WorldWideScience

Sample records for neural circuit analysis

  1. Document analysis with neural net circuits

    Science.gov (United States)

    Graf, Hans Peter

    1994-01-01

    Document analysis is one of the main applications of machine vision today and offers great opportunities for neural net circuits. Despite more and more data processing with computers, the number of paper documents is still increasing rapidly. A fast translation of data from paper into electronic format is needed almost everywhere, and when done manually, this is a time consuming process. Markets range from small scanners for personal use to high-volume document analysis systems, such as address readers for the postal service or check processing systems for banks. A major concern with present systems is the accuracy of the automatic interpretation. Today's algorithms fail miserably when noise is present, when print quality is poor, or when the layout is complex. A common approach to circumvent these problems is to restrict the variations of the documents handled by a system. In our laboratory, we had the best luck with circuits implementing basic functions, such as convolutions, that can be used in many different algorithms. To illustrate the flexibility of this approach, three applications of the NET32K circuit are described in this short viewgraph presentation: locating address blocks, cleaning document images by removing noise, and locating areas of interest in personal checks to improve image compression. Several of the ideas realized in this circuit that were inspired by neural nets, such as analog computation with a low resolution, resulted in a chip that is well suited for real-world document analysis applications and that compares favorably with alternative, 'conventional' circuits.

  2. Large scale neural circuit mapping data analysis accelerated with the graphical processing unit (GPU)

    Science.gov (United States)

    Shi, Yulin; Veidenbaum, Alexander V.; Nicolau, Alex; Xu, Xiangmin

    2014-01-01

    Background Modern neuroscience research demands computing power. Neural circuit mapping studies such as those using laser scanning photostimulation (LSPS) produce large amounts of data and require intensive computation for post-hoc processing and analysis. New Method Here we report on the design and implementation of a cost-effective desktop computer system for accelerated experimental data processing with recent GPU computing technology. A new version of Matlab software with GPU enabled functions is used to develop programs that run on Nvidia GPUs to harness their parallel computing power. Results We evaluated both the central processing unit (CPU) and GPU-enabled computational performance of our system in benchmark testing and practical applications. The experimental results show that the GPU-CPU co-processing of simulated data and actual LSPS experimental data clearly outperformed the multi-core CPU with up to a 22x speedup, depending on computational tasks. Further, we present a comparison of numerical accuracy between GPU and CPU computation to verify the precision of GPU computation. In addition, we show how GPUs can be effectively adapted to improve the performance of commercial image processing software such as Adobe Photoshop. Comparison with Existing Method(s) To our best knowledge, this is the first demonstration of GPU application in neural circuit mapping and electrophysiology-based data processing. Conclusions Together, GPU enabled computation enhances our ability to process large-scale data sets derived from neural circuit mapping studies, allowing for increased processing speeds while retaining data precision. PMID:25277633

  3. Large-scale neural circuit mapping data analysis accelerated with the graphical processing unit (GPU).

    Science.gov (United States)

    Shi, Yulin; Veidenbaum, Alexander V; Nicolau, Alex; Xu, Xiangmin

    2015-01-15

    Modern neuroscience research demands computing power. Neural circuit mapping studies such as those using laser scanning photostimulation (LSPS) produce large amounts of data and require intensive computation for post hoc processing and analysis. Here we report on the design and implementation of a cost-effective desktop computer system for accelerated experimental data processing with recent GPU computing technology. A new version of Matlab software with GPU enabled functions is used to develop programs that run on Nvidia GPUs to harness their parallel computing power. We evaluated both the central processing unit (CPU) and GPU-enabled computational performance of our system in benchmark testing and practical applications. The experimental results show that the GPU-CPU co-processing of simulated data and actual LSPS experimental data clearly outperformed the multi-core CPU with up to a 22× speedup, depending on computational tasks. Further, we present a comparison of numerical accuracy between GPU and CPU computation to verify the precision of GPU computation. In addition, we show how GPUs can be effectively adapted to improve the performance of commercial image processing software such as Adobe Photoshop. To our best knowledge, this is the first demonstration of GPU application in neural circuit mapping and electrophysiology-based data processing. Together, GPU enabled computation enhances our ability to process large-scale data sets derived from neural circuit mapping studies, allowing for increased processing speeds while retaining data precision. Copyright © 2014 Elsevier B.V. All rights reserved.

  4. Next-generation transgenic mice for optogenetic analysis of neural circuits

    Directory of Open Access Journals (Sweden)

    Brent eAsrican

    2013-11-01

    Full Text Available Here we characterize several new lines of transgenic mice useful for optogenetic analysis of brain circuit function. These mice express optogenetic probes, such as enhanced halorhodopsin or several different versions of channelrhodopsins, behind various neuron-specific promoters. These mice permit photoinhibition or photostimulation both in vitro and in vivo. Our results also reveal the important influence of fluorescent tags on optogenetic probe expression and function in transgenic mice.

  5. Evolution and analysis of minimal neural circuits for klinotaxis in Caenorhabditis elegans.

    Science.gov (United States)

    Izquierdo, Eduardo J; Lockery, Shawn R

    2010-09-29

    Chemotaxis during sinusoidal locomotion in nematodes captures in simplified form the general problem of how dynamical interactions between the nervous system, body, and environment are exploited in the generation of adaptive behavior. We used an evolutionary algorithm to generate neural networks that exhibit klinotaxis, a common form of chemotaxis in which the direction of locomotion in a chemical gradient closely follows the line of steepest ascent. Sensory inputs and motor outputs of the model networks were constrained to match the inputs and outputs of the Caenorhabditis elegans klinotaxis network. We found that a minimalistic neural network, comprised of an ON-OFF pair of chemosensory neurons and a pair of neck muscle motor neurons, is sufficient to generate realistic klinotaxis behavior. Importantly, emergent properties of model networks reproduced two key experimental observations that they were not designed to fit, suggesting that the model may be operating according to principles similar to those of the biological network. A dynamical systems analysis of 77 evolved networks revealed a novel neural mechanism for spatial orientation behavior. This mechanism provides a testable hypothesis that is likely to accelerate the discovery and analysis of the biological circuitry for chemotaxis in C. elegans.

  6. Circuit analysis for dummies

    CERN Document Server

    Santiago, John

    2013-01-01

    Circuits overloaded from electric circuit analysis? Many universities require that students pursuing a degree in electrical or computer engineering take an Electric Circuit Analysis course to determine who will ""make the cut"" and continue in the degree program. Circuit Analysis For Dummies will help these students to better understand electric circuit analysis by presenting the information in an effective and straightforward manner. Circuit Analysis For Dummies gives you clear-cut information about the topics covered in an electric circuit analysis courses to help

  7. Analysis of the function and intracellular signal transduction mechanism of secreted semaphorins during neural circuit development

    NARCIS (Netherlands)

    Gunput, R.F.

    2011-01-01

    Our ability to perceive, to act and to remember is a reflection of the elaborate synaptic connections and neuronal circuits that make up the brain. The formation of these connections relies on a series of developmental events including axon growth and guidance, synapse formation and cell death. The

  8. Complexity and competition in appetitive and aversive neural circuits

    Directory of Open Access Journals (Sweden)

    Crista L. Barberini

    2012-11-01

    Full Text Available Decision-making often involves using sensory cues to predict possible rewarding or punishing reinforcement outcomes before selecting a course of action. Recent work has revealed complexity in how the brain learns to predict rewards and punishments. Analysis of neural signaling during and after learning in the amygdala and orbitofrontal cortex, two brain areas that process appetitive and aversive stimuli, reveals a dynamic relationship between appetitive and aversive circuits. Specifically, the relationship between signaling in appetitive and aversive circuits in these areas shifts as a function of learning. Furthermore, although appetitive and aversive circuits may often drive opposite behaviors – approaching or avoiding reinforcement depending upon its valence – these circuits can also drive similar behaviors, such as enhanced arousal or attention; these processes also may influence choice behavior. These data highlight the formidable challenges ahead in dissecting how appetitive and aversive neural circuits interact to produce a complex and nuanced range of behaviors.

  9. Astrocytes: Tailored to Support the Demand of Neural Circuits?

    DEFF Research Database (Denmark)

    Rasmussen, Rune

    2017-01-01

    Anatomy, physiology, proteomics, and genomics reveal the prospect of distinct highly specialized astrocyte subtypes within neural circuits.......Anatomy, physiology, proteomics, and genomics reveal the prospect of distinct highly specialized astrocyte subtypes within neural circuits....

  10. Controlling the elements: an optogenetic approach to understanding the neural circuits of fear.

    Science.gov (United States)

    Johansen, Joshua P; Wolff, Steffen B E; Lüthi, Andreas; LeDoux, Joseph E

    2012-06-15

    Neural circuits underlie our ability to interact in the world and to learn adaptively from experience. Understanding neural circuits and how circuit structure gives rise to neural firing patterns or computations is fundamental to our understanding of human experience and behavior. Fear conditioning is a powerful model system in which to study neural circuits and information processing and relate them to learning and behavior. Until recently, technological limitations have made it difficult to study the causal role of specific circuit elements during fear conditioning. However, newly developed optogenetic tools allow researchers to manipulate individual circuit components such as anatomically or molecularly defined cell populations, with high temporal precision. Applying these tools to the study of fear conditioning to control specific neural subpopulations in the fear circuit will facilitate a causal analysis of the role of these circuit elements in fear learning and memory. By combining this approach with in vivo electrophysiological recordings in awake, behaving animals, it will also be possible to determine the functional contribution of specific cell populations to neural processing in the fear circuit. As a result, the application of optogenetics to fear conditioning could shed light on how specific circuit elements contribute to neural coding and to fear learning and memory. Furthermore, this approach may reveal general rules for how circuit structure and neural coding within circuits gives rise to sensory experience and behavior. Copyright © 2012 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  11. A neural circuit for angular velocity computation

    Directory of Open Access Journals (Sweden)

    Samuel B Snider

    2010-12-01

    Full Text Available In one of the most remarkable feats of motor control in the animal world, some Diptera, such as the housefly, can accurately execute corrective flight maneuvers in tens of milliseconds. These reflexive movements are achieved by the halteres, gyroscopic force sensors, in conjunction with rapidly-tunable wing-steering muscles. Specifically, the mechanosensory campaniform sensilla located at the base of the halteres transduce and transform rotation-induced gyroscopic forces into information about the angular velocity of the fly's body. But how exactly does the fly's neural architecture generate the angular velocity from the lateral strain forces on the left and right halteres? To explore potential algorithms, we built a neuro-mechanical model of the rotation detection circuit. We propose a neurobiologically plausible method by which the fly could accurately separate and measure the three-dimensional components of an imposed angular velocity. Our model assumes a single sign-inverting synapse and formally resembles some models of directional selectivity by the retina. Using multidimensional error analysis, we demonstrate the robustness of our model under a variety of input conditions. Our analysis reveals the maximum information available to the fly given its physical architecture and the mathematics governing the rotation-induced forces at the haltere's end knob.

  12. A neural circuit for angular velocity computation.

    Science.gov (United States)

    Snider, Samuel B; Yuste, Rafael; Packer, Adam M

    2010-01-01

    In one of the most remarkable feats of motor control in the animal world, some Diptera, such as the housefly, can accurately execute corrective flight maneuvers in tens of milliseconds. These reflexive movements are achieved by the halteres, gyroscopic force sensors, in conjunction with rapidly tunable wing steering muscles. Specifically, the mechanosensory campaniform sensilla located at the base of the halteres transduce and transform rotation-induced gyroscopic forces into information about the angular velocity of the fly's body. But how exactly does the fly's neural architecture generate the angular velocity from the lateral strain forces on the left and right halteres? To explore potential algorithms, we built a neuromechanical model of the rotation detection circuit. We propose a neurobiologically plausible method by which the fly could accurately separate and measure the three-dimensional components of an imposed angular velocity. Our model assumes a single sign-inverting synapse and formally resembles some models of directional selectivity by the retina. Using multidimensional error analysis, we demonstrate the robustness of our model under a variety of input conditions. Our analysis reveals the maximum information available to the fly given its physical architecture and the mathematics governing the rotation-induced forces at the haltere's end knob.

  13. Circuit analysis with Multisim

    CERN Document Server

    Baez-Lopez, David

    2011-01-01

    This book is concerned with circuit simulation using National Instruments Multisim. It focuses on the use and comprehension of the working techniques for electrical and electronic circuit simulation. The first chapters are devoted to basic circuit analysis.It starts by describing in detail how to perform a DC analysis using only resistors and independent and controlled sources. Then, it introduces capacitors and inductors to make a transient analysis. In the case of transient analysis, it is possible to have an initial condition either in the capacitor voltage or in the inductor current, or bo

  14. Robust information propagation through noisy neural circuits.

    Science.gov (United States)

    Zylberberg, Joel; Pouget, Alexandre; Latham, Peter E; Shea-Brown, Eric

    2017-04-01

    Sensory neurons give highly variable responses to stimulation, which can limit the amount of stimulus information available to downstream circuits. Much work has investigated the factors that affect the amount of information encoded in these population responses, leading to insights about the role of covariability among neurons, tuning curve shape, etc. However, the informativeness of neural responses is not the only relevant feature of population codes; of potentially equal importance is how robustly that information propagates to downstream structures. For instance, to quantify the retina's performance, one must consider not only the informativeness of the optic nerve responses, but also the amount of information that survives the spike-generating nonlinearity and noise corruption in the next stage of processing, the lateral geniculate nucleus. Our study identifies the set of covariance structures for the upstream cells that optimize the ability of information to propagate through noisy, nonlinear circuits. Within this optimal family are covariances with "differential correlations", which are known to reduce the information encoded in neural population activities. Thus, covariance structures that maximize information in neural population codes, and those that maximize the ability of this information to propagate, can be very different. Moreover, redundancy is neither necessary nor sufficient to make population codes robust against corruption by noise: redundant codes can be very fragile, and synergistic codes can-in some cases-optimize robustness against noise.

  15. Robust information propagation through noisy neural circuits.

    Directory of Open Access Journals (Sweden)

    Joel Zylberberg

    2017-04-01

    Full Text Available Sensory neurons give highly variable responses to stimulation, which can limit the amount of stimulus information available to downstream circuits. Much work has investigated the factors that affect the amount of information encoded in these population responses, leading to insights about the role of covariability among neurons, tuning curve shape, etc. However, the informativeness of neural responses is not the only relevant feature of population codes; of potentially equal importance is how robustly that information propagates to downstream structures. For instance, to quantify the retina's performance, one must consider not only the informativeness of the optic nerve responses, but also the amount of information that survives the spike-generating nonlinearity and noise corruption in the next stage of processing, the lateral geniculate nucleus. Our study identifies the set of covariance structures for the upstream cells that optimize the ability of information to propagate through noisy, nonlinear circuits. Within this optimal family are covariances with "differential correlations", which are known to reduce the information encoded in neural population activities. Thus, covariance structures that maximize information in neural population codes, and those that maximize the ability of this information to propagate, can be very different. Moreover, redundancy is neither necessary nor sufficient to make population codes robust against corruption by noise: redundant codes can be very fragile, and synergistic codes can-in some cases-optimize robustness against noise.

  16. The neural circuit basis of learning

    Science.gov (United States)

    Patrick, Kaifosh William John

    The astounding capacity for learning ranks among the nervous system's most impressive features. This thesis comprises studies employing varied approaches to improve understanding, at the level of neural circuits, of the brain's capacity for learning. The first part of the thesis contains investigations of hippocampal circuitry -- both theoretical work and experimental work in the mouse Mus musculus -- as a model system for declarative memory. To begin, Chapter 2 presents a theory of hippocampal memory storage and retrieval that reflects nonlinear dendritic processing within hippocampal pyramidal neurons. As a prelude to the experimental work that comprises the remainder of this part, Chapter 3 describes an open source software platform that we have developed for analysis of data acquired with in vivo Ca2+ imaging, the main experimental technique used throughout the remainder of this part of the thesis. As a first application of this technique, Chapter 4 characterizes the content of signaling at synapses between GABAergic neurons of the medial septum and interneurons in stratum oriens of hippocampal area CA1. Chapter 5 then combines these techniques with optogenetic, pharmacogenetic, and pharmacological manipulations to uncover inhibitory circuit mechanisms underlying fear learning. The second part of this thesis focuses on the cerebellum-like electrosensory lobe in the weakly electric mormyrid fish Gnathonemus petersii, as a model system for non-declarative memory. In Chapter 6, we study how short-duration EOD motor commands are recoded into a complex temporal basis in the granule cell layer, which can be used to cancel Purkinje-like cell firing to the longer duration and temporally varying EOD-driven sensory responses. In Chapter 7, we consider not only the temporal aspects of the granule cell code, but also the encoding of body position provided from proprioceptive and efference copy sources. Together these studies clarify how the cerebellum-like circuitry of the

  17. Neural circuit mechanisms of posttraumatic epilepsy

    Directory of Open Access Journals (Sweden)

    Robert F Hunt

    2013-06-01

    Full Text Available Traumatic brain injury (TBI greatly increases the risk for a number of mental health problems and is one of the most common causes of medically intractable epilepsy in humans. Several models of TBI have been developed to investigate the relationship between trauma, seizures, and epilepsy-related changes in neural circuit function. These studies have shown that the brain initiates immediate neuronal and glial responses following an injury, usually leading to significant cell loss in areas of the injured brain. Over time, long-term changes in the organization of neural circuits, particularly in neocortex and hippocampus, lead to an imbalance between excitatory and inhibitory neurotransmission and increased risk for spontaneous seizures. These include alterations to inhibitory interneurons and formation of new, excessive recurrent excitatory synaptic connectivity. Here, we review in vivo models of TBI as well as key cellular mechanisms of synaptic reorganization associated with posttraumatic epilepsy. The potential role of inflammation and increased blood brain barrier permeability in the pathophysiology of posttraumatic epilepsy is also discussed. A better understanding of mechanisms that promote the generation of epileptic activity versus those that promote compensatory brain repair and functional recovery should aid development of successful new therapies for posttraumatic epilepsy.

  18. Marginalization in neural circuits with divisive normalization

    Science.gov (United States)

    Beck, J.M.; Latham, P.E.; Pouget, A.

    2011-01-01

    A wide range of computations performed by the nervous system involves a type of probabilistic inference known as marginalization. This computation comes up in seemingly unrelated tasks, including causal reasoning, odor recognition, motor control, visual tracking, coordinate transformations, visual search, decision making, and object recognition, to name just a few. The question we address here is: how could neural circuits implement such marginalizations? We show that when spike trains exhibit a particular type of statistics – associated with constant Fano factors and gain-invariant tuning curves, as is often reported in vivo – some of the more common marginalizations can be achieved with networks that implement a quadratic nonlinearity and divisive normalization, the latter being a type of nonlinear lateral inhibition that has been widely reported in neural circuits. Previous studies have implicated divisive normalization in contrast gain control and attentional modulation. Our results raise the possibility that it is involved in yet another, highly critical, computation: near optimal marginalization in a remarkably wide range of tasks. PMID:22031877

  19. Explicit logic circuits discriminate neural states.

    Directory of Open Access Journals (Sweden)

    Lane Yoder

    Full Text Available The magnitude and apparent complexity of the brain's connectivity have left explicit networks largely unexplored. As a result, the relationship between the organization of synaptic connections and how the brain processes information is poorly understood. A recently proposed retinal network that produces neural correlates of color vision is refined and extended here to a family of general logic circuits. For any combination of high and low activity in any set of neurons, one of the logic circuits can receive input from the neurons and activate a single output neuron whenever the input neurons have the given activity state. The strength of the output neuron's response is a measure of the difference between the smallest of the high inputs and the largest of the low inputs. The networks generate correlates of known psychophysical phenomena. These results follow directly from the most cost-effective architectures for specific logic circuits and the minimal cellular capabilities of excitation and inhibition. The networks function dynamically, making their operation consistent with the speed of most brain functions. The networks show that well-known psychophysical phenomena do not require extraordinarily complex brain structures, and that a single network architecture can produce apparently disparate phenomena in different sensory systems.

  20. The Complexity of Dynamics in Small Neural Circuits.

    Directory of Open Access Journals (Sweden)

    Diego Fasoli

    2016-08-01

    Full Text Available Mean-field approximations are a powerful tool for studying large neural networks. However, they do not describe well the behavior of networks composed of a small number of neurons. In this case, major differences between the mean-field approximation and the real behavior of the network can arise. Yet, many interesting problems in neuroscience involve the study of mesoscopic networks composed of a few tens of neurons. Nonetheless, mathematical methods that correctly describe networks of small size are still rare, and this prevents us to make progress in understanding neural dynamics at these intermediate scales. Here we develop a novel systematic analysis of the dynamics of arbitrarily small networks composed of homogeneous populations of excitatory and inhibitory firing-rate neurons. We study the local bifurcations of their neural activity with an approach that is largely analytically tractable, and we numerically determine the global bifurcations. We find that for strong inhibition these networks give rise to very complex dynamics, caused by the formation of multiple branching solutions of the neural dynamics equations that emerge through spontaneous symmetry-breaking. This qualitative change of the neural dynamics is a finite-size effect of the network, that reveals qualitative and previously unexplored differences between mesoscopic cortical circuits and their mean-field approximation. The most important consequence of spontaneous symmetry-breaking is the ability of mesoscopic networks to regulate their degree of functional heterogeneity, which is thought to help reducing the detrimental effect of noise correlations on cortical information processing.

  1. Dynamical foundations of the neural circuit for bayesian decision making.

    Science.gov (United States)

    Morita, Kenji

    2009-07-01

    On the basis of accumulating behavioral and neural evidences, it has recently been proposed that the brain neural circuits of humans and animals are equipped with several specific properties, which ensure that perceptual decision making implemented by the circuits can be nearly optimal in terms of Bayesian inference. Here, I introduce the basic ideas of such a proposal and discuss its implications from the standpoint of biophysical modeling developed in the framework of dynamical systems.

  2. Neural Control of Energy Balance: Translating Circuits to Therapies

    OpenAIRE

    Gautron, Laurent; Elmquist, Joel K.; Williams, Kevin W.

    2015-01-01

    Recent insights into the neural circuits controlling energy balance and glucose homeostasis have rekindled the hope for development of novel treatments for obesity and diabetes. However, many therapies contribute relatively modest beneficial gains with accompanying side effects, and the mechanisms of action for other interventions remain undefined. This Review summarizes current knowledge linking the neural circuits regulating energy and glucose balance with current and potential pharmacother...

  3. Classes of feedforward neural networks and their circuit complexity

    NARCIS (Netherlands)

    Shawe-Taylor, John S.; Anthony, Martin H.G.; Kern, Walter

    1992-01-01

    This paper aims to place neural networks in the context of boolean circuit complexity. We define appropriate classes of feedforward neural networks with specified fan-in, accuracy of computation and depth and using techniques of communication complexity proceed to show that the classes fit into a

  4. Color Coding of Circuit Quantities in Introductory Circuit Analysis Instruction

    Science.gov (United States)

    Reisslein, Jana; Johnson, Amy M.; Reisslein, Martin

    2015-01-01

    Learning the analysis of electrical circuits represented by circuit diagrams is often challenging for novice students. An open research question in electrical circuit analysis instruction is whether color coding of the mathematical symbols (variables) that denote electrical quantities can improve circuit analysis learning. The present study…

  5. Localizing complex neural circuits with MEG data.

    Science.gov (United States)

    Belardinelli, P; Ciancetta, L; Pizzella, V; Del Gratta, C; Romani, G L

    2006-03-01

    During cognitive processing, the various cortical areas, with specialized functions, supply for different tasks. In most cases then, the information flows are processed in a parallel way by brain networks which work together integrating the single performances for a common goal. Such a step is generally performed at higher processing levels in the associative areas. The frequency range at which neuronal pools oscillate is generally wider than the one which is detectable by bold changes in fMRI studies. A high time resolution technique like magnetoencephalography or electroencephalography is therefore required as well as new data processing algorithms for detecting different coherent brain areas cooperating for one cognitive task. Our experiments show that no algorithm for the inverse problem solution is immune from bias. We propose therefore, as a possible solution, our software LOCANTO (LOcalization and Coherence ANalysis TOol). This new package features a set of tools for the detection of coherent areas. For such a task, as a default, it employs the algorithm with best performances for the neural landscape to be detected. If the neural landscape under attention involves more than two interacting areas the SLoreta algorithm is used. Our study shows in fact that SLoreta performance is not biased when the correlation among multiple sources is high. On the other hand, the Beamforming algorithm is more precise than SLoreta at localizing single or double sources but it gets a relevant localization bias when the sources are more than three and are highly correlated.

  6. Japanese studies on neural circuits and behavior of Caenorhabditis elegans

    Science.gov (United States)

    Sasakura, Hiroyuki; Tsukada, Yuki; Takagi, Shin; Mori, Ikue

    2013-01-01

    The nematode Caenorhabditis elegans is an ideal organism for studying neural plasticity and animal behaviors. A total of 302 neurons of a C. elegans hermaphrodite have been classified into 118 neuronal groups. This simple neural circuit provides a solid basis for understanding the mechanisms of the brains of higher animals, including humans. Recent studies that employ modern imaging and manipulation techniques enable researchers to study the dynamic properties of nervous systems with great precision. Behavioral and molecular genetic analyses of this tiny animal have contributed greatly to the advancement of neural circuit research. Here, we will review the recent studies on the neural circuits of C. elegans that have been conducted in Japan. Several laboratories have established unique and clever methods to study the underlying neuronal substrates of behavioral regulation in C. elegans. The technological advances applied to studies of C. elegans have allowed new approaches for the studies of complex neural systems. Through reviewing the studies on the neuronal circuits of C. elegans in Japan, we will analyze and discuss the directions of neural circuit studies. PMID:24348340

  7. Anomalous neural circuit function in schizophrenia during a virtual Morris water task.

    Science.gov (United States)

    Folley, Bradley S; Astur, Robert; Jagannathan, Kanchana; Calhoun, Vince D; Pearlson, Godfrey D

    2010-02-15

    Previous studies have reported learning and navigation impairments in schizophrenia patients during virtual reality allocentric learning tasks. The neural bases of these deficits have not been explored using functional MRI despite well-explored anatomic characterization of these paradigms in non-human animals. Our objective was to characterize the differential distributed neural circuits involved in virtual Morris water task performance using independent component analysis (ICA) in schizophrenia patients and controls. Additionally, we present behavioral data in order to derive relationships between brain function and performance, and we have included a general linear model-based analysis in order to exemplify the incremental and differential results afforded by ICA. Thirty-four individuals with schizophrenia and twenty-eight healthy controls underwent fMRI scanning during a block design virtual Morris water task using hidden and visible platform conditions. Independent components analysis was used to deconstruct neural contributions to hidden and visible platform conditions for patients and controls. We also examined performance variables, voxel-based morphometry and hippocampal subparcellation, and regional BOLD signal variation. Independent component analysis identified five neural circuits. Mesial temporal lobe regions, including the hippocampus, were consistently task-related across conditions and groups. Frontal, striatal, and parietal circuits were recruited preferentially during the visible condition for patients, while frontal and temporal lobe regions were more saliently recruited by controls during the hidden platform condition. Gray matter concentrations and BOLD signal in hippocampal subregions were associated with task performance in controls but not patients. Patients exhibited impaired performance on the hidden and visible conditions of the task, related to negative symptom severity. While controls showed coupling between neural circuits, regional

  8. Functional neural circuits that underlie developmental stuttering.

    Directory of Open Access Journals (Sweden)

    Jianping Qiao

    Full Text Available The aim of this study was to identify differences in functional and effective brain connectivity between persons who stutter (PWS and typically developing (TD fluent speakers, and to assess whether those differences can serve as biomarkers to distinguish PWS from TD controls. We acquired resting-state functional magnetic resonance imaging data in 44 PWS and 50 TD controls. We then used Independent Component Analysis (ICA together with Hierarchical Partner Matching (HPM to identify networks of robust, functionally connected brain regions that were highly reproducible across participants, and we assessed whether connectivity differed significantly across diagnostic groups. We then used Granger Causality (GC to study the causal interactions (effective connectivity between the regions that ICA and HPM identified. Finally, we used a kernel support vector machine to assess how well these measures of functional connectivity and granger causality discriminate PWS from TD controls. Functional connectivity was stronger in PWS compared with TD controls in the supplementary motor area (SMA and primary motor cortices, but weaker in inferior frontal cortex (IFG, Broca's area, caudate, putamen, and thalamus. Additionally, causal influences were significantly weaker in PWS from the IFG to SMA, and from the basal ganglia to IFG through the thalamus, compared to TD controls. ICA and GC indices together yielded an accuracy of 92.7% in classifying PWS from TD controls. Our findings suggest the presence of dysfunctional circuits that support speech planning and timing cues for the initiation and execution of motor sequences in PWS. Our high accuracy of classification further suggests that these aberrant brain features may serve as robust biomarkers for PWS.

  9. Functional neural circuits that underlie developmental stuttering

    Science.gov (United States)

    Zhao, Guihu; Huo, Yuankai; Herder, Carl L.; Sikora, Chamonix O.; Peterson, Bradley S.

    2017-01-01

    The aim of this study was to identify differences in functional and effective brain connectivity between persons who stutter (PWS) and typically developing (TD) fluent speakers, and to assess whether those differences can serve as biomarkers to distinguish PWS from TD controls. We acquired resting-state functional magnetic resonance imaging data in 44 PWS and 50 TD controls. We then used Independent Component Analysis (ICA) together with Hierarchical Partner Matching (HPM) to identify networks of robust, functionally connected brain regions that were highly reproducible across participants, and we assessed whether connectivity differed significantly across diagnostic groups. We then used Granger Causality (GC) to study the causal interactions (effective connectivity) between the regions that ICA and HPM identified. Finally, we used a kernel support vector machine to assess how well these measures of functional connectivity and granger causality discriminate PWS from TD controls. Functional connectivity was stronger in PWS compared with TD controls in the supplementary motor area (SMA) and primary motor cortices, but weaker in inferior frontal cortex (IFG, Broca’s area), caudate, putamen, and thalamus. Additionally, causal influences were significantly weaker in PWS from the IFG to SMA, and from the basal ganglia to IFG through the thalamus, compared to TD controls. ICA and GC indices together yielded an accuracy of 92.7% in classifying PWS from TD controls. Our findings suggest the presence of dysfunctional circuits that support speech planning and timing cues for the initiation and execution of motor sequences in PWS. Our high accuracy of classification further suggests that these aberrant brain features may serve as robust biomarkers for PWS. PMID:28759567

  10. Neural control of energy balance: translating circuits to therapies.

    Science.gov (United States)

    Gautron, Laurent; Elmquist, Joel K; Williams, Kevin W

    2015-03-26

    Recent insights into the neural circuits controlling energy balance and glucose homeostasis have rekindled the hope for development of novel treatments for obesity and diabetes. However, many therapies contribute relatively modest beneficial gains with accompanying side effects, and the mechanisms of action for other interventions remain undefined. This Review summarizes current knowledge linking the neural circuits regulating energy and glucose balance with current and potential pharmacotherapeutic and surgical interventions for the treatment of obesity and diabetes. Copyright © 2015 Elsevier Inc. All rights reserved.

  11. Genetic control of active neural circuits

    Directory of Open Access Journals (Sweden)

    Leon Reijmers

    2009-12-01

    Full Text Available The use of molecular tools to study the neurobiology of complex behaviors has been hampered by an inability to target the desired changes to relevant groups of neurons. Specific memories and specific sensory representations are sparsely encoded by a small fraction of neurons embedded in a sea of morphologically and functionally similar cells. In this review we discuss genetics techniques that are being developed to address this difficulty. In several studies the use of promoter elements that are responsive to neural activity have been used to drive long lasting genetic alterations into neural ensembles that are activated by natural environmental stimuli. This approach has been used to examine neural activity patterns during learning and retrieval of a memory, to examine the regulation of receptor trafficking following learning and to functionally manipulate a specific memory trace. We suggest that these techniques will provide a general approach to experimentally investigate the link between patterns of environmentally activated neural firing and cognitive processes such as perception and memory.

  12. Adaptive Neurotechnology for Making Neural Circuits Functional .

    Science.gov (United States)

    Jung, Ranu

    2008-03-01

    Two of the most important trends in recent technological developments are that technology is increasingly integrated with biological systems and that it is increasingly adaptive in its capabilities. Neuroprosthetic systems that provide lost sensorimotor function after a neural disability offer a platform to investigate this interplay between biological and engineered systems. Adaptive neurotechnology (hardware and software) could be designed to be biomimetic, guided by the physical and programmatic constraints observed in biological systems, and allow for real-time learning, stability, and error correction. An example will present biomimetic neural-network hardware that can be interfaced with the isolated spinal cord of a lower vertebrate to allow phase-locked real-time neural control. Another will present adaptive neural network control algorithms for functional electrical stimulation of the peripheral nervous system to provide desired movements of paralyzed limbs in rodents or people. Ultimately, the frontier lies in being able to utilize the adaptive neurotechnology to promote neuroplasticity in the living system on a long-time scale under co-adaptive conditions.

  13. Integrated Circuit For Simulation Of Neural Network

    Science.gov (United States)

    Thakoor, Anilkumar P.; Moopenn, Alexander W.; Khanna, Satish K.

    1988-01-01

    Ballast resistors deposited on top of circuit structure. Cascadable, programmable binary connection matrix fabricated in VLSI form as basic building block for assembly of like units into content-addressable electronic memory matrices operating somewhat like networks of neurons. Connections formed during storage of data, and data recalled from memory by prompting matrix with approximate or partly erroneous signals. Redundancy in pattern of connections causes matrix to respond with correct stored data.

  14. Integrating Neural Circuits Controlling Female Sexual Behavior

    Directory of Open Access Journals (Sweden)

    Paul E. Micevych

    2017-06-01

    Full Text Available The hypothalamus is most often associated with innate behaviors such as is hunger, thirst and sex. While the expression of these behaviors important for survival of the individual or the species is nested within the hypothalamus, the desire (i.e., motivation for them is centered within the mesolimbic reward circuitry. In this review, we will use female sexual behavior as a model to examine the interaction of these circuits. We will examine the evidence for a hypothalamic circuit that regulates consummatory aspects of reproductive behavior, i.e., lordosis behavior, a measure of sexual receptivity that involves estradiol membrane-initiated signaling in the arcuate nucleus (ARH, activating β-endorphin projections to the medial preoptic nucleus (MPN, which in turn modulate ventromedial hypothalamic nucleus (VMH activity—the common output from the hypothalamus. Estradiol modulates not only a series of neuropeptides, transmitters and receptors but induces dendritic spines that are for estrogenic induction of lordosis behavior. Simultaneously, in the nucleus accumbens of the mesolimbic system, the mating experience produces long term changes in dopamine signaling and structure. Sexual experience sensitizes the response of nucleus accumbens neurons to dopamine signaling through the induction of a long lasting early immediate gene. While estrogen alone increases spines in the ARH, sexual experience increases dendritic spine density in the nucleus accumbens. These two circuits appear to converge onto the medial preoptic area where there is a reciprocal influence of motivational circuits on consummatory behavior and vice versa. While it has not been formally demonstrated in the human, such circuitry is generally highly conserved and thus, understanding the anatomy, neurochemistry and physiology can provide useful insight into the motivation for sexual behavior and other innate behaviors in humans.

  15. Classical Conditioning with Pulsed Integrated Neural Networks: Circuits and System

    DEFF Research Database (Denmark)

    Lehmann, Torsten

    1998-01-01

    In this paper we investigate on-chip learning for pulsed, integrated neural networks. We discuss the implementational problems the technology imposes on learning systems and we find that abiologically inspired approach using simple circuit structures is most likely to bring success. We develop a ...... chip to solve simple classical conditioning tasks, thus verifying the design methodologies put forward in the paper....

  16. Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks

    NARCIS (Netherlands)

    de Bruin, T.D.; Verbert, K.A.J.; Babuska, R.

    2017-01-01

    Timely detection and identification of faults in railway track circuits are crucial for the safety and availability of railway networks. In this paper, the use of the long-short-term memory (LSTM) recurrent neural network is proposed to accomplish these tasks based on the commonly available

  17. Railway track circuit fault diagnosis using recurrent neural networks

    NARCIS (Netherlands)

    de Bruin, T.D.; Verbert, K.A.J.; Babuska, R.

    2017-01-01

    Timely detection and identification of faults in railway track circuits are crucial for the safety and availability of railway networks. In this paper, the use of the long-short-term memory (LSTM) recurrent neural network is proposed to accomplish these tasks based on the commonly available

  18. Distinct neural circuits subserve interpersonal and non-interpersonal emotions.

    Science.gov (United States)

    Landa, Alla; Wang, Zhishun; Russell, James A; Posner, Jonathan; Duan, Yunsuo; Kangarlu, Alayar; Huo, Yuankai; Fallon, Brian A; Peterson, Bradley S

    2013-01-01

    Emotions elicited by interpersonal versus non-interpersonal experiences have different effects on neurobiological functioning in both animals and humans. However, the extent to which the brain circuits underlying interpersonal and non-interpersonal emotions are distinct still remains unclear. The goal of our study was to assess whether different neural circuits are implicated in the processing of arousal and valence of interpersonal versus non-interpersonal emotions. During functional magnetic resonance imaging, participants imagined themselves in emotion-eliciting interpersonal or non-interpersonal situations and then rated the arousal and valence of emotions they experienced. We identified (1) separate neural circuits that are implicated in the arousal and valence dimensions of interpersonal versus non-interpersonal emotions, (2) circuits that are implicated in arousal and valence for both types of emotion, and (3) circuits that are responsive to the type of emotion, regardless of the valence or arousal level of the emotion. We found extensive recruitment of limbic (for arousal) and temporal-parietal (for valence) systems associated with processing of specifically interpersonal emotions compared to non-interpersonal ones. The neural bases of interpersonal and non-interpersonal emotions may, therefore, be largely distinct.

  19. Distinct Neural Circuits Subserve Interpersonal and Non-interpersonal Emotions

    Science.gov (United States)

    Landa, Alla; Wang, Zhishun; Russell, James A.; Posner, Jonathan; Duan, Yunsuo; Kangarlu, Alayar; Huo, Yuankai; Fallon, Brian A.; Peterson, Bradley S.

    2013-01-01

    Emotions elicited by interpersonal versus non-interpersonal experiences have different effects on neurobiological functioning in both animals and humans. However, the extent to which the brain circuits underlying interpersonal and non-interpersonal emotions are distinct still remains unclear. The goal of our study was to assess whether different neural circuits are implicated in the processing of arousal and valence of interpersonal versus non-interpersonal emotions. During functional magnetic resonance imaging, participants imagined themselves in emotion-eliciting interpersonal or non-interpersonal situations and then rated the arousal and valence of emotions they experienced. We identified (a) separate neural circuits that are implicated in the arousal and valence dimensions of interpersonal versus non-interpersonal emotions, (b) circuits that are implicated in arousal and valence for both types of emotion, and (c) circuits that are responsive to the type of emotion, regardless of the valence or arousal level of the emotion. We found extensive recruitment of limbic (for arousal) and temporal-parietal (for valence) systems associated with processing of specifically interpersonal emotions compared to non-interpersonal ones. The neural bases of interpersonal and non-interpersonal emotions may, therefore, be largely distinct. PMID:24028312

  20. Hox genes: choreographers in neural development, architects of circuit organization.

    Science.gov (United States)

    Philippidou, Polyxeni; Dasen, Jeremy S

    2013-10-02

    The neural circuits governing vital behaviors, such as respiration and locomotion, are comprised of discrete neuronal populations residing within the brainstem and spinal cord. Work over the past decade has provided a fairly comprehensive understanding of the developmental pathways that determine the identity of major neuronal classes within the neural tube. However, the steps through which neurons acquire the subtype diversities necessary for their incorporation into a particular circuit are still poorly defined. Studies on the specification of motor neurons indicate that the large family of Hox transcription factors has a key role in generating the subtypes required for selective muscle innervation. There is also emerging evidence that Hox genes function in multiple neuronal classes to shape synaptic specificity during development, suggesting a broader role in circuit assembly. This Review highlights the functions and mechanisms of Hox gene networks and their multifaceted roles during neuronal specification and connectivity. Copyright © 2013 Elsevier Inc. All rights reserved.

  1. Neuronify: An Educational Simulator for Neural Circuits.

    Science.gov (United States)

    Dragly, Svenn-Arne; Hobbi Mobarhan, Milad; Våvang Solbrå, Andreas; Tennøe, Simen; Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne; Hafting, Torkel; Einevoll, Gaute T

    2017-01-01

    Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker). We aim to provide a low entry point to simulation-based neuroscience by allowing students with no programming experience to create and simulate neural networks. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt and runs on smart phones (Android, iOS) and tablet computers as well personal computers (Windows, Mac, Linux).

  2. Clustered Protocadherins Are Required for Building Functional Neural Circuits

    Science.gov (United States)

    Hasegawa, Sonoko; Kobayashi, Hiroaki; Kumagai, Makiko; Nishimaru, Hiroshi; Tarusawa, Etsuko; Kanda, Hiro; Sanbo, Makoto; Yoshimura, Yumiko; Hirabayashi, Masumi; Hirabayashi, Takahiro; Yagi, Takeshi

    2017-01-01

    Neuronal identity is generated by the cell-surface expression of clustered protocadherin (Pcdh) isoforms. In mice, 58 isoforms from three gene clusters, Pcdhα, Pcdhβ, and Pcdhγ, are differentially expressed in neurons. Since cis-heteromeric Pcdh oligomers on the cell surface interact homophilically with that in other neurons in trans, it has been thought that the Pcdh isoform repertoire determines the binding specificity of synapses. We previously described the cooperative functions of isoforms from all three Pcdh gene clusters in neuronal survival and synapse formation in the spinal cord. However, the neuronal loss and the following neonatal lethality prevented an analysis of the postnatal development and characteristics of the clustered-Pcdh-null (Δαβγ) neural circuits. Here, we used two methods, one to generate the chimeric mice that have transplanted Δαβγ neurons into mouse embryos, and the other to generate double mutant mice harboring null alleles of both the Pcdh gene and the proapoptotic gene Bax to prevent neuronal loss. First, our results showed that the surviving chimeric mice that had a high contribution of Δαβγ cells exhibited paralysis and died in the postnatal period. An analysis of neuronal survival in postnatally developing brain regions of chimeric mice clarified that many Δαβγ neurons in the forebrain were spared from apoptosis, unlike those in the reticular formation of the brainstem. Second, in Δαβγ/Bax null double mutants, the central pattern generator (CPG) for locomotion failed to create a left-right alternating pattern even in the absence of neurodegeneraton. Third, calcium imaging of cultured hippocampal neurons showed that the network activity of Δαβγ neurons tended to be more synchronized and lost the variability in the number of simultaneously active neurons observed in the control network. Lastly, a comparative analysis for trans-homophilic interactions of the exogenously introduced single Pcdh-γA3 isoforms

  3. Clustered Protocadherins Are Required for Building Functional Neural Circuits

    Directory of Open Access Journals (Sweden)

    Takeshi Yagi

    2017-04-01

    Full Text Available Neuronal identity is generated by the cell-surface expression of clustered protocadherin (Pcdh isoforms. In mice, 58 isoforms from three gene clusters, Pcdhα, Pcdhβ, and Pcdhγ, are differentially expressed in neurons. Since cis-heteromeric Pcdh oligomers on the cell surface interact homophilically with that in other neurons in trans, it has been thought that the Pcdh isoform repertoire determines the binding specificity of synapses. We previously described the cooperative functions of isoforms from all three Pcdh gene clusters in neuronal survival and synapse formation in the spinal cord. However, the neuronal loss and the following neonatal lethality prevented an analysis of the postnatal development and characteristics of the clustered-Pcdh-null (Δαβγ neural circuits. Here, we used two methods, one to generate the chimeric mice that have transplanted Δαβγ neurons into mouse embryos, and the other to generate double mutant mice harboring null alleles of both the Pcdh gene and the proapoptotic gene Bax to prevent neuronal loss. First, our results showed that the surviving chimeric mice that had a high contribution of Δαβγ cells exhibited paralysis and died in the postnatal period. An analysis of neuronal survival in postnatally developing brain regions of chimeric mice clarified that many Δαβγ neurons in the forebrain were spared from apoptosis, unlike those in the reticular formation of the brainstem. Second, in Δαβγ/Bax null double mutants, the central pattern generator (CPG for locomotion failed to create a left-right alternating pattern even in the absence of neurodegeneraton. Third, calcium imaging of cultured hippocampal neurons showed that the network activity of Δαβγ neurons tended to be more synchronized and lost the variability in the number of simultaneously active neurons observed in the control network. Lastly, a comparative analysis for trans-homophilic interactions of the exogenously introduced single

  4. Developmental plasticity in neural circuits for a learned behavior.

    Science.gov (United States)

    Bottjer, S W; Arnold, A P

    1997-01-01

    The neural substrate underlying learned vocal behavior in songbirds provides a textbook illustration of anatomical localization of function for a complex learned behavior in vertebrates. The song-control system has become an important model for studying neural systems related to learning, behavior, and development. The song system of zebra finches is characterized by a heightened capacity for both neural and behavioral change during development and has taught us valuable information regarding sensitive periods, rearrangement of synaptic connections, topographic specificity, cell death and neurogenesis, experience-dependent neural plasticity, and sexual differentiation. The song system differs in some interesting ways from some well-studied mammalian model systems and thus offers fresh perspectives on specific theoretical issues. In this highly selective review, we concentrate on two major questions: What are the developmental changes in the song system responsible for song learning and the restriction of learning to a sensitive period, and what factors explain the highly sexually dimorphic development of this system? We discuss the important role of sex steroid hormones and of neurotrophins in creating a male-typical neural song circuit (which can learn to produce complex vocalizations) instead of a reduced, female-typical song circuit that does not produce learned song.

  5. Synchrony and neural coding in cerebellar circuits

    Directory of Open Access Journals (Sweden)

    Abigail L Person

    2012-12-01

    circuits.

  6. Breathtaking Songs: Coordinating the Neural Circuits for Breathing and Singing.

    Science.gov (United States)

    Schmidt, Marc F; Goller, Franz

    2016-11-01

    The vocal behavior of birds is remarkable for its diversity, and songs can feature elaborate characteristics such as long duration, rapid temporal pattern, and broad frequency range. The respiratory system plays a central role in generating the complex song patterns that must be integrated with its life-sustaining functions. Here, we explore how precise coordination between the neural circuits for breathing and singing is fundamental to production of these remarkable behaviors. ©2016 Int. Union Physiol. Sci./Am. Physiol. Soc.

  7. Oxytocin modulation of neural circuits for social behavior.

    Science.gov (United States)

    Marlin, Bianca J; Froemke, Robert C

    2017-02-01

    Oxytocin is a hypothalamic neuropeptide that has gained attention for the effects on social behavior. Recent findings shed new light on the mechanisms of oxytocin in synaptic plasticity and adaptively modifying neural circuits for social interactions such as conspecific recognition, pair bonding, and maternal care. Here, we review several of these newer studies on oxytocin in the context of previous findings, with an emphasis on social behavior and circuit plasticity in various brain regions shown to be enriched for oxytocin receptors. We provide a framework that highlights current circuit-level mechanisms underlying the widespread action of oxytocin. © 2016 Wiley Periodicals, Inc. Develop Neurobiol 77: 169-189, 2017. © 2016 Wiley Periodicals, Inc.

  8. KCNQ potassium channels in sensory system and neural circuits.

    Science.gov (United States)

    Wang, Jing-jing; Li, Yang

    2016-01-01

    M channels, an important regulator of neural excitability, are composed of four subunits of the Kv7 (KCNQ) K(+) channel family. M channels were named as such because their activity was suppressed by stimulation of muscarinic acetylcholine receptors. These channels are of particular interest because they are activated at the subthreshold membrane potentials. Furthermore, neural KCNQ channels are drug targets for the treatments of epilepsy and a variety of neurological disorders, including chronic and neuropathic pain, deafness, and mental illness. This review will update readers on the roles of KCNQ channels in the sensory system and neural circuits as well as discuss their respective mechanisms and the implications for physiology and medicine. We will also consider future perspectives and the development of additional pharmacological models, such as seizure, stroke, pain and mental illness, which work in combination with drug-design targeting of KCNQ channels. These models will hopefully deepen our understanding of KCNQ channels and provide general therapeutic prospects of related channelopathies.

  9. Neural processing of gustatory information in insular circuits.

    Science.gov (United States)

    Maffei, Arianna; Haley, Melissa; Fontanini, Alfredo

    2012-08-01

    The insular cortex is the primary cortical site devoted to taste processing. A large body of evidence is available for how insular neurons respond to gustatory stimulation in both anesthetized and behaving animals. Most of the reports describe broadly tuned neurons that are involved in processing the chemosensory, physiological and psychological aspects of gustatory experience. However little is known about how these neural responses map onto insular circuits. Particularly mysterious is the functional role of the three subdivisions of the insular cortex: the granular, the dysgranular and the agranular insular cortices. In this article we review data on the organization of the local and long-distance circuits in the three subdivisions. The functional significance of these results is discussed in light of the latest electrophysiological data. A view of the insular cortex as a functionally integrated system devoted to processing gustatory, multimodal, cognitive and affective information is proposed. Copyright © 2012 Elsevier Ltd. All rights reserved.

  10. Emotion and decision making: multiple modulatory neural circuits.

    Science.gov (United States)

    Phelps, Elizabeth A; Lempert, Karolina M; Sokol-Hessner, Peter

    2014-01-01

    Although the prevalent view of emotion and decision making is derived from the notion that there are dual systems of emotion and reason, a modulatory relationship more accurately reflects the current research in affective neuroscience and neuroeconomics. Studies show two potential mechanisms for affect's modulation of the computation of subjective value and decisions. Incidental affective states may carry over to the assessment of subjective value and the decision, and emotional reactions to the choice may be incorporated into the value calculation. In addition, this modulatory relationship is reciprocal: Changing emotion can change choices. This research suggests that the neural mechanisms mediating the relation between affect and choice vary depending on which affective component is engaged and which decision variables are assessed. We suggest that a detailed and nuanced understanding of emotion and decision making requires characterizing the multiple modulatory neural circuits underlying the different means by which emotion and affect can influence choices.

  11. Pulse coded biologically motivated neural-type MOS circuits

    Science.gov (United States)

    1991-11-01

    This project has two aspects, one for ONR and one for AFOSR. The ONR portion is devoted to obtaining hardware implementations for the physiological representations used in the program SYNETSIM developed by the neurophysiologist Dr. D. Hartline of Bekesy Laboratories. The AFOSR portion is for evaluation capabilities of the pulse code philosophy of neural networks. On the ONR portion of the research, several chips have been fabricated for SYNETSIM pools and a neural arithmetic unit based upon the pools. Also, a number of modifications have been made to SYNETSIM to make it a much more user-friendly program. Several papers have been presented at international conferences and the DRIVER module is under continued investigation for VLSI realization. The means to implement long term potentiation are also under continued investigation. On the AFOSR portion, a means of realizing any Hopfield-type network via pulse coded circuits was obtained.

  12. Generating three-qubit quantum circuits with neural networks

    Science.gov (United States)

    Swaddle, Michael; Noakes, Lyle; Smallbone, Harry; Salter, Liam; Wang, Jingbo

    2017-10-01

    A new method for compiling quantum algorithms is proposed and tested for a three qubit system. The proposed method is to decompose a unitary matrix U, into a product of simpler Uj via a neural network. These Uj can then be decomposed into product of known quantum gates. Key to the effectiveness of this approach is the restriction of the set of training data generated to paths which approximate minimal normal subRiemannian geodesics, as this removes unnecessary redundancy and ensures the products are unique. The two neural networks are shown to work effectively, each individually returning low loss values on validation data after relatively short training periods. The two networks are able to return coefficients that are sufficiently close to the true coefficient values to validate this method as an approach for generating quantum circuits. There is scope for more work in scaling this approach for larger quantum systems.

  13. A breathing circuit alarm system based on neural networks.

    Science.gov (United States)

    Orr, J A; Westenskow, D R

    1994-03-01

    The objectives of our study were (1) to implement intelligent respiratory alarms with a neural network; and (2) to increase alarm specificity and decrease false-alarm rates compared with current alarms. We trained a neural network to recognize 13 faults in an anesthesia breathing circuit. The system extracted 30 breath-to-breath features from the airway CO2, flow, and pressure signals. We created training data for the network by introducing 13 faults repeatedly in 5 dogs (616 total faults). We used the data to train the neural network using the backward error propagation algorithm. In animals, the trained network reported the alarms correctly for 95.0% of the faults when tested during controlled ventilation, and for 86.9% of the faults during spontaneous breathing. When tested in the operating room, the system found and correctly reported 54 of 57 faults that occurred during 43.6 hr of use. The alarm system produced a total of 74 false alarms during 43.6 hr of monitoring. Neural networks may be useful in creating intelligent anesthesia alarm systems.

  14. Spatiotemporal imaging of glutamate-induced biophotonic activities and transmission in neural circuits.

    Directory of Open Access Journals (Sweden)

    Rendong Tang

    Full Text Available The processing of neural information in neural circuits plays key roles in neural functions. Biophotons, also called ultra-weak photon emissions (UPE, may play potential roles in neural signal transmission, contributing to the understanding of the high functions of nervous system such as vision, learning and memory, cognition and consciousness. However, the experimental analysis of biophotonic activities (emissions in neural circuits has been hampered due to technical limitations. Here by developing and optimizing an in vitro biophoton imaging method, we characterize the spatiotemporal biophotonic activities and transmission in mouse brain slices. We show that the long-lasting application of glutamate to coronal brain slices produces a gradual and significant increase of biophotonic activities and achieves the maximal effect within approximately 90 min, which then lasts for a relatively long time (>200 min. The initiation and/or maintenance of biophotonic activities by glutamate can be significantly blocked by oxygen and glucose deprivation, together with the application of a cytochrome c oxidase inhibitor (sodium azide, but only partly by an action potential inhibitor (TTX, an anesthetic (procaine, or the removal of intracellular and extracellular Ca(2+. We also show that the detected biophotonic activities in the corpus callosum and thalamus in sagittal brain slices mostly originate from axons or axonal terminals of cortical projection neurons, and that the hyperphosphorylation of microtubule-associated protein tau leads to a significant decrease of biophotonic activities in these two areas. Furthermore, the application of glutamate in the hippocampal dentate gyrus results in increased biophotonic activities in its intrahippocampal projection areas. These results suggest that the glutamate-induced biophotonic activities reflect biophotonic transmission along the axons and in neural circuits, which may be a new mechanism for the processing of

  15. Activity-dependent modulation of neural circuit synaptic connectivity

    Directory of Open Access Journals (Sweden)

    Charles R Tessier

    2009-07-01

    Full Text Available In many nervous systems, the establishment of neural circuits is known to proceed via a two-stage process; 1 early, activity-independent wiring to produce a rough map characterized by excessive synaptic connections, and 2 subsequent, use-dependent pruning to eliminate inappropriate connections and reinforce maintained synapses. In invertebrates, however, evidence of the activity-dependent phase of synaptic refinement has been elusive, and the dogma has long been that invertebrate circuits are “hard-wired” in a purely activity-independent manner. This conclusion has been challenged recently through the use of new transgenic tools employed in the powerful Drosophila system, which have allowed unprecedented temporal control and single neuron imaging resolution. These recent studies reveal that activity-dependent mechanisms are indeed required to refine circuit maps in Drosophila during precise, restricted windows of late-phase development. Such mechanisms of circuit refinement may be key to understanding a number of human neurological diseases, including developmental disorders such as Fragile X syndrome (FXS and autism, which are hypothesized to result from defects in synaptic connectivity and activity-dependent circuit function. This review focuses on our current understanding of activity-dependent synaptic connectivity in Drosophila, primarily through analyzing the role of the fragile X mental retardation protein (FMRP in the Drosophila FXS disease model. The particular emphasis of this review is on the expanding array of new genetically-encoded tools that are allowing cellular events and molecular players to be dissected with ever greater precision and detail.

  16. PCSIM: A Parallel Simulation Environment for Neural Circuits Fully Integrated with Python

    Science.gov (United States)

    Pecevski, Dejan; Natschläger, Thomas; Schuch, Klaus

    2008-01-01

    The Parallel Circuit SIMulator (PCSIM) is a software package for simulation of neural circuits. It is primarily designed for distributed simulation of large scale networks of spiking point neurons. Although its computational core is written in C++, PCSIM's primary interface is implemented in the Python programming language, which is a powerful programming environment and allows the user to easily integrate the neural circuit simulator with data analysis and visualization tools to manage the full neural modeling life cycle. The main focus of this paper is to describe PCSIM's full integration into Python and the benefits thereof. In particular we will investigate how the automatically generated bidirectional interface and PCSIM's object-oriented modular framework enable the user to adopt a hybrid modeling approach: using and extending PCSIM's functionality either employing pure Python or C++ and thus combining the advantages of both worlds. Furthermore, we describe several supplementary PCSIM packages written in pure Python and tailored towards setting up and analyzing neural simulations. PMID:19543450

  17. Photovoltaic Pixels for Neural Stimulation: Circuit Models and Performance.

    Science.gov (United States)

    Boinagrov, David; Lei, Xin; Goetz, Georges; Kamins, Theodore I; Mathieson, Keith; Galambos, Ludwig; Harris, James S; Palanker, Daniel

    2016-02-01

    Photovoltaic conversion of pulsed light into pulsed electric current enables optically-activated neural stimulation with miniature wireless implants. In photovoltaic retinal prostheses, patterns of near-infrared light projected from video goggles onto subretinal arrays of photovoltaic pixels are converted into patterns of current to stimulate the inner retinal neurons. We describe a model of these devices and evaluate the performance of photovoltaic circuits, including the electrode-electrolyte interface. Characteristics of the electrodes measured in saline with various voltages, pulse durations, and polarities were modeled as voltage-dependent capacitances and Faradaic resistances. The resulting mathematical model of the circuit yielded dynamics of the electric current generated by the photovoltaic pixels illuminated by pulsed light. Voltages measured in saline with a pipette electrode above the pixel closely matched results of the model. Using the circuit model, our pixel design was optimized for maximum charge injection under various lighting conditions and for different stimulation thresholds. To speed discharge of the electrodes between the pulses of light, a shunt resistor was introduced and optimized for high frequency stimulation.

  18. An integrated modelling framework for neural circuits with multiple neuromodulators.

    Science.gov (United States)

    Joshi, Alok; Youssofzadeh, Vahab; Vemana, Vinith; McGinnity, T M; Prasad, Girijesh; Wong-Lin, KongFatt

    2017-01-01

    Neuromodulators are endogenous neurochemicals that regulate biophysical and biochemical processes, which control brain function and behaviour, and are often the targets of neuropharmacological drugs. Neuromodulator effects are generally complex partly owing to the involvement of broad innervation, co-release of neuromodulators, complex intra- and extrasynaptic mechanism, existence of multiple receptor subtypes and high interconnectivity within the brain. In this work, we propose an efficient yet sufficiently realistic computational neural modelling framework to study some of these complex behaviours. Specifically, we propose a novel dynamical neural circuit model that integrates the effective neuromodulator-induced currents based on various experimental data (e.g. electrophysiology, neuropharmacology and voltammetry). The model can incorporate multiple interacting brain regions, including neuromodulator sources, simulate efficiently and easily extendable to large-scale brain models, e.g. for neuroimaging purposes. As an example, we model a network of mutually interacting neural populations in the lateral hypothalamus, dorsal raphe nucleus and locus coeruleus, which are major sources of neuromodulator orexin/hypocretin, serotonin and norepinephrine/noradrenaline, respectively, and which play significant roles in regulating many physiological functions. We demonstrate that such a model can provide predictions of systemic drug effects of the popular antidepressants (e.g. reuptake inhibitors), neuromodulator antagonists or their combinations. Finally, we developed user-friendly graphical user interface software for model simulation and visualization for both fundamental sciences and pharmacological studies. © 2017 The Authors.

  19. Shared neural circuits for mentalizing about the self and others.

    Science.gov (United States)

    Lombardo, Michael V; Chakrabarti, Bhismadev; Bullmore, Edward T; Wheelwright, Sally J; Sadek, Susan A; Suckling, John; Baron-Cohen, Simon

    2010-07-01

    Although many examples exist for shared neural representations of self and other, it is unknown how such shared representations interact with the rest of the brain. Furthermore, do high-level inference-based shared mentalizing representations interact with lower level embodied/simulation-based shared representations? We used functional neuroimaging (fMRI) and a functional connectivity approach to assess these questions during high-level inference-based mentalizing. Shared mentalizing representations in ventromedial prefrontal cortex, posterior cingulate/precuneus, and temporo-parietal junction (TPJ) all exhibited identical functional connectivity patterns during mentalizing of both self and other. Connectivity patterns were distributed across low-level embodied neural systems such as the frontal operculum/ventral premotor cortex, the anterior insula, the primary sensorimotor cortex, and the presupplementary motor area. These results demonstrate that identical neural circuits are implementing processes involved in mentalizing of both self and other and that the nature of such processes may be the integration of low-level embodied processes within higher level inference-based mentalizing.

  20. Neural circuits mediating olfactory-driven behavior in fish

    Science.gov (United States)

    Kermen, Florence; Franco, Luis M.; Wyatt, Cameron; Yaksi, Emre

    2013-01-01

    The fish olfactory system processes odor signals and mediates behaviors that are crucial for survival such as foraging, courtship, and alarm response. Although the upstream olfactory brain areas (olfactory epithelium and olfactory bulb) are well-studied, less is known about their target brain areas and the role they play in generating odor-driven behaviors. Here we review a broad range of literature on the anatomy, physiology, and behavioral output of the olfactory system and its target areas in a wide range of teleost fish. Additionally, we discuss how applying recent technological advancements to the zebrafish (Danio rerio) could help in understanding the function of these target areas. We hope to provide a framework for elucidating the neural circuit computations underlying the odor-driven behaviors in this small, transparent, and genetically amenable vertebrate. PMID:23596397

  1. Two multichannel integrated circuits for neural recording and signal processing.

    Science.gov (United States)

    Obeid, Iyad; Morizio, James C; Moxon, Karen A; Nicolelis, Miguel A L; Wolf, Patrick D

    2003-02-01

    We have developed, manufactured, and tested two analog CMOS integrated circuit "neurochips" for recording from arrays of densely packed neural electrodes. Device A is a 16-channel buffer consisting of parallel noninverting amplifiers with a gain of 2 V/V. Device B is a 16-channel two-stage analog signal processor with differential amplification and high-pass filtering. It features selectable gains of 250 and 500 V/V as well as reference channel selection. The resulting amplifiers on Device A had a mean gain of 1.99 V/V with an equivalent input noise of 10 microV(rms). Those on Device B had mean gains of 53.4 and 47.4 dB with a high-pass filter pole at 211 Hz and an equivalent input noise of 4.4 microV(rms). Both devices were tested in vivo with electrode arrays implanted in the somatosensory cortex.

