)

    Kopec, Ashley M.; Carew, Thomas J.

    2013-01-01

    Growth factor (GF) signaling is critically important for developmental plasticity. It also plays a crucial role in adult plasticity, such as that required for memory formation. Although different GFs interact with receptors containing distinct types of kinase domains, they typically signal through converging intracellular cascades (e.g.,…

  17. Emotion perception and executive control interact in the salience network during emotionally charged working memory processing

    NARCIS (Netherlands)

    Luo, Y.; Qin, S.; Fernandez, G.S.E.; Zhang, Y.; Klumpers, F.; Li, H.

    2014-01-01

    Processing of emotional stimuli can either hinder or facilitate ongoing working memory (WM); however, the neural basis of these effects remains largely unknown. Here we examined the neural mechanisms of these paradoxical effects by implementing a novel emotional WM task in an fMRI study. Twenty-five

  18. Selective verbal recognition memory impairments are associated with atrophy of the language network in non-semantic variants of primary progressive aphasia.

    Science.gov (United States)

    Nilakantan, Aneesha S; Voss, Joel L; Weintraub, Sandra; Mesulam, M-Marsel; Rogalski, Emily J

    2017-06-01

    Primary progressive aphasia (PPA) is clinically defined by an initial loss of language function and preservation of other cognitive abilities, including episodic memory. While PPA primarily affects the left-lateralized perisylvian language network, some clinical neuropsychological tests suggest concurrent initial memory loss. The goal of this study was to test recognition memory of objects and words in the visual and auditory modality to separate language-processing impairments from retentive memory in PPA. Individuals with non-semantic PPA had longer reaction times and higher false alarms for auditory word stimuli compared to visual object stimuli. Moreover, false alarms for auditory word recognition memory were related to cortical thickness within the left inferior frontal gyrus and left temporal pole, while false alarms for visual object recognition memory was related to cortical thickness within the right-temporal pole. This pattern of results suggests that specific vulnerability in processing verbal stimuli can hinder episodic memory in PPA, and provides evidence for differential contributions of the left and right temporal poles in word and object recognition memory. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. In Vitro Construction of Scaffold-Free Bilayered Tissue-Engineered Skin Containing Capillary Networks

    Directory of Open Access Journals (Sweden)

    Yuan Liu

    2013-01-01

    Full Text Available Many types of skin substitutes have been constructed using exogenous materials. Angiogenesis is an important factor for tissue-engineered skin constructs. In this study, we constructed a scaffold-free bilayered tissue-engineered skin containing a capillary network. First, we cocultured dermal fibroblasts with dermal microvascular endothelial cells at a ratio of 2 : 1. A fibrous sheet was formed by the interactions between the fibroblasts and the endothelial cells, and capillary-like structures were observed after 20 days of coculture. Epithelial cells were then seeded on the fibrous sheet to assemble the bilayered tissue. HE staining showed that tissue-engineered skin exhibited a stratified epidermis after 7 days. Immunostaining showed that the epithelium promoted the formation of capillary-like structures. Transmission electron microscopy (TEM analysis showed that the capillary-like structures were typical microblood vessels. ELISA demonstrated that vascularization was promoted by significant upregulation of vascularization associated growth factors due to interactions among the 3 types of cells in the bilayer, as compared to cocultures of fibroblast and endothelial cells and monocultures.

  20. Dissecting and engineering metabolic and regulatory networks of thermophilic bacteria for biofuel production.

    Science.gov (United States)

    Lin, Lu; Xu, Jian

    2013-11-01

    Interest in thermophilic bacteria as live-cell catalysts in biofuel and biochemical industry has surged in recent years, due to their tolerance of high temperature and wide spectrum of carbon-sources that include cellulose. However their direct employment as microbial cellular factories in the highly demanding industrial conditions has been hindered by uncompetitive biofuel productivity, relatively low tolerance to solvent and osmic stresses, and limitation in genome engineering tools. In this work we review recent advances in dissecting and engineering the metabolic and regulatory networks of thermophilic bacteria for improving the traits of key interest in biofuel industry: cellulose degradation, pentose-hexose co-utilization, and tolerance of thermal, osmotic, and solvent stresses. Moreover, new technologies enabling more efficient genetic engineering of thermophiles were discussed, such as improved electroporation, ultrasound-mediated DNA delivery, as well as thermo-stable plasmids and functional selection systems. Expanded applications of such technological advancements in thermophilic microbes promise to substantiate a synthetic biology perspective, where functional parts, module, chassis, cells and consortia were modularly designed and rationally assembled for the many missions at industry and nature that demand the extraordinary talents of these extremophiles. Copyright © 2013 Elsevier Inc. All rights reserved.

