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  1. Coupled Neural Associative Memories

    OpenAIRE

    Karbasi, Amin; Salavati, Amir Hesam; Shokrollahi, Amin

    2013-01-01

    We propose a novel architecture to design a neural associative memory that is capable of learning a large number of patterns and recalling them later in presence of noise. It is based on dividing the neurons into local clusters and parallel plains, very similar to the architecture of the visual cortex of macaque brain. The common features of our proposed architecture with those of spatially-coupled codes enable us to show that the performance of such networks in eliminating noise is drastical...

  2. A Study on Associative Neural Memories

    OpenAIRE

    B.D.C.N.Prasad; P. E. S. N. Krishna Prasad; Sagar Yeruva; P Sita Rama Murty

    2011-01-01

    Memory plays a major role in Artificial Neural Networks. Without memory, Neural Network can not be learned itself. One of the primary concepts of memory in neural networks is Associative neural memories. A survey has been made on associative neural memories such as Simple associative memories (SAM), Dynamic associative memories (DAM), Bidirectional Associative memories (BAM), Hopfield memories, Context Sensitive Auto-associative memories (CSAM) and so on. These memories can be applied in vari...

  3. On neural networks that design neural associative memories.

    Science.gov (United States)

    Chan, H Y; Zak, S H

    1997-01-01

    The design problem of generalized brain-state-in-a-box (GBSB) type associative memories is formulated as a constrained optimization program, and "designer" neural networks for solving the program in real time are proposed. The stability of the designer networks is analyzed using Barbalat's lemma. The analyzed and synthesized neural associative memories do not require symmetric weight matrices. Two types of the GBSB-based associative memories are analyzed, one when the network trajectories are constrained to reside in the hypercube [-1, 1](n) and the other type when the network trajectories are confined to stay in the hypercube [0, 1](n). Numerical examples and simulations are presented to illustrate the results obtained.

  4. Neural associative memories and sparse coding.

    Science.gov (United States)

    Palm, Günther

    2013-01-01

    The theoretical, practical and technical development of neural associative memories during the last 40 years is described. The importance of sparse coding of associative memory patterns is pointed out. The use of associative memory networks for large scale brain modeling is also mentioned.

  5. Optical neural computing for associative memories

    Energy Technology Data Exchange (ETDEWEB)

    Hsu, Ken Yuh.

    1990-01-01

    Optical techniques for implementing neural computers are presented. In particular, holographic associative memories with feedback are investigated. Characteristics of optical neurons and optical interconnections are discussed. An LCLV is used for simulating a 2-D array of approximately 160,000 optical neurons. Thermoplastic plates are used for providing holographic interconnections among these neurons. The problem of degenerate readout in holographic interconnections and the method of sampling grids to solve this problem are presented. Two optical neural networks for associative memories are implemented and demonstrated. The first one is an optical implementation of the Hopfield network. It performs the function of auto-association that recognizes 2-D images from a distorted or partially blocked input. The trade-off between distortion tolerance and discrimination capability against new images is discussed. The second optical loop is a 2-layer network with feedback. It performs the function of hetero-association, which locks the recognized input and its associated image as a stable state in the loop. In both optical loops, it is shown that the neural gain and the similarity between the input and the stored images are the main factors that determine the dynamics of the network. Neural network models for the optical loops are presented. Equations of motion for describing the dynamical behavior of the systems are derived. The reciprocal vector basis corresponding to stored images is derived. A geometrical method is then introduced which allows us to inspect the convergence property of the system. It is also shown that the main factors that determine the system dynamics are the neural gain and the initial conditions. Photorefractive holography for optical interconnections and sampling grids for volume holographic interconnections are presented.

  6. Auto-associative nanoelectronic neural network

    Energy Technology Data Exchange (ETDEWEB)

    Nogueira, C. P. S. M.; Guimarães, J. G. [Departamento de Engenharia Elétrica - Laboratório de Dispositivos e Circuito Integrado, Universidade de Brasília, CP 4386, CEP 70904-970 Brasília DF (Brazil)

    2014-05-15

    In this paper, an auto-associative neural network using single-electron tunneling (SET) devices is proposed and simulated at low temperature. The nanoelectronic auto-associative network is able to converge to a stable state, previously stored during training. The recognition of the pattern involves decreasing the energy of the input state until it achieves a point of local minimum energy, which corresponds to one of the stored patterns.

  7. Are There Disorders or Conditions Associated with Neural Tube Defects?

    Science.gov (United States)

    ... Publications Are there disorders or conditions associated with neural tube defects? Skip sharing on social media links Share this: Page Content Infants born with neural tube defects that are not immediately fatal may have ...

  8. Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks.

    Science.gov (United States)

    Pu, Yi-Fei; Yi, Zhang; Zhou, Ji-Liu

    2016-07-14

    This paper mainly discusses a novel conceptual framework: fractional Hopfield neural networks (FHNN). As is commonly known, fractional calculus has been incorporated into artificial neural networks, mainly because of its long-term memory and nonlocality. Some researchers have made interesting attempts at fractional neural networks and gained competitive advantages over integer-order neural networks. Therefore, it is naturally makes one ponder how to generalize the first-order Hopfield neural networks to the fractional-order ones, and how to implement FHNN by means of fractional calculus. We propose to introduce a novel mathematical method: fractional calculus to implement FHNN. First, we implement fractor in the form of an analog circuit. Second, we implement FHNN by utilizing fractor and the fractional steepest descent approach, construct its Lyapunov function, and further analyze its attractors. Third, we perform experiments to analyze the stability and convergence of FHNN, and further discuss its applications to the defense against chip cloning attacks for anticounterfeiting. The main contribution of our work is to propose FHNN in the form of an analog circuit by utilizing a fractor and the fractional steepest descent approach, construct its Lyapunov function, prove its Lyapunov stability, analyze its attractors, and apply FHNN to the defense against chip cloning attacks for anticounterfeiting. A significant advantage of FHNN is that its attractors essentially relate to the neuron's fractional order. FHNN possesses the fractional-order-stability and fractional-order-sensitivity characteristics.

  9. Classification of Chronic Whiplash Associated Disorders With Artificial Neural Networks

    Science.gov (United States)

    2007-11-02

    question is how to analyse a multiple of features in an appropriate way. Different Artificial Neural Networks (ANN) have been developed during the past...sample IR-light, at 60 Hz, reflected by the retro-reflective markers. CLASSIFICATION OF CHRONIC WHIPLASH ASSOCIATED DISORDERS WITH ARTIFICIAL NEURAL NETWORKS F...Associated Disorders With Artificial Neural Networks Contract Number Grant Number Program Element Number Author(s) Project Number Task Number

  10. A new model of neural associative memories.

    Science.gov (United States)

    Hao, J; Vandewalle, J

    1994-03-01

    In this paper, we present a new model of discrete neural associative memories and its design rule. The most important feature of this new model is that a static mapping instead of the dynamic convergent process is used to retrieve the stored messages. The new model features a two-layer structure, with feedforward connections only and uses two kinds of neurons which implement different output functions. Another important feature is that this new model employs an extremely simple weight setup rule and all the resulted weights can only assume two different values, -1 and +1, which facilitates the VLSI implementation. Compared to the famous discrete Hopfield model designed with the well-known Hebbian rule or any other rule, the new model can guarantee all the given patterns to be stored as fixed points. Moreover, each fixed point is surrounded by an attraction basin (which is a ball in the Hamming distance sense) with the maximal possible radius. The performances of the new model are compared through some illustrative examples with those of the Hopfield associative memory designed using different methods.

  11. An evolutionary approach to associative memory in recurrent neural networks

    CERN Document Server

    Fujita, Sh; Fujita, Sh; Nishimura, H

    1994-01-01

    Abstract: In this paper, we investigate the associative memory in recurrent neural networks, based on the model of evolving neural networks proposed by Nolfi, Miglino and Parisi. Experimentally developed network has highly asymmetric synaptic weights and dilute connections, quite different from those of the Hopfield model. Some results on the effect of learning efficiency on the evolution are also presented.

  12. ABOUT HYBRID BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH DISCRETE DELAYS

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    In this paper, hybrid bidirectional associative memory neural networks with discrete delays is considered. By ingeniously importing real parameters di > 0(i = 1,2,···,n) which can be adjusted, we establish some new sufficient conditions for the dynamical characteristics of hybrid bidirectional associative memory neural networks with discrete delays by the method of variation of parameters and some analysis techniques. Our results generalize and improve the related results in [10,11]. Our work is significant...

  13. Temporal association in asymmetric neural networks

    Science.gov (United States)

    Sompolinsky, H.; Kanter, I.

    1986-12-01

    A neural network model which is capable of recalling time sequences and cycles of patterns is introduced. In this model, some of the synaptic connections, Jij, between pairs of neurons are asymmetric (Jij≠Jji) and have slow dynamic response. The effects of thermal noise on the generated sequences are discussed. Simulation results demonstrating the performance of the network are presented. The model may be also useful in understanding the generation of rhythmic patterns in biological motor systems.

  14. Neural correlates underlying true and false associative memories.

    Science.gov (United States)

    Dennis, Nancy A; Johnson, Christina E; Peterson, Kristina M

    2014-07-01

    Despite the fact that associative memory studies produce a large number of false memories, neuroimaging analyses utilizing this paradigm typically focus only on neural activity mediating successful retrieval. The current study sought to expand on this prior research by examining the neural basis of both true and false associative memories. Though associative false memories are substantially different than those found in semantic or perceptual false memory paradigms, results suggest that associative false memories are mediated by similar neural mechanisms. Specifically, we found increased frontal activity that likely represents enhanced monitoring and evaluation compared to that needed for true memories and correct rejections. Results also indicated that true, and not false associative memories, are mediated by neural activity in the MTL, specifically the hippocampus. Finally, while activity in early visual cortex distinguished true from false memories, a lack of neural differences between hits and correct rejections failed to support previous findings suggesting that activity in early visual cortex represents sensory reactivation of encoding-related processing.

  15. Real-time synthesis of sparsely interconnected neural associative memories.

    Science.gov (United States)

    Chan, Hubert Y.; Zak, Stanislaw H.

    1998-06-01

    The problem of implementing associative memories using sparsely interconnected generalized Brain-State-in-a-Box (gBSB) network is addressed in this paper. In particular, a "designer" neural network that synthesizes the associative memories is proposed. An upper bound on the time required for the designer network to reach a solution is determined. A neighborhood criterion with toroidal geometry for the cellular gBSB network is analyzed, in which the number of adjacent cells is independent of the generic cell location. A design method of neural associative memories with prespecified interconnecting weights is presented. The effectiveness of the proposed synthesis method is demonstrated with numerical examples.

  16. Design of GBSB neural associative memories using semidefinite programming.

    Science.gov (United States)

    Park, J; Cho, H; Park, D

    1999-01-01

    This paper concerns reliable search for the optimally performing GBSB (generalized brain-state-in-a-box) neural associative memory given a set of prototype patterns to be stored as stable equilibrium points. First, we observe some new qualitative properties of the GBSB model. Next, we formulate the synthesis of GBSB neural associative memories as a constrained optimization problem. Finally, we convert the optimization problem into a semidefinite program (SDP), which can be solved efficiently by recently developed interior point methods. The validity of this approach is illustrated by a design example.

  17. Multi-Valued Associative Memory Neural Network

    Institute of Scientific and Technical Information of China (English)

    修春波; 刘向东; 张宇河

    2003-01-01

    A novel learning method for multi-valued associative memory network is introduced, which is based on Hebb rule, but utilizes more information. According to the current probe vector, the connection weights matrix could be chosen dynamically. Double-valued and multi-valued associative memory are all realized in our simulation experiment. The experimental results show that the method could enhance the associative success rate.

  18. Double-pattern associative memory neural network with pattern loop

    Institute of Scientific and Technical Information of China (English)

    Jian WANG; Zongyuan MAO

    2004-01-01

    A double-patrern associative memory neural network with "pattern loop" is proposed. It can store 2N bit bipolar binary Patterns up to the order of 22N, retrieve part or all of the stored patrems which all have the minimum Hamming distance with input Pattern, completely eliminate spurious Patterns, and has higher storing efficiency and reliability than conventional associative memory. The length of a Pattern stored in this associative memory can be easily extended from 2N to kN.

  19. Neural coding in graphs of bidirectional associative memories.

    Science.gov (United States)

    Bouchain, A David; Palm, Günther

    2012-01-24

    In the last years we have developed large neural network models for the realization of complex cognitive tasks in a neural network architecture that resembles the network of the cerebral cortex. We have used networks of several cortical modules that contain two populations of neurons (one excitatory, one inhibitory). The excitatory populations in these so-called "cortical networks" are organized as a graph of Bidirectional Associative Memories (BAMs), where edges of the graph correspond to BAMs connecting two neural modules and nodes of the graph correspond to excitatory populations with associative feedback connections (and inhibitory interneurons). The neural code in each of these modules consists essentially of the firing pattern of the excitatory population, where mainly it is the subset of active neurons that codes the contents to be represented. The overall activity can be used to distinguish different properties of the patterns that are represented which we need to distinguish and control when performing complex tasks like language understanding with these cortical networks. The most important pattern properties or situations are: exactly fitting or matching input, incomplete information or partially matching pattern, superposition of several patterns, conflicting information, and new information that is to be learned. We show simple simulations of these situations in one area or module and discuss how to distinguish these situations based on the overall internal activation of the module. This article is part of a Special Issue entitled "Neural Coding".

  20. Electronic implementation of associative memory based on neural network models

    Science.gov (United States)

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

    1987-01-01

    An electronic embodiment of a neural network based associative memory in the form of a binary connection matrix is described. The nature of false memory errors, their effect on the information storage capacity of binary connection matrix memories, and a novel technique to eliminate such errors with the help of asymmetrical extra connections are discussed. The stability of the matrix memory system incorporating a unique local inhibition scheme is analyzed in terms of local minimization of an energy function. The memory's stability, dynamic behavior, and recall capability are investigated using a 32-'neuron' electronic neural network memory with a 1024-programmable binary connection matrix.

  1. Associative memory realized by a reconfigurable memristive Hopfield neural network.

    Science.gov (United States)

    Hu, S G; Liu, Y; Liu, Z; Chen, T P; Wang, J J; Yu, Q; Deng, L J; Yin, Y; Hosaka, Sumio

    2015-06-25

    Although synaptic behaviours of memristors have been widely demonstrated, implementation of an even simple artificial neural network is still a great challenge. In this work, we demonstrate the associative memory on the basis of a memristive Hopfield network. Different patterns can be stored into the memristive Hopfield network by tuning the resistance of the memristors, and the pre-stored patterns can be successfully retrieved directly or through some associative intermediate states, being analogous to the associative memory behaviour. Both single-associative memory and multi-associative memories can be realized with the memristive Hopfield network.

  2. Strategies to associate memories by unsupervised learning in neural networks

    Science.gov (United States)

    Agnes, E. J.; Mizusaki, B. E. P.; Erichsen, R., Jr.; Brunnet, L. G.

    2013-01-01

    In this work we study the effects of three different strategies to associate memories in a neural network composed by both excitatory and inhibitory spiking neurons, which are randomly connected through recurrent excitatory and inhibitory synapses. The system is intended to store a number of memories, associated to spatial external inputs. The strategies consist in the presentation of the input patterns through trials in: i) ordered sequence; ii) random sequence; iii) clustered sequences. In addition, an order parameter indicating the correlation between the trials' activities is introduced to compute associative memory capacities and the quality of memory retrieval.

  3. Evolving neural networks with iterative learning scheme for associative memory

    CERN Document Server

    Fujita, Sh

    1995-01-01

    A locally iterative learning (LIL) rule is adapted to a model of the associative memory based on the evolving recurrent-type neural networks composed of growing neurons. There exist extremely different scale parameters of time, the individual learning time and the generation in evolution. This model allows us definite investigation on the interaction between learning and evolution. And the reinforcement of the robustness against the noise is also achieved in the evolutional scheme.

  4. Chromosomal Abnormalities Associated with Neural Tube Defects (I: Full Aneuploidy

    Directory of Open Access Journals (Sweden)

    Chih-Ping Chen

    2007-12-01

    Full Text Available Fetuses with neural tube defects (NTDs carry a risk of chromosomal abnormalities. The risk varies with maternal age, gestational age at diagnosis, association with other structural abnormalities, and family history of chromosome aberrations. This article provides an overview of chromosomal abnormalities associated with NTDs in embryos, fetuses, and newborn patients, and a comprehensive review of numerical chromosomal abnormalities associated with NTDs, such as trisomy 18, trisomy 13, triploidy, trisomy 9, trisomy 2, trisomy 21, trisomy 7, trisomy 8, trisomy 14, trisomy 15, trisomy 16, trisomy 5 mosaicism, trisomy 11 mosaicism, trisomy 20 mosaicism, monosomy X, and tetraploidy. NTDs may be associated with aneuploidy. Perinatal identification of NTDs should alert one to the possibility of chromosomal abnormalities and prompt a thorough cytogenetic investigation and genetic counseling.

  5. GLOBAL DYNAMICS OF DELAYED BIDIRECTIONAL ASSOCIATIVE MEMORY (BAM) NEURAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    ZHOU Jin; LIU Zeng-rong; XIANG Lan

    2005-01-01

    Without assuming the smoothness, monotonicity and boundedness of the activation functions, some novel criteria on the existence and global exponential stability of equilibrium point for delayed bidirectional associative memory (BAM) neural networks are established by applying the Liapunov functional methods and matrix-algebraic techniques. It is shown that the new conditions presented in terms of a nonsingular M matrix described by the networks parameters, the connection matrix and the Lipschitz constant of the activation functions, are not only simple and practical, but also easier to check and less conservative than those imposed by similar results in recent literature.

  6. Neural network implementations of data association algorithms for sensor fusion

    Science.gov (United States)

    Brown, Donald E.; Pittard, Clarence L.; Martin, Worthy N.

    1989-01-01

    The paper is concerned with locating a time varying set of entities in a fixed field when the entities are sensed at discrete time instances. At a given time instant a collection of bivariate Gaussian sensor reports is produced, and these reports estimate the location of a subset of the entities present in the field. A database of reports is maintained, which ideally should contain one report for each entity sensed. Whenever a collection of sensor reports is received, the database must be updated to reflect the new information. This updating requires association processing between the database reports and the new sensor reports to determine which pairs of sensor and database reports correspond to the same entity. Algorithms for performing this association processing are presented. Neural network implementation of the algorithms, along with simulation results comparing the approaches are provided.

  7. Functional neural networks underlying semantic encoding of associative memories.

    Science.gov (United States)

    Crespo-Garcia, M; Cantero, J L; Pomyalov, A; Boccaletti, S; Atienza, M

    2010-04-15

    Evidence suggests that theta oscillations recruit distributed cortical representations to improve associative encoding under semantically congruent conditions. Here we show that positive effects of semantic context on encoding and retrieval of associations are mediated by changes in the coupling pattern between EEG theta sources. During successful encoding of semantically congruent face-location associations, the right superior parietal lobe showed enhanced theta phase synchronization with other regions within the lateral posterior parietal lobe (PPL) and left medial temporal lobe (MTL). However, functional coordination involving the inferior parietal lobe was higher in the incongruent condition. These results suggest a differential engagement of top-down and bottom-up mechanisms during encoding of semantically congruent and incongruent episodic associations, respectively. Although retrieval processes operated on a similar neural network, the main difference with the study phase was the larger amount of functional links shown by the lateral prefrontal cortex with regions of the MTL and PPL. All together, these results suggest that theta oscillations mediate, at least partially, the positive effect of semantic congruence on associative memory by (i) optimizing top-down attentional mechanisms through enhanced theta phase synchronization between dorsal regions of the PPL and MTL and (ii) by adjusting the control of automatic attention to sensory and contextual information reactivated in the MTL through functional connections with the inferior parietal lobe during both encoding and retrieval processes.

  8. State-dependent weights for neural associative memories.

    Science.gov (United States)

    Kothari, R; Lotlikar, R; Cahay, M

    1998-01-01

    In this article we study the effect of dynamically modifying the weight matrix on the performance of a neural associative memory. The dynamic modification is implemented by adding, at each step, the outer product of the current state, scaled by a suitable constant eta, to the correlation weight matrix. For single-shot synchronous dynamics, we analytically obtain the optimal value of eta. Although knowledge of the noise percentage is required for calculating the optimal value of eta, a fairly good choice of eta can be made even when the amount of noise is not known. Experimental results are provided in support of the analysis. The efficacy of the proposed modification is also experimentally verified for the case of asynchronous updating with transient length > 1.

  9. Neural Signaling of Food Healthiness Associated with Emotion Processing

    Science.gov (United States)

    Herwig, Uwe; Dhum, Matthias; Hittmeyer, Anna; Opialla, Sarah; Scherpiet, Sigrid; Keller, Carmen; Brühl, Annette B.; Siegrist, Michael

    2016-01-01

    The ability to differentiate healthy from unhealthy foods is important in order to promote good health. Food, however, may have an emotional connotation, which could be inversely related to healthiness. The neurobiological background of differentiating healthy and unhealthy food and its relations to emotion processing are not yet well understood. We addressed the neural activations, particularly considering the single subject level, when one evaluates a food item to be of a higher, compared to a lower grade of healthiness with a particular view on emotion processing brain regions. Thirty-seven healthy subjects underwent functional magnetic resonance imaging while evaluating the healthiness of food presented as photographs with a subsequent rating on a visual analog scale. We compared individual evaluations of high and low healthiness of food items and also considered gender differences. We found increased activation when food was evaluated to be healthy in the left dorsolateral prefrontal cortex and precuneus in whole brain analyses. In ROI analyses, perceived and rated higher healthiness was associated with lower amygdala activity and higher ventral striatal and orbitofrontal cortex activity. Females exerted a higher activation in midbrain areas when rating food items as being healthy. Our results underline the close relationship between food and emotion processing, which makes sense considering evolutionary aspects. Actively evaluating and deciding whether food is healthy is accompanied by neural signaling associated with reward and self-relevance, which could promote salutary nutrition behavior. The involved brain regions may be amenable to mechanisms of emotion regulation in the context of psychotherapeutic regulation of food intake. PMID:26903859

  10. Neural Signaling of Food Healthiness Associated with Emotion Processing.

    Science.gov (United States)

    Herwig, Uwe; Dhum, Matthias; Hittmeyer, Anna; Opialla, Sarah; Scherpiet, Sigrid; Keller, Carmen; Brühl, Annette B; Siegrist, Michael

    2016-01-01

    The ability to differentiate healthy from unhealthy foods is important in order to promote good health. Food, however, may have an emotional connotation, which could be inversely related to healthiness. The neurobiological background of differentiating healthy and unhealthy food and its relations to emotion processing are not yet well understood. We addressed the neural activations, particularly considering the single subject level, when one evaluates a food item to be of a higher, compared to a lower grade of healthiness with a particular view on emotion processing brain regions. Thirty-seven healthy subjects underwent functional magnetic resonance imaging while evaluating the healthiness of food presented as photographs with a subsequent rating on a visual analog scale. We compared individual evaluations of high and low healthiness of food items and also considered gender differences. We found increased activation when food was evaluated to be healthy in the left dorsolateral prefrontal cortex and precuneus in whole brain analyses. In ROI analyses, perceived and rated higher healthiness was associated with lower amygdala activity and higher ventral striatal and orbitofrontal cortex activity. Females exerted a higher activation in midbrain areas when rating food items as being healthy. Our results underline the close relationship between food and emotion processing, which makes sense considering evolutionary aspects. Actively evaluating and deciding whether food is healthy is accompanied by neural signaling associated with reward and self-relevance, which could promote salutary nutrition behavior. The involved brain regions may be amenable to mechanisms of emotion regulation in the context of psychotherapeutic regulation of food intake.