  2. Acute Stress Influences Neural Circuits of Reward Processing

    Directory of Open Access Journals (Sweden)

    Anthony John Porcelli

    2012-11-01

    Full Text Available People often make decisions under aversive conditions such as acute stress. Yet, less is known about the process in which acute stress can influence decision-making. A growing body of research has established that reward-related information associated with the outcomes of decisions exerts a powerful influence over the choices people make and that an extensive network of brain regions, prominently featuring the striatum, is involved in the processing of this reward-related information. Thus, an important step in research on the nature of acute stress’ influence over decision-making is to examine how it may modulate responses to rewards and punishments within reward-processing neural circuitry. In the current experiment, we employed a simple reward processing paradigm – where participants received monetary rewards and punishments – known to evoke robust striatal responses. Immediately prior to performing each of two task runs, participants were exposed to acute stress (i.e., cold pressor or a no stress control procedure in a between-subjects fashion. No stress group participants exhibited a pattern of activity within the dorsal striatum and orbitofrontal cortex consistent with past research on outcome processing – specifically, differential responses for monetary rewards over punishments. In contrast, acute stress group participants’ dorsal striatum and orbitofrontal cortex demonstrated decreased sensitivity to monetary outcomes and a lack of differential activity. These findings provide insight into how neural circuits may process rewards and punishments associated with simple decisions under acutely stressful conditions.

  3. Long-Lasting Neural Circuit Dysfunction Following Developmental Ethanol Exposure

    Directory of Open Access Journals (Sweden)

    Mariko Saito

    2013-04-01

    Full Text Available Fetal Alcohol Spectrum Disorder (FASD is a general diagnosis for those exhibiting long-lasting neurobehavioral and cognitive deficiencies as a result of fetal alcohol exposure. It is among the most common causes of mental deficits today. Those impacted are left to rely on advances in our understanding of the nature of early alcohol-induced disorders toward human therapies. Research findings over the last decade have developed a model where ethanol-induced neurodegeneration impacts early neural circuit development, thereby perpetuating subsequent integration and plasticity in vulnerable brain regions. Here we review our current knowledge of FASD neuropathology based on discoveries of long-lasting neurophysiological effects of acute developmental ethanol exposure in animal models. We discuss the important balance between synaptic excitation and inhibition in normal neural network function, and relate the significance of that balance to human FASD as well as related disease states. Finally, we postulate that excitation/inhibition imbalance caused by early ethanol-induced neurodegeneration results in perturbed local and regional network signaling and therefore neurobehavioral pathology.

  4. Inexact Subgraph Matching for Digital Circuit Analysis

    Science.gov (United States)

    2017-03-01

    circuit graph perspective. As a result, attempting to locate library structures within the larger circuit context via static analysis becomes an...e.g. imaging, biology, and large data set feature analysis ), however, and practical applications of it have been studied for some time. Many of...Digital Circuit Analysis Whitney Batchelor, Frederic Tuttle, Stephen Baka, Scott Harper Secure Computing & Communications Division MacAulay-Brown

  5. Timing matters: Using optogenetics to chronically manipulate neural circuits and rhythms

    Directory of Open Access Journals (Sweden)

    Michelle M Sidor

    2014-02-01

    Full Text Available The ability to probe defined neural circuits with both the spatial and temporal resolution imparted by optogenetics has transformed the field of neuroscience. Although much attention has been paid to the advantages of manipulating neural activity at millisecond timescales in order to elicit time-locked neural responses, little consideration has been given to the manipulation of circuit activity at physiologically relevant times of day, across multiple days. Nearly all biological events are governed by the circadian clock and exhibit 24-hour rhythms in activity. Indeed, neural circuit activity itself exhibits a daily rhythm with distinct temporal peaks in activity occurring at specific times of the day. Therefore, experimentally probing circuit function within and across physiologically relevant time windows (minutes to hours in behaving animals is fundamental to understanding the function of any one particular circuit within the intact brain. Furthermore, understanding how circuit function changes with repeated manipulation is important for modeling the circuit-wide disruptions that occur with chronic disease states. Here, we review recent advances in optogenetic technology that allow for chronic, temporally specific, control of circuit activity and provide examples of chronic optogenetic paradigms that have been utilized in the search for the neural circuit basis of behaviors relevant to human neuropsychiatric disease.

  6. A neural circuit covarying with social hierarchy in macaques.

    Directory of Open Access Journals (Sweden)

    MaryAnn P Noonan

    2014-09-01

    Full Text Available Despite widespread interest in social dominance, little is known of its neural correlates in primates. We hypothesized that social status in primates might be related to individual variation in subcortical brain regions implicated in other aspects of social and emotional behavior in other mammals. To examine this possibility we used magnetic resonance imaging (MRI, which affords the taking of quantitative measurements noninvasively, both of brain structure and of brain function, across many regions simultaneously. We carried out a series of tests of structural and functional MRI (fMRI data in 25 group-living macaques. First, a deformation-based morphometric (DBM approach was used to show that gray matter in the amygdala, brainstem in the vicinity of the raphe nucleus, and reticular formation, hypothalamus, and septum/striatum of the left hemisphere was correlated with social status. Second, similar correlations were found in the same areas in the other hemisphere. Third, similar correlations were found in a second data set acquired several months later from a subset of the same animals. Fourth, the strength of coupling between fMRI-measured activity in the same areas was correlated with social status. The network of subcortical areas, however, had no relationship with the sizes of individuals' social networks, suggesting the areas had a simple and direct relationship with social status. By contrast a second circuit in cortex, comprising the midsuperior temporal sulcus and anterior and dorsal prefrontal cortex, covaried with both individuals' social statuses and the social network sizes they experienced. This cortical circuit may be linked to the social cognitive processes that are taxed by life in more complex social networks and that must also be used if an animal is to achieve a high social status.

  7. Neural circuit mechanisms of short-term memory

    Science.gov (United States)

    Goldman, Mark

    Memory over time scales of seconds to tens of seconds is thought to be maintained by neural activity that is triggered by a memorized stimulus and persists long after the stimulus is turned off. This presents a challenge to current models of memory-storing mechanisms, because the typical time scales associated with cellular and synaptic dynamics are two orders of magnitude smaller than this. While such long time scales can easily be achieved by bistable processes that toggle like a flip-flop between a baseline and elevated-activity state, many neuronal systems have been observed experimentally to be capable of maintaining a continuum of stable states. For example, in neural integrator networks involved in the accumulation of evidence for decision making and in motor control, individual neurons have been recorded whose activity reflects the mathematical integral of their inputs; in the absence of input, these neurons sustain activity at a level proportional to the running total of their inputs. This represents an analog form of memory whose dynamics can be conceptualized through an energy landscape with a continuum of lowest-energy states. Such continuous attractor landscapes are structurally non-robust, in seeming violation of the relative robustness of biological memory systems. In this talk, I will present and compare different biologically motivated circuit motifs for the accumulation and storage of signals in short-term memory. Challenges to generating robust memory maintenance will be highlighted and potential mechanisms for ameliorating the sensitivity of memory networks to perturbations will be discussed. Funding for this work was provided by NIH R01 MH065034, NSF IIS-1208218, Simons Foundation 324260, and a UC Davis Ophthalmology Research to Prevent Blindness Grant.

  8. Noise Analysis of Switched-Capacitor Circuits

    Science.gov (United States)

    Retdian, Nicodimus; Takagi, Shigetaka

    Switched-capacitor (SC) circuit has been a well known analog circuit block. Since their characteristics are determined by capacitance ratio, SC circuits are suitable for on-chip implementations. Eventhough there are so many publications on the design of SC circuits, only a few of them discuss the property of noise in SC circuits. Linear noise analysis are common in continuous-time systems. However, SC circuits are operating in discrete-time domain and their noise properties are known to be a non-linear phenomenon and dificult to be analyzed by hand. This paper will show a noise analysis of an SC integrator as an example for a better understanding of the analysis method. It will be shown by simulation results that the proposed formula gives a better approximation of noise power spectral density (PSD).

  9. The generation effect: activating broad neural circuits during memory encoding.

    Science.gov (United States)

    Rosner, Zachary A; Elman, Jeremy A; Shimamura, Arthur P

    2013-01-01

    The generation effect is a robust memory phenomenon in which actively producing material during encoding acts to improve later memory performance. In a functional magnetic resonance imaging (fMRI) analysis, we explored the neural basis of this effect. During encoding, participants generated synonyms from word-fragment cues (e.g., GARBAGE-W_ST_) or read other synonym pairs (e.g., GARBAGE-WASTE). Compared to simply reading target words, generating target words significantly improved later recognition memory performance. During encoding, this benefit was associated with a broad neural network that involved both prefrontal (inferior frontal gyrus, middle frontal gyrus) and posterior cortex (inferior temporal gyrus, lateral occipital cortex, parahippocampal gyrus, ventral posterior parietal cortex). These findings define the prefrontal-posterior cortical dynamics associated with the mnemonic benefits underlying the generation effect. Copyright © 2012 Elsevier Ltd. All rights reserved.

  10. Rapid neural circuit switching mediated by synaptic plasticity during neural morphallactic regeneration.

    Science.gov (United States)

    Lybrand, Zane R; Zoran, Mark J

    2012-09-01

    The aquatic oligochaete, Lumbriculus variegatus (Lumbriculidae), undergoes a rapid regenerative transformation of its neural circuits following body fragmentation. This type of nervous system plasticity, called neural morphallaxis, involves the remodeling of the giant fiber pathways that mediate rapid head and tail withdrawal behaviors. Extra- and intracellular electrophysiological recordings demonstrated that changes in cellular properties and synaptic connections underlie neurobehavioral plasticity during morphallaxis. Sensory-to-giant interneuron connections, undetectable prior to body injury, emerged within hours of segment amputation. The appearance of functional synaptic transmission was followed by interneuron activation, coupling of giant fiber spiking to motor outputs and overt segmental shortening. The onset of morphallactic plasticity varied along the body axis and emerged more rapidly in segments closer to regions of sensory field overlap between the two giant fiber pathways. The medial and lateral giant fibers were simultaneously activated during a transient phase of network remodeling. Thus, synaptic plasticity at sensory-to-giant interneuron connections mediates escape circuit morphallaxis in this regenerating annelid worm. Copyright © 2011 Wiley Periodicals, Inc.

  11. The analysis and design of linear circuits

    CERN Document Server

    Thomas, Roland E; Toussaint, Gregory J

    2009-01-01

    The Analysis and Design of Linear Circuits, 6e gives the reader the opportunity to not only analyze, but also design and evaluate linear circuits as early as possible. The text's abundance of problems, applications, pedagogical tools, and realistic examples helps engineers develop the skills needed to solve problems, design practical alternatives, and choose the best design from several competing solutions. Engineers searching for an accessible introduction to resistance circuits will benefit from this book that emphasizes the early development of engineering judgment.

  12. Monitoring activity in neural circuits with genetically encoded indicators

    Directory of Open Access Journals (Sweden)

    Gerard Joseph Broussard

    2014-12-01

    Full Text Available Recent developments in genetically encoded indicators of neural activity (GINAs have greatly advanced the field of systems neuroscience. As they are encoded by DNA, GINAs can be targeted to genetically defined cellular populations. Combined with fluorescence microscopy, most notably multi-photon imaging, GINAs allow chronic simultaneous optical recordings from large populations of neurons or glial cells in awake, behaving mammals, particularly rodents. This large-scale recording of neural activity at multiple temporal and spatial scales has greatly advanced our understanding of the dynamics of neural circuitry underlying behavior—a critical first step toward understanding the complexities of brain function, such as sensorimotor integration and learning.Here, we summarize the recent development and applications of the major classes of GINAs. In particular, we take an in-depth look at the design of available GINA families with a particular focus on genetically encoded calcium indicators, sensors probing synaptic activity, and genetically encoded voltage indicators. Using the family of the genetically encoded calcium indicator GCaMP as an example, we review established sensor optimization pipelines. We also discuss practical considerations for end users of GINAs about experimental methods including approaches for gene delivery, imaging system requirements, and data analysis techniques. With the growing toolbox of GINAs and with new microscopy techniques pushing beyond their current limits, the age of light can finally achieve the goal of broad and dense sampling of neuronal activity across time and brain structures to obtain a dynamic picture of brain function.

  13. Self-Organizing Neural Circuits for Sensory-Guided Motor Control

    National Research Council Canada - National Science Library

    Grossberg, Stephen

    1999-01-01

    The reported projects developed mathematical models to explain how self-organizing neural circuits that operate under continuous or intermittent sensory guidance achieve flexible and accurate control of human movement...

  14. Analysis of neural data

    CERN Document Server

    Kass, Robert E; Brown, Emery N

    2014-01-01

    Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.

  15. Ultra low-power integrated circuit design for wireless neural interfaces

    CERN Document Server

    Holleman, Jeremy; Otis, Brian

    2014-01-01

    Presenting results from real prototype systems, this volume provides an overview of ultra low-power integrated circuits and systems for neural signal processing and wireless communication. Topics include analog, radio, and signal processing theory and design for ultra low-power circuits.

  16. Neural Circuits via Which Single Prolonged Stress Exposure Leads to Fear Extinction Retention Deficits

    Science.gov (United States)

    Knox, Dayan; Stanfield, Briana R.; Staib, Jennifer M.; David, Nina P.; Keller, Samantha M.; DePietro, Thomas

    2016-01-01

    Single prolonged stress (SPS) has been used to examine mechanisms via which stress exposure leads to post-traumatic stress disorder symptoms. SPS induces fear extinction retention deficits, but neural circuits critical for mediating these deficits are unknown. To address this gap, we examined the effect of SPS on neural activity in brain regions…

  17. Demonstration of a neural circuit critical for imprinting behavior in chicks.

    Science.gov (United States)

    Nakamori, Tomoharu; Sato, Katsushige; Atoji, Yasuro; Kanamatsu, Tomoyuki; Tanaka, Kohichi; Ohki-Hamazaki, Hiroko

    2010-03-24

    Imprinting behavior in birds is elicited by visual and/or auditory cues. It has been demonstrated previously that visual cues are recognized and processed in the visual Wulst (VW), and imprinting memory is stored in the intermediate medial mesopallium (IMM) of the telencephalon. Alteration of neural responses in these two regions according to imprinting has been reported, yet direct evidence of the neural circuit linking these two regions is lacking. Thus, it remains unclear how memory is formed and expressed in this circuit. Here, we present anatomical as well as physiological evidence of the neural circuit connecting the VW and IMM and show that imprinting training during the critical period strengthens and refines this circuit. A functional connection established by imprint training resulted in an imprinting behavior. After the closure of the critical period, training could not activate this circuit nor induce the imprinting behavior. Glutamatergic neurons in the ventroposterior region of the VW, the core region of the hyperpallium densocellulare (HDCo), sent their axons to the periventricular part of the HD, just dorsal and afferent to the IMM. We found that the HDCo is important in imprinting behavior. The refinement and/or enhancement of this neural circuit are attributed to increased activity of HDCo cells, and the activity depended on NR2B-containing NMDA receptors. These findings show a neural connection in the telencephalon in Aves and demonstrate that NR2B function is indispensable for the plasticity of HDCo cells, which are key mediators of imprinting.

  18. Self-control of chaos in neural circuits with plastic electrical synapses

    Science.gov (United States)

    Zhigulin, V. P.; Rabinovich, M. I.

    2004-10-01

    Two kinds of connections are known to exist in neural circuits: electrical (also called gap junctions) and chemical. Whereas chemical synapses are known to be plastic (i. e., modifiable), but slow, electrical transmission through gap junctions is not modifiable, but is very fast. We suggest the new artificial synapse that combines the best properties of both: the fast reaction of a gap junction and the plasticity of a chemical synapse. Such a plastic electrical synapse can be used in hybrid neural circuits and for the development of neural prosthetics, i.e., implanted devices that can interact with the real nervous system. Based on the computer modelling we show that such a plastic electrical synapse regularizes chaos in the minimal neural circuit consisting of two chaotic bursting neurons.

  19. Equivalent circuit analysis of terahertz metamaterial filters

    KAUST Repository

    Zhang, Xueqian

    2011-01-01

    An equivalent circuit model for the analysis and design of terahertz (THz) metamaterial filters is presented. The proposed model, derived based on LMC equivalent circuits, takes into account the detailed geometrical parameters and the presence of a dielectric substrate with the existing analytic expressions for self-inductance, mutual inductance, and capacitance. The model is in good agreement with the experimental measurements and full-wave simulations. Exploiting the circuit model has made it possible to predict accurately the resonance frequency of the proposed structures and thus, quick and accurate process of designing THz device from artificial metamaterials is offered. ©2011 Chinese Optics Letters.

  20. Operational amplifier circuits analysis and design

    CERN Document Server

    Nelson, J C C

    1995-01-01

    This book, a revised and updated version of the author's Basic Operational Amplifiers (Butterworths 1986), enables the non-specialist to make effective use of readily available integrated circuit operational amplifiers for a range of applications, including instrumentation, signal generation and processing.It is assumed the reader has a background in the basic techniques of circuit analysis, particularly the use of j notation for reactive circuits, with a corresponding level of mathematical ability. The underlying theory is explained with sufficient but not excessive, detail. A range of compu

  1. An Implantable Mixed Analog/Digital Neural Stimulator Circuit

    DEFF Research Database (Denmark)

    Gudnason, Gunnar; Bruun, Erik; Haugland, Morten

    1999-01-01

    This paper describes a chip for a multichannel neural stimulator for functional electrical stimulation. The chip performs all the signal processing required in an implanted neural stimulator. The power and signal transmission to the stimulator is carried out via an inductive link. From the signals...

  2. Nonlinear resonances and multi-stability in simple neural circuits

    Science.gov (United States)

    Alonso, Leandro M.

    2017-01-01

    This article describes a numerical procedure designed to tune the parameters of periodically driven dynamical systems to a state in which they exhibit rich dynamical behavior. This is achieved by maximizing the diversity of subharmonic solutions available to the system within a range of the parameters that define the driving. The procedure is applied to a problem of interest in computational neuroscience: a circuit composed of two interacting populations of neurons under external periodic forcing. Depending on the parameters that define the circuit, such as the weights of the connections between the populations, the response of the circuit to the driving can be strikingly rich and diverse. The procedure is employed to find circuits that, when driven by external input, exhibit multiple stable patterns of periodic activity organized in complex tuning diagrams and signatures of low dimensional chaos.

  3. The Vite Model: A Neural Command Circuit for Generating Arm and Articulator Trajectories,

    Science.gov (United States)

    1988-03-01

    associative map, looking at an object can activate a TPC of the hand-arm system, as Piaget (1963) noted. Then a VITE circuit can translate this latter TPC...two ways: by comparing trajectories of the neural circuit’s output stage with actual arm trajectories, and by checking for the existence of the...in precentral motor cortex could be analysed as an in vivo analogue of model DV stage neurons. Additional physiological support for the VITE model

  4. The neurobiology of sound-specific auditory plasticity: a core neural circuit.

    Science.gov (United States)

    Xiong, Ying; Zhang, Yonghai; Yan, Jun

    2009-09-01

    Auditory learning or experience induces large-scale neural plasticity in not only the auditory cortex but also in the auditory thalamus and midbrain. Such plasticity is guided by acquired sound (sound-specific auditory plasticity). The mechanisms involved in this process have been studied from various approaches and support the presence of a core neural circuit consisting of a subcortico-cortico-subcortical tonotopic loop supplemented by neuromodulatory (e.g., cholinergic) inputs. This circuit has three key functions essential for establishing large-scale and sound-specific plasticity in the auditory cortex, auditory thalamus and auditory midbrain. They include the presence of sound information for guiding the plasticity, the communication between the cortex, thalamus and midbrain for coordinating the plastic changes and the adjustment of the circuit status for augmenting the plasticity. This review begins with an overview of sound-specific auditory plasticity in the central auditory system. It then introduces the core neural circuit which plays an essential role in inducing sound-specific auditory plasticity. Finally, the core neural circuit and its relationship to auditory learning and experience are discussed.

  5. Distributed dynamical computation in neural circuits with propagating coherent activity patterns.

    Directory of Open Access Journals (Sweden)

    Pulin Gong

    2009-12-01

    Full Text Available Activity in neural circuits is spatiotemporally organized. Its spatial organization consists of multiple, localized coherent patterns, or patchy clusters. These patterns propagate across the circuits over time. This type of collective behavior has ubiquitously been observed, both in spontaneous activity and evoked responses; its function, however, has remained unclear. We construct a spatially extended, spiking neural circuit that generates emergent spatiotemporal activity patterns, thereby capturing some of the complexities of the patterns observed empirically. We elucidate what kind of fundamental function these patterns can serve by showing how they process information. As self-sustained objects, localized coherent patterns can signal information by propagating across the neural circuit. Computational operations occur when these emergent patterns interact, or collide with each other. The ongoing behaviors of these patterns naturally embody both distributed, parallel computation and cascaded logical operations. Such distributed computations enable the system to work in an inherently flexible and efficient way. Our work leads us to propose that propagating coherent activity patterns are the underlying primitives with which neural circuits carry out distributed dynamical computation.

  6. Implantable neurotechnologies: bidirectional neural interfaces--applications and VLSI circuit implementations.

    Science.gov (United States)

    Greenwald, Elliot; Masters, Matthew R; Thakor, Nitish V

    2016-01-01

    A bidirectional neural interface is a device that transfers information into and out of the nervous system. This class of devices has potential to improve treatment and therapy in several patient populations. Progress in very large-scale integration has advanced the design of complex integrated circuits. System-on-chip devices are capable of recording neural electrical activity and altering natural activity with electrical stimulation. Often, these devices include wireless powering and telemetry functions. This review presents the state of the art of bidirectional circuits as applied to neuroprosthetic, neurorepair, and neurotherapeutic systems.

  7. An overview of Bayesian methods for neural spike train analysis.

    Science.gov (United States)

    Chen, Zhe

    2013-01-01

    Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.

  8. An Overview of Bayesian Methods for Neural Spike Train Analysis

    Directory of Open Access Journals (Sweden)

    Zhe Chen

    2013-01-01

    Full Text Available Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.

  9. Deconstruction and Control of Neural Circuits in Posttraumatic Epilepsy

    Science.gov (United States)

    2017-10-01

    Holden and Frances Cho –received awards that allowed them to present their work at multiple national and international conferences. These awards...Stephanie Holden and Frances Cho – whose work focuses on this DoD-funded project, received multiple awards that allowed them to present their work at...epileptogenesis. Stephanie and Frances presented their work at multiple conferences: 8. Holden S, Paz JT (2017) Deconstruction of thalamic circuits in a mouse

  10. Artwork Analysis Tools for VLSI Circuits.

    Science.gov (United States)

    1980-06-01

    derived frcm the art- work.i~nFo :.- Is zr Code DI t pecal Sculnfv CLA a uPICAT OP T0416 PA*6WM Dine Bftee AMA& -’M Artwork Analysis Tools for VLSI Circuits... code of the program and in pre-generated bit tables. The design rules thcmselves are not input directly into the checker. The rules were interpreted...circuit simulation is swich -level sintulation. In this type, transistors are modeled as switches that are either on or off. Fixed delays are a%.ociated

  11. Neural circuit remodeling and structural plasticity in the cortex during chronic pain.

    Science.gov (United States)

    Kim, Woojin; Kim, Sun Kwang

    2016-01-01

    Damage in the periphery or spinal cord induces maladaptive plastic changes along the somatosensory nervous system from the periphery to the cortex, often leading to chronic pain. Although the role of neural circuit remodeling and structural synaptic plasticity in the 'pain matrix' cortices in chronic pain has been thought as a secondary epiphenomenon to altered nociceptive signaling in the spinal cord, progress in whole brain imaging studies on human patients and animal models has suggested a possibility that plastic changes in cortical neural circuits may actively contribute to chronic pain symptoms. Furthermore, recent development in two-photon microscopy and fluorescence labeling techniques have enabled us to longitudinally trace the structural and functional changes in local circuits, single neurons and even individual synapses in the brain of living animals. These technical advances has started to reveal that cortical structural remodeling following tissue or nerve damage could rapidly occur within days, which are temporally correlated with functional plasticity of cortical circuits as well as the development and maintenance of chronic pain behavior, thereby modifying the previous concept that it takes much longer periods (e.g. months or years). In this review, we discuss the relation of neural circuit plasticity in the 'pain matrix' cortices, such as the anterior cingulate cortex, prefrontal cortex and primary somatosensory cortex, with chronic pain. We also introduce how to apply long-term in vivo two-photon imaging approaches for the study of pathophysiological mechanisms of chronic pain.

  12. Priming Neural Circuits to Modulate Spinal Reflex Excitability

    OpenAIRE

    Estes, Stephen P.; Iddings, Jennifer A.; Field-Fote, Edelle C.

    2017-01-01

    While priming is most often thought of as a strategy for modulating neural excitability to facilitate voluntary motor control, priming stimulation can also be utilized to target spinal reflex excitability. In this application, priming can be used to modulate the involuntary motor output that often follows central nervous system injury. Individuals with spinal cord injury (SCI) often experience spasticity, for which antispasmodic medications are the most common treatment. Physical therapeutic/...

  13. Energy efficient neural stimulation: coupling circuit design and membrane biophysics.

    Science.gov (United States)

    Foutz, Thomas J; Ackermann, D Michael; Kilgore, Kevin L; McIntyre, Cameron C

    2012-01-01

    The delivery of therapeutic levels of electrical current to neural tissue is a well-established treatment for numerous indications such as Parkinson's disease and chronic pain. While the neuromodulation medical device industry has experienced steady clinical growth over the last two decades, much of the core technology underlying implanted pulse generators remain unchanged. In this study we propose some new methods for achieving increased energy-efficiency during neural stimulation. The first method exploits the biophysical features of excitable tissue through the use of a centered-triangular stimulation waveform. Neural activation with this waveform is achieved with a statistically significant reduction in energy compared to traditional rectangular waveforms. The second method demonstrates energy savings that could be achieved by advanced circuitry design. We show that the traditional practice of using a fixed compliance voltage for constant-current stimulation results in substantial energy loss. A portion of this energy can be recuperated by adjusting the compliance voltage to real-time requirements. Lastly, we demonstrate the potential impact of axon fiber diameter on defining the energy-optimal pulse-width for stimulation. When designing implantable pulse generators for energy efficiency, we propose that the future combination of a variable compliance system, a centered-triangular stimulus waveform, and an axon diameter specific stimulation pulse-width has great potential to reduce energy consumption and prolong battery life in neuromodulation devices.

  14. A neural space vector fault location for parallel double-circuit distribution lines

    Energy Technology Data Exchange (ETDEWEB)

    Sousa Martins, L.; Martins, J.F.; Fernao Pires, V. [Politecnico de Setubal (Portugal). Escola Sup. Tecnol.; Alegria, C.M. [Instituto Superior Tecnico, Lisbon (Portugal)

    2005-03-01

    A new approach to fault location for parallel double-circuit distribution power lines is presented. This approach uses the Clark-Concordia transformation and an artificial neural network based learning algorithm. The {alpha}, {beta}, 0 components of double line currents resulting from the Clarke-Concordia transformation are used to characterize different states of the system. The neural network is trained to map the non-linear relationship existing between fault location and characteristic eigenvalue. The proposed approach is able to identify and to locate different types of faults such as: phase-to-earth, phase-to-phase, two-phase-to-earth and three-phase. Using the eigenvalue as neural network inputs the proposed algorithm locates the fault distance. Results are presented which show the effectiveness of the proposed algorithm for a correct fault location on a parallel double-circuit distribution line. (author)

  15. Optogenetic manipulation of neural circuits in awake marmosets.

    Science.gov (United States)

    MacDougall, Matthew; Nummela, Samuel U; Coop, Shanna; Disney, Anita; Mitchell, Jude F; Miller, Cory T

    2016-09-01

    Optogenetics has revolutionized the study of functional neuronal circuitry (Boyden ES, Zhang F, Bamberg E, Nagel G, Deisseroth K. Nat Neurosci 8: 1263-1268, 2005; Deisseroth K. Nat Methods 8: 26-29, 2011). Although these techniques have been most successfully implemented in rodent models, they have the potential to be similarly impactful in studies of nonhuman primate brains. Common marmosets (Callithrix jacchus) have recently emerged as a candidate primate model for gene editing, providing a potentially powerful model for studies of neural circuitry and disease in primates. The application of viral transduction methods in marmosets for identifying and manipulating neuronal circuitry is a crucial step in developing this species for neuroscience research. In the present study we developed a novel, chronic method to successfully induce rapid photostimulation in individual cortical neurons transduced by adeno-associated virus to express channelrhodopsin (ChR2) in awake marmosets. We found that large proportions of neurons could be effectively photoactivated following viral transduction and that this procedure could be repeated for several months. These data suggest that techniques for viral transduction and optical manipulation of neuronal populations are suitable for marmosets and can be combined with existing behavioral preparations in the species to elucidate the functional neural circuitry underlying perceptual and cognitive processes. Copyright © 2016 the American Physiological Society.

  16. Ontogeny of neural circuits underlying spatial memory in the rat

    Directory of Open Access Journals (Sweden)

    James Alexander Ainge

    2012-03-01

    Full Text Available Spatial memory is a well characterised psychological function in both humans and rodents. The combined computations of a network of systems including place cells in the hippocampus, grid cells in the medial entorhinal cortex and head direction cells found in numerous structures in the brain have been suggested to form the neural instantiation of the cognitive map as first described by Tolman in 1948. However, while our understanding of the neural mechanisms underlying spatial representations in adults is relatively sophisticated, we know substantially less about how this network develops in young animals. In this article we review studies examining the developmental timescale that these systems follow. Electrophysiological recordings from very young rats show that directional information is at adult levels at the outset of navigational experience. The systems supporting allocentric memory, however, take longer to mature. This is consistent with behavioural studies of young rats which show that spatial memory based on head direction develops very early but that allocentric spatial memory takes longer to mature. We go on to report new data demonstrating that memory for associations between objects and their spatial locations is slower to develop than memory for objects alone. This is again consistent with previous reports suggesting that adult like spatial representations have a protracted development in rats and also suggests that the systems involved in processing non-spatial stimuli come online earlier.

  17. How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits?

    Science.gov (United States)

    Brinkman, Braden A W; Weber, Alison I; Rieke, Fred; Shea-Brown, Eric

    2016-10-01

    Neural circuits reliably encode and transmit signals despite the presence of noise at multiple stages of processing. The efficient coding hypothesis, a guiding principle in computational neuroscience, suggests that a neuron or population of neurons allocates its limited range of responses as efficiently as possible to best encode inputs while mitigating the effects of noise. Previous work on this question relies on specific assumptions about where noise enters a circuit, limiting the generality of the resulting conclusions. Here we systematically investigate how noise introduced at different stages of neural processing impacts optimal coding strategies. Using simulations and a flexible analytical approach, we show how these strategies depend on the strength of each noise source, revealing under what conditions the different noise sources have competing or complementary effects. We draw two primary conclusions: (1) differences in encoding strategies between sensory systems-or even adaptational changes in encoding properties within a given system-may be produced by changes in the structure or location of neural noise, and (2) characterization of both circuit nonlinearities as well as noise are necessary to evaluate whether a circuit is performing efficiently.

  18. How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits?

    Directory of Open Access Journals (Sweden)

    Braden A W Brinkman

    2016-10-01

    Full Text Available Neural circuits reliably encode and transmit signals despite the presence of noise at multiple stages of processing. The efficient coding hypothesis, a guiding principle in computational neuroscience, suggests that a neuron or population of neurons allocates its limited range of responses as efficiently as possible to best encode inputs while mitigating the effects of noise. Previous work on this question relies on specific assumptions about where noise enters a circuit, limiting the generality of the resulting conclusions. Here we systematically investigate how noise introduced at different stages of neural processing impacts optimal coding strategies. Using simulations and a flexible analytical approach, we show how these strategies depend on the strength of each noise source, revealing under what conditions the different noise sources have competing or complementary effects. We draw two primary conclusions: (1 differences in encoding strategies between sensory systems-or even adaptational changes in encoding properties within a given system-may be produced by changes in the structure or location of neural noise, and (2 characterization of both circuit nonlinearities as well as noise are necessary to evaluate whether a circuit is performing efficiently.

  19. A simple structure wavelet transform circuit employing function link neural networks and SI filters

    Science.gov (United States)

    Mu, Li; Yigang, He

    2016-12-01

    Signal processing by means of analog circuits offers advantages from a power consumption viewpoint. Implementing wavelet transform (WT) using analog circuits is of great interest when low-power consumption becomes an important issue. In this article, a novel simple structure WT circuit in analog domain is presented by employing functional link neural network (FLNN) and switched-current (SI) filters. First, the wavelet base is approximated using FLNN algorithms for giving a filter transfer function that is suitable for simple structure WT circuit implementation. Next, the WT circuit is constructed with the wavelet filter bank, whose impulse response is the approximated wavelet and its dilations. The filter design that follows is based on a follow-the-leader feedback (FLF) structure with multiple output bilinear SI integrators and current mirrors as the main building blocks. SI filter is well suited for this application since the dilation constant across different scales of the transform can be precisely implemented and controlled by the clock frequency of the circuit with the same system architecture. Finally, to illustrate the design procedure, a seventh-order FLNN-approximated Gaussian wavelet is implemented as an example. Simulations have successfully verified that the designed simple structure WT circuit has low sensitivity, low-power consumption and litter effect to the imperfections.

  20. Massively parallel neural circuits for stereoscopic color vision: encoding, decoding and identification.

    Science.gov (United States)

    Lazar, Aurel A; Slutskiy, Yevgeniy B; Zhou, Yiyin

    2015-03-01

    Past work demonstrated how monochromatic visual stimuli could be faithfully encoded and decoded under Nyquist-type rate conditions. Color visual stimuli were then traditionally encoded and decoded in multiple separate monochromatic channels. The brain, however, appears to mix information about color channels at the earliest stages of the visual system, including the retina itself. If information about color is mixed and encoded by a common pool of neurons, how can colors be demixed and perceived? We present Color Video Time Encoding Machines (Color Video TEMs) for encoding color visual stimuli that take into account a variety of color representations within a single neural circuit. We then derive a Color Video Time Decoding Machine (Color Video TDM) algorithm for color demixing and reconstruction of color visual scenes from spikes produced by a population of visual neurons. In addition, we formulate Color Video Channel Identification Machines (Color Video CIMs) for functionally identifying color visual processing performed by a spiking neural circuit. Furthermore, we derive a duality between TDMs and CIMs that unifies the two and leads to a general theory of neural information representation for stereoscopic color vision. We provide examples demonstrating that a massively parallel color visual neural circuit can be first identified with arbitrary precision and its spike trains can be subsequently used to reconstruct the encoded stimuli. We argue that evaluation of the functional identification methodology can be effectively and intuitively performed in the stimulus space. In this space, a signal reconstructed from spike trains generated by the identified neural circuit can be compared to the original stimulus. Copyright © 2014 Elsevier Ltd. All rights reserved.

  1. In search of the neural circuits of intrinsic motivation

    Directory of Open Access Journals (Sweden)

    Frederic Kaplan

    2007-10-01

    Full Text Available Children seem to acquire new know-how in a continuous and open-ended manner. In this paper, we hypothesize that an intrinsic motivation to progress in learning is at the origins of the remarkable structure of children's developmental trajectories. In this view, children engage in exploratory and playful activities for their own sake, not as steps toward other extrinsic goals. The central hypothesis of this paper is that intrinsically motivating activities correspond to expected decrease in prediction error. This motivation system pushes the infant to avoid both predictable and unpredictable situations in order to focus on the ones that are expected to maximize progress in learning. Based on a computational model and a series of robotic experiments, we show how this principle can lead to organized sequences of behavior of increasing complexity characteristic of several behavioral and developmental patterns observed in humans. We then discuss the putative circuitry underlying such an intrinsic motivation system in the brain and formulate two novel hypotheses. The first one is that tonic dopamine acts as a learning progress signal. The second is that this progress signal is directly computed through a hierarchy of microcortical circuits that act both as prediction and metaprediction systems.

  2. A Simple Short Circuit Analysis for Power Networks

    Directory of Open Access Journals (Sweden)

    Koşalay İlhan

    2016-01-01

    Full Text Available This study investigates the transient behavior of short circuits in power circuits. The circuit consists of two part; input part and load part. These two parts are connected with a circuit breaker switch. The circuit works in two modes; first mode is when the switch is open and second mode is when the switch is closed. This study analyses the circuit when the switch is closed. The analysis is done with different types of closing angle. The analysis is done by forming state equations and those equations are solved numerically by using Matlab. The analysis and conclusion is performed by observing the behaviors of the graphs.

  3. Neural circuits involved in the renewal of extinguished fear.

    Science.gov (United States)

    Chen, Weihai; Wang, Yan; Wang, Xiaqing; Li, Hong

    2017-07-01

    The last 10 years have witnessed a substantial progress in understanding the neural mechanisms for the renewal of the extinguished fear memory. Based on the theory of fear extinction, exposure therapy has been developed as a typical cognitive behavioral therapy for posttraumatic stress disorder. Although the fear memory can be extinguished by repeated presentation of conditioned stimulus without unconditioned stimulus, the fear memory is not erased and tends to relapse outside of extinction context, which is referred to as renewal. Therefore, the renewal is regarded as a great obstruction interfering with the effect of exposure therapy. In recent years, there has been a great deal of studies in understanding the neurobiological underpinnings of fear renewal. These offer a foundation upon which novel therapeutic interventions for the renewal may be built. This review focuses on behavioral, anatomical and electrophysiological studies that interpret roles of the hippocampus, prelimbic cortex and amygdala as well as the connections between them for the renewal of the extinguished fear. Additionally, this review suggests the possible pathways for the renewal: (1) the prelimbic cortex may integrate contextual information from hippocampal inputs and project to the basolateral amygdala to mediate the renewal of extinguished fear memory; the ventral hippocampus may innervate the activities of the basolateral amygdala or the central amygdala directly for the renewal. © 2017 IUBMB Life, 69(7):470-478, 2017. © 2017 International Union of Biochemistry and Molecular Biology.

  4. Nanowire electrodes for high-density stimulation and measurement of neural circuits

    Directory of Open Access Journals (Sweden)

    Jacob T. Robinson

    2013-03-01

    Full Text Available Brain-machine interfaces (BMIs that can precisely monitor and control neural activity will likely require new hardware with improved resolution and specificity. New nanofabricated electrodes with feature sizes and densities comparable to neural circuits may lead to such improvements. In this perspective, we review the recent development of vertical nanowire (NW electrodes that could provide highly parallel single-cell recording and stimulation for future BMIs. We compare the advantages of these devices and discuss some of the technical challenges that must be overcome for this technology to become a platform for next-generation closed-loop BMIs.

  5. Neuromodulation of the neural circuits controlling the lower urinary tract.

    Science.gov (United States)

    Gad, Parag N; Roy, Roland R; Zhong, Hui; Gerasimenko, Yury P; Taccola, Giuliano; Edgerton, V Reggie

    2016-11-01

    The inability to control timely bladder emptying is one of the most serious challenges among the many functional deficits that occur after a spinal cord injury. We previously demonstrated that electrodes placed epidurally on the dorsum of the spinal cord can be used in animals and humans to recover postural and locomotor function after complete paralysis and can be used to enable voiding in spinal rats. In the present study, we examined the neuromodulation of lower urinary tract function associated with acute epidural spinal cord stimulation, locomotion, and peripheral nerve stimulation in adult rats. Herein we demonstrate that electrically evoked potentials in the hindlimb muscles and external urethral sphincter are modulated uniquely when the rat is stepping bipedally and not voiding, immediately pre-voiding, or when voiding. We also show that spinal cord stimulation can effectively neuromodulate the lower urinary tract via frequency-dependent stimulation patterns and that neural peripheral nerve stimulation can activate the external urethral sphincter both directly and via relays in the spinal cord. The data demonstrate that the sensorimotor networks controlling bladder and locomotion are highly integrated neurophysiologically and behaviorally and demonstrate how these two functions are modulated by sensory input from the tibial and pudental nerves. A more detailed understanding of the high level of interaction between these networks could lead to the integration of multiple neurophysiological strategies to improve bladder function. These data suggest that the development of strategies to improve bladder function should simultaneously engage these highly integrated networks in an activity-dependent manner. Copyright © 2016. Published by Elsevier Inc.

  6. [Progress in activity-dependent structural plasticity of neural circuits in cortex].

    Science.gov (United States)

    Rao, Xiao-Ping; Xu, Zhi-Xiang; Xu, Fu-Qiang

    2012-10-01

    Neural circuits of mammalian cerebral cortex have exhibited amazing abilities of structural and functional plasticity in development, learning and memory, neurological and psychiatric diseases. With the new imaging techniques and the application of molecular biology methods, observation neural circuits' structural dynamics within the cortex in vivo at the cellular and synaptic level was possible, so there were many great progresses in the field of the activity-dependent structural plasticity over the past decade. This paper reviewed some of the aspects of the experimental results, focused on the characteristics of dendritic structural plasticity in individual growth and development, rich environment, sensory deprivation, and pathological conditions, as well as learning and memory, especially the dynamics of dendritic spines on morphology and quantity; after that, we introduced axonal structural plasticity, the molecular and cellular mechanisms of structural plasticity, and proposed some future problems to be solved at last.

  7. Automated cell-specific laser detection and ablation of neural circuits in neonatal brain tissue

    Science.gov (United States)

    Wang, Xueying; Hayes, John A; Picardo, Maria Cristina D; Del Negro, Christopher A

    2013-01-01

    A key feature of neurodegenerative disease is the pathological loss of neurons that participate in generating behaviour. To investigate network properties of neural circuits and provide a complementary tool to study neurodegeneration in vitro or in situ, we developed an automated cell-specific laser detection and ablation system. The instrument consists of a two-photon and visible-wavelength confocal imaging setup, controlled by executive software, that identifies neurons in preparations based on genetically encoded fluorescent proteins or Ca2+ imaging, and then sequentially ablates cell targets while monitoring network function concurrently. Pathological changes in network function can be directly attributed to ablated cells, which are logged in real time. Here, we investigated brainstem respiratory circuits to demonstrate single-cell precision in ablation during physiological network activity, but the technique could be applied to interrogate network properties in neural systems that retain network functionality in reduced preparations in vitro or in situ. PMID:23440965

  8. A decision-making model based on a spiking neural circuit and synaptic plasticity.

    Science.gov (United States)

    Wei, Hui; Bu, Yijie; Dai, Dawei

    2017-10-01

    To adapt to the environment and survive, most animals can control their behaviors by making decisions. The process of decision-making and responding according to cues in the environment is stable, sustainable, and learnable. Understanding how behaviors are regulated by neural circuits and the encoding and decoding mechanisms from stimuli to responses are important goals in neuroscience. From results observed in Drosophila experiments, the underlying decision-making process is discussed, and a neural circuit that implements a two-choice decision-making model is proposed to explain and reproduce the observations. Compared with previous two-choice decision making models, our model uses synaptic plasticity to explain changes in decision output given the same environment. Moreover, biological meanings of parameters of our decision-making model are discussed. In this paper, we explain at the micro-level (i.e., neurons and synapses) how observable decision-making behavior at the macro-level is acquired and achieved.

  9. Engagement of neural circuits underlying 2D spatial navigation in a rodent virtual reality system.

    Science.gov (United States)

    Aronov, Dmitriy; Tank, David W

    2014-10-22

    Virtual reality (VR) enables precise control of an animal's environment and otherwise impossible experimental manipulations. Neural activity in rodents has been studied on virtual 1D tracks. However, 2D navigation imposes additional requirements, such as the processing of head direction and environment boundaries, and it is unknown whether the neural circuits underlying 2D representations can be sufficiently engaged in VR. We implemented a VR setup for rats, including software and large-scale electrophysiology, that supports 2D navigation by allowing rotation and walking in any direction. The entorhinal-hippocampal circuit, including place, head direction, and grid cells, showed 2D activity patterns similar to those in the real world. Furthermore, border cells were observed, and hippocampal remapping was driven by environment shape, suggesting functional processing of virtual boundaries. These results illustrate that 2D spatial representations can be engaged by visual and rotational vestibular stimuli alone and suggest a novel VR tool for studying rat navigation.

  10. The neural circuits recruited for the production of signs and fingerspelled words.

    Science.gov (United States)

    Emmorey, Karen; Mehta, Sonya; McCullough, Stephen; Grabowski, Thomas J

    2016-09-01

    Signing differs from typical non-linguistic hand actions because movements are not visually guided, finger movements are complex (particularly for fingerspelling), and signs are not produced as holistic gestures. We used positron emission tomography to investigate the neural circuits involved in the production of American Sign Language (ASL). Different types of signs (one-handed (articulated in neutral space), two-handed (neutral space), and one-handed body-anchored signs) were elicited by asking deaf native signers to produce sign translations of English words. Participants also fingerspelled (one-handed) printed English words. For the baseline task, participants indicated whether a word contained a descending letter. Fingerspelling engaged ipsilateral motor cortex and cerebellar cortex in contrast to both one-handed signs and the descender baseline task, which may reflect greater timing demands and complexity of handshape sequences required for fingerspelling. Greater activation in the visual word form area was also observed for fingerspelled words compared to one-handed signs. Body-anchored signs engaged bilateral superior parietal cortex to a greater extent than the descender baseline task and neutral space signs, reflecting the motor control and proprioceptive monitoring required to direct the hand toward a specific location on the body. Less activation in parts of the motor circuit was observed for two-handed signs compared to one-handed signs, possibly because, for half of the signs, handshape and movement goals were spread across the two limbs. Finally, the conjunction analysis comparing each sign type with the descender baseline task revealed common activation in the supramarginal gyrus bilaterally, which we interpret as reflecting phonological retrieval and encoding processes. Copyright © 2016 Elsevier Inc. All rights reserved.

  11. Homology and homoplasy of swimming behaviors and neural circuits in the Nudipleura (Mollusca, Gastropoda, Opisthobranchia)

    Science.gov (United States)

    Newcomb, James M.; Sakurai, Akira; Lillvis, Joshua L.; Gunaratne, Charuni A.; Katz, Paul S.

    2012-01-01

    How neural circuit evolution relates to behavioral evolution is not well understood. Here the relationship between neural circuits and behavior is explored with respect to the swimming behaviors of the Nudipleura (Mollusca, Gastropoda, Opithobranchia). Nudipleura is a diverse monophyletic clade of sea slugs among which only a small percentage of species can swim. Swimming falls into a limited number of categories, the most prevalent of which are rhythmic left–right body flexions (LR) and rhythmic dorsal–ventral body flexions (DV). The phylogenetic distribution of these behaviors suggests a high degree of homoplasy. The central pattern generator (CPG) underlying DV swimming has been well characterized in Tritonia diomedea and in Pleurobranchaea californica. The CPG for LR swimming has been elucidated in Melibe leonina and Dendronotus iris, which are more closely related. The CPGs for the categorically distinct DV and LR swimming behaviors consist of nonoverlapping sets of homologous identified neurons, whereas the categorically similar behaviors share some homologous identified neurons, although the exact composition of neurons and synapses in the neural circuits differ. The roles played by homologous identified neurons in categorically distinct behaviors differ. However, homologous identified neurons also play different roles even in the swim CPGs of the two LR swimming species. Individual neurons can be multifunctional within a species. Some of those functions are shared across species, whereas others are not. The pattern of use and reuse of homologous neurons in various forms of swimming and other behaviors further demonstrates that the composition of neural circuits influences the evolution of behaviors. PMID:22723353

  12. Priming Neural Circuits to Modulate Spinal Reflex Excitability

    Science.gov (United States)

    Estes, Stephen P.; Iddings, Jennifer A.; Field-Fote, Edelle C.

    2017-01-01

    While priming is most often thought of as a strategy for modulating neural excitability to facilitate voluntary motor control, priming stimulation can also be utilized to target spinal reflex excitability. In this application, priming can be used to modulate the involuntary motor output that often follows central nervous system injury. Individuals with spinal cord injury (SCI) often experience spasticity, for which antispasmodic medications are the most common treatment. Physical therapeutic/electroceutic interventions offer an alternative treatment for spasticity, without the deleterious side effects that can accompany pharmacological interventions. While studies of physical therapeutic/electroceutic interventions have been published, a systematic comparison of these approaches has not been performed. The purpose of this study was to compare four non-pharmacological interventions to a sham-control intervention to assess their efficacy for spasticity reduction. Participants were individuals (n = 10) with chronic SCI (≥1 year) who exhibited stretch-induced quadriceps spasticity. Spasticity was quantified using the pendulum test before and at two time points after (immediate, 45 min delayed) each of four different physical therapeutic/electroceutic interventions, plus a sham-control intervention. Interventions included stretching, cyclic passive movement (CPM), transcutaneous spinal cord stimulation (tcSCS), and transcranial direct current stimulation (tDCS). The sham-control intervention consisted of a brief ramp-up and ramp-down of knee and ankle stimulation while reclined with legs extended. The order of interventions was randomized, and each was tested on a separate day with at least 48 h between sessions. Compared to the sham-control intervention, stretching, CPM, and tcSCS were associated with a significantly greater reduction in spasticity immediately after treatment. While the immediate effect was largest for stretching, the reduction persisted

  13. Architecture Analysis of an FPGA-Based Hopfield Neural Network

    Directory of Open Access Journals (Sweden)

    Miguel Angelo de Abreu de Sousa

    2014-01-01

    Full Text Available Interconnections between electronic circuits and neural computation have been a strongly researched topic in the machine learning field in order to approach several practical requirements, including decreasing training and operation times in high performance applications and reducing cost, size, and energy consumption for autonomous or embedded developments. Field programmable gate array (FPGA hardware shows some inherent features typically associated with neural networks, such as, parallel processing, modular executions, and dynamic adaptation, and works on different types of FPGA-based neural networks were presented in recent years. This paper aims to address different aspects of architectural characteristics analysis on a Hopfield Neural Network implemented in FPGA, such as maximum operating frequency and chip-area occupancy according to the network capacity. Also, the FPGA implementation methodology, which does not employ multipliers in the architecture developed for the Hopfield neural model, is presented, in detail.

  14. Biologically based neural circuit modelling for the study of fear learning and extinction

    Science.gov (United States)

    Nair, Satish S.; Paré, Denis; Vicentic, Aleksandra

    2016-11-01

    The neuronal systems that promote protective defensive behaviours have been studied extensively using Pavlovian conditioning. In this paradigm, an initially neutral-conditioned stimulus is paired with an aversive unconditioned stimulus leading the subjects to display behavioural signs of fear. Decades of research into the neural bases of this simple behavioural paradigm uncovered that the amygdala, a complex structure comprised of several interconnected nuclei, is an essential part of the neural circuits required for the acquisition, consolidation and expression of fear memory. However, emerging evidence from the confluence of electrophysiological, tract tracing, imaging, molecular, optogenetic and chemogenetic methodologies, reveals that fear learning is mediated by multiple connections between several amygdala nuclei and their distributed targets, dynamical changes in plasticity in local circuit elements as well as neuromodulatory mechanisms that promote synaptic plasticity. To uncover these complex relations and analyse multi-modal data sets acquired from these studies, we argue that biologically realistic computational modelling, in conjunction with experiments, offers an opportunity to advance our understanding of the neural circuit mechanisms of fear learning and to address how their dysfunction may lead to maladaptive fear responses in mental disorders.

  15. Neural learning circuits utilizing nano-crystalline silicon transistors and memristors.

    Science.gov (United States)

    Cantley, Kurtis D; Subramaniam, Anand; Stiegler, Harvey J; Chapman, Richard A; Vogel, Eric M

    2012-04-01

    Properties of neural circuits are demonstrated via SPICE simulations and their applications are discussed. The neuron and synapse subcircuits include ambipolar nano-crystalline silicon transistor and memristor device models based on measured data. Neuron circuit characteristics and the Hebbian synaptic learning rule are shown to be similar to biology. Changes in the average firing rate learning rule depending on various circuit parameters are also presented. The subcircuits are then connected into larger neural networks that demonstrate fundamental properties including associative learning and pulse coincidence detection. Learned extraction of a fundamental frequency component from noisy inputs is demonstrated. It is then shown that if the fundamental sinusoid of one neuron input is out of phase with the rest, its synaptic connection changes differently than the others. Such behavior indicates that the system can learn to detect which signals are important in the general population, and that there is a spike-timing-dependent component of the learning mechanism. Finally, future circuit design and considerations are discussed, including requirements for the memristive device.

  16. Analysis of neural networks

    CERN Document Server

    Heiden, Uwe

    1980-01-01

    The purpose of this work is a unified and general treatment of activity in neural networks from a mathematical pOint of view. Possible applications of the theory presented are indica­ ted throughout the text. However, they are not explored in de­ tail for two reasons : first, the universal character of n- ral activity in nearly all animals requires some type of a general approach~ secondly, the mathematical perspicuity would suffer if too many experimental details and empirical peculiarities were interspersed among the mathematical investigation. A guide to many applications is supplied by the references concerning a variety of specific issues. Of course the theory does not aim at covering all individual problems. Moreover there are other approaches to neural network theory (see e.g. Poggio-Torre, 1978) based on the different lev­ els at which the nervous system may be viewed. The theory is a deterministic one reflecting the average be­ havior of neurons or neuron pools. In this respect the essay is writt...

  17. Circuit models and experimental noise measurements of micropipette amplifiers for extracellular neural recordings from live animals.

    Science.gov (United States)

    Chen, Chang Hao; Pun, Sio Hang; Mak, Peng Un; Vai, Mang I; Klug, Achim; Lei, Tim C

    2014-01-01

    Glass micropipettes are widely used to record neural activity from single neurons or clusters of neurons extracellularly in live animals. However, to date, there has been no comprehensive study of noise in extracellular recordings with glass micropipettes. The purpose of this work was to assess various noise sources that affect extracellular recordings and to create model systems in which novel micropipette neural amplifier designs can be tested. An equivalent circuit of the glass micropipette and the noise model of this circuit, which accurately describe the various noise sources involved in extracellular recordings, have been developed. Measurement schemes using dead brain tissue as well as extracellular recordings from neurons in the inferior colliculus, an auditory brain nucleus of an anesthetized gerbil, were used to characterize noise performance and amplification efficacy of the proposed micropipette neural amplifier. According to our model, the major noise sources which influence the signal to noise ratio are the intrinsic noise of the neural amplifier and the thermal noise from distributed pipette resistance. These two types of noise were calculated and measured and were shown to be the dominating sources of background noise for in vivo experiments.

  18. Circuit Models and Experimental Noise Measurements of Micropipette Amplifiers for Extracellular Neural Recordings from Live Animals

    Directory of Open Access Journals (Sweden)

    Chang Hao Chen

    2014-01-01

    Full Text Available Glass micropipettes are widely used to record neural activity from single neurons or clusters of neurons extracellularly in live animals. However, to date, there has been no comprehensive study of noise in extracellular recordings with glass micropipettes. The purpose of this work was to assess various noise sources that affect extracellular recordings and to create model systems in which novel micropipette neural amplifier designs can be tested. An equivalent circuit of the glass micropipette and the noise model of this circuit, which accurately describe the various noise sources involved in extracellular recordings, have been developed. Measurement schemes using dead brain tissue as well as extracellular recordings from neurons in the inferior colliculus, an auditory brain nucleus of an anesthetized gerbil, were used to characterize noise performance and amplification efficacy of the proposed micropipette neural amplifier. According to our model, the major noise sources which influence the signal to noise ratio are the intrinsic noise of the neural amplifier and the thermal noise from distributed pipette resistance. These two types of noise were calculated and measured and were shown to be the dominating sources of background noise for in vivo experiments.

  19. Changes in the spinal neural circuits are dependent on the movement speed of the visuomotor task

    Directory of Open Access Journals (Sweden)

    Shinji eKubota

    2015-12-01

    Full Text Available Previous studies have shown that spinal neural circuits are modulated by motor skill training. However, the effects of task movement speed on changes in spinal neural circuits have not been clarified. The aim of this research was to investigate whether spinal neural circuits were affected by task movement speed. Thirty-eight healthy subjects participated in this study. In experiment 1, the effects of task movement speed on the spinal neural circuits were examined. 18 subjects performed a visuomotor task involving ankle muscle slow (9 subjects or fast (9 subjects movement speed. Another 9 subjects performed a non-visuomotor task (controls in fast movement speed. The motor task training lasted for 20 min. The amounts of D1 inhibition and reciprocal Ia inhibition were measured using H-relfex condition-test paradigm and recorded before, and at 5, 15, and 30 min after the training session. In experiment 2, using transcranial magnetic stimulation (TMS, the effects of corticospinal descending inputs on the presynaptic inhibitory pathway were examined before and after performing either a visuomotor (8 subjects or a control task (8 subjects. All measurements were taken under resting conditions. The amount of D1 inhibition increased after the visuomotor task irrespective of movement speed (P < 0.01. The amount of reciprocal Ia inhibition increased with fast movement speed conditioning (P < 0.01, but was unchanged by slow movement speed conditioning. These changes lasted up to 15 min in D1 inhibition and 5 min in reciprocal Ia inhibition after the training session. The control task did not induce changes in D1 inhibition and reciprocal Ia inhibition. The TMS conditioned inhibitory effects of presynaptic inhibitory pathways decreased following visuomotor tasks (P < 0.01. The size of test H-reflex was almost the same size throughout experiments. The results suggest that supraspinal descending inputs for controlling joint movement are responsible for changes

  20. A neuroplasticity-inspired neural circuit for acoustic navigation with obstacle avoidance that learns smooth motion paths

    DEFF Research Database (Denmark)

    Shaikh, Danish; Manoonpong, Poramate

    2018-01-01

    avoiding obstacles. We have reported earlier on a neural circuit for acoustic navigation, inspired by neuroplasticity mechanisms, which learned stable robot motion paths for a simulated mobile robot. The circuit realised a reactive behaviour-based navigation architecture where a phonotaxis behaviour...

  1. An implantable wireless neural interface for recording cortical circuit dynamics in moving primates.

    Science.gov (United States)

    Borton, David A; Yin, Ming; Aceros, Juan; Nurmikko, Arto

    2013-04-01

    Neural interface technology suitable for clinical translation has the potential to significantly impact the lives of amputees, spinal cord injury victims and those living with severe neuromotor disease. Such systems must be chronically safe, durable and effective. We have designed and implemented a neural interface microsystem, housed in a compact, subcutaneous and hermetically sealed titanium enclosure. The implanted device interfaces the brain with a 510k-approved, 100-element silicon-based microelectrode array via a custom hermetic feedthrough design. Full spectrum neural signals were amplified (0.1 Hz to 7.8 kHz, 200× gain) and multiplexed by a custom application specific integrated circuit, digitized and then packaged for transmission. The neural data (24 Mbps) were transmitted by a wireless data link carried on a frequency-shift-key-modulated signal at 3.2 and 3.8 GHz to a receiver 1 m away by design as a point-to-point communication link for human clinical use. The system was powered by an embedded medical grade rechargeable Li-ion battery for 7 h continuous operation between recharge via an inductive transcutaneous wireless power link at 2 MHz. Device verification and early validation were performed in both swine and non-human primate freely-moving animal models and showed that the wireless implant was electrically stable, effective in capturing and delivering broadband neural data, and safe for over one year of testing. In addition, we have used the multichannel data from these mobile animal models to demonstrate the ability to decode neural population dynamics associated with motor activity. We have developed an implanted wireless broadband neural recording device evaluated in non-human primate and swine. The use of this new implantable neural interface technology can provide insight into how to advance human neuroprostheses beyond the present early clinical trials. Further, such tools enable mobile patient use, have the potential for wider diagnosis of

  2. Changed Synaptic Plasticity in Neural Circuits of Depressive-Like and Escitalopram-Treated Rats.

    Science.gov (United States)

    Li, Xiao-Li; Yuan, Yong-Gui; Xu, Hua; Wu, Di; Gong, Wei-Gang; Geng, Lei-Yu; Wu, Fang-Fang; Tang, Hao; Xu, Lin; Zhang, Zhi-Jun

    2015-04-21

    Although progress has been made in the detection and characterization of neural plasticity in depression, it has not been fully understood in individual synaptic changes in the neural circuits under chronic stress and antidepressant treatment. Using electron microscopy and Western-blot analyses, the present study quantitatively examined the changes in the Gray's Type I synaptic ultrastructures and the expression of synapse-associated proteins in the key brain regions of rats' depressive-related neural circuit after chronic unpredicted mild stress and/or escitalopram administration. Meanwhile, their depressive behaviors were also determined by several tests. The Type I synapses underwent considerable remodeling after chronic unpredicted mild stress, which resulted in the changed width of the synaptic cleft, length of the active zone, postsynaptic density thickness, and/or synaptic curvature in the subregions of medial prefrontal cortex and hippocampus, as well as the basolateral amygdaloid nucleus of the amygdala, accompanied by changed expression of several synapse-associated proteins. Chronic escitalopram administration significantly changed the above alternations in the chronic unpredicted mild stress rats but had little effect on normal controls. Also, there was a positive correlation between the locomotor activity and the maximal synaptic postsynaptic density thickness in the stratum radiatum of the Cornu Ammonis 1 region and a negative correlation between the sucrose preference and the length of the active zone in the basolateral amygdaloid nucleus region in chronic unpredicted mild stress rats. These findings strongly indicate that chronic stress and escitalopram can alter synaptic plasticity in the neural circuits, and the remodeled synaptic ultrastructure was correlated with the rats' depressive behaviors, suggesting a therapeutic target for further exploration. © The Author 2015. Published by Oxford University Press on behalf of CINP.

  3. Changed Synaptic Plasticity in Neural Circuits of Depressive-Like and Escitalopram-Treated Rats

    Science.gov (United States)

    Li, Xiao-Li; Yuan, Yong-Gui; Xu, Hua; Wu, Di; Gong, Wei-Gang; Geng, Lei-Yu; Wu, Fang-Fang; Tang, Hao; Xu, Lin

    2015-01-01

    Background: Although progress has been made in the detection and characterization of neural plasticity in depression, it has not been fully understood in individual synaptic changes in the neural circuits under chronic stress and antidepressant treatment. Methods: Using electron microscopy and Western-blot analyses, the present study quantitatively examined the changes in the Gray’s Type I synaptic ultrastructures and the expression of synapse-associated proteins in the key brain regions of rats’ depressive-related neural circuit after chronic unpredicted mild stress and/or escitalopram administration. Meanwhile, their depressive behaviors were also determined by several tests. Results: The Type I synapses underwent considerable remodeling after chronic unpredicted mild stress, which resulted in the changed width of the synaptic cleft, length of the active zone, postsynaptic density thickness, and/or synaptic curvature in the subregions of medial prefrontal cortex and hippocampus, as well as the basolateral amygdaloid nucleus of the amygdala, accompanied by changed expression of several synapse-associated proteins. Chronic escitalopram administration significantly changed the above alternations in the chronic unpredicted mild stress rats but had little effect on normal controls. Also, there was a positive correlation between the locomotor activity and the maximal synaptic postsynaptic density thickness in the stratum radiatum of the Cornu Ammonis 1 region and a negative correlation between the sucrose preference and the length of the active zone in the basolateral amygdaloid nucleus region in chronic unpredicted mild stress rats. Conclusion: These findings strongly indicate that chronic stress and escitalopram can alter synaptic plasticity in the neural circuits, and the remodeled synaptic ultrastructure was correlated with the rats’ depressive behaviors, suggesting a therapeutic target for further exploration. PMID:25899067

  4. Implementing a Bayes Filter in a Neural Circuit: The Case of Unknown Stimulus Dynamics.

    Science.gov (United States)

    Sokoloski, Sacha

    2017-09-01

    In order to interact intelligently with objects in the world, animals must first transform neural population responses into estimates of the dynamic, unknown stimuli that caused them. The Bayesian solution to this problem is known as a Bayes filter, which applies Bayes' rule to combine population responses with the predictions of an internal model. The internal model of the Bayes filter is based on the true stimulus dynamics, and in this note, we present a method for training a theoretical neural circuit to approximately implement a Bayes filter when the stimulus dynamics are unknown. To do this we use the inferential properties of linear probabilistic population codes to compute Bayes' rule and train a neural network to compute approximate predictions by the method of maximum likelihood. In particular, we perform stochastic gradient descent on the negative log-likelihood of the neural network parameters with a novel approximation of the gradient. We demonstrate our methods on a finite-state, a linear, and a nonlinear filtering problem and show how the hidden layer of the neural network develops tuning curves consistent with findings in experimental neuroscience.