  1. Networking automation of ECI’s G-204 electronic engineering laboratory

    Directory of Open Access Journals (Sweden)

    Hernán Paz Penagos

    2010-04-01

    Full Text Available Increased use (by students and teachers of the “Escuela Colombiana de Ingeniería Julio Garavito” Electronic Engineering laboratories during the last year has congested access to these laboratories; the School’s Electronic Engineering (Ecitronica programme’s applied Electronic studies’ centre research group thus proposed, designed and developed a research prolect taking advantage of G building’s electrical distribution to offer access facilities, laboratory equipment control, energy saving and improved service quality. The G-204 laboratory’s network sys- tem will have an access control subsystem with client–main computer architecture. The latter consists of a user, schedule, group and work-bank database; the user is connected from any computer (client to the main computer through Internet to reserve his/her turn at laboratory practice by selecting the schedule, group, work-bank, network type required (1Ф or 3Ф and registering co-workers. Access to the G-204 laboratory on the day and time of practice is made by means of an intelligent card reader. Information of public interest produced and controlled by the main computer is displayed on three LCD screens located on one of G building’s second floor walls, as is an electronic clock. The G-204 laboratory temperature and time are continually updated. Work-banks are enabled or disabled by the main computer; the work banks are provided with power (beginning of practice or disconnected (end of practice or due to eventualities to protect the equipment, save energy, facilitate monitors and supervise the logistics of the state of the equipment at the end of each practice. The research group was organised into Transmission Line and Applications sub-groups. Power Line Communications PLC technology was used for to exploring digital modulation alternatives, coding and detecting errors, coupling, data transmission protocols and new applications, all based on channel estimation (networking

  2. Sevoflurane anesthesia induces neither contextual fear memory impairment nor alterations in local population connectivity of medial prefrontal cortex local field potentials networks in aged rats.

    Science.gov (United States)

    Xu, Xinyu; Zhang, Qian; Tian, Xin; Wang, Guolin

    2016-08-01

    Sevoflurane has been found to increase apoptosis and pathologic markers associated with Alzheimer disease, provoking concern over their potential contribution to postoperative cognitive dysfunction. This study aimed to determine the effects of sevoflurane on contextual fear memory of aged rats and to characterize local population connectivity of local field potentials (LFPs) in medial prefrontal cortex (mPFC) of aged rats during contextual fear memory. Eighteen-month-old male SD rats were implanted with one multichannel electrode array in mPFC. The aged rats were divided into control group, sevoflurane group (1 MAC sevoflurane for 2 h) and surgical group with 1.0 MAC sevoflurane for 2 h. We then assessed the effect of the anesthesia on contextual fear memory, and alterations in the local population connectivity of mPFC LFP networks by partial directed coherence (PDC). Surgery impaired contextual fear memory and reduced local population connectivity of mPFC LFP networks in aged rats at day 1 after the surgery and anesthesia. 1 MAC Sevoflurane anesthesia induced neither contextual fear memory impairment nor alterations in local population connectivity of mPFC LFP networks in aged rats when tested 1, 7, 15 and 30 days after exposure (P > 0.05). PDC values of theta band mPFC LFPs became strongly increased during contextual fear memory at 1, 7, 15, and 30 days after anesthesia. Our results suggest that 1 MAC sevoflurane anesthesia does not induce contextual fear memory impairment in aged rats and suggest that the increased local population connectivity in theta bands LFPs of mPFC plays a role in contextual fear memory. © 2016 Société Française de Pharmacologie et de Thérapeutique.

  3. Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems

    Science.gov (United States)

    Perez-Garcia, Octavio; Lear, Gavin; Singhal, Naresh

    2016-01-01

    We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN) models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms, and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA), experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e., (i) lumped networks, (ii) compartment per guild networks, (iii) bi-level optimization simulations, and (iv) dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach) are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial interactions can

  4. Metabolic network modeling of microbial interactions in natural and engineered environmental systems

    Directory of Open Access Journals (Sweden)

    Octavio ePerez-Garcia

    2016-05-01

    Full Text Available We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA, experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e. i lumped networks, ii compartment per guild networks, iii bi-level optimization simulations and iv dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial

  5. Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering

    Science.gov (United States)

    Dhaeseleer, Patrik; Liang, Shoudan; Somogyi, Roland

    2000-01-01

    Advances in molecular biological, analytical, and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-duster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e., who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting, and bioengineering.