  11. Neural signalling of food healthiness associated with emotion processing

    Directory of Open Access Journals (Sweden)

    Uwe eHerwig

    2016-02-01

    Full Text Available The ability to differentiate healthy from unhealthy foods is important in order to promote good health. Food, however, may have an emotional connotation, which could be inversely related to healthiness. The neurobiological background of differentiating healthy and unhealthy food and its relations to emotion processing are not yet well understood. We addressed the neural activations, particularly considering the single subject level, when one evaluates a food item to be of a higher, compared to a lower grade of healthiness with a particular view on emotion processing brain regionsThirty-seven healthy subjects underwent functional magnetic resonance imaging while evaluating the healthiness of food presented as photographs with a subsequent rating on a visual analogue scale. We compared individual evaluations of high and low healthiness of food items and also considered gender differences.We found increased activation when food was evaluated to be healthy in the left dorsolateral prefrontal cortex and precuneus in whole brain analyses. In ROI analyses, perceived and rated higher healthiness was associated with lower amygdala activity and higher ventral striatal and orbitofrontal cortex activity. Females exerted a higher activation in midbrain areas when rating food items as being healthy.Our results underline the close relationship between food and emotion processing, which makes sense considering evolutionary aspects. Actively evaluating and deciding whether food is healthy is accompanied by neural signalling associated with reward and self-relevance, which could promote salutary nutrition behaviour. The involved brain regions may be amenable to mechanisms of emotion regulation in the context of psychotherapeutic regulation of food intake.

  12. Reduced order multiport parallel and multidirectional neural associative memories.

    Science.gov (United States)

    Bhatti, Abdul Aziz

    2009-05-01

    This paper proposes multiport parallel and multidirectional intraconnected associative memories of outer product type with reduced interconnections. Some new reduced order memory architectures such as k-directional and k-port parallel memories are suggested. These architectures are, also, very suitable for implementation of spatio-temporal sequences and multiassociative memories. It is shown that in the proposed memory architectures, a substational reduction in interconnections is achieved if the actual length of original N-bit long vectors is subdivided into k sublengths. Using these sublengths, submemory matrices, T ( s ) or W ( s ), are computed, which are then intraconnected to form k-port parallel or k-directional memories. The subdivisions of N-bit long vectors into k sublengths save ((k-1) x 100) / k % of interconnections. It is shown, by means of an example, that more than 80% reduction in interconnections is achieved. Minimum limit in bits on k as well as maximum limit on subdivisions in k is determined. The topologies of reduced interconnectivity developed in this paper are symmetric in structure and can be used to scale up to larger systems. The underlying principal of construction, storage and retrieval processes of such associative memories has been analyzed. The effect of complexity of different levels of reduced interconnectivity on the quality of retrieval, signal to noise ratio, and storage capacity has been investigated. The model possesses analogies to biological neural structures and digital parallel port memories commonly used in parallel and multiprocessing systems.

  13. Neural dynamics of learning sound-action associations.

    Directory of Open Access Journals (Sweden)

    Adam McNamara

    Full Text Available A motor component is pre-requisite to any communicative act as one must inherently move to communicate. To learn to make a communicative act, the brain must be able to dynamically associate arbitrary percepts to the neural substrate underlying the pre-requisite motor activity. We aimed to investigate whether brain regions involved in complex gestures (ventral pre-motor cortex, Brodmann Area 44 were involved in mediating association between novel abstract auditory stimuli and novel gestural movements. In a functional resonance imaging (fMRI study we asked participants to learn associations between previously unrelated novel sounds and meaningless gestures inside the scanner. We use functional connectivity analysis to eliminate the often present confound of 'strategic covert naming' when dealing with BA44 and to rule out effects of non-specific reductions in signal. Brodmann Area 44, a region incorporating Broca's region showed strong, bilateral, negative correlation of BOLD (blood oxygen level dependent response with learning of sound-action associations during data acquisition. Left-inferior-parietal-lobule (l-IPL and bilateral loci in and around visual area V5, right-orbital-frontal-gyrus, right-hippocampus, left-para-hippocampus, right-head-of-caudate, right-insula and left-lingual-gyrus also showed decreases in BOLD response with learning. Concurrent with these decreases in BOLD response, an increasing connectivity between areas of the imaged network as well as the right-middle-frontal-gyrus with rising learning performance was revealed by a psychophysiological interaction (PPI analysis. The increasing connectivity therefore occurs within an increasingly energy efficient network as learning proceeds. Strongest learning related connectivity between regions was found when analysing BA44 and l-IPL seeds. The results clearly show that BA44 and l-IPL is dynamically involved in linking gesture and sound and therefore provides evidence that one of

  14. Typing mineral deposits using their associated rocks, grades and tonnages using a probabilistic neural network

    Science.gov (United States)

    Singer, D.A.

    2006-01-01

    A probabilistic neural network is employed to classify 1610 mineral deposits into 18 types using tonnage, average Cu, Mo, Ag, Au, Zn, and Pb grades, and six generalized rock types. The purpose is to examine whether neural networks might serve for integrating geoscience information available in large mineral databases to classify sites by deposit type. Successful classifications of 805 deposits not used in training - 87% with grouped porphyry copper deposits - and the nature of misclassifications demonstrate the power of probabilistic neural networks and the value of quantitative mineral-deposit models. The results also suggest that neural networks can classify deposits as well as experienced economic geologists. ?? International Association for Mathematical Geology 2006.

  15. MR findings of spondylolisthesis: assessment of associated spinal and neural foraminal stenosis

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jae Seung; Kang, Heung Sik; Yoon, Hye Kyung; Kim, Chu Wan [Seoul National University College of Medicine, Seoul (Korea, Republic of)

    1994-02-15

    To assess the spinal canal and neural foraminal stenosis associated with spondylolisthesis on MR imaging. We retrospectively analysed MR findings of 63 cases of spondylolisthesis(degenerative type: 23 cases, isthmic type: 40 cases) regarding the type and grade of spondylolisthesis, presence or absence of associated spinal canal stenosis, and the severity of associated neural foraminal stenosis. Central canal stenosis were more frequent in degenerative type(91%) than isthmic type(33%), and more frequent in grade II spondylolisthesis of degenerative type(100%) and isthmic type(89%) than in grade I spondylolisthesis of degenerative type(45%) and isthmic type(20%). There was positive correlation between the severity of neural foraminal stenosis and the grade of spondylolisthesis, whereas there was no significant difference between degenerative and isthmic types. Degenerative spondylolisthesis were frequently associated with central canal stenosis more than isthmic type. When the grade of spondylolisthesis was higher, it was more frequently associated with central canal stenosis and severe neural foraminal stenosis.

  16. Yes-associated protein 65 (YAP expands neural progenitors and regulates Pax3 expression in the neural plate border zone.

    Directory of Open Access Journals (Sweden)

    Stephen T Gee

    Full Text Available Yes-associated protein 65 (YAP contains multiple protein-protein interaction domains and functions as both a transcriptional co-activator and as a scaffolding protein. Mouse embryos lacking YAP did not survive past embryonic day 8.5 and showed signs of defective yolk sac vasculogenesis, chorioallantoic fusion, and anterior-posterior (A-P axis elongation. Given that the YAP knockout mouse defects might be due in part to nutritional deficiencies, we sought to better characterize a role for YAP during early development using embryos that develop externally. YAP morpholino (MO-mediated loss-of-function in both frog and fish resulted in incomplete epiboly at gastrulation and impaired axis formation, similar to the mouse phenotype. In frog, germ layer specific genes were expressed, but they were temporally delayed. YAP MO-mediated partial knockdown in frog allowed a shortened axis to form. YAP gain-of-function in Xenopus expanded the progenitor populations in the neural plate (sox2(+ and neural plate border zone (pax3(+, while inhibiting the expression of later markers of tissues derived from the neural plate border zone (neural crest, pre-placodal ectoderm, hatching gland, as well as epidermis and somitic muscle. YAP directly regulates pax3 expression via association with TEAD1 (N-TEF at a highly conserved, previously undescribed, TEAD-binding site within the 5' regulatory region of pax3. Structure/function analyses revealed that the PDZ-binding motif of YAP contributes to the inhibition of epidermal and somitic muscle differentiation, but a complete, intact YAP protein is required for expansion of the neural plate and neural plate border zone progenitor pools. These results provide a thorough analysis of YAP mediated gene expression changes in loss- and gain-of-function experiments. Furthermore, this is the first report to use YAP structure-function analyzes to determine which portion of YAP is involved in specific gene expression changes and the

  17. EXPONENTIAL STABILITY AND PERIODIC SOLUTION OF HYBRID BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH DISCRETE DELAYS

    Institute of Scientific and Technical Information of China (English)

    谢惠琴; 王全义

    2004-01-01

    In this paper, we study the existence, uniqueness, and the global exponential stability of the periodic solution and equilibrium of hybrid bidirectional associative memory neural networks with discrete delays. By ingeniously importing real parameters di > 0 (i = 1, 2,..., n) which can be adjusted, making use of the Lyapunov functional method and some analysis techniques, some new sufficient conditions are established. Our results generalize and improve the related results in [9]. These conditions can be used both to design globally exponentially stable and periodical oscillatory hybrid bidirectional associative neural networks with discrete delays, and to enlarge the area of designing neural networks. Our work has important significance in related theory and its application.

  18. On the design of dynamic associative neural memories.

    Science.gov (United States)

    Savran, M E; Morgul, O

    1994-01-01

    We consider the design problem for a class of discrete-time and continuous-time neural networks. We obtain a characterization of all connection weights that store a given set of vectors into the network, that is, each given vector becomes an equilibrium point of the network. We also give sufficient conditions that guarantee the asymptotic stability of these equilibrium points.

  19. Synthesization of high-capacity auto-associative memories using complex-valued neural networks

    Science.gov (United States)

    Huang, Yu-Jiao; Wang, Xiao-Yan; Long, Hai-Xia; Yang, Xu-Hua

    2016-12-01

    In this paper, a novel design procedure is proposed for synthesizing high-capacity auto-associative memories based on complex-valued neural networks with real-imaginary-type activation functions and constant delays. Stability criteria dependent on external inputs of neural networks are derived. The designed networks can retrieve the stored patterns by external inputs rather than initial conditions. The derivation can memorize the desired patterns with lower-dimensional neural networks than real-valued neural networks, and eliminate spurious equilibria of complex-valued neural networks. One numerical example is provided to show the effectiveness and superiority of the presented results. Project supported by the National Natural Science Foundation of China (Grant Nos. 61503338, 61573316, 61374152, and 11302195) and the Natural Science Foundation of Zhejiang Province, China (Grant No. LQ15F030005).

  20. Neural network data association with application to multiple-target tracking

    Science.gov (United States)

    Leung, Henry

    1996-03-01

    Data association is the process of relating sensor measurements in a data fusion system. It can be structured in a basic framework very similar to that of the classic traveling salesman problem. The derivation of the energy function is presented, and the solution is based on a modified Hopfield network which uses the Runge-Kutta method and Aiyer's network structure. The neural data association is then applied to the problem of multiple-target tracking (MTT). The proposed neural MTT system consists of a modified Hough transform track initiator, a Kalman filter state estimator and the Hopfield probabilistic data association. Real- life air surveillance data are used to evaluate the practicality of the neural MTT system, and the results show that the neural system works efficiently in real-life tracking environments.

  1. Neural activity supporting the formation of associative memory versus source memory.

    Science.gov (United States)

    Park, Heekyeong; Shannon, Vale; Biggan, John; Spann, Catherine

    2012-08-30

    The ability to form a new association with discontiguous elements constitutes the very crux of episodic memory. However, it is not fully understood whether different types of associations rely on common neural correlates for encoding associations. In the present study, we investigated whether the formation of associative memory (associations between items) and source memory (associations between an item and its context) recruits common neural activity during encoding, or whether each type of association requires different neural activity for subsequent memory. During study, participants were visually presented a list of object pairs in the scanner while the names of objects were simultaneously presented either in a male or female voice. Participants completed a post-scan recognition test for associative and source memories for object pairs and their contexts. Associative memory was predicted in the left inferior prefrontal cortex, the fusiform gyrus and the medial temporal lobe including both perirhinal and parahippocampal cortices and the posterior hippocampus. Encoding activity for source memory was identified in the right insula and the right anterior hippocampus. Further, neural activity in the right posterior hippocampus was recruited for successful formation of both associative and source memories. Collectively, these findings highlight the pivotal role of the hippocampus in successful encoding of associative and source memories and add more weight to the role of the perirhinal cortex in associative encoding of objects. The present findings have implications for roles of the medial temporal lobe sub-regions for successful formation of associative and source memories.

  2. Neural associative memories for the integration of language, vision and action in an autonomous agent.

    Science.gov (United States)

    Markert, H; Kaufmann, U; Kara Kayikci, Z; Palm, G

    2009-03-01

    Language understanding is a long-standing problem in computer science. However, the human brain is capable of processing complex languages with seemingly no difficulties. This paper shows a model for language understanding using biologically plausible neural networks composed of associative memories. The model is able to deal with ambiguities on the single word and grammatical level. The language system is embedded into a robot in order to demonstrate the correct semantical understanding of the input sentences by letting the robot perform corresponding actions. For that purpose, a simple neural action planning system has been combined with neural networks for visual object recognition and visual attention control mechanisms.

  3. Optimal and robust design of brain-state-in-a-box neural associative memories.

    Science.gov (United States)

    Park, Yonmook

    2010-03-01

    This paper presents a new optimization approach to the design of associative memories via the brain-state-in-a-box (BSB) neural network. The optimization approach proposed in this paper provides the large and uniform domains of attraction of the prototype patterns, the large robustness margin for the weight matrix of the perturbed BSB neural network, the asymptotic stability of the prototype patterns, and the global stability of the BSB neural network. Based on some known qualitative properties of the BSB neural network and theoretical results presented in this paper, a synthesis method of the BSB-based associative memory is established. The synthesis method presented in this paper is given in the form of a linear matrix inequality-based optimization problem, which can be efficiently solved by a readily available software. Design examples are given to demonstrate the applicability of the proposed method and to compare with the existing synthesis methods.

  4. Two-Layer Feedback Neural Networks with Associative Memories

    Institute of Scientific and Technical Information of China (English)

    WU Gui-Kun; ZHAO Hong

    2008-01-01

    We construct a two-layer feedback neural network by a Monte Carlo based algorithm to store memories as fixed-point attractors or as limit-cycle attractors. Special attention is focused on comparing the dynamics of the network with limit-cycle attractors and with fixed-point attractors. It is found that the former has better retrieval property than the latter. Particularly, spurious memories may be suppressed completely when the memories are stored as a long-limit cycle. Potential application of limit-cycle-attractor networks is discussed briefly.

  5. Learning and Forgetting in Generalized Brain-state-in-a-box (BSB) Neural Associative Memories.

    Science.gov (United States)

    Hui, Stefen; Lillo, Walter E.; Zak, Stanislaw H.

    1996-07-01

    We propose learning and forgetting techniques for the generalized brain-state-in-a-box (BSB) based associative memories. A generalization of the BSB model allows each neuron to have its own bias and the synaptic weight matrix does not have to be symmetric. A pattern is learned by a memory if its noisy or an incomplete version presented to the memory is mapped back to this pattern. A pattern, previously stored, is forgotten or deleted from the memory if a stimulus that is a perturbed version of the pattern, when presented to the memory, is not mapped back to this pattern. In this paper we propose "on-line" memory storage and deletion methods using an iterative method of computing the pseudo-inverse of a given matrix. The proposed methods allow one to "add" or "delete" a memory pattern by updating, rather than recomputing from scratch, the current synaptic weight matrix in a single step. We first analyze the desired characteristics of neural network associative memories. After that, we review the existing methods for design of neural associative memories. Then we discuss the generalized BSB neural model and its possible function as an associative memory and proffer arguments in support of using such models for neural associative memories. In particular, the generalized BSB type models are easier to analyze, synthesize, and implement than other neural networks. The results obtained are illustrated by numerical examples. Copyright 1996 Elsevier Science Ltd

  6. Neural associations of the early retinotopic cortex with the lateral occipital complex during visual perception.

    Directory of Open Access Journals (Sweden)

    Delong Zhang

    Full Text Available Previous studies have demonstrated that the early retinotopic cortex (ERC, i.e., V1/V2/V3 is highly associated with the lateral occipital complex (LOC during visual perception. However, it remains largely unclear how to evaluate their associations in quantitative way. The present study tried to apply a multivariate pattern analysis (MVPA to quantify the neural activity in ERC and its association with that of the LOC when participants saw visual images. To this end, we assessed whether low-level visual features (Gabor features could predict the neural activity in the ERC and LOC according to a voxel-based encoding model (VBEM, and then quantified the association of the neural activity between these regions by using an analogical VBEM. We found that the Gabor features remarkably predicted the activity of the ERC (e.g., the predicted accuracy was 52.5% for a participant instead of that of the LOC (4.2%. Moreover, the MVPA approach can also be used to establish corresponding relationships between the activity patterns in the LOC and those in the ERC (64.2%. In particular, we found that the integration of the Gabor features and LOC visual information could dramatically improve the 'prediction' of ERC activity (88.3%. Overall, the present study provides new evidences for the possibility of quantifying the association of the neural activity between the regions of ERC and LOC. This approach will help to provide further insights into the neural substrates of the visual processing.

  7. Neural Pattern Similarity in the Left IFG and Fusiform Is Associated with Novel Word Learning

    Directory of Open Access Journals (Sweden)

    Jing Qu

    2017-08-01

    Full Text Available Previous studies have revealed that greater neural pattern similarity across repetitions is associated with better subsequent memory. In this study, we used an artificial language training paradigm and representational similarity analysis to examine whether neural pattern similarity across repetitions before training was associated with post-training behavioral performance. Twenty-four native Chinese speakers were trained to learn a logographic artificial language for 12 days and behavioral performance was recorded using the word naming and picture naming tasks. Participants were scanned while performing a passive viewing task before training, after 4-day training and after 12-day training. Results showed that pattern similarity in the left pars opercularis (PO and fusiform gyrus (FG before training was negatively associated with reaction time (RT in both word naming and picture naming tasks after training. These results suggest that neural pattern similarity is an effective neurofunctional predictor of novel word learning in addition to word memory.

  8. Hopf bifurcation of an (n + 1) -neuron bidirectional associative memory neural network model with delays.

    Science.gov (United States)

    Xiao, Min; Zheng, Wei Xing; Cao, Jinde

    2013-01-01

    Recent studies on Hopf bifurcations of neural networks with delays are confined to simplified neural network models consisting of only two, three, four, five, or six neurons. It is well known that neural networks are complex and large-scale nonlinear dynamical systems, so the dynamics of the delayed neural networks are very rich and complicated. Although discussing the dynamics of networks with a few neurons may help us to understand large-scale networks, there are inevitably some complicated problems that may be overlooked if simplified networks are carried over to large-scale networks. In this paper, a general delayed bidirectional associative memory neural network model with n + 1 neurons is considered. By analyzing the associated characteristic equation, the local stability of the trivial steady state is examined, and then the existence of the Hopf bifurcation at the trivial steady state is established. By applying the normal form theory and the center manifold reduction, explicit formulae are derived to determine the direction and stability of the bifurcating periodic solution. Furthermore, the paper highlights situations where the Hopf bifurcations are particularly critical, in the sense that the amplitude and the period of oscillations are very sensitive to errors due to tolerances in the implementation of neuron interconnections. It is shown that the sensitivity is crucially dependent on the delay and also significantly influenced by the feature of the number of neurons. Numerical simulations are carried out to illustrate the main results.

  9. The Neural Correlates of Worry in Association with Individual Differences in Neuroticism

    NARCIS (Netherlands)

    Servaas, Michelle Nadine; Riese, Harriette; Ormel, Johan; Aleman, Andre

    2014-01-01

    The tendency to worry is a facet of neuroticism that has been shown to mediate the relationship between neuroticism and symptoms of depression and anxiety. The aim of the current study was to investigate the neural correlates of state worry in association with neuroticism. One-hundred twenty partici

  10. GLOBAL EXPONENTIAL STABILITY IN HOPFIELD AND BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH TIME DELAYS

    Institute of Scientific and Technical Information of China (English)

    RONG LIBIN; LU WENLIAN; CHEN TIANPING

    2004-01-01

    Without assuming the boundedness, strict monotonicity and differentiability of the activation functions, the authors utilize the Lyapunov functional method to analyze the global convergence of some delayed models. For the Hopfield neural network with time delays, a new sufficient condition ensuring the existence, uniqueness and global exponential stability of the equilibrium point is derived. This criterion concerning the signs of entries in the connection matrix imposes constraints on the feedback matrix independently of the delay parameters. From a new viewpoint, the bidirectional associative memory neural network with time delays is investigated and a new global exponential stability result is given.

  11. [Complete lower urinary tract duplication with true diphallia associated to anorrectal and neural malformations].

    Science.gov (United States)

    Guirao, M J; Zambudio, G; Nortes, L; Jiménez, J I Ruiz

    2008-10-01

    We report a case of complete urinary tract duplication with true diphallia associated to intestinal and neural anomalies. Complete penile duplication with hypospadias and bifidum scrotum were showed. Moreover, he had got anorrectal disease (anterior anus) and neural tube defects (myelomeningocele). Radiological and functional studies were performed and complete duplication lower urinary tract with coordinate miction were found. Combined surgical approach were used: perineal to remove lateralized and hypospadic penile and abdominal for cystoplasty. We report a case due to the extremely low prevalence. Only 15 cases have been described in the literature.

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

    Directory of Open Access Journals (Sweden)

    J. Thipcha

    2013-01-01

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

  13. Age-related changes in neural activity associated with familiarity, recollection and false recognition.

    Science.gov (United States)

    Duarte, Audrey; Graham, Kim S; Henson, Richard N

    2010-10-01

    Older adults often exhibit elevated false recognition for events that never occurred, while simultaneously experiencing difficulty in recognizing events that actually occurred. It has been proposed that reduced recollection in conjunction with an over-reliance on familiarity may contribute to this pattern of results. This explanation is somewhat inconsistent, however, with recent evidence suggesting that familiarity and associated neural activity are reduced in healthy aging. Alternatively, given that illusory memory may be based, in part, on veridical memory processes (recollection/familiarity), one might predict that older adults exhibit enhanced false alarm rates because the neural signatures associated with true recognition (hits) and false recognition (false alarms) are less distinguishable in old than in young adults. Here, we used event-related fMRI to measure the effects of aging on neural activity associated with recollection, familiarity and familiarity-based false alarms for objects in young and older adults. Compared to young adults, older adults exhibited elevated false alarm rates and impaired behavioral indices of recollection and familiarity. Imaging data showed that older adults exhibited reduced recollection effects in the left parietoccipital cortex. Furthermore, while similar regions in frontal, parietal, lateral and inferior temporal cortices contributed to familiarity-based true and false recognition, reduced familiarity-related activity in frontal and inferior temporal regions in the older adults resulted in decreased differentiation between true and false recognition effects in this group. Our results suggest that reductions in neural activity associated with both recollection and familiarity for studied items may contribute to elevated false recognition in older adults, via reduced differentiation between the neural activity associated with true and false memory.