  5. Genetic manipulation of specific neural circuits by use of a viral vector system.

    Science.gov (United States)

    Kobayashi, Kenta; Kato, Shigeki; Kobayashi, Kazuto

    2017-01-05

    To understand the mechanisms underlying higher brain functions, we need to analyze the roles of specific neuronal pathways or cell types forming the complex neural networks. In the neuroscience field, the transgenic approach has provided a useful gene engineering tool for experimental studies of neural functions. The conventional transgenic technique requires the appropriate promoter regions that drive a neuronal type-specific gene expression, but the promoter sequences specifically functioning in each neuronal type are limited. Previously, we developed novel types of lentiviral vectors showing high efficiency of retrograde gene transfer in the central nervous system, termed highly efficient retrograde gene transfer (HiRet) vector and neuron-specific retrograde gene transfer (NeuRet) vector. The HiRet and NeuRet vectors enable genetical manipulation of specific neural pathways in diverse model animals in combination with conditional cell targeting, synaptic transmission silencing, and gene expression systems. These newly developed vectors provide powerful experimental strategies to investigate, more precisely, the machineries exerting various neural functions. In this review, we give an outline of the HiRet and NeuRet vectors and describe recent representative applications of these viral vectors for studies on neural circuits.

  6. Spiking neural circuits with dendritic stimulus processors : encoding, decoding, and identification in reproducing kernel Hilbert spaces.

    Science.gov (United States)

    Lazar, Aurel A; Slutskiy, Yevgeniy B

    2015-02-01

    We present a multi-input multi-output neural circuit architecture for nonlinear processing and encoding of stimuli in the spike domain. In this architecture a bank of dendritic stimulus processors implements nonlinear transformations of multiple temporal or spatio-temporal signals such as spike trains or auditory and visual stimuli in the analog domain. Dendritic stimulus processors may act on both individual stimuli and on groups of stimuli, thereby executing complex computations that arise as a result of interactions between concurrently received signals. The results of the analog-domain computations are then encoded into a multi-dimensional spike train by a population of spiking neurons modeled as nonlinear dynamical systems. We investigate general conditions under which such circuits faithfully represent stimuli and demonstrate algorithms for (i) stimulus recovery, or decoding, and (ii) identification of dendritic stimulus processors from the observed spikes. Taken together, our results demonstrate a fundamental duality between the identification of the dendritic stimulus processor of a single neuron and the decoding of stimuli encoded by a population of neurons with a bank of dendritic stimulus processors. This duality result enabled us to derive lower bounds on the number of experiments to be performed and the total number of spikes that need to be recorded for identifying a neural circuit.

  7. Neural circuit dynamics underlying accumulation of time-varying evidence during perceptual decision making

    Directory of Open Access Journals (Sweden)

    Kong-Fatt Wong

    2007-11-01

    Full Text Available How do neurons in a decision circuit integrate time-varying signals, in favor of or against alternative choice options? To address this question, we used a recurrent neural circuit model to simulate an experiment in which monkeys performed a direction-discrimination task on a visual motion stimulus. In a recent study, it was found that brief pulses of motion perturbed neural activity in the lateral intraparietal area (LIP, and exerted corresponding effects on the monkey's choices and response times. Our model reproduces the behavioral observations and replicates LIP activity which, depending on whether the direction of the pulse is the same or opposite to that of a preferred motion stimulus, increases or decreases persistently over a few hundred milliseconds. Furthermore, our model accounts for the observation that the pulse exerts a weaker influence on LIP neuronal responses when the pulse is late relative to motion stimulus onset. We show that this violation of time-shift invariance (TSI is consistent with a recurrent circuit mechanism of time integration. We further examine time integration using two consecutive pulses of the same or opposite motion directions. The induced changes in the performance are not additive, and the second of the paired pulses is less effective than its standalone impact, a prediction that is experimentally testable. Taken together, these findings lend further support for an attractor network model of time integration in perceptual decision making.

  8. Dynamical systems, attractors, and neural circuits [version 1; referees: 3 approved

    Directory of Open Access Journals (Sweden)

    Paul Miller

    2016-05-01

    Full Text Available Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic—they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.

  9. A Circuit-Based Neural Network with Hybrid Learning of Backpropagation and Random Weight Change Algorithms

    Science.gov (United States)

    Yang, Changju; Kim, Hyongsuk; Adhikari, Shyam Prasad; Chua, Leon O.

    2016-01-01

    A hybrid learning method of a software-based backpropagation learning and a hardware-based RWC learning is proposed for the development of circuit-based neural networks. The backpropagation is known as one of the most efficient learning algorithms. A weak point is that its hardware implementation is extremely difficult. The RWC algorithm, which is very easy to implement with respect to its hardware circuits, takes too many iterations for learning. The proposed learning algorithm is a hybrid one of these two. The main learning is performed with a software version of the BP algorithm, firstly, and then, learned weights are transplanted on a hardware version of a neural circuit. At the time of the weight transplantation, a significant amount of output error would occur due to the characteristic difference between the software and the hardware. In the proposed method, such error is reduced via a complementary learning of the RWC algorithm, which is implemented in a simple hardware. The usefulness of the proposed hybrid learning system is verified via simulations upon several classical learning problems. PMID:28025566

  10. Cell biology in neuroscience: Architects in neural circuit design: glia control neuron numbers and connectivity.

    Science.gov (United States)

    Corty, Megan M; Freeman, Marc R

    2013-11-11

    Glia serve many important functions in the mature nervous system. In addition, these diverse cells have emerged as essential participants in nearly all aspects of neural development. Improved techniques to study neurons in the absence of glia, and to visualize and manipulate glia in vivo, have greatly expanded our knowledge of glial biology and neuron-glia interactions during development. Exciting studies in the last decade have begun to identify the cellular and molecular mechanisms by which glia exert control over neuronal circuit formation. Recent findings illustrate the importance of glial cells in shaping the nervous system by controlling the number and connectivity of neurons.

  11. Distinct neural circuits underlie assessment of a diversity of natural dangers by American crows

    Science.gov (United States)

    Cross, Donna J.; Marzluff, John M.; Palmquist, Ila; Minoshima, Satoshi; Shimizu, Toru; Miyaoka, Robert

    2013-01-01

    Social animals encountering natural dangers face decisions such as whether to freeze, flee or harass the threat. The American crow, Corvus brachyrhynchos, conspicuously mobs dangers. We used positron emission tomography to test the hypothesis that distinct neuronal substrates underlie the crow's consistent behavioural response to different dangers. We found that crows activated brain regions associated with attention and arousal (nucleus isthmo-opticus/locus coeruleus), and with motor response (arcopallium), as they fixed their gaze on a threat. However, despite this consistent behavioural and neural response, the sight of a person who previously captured the crow, a person holding a dead crow and a taxidermy-mounted hawk activated distinct forebrain regions (amygdala, hippocampus and portion of the caudal nidopallium, respectively). We suggest that aspects of mobbing behaviour are guided by unique neural circuits that respond to differences in mental processing—learning, memory formation and multisensory discrimination—required to appropriately nuance a risky behaviour to specific dangers. PMID:23825209

  12. Distinct neural circuits underlie assessment of a diversity of natural dangers by American crows.

    Science.gov (United States)

    Cross, Donna J; Marzluff, John M; Palmquist, Ila; Minoshima, Satoshi; Shimizu, Toru; Miyaoka, Robert

    2013-08-22

    Social animals encountering natural dangers face decisions such as whether to freeze, flee or harass the threat. The American crow, Corvus brachyrhynchos, conspicuously mobs dangers. We used positron emission tomography to test the hypothesis that distinct neuronal substrates underlie the crow's consistent behavioural response to different dangers. We found that crows activated brain regions associated with attention and arousal (nucleus isthmo-opticus/locus coeruleus), and with motor response (arcopallium), as they fixed their gaze on a threat. However, despite this consistent behavioural and neural response, the sight of a person who previously captured the crow, a person holding a dead crow and a taxidermy-mounted hawk activated distinct forebrain regions (amygdala, hippocampus and portion of the caudal nidopallium, respectively). We suggest that aspects of mobbing behaviour are guided by unique neural circuits that respond to differences in mental processing-learning, memory formation and multisensory discrimination-required to appropriately nuance a risky behaviour to specific dangers.

  13. Effects of intranasal oxytocin on neural processing within a socially relevant neural circuit.

    Science.gov (United States)

    Singh, Fiza; Nunag, Jason; Muldoon, Glennis; Cadenhead, Kristin S; Pineda, Jaime A; Feifel, David

    2016-03-01

    Dysregulation of the Mirror Neuron System (MNS) in schizophrenia (SCZ) may underlie the cognitive and behavioral manifestations of social dysfunction associated with that disorder. In healthy subjects intranasal (IN) oxytocin (OT) improves neural processing in the MNS and is associated with improved social cognition. OT's brain effects can be measured through its modulation of the MNS by suppressing EEG mu-band electrical activity (8-13Hz) in response to motion perception. Although IN OT's effects on social cognition have been tested in SCZ, OT's impact on the MNS has not been evaluated to date. Therefore, we designed a study to investigate the effects of two different OT doses on biological motion-induced mu suppression in SCZ and healthy subjects. EEG recordings were taken after each subject received a single IN administration of placebo, OT-24IU and OT-48IU in randomized order in a double-blind crossover design. The results provide support for OT's regulation of the MNS in both healthy and SCZ subjects, with the optimal dose dependent on diagnostic group and sex of subject. A statistically significant response was seen in SCZ males only, indicating a heightened sensitivity to those effects, although sex hormone related effects cannot be ruled out. In general, OT appears to have positive effects on neural circuitry that supports social cognition and socially adaptive behaviors. Published by Elsevier B.V.

  14. Timing Analysis of Genetic Logic Circuits using D-VASim

    DEFF Research Database (Denmark)

    Baig, Hasan; Madsen, Jan

    delay analysis may play a very significant role in the designing of genetic logic circuits. In thisdemonstration, we present the capability of D-VASim (Dynamic Virtual Analyzer and Simulator) to perform the timing and propagationdelay analysis of genetic logic circuits. Using D-VASim, the timing......A genetic logic circuit is a gene regulator network implemented by re-engineering the DNA of a cell, in order to controlgene expression or metabolic pathways, through a logic combination of external signals, such as chemicals or proteins. As for electroniclogic circuits, timing and propagation...... and propagation delay analysis of single as well as cascaded geneticlogic circuits can be performed. D-VASim allows user to change the circuit parameters during runtime simulation to observe its effectson circuit’s timing behavior. The results obtained from D-VASim can be used not only to characterize the timing...

  15. Neural circuits of disgust induced by sexual stimuli in homosexual and heterosexual men: An fMRI study

    Energy Technology Data Exchange (ETDEWEB)

    Zhang Minming [Department of Radiology, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou (China); Hu Shaohua [Department of Mental Health, First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qing Chun Road, Hangzhou, Zhejiang Province 310003 (China); Xu Lijuan [National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing (China); Wang Qidong [Department of Radiology, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou (China); Xu Xiaojun [Department of Radiology, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou (China); Wei Erqing [College of Pharmacology, Zhejiang University (China); Yan Leqin [MD Anderson Cancer Center, Virginia Harris Cockrell Cancer Research Center, University of Texas, Austin (United States); Hu Jianbo; Wei Ning; Zhou Weihua; Huang Manli [Department of Mental Health, First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qing Chun Road, Hangzhou, Zhejiang Province 310003 (China); Xu Yi, E-mail: xuyi61@yahoo.com.cn [Department of Mental Health, First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qing Chun Road, Hangzhou, Zhejiang Province 310003 (China)

    2011-11-15

    Few studies demonstrated neural circuits related to disgust were influenced by internal sexual orientation in male. Here we used fMRI to study the neural responses to disgust in homosexual and heterosexual men to investigate that issue. Thirty-two healthy male volunteers (sixteen homosexual and sixteen heterosexual) were scanned while viewing alternating blocks of three types of erotic film: heterosexual couples (F-M), male homosexual couples (M-M), and female homosexual couples (F-F) engaged in sexual activity. All the participants rated their level of disgust and sexual arousal as well. The F-F and M-M stimuli induced disgust in homosexual and heterosexual men, respectively. The common activations related to disgusting stimuli included: bilateral frontal gyrus and occipital gyrus, right middle temporal gyrus, left superior temporal gyrus, right cerebellum, and right thalamus. Homosexual men had greater neural responses in the left medial frontal gyrus than did heterosexual men to the sexual disgusting stimuli; in contrast, heterosexual men showed significantly greater activation than homosexual men in the left cuneus. ROI analysis showed that negative correlation were found between the magnitude of MRI signals in the left medial frontal gyrus and scores of disgust in homosexual subjects (p < 0.05). This study indicated that there were regions in common as well as regions specific for each type of erotic stimuli during disgust of homosexual and heterosexual men.

  16. An implantable wireless neural interface for recording cortical circuit dynamics in moving primates

    Science.gov (United States)

    Borton, David A.; Yin, Ming; Aceros, Juan; Nurmikko, Arto

    2013-04-01

    Objective. Neural interface technology suitable for clinical translation has the potential to significantly impact the lives of amputees, spinal cord injury victims and those living with severe neuromotor disease. Such systems must be chronically safe, durable and effective. Approach. We have designed and implemented a neural interface microsystem, housed in a compact, subcutaneous and hermetically sealed titanium enclosure. The implanted device interfaces the brain with a 510k-approved, 100-element silicon-based microelectrode array via a custom hermetic feedthrough design. Full spectrum neural signals were amplified (0.1 Hz to 7.8 kHz, 200× gain) and multiplexed by a custom application specific integrated circuit, digitized and then packaged for transmission. The neural data (24 Mbps) were transmitted by a wireless data link carried on a frequency-shift-key-modulated signal at 3.2 and 3.8 GHz to a receiver 1 m away by design as a point-to-point communication link for human clinical use. The system was powered by an embedded medical grade rechargeable Li-ion battery for 7 h continuous operation between recharge via an inductive transcutaneous wireless power link at 2 MHz. Main results. Device verification and early validation were performed in both swine and non-human primate freely-moving animal models and showed that the wireless implant was electrically stable, effective in capturing and delivering broadband neural data, and safe for over one year of testing. In addition, we have used the multichannel data from these mobile animal models to demonstrate the ability to decode neural population dynamics associated with motor activity. Significance. We have developed an implanted wireless broadband neural recording device evaluated in non-human primate and swine. The use of this new implantable neural interface technology can provide insight into how to advance human neuroprostheses beyond the present early clinical trials. Further, such tools enable mobile

  17. Oscillator circuits frontiers in design, analysis and applications

    CERN Document Server

    2016-01-01

    This book surveys recent developments in the design, analysis and applications of oscillator circuit design. It highlights developments in the analysis of synchronization and wave phenomena, new analytical and design methods and their application, and novel engineering applications of oscillator circuits.

  18. Impaired activity-dependent neural circuit assembly and refinement in autism spectrum disorder genetic models

    Directory of Open Access Journals (Sweden)

    Caleb Andrew Doll

    2014-02-01

    Full Text Available Early-use activity during circuit-specific critical periods refines brain circuitry by the coupled processes of eliminating inappropriate synapses and strengthening maintained synapses. We theorize these activity-dependent developmental processes are specifically impaired in autism spectrum disorders (ASDs. ASD genetic models in both mouse and Drosophila have pioneered our insights into normal activity-dependent neural circuit assembly and consolidation, and how these developmental mechanisms go awry in specific genetic conditions. The monogenic Fragile X syndrome (FXS, a common cause of heritable ASD and intellectual disability, has been particularly well linked to defects in activity-dependent critical period processes. The Fragile X Mental Retardation Protein (FMRP is positively activity-regulated in expression and function, in turn regulates excitability and activity in a negative feedback loop, and appears to be required for the activity-dependent remodeling of synaptic connectivity during early-use critical periods. The Drosophila FXS model has been shown to functionally conserve the roles of human FMRP in synaptogenesis, and has been centrally important in generating our current mechanistic understanding of the FXS disease state. Recent advances in Drosophila optogenetics, transgenic calcium reporters, highly-targeted transgenic drivers for individually-identified neurons, and a vastly improved connectome of the brain are now being combined to provide unparalleled opportunities to both manipulate and monitor activity-dependent processes during critical period brain development in defined neural circuits. The field is now poised to exploit this new Drosophila transgenic toolbox for the systematic dissection of activity-dependent mechanisms in normal versus ASD brain development, particularly utilizing the well-established Drosophila FXS disease model.

  19. Gate-Level Circuit Reliability Analysis: A Survey

    Directory of Open Access Journals (Sweden)

    Ran Xiao

    2014-01-01

    Full Text Available Circuit reliability has become a growing concern in today’s nanoelectronics, which motivates strong research interest over the years in reliability analysis and reliability-oriented circuit design. While quite a few approaches for circuit reliability analysis have been reported, there is a lack of comparative studies on their pros and cons in terms of both accuracy and efficiency. This paper provides an overview of some typical methods for reliability analysis with focus on gate-level circuits, large or small, with or without reconvergent fanouts. It is intended to help the readers gain an insight into the reliability issues, and their complexity as well as optional solutions. Understanding the reliability analysis is also a first step towards advanced circuit designs for improved reliability in the future research.

  20. A Computer Program for Short Circuit Analysis of Electric Power ...

    African Journals Online (AJOL)

    This paper described the mathematical basis and computational framework of a computer program developed for short circuit studies of electric power systems. The Short Circuit Analysis Program (SCAP) is to be used to assess the composite effects of unbalanced and balanced faults on the overall reliability of electric ...

  1. Symbolic Analysis of OTRAs-Based Circuits

    Directory of Open Access Journals (Sweden)

    C. Sánchez-López

    2011-04-01

    Full Text Available A new nullor-based model to describe the behavior of Operational Transresistance Amplifiers (OTRAs is introduced.The new model is composed of four nullors and three grounded resistors. As a consequence, standard nodal analysiscan be applied to compute fully-symbolic small-signal characteristics of OTRA-based analog circuits, and the nullorbasedOTRAs model can be used in CAD tools. In this manner, the fully-symbolic transfer functions of severalapplication circuits, such as filters and oscillators can easily be approximated.

  2. The neural circuits and sensory channels mediating harsh touch sensation in Caenorhabditis elegans.

    Science.gov (United States)

    Li, Wei; Kang, Lijun; Piggott, Beverly J; Feng, Zhaoyang; Xu, X Z Shawn

    2011-01-01

    Most animals can distinguish two distinct types of touch stimuli: gentle (innocuous) and harsh (noxious/painful) touch, however, the underlying mechanisms are not well understood. Caenorhabditis elegans is a useful model for the study of gentle touch sensation. However, little is known about harsh touch sensation in this organism. Here we characterize harsh touch sensation in C. elegans. We show that C. elegans exhibits differential behavioural responses to harsh touch and gentle touch. Laser ablations identify distinct sets of sensory neurons and interneurons required for harsh touch sensation at different body segments. Optogenetic stimulation of the circuitry can drive behaviour. Patch-clamp recordings reveal that TRP family and amiloride-sensitive Na(+) channels mediate touch-evoked currents in different sensory neurons. Our work identifies the neural circuits and characterizes the sensory channels mediating harsh touch sensation in C. elegans, establishing it as a genetic model for studying this sensory modality.

  3. Cross-talk between the epigenome and neural circuits in drug addiction.

    Science.gov (United States)

    Mews, Philipp; Calipari, Erin S

    2017-01-01

    Drug addiction is a behavioral disorder characterized by dysregulated learning about drugs and associated cues that result in compulsive drug seeking and relapse. Learning about drug rewards and predictive cues is a complex process controlled by a computational network of neural connections interacting with transcriptional and molecular mechanisms within each cell to precisely guide behavior. The interplay between rapid, temporally specific neuronal activation, and longer-term changes in transcription is of critical importance in the expression of appropriate, or in the case of drug addiction, inappropriate behaviors. Thus, these factors and their interactions must be considered together, especially in the context of treatment. Understanding the complex interplay between epigenetic gene regulation and circuit connectivity will allow us to formulate novel therapies to normalize maladaptive reward behaviors, with a goal of modulating addictive behaviors, while leaving natural reward-associated behavior unaffected. © 2017 Elsevier B.V. All rights reserved.

  4. Multiple conserved cell adhesion protein interactions mediate neural wiring of a sensory circuit in C. elegans.

    Science.gov (United States)

    Kim, Byunghyuk; Emmons, Scott W

    2017-09-13

    Nervous system function relies on precise synaptic connections. A number of widely-conserved cell adhesion proteins are implicated in cell recognition between synaptic partners, but how these proteins act as a group to specify a complex neural network is poorly understood. Taking advantage of known connectivity in C. elegans, we identified and studied cell adhesion genes expressed in three interacting neurons in the mating circuits of the adult male. Two interacting pairs of cell surface proteins independently promote fasciculation between sensory neuron HOA and its postsynaptic target interneuron AVG: BAM-2/neurexin-related in HOA binds to CASY-1/calsyntenin in AVG; SAX-7/L1CAM in sensory neuron PHC binds to RIG-6/contactin in AVG. A third, basal pathway results in considerable HOA-AVG fasciculation and synapse formation in the absence of the other two. The features of this multiplexed mechanism help to explain how complex connectivity is encoded and robustly established during nervous system development.

  5. Application of viral vectors to the study of neural connectivities and neural circuits in the marmoset brain.

    Science.gov (United States)

    Watakabe, Akiya; Sadakane, Osamu; Hata, Katsusuke; Ohtsuka, Masanari; Takaji, Masafumi; Yamamori, Tetsuo

    2017-03-01

    It is important to study the neural connectivities and functions in primates. For this purpose, it is critical to be able to transfer genes to certain neurons in the primate brain so that we can image the neuronal signals and analyze the function of the transferred gene. Toward this end, our team has been developing gene transfer systems using viral vectors. In this review, we summarize our current achievements as follows. 1) We compared the features of gene transfer using five different AAV serotypes in combination with three different promoters, namely, CMV, mouse CaMKII (CaMKII), and human synapsin 1 (hSyn1), in the marmoset cortex with those in the mouse and macaque cortices. 2) We used target-specific double-infection techniques in combination with TET-ON and TET-OFF using lentiviral retrograde vectors for enhanced visualization of neural connections. 3) We used an AAV-mediated gene transfer method to study the transcriptional control for amplifying fluorescent signals using the TET/TRE system in the primate neocortex. We also established systems for shRNA mediated gene targeting in a neocortical region where a gene is significantly expressed and for expressing the gene using the CMV promoter for an unexpressed neocortical area in the primate cortex using AAV vectors to understand the regulation of downstream genes. Our findings have demonstrated the feasibility of using viral vector mediated gene transfer systems for the study of primate cortical circuits using the marmoset as an animal model. © 2016 Wiley Periodicals, Inc. Develop Neurobiol 77: 354-372, 2017. © 2016 The Authors. Developmental Neurobiology Published by Wiley Periodicals, Inc.

  6. Satellite image analysis using neural networks

    Science.gov (United States)

    Sheldon, Roger A.

    1990-01-01

    The tremendous backlog of unanalyzed satellite data necessitates the development of improved methods for data cataloging and analysis. Ford Aerospace has developed an image analysis system, SIANN (Satellite Image Analysis using Neural Networks) that integrates the technologies necessary to satisfy NASA's science data analysis requirements for the next generation of satellites. SIANN will enable scientists to train a neural network to recognize image data containing scenes of interest and then rapidly search data archives for all such images. The approach combines conventional image processing technology with recent advances in neural networks to provide improved classification capabilities. SIANN allows users to proceed through a four step process of image classification: filtering and enhancement, creation of neural network training data via application of feature extraction algorithms, configuring and training a neural network model, and classification of images by application of the trained neural network. A prototype experimentation testbed was completed and applied to climatological data.

  7. Circuit Distortion Analysis Based on the Simplified Newton's Method

    Directory of Open Access Journals (Sweden)

    M. M. Gourary

    2011-01-01

    Full Text Available A new computational technique for distortion analysis of nonlinear circuits is presented. The new technique is applicable to the same class of circuits, namely, weakly nonlinear and time-varying circuits, as the periodic Volterra series. However, unlike the Volterra series, it does not require the computation of the second and third derivatives of device models. The new method is computationally efficient compared with a complete multitone nonlinear steady-state analysis such as harmonic balance. Moreover, the new technique naturally allows computing and characterizing the contributions of individual circuit components to the overall circuit distortion. This paper presents the theory of the new technique, a discussion of the numerical aspects, and numerical results.

  8. An Integrated Circuit for Simultaneous Extracellular Electrophysiology Recording and Optogenetic Neural Manipulation.

    Science.gov (United States)

    Chen, Chang Hao; McCullagh, Elizabeth A; Pun, Sio Hang; Mak, Peng Un; Vai, Mang I; Mak, Pui In; Klug, Achim; Lei, Tim C

    2017-03-01

    The ability to record and to control action potential firing in neuronal circuits is critical to understand how the brain functions. The objective of this study is to develop a monolithic integrated circuit (IC) to record action potentials and simultaneously control action potential firing using optogenetics. A low-noise and high input impedance (or low input capacitance) neural recording amplifier is combined with a high current laser/light-emitting diode (LED) driver in a single IC. The low input capacitance of the amplifier (9.7 pF) was achieved by adding a dedicated unity gain stage optimized for high impedance metal electrodes. The input referred noise of the amplifier is [Formula: see text], which is lower than the estimated thermal noise of the metal electrode. Thus, the action potentials originating from a single neuron can be recorded with a signal-to-noise ratio of at least 6.6. The LED/laser current driver delivers a maximum current of 330 mA, which is adequate for optogenetic control. The functionality of the IC was tested with an anesthetized Mongolian gerbil and auditory stimulated action potentials were recorded from the inferior colliculus. Spontaneous firings of fifth (trigeminal) nerve fibers were also inhibited using the optogenetic protein Halorhodopsin. Moreover, a noise model of the system was derived to guide the design. A single IC to measure and control action potentials using optogenetic proteins is realized so that more complicated behavioral neuroscience research and the translational neural disorder treatments become possible in the future.

  9. Programmable delay circuit for sparker signal analysis

    Digital Repository Service at National Institute of Oceanography (India)

    Pathak, D.

    on it to classify the seafloor sediment properties. A specific purpose oriented programmable delay circuit was developed to generate the necessary delay so that the A/D conversion could start just before the arrival of the echo from the water bottom interface...

  10. Circuit Analysis in Metal-Optics

    CERN Document Server

    Staffaroni, Matteo; Vedantam, Shantha; Tang, Japeck; Yablonovitch, Eli

    2010-01-01

    We provide electrical circuit descriptions for bulk plasmons, single surface plasmons, and parallel-plate plasmons. Simple circuits can reproduce the exactly known frequency versus wave-vector dispersion relations for all these cases, with reasonable accuracy. The circuit paradigm directly provides a characteristic wave-impedance, Zo, that is rarely discussed in the context of plasmonics. The case of a single-surface-plasmon is particularly interesting since it can be modeled as a transmission line, even though there is no return current conductor. The capacitance/unit length and the Faraday inductance/unit length, of a flat metal surface, are C'=2epsilon_okW, and L'=epsilon_o/2kW respectively, (where k is wave-vector, and W is the width of the flat metal surface). We believe that many other metal-optic geometries can be described within the circuit paradigm, with the prerequisite that the distributed capacitance and inductance must be calculated for each particular geometry

  11. Circuit analysis in metal-optics

    Science.gov (United States)

    Staffaroni, M.; Conway, J.; Vedantam, S.; Tang, J.; Yablonovitch, E.

    2012-01-01

    We provide electrical circuit descriptions for bulk plasmons, single surface plasmons, and parallel-plate plasmons. Simple circuits can reproduce the exactly known frequency versus wave-vector dispersion relations for all these cases, with reasonable accuracy. The circuit paradigm directly provides a characteristic wave-impedance, Zo, that is rarely discussed in the context of plasmonics. The case of a single-surface-plasmon is particularly interesting since it can be modeled as a transmission line, even though there is no return current conductor. The capacitance/unit length and the Faraday inductance/unit length, of a flat metal surface, are C‧ = 2ɛokW, and L‧ = μo/2kW, respectively (where k is the wave-vector, and W is the width of the flat metal surface). We believe that many other metal-optic geometries can be described within the circuit paradigm, with the prerequisite that the distributed capacitance and inductance must be calculated for each particular geometry.

  12. Equivalent Circuit Parameters Estimation for PEM Fuel Cell Using RBF Neural Network and Enhanced Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Wen-Yeau Chang

    2013-01-01

    Full Text Available This paper proposes an equivalent circuit parameters measurement and estimation method for proton exchange membrane fuel cell (PEMFC. The parameters measurement method is based on current loading technique; in current loading test a no load PEMFC is suddenly turned on to obtain the waveform of the transient terminal voltage. After the equivalent circuit parameters were measured, a hybrid method that combines a radial basis function (RBF neural network and enhanced particle swarm optimization (EPSO algorithm is further employed for the equivalent circuit parameters estimation. The RBF neural network is adopted such that the estimation problem can be effectively processed when the considered data have different features and ranges. In the hybrid method, EPSO algorithm is used to tune the connection weights, the centers, and the widths of RBF neural network. Together with the current loading technique, the proposed hybrid estimation method can effectively estimate the equivalent circuit parameters of PEMFC. To verify the proposed approach, experiments were conducted to demonstrate the equivalent circuit parameters estimation of PEMFC. A practical PEMFC stack was purposely created to produce the common current loading activities of PEMFC for the experiments. The practical results of the proposed method were studied in accordance with the conditions for different loading conditions.

  13. Estimating neural background input with controlled and fast perturbations: A bandwidth comparison between inhibitory opsins and neural circuits

    Directory of Open Access Journals (Sweden)

    David Eriksson

    2016-08-01

    Full Text Available To test the importance of a certain cell type or brain area it is common to make a lack of function experiment in which the neuronal population of interest is inhibited. Here we review physiological and methodological constraints for making controlled perturbations using the corticothalamic circuit as an example. The brain with its many types of cells and rich interconnectivity offers many paths through which a perturbation can spread within a short time. To understand the side effects of the perturbation one should record from those paths. We find that ephaptic effects, gap-junctions, and fast chemical synapses are so fast that they can react to the perturbation during the few milliseconds it takes for an opsin to change the membrane potential. The slow chemical synapses, astrocytes, extracellular ions and vascular signals, will continue to give their physiological input for around 20 milliseconds before they also react to the perturbation. Although we show that some pathways can react within milliseconds the strength/speed reported in this review should be seen as an upper bound since we have omitted how polysynaptic signals are attenuated. Thus the number of additional recordings that has to be made to control for the perturbation side effects is expected to be fewer than proposed here. To summarize, the reviewed literature not only suggests that it is possible to make controlled lack of function experiments, but, it also suggests that such a lack of function experiment can be used to measure the context of local neural computations.

  14. Analgesic Neural Circuits Are Activated by Electroacupuncture at Two Sets of Acupoints

    Directory of Open Access Journals (Sweden)

    Man-Li Hu

    2016-01-01

    Full Text Available To investigate analgesic neural circuits activated by electroacupuncture (EA at different sets of acupoints in the brain, goats were stimulated by EA at set of Baihui-Santai acupoints or set of Housanli acupoints for 30 min. The pain threshold was measured using the potassium iontophoresis method. The levels of c-Fos were determined with Streptavidin-Biotin Complex immunohistochemistry. The results showed pain threshold induced by EA at set of Baihui-Santai acupoints was 44.74%±4.56% higher than that by EA at set of Housanli acupoints (32.64%±5.04%. Compared with blank control, EA at two sets of acupoints increased c-Fos expression in the medial septal nucleus (MSN, the arcuate nucleus (ARC, the nucleus amygdala basalis (AB, the lateral habenula nucleus (HL, the ventrolateral periaqueductal grey (vlPAG, the locus coeruleus (LC, the nucleus raphe magnus (NRM, the pituitary gland, and spinal cord dorsal horn (SDH. Compared with EA at set of Housanli points, EA at set of Baihui-Santai points induced increased c-Fos expression in AB but decrease in MSN, the paraventricular nucleus of the hypothalamus, HL, and SDH. It suggests that ARC-PAG-NRM/LC-SDH and the hypothalamus-pituitary may be the common activated neural pathways taking part in EA-induced analgesia at the two sets of acupoints.

  15. Information processing in micro and meso-scale neural circuits during normal and disease states

    Science.gov (United States)

    Luongo, Francisco

    Neural computation can occur at multiple spatial and temporal timescales. The sum total of all of these processes is to guide optimal behaviors within the context of the constraints imposed by the physical world. How the circuits of the brain achieves this goal represents a central question in systems neuroscience. Here I explore the many ways in which the circuits of the brain can process information at both the micro and meso scale. Understanding the way information is represented and processed in the brain could shed light on the neuropathology underlying complex neuropsychiatric diseases such as autism and schizophrenia. Chapter 2 establishes an experimental paradigm for assaying patterns of microcircuit activity and examines the role of dopaminergic modulation on prefrontal microcircuits. We find that dopamine type 2 (D2) receptor activation results in an increase in spontaneous activity while dopamine type 1 (D1) activation does not. Chapter 3 of this dissertation presents a study that illustrates how cholingergic activation normally produces what has been suggested as a neural substrate of attention; pairwise decorrelation in microcircuit activity. This study also shows that in two etiologicall distinct mouse models of autism, FMR1 knockout mice and Valproic Acid exposed mice, this ability to decorrelate in the presence of cholinergic activation is lost. This represents a putative microcircuit level biomarker of autism. Chapter 4 examines the structure/function relationship within the prefrontal microcircuit. Spontaneous activity in prefrontal microcircuits is shown to be organized according to a small world architecture. Interestingly, this architecture is important for one concrete function of neuronal microcircuits; the ability to produce temporally stereotyped patterns of activation. In the final chapter, we identify subnetworks in chronic intracranial electrocorticographic (ECoG) recordings using pairwise electrode coherence and dimensionality reduction

  16. Antagonistic Serotonergic and Octopaminergic Neural Circuits Mediate Food-Dependent Locomotory Behavior in Caenorhabditis elegans.

    Science.gov (United States)

    Churgin, Matthew A; McCloskey, Richard J; Peters, Emily; Fang-Yen, Christopher

    2017-08-16

    Biogenic amines are conserved signaling molecules that link food cues to behavior and metabolism in a wide variety of organisms. In the nematode Caenorhabditis elegans, the biogenic amines serotonin (5-HT) and octopamine regulate a number of food-related behaviors. Using a novel method for long-term quantitative behavioral imaging, we show that 5-HT and octopamine jointly influence locomotor activity and quiescence in feeding and fasting hermaphrodites, and we define the neural circuits through which this modulation occurs. We show that 5-HT produced by the ADF neurons acts via the SER-5 receptor in muscles and neurons to suppress quiescent behavior and promote roaming in fasting worms, whereas 5-HT produced by the NSM neurons acts on the MOD-1 receptor in AIY neurons to promote low-amplitude locomotor behavior characteristic of well fed animals. Octopamine, produced by the RIC neurons, acts via SER-3 and SER-6 receptors in SIA neurons to promote roaming behaviors characteristic of fasting animals. We find that 5-HT signaling is required for animals to assume food-appropriate behavior, whereas octopamine signaling is required for animals to assume fasting-appropriate behavior. The requirement for both neurotransmitters in both the feeding and fasting states enables increased behavioral adaptability. Our results define the molecular and neural pathways through which parallel biogenic amine signaling tunes behavior appropriately to nutrient conditions.SIGNIFICANCE STATEMENT Animals adjust behavior in response to environmental changes, such as fluctuations in food abundance, to maximize survival and reproduction. Biogenic amines, such as like serotonin, are conserved neurotransmitters that regulate behavior and metabolism in relation to energy status. Disruptions of biogenic amine signaling contribute to human neurological diseases of mood, appetite, and movement. In this study, we investigated the roles of the biogenic amines serotonin and octopamine in regulating

  17. Nonlinear analysis of ring oscillator circuits

    KAUST Repository

    Ge, Xiaoqing

    2010-06-01

    Using nonlinear systems techniques, we analyze the stability properties and synchronization conditions for ring oscillator circuits, which are essential building blocks in digital systems. By making use of its cyclic structure, we investigate local and global stability properties of an n-stage ring oscillator. We present a sufficient condition for global asymptotic stability of the origin and obtain necessity if the ring oscillator consists of identical inverter elements. We then give a synchronization condition for identical interconnected ring oscillators.

  18. Texture Based Image Analysis With Neural Nets

    Science.gov (United States)

    Ilovici, Irina S.; Ong, Hoo-Tee; Ostrander, Kim E.

    1990-03-01

    In this paper, we combine direct image statistics and spatial frequency domain techniques with a neural net model to analyze texture based images. The resultant optimal texture features obtained from the direct and transformed image form the exemplar pattern of the neural net. The proposed approach introduces an automated texture analysis applied to metallography for determining the cooling rate and mechanical working of the materials. The results suggest that the proposed method enhances the practical applications of neural nets and texture extraction features.

  19. NEURAL CORRELATES FOR APATHY: FRONTAL - PREFRONTAL AND PARIETAL CORTICAL - SUBCORTICAL CIRCUITS

    Directory of Open Access Journals (Sweden)

    Rita Moretti

    2016-12-01

    Full Text Available Apathy is an uncertain nosographical entity, which includes reduced motivation, abulia, decreased empathy, and lack of emotional invovlement; it is an important and heavy-burden clinical condition which strongly impacts in every day life events, affects the common daily living abilities, reduced the inner goal directed behavior, and gives the heaviest burden on caregivers. Is a quite common comorbidity of many neurological disease, However, there is no definite consensus on the role of apathy in clinical practice, no definite data on anatomical circuits involved in its development, and no definite instrument to detect it at bedside. As a general observation, the occurrence of apathy is connected to damage of prefrontal cortex (PFC and basal ganglia; emotional affective apathy may be related to the orbitomedial PFC and ventral striatum; cognitive apathy may be associated with dysfunction of lateral PFC and dorsal caudate nuclei; deficit of autoactivation may be due to bilateral lesions of the internal portion of globus pallidus, bilateral paramedian thalamic lesions, or the dorsomedial portion of PFC. On the other hand, apathy severity has been connected to neurofibrillary tangles density in the anterior cingulate gyrus and to grey matter atrophy in the anterior cingulate (ACC and in the left medial frontal cortex, confirmed by functional imaging studies. These neural networks are linked to projects, judjing and planning, execution and selection common actions, and through the basolateral amygdala and nucleus accumbens projects to the frontostriatal and to the dorsolateral prefrontal cortex. Therefore, an alteration of these circuitry caused a lack of insight, a reduction of decision-making strategies and a reduced speedness in action decsion, major resposnible for apathy. Emergent role concerns also the parietal cortex, with its direct action motivation control.We will discuss the importance of these circuits in different pathologies

  20. Neural Correlates for Apathy: Frontal-Prefrontal and Parietal Cortical- Subcortical Circuits

    Science.gov (United States)

    Moretti, Rita; Signori, Riccardo

    2016-01-01

    Apathy is an uncertain nosographical entity, which includes reduced motivation, abulia, decreased empathy, and lack of emotional involvement; it is an important and heavy-burden clinical condition which strongly impacts in everyday life events, affects the common daily living abilities, reduced the inner goal directed behavior, and gives the heaviest burden on caregivers. Is a quite common comorbidity of many neurological disease, However, there is no definite consensus on the role of apathy in clinical practice, no definite data on anatomical circuits involved in its development, and no definite instrument to detect it at bedside. As a general observation, the occurrence of apathy is connected to damage of prefrontal cortex (PFC) and basal ganglia; “emotional affective” apathy may be related to the orbitomedial PFC and ventral striatum; “cognitive apathy” may be associated with dysfunction of lateral PFC and dorsal caudate nuclei; deficit of “autoactivation” may be due to bilateral lesions of the internal portion of globus pallidus, bilateral paramedian thalamic lesions, or the dorsomedial portion of PFC. On the other hand, apathy severity has been connected to neurofibrillary tangles density in the anterior cingulate gyrus and to gray matter atrophy in the anterior cingulate (ACC) and in the left medial frontal cortex, confirmed by functional imaging studies. These neural networks are linked to projects, judjing and planning, execution and selection common actions, and through the basolateral amygdala and nucleus accumbens projects to the frontostriatal and to the dorsolateral prefrontal cortex. Therefore, an alteration of these circuitry caused a lack of insight, a reduction of decision-making strategies, and a reduced speedness in action decision, major responsible for apathy. Emergent role concerns also the parietal cortex, with its direct action motivation control. We will discuss the importance of these circuits in different pathologies

  1. Navigating Monogamy: Nonapeptide Sensitivity in a Memory Neural Circuit May Shape Social Behavior and Mating Decisions

    Directory of Open Access Journals (Sweden)

    Alexander G. Ophir

    2017-07-01

    Full Text Available The role of memory in mating systems is often neglected despite the fact that most mating systems are defined in part by how animals use space. Monogamy, for example, is usually characterized by affiliative (e.g., pairbonding and defensive (e.g., mate guarding behaviors, but a high degree of spatial overlap in home range use is the easiest defining feature of monogamous animals in the wild. The nonapeptides vasopressin and oxytocin have been the focus of much attention for their importance in modulating social behavior, however this work has largely overshadowed their roles in learning and memory. To date, the understanding of memory systems and mechanisms governing social behavior have progressed relatively independently. Bridging these two areas will provide a deeper appreciation for understanding behavior, and in particular the mechanisms that mediate reproductive decision-making. Here, I argue that the ability to mate effectively as monogamous individuals is linked to the ability to track conspecifics in space. I discuss the connectivity across some well-known social and spatial memory nuclei, and propose that the nonapeptide receptors within these structures form a putative “socio-spatial memory neural circuit.” This purported circuit may function to integrate social and spatial information to shape mating decisions in a context-dependent fashion. The lateral septum and/or the nucleus accumbens, and neuromodulation therein, may act as an intermediary to relate socio-spatial information with social behavior. Identifying mechanisms responsible for relating information about the social world with mechanisms mediating mating tactics is crucial to fully appreciate the suite of factors driving reproductive decisions and social decision-making.

  2. Navigating Monogamy: Nonapeptide Sensitivity in a Memory Neural Circuit May Shape Social Behavior and Mating Decisions.

    Science.gov (United States)

    Ophir, Alexander G

    2017-01-01

    The role of memory in mating systems is often neglected despite the fact that most mating systems are defined in part by how animals use space. Monogamy, for example, is usually characterized by affiliative (e.g., pairbonding) and defensive (e.g., mate guarding) behaviors, but a high degree of spatial overlap in home range use is the easiest defining feature of monogamous animals in the wild. The nonapeptides vasopressin and oxytocin have been the focus of much attention for their importance in modulating social behavior, however this work has largely overshadowed their roles in learning and memory. To date, the understanding of memory systems and mechanisms governing social behavior have progressed relatively independently. Bridging these two areas will provide a deeper appreciation for understanding behavior, and in particular the mechanisms that mediate reproductive decision-making. Here, I argue that the ability to mate effectively as monogamous individuals is linked to the ability to track conspecifics in space. I discuss the connectivity across some well-known social and spatial memory nuclei, and propose that the nonapeptide receptors within these structures form a putative "socio-spatial memory neural circuit." This purported circuit may function to integrate social and spatial information to shape mating decisions in a context-dependent fashion. The lateral septum and/or the nucleus accumbens, and neuromodulation therein, may act as an intermediary to relate socio-spatial information with social behavior. Identifying mechanisms responsible for relating information about the social world with mechanisms mediating mating tactics is crucial to fully appreciate the suite of factors driving reproductive decisions and social decision-making.

  3. Structural basis for cholinergic regulation of neural circuits in the mouse olfactory bulb.

    Science.gov (United States)

    Hamamoto, Masakazu; Kiyokage, Emi; Sohn, Jaerin; Hioki, Hiroyuki; Harada, Tamotsu; Toida, Kazunori

    2017-02-15

    Odor information is regulated by olfactory inputs, bulbar interneurons, and centrifugal inputs in the olfactory bulb (OB). Cholinergic neurons projecting from the nucleus of the horizontal limb of the diagonal band of Broca and the magnocellular preoptic nucleus are one of the primary centrifugal inputs to the OB. In this study, we focused on cholinergic regulation of the OB and analyzed neural morphology with a particular emphasis on the projection pathways of cholinergic neurons. Single-cell imaging of a specific neuron within dense fibers is critical to evaluate the structure and function of the neural circuits. We labeled cholinergic neurons by infection with virus vector and then reconstructed them three-dimensionally. We also examined the ultramicrostructure of synapses by electron microscopy tomography. To further clarify the function of cholinergic neurons, we performed confocal laser scanning microscopy to investigate whether other neurotransmitters are present within cholinergic axons in the OB. Our results showed the first visualization of complete cholinergic neurons, including axons projecting to the OB, and also revealed frequent axonal branching within the OB where it innervated multiple glomeruli in different areas. Furthermore, electron tomography demonstrated that cholinergic axons formed asymmetrical synapses with a morphological variety of thicknesses of the postsynaptic density. Although we have not yet detected the presence of other neurotransmitters, the range of synaptic morphology suggests multiple modes of transmission. The present study elucidates the ways that cholinergic neurons could contribute to the elaborate mechanisms involved in olfactory processing in the OB. J. Comp. Neurol. 525:574-591, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  4. The malleable brain: plasticity of neural circuits and behavior - a review from students to students.

    Science.gov (United States)

    Schaefer, Natascha; Rotermund, Carola; Blumrich, Eva-Maria; Lourenco, Mychael V; Joshi, Pooja; Hegemann, Regina U; Jamwal, Sumit; Ali, Nilufar; García Romero, Ezra Michelet; Sharma, Sorabh; Ghosh, Shampa; Sinha, Jitendra K; Loke, Hannah; Jain, Vishal; Lepeta, Katarzyna; Salamian, Ahmad; Sharma, Mahima; Golpich, Mojtaba; Nawrotek, Katarzyna; Paidi, Ramesh K; Shahidzadeh, Sheila M; Piermartiri, Tetsade; Amini, Elham; Pastor, Veronica; Wilson, Yvette; Adeniyi, Philip A; Datusalia, Ashok K; Vafadari, Benham; Saini, Vedangana; Suárez-Pozos, Edna; Kushwah, Neetu; Fontanet, Paula; Turner, Anthony J

    2017-06-20

    One of the most intriguing features of the brain is its ability to be malleable, allowing it to adapt continually to changes in the environment. Specific neuronal activity patterns drive long-lasting increases or decreases in the strength of synaptic connections, referred to as long-term potentiation and long-term depression, respectively. Such phenomena have been described in a variety of model organisms, which are used to study molecular, structural, and functional aspects of synaptic plasticity. This review originated from the first International Society for Neurochemistry (ISN) and Journal of Neurochemistry (JNC) Flagship School held in Alpbach, Austria (Sep 2016), and will use its curriculum and discussions as a framework to review some of the current knowledge in the field of synaptic plasticity. First, we describe the role of plasticity during development and the persistent changes of neural circuitry occurring when sensory input is altered during critical developmental stages. We then outline the signaling cascades resulting in the synthesis of new plasticity-related proteins, which ultimately enable sustained changes in synaptic strength. Going beyond the traditional understanding of synaptic plasticity conceptualized by long-term potentiation and long-term depression, we discuss system-wide modifications and recently unveiled homeostatic mechanisms, such as synaptic scaling. Finally, we describe the neural circuits and synaptic plasticity mechanisms driving associative memory and motor learning. Evidence summarized in this review provides a current view of synaptic plasticity in its various forms, offers new insights into the underlying mechanisms and behavioral relevance, and provides directions for future research in the field of synaptic plasticity. Read the Editorial Highlight for this article on doi: 10.1111/jnc.14102. © 2017 International Society for Neurochemistry.

  5. Uncertainty-Dependent Extinction of Fear Memory in an Amygdala-mPFC Neural Circuit Model

    Science.gov (United States)

    Li, Yuzhe; Nakae, Ken; Ishii, Shin; Naoki, Honda

    2016-01-01

    Uncertainty of fear conditioning is crucial for the acquisition and extinction of fear memory. Fear memory acquired through partial pairings of a conditioned stimulus (CS) and an unconditioned stimulus (US) is more resistant to extinction than that acquired through full pairings; this effect is known as the partial reinforcement extinction effect (PREE). Although the PREE has been explained by psychological theories, the neural mechanisms underlying the PREE remain largely unclear. Here, we developed a neural circuit model based on three distinct types of neurons (fear, persistent and extinction neurons) in the amygdala and medial prefrontal cortex (mPFC). In the model, the fear, persistent and extinction neurons encode predictions of net severity, of unconditioned stimulus (US) intensity, and of net safety, respectively. Our simulation successfully reproduces the PREE. We revealed that unpredictability of the US during extinction was represented by the combined responses of the three types of neurons, which are critical for the PREE. In addition, we extended the model to include amygdala subregions and the mPFC to address a recent finding that the ventral mPFC (vmPFC) is required for consolidating extinction memory but not for memory retrieval. Furthermore, model simulations led us to propose a novel procedure to enhance extinction learning through re-conditioning with a stronger US; strengthened fear memory up-regulates the extinction neuron, which, in turn, further inhibits the fear neuron during re-extinction. Thus, our models increased the understanding of the functional roles of the amygdala and vmPFC in the processing of uncertainty in fear conditioning and extinction. PMID:27617747

  6. Acute genetic manipulation of neuronal activity for the functional dissection of neural circuits-a dream come true for the pioneers of behavioral genetics.

    Science.gov (United States)

    Yoshihara, Moto; Ito, Kei

    2012-03-01

    Abstract: This review summarizes technical development of the functional manipulation of specific neural circuits through genetic techniques in Drosophila. Long after pioneers' efforts for the genetic dissection of behavior using this organism as a model, analyses with acute activation of specific neural circuits have finally become feasible using transgenic Drosophila that expresses light-, heat-, or cold-activatable cation channels by xxx/upstream activation sequence (Gal4/UAS)-based induction system. This methodology opened a new avenue to dissect functions of neural circuits to make dreams of the pioneers into reality.

  7. Hybrid Spintronic-CMOS Spiking Neural Network with On-Chip Learning: Devices, Circuits, and Systems

    Science.gov (United States)

    Sengupta, Abhronil; Banerjee, Aparajita; Roy, Kaushik

    2016-12-01

    Over the past decade, spiking neural networks (SNNs) have emerged as one of the popular architectures to emulate the brain. In SNNs, information is temporally encoded and communication between neurons is accomplished by means of spikes. In such networks, spike-timing-dependent plasticity mechanisms require the online programing of synapses based on the temporal information of spikes transmitted by spiking neurons. In this work, we propose a spintronic synapse with decoupled spike-transmission and programing-current paths. The spintronic synapse consists of a ferromagnet-heavy-metal heterostructure where the programing current through the heavy metal generates spin-orbit torque to modulate the device conductance. Low programing energy and fast programing times demonstrate the efficacy of the proposed device as a nanoelectronic synapse. We perform a simulation study based on an experimentally benchmarked device-simulation framework to demonstrate the interfacing of such spintronic synapses with CMOS neurons and learning circuits operating in the transistor subthreshold region to form a network of spiking neurons that can be utilized for pattern-recognition problems.

  8. Impact of adolescent social experiences on behavior and neural circuits implicated in mental illnesses.

    Science.gov (United States)

    Burke, Andrew R; McCormick, Cheryl M; Pellis, Sergio M; Lukkes, Jodi L

    2017-05-01

    Negative social experiences during adolescence are central features for several stress-related mental illnesses. Social play fighting behavior in rats peaks during early adolescence and is essential for the final maturation of brain and behavior. Manipulation of the rat adolescent social experience alters many neurobehavioral measurements implicated in anxiety, depression, and substance abuse. In this review, we will highlight the importance of social play and the use of three separate social stress models (isolation-rearing, social defeat, and social instability stress) to disrupt the acquisition of this adaptive behavior. Social stress during adolescence leads to the development of anxiety and depressive behavior as well as escalated drug use in adulthood. Furthermore, sex- and age-dependent effects on the hormonal stress response following adolescent social stress are also observed. Finally, manipulation of the social experience during adolescence alters stress-related neural circuits and monoaminergic systems. Overall, positive social experiences among age-matched conspecifics during rat adolescence are critical for healthy neurobehavioral maturation. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Segregated and overlapping neural circuits exist for the production of static and dynamic precision grip force

    Science.gov (United States)

    Neely, Kristina A.; Coombes, Stephen A.; Planetta, Peggy J.; Vaillancourt, David E.

    2011-01-01

    A central topic in sensorimotor neuroscience is the static-dynamic dichotomy that exists throughout the nervous system. Previous work examining motor unit synchronization reports that the activation strategy and timing of motor units differ for static and dynamic tasks. However, it remains unclear whether segregated or overlapping blood-oxygen-level-dependent (BOLD) activity exists in the brain for static and dynamic motor control. This study compared the neural circuits associated with the production of static force to those associated with the production of dynamic force pulses. To that end, healthy young adults (n = 17) completed static and dynamic precision grip force tasks during functional magnetic resonance imaging (fMRI). Both tasks activated core regions within the visuomotor network, including primary and sensory motor cortices, premotor cortices, multiple visual areas, putamen, and cerebellum. Static force was associated with unique activity in a right-lateralized cortical network including inferior parietal lobe, ventral premotor cortex, and dorsolateral prefrontal cortex. In contrast, dynamic force was associated with unique activity in left-lateralized and midline cortical regions, including supplementary motor area, superior parietal lobe, fusiform gyrus, and visual area V3. These findings provide the first neuroimaging evidence supporting a lateralized pattern of brain activity for the production of static and dynamic precision grip force. PMID:22109998

  10. The primary visual cortex in the neural circuit for visual orienting

    Science.gov (United States)

    Zhaoping, Li

    The primary visual cortex (V1) is traditionally viewed as remote from influencing brain's motor outputs. However, V1 provides the most abundant cortical inputs directly to the sensory layers of superior colliculus (SC), a midbrain structure to command visual orienting such as shifting gaze and turning heads. I will show physiological, anatomical, and behavioral data suggesting that V1 transforms visual input into a saliency map to guide a class of visual orienting that is reflexive or involuntary. In particular, V1 receives a retinotopic map of visual features, such as orientation, color, and motion direction of local visual inputs; local interactions between V1 neurons perform a local-to-global computation to arrive at a saliency map that highlights conspicuous visual locations by higher V1 responses. The conspicuous location are usually, but not always, where visual input statistics changes. The population V1 outputs to SC, which is also retinotopic, enables SC to locate, by lateral inhibition between SC neurons, the most salient location as the saccadic target. Experimental tests of this hypothesis will be shown. Variations of the neural circuit for visual orienting across animal species, with more or less V1 involvement, will be discussed. Supported by the Gatsby Charitable Foundation.

  11. The Neuropsychiatry of Hyperkinetic Movement Disorders: Insights from Neuroimaging into the Neural Circuit Bases of Dysfunction

    Directory of Open Access Journals (Sweden)

    Bradleigh D. Hayhow

    2013-09-01

    Full Text Available Background: Movement disorders, particularly those associated with basal ganglia disease, have a high rate of comorbid neuropsychiatric illness.Methods: We consider the pathophysiological basis of the comorbidity between movement disorders and neuropsychiatric illness by 1 reviewing the epidemiology of neuropsychiatric illness in a range of hyperkinetic movement disorders, and 2 correlating findings to evidence from studies that have utilized modern neuroimaging techniques to investigate these disorders. In addition to diseases classically associated with basal ganglia pathology, such as Huntington disease, Wilson disease, the neuroacanthocytoses, and diseases of brain iron accumulation, we include diseases associated with pathology of subcortical white matter tracts, brain stem nuclei, and the cerebellum, such as metachromatic leukodystrophy, dentatorubropallidoluysian atrophy, and the spinocerebellar ataxias.Conclusions: Neuropsychiatric symptoms are integral to a thorough phenomenological account of hyperkinetic movement disorders. Drawing on modern theories of cortico-subcortical circuits, we argue that these disorders can be conceptualized as disorders of complex subcortical networks with distinct functional architectures. Damage to any component of these complex information-processing networks can have variable and often profound consequences for the function of more remote neural structures, creating a diverse but nonetheless rational pattern of clinical symptomatology.

  12. Advancements in Automated Circuit Grouping for Intellectual Property Trust Analysis

    Science.gov (United States)

    2017-03-20

    Advancements in Automated Circuit Grouping for Intellectual Property Trust Analysis James Inge, Matthew Kwiec, Stephen Baka, John Hallman...grouping assembles related circuitry into hierarchical blocks of functions that can be independently analyzed. Keywords: Trust ; analysis...intellectual property; automation; functional discovery Introduction In today’s global supply chain, trusting intellectual property that makes up our most

  13. Medical image analysis with artificial neural networks.

    Science.gov (United States)

    Jiang, J; Trundle, P; Ren, J

    2010-12-01

    Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. Copyright © 2010 Elsevier Ltd. All rights reserved.

  14. Modulatory effects of modafinil on neural circuits regulating emotion and cognition.

    Science.gov (United States)

    Rasetti, Roberta; Mattay, Venkata S; Stankevich, Beth; Skjei, Kelsey; Blasi, Giuseppe; Sambataro, Fabio; Arrillaga-Romany, Isabel C; Goldberg, Terry E; Callicott, Joseph H; Apud, José A; Weinberger, Daniel R

    2010-09-01

    Modafinil differs from other arousal-enhancing agents in chemical structure, neurochemical profile, and behavioral effects. Most functional neuroimaging studies to date examined the effect of modafinil only on information processing underlying executive cognition, but cognitive enhancers in general have been shown to have pronounced effects on emotional behavior, too. We examined the effect of modafinil on neural circuits underlying affective processing and cognitive functions. Healthy volunteers were enrolled in this double-blinded placebo-controlled trial (100 mg/day for 7 days). They underwent BOLD fMRI while performing an emotion information-processing task that activates the amygdala and two prefrontally dependent cognitive tasks-a working memory (WM) task and a variable attentional control (VAC) task. A clinical assessment that included measurement of blood pressure, heart rate, the Hamilton anxiety scale, and the profile of mood state (POMS) questionnaire was also performed on each test day. BOLD fMRI revealed significantly decreased amygdala reactivity to fearful stimuli on modafinil compared with the placebo condition. During executive cognition tasks, a WM task and a VAC task, modafinil reduced BOLD signal in the prefrontal cortex and anterior cingulate. Although not statistically significant, there were trends for reduced anxiety, for decreased fatigue-inertia and increased vigor-activity, as well as decreased anger-hostility on modafinil. Modafinil in low doses has a unique physiologic profile compared with stimulant drugs: it enhances the efficiency of prefrontal cortical cognitive information processing, while dampening reactivity to threatening stimuli in the amygdala, a brain region implicated in anxiety.

  15. Modulatory Effects of Modafinil on Neural Circuits Regulating Emotion and Cognition

    Science.gov (United States)

    Rasetti, Roberta; Mattay, Venkata S; Stankevich, Beth; Skjei, Kelsey; Blasi, Giuseppe; Sambataro, Fabio; Arrillaga-Romany, Isabel C; Goldberg, Terry E; Callicott, Joseph H; Apud, José A; Weinberger, Daniel R

    2010-01-01

    Modafinil differs from other arousal-enhancing agents in chemical structure, neurochemical profile, and behavioral effects. Most functional neuroimaging studies to date examined the effect of modafinil only on information processing underlying executive cognition, but cognitive enhancers in general have been shown to have pronounced effects on emotional behavior, too. We examined the effect of modafinil on neural circuits underlying affective processing and cognitive functions. Healthy volunteers were enrolled in this double-blinded placebo-controlled trial (100 mg/day for 7 days). They underwent BOLD fMRI while performing an emotion information-processing task that activates the amygdala and two prefrontally dependent cognitive tasks—a working memory (WM) task and a variable attentional control (VAC) task. A clinical assessment that included measurement of blood pressure, heart rate, the Hamilton anxiety scale, and the profile of mood state (POMS) questionnaire was also performed on each test day. BOLD fMRI revealed significantly decreased amygdala reactivity to fearful stimuli on modafinil compared with the placebo condition. During executive cognition tasks, a WM task and a VAC task, modafinil reduced BOLD signal in the prefrontal cortex and anterior cingulate. Although not statistically significant, there were trends for reduced anxiety, for decreased fatigue-inertia and increased vigor-activity, as well as decreased anger-hostility on modafinil. Modafinil in low doses has a unique physiologic profile compared with stimulant drugs: it enhances the efficiency of prefrontal cortical cognitive information processing, while dampening reactivity to threatening stimuli in the amygdala, a brain region implicated in anxiety. PMID:20555311

  16. Normalization of Intrinsic Neural Circuits Governing Tourette's Syndrome Using Cranial Electrotherapy Stimulation.

    Science.gov (United States)

    Qiao, Jianping; Weng, Shenhong; Wang, Pengwei; Long, Jun; Wang, Zhishun

    2015-05-01

    The aim of this study was to investigate the normalization of the intrinsic functional activity and connectivity of TS adolescents before and after the cranial electrotherapy stimulation (CES) with alpha stim device. We performed resting-state functional magnetic resonance imaging on eight adolescents before and after CES with mean age of about nine-years old who had Tourette's syndrome with moderate to severe tics symptom. Independent component analysis (ICA) with hierarchical partner matching method was used to examine the functional connectivity between regions within cortico-striato-thalamo-cortical (CSTC) circuit. Granger causality was used to investigate effective connectivity among these regions detected by ICA. We then performed pattern classification on independent components with significant group differences that served as endophenotype markers to distinguish the adolescents between TS and the normalized ones after CES. Results showed that TS adolescents after CES treatment had stronger functional activity and connectivity in anterior cingulate cortex (ACC), caudate and posterior cingulate cortex while had weaker activity in supplementary motor area within the motor pathway compared with TS before CES. The results suggest that the functional activity and connectivity in motor pathway was suppressed while activities in the control portions within CSTC loop including ACC and caudate were increased in TS adolescents after CES compared with adolescents before CES. The normalization of the balance between motor and control portions of the CSTC circuit may result in the recovery of TS adolescents.

  17. Modelling and analysis of local field potentials for studying the function of cortical circuits.

    Science.gov (United States)

    Einevoll, Gaute T; Kayser, Christoph; Logothetis, Nikos K; Panzeri, Stefano

    2013-11-01

    The past decade has witnessed a renewed interest in cortical local field potentials (LFPs)--that is, extracellularly recorded potentials with frequencies of up to ~500 Hz. This is due to both the advent of multielectrodes, which has enabled recording of LFPs at tens to hundreds of sites simultaneously, and the insight that LFPs offer a unique window into key integrative synaptic processes in cortical populations. However, owing to its numerous potential neural sources, the LFP is more difficult to interpret than are spikes. Careful mathematical modelling and analysis are needed to take full advantage of the opportunities that this signal offers in understanding signal processing in cortical circuits and, ultimately, the neural basis of perception and cognition.

  18. Harmonic balance analysis of the generalized chua's circuit

    OpenAIRE

    Savacı, Ferit Acar; Günel, Serkan

    2006-01-01

    In this paper, the harmonic balance analysis of Generalized Chua's circuit exhibiting n-scroll attractors has been accomplished. The dual-input describing functions of the piecewise-linear characteristics of Chua's diode have been obtained and based on the harmonic balance principle, the existence and the locations of the n-scrolls have been verified.