  6. Applications of Artificial Neural Networks in Structural Engineering with Emphasis on Continuum Models

    Science.gov (United States)

    Kapania, Rakesh K.; Liu, Youhua

    1998-01-01

    The use of continuum models for the analysis of discrete built-up complex aerospace structures is an attractive idea especially at the conceptual and preliminary design stages. But the diversity of available continuum models and hard-to-use qualities of these models have prevented them from finding wide applications. In this regard, Artificial Neural Networks (ANN or NN) may have a great potential as these networks are universal approximators that can realize any continuous mapping, and can provide general mechanisms for building models from data whose input-output relationship can be highly nonlinear. The ultimate aim of the present work is to be able to build high fidelity continuum models for complex aerospace structures using the ANN. As a first step, the concepts and features of ANN are familiarized through the MATLAB NN Toolbox by simulating some representative mapping examples, including some problems in structural engineering. Then some further aspects and lessons learned about the NN training are discussed, including the performances of Feed-Forward and Radial Basis Function NN when dealing with noise-polluted data and the technique of cross-validation. Finally, as an example of using NN in continuum models, a lattice structure with repeating cells is represented by a continuum beam whose properties are provided by neural networks.

  7. Robust quantum-network memory using decoherence-protected subspaces of nuclear spins

    NARCIS (Netherlands)

    Reiserer, A.A.; Kalb, N.; Blok, M.S.; van Bemmelen, Koen J M; Taminiau, T.H.; Hanson, R.; Twitchen, Daniel J.; Markham, Matthew

    2016-01-01

    The realization of a network of quantum registers is an outstanding challenge in quantum science and technology. We experimentally investigate a network node that consists of a single nitrogen-vacancy center electronic spin hyperfine coupled to nearby nuclear spins. We demonstrate individual

  8. Prenatal drug exposure to illicit drugs alters working memory-related brain activity and underlying network properties in adolescence.

    Science.gov (United States)

    Schweitzer, Julie B; Riggins, Tracy; Liang, Xia; Gallen, Courtney; Kurup, Pradeep K; Ross, Thomas J; Black, Maureen M; Nair, Prasanna; Salmeron, Betty Jo

    2015-01-01

    The persistence of effects of prenatal drug exposure (PDE) on brain functioning during adolescence is poorly understood. We explored neural activation to a visuospatial working memory (VSWM) versus a control task using functional magnetic resonance imaging (fMRI) in adolescents with PDE and a community comparison group (CC) of non-exposed adolescents. We applied graph theory metrics to resting state data using a network of nodes derived from the VSWM task activation map to further explore connectivity underlying WM functioning. Participants (ages 12-15 years) included 47 adolescents (27 PDE and 20 CC). All analyses controlled for potentially confounding differences in birth characteristics and postnatal environment. Significant group by task differences in brain activation emerged in the left middle frontal gyrus (BA 6) with the CC group, but not the PDE group, activating this region during VSWM. The PDE group deactivated the culmen, whereas the CC group activated it during the VSWM task. The CC group demonstrated a significant relation between reaction time and culmen activation, not present in the PDE group. The network analysis underlying VSWM performance showed that PDE group had lower global efficiency than the CC group and a trend level reduction in local efficiency. The network node corresponding to the BA 6 group by task interaction showed reduced nodal efficiency and fewer direct connections to other nodes in the network. These results suggest that adolescence reveals altered neural functioning related to response planning that may reflect less efficient network functioning in youth with PDE. Copyright © 2015 Elsevier Inc. All rights reserved.