  14. Dynamical Associative Memory: The Properties of the New Weighted Chaotic Adachi Neural Network

    Science.gov (United States)

    Luo, Guangchun; Ren, Jinsheng; Qin, Ke

    A new training algorithm for the chaotic Adachi Neural Network (AdNN) is investigated. The classical training algorithm for the AdNN and it's variants is usually a “one-shot” learning, for example, the Outer Product Rule (OPR) is the most used. Although the OPR is effective for conventional neural networks, its effectiveness and adequateness for Chaotic Neural Networks (CNNs) have not been discussed formally. As a complementary and tentative work in this field, we modified the AdNN's weights by enforcing an unsupervised Hebbian rule. Experimental analysis shows that the new weighted AdNN yields even stronger dynamical associative memory and pattern recognition phenomena for different settings than the primitive AdNN.

  15. Neural recruitment associated with anomia treatment in aphasia.

    Science.gov (United States)

    Fridriksson, Julius; Morrow-Odom, Leigh; Moser, Dana; Fridriksson, Astrid; Baylis, Gordon

    2006-09-01

    The purpose of this study was to investigate changes in the spatial distribution of cortical activity associated with anomia treatment in three persons with aphasia. Participants underwent three fMRI sessions before and after a period of intensive language treatment focused on object naming. The results revealed bilateral hemispheric recruitment associated with improved ability to name items targeted in treatment. This is the first study to employ multiple pre- and post-treatment fMRI sessions in the study of treatment-induced recovery from aphasia and has implications for future studies of brain plasticity in stroke.

  16. Explaining neural signals in human visual cortex with an associative learning model.

    Science.gov (United States)

    Jiang, Jiefeng; Schmajuk, Nestor; Egner, Tobias

    2012-08-01

    "Predictive coding" models posit a key role for associative learning in visual cognition, viewing perceptual inference as a process of matching (learned) top-down predictions (or expectations) against bottom-up sensory evidence. At the neural level, these models propose that each region along the visual processing hierarchy entails one set of processing units encoding predictions of bottom-up input, and another set computing mismatches (prediction error or surprise) between predictions and evidence. This contrasts with traditional views of visual neurons operating purely as bottom-up feature detectors. In support of the predictive coding hypothesis, a recent human neuroimaging study (Egner, Monti, & Summerfield, 2010) showed that neural population responses to expected and unexpected face and house stimuli in the "fusiform face area" (FFA) could be well-described as a summation of hypothetical face-expectation and -surprise signals, but not by feature detector responses. Here, we used computer simulations to test whether these imaging data could be formally explained within the broader framework of a mathematical neural network model of associative learning (Schmajuk, Gray, & Lam, 1996). Results show that FFA responses could be fit very closely by model variables coding for conditional predictions (and their violations) of stimuli that unconditionally activate the FFA. These data document that neural population signals in the ventral visual stream that deviate from classic feature detection responses can formally be explained by associative prediction and surprise signals.

  17. Neural Correlates of Associative Memory in the Elderly: A Resting-State Functional MRI Study.

    Science.gov (United States)

    Ren, Weicong; Li, Rui; Zheng, Zhiwei; Li, Juan

    2015-01-01

    The neural correlates of associative memory in healthy older adults were investigated by examining the correlation of associative memory performance with spontaneous brain oscillations. Eighty healthy older adults underwent a resting-state functional MRI and took a paired-associative learning test (PALT). Correlations between the amplitude of low-frequency fluctuations (ALFF) as well as fractional ALFF (fALFF) in the whole brain and PALT scores were calculated. We found that spontaneous activity as indexed by both ALFF and fALFF in the parahippocampal gyrus (PHG) was significantly positively correlated with associative memory performance, suggesting that the PHG plays a critical role in associative memory in older people.

  18. Stromal SLIT2 impacts on pancreatic cancer-associated neural remodeling.

    Science.gov (United States)

    Secq, V; Leca, J; Bressy, C; Guillaumond, F; Skrobuk, P; Nigri, J; Lac, S; Lavaut, M-N; Bui, T-T; Thakur, A K; Callizot, N; Steinschneider, R; Berthezene, P; Dusetti, N; Ouaissi, M; Moutardier, V; Calvo, E; Bousquet, C; Garcia, S; Bidaut, G; Vasseur, S; Iovanna, J L; Tomasini, R

    2015-01-15

    Pancreatic ductal adenocarcinoma (PDA) is a critical health issue in the field of cancer, with few therapeutic options. Evidence supports an implication of the intratumoral microenvironment (stroma) on PDA progression. However, its contribution to the role of neuroplastic changes within the pathophysiology and clinical course of PDA, through tumor recurrence and neuropathic pain, remains unknown, neglecting a putative, therapeutic window. Here, we report that the intratumoral microenvironment is a mediator of PDA-associated neural remodeling (PANR), and we highlight factors such as 'SLIT2' (an axon guidance molecule), which is expressed by cancer-associated fibroblasts (CAFs), that impact on neuroplastic changes in human PDA. We showed that 'CAF-secreted SLIT2' increases neurite outgrowth from dorsal root ganglia neurons as well as from Schwann cell migration/proliferation by modulating N-cadherin/β-catenin signaling. Importantly, SLIT2/ROBO signaling inhibition disrupts this stromal/neural connection. Finally, we revealed that SLIT2 expression and CAFs are correlated with neural remodeling within human and mouse PDA. All together, our data demonstrate the implication of CAFs, through the secretion of axon guidance molecule, in PANR. Furthermore, it provides rationale to investigate the disruption of the stromal/neural compartment connection with SLIT2/ROBO inhibitors for the treatment of pancreatic cancer recurrence and pain.

  19. Contributions of Matrix Metalloproteinases to Neural Plasticity, Habituation, Associative Learning and Drug Addiction

    Directory of Open Access Journals (Sweden)

    John W. Wright

    2009-01-01

    Full Text Available The premise of this paper is that increased expression of matrix metalloproteinases (MMPs permits the reconfiguration of synaptic connections (i.e., neural plasticity by degrading cell adhesion molecules (CAMs designed to provide stability to those extracellular matrix (ECM proteins that form scaffolding supporting neurons and glia. It is presumed that while these ECM proteins are weakened, and/or detached, synaptic connections can form resulting in new neural pathways. Tissue inhibitors of metalloproteinases (TIMPs are designed to deactivate MMPs permitting the reestablishment of CAMs, thus returning the system to a reasonably fixed state. This review considers available findings concerning the roles of MMPs and TIMPs in reorganizing ECM proteins thus facilitating the neural plasticity underlying long-term potentiation (LTP, habituation, and associative learning. We conclude with a consideration of the influence of these phenomena on drug addiction, given that these same processes may be instrumental in the formation of addiction and subsequent relapse. However, our knowledge concerning the precise spatial and temporal relationships among the mechanisms of neural plasticity, habituation, associative learning, and memory consolidation is far from complete and the possibility that these phenomena mediate drug addiction is a new direction of research.

  20. Neural circuits associated with positive and negative self-appraisal.

    Science.gov (United States)

    Brühl, A B; Rufer, M; Kaffenberger, T; Baur, V; Herwig, U

    2014-04-18

    Self-worth is particularly influenced by self-appraisal, which is negatively biased in many psychiatric disorders. Positive and negative self-appraisals also shape current emotional states or even evoke defensive reactions, when they are incongruent with a subject's current state. Prior studies have mainly used externally given evaluative appraisals. In this study, 30 subjects used individual negative and positive self-appraisals during functional magnetic resonance imaging. We additionally investigated the effects of such self-appraisals onto the subsequent perception of photos of the individual subjects. Both self-appraisal conditions activated dorsomedial and dorsolateral prefrontal cortex compared to the neutral condition. Positive self-appraisal evoked stronger activity than negative self-appraisal in the amygdala, ventral striatum and anterior cingulate cortex, whereas negative self-appraisal was associated with increased activity in the occipital regions. Positive self-appraisal had no effect on the perception of a photo of oneself, whereas negative appraisal increased activity in the anterior insula and parietal regions. Overall, positive self-appraisal activated more emotion-related brain regions, whereas negative self-appraisal had a relatively stronger influence on perception-related brain activity. These findings could on the one hand explain the effect of negative self-appraisal on the behavior in the real world and on the other hand support a stronger focus of psychotherapy on enhancing positive self-appraisals.

  1. Detection of Malignancy Associated Changes in Cervical Cell Nuclei Using Feed-Forward Neural Networks

    Directory of Open Access Journals (Sweden)

    Roger A. Kemp

    1997-01-01

    Full Text Available Normal cells in the presence of a precancerous lesion undergo subtle changes of their DNA distribution when observed by visible microscopy. These changes have been termed Malignancy Associated Changes (MACs. Using statistical models such as neural networks and discriminant functions it is possible to design classifiers that can separate these objects from truly normal cells. The correct classification rate using feed‐forward neural networks is compared to linear discriminant analysis when applied to detecting MACs. Classifiers were designed using 53 nuclear features calculated from images for each of 25,360 normal appearing cells taken from 344 slides diagnosed as normal or containing severe dysplasia. A linear discriminant function achieved a correct classification rate of 61.6% on the test data while neural networks scored as high as 72.5% on a cell‐by‐cell basis. The cell classifiers were applied to a library of 93,494 cells from 395 slides, and the results were jackknifed using a single slide feature. The discriminant function achieved a correct classification rate of 67.6% while the neural networks managed as high as 76.2%.

  2. Analogue spin-orbit torque device for artificial-neural-network-based associative memory operation

    Science.gov (United States)

    Borders, William A.; Akima, Hisanao; Fukami, Shunsuke; Moriya, Satoshi; Kurihara, Shouta; Horio, Yoshihiko; Sato, Shigeo; Ohno, Hideo

    2017-01-01

    We demonstrate associative memory operations reminiscent of the brain using nonvolatile spintronics devices. Antiferromagnet-ferromagnet bilayer-based Hall devices, which show analogue-like spin-orbit torque switching under zero magnetic fields and behave as artificial synapses, are used. An artificial neural network is used to associate memorized patterns from their noisy versions. We develop a network consisting of a field-programmable gate array and 36 spin-orbit torque devices. An effect of learning on associative memory operations is successfully confirmed for several 3 × 3-block patterns. A discussion on the present approach for realizing spintronics-based artificial intelligence is given.

  3. Global stability of bidirectional associative memory neural networks with continuously distributed delays

    Institute of Scientific and Technical Information of China (English)

    张强; 马润年; 许进

    2003-01-01

    Global asymptotic stability of the equilibrium point of bidirectional associative memory (BAM) neural networks with continuously distributed delays is studied. Under two mild assumptions on the acti-vation functions, two sufficient conditions ensuring global stability of such networks are derived by utiliz-ing Lyapunov functional and some inequality analysis technique. The results here extend some previous results. A numerical example is given showing the validity of our method.

  4. Can the neural-cortisol association be moderated by experience-induced changes in awareness?

    Science.gov (United States)

    Lau, Way K W; Leung, Mei-Kei; Chan, Chetwyn C H; Wong, Samuel S Y; Lee, Tatia M C

    2015-11-18

    Cortisol homeostasis is important for cognitive and affective functions that depend on cortisol-sensitive brain regions including the hippocampus and prefrontal cortex. Recent studies have shown that training induces changes in the brain. We report the findings of a longitudinal study that verified the moderation effect of experience-induced changes in awareness on the neural-cortisol association in cortisol-sensitive brain regions. These findings provide the first piece of evidence that planned behavioral experience can moderate the neural-cortisol association. A range of changes in awareness was achieved in a sample of 21 Chinese participants, divided into two groups: Awareness-based compassion meditation (ABCM) (n = 10) and relaxation (n = 11). We observed that changes in awareness were significant moderators of hippocampal-cortisol changes. Furthermore, a significant negative association between changes in plasma cortisol level and the resting-state synchrony of the right hippocampal and insular-frontal-operculum regions was observed. These novel findings shed light on the inter-relationships between changes in hippocampal-cortisol levels and changes in awareness and preliminarily identify the neural underpinnings of interventions for cortisol-related abnormal functioning for further study.

  5. Associations between maternal negative affect and adolescent's neural response to peer evaluation

    Directory of Open Access Journals (Sweden)

    Patricia Z. Tan

    2014-04-01

    Full Text Available Parenting is often implicated as a potential source of individual differences in youths’ emotional information processing. The present study examined whether parental affect is related to an important aspect of adolescent emotional development, response to peer evaluation. Specifically, we examined relations between maternal negative affect, observed during parent–adolescent discussion of an adolescent-nominated concern with which s/he wants parental support, and adolescent neural responses to peer evaluation in 40 emotionally healthy and depressed adolescents. We focused on a network of ventral brain regions involved in affective processing of social information: the amygdala, anterior insula, nucleus accumbens, and subgenual anterior cingulate, as well as the ventrolateral prefrontal cortex. Maternal negative affect was not associated with adolescent neural response to peer rejection. However, longer durations of maternal negative affect were associated with decreased responsivity to peer acceptance in the amygdala, left anterior insula, subgenual anterior cingulate, and left nucleus accumbens. These findings provide some of the first evidence that maternal negative affect is associated with adolescents’ neural processing of social rewards. Findings also suggest that maternal negative affect could contribute to alterations in affective processing, specifically, dampening the saliency and/or reward of peer interactions during adolescence.

  6. Associations between maternal negative affect and adolescent's neural response to peer evaluation.

    Science.gov (United States)

    Tan, Patricia Z; Lee, Kyung Hwa; Dahl, Ronald E; Nelson, Eric E; Stroud, Laura J; Siegle, Greg J; Morgan, Judith K; Silk, Jennifer S

    2014-04-01

    Parenting is often implicated as a potential source of individual differences in youths' emotional information processing. The present study examined whether parental affect is related to an important aspect of adolescent emotional development, response to peer evaluation. Specifically, we examined relations between maternal negative affect, observed during parent-adolescent discussion of an adolescent-nominated concern with which s/he wants parental support, and adolescent neural responses to peer evaluation in 40 emotionally healthy and depressed adolescents. We focused on a network of ventral brain regions involved in affective processing of social information: the amygdala, anterior insula, nucleus accumbens, and subgenual anterior cingulate, as well as the ventrolateral prefrontal cortex. Maternal negative affect was not associated with adolescent neural response to peer rejection. However, longer durations of maternal negative affect were associated with decreased responsivity to peer acceptance in the amygdala, left anterior insula, subgenual anterior cingulate, and left nucleus accumbens. These findings provide some of the first evidence that maternal negative affect is associated with adolescents' neural processing of social rewards. Findings also suggest that maternal negative affect could contribute to alterations in affective processing, specifically, dampening the saliency and/or reward of peer interactions during adolescence.

  7. Lack of association between folate-receptor autoantibodies and neural-tube defects.

    LENUS (Irish Health Repository)

    Molloy, Anne M

    2009-07-09

    BACKGROUND: A previous report described the presence of autoantibodies against folate receptors in 75% of serum samples from women with a history of pregnancy complicated by a neural-tube defect, as compared with 10% of controls. We sought to confirm this finding in an Irish population, which traditionally has had a high prevalence of neural-tube defects. METHODS: We performed two studies. Study 1 consisted of analysis of stored frozen blood samples collected from 1993 through 1994 from 103 mothers with a history of pregnancy complicated by a neural-tube defect (case mothers), 103 mothers with a history of pregnancy but no complication by a neural-tube defect (matched with regard to number of pregnancies and sampling dates), 58 women who had never been pregnant, and 36 men. Study 2, conducted to confirm that the storage of samples did not influence the folate-receptor autoantibodies, included fresh samples from 37 case mothers, 22 control mothers, 10 women who had never been pregnant, and 9 men. All samples were assayed for blocking and binding autoantibodies against folate receptors. RESULTS: In Study 1, blocking autoantibodies were found in 17% of case mothers, as compared with 13% of control mothers (odds ratio, 1.54; 95% confidence interval [CI], 0.70 to 3.39), and binding autoantibodies in 29%, as compared with 32%, respectively (odds ratio, 0.82; 95% CI, 0.44 to 1.50). Study 2 showed similar results, indicating that sample degradation was unlikely. CONCLUSIONS: The presence and titer of maternal folate-receptor autoantibodies were not significantly associated with a neural-tube defect-affected pregnancy in this Irish population.

  8. Seeing human: distinct and overlapping neural signatures associated with two forms of dehumanization.

    Science.gov (United States)

    Jack, Anthony I; Dawson, Abigail J; Norr, Megan E

    2013-10-01

    The process of dehumanization, or thinking of others as less than human, is a phenomenon with significant societal implications. According to Haslam's (2006) model, two concepts of humanness derive from comparing humans with either animals or machines: individuals may be dehumanized by likening them to either animals or machines, or humanized by emphasizing differences from animals or machines. Recent work in cognitive neuroscience emphasizes understanding cognitive processes in terms of interactions between distributed cortical networks. It has been found that reasoning about internal mental states is associated with activation of the default mode network (DMN) and deactivation of the task positive network (TPN); whereas reasoning about mechanical processes produces the opposite pattern. We conducted two neuroimaging studies. The first examined the neural bases of dehumanization and its relation to these two brain networks, using images and voice-over social narratives which either implicitly contrasted or implicitly likened humans to either animals or machines. The second study addressed a discrepancy between findings from the first study and prior work on the neural correlates of dehumanization: using a design similar to prior work we examined neural responses to pictures of humans, animals and machines, presented without any social context. In both studies, human and humanizing conditions were associated with relatively high activity in the DMN and relatively low activity in the TPN. However, the non-human and dehumanizing conditions deviated in different ways: they demonstrated more marked changes either in the DMN or in the TPN. Notably, differences between the animal dehumanizing and humanizing conditions were most evident in regions associated with mechanistic reasoning, not in the mentalizing network. Conjunction analysis of contrasts from both paradigms revealed that only one region was consistently more active when participants saw human, a medial

  9. Social anhedonia is associated with neural abnormalities during face emotion processing.

    Science.gov (United States)

    Germine, Laura T; Garrido, Lucia; Bruce, Lori; Hooker, Christine

    2011-10-01

    Human beings are social organisms with an intrinsic desire to seek and participate in social interactions. Social anhedonia is a personality trait characterized by a reduced desire for social affiliation and reduced pleasure derived from interpersonal interactions. Abnormally high levels of social anhedonia prospectively predict the development of schizophrenia and contribute to poorer outcomes for schizophrenia patients. Despite the strong association between social anhedonia and schizophrenia, the neural mechanisms that underlie individual differences in social anhedonia have not been studied and are thus poorly understood. Deficits in face emotion recognition are related to poorer social outcomes in schizophrenia, and it has been suggested that face emotion recognition deficits may be a behavioral marker for schizophrenia liability. In the current study, we used functional magnetic resonance imaging (fMRI) to see whether there are differences in the brain networks underlying basic face emotion processing in a community sample of individuals low vs. high in social anhedonia. We isolated the neural mechanisms related to face emotion processing by comparing face emotion discrimination with four other baseline conditions (identity discrimination of emotional faces, identity discrimination of neutral faces, object discrimination, and pattern discrimination). Results showed a group (high/low social anhedonia) × condition (emotion discrimination/control condition) interaction in the anterior portion of the rostral medial prefrontal cortex, right superior temporal gyrus, and left somatosensory cortex. As predicted, high (relative to low) social anhedonia participants showed less neural activity in face emotion processing regions during emotion discrimination as compared to each control condition. The findings suggest that social anhedonia is associated with abnormalities in networks responsible for basic processes associated with social cognition, and provide a

  10. Clique-Based Neural Associative Memories with Local Coding and Precoding.

    Science.gov (United States)

    Mofrad, Asieh Abolpour; Parker, Matthew G; Ferdosi, Zahra; Tadayon, Mohammad H

    2016-08-01

    Techniques from coding theory are able to improve the efficiency of neuroinspired and neural associative memories by forcing some construction and constraints on the network. In this letter, the approach is to embed coding techniques into neural associative memory in order to increase their performance in the presence of partial erasures. The motivation comes from recent work by Gripon, Berrou, and coauthors, which revisited Willshaw networks and presented a neural network with interacting neurons that partitioned into clusters. The model introduced stores patterns as small-size cliques that can be retrieved in spite of partial error. We focus on improving the success of retrieval by applying two techniques: doing a local coding in each cluster and then applying a precoding step. We use a slightly different decoding scheme, which is appropriate for partial erasures and converges faster. Although the ideas of local coding and precoding are not new, the way we apply them is different. Simulations show an increase in the pattern retrieval capacity for both techniques. Moreover, we use self-dual additive codes over field [Formula: see text], which have very interesting properties and a simple-graph representation.

  11. On the design of BSB neural associative memories using semidefinite programming.

    Science.gov (United States)

    Park, J; Cho, H; Park, D

    1999-11-15

    This article is concerned with the reliable search for optimally performing BSB (brain state in a box) neural associative memories given a set of prototype patterns to be stored as stable equilibrium points. By converting and/or modifying the nonlinear constraints of a known formulation for the synthesis of BSB-based associative memories into linear matrix inequalities, we recast the synthesis into semidefinite programming problems and solve them by recently developed interior point methods. The validity of this approach is illustrated by a design example.

  12. Optimization neural net for multiple-target data association: real-time optical lab results

    Science.gov (United States)

    Yee, Mark L.; Casasent, David P.

    1991-08-01

    The Hopfield neural network was first used for optimization in solving the famous Traveling Salesman Problem. A similar approach has been applied to the solution of another problem, namely, data association for multiple targets. Simulation data are presented which demonstrate the network''s ability to successfully determine the optimum data association solutions, with target noise present. Simulations also indicate the ability to solve the problem on a low accuracy (analog optical) processor. Optical implementation issues are discussed, and an optical architecture is presented with laboratory results.

  13. Associated decrements in rate of force development and neural drive after maximal eccentric exercise.

    Science.gov (United States)

    Farup, J; Rahbek, S K; Bjerre, J; de Paoli, F; Vissing, K

    2016-05-01

    The present study investigated the changes in contractile rate of force development (RFD) and the neural drive following a single bout of eccentric exercise. Twenty-four subjects performed 15 × 10 maximal isokinetic eccentric knee extensor contractions. Prior to and at 24, 48, 72, 96, and 168 h during post-exercise recovery, isometric RFD (30, 50 100, and 200 ms), normalized RFD [1/6,1/2, and 2/3 of maximal voluntary contraction (MVC)] and rate of electromyography rise (RER; 30, 50, and 75 ms) were measured. RFD decreased by 28-42% peaking at 48 h (P eccentric exercise. This association suggests that exercise-induced decrements in RFD can, in part, be explained decrements in neural drive. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  14. Distinct neural activity associated with focused-attention meditation and loving-kindness meditation.