  19. Refinement and Pattern Formation in Neural Circuits by the Interaction of Traveling Waves with Spike-Timing Dependent Plasticity

    Science.gov (United States)

    Bennett, James E. M.; Bair, Wyeth

    2015-01-01

    Traveling waves in the developing brain are a prominent source of highly correlated spiking activity that may instruct the refinement of neural circuits. A candidate mechanism for mediating such refinement is spike-timing dependent plasticity (STDP), which translates correlated activity patterns into changes in synaptic strength. To assess the potential of these phenomena to build useful structure in developing neural circuits, we examined the interaction of wave activity with STDP rules in simple, biologically plausible models of spiking neurons. We derive an expression for the synaptic strength dynamics showing that, by mapping the time dependence of STDP into spatial interactions, traveling waves can build periodic synaptic connectivity patterns into feedforward circuits with a broad class of experimentally observed STDP rules. The spatial scale of the connectivity patterns increases with wave speed and STDP time constants. We verify these results with simulations and demonstrate their robustness to likely sources of noise. We show how this pattern formation ability, which is analogous to solutions of reaction-diffusion systems that have been widely applied to biological pattern formation, can be harnessed to instruct the refinement of postsynaptic receptive fields. Our results hold for rich, complex wave patterns in two dimensions and over several orders of magnitude in wave speeds and STDP time constants, and they provide predictions that can be tested under existing experimental paradigms. Our model generalizes across brain areas and STDP rules, allowing broad application to the ubiquitous occurrence of traveling waves and to wave-like activity patterns induced by moving stimuli. PMID:26308406

  20. Neural Nets for Scene Analysis

    Science.gov (United States)

    1992-09-01

    decision boundaries produced for the arificial database when prototypes are Se- feature 1 lected from reduced training set. ly selected from the 383...CLASSIFIER HIT MISS MOPOGIA CORRELATION LOW-LEVEL VISION IVARL&MCE NEURAL NE. (O D ILER) SE CORRELATION REUCE ETC.(OR I F RS)DI4ENSIONAIM AND TRAINING...A) = J11’, + tOi2Z2 + 61311’ (4) SPE Vol. 1608 mitalwg’t Robots and Coniutef Vision X (991)/501 - "X,, ,v ) X 1112 1P Pa P2 P2 .. 2 33 CL AS INPUT

  1. Transient stability analysis of electric energy systems via a fuzzy ART-ARTMAP neural network

    Energy Technology Data Exchange (ETDEWEB)

    Ferreira, Wagner Peron; Silveira, Maria do Carmo G.; Lotufo, AnnaDiva P.; Minussi, Carlos. R. [Department of Electrical Engineering, Sao Paulo State University (UNESP), P.O. Box 31, 15385-000, Ilha Solteira, SP (Brazil)

    2006-04-15

    This work presents a methodology to analyze transient stability (first oscillation) of electric energy systems, using a neural network based on ART architecture (adaptive resonance theory), named fuzzy ART-ARTMAP neural network for real time applications. The security margin is used as a stability analysis criterion, considering three-phase short circuit faults with a transmission line outage. The neural network operation consists of two fundamental phases: the training and the analysis. The training phase needs a great quantity of processing for the realization, while the analysis phase is effectuated almost without computation effort. This is, therefore the principal purpose to use neural networks for solving complex problems that need fast solutions, as the applications in real time. The ART neural networks have as primordial characteristics the plasticity and the stability, which are essential qualities to the training execution and to an efficient analysis. The fuzzy ART-ARTMAP neural network is proposed seeking a superior performance, in terms of precision and speed, when compared to conventional ARTMAP, and much more when compared to the neural networks that use the training by backpropagation algorithm, which is a benchmark in neural network area. (author)

  2. Design and Simulation of Electrocardiogram Circuit with Automatic Analysis of ECG Signal

    Directory of Open Access Journals (Sweden)

    Tosin Jemilehin

    2016-10-01

    Full Text Available An electrocardiogram (ECG is the graphical record of bioelectric signal generated by the human body during cardiac cycle, it tells a lot about the medical status of an individual. A typical ECG waveform consist of the P, Q, R, S and T wave. The automatic ECG signal analysis comprises of using computational method/approach in extracting important features and classification of ECG waveform. This paper presents a concise ECG circuit design using an instrumentation amplifier and a band-pass passive filter. It also present the process involved in analysis of ECG signal. The first stage is the pre-filtering stage, followed by feature extraction of the signal. QRS complex is first extracted followed by P and T wave detection, also the FFT of the signal is also extracted. These features are fed into the classifier for proper classification. A pattern recognition neural network is used for classification, prior to the full deployment of the neural network, it is trained by pre-recorded ECG signal downloaded from the MIT/BIH Arrhythmias database. The neural network gave a satisfactory result with accuracy of around 87%.The whole ECG signal analysis is packaged into a MATLAB GUI for ease of use

  3. Ferroelectric Field-Effect Transistor Differential Amplifier Circuit Analysis

    Science.gov (United States)

    Phillips, Thomas A.; MacLeod, Todd C.; Ho, Fat D.

    2008-01-01

    There has been considerable research investigating the Ferroelectric Field-Effect Transistor (FeFET) in memory circuits. However, very little research has been performed in applying the FeFET to analog circuits. This paper investigates the use of FeFETs in a common analog circuit, the differential amplifier. The two input Metal-Oxide-Semiconductor (MOS) transistors in a general MOS differential amplifier circuit are replaced with FeFETs. Resistors are used in place of the other three MOS transistors. The FeFET model used in the analysis has been previously reported and was based on experimental device data. Because of the FeFET hysteresis, the FeFET differential amplifier has four different operating modes depending on whether the FeFETs are positively or negatively polarized. The FeFET differential amplifier operation in the different modes was analyzed by calculating the amplifier voltage transfer and gain characteristics shown in figures 2 through 5. Comparisons were made between the FeFET differential amplifier and the standard MOS differential amplifier. Possible applications and benefits of the FeFET differential amplifier are discussed.

  4. Chaos in Electronic Circuits: Nonlinear Time Series Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Wheat, Jr., Robert M. [Kennedy Western Univ., Cheyenne, WY (United States)

    2003-07-01

    Chaos in electronic circuits is a phenomenon that has been largely ignored by engineers, manufacturers, and researchers until the early 1990’s and the work of Chua, Matsumoto, and others. As the world becomes more dependent on electronic devices, the detrimental effects of non-normal operation of these devices becomes more significant. Developing a better understanding of the mechanisms involved in the chaotic behavior of electronic circuits is a logical step toward the prediction and prevention of any potentially catastrophic occurrence of this phenomenon. Also, a better understanding of chaotic behavior, in a general sense, could potentially lead to better accuracy in the prediction of natural events such as weather, volcanic activity, and earthquakes. As a first step in this improvement of understanding, and as part of the research being reported here, methods of computer modeling, identifying and analyzing, and producing chaotic behavior in simple electronic circuits have been developed. The computer models were developed using both the Alternative Transient Program (ATP) and Spice, the analysis techniques have been implemented using the C and C++ programming languages, and the chaotically behaving circuits developed using “off the shelf” electronic components.

  5. A neural circuit model of emotional learning using two pathways with different granularity and speed of information processing.

    Science.gov (United States)

    Murakoshi, Kazushi; Saito, Mayuko

    2009-02-01

    We propose a neural circuit model of emotional learning using two pathways with different granularity and speed of information processing. In order to derive a precise time process, we utilized a spiking model neuron proposed by Izhikevich and spike-timing-dependent synaptic plasticity (STDP) of both excitatory and inhibitory synapses. We conducted computer simulations to evaluate the proposed model. We demonstrate some aspects of emotional learning from the perspective of the time process. The agreement of the results with the previous behavioral experiments suggests that the structure and learning process of the proposed model are appropriate.

  6. Neural circuit of verbal humor comprehension in schizophrenia - an fMRI study

    Directory of Open Access Journals (Sweden)

    Przemysław Adamczyk

    2017-01-01

    Full Text Available Individuals with schizophrenia exhibit problems with understanding the figurative meaning of language. This study evaluates neural correlates of diminished humor comprehension observed in schizophrenia. The study included chronic schizophrenia (SCH outpatients (n = 20, and sex, age and education level matched healthy controls (n = 20. The fMRI punchline based humor comprehension task consisted of 60 stories of which 20 had funny, 20 nonsensical and 20 neutral (not funny punchlines. After the punchlines were presented, the participants were asked to indicate whether the story was comprehensible and how funny it was. Three contrasts were analyzed in both groups reflecting stages of humor processing: abstract vs neutral stories - incongruity detection; funny vs abstract - incongruity resolution and elaboration; and funny vs neutral – complete humor processing. Additionally, parametric modulation analysis was performed using both subjective ratings separately. Between-group comparisons revealed that the SCH subjects had attenuated activation in the right posterior superior temporal gyrus (BA 41 in case of irresolvable incongruity processing of nonsensical puns; in the left dorsomedial middle and superior frontal gyri (BA 8/9 in case of incongruity resolution and elaboration processing of funny puns; and in the interhemispheric dorsal anterior cingulate cortex (BA 24 in case of complete processing of funny puns. Additionally, during comprehensibility ratings the SCH group showed a suppressed activity in the left dorsomedial middle and superior frontal gyri (BA 8/9 and revealed weaker activation during funniness ratings in the left dorsal anterior cingulate cortex (BA 24. Interestingly, these differences in the SCH group were accompanied behaviorally by a protraction of time in both types of rating responses and by indicating funny punchlines less comprehensible. Summarizing, our results indicate neural substrates of humor comprehension

  7. Neural circuit of verbal humor comprehension in schizophrenia - an fMRI study.

    Science.gov (United States)

    Adamczyk, Przemysław; Wyczesany, Miroslaw; Domagalik, Aleksandra; Daren, Artur; Cepuch, Kamil; Błądziński, Piotr; Cechnicki, Andrzej; Marek, Tadeusz

    2017-01-01

    Individuals with schizophrenia exhibit problems with understanding the figurative meaning of language. This study evaluates neural correlates of diminished humor comprehension observed in schizophrenia. The study included chronic schizophrenia (SCH) outpatients (n = 20), and sex, age and education level matched healthy controls (n = 20). The fMRI punchline based humor comprehension task consisted of 60 stories of which 20 had funny, 20 nonsensical and 20 neutral (not funny) punchlines. After the punchlines were presented, the participants were asked to indicate whether the story was comprehensible and how funny it was. Three contrasts were analyzed in both groups reflecting stages of humor processing: abstract vs neutral stories - incongruity detection; funny vs abstract - incongruity resolution and elaboration; and funny vs neutral - complete humor processing. Additionally, parametric modulation analysis was performed using both subjective ratings separately. Between-group comparisons revealed that the SCH subjects had attenuated activation in the right posterior superior temporal gyrus (BA 41) in case of irresolvable incongruity processing of nonsensical puns; in the left dorsomedial middle and superior frontal gyri (BA 8/9) in case of incongruity resolution and elaboration processing of funny puns; and in the interhemispheric dorsal anterior cingulate cortex (BA 24) in case of complete processing of funny puns. Additionally, during comprehensibility ratings the SCH group showed a suppressed activity in the left dorsomedial middle and superior frontal gyri (BA 8/9) and revealed weaker activation during funniness ratings in the left dorsal anterior cingulate cortex (BA 24). Interestingly, these differences in the SCH group were accompanied behaviorally by a protraction of time in both types of rating responses and by indicating funny punchlines less comprehensible. Summarizing, our results indicate neural substrates of humor comprehension processing

  8. Modification of tenascin-R expression following unilateral labyrinthectomy in rats indicates its possible role in neural plasticity of the vestibular neural circuit.

    Science.gov (United States)

    Gaal, Botond; Jóhannesson, Einar Örn; Dattani, Amit; Magyar, Agnes; Wéber, Ildikó; Matesz, Clara

    2015-09-01

    We have previously found that unilateral labyrinthectomy is accompanied by modification of hyaluronan and chondroitin sulfate proteoglycan staining in the lateral vestibular nucleus of rats and the time course of subsequent reorganization of extracellular matrix assembly correlates to the restoration of impaired vestibular function. The tenascin-R has repelling effect on pathfinding during axonal growth/regrowth, and thus inhibits neural circuit repair. By using immunohistochemical method, we studied the modification of tenascin-R expression in the superior, medial, lateral, and descending vestibular nuclei of the rat following unilateral labyrinthectomy. On postoperative day 1, tenascin-R reaction in the perineuronal nets disappeared on the side of labyrinthectomy in the superior, lateral, medial, and rostral part of the descending vestibular nuclei. On survival day 3, the staining intensity of tenascin-R reaction in perineuronal nets recovered on the operated side of the medial vestibular nucleus, whereas it was restored by the time of postoperative day 7 in the superior, lateral and rostral part of the descending vestibular nuclei. The staining intensity of tenascin-R reaction remained unchanged in the caudal part of the descending vestibular nucleus bilaterally. Regional differences in the modification of tenascin-R expression presented here may be associated with different roles of individual vestibular nuclei in the compensatory processes. The decreased expression of the tenascin-R may suggest the extracellular facilitation of plastic modifications in the vestibular neural circuit after lesion of the labyrinthine receptors.

  9. A Circuit-Theoretic Anomaly Resolved by Nonstandard Analysis

    OpenAIRE

    Zemanian, A.H

    2005-01-01

    An anomaly in electrical circuit theory is the disappearance of some of the energy when two capacitors, one charged and the other uncharged, are connected together through resistanceless wires. Nonstandard analysis shows that, when the wires are taken to have infinitesimally small but nonzero resistance, the energy dissipated in the wires equals that substantial amount of enregy that had disappeared, and that all but an infinitesimal amount of this dissipation occurs during an infinitesimal i...

  10. Nonlinear Time Series Analysis via Neural Networks

    Science.gov (United States)

    Volná, Eva; Janošek, Michal; Kocian, Václav; Kotyrba, Martin

    This article deals with a time series analysis based on neural networks in order to make an effective forex market [Moore and Roche, J. Int. Econ. 58, 387-411 (2002)] pattern recognition. Our goal is to find and recognize important patterns which repeatedly appear in the market history to adapt our trading system behaviour based on them.

  11. Reversible and quantum circuits optimization and complexity analysis

    CERN Document Server

    Abdessaied, Nabila

    2016-01-01

    This book presents a new optimization flow for quantum circuits realization. At the reversible level, optimization algorithms are presented to reduce the quantum cost. Then, new mapping approaches to decompose reversible circuits to quantum circuits using different quantum libraries are described. Finally, optimization techniques to reduce the quantum cost or the delay are applied to the resulting quantum circuits. Furthermore, this book studies the complexity of reversible circuits and quantum circuits from a theoretical perspective.

  12. Fiberless multicolor neural optoelectrode for in vivo circuit analysis

    National Research Council Canada - National Science Library

    Kampasi, Komal; Stark, Eran; Seymour, John; Na, Kyounghwan; Winful, Herbert G; Buzsáki, György; Wise, Kensall D; Yoon, Euisik

    2016-01-01

    ... recording electrical data from those neurons. Here, we present the first fiber-less optoelectrode with a monolithically integrated optical waveguide mixer that can deliver multicolor light at a common waveguide port to achieve multicolor...

  13. An integrated multichannel neural recording analog front-end ASIC with area-efficient driven right leg circuit.

    Science.gov (United States)

    Tao Tang; Wang Ling Goh; Lei Yao; Jia Hao Cheong; Yuan Gao

    2017-07-01

    This paper describes an integrated multichannel neural recording analog front end (AFE) with a novel area-efficient driven right leg (DRL) circuit to improve the system common mode rejection ratio (CMRR). The proposed AFE consists of an AC-coupled low-noise programmable-gain amplifier, an area-efficient DRL block and a 10-bit SAR ADC. Compared to conventional DRL circuit, the proposed capacitor-less DRL design achieves 90% chip area reduction with enhanced CMRR performance, making it ideal for multichannel biomedical recording applications. The AFE circuit has been designed in a standard 0.18-μm CMOS process. Post-layout simulation results show that the AFE provides two gain settings of 54dB/60dB while consuming 1 μA per channel under a supply voltage of 1 V. The input-referred noise of the AFE integrated from 1 Hz to 10k Hz is only 4 μVrms and the CMRR is 110 dB.

  14. Synaptic plasticity, neural circuits, and the emerging role of altered short-term information processing in schizophrenia.

    Science.gov (United States)

    Crabtree, Gregg W; Gogos, Joseph A

    2014-01-01

    Synaptic plasticity alters the strength of information flow between presynaptic and postsynaptic neurons and thus modifies the likelihood that action potentials in a presynaptic neuron will lead to an action potential in a postsynaptic neuron. As such, synaptic plasticity and pathological changes in synaptic plasticity impact the synaptic computation which controls the information flow through the neural microcircuits responsible for the complex information processing necessary to drive adaptive behaviors. As current theories of neuropsychiatric disease suggest that distinct dysfunctions in neural circuit performance may critically underlie the unique symptoms of these diseases, pathological alterations in synaptic plasticity mechanisms may be fundamental to the disease process. Here we consider mechanisms of both short-term and long-term plasticity of synaptic transmission and their possible roles in information processing by neural microcircuits in both health and disease. As paradigms of neuropsychiatric diseases with strongly implicated risk genes, we discuss the findings in schizophrenia and autism and consider the alterations in synaptic plasticity and network function observed in both human studies and genetic mouse models of these diseases. Together these studies have begun to point toward a likely dominant role of short-term synaptic plasticity alterations in schizophrenia while dysfunction in autism spectrum disorders (ASDs) may be due to a combination of both short-term and long-term synaptic plasticity alterations.

  15. Two-photon imaging and analysis of neural network dynamics

    Science.gov (United States)

    Lütcke, Henry; Helmchen, Fritjof

    2011-08-01

    The glow of a starry night sky, the smell of a freshly brewed cup of coffee or the sound of ocean waves breaking on the beach are representations of the physical world that have been created by the dynamic interactions of thousands of neurons in our brains. How the brain mediates perceptions, creates thoughts, stores memories and initiates actions remains one of the most profound puzzles in biology, if not all of science. A key to a mechanistic understanding of how the nervous system works is the ability to measure and analyze the dynamics of neuronal networks in the living organism in the context of sensory stimulation and behavior. Dynamic brain properties have been fairly well characterized on the microscopic level of individual neurons and on the macroscopic level of whole brain areas largely with the help of various electrophysiological techniques. However, our understanding of the mesoscopic level comprising local populations of hundreds to thousands of neurons (so-called 'microcircuits') remains comparably poor. Predominantly, this has been due to the technical difficulties involved in recording from large networks of neurons with single-cell spatial resolution and near-millisecond temporal resolution in the brain of living animals. In recent years, two-photon microscopy has emerged as a technique which meets many of these requirements and thus has become the method of choice for the interrogation of local neural circuits. Here, we review the state-of-research in the field of two-photon imaging of neuronal populations, covering the topics of microscope technology, suitable fluorescent indicator dyes, staining techniques, and in particular analysis techniques for extracting relevant information from the fluorescence data. We expect that functional analysis of neural networks using two-photon imaging will help to decipher fundamental operational principles of neural microcircuits.

  16. Two-photon imaging and analysis of neural network dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Luetcke, Henry; Helmchen, Fritjof [Brain Research Institute, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich (Switzerland)

    2011-08-15

    The glow of a starry night sky, the smell of a freshly brewed cup of coffee or the sound of ocean waves breaking on the beach are representations of the physical world that have been created by the dynamic interactions of thousands of neurons in our brains. How the brain mediates perceptions, creates thoughts, stores memories and initiates actions remains one of the most profound puzzles in biology, if not all of science. A key to a mechanistic understanding of how the nervous system works is the ability to measure and analyze the dynamics of neuronal networks in the living organism in the context of sensory stimulation and behavior. Dynamic brain properties have been fairly well characterized on the microscopic level of individual neurons and on the macroscopic level of whole brain areas largely with the help of various electrophysiological techniques. However, our understanding of the mesoscopic level comprising local populations of hundreds to thousands of neurons (so-called 'microcircuits') remains comparably poor. Predominantly, this has been due to the technical difficulties involved in recording from large networks of neurons with single-cell spatial resolution and near-millisecond temporal resolution in the brain of living animals. In recent years, two-photon microscopy has emerged as a technique which meets many of these requirements and thus has become the method of choice for the interrogation of local neural circuits. Here, we review the state-of-research in the field of two-photon imaging of neuronal populations, covering the topics of microscope technology, suitable fluorescent indicator dyes, staining techniques, and in particular analysis techniques for extracting relevant information from the fluorescence data. We expect that functional analysis of neural networks using two-photon imaging will help to decipher fundamental operational principles of neural microcircuits.

  17. Analysis and application of analog electronic circuits to biomedical instrumentation

    CERN Document Server

    Northrop, Robert B

    2003-01-01

    This book introduces the basic mathematical tools used to describe noise and its propagation through linear systems and provides a basic description of the improvement of signal-to-noise ratio by signal averaging and linear filtering. The text also demonstrates how op amps are the keystone of modern analog signal conditioning systems design, and illustrates their use in isolation and instrumentation amplifiers, active filters, and numerous biomedical instrumentation systems and subsystems. It examines the properties of the ideal op amp and applies this model to the analysis of various circuits

  18. A feed-forward spinal cord glycinergic neural circuit gates mechanical allodynia.

    Science.gov (United States)

    Lu, Yan; Dong, Hailong; Gao, Yandong; Gong, Yuanyuan; Ren, Yingna; Gu, Nan; Zhou, Shudi; Xia, Nan; Sun, Yan-Yan; Ji, Ru-Rong; Xiong, Lize

    2013-09-01

    Neuropathic pain is characterized by mechanical allodynia induced by low-threshold myelinated Aβ-fiber activation. The original gate theory of pain proposes that inhibitory interneurons in the lamina II of the spinal dorsal horn (DH) act as "gate control" units for preventing the interaction between innocuous and nociceptive signals. However, our understanding of the neuronal circuits underlying pain signaling and modulation in the spinal DH is incomplete. Using a rat model, we have shown that the convergence of glycinergic inhibitory and excitatory Aβ-fiber inputs onto PKCγ+ neurons in the superficial DH forms a feed-forward inhibitory circuit that prevents Aβ input from activating the nociceptive pathway. This feed-forward inhibition was suppressed following peripheral nerve injury or glycine blockage, leading to inappropriate induction of action potential outputs in the nociceptive pathway by Aβ-fiber stimulation. Furthermore, spinal blockage of glycinergic synaptic transmission in vivo induced marked mechanical allodynia. Our findings identify a glycinergic feed-forward inhibitory circuit that functions as a gate control to separate the innocuous mechanoreceptive pathway and the nociceptive pathway in the spinal DH. Disruption of this glycinergic inhibitory circuit after peripheral nerve injury has the potential to elicit mechanical allodynia, a cardinal symptom of neuropathic pain.

  19. Logic analysis and verification of n-input genetic logic circuits

    DEFF Research Database (Denmark)

    Baig, Hasan; Madsen, Jan

    2017-01-01

    accordingly. As compared to electronic circuits, genetic circuits exhibit stochastic behavior and do not always behave as intended. Therefore, there is a growing interest in being able to analyze and verify the logical behavior of a genetic circuit model, prior to its physical implementation in a laboratory....... In this paper, we present an approach to analyze and verify the Boolean logic of a genetic circuit from the data obtained through stochastic analog circuit simulations. The usefulness of this analysis is demonstrated through different case studies illustrating how our approach can be used to verify the expected...... behavior of an n-input genetic logic circuit....

  20. Effects of ion channel noise on neural circuits: an application to the respiratory pattern generator to investigate breathing variability.

    Science.gov (United States)

    Yu, Haitao; Dhingra, Rishi R; Dick, Thomas E; Galán, Roberto F

    2017-01-01

    Neural activity generally displays irregular firing patterns even in circuits with apparently regular outputs, such as motor pattern generators, in which the output frequency fluctuates randomly around a mean value. This "circuit noise" is inherited from the random firing of single neurons, which emerges from stochastic ion channel gating (channel noise), spontaneous neurotransmitter release, and its diffusion and binding to synaptic receptors. Here we demonstrate how to expand conductance-based network models that are originally deterministic to include realistic, physiological noise, focusing on stochastic ion channel gating. We illustrate this procedure with a well-established conductance-based model of the respiratory pattern generator, which allows us to investigate how channel noise affects neural dynamics at the circuit level and, in particular, to understand the relationship between the respiratory pattern and its breath-to-breath variability. We show that as the channel number increases, the duration of inspiration and expiration varies, and so does the coefficient of variation of the breath-to-breath interval, which attains a minimum when the mean duration of expiration slightly exceeds that of inspiration. For small channel numbers, the variability of the expiratory phase dominates over that of the inspiratory phase, and vice versa for large channel numbers. Among the four different cell types in the respiratory pattern generator, pacemaker cells exhibit the highest sensitivity to channel noise. The model shows that suppressing input from the pons leads to longer inspiratory phases, a reduction in breathing frequency, and larger breath-to-breath variability, whereas enhanced input from the raphe nucleus increases breathing frequency without changing its pattern. A major source of noise in neuronal circuits is the "flickering" of ion currents passing through the neurons' membranes (channel noise), which cannot be suppressed experimentally. Computational

  1. Reverse Engineering Integrated Circuits Using Finite State Machine Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Oler, Kiri J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Miller, Carl H. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2016-04-12

    In this paper, we present a methodology for reverse engineering integrated circuits, including a mathematical verification of a scalable algorithm used to generate minimal finite state machine representations of integrated circuits.

  2. A Neural Circuit for Acoustic Navigation combining Heterosynaptic and Non-synaptic Plasticity that learns Stable Trajectories

    DEFF Research Database (Denmark)

    Shaikh, Danish; Manoonpong, Poramate

    2017-01-01

    Reactive spatial robot navigation in goal-directed tasks such as phonotaxis requires generating consistent and stable trajectories towards an acoustic target while avoiding obstacles. High-level goal-directed steering behaviour can steer a robot towards the target by mapping sound direction...... controllers be resolved in a manner that generates consistent and stable robot trajectories? We propose a neural circuit that minimises this conflict by learning sensorimotor mappings as neuronal transfer functions between the perceived sound direction and wheel velocities of a simulated non-holonomic mobile...... robot. These mappings constitute the high-level goal-directed steering behaviour. Sound direction information is obtained from a model of the lizard peripheral auditory system. The parameters of the transfer functions are learned via an online unsupervised correlation learning algorithm through...

  3. On the SCTC-OCTC Method for the Analysis and Design of Circuits

    Science.gov (United States)

    Salvatori, S.; Conte, G.

    2009-01-01

    This paper discusses guidelines that emphasize the relevance of short-circuit- and open-circuit-time constant (SCTC and OCTC, respectively) methods in the analysis and design of electronic amplifiers. It is demonstrated that it is only necessary to grasp a few concepts in order to understand that the two short- and open-circuit cases fall into a…

  4. Sites of Plasticity in the Neural Circuit Mediating Tentacle Withdrawal in the Snail Helix aspersa: Implications for Behavioral Change and Learning Kinetics

    Science.gov (United States)

    Prescott, Steven A.; Chase, Ronald

    1999-01-01

    The tentacle withdrawal reflex of the snail Helix aspersa exhibits a complex combination of habituation and sensitization consistent with the dual-process theory of plasticity. Habituation, sensitization, or a combination of both were elicited by varying stimulation parameters and lesion condition. Analysis of response plasticity shows that the late phase of the response is selectively enhanced by sensitization, whereas all phases are decreased by habituation. Previous data have shown that tentacle withdrawal is mediated conjointly by parallel monosynaptic and polysynaptic pathways. The former mediates the early phase, whereas the latter mediates the late phase of the response. Plastic loci were identified by stimulating and recording at different points within the neural circuit, in combination with selective lesions. Results indicate that depression occurs at an upstream locus, before circuit divergence, and is therefore expressed in all pathways, whereas facilitation requires downstream facilitatory neurons and is selectively expressed in polysynaptic pathways. Differential expression of plasticity between pathways helps explain the behavioral manifestation of depression and facilitation. A simple mathematical model is used to show how serial positioning of depression and facilitation can explain the kinetics of dual-process learning. These results illustrate how the position of cellular plasticity in the network affects behavioral change and how forms of plasticity can interact to determine the kinetics of the net changes. PMID:10509707

  5. Rules and mechanisms for efficient two-stage learning in neural circuits.

    Science.gov (United States)

    Teşileanu, Tiberiu; Ölveczky, Bence; Balasubramanian, Vijay

    2017-04-04

    Trial-and-error learning requires evaluating variable actions and reinforcing successful variants. In songbirds, vocal exploration is induced by LMAN, the output of a basal ganglia-related circuit that also contributes a corrective bias to the vocal output. This bias is gradually consolidated in RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using stochastic gradient descent, we derive how the activity in 'tutor' circuits ( e.g., LMAN) should match plasticity mechanisms in 'student' circuits ( e.g., RA) to achieve efficient learning. We further describe a reinforcement learning framework through which the tutor can build its teaching signal. We show that mismatches between the tutor signal and the plasticity mechanism can impair learning. Applied to birdsong, our results predict the temporal structure of the corrective bias from LMAN given a plasticity rule in RA. Our framework can be applied predictively to other paired brain areas showing two-stage learning.

  6. Fluorescence-based monitoring of in vivo neural activity using a circuit-tracing pseudorabies virus.

    Directory of Open Access Journals (Sweden)

    Andrea E Granstedt

    Full Text Available The study of coordinated activity in neuronal circuits has been challenging without a method to simultaneously report activity and connectivity. Here we present the first use of pseudorabies virus (PRV, which spreads through synaptically connected neurons, to express a fluorescent calcium indicator protein and monitor neuronal activity in a living animal. Fluorescence signals were proportional to action potential number and could reliably detect single action potentials in vitro. With two-photon imaging in vivo, we observed both spontaneous and stimulated activity in neurons of infected murine peripheral autonomic submandibular ganglia (SMG. We optically recorded the SMG response in the salivary circuit to direct electrical stimulation of the presynaptic axons and to physiologically relevant sensory stimulation of the oral cavity. During a time window of 48 hours after inoculation, few spontaneous transients occurred. By 72 hours, we identified more frequent and prolonged spontaneous calcium transients, suggestive of neuronal or tissue responses to infection that influence calcium signaling. Our work establishes in vivo investigation of physiological neuronal circuit activity and subsequent effects of infection with single cell resolution.

  7. Gene circuit analysis of the terminal gap gene huckebein.

    Directory of Open Access Journals (Sweden)

    Maksat Ashyraliyev

    2009-10-01

    Full Text Available The early embryo of Drosophila melanogaster provides a powerful model system to study the role of genes in pattern formation. The gap gene network constitutes the first zygotic regulatory tier in the hierarchy of the segmentation genes involved in specifying the position of body segments. Here, we use an integrative, systems-level approach to investigate the regulatory effect of the terminal gap gene huckebein (hkb on gap gene expression. We present quantitative expression data for the Hkb protein, which enable us to include hkb in gap gene circuit models. Gap gene circuits are mathematical models of gene networks used as computational tools to extract regulatory information from spatial expression data. This is achieved by fitting the model to gap gene expression patterns, in order to obtain estimates for regulatory parameters which predict a specific network topology. We show how considering variability in the data combined with analysis of parameter determinability significantly improves the biological relevance and consistency of the approach. Our models are in agreement with earlier results, which they extend in two important respects: First, we show that Hkb is involved in the regulation of the posterior hunchback (hb domain, but does not have any other essential function. Specifically, Hkb is required for the anterior shift in the posterior border of this domain, which is now reproduced correctly in our models. Second, gap gene circuits presented here are able to reproduce mutants of terminal gap genes, while previously published models were unable to reproduce any null mutants correctly. As a consequence, our models now capture the expression dynamics of all posterior gap genes and some variational properties of the system correctly. This is an important step towards a better, quantitative understanding of the developmental and evolutionary dynamics of the gap gene network.

  8. Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

    Science.gov (United States)

    Chernoded, Andrey; Dudko, Lev; Myagkov, Igor; Volkov, Petr

    2017-10-01

    Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

  9. Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

    Directory of Open Access Journals (Sweden)

    Chernoded Andrey

    2017-01-01

    Full Text Available Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

  10. Functional analysis of molecular interactions in synthetic auxin response circuits.

    Science.gov (United States)

    Pierre-Jerome, Edith; Moss, Britney L; Lanctot, Amy; Hageman, Amber; Nemhauser, Jennifer L

    2016-10-04

    Auxin-regulated transcription pivots on the interaction between the AUXIN/INDOLE-3-ACETIC ACID (Aux/IAA) repressor proteins and the AUXIN RESPONSE FACTOR (ARF) transcription factors. Recent structural analyses of ARFs and Aux/IAAs have raised questions about the functional complexes driving auxin transcriptional responses. To parse the nature and significance of ARF-DNA and ARF-Aux/IAA interactions, we analyzed structure-guided variants of synthetic auxin response circuits in the budding yeast Saccharomyces cerevisiae Our analysis revealed that promoter architecture could specify ARF activity and that ARF19 required dimerization at two distinct domains for full transcriptional activation. In addition, monomeric Aux/IAAs were able to repress ARF activity in both yeast and plants. This systematic, quantitative structure-function analysis identified a minimal complex-comprising a single Aux/IAA repressing a pair of dimerized ARFs-sufficient for auxin-induced transcription.

  11. Simulation Approach for Timing Analysis of Genetic Logic Circuits

    DEFF Research Database (Denmark)

    Baig, Hasan; Madsen, Jan

    2017-01-01

    in a manner similar to electronic logic circuits, but they are much more stochastic and hence much harder to characterize. In this article, we introduce an approach to analyze the threshold value and timing of genetic logic circuits. We show how this approach can be used to analyze the timing behavior...... of single and cascaded genetic logic circuits. We further analyze the timing sensitivity of circuits by varying the degradation rates and concentrations. Our approach can be used not only to characterize the timing behavior but also to analyze the timing constraints of cascaded genetic logic circuits...

  12. Visual motion imagery neurofeedback based on the hMT+/V5 complex: evidence for a feedback-specific neural circuit involving neocortical and cerebellar regions

    Science.gov (United States)

    Banca, Paula; Sousa, Teresa; Catarina Duarte, Isabel; Castelo-Branco, Miguel

    2015-12-01

    Objective. Current approaches in neurofeedback/brain-computer interface research often focus on identifying, on a subject-by-subject basis, the neural regions that are best suited for self-driven modulation. It is known that the hMT+/V5 complex, an early visual cortical region, is recruited during explicit and implicit motion imagery, in addition to real motion perception. This study tests the feasibility of training healthy volunteers to regulate the level of activation in their hMT+/V5 complex using real-time fMRI neurofeedback and visual motion imagery strategies. Approach. We functionally localized the hMT+/V5 complex to further use as a target region for neurofeedback. An uniform strategy based on motion imagery was used to guide subjects to neuromodulate hMT+/V5. Main results. We found that 15/20 participants achieved successful neurofeedback. This modulation led to the recruitment of a specific network as further assessed by psychophysiological interaction analysis. This specific circuit, including hMT+/V5, putative V6 and medial cerebellum was activated for successful neurofeedback runs. The putamen and anterior insula were recruited for both successful and non-successful runs. Significance. Our findings indicate that hMT+/V5 is a region that can be modulated by focused imagery and that a specific cortico-cerebellar circuit is recruited during visual motion imagery leading to successful neurofeedback. These findings contribute to the debate on the relative potential of extrinsic (sensory) versus intrinsic (default-mode) brain regions in the clinical application of neurofeedback paradigms. This novel circuit might be a good target for future neurofeedback approaches that aim, for example, the training of focused attention in disorders such as ADHD.

  13. Failure analysis of collector circuits associated with wind farms

    Directory of Open Access Journals (Sweden)

    Clifton Ashley P.

    2017-01-01

    Full Text Available Wind farm collector circuits generally comprise several wind turbine generators (WTG’s. WTG’s are connected in parallel to a substation. This connection acts as the point-of-connection to the national electricity grid. The electrical load in these circuits is close to component (power cables and accessories ratings. The objective of this paper is to identify cable joint failure paths; and, develop an understanding of specific contributing factors. All findings presented were established from literature review involving data analysis and discussion with industry experts working across the wind industry. Application of forces, inadequate workmanship, incorrect thermal resistance or other contributing factors, all contribute to high conductor operating temperatures. High conductor operating temperatures highlight issues including insufficient environmental heat transfer due to the use of inadequate cable trenching materials. This in turn results in the imbalanced application of force, experienced at the cable joint, as a direct result of frequent thermal expansion and contraction. For most cable joint failures, the root cause is insulation breakdown due to sustained deterioration of the cross-linked polyethylene insulation. This is a direct result from excessive operating temperatures.

  14. Simulation Approach for Timing Analysis of Genetic Logic Circuits

    DEFF Research Database (Denmark)

    Baig, Hasan; Madsen, Jan

    2017-01-01

    in a manner similar to electronic logic circuits, but they are much more stochastic and hence much harder to characterize. In this article, we introduce an approach to analyze the threshold value and timing of genetic logic circuits. We show how this approach can be used to analyze the timing behavior...... of single and cascaded genetic logic circuits. We further analyze the timing sensitivity of circuits by varying the degradation rates and concentrations. Our approach can be used not only to characterize the timing behavior but also to analyze the timing constraints of cascaded genetic logic circuits......Constructing genetic logic circuits is an application of synthetic biology in which parts of the DNA of a living cell are engineered to perform a dedicated Boolean function triggered by an appropriate concentration of certain proteins or by different genetic components. These logic circuits work...

  15. A multichannel integrated circuit for electrical recording of neural activity, with independent channel programmability.

    Science.gov (United States)

    Mora Lopez, Carolina; Prodanov, Dimiter; Braeken, Dries; Gligorijevic, Ivan; Eberle, Wolfgang; Bartic, Carmen; Puers, Robert; Gielen, Georges

    2012-04-01

    Since a few decades, micro-fabricated neural probes are being used, together with microelectronic interfaces, to get more insight in the activity of neuronal networks. The need for higher temporal and spatial recording resolutions imposes new challenges on the design of integrated neural interfaces with respect to power consumption, data handling and versatility. In this paper, we present an integrated acquisition system for in vitro and in vivo recording of neural activity. The ASIC consists of 16 low-noise, fully-differential input channels with independent programmability of its amplification (from 100 to 6000 V/V) and filtering (1-6000 Hz range) capabilities. Each channel is AC-coupled and implements a fourth-order band-pass filter in order to steeply attenuate out-of-band noise and DC input offsets. The system achieves an input-referred noise density of 37 nV/√Hz, a NEF of 5.1, a CMRR > 60 dB, a THD noise ratios.

  16. Neural circuits underlying mother's voice perception predict social communication abilities in children.

    Science.gov (United States)

    Abrams, Daniel A; Chen, Tianwen; Odriozola, Paola; Cheng, Katherine M; Baker, Amanda E; Padmanabhan, Aarthi; Ryali, Srikanth; Kochalka, John; Feinstein, Carl; Menon, Vinod

    2016-05-31

    The human voice is a critical social cue, and listeners are extremely sensitive to the voices in their environment. One of the most salient voices in a child's life is mother's voice: Infants discriminate their mother's voice from the first days of life, and this stimulus is associated with guiding emotional and social function during development. Little is known regarding the functional circuits that are selectively engaged in children by biologically salient voices such as mother's voice or whether this brain activity is related to children's social communication abilities. We used functional MRI to measure brain activity in 24 healthy children (mean age, 10.2 y) while they attended to brief (social function. Compared to female control voices, mother's voice elicited greater activity in primary auditory regions in the midbrain and cortex; voice-selective superior temporal sulcus (STS); the amygdala, which is crucial for processing of affect; nucleus accumbens and orbitofrontal cortex of the reward circuit; anterior insula and cingulate of the salience network; and a subregion of fusiform gyrus associated with face perception. The strength of brain connectivity between voice-selective STS and reward, affective, salience, memory, and face-processing regions during mother's voice perception predicted social communication skills. Our findings provide a novel neurobiological template for investigation of typical social development as well as clinical disorders, such as autism, in which perception of biologically and socially salient voices may be impaired.

  17. Neural reuse of action perception circuits for language, concepts and communication.

    Science.gov (United States)

    Pulvermüller, Friedemann

    2018-01-01

    Neurocognitive and neurolinguistics theories make explicit statements relating specialized cognitive and linguistic processes to specific brain loci. These linking hypotheses are in need of neurobiological justification and explanation. Recent mathematical models of human language mechanisms constrained by fundamental neuroscience principles and established knowledge about comparative neuroanatomy offer explanations for where, when and how language is processed in the human brain. In these models, network structure and connectivity along with action- and perception-induced correlation of neuronal activity co-determine neurocognitive mechanisms. Language learning leads to the formation of action perception circuits (APCs) with specific distributions across cortical areas. Cognitive and linguistic processes such as speech production, comprehension, verbal working memory and prediction are modelled by activity dynamics in these APCs, and combinatorial and communicative-interactive knowledge is organized in the dynamics within, and connections between APCs. The network models and, in particular, the concept of distributionally-specific circuits, can account for some previously not well understood facts about the cortical 'hubs' for semantic processing and the motor system's role in language understanding and speech sound recognition. A review of experimental data evaluates predictions of the APC model and alternative theories, also providing detailed discussion of some seemingly contradictory findings. Throughout, recent disputes about the role of mirror neurons and grounded cognition in language and communication are assessed critically. Copyright © 2017 The Author. Published by Elsevier Ltd.. All rights reserved.

  18. A low-power, low-noise neural-signal amplifier circuit in 90-nm CMOS.

    Science.gov (United States)

    Zarifi, M H; Frounchi, J; Farshchi, S; Judy, J W

    2008-01-01

    A fully-differential low-power low-noise preamplifier for biopotential and neural-recording applications is presented. This design, which has been simulated in a standard 90-nm CMOS process, consumes 30 microW from a 3-V power supply. The simulated integrated input-referred noise is 2.3 microV over 0.1 Hz to 20 kHz. The amplifier also provides an output swing of +/- 0.9 V with a THD of less than 0.1%

  19. Negative emotional distraction on neural circuits for working memory in patients with posttraumatic stress disorder.

    Science.gov (United States)

    Zhang, Jing-na; Xiong, Kun-lining; Qiu, Ming-guo; Zhang, Ye; Xie, Bing; Wang, Jian; Li, Min; Chen, Han; Zhang, Yu; Zhang, Jia-jia

    2013-09-19

    To study the neural mechanism for the impact of negative emotional distraction on working memory in patients with posttraumatic stress disorder (PTSD) resulting from exposure to motor vehicle accidents. Twenty PTSD patients and 20 healthy subjects were recruited. Event-related functional magnetic resonance imaging (fMRI) was used to investigate the effects of negative and neutral distractors on a delayed-response working memory task. All experiments were performed on a 3.0T MRI scanner, and the functional imaging data were analyzed using SPM8 software. The PTSD group showed poorer performance than the control group when the negative distractors were presented during the delay phase of working memory. The functional imaging indicated that, in the presence of negative relative to neutral distractors, the PTSD group showed higher activation in the emotion processing regions, including amygdala, precuneus and fusiform gyrus, but lower activation in the inferior frontal cortex, insula and left supramarginal gyrus than the control group. Based on the results that activation in the PTSD patients in the presence of negative distractors increased in the emotion-related brain regions but decreased in the working memory-related brain regions, we may conclude that the neural basis of working memory is impaired by negative emotion in PTSD patients. © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

  20. Neural circuits in anxiety and stress disorders: a focused review

    Directory of Open Access Journals (Sweden)

    Duval ER

    2015-01-01

    Full Text Available Elizabeth R Duval, Arash Javanbakht, Israel LiberzonDepartment of Psychiatry, University of Michigan Health System, Ann Arbor, MI, USAAbstract: Anxiety and stress disorders are among the most prevalent neuropsychiatric disorders. In recent years, multiple studies have examined brain regions and networks involved in anxiety symptomatology in an effort to better understand the mechanisms involved and to develop more effective treatments. However, much remains unknown regarding the specific abnormalities and interactions between networks of regions underlying anxiety disorder presentations. We examined recent neuroimaging literature that aims to identify neural mechanisms underlying anxiety, searching for patterns of neural dysfunction that might be specific to different anxiety disorder categories. Across different anxiety and stress disorders, patterns of hyperactivation in emotion-generating regions and hypoactivation in prefrontal/regulatory regions are common in the literature. Interestingly, evidence of differential patterns is also emerging, such that within a spectrum of disorders ranging from more fear-based to more anxiety-based, greater involvement of emotion-generating regions is reported in panic disorder and specific phobia, and greater involvement of prefrontal regions is reported in generalized anxiety disorder and posttraumatic stress disorder. We summarize the pertinent literature and suggest areas for continued investigation.Keywords: fear, anxiety, neuroimaging

  1. PDF-1 neuropeptide signaling modulates a neural circuit for mate-searching behavior in C. elegans.

    Science.gov (United States)

    Barrios, Arantza; Ghosh, Rajarshi; Fang, Chunhui; Emmons, Scott W; Barr, Maureen M

    2012-12-01

    Appetitive behaviors require complex decision making that involves the integration of environmental stimuli and physiological needs. C. elegans mate searching is a male-specific exploratory behavior regulated by two competing needs: food and reproductive appetite. We found that the pigment dispersing factor receptor (PDFR-1) modulates the circuit that encodes the male reproductive drive that promotes male exploration following mate deprivation. PDFR-1 and its ligand, PDF-1, stimulated mate searching in the male, but not in the hermaphrodite. pdf-1 was required in the gender-shared interneuron AIM, and the receptor acted in internal and external environment-sensing neurons of the shared nervous system (URY, PQR and PHA) to produce mate-searching behavior. Thus, the pdf-1 and pdfr-1 pathway functions in non-sex-specific neurons to produce a male-specific, goal-oriented exploratory behavior. Our results indicate that secretin neuropeptidergic signaling is involved in regulating motivational internal states.

  2. SEMICONDUCTOR INTEGRATED CIRCUITS: A four-channel microelectronic system for neural signal regeneration

    Science.gov (United States)

    Shushan, Xie; Zhigong, Wang; Xiaoying, Lü; Wenyuan, Li; Haixian, Pan

    2009-12-01

    This paper presents a microelectronic system which is capable of making a signal record and functional electric stimulation of an injured spinal cord. As a requirement of implantable engineering for the regeneration microelectronic system, the system is of low noise, low power, small size and high performance. A front-end circuit and two high performance OPAs (operational amplifiers) have been designed for the system with different functions, and the two OPAs are a low-noise low-power two-stage OPA and a constant-gm RTR input and output OPA. The system has been realized in CSMC 0.5-μm CMOS technology. The test results show that the system satisfies the demands of neuron signal regeneration.

  3. Analysis of neural networks through base functions

    NARCIS (Netherlands)

    van der Zwaag, B.J.; Slump, Cornelis H.; Spaanenburg, L.

    Problem statement. Despite their success-story, neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more

  4. Neural circuits in the brain that are activated when mitigating criminal sentences.

    Science.gov (United States)

    Yamada, Makiko; Camerer, Colin F; Fujie, Saori; Kato, Motoichiro; Matsuda, Tetsuya; Takano, Harumasa; Ito, Hiroshi; Suhara, Tetsuya; Takahashi, Hidehiko

    2012-03-27

    In sentencing guilty defendants, jurors and judges weigh 'mitigating circumstances', which create sympathy for a defendant. Here we use functional magnetic resonance imaging to measure neural activity in ordinary citizens who are potential jurors, as they decide on mitigation of punishment for murder. We found that sympathy activated regions associated with mentalising and moral conflict (dorsomedial prefrontal cortex, precuneus and temporo-parietal junction). Sentencing also activated precuneus and anterior cingulate cortex, suggesting that mitigation is based on negative affective responses to murder, sympathy for mitigating circumstances and cognitive control to choose numerical punishments. Individual differences on the inclination to mitigate, the sentence reduction per unit of judged sympathy, correlated with activity in the right middle insula, an area known to represent interoception of visceral states. These results could help the legal system understand how potential jurors actually decide, and contribute to growing knowledge about whether emotion and cognition are integrated sensibly in difficult judgments.

  5. Altered neural connectivity in excitatory and inhibitory cortical circuits in autism

    Directory of Open Access Journals (Sweden)

    Basilis eZikopoulos

    2013-09-01

    Full Text Available Converging evidence from diverse studies suggests that atypical brain connectivity in autism affects in distinct ways short- and long-range cortical pathways, disrupting neural communication and the balance of excitation and inhibition. This hypothesis is based mostly on functional non-invasive studies that show atypical synchronization and connectivity patterns between cortical areas in children and adults with autism. Indirect methods to study the course and integrity of major brain pathways at low resolution show changes in fractional anisotropy or diffusivity of the white matter in autism. Findings in post-mortem brains of adults with autism provide evidence of changes in the fine structure of axons below prefrontal cortices, which communicate over short- or long-range pathways with other cortices and subcortical structures. Here we focus on evidence of cellular and axon features that likely underlie the changes in short- and long-range communication in autism. We review recent findings of changes in the shape, thickness, and volume of brain areas, cytoarchitecture, neuronal morphology, cellular elements, and structural and neurochemical features of individual axons in the white matter, where pathology is evident even in gross images. We relate cellular and molecular features to imaging and genetic studies that highlight a variety of polymorphisms and epigenetic factors that primarily affect neurite growth and synapse formation and function in autism. We report preliminary findings of changes in autism in the ratio of distinct types of inhibitory neurons in prefrontal cortex, known to shape network dynamics and the balance of excitation and inhibition. Finally we present a model that synthesizes diverse findings by relating them to developmental events, with a goal to identify common processes that perturb development in autism and affect neural communication, reflected in altered patterns of attention, social interactions, and language.

  6. High Resolution PV Power Modeling for Distribution Circuit Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Norris, B. L.; Dise, J. H.

    2013-09-01

    NREL has contracted with Clean Power Research to provide 1-minute simulation datasets of PV systems located at three high penetration distribution feeders in the service territory of Southern California Edison (SCE): Porterville, Palmdale, and Fontana, California. The resulting PV simulations will be used to separately model the electrical circuits to determine the impacts of PV on circuit operations.

  7. Remediation of Childhood Math Anxiety and Associated Neural Circuits through Cognitive Tutoring

    Science.gov (United States)

    Iuculano, Teresa; Chen, Lang

    2015-01-01

    Math anxiety is a negative emotional reaction that is characterized by feelings of stress and anxiety in situations involving mathematical problem solving. High math-anxious individuals tend to avoid situations involving mathematics and are less likely to pursue science, technology, engineering, and math-related careers than those with low math anxiety. Math anxiety during childhood, in particular, has adverse long-term consequences for academic and professional success. Identifying cognitive interventions and brain mechanisms by which math anxiety can be ameliorated in children is therefore critical. Here we investigate whether an intensive 8 week one-to-one cognitive tutoring program designed to improve mathematical skills reduces childhood math anxiety, and we identify the neurobiological mechanisms by which math anxiety can be reduced in affected children. Forty-six children in grade 3, a critical early-onset period for math anxiety, participated in the cognitive tutoring program. High math-anxious children showed a significant reduction in math anxiety after tutoring. Remarkably, tutoring remediated aberrant functional responses and connectivity in emotion-related circuits anchored in the basolateral amygdala. Crucially, children with greater tutoring-induced decreases in amygdala reactivity had larger reductions in math anxiety. Our study demonstrates that sustained exposure to mathematical stimuli can reduce math anxiety and highlights the key role of the amygdala in this process. Our findings are consistent with models of exposure-based therapy for anxiety disorders and have the potential to inform the early treatment of a disability that, if left untreated in childhood, can lead to significant lifelong educational and socioeconomic consequences in affected individuals. SIGNIFICANCE STATEMENT Math anxiety during early childhood has adverse long-term consequences for academic and professional success. It is therefore important to identify ways to alleviate

  8. Remediation of Childhood Math Anxiety and Associated Neural Circuits through Cognitive Tutoring.

    Science.gov (United States)

    Supekar, Kaustubh; Iuculano, Teresa; Chen, Lang; Menon, Vinod

    2015-09-09

    Math anxiety is a negative emotional reaction that is characterized by feelings of stress and anxiety in situations involving mathematical problem solving. High math-anxious individuals tend to avoid situations involving mathematics and are less likely to pursue science, technology, engineering, and math-related careers than those with low math anxiety. Math anxiety during childhood, in particular, has adverse long-term consequences for academic and professional success. Identifying cognitive interventions and brain mechanisms by which math anxiety can be ameliorated in children is therefore critical. Here we investigate whether an intensive 8 week one-to-one cognitive tutoring program designed to improve mathematical skills reduces childhood math anxiety, and we identify the neurobiological mechanisms by which math anxiety can be reduced in affected children. Forty-six children in grade 3, a critical early-onset period for math anxiety, participated in the cognitive tutoring program. High math-anxious children showed a significant reduction in math anxiety after tutoring. Remarkably, tutoring remediated aberrant functional responses and connectivity in emotion-related circuits anchored in the basolateral amygdala. Crucially, children with greater tutoring-induced decreases in amygdala reactivity had larger reductions in math anxiety. Our study demonstrates that sustained exposure to mathematical stimuli can reduce math anxiety and highlights the key role of the amygdala in this process. Our findings are consistent with models of exposure-based therapy for anxiety disorders and have the potential to inform the early treatment of a disability that, if left untreated in childhood, can lead to significant lifelong educational and socioeconomic consequences in affected individuals. Significance statement: Math anxiety during early childhood has adverse long-term consequences for academic and professional success. It is therefore important to identify ways to alleviate

  9. Disrupted insula-based neural circuit organization and conflict interference in trauma-exposed youth

    Directory of Open Access Journals (Sweden)

    Hilary A. Marusak

    2015-01-01

    Full Text Available Childhood trauma exposure is a potent risk factor for psychopathology. Emerging research suggests that aberrant saliency processing underlies the link between early trauma exposure and later cognitive and socioemotional deficits that are hallmark of several psychiatric disorders. Here, we examine brain and behavioral responses during a face categorization conflict task, and relate these to intrinsic connectivity of the salience network (SN. The results demonstrate a unique pattern of SN dysfunction in youth exposed to trauma (n = 14 relative to comparison youth (n = 19 matched on age, sex, IQ, and sociodemographic risk. We find that trauma-exposed youth are more susceptible to conflict interference and this correlates with higher fronto-insular responses during conflict. Resting-state functional connectivity data collected in the same participants reveal increased connectivity of the insula to SN seed regions that is associated with diminished reward sensitivity, a critical risk/resilience trait following stress. In addition to altered intrinsic connectivity of the SN, we observed altered connectivity between the SN and default mode network (DMN in trauma-exposed youth. These data uncover network-level disruptions in brain organization following one of the strongest predictors of illness, early life trauma, and demonstrate the relevance of observed neural effects for behavior and specific symptom dimensions. SN dysfunction may serve as a diathesis that contributes to illness and negative outcomes following childhood trauma.

  10. Neural bases of food-seeking: affect, arousal and reward in corticostriatolimbic circuits.

    Science.gov (United States)

    Balleine, Bernard W

    2005-12-15

    Recent studies suggest that there are multiple 'reward' or 'reward-like' systems that control food seeking; evidence points to two distinct learning processes and four modulatory processes that contribute to the performance of food-related instrumental actions. The learning processes subserve the acquisition of goal-directed and habitual actions and involve the dorsomedial and dorsolateral striatum, respectively. Access to food can function both to reinforce habits and as a reward or goal for actions. Encoding and retrieving the value of a goal appears to be mediated by distinct processes that, contrary to the somatic marker hypothesis, do not appear to depend on a common mechanism but on emotional and more abstract evaluative processes, respectively. The anticipation of reward on the basis of environmental events exerts a further modulatory influence on food seeking that can be dissociated from that of reward itself; earning a reward and anticipating a reward appear to be distinct processes and have been doubly dissociated at the level of the nucleus accumbens. Furthermore, the excitatory influence of reward-related cues can be both quite specific, based on the identity of the reward anticipated, or more general based on its motivational significance. The influence of these two processes on instrumental actions has also been doubly dissociated at the level of the amygdala. Although the complexity of food seeking provides a hurdle for the treatment of eating disorders, the suggestion that these apparently disparate determinants are functionally integrated within larger neural systems may provide novel approaches to these problems.

  11. Stress-protective neural circuits: not all roads lead through the prefrontal cortex.

    Science.gov (United States)

    Christianson, John P; Greenwood, Benjamin N

    2014-01-01

    Exposure to an uncontrollable stressor elicits a constellation of physiological and behavioral sequel in laboratory rats that often reflect aspects of anxiety and other emotional disruptions. We review evidence suggesting that plasticity within the serotonergic dorsal raphe nucleus (DRN) is critical to the expression of uncontrollable stressor-induced anxiety. Specifically, after uncontrollable stressor exposure subsequent anxiogenic stimuli evoke greater 5-HT release in DRN terminal regions including the amygdala and striatum; and pharmacological blockade of postsynaptic 5-HT(2C) receptors in these regions prevents expression of stressor-induced anxiety. Importantly, the controllability of stress, the presence of safety signals, and a history of exercise mitigate the expression of stressor-induced anxiety. These stress-protective factors appear to involve distinct neural substrates; with stressor controllability requiring the medial prefrontal cortex, safety signals the insular cortex and exercise affecting the 5-HT system directly. Knowledge of the distinct yet converging mechanisms underlying these stress-protective factors could provide insight into novel strategies for the treatment and prevention of stress-related psychiatric disorders.

  12. With a little help from my friends: androgens tap BDNF signaling pathways to alter neural circuits.

    Science.gov (United States)

    Ottem, E N; Bailey, D J; Jordan, C L; Breedlove, S M

    2013-06-03

    Gonadal androgens are critical for the development and maintenance of sexually dimorphic regions of the male nervous system, which is critical for male-specific behavior and physiological functioning. In rodents, the motoneurons of the spinal nucleus of the bulbocavernosus (SNB) provide a useful example of a neural system dependent on androgen. Unless rescued by perinatal androgens, the SNB motoneurons will undergo apoptotic cell death. In adulthood, SNB motoneurons remain dependent on androgen, as castration leads to somal atrophy and dendritic retraction. In a second vertebrate model, the zebra finch, androgens are critical for the development of several brain nuclei involved in song production in males. Androgen deprivation during a critical period during postnatal development disrupts song acquisition and dimorphic size-associated nuclei. Mechanisms by which androgens exert masculinizing effects in each model system remain elusive. Recent studies suggest that brain-derived neurotrophic factor (BDNF) may play a role in androgen-dependent masculinization and maintenance of both SNB motoneurons and song nuclei of birds. This review aims to summarize studies demonstrating that BDNF signaling via its tyrosine receptor kinase (TrkB) receptor may work cooperatively with androgens to maintain somal and dendritic morphology of SNB motoneurons. We further describe studies that suggest the cellular origin of BDNF is of particular importance in androgen-dependent regulation of SNB motoneurons. We review evidence that androgens and BDNF may synergistically influence song development and plasticity in bird species. Finally, we provide hypothetical models of mechanisms that may underlie androgen- and BDNF-dependent signaling pathways. Copyright © 2012 IBRO. Published by Elsevier Ltd. All rights reserved.

  13. Nitric oxide in the flocculus works the inhibitory neural circuits after unilateral labyrinthectomy.

    Science.gov (United States)

    Kitahara, T; Takeda, N; Kubo, T; Kiyama, H

    1999-01-09

    We previously reported that nitric oxide (NO) production in the unipolar brush (UB) cells is involved in vestibular compensation [T. Kitahara, N. Takeda, P.C. Emson, T. Kubo, H. Kiyama, Changes in nitric oxide synthase-like immunoreactivities in unipolar brush cells in the rat cerebellar flocculus after unilateral labyrinthectomy, Brain Res. 765 (1997) 1-6]. To further elucidate the role of NO-mediated signaling in flocculus after unilateral labyrinthectomy (UL), we examined UL-induced Fos expression, a marker of neural activity, in vestibular brainstem with continuous floccular infusions of Nomega-nitro-l-arginine methyl ester (l-NAME), an inhibitor of NO synthase (NOS). After UL with floccular l-NAME infusions, Fos expression appeared in bilateral medial vestibular (MVe) and prepositus hypoglossal (PrH) nuclei. After UL with floccular saline infusions, however, Fos expression was observed only in the ipsi-MVe and contra-PrH. Furthermore, it has been revealed that UL with l-NAME infusions caused more severe vestibulo-ocular disturbances than UL with saline infusions at the initial stage [Kitahara et al. Brain Res. 765 (1997) 1-6]. Therefore, it is suggested that UL with floccular l-NAME infusions activates the contra-MVe and ipsi-PrH neurons and causes more severe imbalance between intervestibular nuclear activities at the initial stage. NO-mediated signaling in flocculus could be a possible driving force of the flocculus-mediated inhibition on the contra-MVe and ipsi-PrH at the initial stage of vestibular compensation. Copyright 1999 Elsevier Science B.V.

  14. Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity

    Directory of Open Access Journals (Sweden)

    Wiebke ePotjans

    2010-11-01

    Full Text Available A major puzzle in the field of computational neuroscience is how to relate system-level learning in higher organisms to synaptic plasticity. Recently, plasticity rules depending not only on pre- and post-synaptic activity but also on a third, non-local neuromodulatory signal have emerged as key candidates to bridge the gap between the macroscopic and the microscopic level of learning. Crucial insights into this topic are expected to be gained from simulations of neural systems, as these allow the simultaneous study of the multiple spatial and temporal scales that are involved in the problem. In particular, synaptic plasticity can be studied during the whole learning process, i.e. on a time scale of minutes to hours and across multiple brain areas. Implementing neuromodulated plasticity in large-scale network simulations where the neuromodulatory signal is dynamically generated by the network itself is challenging, because the network structure is commonly defined purely by the connectivity graph without explicit reference to the embedding of the nodes in physical space. Furthermore, the simulation of networks with realistic connectivity entails the use of distributed computing. A neuromodulated synapse must therefore be informed in an efficient way about the neuromodulatory signal, which is typically generated by a population of neurons located on different machines than either the pre- or post-synaptic neuron. Here, we develop a general framework to solve the problem of implementing neuromodulated plasticity in a time-driven distributed simulation, without reference to a particular implementation language, neuromodulator or neuromodulated plasticity mechanism. We implement our framework in the simulator NEST and demonstrate excellent scaling up to 1024 processors for simulations of a recurrent network incorporating neuromodulated spike-timing dependent plasticity.

  15. Numerical analysis of modeling based on improved Elman neural network.

    Science.gov (United States)

    Jie, Shao; Li, Wang; WeiSong, Zhao; YaQin, Zhong; Malekian, Reza

    2014-01-01

    A modeling based on the improved Elman neural network (IENN) is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE) varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA) with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL) model, Chebyshev neural network (CNN) model, and basic Elman neural network (BENN) model, the proposed model has better performance.

  16. Numerical Analysis of Modeling Based on Improved Elman Neural Network

    Directory of Open Access Journals (Sweden)

    Shao Jie

    2014-01-01

    Full Text Available A modeling based on the improved Elman neural network (IENN is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL model, Chebyshev neural network (CNN model, and basic Elman neural network (BENN model, the proposed model has better performance.

  17. Conditioning Circuit Analysis for Slimes Management in Quarries

    OpenAIRE

    Bourgeois, Florent; Baudet, Gérard; Bizi, Mohamed; Gaboriau, Hervé

    2003-01-01

    Driven by increasingly stringent environmental regulations on water usage, many European quarries are in the process of adding thickening circuits to their wash plants for managing clayey tailings. One of the critical components of this circuit is slurry conditioning. With typical water consumption over 1 m3 per ton of washed aggregates, quarries produce dilute streams that are conditioned with high flocculant dosages to maintain water clarification rates as high as possible. At present, cond...