  9. Genome-scale metabolic network guided engineering of Streptomyces tsukubaensis for FK506 production improvement.

    Science.gov (United States)

    Huang, Di; Li, Shanshan; Xia, Menglei; Wen, Jianping; Jia, Xiaoqiang

    2013-05-24

    FK506 is an important immunosuppressant, which can be produced by Streptomyces tsukubaensis. However, the production capacity of the strain is very low. Hereby, a computational guided engineering approach was proposed in order to improve the intracellular precursor and cofactor availability of FK506 in S. tsukubaensis. First, a genome-scale metabolic model of S. tsukubaensis was constructed based on its annotated genome and biochemical information. Subsequently, several potential genetic targets (knockout or overexpression) that guaranteed an improved yield of FK506 were identified by the recently developed methodology. To validate the model predictions, each target gene was manipulated in the parent strain D852, respectively. All the engineered strains showed a higher FK506 production, compared with D852. Furthermore, the combined effect of the genetic modifications was evaluated. Results showed that the strain HT-ΔGDH-DAZ with gdhA-deletion and dahp-, accA2-, zwf2-overexpression enhanced FK506 concentration up to 398.9 mg/L, compared with 143.5 mg/L of the parent strain D852. Finally, fed-batch fermentations of HT-ΔGDH-DAZ were carried out, which led to the FK506 production of 435.9 mg/L, 1.47-fold higher than the parent strain D852 (158.7 mg/L). Results confirmed that the promising targets led to an increase in FK506 titer. The present work is the first attempt to engineer the primary precursor pathways to improve FK506 production in S. tsukubaensis with genome-scale metabolic network guided metabolic engineering. The relationship between model prediction and experimental results demonstrates the rationality and validity of this approach for target identification. This strategy can also be applied to the improvement of other important secondary metabolites.

  10. Estimation of operational parameters for a direct injection turbocharged spark ignition engine by using regression analysis and artificial neural network

    Directory of Open Access Journals (Sweden)

    Tosun Erdi

    2017-01-01

    Full Text Available This study was aimed at estimating the variation of several engine control parameters within the rotational speed-load map, using regression analysis and artificial neural network techniques. Duration of injection, specific fuel consumption, exhaust gas at turbine inlet, and within the catalytic converter brick were chosen as the output parameters for the models, while engine speed and brake mean effective pressure were selected as independent variables for prediction. Measurements were performed on a turbocharged direct injection spark ignition engine fueled with gasoline. A three-layer feed-forward structure and back-propagation algorithm was used for training the artificial neural network. It was concluded that this technique is capable of predicting engine parameters with better accuracy than linear and non-linear regression techniques.

  11. Daily carnosine and anserine supplementation alters verbal episodic memory and resting state network connectivity in healthy elderly adults

    Directory of Open Access Journals (Sweden)

    Jaroslav eRokicki

    2015-11-01

    Full Text Available Carnosine and anserine are strong antioxidants, previously demonstrated to reduce cognitive decline in animal studies. We aimed to investigate their cognitive and neurophysiological effects, using functional MRI, on humans.Thirty-one healthy participants (age 40-78, 10~male/21~female were recruited to a double-blind placebo-controlled study. Participants were assigned to twice-daily doses of imidazole dipeptide formula ($n = 14$, containing 500~mg (carnosine/anserine, ratio 1/3 or an identical placebo ($n = 17$. Functional MRI and neuropsychological assessments were carried out at baseline and after 3 months of supplementation. We analyzed resting state functional connectivity with the FSL fMRI analysis package. There were no differences in neuropsychological scores between the groups at baseline. After 3 months of supplementation, the carnosine/anserine group had better verbal episodic memory performance and decreased connectivity in the Default Mode Network, the Posterior Cingulate Cortex and the Right Fronto Parietal Network, as compared with the placebo group. Furthermore, there was a correlation between the extents of cognitive and neuroimaging changes. These results suggest that daily carnosine/anserine supplementation can impact cognitive function and that network connectivity changes are associated with its effects.

  12. Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Ghobadian, B.; Rahimi, H.; Nikbakht, A.M.; Najafi, G. [Tarbiat Modares University, P.O. Box 14115-111, Tehran (Iran); Yusaf, T.F. [University of Southern Queensland, Toowoomba 4350 QLD (Australia)

    2009-04-15

    This study deals with artificial neural network (ANN) modeling of a diesel engine using waste cooking biodiesel fuel to predict the brake power, torque, specific fuel consumption and exhaust emissions of the engine. To acquire data for training and testing the proposed ANN, a two cylinders, four-stroke diesel engine was fuelled with waste vegetable cooking biodiesel and diesel fuel blends and operated at different engine speeds. The properties of biodiesel produced from waste vegetable oil was measured based on ASTM standards. The experimental results revealed that blends of waste vegetable oil methyl ester with diesel fuel provide better engine performance and improved emission characteristics. Using some of the experimental data for training, an ANN model was developed based on standard Back-Propagation algorithm