    Directory of Open Access Journals (Sweden)

    Tatia M C Lee

    Full Text Available This study examined the dissociable neural effects of ānāpānasati (focused-attention meditation, FAM and mettā (loving-kindness meditation, LKM on BOLD signals during cognitive (continuous performance test, CPT and affective (emotion-processing task, EPT, in which participants viewed affective pictures processing. Twenty-two male Chinese expert meditators (11 FAM experts, 11 LKM experts and 22 male Chinese novice meditators (11 FAM novices, 11 LKM novices had their brain activity monitored by a 3T MRI scanner while performing the cognitive and affective tasks in both meditation and baseline states. We examined the interaction between state (meditation vs. baseline and expertise (expert vs. novice separately during LKM and FAM, using a conjunction approach to reveal common regions sensitive to the expert meditative state. Additionally, exclusive masking techniques revealed distinct interactions between state and group during LKM and FAM. Specifically, we demonstrated that the practice of FAM was associated with expertise-related behavioral improvements and neural activation differences in attention task performance. However, the effect of state LKM meditation did not carry over to attention task performance. On the other hand, both FAM and LKM practice appeared to affect the neural responses to affective pictures. For viewing sad faces, the regions activated for FAM practitioners were consistent with attention-related processing; whereas responses of LKM experts to sad pictures were more in line with differentiating emotional contagion from compassion/emotional regulation processes. Our findings provide the first report of distinct neural activity associated with forms of meditation during sustained attention and emotion processing.

  15. Distinct neural activity associated with focused-attention meditation and loving-kindness meditation.

    Science.gov (United States)

    Lee, Tatia M C; Leung, Mei-Kei; Hou, Wai-Kai; Tang, Joey C Y; Yin, Jing; So, Kwok-Fai; Lee, Chack-Fan; Chan, Chetwyn C H

    2012-01-01

    This study examined the dissociable neural effects of ānāpānasati (focused-attention meditation, FAM) and mettā (loving-kindness meditation, LKM) on BOLD signals during cognitive (continuous performance test, CPT) and affective (emotion-processing task, EPT, in which participants viewed affective pictures) processing. Twenty-two male Chinese expert meditators (11 FAM experts, 11 LKM experts) and 22 male Chinese novice meditators (11 FAM novices, 11 LKM novices) had their brain activity monitored by a 3T MRI scanner while performing the cognitive and affective tasks in both meditation and baseline states. We examined the interaction between state (meditation vs. baseline) and expertise (expert vs. novice) separately during LKM and FAM, using a conjunction approach to reveal common regions sensitive to the expert meditative state. Additionally, exclusive masking techniques revealed distinct interactions between state and group during LKM and FAM. Specifically, we demonstrated that the practice of FAM was associated with expertise-related behavioral improvements and neural activation differences in attention task performance. However, the effect of state LKM meditation did not carry over to attention task performance. On the other hand, both FAM and LKM practice appeared to affect the neural responses to affective pictures. For viewing sad faces, the regions activated for FAM practitioners were consistent with attention-related processing; whereas responses of LKM experts to sad pictures were more in line with differentiating emotional contagion from compassion/emotional regulation processes. Our findings provide the first report of distinct neural activity associated with forms of meditation during sustained attention and emotion processing.

  16. Distinct Neural Activity Associated with Focused-Attention Meditation and Loving-Kindness Meditation

    Science.gov (United States)

    Lee, Tatia M. C.; Leung, Mei-Kei; Hou, Wai-Kai; Tang, Joey C. Y.; Yin, Jing; So, Kwok-Fai; Lee, Chack-Fan; Chan, Chetwyn C. H.

    2012-01-01

    This study examined the dissociable neural effects of ānāpānasati (focused-attention meditation, FAM) and mettā (loving-kindness meditation, LKM) on BOLD signals during cognitive (continuous performance test, CPT) and affective (emotion-processing task, EPT, in which participants viewed affective pictures) processing. Twenty-two male Chinese expert meditators (11 FAM experts, 11 LKM experts) and 22 male Chinese novice meditators (11 FAM novices, 11 LKM novices) had their brain activity monitored by a 3T MRI scanner while performing the cognitive and affective tasks in both meditation and baseline states. We examined the interaction between state (meditation vs. baseline) and expertise (expert vs. novice) separately during LKM and FAM, using a conjunction approach to reveal common regions sensitive to the expert meditative state. Additionally, exclusive masking techniques revealed distinct interactions between state and group during LKM and FAM. Specifically, we demonstrated that the practice of FAM was associated with expertise-related behavioral improvements and neural activation differences in attention task performance. However, the effect of state LKM meditation did not carry over to attention task performance. On the other hand, both FAM and LKM practice appeared to affect the neural responses to affective pictures. For viewing sad faces, the regions activated for FAM practitioners were consistent with attention-related processing; whereas responses of LKM experts to sad pictures were more in line with differentiating emotional contagion from compassion/emotional regulation processes. Our findings provide the first report of distinct neural activity associated with forms of meditation during sustained attention and emotion processing. PMID:22905090

  17. Syndromes, Disorders and Maternal Risk Factors Associated With Neural Tube Defects (VI

    Directory of Open Access Journals (Sweden)

    Chih-Ping Chen

    2008-09-01

    Full Text Available Neural tube defects (NTDs may be associated with syndromes, disorders, and maternal and fetal risk factors. This article provides a comprehensive review of the syndromes, disorders, and maternal and fetal risk factors associated with NTDs, including maternal fumonisin consumption, periconceptional zinc deficiency, parental occupational exposure and residential proximity to pesticides, lower socioeconomic status, fetal alcohol syndrome, mutations in the VANGL1 gene, human athymic Nude/SCID fetus, and single nucleotide polymorphism in the NOS3 gene. NTDs associated with these syndromes, disorders, and maternal and fetal risk factors are a rare but important cause of NTDs. The recurrence risk and the preventive effect of maternal folic acid intake in NTDs associated with syndromes, disorders and maternal risk factors may be different from those of nonsyndromic multifactorial NTDs. Perinatal diagnosis of NTDs should alert doctors to the syndromes, disorders, and maternal and fetal risk factors associated with NTDs, and prompt thorough etiologic investigation and genetic counseling.

  18. Identifying temporal and causal contributions of neural processes underlying the Implicit Association Test (IAT

    Directory of Open Access Journals (Sweden)

    Chad Edward Forbes

    2012-11-01

    Full Text Available The Implicit Association Test (IAT is a popular behavioral measure that assesses the associative strength between outgroup members and stereotypical and counterstereotypical traits. Less is known, however, about the degree to which the IAT reflects automatic processing. Two studies examined automatic processing contributions to a gender-IAT using a data driven, social neuroscience approach. Performance on congruent (e.g., categorizing male names with synonyms of strength and incongruent (e.g., categorizing female names with synonyms of strength IAT blocks were separately analyzed using EEG (event-related potentials, or ERPs, and coherence; Study 1 and lesion (Study 2 methodologies. Compared to incongruent blocks, performance on congruent IAT blocks was associated with more positive ERPs that manifested in frontal and occipital regions at automatic processing speeds, occipital regions at more controlled processing speeds and was compromised by volume loss in the anterior temporal lobe, insula and medial PFC. Performance on incongruent blocks was associated with volume loss in supplementary motor areas, cingulate gyrus and a region in medial PFC similar to that found for congruent blocks. Greater coherence was found between frontal and occipital regions to the extent individuals exhibited more bias. This suggests there are separable neural contributions to congruent and incongruent blocks of the IAT but there is also a surprising amount of overlap. Given the temporal and regional neural distinctions, these results provide converging evidence that stereotypic associative strength assessed by the IAT indexes automatic processing to a degree.

  19. The effects of item familiarity on the neural correlates of successful associative memory encoding.

    Science.gov (United States)

    Dennis, Nancy A; Turney, Indira C; Webb, Christina E; Overman, Amy A

    2015-12-01

    Associative memory is considered to be resource-demanding, requiring individuals to learn individual items and the specific relationships between those items. Previous research has shown that prior studying of items aids in associative memory for pairs composed of those same items, as compared to pairs of items that have not been prelearned (e.g., Kilb & Naveh-Benjamin, 2011). In the present study, we sought to elucidate the neural correlates mediating this memory facilitation. After being trained on individual items, participants were scanned while encoding item pairs composed of items from the pretrained phase (familiarized-item pairs) and pairs whose items had not been previously learned (unfamiliarized-item pairs). Consistent with previous findings, the overall subsequent recollection showed the engagement of bilateral parahippocampal gyrus (PHG) and hippocampus, when compared to subsequent forgetting. However, a direct comparison between familiarized- and unfamiliarized-item pairs showed that subsequently recollected familiarized-item pairs were associated with decreased activity across much of the encoding network, including bilateral PHG, hippocampus, prefrontal cortex, and regions associated with item-specific processing within occipital cortex. Increased activity for familiarized-item pairs was found in a more limited set of regions, including bilateral parietal cortex, which has been associated with the formation of novel associations. Additionally, activity in the right parietal cortex correlated with associative memory success in the familiarized condition. Taken together, these results suggest that prior exposure to items can reduce the demands incurred on neural processing throughout the associative encoding network and can enhance associative memory performance by focusing resources within regions supporting the formation of associative links.

  20. Adolescents’ emotional competence is associated with parents’ neural sensitivity to emotions

    Directory of Open Access Journals (Sweden)

    Eva H Telzer

    2014-07-01

    Full Text Available An essential component of youths’ successful development is learning to appropriately respond to emotions, including the ability to recognize, identify, and describe one’s feelings. Such emotional competence is thought to arise through the parent-child relationship. Yet, the mechanisms by which parents transmit emotional competence to their children are difficult to measure because they are often implicit, idiosyncratic, and not easily articulated by parents or children. In the current study, we used a multifaceted approach that went beyond self-report measures and examined whether parental neural sensitivity to emotions predicted their child’s emotional competence. Twenty-two adolescent-parent dyads completed an fMRI scan during which they labeled the emotional expressions of negatively valenced faces. Results indicate that parents who recruited the amygdala, VLPFC, and brain regions involved in mentalizing (i.e., inferring others’ emotional states had adolescent children with greater emotional competence. These results held after controlling for parents’ self-reports of emotional expressivity and adolescents’ self-reports of the warmth and support of their parent relationships. In addition, adolescents recruited neural regions involved in mentalizing during affect labeling, which significantly mediated the associated between parental neural sensitivity and adolescents’ emotional competence, suggesting that youth are modeling or referencing their parents’ emotional profiles, thereby contributing to better emotional competence.

  1. Adolescents' emotional competence is associated with parents' neural sensitivity to emotions.

    Science.gov (United States)

    Telzer, Eva H; Qu, Yang; Goldenberg, Diane; Fuligni, Andrew J; Galván, Adriana; Lieberman, Matthew D

    2014-01-01

    An essential component of youths' successful development is learning to appropriately respond to emotions, including the ability to recognize, identify, and describe one's feelings. Such emotional competence is thought to arise through the parent-child relationship. Yet, the mechanisms by which parents transmit emotional competence to their children are difficult to measure because they are often implicit, idiosyncratic, and not easily articulated by parents or children. In the current study, we used a multifaceted approach that went beyond self-report measures and examined whether parental neural sensitivity to emotions predicted their child's emotional competence. Twenty-two adolescent-parent dyads completed an fMRI scan during which they labeled the emotional expressions of negatively valenced faces. Results indicate that parents who recruited the amygdala, VLPFC, and brain regions involved in mentalizing (i.e., inferring others' emotional states) had adolescent children with greater emotional competence. These results held after controlling for parents' self-reports of emotional expressivity and adolescents' self-reports of the warmth and support of their parent relationships. In addition, adolescents recruited neural regions involved in mentalizing during affect labeling, which significantly mediated the associated between parental neural sensitivity and adolescents' emotional competence, suggesting that youth are modeling or referencing their parents' emotional profiles, thereby contributing to better emotional competence.

  2. Genomic DNA hypomethylation is associated with neural tube defects induced by methotrexate inhibition of folate metabolism.

    Directory of Open Access Journals (Sweden)

    Xiuwei Wang

    Full Text Available DNA methylation is thought to be involved in the etiology of neural tube defects (NTDs. However, the exact mechanism between DNA methylation and NTDs remains unclear. Herein, we investigated the change of methylation in mouse model of NTDs associated with folate dysmetabolism by use of ultraperformance liquid chromatography tandem mass spectrometry (UPLC/MS/MS, liquid chromatography-electrospray ionization tandem mass spectrometry (LC-MS/MS, microarray, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and Real time quantitative PCR. Results showed that NTD neural tube tissues had lower concentrations of 5-methyltetrahydrofolate (5-MeTHF, P = 0.005, 5-formyltetrahydrofolate (5-FoTHF, P = 0.040, S-adenosylmethionine (SAM, P = 0.004 and higher concentrations of folic acid (P = 0.041, homocysteine (Hcy, P = 0.006 and S-adenosylhomocysteine (SAH, P = 0.045 compared to control. Methylation levels of genomic DNA decreased significantly in the embryonic neural tube tissue of NTD samples. 132 differentially methylated regions (35 low methylated regions and 97 high methylated regions were selected by microarray. Two genes (Siah1b, Prkx in Wnt signal pathway demonstrated lower methylated regions (peak and higher expression in NTDs (P<0.05; P<0.05. Results suggest that DNA hypomethylation was one of the possible epigenetic variations correlated with the occurrence of NTDs induced by folate dysmetabolism and that Siah1b, Prkx in Wnt pathway may be candidate genes for NTDs.

  3. Genomic DNA hypomethylation is associated with neural tube defects induced by methotrexate inhibition of folate metabolism.

    Science.gov (United States)

    Wang, Xiuwei; Guan, Zhen; Chen, Yan; Dong, Yanting; Niu, Yuhu; Wang, Jianhua; Zhang, Ting; Niu, Bo

    2015-01-01

    DNA methylation is thought to be involved in the etiology of neural tube defects (NTDs). However, the exact mechanism between DNA methylation and NTDs remains unclear. Herein, we investigated the change of methylation in mouse model of NTDs associated with folate dysmetabolism by use of ultraperformance liquid chromatography tandem mass spectrometry (UPLC/MS/MS), liquid chromatography-electrospray ionization tandem mass spectrometry (LC-MS/MS), microarray, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and Real time quantitative PCR. Results showed that NTD neural tube tissues had lower concentrations of 5-methyltetrahydrofolate (5-MeTHF, P = 0.005), 5-formyltetrahydrofolate (5-FoTHF, P = 0.040), S-adenosylmethionine (SAM, P = 0.004) and higher concentrations of folic acid (P = 0.041), homocysteine (Hcy, P = 0.006) and S-adenosylhomocysteine (SAH, P = 0.045) compared to control. Methylation levels of genomic DNA decreased significantly in the embryonic neural tube tissue of NTD samples. 132 differentially methylated regions (35 low methylated regions and 97 high methylated regions) were selected by microarray. Two genes (Siah1b, Prkx) in Wnt signal pathway demonstrated lower methylated regions (peak) and higher expression in NTDs (P<0.05; P<0.05). Results suggest that DNA hypomethylation was one of the possible epigenetic variations correlated with the occurrence of NTDs induced by folate dysmetabolism and that Siah1b, Prkx in Wnt pathway may be candidate genes for NTDs.

  4. Neural processing associated with cognitive and affective Theory of Mind in adolescents and adults

    Science.gov (United States)

    Fontaine, Nathalie M. G.; Bird, Geoffrey; Blakemore, Sarah-Jayne; De Brito, Stephane A.; McCrory, Eamon J. P.; Viding, Essi

    2012-01-01

    Theory of Mind (ToM) is the ability to attribute thoughts, intentions and beliefs to others. This involves component processes, including cognitive perspective taking (cognitive ToM) and understanding emotions (affective ToM). This study assessed the distinction and overlap of neural processes involved in these respective components, and also investigated their development between adolescence and adulthood. While data suggest that ToM develops between adolescence and adulthood, these populations have not been compared on cognitive and affective ToM domains. Using fMRI with 15 adolescent (aged 11–16 years) and 15 adult (aged 24–40 years) males, we assessed neural responses during cartoon vignettes requiring cognitive ToM, affective ToM or physical causality comprehension (control). An additional aim was to explore relationships between fMRI data and self-reported empathy. Both cognitive and affective ToM conditions were associated with neural responses in the classic ToM network across both groups, although only affective ToM recruited medial/ventromedial PFC (mPFC/vmPFC). Adolescents additionally activated vmPFC more than did adults during affective ToM. The specificity of the mPFC/vmPFC response during affective ToM supports evidence from lesion studies suggesting that vmPFC may integrate affective information during ToM. Furthermore, the differential neural response in vmPFC between adult and adolescent groups indicates developmental changes in affective ToM processing. PMID:21467048

  5. The influence of emotional associations on the neural correlates of semantic priming.

    Science.gov (United States)

    Sass, Katharina; Habel, Ute; Sachs, Olga; Huber, Walter; Gauggel, Siegfried; Kircher, Tilo

    2012-03-01

    Emotions influence our everyday life in several ways. With the present study, we wanted to examine the impact of emotional information on neural correlates of semantic priming, a well-established technique to investigate semantic processing. Stimuli were presented with a short SOA of 200 ms as subjects performed a lexical decision task during fMRI measurement. Seven experimental conditions were compared: positive/negative/neutral related, positive/negative/neutral unrelated, nonwords (all words were nouns). Behavioral data revealed a valence specific semantic priming effect (i.e., unrelated > related) only for neutral and positive related word pairs. On a neural level, the comparison of emotional over neutral relations showed activation in left anterior medial frontal cortex, superior frontal gyrus, and posterior cingulate. Interactions for the different relations were located in left anterior part of the medial frontal cortex, cingulate regions, and right hippocampus (positive > neutral + negative) and left posterior part of medial frontal cortex (negative > neutral + positive). The results showed that emotional information have an influence on semantic association processes. While positive and neutral information seem to share a semantic network, negative relations might induce compensatory mechanisms that inhibit the spread of activation between related concepts. The neural correlates highlighted a distributed neural network, primarily involving attention, memory and emotion related processing areas in medial fronto-parietal cortices. The differentiation between anterior (positive) and posterior part (negative) of the medial frontal cortex was linked to the type of affective manipulation with more cognitive demands being involved in the automatic processing of negative information.

  6. Informing others is associated with behavioral and neural signatures of value.

    Science.gov (United States)

    Tamir, Diana I; Zaki, Jamil; Mitchell, Jason P

    2015-12-01

    One of the many proclivities of our species is the drive to share information with others. What drives this unusual proclivity for propagating knowledge? Here, we test a common prediction made by recent theories in this domain: that individuals value opportunities to inform others. Two sets of studies supported this hypothesis. Behaviorally, individuals gave up money to inform others, even in "minimalistic" settings under which informing neither improved participants' reputation nor provided material benefits to information recipients. Neurally, opportunities to inform others engaged brain regions associated with motivation and reward, including the nucleus accumbens and ventromedial prefrontal cortex. Together, these findings suggest that people place intrinsic value on sharing information with others.

  7. A synthesis procedure for associative memories based on space-varying cellular neural networks.

    Science.gov (United States)

    Park, J; Kim, H Y; Park, Y; Lee, S W

    2001-01-01

    In this paper, we consider the problem of realizing associative memories via space-varying CNNs (cellular neural networks). Based on some known results and a newly derived theorem for the CNN model, we propose a synthesis procedure for obtaining a space-varying CNN that can store given bipolar vectors with certain desirable properties. The major part of our synthesis procedure consists of solving generalized eigenvalue problems and/or linear matrix inequality problems, which can be efficiently solved by recently developed interior point methods. The validity of the proposed approach is illustrated by a design example.

  8. Neural correlates associated with superior tactile symmetry perception in the early blind.

    Science.gov (United States)

    Bauer, Corinna; Yazzolino, Lindsay; Hirsch, Gabriella; Cattaneo, Zaira; Vecchi, Tomaso; Merabet, Lotfi B

    2015-02-01

    Symmetry is an organizational principle that is ubiquitous throughout the visual world. However, this property can also be detected through non-visual modalities such as touch. The role of prior visual experience on detecting tactile patterns containing symmetry remains unclear. We compared the behavioral performance of early blind and sighted (blindfolded) controls on a tactile symmetry detection task. The tactile patterns used were similar in design and complexity as in previous visual perceptual studies. The neural correlates associated with this behavioral task were identified with functional magnetic resonance imaging (fMRI). In line with growing evidence demonstrating enhanced tactile processing abilities in the blind, we found that early blind individuals showed significantly superior performance in detecting tactile symmetric patterns compared to sighted controls. Furthermore, comparing patterns of activation between these two groups identified common areas of activation (e.g. superior parietal cortex) but key differences also emerged. In particular, tactile symmetry detection in the early blind was also associated with activation that included peri-calcarine cortex, lateral occipital (LO), and middle temporal (MT) cortex, as well as inferior temporal and fusiform cortex. These results contribute to the growing evidence supporting superior behavioral abilities in the blind, and the neural correlates associated with crossmodal neuroplasticity following visual deprivation.

  9. Learning to Associate Auditory and Visual Stimuli: Behavioral and Neural Mechanisms

    Science.gov (United States)

    Altieri, Nicholas; Stevenson, Ryan; Wallace, Mark T.; Wenger, Michael J.

    2014-01-01

    The ability to effectively combine sensory inputs across modalities is vital for acquiring a unified percept of events. For example, watching a hammer hit a nail while simultaneously identifying the sound as originating from the event requires the ability to identify spatio-temporal congruencies and statistical regularities. In this study, we applied a reaction time (RT) and hazard function measure known as capacity (e.g., Townsend and Ashby, 1978) to quantify the extent to which observers learn paired associations between simple auditory and visual patterns in a model theoretic manner. As expected, results showed that learning was associated with an increase in accuracy, but more significantly, an increase in capacity. The aim of this study was to associate capacity measures of multisensory learning, with neural based measures, namely mean Global Field Power (GFP). We observed a co-variation between an increase in capacity, and a decrease in GFP amplitude as learning occurred. This suggests that capacity constitutes a reliable behavioral index of efficient energy expenditure in the neural domain. PMID:24276220

  10. Fetal DNA hypermethylation in tight junction pathway is associated with neural tube defects: A genome-wide DNA methylation analysis.

    Science.gov (United States)

    Wang, Linlin; Lin, Shanshan; Zhang, Ji; Tian, Tian; Jin, Lei; Ren, Aiguo

    2017-02-01

    Neural tube defects (NTDs) are a spectrum of severe congenital malformations of fusion failure of the neural tube during early embryogenesis. Evidence on aberrant DNA methylation in NTD development remains scarce, especially when exposure to environmental pollutant is taken into consideration. DNA methylation profiling was quantified using the Infinium HumanMethylation450 array in neural tissues from 10 NTD cases and 8 non-malformed controls (stage 1). Subsequent validation was performed using a Sequenom MassARRAY system in neural tissues from 20 NTD cases and 20 non-malformed controls (stage 2). Correlation analysis of differentially methylated CpG sites in fetal neural tissues and polycyclic aromatic hydrocarbons concentrations in fetal neural tissues and maternal serum was conducted. Differentially methylated CpG sites of neural tissues were further validated in fetal mice with NTDs induced by benzo(a)pyrene given to pregnant mice. Differentially hypermethylated CpG sites in neural tissues from 17 genes and 6 pathways were identified in stage 1. Subsequently, differentially hypermethylated CpG sites in neural tissues from 6 genes (BDKRB2, CTNNA1, CYFIP2, MMP7, MYH2, and TIAM2) were confirmed in stage 2. Correlation analysis showed that methylated CpG sites in CTNNA1 and MYH2 from NTD cases were positively correlated to polycyclic aromatic hydrocarbon level in fetal neural tissues and maternal serum. The correlation was confirmed in NTD-affected fetal mice that were exposed to benzo(a)pyrene in utero. In conclusion, hypermethylation of the CTNNA1 and MYH2 genes in tight junction pathway is associated with the risk for NTDs, and the DNA methylation aberration may be caused by exposure to benzo(a)pyrene.