  18. Neural networks analysis on SSME vibration simulation data

    Science.gov (United States)

    Lo, Ching F.; Wu, Kewei

    1993-01-01

    The neural networks method is applied to investigate the feasibility in detecting anomalies in turbopump vibration of SSME to supplement the statistical method utilized in the prototype system. The investigation of neural networks analysis is conducted using SSME vibration data from a NASA developed numerical simulator. The limited application of neural networks to the HPFTP has also shown the effectiveness in diagnosing the anomalies of turbopump vibrations.

  19. Altered behavioral performance and live imaging of circuit-specific neural deficiencies in a zebrafish model for psychomotor retardation.

    Directory of Open Access Journals (Sweden)

    David Zada

    2014-09-01

    Full Text Available The mechanisms and treatment of psychomotor retardation, which includes motor and cognitive impairment, are indefinite. The Allan-Herndon-Dudley syndrome (AHDS is an X-linked psychomotor retardation characterized by delayed development, severe intellectual disability, muscle hypotonia, and spastic paraplegia, in combination with disturbed thyroid hormone (TH parameters. AHDS has been associated with mutations in the monocarboxylate transporter 8 (mct8/slc16a2 gene, which is a TH transporter. In order to determine the pathophysiological mechanisms of AHDS, MCT8 knockout mice were intensively studied. Although these mice faithfully replicated the abnormal serum TH levels, they failed to exhibit the neurological and behavioral symptoms of AHDS patients. Here, we generated an mct8 mutant (mct8-/- zebrafish using zinc-finger nuclease (ZFN-mediated targeted gene editing system. The elimination of MCT8 decreased the expression levels of TH receptors; however, it did not affect the expression of other TH-related genes. Similar to human patients, mct8-/- larvae exhibited neurological and behavioral deficiencies. High-throughput behavioral assays demonstrated that mct8-/- larvae exhibited reduced locomotor activity, altered response to external light and dark transitions and an increase in sleep time. These deficiencies in behavioral performance were associated with altered expression of myelin-related genes and neuron-specific deficiencies in circuit formation. Time-lapse imaging of single-axon arbors and synapses in live mct8-/- larvae revealed a reduction in filopodia dynamics and axon branching in sensory neurons and decreased synaptic density in motor neurons. These phenotypes enable assessment of the therapeutic potential of three TH analogs that can enter the cells in the absence of MCT8. The TH analogs restored the myelin and axon outgrowth deficiencies in mct8-/- larvae. These findings suggest a mechanism by which MCT8 regulates neural circuit

  20. Decision-making neural circuits mediating social behaviors : An attractor network model.

    Science.gov (United States)

    Hurtado-López, Julián; Ramirez-Moreno, David F; Sejnowski, Terrence J

    2017-06-29

    We propose a mathematical model of a continuous attractor network that controls social behaviors. The model is examined with bifurcation analysis and computer simulations. The results show that the model exhibits stable steady states and thresholds for steady state transitions corresponding to some experimentally observed behaviors, such as aggression control. The performance of the model and the relation with experimental evidence are discussed.

  1. Analysis of bilinear noise models in circuits and devices

    Science.gov (United States)

    Willsky, A. S.; Marcus, S. I.

    1976-01-01

    There are a number of applications in which linear noise models are inappropriate. In the paper, the use of bilinear noise models in circuits and devices is considered. Several physical problems are studied in this framework. These include circuits involving varying parameters (such as variable resistance circuits constructed using field-effect transistors), the effect of switching jitter on sampled data system performance and communication systems involving voltage-controlled oscillators and phase-lock loops. In addition, several types of analytical techniques for stochastic bilinear systems are considered. Specifically, the moment equations of Brockett for bilinear systems driven by white noise are discussed, and closed-form expressions for certain bilinear systems (those that evolve an Abelian or solvable Lie groups) driven by white or colored noise are derived. In addition, an approximate statistical technique involving the use of harmonic expansions is described.

  2. Power analysis dataset for QCA based multiplexer circuits

    Directory of Open Access Journals (Sweden)

    Md. Abdullah-Al-Shafi

    2017-04-01

    Full Text Available Power consumption in irreversible QCA logic circuits is a vital and a major issue; however in the practical cases, this focus is mostly omitted.The complete power depletion dataset of different QCA multiplexers have been worked out in this paper. At −271.15 °C temperature, the depletion is evaluated under three separate tunneling energy levels. All the circuits are designed with QCADesigner, a broadly used simulation engine and QCAPro tool has been applied for estimating the power dissipation.

  3. Google matrix analysis of C.elegans neural network

    Science.gov (United States)

    Kandiah, V.; Shepelyansky, D. L.

    2014-05-01

    We study the structural properties of the neural network of the C.elegans (worm) from a directed graph point of view. The Google matrix analysis is used to characterize the neuron connectivity structure and node classifications are discussed and compared with physiological properties of the cells. Our results are obtained by a proper definition of neural directed network and subsequent eigenvector analysis which recovers some results of previous studies. Our analysis highlights particular sets of important neurons constituting the core of the neural system. The applications of PageRank, CheiRank and ImpactRank to characterization of interdependency of neurons are discussed.

  4. Modelling, analysis, and acceleration of a printed circuit board ...

    Indian Academy of Sciences (India)

    Springer Verlag Heidelberg #4 2048 1996 Dec 15 10:16:45

    reviewed for their correctness, then contact films are developed from the circuit films. These contact films are used in fabrication process. If the documents are not suitable for fabrication, it is returned back to the customer. 2.2 Panel cutting. Copper clads with thin layer of copper on both sides are used for fabrication of double.

  5. [Robustness analysis of adaptive neural network model based on spike timing-dependent plasticity].

    Science.gov (United States)

    Chen, Yunzhi; Xu, Guizhi; Zhou, Qian; Guo, Miaomiao; Guo, Lei; Wan, Xiaowei

    2015-02-01

    To explore the self-organization robustness of the biological neural network, and thus to provide new ideas and methods for the electromagnetic bionic protection, we studied both the information transmission mechanism of neural network and spike timing-dependent plasticity (STDP) mechanism, and then investigated the relationship between synaptic plastic and adaptive characteristic of biology. Then a feedforward neural network with the Izhikevich model and the STDP mechanism was constructed, and the adaptive robust capacity of the network was analyzed. Simulation results showed that the neural network based on STDP mechanism had good rubustness capacity, and this characteristics is closely related to the STDP mechanisms. Based on this simulation work, the cell circuit with neurons and synaptic circuit which can simulate the information processing mechanisms of biological nervous system will be further built, then the electronic circuits with adaptive robustness will be designed based on the cell circuit.

  6. Neural Network Reorganization Analysis During an Auditory Oddball Task in Schizophrenia Using Wavelet Entropy

    Directory of Open Access Journals (Sweden)

    Javier Gomez-Pilar

    2015-07-01

    Full Text Available The aim of the present study was to characterize the neural network reorganization during a cognitive task in schizophrenia (SCH by means of wavelet entropy (WE. Previous studies suggest that the cognitive impairment in patients with SCH could be related to the disrupted integrative functions of neural circuits. Nevertheless, further characterization of this effect is needed, especially in the time-frequency domain. This characterization is sensitive to fast neuronal dynamics and their synchronization that may be an important component of distributed neuronal interactions; especially in light of the disconnection hypothesis for SCH and its electrophysiological correlates. In this work, the irregularity dynamics elicited by an auditory oddball paradigm were analyzed through synchronized-averaging (SA and single-trial (ST analyses. They provide complementary information on the spatial patterns involved in the neural network reorganization. Our results from 20 healthy controls and 20 SCH patients showed a WE decrease from baseline to response both in controls and SCH subjects. These changes were significantly more pronounced for healthy controls after ST analysis, mainly in central and frontopolar areas. On the other hand, SA analysis showed more widespread spatial differences than ST results. These findings suggest that the activation response is weakly phase-locked to stimulus onset in SCH and related to the default mode and salience networks. Furthermore, the less pronounced changes in WE from baseline to response for SCH patients suggest an impaired ability to reorganize neural dynamics during an oddball task.

  7. Failure analysis of cracked brass turrets used in electronic circuits

    Directory of Open Access Journals (Sweden)

    Nirmal Ninan

    2014-10-01

    Full Text Available Copper alloys are widely used in electrical circuits mainly due to their electrical conductivity. In electrical circuits, solder terminals are used to provide multiple electrical connections and swage type turret terminals are preferred owing to their high reliability under dynamic environments where the electrical systems are subjected to higher vibrations and line currents, as in the case of satellite launch vehicles. Owing to high machinability, free-cutting brass is preferred in the manufacture of such small sized terminals, which will be cold worked/swaged to the printed circuit boards. Improper selection of the composition of free-cutting brass can lead to cracking of the terminals, thereby affecting the reliability of the system. This paper describes cracking of turret terminals made of free cutting brass during swaging operation. The crack propagation tendency during cold working of brass turret terminals made from different compositions is studied through optical and scanning electron microscopy. Finally, composition for obtaining crack free terminals during swaging is suggested for applications involving cold swaging.

  8. Mass analysis of the components separated from printed circuit boards

    Directory of Open Access Journals (Sweden)

    Hana Charvátová

    2010-06-01

    Full Text Available Methods of effective and ecological recycling of printed circuit boards (PCBs are searched all over the world at this time.The material composition and temperature properties of PCB are necessary to be known for an optimal recycling technology. For thispurpose we analyzed weight ratio of the electronic components moulded on the selected kinds of PCBs and next we formulatedmathematic model of temperature field in PCB during a grinding process in that the metal layers are separated from the plasticelements. We present the obtained results in this paper.

  9. Electrical circuit modeling and analysis of microwave acoustic interaction with biological tissues.

    Science.gov (United States)

    Gao, Fei; Zheng, Qian; Zheng, Yuanjin

    2014-05-01

    Numerical study of microwave imaging and microwave-induced thermoacoustic imaging utilizes finite difference time domain (FDTD) analysis for simulation of microwave and acoustic interaction with biological tissues, which is time consuming due to complex grid-segmentation and numerous calculations, not straightforward due to no analytical solution and physical explanation, and incompatible with hardware development requiring circuit simulator such as SPICE. In this paper, instead of conventional FDTD numerical simulation, an equivalent electrical circuit model is proposed to model the microwave acoustic interaction with biological tissues for fast simulation and quantitative analysis in both one and two dimensions (2D). The equivalent circuit of ideal point-like tissue for microwave-acoustic interaction is proposed including transmission line, voltage-controlled current source, envelop detector, and resistor-inductor-capacitor (RLC) network, to model the microwave scattering, thermal expansion, and acoustic generation. Based on which, two-port network of the point-like tissue is built and characterized using pseudo S-parameters and transducer gain. Two dimensional circuit network including acoustic scatterer and acoustic channel is also constructed to model the 2D spatial information and acoustic scattering effect in heterogeneous medium. Both FDTD simulation, circuit simulation, and experimental measurement are performed to compare the results in terms of time domain, frequency domain, and pseudo S-parameters characterization. 2D circuit network simulation is also performed under different scenarios including different sizes of tumors and the effect of acoustic scatterer. The proposed circuit model of microwave acoustic interaction with biological tissue could give good agreement with FDTD simulated and experimental measured results. The pseudo S-parameters and characteristic gain could globally evaluate the performance of tumor detection. The 2D circuit network

  10. Mirrored Modified Howland Circuit for Bioimpedance Applications: Analytical Analysis

    Science.gov (United States)

    Bertemes-Filho, P.; Negri, L. H.; Felipe, A.; Vincence, V. C.

    2012-12-01

    Multifrequency Electrical Bioimpedance (MEB) has been widely used as a non-invasive technique for characterizing tissues. Most MEB systems use wideband current sources for injecting current to load and instrumentation amplifiers for measuring the resultant potential. Current sources should present intrinsically high output impedance in a very wide frequency range. The objective of this work is to investigate the performance of the Mirrored Modified Howland Current Source (MMHCS) by comparing the analytical solution with the SPICE simulation. It was implemented four MMHCS circuits by using four different operational amplifiers each one. The output current was set to 1 mAp (peak) in the frequency from 1 Hz to 100 MHz. Both techniques presented similar results at lower frequencies. It can be concluded that the output impedance of the circuit is highly dependent of the open-loop gain of the operation amplifier. The analytical solution showed that it is possible to project a current source by using only theirs output current and impedance equations.

  11. TRANSIENT ANALYSIS OF ELECTRIC POWER CIRCUITS BY THE CLASSICAL METHOD IN THE EXAMPLES

    OpenAIRE

    A.A.ZELENKOV

    2009-01-01

    CONTENTS PREFACE CHAPTER 1. CLASSICAL APPROACH TO TRANSIENT ANALYSIS 1.1. Introduction 6 1.2. Appearance of transients in electrical circuits 8 1.3. Differential equations describing electrical circuits 11 1.3.1. Exponential solution of a simple differential equation 14 1.4. Natural and forced responses 19 1.5. Characteristic equation and its determination 22 1.6. Roots of the characteristic equation and different kinds of transient responses 29 1.6.1. Firs...

  12. Investment Valuation Analysis with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Hüseyin İNCE

    2017-07-01

    Full Text Available This paper shows that discounted cash flow and net present value, which are traditional investment valuation models, can be combined with artificial neural network model forecasting. The main inputs for the valuation models, such as revenue, costs, capital expenditure, and their growth rates, are heavily related to sector dynamics and macroeconomics. The growth rates of those inputs are related to inflation and exchange rates. Therefore, predicting inflation and exchange rates is a critical issue for the valuation output. In this paper, the Turkish economy’s inflation rate and the exchange rate of USD/TRY are forecast by artificial neural networks and implemented to the discounted cash flow model. Finally, the results are benchmarked with conventional practices.

  13. Short circuit analysis of distribution system with integration of DG

    DEFF Research Database (Denmark)

    Su, Chi; Liu, Zhou; Chen, Zhe

    2014-01-01

    Integration of distributed generation (DG) such as wind turbines into distribution system is increasing all around the world, because of the flexible and environmentally friendly characteristics. However, DG integration may change the pattern of the fault currents in the distribution system...... during both balanced and unbalanced faults. Major factors such as external grid short circuit power capacity, WT integration location, connection type of WT integration transformer are taken into account. In turn, the challenges brought to the protection system in the distribution network are presented...... and as a result bring challenges to the network protection system. This problem has been frequently discussed in the literature, but mostly considering only the balanced fault situation. This paper presents an investigation on the influence of full converter based wind turbine (WT) integration on fault currents...

  14. Application of damage function analysis to reactor coolant circuits

    Energy Technology Data Exchange (ETDEWEB)

    MacDonald, D.D. [Center for Electrochemical Science and Technology, Pennsylvania State Univ., University Park, PA (United States)

    2002-07-01

    The application of deterministic models for simulating stress corrosion cracking phenomena in Boiling Water Reactor primary coolant circuits is described. The first generation code, DAMAGE-PREDICTOR, has been used to model the radiolysis of the coolant, to estimate the electrochemical corrosion potential (ECP), and to calculate the crack growth rate (CGR) at fixed state points during reactor operation in about a dozen plants worldwide. This code has been validated in ''double-blind'' comparisons between the calculated and measured hydrogen concentration, oxygen concentration, and ECP in the recirculation system of the Leibstadt BWR in Switzerland, as well as through less formal comparisons with data from other plants. Second generation codes have now been developed, including REMAIN for simulating BWRs with internal coolant pumps and the ALERT series for modeling reactors with external pumps. One of this series, ALERT, yields the integrated damage function (IDF), which is the crack length versus time, on a component-by-component basis for a specified future operating scenario. This code therefore allows one to explore proposed future operating protocols, with the objective of identifying those that are most cost-effective and which minimizes the risk of failure of components in the coolant circuit by stress corrosion cracking. The application of this code is illustrated by exploring the benefits of partial hydrogen water chemistry (HWC) for an actual reactor, in which hydrogen is added to the feedwater over only limited periods during operation. The simulations show that the benefits, in terms of reduction in the IDFs for various components, are sensitive to when HWC was initiated in the plant life and to the length of time over which it is applied. (author)

  15. A Pruning Neural Network Model in Credit Classification Analysis

    Directory of Open Access Journals (Sweden)

    Yajiao Tang

    2018-01-01

    Full Text Available Nowadays, credit classification models are widely applied because they can help financial decision-makers to handle credit classification issues. Among them, artificial neural networks (ANNs have been widely accepted as the convincing methods in the credit industry. In this paper, we propose a pruning neural network (PNN and apply it to solve credit classification problem by adopting the well-known Australian and Japanese credit datasets. The model is inspired by synaptic nonlinearity of a dendritic tree in a biological neural model. And it is trained by an error back-propagation algorithm. The model is capable of realizing a neuronal pruning function by removing the superfluous synapses and useless dendrites and forms a tidy dendritic morphology at the end of learning. Furthermore, we utilize logic circuits (LCs to simulate the dendritic structures successfully which makes PNN be implemented on the hardware effectively. The statistical results of our experiments have verified that PNN obtains superior performance in comparison with other classical algorithms in terms of accuracy and computational efficiency.

  16. Short Circuit Ratio analysis of multi-infeed HVDC system with a VSC-HVDC link

    DEFF Research Database (Denmark)

    Liu, Yan; Chen, Zhe

    2011-01-01

    -infeed HVDC system stability has been analyzed a lot but the study on ESCR of this kind of system is still insufficient. This paper presents a calculation method for ESCR of the hybrid multi infeed HVDC system based on a simple two-infeed HVDC system model. The equivalent circuit of this system under short......As an important indicator of system stability, Short Circuit Ratio (SCR) is commonly used in power system analysis. For systems include HVDC link connection, the Effective SCR (ESCR) is mostly applied to indicate the strength of HVDC infeed bus. The contribution of VSC-HVDC link to multi...... circuit situation is firstly obtained based on the model. Then its Thevenin equivalent circuit is derived and system ESCR can be calculated. At last, simulation study verified that the calculated ESCR value under different cases can indicate the change of system stability....

  17. Performance Analysis of Modified Drain Gating Techniques for Low Power and High Speed Arithmetic Circuits

    Directory of Open Access Journals (Sweden)

    Shikha Panwar

    2014-01-01

    Full Text Available This paper presents several high performance and low power techniques for CMOS circuits. In these design methodologies, drain gating technique and its variations are modified by adding an additional NMOS sleep transistor at the output node which helps in faster discharge and thereby providing higher speed. In order to achieve high performance, the proposed design techniques trade power for performance in the delay critical sections of the circuit. Intensive simulations are performed using Cadence Virtuoso in a 45 nm standard CMOS technology at room temperature with supply voltage of 1.2 V. Comparative analysis of the present circuits with standard CMOS circuits shows smaller propagation delay and lesser power consumption.

  18. Frequency analysis for the thermal hydraulic characterization of a natural circulation circuit

    Energy Technology Data Exchange (ETDEWEB)

    Torres, Walmir M.; Macedo, Luiz A.; Sabundjian, Gaiane; Andrade, Delvonei A.; Umbehaun, Pedro E.; Conti, Thadeu N.; Mesquita, Roberto N.; Masotti, Paulo H.; Angelo, Gabriel, E-mail: wmtorres@ipen.b, E-mail: lamacedo@ipen.b, E-mail: gdjian@ipen.b, E-mail: delvonei@ipen.b, E-mail: umbehaun@ipen.b, E-mail: tnconti@ipen.b, E-mail: , E-mail: rnavarro@ipen.b, E-mail: pmasotti@ipen.b, E-mail: gabriel.angelo@usp.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2011-07-01

    This paper presents the frequency analysis studies of the pressure signals from an experimental natural circulation circuit during a heating process. The main objective is to identify the characteristic frequencies of this process using fast Fourier transform. Video images are used to associate these frequencies to the observed phenomenology in the circuit during the process. Sub-cooled and saturated flow boiling, heaters vibrations, overall circuit vibrations, chugging and geysering were observed. Each phenomenon has its specific frequency associated. Some phenomena and their frequencies must be avoided or attenuated since they can cause damages to the natural circulation circuit and its components. Special operation procedures and devices can be developed to avoid these undesirable frequencies. (author)

  19. The Use of Modular, Electronic Neuron Simulators for Neural Circuit Construction Produces Learning Gains in an Undergraduate Anatomy and Physiology Course.

    Science.gov (United States)

    Petto, Andrew; Fredin, Zachary; Burdo, Joseph

    2017-01-01

    During the spring of 2016 at the University of Wisconsin-Milwaukee, we implemented a novel educational technology designed to teach undergraduates about the nervous system while allowing them to physically construct their own neural circuits. Modular, electronic neuron simulators called NeuroBytes were used by the students in BIOSCI202 Anatomy and Physiology I, a four-credit course consisting of three hours per week each of lecture and laboratory time. 162 students participated in the laboratory sessions that covered reflexes; 83 in the experimental sections used the NeuroBytes to build a model of the patellar tendon reflex, while 79 in the control sections participated in alternate reflex curricula. To address the question of whether or not the NeuroBytes-based patellar tendon reflex simulation brought about learning gains, the control and experimental group students underwent pre/post testing before and after their laboratory sections. We found that for several of the neuroscience and physiology concepts assessed on the test, the experimental group students had significantly greater declarative learning gains between the pre- and post-test as compared to the control group students. While there are numerous virtual neuroscience education tools available to undergraduate educators, there are relatively few designed to engage students in the basics of electrophysiology and neural circuitry using physical manipulatives, and none to our knowledge that allow them to build circuits from functioning hand-held "neurons."

  20. Multi-array silicon probes with integrated optical fibers: light-assisted perturbation and recording of local neural circuits in the behaving animal.

    Science.gov (United States)

    Royer, Sébastien; Zemelman, Boris V; Barbic, Mladen; Losonczy, Attila; Buzsáki, György; Magee, Jeffrey C

    2010-06-01

    Recordings of large neuronal ensembles and neural stimulation of high spatial and temporal precision are important requisites for studying the real-time dynamics of neural networks. Multiple-shank silicon probes enable large-scale monitoring of individual neurons. Optical stimulation of genetically targeted neurons expressing light-sensitive channels or other fast (milliseconds) actuators offers the means for controlled perturbation of local circuits. Here we describe a method to equip the shanks of silicon probes with micron-scale light guides for allowing the simultaneous use of the two approaches. We then show illustrative examples of how these compact hybrid electrodes can be used in probing local circuits in behaving rats and mice. A key advantage of these devices is the enhanced spatial precision of stimulation that is achieved by delivering light close to the recording sites of the probe. When paired with the expression of light-sensitive actuators within genetically specified neuronal populations, these devices allow the relatively straightforward and interpretable manipulation of network activity.

  1. Artificial Neural Network Analysis of Xinhui Pericarpium Citri ...

    African Journals Online (AJOL)

    Artificial Neural Network Analysis of Xinhui Pericarpium ... Results: The Root Mean Square (RMS) error of GRNN was 0.22, less than the error MLFN at different .... Statistical analysis. To quantify the results of the model, the judgments generated by ANN model were presented as "1" or "0". "1" represents the characteristics of ...

  2. Measuring user similarity using electric circuit analysis: application to collaborative filtering.

    Directory of Open Access Journals (Sweden)

    Joonhyuk Yang

    Full Text Available We propose a new technique of measuring user similarity in collaborative filtering using electric circuit analysis. Electric circuit analysis is used to measure the potential differences between nodes on an electric circuit. In this paper, by applying this method to transaction networks comprising users and items, i.e., user-item matrix, and by using the full information about the relationship structure of users in the perspective of item adoption, we overcome the limitations of one-to-one similarity calculation approach, such as the Pearson correlation, Tanimoto coefficient, and Hamming distance, in collaborative filtering. We found that electric circuit analysis can be successfully incorporated into recommender systems and has the potential to significantly enhance predictability, especially when combined with user-based collaborative filtering. We also propose four types of hybrid algorithms that combine the Pearson correlation method and electric circuit analysis. One of the algorithms exceeds the performance of the traditional collaborative filtering by 37.5% at most. This work opens new opportunities for interdisciplinary research between physics and computer science and the development of new recommendation systems.

  3. Neural network analysis for hazardous waste characterization

    Energy Technology Data Exchange (ETDEWEB)

    Misra, M.; Pratt, L.Y.; Farris, C. [Colorado School of Mines, Golden, CO (United States)] [and others

    1995-12-31

    This paper is a summary of our work in developing a system for interpreting electromagnetic (EM) and magnetic sensor information from the dig face characterization experimental cell at INEL to determine the depth and nature of buried objects. This project contained three primary components: (1) development and evaluation of several geophysical interpolation schemes for correcting missing or noisy data, (2) development and evaluation of several wavelet compression schemes for removing redundancies from the data, and (3) construction of two neural networks that used the results of steps (1) and (2) to determine the depth and nature of buried objects. This work is a proof-of-concept study that demonstrates the feasibility of this approach. The resulting system was able to determine the nature of buried objects correctly 87% of the time and was able to locate a buried object to within an average error of 0.8 feet. These statistics were gathered based on a large test set and so can be considered reliable. Considering the limited nature of this study, these results strongly indicate the feasibility of this approach, and the importance of appropriate preprocessing of neural network input data.

  4. Analysis of the experimental positron lifetime spectra by neural networks

    Directory of Open Access Journals (Sweden)

    Avdić Senada

    2003-01-01

    Full Text Available This paper deals with the analysis of experimental positron lifetime spectra in polymer materials by using various algorithms of neural networks. A method based on the use of artificial neural networks for unfolding the mean lifetime and intensity of the spectral components of simulated positron lifetime spectra was previously suggested and tested on simulated data [Pžzsitetal, Applied Surface Science, 149 (1998, 97]. In this work, the applicability of the method to the analysis of experimental positron spectra has been verified in the case of spectra from polymer materials with three components. It has been demonstrated that the backpropagation neural network can determine the spectral parameters with a high accuracy and perform the decomposi-tion of lifetimes which differ by 10% or more. The backpropagation network has not been suitable for the identification of both the parameters and the number of spectral components. Therefore, a separate artificial neural network module has been designed to solve the classification problem. Module types based on self-organizing map and learning vector quantization algorithms have been tested. The learning vector quantization algorithm was found to have better performance and reliability. A complete artificial neural network analysis tool of positron lifetime spectra has been constructed to include a spectra classification module and parameter evaluation modules for spectra with a different number of components. In this way, both flexibility and high resolution can be achieved.

  5. Linear circuit analysis program for IBM 1620 Monitor 2, 1311/1443 data processing system /CIRCS/

    Science.gov (United States)

    Hatfield, J.

    1967-01-01

    CIRCS is modification of IBSNAP Circuit Analysis Program, for use on smaller systems. This data processing system retains the basic dc, transient analysis, and FORTRAN 2 formats. It can be used on the IBM 1620/1311 Monitor I Mod 5 system, and solves a linear network containing 15 nodes and 45 branches.

  6. Analysis of input variables of an artificial neural network using bivariate correlation and canonical correlation

    Energy Technology Data Exchange (ETDEWEB)

    Costa, Valter Magalhaes; Pereira, Iraci Martinez, E-mail: valter.costa@usp.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2011-07-01

    The monitoring of variables and diagnosis of sensor fault in nuclear power plants or processes industries is very important because a previous diagnosis allows the correction of the fault and, like this, to prevent the production stopped, improving operator's security and it's not provoking economics losses. The objective of this work is to build a set, using bivariate correlation and canonical correlation, which will be the set of input variables of an artificial neural network to monitor the greater number of variables. This methodology was applied to the IEA-R1 Research Reactor at IPEN. Initially, for the input set of neural network we selected the variables: nuclear power, primary circuit flow rate, control/safety rod position and difference in pressure in the core of the reactor, because almost whole of monitoring variables have relation with the variables early described or its effect can be result of the interaction of two or more. The nuclear power is related to the increasing and decreasing of temperatures as well as the amount radiation due fission of the uranium; the rods are controls of power and influence in the amount of radiation and increasing and decreasing of temperatures; the primary circuit flow rate has the function of energy transport by removing the nucleus heat. An artificial neural network was trained and the results were satisfactory since the IEA-R1 Data Acquisition System reactor monitors 64 variables and, with a set of 9 input variables resulting from the correlation analysis, it was possible to monitor 51 variables. (author)

  7. E-learning platform for automated testing of electronic circuits using signature analysis method

    Science.gov (United States)

    Gherghina, Cǎtǎlina; Bacivarov, Angelica; Bacivarov, Ioan C.; Petricǎ, Gabriel

    2016-12-01

    Dependability of electronic circuits can be ensured only through testing of circuit modules. This is done by generating test vectors and their application to the circuit. Testability should be viewed as a concerted effort to ensure maximum efficiency throughout the product life cycle, from conception and design stage, through production to repairs during products operating. In this paper, is presented the platform developed by authors for training for testability in electronics, in general and in using signature analysis method, in particular. The platform allows highlighting the two approaches in the field namely analog and digital signature of circuits. As a part of this e-learning platform, it has been developed a database for signatures of different electronic components meant to put into the spotlight different techniques implying fault detection, and from this there were also self-repairing techniques of the systems with this kind of components. An approach for realizing self-testing circuits based on MATLAB environment and using signature analysis method is proposed. This paper analyses the benefits of signature analysis method and simulates signature analyzer performance based on the use of pseudo-random sequences, too.

  8. In Vivo Electrochemical Analysis of a PEDOT/MWCNT Neural Electrode Coating

    Directory of Open Access Journals (Sweden)

    Nicolas A. Alba

    2015-10-01

    Full Text Available Neural electrodes hold tremendous potential for improving understanding of brain function and restoring lost neurological functions. Multi-walled carbon nanotube (MWCNT and dexamethasone (Dex-doped poly(3,4-ethylenedioxythiophene (PEDOT coatings have shown promise to improve chronic neural electrode performance. Here, we employ electrochemical techniques to characterize the coating in vivo. Coated and uncoated electrode arrays were implanted into rat visual cortex and subjected to daily cyclic voltammetry (CV and electrochemical impedance spectroscopy (EIS for 11 days. Coated electrodes experienced a significant decrease in 1 kHz impedance within the first two days of implantation followed by an increase between days 4 and 7. Equivalent circuit analysis showed that the impedance increase is the result of surface capacitance reduction, likely due to protein and cellular processes encapsulating the porous coating. Coating’s charge storage capacity remained consistently higher than uncoated electrodes, demonstrating its in vivo electrochemical stability. To decouple the PEDOT/MWCNT material property changes from the tissue response, in vitro characterization was conducted by soaking the coated electrodes in PBS for 11 days. Some coated electrodes exhibited steady impedance while others exhibiting large increases associated with large decreases in charge storage capacity suggesting delamination in PBS. This was not observed in vivo, as scanning electron microscopy of explants verified the integrity of the coating with no sign of delamination or cracking. Despite the impedance increase, coated electrodes successfully recorded neural activity throughout the implantation period.

  9. On the neural networks of empathy: A principal component analysis of an fMRI study

    Directory of Open Access Journals (Sweden)

    Wittsack Hans-Jörg

    2008-09-01

    Full Text Available Abstract Background Human emotional expressions serve an important communicatory role allowing the rapid transmission of valence information among individuals. We aimed at exploring the neural networks mediating the recognition of and empathy with human facial expressions of emotion. Methods A principal component analysis was applied to event-related functional magnetic imaging (fMRI data of 14 right-handed healthy volunteers (29 +/- 6 years. During scanning, subjects viewed happy, sad and neutral face expressions in the following conditions: emotion recognition, empathizing with emotion, and a control condition of simple object detection. Functionally relevant principal components (PCs were identified by planned comparisons at an alpha level of p Results Four PCs revealed significant differences in variance patterns of the conditions, thereby revealing distinct neural networks: mediating facial identification (PC 1, identification of an expressed emotion (PC 2, attention to an expressed emotion (PC 12, and sense of an emotional state (PC 27. Conclusion Our findings further the notion that the appraisal of human facial expressions involves multiple neural circuits that process highly differentiated cognitive aspects of emotion.

  10. Epidemiological analysis of doping offences in the professional tennis circuit

    Directory of Open Access Journals (Sweden)

    Maquirriain Javier

    2010-12-01

    Full Text Available Abstract Introduction Tennis is a professional sport under a strict anti-doping control. However, since the first violation of the code, the positive cases have not been statistically studied. The objective of this study was to analyze doping offences in the international professional tennis circuit. Methods All offences to the Doping Code committed by tennis players during 2003-2009 were collected from the ITF official webpage, registered and analyzed. Results An average of 1905.7 (±174.5 samples was obtained per year. Fifty-two doping offences were reported and the overall incidence of positive doping samples accounted for 0.38% and 7.4 (±4.1 cases/year. Male players showed higher incidence doping offences than females (p = 0.0004. The incidence in wheelchair players was higher than in non-handicapped subjects (p = 0.0001 Banned substance distribution showed: stimulants 32.69%, cannabis 23.07%; anabolic 11.53%, diuretics and masking agents 11.53, β2-agonists 9.61%; corticosteroids 3.84%, others 3.84%. The overall incidence of 'social drugs' (cocaine, cannabis was 36.53%. All EPO and blood samples were normal, while the incidence of 'out-of-competition' offences was 0.12%. The lower incidence of doping was found in Grand Slams tournaments. Conclusions The incidence of positive doping samples among professional tennis players is quite low supporting the assumption that there is no evidence of systematic doping in Tennis. "Social drugs" misuse constitutes the main problem of doping in tennis. Male and wheelchair tennis players showed higher risk of infringing the doping code than their females and non-handicapped counterparts. Findings of this study should help to determine the direction of the ongoing strategy in the fight against doping in Tennis.

  11. Communicative interaction in natural sciences lessons. A didactic analysis based on discursive circuits

    Directory of Open Access Journals (Sweden)

    De Longhi, A.L.;

    2012-04-01

    Full Text Available This work argues the role of communication in natural sciences classrooms and delimits a way of analysis in relation to interactive discursive circuits. Three types of circuits for experimental sciences lessons are compared: open exposition, guided dialog and problematic dialogic inquiry. The data summary the records of a group of interpretative research developed by the team in the last years ten years, which analyses the dialogue associated with science topics and cycles of activity. The resulting scale of analysis and types of intervention sequences held by teachers and students are presented. Based on them, communication circuits are modeled ranging from the traditional classroom to a constructivist one. It is concluded in relation to the need of including the problem of communication in the classroom throughout the educational practice and the processes of teacher training, making it part of the design, development and evaluation of the teaching task.

  12. A neural circuit transforming temporal periodicity information into a rate-based representation in the mammalian auditory system

    DEFF Research Database (Denmark)

    Dicke, Ulrike; Ewert, Stephan D.; Dau, Torsten

    2007-01-01

    to previous modeling studies, the present circuit does not employ a continuously changing temporal parameter to obtain different best modulation frequencies BMFs of the IC bandpass units. Instead, different BMFs are yielded from varying the number of input units projecting onto different bandpass units...

  13. Analysis of wave directional spreading using neural networks

    Digital Repository Service at National Institute of Oceanography (India)

    Deo, M.C.; Gondane, D.S.; SanilKumar, V.

    !. ‘‘Analysis of directional wave energy using neu- ral networks.’’ MS thesis, Indian Institute of Technology, Bombay, India. Kosko, B. ~1992!. Neural networks and fuzzy systems, Prentice Hall, Englewood Cliffs, N.J. Kuik, A. J., Vledder, G., and Holthuijsen, L...

  14. Neural network analysis of varying trends in real exchange rates

    NARCIS (Netherlands)

    J.F. Kaashoek (Johan); H.K. van Dijk (Herman)

    1999-01-01

    textabstractIn this paper neural networks are fitted to the real exchange rates of seven industrialized countries. The size and topology of the used networks is found by reducing the size of the network through the use of multiple correlation coefficients, principal component analysis of residuals

  15. A comparative analysis of neural taste processing in animals

    Science.gov (United States)

    de Brito Sanchez, Gabriela; Giurfa, Martin

    2011-01-01

    Understanding taste processing in the nervous system is a fundamental challenge of modern neuroscience. Recent research on the neural bases of taste coding in invertebrates and vertebrates allows discussion of whether labelled-line or across-fibre pattern encoding applies to taste perception. While the former posits that each gustatory receptor responds to one stimulus or a very limited range of stimuli and sends a direct ‘line’ to the central nervous system to communicate taste information, the latter postulates that each gustatory receptor responds to a wider range of stimuli so that the entire population of taste-responsive neurons participates in the taste code. Tastes are represented in the brain of the fruitfly and of the rat by spatial patterns of neural activity containing both distinct and overlapping regions, which are in accord with both labelled-line and across-fibre pattern processing of taste, respectively. In both animal models, taste representations seem to relate to the hedonic value of the tastant (e.g. palatable versus non-palatable). Thus, although the labelled-line hypothesis can account for peripheral taste processing, central processing remains either unknown or differs from a pure labelled-line coding. The essential task for a neuroscience of taste is, therefore, to determine the connectivity of taste-processing circuits in central nervous systems. Such connectivity may determine coding strategies that differ significantly from both the labelled-line and the across-fibre pattern models. PMID:21690133

  16. MHC haplotype analysis by artificial neural networks.

    Science.gov (United States)

    Bellgard, M I; Tay, G K; Hiew, H L; Witt, C S; Ketheesan, N; Christiansen, F T; Dawkins, R L

    1998-01-01

    Conventional matching is based on numbers of alleles shared between donor and recipient. This approach, however, ignores the degree of relationship between alleles and haplotypes, and therefore the actual degree of difference. To address this problem, we have compared family members using a block matching technique which reflects differences in genomic sequences. All parents and siblings had been genotyped using conventional MHC typing so that haplotypes could be assigned and relatives could be classified as sharing 0, 1 or 2 haplotypes. We trained an Artificial Neural Network (ANN) with subjects from 6 families (85 comparisons) to distinguish between relatives. Using the outputs of the ANN, we developed a score, the Histocompatibility Index (HI), as a measure of the degree of difference. Subjects from a further 3 families (106 profile comparisons) were tested. The HI score for each comparison was plotted. We show that the HI score is trimodal allowing the definition of three populations corresponding to approximately 0, 1 or 2 haplotype sharing. The means and standard deviations of the three populations were found. As expected, comparisons between family members sharing 2 haplotypes resulted in high HI scores with one exception. More interestingly, this approach distinguishes between the 1 and 0 haplotype groups, with some informative exceptions. This distinction was considered too difficult to attempt visually. The approach provides promise in the quantification of degrees of histocompatibility.

  17. Analysis of convergence performance of neural networks ranking algorithm.

    Science.gov (United States)

    Zhang, Yongquan; Cao, Feilong

    2012-10-01

    The ranking problem is to learn a real-valued function which gives rise to a ranking over an instance space, which has gained much attention in machine learning in recent years. This article gives analysis of the convergence performance of neural networks ranking algorithm by means of the given samples and approximation property of neural networks. The upper bounds of convergence rate provided by our results can be considerably tight and independent of the dimension of input space when the target function satisfies some smooth condition. The obtained results imply that neural networks are able to adapt to ranking function in the instance space. Hence the obtained results are able to circumvent the curse of dimensionality on some smooth condition. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.

  18. a computer program for short circuit analysis of electric power systems

    African Journals Online (AJOL)

    ES Obe

    1981-03-01

    Mar 1, 1981 ... system. Specifically in short circuit studies, the power system network is subjected to postulated fault conditions and the resulting faulted network is solved to determine the phase (and sequence) voltages, currents and power of any bus or transmission line of the system. From this analysis, the power system.

  19. Circuit Board Analysis for Lead by Atomic Absorption Spectroscopy in a Course for Nonscience Majors

    Science.gov (United States)

    Weidenhammer, Jeffrey D.

    2007-01-01

    A circuit board analysis of the atomic absorption spectroscopy, which is used to measure lead content in a course for nonscience majors, is being presented. The experiment can also be used to explain the potential environmental hazards of unsafe disposal of various used electronic equipments.

  20. Quasi-linear vacancy dynamics modeling and circuit analysis of the bipolar memristor.

    Directory of Open Access Journals (Sweden)

    Isaac Abraham

    Full Text Available The quasi-linear transport equation is investigated for modeling the bipolar memory resistor. The solution accommodates vacancy and circuit level perspectives on memristance. For the first time in literature the component resistors that constitute the contemporary dual variable resistor circuit model are quantified using vacancy parameters and derived from a governing partial differential equation. The model describes known memristor dynamics even as it generates new insight about vacancy migration, bottlenecks to switching speed and elucidates subtle relationships between switching resistance range and device parameters. The model is shown to comply with Chua's generalized equations for the memristor. Independent experimental results are used throughout, to validate the insights obtained from the model. The paper concludes by implementing a memristor-capacitor filter and compares its performance to a reference resistor-capacitor filter to demonstrate that the model is usable for practical circuit analysis.

  1. Analysis of ringing effects due to magnetic core materials in pulsed nuclear magnetic resonance circuits

    Energy Technology Data Exchange (ETDEWEB)

    Prabhu Gaunkar, N., E-mail: neelampg@iastate.edu; Bouda, N. R. Y.; Nlebedim, I. C.; Hadimani, R. L.; Mina, M.; Jiles, D. C. [Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa 50011 (United States); Bulu, I.; Ganesan, K.; Song, Y. Q. [Schlumberger-Doll Research, Cambridge, Massachusetts 02139 (United States)

    2015-05-07

    This work presents investigations and detailed analysis of ringing in a non-resonant pulsed nuclear magnetic resonance (NMR) circuit. Ringing is a commonly observed phenomenon in high power switching circuits. The oscillations described as ringing impede measurements in pulsed NMR systems. It is therefore desirable that those oscillations decay fast. It is often assumed that one of the causes behind ringing is the role of the magnetic core used in the antenna (acting as an inductive load). We will demonstrate that an LRC subcircuit is also set-up due to the inductive load and needs to be considered due to its parasitic effects. It is observed that the parasitics associated with the inductive load become important at certain frequencies. The output response can be related to the response of an under-damped circuit and to the magnetic core material. This research work demonstrates and discusses ways of controlling ringing by considering interrelationships between different contributing factors.

  2. Analysis of the Cold Gas Flow in Puffer Type Circuit Breaker

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Hong Kyu; Jung, Hyun Kyo [Seoul National University (Korea); Shin, Seong Rok [Samsung SDI R and D Center (Korea); Kim, Doo Sung; Kweon, KI Yeoung [Hyosung Heavy Industry (Korea)

    2000-04-01

    There are many difficult problems in analyzing the gas flow in puffer type circuit breaker such as complex geometry, moving boundary, shock wave and so on. To predict the interruption performance accurately, these should be considered in the simulation. In this paper, the analysis procedure of the cold gas flow in the circuit breaker is presented. Euler equation is solved by FVFLIC method which is an explicit time difference scheme for an unsteady flow computation. Moving boundaries are treated with a cell elimination-addition technique. The pressure and density in front of piston are calculated from the rate of the cell volume change. The presented method is applied to the real circuit breaker model and the pressure in front of the piston is good agreement with the experimental one. (author). 8 refs., 13 figs., 1 tab.

  3. Dynamic transcriptional signature and cell fate analysis reveals plasticity of individual neural plate border cells.

    Science.gov (United States)

    Roellig, Daniela; Tan-Cabugao, Johanna; Esaian, Sevan; Bronner, Marianne E

    2017-03-29

    The 'neural plate border' of vertebrate embryos contains precursors of neural crest and placode cells, both defining vertebrate characteristics. How these lineages segregate from neural and epidermal fates has been a matter of debate. We address this by performing a fine-scale quantitative temporal analysis of transcription factor expression in the neural plate border of chick embryos. The results reveal significant overlap of transcription factors characteristic of multiple lineages in individual border cells from gastrula through neurula stages. Cell fate analysis using a Sox2 (neural) enhancer reveals that cells that are initially Sox2+ cells can contribute not only to neural tube but also to neural crest and epidermis. Moreover, modulating levels of Sox2 or Pax7 alters the apportionment of neural tube versus neural crest fates. Our results resolve a long-standing question and suggest that many individual border cells maintain ability to contribute to multiple ectodermal lineages until or beyond neural tube closure.

  4. A Leptin Analog Locally Produced in the Brain Acts via a Conserved Neural Circuit to Modulate Obesity-Linked Behaviors in Drosophila.

    Science.gov (United States)

    Beshel, Jennifer; Dubnau, Josh; Zhong, Yi

    2017-01-10

    Leptin, a typically adipose-derived "satiety hormone," has a well-established role in weight regulation. Here we describe a functionally conserved model of genetically induced obesity in Drosophila by manipulating the fly leptin analog unpaired 1 (upd1). Unexpectedly, cell-type-specific knockdown reveals upd1 in the brain, not the adipose tissue, mediates obesity-related traits. Disrupting brain-derived upd1 in flies leads to all the hallmarks of mammalian obesity: increased attraction to food cues, increased food intake, and increased weight. These effects are mediated by domeless receptors on neurons expressing Drosophila neuropeptide F, the orexigenic mammalian neuropeptide Y homolog. In vivo two-photon imaging reveals upd1 and domeless inhibit this hedonic signal in fed animals. Manipulations along this central circuit also create hypersensitivity to obesogenic conditions, emphasizing the critical interplay between biological predisposition and environment in overweight and obesity prevalence. We propose adipose- and brain-derived upd/leptin may control differing features of weight regulation through distinct neural circuits. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. Equivalent Quantum Circuits

    OpenAIRE

    Garcia-Escartin, Juan Carlos; Chamorro-Posada, Pedro

    2011-01-01

    Quantum algorithms and protocols are often presented as quantum circuits for a better understanding. We give a list of equivalence rules which can help in the analysis and design of quantum circuits. As example applications we study quantum teleportation and dense coding protocols in terms of a simple XOR swapping circuit and give an intuitive picture of a basic gate teleportation circuit.

  6. Neural Mobilization: A Systematic Review of Randomized Controlled Trials with an Analysis of Therapeutic Efficacy

    Science.gov (United States)

    Ellis, Richard F.; Hing, Wayne A.

    2008-01-01

    Neural mobilization is a treatment modality used in relation to pathologies of the nervous system. It has been suggested that neural mobilization is an effective treatment modality, although support of this suggestion is primarily anecdotal. The purpose of this paper was to provide a systematic review of the literature pertaining to the therapeutic efficacy of neural mobilization. A search to identify randomized controlled trials investigating neural mobilization was conducted using the key words neural mobilisation/mobilization, nerve mobilisation/mobilization, neural manipulative physical therapy, physical therapy, neural/nerve glide, nerve glide exercises, nerve/neural treatment, nerve/neural stretching, neurodynamics, and nerve/neural physiotherapy. The titles and abstracts of the papers identified were reviewed to select papers specifically detailing neural mobilization as a treatment modality. The PEDro scale, a systematic tool used to critique RCTs and grade methodological quality, was used to assess these trials. Methodological assessment allowed an analysis of research investigating therapeutic efficacy of neural mobilization. Ten randomized clinical trials (discussed in 11 retrieved articles) were identified that discussed the therapeutic effect of neural mobilization. This review highlights the lack in quantity and quality of the available research. Qualitative analysis of these studies revealed that there is only limited evidence to support the use of neural mobilization. Future research needs to re-examine the application of neural mobilization with use of more homogeneous study designs and pathologies; in addition, it should standardize the neural mobilization interventions used in the study. PMID:19119380

  7. ANALYSIS OF CIRCUITS WITH MULTIPLE COUPLED LOSSY TRANSMISSION LINES

    DEFF Research Database (Denmark)

    Dalby, Arne Brejning

    1997-01-01

    The analysis of coupled lossy transmission line structures like microwave directional couplers and filters, 3-phase power cable networks or a databus is not simple. In this paper it is shown that the admittance parameters for the 2N port network, N coupled lossy transmission lines form, may...... be found using a set of almost arbitrary (orthogonal) quasi propagation modes instead of the true modes, if the up-per frequency is so low that the electrical length of the lines is smaller than about 1/3 of the wavelength for the propagation mode with the lowest phase velocity....

  8. Magnetic Circuit & Torque Analysis Of Brushless DC Motor

    Directory of Open Access Journals (Sweden)

    Arif J. Abbas

    2013-05-01

    Full Text Available       This work is concerned with magnetic and torque analysis of BLDCM and with development of a method of designing BLDCM that have symmetric winding on the rotor .make significant contribution to the rotor inductance position difficult. It is also show that the prediction detent torque can be extremely sensitive to the permanent magnet by altering magnet arc width. Finally, simple lumped models that allow one to predict motor performance and characteristics as a function of main dimension, magnet residual flux density and phase current are developed. These models are used as a basis for an approach to designing BLDCM

  9. Curcumin Alters Neural Plasticity and Viability of Intact Hippocampal Circuits and Attenuates Behavioral Despair and COX-2 Expression in Chronically Stressed Rats.

    Science.gov (United States)

    Choi, Ga-Young; Kim, Hyun-Bum; Hwang, Eun-Sang; Lee, Seok; Kim, Min-Ji; Choi, Ji-Young; Lee, Sung-Ok; Kim, Sang-Seong; Park, Ji-Ho

    2017-01-01

    Curcumin is a major diarylheptanoid component of Curcuma longa with traditional usage for anxiety and depression. It has been known for the anti-inflammatory, antistress, and neurotropic effects. Here we examined curcumin effect in neural plasticity and cell viability. 60-channel multielectrode array was applied on organotypic hippocampal slice cultures (OHSCs) to monitor the effect of 10 μM curcumin in long-term depression (LTD) through low-frequency stimulation (LFS) to the Schaffer collaterals and commissural pathways. Cell viability was assayed by propidium iodide uptake test in OHSCs. In addition, the influence of oral curcumin administration on rat behavior was assessed with the forced swim test (FST). Finally, protein expression levels of brain-derived neurotrophic factor (BDNF) and cyclooxygenase-2 (COX-2) were measured by Western blot in chronically stressed rats. Our results demonstrated that 10 μM curcumin attenuated LTD and reduced cell death. It also recovered the behavior immobility of FST, rescued the attenuated BDNF expression, and inhibited the enhancement of COX-2 expression in stressed animals. These findings indicate that curcumin can enhance postsynaptic electrical reactivity and cell viability in intact neural circuits with antidepressant-like effects, possibly through the upregulation of BDNF and reduction of inflammatory factors in the brain.

  10. Curcumin Alters Neural Plasticity and Viability of Intact Hippocampal Circuits and Attenuates Behavioral Despair and COX-2 Expression in Chronically Stressed Rats

    Directory of Open Access Journals (Sweden)

    Ga-Young Choi

    2017-01-01

    Full Text Available Curcumin is a major diarylheptanoid component of Curcuma longa with traditional usage for anxiety and depression. It has been known for the anti-inflammatory, antistress, and neurotropic effects. Here we examined curcumin effect in neural plasticity and cell viability. 60-channel multielectrode array was applied on organotypic hippocampal slice cultures (OHSCs to monitor the effect of 10 μM curcumin in long-term depression (LTD through low-frequency stimulation (LFS to the Schaffer collaterals and commissural pathways. Cell viability was assayed by propidium iodide uptake test in OHSCs. In addition, the influence of oral curcumin administration on rat behavior was assessed with the forced swim test (FST. Finally, protein expression levels of brain-derived neurotrophic factor (BDNF and cyclooxygenase-2 (COX-2 were measured by Western blot in chronically stressed rats. Our results demonstrated that 10 μM curcumin attenuated LTD and reduced cell death. It also recovered the behavior immobility of FST, rescued the attenuated BDNF expression, and inhibited the enhancement of COX-2 expression in stressed animals. These findings indicate that curcumin can enhance postsynaptic electrical reactivity and cell viability in intact neural circuits with antidepressant-like effects, possibly through the upregulation of BDNF and reduction of inflammatory factors in the brain.

  11. Combined analysis of energy band diagram and equivalent circuit on nanocrystal solid

    Energy Technology Data Exchange (ETDEWEB)

    Kano, Shinya, E-mail: kano@eedept.kobe-u.ac.jp, E-mail: fujii@eedept.kobe-u.ac.jp; Sasaki, Masato; Fujii, Minoru, E-mail: kano@eedept.kobe-u.ac.jp, E-mail: fujii@eedept.kobe-u.ac.jp [Department of Electrical and Electronic Engineering, Graduate School of Engineering, Kobe University, Rokkodai, Nada, Kobe 657-8501 (Japan)

    2016-06-07

    We investigate a combined analysis of an energy band diagram and an equivalent circuit on nanocrystal (NC) solids. We prepared a flat silicon-NC solid in order to carry out the analysis. An energy band diagram of a NC solid is determined from DC transport properties. Current-voltage characteristics, photocurrent measurements, and conductive atomic force microscopy images indicate that a tunneling transport through a NC solid is dominant. Impedance spectroscopy gives an equivalent circuit: a series of parallel resistor-capacitors corresponding to NC/metal and NC/NC interfaces. The equivalent circuit also provides an evidence that the NC/NC interface mainly dominates the carrier transport through NC solids. Tunneling barriers inside a NC solid can be taken into account in a combined capacitance. Evaluated circuit parameters coincide with simple geometrical models of capacitances. As a result, impedance spectroscopy is also a useful technique to analyze semiconductor NC solids as well as usual DC transport. The analyses provide indispensable information to implement NC solids into actual electronic devices.

  12. Analysis of pressure losses in the hemodialysis graft vascular circuit using finite element analysis.

    Science.gov (United States)

    Beasley, Madison P; Conrad, Steven A

    2007-01-01

    Vascular access complications (stenosis and thrombosis) present the greatest morbidity associated with chronic hemodialysis. Understanding the hemodynamics of the vascular access circuit is essential identify problems. We developed a finite element model based on a previously-reported experimental vascular circuit model, and compared pressure losses at segments within the circuit determined by the finite element model with experimental results and with an analytical model based on the circuit. In most cases the finite element model better predicted the pressure losses than did the analytical model. The finite element model also revealed the complex flow patterns that can occur in hemodialysis vascular access circuits that may explain some of the variance with the analytical model.

  13. Analysis of recreational closed-circuit rebreather deaths 1998-2010.

    Science.gov (United States)

    Fock, Andrew W

    2013-06-01

    Since the introduction of recreational closed-circuit rebreathers (CCRs) in 1998, there have been many recorded deaths. Rebreather deaths have been quoted to be as high as 1 in 100 users. Rebreather fatalities between 1998 and 2010 were extracted from the Deeplife rebreather mortality database, and inaccuracies were corrected where known. Rebreather absolute numbers were derived from industry discussions and training agency statistics. Relative numbers and brands were extracted from the Rebreather World website database and a Dutch rebreather survey. Mortality was compared with data from other databases. A fault-tree analysis of rebreathers was compared to that of open-circuit scuba of various configurations. Finally, a risk analysis was applied to the mortality database. The 181 recorded recreational rebreather deaths occurred at about 10 times the rate of deaths amongst open-circuit recreational scuba divers. No particular brand or type of rebreather was over-represented. Closed-circuit rebreathers have a 25-fold increased risk of component failure compared to a manifolded twin-cylinder open-circuit system. This risk can be offset by carrying a redundant 'bailout' system. Two-thirds of fatal dives were associated with a high-risk dive or high-risk behaviour. There are multiple points in the human-machine interface (HMI) during the use of rebreathers that can result in errors that may lead to a fatality. While rebreathers have an intrinsically higher risk of mechanical failure as a result of their complexity, this can be offset by good design incorporating redundancy and by carrying adequate 'bailout' or alternative gas sources for decompression in the event of a failure. Designs that minimize the chances of HMI errors and training that highlights this area may help to minimize fatalities.

  14. Transient Analysis of Lumped Circuit Networks Loaded Thin Wires By DGTD Method

    KAUST Repository

    Li, Ping

    2016-03-31

    With the purpose of avoiding very fine mesh cells in the proximity of a thin wire, the modified telegrapher’s equations (MTEs) are employed to describe the thin wire voltage and current distributions, which consequently results in reduced number of unknowns and augmented Courant-Friedrichs-Lewy (CFL) number. As hyperbolic systems, both the MTEs and the Maxwell’s equations are solved by the discontinuous Galerkin time-domain (DGTD) method. In realistic situations, the thin wires could be either driven or loaded by circuit networks. The thin wire-circuit interface performs as a boundary condition for the thin wire solver, where the thin wire voltage and current used for the incoming flux evaluation involved in the DGTD analyzed MTEs are not available. To obtain this voltage and current, an auxiliary current flowing through the thin wire-circuit interface is introduced at each interface. Corresponding auxiliary equations derived from the invariable property of characteristic variable for hyperbolic systems are developed and solved together with the circuit equations established by the modified nodal analysis (MNA) modality. Furthermore, in order to characterize the field and thin wire interactions, a weighted electric field and a volume current density are added into the MTEs and Maxwell-Ampere’s law equation, respectively. To validate the proposed algorithm, three representative examples are presented.

  15. Forgetting the best when predicting the worst: Preliminary observations on neural circuit function in adolescent social anxiety

    Directory of Open Access Journals (Sweden)

    Johanna M. Jarcho

    2015-06-01

    Full Text Available Social anxiety disorder typically begins in adolescence, a sensitive period for brain development, when increased complexity and salience of peer relationships requires novel forms of social learning. Disordered social learning in adolescence may explain how brain dysfunction promotes social anxiety. Socially anxious adolescents (n = 15 and adults (n = 19 and non-anxious adolescents (n = 24 and adults (n = 32 predicted, then received, social feedback from high and low-value peers while undergoing functional magnetic resonance imaging (fMRI. A surprise recall task assessed memory biases for feedback. Neural correlates of social evaluation prediction errors (PEs were assessed by comparing engagement to expected and unexpected positive and negative feedback. For socially anxious adolescents, but not adults or healthy participants of either age group, PEs elicited heightened striatal activity and negative fronto-striatal functional connectivity. This occurred selectively to unexpected positive feedback from high-value peers and corresponded with impaired memory for social feedback. While impaired memory also occurred in socially-anxious adults, this impairment was unrelated to brain-based PE activity. Thus, social anxiety in adolescence may relate to altered neural correlates of PEs that contribute to impaired learning about social feedback. Small samples necessitate replication. Nevertheless, results suggest that the relationship between learning and fronto-striatal function may attenuate as development progresses.

  16. Large-scale functional neural network correlates of response inhibition: an fMRI meta-analysis.

    Science.gov (United States)

    Zhang, Ruibin; Geng, Xiujuan; Lee, Tatia M C

    2017-12-01

    An influential hypothesis from the last decade proposed that regions within the right inferior frontal cortex of the human brain were dedicated to supporting response inhibition. There is growing evidence, however, to support an alternative model, which proposes that neural areas associated with specific inhibitory control tasks co-exist as common network mechanisms, supporting diverse cognitive processes. This meta-analysis of 225 studies comprising 323 experiments examined the common and distinct neural correlates of cognitive processes for response inhibition, namely interference resolution, action withholding, and action cancellation. Activation coordinates for each subcategory were extracted using multilevel kernel density analysis (MKDA). The extracted activity patterns were then mapped onto the brain functional network atlas to derive the common (i.e., process-general) and distinct (i.e., domain-oriented) neural network correlates of these processes. Independent of the task types, activation of the right hemispheric regions (inferior frontal gyrus, insula, median cingulate, and paracingulate gyri) and superior parietal gyrus was common across the cognitive processes studied. Mapping the activation patterns to a brain functional network atlas revealed that the fronto-parietal and ventral attention networks were the core neural systems that were commonly engaged in different processes of response inhibition. Subtraction analyses elucidated the distinct neural substrates of interference resolution, action withholding, and action cancellation, revealing stronger activation in the ventral attention network for interference resolution than action inhibition. On the other hand, action withholding/cancellation primarily engaged the fronto-striatal circuit. Overall, our results suggest that response inhibition is a multidimensional cognitive process involving multiple neural regions and networks for coordinating optimal performance. This finding has significant

  17. Transient and Steady-State Analysis of Nonlinear RF and Microwave Circuits

    Directory of Open Access Journals (Sweden)

    2006-01-01

    Full Text Available This paper offers a review of simulation methods currently available for the transient and steady-state analysis of nonlinear RF and microwave circuits. The most general method continues to be the time-marching approach used in Spice, but more recent methods based on multiple time dimensions are particularly effective for RF and microwave circuits. We derive nodal formulations for the most widely used multiple time dimension methods. We put special emphasis on methods for the analysis of oscillators based in the warped multitime partial differential equations (WaMPDE approach. Case studies of a Colpitts oscillator and a voltage controlled Clapp-Gouriet oscillator are presented and discussed. The accuracy of the amplitude and phase of these methods is investigated. It is shown that the exploitation of frequency-domain latency reduces the computational effort.

  18. Static Voltage Stability Analysis by Using SVM and Neural Network

    Directory of Open Access Journals (Sweden)

    Mehdi Hajian

    2013-01-01

    Full Text Available Voltage stability is an important problem in power system networks. In this paper, in terms of static voltage stability, and application of Neural Networks (NN and Supported Vector Machine (SVM for estimating of voltage stability margin (VSM and predicting of voltage collapse has been investigated. This paper considers voltage stability in power system in two parts. The first part calculates static voltage stability margin by Radial Basis Function Neural Network (RBFNN. The advantage of the used method is high accuracy in online detecting the VSM. Whereas the second one, voltage collapse analysis of power system is performed by Probabilistic Neural Network (PNN and SVM. The obtained results in this paper indicate, that time and number of training samples of SVM, are less than NN. In this paper, a new model of training samples for detection system, using the normal distribution load curve at each load feeder, has been used. Voltage stability analysis is estimated by well-know L and VSM indexes. To demonstrate the validity of the proposed methods, IEEE 14 bus grid and the actual network of Yazd Province are used.

  19. Stability analysis of embedded nonlinear predictor neural generalized predictive controller

    Directory of Open Access Journals (Sweden)

    Hesham F. Abdel Ghaffar

    2014-03-01

    Full Text Available Nonlinear Predictor-Neural Generalized Predictive Controller (NGPC is one of the most advanced control techniques that are used with severe nonlinear processes. In this paper, a hybrid solution from NGPC and Internal Model Principle (IMP is implemented to stabilize nonlinear, non-minimum phase, variable dead time processes under high disturbance values over wide range of operation. Also, the superiority of NGPC over linear predictive controllers, like GPC, is proved for severe nonlinear processes over wide range of operation. The necessary conditions required to stabilize NGPC is derived using Lyapunov stability analysis for nonlinear processes. The NGPC stability conditions and improvement in disturbance suppression are verified by both simulation using Duffing’s nonlinear equation and real-time using continuous stirred tank reactor. Up to our knowledge, the paper offers the first hardware embedded Neural GPC which has been utilized to verify NGPC–IMP improvement in realtime.

  20. The analysis of VERITAS muon images using convolutional neural networks

    Science.gov (United States)

    Feng, Qi; Lin, Tony T. Y.; VERITAS Collaboration

    2017-06-01

    Imaging atmospheric Cherenkov telescopes (IACTs) are sensitive to rare gamma-ray photons, buried in the background of charged cosmic-ray (CR) particles, the flux of which is several orders of magnitude greater. The ability to separate gamma rays from CR particles is important, as it is directly related to the sensitivity of the instrument. This gamma-ray/CR-particle classification problem in IACT data analysis can be treated with the rapidly-advancing machine learning algorithms, which have the potential to outperform the traditional box-cut methods on image parameters. We present preliminary results of a precise classification of a small set of muon events using a convolutional neural networks model with the raw images as input features. We also show the possibility of using the convolutional neural networks model for regression problems, such as the radius and brightness measurement of muon events, which can be used to calibrate the throughput efficiency of IACTs.