  11. CMOS current-mode neural associative memory design with on-chip learning.

    Science.gov (United States)

    Wu, C Y; Lan, J F

    1996-01-01

    Based on the Grossberg mathematical model called the outstar, a modular neural net with on-chip learning and memory is designed and analyzed. The outstar is the minimal anatomy that can interpret the classical conditioning or associative memory. It can also be served as a general-purpose pattern learning device. To realize the outstar, CMOS (complimentary metal-oxide semiconductor) current-mode analog dividers are developed to implement the special memory called the ratio-type memory. Furthermore, a CMOS current-mode analog multiplier is used to implement the correlation. The implemented CMOS outstar can on-chip store the relative ratio values of the trained weights for a long time. It can also be modularized to construct general neural nets. HSPICE (a circuit simulator of Meta Software, Inc.) simulation results of the CMOS outstar circuits as associative memory and pattern learner have successfully verified their functions. The measured results of the fabricated CMOS outstar circuits have also successfully confirmed the ratio memory and on-chip learning capability of the circuits. Furthermore, it has been shown that the storage time of the ratio memory can be as long as five minutes without refreshment. Also the outstar can enhance the contrast of the stored pattern within a long period. This makes the outstar circuits quite feasible in many applications.

  12. Application of neural network to humanoid robots-development of co-associative memory model.

    Science.gov (United States)

    Itoh, Kazuko; Miwa, Hiroyasu; Takanobu, Hideaki; Takanishi, Atsuo

    2005-01-01

    We have been studying a system of many harmonic oscillators (neurons) interacting via a chaotic force since 2002. Each harmonic oscillator is driven by chaotic force whose bifurcation parameter is modulated by the position of the harmonic oscillator. Moreover, a system of mutually coupled chaotic neural networks was investigated. Different patterns were stored in each network and the associative memory problem was discussed in these networks. Each network can retrieve the pattern stored in the other network. On the other hand, we have been developing new mechanisms and functions for a humanoid robot with the ability to express emotions and communicate with humans in a human-like manner. We introduced a mental model which consisted of the mental space, the mood, the equations of emotion, the robot personality, the need model, the consciousness model and the behavior model. This type of mental model was implemented in Emotion Expression Humanoid Robot WE-4RII (Waseda Eye No.4 Refined II). In this paper, an associative memory model using mutually coupled chaotic neural networks is proposed for retrieving optimum memory (recognition) in response to a stimulus. We implemented this model in Emotion Expression Humanoid Robot WE-4RII (Waseda Eye No.4 Refined II).

  13. Age associations with neural processing of reward anticipation in adolescents with bipolar disorders

    Directory of Open Access Journals (Sweden)

    Snežana Urošević

    2016-01-01

    Full Text Available Reward/behavioral approach system hypersensitivity is implicated in bipolar disorders (BD and in normative development during adolescence. Pediatric onset of BD is associated with a more severe illness course. However, little is known about neural processing of rewards in adolescents with BD or developmental (i.e., age associations with activation of these neural systems. The present study aims to address this knowledge gap. The present sample included 21 adolescents with BD and 26 healthy adolescents, ages 13 to 19. Participants completed a functional magnetic resonance imaging (fMRI protocol using the Monetary Incentive Delay (MID task. Behavioral performance was similar between groups. Group differences in BOLD activation during target anticipation and feedback anticipation periods of the task were examined using whole-brain analyses, as were group differences in age effects. During both target anticipation and feedback anticipation, adolescents with BD, compared to adolescents without psychopathology, exhibited decreased engagement of frontal regions involved in cognitive control (i.e., dorsolateral prefrontal cortex. Healthy adolescents exhibited age-related decreases, while adolescents with BD exhibited age-related increases, in activity of other cognitive control frontal areas (i.e., right inferior frontal gyrus, suggesting altered development in the BD group. Longitudinal research is needed to examine potentially abnormal development of cognitive control during reward pursuit in adolescent BD and whether early therapeutic interventions can prevent these potential deviations from normative development.

  14. Age associations with neural processing of reward anticipation in adolescents with bipolar disorders.

    Science.gov (United States)

    Urošević, Snežana; Luciana, Monica; Jensen, Jonathan B; Youngstrom, Eric A; Thomas, Kathleen M

    2016-01-01

    Reward/behavioral approach system hypersensitivity is implicated in bipolar disorders (BD) and in normative development during adolescence. Pediatric onset of BD is associated with a more severe illness course. However, little is known about neural processing of rewards in adolescents with BD or developmental (i.e., age) associations with activation of these neural systems. The present study aims to address this knowledge gap. The present sample included 21 adolescents with BD and 26 healthy adolescents, ages 13 to 19. Participants completed a functional magnetic resonance imaging (fMRI) protocol using the Monetary Incentive Delay (MID) task. Behavioral performance was similar between groups. Group differences in BOLD activation during target anticipation and feedback anticipation periods of the task were examined using whole-brain analyses, as were group differences in age effects. During both target anticipation and feedback anticipation, adolescents with BD, compared to adolescents without psychopathology, exhibited decreased engagement of frontal regions involved in cognitive control (i.e., dorsolateral prefrontal cortex). Healthy adolescents exhibited age-related decreases, while adolescents with BD exhibited age-related increases, in activity of other cognitive control frontal areas (i.e., right inferior frontal gyrus), suggesting altered development in the BD group. Longitudinal research is needed to examine potentially abnormal development of cognitive control during reward pursuit in adolescent BD and whether early therapeutic interventions can prevent these potential deviations from normative development.

  15. An Incremental Time-delay Neural Network for Dynamical Recurrent Associative Memory

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    An incremental time-delay neural network based on synapse growth, which is suitable for dynamic control and learning of autonomous robots, is proposed to improve the learning and retrieving performance of dynamical recurrent associative memory architecture. The model allows steady and continuous establishment of associative memory for spatio-temporal regularities and time series in discrete sequence of inputs. The inserted hidden units can be taken as the long-term memories that expand the capacity of network and sometimes may fade away under certain condition. Preliminary experiment has shown that this incremental network may be a promising approach to endow autonomous robots with the ability of adapting to new data without destroying the learned patterns. The system also benefits from its potential chaos character for emergence.

  16. Multidimensional analysis of the abnormal neural oscillations associated with lexical processing in schizophrenia.

    Science.gov (United States)

    Xu, Tingting; Stephane, Massoud; Parhi, Keshab K

    2013-04-01

    The neural mechanisms of language abnormalities, the core symptoms in schizophrenia, remain unclear. In this study, a new experimental paradigm, combining magnetoencephalography (MEG) techniques and machine intelligence methodologies, was designed to gain knowledge about the frequency, brain location, and time of occurrence of the neural oscillations that are associated with lexical processing in schizophrenia. The 248-channel MEG recordings were obtained from 12 patients with schizophrenia and 10 healthy controls, during a lexical processing task, where the patients discriminated correct from incorrect lexical stimuli that were visually presented. Event-related desynchronization/synchronization (ERD/ERS) was computed along the frequency, time, and space dimensions combined, that resulted in a large spectral-spatial-temporal ERD/ERS feature set. Machine intelligence techniques were then applied to select a small subset of oscillation patterns that are abnormal in patients with schizophrenia, according to their discriminating power in patient and control classification. Patients with schizophrenia showed abnormal ERD/ERS patterns during both lexical encoding and post-encoding periods. The top-ranked features were located at the occipital and left frontal-temporal areas, and covered a wide frequency range, including δ (1-4 Hz), α (8-12 Hz), β (12-32 Hz), and γ (32-48 Hz) bands. These top features could discriminate the patient group from the control group with 90.91% high accuracy, which demonstrates significant brain oscillation abnormalities in patients with schizophrenia at the specific frequency, time, and brain location indicated by these top features. As neural oscillation abnormality may be due to the mechanisms of the disease, the spectral, spatial, and temporal content of the discriminating features can offer useful information for helping understand the physiological basis of the language disorder in schizophrenia, as well as the pathology of the

  17. Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts

    CERN Document Server

    Kim, Kyungmin; Hodge, Kari A; Kim, Young-Min; Lee, Chang-Hwan; Lee, Hyun Kyu; Oh, John J; Oh, Sang Hoon; Son, Edwin J

    2014-01-01

    We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. In order to demonstrate the performance, we also evaluate a few seconds of gravitational-wave data segment using the trained networks and obtain the false alarm probability. We suggest that the artificial neural network can be a complementary method to the conventio...

  18. Equilibrium and attractivity analysis for a class of hetero-associative neural memories.

    Science.gov (United States)

    Lee, D L; Wang, W J

    1996-07-01

    Based on the natural structure of Kosko's Bidirectional Associative Memories (BAM), a high-performance, high-capacity associative neural model is proposed which is capable of simultaneous hetero-associative recall. The proposed model, Modified Bidirectional Decoding Strategy (MBDS), improves the recall rate by adding some association fascicles to Kosko's BAM. The association fascicles are sparse coding neuron structures that provide activating strengths between two neuron fields (say, field X and field Y). The sufficient conditions for a state to become an equilibrium state of the MBDS network is derived. Based on these results, we discuss the basins of attraction of the training pairs in one iteration. The upper bound of the number of error bits which can be tolerated by MBDS is also derived. Because the attractivity of a stored training pair can be increased markedly with the aid of its corresponding association fascicles, we recommend a high capacity realization of MBDS, Bidirectional Holographic Memory (BHM), so that each training pair is stored uniquely and directly in the connection weights rather than encoded in a correlation matrix. Finally, computer simulations demonstrate the attractiveness of three different realizations of MBDS to verify our results.

  19. Chronic Childhood Peer Rejection is Associated with Heightened Neural Responses to Social Exclusion During Adolescence.

    Science.gov (United States)

    Will, Geert-Jan; van Lier, Pol A C; Crone, Eveline A; Güroğlu, Berna

    2016-01-01

    This functional Magnetic Resonance Imaging (fMRI) study examined subjective and neural responses to social exclusion in adolescents (age 12-15) who either had a stable accepted (n = 27; 14 males) or a chronic rejected (n = 19; 12 males) status among peers from age 6 to 12. Both groups of adolescents reported similar increases in distress after being excluded in a virtual ball-tossing game (Cyberball), but adolescents with a history of chronic peer rejection showed higher activity in brain regions previously linked to the detection of, and the distress caused by, social exclusion. Specifically, compared with stably accepted adolescents, chronically rejected adolescents displayed: 1) higher activity in the dorsal anterior cingulate cortex (dACC) during social exclusion and 2) higher activity in the dACC and anterior prefrontal cortex when they were incidentally excluded in a social interaction in which they were overall included. These findings demonstrate that chronic childhood peer rejection is associated with heightened neural responses to social exclusion during adolescence, which has implications for understanding the processes through which peer rejection may lead to adverse effects on mental health over time.

  20. Neural correlates of stimulus-response and response-outcome associations in dorsolateral versus dorsomedial striatum

    Directory of Open Access Journals (Sweden)

    Thomas A Stalnaker

    2010-05-01

    Full Text Available Considerable evidence suggests that there is functional heterogeneity in the control of behavior by the dorsal striatum. Dorsomedial striatum may support goal-directed behavior by representing associations between responses and outcomes (R-O associations. The dorsolateral striatum, in contrast, may support motor habits by encoding associations between stimuli and responses (S-R associations. To test whether neural correlates in striatum in fact conform to this pattern, we recorded single-units in dorsomedial and dorsolateral striatum of rats performing a task in which R-O contingencies were manipulated independently of S-R contingencies. Among response-selective neurons in both regions, activity was significantly modulated by the initial stimulus, providing evidence of S-R encoding. Similarly, response selectivity was significantly modulated by the associated outcome in both regions, providing evidence of R-O encoding. In both regions, this outcome-modulation did not seem to reflect the relative value of the expected outcome, but rather its specific identity. Finally, in both regions we found correlates of the available action-outcome contingencies reflected in the baseline activity of many neurons. These results suggest that differences in information content in these two regions may not determine the differential roles they play in controlling behavior, demonstrated in previous studies.

  1. Syndromes, Disorders and Maternal Risk Factors Associated With Neural Tube Defects (VII

    Directory of Open Access Journals (Sweden)

    Chih-Ping Chen

    2008-09-01

    Full Text Available Neural tube defects (NTDs may be associated with syndromes, disorders and maternal risk factors. This article provides a comprehensive review of the syndromes, disorders and maternal risk factors associated with NTDs, including DK phocomelia syndrome (von Voss-Cherstvoy syndrome, Siegel-Bartlet syndrome, fetal warfarin syndrome, craniotelencephalic dysplasia, Czeizel-Losonci syndrome, maternal cocaine abuse, Weissenbacher-Zweymüller syndrome, parietal foramina (cranium bifidum, Apert syndrome, craniomicromelic syndrome, XX-agonadism with multiple dysraphic lesions including omphalocele and NTDs, Fryns microphthalmia syndrome, Gershoni-Baruch syndrome, PHAVER syndrome, periconceptional vitamin B6 deficiency, and autosomal dominant Dandy-Walker malformation with occipital cephalocele. NTDs associated with these syndromes, disorders and maternal risk factors are a rare but important cause of NTDs. The recurrence risk and the preventive effect of maternal folic acid intake in NTDs associated with syndromes, disorders and maternal risk factors may be different from those of nonsyndromic multifactorial NTDs. Perinatal diagnosis of NTDs should alert doctors to the syndromes, disorders and maternal risk factors associated with NTDs, and prompt thorough etiologic investigation and genetic counseling.

  2. Syndromes, Disorders and Maternal Risk Factors Associated with Neural Tube Defects (III

    Directory of Open Access Journals (Sweden)

    Chih-Ping Chen

    2008-06-01

    Full Text Available Fetuses with neural tube defects (NTDs may be associated with syndromes, disorders, and maternal and fetal risk factors. This article provides a comprehensive review of syndromes, disorders, and maternal and fetal risk factors associated with NTDs, such as omphalocele, OEIS (omphalocele-exstrophy-imperforate anus-spinal defects complex, pentalogy of Cantrell, amniotic band sequence, limb-body wall complex, Meckel syndrome, Joubert syndrome, skeletal dysplasia, diabetic embryopathy, and single nucleotide polymorphisms in genes of glucose metabolism. NTDs associated with syndromes, disorders, and maternal and fetal risk factors are a rare but important cause of NTDs. The recurrence risk and the preventive effect of maternal folic acid intake in NTDs associated with syndromes, disorders and maternal risk factors may be different from those of nonsyndromic multi facto rial NTDs. Perinatal identification of NTDs should alert the clinician to the syndromes, disorders, and maternal and fetal risk factors associated with NTDs, and prompt a thorough etiologic investigation and genetic counseling. [Taiwan J Obstet Cynecol 2008;47(2:131-140

  3. Multiscale Modeling of Gene-Behavior Associations in an Artificial Neural Network Model of Cognitive Development.

    Science.gov (United States)

    Thomas, Michael S C; Forrester, Neil A; Ronald, Angelica

    2016-01-01

    In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. Given that they have been detected, how do such associations inform cognitive-level theories? To investigate this question, we employed a multiscale computational model of development, using a sample domain drawn from the field of language acquisition. The model comprised an artificial neural network model of past-tense acquisition trained using the backpropagation learning algorithm, extended to incorporate population modeling and genetic algorithms. It included five levels of description-four internal: genetic, network, neurocomputation, behavior; and one external: environment. Since the mechanistic assumptions of the model were known and its operation was relatively transparent, we could evaluate whether cross-level associations gave an accurate picture of causal processes. We established that associations could be detected between artificial genes and behavioral variation, even under polygenic assumptions of a many-to-one relationship between genes and neurocomputational parameters, and when an experience-dependent developmental process interceded between the action of genes and the emergence of behavior. We evaluated these associations with respect to their specificity (to different behaviors, to function vs. structure), to their developmental stability, and to their replicability, as well as considering issues of missing heritability and gene-environment interactions. We argue that gene

  4. Singular-Value-Decomposition Analysis of Associative Memory in a Neural Network

    Science.gov (United States)

    Kumamoto, Tatsuya; Suzuki, Mao; Matsueda, Hiroaki

    2017-02-01

    We evaluate performance of associative memory in a neural network by based on the singular value decomposition (SVD) of image data stored in the network. We consider the situation in which the original image and its highly coarse-grained one by SVD are stored in the network and the intermediate one is taken as an input. We find that the performance is characterized by the snapshot-entropy scaling inherent in the SVD: the network retrieves the original image when the entropy of the input image is larger than the critical value determined from the scaling. The result indicates efficiency of the SVD as a criterion of the performance and also indicates universality of the scaling for realistic problems beyond theoretical physics.

  5. Dynamic synchronization and chaos in an associative neural network with multiple active memories.

    Science.gov (United States)

    Raffone, Antonino; van Leeuwen, Cees

    2003-09-01

    Associative memory dynamics in neural networks are generally based on attractors. Retrieval based on fixed-point attractors works if only one memory pattern is retrieved at the time, but cannot enable the simultaneous retrieval of more than one pattern. Stable phase-locking of periodic oscillations or limit cycle attractors leads to incorrect feature bindings if the simultaneously retrieved patterns share some of their features. We investigate retrieval dynamics of multiple active patterns in a network of chaotic model neurons. Several memory patterns are kept simultaneously active and separated from each other by a dynamic itinerant synchronization between neurons. Neurons representing shared features alternate their synchronization between patterns, thus multiplexing their binding relationships. Our model includes a mechanism for self-organized readout or decoding of memory pattern coherence in terms of short-term potentiation and short-term depression of synaptic weights.

  6. ECG processing techniques based on neural networks and bidirectional associative memories.

    Science.gov (United States)

    Maglaveras, N; Stamkopoulos, T; Pappas, C; Strintzis, M

    1998-01-01

    Two ECG processing techniques are described for the classification of QRSs, PVCs and normal and ischaemic beats. The techniques use neural network (NN) technology in two ways. The first technique, uses nonlinear ECG mapping preprocessing and subsequently for classification uses a shrinking algorithm based on NNs. This technique is applied to the QRS/PVC problem with good result. The second technique is based on the Bidirectional Associative Memory (BAM) NN and is used to distinguish normal from ischaemic beats. In this technique the ECG beat is treated as a digitized image which is then transformed into a bipolar vector suitable for input in the BAM. The results show that this method, if properly calibrated, can result in a fast and reliable ischaemic beat detection algorithm.

  7. An optimization approach to design of generalized BSB neural associative memories.

    Science.gov (United States)

    Park, J; Park, Y

    2000-06-01

    This article is concerned with the synthesis of the optimally performing GBSB (generalized brain-state-in-a-box) neural associative memory given a set of desired binary patterns to be stored as asymptotically stable equilibrium points. Based on some known qualitative properties and newly observed fundamental properties of the GBSB model, the synthesis problem is formulated as a constrained optimization problem. Next, we convert this problem into a quasi-convex optimization problem called GEVP (generalized eigenvalue problem). This conversion is particularly useful in practice, because GEVPs can be efficiently solved by recently developed interior point methods. Design examples are given to illustrate the proposed approach and to compare with existing synthesis methods.

  8. Reconstruction based approach to sensor fault diagnosis using auto-associative neural networks

    Institute of Scientific and Technical Information of China (English)

    Mousavi Hamidreza; Shahbazian Mehdi; Jazayeri-Rad Hooshang; Nekounam Aliakbar

    2014-01-01

    Fault diagnostics is an important research area including different techniques. Principal component analysis (PCA) is a linear technique which has been widely used. For nonlinear processes, however, the nonlinear principal component analysis (NLPCA) should be applied. In this work, NLPCA based on auto-associative neural network (AANN) was applied to model a chemical process using historical data. First, the residuals generated by the AANN were used for fault detection and then a reconstruction based approach called enhanced AANN (E-AANN) was presented to isolate and reconstruct the faulty sensor simultaneously. The proposed method was implemented on a continuous stirred tank heater (CSTH) and used to detect and isolate two types of faults (drift and offset) for a sensor. The results show that the proposed method can detect, isolate and reconstruct the occurred fault properly.

  9. Neural activity associated with semantic versus phonological anomia treatments in aphasia.

    Science.gov (United States)

    van Hees, Sophia; McMahon, Katie; Angwin, Anthony; de Zubicaray, Greig; Copland, David A

    2014-02-01

    Naming impairments in aphasia are typically targeted using semantic and/or phonologically based tasks. However, it is not known whether these treatments have different neural mechanisms. Eight participants with aphasia received twelve treatment sessions using an alternating treatment design, with fMRI scans pre- and post-treatment. Half the sessions employed Phonological Components Analysis (PCA), and half the sessions employed Semantic Feature Analysis (SFA). Pre-treatment activity in the left caudate correlated with greater immediate treatment success for items treated with SFA, whereas recruitment of the left supramarginal gyrus and right precuneus post-treatment correlated with greater immediate treatment success for items treated with PCA. The results support previous studies that have found greater treatment outcome to be associated with activity in predominantly left hemisphere regions, and suggest that different mechanisms may be engaged dependent on the type of treatment employed.

  10. The neural correlates of worry in association with individual differences in neuroticism.

    Science.gov (United States)

    Servaas, Michelle Nadine; Riese, Harriëtte; Ormel, Johan; Aleman, André

    2014-09-01

    The tendency to worry is a facet of neuroticism that has been shown to mediate the relationship between neuroticism and symptoms of depression and anxiety. The aim of the current study was to investigate the neural correlates of state worry in association with neuroticism. One-hundred twenty participants were selected from an initially recruited sample of 240 women based on their neuroticism score. First, participants completed a questionnaire to assess the excessiveness and uncontrollability of pathological worry. Second, we measured brain activation with functional magnetic resonance imaging (fMRI) while participants were randomly presented with 12 worry-inducing sentences and 12 neutral sentences in a mood induction paradigm. Individuals scoring higher on neuroticism reported to worry more in daily life and to have generated more worry-related thoughts after the presentation of a worry-inducing sentence. Furthermore, imaging results showed the involvement of default mode and emotional brain areas during worry, previously associated with self-related processing and emotion regulation. Specifically, cortical midline structures and the anterior insula showed more activation during worry, when individuals indicated to have generated more worry-related thoughts. Activation in the retrosplenial and visual cortex was decreased in individuals scoring higher on neuroticism during worry, possibly suggesting reduced autobiographical specificity and visual mental imagery. In the literature, both these processes have been related to the cognitive avoidance of emotional distress. Excessive worry features in a number of emotional disorders and results from studies that elucidate its neural basis may help explain how and why neuroticism contributes to vulnerability for psychopathology.

  11. Cross-Modal Decoding of Neural Patterns Associated with Working Memory: Evidence for Attention-Based Accounts of Working Memory.

    Science.gov (United States)

    Majerus, Steve; Cowan, Nelson; Péters, Frédéric; Van Calster, Laurens; Phillips, Christophe; Schrouff, Jessica

    2016-01-01

    Recent studies suggest common neural substrates involved in verbal and visual working memory (WM), interpreted as reflecting shared attention-based, short-term retention mechanisms. We used a machine-learning approach to determine more directly the extent to which common neural patterns characterize retention in verbal WM and visual WM. Verbal WM was assessed via a standard delayed probe recognition task for letter sequences of variable length. Visual WM was assessed via a visual array WM task involving the maintenance of variable amounts of visual information in the focus of attention. We trained a classifier to distinguish neural activation patterns associated with high- and low-visual WM load and tested the ability of this classifier to predict verbal WM load (high-low) from their associated neural activation patterns, and vice versa. We observed significant between-task prediction of load effects during WM maintenance, in posterior parietal and superior frontal regions of the dorsal attention network; in contrast, between-task prediction in sensory processing cortices was restricted to the encoding stage. Furthermore, between-task prediction of load effects was strongest in those participants presenting the highest capacity for the visual WM task. This study provides novel evidence for common, attention-based neural patterns supporting verbal and visual WM. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  12. The relationships between age, associative memory performance, and the neural correlates of successful associative memory encoding.

    Science.gov (United States)

    de Chastelaine, Marianne; Mattson, Julia T; Wang, Tracy H; Donley, Brian E; Rugg, Michael D

    2016-06-01

    Using functional magnetic resonance imaging, subsequent memory effects (greater activity for later remembered than later forgotten study items) predictive of associative encoding were compared across samples of young, middle-aged, and older adults (total N = 136). During scanning, participants studied visually presented word pairs. In a later test phase, they discriminated between studied pairs, "rearranged" pairs (items studied on different trials), and new pairs. Subsequent memory effects were identified by contrasting activity elicited by study pairs that went on to be correctly judged intact or incorrectly judged rearranged. Effects in the hippocampus were age-invariant and positively correlated across participants with associative memory performance. Subsequent memory effects in the right inferior frontal gyrus (IFG) were greater in the older than the young group. In older participants only, both left and, in contrast to prior reports, right IFG subsequent memory effects correlated positively with memory performance. We suggest that the IFG is especially vulnerable to age-related decline in functional integrity and that the relationship between encoding-related activity in right IFG and memory performance depends on the experimental context.

  13. Priming for novel object associations: Neural differences from object item priming and equivalent forms of recognition.

    Science.gov (United States)

    Gomes, Carlos Alexandre; Figueiredo, Patrícia; Mayes, Andrew

    2016-04-01

    The neural substrates of associative and item priming and recognition were investigated in a functional magnetic resonance imaging study over two separate sessions. In the priming session, participants decided which object of a pair was bigger during both study and test phases. In the recognition session, participants saw different object pairs and performed the same size-judgement task followed by an associative recognition memory task. Associative priming was accompanied by reduced activity in the right middle occipital gyrus as well as in bilateral hippocampus. Object item priming was accompanied by reduced activity in extensive priming-related areas in the bilateral occipitotemporofrontal cortex, as well as in the perirhinal cortex, but not in the hippocampus. Associative recognition was characterized by activity increases in regions linked to recollection, such as the hippocampus, posterior cingulate cortex, anterior medial frontal gyrus and posterior parahippocampal cortex. Item object priming and recognition recruited broadly overlapping regions (e.g., bilateral middle occipital and prefrontal cortices, left fusiform gyrus), even though the BOLD response was in opposite directions. These regions along with the precuneus, where both item priming and recognition were accompanied by activation, have been found to respond to object familiarity. The minimal structural overlap between object associative priming and recollection-based associative recognition suggests that they depend on largely different stimulus-related information and that the different directions of the effects indicate distinct retrieval mechanisms. In contrast, item priming and familiarity-based recognition seemed mainly based on common memory information, although the extent of common processing between priming and familiarity remains unclear. Further implications of these findings are discussed.

  14. Associative memories based on continuous-time cellular neural networks designed using space-invariant cloning templates.

    Science.gov (United States)

    Zeng, Zhigang; Wang, Jun

    2009-01-01

    Associative memories are brain-style devices designed to store a set of patterns as stable equilibria such that the stored patterns can be reliably retrieved with the initial probes containing sufficient information about the patterns. This paper presents a new design procedure for synthesizing associative memories based on continuous-time cellular neural networks with time delays characterized by input and output matrices obtained using two-dimensional space-invariant cloning templates. The design procedure enables hetero-associative or auto-associative memories to be synthesized by solving a set of linear inequalities with few design parameters and retrieval probes feeding from external inputs instead of initial states. The designed associative memories are robust in terms of design parameter selection. In addition, the hosting cellular neural networks are guaranteed to be globally exponentially stable. Simulation and experimental results of illustrative examples and Monte Carlo tests demonstrate the applicability and superiority of the methodology.

  15. Pattern Association For Character Recognition By Back-Propagation Algorithm Using Neural Network Approach

    Directory of Open Access Journals (Sweden)

    S.P.Kosbatwar

    2012-03-01

    Full Text Available The use of artificial neural network in applications can dramatically simplify the code and improve quality of recognition while achieving good performance. Another benefit of using neural network in application is extensibility of the system – ability to recognize more character sets than initially defined. Most of traditional systems are not extensible enough. In this paper recognition ofcharacters is possible by using neural network back propagation algorithm.

  16. Estimating the geoeffectiveness of halo CMEs from associated solar and IP parameters using neural networks

    Directory of Open Access Journals (Sweden)

    J. Uwamahoro

    2012-06-01

    Full Text Available Estimating the geoeffectiveness of solar events is of significant importance for space weather modelling and prediction. This paper describes the development of a neural network-based model for estimating the probability occurrence of geomagnetic storms following halo coronal mass ejection (CME and related interplanetary (IP events. This model incorporates both solar and IP variable inputs that characterize geoeffective halo CMEs. Solar inputs include numeric values of the halo CME angular width (AW, the CME speed (Vcme, and the comprehensive flare index (cfi, which represents the flaring activity associated with halo CMEs. IP parameters used as inputs are the numeric peak values of the solar wind speed (Vsw and the southward Z-component of the interplanetary magnetic field (IMF or Bs. IP inputs were considered within a 5-day time window after a halo CME eruption. The neural network (NN model training and testing data sets were constructed based on 1202 halo CMEs (both full and partial halo and their properties observed between 1997 and 2006. The performance of the developed NN model was tested using a validation data set (not part of the training data set covering the years 2000 and 2005. Under the condition of halo CME occurrence, this model could capture 100% of the subsequent intense geomagnetic storms (Dst ≤ −100 nT. For moderate storms (−100 < Dst ≤ −50, the model is successful up to 75%. This model's estimate of the storm occurrence rate from halo CMEs is estimated at a probability of 86%.

  17. Using perfusion MRI to measure the dynamic changes in neural activation associated with tonic muscular pain.

    Science.gov (United States)

    Owen, Daron G; Clarke, Collin F; Ganapathy, Sugantha; Prato, Frank S; St Lawrence, Keith S

    2010-03-01

    Knowledge regarding neural pain processing is primarily the result of studies involving models of brief cutaneous pain; however, clinical pain generally originates in deep tissue and is prolonged. This study measured the dynamic neural activation associated with a muscular pain model incorporating both acute and tonic states. Hypertonic saline (5% NaCl) was infused into the brachioradialis muscle of eleven healthy volunteers for 15min after an initial bolus of 0.5mL. Ten controls followed the same protocol with normal saline (0.9% NaCl). Magnetic resonance images of cerebral blood flow (CBF) were acquired using an arterial spin labelling method. The imaging volume extended from the thalamus to the primary somatosensory cortices, but did not include the brainstem and cerebellum. Using a numerical scale from 0 to 10, ratings of pain intensity peaked at 5.9+/-0.6 and remained near 5 for the remainder of the trial. Controls experienced minimal pain, reporting a peak value of 1.8+/-0.4. Significant CBF increases in rostral and caudal anterior insula bilaterally, anterior mid-cingulate cortex (aMCC), bilateral thalamus, and contralateral posterior insula were observed. The time courses of CBF revealed significant differences in the activation pattern during tonic pain. In particular, a more rapid return to baseline in aMCC versus insula was interpreted as a preferential decrease in the affective component of pain. This conclusion was supported by the strong correlation between pain intensity ratings and CBF in the contralateral insula (R(2)=0.911, p<0.01), which is a region believed to be responsible for pain intensity processing.

  18. A Hopfield-like hippocampal CA3 neural network model for studying associative memory in Alzheimer's disease

    Institute of Scientific and Technical Information of China (English)

    Wangxiong Zhao; Qingli Qiao; Dan Wang

    2010-01-01

    Associative memory, one of the major cognitive functions in the hippocampal CA3 region, includes auto-associative memory and hetero-associative memory. Many previous studies have shown that Alzheimer's disease (AD) can lead to loss of functional synapses in the central nervous system, and associative memory functions in patients with AD are often impaired, but few studies have addressed the effect of AD on hetero-associative memory in the hippocampal CA3 region. In this study, based on a simplified anatomical structure and synaptic connections in the hippocampal CA3 region, a three-layered Hopfield-like neural network model of hippocampal CA3 was proposed and then used to simulate associative memory functions in three circumstances: normal, synaptic deletion and synaptic compensation, according to Ruppin's synaptic deletion and compensation theory. The influences of AD on hetero-associative memory were further analyzed. The simulated results showed that the established three-layered Hopfield-like neural network model of hippocampal CA3 has both auto-associative and hetero-associative memory functions. With increasing synaptic deletion level, both associative memory functions were gradually impaired and the mean firing rates of the neurons within the network model were decreased. With gradual increasing synaptic compensation, the associative memory functions of the network were improved and the mean firing rates were increased. The simulated results suggest that the Hopfield-like neural network model can effectively simulate both associative memory functions of the hippocampal CA3 region. Synaptic deletion affects both auto-associative and hetero-associative memory functions in the hippocampal CA3 region, and can also result in memory dysfunction. To some extent, synaptic compensation measures can offset two kinds of associative memory dysfunction caused by synaptic deletion in the hippocampal CA3 area.

  19. Neural correlates of retaliatory and prosocial reactions to social exclusion: Associations with chronic peer rejection

    Directory of Open Access Journals (Sweden)

    Geert-Jan Will

    2016-06-01

    Full Text Available Social exclusion is a distressing experience and can lead to both retaliatory and prosocial reactions toward the sources of exclusion. The way people react to social exclusion has been hypothesized to be shaped through chronic exposure to peer rejection. This functional Magnetic Resonance Imaging study examined associations between chronic peer rejection and retaliatory (i.e. punishing and prosocial (i.e. forgiving reactions to social exclusion and the neural processes underlying them. Chronically rejected (n = 19 and stably highly accepted adolescents (n = 27 distributed money between themselves and unknown others who previously included or excluded them in a virtual ball-tossing game (Cyberball. Decreasing the excluders’ monetary profits (i.e., punishment was associated with increased activity in the ventral striatum, dorsolateral prefrontal cortex (PFC and parietal cortex in both groups. Compared to stably highly accepted adolescents, chronically rejected adolescents exhibited higher activity in the dorsal striatum and lateral prefrontal cortex – brain regions implicated in cognitive control – when they refrained from punishment and shared their money equally with (i.e. forgave the excluders. These results provide insights into processes that might underlie the maintenance of peer rejection across development, such as difficulties controlling the urge to retaliate after exclusion.

  20. Cognitive inflexibility in obsessive-compulsive disorder and major depression is associated with distinct neural correlates.

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    Peter L Remijnse

    Full Text Available Obsessive-compulsive disorder (OCD and major depressive disorder (MDD are frequently co-morbid, and dysfunctional frontal-striatal circuits have been implicated in both disorders. Neurobiological distinctions between OCD and MDD are insufficiently clear, and comparative neuroimaging studies are extremely scarce. OCD and MDD may be characterized by cognitive rigidity at the phenotype level, and frontal-striatal brain circuits constitute the neural substrate of intact cognitive flexibility. In the present study, 18 non-medicated MDD-free patients with OCD, 19 non-medicated OCD-free patients with MDD, and 29 matched healthy controls underwent functional magnetic resonance imaging during performance of a self-paced letter/digit task switching paradigm. Results showed that both patient groups responded slower relative to controls during repeat events, but only in OCD patients slowing was associated with decreased error rates. During switching, patients with OCD showed increased activation of the putamen, anterior cingulate and insula, whereas MDD patients recruited inferior parietal cortex and precuneus to a lesser extent. Patients with OCD and MDD commonly failed to reveal anterior prefrontal cortex activation during switching. This study shows subtle behavioral abnormalities on a measure of cognitive flexibility in MDD and OCD, associated with differential frontal-striatal brain dysfunction in both disorders. These findings may add to the development of biological markers that more precisely characterize frequently co-morbid neuropsychiatric disorders such as OCD and MDD.

  1. Neural events leading to and associated with detection of sounds under high processing load.

    Science.gov (United States)

    Sabri, Merav; Humphries, Colin; Binder, Jeffrey R; Liebenthal, Einat

    2013-03-01

    The neural events that lead to successful or failed detection of suprathreshold sounds are not well established. In this experiment, event-related potentials (ERPs) and functional magnetic resonance imaging (fMRI) were recorded while participants performed two tasks: a primary difficult duration judgment task on a sequence of tones presented to one ear, and a secondary target detection task on an auditory oddball stream presented to the other ear. The paradigm was designed to elicit competition and variability in detection of auditory targets despite identical input. Successful detection of auditory targets was associated mainly with greater fMRI activity in superior parietal cortex and thalamus. In the ERPs, successful detection was linked with a larger fronto-central negativity at 200-400 ms, and a later centro-posterior positivity. Failure to detect targets was associated with greater fMRI signal in the default mode network, a significantly smaller electrical fronto-central negativity and no late positivity. These findings demonstrate that variability in auditory detection is related to modulation of activity in multimodal parietal and frontal networks active ∼ 200 ms after target onset. Results are consistent with a limited capacity and late selection view of attention.

  2. Neural correlates of retaliatory and prosocial reactions to social exclusion: Associations with chronic peer rejection.

    Science.gov (United States)

    Will, Geert-Jan; Crone, Eveline A; van Lier, Pol A C; Güroğlu, Berna

    2016-06-01

    Social exclusion is a distressing experience and can lead to both retaliatory and prosocial reactions toward the sources of exclusion. The way people react to social exclusion has been hypothesized to be shaped through chronic exposure to peer rejection. This functional Magnetic Resonance Imaging study examined associations between chronic peer rejection and retaliatory (i.e. punishing) and prosocial (i.e. forgiving) reactions to social exclusion and the neural processes underlying them. Chronically rejected (n=19) and stably highly accepted adolescents (n=27) distributed money between themselves and unknown others who previously included or excluded them in a virtual ball-tossing game (Cyberball). Decreasing the excluders' monetary profits (i.e., punishment) was associated with increased activity in the ventral striatum, dorsolateral prefrontal cortex (PFC) and parietal cortex in both groups. Compared to stably highly accepted adolescents, chronically rejected adolescents exhibited higher activity in the dorsal striatum and lateral prefrontal cortex - brain regions implicated in cognitive control - when they refrained from punishment and shared their money equally with (i.e. forgave) the excluders. These results provide insights into processes that might underlie the maintenance of peer rejection across development, such as difficulties controlling the urge to retaliate after exclusion.

  3. Comparison of artificial neural network analysis with other multimarker methods for detecting genetic association

    Directory of Open Access Journals (Sweden)

    Curtis David

    2007-07-01

    Full Text Available 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. Results Of several methods studied and applied to simulated SNP datasets, heterogeneity testing of estimated haplotype frequencies using asymptotic p values rather than permutation testing had the lowest power of the methods studied and ANN analysis had the highest power. The difference in power to detect association between these two methods was statistically significant (p = 0.001 but other comparisons between methods were not significant. The raw t statistic obtained from ANN analysis correlated highly with the empirical statistical significance obtained from permutation testing of the ANN results and with the p value obtained from the heterogeneity test. Conclusion Although ANN analysis was more powerful than the standard haplotype-based test it is unlikely to be taken up widely. The permutation testing necessary to obtain a valid p value makes it slow to perform and it is not underpinned by a theoretical model relating marker genotypes to disease phenotype. Nevertheless, the superior performance of this method does imply that the widely-used haplotype-based methods for detecting association with multiple markers are not optimal and efforts could be made to improve upon them. The fact that the t statistic obtained from ANN analysis is highly correlated with the statistical significance does suggest a possibility to use ANN analysis in situations where large numbers of markers have been genotyped, since the t value could be used as a proxy for the p value in

  4. Differential diagnosis of dumbbell lesions associated with spinal neural foraminal widening: Imaging features

    Energy Technology Data Exchange (ETDEWEB)

    Kivrak, Ali Sami [Selcuk University, Meram Medical Faculty, Department of Radiology, 42080 Konya (Turkey)], E-mail: alisamikivrak@hotmail.com; Koc, Osman; Emlik, Dilek; Kiresi, Demet; Odev, Kemal [Selcuk University, Meram Medical Faculty, Department of Radiology, 42080 Konya (Turkey); Kalkan, Erdal [Selcuk University, Meram Medical Faculty, Department of Neurosurgery, Konya (Turkey)

    2009-07-15

    Computed tomography (CT) and magnetic resonance imaging (MRI) reliably demonstrate typical features of schwannomas or neurofibromas in the vast majority of dumbbell lesions responsible for neural foraminal widening. However, a large variety of unusual lesions which are causes of neural foraminal widening can also be encountered in the spinal neural foramen. Radiologic findings can be helpful in differential diagnosis of lesions of spinal neural foramen including neoplastic lesions such as benign/malign peripheral nerve sheath tumors (PNSTs), solitary bone plasmacytoma (SBP), chondroid chordoma, superior sulcus tumor, metastasis and non-neoplastic lesions such as infectious process (tuberculosis, hydatid cyst), aneurysmal bone cyst (ABC), synovial cyst, traumatic pseudomeningocele, arachnoid cyst, vertebral artery tortuosity. In this article, we discuss CT and MRI findings of dumbbell lesions which are causes of neural foraminal widening.

  5. Light evokes melanopsin-dependent vocalization and neural activation associated with aversive experience in neonatal mice.

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    Anton Delwig

    Full Text Available Melanopsin-expressing intrinsically photosensitive retinal ganglion cells (ipRGCs are the only functional photoreceptive cells in the eye of newborn mice. Through postnatal day 9, in the absence of functional rods and cones, these ipRGCs mediate a robust avoidance behavior to a light source, termed negative phototaxis. To determine whether this behavior is associated with an aversive experience in neonatal mice, we characterized light-induced vocalizations and patterns of neuronal activation in regions of the brain involved in the processing of aversive and painful stimuli. Light evoked distinct melanopsin-dependent ultrasonic vocalizations identical to those emitted under stressful conditions, such as isolation from the litter. In contrast, light did not evoke the broad-spectrum calls elicited by acute mechanical pain. Using markers of neuronal activation, we found that light induced the immediate-early gene product Fos in the posterior thalamus, a brain region associated with the enhancement of responses to mechanical stimulation of the dura by light, and thought to be the basis for migrainous photophobia. Additionally, light induced the phosphorylation of extracellular-related kinase (pERK in neurons of the central amygdala, an intracellular signal associated with the processing of the aversive aspects of pain. However, light did not activate Fos expression in the spinal trigeminal nucleus caudalis, the primary receptive field for painful stimulation to the head. We conclude that these light-evoked vocalizations and the distinct pattern of brain activation in neonatal mice are consistent with a melanopsin-dependent neural pathway involved in processing light as an aversive but not acutely painful stimulus.

  6. Neural adaptations associated with interlimb transfer in a ballistic wrist flexion task.

    Directory of Open Access Journals (Sweden)

    Kathy L Ruddy

    2016-05-01

    Full Text Available Cross education is the process whereby training of one limb gives rise to increases in the subsequent performance of its opposite counterpart. The execution of many unilateral tasks is associated with increased excitability of corticospinal projections from primary motor cortex (M1 to the opposite limb. It has been proposed that these effects are causally related. Our aim was to establish whether changes in corticospinal excitability arising from prior training of the opposite limb determine levels of interlimb transfer. We used three vision conditions shown previously to modulate the excitability of corticospinal projections to the inactive (right limb during wrist flexion movements performed by the training (left limb. These were: mirrored visual feedback of the training limb; no visual feedback of either limb; and visual feedback of the inactive limb. Training comprised 300 discrete, ballistic wrist flexion movements executed as rapidly as possible. Performance of the right limb on the same task was assessed prior to, at the mid point of, and following left limb training. There was no evidence that variations in the excitability of corticospinal projections (assessed by transcranial magnetic stimulation (TMS to the inactive limb were associated with, or predictive of, the extent of interlimb transfer that was expressed. There were however associations between alterations in muscle activation dynamics observed for the untrained limb, and the degree of positive transfer that arose from training of the opposite limb. The results suggest that the acute adaptations that mediate the bilateral performance gains realised through unilateral practice of this ballistic wrist flexion task are mediated by neural elements other than those within M1 that are recruited at rest by single-pulse TMS.

  7. Neural Adaptations Associated with Interlimb Transfer in a Ballistic Wrist Flexion Task.

    Science.gov (United States)

    Ruddy, Kathy L; Rudolf, Anne K; Kalkman, Barbara; King, Maedbh; Daffertshofer, Andreas; Carroll, Timothy J; Carson, Richard G

    2016-01-01

    Cross education is the process whereby training of one limb gives rise to increases in the subsequent performance of its opposite counterpart. The execution of many unilateral tasks is associated with increased excitability of corticospinal projections from primary motor cortex (M1) to the opposite limb. It has been proposed that these effects are causally related. Our aim was to establish whether changes in corticospinal excitability (CSE) arising from prior training of the opposite limb determine levels of interlimb transfer. We used three vision conditions shown previously to modulate the excitability of corticospinal projections to the inactive (right) limb during wrist flexion movements performed by the training (left) limb. These were: (1) mirrored visual feedback of the training limb; (2) no visual feedback of either limb; and (3) visual feedback of the inactive limb. Training comprised 300 discrete, ballistic wrist flexion movements executed as rapidly as possible. Performance of the right limb on the same task was assessed prior to, at the mid point of, and following left limb training. There was no evidence that variations in the excitability of corticospinal projections (assessed by transcranial magnetic stimulation (TMS)) to the inactive limb were associated with, or predictive of, the extent of interlimb transfer that was expressed. There were however associations between alterations in muscle activation dynamics observed for the untrained limb, and the degree of positive transfer that arose from training of the opposite limb. The results suggest that the acute adaptations that mediate the bilateral performance gains realized through unilateral practice of this ballistic wrist flexion task are mediated by neural elements other than those within M1 that are recruited at rest by single-pulse TMS.

  8. Adding insult to injury: neural sensitivity to social exclusion is associated with internalizing symptoms in chronically peer-victimized girls.