  1. Conflict Resolution as Near-Threshold Decision-Making: A Spiking Neural Circuit Model with Two-Stage Competition for Antisaccadic Task.

    Science.gov (United States)

    Lo, Chung-Chuan; Wang, Xiao-Jing

    2016-08-01

    Automatic responses enable us to react quickly and effortlessly, but they often need to be inhibited so that an alternative, voluntary action can take place. To investigate the brain mechanism of controlled behavior, we investigated a biologically-based network model of spiking neurons for inhibitory control. In contrast to a simple race between pro- versus anti-response, our model incorporates a sensorimotor remapping module, and an action-selection module endowed with a "Stop" process through tonic inhibition. Both are under the modulation of rule-dependent control. We tested the model by applying it to the well known antisaccade task in which one must suppress the urge to look toward a visual target that suddenly appears, and shift the gaze diametrically away from the target instead. We found that the two-stage competition is crucial for reproducing the complex behavior and neuronal activity observed in the antisaccade task across multiple brain regions. Notably, our model demonstrates two types of errors: fast and slow. Fast errors result from failing to inhibit the quick automatic responses and therefore exhibit very short response times. Slow errors, in contrast, are due to incorrect decisions in the remapping process and exhibit long response times comparable to those of correct antisaccade responses. The model thus reveals a circuit mechanism for the empirically observed slow errors and broad distributions of erroneous response times in antisaccade. Our work suggests that selecting between competing automatic and voluntary actions in behavioral control can be understood in terms of near-threshold decision-making, sharing a common recurrent (attractor) neural circuit mechanism with discrimination in perception.

  2. Electronic devices and circuits

    CERN Document Server

    Pridham, Gordon John

    1972-01-01

    Electronic Devices and Circuits, Volume 3 provides a comprehensive account on electronic devices and circuits and includes introductory network theory and physics. The physics of semiconductor devices is described, along with field effect transistors, small-signal equivalent circuits of bipolar transistors, and integrated circuits. Linear and non-linear circuits as well as logic circuits are also considered. This volume is comprised of 12 chapters and begins with an analysis of the use of Laplace transforms for analysis of filter networks, followed by a discussion on the physical properties of

  3. Measurement and Analysis of Multiple Output Transient Propagation in BJT Analog Circuits

    Science.gov (United States)

    Roche, Nicolas J.-H.; Khachatrian, A.; Warner, J. H.; Buchner, S. P.; McMorrow, D.; Clymer, D. A.

    2016-08-01

    The propagation of Analog Single Event Transients (ASETs) to multiple outputs of Bipolar Junction Transistor (BJTs) Integrated Circuits (ICs) is reported for the first time. The results demonstrate that ASETs can appear at several outputs of a BJT amplifier or comparator as a result of a single ion or single laser pulse strike at a single physical location on the chip of a large-scale integrated BJT analog circuit. This is independent of interconnect cross-talk or charge-sharing effects. Laser experiments, together with SPICE simulations and analysis of the ASET's propagation in the s-domain are used to explain how multiple-output transients (MOTs) are generated and propagate in the device. This study demonstrates that both the charge collection associated with an ASET and the ASET's shape, commonly used to characterize the propagation of SETs in devices and systems, are unable to explain quantitatively how MOTs propagate through an integrated analog circuit. The analysis methodology adopted here involves combining the Fourier transform of the propagating signal and the current-source transfer function in the s-domain. This approach reveals the mechanisms involved in the transient signal propagation from its point of generation to one or more outputs without the signal following a continuous interconnect path.

  4. Simplified thermal-hydraulic analysis of single phase natural circulation circuit with two heat exchangers

    Energy Technology Data Exchange (ETDEWEB)

    Pinheiro, Larissa Cunha; Su, Jian, E-mail: larissa@lasme.coppe.ufrj.br, E-mail: sujian@lasme.coppe.ufrj.br [Coordenacao dos Programas de Pos-Graduacao em Engenharia (COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Programa de Engenhraria Nuclear; Cotta, Renato Machado, E-mail: cotta@mecanica.coppe.ufrj.br [Coordenacao dos Programas de Pos-Graduacao em Engenharia (POLI/COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Dept. de Engenharia Mecanica

    2015-07-01

    Single phase natural circulation circuits composed of two convective heat exchangers and connecting tubes are important for the passive heat removal from spent fuel pools (SFP). To keep the structural integrity of the stored spent fuel assemblies, continuously cooling has to be provided in order to avoid increase at the pool temperature and subsequent uncovering of the fuel and enhanced reaction between water and metal releasing hydrogen. Decay heat can achieve considerably high amounts of energy e.g. in the AP1000, considering the emergency fuel assemblies, the maximum heat decay will reach 13 MW in the 15th day (Westinghouse Electric Company, 2010). A highly efficient alternative to do so is by means of natural circulation, which is cost-effective compared to active cooling systems and is inherently safer since presents less associated devices and no external work is required. Many researchers have investigated safety and stability aspects of natural circulation loops (NCL). However, there is a lack of literature concerning the improvement of NCL through a standard unified methodology, especially for natural circulation circuits with two heat exchangers. In the present study, a simplified thermal-hydraulic analysis of single phase natural circulation circuit with two heat exchanges is presented. Relevant dimensionless key groups were proposed to for the design and safety analysis of a scaled NCL for the cooling of spent fuel storage pool with convective cooling and heating. (author)

  5. D-VASim: an interactive virtual laboratory environment for the simulation and analysis of genetic circuits.

    Science.gov (United States)

    Baig, Hasan; Madsen, Jan

    2017-01-15

    Simulation and behavioral analysis of genetic circuits is a standard approach of functional verification prior to their physical implementation. Many software tools have been developed to perform in silico analysis for this purpose, but none of them allow users to interact with the model during runtime. The runtime interaction gives the user a feeling of being in the lab performing a real world experiment. In this work, we present a user-friendly software tool named D-VASim (Dynamic Virtual Analyzer and Simulator), which provides a virtual laboratory environment to simulate and analyze the behavior of genetic logic circuit models represented in an SBML (Systems Biology Markup Language). Hence, SBML models developed in other software environments can be analyzed and simulated in D-VASim. D-VASim offers deterministic as well as stochastic simulation; and differs from other software tools by being able to extract and validate the Boolean logic from the SBML model. D-VASim is also capable of analyzing the threshold value and propagation delay of a genetic circuit model. D-VASim is available for Windows and Mac OS and can be downloaded from bda.compute.dtu.dk/downloads/. haba@dtu.dk, jama@dtu.dk. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  6. Experiments and kinematics analysis of a hand rehabilitation exoskeleton with circuitous joints.

    Science.gov (United States)

    Zhang, Fuhai; Fu, Yili; Zhang, Qinchao; Wang, Shuguo

    2015-01-01

    Aiming at the hand rehabilitation of stroke patients, a wearable hand exoskeleton with circuitous joint is proposed. The circuitous joint adopts the symmetric pinion and rack mechanism (SPRM) with the parallel mechanism. The exoskeleton finger is a serial mechanism composed of three closed-chain SPRM joints in series. The kinematic equations of the open chain of the finger and the closed chains of the SPRM joints were built to analyze the kinematics of the hand rehabilitation exoskeleton. The experimental setup of the hand rehabilitation exoskeleton was built and the continuous passive motion (CPM) rehabilitation experiment and the test of human-robot interaction force measurement were conducted. Experiment results show that the mechanical design of the hand rehabilitation robot is reasonable and that the kinematic analysis is correct, thus the exoskeleton can be used for the hand rehabilitation of stroke patients.

  7. Analysis of the transients on the multi-circuit overhead transmission line

    Science.gov (United States)

    Nazarčík, Tomáš; Benešová, Zdeňka

    2017-11-01

    In this paper a method for a transient analysis on the multi-circuit overhead transmission line with different voltage levels (EHV and HV) is presented. The influence of inductive and capacitive couplings between the parallel circuits placed on the same tower is studied. The transmission line model consists of mutual coupled two-port cascades which can be described by a system of ordinary differential equations (ODE). This system has been solved numerically in MATLAB, the obtained results provide the time distribution of currents and voltages in all conductors along the transmission line. The presented algorithm allows solving various types of transients which can occur during switching off- and on-operations, shortcircuits etc.

  8. Aspects of the numerical analysis of neural networks

    Science.gov (United States)

    Ellacott, S. W.

    This article starts with a brief introduction to neural networks for those unfamiliar with the basic concepts, together with a very brief overview of mathematical approaches to the subject. This is followed by a more detailed look at three areas of research which are of particular interest to numerical analysts.The first area is approximation theory. If K is a compact set in n, for some n, then it is proved that a semilinear feedforward network with one hidden layer can uniformly approximate any continuous function in C(K) to any required accuracy. A discussion of known results and open questions on the degree of approximation is included. We also consider the relevance of radial basis functions to neural networks.The second area considered is that of learning algorithms. A detailed analysis of one popular algorithm (the delta rule) will be given, indicating why one implementation leads to a stable numerical process, whereas an initially attractive variant (essentially a form of steepest descent) does not. Similar considerations apply to the backpropagation algorithm. The effect of filtering and other preprocessing of the input data will also be discussed systematically.Finally some applications of neural networks to numerical computation are considered.

  9. Deep neural networks for texture classification-A theoretical analysis.

    Science.gov (United States)

    Basu, Saikat; Mukhopadhyay, Supratik; Karki, Manohar; DiBiano, Robert; Ganguly, Sangram; Nemani, Ramakrishna; Gayaka, Shreekant

    2018-01-01

    We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Sensitivity analysis of linear programming problem through a recurrent neural network

    Science.gov (United States)

    Das, Raja

    2017-11-01

    In this paper we study the recurrent neural network for solving linear programming problems. To achieve optimality in accuracy and also in computational effort, an algorithm is presented. We investigate the sensitivity analysis of linear programming problem through the neural network. A detailed example is also presented to demonstrate the performance of the recurrent neural network.

  11. The interaction of bayesian priors and sensory data and its neural circuit implementation in visually guided movement.

    Science.gov (United States)

    Yang, Jin; Lee, Joonyeol; Lisberger, Stephen G

    2012-12-05

    Sensory-motor behavior results from a complex interaction of noisy sensory data with priors based on recent experience. By varying the stimulus form and contrast for the initiation of smooth pursuit eye movements in monkeys, we show that visual motion inputs compete with two independent priors: one prior biases eye speed toward zero; the other prior attracts eye direction according to the past several days' history of target directions. The priors bias the speed and direction of the initiation of pursuit for the weak sensory data provided by the motion of a low-contrast sine wave grating. However, the priors have relatively little effect on pursuit speed and direction when the visual stimulus arises from the coherent motion of a high-contrast patch of dots. For any given stimulus form, the mean and variance of eye speed covary in the initiation of pursuit, as expected for signal-dependent noise. This relationship suggests that pursuit implements a trade-off between movement accuracy and variation, reducing both when the sensory signals are noisy. The tradeoff is implemented as a competition of sensory data and priors that follows the rules of Bayesian estimation. Computer simulations show that the priors can be understood as direction-specific control of the strength of visual-motor transmission, and can be implemented in a neural-network model that makes testable predictions about the population response in the smooth eye movement region of the frontal eye fields.

  12. The Physics of Decision Making:. Stochastic Differential Equations as Models for Neural Dynamics and Evidence Accumulation in Cortical Circuits

    Science.gov (United States)

    Holmes, Philip; Eckhoff, Philip; Wong-Lin, K. F.; Bogacz, Rafal; Zacksenhouse, Miriam; Cohen, Jonathan D.

    2010-03-01

    We describe how drift-diffusion (DD) processes - systems familiar in physics - can be used to model evidence accumulation and decision-making in two-alternative, forced choice tasks. We sketch the derivation of these stochastic differential equations from biophysically-detailed models of spiking neurons. DD processes are also continuum limits of the sequential probability ratio test and are therefore optimal in the sense that they deliver decisions of specified accuracy in the shortest possible time. This leaves open the critical balance of accuracy and speed. Using the DD model, we derive a speed-accuracy tradeoff that optimizes reward rate for a simple perceptual decision task, compare human performance with this benchmark, and discuss possible reasons for prevalent sub-optimality, focussing on the question of uncertain estimates of key parameters. We present an alternative theory of robust decisions that allows for uncertainty, and show that its predictions provide better fits to experimental data than a more prevalent account that emphasises a commitment to accuracy. The article illustrates how mathematical models can illuminate the neural basis of cognitive processes.

  13. The interaction of Bayesian priors and sensory data and its neural circuit implementation in visually-guided movement

    Science.gov (United States)

    Yang, Jin; Lee, Joonyeol; Lisberger, Stephen G.

    2012-01-01

    Sensory-motor behavior results from a complex interaction of noisy sensory data with priors based on recent experience. By varying the stimulus form and contrast for the initiation of smooth pursuit eye movements in monkeys, we show that visual motion inputs compete with two independent priors: one prior biases eye speed toward zero; the other prior attracts eye direction according to the past several days’ history of target directions. The priors bias the speed and direction of the initiation of pursuit for the weak sensory data provided by the motion of a low-contrast sine wave grating. However, the priors have relatively little effect on pursuit speed and direction when the visual stimulus arises from the coherent motion of a high-contrast patch of dots. For any given stimulus form, the mean and variance of eye speed co-vary in the initiation of pursuit, as expected for signal-dependent noise. This relationship suggests that pursuit implements a trade-off between movement accuracy and variation, reducing both when the sensory signals are noisy. The tradeoff is implemented as a competition of sensory data and priors that follows the rules of Bayesian estimation. Computer simulations show that the priors can be understood as direction specific control of the strength of visual-motor transmission, and can be implemented in a neural-network model that makes testable predictions about the population response in the smooth eye movement region of the frontal eye fields. PMID:23223286

  14. Landslide susceptibility analysis using an artificial neural network model

    Science.gov (United States)

    Mansor, Shattri; Pradhan, Biswajeet; Daud, Mohamed; Jamaludin, Normalina; Khuzaimah, Zailani

    2007-10-01

    This paper deals with landslide susceptibility analysis using an artificial neural network model for Cameron Highland, Malaysia. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for the landslide hazards. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide hazard was analyzed using landslide occurrence factors employing the logistic regression model. The results of the analysis were verified using the landslide location data and compared with logistic regression model. The accuracy of hazard map observed was 85.73%. The qualitative landslide susceptibility analysis was carried out using an artificial neural network model by doing map overlay analysis in GIS environment. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.

  15. Analysis of the discharge of the ATLAS barrel toroid and end cap toroids with different configurations of the protection circuit

    CERN Document Server

    Acerbi, E; Broggi, F; Sorbi, M; Volpini, G

    2001-01-01

    An analysis of the discharge of the barrel toroid and end cap toroids with different protection circuits has been carried out in order to verify the possibility of a new simplified and cheaper configuration of the components of the circuit. In the study also the presence of short circuits has been considered. The comparison of the results and the analysis of the advantages and risks of the different configurations should allow the choice of the best solution for the economy and safety of the toroids. (4 refs).

  16. Electric circuits essentials

    CERN Document Server

    REA, Editors of

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Electric Circuits I includes units, notation, resistive circuits, experimental laws, transient circuits, network theorems, techniques of circuit analysis, sinusoidal analysis, polyph

  17. System Theoretic Dependability Analysis of the LHC Superconducting Magnet Circuit Protection

    CERN Document Server

    AUTHOR|(CDS)2254970

    Subject of the present work is the application of the methods STPA (System Theoretic Process Analysis) and CAST (Causal Analysis based on STAMP) to analyze the protection systems of the superconducting magnet circuit of the LHC at CERN, Geneva. The named methods are derived from the at MIT developed STAMP (System Theoretic Accident Model and Processes) accident model. The CAST method was applied to the analysis of the 2008 Incident during the Hardware Commissioning. An incorrect interconnection between two magnets damaged the accelerator severely. The analysis defines the control structure of the Commissioning and investigates every subsystem and the interaction between the components. The results were social and technical requirements. Among others, it shows the necessity for safety culture at CERN and a revision of the magnet interconnection process. The present analysis found the same root causes for the incident than a task force did in 2009. Further, the CAST analysis found more, socio-technica...

  18. Device and circuit performance analysis of double gate junctionless transistors at L(g = 18 nm

    Directory of Open Access Journals (Sweden)

    Chitrakant Sahu

    2014-04-01

    Full Text Available The design and characteristics of double-gate (DG junctionless (JL devices are compared with the DG inversion-mode (IM field effect transistors (FETs at 45 nm technology node with effective channel length of 18 nm. The comparison are performed at iso-V(th for both n- and p-type of devices. The JL device shows lower drain-induced barrier lowering, steep subthreshold slope and lower OFF state current. For the first time, the authors demonstrate a pass gate (PG logic, inverter circuit and static random access memory (SRAM stability analysis using JL devices, rather than a complementary metal-oxide semiconductor (CMOS configuration. They observed that transient response of JL PG configuration is similar to that of conventional CMOS PGs. JL inverter also shows similar transient characteristics with 25% reduction in delay and 12% improvement in 6 T SRAM cell stability compared with IMFETs, which shows large potential in digital circuit applications. The simulations were performed using coupled device-circuit methodology in ATLAS technology aided computer design (TCAD mixed-mode simulator.

  19. Global dissipativity analysis for delayed quaternion-valued neural networks.

    Science.gov (United States)

    Tu, Zhengwen; Cao, Jinde; Alsaedi, Ahmed; Hayat, Tasawar

    2017-05-01

    The problem of global dissipativity analysis for quaternion-valued neural networks (QVNNs) with time-varying delays is firstly investigated in this paper. The QVNN is studied as a single entirety without any decomposition. Several algebraic conditions ensuring the global dissipativity and globally exponential dissipativity for QVNNs are derived by employing Lyapunov theory and some analytic techniques. Furthermore, the positive invariant sets, globally attractive sets and globally exponentially attractive sets are figured out as well. Finally, the effectiveness is notarized by deducing two simulation examples. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. An effective convolutional neural network model for Chinese sentiment analysis

    Science.gov (United States)

    Zhang, Yu; Chen, Mengdong; Liu, Lianzhong; Wang, Yadong

    2017-06-01

    Nowadays microblog is getting more and more popular. People are increasingly accustomed to expressing their opinions on Twitter, Facebook and Sina Weibo. Sentiment analysis of microblog has received significant attention, both in academia and in industry. So far, Chinese microblog exploration still needs lots of further work. In recent years CNN has also been used to deal with NLP tasks, and already achieved good results. However, these methods ignore the effective use of a large number of existing sentimental resources. For this purpose, we propose a Lexicon-based Sentiment Convolutional Neural Networks (LSCNN) model focus on Weibo's sentiment analysis, which combines two CNNs, trained individually base on sentiment features and word embedding, at the fully connected hidden layer. The experimental results show that our model outperforms the CNN model only with word embedding features on microblog sentiment analysis task.

  1. Feedback in analog circuits

    CERN Document Server

    Ochoa, Agustin

    2016-01-01

    This book describes a consistent and direct methodology to the analysis and design of analog circuits with particular application to circuits containing feedback. The analysis and design of circuits containing feedback is generally presented by either following a series of examples where each circuit is simplified through the use of insight or experience (someone else’s), or a complete nodal-matrix analysis generating lots of algebra. Neither of these approaches leads to gaining insight into the design process easily. The author develops a systematic approach to circuit analysis, the Driving Point Impedance and Signal Flow Graphs (DPI/SFG) method that does not require a-priori insight to the circuit being considered and results in factored analysis supporting the design function. This approach enables designers to account fully for loading and the bi-directional nature of elements both in the feedback path and in the amplifier itself, properties many times assumed negligible and ignored. Feedback circuits a...

  2. 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...

  3. ANNA: A Convolutional Neural Network Code for Spectroscopic Analysis

    Science.gov (United States)

    Lee-Brown, Donald; Anthony-Twarog, Barbara J.; Twarog, Bruce A.

    2018-01-01

    We present ANNA, a Python-based convolutional neural network code for the automated analysis of stellar spectra. ANNA provides a flexible framework that allows atmospheric parameters such as temperature and metallicity to be determined with accuracies comparable to those of established but less efficient techniques. ANNA performs its parameterization extremely quickly; typically several thousand spectra can be analyzed in less than a second. Additionally, the code incorporates features which greatly speed up the training process necessary for the neural network to measure spectra accurately, resulting in a tool that can easily be run on a single desktop or laptop computer. Thus, ANNA is useful in an era when spectrographs increasingly have the capability to collect dozens to hundreds of spectra each night. This talk will cover the basic features included in ANNA and demonstrate its performance in two use cases: an open cluster abundance analysis involving several hundred spectra, and a metal-rich field star study. Applicability of the code to large survey datasets will also be discussed.

  4. ANALYSIS OF INDUCTION MOTOR WITH BROKEN BARS AND CONSTANT SPEED USING CIRCUIT-FIELD COUPLED METHOD

    Directory of Open Access Journals (Sweden)

    N. Halem

    2015-07-01

    Full Text Available The paper presents the use of the two-dimensional finite element method for modeling the three-phase squirrel-cage induction motor by using circuit coupled method. In order to analyze the machine performances, the voltage source is considered. The Ansys magnetic analysis software is used for calculating the magnetic field of an induction motor having a cage fault. The experimental results prove that the proposed approach constitutes a useful tool for the study and diagnostics of induction motors.

  5. Parametric Analysis of Input Circuits of Galvanostatic Type Impedance Measuring Transducers

    Directory of Open Access Journals (Sweden)

    Barylo Grygoriy

    2017-07-01

    Full Text Available The main approaches of parametric analysis of impedance measuring transducers on the base of SPICE modeling are considered in paper. In the modeling process, the galvanostatic measurement method is used, which is on the base of the determination of the ratio and phase delay between the voltage and the harmonic current with the specified amplitude and frequency. As a result of simulation, the possibility of significant increase of the measurement transformation accuracy is found by means of modifying the input circuits of the impedance measuring transducers.

  6. Experimental circuit realization of zero-dispersion nonlinear resonance via the generic input-output analysis

    Energy Technology Data Exchange (ETDEWEB)

    Ho, Ming-Chung, E-mail: t1603@nknucc.nknu.edu.t [Department of Physics, National Kaohsiung Normal University, Kaohsiung 824, Taiwan (China); Lin, Chien-Hui [Department of Physics, National Sun Yat-Sen University, Kaohsiung 804, Taiwan (China); Lu, Ming-Chi [Department of Physics, National Kaohsiung Normal University, Kaohsiung 824, Taiwan (China); Hsu, Tzu-Fang [Department of Applied Physics, National Pingtung University of Education, Pingtung 900, Taiwan (China); Liu, Chia-Ju [Graduate Institute of Science Education, National Kaohsiung Normal University, Kaohsiung 824, Taiwan (China); Kuo, Chie-Tong; Jiang, I-Min [Department of Physics, National Sun Yat-Sen University, Kaohsiung 804, Taiwan (China)

    2010-06-07

    When a nonlinear oscillator is driven by an external force which is calculated on the basis of the zero-dispersion nonlinear resonance (ZDNR) theory, it can be transparent, i.e., the output of the response system is the same as the magnitude of driving force without any dispersion or distortion. In fact, this is a unique phenomenon, called zero-dispersion nonlinear resonance (ZDNR). Herein, we used the generic input-output analysis to realize the ZDNR circuit experiment of two unidirectional coupling self-excited oscillators. The experimental observations also show good agreement with numerical simulations.

  7. A classic learning opportunity from Arthur Guyton and colleagues (1955): circuit analysis of venous return.

    Science.gov (United States)

    Curran-Everett, Douglas

    2007-06-01

    The circuit analysis of an electric analog of the systemic circulation, the focus of a classic paper by Guyton, Lindsey, and Kaufmann, provides a framework for understanding the factors that impact venous return and for appreciating the value of modeling physiological systems. The classic 1955 paper by Guyton, Lindsey, and Kaufmann gives your students an opportunity to learn about modeling from the physiologist who pioneered it (Guyton) and demonstrates that mathematics and data graphics are fundamental tools with which to learn about the regulation of the cardiovascular system. In this essay, I outline avenues of discovery by which your students can explore the factors that impact venous return.

  8. A survey of SiC power MOSFETs short-circuit robustness and failure mode analysis

    DEFF Research Database (Denmark)

    Ceccarelli, L.; Reigosa, P. D.; Iannuzzo, F.

    2017-01-01

    The aim of this paper is to provide an extensive overview about the state-of-art commercially available SiC power MOSFET, focusing on their short-circuit ruggedness. A detailed literature investigation has been carried out, in order to collect and understand the latest research contribution within...... this topic and create a survey of the present scenario of SiC MOSFETs reliability evaluation and failure mode analysis, pointing out the evolution and improvements as well as the future challenges in this promising device technology....

  9. Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: a theoretical review of the evidence and future directions for clinical translation.

    Science.gov (United States)

    Williams, Leanne M

    2017-01-01

    Complex emotional, cognitive and self-reflective functions rely on the activation and connectivity of large-scale neural circuits. These circuits offer a relevant scale of focus for conceptualizing a taxonomy for depression and anxiety based on specific profiles (or biotypes) of neural circuit dysfunction. Here, the theoretical review first outlines the current consensus as to what constitutes the organization of large-scale circuits in the human brain identified using parcellation and meta-analysis. The focus is on neural circuits implicated in resting reflection (default mode), detection of "salience," affective processing ("threat" and "reward"), "attention," and "cognitive control." Next, the current evidence regarding which type of dysfunctions in these circuits characterize depression and anxiety disorders is reviewed, with an emphasis on published meta-analyses and reviews of circuit dysfunctions that have been identified in at least two well-powered case:control studies. Grounded in the review of these topics, a conceptual framework is proposed for considering neural circuit-defined "biotypes." In this framework, biotypes are defined by profiles of extent of dysfunction on each large-scale circuit. The clinical implications of a biotype approach for guiding classification and treatment of depression and anxiety is considered. Future research directions will develop the validity and clinical utility of a neural circuit biotype model that spans diagnostic categories and helps to translate neuroscience into clinical practice in the real world. © 2016 Wiley Periodicals, Inc.

  10. Research on artificial neural network intrusion detection photochemistry based on the improved wavelet analysis and transformation

    Science.gov (United States)

    Li, Hong; Ding, Xue

    2017-03-01

    This paper combines wavelet analysis and wavelet transform theory with artificial neural network, through the pretreatment on point feature attributes before in intrusion detection, to make them suitable for improvement of wavelet neural network. The whole intrusion classification model gets the better adaptability, self-learning ability, greatly enhances the wavelet neural network for solving the problem of field detection invasion, reduces storage space, contributes to improve the performance of the constructed neural network, and reduces the training time. Finally the results of the KDDCup99 data set simulation experiment shows that, this method reduces the complexity of constructing wavelet neural network, but also ensures the accuracy of the intrusion classification.

  11. Analysis of neural networks in terms of domain functions

    NARCIS (Netherlands)

    van der Zwaag, B.J.; Slump, Cornelis H.; Spaanenburg, Lambert

    Despite their success-story, artificial neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more as a

  12. Artificial Neural Network Analysis of Xinhui Pericarpium Citri ...

    African Journals Online (AJOL)

    Purpose: To develop an effective analytical method to distinguish old peels of Xinhui Pericarpium citri reticulatae (XPCR) stored for > 3 years from new peels stored for < 3 years. Methods: Artificial neural networks (ANN) models, including general regression neural network (GRNN) and multi-layer feedforward neural ...

  13. Stability Analysis of Neural Networks-Based System Identification

    Directory of Open Access Journals (Sweden)

    Talel Korkobi

    2008-01-01

    Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.

  14. Ancestral regulatory circuits governing ectoderm patterning downstream of Nodal and BMP2/4 revealed by gene regulatory network analysis in an echinoderm.

    Directory of Open Access Journals (Sweden)

    Alexandra Saudemont

    2010-12-01

    Full Text Available Echinoderms, which are phylogenetically related to vertebrates and produce large numbers of transparent embryos that can be experimentally manipulated, offer many advantages for the analysis of the gene regulatory networks (GRN regulating germ layer formation. During development of the sea urchin embryo, the ectoderm is the source of signals that pattern all three germ layers along the dorsal-ventral axis. How this signaling center controls patterning and morphogenesis of the embryo is not understood. Here, we report a large-scale analysis of the GRN deployed in response to the activity of this signaling center in the embryos of the Mediterranean sea urchin Paracentrotus lividus, in which studies with high spatial resolution are possible. By using a combination of in situ hybridization screening, overexpression of mRNA, recombinant ligand treatments, and morpholino-based loss-of-function studies, we identified a cohort of transcription factors and signaling molecules expressed in the ventral ectoderm, dorsal ectoderm, and interposed neurogenic ("ciliary band" region in response to the known key signaling molecules Nodal and BMP2/4 and defined the epistatic relationships between the most important genes. The resultant GRN showed a number of striking features. First, Nodal was found to be essential for the expression of all ventral and dorsal marker genes, and BMP2/4 for all dorsal genes. Second, goosecoid was identified as a central player in a regulatory sub-circuit controlling mouth formation, while tbx2/3 emerged as a critical factor for differentiation of the dorsal ectoderm. Finally, and unexpectedly, a neurogenic ectoderm regulatory circuit characterized by expression of "ciliary band" genes was triggered in the absence of TGF beta signaling. We propose a novel model for ectoderm regionalization, in which neural ectoderm is the default fate in the absence of TGF beta signaling, and suggest that the stomodeal and neural subcircuits that we

  15. Neural network adapted to wound cell analysis in surgical patients.

    Science.gov (United States)

    Viljanto, Jouko; Koski, Antti

    2011-01-01

    Assessment of the real state of wound healing of closed surgical wounds is uncertain both clinically and from conventional laboratory tests. Therefore, a novel approach based on early analysis of exactly timed wound cells, computerized further with an artificial neural network, was developed. At the end of routine surgery performed on 481 children under 18 years of age, a specific wound drain Cellstick™ was inserted subcutaneously between the wound edges to harvest wound cells. The Cellsticks™ were removed from 1 to 50 hours, mainly at hour 3 or 24 postsurgery. Immediately, the cellular contents were washed out using a pump constructed for the purpose. After cytocentrifugation, the cells were stained and counted differentially. Based on their relative proportions at selected time intervals, an artificial self-organizing neural map was developed. This was further transformed to a unidirectional linear graph where each node represents one set of relative cell quantities. As early as 3 hours, but more precisely 24 hours after surgery, the location of the nodes on this graph showed individually the patients' initial speed of wound inflammatory cell response. Similarly, timed Cellstick™ specimens from new surgical patients could be analyzed, computerized, and compared with these node values to assess their initial speed in wound inflammatory cell response. Location of the node on the graph does not express the time lapse after surgery but the speed of wound inflammatory cell response in relation to that of other patients. © 2011 by the Wound Healing Society.

  16. Reliability and risk analysis using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Robinson, D.G. [Sandia National Labs., Albuquerque, NM (United States)

    1995-12-31

    This paper discusses preliminary research at Sandia National Laboratories into the application of artificial neural networks for reliability and risk analysis. The goal of this effort is to develop a reliability based methodology that captures the complex relationship between uncertainty in material properties and manufacturing processes and the resulting uncertainty in life prediction estimates. The inputs to the neural network model are probability density functions describing system characteristics and the output is a statistical description of system performance. The most recent application of this methodology involves the comparison of various low-residue, lead-free soldering processes with the desire to minimize the associated waste streams with no reduction in product reliability. Model inputs include statistical descriptions of various material properties such as the coefficients of thermal expansion of solder and substrate. Consideration is also given to stochastic variation in the operational environment to which the electronic components might be exposed. Model output includes a probabilistic characterization of the fatigue life of the surface mounted component.

  17. Topological analysis of chaotic solution of three-element memristive circuit

    OpenAIRE

    Ginoux, Jean-Marc; Letellier, Christophe; Chua, Leon

    2010-01-01

    International audience; The simplest electronic circuit with a memristor was recently proposed. Chaotic attractors solution to this memristive circuit are topologically characterized and compared to Rossler-like attractors.

  18. Automatic Analysis at the Commissioning of the LHC Superconducting Electrical Circuits

    CERN Document Server

    Reymond, H; Charrondiere, C; Rijllart, A; Zerlauth, M

    2011-01-01

    Since the beginning of 2010 the LHC has been operating in a routinely manner, starting with a commissioning phase and then an operation for physics phase. The commissioning of the superconducting electrical circuits requires rigorous test procedures before entering into operation. To maximize the beam operation time of the LHC, these tests should be done as fast as procedures allow. A full commissioning need 12000 tests and is required after circuits have been warmed above liquid nitrogen temperature. Below this temperature, after an end of year break of two months, commissioning needs about 6000 tests. As the manual analysis of the tests takes a major part of the commissioning time, we automated existing analysis tools. We present here how these LabVIEW™ applications were automated, the evaluation of the gain in commissioning time and reduction of experts on night shift observed during the LHC hardware commissioning campaign of 2011 compared to 2010. We end with an outlook at what can be further optimized.

  19. Analysis of the developing neural system using an in vitro model by Raman spectroscopy.

    Science.gov (United States)

    Hashimoto, Kosuke; Kudoh, Suguru N; Sato, Hidetoshi

    2015-04-07

    We developed an in vitro model of early neural cell development. The maturation of a normal neural cell was studied in vitro using Raman spectroscopy for 120 days. The Raman spectra datasets were analyzed by principal component analysis (PCA) to investigate the relationship between maturation stages and molecular composition changes in neural cells. According to the PCA, the Raman spectra datasets can be classified into four larger groups. Previous electrophysiological studies have suggested that a normal neural cell goes through three maturation states. The groups we observed by Raman analysis showed good agreement with the electrophysiological results, except with the addition of a fourth state. The results demonstrated that Raman analysis was powerful to investigate the daily changes in molecular composition of the growing neural cell. This in vitro model system may be useful for future studies of the effects of endocrine disrupters in the developing early neural system.

  20. Radial basis function (RBF) neural network control for mechanical systems design, analysis and Matlab simulation

    CERN Document Server

    Liu, Jinkun

    2013-01-01

    Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design.   This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...

  1. Measuring circuits

    CERN Document Server

    Graf, Rudolf F

    1996-01-01

    This series of circuits provides designers with a quick source for measuring circuits. Why waste time paging through huge encyclopedias when you can choose the topic you need and select any of the specialized circuits sorted by application?This book in the series has 250-300 practical, ready-to-use circuit designs, with schematics and brief explanations of circuit operation. The original source for each circuit is listed in an appendix, making it easy to obtain additional information.Ready-to-use circuits.Grouped by application for easy look-up.Circuit source listings

  2. Oscillator circuits

    CERN Document Server

    Graf, Rudolf F

    1996-01-01

    This series of circuits provides designers with a quick source for oscillator circuits. Why waste time paging through huge encyclopedias when you can choose the topic you need and select any of the specialized circuits sorted by application?This book in the series has 250-300 practical, ready-to-use circuit designs, with schematics and brief explanations of circuit operation. The original source for each circuit is listed in an appendix, making it easy to obtain additional information.Ready-to-use circuits.Grouped by application for easy look-up.Circuit source listing

  3. Neural Circuits for Peristaltic Wave Propagation in Crawling Drosophila Larvae: Analysis and Modeling

    Directory of Open Access Journals (Sweden)

    Julijana eGjorgjieva

    2013-04-01

    Full Text Available Drosophila larvae crawl by peristaltic waves of muscle contractions, which propagate along the animal body and involve the simultaneous contraction of the left and right side of each segment. Coordinated propagation of contraction does not require sensory input, suggesting that movement is generated by a central pattern generator (CPG. We characterized crawling behavior of newly hatched Drosophila larvae by quantifying timing and duration of segmental boundary contractions. We developed a CPG network model that recapitulates these patterns based on segmentally repeated units of excitatory and inhibitory neuronal populations coupled with immediate neighboring segments. A single network with symmetric coupling between neighboring segments succeeded in generating both forward and backward propagation of activity. The CPG network was robust to changes in amplitude and variability of connectivity strength. Introducing sensory feedback via `stretch-sensitive' neurons improved wave propagation properties such as speed of propagation and segmental contraction duration as observed experimentally. Sensory feedback also restored propagating activity patterns when an inappropriately tuned CPG network failed to generate waves. Finally, in a two-sided CPG model we demonstrated that two types of connectivity could synchronize the activity of two independent networks: connections from excitatory neurons on one side to excitatory contralateral neurons (E to E, and connections from inhibitory neurons on one side to excitatory contralateral neurons (I to E. To our knowledge, such I to E connectivity has not yet been found in any experimental system; however, it provides the most robust mechanism to synchronize activity between contralateral CPGs in our model. Our model provides a general framework for studying the conditions under which a single locally coupled network generates bilaterally synchronized and longitudinally propagating waves in either direction.

  4. Simulation Analysis for Opening Performance of Medium Voltage Vacuum Circuit Breaker Based on ADAMS and Maxwell

    Directory of Open Access Journals (Sweden)

    Ding Xianliang

    2017-01-01

    Full Text Available The circuit breakers play a important role in control and protect the power systemand the vacuum circuit breaker has beenwidely used in the field of medium voltage with its excellent opening performance.Virtual prototyping technology is alsobecamemore and more popularin design and optimization of the vacuum circuit breaker. In this paper, the electromagnetic simulation software Ansoft Maxwell is used to analyze the electric repulsion of the circuit breaker in the case of open the rated short circuit breaking current. The 3D model that wasbuilt by CREOis imported into ADAMS. Thenconstraints, contact force, and the electric repulsion forcethat was analysezed in Ansoft Maxwell is added into the 3D model.Therefore, we can carry on the multi-body dynamics simulation to the 3D model. Then We can get the openingperformance of the vacuum circuit breakerin the condition of open circuit rated short circuit breaking current. The simulation results show that the circuit breaker can still meet the performance requirements in the condition of open circuit rated short circuit breaking current.

  5. Mineralogical analysis of dust collected from typical recycling line of waste printed circuit boards.

    Science.gov (United States)

    Wang, Fangfang; Zhao, Yuemin; Zhang, Tao; Duan, Chenlong; Wang, Lizhang

    2015-09-01

    As dust is one of the byproducts originating in the mechanical recycling process of waste printed circuit boards such as crushing and separating, from the viewpoints of resource reuse and environmental protection, an effective recycling method to recover valuable materials from this kind of dust is in urgent need. In this paper, detailed mineralogical analysis on the dust collected from a typical recycling line of waste printed circuit boards is investigated by coupling several analytical techniques. The results demonstrate that there are 73.1wt.% organic matters, 4.65wt.% Al, 4.55wt.% Fe, 2.67wt.% Cu and 1.06wt.% Pb in the dust, which reveals the dust is worthy of reuse and harmful to environment. The concentration ratios of Fe, Mn and Zn can reach 12.35, 12.33 and 6.67 respectively by magnetic separation. The yield of dust in each size fraction is nonuniform, while the yield of -0.75mm size fraction is up to 51.15wt.%; as the particle size decreases, the content of liberated metals and magnetic materials increase, and metals are mainly in elemental forms. The F, Cl and Br elements combing to C in the dust would make thermal treatment dangerous to the environment. Based on these results, a flowsheet to recycle the dust is proposed. Copyright © 2015 Elsevier Ltd. All rights reserved.

  6. Magnetic field analysis of Lorentz motors using a novel segmented magnetic equivalent circuit method.

    Science.gov (United States)

    Qian, Junbing; Chen, Xuedong; Chen, Han; Zeng, Lizhan; Li, Xiaoqing

    2013-01-28

    A simple and accurate method based on the magnetic equivalent circuit (MEC) model is proposed in this paper to predict magnetic flux density (MFD) distribution of the air-gap in a Lorentz motor (LM). In conventional MEC methods, the permanent magnet (PM) is treated as one common source and all branches of MEC are coupled together to become a MEC network. In our proposed method, every PM flux source is divided into three sub-sections (the outer, the middle and the inner). Thus, the MEC of LM is divided correspondingly into three independent sub-loops. As the size of the middle sub-MEC is small enough, it can be treated as an ideal MEC and solved accurately. Combining with decoupled analysis of outer and inner MECs, MFD distribution in the air-gap can be approximated by a quadratic curve, and the complex calculation of reluctances in MECs can be avoided. The segmented magnetic equivalent circuit (SMEC) method is used to analyze a LM, and its effectiveness is demonstrated by comparison with FEA, conventional MEC and experimental results.

  7. Magnetic Field Analysis of Lorentz Motors Using a Novel Segmented Magnetic Equivalent Circuit Method

    Directory of Open Access Journals (Sweden)

    Xiaoqing Li

    2013-01-01

    Full Text Available A simple and accurate method based on the magnetic equivalent circuit (MEC model is proposed in this paper to predict magnetic flux density (MFD distribution of the air-gap in a Lorentz motor (LM. In conventional MEC methods, the permanent magnet (PM is treated as one common source and all branches of MEC are coupled together to become a MEC network. In our proposed method, every PM flux source is divided into three sub-sections (the outer, the middle and the inner. Thus, the MEC of LM is divided correspondingly into three independent sub-loops. As the size of the middle sub-MEC is small enough, it can be treated as an ideal MEC and solved accurately. Combining with decoupled analysis of outer and inner MECs, MFD distribution in the air-gap can be approximated by a quadratic curve, and the complex calculation of reluctances in MECs can be avoided. The segmented magnetic equivalent circuit (SMEC method is used to analyze a LM, and its effectiveness is demonstrated by comparison with FEA, conventional MEC and experimental results.

  8. Magnetic Field Analysis of Lorentz Motors Using a Novel Segmented Magnetic Equivalent Circuit Method

    Science.gov (United States)

    Qian, Junbing; Chen, Xuedong; Chen, Han; Zeng, Lizhan; Li, Xiaoqing

    2013-01-01

    A simple and accurate method based on the magnetic equivalent circuit (MEC) model is proposed in this paper to predict magnetic flux density (MFD) distribution of the air-gap in a Lorentz motor (LM). In conventional MEC methods, the permanent magnet (PM) is treated as one common source and all branches of MEC are coupled together to become a MEC network. In our proposed method, every PM flux source is divided into three sub-sections (the outer, the middle and the inner). Thus, the MEC of LM is divided correspondingly into three independent sub-loops. As the size of the middle sub-MEC is small enough, it can be treated as an ideal MEC and solved accurately. Combining with decoupled analysis of outer and inner MECs, MFD distribution in the air-gap can be approximated by a quadratic curve, and the complex calculation of reluctances in MECs can be avoided. The segmented magnetic equivalent circuit (SMEC) method is used to analyze a LM, and its effectiveness is demonstrated by comparison with FEA, conventional MEC and experimental results. PMID:23358368

  9. Dynamic transcriptional signature and cell fate analysis reveals plasticity of individual neural plate border cells

    Science.gov (United States)

    Roellig, Daniela; Tan-Cabugao, Johanna; Esaian, Sevan; Bronner, Marianne E

    2017-01-01

    The ‘neural plate border’ of vertebrate embryos contains precursors of neural crest and placode cells, both defining vertebrate characteristics. How these lineages segregate from neural and epidermal fates has been a matter of debate. We address this by performing a fine-scale quantitative temporal analysis of transcription factor expression in the neural plate border of chick embryos. The results reveal significant overlap of transcription factors characteristic of multiple lineages in individual border cells from gastrula through neurula stages. Cell fate analysis using a Sox2 (neural) enhancer reveals that cells that are initially Sox2+ cells can contribute not only to neural tube but also to neural crest and epidermis. Moreover, modulating levels of Sox2 or Pax7 alters the apportionment of neural tube versus neural crest fates. Our results resolve a long-standing question and suggest that many individual border cells maintain ability to contribute to multiple ectodermal lineages until or beyond neural tube closure. DOI: http://dx.doi.org/10.7554/eLife.21620.001 PMID:28355135

  10. Regional frequency analysis using Growing Neural Gas network

    Science.gov (United States)

    Abdi, Amin; Hassanzadeh, Yousef; Ouarda, Taha B. M. J.

    2017-07-01

    The delineation of hydrologically homogeneous regions is an important issue in regional hydrological frequency analysis. In the present study, an application of the Growing Neural Gas (GNG) network for hydrological data clustering is presented. The GNG is an incremental and unsupervised neural network, which is able to adapt its structure during the training procedure without using a prior knowledge of the size and shape of the network. In the GNG algorithm, the Minimum Description Length (MDL) measure as the cluster validity index is utilized for determining the optimal number of clusters (sub-regions). The capability of the proposed algorithm is illustrated by regionalizing drought severities for 40 synoptic weather stations in Iran. To fulfill this aim, first a clustering method is applied to form the sub-regions and then a heterogeneity measure is used to test the degree of heterogeneity of the delineated sub-regions. According to the MDL measure and considering two different indices namely CS and Davies-Bouldin (DB) in the GNG network, the entire study area is subdivided in two sub-regions located in the eastern and western sides of Iran. In order to evaluate the performance of the GNG algorithm, a number of other commonly used clustering methods, like K-means, fuzzy C-means, self-organizing map and Ward method are utilized in this study. The results of the heterogeneity measure based on the L-moments approach reveal that only the GNG algorithm successfully yields homogeneous sub-regions in comparison to the other methods.

  11. Electric circuits and signals

    CERN Document Server

    Sabah, Nassir H

    2007-01-01

    Circuit Variables and Elements Overview Learning Objectives Electric Current Voltage Electric Power and Energy Assigned Positive Directions Active and Passive Circuit Elements Voltage and Current Sources The Resistor The Capacitor The Inductor Concluding Remarks Summary of Main Concepts and Results Learning Outcomes Supplementary Topics on CD Problems and Exercises Basic Circuit Connections and Laws Overview Learning Objectives Circuit Terminology Kirchhoff's Laws Voltage Division and Series Connection of Resistors Current Division and Parallel Connection of Resistors D-Y Transformation Source Equivalence and Transformation Reduced-Voltage Supply Summary of Main Concepts and Results Learning Outcomes Supplementary Topics and Examples on CD Problems and Exercises Basic Analysis of Resistive Circuits Overview Learning Objectives Number of Independent Circuit Equations Node-Voltage Analysis Special Considerations in Node-Voltage Analysis Mesh-Current Analysis Special Conside...

  12. Obtaining real-time Bode (analysis of electronic circuits with LabVIEW

    Directory of Open Access Journals (Sweden)

    Akif Akgül

    2017-08-01

    Full Text Available Bode diagram is a term used in many fields of science and engineering that can be described as frequency response analysis. LabVIEW is a multifunctional program for acquiring, processing and analyzing data. In this paper, real time Bode diagram of electronic circuits are obtained in LabVIEW platform without using special VIs(virtual instruments such as SI Bode Plot VI. In addition to this, the realized study can be easily integrated in to other commonly used platforms such as MATLAB and C to obtain bode diagram results in real time. To evaluate performance of the realized study, Bode diagram results obtained from LabVIEW is compared with the results obtained from ORCAD-PSpice simulations.

  13. Multi-Level Simulated Fault Injection for Data Dependent Reliability Analysis of RTL Circuit Descriptions

    Directory of Open Access Journals (Sweden)

    NIMARA, S.

    2016-02-01

    Full Text Available This paper proposes data-dependent reliability evaluation methodology for digital systems described at Register Transfer Level (RTL. It uses a hybrid hierarchical approach, combining the accuracy provided by Gate Level (GL Simulated Fault Injection (SFI and the low simulation overhead required by RTL fault injection. The methodology comprises the following steps: the correct simulation of the RTL system, according to a set of input vectors, hierarchical decomposition of the system into basic RTL blocks, logic synthesis of basic RTL blocks, data-dependent SFI for the GL netlists, and RTL SFI. The proposed methodology has been validated in terms of accuracy on a medium sized circuit – the parallel comparator used in Check Node Unit (CNU of the Low-Density Parity-Check (LDPC decoders. The methodology has been applied for the reliability analysis of a 128-bit Advanced Encryption Standard (AES crypto-core, for which the GL simulation was prohibitive in terms of required computational resources.

  14. Driver circuit

    Science.gov (United States)

    Matsumoto, Raymond T. (Inventor); Higashi, Stanley T. (Inventor)

    1976-01-01

    A driver circuit which has low power requirements, a relatively small number of components and provides flexibility in output voltage setting. The driver circuit comprises, essentially, two portions which are selectively activated by the application of input signals. The output signal is determined by which of the two circuit portions is activated. While each of the two circuit portions operates in a manner similar to silicon controlled rectifiers (SCR), the circuit portions are on only when an input signal is supplied thereto.

  15. Contextual neural gas for spatial clustering and analysis

    NARCIS (Netherlands)

    Hagenauer, J.; Helbich, M.

    2013-01-01

    This study aims to introduce contextual Neural Gas (CNG), a variant of the Neural Gas algorithm, which explicitly accounts for spatial dependencies within spatial data. The main idea of the CNG is to map spatially close observations to neurons, which are close with respect to their rank distance.

  16. Electronic circuit for use in an analog circuit analysis program for the simulation of the temperature behaviour of a heating section; Elektrische Schaltung zur Simulation des Temperaturverhaltens einer Heizstrecke fuer den Einsatz in einem Simulationsprogramm

    Energy Technology Data Exchange (ETDEWEB)

    Rapp, W.

    1994-01-01

    A circuit simulating the temperature behavior of an object as a function of the heating or cooling energy supplied has been developed for use in an analog electronic circuit analysis system. It was attempted to design this circuit in a descriptive manner without applying the methods of conventional control engineering. As is evident from the comparison of the measured temperature behavior of a heated object and the initial function of the electronic simulation circuit, the curve properties are in good agreement. The circuit has been developed and applied within the framework of the ``Electronic Circuit Analysis Program`` MICRO-CapIV of the Spectrum company with the objective of investigating and optimizing various control systems. (orig.) [Deutsch] Fuer den Einsatz in einem analogen elektrischen Schaltungssimulationssystem wurde eine Schaltung entwickelt, welche das Temperaturverhalten eines Objektes als Funktion der zugefuehrten Heiz- oder Kuehlleistung simuliert. Es wurde versucht, diese Schaltung auf anschauliche Weise, ohne die Methoden der konventionellen Regelungstechnik, zu konstruieren. Der Vergleich zwischen dem gemessenen Temperaturverlauf eines beheizten Objektes mit dem Verlauf der Ausgangsfunktion der elektrischen Simulationsschaltung zeigt eine gute Uebereinstimmung der Kurveneigenschaften. Die Schaltung ist mit dem ``Electronic Circuit Analysis Program`` MICRO-CapIV der Firma Spectrum realisiert und eingesetzt worden. Das Ziel ist die Untersuchung und Optimierung verschiedener Regelungssysteme. (orig.)

  17. Analysis of electrical circuits with variable load regime parameters projective geometry method

    CERN Document Server

    Penin, A

    2015-01-01

    This book introduces electric circuits with variable loads and voltage regulators. It allows to define invariant relationships for various parameters of regime and circuit sections and to prove the concepts characterizing these circuits. Generalized equivalent circuits are introduced. Projective geometry is used for the interpretation of changes of operating regime parameters. Expressions of normalized regime parameters and their changes are presented. Convenient formulas for the calculation of currents are given. Parallel voltage sources and the cascade connection of multi-port networks are d

  18. Secretome analysis of human oligodendrocytes derived from neural stem cells.

    Directory of Open Access Journals (Sweden)

    Woo Kyung Kim

    Full Text Available In this study, we investigated the secretome of human oligodendrocytes (F3.Olig2 cells generated from human neural stem cells by transduction with the gene encoding the Olig2 transcription factor. Using mRNA sequencing and protein cytokine arrays, we identified a number of biologically important secretory proteins whose expression has not been previously reported in oligodendrocytes. We found that F3.Olig2 cells secrete IL-6, PDGF-AA, GRO, GM-CSF, and M-CSF, and showed prominent expression of their corresponding receptors. Co-expression of ligands and receptors suggests that autocrine signaling loops may play important roles in both differentiation and maintenance of oligodendrocytes. We also found that F3.Olig2 cells secrete matrix metalloproteinases and matrix metalloproteinase-associated proteins associated with functional competence of oligodendrocytes. The results of our secretome analysis provide insights into the functional and molecular details of human oligodendrocytes. To the best of our knowledge, this is the first systematic analysis of the secretome of oligodendrocytes.

  19. Analysis of Transformer-Isolated Diode Rectifier Circuits in Three-Phase Rectangular-Waveform Distribution System

    Science.gov (United States)

    Kito, Minoru; Watanabe, Jumpei; Takeshita, Takaharu; Nishida, Yasuyuki

    This paper presents an analysis of the steady and transient characteristics of capacitor-input-type diode rectifier circuits connected to a delta-star transformer in a three-phase rectangular-waveform distribution system. Equations are derived for the input current, output voltage, and total input power factor of the rectifier circuits. The total input power factor can be improved from 0.91 to 0.98 by using the delta-star transformer. Further, design methods are developed for both the input reactor and output capacitor of the rectifier circuits. A small input reactor is desirable for obtaining a large total input power factor and a constant output voltage. The output capacitor can be designed to obtain the required output voltage variation in steady and transient states. The effectiveness of the proposed analysis is experimentally verified by using a 2.4kW prototype.

  20. Spinal nociceptive circuit analysis with recombinant adeno-associated viruses: the impact of serotypes and promoters.

    Science.gov (United States)

    Haenraets, Karen; Foster, Edmund; Johannssen, Helge; Kandra, Vinnie; Frezel, Noémie; Steffen, Timothy; Jaramillo, Valeria; Paterna, Jean-Charles; Zeilhofer, Hanns Ulrich; Wildner, Hendrik

    2017-09-01

    Recombinant adeno-associated virus (rAAV) vector-mediated gene transfer into genetically defined neuron subtypes has become a powerful tool to study the neuroanatomy of neuronal circuits in the brain and to unravel their functions. More recently, this methodology has also become popular for the analysis of spinal cord circuits. To date, a variety of naturally occurring AAV serotypes and genetically modified capsid variants are available but transduction efficiency in spinal neurons, target selectivity, and the ability for retrograde tracing are only incompletely characterized. Here, we have compared the transduction efficiency of seven commonly used AAV serotypes after intraspinal injection. We specifically analyzed local transduction of different types of dorsal horn neurons, and retrograde transduction of dorsal root ganglia (DRG) neurons and of neurons in the rostral ventromedial medulla (RVM) and the somatosensory cortex (S1). Our results show that most of the tested rAAV vectors have similar transduction efficiency in spinal neurons. All serotypes analyzed were also able to transduce DRG neurons and descending RVM and S1 neurons via their spinal axon terminals. When comparing the commonly used rAAV serotypes to the recently developed serotype 2 capsid variant rAAV2retro, a > 20-fold increase in transduction efficiency of descending supraspinal neurons was observed. Conversely, transgene expression in retrogradely transduced neurons was strongly reduced when the human synapsin 1 (hSyn1) promoter was used instead of the strong ubiquitous hybrid cytomegalovirus enhancer/chicken β-actin promoter (CAG) or cytomegalovirus (CMV) promoter fragments. We conclude that the use of AAV2retro greatly increases transduction of neurons connected to the spinal cord via their axon terminals, while the hSyn1 promoter can be used to minimize transgene expression in retrogradely connected neurons of the DRG or brainstem. Cover Image for this issue: doi. 10.1111/jnc.13813.

  1. Neural Network Analysis and Evaluation of the Fetal Heart Rate

    Directory of Open Access Journals (Sweden)

    Yasuaki Noguchi

    2009-01-01

    Full Text Available The aim of the present study is to obtain a highly objective automatic fetal heart rate (FHR diagnosis. The neural network software was composed of three layers with the back propagation, to which 8 FHR data, including sinusoidal FHR, were input and the system was educated by the data of 20 cases with a known outcome. The output was the probability of a normal, intermediate, or pathologic outcome. The neural index studied prolonged monitoring. The neonatal states and the FHR score strongly correlated with the outcome probability. The neural index diagnosis was correct. The completed software was transferred to other computers, where the system function was correct.

  2. Qualitative analysis and control of complex neural networks with delays

    CERN Document Server

    Wang, Zhanshan; Zheng, Chengde

    2016-01-01

    This book focuses on the stability of the dynamical neural system, synchronization of the coupling neural system and their applications in automation control and electrical engineering. The redefined concept of stability, synchronization and consensus are adopted to provide a better explanation of the complex neural network. Researchers in the fields of dynamical systems, computer science, electrical engineering and mathematics will benefit from the discussions on complex systems. The book will also help readers to better understand the theory behind the control technique and its design.

  3. Circuit and Noise Analysis of Odorant Gas Sensors in an E-Nose

    OpenAIRE

    Tian, Fengchun; Yang, Simon X.; Dong, Kevin

    2005-01-01

    In this paper, the relationship between typical circuit structures of gas sensor circuits and their output noise is analyzed. By using averaged segmenting periodical graph and improved histogram estimation methods, we estimated their noise power spectra and optimal probability distribution functions (pdf). The results were confirmed through experiment studies.

  4. Experimental and theoretical analysis of vacuum circuit breaker prestrike effect on a transformer

    NARCIS (Netherlands)

    Popov, M.; Smeets, R.P.P.; Van der Sluis, L.; De Herdt, H.; Declerq, J.

    2009-01-01

    The work presented in this paper deals with the investigation of circuit breaker prestrike effect that occurs during energizing a distribution transformer. An experimental test setup that consists of a supply transformer, a vacuum circuit breaker (VCB), a cable and a test transformer is built, and

  5. Methods and Tools for the Analysis, Verification and Synthesis of Genetic Logic Circuits,

    DEFF Research Database (Denmark)

    Baig, Hasan

    2017-01-01

    Synthetic biology has emerged as an important discipline in which engineers and biologists are working together to design new and useful biological systems composed of genetic circuits. The purpose of developing genetic circuits is to carry out desired logical functions inside a living cell. This...

  6. Neural activity associated with metaphor comprehension: spatial analysis.

    Science.gov (United States)

    Sotillo, María; Carretié, Luis; Hinojosa, José A; Tapia, Manuel; Mercado, Francisco; López-Martín, Sara; Albert, Jacobo

    2005-01-03

    Though neuropsychological data indicate that the right hemisphere (RH) plays a major role in metaphor processing, other studies suggest that, at least during some phases of this processing, a RH advantage may not exist. The present study explores, through a temporally agile neural signal--the event-related potentials (ERPs)--, and through source-localization algorithms applied to ERP recordings, whether the crucial phase of metaphor comprehension presents or not a RH advantage. Participants (n=24) were submitted to a S1-S2 experimental paradigm. S1 consisted of visually presented metaphoric sentences (e.g., "Green lung of the city"), followed by S2, which consisted of words that could (i.e., "Park") or could not (i.e., "Semaphore") be defined by S1. ERPs elicited by S2 were analyzed using temporal principal component analysis (tPCA) and source-localization algorithms. These analyses revealed that metaphorically related S2 words showed significantly higher N400 amplitudes than non-related S2 words. Source-localization algorithms showed differential activity between the two S2 conditions in the right middle/superior temporal areas. These results support the existence of an important RH contribution to (at least) one phase of metaphor processing and, furthermore, implicate the temporal cortex with respect to that contribution.

  7. Full Wave Analysis of Passive Microwave Monolithic Integrated Circuit Devices Using a Generalized Finite Difference Time Domain (GFDTD) Algorithm

    Science.gov (United States)

    Lansing, Faiza S.; Rascoe, Daniel L.

    1993-01-01

    This paper presents a modified Finite-Difference Time-Domain (FDTD) technique using a generalized conformed orthogonal grid. The use of the Conformed Orthogonal Grid, Finite Difference Time Domain (GFDTD) enables the designer to match all the circuit dimensions, hence eliminating a major source o error in the analysis.

  8. Neural signal registration and analysis of axons grown in microchannels

    Science.gov (United States)

    Pigareva, Y.; Malishev, E.; Gladkov, A.; Kolpakov, V.; Bukatin, A.; Mukhina, I.; Kazantsev, V.; Pimashkin, A.

    2016-08-01

    Registration of neuronal bioelectrical signals remains one of the main physical tools to study fundamental mechanisms of signal processing in the brain. Neurons generate spiking patterns which propagate through complex map of neural network connectivity. Extracellular recording of isolated axons grown in microchannels provides amplification of the signal for detailed study of spike propagation. In this study we used neuronal hippocampal cultures grown in microfluidic devices combined with microelectrode arrays to investigate a changes of electrical activity during neural network development. We found that after 5 days in vitro after culture plating the spiking activity appears first in microchannels and on the next 2-3 days appears on the electrodes of overall neural network. We conclude that such approach provides a convenient method to study neural signal processing and functional structure development on a single cell and network level of the neuronal culture.

  9. Analysis of fractal electrodes for efficient neural stimulation.

    Science.gov (United States)

    Golestanirad, Laleh; Elahi, Behzad; Molina, Alberto; Mosig, Juan R; Pollo, Claudio; Chen, Robert; Graham, Simon J

    2013-01-01

    Planar electrodes are increasingly used in therapeutic neural stimulation techniques such as functional electrical stimulation, epidural spinal cord stimulation (ESCS), and cortical stimulation. Recently, optimized electrode geometries have been shown to increase the efficiency of neural stimulation by increasing the variation of current density on the electrode surface. In the present work, a new family of modified fractal electrode geometries is developed to enhance the efficiency of neural stimulation. It is shown that a promising approach in increasing the neural activation function is to increase the "edginess" of the electrode surface, a concept that is explained and quantified by fractal mathematics. Rigorous finite element simulations were performed to compute electric potential produced by proposed modified fractal geometries. The activation of 256 model axons positioned around the electrodes was then quantified, showing that modified fractal geometries required a 22% less input power while maintaining the same level of neural activation. Preliminary in vivo experiments investigating muscle evoked potentials due to median nerve stimulation showed encouraging results, supporting the feasibility of increasing neural stimulation efficiency using modified fractal geometries.

  10. Multistationarity, the basis of cell differentiation and memory. II. Logical analysis of regulatory networks in terms of feedback circuits

    Science.gov (United States)

    Thomas, R.; Kaufman, M.

    2001-03-01

    Circuits and their involvement in complex dynamics are described in differential terms in Part I of this work. Here, we first explain why it may be appropriate to use a logical description, either by itself or in symbiosis with the differential description. The major problem of a logical description is to find an adequate way to involve time. The procedure we adopted differs radically from the classical one by its fully asynchronous character. In Sec. II we describe our "naive" logical approach, and use it to illustrate the major laws of circuitry (namely, the involvement of positive circuits in multistationarity and of negative circuits in periodicity) and in a biological example. Already in the naive description, the major steps of the logical description are to: (i) describe a model as a set of logical equations, (ii) derive the state table from the equations, (iii) derive the graph of the sequences of states from the state table, and (iv) determine which of the possible pathways will be actually followed in terms of time delays. In the following sections we consider multivalued variables where required, the introduction of logical parameters and of logical values ascribed to the thresholds, and the concept of characteristic state of a circuit. This generalized logical description provides an image whose qualitative fit with the differential description is quite remarkable. A major interest of the generalized logical description is that it implies a limited and often quite small number of possible combinations of values of the logical parameters. The space of the logical parameters is thus cut into a limited number of boxes, each of which is characterized by a defined qualitative behavior of the system. Our analysis tells which constraints on the logical parameters must be fulfilled in order for any circuit (or combination of circuits) to be functional. Functionality of a circuit will result in multistationarity (in the case of a positive circuit) or in a cycle

  11. The development of a tool for assessing the quality of closed circuit camera footage for use in forensic gait analysis.