    Science.gov (United States)

    Rudolph, Karen D; Miernicki, Michelle E; Troop-Gordon, Wendy; Davis, Megan M; Telzer, Eva H

    2016-05-01

    Despite evidence documenting activation of the social pain network in response to social rejection and its link to temporary distress, far less is known regarding its role in pervasive emotional difficulties. Moreover, research has not considered the intersection between neural activation to experimentally induced social exclusion and naturally occurring social adversity. This study examined an integrated social pain model of internalizing symptoms, which posits that (i) neural sensitivity in the social pain network is associated with internalizing symptoms, (ii) this linkage is more robust in youth with than without a history of social adversity, and (iii) heightened avoidance motivation serves as one pathway linking neural sensitivity and internalizing symptoms. During a functional magnetic resonance imaging scan, 47 adolescent girls (M age = 15.46 years, SD = .35) with well-characterized histories of peer victimization were exposed to social exclusion. Whole-brain analyses revealed that activation to exclusion in the social pain network was associated with internalizing symptoms. As anticipated, this linkage was stronger in chronically victimized than non-victimized girls and was partially accounted for by avoidance motivation. This research indicates the importance of integrating neural, social and psychological systems of development in efforts to elucidate risk for internalizing symptoms among adolescent girls.

  9. Epileptic Seizure Prediction by a System of Particle Filter Associated with a Neural Network

    Science.gov (United States)

    Liu, Derong; Pang, Zhongyu; Wang, Zhuo

    2009-12-01

    None of the current epileptic seizure prediction methods can widely be accepted, due to their poor consistency in performance. In this work, we have developed a novel approach to analyze intracranial EEG data. The energy of the frequency band of 4-12 Hz is obtained by wavelet transform. A dynamic model is introduced to describe the process and a hidden variable is included. The hidden variable can be considered as indicator of seizure activities. The method of particle filter associated with a neural network is used to calculate the hidden variable. Six patients' intracranial EEG data are used to test our algorithm including 39 hours of ictal EEG with 22 seizures and 70 hours of normal EEG recordings. The minimum least square error algorithm is applied to determine optimal parameters in the model adaptively. The results show that our algorithm can successfully predict 15 out of 16 seizures and the average prediction time is 38.5 minutes before seizure onset. The sensitivity is about 93.75% and the specificity (false prediction rate) is approximately 0.09 FP/h. A random predictor is used to calculate the sensitivity under significance level of 5%. Compared to the random predictor, our method achieved much better performance.

  10. Associations between parental ideology and neural sensitivity to cognitive conflict in children.

    Science.gov (United States)

    Dennis, Tracy A; Amodio, David M; O'Toole, Laura J

    2015-04-01

    Processes through which parental ideology is transmitted to children-especially at a young age prior to the formation of political beliefs-remain poorly understood. Given recent evidence that political ideology is associated with neural responses to cognitive conflict in adults, we tested the exploratory hypothesis that children's neurocognitive responses to conflict may also differ depending on their parents' ideology. We assessed relations between parental political ideology and children's neurocognitive responses to conflict, as measured by the N2 component of the event-related potential. Children aged 5-7 completed an age-appropriate flanker task while electroencephalography was recorded, and the N2 was scored to incongruent versus congruent flankers to index conflict processing. Because previous research documents heightened liberal-conservative differences in threat-relevant contexts, each trial of the task was preceded by an angry face (threat-relevant) or comparison face (happy or neutral). An effect of parental ideology on the conflict-related N2 emerged in the threat condition, such that the N2 was larger among children of liberals compared with children of moderates and conservatives. These findings suggest that individual differences in neurocognitive responses to conflict, heightened in the context of threat, may reflect a more general pattern of individual differences that, in adults, relates to political ideology.

  11. Magnetically enhanced adeno-associated viral vector delivery for human neural stem cell infection.

    Science.gov (United States)

    Kim, Eunmi; Oh, Ji-Seon; Ahn, Ik-Sung; Park, Kook In; Jang, Jae-Hyung

    2011-11-01

    Gene therapy technology is a powerful tool to elucidate the molecular cues that precisely regulate stem cell fates, but developing safe vehicles or mechanisms that are capable of delivering genes to stem cells with high efficiency remains a challenge. In this study, we developed a magnetically guided adeno-associated virus (AAV) delivery system for gene delivery to human neural stem cells (hNSCs). Magnetically guided AAV delivery resulted in rapid accumulation of vectors on target cells followed by forced penetration of the vectors across the plasma membrane, ultimately leading to fast and efficient cellular transduction. To combine AAV vectors with the magnetically guided delivery, AAV was genetically modified to display hexa-histidine (6xHis) on the physically exposed loop of the AAV2 capsid (6xHis AAV), which interacted with nickel ions chelated on NTA-biotin conjugated to streptavidin-coated superparamagnetic iron oxide nanoparticles (NiStNPs). NiStNP-mediated 6xHis AAV delivery under magnetic fields led to significantly enhanced cellular transduction in a non-permissive cell type (i.e., hNSCs). In addition, this delivery method reduced the viral exposure times required to induce a high level of transduction by as much as to 2-10 min of hNSC infection, thus demonstrating the great potential of magnetically guided AAV delivery for numerous gene therapy and stem cell applications.

  12. CONVERTING RETRIEVED SPOKEN DOCUMENTS INTO TEXT USING AN AUTO ASSOCIATIVE NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    J. SANGEETHA

    2016-06-01

    Full Text Available This paper frames a novel methodology for spoken document information retrieval to the spontaneous speech corpora and converting the retrieved document into the corresponding language text. The proposed work involves the three major areas namely spoken keyword detection, spoken document retrieval and automatic speech recognition. The keyword spotting is concerned with the exploit of the distribution capturing capability of the Auto Associative Neural Network (AANN for spoken keyword detection. It involves sliding a frame-based keyword template along the audio documents and by means of confidence score acquired from the normalized squared error of AANN to search for a match. This work benevolences a new spoken keyword spotting algorithm. Based on the match the spoken documents are retrieved and clustered together. In speech recognition step, the retrieved documents are converted into the corresponding language text using the AANN classifier. The experiments are conducted using the Dravidian language database and the results recommend that the proposed method is promising for retrieving the relevant documents of a spoken query as a key and transform it into the corresponding language.

  13. Adaptive Predistortions Based on Neural Networks Associated with Levenberg-Marquardt Algorithm for Satellite Down Links

    Directory of Open Access Journals (Sweden)

    Roviras Daniel

    2008-01-01

    Full Text Available Abstract This paper presents adaptive predistortion techniques based on a feed-forward neural network (NN to linearize power amplifiers such as those used in satellite communications. Indeed, it presents the suitable NN structures which give the best performances for three satellite down links. The first link is a stationary memoryless travelling wave tube amplifier (TWTA, the second one is a nonstationary memoryless TWT amplifier while the third is an amplifier with memory modeled by a memoryless amplifier followed by a linear filter. Equally important, it puts forward the studies concerning the application of different NN training algorithms in order to determine the most prefermant for adaptive predistortions. This comparison examined through computer simulation for 64 carriers and 16-QAM OFDM system, with a Saleh's TWT amplifier, is based on some quality measure (mean square error, the required training time to reach a particular quality level, and computation complexity. The chosen adaptive predistortions (NN structures associated with an adaptive algorithm have a low complexity, fast convergence, and best performance.

  14. Neural compensation in adulthood following very preterm birth demonstrated during a visual paired associates learning task

    Directory of Open Access Journals (Sweden)

    Philip J. Brittain

    2014-01-01

    was not significantly associated with functional activation. These results demonstrate that although cognitive task performance between VPT individuals and controls may be comparable on certain measures, differences in BOLD signal may also be evident, some of which could represent compensatory neural processes following VPT-related brain insult.

  15. Genome-wide screen for differential DNA methylation associated with neural cell differentiation in mouse.

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    Rene Cortese

    Full Text Available Cellular differentiation involves widespread epigenetic reprogramming, including modulation of DNA methylation patterns. Using Differential Methylation Hybridization (DMH in combination with a custom DMH array containing 51,243 features covering more than 16,000 murine genes, we carried out a genome-wide screen for cell- and tissue-specific differentially methylated regions (tDMRs in undifferentiated embryonic stem cells (ESCs, in in-vitro induced neural stem cells (NSCs and 8 differentiated embryonic and adult tissues. Unsupervised clustering of the generated data showed distinct cell- and tissue-specific DNA methylation profiles, revealing 202 significant tDMRs (p1.96 enrichment for genes involved in neural differentiation, including, for example, Jag1 and Tcf4. Our results provide robust evidence for the relevance of DNA methylation in early neural development and identify novel marker candidates for neural cell differentiation.

  16. Design and analysis of high-capacity associative memories based on a class of discrete-time recurrent neural networks.

    Science.gov (United States)

    Zeng, Zhigang; Wang, Jun

    2008-12-01

    This paper presents a design method for synthesizing associative memories based on discrete-time recurrent neural networks. The proposed procedure enables both hetero- and autoassociative memories to be synthesized with high storage capacity and assured global asymptotic stability. The stored patterns are retrieved by feeding probes via external inputs rather than initial conditions. As typical representatives, discrete-time cellular neural networks (CNNs) designed with space-invariant cloning templates are examined in detail. In particular, it is shown that procedure herein can determine the input matrix of any CNN based on a space-invariant cloning template which involves only a few design parameters. Two specific examples and many experimental results are included to demonstrate the characteristics and performance of the designed associative memories.

  17. Detection of copy number variants reveals association of cilia genes with neural tube defects.

    Directory of Open Access Journals (Sweden)

    Xiaoli Chen

    Full Text Available BACKGROUND: Neural tube defects (NTDs are one of the most common birth defects caused by a combination of genetic and environmental factors. Currently, little is known about the genetic basis of NTDs although up to 70% of human NTDs were reported to be attributed to genetic factors. Here we performed genome-wide copy number variants (CNVs detection in a cohort of Chinese NTD patients in order to exam the potential role of CNVs in the pathogenesis of NTDs. METHODS: The genomic DNA from eighty-five NTD cases and seventy-five matched normal controls were subjected for whole genome CNVs analysis. Non-DGV (the Database of Genomic Variants CNVs from each group were further analyzed for their associations with NTDs. Gene content in non-DGV CNVs as well as participating pathways were examined. RESULTS: Fifty-five and twenty-six non-DGV CNVs were detected in cases and controls respectively. Among them, forty and nineteen CNVs involve genes (genic CNV. Significantly more non-DGV CNVs and non-DGV genic CNVs were detected in NTD patients than in control (41.2% vs. 25.3%, p<0.05 and 37.6% vs. 20%, p<0.05. Non-DGV genic CNVs are associated with a 2.65-fold increased risk for NTDs (95% CI: 1.24-5.87. Interestingly, there are 41 cilia genes involved in non-DGV CNVs from NTD patients which is significantly enriched in cases compared with that in controls (24.7% vs. 9.3%, p<0.05, corresponding with a 3.19-fold increased risk for NTDs (95% CI: 1.27-8.01. Pathway analyses further suggested that two ciliogenesis pathways, tight junction and protein kinase A signaling, are top canonical pathways implicated in NTD-specific CNVs, and these two novel pathways interact with known NTD pathways. CONCLUSIONS: Evidence from the genome-wide CNV study suggests that genic CNVs, particularly ciliogenic CNVs are associated with NTDs and two ciliogenesis pathways, tight junction and protein kinase A signaling, are potential pathways involved in NTD pathogenesis.

  18. VANGL1 rare variants associated with neural tube defects affect convergent extension in zebrafish.

    Science.gov (United States)

    Reynolds, Annie; McDearmid, Jonathan R; Lachance, Stephanie; De Marco, Patrizia; Merello, Elisa; Capra, Valeria; Gros, Philippe; Drapeau, Pierre; Kibar, Zoha

    2010-01-01

    In humans, rare non-synonymous variants in the planar cell polarity gene VANGL1 are associated with neural tube defects (NTDs). These variants were hypothesized to be pathogenic based mainly on genetic studies in a large cohort of NTD patients. In this study, we validate the potential pathogenic effect of these mutations in vivo by investigating their effect on convergent extension in zebrafish. Knocking down the expression of tri, the ortholog of Vangl2, using an antisense morpholino (MO), as shown previously, led to a defective convergent extension (CE) manifested by a shortened body axis and widened somites. Co-injection of the human VANGL1 with the tri-MO was able to partially rescue the tri-MO induced phenotype in zebrafish. In contrast, co-injection of two human VANGL1 variants, p.Val239Ile and p.Met328Thr, failed to rescue this phenotype. We next carried out overexpression studies where we measured the ability of the human VANGL1 alleles to induce a CE phenotype when injected at high doses in zebrafish embryos. While overexpressing the wild-type allele led to a severely defective CE, overexpression of either p.Val239Ile or p.Met328Thr variant failed to do so. Results from both tri-MO knockdown/rescue results and overexpression assays suggest that these two variants most likely represent "loss-of-function" alleles that affect protein function during embryonic development. Our study demonstrates a high degree of functional conservation of VANGL genes across evolution and provides a model system for studying potential variants identified in human NTDs.

  19. Lymphotropic Virions Affect Chemokine Receptor-Mediated Neural Signaling and Apoptosis: Implications for Human Immunodeficiency Virus Type 1-Associated Dementia

    Science.gov (United States)

    Zheng, Jialin; Ghorpade, Anuja; Niemann, Douglas; Cotter, Robin L.; Thylin, Michael R.; Epstein, Leon; Swartz, Jennifer M.; Shepard, Robin B.; Liu, Xiaojuan; Nukuna, Adeline; Gendelman, Howard E.

    1999-01-01

    Chemokine receptors pivotal for human immunodeficiency virus type 1 (HIV-1) infection in lymphocytes and macrophages (CCR3, CCR5, and CXCR4) are expressed on neural cells (microglia, astrocytes, and/or neurons). It is these cells which are damaged during progressive HIV-1 infection of the central nervous system. We theorize that viral coreceptors could effect neural cell damage during HIV-1-associated dementia (HAD) without simultaneously affecting viral replication. To these ends, we studied the ability of diverse viral strains to affect intracellular signaling and apoptosis of neurons, astrocytes, and monocyte-derived macrophages. Inhibition of cyclic AMP, activation of inositol 1,4,5-trisphosphate, and apoptosis were induced by diverse HIV-1 strains, principally in neurons. Virions from T-cell-tropic (T-tropic) strains (MN, IIIB, and Lai) produced the most significant alterations in signaling of neurons and astrocytes. The HIV-1 envelope glycoprotein, gp120, induced markedly less neural damage than purified virions. Macrophage-tropic (M-tropic) strains (ADA, JR-FL, Bal, MS-CSF, and DJV) produced the least neural damage, while 89.6, a dual-tropic HIV-1 strain, elicited intermediate neural cell damage. All T-tropic strain-mediated neuronal impairments were blocked by the CXCR4 antibody, 12G5. In contrast, the M-tropic strains were only partially blocked by 12G5. CXCR4-mediated neuronal apoptosis was confirmed in pure populations of rat cerebellar granule neurons and was blocked by HA1004, an inhibitor of calcium/calmodulin-dependent protein kinase II, protein kinase A, and protein kinase C. Taken together, these results suggest that progeny HIV-1 virions can influence neuronal signal transduction and apoptosis. This process occurs, in part, through CXCR4 and is independent of CD4 binding. T-tropic viruses that traffic in and out of the brain during progressive HIV-1 disease may play an important role in HAD neuropathogenesis. PMID:10482576

  20. Validation of artificial neural network models for predicting biochemical markers associated with male infertility.

    Science.gov (United States)

    Vickram, A S; Kamini, A Rao; Das, Raja; Pathy, M Ramesh; Parameswari, R; Archana, K; Sridharan, T B

    2016-08-01

    Seminal fluid is the secretion from many glands comprised of several organic and inorganic compounds including free amino acids, proteins, fructose, glucosidase, zinc, and other scavenging elements like Mg(2+), Ca(2+), K(+), and Na(+). Therefore, in the view of development of novel approaches and proper diagnosis to male infertility, overall understanding of the biochemical and molecular composition and its role in regulation of sperm quality is highly desirable. Perhaps this can be achieved through artificial intelligence. This study was aimed to elucidate and predict various biochemical markers present in human seminal plasma with three different neural network models. A total of 177 semen samples were collected for this research (both fertile and infertile samples) and immediately processed to prepare a semen analysis report, based on the protocol of the World Health Organization (WHO [2010]). The semen samples were then categorized into oligoasthenospermia (n=35), asthenospermia (n=35), azoospermia (n=22), normospermia (n=34), oligospermia (n=34), and control (n=17). The major biochemical parameters like total protein content, fructose, glucosidase, and zinc content were elucidated by standard protocols. All the biochemical markers were predicted by using three different artificial neural network (ANN) models with semen parameters as inputs. Of the three models, the back propagation neural network model (BPNN) yielded the best results with mean absolute error 0.025, -0.080, 0.166, and -0.057 for protein, fructose, glucosidase, and zinc, respectively. This suggests that BPNN can be used to predict biochemical parameters for the proper diagnosis of male infertility in assisted reproductive technology (ART) centres. AAS: absorption spectroscopy; AI: artificial intelligence; ANN: artificial neural networks; ART: assisted reproductive technology; BPNN: back propagation neural network model; DT: decision tress; MLP: multilayer perceptron; PESA: percutaneous

  1. Mutations in the Motile Cilia Gene DNAAF1 Are Associated with Neural Tube Defects in Humans

    Directory of Open Access Journals (Sweden)

    Chunyue Miao

    2016-10-01

    Full Text Available Neural tube defects (NTDs are severe malformations of the central nervous system caused by complex genetic and environmental factors. Among genes involved in NTD, cilia-related genes have been well defined and found to be essential for the completion of neural tube closure (NTC. We have carried out next-generation sequencing on target genes in 373 NTDs and 222 healthy controls, and discovered eight disease-specific rare mutations in cilia-related gene DNAAF1. DNAAF1 plays a central role in cytoplasmic preassembly of distinct dynein-arm complexes, and is expressed in some key tissues involved in neural system development, such as neural tube, floor plate, embryonic node, and brain ependyma epithelial cells in zebrafish and mouse. Therefore, we evaluated the expression and functions of mutations in DNAAF1 in transfected cells to analyze the potential correlation of these mutants to NTDs in humans. One rare frameshift mutation (p.Gln341Argfs*10 resulted in significantly diminished DNAAF1 protein expression, compared to the wild type. Another mutation, p.Lys231Gln, disrupted cytoplasmic preassembly of the dynein-arm complexes in cellular assay. Furthermore, results from NanoString assay on mRNA from NTD samples indicated that DNAAF1 mutants altered the expression level of NTC-related genes. Altogether, these findings suggest that the rare mutations in DNAAF1 may contribute to the susceptibility for NTDs in humans.

  2. Chronic childhood peer rejection is associated with heightened neural responses to social exclusion during adolescence.

    NARCIS (Netherlands)

    Will, G.J.; Van, Lier P.A.; Crone, E.A.; Guroglu, B.

    2016-01-01

    This functional Magnetic Resonance Imaging (fMRI) study examined subjective and neural responses to social exclusion in adolescents (age 12-15) who either had a stable accepted (n = 27; 14 males) or a chronic rejected (n = 19; 12 males) status among peers from age 6 to 12. Both groups of adolescents

  3. Search for Standard Model Higgs Boson Production in Association with a W Boson using a Neural Network

    Energy Technology Data Exchange (ETDEWEB)

    Aaltonen, T.; /Helsinki Inst. of Phys.; Adelman, Jahred A.; /Chicago U., EFI; Akimoto, T.; /Tsukuba U.; Alvarez Gonzalez, B.; /Cantabria Inst. of Phys.; Amerio, S.; /INFN, Padua; Amidei, Dante E.; /Michigan U.; Anastassov, A.; /Northwestern U.; Annovi, Alberto; /Frascati; Antos, Jaroslav; /Comenius U.; Apollinari, G.; /Fermilab; Apresyan, A.; /Purdue U. /Waseda U.

    2009-05-01

    We present a search for standard model Higgs boson production in association with a W boson in proton-antiproton collisions (p{bar p} {yields} W{sup {+-}}H {yields} {ell}{nu}b{bar b}) at a center of mass energy of 1.96 TeV. The search employs data collected with the CDF II detector that correspond to an integrated luminosity of approximately 1.9 fb{sup -1}. We select events consistent with a signature of a single charged lepton (e{sup {+-}}/{mu}{sup {+-}}), missing transverse energy, and two jets. Jets corresponding to bottom quarks are identified with a secondary vertex tagging method, a jet probability tagging method, and a neural network filter. We use kinematic information in an artificial neural network to improve discrimination between signal and background compared to previous analyses. The observed number of events and the neural network output distributions are consistent with the standard model background expectations, and we set 95% confidence level upper limits on the production cross section times branching fraction ranging from 1.2 to 1.1 pb or 7.5 to 102 times the standard model expectation for Higgs boson masses from 110 to 150 GeV/c{sup 2}, respectively.

  4. Neural changes associated to procedural learning and automatization process in Developmental Coordination Disorder and/or Developmental Dyslexia.

    Science.gov (United States)

    Biotteau, Maëlle; Péran, Patrice; Vayssière, Nathalie; Tallet, Jessica; Albaret, Jean-Michel; Chaix, Yves

    2017-03-01

    Recent theories hypothesize that procedural learning may support the frequent overlap between neurodevelopmental disorders. The neural circuitry supporting procedural learning includes, among others, cortico-cerebellar and cortico-striatal loops. Alteration of these loops may account for the frequent comorbidity between Developmental Coordination Disorder (DCD) and Developmental Dyslexia (DD). The aim of our study was to investigate cerebral changes due to the learning and automatization of a sequence learning task in children with DD, or DCD, or both disorders. fMRI on 48 children (aged 8-12) with DD, DCD or DD + DCD was used to explore their brain activity during procedural tasks, performed either after two weeks of training or in the early stage of learning. Firstly, our results indicate that all children were able to perform the task with the same level of automaticity, but recruit different brain processes to achieve the same performance. Secondly, our fMRI results do not appear to confirm Nicolson and Fawcett's model. The neural correlates recruited for procedural learning by the DD and the comorbid groups are very close, while the DCD group presents distinct characteristics. This provide a promising direction on the neural mechanisms associated with procedural learning in neurodevelopmental disorders and for understanding comorbidity. Published by Elsevier Ltd.

  5. The effects of age, memory performance, and callosal integrity on the neural correlates of successful associative encoding.

    Science.gov (United States)

    de Chastelaine, Marianne; Wang, Tracy H; Minton, Brian; Muftuler, L Tugan; Rugg, Michael D

    2011-09-01

    This functional magnetic resonance imaging study investigated the relationship between the neural correlates of associative memory encoding, callosal integrity, and memory performance in older adults. Thirty-six older and 18 young subjects were scanned while making relational judgments on word pairs. Neural correlates of successful encoding (subsequent memory effects) were identified by contrasting the activity elicited by study pairs that were correctly identified as having been studied together with the activity elicited by pairs wrongly judged to have come from different study trials. Subsequent memory effects common to the 2 age groups were identified in several regions, including left inferior frontal gyrus and bilateral hippocampus. Negative effects (greater activity for forgotten than for remembered items) in default network regions in young subjects were reversed in the older group, and the amount of reversal correlated negatively with memory performance. Additionally, older subjects' subsequent memory effects in right frontal cortex correlated positively with anterior callosal integrity and negatively with memory performance. It is suggested that recruitment of right frontal cortex during verbal memory encoding may reflect the engagement of processes that compensate only partially for age-related neural degradation.

  6. Further EST analysis of endocrine genes that are preferentially expressed in the neural complex of Ciona intestinalis: receptor and enzyme genes associated with endocrine system in the neural complex.