    Science.gov (United States)

    Birch, Ivan; Vernon, Wesley; Walker, Jeremy; Saxelby, Jai

    2013-10-01

    Gait analysis from closed circuit camera footage is now commonly used as evidence in criminal trials. The biomechanical analysis of human gait is a well established science in both clinical and laboratory settings. However, closed circuit camera footage is rarely of the quality of that taken in the more controlled clinical and laboratory environments. The less than ideal quality of much of this footage for use in gait analysis is associated with a range of issues, the combination of which can often render the footage unsuitable for use in gait analysis. The aim of this piece of work was to develop a tool for assessing the suitability of closed circuit camera footage for the purpose of forensic gait analysis. A Delphi technique was employed with a small sample of expert forensic gait analysis practitioners, to identify key quality elements of CCTV footage used in legal proceedings. Five elements of the footage were identified and then subdivided into 15 contributing sub-elements, each of which was scored using a 5-point Likert scale. A Microsoft Excel worksheet was developed to calculate automatically an overall score from the fifteen sub-element scores. Five expert witnesses experienced in using CCTV footage for gait analysis then trialled the prototype tool on current case footage. A repeatability study was also undertaken using standardized CCTV footage. The results showed the tool to be a simple and repeatable means of assessing the suitability of closed circuit camera footage for use in forensic gait analysis. The inappropriate use of poor quality footage could lead to challenges to the practice of forensic gait analysis. All parties involved in criminal proceedings must therefore understand the fitness for purpose of any footage used. The development of this tool could offer a method of achieving this goal, and help to assure the continued role of forensic gait analysis as an aid to the identification process. Copyright © 2013 Elsevier Ltd and Faculty of

  12. Brain-machine interface circuits and systems

    CERN Document Server

    Zjajo, Amir

    2016-01-01

    This book provides a complete overview of significant design challenges in respect to circuit miniaturization and power reduction of the neural recording system, along with circuit topologies, architecture trends, and (post-silicon) circuit optimization algorithms. The introduced novel circuits for signal conditioning, quantization, and classification, as well as system configurations focus on optimized power-per-area performance, from the spatial resolution (i.e. number of channels), feasible wireless data bandwidth and information quality to the delivered power of implantable system.

  13. Feature Acquisition and Analysis for Facial Expression Recognition Using Convolutional Neural Networks

    National Research Council Canada - National Science Library

    Taiki Nishime; Satoshi Endo; Naruaki Toma; Koji Yamada; Yuhei Akamine

    2017-01-01

    .... Therefore, it is difficult to evaluate the reliability of the result from recognition accuracy alone, and the analysis for explaining the result and feature learned by Convolutional Neural Networks (CNN...

  14. Analysis of surface ozone using a recurrent neural network.

    Science.gov (United States)

    Biancofiore, Fabio; Verdecchia, Marco; Di Carlo, Piero; Tomassetti, Barbara; Aruffo, Eleonora; Busilacchio, Marcella; Bianco, Sebastiano; Di Tommaso, Sinibaldo; Colangeli, Carlo

    2015-05-01

    Hourly concentrations of ozone (O₃) and nitrogen dioxide (NO₂) have been measured for 16 years, from 1998 to 2013, in a seaside town in central Italy. The seasonal trends of O₃ and NO₂ recorded in this period have been studied. Furthermore, we used the data collected during one year (2005), to define the characteristics of a multiple linear regression model and a neural network model. Both models are used to model the hourly O₃ concentration, using, two scenarios: 1) in the first as inputs, only meteorological parameters and 2) in the second adding photochemical parameters at those of the first scenario. In order to evaluate the performance of the model four statistical criteria are used: correlation coefficient, fractional bias, normalized mean squared error and a factor of two. All the criteria show that the neural network gives better results, compared to the regression model, in all the model scenarios. Predictions of O₃ have been carried out by many authors using a feed forward neural architecture. In this paper we show that a recurrent architecture significantly improves the performances of neural predictors. Using only the meteorological parameters as input, the recurrent architecture shows performance better than the multiple linear regression model that uses meteorological and photochemical data as input, making the neural network model with recurrent architecture a more useful tool in areas where only weather measurements are available. Finally, we used the neural network model to forecast the O₃ hourly concentrations 1, 3, 6, 12, 24 and 48 h ahead. The performances of the model in predicting O₃ levels are discussed. Emphasis is given to the possibility of using the neural network model in operational ways in areas where only meteorological data are available, in order to predict O₃ also in sites where it has not been measured yet. Copyright © 2015 Elsevier B.V. All rights reserved.

  15. Healthcare failure mode effect analysis of a miniaturized extracorporeal bypass circuit.

    Science.gov (United States)

    Overdevest, Ep; van Hees, Jwh; Lagerburg, V; Kloeze, C; van Straten, Ahm

    2014-07-01

    The introduction of new and more advanced technology in healthcare occurs with an increasing speed. Therefore, more attention is needed for safety evaluation of new devices or techniques from an end-user perspective, especially when (inter-) national perfusion safety standards are lacking. A recently increased awareness of the safety risks as a consequence of technical or human error has provoked interest in optimisation of perfusion methodology and devices. To prevent or reduce the severity or likelihood of failures of new technology, 'failure mode effect analysis' is a proven proactive technique. When it is used as a qualitative analysis for possible hazards in patient treatment associated with the use of medical devices, it's called healthcare failure mode effect analysis (hFMEA). To evaluate the safety of the Extra Corporeal Circulation Optimized (ECCO, Sorin Group, Mirandola, Italy) miniaturized bypass circuit, hFMEA was used. A multi disciplinary team that consisted of two clinical perfusionists, a clinical physicist, a clinical physicist trainee and a technician has performed this analysis. The hFMEA demonstrated that failure of the bubble sensor for the electric remote clamping system on the arterial line (Figure 1), activated by air passing the venous bubble trap, had the highest risk score of all failure modes. This has led to the implementation of an extra low-level sensor in the system to prevent air passing through into the centrifugal pump. The hFMEA has also indicated that extra individual simulation training is needed for handling critical failures during the use of the miniature bypass system. Early identification of possible technology failures in any process or device can avoid adverse patient outcomes. The technique of hFMEA is a valuable tool in evaluating the use of high-risk apparatus, such as an extracorporeal bypass system, in patient treatment in order to increase patient safety. © The Author(s) 2014.

  16. Integrating Independent Component Analysis and Principal Component Analysis with Neural Network to Predict Chinese Stock Market

    Directory of Open Access Journals (Sweden)

    Haifan Liu

    2011-01-01

    Full Text Available We investigate the statistical behaviors of Chinese stock market fluctuations by independent component analysis. The independent component analysis (ICA method is integrated into the neural network model. The proposed approach uses ICA method to analyze the input data of neural network and can obtain the latent independent components (ICs. After analyzing and removing the IC that represents noise, the rest of ICs are used as the input of neural network. In order to forect the fluctuations of Chinese stock market, the data of Shanghai Composite Index is selected and analyzed, and we compare the forecasting performance of the proposed model with those of common BP model integrating principal component analysis (PCA and single BP model. Experimental results show that the proposed model outperforms the other two models no matter in relatively small or relatively large sample, and the performance of BP model integrating PCA is closer to that of the proposed model in relatively large sample. Further, the prediction results on the points where the prices fluctuate violently by the above three models relatively deviate from the corresponding real market data.

  17. Circuits and filters handbook

    CERN Document Server

    Chen, Wai-Kai

    2003-01-01

    A bestseller in its first edition, The Circuits and Filters Handbook has been thoroughly updated to provide the most current, most comprehensive information available in both the classical and emerging fields of circuits and filters, both analog and digital. This edition contains 29 new chapters, with significant additions in the areas of computer-aided design, circuit simulation, VLSI circuits, design automation, and active and digital filters. It will undoubtedly take its place as the engineer's first choice in looking for solutions to problems encountered in the design, analysis, and behavi

  18. Analysis of signal processing in vestibular circuits with a novel light-emitting diodes-based fluorescence microscope.

    Science.gov (United States)

    Direnberger, Stephan; Banchi, Roberto; Brosel, Sonja; Seebacher, Christian; Laimgruber, Stefan; Uhl, Rainer; Felmy, Felix; Straka, Hans; Kunz, Lars

    2015-05-01

    Optical visualization of neural network activity is limited by imaging system-dependent technical tradeoffs. To overcome these constraints, we have developed a powerful low-cost and flexible imaging system with high spectral variability and unique spatio-temporal precision for simultaneous optical recording and manipulation of neural activity of large cell groups. The system comprises eight high-power light-emitting diodes, a camera with a large metal-oxide-semiconductor sensor and a high numerical aperture water-dipping objective. It allows fast and precise control of excitation and simultaneous low noise imaging at high resolution. Adjustable apertures generated two independent areas of variable size and position for simultaneous optical activation and image capture. The experimental applicability of this system was explored in semi-isolated preparations of larval axolotl (Ambystoma mexicanum) with intact inner ear organs and central nervous circuits. Cyclic galvanic stimulation of semicircular canals together with glutamate- and γ-aminobutyric acid (GABA)-uncaging caused a corresponding modulation of Ca(2+) transients in central vestibular neurons. These experiments revealed specific cellular properties as well as synaptic interactions between excitatory and inhibitory inputs, responsible for spatio-temporal-specific sensory signal processing. Location-specific GABA-uncaging revealed a potent inhibitory shunt of vestibular nerve afferent input in the predominating population of tonic vestibular neurons, indicating a considerable impact of local and commissural inhibitory circuits on the processing of head/body motion-related signals. The discovery of these previously unknown properties of vestibular computations demonstrates the merits of our novel microscope system for experimental applications in the field of neurobiology. © 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  19. PULLING THE TRIGGER: AN ANALYSIS OF CIRCUIT COURT REVIEW OF THE "PERSECUTOR BAR"

    National Research Council Canada - National Science Library

    Martine Forneret

    2013-01-01

    .... Use of the persecutor bar by the Board of Immigration Appeals and circuit courts leads to situations of legal complexity because neither the judiciary nor the legislature has concretely defined "persecution...

  20. Characteristic Analysis of Fractional-Order 4D Hyperchaotic Memristive Circuit

    Directory of Open Access Journals (Sweden)

    Jun Mou

    2017-01-01

    Full Text Available Dynamical behaviors of the 4D hyperchaotic memristive circuit are analyzed with the system parameter. Based on the definitions of fractional-order differential and Adomian decomposition algorithm, the numerical solution of fractional-order 4D hyperchaotic memristive circuit is investigated. The distribution of stable and unstable regions of the fractional-order 4D hyperchaotic memristive circuit is determined, and dynamical characteristics are studied by phase portraits, Lyapunov exponents spectrum, and bifurcation diagram. Complexities are calculated by employing the spectral entropy (SE algorithm and C0 algorithm. Complexity results are consistent with that of the bifurcation diagrams, and this means that complexity can also reflect the dynamic characteristics of a chaotic system. Results of this paper provide a theoretical and experimental basis for the application of fractional-order 4D hyperchaotic memristive circuit in the field of encryption and secure communication.

  1. Parameter estimation and determinability analysis applied to Drosophila gap gene circuits

    Directory of Open Access Journals (Sweden)

    Jaeger Johannes

    2008-09-01

    Full Text Available Abstract Background Mathematical modeling of real-life processes often requires the estimation of unknown parameters. Once the parameters are found by means of optimization, it is important to assess the quality of the parameter estimates, especially if parameter values are used to draw biological conclusions from the model. Results In this paper we describe how the quality of parameter estimates can be analyzed. We apply our methodology to assess parameter determinability for gene circuit models of the gap gene network in early Drosophila embryos. Conclusion Our analysis shows that none of the parameters of the considered model can be determined individually with reasonable accuracy due to correlations between parameters. Therefore, the model cannot be used as a tool to infer quantitative regulatory weights. On the other hand, our results show that it is still possible to draw reliable qualitative conclusions on the regulatory topology of the gene network. Moreover, it improves previous analyses of the same model by allowing us to identify those interactions for which qualitative conclusions are reliable, and those for which they are ambiguous.

  2. Magnetic Circuit Design and Multiphysics Analysis of a Novel MR Damper for Applications under High Velocity

    Directory of Open Access Journals (Sweden)

    Jiajia Zheng

    2014-02-01

    Full Text Available A novel magnetorheological (MR damper with a multistage piston and independent input currents is designed and analyzed. The equivalent magnetic circuit model is investigated along with the relation between magnetic induction density in the working gap and input currents of the electromagnetic coils. Finite element method (FEM is used to analyze the distribution of magnetic field through the MR fluid region. Considering the real situation, coupling equations are presented to analyze the electromagnetic-thermal-flow coupling problems. Software COMSOL is used to analyze the multiphysics, that is, electromagnetic, thermal dynamic, and fluid mechanic. A measurement index involving total damping force, dynamic range, and induction time needed for magnetic coil is put forward to evaluate the performance of the novel multistage MR damper. The simulation results show that it is promising for applications under high velocity and works better when more electromagnetic coils are applied with input currents separately. Besides, in order to reduce energy consumption, it is recommended to apply more electromagnetic coils with relative low currents based on the analysis of pressure drop along the annular gap.

  3. Dynamic analysis and electronic circuit implementation of a novel 3D autonomous system without linear terms

    Science.gov (United States)

    Kengne, J.; Jafari, S.; Njitacke, Z. T.; Yousefi Azar Khanian, M.; Cheukem, A.

    2017-11-01

    Mathematical models (ODEs) describing the dynamics of almost all continuous time chaotic nonlinear systems (e.g. Lorenz, Rossler, Chua, or Chen system) involve at least a nonlinear term in addition to linear terms. In this contribution, a novel (and singular) 3D autonomous chaotic system without linear terms is introduced. This system has an especial feature of having two twin strange attractors: one ordinary and one symmetric strange attractor when the time is reversed. The complex behavior of the model is investigated in terms of equilibria and stability, bifurcation diagrams, Lyapunov exponent plots, time series and Poincaré sections. Some interesting phenomena are found including for instance, period-doubling bifurcation, antimonotonicity (i.e. the concurrent creation and annihilation of periodic orbits) and chaos while monitoring the system parameters. Compared to the (unique) case previously reported by Xu and Wang (2014) [31], the system considered in this work displays a more 'elegant' mathematical expression and experiences richer dynamical behaviors. A suitable electronic circuit (i.e. the analog simulator) is designed and used for the investigations. Pspice based simulation results show a very good agreement with the theoretical analysis.

  4. New tetrapolar method for complex bioimpedance measurement: theoretical analysis and circuit realization.

    Science.gov (United States)

    Yang, Yuxiang; Wang, Jue

    2005-01-01

    This paper describes the theory and practical implementation of a new tetrapolar (four-electrode) method for measuring complex bioimpedance of local tissue. The new tetrapolar method adopts three independent voltages Vin+,Vin-,VR respectively from the two voltage electrodes and the sample resistor R, which is connected in serial with one of the two current electrodes. Theoretical analysis shows that the real part Zreal and imaginary part Zimag of the complex impedance between two voltage electrodes can be acquired from the modular ratios (Vin/Vr), (Vin-/Vr) and phase differences (thetaIN+-thetaR), (thetaIN-thetaR) through simple calculation. Realization of this method, based on the newest generation of highly integrated electronic components for directly generating relevant amplitude ratios and phase differences, is simple. Compared with the conventional tetrapolar method, the new tetrapolar method shows its advantage of simplicity in circuit realization, enables complete microprocessor control of localized bioimpedance measurements, and may liberate the traditional bioimpedance measuring system from the complexity of electronics.

  5. THE ANALYSIS OF STRUCTURAL RELIABILITY OF THE MAIN ELECTRIC CONNECTION CIRCUITS OF NUCLEAR POWER PLANTS

    Directory of Open Access Journals (Sweden)

    M. A. Korotkevich

    2017-01-01

    Full Text Available The reliability of the main circuit of electrical connections at a nuclear electric power plant that has two units with a capacity of 1,200 MW each has been determined. Reliability, economical, maneuverable properties of the atomic power plant under study are largely determined by its main circuit, so the choice of the circuit for the design and its status in the process of operation occur to be critical objectives. Main electrical connection circuits in nuclear electric power plants are selected on the basis of the schematic networks of the energy system and the land attached to the plant. The circuit of the connection of a nuclear power plant to the grid in the original normal operating modes at all stages of the construction of such a plant should provide the outcome of the full added capacity of a nuclear power plant and the preservation of its stability in the power system without the influence of the emergency system automatics when any outgoing transmission line is disabled. When selecting the main circuit the individual capacity of the installed units and their number are taken into account as well as the order of development of the plant and power supply system; the voltage on which the power of a plant is delivered; a shortcircuit current for switchgear high voltage and the need for their limitation by circuit means; the most power that can be lost when damage to any switch. A model of reliability of the main circuit of electrical connections is designed to detect all types of accidents that are possible at the coincidence of failures of elements with the repair and operational modes that differs in composition and damageability of the equipment, as well as under conditions of the development of accidents due to failure of operation of devices of relay protection and automation.

  6. Full analysis of small hypercycles with short-circuits in prebiotic evolution

    Science.gov (United States)

    Sardanyés, Josep; Tomás Lázaro, J.; Guillamon, Antoni; Fontich, Ernest

    2017-05-01

    It is known that hypercycles are sensitive to the so-called parasites and short-circuits. While the impact of parasites has been widely investigated for well-mixed and spatial hypercycles, the effect of short-circuits in hypercycles remains poorly understood. In this article we analyze the mean field and spatial dynamics of two small, asymmetric hypercycles with short-circuits. Specifically, we analyze a two-member hypercycle where one of the species contains an auto-catalytic loop, as the simplest hypercycle with a short-circuit. Then, we extend this system by adding another species that closes a three-member hypercycle while keeping the auto-catalytic short-circuit and the two-member cycle. The mean field model allows us to discard the presence of stable or unstable periodic orbits for both systems. We characterize the bifurcations and transitions involved in the dominance of the short-circuits i.e., in the reduction of the hypercycles' size. The spatial simulations reveal a random-like and mixed distribution of the replicators in the all-species coexistence, ruling out the presence of large-scale spatial patterns such as spirals or spots typical of larger, oscillating hypercycles. A Monte Carlo sampling of the parameter space for the well-mixed and the spatial models reveals that the probability of finding stable hypercycles with short-circuits drastically diminishes from the two-member to the three-member system, especially at growing degradation rates of the replicators. These findings pose a big constraint in the increase of hypercycle's size and complexity under the presence of inner cycles, suggesting the importance of a rapid growth of hypercycles able to generate spatial structures (e.g., rotating spirals) prior to the emergence of inner cycles. Our results can also be useful for the future design and implementation of synthetic cooperative systems containing catalytic short-circuits.

  7. Dynamic Analysis and Circuit Design of a Novel Hyperchaotic System with Fractional-Order Terms

    Directory of Open Access Journals (Sweden)

    Abir Lassoued

    2017-01-01

    Full Text Available A novel hyperchaotic system with fractional-order (FO terms is designed. Its highly complex dynamics are investigated in terms of equilibrium points, Lyapunov spectrum, and attractor forms. It will be shown that the proposed system exhibits larger Lyapunov exponents than related hyperchaotic systems. Finally, to enhance its potential application, a related circuit is designed by using the MultiSIM Software. Simulation results verify the effectiveness of the suggested circuit.

  8. Analysis Resilient Algorithm on Artificial Neural Network Backpropagation

    Science.gov (United States)

    Saputra, Widodo; Tulus; Zarlis, Muhammad; Widia Sembiring, Rahmat; Hartama, Dedy

    2017-12-01

    Prediction required by decision makers to anticipate future planning. Artificial Neural Network (ANN) Backpropagation is one of method. This method however still has weakness, for long training time. This is a reason to improve a method to accelerate the training. One of Artificial Neural Network (ANN) Backpropagation method is a resilient method. Resilient method of changing weights and bias network with direct adaptation process of weighting based on local gradient information from every learning iteration. Predicting data result of Istanbul Stock Exchange training getting better. Mean Square Error (MSE) value is getting smaller and increasing accuracy.

  9. A functional clustering algorithm for the analysis of neural relationships

    CERN Document Server

    Feldt, S; Hetrick, V L; Berke, J D; Zochowski, M

    2008-01-01

    We formulate a novel technique for the detection of functional clusters in neural data. In contrast to prior network clustering algorithms, our procedure progressively combines spike trains and derives the optimal clustering cutoff in a simple and intuitive manner. To demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both simulated data and real neural data obtained from the mouse hippocampus during exploration and slow-wave sleep. We observe state-dependent clustering patterns consistent with known neurophysiological processes involved in memory consolidation.

  10. Analysis of some meteorological parameters using artificial neural ...

    African Journals Online (AJOL)

    The mean daily data for sunshine hours, maximum temperature, cloud cover and relative humidity data, were used to estimate monthly average global solar irradiation on a horizontal surface for Makurdi, Nigeria. The study used artificial neural networks (ANN) for the estimation. Results showed good agreement between ...

  11. Use of artificial neural network for spatial rainfall analysis

    Indian Academy of Sciences (India)

    In the present study, the precipitation data measured at 23 rain gauge stations over the Achaia County, Greece, were used to estimate the spatial distribution of the mean annual precipitation values over a specific catchment area. The objective of this work was achieved by programming an Artificial Neural Network (ANN) ...

  12. Coherency and connectivity in oscillating neural networks: linear partialization analysis

    NARCIS (Netherlands)

    Kalitzin, S.; van Dijk, B. W.; Spekreijse, H.; van Leeuwen, W. A.

    1997-01-01

    This paper studies the relation between the functional synaptic connections between two artificial neural networks and the correlation of their spiking activities. The model neurons had realistic non-oscillatory dynamic properties and the networks showed oscillatory behavior as a result of their

  13. Analysis of Neural-BOLD Coupling through Four Models of the Neural Metabolic Demand

    Directory of Open Access Journals (Sweden)

    Christopher W Tyler

    2015-12-01

    Full Text Available The coupling of the neuronal energetics to the blood-oxygen-level-dependent (BOLD response is still incompletely understood. To address this issue, we compared the fits of four plausible models of neurometabolic coupling dynamics to available data for simultaneous recordings of the local field potential (LFP and the local BOLD response recorded from monkey primary visual cortex over a wide range of stimulus durations. The four models of the metabolic demand driving the BOLD response were: direct coupling with the overall LFP; rectified coupling to the LFP; coupling with a slow adaptive component of the implied neural population response; and coupling with the non-adaptive intracellular input signal defined by the stimulus time course. Taking all stimulus durations into account, the results imply that the BOLD response is most closely coupled with metabolic demand derived from the intracellular input waveform, without significant influence from the adaptive transients and nonlinearities exhibited by the LFP waveform.

  14. Quantitative Analysis of Human Pluripotency and Neural Specification by In-Depth (PhosphoProteomic Profiling

    Directory of Open Access Journals (Sweden)

    Ilyas Singec

    2016-09-01

    Full Text Available Controlled differentiation of human embryonic stem cells (hESCs can be utilized for precise analysis of cell type identities during early development. We established a highly efficient neural induction strategy and an improved analytical platform, and determined proteomic and phosphoproteomic profiles of hESCs and their specified multipotent neural stem cell derivatives (hNSCs. This quantitative dataset (nearly 13,000 proteins and 60,000 phosphorylation sites provides unique molecular insights into pluripotency and neural lineage entry. Systems-level comparative analysis of proteins (e.g., transcription factors, epigenetic regulators, kinase families, phosphorylation sites, and numerous biological pathways allowed the identification of distinct signatures in pluripotent and multipotent cells. Furthermore, as predicted by the dataset, we functionally validated an autocrine/paracrine mechanism by demonstrating that the secreted protein midkine is a regulator of neural specification. This resource is freely available to the scientific community, including a searchable website, PluriProt.

  15. Dynamics, Analysis and Implementation of a Multiscroll Memristor-Based Chaotic Circuit

    Science.gov (United States)

    Alombah, N. Henry; Fotsin, Hilaire; Ngouonkadi, E. B. Megam; Nguazon, Tekou

    This article introduces a novel four-dimensional autonomous multiscroll chaotic circuit which is derived from the actual simplest memristor-based chaotic circuit. A fourth circuit element — another inductor — is introduced to generate the complex behavior observed. A systematic study of the chaotic behavior is performed with the help of some nonlinear tools such as Lyapunov exponents, phase portraits, and bifurcation diagrams. Multiple scroll attractors are observed in Matlab, Pspice environments and also experimentally. We also observe the phenomenon of antimonotonicity, periodic and chaotic bubbles, multiple periodic-doubling bifurcations, Hopf bifurcations, crises and the phenomenon of intermittency. The chaotic dynamics of this circuit is realized by laboratory experiments, Pspice simulations, numerical and analytical investigations. It is observed that the results from the three environments agree to a great extent. This topology is likely convenient to be used to intentionally generate chaos in memristor-based chaotic circuit applications, given the fact that multiscroll chaotic systems have found important applications as broadband signal generators, pseudorandom number generators for communication engineering and also in biometric authentication.

  16. A Detailed Circuit Analysis of the Lawrence Livermore National Laboratory Building 141 Detonator Test Facility

    Energy Technology Data Exchange (ETDEWEB)

    Mayhall, D J; Wilson, M J; Wilson, J H

    2003-10-01

    A detailed electrical equivalent circuit of an as-built utility fault simulator is presented. Standard construction techniques for light industrial facilities were used to build a test-bed for evaluating utility power level faults into unintentional victims. The initial components or victims of interest are commercial detonators. Other possible candidates for fault response analyses include motors, power supplies, control systems, computers, or other electronic equipment. Measured Thevenin parameters of all interconnections provide the selected component values used in the model. Included in the model is an opening 10 HP motor circuit demonstrating voltage transients commonly seen on branch circuits from inductive loads common to industrial installations. Complex transmission lines were developed to represent real world transmission line effects possible from the associated branch circuits. To reduce the initial circuit stabilization delay a set of non-linear resistive elements are employed. The resulting model has assisted in confirming previous detonator safety work and supported the definition of critical parameters needed for continued safety assessment of victims to utility type power sources.

  17. Linear analysis near a steady-state of biochemical networks: control analysis, correlation metrics and circuit theory

    Directory of Open Access Journals (Sweden)

    Qian Hong

    2008-05-01

    Full Text Available Abstract Background: Several approaches, including metabolic control analysis (MCA, flux balance analysis (FBA, correlation metric construction (CMC, and biochemical circuit theory (BCT, have been developed for the quantitative analysis of complex biochemical networks. Here, we present a comprehensive theory of linear analysis for nonequilibrium steady-state (NESS biochemical reaction networks that unites these disparate approaches in a common mathematical framework and thermodynamic basis. Results: In this theory a number of relationships between key matrices are introduced: the matrix A obtained in the standard, linear-dynamic-stability analysis of the steady-state can be decomposed as A = SRT where R and S are directly related to the elasticity-coefficient matrix for the fluxes and chemical potentials in MCA, respectively; the control-coefficients for the fluxes and chemical potentials can be written in terms of RT BS and ST BS respectively where matrix B is the inverse of A; the matrix S is precisely the stoichiometric matrix in FBA; and the matrix eAt plays a central role in CMC. Conclusion: One key finding that emerges from this analysis is that the well-known summation theorems in MCA take different forms depending on whether metabolic steady-state is maintained by flux injection or concentration clamping. We demonstrate that if rate-limiting steps exist in a biochemical pathway, they are the steps with smallest biochemical conductances and largest flux control-coefficients. We hypothesize that biochemical networks for cellular signaling have a different strategy for minimizing energy waste and being efficient than do biochemical networks for biosynthesis. We also discuss the intimate relationship between MCA and biochemical systems analysis (BSA.

  18. Digital-circuit analysis of short-gate tunnel FETs for low-voltage applications

    Science.gov (United States)

    Zhuge, Jing; Verhulst, Anne S.; Vandenberghe, William G.; Dehaene, Wim; Huang, Ru; Wang, Yangyuan; Groeseneken, Guido

    2011-08-01

    This paper investigates the potential of tunnel field-effect transistors (TFETs), with emphasis on short-gate TFETs, by simulation for low-power digital applications having a supply voltage lower than 0.5 V. A transient study shows that the tunneling current has a negligible contribution in charging and discharging the gate capacitance of TFETs. In spite of a higher resistance region in the short-gate TFET, the gate (dis)charging speed still meets low-voltage application requirements. A circuit analysis is performed on short-gate TFETs with different materials, such as Si, Ge and heterostructures in terms of voltage overshoot, delay, static power, energy consumption and energy delay product (EDP). These results are compared to MOSFET and full-gate TFET performance. It is concluded that short-gate heterostructure TFETs (Ge-source for nTFET, In0.6Ga0.4As-source for pTFET) are promising candidates to extend the supply voltage to lower than 0.5 V because they combine the advantage of a low Miller capacitance, due to the short-gate structures, and strong drive current in TFETs, due to the narrow bandgap material in the source. At a supply voltage of 0.4 V and for an EOT and channel length of 0.6 nm and 40 nm, respectively, a three-stage inverter chain based on short-gate heterostructure TFETs saves 40% energy consumption per cycle at the same delay and shows 60%-75% improvement of EDP at the same static power, compared to its full-gate counterpart. When compared to the MOSFET, better EDP can be achieved in the heterostructure TFET especially at low static power consumption.

  19. A Voltage Mode Memristor Bridge Synaptic Circuit with Memristor Emulators

    Directory of Open Access Journals (Sweden)

    Leon Chua

    2012-03-01

    Full Text Available A memristor bridge neural circuit which is able to perform signed synaptic weighting was proposed in our previous study, where the synaptic operation was verified via software simulation of the mathematical model of the HP memristor. This study is an extension of the previous work advancing toward the circuit implementation where the architecture of the memristor bridge synapse is built with memristor emulator circuits. In addition, a simple neural network which performs both synaptic weighting and summation is built by combining memristor emulators-based synapses and differential amplifier circuits. The feasibility of the memristor bridge neural circuit is verified via SPICE simulations.

  20. Recognition of Protozoa and Metazoa using image analysis tools, discriminant analysis, neural networks and decision trees.

    Science.gov (United States)

    Ginoris, Y P; Amaral, A L; Nicolau, A; Coelho, M A Z; Ferreira, E C

    2007-07-09

    Protozoa and metazoa are considered good indicators of the treatment quality in activated sludge systems due to the fact that these organisms are fairly sensitive to physical, chemical and operational processes. Therefore, it is possible to establish close relationships between the predominance of certain species or groups of species and several operational parameters of the plant, such as the biotic indices, namely the Sludge Biotic Index (SBI). This procedure requires the identification, classification and enumeration of the different species, which is usually achieved manually implying both time and expertise availability. Digital image analysis combined with multivariate statistical techniques has proved to be a useful tool to classify and quantify organisms in an automatic and not subjective way. This work presents a semi-automatic image analysis procedure for protozoa and metazoa recognition developed in Matlab language. The obtained morphological descriptors were analyzed using discriminant analysis, neural network and decision trees multivariable statistical techniques to identify and classify each protozoan or metazoan. The obtained procedure was quite adequate for distinguishing between the non-sessile protozoa classes and also for the metazoa classes, with high values for the overall species recognition with the exception of sessile protozoa. In terms of the wastewater conditions assessment the obtained results were found to be suitable for the prediction of these conditions. Finally, the discriminant analysis and neural networks results were found to be quite similar whereas the decision trees technique was less appropriate.

  1. Fabric Defect Detection Using Local Homogeneity Analysis and Neural Network

    Directory of Open Access Journals (Sweden)

    Ali Rebhi

    2015-01-01

    Full Text Available In the textile manufacturing industry, fabric defect detection becomes a necessary and essential step in quality control. The investment in this field is more than economical when reduction in labor cost and associated benefits are considered. Moreover, the development of a wholly automated inspection system requires efficient and robust algorithms. To overcome this problem, in this paper, we present a new fabric defect detection scheme which uses the local homogeneity and neural network. Its first step consists in computing a new homogeneity image denoted as H-image. The second step is devoted to the application of the discrete cosine transform (DCT to the H-image and the extraction of different representative energy features of each DCT block. These energy features are used by the back-propagation neural network to judge the existence of fabric defect. Simulations on different fabric images and different defect aspects show that the proposed method achieves an average accuracy of 97.35%.

  2. Analysis of Natural Gradient Descent for Multilayer Neural Networks

    OpenAIRE

    Rattray, Magnus; Saad, David

    1999-01-01

    Natural gradient descent is a principled method for adapting the parameters of a statistical model on-line using an underlying Riemannian parameter space to redefine the direction of steepest descent. The algorithm is examined via methods of statistical physics which accurately characterize both transient and asymptotic behavior. A solution of the learning dynamics is obtained for the case of multilayer neural network training in the limit of large input dimension. We find that natural gradie...

  3. Artificial Neural Networks Application in Modal Analysis of Tires

    Science.gov (United States)

    Koštial, P.; Jančíková, Z.; Bakošová, D.; Valíček, J.; Harničárová, M.; Špička, I.

    2013-10-01

    The paper deals with the application of artificial neural networks (ANN) to tires' own frequency (OF) prediction depending on a tire construction. Experimental data of OF were obtained by electronic speckle pattern interferometry (ESPI). A very good conformity of both experimental and predicted data sets is presented here. The presented ANN method applied to ESPI experimental data can effectively help designers to optimize dimensions of tires from the point of view of their noise.

  4. Integrated Circuits for Analog Signal Processing

    CERN Document Server

    2013-01-01

      This book presents theory, design methods and novel applications for integrated circuits for analog signal processing.  The discussion covers a wide variety of active devices, active elements and amplifiers, working in voltage mode, current mode and mixed mode.  This includes voltage operational amplifiers, current operational amplifiers, operational transconductance amplifiers, operational transresistance amplifiers, current conveyors, current differencing transconductance amplifiers, etc.  Design methods and challenges posed by nanometer technology are discussed and applications described, including signal amplification, filtering, data acquisition systems such as neural recording, sensor conditioning such as biomedical implants, actuator conditioning, noise generators, oscillators, mixers, etc.   Presents analysis and synthesis methods to generate all circuit topologies from which the designer can select the best one for the desired application; Includes design guidelines for active devices/elements...

  5. CMOS analog circuit design

    CERN Document Server

    Allen, Phillip E

    1987-01-01

    This text presents the principles and techniques for designing analog circuits to be implemented in a CMOS technology. The level is appropriate for seniors and graduate students familiar with basic electronics, including biasing, modeling, circuit analysis, and some familiarity with frequency response. Students learn the methodology of analog integrated circuit design through a hierarchically-oriented approach to the subject that provides thorough background and practical guidance for designing CMOS analog circuits, including modeling, simulation, and testing. The authors' vast industrial experience and knowledge is reflected in the circuits, techniques, and principles presented. They even identify the many common pitfalls that lie in the path of the beginning designer--expert advice from veteran designers. The text mixes the academic and practical viewpoints in a treatment that is neither superficial nor overly detailed, providing the perfect balance.

  6. Design and Analysis of Compact DNA Strand Displacement Circuits for Analog Computation Using Autocatalytic Amplifiers.

    Science.gov (United States)

    Song, Tianqi; Garg, Sudhanshu; Mokhtar, Reem; Bui, Hieu; Reif, John

    2017-12-04

    A main goal in DNA computing is to build DNA circuits to compute designated functions using minimal number of DNA strands. Here, we propose a novel architecture to build compact DNA strand displacement circuits to compute a broad scope of functions in an analog fashion. A circuit by this architecture is composed of three autocatalytic amplifiers, and the amplifiers interact to perform computation. We show DNA circuits to compute functions sqrt(x), ln(x) and exp(x) for x in tunable ranges with simulation results. A key innovation in our architecture, inspired by Napier's use of logarithm transforms to compute square roots on a slide rule, is to make use of autocatalytic amplifiers to do logarithmic and exponential transforms in concentration and time. In particular, we convert from the input that is encoded by the initial concentration of the input DNA strand, to time, and then back again to the output encoded by the concentration of the output DNA strand at equilibrium. This combined use of strand concentration and time encoding of computational values may have impact on other forms of molecular computation.

  7. Analysis of Wave Propagation in Stratified Structures Using Circuit Analogues, with Application to Electromagnetic Absorbers

    Science.gov (United States)

    Sjoberg, Daniel

    2008-01-01

    This paper presents an overview of how circuit models can be used for analysing wave propagation in stratified structures. Relatively complex structures can be analysed using models which are accessible to undergraduate students. Homogeneous slabs are modelled as transmission lines, and thin sheets between the slabs are modelled as lumped…

  8. Preliminary Analysis and Test Results for Circuit Cards in X-Ray and Gamma Ray Environments.

    Science.gov (United States)

    1986-01-10

    agreement. A prediction for the plane-box short circuit current source is given by Isc -- 1 f(t) AA [QF - QRB ) + - QFB ) (amps) (1) ,’V-#" where AA is the...box wall, QR is the reverse emission from the box wall, QFB is the forward emission from the card, and QRB is the reverse emission from the card. 10

  9. Battery open-circuit voltage estimation by a method of statistical analysis

    NARCIS (Netherlands)

    Snihir, Iryna; Rey, William; Verbitskiy, Evgeny; Belfadhel-Ayeb, Afifa; Notten, Peter H.L.

    2006-01-01

    The basic task of a battery management system (BMS) is the optimal utilization of the stored energy and minimization of degradation effects. It is critical for a BMS that the state-of-charge (SoC) be accurately determined. Open-circuit voltage (OCV) is directly related to the state-of-charge of the

  10. Load Flow and Short Circuit Analysis of the Class III Power System of HANARO

    Energy Technology Data Exchange (ETDEWEB)

    Kim, H. K.; Jung, H. S

    2005-12-15

    The planning, design, and operation of electric power system require engineering studies to assist in the evaluation of the system performance, reliability, safety and economics. The Class III power of HANARO supplies power for not only HANARO but also RIPF and IMEF. The starting current of most ac motors is five to ten times normal full load current. The loads of the Class III power are connected in consecutive orders at an interval for 10 seconds to avoid excessive voltage drop. This technical report deals with the load flow study and motor starting study for the Class III power of HANARO using ETAP(Electrical Transient Analyzer Program) to verify the capacity of the diesel generator. Short-circuit studies are done to determine the magnitude of the prospective currents flowing throughout the power system at various time intervals after a fault occurs. Short-circuit studies can be performed at the planning stage in order to help finalize the system layout, determine voltage levels, and size cables, transformers, and conductors. From this study, we verify the short circuit current capacity of air circuit breaker(ACB) and automatic transfer switch(ATS) of the Class III power.

  11. Performance analysis of an ultralow power circuit using single halo CNTFETs

    Science.gov (United States)

    Wang, Wei; Wang, Huan; Liu, Jichao; Li, Na; Zhang, Ting; Jiang, Sitao; Zhang, Lu; Xu, Min; Gao, Jian

    2015-05-01

    This work presents a comprehensive study of the influence of channel engineering on the switching, high frequency characteristics, and circuit-level performance of carbon nanotube field-effect transistors (CNTFETs). At the device level, new CNTFETs with single halo doping (SH-CNTFETs) have been proposed. The impact of SH implantation on the cutoff frequency (fT), switching delay (τ), and on/off current ratio (Ion/Ioff) has been explored and it is revealed that SH-CNTFETs have improved radio frequency (RF) and switching characteristics. At the circuit level, using the Hailey Simulation Program with IC Emphasis (HSPICE) with lookup table (LUT)-based Verilog-A models, the performance parameters of the circuit have been calculated. Results show that compared to a conventional CNTFET (C-CNTFET)-based inverter, the SH-CNTFET-based inverter exhibits better performance. In addition, we evaluate the stability and performance of six-transistor (6T) CNTFET static random access memory (SRAM) cells with SH-CNTFETs. The performance parameters such as static noise margin (SNM) and write delay as well as power-delay product (PDP) and SNM/write delay (SWD) have been calculated and optimized. It is shown that SH-CNTFET SRAMs have an improved performance in SNM, PDP, and write power compared to C-CNTFET SRAMs and the optimum halo doping level has been concluded. Our results may be useful for designing and optimizing CNTFET devices and circuits.

  12. D-VASim: An Interactive Virtual Laboratory Environment for the Simulation and Analysis of Genetic Circuits

    DEFF Research Database (Denmark)

    Baig, Hasan; Madsen, Jan

    2016-01-01

    the behavior of genetic logic circuit models represented in an SBML (Systems Biology Markup Language). Hence, SBML models developed in other software environments can be analyzed and simulated in D-VASim. D-VASim offers deterministic as well as stochastic simulation; and differs from other software tools...

  13. USING ARTIFICIAL NEURAL NETWORKS AS STATISTICAL TOOLS FOR ANALYSIS OF MEDICAL DATA

    Directory of Open Access Journals (Sweden)

    ANOUSHIRAVAN KAZEMNEZHAD

    2003-06-01

    Full Text Available Introduction: Artificial neural networks mimic brains behavior. They are able to predict and feature recognition and classification. Therefore, neural networks seem to serious rivals for statistical models like regression and discriminant analysis. Methods: We have introduced biological neuron and generalized their function for artificial neurons and described back propagation error algoritm for training of networks in details. Result: Based on two simulated data and one real data we built neural networks by using back propagation and compared them by regression models. Discussion: Neural networks can be considered as a non parametric method for data modeling and seem that they are potentially. more powerful than regression for modeling, but more ambiguous in notation.

  14. Harmonic analysis approach to the 'TunneLadder' - A modified Karp circuit for millimeter-wave TWTA's

    Science.gov (United States)

    Kosmahl, H. G.; Palmer, R. W.

    1982-05-01

    A field approach to the summed harmonic analysis of the TunneLadder structure, or modified forward-wave Karp circuit, is developed by combining TM(01) and TE(11) modes. Results suggest the suitability of this structure as a high-impedance, about 1-% bandwidth circuit, millimeter-wave forward-wave-type amplifier that is voltage tunable over about a 5-% frequency range and has excellent power handling ability. Theory gives good agreement with experimental results obtained by Karp in omega-beta dispersion and predicts qualitatively the appearances of the antisymmetric mode discussed and of the so called Hightron mode that was discussed earlier in White, Enderby and Birdsall (1964), and Enderby (1964), in addition to the desired symmetric mode.

  15. Harmonic analysis approach to the 'TunneLadder' - A modified Karp circuit for millimeter-wave TWTA's

    Science.gov (United States)

    Kosmahl, H. G.; Palmer, R. W.

    1982-01-01

    A field approach to the summed harmonic analysis of the TunneLadder structure, or modified forward-wave Karp circuit, is developed by combining TM(01) and TE(11) modes. Results suggest the suitability of this structure as a high-impedance, about 1-% bandwidth circuit, millimeter-wave forward-wave-type amplifier that is voltage tunable over about a 5-% frequency range and has excellent power handling ability. Theory gives good agreement with experimental results obtained by Karp in omega-beta dispersion and predicts qualitatively the appearances of the antisymmetric mode discussed and of the so called Hightron mode that was discussed earlier in White, Enderby and Birdsall (1964), and Enderby (1964), in addition to the desired symmetric mode.

  16. A computational analysis of the neural bases of Bayesian inference.

    Science.gov (United States)

    Kolossa, Antonio; Kopp, Bruno; Fingscheidt, Tim

    2015-02-01

    Empirical support for the Bayesian brain hypothesis, although of major theoretical importance for cognitive neuroscience, is surprisingly scarce. This hypothesis posits simply that neural activities code and compute Bayesian probabilities. Here, we introduce an urn-ball paradigm to relate event-related potentials (ERPs) such as the P300 wave to Bayesian inference. Bayesian model comparison is conducted to compare various models in terms of their ability to explain trial-by-trial variation in ERP responses at different points in time and over different regions of the scalp. Specifically, we are interested in dissociating specific ERP responses in terms of Bayesian updating and predictive surprise. Bayesian updating refers to changes in probability distributions given new observations, while predictive surprise equals the surprise about observations under current probability distributions. Components of the late positive complex (P3a, P3b, Slow Wave) provide dissociable measures of Bayesian updating and predictive surprise. Specifically, the updating of beliefs about hidden states yields the best fit for the anteriorly distributed P3a, whereas the updating of predictions of observations accounts best for the posteriorly distributed Slow Wave. In addition, parietally distributed P3b responses are best fit by predictive surprise. These results indicate that the three components of the late positive complex reflect distinct neural computations. As such they are consistent with the Bayesian brain hypothesis, but these neural computations seem to be subject to nonlinear probability weighting. We integrate these findings with the free-energy principle that instantiates the Bayesian brain hypothesis. Copyright © 2014 Elsevier Inc. All rights reserved.

  17. Topological analysis of chaos in neural spike train bursts

    Energy Technology Data Exchange (ETDEWEB)

    Gilmore, R. (Department of Physics and Atmospheric Science, Drexel University, Philadelphia, Pennsylvania 19104 (United States)); Pei, X.; Moss, F. (Center for Neurodynamics, University of Missouri, St. Louis, Missouri 63121 (United States))

    1999-09-01

    We show how a topological model which describes the stretching and squeezing mechanisms responsible for creating chaotic behavior can be extracted from the neural spike train data. The mechanism we have identified is the same one ([open quotes]gateau roul[acute e],[close quotes] or jelly-roll) which has previously been identified in the Duffing oscillator [Gilmore and McCallum, Phys. Rev. E [bold 51], 935 (1995)] and in a YAG laser [Boulant [ital et al.], Phys. Rev. E [bold 55], 5082 (1997)]. [copyright] [ital 1999 American Institute of Physics.

  18. Mechanisms of hierarchical reinforcement learning in cortico-striatal circuits 2: evidence from fMRI.

    Science.gov (United States)

    Badre, David; Frank, Michael J

    2012-03-01

    The frontal lobes may be organized hierarchically such that more rostral frontal regions modulate cognitive control operations in caudal regions. In our companion paper (Frank MJ, Badre D. 2011. Mechanisms of hierarchical reinforcement learning in corticostriatal circuits I: computational analysis. 22:509-526), we provide novel neural circuit and algorithmic models of hierarchical cognitive control in cortico-striatal circuits. Here, we test key model predictions using functional magnetic resonance imaging (fMRI). Our neural circuit model proposes that contextual representations in rostral frontal cortex influence the striatal gating of contextual representations in caudal frontal cortex. Reinforcement learning operates at each level, such that the system adaptively learns to gate higher order contextual information into rostral regions. Our algorithmic Bayesian "mixture of experts" model captures the key computations of this neural model and provides trial-by-trial estimates of the learner's latent hypothesis states. In the present paper, we used these quantitative estimates to reanalyze fMRI data from a hierarchical reinforcement learning task reported in Badre D, Kayser AS, D'Esposito M. 2010. Frontal cortex and the discovery of abstract action rules. Neuron. 66:315--326. Results validate key predictions of the models and provide evidence for an individual cortico-striatal circuit for reinforcement learning of hierarchical structure at a specific level of policy abstraction. These findings are initially consistent with the proposal that hierarchical control in frontal cortex may emerge from interactions among nested cortico-striatal circuits at different levels of abstraction.

  19. Multi-channel holographic birfurcative neural network system for real-time adaptive EOS data analysis

    Science.gov (United States)

    Liu, Hua-Kuang; Diep, J.; Huang, K.

    1991-01-01

    Viewgraphs on multi-channel holographic bifurcative neural network system for real-time adaptive Earth Observing System (EOS) data analysis are presented. The objective is to research and develop an optical bifurcating neuromorphic pattern recognition system for making optical data array comparisons and to evaluate the use of the system for EOS data classification, reduction, analysis, and other applications.

  20. Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

    Science.gov (United States)

    Cheng, K.; Guo, L. M.; Wang, Y. K.; Zafar, M. T.

    2017-11-01

    In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.

  1. Inherently stochastic spiking neurons for probabilistic neural computation

    KAUST Repository

    Al-Shedivat, Maruan

    2015-04-01

    Neuromorphic engineering aims to design hardware that efficiently mimics neural circuitry and provides the means for emulating and studying neural systems. In this paper, we propose a new memristor-based neuron circuit that uniquely complements the scope of neuron implementations and follows the stochastic spike response model (SRM), which plays a cornerstone role in spike-based probabilistic algorithms. We demonstrate that the switching of the memristor is akin to the stochastic firing of the SRM. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards memristive, scalable and efficient stochastic neuromorphic platforms. © 2015 IEEE.

  2. Mammographic Image Analysis of Breast Using Neural Network

    Directory of Open Access Journals (Sweden)

    Lesa MAMBWE

    2015-07-01

    Full Text Available This paper discusses the various stages of detecting tumours of the breast mammogram images. A Neural Network algorithm is applied for obtaining the complete classification of the tumour into normal or abnormal. The most important procedure or technique for obtaining the classification is the feature extraction, by extracting a few of discriminative features, first-order statistical intensities and gradients. The Image Pre-processing technique is essential prior to Image Segmentation in order to obtain accurate segmentation. Thus mass detection can be carried out. The processes involved in achieving the three techniques mentioned above include global equalization transformation, denoising, binarization, breast orientation determination and the pectoral muscle suppression. The presented feature difference matrices could be created by five features extracted from a suspicious region of interest (ROI. Grey Level Co-occurrence Matrix (GLCM aids the obtaining of statistical features such as correlation, energy, entropy and homogeneity. The other statistical to features to obtain are area, moment, variance, entropy, standard deviation and moment. The Neural network technique yields results of abnormal mammograms.

  3. Analysis of surface insulation resistance related failures in electronics by circuit simulation

    DEFF Research Database (Denmark)

    Verdingovas, Vadimas; Joshy, Salil; Jellesen, Morten Stendahl

    2017-01-01

    conduction medium. Findings-This paper provides a summary of the effects of contamination with various weak organic acids representing the active components in no-clean solder flux residue, and demonstrates the effect of humidity and contamination on the possible malfunctions and errors in electronic...... of the circuits using a range of empirical leakage resistance values combined with the knowledge of the humidity and contamination profile of the electronics can be used for the robust design of a device, which is also important for electronic products relying on low current consumption for long battery lifetime....... Originality/value-Examples provide a basic link between the combined effect of humidity and contamination and the performance of electronic circuits. The methodology shown provides the possibility of addressing the climatic reliability of an electronic device at the early stage of device design by using...

  4. A nonlinear equivalent circuit method for analysis of passive intermodulation of mesh reflectors

    Directory of Open Access Journals (Sweden)

    Jiang Jie

    2014-08-01

    Full Text Available Passive intermodulation (PIM has gradually become a serious electromagnetic interference due to the development of high-power and high-sensitivity RF/microwave communication systems, especially large deployable mesh reflector antennas. This paper proposes a field-circuit coupling method to analyze the PIM level of mesh reflectors. With the existence of many metal–metal (MM contacts in mesh reflectors, the contact nonlinearity becomes the main reason for PIM generation. To analyze these potential PIM sources, an equivalent circuit model including nonlinear components is constructed to model a single MM contact so that the transient current through the MM contact point induced by incident electromagnetic waves can be calculated. Taking the electric current as a new electromagnetic wave source, the far-field scattering can be obtained by the use of electromagnetic numerical methods or the communication link method. Finally, a comparison between simulation and experimental results is illustrated to verify the validity of the proposed method.

  5. Analysis and design of wire-based metamaterial absorbers using equivalent circuit approach

    Science.gov (United States)

    Pang, Yongqiang; Cheng, Haifeng; Zhou, Yongjiang; Wang, Jun

    2013-03-01

    Wire-based metamaterial absorbers, as a kind of simple but versatile artificial structures, have been widely investigated from microwave to optical frequencies. In order to completely understand how the constitutive parameters affect the absorption properties, an equivalent circuit model is developed in this paper. The analytical, numerical, and experimental results show that the absorption frequency is determined by the wire length and the spacer electromagnetic parameters, whereas the absorption level by the thickness of spacers and losses which include Ohmic loss of the metal as well as dielectric loss of the spacers. It is also explained why the small losses have hardly any effect on the absorption frequency, but can result in the near-unity absorption. Based on the circuit model, some strategies for designing broadband absorbers are discussed finally.

  6. Analysis of Constrained Optimization Variants of the Map-Seeking Circuit Algorithm

    Energy Technology Data Exchange (ETDEWEB)

    S.R. Harker; C.R. Vogel; T. Gedeon

    2005-09-05

    The map-seeking circuit algorithm (MSC) was developed by Arathorn to efficiently solve the combinatorial problem of correspondence maximization, which arises in applications like computer vision, motion estimation, image matching, and automatic speech recognition [D. W. Arathorn, Map-Seeking Circuits in Visual Cognition: A Computational Mechanism for Biological and Machine Vision, Stanford University Press, 2002]. Given an input image, a template image, and a discrete set of transformations, the goal is to find a composition of transformations which gives the best fit between the transformed input and the template. We imbed the associated combinatorial search problem within a continuous framework by using superposition, and we analyze a resulting constrained optimization problem. We present several numerical schemes to compute local solutions, and we compare their performance on a pair of test problems: an image matching problem and the challenging problem of automatically solving a Rubik's cube.

  7. Experimental and theoretical analysis of integrated circuit (IC) chips on flexible substrates subjected to bending

    Science.gov (United States)

    Chen, Ying; Yuan, Jianghong; Zhang, Yingchao; Huang, Yonggang; Feng, Xue

    2017-10-01

    The interfacial failure of integrated circuit (IC) chips integrated on flexible substrates under bending deformation has been studied theoretically and experimentally. A compressive buckling test is used to impose the bending deformation onto the interface between the IC chip and the flexible substrate quantitatively, after which the failed interface is investigated using scanning electron microscopy. A theoretical model is established based on the beam theory and a bi-layer interface model, from which an analytical expression of the critical curvature in relation to the interfacial failure is obtained. The relationships between the critical curvature, the material, and the geometric parameters of the device are discussed in detail, providing guidance for future optimization flexible circuits based on IC chips.

  8. Nonlinear Analysis of Ring Oscillator and Cross-Coupled Oscillator Circuits

    KAUST Repository

    Ge, Xiaoqing

    2010-12-01

    Hassan Khalil’s research results and beautifully written textbook on nonlinear systems have influenced generations of researchers, including the authors of this paper. Using nonlinear systems techniques, this paper analyzes ring oscillator and cross-coupled oscillator circuits, which are essential building blocks in digital systems. The paper first investigates local and global stability properties of an n-stage ring oscillator by making use of its cyclic structure. It next studies global stability properties of a class of cross-coupled oscillators which admit the representation of a dynamic system in feedback with a static nonlinearity, and presents su cient conditions for almost global convergence of the solutions to a limit cycle when the feedback gain is in the vicinity of a bifurcation point. The result are also extended to the synchronization of interconnected identical oscillator circuits.

  9. Analysis of generator bearing vibration data for diagnosing rotor circuit malfunction in DFIGs

    DEFF Research Database (Denmark)

    Skrimpas, Georgios Alexandros; Sweeney, Christian W.; Jensen, Bogi Bech

    2014-01-01

    Doubly fed induction generators (DFIGs) are the most popular configuration met in the wind energy sector occu- pying approximately 65 % of the total market share. Condition monitoring of wind turbine generators is performed based on vibration data collected from accelerometers mounted on the drive...... the slip ring unit and the rotor circuit is manifested as excessive vibration seen in both ends of the generator. The consistent and accurate diagnosis of a rotor circuit malfunction is of crucial importance in regards to proper troubleshooting of the fault and the overall health of the generator....... In this paper generator bearing vibration signature for a DFIG under operation with one rotor phase coil open is analysed and presented. Further this failure mode is compared to rotor dynamics fault, such as rotational looseness, and the difference in signature is discussed. Vibration data from a multi...

  10. Neural analysis of bovine ovaries ultrasound images in the identification process of the corpus luteum

    Science.gov (United States)

    Górna, K.; Jaśkowski, B. M.; Okoń, P.; Czechlowski, M.; Koszela, K.; Zaborowicz, M.; Idziaszek, P.

    2017-07-01

    The aim of the paper is to shown the neural image analysis as a method useful for identifying the development stage of the domestic bovine corpus luteum on digital USG (UltraSonoGraphy) images. Corpus luteum (CL) is a transient endocrine gland that develops after ovulation from the follicle secretory cells. The aim of CL is the production of progesterone, which regulates many reproductive functions. In the presented studies, identification of the corpus luteum was carried out on the basis of information contained in ultrasound digital images. Development stage of the corpus luteum was considered in two aspects: just before and middle of domination phase and luteolysis and degradation phase. Prior to the classification, the ultrasound images have been processed using a GLCM (Gray Level Co-occurence Matrix). To generate a classification model, a Neural Networks module implemented in the STATISTICA was used. Five representative parameters describing the ultrasound image were used as learner variables. On the output of the artificial neural network was generated information about the development stage of the corpus luteum. Results of this study indicate that neural image analysis combined with GLCM texture analysis may be a useful tool for identifying the bovine corpus luteum in the context of its development phase. Best-generated artificial neural network model was the structure of MLP (Multi Layer Perceptron) 5:5-17-1:1.

  11. Simulation-based analysis for NBTI degradation in combinational CMOS VLSI circuits

    OpenAIRE

    Georgiev, Zdravko

    2013-01-01

    The negative-bias temperature instability (NBTI) is one of the dominant aging degradation mechanisms in today Very Large Scale Integration (VLSI) Integrated Circuits (IC). With the further decreasing of the transistor dimensions and reduction of supply voltage, the NBTI degradation may become a critical reliability threat. Nevertheless, most of the EDA tools lack in the ability to predict and analyse the impact of the NBTI. Other tools able to analyse the NBTI, are often on very low design le...

  12. Double-layer rotor magnetic shield performance analysis in high temperature superconducting synchronous generators under short circuit fault conditions

    Science.gov (United States)

    Hekmati, Arsalan; Aliahmadi, Mehdi

    2016-12-01

    High temperature superconducting, HTS, synchronous machines benefit from a rotor magnetic shield in order to protect superconducting coils against asynchronous magnetic fields. This magnetic shield, however, suffers from exerted Lorentz forces generated in light of induced eddy currents during transient conditions, e.g. stator windings short-circuit fault. In addition, to the exerted electromagnetic forces, eddy current losses and the associated effects on the cryogenic system are the other consequences of shielding HTS coils. This study aims at investigating the Rotor Magnetic Shield, RMS, performance in HTS synchronous generators under stator winding short-circuit fault conditions. The induced eddy currents in different circumferential positions of the rotor magnetic shield along with associated Joule heating losses would be studied using 2-D time-stepping Finite Element Analysis, FEA. The investigation of Lorentz forces exerted on the magnetic shield during transient conditions has also been performed in this paper. The obtained results show that double line-to-ground fault is of the most importance among different types of short-circuit faults. It was revealed that when it comes to the design of the rotor magnetic shields, in addition to the eddy current distribution and the associated ohmic losses, two phase-to-ground fault should be taken into account since the produced electromagnetic forces in the time of fault conditions are more severe during double line-to-ground fault.

  13. PERFORMANCE ANALYSIS OF POWER GATING TECHNIQUES IN 4-BIT SISO SHIFT REGISTER CIRCUITS

    Directory of Open Access Journals (Sweden)

    K. NEHRU

    2017-12-01

    Full Text Available The last few years have witnessed great deal of research activities in the area of reversible logic; the intrinsic functionality to reduce the power dissipation that has been the main requirement in the low power digital circuit design has garnered more attraction to this field. In this paper various power gating techniques for power minimization in adder and 4 bit serial in serial out (SISO shift register circuits is proposed. The work also analyze various leakage reduction approaches such as sleep approach, sleepy stack approach, dual sleep technique and zig-zag technique for gate diffusion input technique, self resetting gate diffusion input technique for complementary metal oxide semi conductor (CMOS technology and forced stack and multiplexer based SISO registers. A 4 bit SISO and full adder was designed in a cadence virtuoso 180 nm technology and the simulated results show the trade-off between power, delay and power for the sequential circuits and the results demonstrate that minimum power consumption can be achieved when the adder and SISO are designed for clock gating.

  14. Analysis of Sensitivity and Power Consumption of Chopping Techniques for Integrated Capacitive Sensor Interface Circuits

    Directory of Open Access Journals (Sweden)

    Parisa Vejdani

    2017-12-01

    Full Text Available In this paper, parameters related to the sensitivity of the interface circuits for capacitive sensors are determined. Both the input referred noise and capacitance of the input transistors are important for capacitive sensitivity. Chopping is an effective technique for signal conditioning circuits because of its capability of reducing circuit noise at low frequencies. The capacitive sensitivity and power consumption of various chopping techniques including the dual chopper amplifier (DCA, single chopper amplifier (SCA and two-stage single chopper amplifier (TCA are extracted for different values of total gain and sensor capacitance. The minimum sensitivity for each technique will be extracted based on the gain and sensor capacitance. It will be shown that designation of the amplifier and distribution of gain in the TCA and DCA are important for sensitivity. A design procedure for chopper amplifiers that illustrates the steps required to achieve either the best or the desired sensitivity while minimizing power consumption will be presented. It will be shown that for a small sensor capacitance and large total gain, the DCA has the best sensitivity, while for a large sensor capacitance and a lower gain, the SCA is preferable. The TCA is the desired architecture for an average total gain and a large sensor capacitance. Moreover, when the power consumption is the key requirement and the maximum sensitivity is not the goal; the TCA works best due to its potential to decrease the power consumption.

  15. Memristor Circuits and Systems

    KAUST Repository

    Zidan, Mohammed A.

    2015-05-01

    Current CMOS-based technologies are facing design challenges related to the continuous scaling down of the minimum feature size, according to Moore’s law. Moreover, conventional computing architecture is no longer an effective way of fulfilling modern applications demands, such as big data analysis, pattern recognition, and vector processing. Therefore, there is an exigent need to shift to new technologies, at both the architecture and the device levels. Recently, memristor devices and structures attracted attention for being promising candidates for this job. Memristor device adds a new dimension for designing novel circuits and systems. In addition, high-density memristor-based crossbar is widely considered to be the essential element for future memory and bio-inspired computing systems. However, numerous challenges need to be addressed before the memristor genuinely replaces current memory and computing technologies, which is the motivation behind this research effort. In order to address the technology challenges, we begin by fabricating and modeling the memristor device. The devices fabricated at our local clean room enriched our understanding of the memristive phenomenon and enabled the experimental testing for our memristor-based circuits. Moreover, our proposed mathematical modeling for memristor behavior is an essential element for the theoretical circuit design stage. Designing and addressing the challenges of memristor systems with practical complexity, however, requires an extra step, which takes the form of a reliable and modular simulation platform. We, therefore, built a new simulation platform for the resistive crossbar, which can simulate realistic size arrays filled with real memory data. In addition, this simulation platform includes various crossbar nonidealities in order to obtain accurate simulation results. Consequently, we were able to address the significant challenges facing the high density memristor crossbar, as the building block for

  16. Analysis of the flow imbalance in the KSTAR PF cryo-circuit

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Hyun-Jung, E-mail: yaeban@nfri.re.kr; Park, Dong-Seong; Kwag, Sang-Woo; Joo, Jae-Jun; Moon, Kyung-Mo; Kim, Nam-Won; Lee, Young-Joo; Park, Young-Min; Yang, Hyung-Lyeol

    2015-12-15

    Highlights: • Investigate of flow imbalance trend for the KSTAR PF superconducting magnet. • Flow imbalance is compared with individual magnet test and integration magnet test. • Intensifying of flow imbalance is proven from the flow monitoring in the KSTAR PF circuit. • Flow behavior is analyzed during magnet charging in the circulator circuit. • Variation of magnet outlet temperature is analyzed due to flow imbalance. - Abstract: The KSTAR PF cryo-circuit is a quasi-closed circulation system in which more than 370 g/s of supercritical helium (SHe) is circulated using a SHe circulator. The heated helium from superconducting magnet is cooled through sub cooler (4.3 K). The circulator is operated at 4.5 K and 6.5 bar, and the pressure drop of the circuit is kept at 2 bar in order to maintain the supercritical state and circulator stability. The circuit is connected with helium refrigerator system, distribution system, and supercritical magnet system. It has a hundred branches to supply supercritical helium to the poloidal field superconducting magnet. The branch was designed to optimize the operation conditions and they are grouped for one cryogenic valve has the same length within the cardinal principle of the optimization. Five cryogenic valves are installed to control the mass flow rate, and seven orifice mass flow meters, differential pressure gauges and temperature sensors were installed in front of the magnet in the distribution because upper magnet and lower magnet is symmetric theoretically. The cryogenic pipe line was manufactured with elevation about 10 m between upper magnet and lower magnet. The inlet and outlet helium feed-through were installed at the coil inside in case of KSTAR PF1–PF5 upper magnet and lower magnet. The flow imbalance is caused by void fraction and it could be changed due to manufacturing process even if it has the same length of cooling channel. This creates an imbalance among cooling channels and temperatures are

  17. Spectral analysis of stellar light curves by means of neural networks

    Science.gov (United States)

    Tagliaferri, R.; Ciaramella, A.; Milano, L.; Barone, F.; Longo, G.

    1999-06-01

    Periodicity analysis of unevenly collected data is a relevant issue in several scientific fields. In astrophysics, for example, we have to find the fundamental period of light or radial velocity curves which are unevenly sampled observations of stars. Classical spectral analysis methods are unsatisfactory to solve the problem. In this paper we present a neural network based estimator system which performs well the frequency extraction in unevenly sampled signals. It uses an unsupervised Hebbian nonlinear neural algorithm to extract, from the interpolated signal, the principal components which, in turn, are used by the MUSIC frequency estimator algorithm to extract the frequencies. The neural network is tolerant to noise and works well also with few points in the sequence. We benchmark the system on synthetic and real signals with the Periodogram and with the Cramer-Rao lower bound. This work was been partially supported by IIASS, by MURST 40\\% and by the Italian Space Agency.

  18. Neuromorphic Silicon Neuron Circuits

    Science.gov (United States)

    Indiveri, Giacomo; Linares-Barranco, Bernabé; Hamilton, Tara Julia; van Schaik, André; Etienne-Cummings, Ralph; Delbruck, Tobi; Liu, Shih-Chii; Dudek, Piotr; Häfliger, Philipp; Renaud, Sylvie; Schemmel, Johannes; Cauwenberghs, Gert; Arthur, John; Hynna, Kai; Folowosele, Fopefolu; Saighi, Sylvain; Serrano-Gotarredona, Teresa; Wijekoon, Jayawan; Wang, Yingxue; Boahen, Kwabena

    2011-01-01

    Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain–machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips. PMID:21747754

  19. Neuromorphic silicon neuron circuits

    Directory of Open Access Journals (Sweden)

    Giacomo eIndiveri

    2011-05-01

    Full Text Available Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance based Hodgkin-Huxley models to bi-dimensional generalized adaptive Integrate and Fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.

  20. Neural reflexes in inflammation and immunity

    National Research Council Canada - National Science Library

    Andersson, Ulf; Tracey, Kevin J

    2012-01-01

    .... Development of advanced neurophysiological and immunological techniques recently enabled the study of reflex neural circuits that maintain immunological homeostasis, and are essential for health in mammals...

  1. CMOS circuits for piezoelectric energy harvesters efficient power extraction, interface modeling and loss analysis

    CERN Document Server

    Hehn, Thorsten

    2014-01-01

    This book deals with the challenge of exploiting ambient vibrational energy which can be used to power small and low-power electronic devices, e.g. wireless sensor nodes. Generally, particularly for low voltage amplitudes, low-loss rectification is required to achieve high conversion efficiency. In the special case of piezoelectric energy harvesting, pulsed charge extraction has the potential to extract more power compared to a single rectifier. For this purpose, a fully autonomous CMOS integrated interface circuit for piezoelectric generators which fulfills these requirements is presented.Due

  2. MATLAB BASED GUIS FOR ELECTROMAGNET CIRCUIT DESİGN AND ANALYSIS

    Directory of Open Access Journals (Sweden)

    Gürsel ŞEFKAT

    2005-01-01

    Full Text Available Nowadays, the usage of computer has become inevitable in the solution of the engineering systems and problems. In this study, the development of easy-to-use and application specific computer programming is considered. The program is based on the GUI of MATLAB technical programming language, which is widely used at the engineering area. As a programming application, a solution of static and dynamic characteristics of different type of electromagnetic circuits is investigated. With the program, user can select the type of magnet and material, and can calculate the size of magnet by entering parameters of the design.

  3. Analysis and estimation of truncation errors in modeling complex resonant circuits with the EMTP

    Energy Technology Data Exchange (ETDEWEB)

    Carbone, R. [Reggio Calabria ' Mediterranea' Univ., D.I.M.E.T., Reggio Calabria (Italy); Dommel, H.W. [British Columbia Univ., Dept. of Electrical Computer Engineering, Vancouver, BC (Canada); Langella, R.; Testa, A. [Univ. degli Studi di Napoli 2, Dip. di Ingegneria dell' Informazione Aversa, Aversa (Italy)

    2002-05-01

    Most common computer programs for time domain simulations of power systems calculate the state of the system step by step, either with fixed or with variable step size. They integrate differential equations by using numerical methods. Whichever method of integration is used, numerical errors in modeling power system impedances will arise, which may not be negligible in the presence of resonances. The problem of analytically describing these errors for complex circuits is analyzed, with particular reference to computer programs of the EMTP type. (Author)

  4. Frontolimbic Neural Circuit Changes in Emotional Processing and Inhibitory Control Associated With Clinical Improvement Following Transference-Focused Psychotherapy in Borderline Personality Disorder

    Science.gov (United States)

    Perez, David L.; Vago, David R.; Pan, Hong; Root, James; Tuescher, Oliver; Fuchs, Benjamin H.; Leung, Lorene; Epstein, Jane; Cain, Nicole M.; Clarkin, John F.; Lenzenweger, Mark F.; Kernberg, Otto F.; Levy, Kenneth N.; Silbersweig, David A.; Stern, Emily

    2015-01-01

    Aim Borderline personality disorder (BPD) is characterized by self-regulation deficits, including impulsivity and affective lability. Transference-Focused Psychotherapy (TFP) is an evidence-based treatment proven to reduce symptoms across multiple cognitive-emotional domains in BPD. This pilot study aims to investigate neural activation associated with, and predictive of, clinical improvement in emotional and behavioral regulation in BPD following TFP. Methods BPD subjects (N=10) were scanned pre- and post-TFP treatment using a within-subjects design. A disorder-specific emotional-linguistic go/no-go fMRI paradigm was used to probe the interaction between negative emotional processing and inhibitory control. Results Analyses demonstrated significant treatment-related effects with relative increased dorsal prefrontal (dorsal anterior cingulate, dorsolateral prefrontal, and frontopolar cortices) activation, and relative decreased ventrolateral prefrontal cortex and hippocampal activation following treatment. Clinical improvement in constraint correlated positively with relative increased left dorsal anterior cingulate cortex activation. Clinical improvement in affective lability correlated positively with left posterior-medial orbitofrontal cortex/ventral striatum activation, and negatively with right amygdala/parahippocampal activation. Post-treatment improvements in constraint were predicted by pre-treatment right dorsal anterior cingulate cortex hypoactivation, and pre-treatment left posterior-medial orbitofrontal cortex/ventral striatum hypoactivation predicted improvements in affective lability. Conclusions These preliminary findings demonstrate potential TFP-associated alterations in frontolimbic circuitry and begin to identify neural mechanisms associated with a psychodynamically-oriented psychotherapy. PMID:26289141

  5. A LabVIEW-based electrical bioimpedance spectroscopic data interpreter (LEBISDI) for biological tissue impedance analysis and equivalent circuit modelling

    KAUST Repository

    Bera, Tushar Kanti

    2016-12-05

    Under an alternating electrical signal, biological tissues produce a complex electrical bioimpedance that is a function of tissue composition and applied signal frequencies. By studying the bioimpedance spectra of biological tissues over a wide range of frequencies, we can noninvasively probe the physiological properties of these tissues to detect possible pathological conditions. Electrical impedance spectroscopy (EIS) can provide the spectra that are needed to calculate impedance parameters within a wide range of frequencies. Before impedance parameters can be calculated and tissue information extracted, impedance spectra should be processed and analyzed by a dedicated software program. National Instruments (NI) Inc. offers LabVIEW, a fast, portable, robust, user-friendly platform for designing dataanalyzing software. We developed a LabVIEW-based electrical bioimpedance spectroscopic data interpreter (LEBISDI) to analyze the electrical impedance spectra for tissue characterization in medical, biomedical and biological applications. Here, we test, calibrate and evaluate the performance of LEBISDI on the impedance data obtained from simulation studies as well as the practical EIS experimentations conducted on electronic circuit element combinations and the biological tissue samples. We analyze the Nyquist plots obtained from the EIS measurements and compare the equivalent circuit parameters calculated by LEBISDI with the corresponding original circuit parameters to assess the accuracy of the program developed. Calibration studies show that LEBISDI not only interpreted the simulated and circuitelement data accurately, but also successfully interpreted tissues impedance data and estimated the capacitive and resistive components produced by the compositions biological cells. Finally, LEBISDI efficiently calculated and analyzed variation in bioimpedance parameters of different tissue compositions, health and temperatures. LEBISDI can also be used for human tissue

  6. Instantaneous charging & discharging cycle analysis of a novel supercapacitor based energy harvesting circuit

    Science.gov (United States)

    Khan, MD Shahrukh Adnan; Kuni, Sharsad Kara; Rajkumar, Rajprasad; Syed, Anas; Hawladar, Masum; Rahman, Md. Moshiur

    2017-12-01

    In this paper, an extensive effort has been made to design and develop a prototype in a laboratory setup environment in order to investigate experimentally the response of a novel Supercapacitor based energy harvesting circuit; particularly the phenomena of instantaneous charging and discharging cycle is analysed. To maximize battery lifespan and storage capacity, charging/discharging cycles need to be optimized in such a way, it ultimately enhances the system performances reliably. Keeping this into focus, an Arduino-MOSFET based control system is developed to charge the Supercapacitor from a low wind Vertical Axis Turbine (VAWT) and discharge it through a 6V battery. With a wind speed of 5m/s, the wind turbine requires approximately 8.1 hours to charge the 6V battery through Supercapacitor bank that constitutes 18 cycles in which each cycle consumes 27 minutes. The overall performance of the proposed system was quite convincing in a sense that the efficiency of the developed Energy Harvesting Circuit EHC raises to 19% in comparison to direct charging of the battery from the Vertical wind turbine. At low wind speed, such value of efficiency margin is quite encouraging which essentially validates the system design.

  7. Bone age detection via carpogram analysis using convolutional neural networks

    Science.gov (United States)

    Torres, Felipe; Bravo, María. Alejandra; Salinas, Emmanuel; Triana, Gustavo; Arbeláez, Pablo

    2017-11-01

    Bone age assessment is a critical factor for determining delayed development in children, which can be a sign of pathologies such as endocrine diseases, growth abnormalities, chromosomal, neurological and congenital disorders among others. In this paper we present BoneNet, a methodology to assess automatically the skeletal maturity state in pediatric patients based on Convolutional Neural Networks. We train and evaluate our algorithm on a database of X-Ray images provided by the hospital Fundacion Santa Fe de Bogot ´ a with around 1500 images of patients between the ages 1 to 18. ´ We compare two different architectures to classify the given data in order to explore the generality of our method. To accomplish this, we define multiple binary age assessment problems, dividing the data by bone age and differentiating the patients by their gender. Thus, exploring several parameters, we develop BoneNet. Our approach is holistic, efficient, and modular, since it is possible for the specialists to use all the networks combined to determine how is the skeletal maturity of a patient. BoneNet achieves over 90% accuracy for most of the critical age thresholds, when differentiating the images between over or under a given age.

  8. Sparse Data Analysis Strategy for Neural Spike Classification

    Directory of Open Access Journals (Sweden)

    Vincent Vigneron

    2014-01-01

    Full Text Available Many of the multichannel extracellular recordings of neural activity consist of attempting to sort spikes on the basis of shared characteristics with some feature detection techniques. Then spikes can be sorted into distinct clusters. There are in general two main statistical issues: firstly, spike sorting can result in well-sorted units, but by with no means one can be sure that one is dealing with single units due to the number of neurons adjacent to the recording electrode. Secondly, the waveform dimensionality is reduced in a small subset of discriminating features. This shortening dimension effort was introduced as an aid to visualization and manual clustering, but also to reduce the computational complexity in automatic classification. We introduce a metric based on common neighbourhood to introduce sparsity in the dataset and separate data into more homogeneous subgroups. The approach is particularly well suited for clustering when the individual clusters are elongated (that is nonspherical. In addition it does need not to select the number of clusters, it is very efficient to visualize clusters in a dataset, it is robust to noise, it can handle imbalanced data, and it is fully automatic and deterministic.

  9. In vitro and in vivo analysis and characterization of engineered spinal neural implants (Conference Presentation)

    Science.gov (United States)

    Shor, Erez; Shoham, Shy; Levenberg, Shulamit

    2016-03-01

    Spinal cord injury is a devastating medical condition. Recent developments in pre-clinical and clinical research have started to yield neural implants inducing functional recovery after spinal cord transection injury. However, the functional performance of the transplants was assessed using histology and behavioral experiments which are unable to study cell dynamics and the therapeutic response. Here, we use neurophotonic tools and optogenetic probes to investigate cellular level morphology and activity characteristics of neural implants over time at the cellular level. These methods were used in-vitro and in-vivo, in a mouse spinal cord injury implant model. Following previous attempts to induce recovery after spinal cord injury, we engineered a pre-vascularized implant to obtain better functional performance. To image network activity of a construct implanted in a mouse spinal cord, we transfected the implant to express GCaMP6 calcium activity indicators and implanted these constructs under a spinal cord chamber enabling 2-photon chronic in vivo neural activity imaging. Activity and morphology analysis image processing software was developed to automatically quantify the behavior of the neural and vascular networks. Our experimental results and analyses demonstrate that vascularized and non-vascularized constructs exhibit very different morphologic and activity patterns at the cellular level. This work enables further optimization of neural implants and also provides valuable tools for continuous cellular level monitoring and evaluation of transplants designed for various neurodegenerative disease models.

  10. Single-trial analysis of the neural correlates of speech quality perception

    Science.gov (United States)

    Porbadnigk, Anne K.; Treder, Matthias S.; Blankertz, Benjamin; Antons, Jan-Niklas; Schleicher, Robert; Möller, Sebastian; Curio, Gabriel; Müller, Klaus-Robert

    2013-10-01

    Objective. Assessing speech quality perception is a challenge typically addressed in behavioral and opinion-seeking experiments. Only recently, neuroimaging methods were introduced, which were used to study the neural processing of quality at group level. However, our electroencephalography (EEG) studies show that the neural correlates of quality perception are highly individual. Therefore, it became necessary to establish dedicated machine learning methods for decoding subject-specific effects. Approach. The effectiveness of our methods is shown by the data of an EEG study that investigates how the quality of spoken vowels is processed neurally. Participants were asked to indicate whether they had perceived a degradation of quality (signal-correlated noise) in vowels, presented in an oddball paradigm. Main results. We find that the P3 amplitude is attenuated with increasing noise. Single-trial analysis allows one to show that this is partly due to an increasing jitter of the P3 component. A novel classification approach helps to detect trials with presumably non-conscious processing at the threshold of perception. We show that this approach uncovers a non-trivial confounder between neural hits and neural misses. Significance. The combined use of EEG signals and machine learning methods results in a significant ‘neural’ gain in sensitivity (in processing quality loss) when compared to standard behavioral evaluation; averaged over 11 subjects, this amounts to a relative improvement in sensitivity of 35%.

  11. Stability analysis and the stabilization of a class of discrete-time dynamic neural networks.

    Science.gov (United States)

    Patan, Krzysztof

    2007-05-01

    This paper deals with problems of stability and the stabilization of discrete-time neural networks. Neural structures under consideration belong to the class of the so-called locally recurrent globally feedforward networks. The single processing unit possesses dynamic behavior. It is realized by introducing into the neuron structure a linear dynamic system in the form of an infinite impulse response filter. In this way, a dynamic neural network is obtained. It is well known that the crucial problem with neural networks of the dynamic type is stability as well as stabilization in learning problems. The paper formulates stability conditions for the analyzed class of neural networks. Moreover, a stabilization problem is defined and solved as a constrained optimization task. In order to tackle this problem two methods are proposed. The first one is based on a gradient projection (GP) and the second one on a minimum distance projection (MDP). It is worth noting that these methods can be easily introduced into the existing learning algorithm as an additional step, and suitable convergence conditions can be developed for them. The efficiency and usefulness of the proposed approaches are justified by using a number of experiments including numerical complexity analysis, stabilization effectiveness, and the identification of an industrial process.

  12. Neuromechanic: a computational platform for simulation and analysis of the neural control of movement

    Science.gov (United States)

    Bunderson, Nathan E.; Bingham, Jeffrey T.; Sohn, M. Hongchul; Ting, Lena H.; Burkholder, Thomas J.

    2015-01-01

    Neuromusculoskeletal models solve the basic problem of determining how the body moves under the influence of external and internal forces. Existing biomechanical modeling programs often emphasize dynamics with the goal of finding a feed-forward neural program to replicate experimental data or of estimating force contributions or individual muscles. The computation of rigid-body dynamics, muscle forces, and activation of the muscles are often performed separately. We have developed an intrinsically forward computational platform (Neuromechanic, www.neuromechanic.com) that explicitly represents the interdependencies among rigid body dynamics, frictional contact, muscle mechanics, and neural control modules. This formulation has significant advantages for optimization and forward simulation, particularly with application to neural controllers with feedback or regulatory features. Explicit inclusion of all state dependencies allows calculation of system derivatives with respect to kinematic states as well as muscle and neural control states, thus affording a wealth of analytical tools, including linearization, stability analyses and calculation of initial conditions for forward simulations. In this review, we describe our algorithm for generating state equations and explain how they may be used in integration, linearization and stability analysis tools to provide structural insights into the neural control of movement. PMID:23027632

  13. Functional neural networks of honesty and dishonesty in children: Evidence from graph theory analysis.

    Science.gov (United States)

    Ding, Xiao Pan; Wu, Si Jia; Liu, Jiangang; Fu, Genyue; Lee, Kang

    2017-09-21

    The present study examined how different brain regions interact with each other during spontaneous honest vs. dishonest communication. More specifically, we took a complex network approach based on the graph-theory to analyze neural response data when children are spontaneously engaged in honest or dishonest acts. Fifty-nine right-handed children between 7 and 12 years of age participated in the study. They lied or told the truth out of their own volition. We found that lying decreased both the global and local efficiencies of children's functional neural network. This finding, for the first time, suggests that lying disrupts the efficiency of children's cortical network functioning. Further, it suggests that the graph theory based network analysis is a viable approach to study the neural development of deception.

  14. Multistability and instability analysis of recurrent neural networks with time-varying delays.

    Science.gov (United States)

    Zhang, Fanghai; Zeng, Zhigang

    2018-01-01

    This paper provides new theoretical results on the multistability and instability analysis of recurrent neural networks with time-varying delays. It is shown that such n-neuronal recurrent neural networks have exactly [Formula: see text] equilibria, [Formula: see text] of which are locally exponentially stable and the others are unstable, where k 0 is a nonnegative integer such that k 0 ≤n. By using the combination method of two different divisions, recurrent neural networks can possess more dynamic properties. This method improves and extends the existing results in the literature. Finally, one numerical example is provided to show the superiority and effectiveness of the presented results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Passivity analysis of memristor-based impulsive inertial neural networks with time-varying delays.

    Science.gov (United States)

    Wan, Peng; Jian, Jigui

    2018-02-15

    This paper focuses on delay-dependent passivity analysis for a class of memristive impulsive inertial neural networks with time-varying delays. By choosing proper variable transformation, the memristive inertial neural networks can be rewritten as first-order differential equations. The memristive model presented here is regarded as a switching system rather than employing the theory of differential inclusion and set-value map. Based on matrix inequality and Lyapunov-Krasovskii functional method, several delay-dependent passivity conditions are obtained to ascertain the passivity of the addressed networks. In addition, the results obtained here contain those on the passivity for the addressed networks without impulse effects as special cases and can also be generalized to other neural networks with more complex pulse interference. Finally, one numerical example is presented to show the validity of the obtained results. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  16. Dissipativity and stability analysis of fractional-order complex-valued neural networks with time delay.

    Science.gov (United States)

    Velmurugan, G; Rakkiyappan, R; Vembarasan, V; Cao, Jinde; Alsaedi, Ahmed

    2017-02-01

    As we know, the notion of dissipativity is an important dynamical property of neural networks. Thus, the analysis of dissipativity of neural networks with time delay is becoming more and more important in the research field. In this paper, the authors establish a class of fractional-order complex-valued neural networks (FCVNNs) with time delay, and intensively study the problem of dissipativity, as well as global asymptotic stability of the considered FCVNNs with time delay. Based on the fractional Halanay inequality and suitable Lyapunov functions, some new sufficient conditions are obtained that guarantee the dissipativity of FCVNNs with time delay. Moreover, some sufficient conditions are derived in order to ensure the global asymptotic stability of the addressed FCVNNs with time delay. Finally, two numerical simulations are posed to ensure that the attention of our main results are valuable. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. Computational Auditory Scene Analysis Based Perceptual and Neural Principles

    National Research Council Canada - National Science Library

    Wang, DeLiang

    2004-01-01

    .... This fundamental process of auditory perception is called auditory scene analysis. of particular importance in auditory scene analysis is the separation of speech from interfering sounds, or speech segregation...

  18. Surge Analysis during Making-Operation of Circuit Breaker in 345kV Network of KEPCO

    Energy Technology Data Exchange (ETDEWEB)

    Jung, Y.H.; Song, W.P.; Ohk, T.H.; Kim, D.S. [HYOSUNG CORPORATION (Korea); Moon, B.S. [KEPCO (Korea); Kho, H.S. [Kyoungnam University (Korea)

    2001-07-01

    We have reviewed the making surge of GCB without closing resistor of Sanchung Power Plant in 345kV network of KEPCO. In 345kV GCB with closing resistor (520Ohm) to suppress the making-surge of circuit breaker. But, in this case of Sanchung power plant, we didn't insert the closing resistor between the contacts of circuit breaker even though it is 345kV network because we thought there was no problem as the length of transmission line between Sanchung power plant and Ueryoung Substation is very short (about 46km). But, KEPCO didn't accept this design and than we had to the making-surge analysis of line between Sanchung and Ueryoung. By the analysis results, we knew that the max making-surge was below the design criteria KEPCO's network (2.3pu) with thinking all con of network, substations and GIS. Consequ KEPCO has accepted the design of HYOSUNG 345kV GIS-CB without closing resistors in network between Sanchung and Ueryoung. (author). 2 refs., 3 figs., 1 tabs.

  19. Frontolimbic neural circuit changes in emotional processing and inhibitory control associated with clinical improvement following transference-focused psychotherapy in borderline personality disorder.

    Science.gov (United States)

    Perez, David L; Vago, David R; Pan, Hong; Root, James; Tuescher, Oliver; Fuchs, Benjamin H; Leung, Lorene; Epstein, Jane; Cain, Nicole M; Clarkin, John F; Lenzenweger, Mark F; Kernberg, Otto F; Levy, Kenneth N; Silbersweig, David A; Stern, Emily

    2016-01-01

    Borderline personality disorder (BPD) is characterized by self-regulation deficits, including impulsivity and affective lability. Transference-focused psychotherapy (TFP) is an evidence-based treatment proven to reduce symptoms across multiple cognitive-emotional domains in BPD. This pilot study aimed to investigate neural activation associated with, and predictive of, clinical improvement in emotional and behavioral regulation in BPD following TFP. BPD subjects (n = 10) were scanned pre- and post-TFP treatment using a within-subjects design. A disorder-specific emotional-linguistic go/no-go functional magnetic resonance imaging paradigm was used to probe the interaction between negative emotional processing and inhibitory control. Analyses demonstrated significant treatment-related effects with relative increased dorsal prefrontal (dorsal anterior cingulate, dorsolateral prefrontal, and frontopolar cortices) activation, and relative decreased ventrolateral prefrontal cortex and hippocampal activation following treatment. Clinical improvement in constraint correlated positively with relative increased left dorsal anterior cingulate cortex activation. Clinical improvement in affective lability correlated positively with left posterior-medial orbitofrontal cortex/ventral striatum activation, and negatively with right amygdala/parahippocampal activation. Post-treatment improvements in constraint were predicted by pre-treatment right dorsal anterior cingulate cortex hypoactivation, and pre-treatment left posterior-medial orbitofrontal cortex/ventral striatum hypoactivation predicted improvements in affective lability. These preliminary findings demonstrate potential TFP-associated alterations in frontolimbic circuitry and begin to identify neural mechanisms associated with a psychodynamically oriented psychotherapy. © 2015 The Authors. Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology.

  20. Disrupted reward circuits is associated with cognitive deficits and depression severity in major depressive disorder.

    Science.gov (United States)

    Gong, Liang; Yin, Yingying; He, Cancan; Ye, Qing; Bai, Feng; Yuan, Yonggui; Zhang, Haisan; Lv, Luxian; Zhang, Hongxing; Xie, Chunming; Zhang, Zhijun

    2017-01-01

    Neuroimaging studies have demonstrated that major depressive disorder (MDD) patients show blunted activity responses to reward-related tasks. However, whether abnormal reward circuits affect cognition and depression in MDD patients remains unclear. Seventy-five drug-naive MDD patients and 42 cognitively normal (CN) subjects underwent a resting-state functional magnetic resonance imaging scan. The bilateral nucleus accumbens (NAc) were selected as seeds to construct reward circuits across all subjects. A multivariate linear regression analysis was employed to investigate the neural substrates of cognitive function and depression severity on the reward circuits in MDD patients. The common pathway underlying cognitive deficits and depression was identified with conjunction analysis. Compared with CN subjects, MDD patients showed decreased reward network connectivity that was primarily located in the prefrontal-striatal regions. Importantly, distinct and common neural pathways underlying cognition and depression were identified, implying the independent and synergistic effects of cognitive deficits and depression severity on reward circuits. This study demonstrated that disrupted topological organization within reward circuits was significantly associated with cognitive deficits and depression severity in MDD patients. These findings suggest that in addition to antidepressant treatment, normalized reward circuits should be a focus and a target for improving depression and cognitive deficits in MDD patients. Copyright © 2016 Elsevier Ltd. All rights reserved.

  1. Analysis of a utility-interactive wind-photovoltaic hybrid system with battery storage using neural network

    Science.gov (United States)

    Giraud, Francois

    1999-10-01

    This dissertation investigates the application of neural network theory to the analysis of a 4-kW Utility-interactive Wind-Photovoltaic System (WPS) with battery storage. The hybrid system comprises a 2.5-kW photovoltaic generator and a 1.5-kW wind turbine. The wind power generator produces power at variable speed and variable frequency (VSVF). The wind energy is converted into dc power by a controlled, tree-phase, full-wave, bridge rectifier. The PV power is maximized by a Maximum Power Point Tracker (MPPT), a dc-to-dc chopper, switching at a frequency of 45 kHz. The whole dc power of both subsystems is stored in the battery bank or conditioned by a single-phase self-commutated inverter to be sold to the utility at a predetermined amount. First, the PV is modeled using Artificial Neural Network (ANN). To reduce model uncertainty, the open-circuit voltage VOC and the short-circuit current ISC of the PV are chosen as model input variables of the ANN. These input variables have the advantage of incorporating the effects of the quantifiable and non-quantifiable environmental variants affecting the PV power. Then, a simplified way to predict accurately the dynamic responses of the grid-linked WPS to gusty winds using a Recurrent Neural Network (RNN) is investigated. The RNN is a single-output feedforward backpropagation network with external feedback, which allows past responses to be fed back to the network input. In the third step, a Radial Basis Functions (RBF) Network is used to analyze the effects of clouds on the Utility-Interactive WPS. Using the irradiance as input signal, the network models the effects of random cloud movement on the output current, the output voltage, the output power of the PV system, as well as the electrical output variables of the grid-linked inverter. Fourthly, using RNN, the combined effects of a random cloud and a wind gusts on the system are analyzed. For short period intervals, the wind speed and the solar radiation are considered as

  2. Multi-qubit joint measurements in circuit QED: stochastic master equation analysis

    Energy Technology Data Exchange (ETDEWEB)

    Criger, Ben; Ciani, Alessandro [RWTH, JARA Institut fuer Quanteninformation, Aachen (Germany); DiVincenzo, David P. [RWTH, JARA Institut fuer Quanteninformation, Aachen (Germany); Forschungszentrum Juelich, Juelich (Germany)

    2016-12-15

    We derive a family of stochastic master equations describing homodyne measurement of multi-qubit diagonal observables in circuit quantum electrodynamics. In the regime where qubit decay can be neglected, our approach replaces the polaron-like transformation of previous work, which required a lengthy calculation for the physically interesting case of three qubits and two resonator modes. The technique introduced here makes this calculation straightforward and manifestly correct. Using this technique, we are able to show that registers larger than one qubit evolve under a non-Markovian master equation. We perform numerical simulations of the three-qubit, two-mode case from previous work, obtaining an average post-measurement state fidelity of ∝94%, limited by measurement-induced decoherence and dephasing. (orig.)

  3. A Circuit Design and its Experimental Analysis for Electromagnetic Flowmeter in Measurement of Sewage

    Directory of Open Access Journals (Sweden)

    Huang Yu-Hang

    2014-02-01

    Full Text Available There are many problems in the traditional electromagnetic flowmeter. The problems involve three aspects. The first one is that the measurement precision is low. The second one is that the measurement range is narrow. The third one is that the test results are susceptible to interference. For the problems a new electromagnetic flowmeter controlled by single-chip microcomputer has been proposed. The medium/large-sized electromagnetic flowmeter is suitable for measurement of sewage. The software and hardware circuit of the electromagnetic flowmeter has been designed and tested. The tested data have been analyzed by the least square method and the error is 0.8 %. The result shown that the electromagnetic flowmeter controlled by single-chip microcomputer for measurement of sewage has reached the advanced level of similar products at home and abroad.

  4. SEMICONDUCTOR INTEGRATED CIRCUITS: Design and noise analysis of a sigma-delta capacitive micromachined accelerometer

    Science.gov (United States)

    Yuntao, Liu; Xiaowei, Liu; Weiping, Chen; Qun, Wu

    2010-05-01

    A single-loop fourth-order sigma-delta (σ Δ) interface circuit for a closed-loop micromachined accelerometer is presented. Two additional electronic integrators are cascaded with the micromachined sensing element to form a fourth-order loop filter. The three main noise sources affecting the overall system resolution of a σ Δ accelerometer, mechanical noise, electronic noise and quantization noise, are analyzed in detail. Accurate mathematical formulas for electronic and quantization noise are established. The ASIC is fabricated in a 0.5 μm two-metal two-poly n-well CMOS process. The test results indicate that the mechanical noise and electronic noise are 1 μg/√Hz and 8 μV/√Hz respectively, and the theoretical models of electronic and quantization noise agree well with the test and simulation results.

  5. Numerical analysis of the influence of circuit arrangement on a fin-and-tube condenser performance

    Directory of Open Access Journals (Sweden)

    Cesare Maria Joppolo

    2015-09-01

    Full Text Available In the present paper a model for the steady-state simulation of fin-and-tube condenser is developed. The model is based on a finite volume approach that divides each tube into small elemental volumes where mass, momentum and energy conservation equations are solved using the effectiveness-NTU method and with appropriate correlations for void fraction, friction factor and heat transfer coefficient calculation. The model is validated against experimental data on two small condensers finding that the calculated heat transfer rate and refrigerant-side pressure drop agree within ±5% and ±21% respectively to the experimental values. The model is then used to numerically analyse the impact of different circuit arrangements on the condenser heat transfer rate, refrigerant-side pressure drop and refrigerant charge.

  6. Analysis and improvement in repulsive force of 630 A frame Moulded Case Circuit Breaker (MCCB

    Directory of Open Access Journals (Sweden)

    Digpalsinh Parmar

    2016-09-01

    Full Text Available Moulded Case Circuit Breaker (MCCB is a protective device used for low voltage protection in the range of 12–1250 A. It is having fast breaking and making ability due to special contact structure. In case of transformer it is generally placed in secondary side and in case of motor protection it is used as backup protection. The existing design is suffering from chattering of contact tip which lead to erosion and in turn affect electrical life. It also reduces interrupting/breaking capacity of MCCB. In this paper, detailed study of the factors and constraints related to repulsion threshold current (Irp and consistency of magnetic release for 630 A MCCB is carried out using Finite Element tool JMAG to find out limitations and scope of design improvements in existing design. The proposed suggestion is validated by testing.

  7. Design and analysis of perfect terahertz metamaterial absorber by a novel dynamic circuit model.

    Science.gov (United States)

    Hokmabadi, Mohammad Parvinnezhad; Wilbert, David S; Kung, Patrick; Kim, Seongsin M

    2013-07-15

    Metamaterial terahertz absorbers composed of a frequency selective layer followed by a spacer and a metallic backplane have recently attracted great attention as a device to detect terahertz radiation. In this work, we present a quasistatic dynamic circuit model that can decently describe operational principle of metamaterial terahertz absorbers based on interference theory of reflected waves. The model comprises two series LC resonance components, one for resonance in frequency selective surface (FSS) and another for resonance inside the spacer. Absorption frequency is dominantly determined by the LC of FSS while the spacer LC changes slightly the magnitude and frequency of absorption. This model fits perfectly for both simulated and experimental data. By using this model, we study our designed absorber and we analyze the effect of changing in spacer thickness and metal conductivity on absorption spectrum.

  8. Analysis of Production and Delivery Center Hydrogen Applied to the Southern Patagonian Circuit

    Directory of Open Access Journals (Sweden)

    Maximiliano Fernando Medina

    2016-08-01

    Full Text Available The Desire department of the province of Santa Cruz, Argentina, presents the greatest potential electrolytic Hydrogen Production Country, From Three primary sources of sustainable energy: wind, solar, biomass. There, the Hydrogen Plant of Pico Truncado has capacity central production of hydrogen 100m3 of H2 / day, enough to supply 353 vehicles with hybrid fuel called HGNC, made by cutting 12% V / V of hydrogen in CNG (in situ at each station. Puerto Deseado, Fitz Roy, Caleta Olivia, Las Heras, Comodoro Rivadavia, Sarmiento and the Ancients: From the production cost, the cost of delivering hydrogen to the Southern Patagonian circuit comprised analyzed. Considering various local parameters are determined as a way of delivering more profitable virtual pipeline, with total cost of hydrogen estimated 6.5 USD / kg H2 and HGNC shipped in the station at 0.50 USD / Nm3.

  9. Comparison of artificial neural network analysis with other multimarker methods for detecting genetic association

    OpenAIRE

    Curtis David

    2007-01-01

    Abstract Background Debate remains as to the optimal method for utilising genotype data obtained from multiple markers in case-control association studies. I and colleagues have previously described a method of association analysis using artificial neural networks (ANNs), whose performance compared favourably to single-marker methods. Here, the perfomance of ANN analysis is compared with other multi-marker methods, comprising different haplotype-based analyses and locus-based analyses. Result...

  10. Classification of fault diagnosis in a gear wheel by used probabilistic neural network, fast Fourier transform and principal component analysis

    Directory of Open Access Journals (Sweden)

    Piotr CZECH

    2007-01-01

    Full Text Available This paper presents the results of an experimental application of artificial neural network as a classifier of the degree of cracking of a tooth root in a gear wheel. The neural classifier was based on the artificial neural network of Probabilistic Neural Network type (PNN. The input data for the classifier was in a form of matrix composedof statistical measures, obtained from fast Fourier transform (FFT and principal component analysis (PCA. The identified model of toothed gear transmission, operating in a circulating power system, served for generation of the teaching and testing set applied for the experiment.

  11. Imaging the neural circuitry and chemical control of aggressive motivation

    Directory of Open Access Journals (Sweden)

    Blanchard D Caroline

    2008-11-01

    Full Text Available Abstract Background With the advent of functional magnetic resonance imaging (fMRI in awake animals it is possible to resolve patterns of neuronal activity across the entire brain with high spatial and temporal resolution. Synchronized changes in neuronal activity across multiple brain areas can be viewed as functional neuroanatomical circuits coordinating the thoughts, memories and emotions for particular behaviors. To this end, fMRI in conscious rats combined with 3D computational analysis was used to identifying the putative distributed neural circuit involved in aggressive motivation and how this circuit is affected by drugs that block aggressive behavior. Results To trigger aggressive motivation, male rats were presented with their female cage mate plus a novel male intruder in the bore of the magnet during image acquisition. As expected, brain areas previously identified as critical in the organization and expression of aggressive behavior were activated, e.g., lateral hypothalamus, medial basal amygdala. Unexpected was the intense activation of the forebrain cortex and anterior thalamic nuclei. Oral administration of a selective vasopressin V1a receptor antagonist SRX251 or the selective serotonin reuptake inhibitor fluoxetine, drugs that block aggressive behavior, both caused a general suppression of the distributed neural circuit involved in aggressive motivation. However, the effect of SRX251, but not fluoxetine, was specific to aggression as brain activation in response to a novel sexually receptive female was unaffected. Conclusion The putative neural circuit of aggressive motivation identified with fMRI includes neural substrates contributing to emotional expression (i.e. cortical and medial amygdala, BNST, lateral hypothalamus, emotional experience (i.e. hippocampus, forebrain cortex, anterior cingulate, retrosplenial cortex and the anterior thalamic nuclei that bridge the motor and cognitive components of aggressive responding

  12. Stability Analysis of Recurrent Neural Networks with Random Delay and Markovian Switching

    Directory of Open Access Journals (Sweden)

    Enwen Zhu

    2010-01-01

    Full Text Available In this paper, the exponential stability analysis problem is considered for a class of recurrent neural networks (RNNs with random delay and Markovian switching. The evolution of the delay is modeled by a continuous-time homogeneous Markov process with a finite number of states. The main purpose of this paper is to establish easily verifiable conditions under which the random delayed recurrent neural network with Markovian switching is exponentially stable. The analysis is based on the Lyapunov-Krasovskii functional and stochastic analysis approach, and the conditions are expressed in terms of linear matrix inequalities, which can be readily checked by using some standard numerical packages such as the Matlab LMI Toolbox. A numerical example is exploited to show the usefulness of the derived LMI-based stability conditions.

  13. Theoretical design and analysis of wideband active hard electromagnetic surfaces using non-Foster circuit loaded anisotropic metasurfaces

    Science.gov (United States)

    Li, Yunbo; Li, Aobo; Sievenpiper, Daniel

    2018-02-01

    The electromagnetic (EM) hard surface which can both support transverse electric and transverse magnetic surface wave modes has the important ability to reduce the EM blockage of metallic obstacles. We propose a method to design an electrically thin hard surface with wide bandwidth by loading with non-Foster elements. The wideband hard surface composed of an anisotropic impedance coating can be considered as a kind of active metasurface. We develop a method to determine the values of the loading non-Foster circuit which can minimize the dispersion of the unit cells. For this method, we derive accurate values for the loading non-Foster elements through theoretical analysis. We also determine the fundamental limitations on the bandwidth due to stability requirements. To verify our theoretical design, we simulate the transmission performance between the two ports on opposite sides of a metallic rhombus-shaped obstacle coated with the non-Foster based metasurface. The simulated results show that the blockage has been largely reduced over a broad bandwidth from 0.2 GHz to 1.5 GHz. Finally, we provide a discussion on how the resistive part of the non-Foster circuit can affect the performance of the wideband hard surface coating.

  14. Artificial neural network for on-site quantitative analysis of soils using laser induced breakdown spectroscopy

    Energy Technology Data Exchange (ETDEWEB)

    El Haddad, J. [Univ. Bordeaux, LOMA, UMR 5798, F-33400 Talence (France); CNRS, LOMA, UMR 5798, F-33400 Talence (France); Villot-Kadri, M.; Ismaël, A.; Gallou, G. [IVEA Solution, Centre Scientifique d' Orsay, Bât 503, 91400 Orsay (France); Michel, K.; Bruyère, D.; Laperche, V. [BRGM, Service Métrologie, Monitoring et Analyse, 3 avenue Claude Guillemin, B.P 36009, 45060 Orléans Cedex (France); Canioni, L. [Univ. Bordeaux, LOMA, UMR 5798, F-33400 Talence (France); CNRS, LOMA, UMR 5798, F-33400 Talence (France); Bousquet, B., E-mail: bruno.bousquet@u-bordeaux1.fr [Univ. Bordeaux, LOMA, UMR 5798, F-33400 Talence (France); CNRS, LOMA, UMR 5798, F-33400 Talence (France)

    2013-01-01

    Nowadays, due to environmental concerns, fast on-site quantitative analyses of soils are required. Laser induced breakdown spectroscopy is a serious candidate to address this challenge and is especially well suited for multi-elemental analysis of heavy metals. However, saturation and matrix effects prevent from a simple treatment of the LIBS data, namely through a regular calibration curve. This paper details the limits of this approach and consequently emphasizes the advantage of using artificial neural networks well suited for non-linear and multi-variate calibration. This advanced method of data analysis is evaluated in the case of real soil samples and on-site LIBS measurements. The selection of the LIBS data as input data of the network is particularly detailed and finally, resulting errors of prediction lower than 20% for aluminum, calcium, copper and iron demonstrate the good efficiency of the artificial neural networks for on-site quantitative LIBS of soils. - Highlights: ► We perform on-site quantitative LIBS analysis of soil samples. ► We demonstrate that univariate analysis is not convenient. ► We exploit artificial neural networks for LIBS analysis. ► Spectral lines other than the ones from the analyte must be introduced.

  15. G gene-deficient single-round rabies viruses for neuronal circuit analysis.

    Science.gov (United States)

    Ghanem, Alexander; Conzelmann, Karl-Klaus

    2016-05-02

    Rhabdoviruses like the neurotropic rabies virus are fully amenable to pseudotyping with homologous and heterologous membrane proteins, which is being harnessed for the study of viral envelope proteins, viral retargeting, or immunization purposes. Particularly, pseudotyped delta G rabies viruses are emerging as safe and superb tools for mapping direct synaptic connections and analyzing neuronal circuits in the central and peripheral nervous system, which is a fundamental pillar of modern neuroscience. Such retrograde rabies mono-transsynaptic tracers in combination with optogenetics and modern in vivo imaging methods are opening entirely new avenues of investigation in neuroscience and help in answering major outstanding questions of connectivity and function of the nervous system. Here, we provide a brief overview on the biology and life cycle of rabies virus with emphasis on neuronal infection via axon ends, transport, and transsynaptic transmission of the virus. Pseudotyping of single-round, G-deleted virus with foreign glycoproteins allows to determine tropism and entry route, resulting in either retro- or anterograde labeling of neurons. Pseudotyping in vitro also allows specific targeting of cells that serve as starter cells for transsynaptic tracing, and pseudotyping in situ for a single (mono-transsynaptic) step of transmission to presynaptic neurons. We describe principle and experimental variations for defining "starter" cells for mono-transsynaptic tracing with ΔG rabies virus and outline open questions and limitations of the approach. Copyright © 2015 Elsevier B.V. All rights reserved.

  16. Analysis of the Diurnal Variation of the Global Electric Circuit Obtained From Different Numerical Models

    Science.gov (United States)

    Jánský, Jaroslav; Lucas, Greg M.; Kalb, Christina; Bayona, Victor; Peterson, Michael J.; Deierling, Wiebke; Flyer, Natasha; Pasko, Victor P.

    2017-12-01

    This work analyzes different current source and conductivity parameterizations and their influence on the diurnal variation of the global electric circuit (GEC). The diurnal variations of the current source parameterizations obtained using electric field and conductivity measurements from plane overflights combined with global Tropical Rainfall Measuring Mission satellite data give generally good agreement with measured diurnal variation of the electric field at Vostok, Antarctica, where reference experimental measurements are performed. An approach employing 85 GHz passive microwave observations to infer currents within the GEC is compared and shows the best agreement in amplitude and phase with experimental measurements. To study the conductivity influence, GEC models solving the continuity equation in 3-D are used to calculate atmospheric resistance using yearly averaged conductivity obtained from the global circulation model Community Earth System Model (CESM). Then, using current source parameterization combining mean currents and global counts of electrified clouds, if the exponential conductivity is substituted by the conductivity from CESM, the peak to peak diurnal variation of the ionospheric potential of the GEC decreases from 24% to 20%. The main reason for the change is the presence of clouds while effects of 222Rn ionization, aerosols, and topography are less pronounced. The simulated peak to peak diurnal variation of the electric field at Vostok is increased from 15% to 18% from the diurnal variation of the global current in the GEC if conductivity from CESM is used.

  17. Regulatory Analysis for the resolution of Generic Issue 142: Leakage through electrical isolators in instrumentation circuits

    Energy Technology Data Exchange (ETDEWEB)

    Rourk, C.J.

    1993-09-01

    Generic Issue (GI) 142 deals with staff concerns about the design of isolation devices used to ensure separation between Class 1E and non-Class 1E electrical control and instrumentation circuits. This issue was initiated in June 1987. Staff reviews of the implementation of the Safety Parameter Display System (SPDS) requirement indicated that some isolation devices used to provide an interface between the non-Class 1E SPDS and the Class 1E safety systems would allow signal leakage if electrically challenged. It was unknown if the amount of leakage posed a hazard to safe operation of the Class 1E system. A review of failure records does not reveal any incidents of system damage caused by isolation device challenge. Furthermore, a review of existing PRA data indicates that the safety significance of ID challenge is low, at the expected challenge event frequency. However, based upon the potential design variations in future control systems resulting from application of computer technology, additional design and qualification test requirements for future plants are recommended.

  18. A Neural Network Based Workstation for Automated Cell Proliferation Analysis

    Science.gov (United States)

    2001-10-25

    proliferation analysis, of cytological microscope images. The software of the system assists the expert biotechnologist during cell proliferation and...work was supported by the Programa de Apoyo a Proyectos de Desarrollo e Investigacíon en Informática REDII 2000. We thank Blanca Itzel Taboada for

  19. Predicting Television Extreme Viewers and Nonviewers: A Neural Network Analysis.

    Science.gov (United States)

    Paik, Haejung; Marzban, Caren

    1995-01-01

    States that in an attempt to better understand the attributes of the "average" viewer, an analysis of data characterizing television nonviewers and extreme viewers was performed. Identifies a set of demographic variables as the strongest predictor of nonviewers and the combination of family-related and lifestyle/social activity-related…

  20. Neural network for automatic analysis of motility data

    DEFF Research Database (Denmark)

    Jakobsen, Erik; Kruse-Andersen, S; Kolberg, Jens Godsk

    1994-01-01

    Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esophageal motor abnormalities. A subsequent automatic analysis of the resulting motility data relies on strict mathematical criteria for recognition of pressure...

  1. Scientific progress regarding neural regeneration in the Web of Science: A 10-year bibliometric analysis.

    Science.gov (United States)

    Pan, Yuntao; Zhang, Yuhua; Gao, Xiaopei; Jia, Jia; Gao, Jiping; Ma, Zheng

    2013-12-25

    Science and Technology programs were the majority. Our bibliometric analysis provides a historical perspective on the progress of neural regeneration research. At present, the number of articles addressing neural regeneration is increasing rapidly; however, through analysis of citations it is clear that there is a long way to go to improve the academic quality.

  2. Dynamic imaging and quantitative analysis of cranial neural tube closure in the mouse embryo using optical coherence tomography.

    Science.gov (United States)

    Wang, Shang; Garcia, Monica D; Lopez, Andrew L; Overbeek, Paul A; Larin, Kirill V; Larina, Irina V

    2017-01-01

    Neural tube closure is a critical feature of central nervous system morphogenesis during embryonic development. Failure of this process leads to neural tube defects, one of the most common forms of human congenital defects. Although molecular and genetic studies in model organisms have provided insights into the genes and proteins that are required for normal neural tube development, complications associated with live imaging of neural tube closure in mammals limit efficient morphological analyses. Here, we report the use of optical coherence tomography (OCT) for dynamic imaging and quantitative assessment of cranial neural tube closure in live mouse embryos in culture. Through time-lapse imaging, we captured two neural tube closure mechanisms in different cranial regions, zipper-like closure of the hindbrain region and button-like closure of the midbrain region. We also used OCT imaging for phenotypic characterization of a neural tube defect in a mouse mutant. These results suggest that the described approach is a useful tool for live dynamic analysis of normal neural tube closure and neural tube defects in the mouse model.

  3. Signaling and transcriptional regulation in neural crest specification and migration: lessons from xenopus embryos.

    Science.gov (United States)

    Pegoraro, Caterina; Monsoro-Burq, Anne H

    2013-01-01

    The neural crest is a population of highly migratory and multipotent cells, which arises from the border of the neural plate in vertebrate embryos. In the last few years, the molecular actors of neural crest early development have been intensively studied, notably by using the frog embryo, as a prime model for the analysis of the earliest embryonic inductions. In addition, tremendous progress has been made in understanding the molecular and cellular basis of Xenopus cranial neural crest migration, by combining in vitro and in vivo analysis. In this review, we examine how the action of previously known neural crest-inducing signals [bone morphogenetic protein (BMP), wingless-int (Wnt), fibroblast growth factor (FGF)] is controlled by newly discovered modulators during early neural plate border patterning and neural crest specification. This regulation controls the induction of key transcription factors that cooperate to pattern the premigratory neural crest progenitors. These data are discussed in the perspective of the gene regulatory network that controls neural and neural crest patterning. We then address recent findings on noncanonical Wnt signaling regulation, cell polarization, and collective cell migration which highlight how cranial neural crest cells populate their target tissue, the branchial arches, in vivo. More than ever, the neural crest stands as a powerful and attractive model to decipher complex vertebrate regulatory circuits in vivo. Copyright © 2012 Wiley Periodicals, Inc.

  4. Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System.

    Science.gov (United States)

    Kim, Sungkon; Lee, Jungwhee; Park, Min-Seok; Jo, Byung-Wan

    2009-01-01

    This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM) systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN) was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

  5. Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System

    Directory of Open Access Journals (Sweden)

    Min-Seok Park

    2009-10-01

    Full Text Available This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

  6. Reliability Analysis of the Chatter Stability during Milling Using a Neural Network

    Directory of Open Access Journals (Sweden)

    Sen Hu

    2016-01-01

    Full Text Available The parameters of a system have the randomness generally in the process of milling, which influences the stability of the milling. This paper uses the neural network to get a comprehensive analysis of the influences of random factors in milling and proposes a method for reliability analysis of the regenerative chatter stability in milling. Dynamic model of milling regenerative chatter is established, and stability lobe diagram is obtained by the full-discretization method (FDM. The neural network is applied to approximate the functional relationship of the limit axial cutting depth; then the reliability is computed with the Monte Carlo simulation method (MCSM and the moment method (MM, respectively. Finally, the results of an example are used to demonstrate the efficiency and accuracy of the proposed method.

  7. Neural network-based survey analysis of risk management practices in new product development

    DEFF Research Database (Denmark)

    Kampianakis, Andreas N.; Oehmen, Josef

    2017-01-01

    The current study investigates the applicability of Artificial Neural Networks (ANNs) to analyse survey data on the effectiveness of risk management practices in product development (PD) projects, and its ability to forecast project outcomes. Moreover, this study presents the relations between risk...... Neural Networks. Dataset used is a filtered survey of 291 product development programs. Answers of this survey are used as training input and target output, in pattern recognition two-layer feed forward networks, using various transfer functions. Using this method, relations among 6 project practices...... and 13 outcome metrics were revealed. Results of this analysis are compared with existent results made through statistical analysis in prior work of one of the authors. Future investigation is needed in order to tackle the lack of data and create an easy to use platform for industrial use....

  8. Fluvial facies reservoir productivity prediction method based on principal component analysis and artificial neural network

    Directory of Open Access Journals (Sweden)

    Pengyu Gao

    2016-03-01

    Full Text Available It is difficult to forecast the well productivity because of the complexity of vertical and horizontal developments in fluvial facies reservoir. This paper proposes a method based on Principal Component Analysis and Artificial Neural Network to predict well productivity of fluvial facies reservoir. The method summarizes the statistical reservoir factors and engineering factors that affect the well productivity, extracts information by applying the principal component analysis method and approximates arbitrary functions of the neural network to realize an accurate and efficient prediction on the fluvial facies reservoir well productivity. This method provides an effective way for forecasting the productivity of fluvial facies reservoir which is affected by multi-factors and complex mechanism. The study result shows that this method is a practical, effective, accurate and indirect productivity forecast method and is suitable for field application.

  9. Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks

    Directory of Open Access Journals (Sweden)

    Luciano Pivoto Specht

    2007-03-01

    Full Text Available It is of a great importance to know binders' viscosity in order to perform handling, mixing, application processes and asphalt mixes compaction in highway surfacing. This paper presents the results of viscosity measurement in asphalt-rubber binders prepared in laboratory. The binders were prepared varying the rubber content, rubber particle size, duration and temperature of mixture, all following a statistical design plan. The statistical analysis and artificial neural networks were used to create mathematical models for prediction of the binders viscosity. The comparison between experimental data and simulated results with the generated models showed best performance of the neural networks analysis in contrast to the statistic models. The results indicated that the rubber content and duration of mixture have major influence on the observed viscosity for the considered interval of parameters variation.

  10. 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.

  11. 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...

  12. All-diamond functional surface micro-electrode arrays for brain-slice neural analysis

    OpenAIRE

    Vahidpour, Farnoosh; Curley, Lowry; Biró, István; McDonald, Matthew; Croux, Dieter; POBEDINSKAS, Paulius; Haenen, Ken; Giugliano, Michele; Zivcova, Zuzana Vlckova; Kavan, Ladislav; Nesladek, Milos

    2017-01-01

    Abstract: Diamond-based microelectrode arrays were fabricated by using nanocrystalline diamond as an insulating layer and conductive boron-doped in order to used them for analysis of brain cortical slices. MEA surface is solely composed of diamond, exposed to the cells. The impedance measurements showed negligible cross-talk between neighbouring diamond microelectrodes. Local field potentials related to neural signals were then successfully recorded from pharmacologically disinhibited rat cor...

  13. Forest Connectivity Regions of Canada Using Circuit Theory and Image Analysis

    Science.gov (United States)

    Pelletier, David; Lapointe, Marc-Élie; Wulder, Michael A.; White, Joanne C.; Cardille, Jeffrey A.

    2017-01-01

    Ecological processes are increasingly well understood over smaller areas, yet information regarding interconnections and the hierarchical nature of ecosystems remains less studied and understood. Information on connectivity over large areas with high resolution source information provides for both local detail and regional context. The emerging capacity to apply circuit theory to create maps of omnidirectional connectivity provides an opportunity for improved and quantitative depictions of forest connectivity, supporting the formation and testing of hypotheses about the density of animal movement, ecosystem structure, and related links to natural and anthropogenic forces. In this research, our goal was to delineate regions where connectivity regimes are similar across the boreal region of Canada using new quantitative analyses for characterizing connectivity over large areas (e.g., millions of hectares). Utilizing the Earth Observation for Sustainable Development of forests (EOSD) circa 2000 Landsat-derived land-cover map, we created and analyzed a national-scale map of omnidirectional forest connectivity at 25m resolution over 10000 tiles of 625 km2 each, spanning the forested regions of Canada. Using image recognition software to detect corridors, pinch points, and barriers to movements at multiple spatial scales in each tile, we developed a simple measure of the structural complexity of connectivity patterns in omnidirectional connectivity maps. We then mapped the Circuitscape resistance distance measure and used it in conjunction with the complexity data to study connectivity characteristics in each forested ecozone. Ecozone boundaries masked substantial systematic patterns in connectivity characteristics that are uncovered using a new classification of connectivity patterns that revealed six clear groups of forest connectivity patterns found in Canada. The resulting maps allow exploration of omnidirectional forest connectivity patterns at full resolution while

  14. Forest Connectivity Regions of Canada Using Circuit Theory and Image Analysis.

    Directory of Open Access Journals (Sweden)

    David Pelletier

    Full Text Available Ecological processes are increasingly well understood over smaller areas, yet information regarding interconnections and the hierarchical nature of ecosystems remains less studied and understood. Information on connectivity over large areas with high resolution source information provides for both local detail and regional context. The emerging capacity to apply circuit theory to create maps of omnidirectional connectivity provides an opportunity for improved and quantitative depictions of forest connectivity, supporting the formation and testing of hypotheses about the density of animal movement, ecosystem structure, and related links to natural and anthropogenic forces. In this research, our goal was to delineate regions where connectivity regimes are similar across the boreal region of Canada using new quantitative analyses for characterizing connectivity over large areas (e.g., millions of hectares. Utilizing the Earth Observation for Sustainable Development of forests (EOSD circa 2000 Landsat-derived land-cover map, we created and analyzed a national-scale map of omnidirectional forest connectivity at 25m resolution over 10000 tiles of 625 km2 each, spanning the forested regions of Canada. Using image recognition software to detect corridors, pinch points, and barriers to movements at multiple spatial scales in each tile, we developed a simple measure of the structural complexity of connectivity patterns in omnidirectional connectivity maps. We then mapped the Circuitscape resistance distance measure and used it in conjunction with the complexity data to study connectivity characteristics in each forested ecozone. Ecozone boundaries masked substantial systematic patterns in connectivity characteristics that are uncovered using a new classification of connectivity patterns that revealed six clear groups of forest connectivity patterns found in Canada. The resulting maps allow exploration of omnidirectional forest connectivity patterns at

  15. Artificial Neural Network Analysis of Immobilized Lipase Catalyzed Synthesis of Biodiesel from Rapeseed Soapstock

    Science.gov (United States)

    Ying, Yanjie; Shao, Ping; Jiang, Shaotong; Sun, Peilong

    Refined vegetable oils are the predominant feedstocks for the production of biodiesel. However, their relatively high costs render the resulting fuels unable to compete with petroleum-derived fuel. Artificial neural network (ANN) analysis of immobilized Candida rugosa lipase (CRL) on chitosan catalyzed preparation of biodiesel from rapeseed soapstock with methanol was carried out. Methanol substrate molar ratio, enzyme amount, water content and reaction temperature were four important parameters employed. Back-Propagation algorithm with momentous factor was adopted to train the neural network. The momentous factor and learning rate were selected as 0.95 and 0.8. ANN analysis showed good correspondence between experimental and predicted values. The coefficient of determination (R2) between experimental and predicted values was 99.20%. Biodiesel conversion of 75.4% was obtained when optimum conditions of immobilized lipase catalysed for biodiesel production were methanol substrate molar ratio of 4.4:1, enzyme amount of 11.6%, water content of 4% and reaction temperature of 45°. Methyl ester content was above 95% after short path distillation process. Biodiesel conversion was increased markedly by neural network analysis.

  16. Analysis of Power Laws, Shape Collapses, and Neural Complexity: New Techniques and MATLAB Support via the NCC Toolbox.

    Science.gov (United States)

    Marshall, Najja; Timme, Nicholas M; Bennett, Nicholas; Ripp, Monica; Lautzenhiser, Edward; Beggs, John M

    2016-01-01

    Neural systems include interactions that occur across many scales. Two divergent methods for characterizing such interactions have drawn on the physical analysis of critical phenomena and the mathematical study of information. Inferring criticality in neural systems has traditionally rested on fitting power laws to the property distributions of "neural avalanches" (contiguous bursts of activity), but the fractal nature of avalanche shapes has recently emerged as another signature of criticality. On the other hand, neural complexity, an information theoretic measure, has been used to capture the interplay between the functional localization of brain regions and their integration for higher cognitive functions. Unfortunately, treatments of all three methods-power-law fitting, avalanche shape collapse, and neural complexity-have suffered from shortcomings. Empirical data often contain biases that introduce deviations from true power law in the tail and head of the distribution, but deviations in the tail have often been unconsidered; avalanche shape collapse has required manual parameter tuning; and the estimation of neural complexity has relied on small data sets or statistical assumptions for the sake of computational efficiency. In this paper we present technical advancements in the analysis of criticality and complexity in neural systems. We use maximum-likelihood estimation to automatically fit power laws with left and right cutoffs, present the first automated shape collapse algorithm, and describe new techniques to account for large numbers of neural variables and small data sets in the calculation of neural complexity. In order to facilitate future research in criticality and complexity, we have made the software utilized in this analysis freely available online in the MATLAB NCC (Neural Complexity and Criticality) Toolbox.

  17. The primary circuit materials properties results analysis performed on archive material used in NPP V-1 and Kola NPP Units 1 and 2

    Energy Technology Data Exchange (ETDEWEB)

    Kupca, L.; Beno, P. [Nuclear Power Plants Research Institute Inc., Trnava (Slovakia)

    1997-04-01

    A very brief summary is provided of a primary circuit piping material properties analysis. The analysis was performed for the Bohunice V-1 reactor and the Kola-1 and -2 reactors. Assessment was performed on Bohunice V-1 archive materials and primary piping material cut from the Kola units after 100,000 hours of operation. Main research program tasks included analysis of mechanical properties, corrosion stability, and microstructural properties. Analysis results are not provided.

  18. Circuit design for reliability

    CERN Document Server

    Cao, Yu; Wirth, Gilson

    2015-01-01

    This book presents physical understanding, modeling and simulation, on-chip characterization, layout solutions, and design techniques that are effective to enhance the reliability of various circuit units.  The authors provide readers with techniques for state of the art and future technologies, ranging from technology modeling, fault detection and analysis, circuit hardening, and reliability management. Provides comprehensive review on various reliability mechanisms at sub-45nm nodes; Describes practical modeling and characterization techniques for reliability; Includes thorough presentation of robust design techniques for major VLSI design units; Promotes physical understanding with first-principle simulations.

  19. INTERNAL MEASUREMENTS FOR FAILURE ANALYSIS AND CHIP VERIFICATION OF VLSI CIRCUITS

    OpenAIRE

    KÖlzer, J.; Otto, J.

    1989-01-01

    Chip verification and failure analysis during the design evaluation of very large scale integrated (VLSI) devices call for highly accurate internal analysis methods. After having characterized the first silicon by automated functional testing, classification and statistical analysis can be carried out : In this way a rough electrical evaluation of the material under investigation can be made. Further clues to a faulty device behavior can only be obtained by internal measurements. Serious malf...

  20. Assessment of neural network, frequency ratio and regression models for landslide susceptibility analysis

    Science.gov (United States)

    Pradhan, B.; Buchroithner, M. F.; Mansor, S.

    2009-04-01

    This paper presents the assessment results of spatially based probabilistic three models using Geoinformation Techniques (GIT) for landslide susceptibility analysis at Penang Island in Malaysia. Landslide locations within the study areas were identified by interpreting aerial photographs, satellite images and supported with field surveys. Maps of the topography, soil type, lineaments and land cover were constructed from the spatial data sets. There are nine landslide related factors were extracted from the spatial database and the neural network, frequency ratio and logistic regression coefficients of each factor was computed. Landslide susceptibility maps were drawn for study area using neural network, frequency ratios and logistic regression models. For verification, the results of the analyses were compared with actual landslide locations in study area. The verification results show that frequency ratio model provides higher prediction accuracy than the ANN and regression models.