    Science.gov (United States)

    Sekiguchi, Toshio; Kawashima, Takeshi; Satou, Yutaka; Satoh, Nori

    2007-01-15

    Identification of orthologs of vertebrate neuropeptides and hypothalamic hormones in the neural complex of ascidians suggests integral roles of the ascidian neural complex in the endocrine system. In the present study, we investigated endocrine-related genes expressed in the neural complex of Ciona intestinalis. Comprehensive analyses of 3'-end sequences of the neural complex cDNAs placed 10,029 clones into 4051 independent clusters or genes, 1524 of them being expressed preferentially in this organ. Comparison of the 1524 genes with the human proteome databank demonstrated that 476 matched previously identified human proteins with distinct functions. Further analyses of sequence similarity of the 476 genes demonstrated that 21 genes are candidates for those involved in the endocrine system. Although we cannot detect hormone or peptide candidates, we found 21 genes such as receptors for peptide ligands, receptor-modulating proteins, and processing enzymes. We then characterized the Ciona prohormone convertase 2 (Ci-PC2) and carboxypeptidase E (Ci-CPE), which are associated with endoproteolytic processing of peptide hormone precursors. Furthermore, genes encoding these transcripts are expressed specifically in the neural complex of young adult ascidians. These data provide the molecular basis for further functional studies of the endocrine role of the neural complex of ascidians.

  7. Perinatal factors associated with neural tube defects (anencephaly [correction of anancephaly], spina bifida and encephalocele).

    Science.gov (United States)

    Ogata, A J; Camano, L; Brunoni, D

    1992-01-01

    The objective of the present study was to determine the presence of risk factors for the occurrence of neural tube defects. Data for 33,535 births which occurred at Hospital do Servidor Público Estadual de São Paulo from July 1973 to December 1986 were collected in a prospective manner as recommended by "Estudo Colaborativo Latino-Americano de Malformações Congênitas" (ECLAMC, Collaborative Latin American Study on Congenital Malformations). Twenty-six cases of neural tube defects were detected (0.77/1000 births). Of these, 11 were cases of spina bifida (0.39/1000 births), 9 of anencephaly (0.27/1000 births) and 6 of encephalocele (0.18/1000 births). We observed a higher frequency of polyhydramnios, premature labor, Apgar scores of less than 7 at the first and fifth minutes, low birth weight and intrauterine growth retardation.

  8. Endogenous testosterone levels are associated with neural activity in men with schizophrenia during facial emotion processing.

    Science.gov (United States)

    Ji, Ellen; Weickert, Cynthia Shannon; Lenroot, Rhoshel; Catts, Stanley V; Vercammen, Ans; White, Christopher; Gur, Raquel E; Weickert, Thomas W

    2015-06-01

    Growing evidence suggests that testosterone may play a role in the pathophysiology of schizophrenia given that testosterone has been linked to cognition and negative symptoms in schizophrenia. Here, we determine the extent to which serum testosterone levels are related to neural activity in affective processing circuitry in men with schizophrenia. Functional magnetic resonance imaging was used to measure blood-oxygen-level-dependent signal changes as 32 healthy controls and 26 people with schizophrenia performed a facial emotion identification task. Whole brain analyses were performed to determine regions of differential activity between groups during processing of angry versus non-threatening faces. A follow-up ROI analysis using a regression model in a subset of 16 healthy men and 16 men with schizophrenia was used to determine the extent to which serum testosterone levels were related to neural activity. Healthy controls displayed significantly greater activation than people with schizophrenia in the left inferior frontal gyrus (IFG). There was no significant difference in circulating testosterone levels between healthy men and men with schizophrenia. Regression analyses between activation in the IFG and circulating testosterone levels revealed a significant positive correlation in men with schizophrenia (r=.63, p=.01) and no significant relationship in healthy men. This study provides the first evidence that circulating serum testosterone levels are related to IFG activation during emotion face processing in men with schizophrenia but not in healthy men, which suggests that testosterone levels modulate neural processes relevant to facial emotion processing that may interfere with social functioning in men with schizophrenia.

  9. Artificial neural network associated to UV/Vis spectroscopy for monitoring bioreactions in biopharmaceutical processes.

    Science.gov (United States)

    Takahashi, Maria Beatriz; Leme, Jaci; Caricati, Celso Pereira; Tonso, Aldo; Fernández Núñez, Eutimio Gustavo; Rocha, José Celso

    2015-06-01

    Currently, mammalian cells are the most utilized hosts for biopharmaceutical production. The culture media for these cell lines include commonly in their composition a pH indicator. Spectroscopic techniques are used for biopharmaceutical process monitoring, among them, UV-Vis spectroscopy has found scarce applications. This work aimed to define artificial neural networks architecture and fit its parameters to predict some nutrients and metabolites, as well as viable cell concentration based on UV-Vis spectral data of mammalian cell bioprocess using phenol red in culture medium. The BHK-21 cell line was used as a mammalian cell model. Off-line spectra of supernatant samples taken from batches performed at different dissolved oxygen concentrations in two bioreactor configurations and with two pH control strategies were used to define two artificial neural networks. According to absolute errors, glutamine (0.13 ± 0.14 mM), glutamate (0.02 ± 0.02 mM), glucose (1.11 ± 1.70 mM), lactate (0.84 ± 0.68 mM) and viable cell concentrations (1.89 10(5) ± 1.90 10(5) cell/mL) were suitably predicted. The prediction error averages for monitored variables were lower than those previously reported using different spectroscopic techniques in combination with partial least squares or artificial neural network. The present work allows for UV-VIS sensor development, and decreases cost related to nutrients and metabolite quantifications.

  10. Differentiation defect in neural crest-derived smooth muscle cells in patients with aortopathy associated with bicuspid aortic valves.

    Science.gov (United States)

    Jiao, Jiao; Xiong, Wei; Wang, Lunchang; Yang, Jiong; Qiu, Ping; Hirai, Hiroyuki; Shao, Lina; Milewicz, Dianna; Chen, Y Eugene; Yang, Bo

    2016-08-01

    Individuals with bicuspid aortic valves (BAV) are at a higher risk of developing thoracic aortic aneurysms (TAA) than patients with trileaflet aortic valves (TAV). The aneurysms associated with BAV most commonly involve the ascending aorta and spare the descending aorta. Smooth muscle cells (SMCs) in the ascending and descending aorta arise from neural crest (NC) and paraxial mesoderm (PM), respectively. We hypothesized defective differentiation of the neural crest stem cells (NCSCs)-derived SMCs but not paraxial mesoderm cells (PMCs)-derived SMCs contributes to the aortopathy associated with BAV. When induced pluripotent stem cells (iPSCs) from BAV/TAA patients were differentiated into NCSC-derived SMCs, these cells demonstrated significantly decreased expression of marker of SMC differentiation (MYH11) and impaired contraction compared to normal control. In contrast, the PMC-derived SMCs were similar to control cells in these aspects. The NCSC-SMCs from the BAV/TAA also showed decreased TGF-β signaling based on phosphorylation of SMAD2, and increased mTOR signaling. Inhibition of mTOR pathway using rapamycin rescued the aberrant differentiation. Our data demonstrates that decreased differentiation and contraction of patient's NCSC-derived SMCs may contribute to that aortopathy associated with BAV.

  11. Differentiation defect in neural crest-derived smooth muscle cells in patients with aortopathy associated with bicuspid aortic valves

    Directory of Open Access Journals (Sweden)

    Jiao Jiao

    2016-08-01

    Full Text Available Individuals with bicuspid aortic valves (BAV are at a higher risk of developing thoracic aortic aneurysms (TAA than patients with trileaflet aortic valves (TAV. The aneurysms associated with BAV most commonly involve the ascending aorta and spare the descending aorta. Smooth muscle cells (SMCs in the ascending and descending aorta arise from neural crest (NC and paraxial mesoderm (PM, respectively. We hypothesized defective differentiation of the neural crest stem cells (NCSCs-derived SMCs but not paraxial mesoderm cells (PMCs-derived SMCs contributes to the aortopathy associated with BAV. When induced pluripotent stem cells (iPSCs from BAV/TAA patients were differentiated into NCSC-derived SMCs, these cells demonstrated significantly decreased expression of marker of SMC differentiation (MYH11 and impaired contraction compared to normal control. In contrast, the PMC-derived SMCs were similar to control cells in these aspects. The NCSC-SMCs from the BAV/TAA also showed decreased TGF-β signaling based on phosphorylation of SMAD2, and increased mTOR signaling. Inhibition of mTOR pathway using rapamycin rescued the aberrant differentiation. Our data demonstrates that decreased differentiation and contraction of patient's NCSC-derived SMCs may contribute to that aortopathy associated with BAV.

  12. Syndromes and disorders associated with omphalocele (III): single gene disorders, neural tube defects, diaphragmatic defects and others.

    Science.gov (United States)

    Chen, Chih-Ping

    2007-06-01

    Omphalocele can be associated with single gene disorders, neural tube defects, diaphragmatic defects, fetal valproate syndrome, and syndromes of unknown etiology. This article provides a comprehensive review of omphalocele-related disorders: otopalatodigital syndrome type II; Melnick-Needles syndrome; Rieger syndrome; neural tube defects; Meckel syndrome; Shprintzen-Goldberg omphalocele syndrome; lethal omphalocele-cleft palate syndrome; cerebro-costo-mandibular syndrome; fetal valproate syndrome; Marshall-Smith syndrome; fibrochondrogenesis; hydrolethalus syndrome; Fryns syndrome; omphalocele, diaphragmatic defects, radial anomalies and various internal malformations; diaphragmatic defects, limb deficiencies and ossification defects of skull; Donnai-Barrow syndrome; CHARGE syndrome; Goltz syndrome; Carpenter syndrome; Toriello-Carey syndrome; familial omphalocele; Cornelia de Lange syndrome; C syndrome; Elejalde syndrome; Malpuech syndrome; cervical ribs, Sprengel anomaly, anal atresia and urethral obstruction; hydrocephalus with associated malformations; Kennerknecht syndrome; lymphedema, atrial septal defect and facial changes; and craniosynostosismental retardation syndrome of Lin and Gettig. Perinatal identification of omphalocele should alert one to the possibility of omphalocele-related disorders and familial inheritance and prompt a thorough genetic counseling for these disorders.

  13. Soft sensor of chemical processes with large numbers of input parameters using auto-associative hierarchical neural network

    Institute of Scientific and Technical Information of China (English)

    Yanlin He; Yuan Xu; Zhiqiang Geng; Qunxiong Zhu

    2015-01-01

    To explore the problems of monitoring chemical processes with large numbers of input parameters, a method based on Auto-associative Hierarchical Neural Network (AHNN) is proposed. AHNN focuses on dealing with datasets in high-dimension. AHNNs consist of two parts:groups of subnets based on well trained Auto-associative Neural Networks (AANNs) and a main net. The subnets play an important role on the performance of AHNN. A simple but effective method of designing the subnets is developed in this paper. In this method, the subnets are designed according to the classification of the data attributes. For getting the classification, an effective method called Extension Data Attributes Classification (EDAC) is adopted. Soft sensor using AHNN based on EDAC (EDAC-AHNN) is introduced. As a case study, the production data of Purified Terephthalic Acid (PTA) solvent system are selected to examine the proposed model. The results of the EDAC-AHNN model are compared with the experimental data extracted from the literature, which shows the efficiency of the proposed model.

  14. Syndromes and Disorders Associated with Omphalocele (III: Single Gene Disorders, Neural Tube Defects, Diaphragmatic Defects and Others

    Directory of Open Access Journals (Sweden)

    Chih-Ping Chen

    2007-06-01

    Full Text Available Omphalocele can be associated with single gene disorders, neural tube defects, diaphragmatic defects, fetal valproate syndrome, and syndromes of unknown etiology. This article provides a comprehensive review of omphalocele-related disorders: otopalatodigital syndrome type II; Melnick–Needles syndrome; Rieger syndrome; neural tube defects; Meckel syndrome; Shprintzen–Goldberg omphalocele syndrome; lethal omphalocele-cleft palate syndrome; cerebro-costo-mandibular syndrome; fetal valproate syndrome; Marshall–Smith syndrome; fibrochondrogenesis; hydrolethalus syndrome; Fryns syndrome; omphalocele, diaphragmatic defects, radial anomalies and various internal malformations; diaphragmatic defects, limb deficiencies and ossification defects of skull; Donnai–Barrow syndrome; CHARGE syndrome; Goltz syndrome; Carpenter syndrome; Toriello–Carey syndrome; familial omphalocele; Cornelia de Lange syndrome; C syndrome; Elejalde syndrome; Malpuech syndrome; cervical ribs, Sprengel anomaly, anal atresia and urethral obstruction; hydrocephalus with associated malformations; Kennerknecht syndrome; lymphedema, atrial septal defect and facial changes; and craniosynostosis- mental retardation syndrome of Lin and Gettig. Perinatal identification of omphalocele should alert one to the possibility of omphalocele-related disorders and familial inheritance and prompt a thorough genetic counseling for these disorders.

  15. Risk factors, organ weight deviation and associated anomalies in neural tube defects: A prospective fetal and perinatal autopsy series

    Directory of Open Access Journals (Sweden)

    Asaranti Kar

    2015-01-01

    Full Text Available Introduction: Neural tube defects (NTD are a group of serious birth defects occurring due to defective closure of neural tube during embryonic development. It comprises of anencephaly, encephalocele and spina bifida. We conducted this prospective fetal autopsy series to study the rate and distribution of NTD, analyze the reproductive factors and risk factors, note any associated anomalies and evaluate the organ weights and their deviation from normal. Materials and Methods: This was a prospective study done over a period of 6 years from August, 2007 to July, 2013. All cases of NTDs delivered as abortion, still born and live born were included. The reproductive and risk factors like age, parity, multiple births, previous miscarriage, obesity, diabetes mellitus, socioeconomic status and use of folic acid during pregnancy were collected.Autopsy was performed according to Virchow′s technique. Detail external and internal examination were carried out to detect any associated anomalies. Gross and microscopic examination of organs were done. Results: Out of 210 cases of fetal and perinatal autopsy done, 72 (34.28% had NTD constituting 49 cases of anencephaly, 16 spina bifida and 7 cases of encephalocele. The mothers in these cases predominantly were within 25-29 years (P = 0.02 and primy (P = 0.01. Female sex was more commonly affected than males (M:F = 25:47, P = 0.0005 There was no history of folate use in majority of cases. Organ weight deviations were >2 standard deviation low in most of the cases. Most common associated anomalies were adrenal hypoplasia and thymic hyperplasia. Conclusion: The authors have made an attempt to study NTD cases in respect to maternal reproductive and risk factors and their association with NTD along with the organ weight deviation and associated anomalies. This so far in our knowledge is an innovative study which was not found in literature even after extensive search.

  16. Inca: a novel p21-activated kinase-associated protein required for cranial neural crest development.

    Science.gov (United States)

    Luo, Ting; Xu, Yanhua; Hoffman, Trevor L; Zhang, Tailin; Schilling, Thomas; Sargent, Thomas D

    2007-04-01

    Inca (induced in neural crest by AP2) is a novel protein discovered in a microarray screen for genes that are upregulated in Xenopus embryos by the transcriptional activator protein Tfap2a. It has no significant similarity to any known protein, but is conserved among vertebrates. In Xenopus, zebrafish and mouse embryos, Inca is expressed predominantly in the premigratory and migrating neural crest (NC). Knockdown experiments in frog and fish using antisense morpholinos reveal essential functions for Inca in a subset of NC cells that form craniofacial cartilage. Cells lacking Inca migrate successfully but fail to condense into skeletal primordia. Overexpression of Inca disrupts cortical actin and prevents formation of actin "purse strings", which are required for wound healing in Xenopus embryos. We show that Inca physically interacts with p21-activated kinase 5 (PAK5), a known regulator of the actin cytoskeleton that is co-expressed with Inca in embryonic ectoderm, including in the NC. These results suggest that Inca and PAK5 cooperate in restructuring cytoskeletal organization and in the regulation of cell adhesion in the early embryo and in NC cells during craniofacial development.

  17. Fetal Alcohol Spectrum Disorder (FASD Associated Neural Defects: Complex Mechanisms and Potential Therapeutic Targets

    Directory of Open Access Journals (Sweden)

    James A. Marrs

    2013-06-01

    Full Text Available Fetal alcohol spectrum disorder (FASD, caused by prenatal alcohol exposure, can result in craniofacial dysmorphism, cognitive impairment, sensory and motor disabilities among other defects. FASD incidences are as high as 2% to 5 % children born in the US, and prevalence is higher in low socioeconomic populations. Despite various mechanisms being proposed to explain the etiology of FASD, the molecular targets of ethanol toxicity during development are unknown. Proposed mechanisms include cell death, cell signaling defects and gene expression changes. More recently, the involvement of several other molecular pathways was explored, including non-coding RNA, epigenetic changes and specific vitamin deficiencies. These various pathways may interact, producing a wide spectrum of consequences. Detailed understanding of these various pathways and their interactions will facilitate the therapeutic target identification, leading to new clinical intervention, which may reduce the incidence and severity of these highly prevalent preventable birth defects. This review discusses manifestations of alcohol exposure on the developing central nervous system, including the neural crest cells and sensory neural placodes, focusing on molecular neurodevelopmental pathways as possible therapeutic targets for prevention or protection.

  18. Dynamic neural network reorganization associated with second language vocabulary acquisition: a multimodal imaging study.

    Science.gov (United States)

    Hosoda, Chihiro; Tanaka, Kanji; Nariai, Tadashi; Honda, Manabu; Hanakawa, Takashi

    2013-08-21

    It remains unsettled whether human language relies exclusively on innately privileged brain structure in the left hemisphere or is more flexibly shaped through experiences, which induce neuroplastic changes in potentially relevant neural circuits. Here we show that learning of second language (L2) vocabulary and its cessation can induce bidirectional changes in the mirror-reverse of the traditional language areas. A cross-sectional study identified that gray matter volume in the inferior frontal gyrus pars opercularis (IFGop) and connectivity of the IFGop with the caudate nucleus and the superior temporal gyrus/supramarginal (STG/SMG), predominantly in the right hemisphere, were positively correlated with L2 vocabulary competence. We then implemented a cohort study involving 16 weeks of L2 training in university students. Brain structure before training did not predict the later gain in L2 ability. However, training intervention did increase IFGop volume and reorganization of white matter including the IFGop-caudate and IFGop-STG/SMG pathways in the right hemisphere. These "positive" plastic changes were correlated with the gain in L2 ability in the trained group but were not observed in the control group. We propose that the right hemispheric network can be reorganized into language-related areas through use-dependent plasticity in young adults, reflecting a repertoire of flexible reorganization of the neural substrates responding to linguistic experiences.

  19. Neural substrates associated with evaluative processing during co-activation of positivity and negativity: a PET investigation.

    Science.gov (United States)

    Jung, Young Chul; An, Suk Kyoon; Seok, Jeong Ho; Kim, Jae Seung; Oh, Seung Jun; Moon, Dae Hyuk; Kim, Jae-Jin

    2006-10-01

    Affective symmetries, such as the positivity offset and negativity bias, have been postulated to be attributable to distinct activation functions of the positive and negative affect systems. We investigated the neural substrates that are engaged when the positive and negative affect systems undergo parallel and integrative processing. Eleven subjects were scanned using H(2)(15)O PET during choosing the subjective feeling produced by a stimulation pair of pictures or words. Four different conditions were designed for contrast: pure positivity, pure negativity, positivity offset, and negativity bias. The dorsolateral prefrontal activation was associated with positivity offset and negativity bias condition, whereas the ventromedial prefrontal activation, together with limbic and subcortical activations, was associated with pure positivity and pure negativity condition. The results indicated that positivity offset and negativity bias are not merely due to asymmetric activations of the positive and negative systems, but integrative processing of higher neocortical levels is involved.

  20. Increased skin conductance responses and neural activity during fear conditioning are associated with a repressive coping style

    Directory of Open Access Journals (Sweden)

    Tim eKlucken

    2015-06-01

    Full Text Available The investigation of individual differences in coping styles in response to fear conditioning is an important issue for a better understanding of the etiology and treatment of psychiatric disorders. It has been assumed that an avoidant (repressive coping style is characterized by increased emotion regulation efforts in context of fearful stimuli as compared to a more vigilant coping style. However, no study so far has investigated the neural correlates of fear conditioning of repressors and sensitizers.In the present fMRI study, 76 participants were classified as repressors or as sensitizers and were exposed to a fear conditioning paradigm, in which the CS+ predicted electrical stimulation, while another neutral stimulus (CS- did not. In addition, skin conductance responses (SCRs were measured continuously.As the main findings, we found increased neural activations in repressors as compared to sensitizers in the ventromedial prefrontal cortex and the anterior cingulate cortex during fear conditioning. In addition, elevated activity to the CS+ in amygdala, insula, occipital, and orbitofrontal cortex as well as conditioned SCRs were found in repressors.The present results demonstrate increased neural activations in structures linked to emotion down-regulation mechanisms like the ventromedial prefrontal cortex, which may reflect the increased coping effort in repressors. At the same time, repressors showed increased activations in arousal and evaluation-associated structures like the amygdala, the occipital cortex, and the orbitofrontal cortex, which is also mirrored in increased SCRs. The present results support recent assumptions about a two-process model of repression postulating a fast vigilant response to fearful stimuli, but also a second emotion down-regulating process.

  1. Parietofrontal integrity determines neural modulation associated with grasping imagery after stroke

    Science.gov (United States)

    Modir Shanechi, Amirali; Fourkas, Alissa D.; Weber, Cornelia; Birbaumer, Niels

    2012-01-01

    Chronic stroke patients with heterogeneous lesions, but no direct damage to the primary sensorimotor cortex, are capable of longitudinally acquiring the ability to modulate sensorimotor rhythms using grasping imagery of the affected hand. Volitional modulation of neural activity can be used to drive grasping functions of the paralyzed hand through a brain–computer interface. The neural substrates underlying this skill are not known. Here, we investigated the impact of individual patient's lesion pathology on functional and structural network integrity related to this volitional skill. Magnetoencephalography data acquired throughout training was used to derive functional networks. Structural network models and local estimates of extralesional white matter microstructure were constructed using T1-weighted and diffusion-weighted magnetic resonance imaging data. We employed a graph theoretical approach to characterize emergent properties of distributed interactions between nodal brain regions of these networks. We report that interindividual variability in patients’ lesions led to differential impairment of functional and structural network characteristics related to successful post-training sensorimotor rhythm modulation skill. Patients displaying greater magnetoencephalography global cost-efficiency, a measure of information integration within the distributed functional network, achieved greater levels of skill. Analysis of lesion damage to structural network connectivity revealed that the impact on nodal betweenness centrality of the ipsilesional primary motor cortex, a measure that characterizes the importance of a brain region for integrating visuomotor information between frontal and parietal cortical regions and related thalamic nuclei, correlated with skill. Edge betweenness centrality, an analogous measure, which assesses the role of specific white matter fibre pathways in network integration, showed a similar relationship between skill and a portion of

  2. Modeling the behavioral substrates of associate learning and memory - Adaptive neural models

    Science.gov (United States)

    Lee, Chuen-Chien

    1991-01-01

    Three adaptive single-neuron models based on neural analogies of behavior modification episodes are proposed, which attempt to bridge the gap between psychology and neurophysiology. The proposed models capture the predictive nature of Pavlovian conditioning, which is essential to the theory of adaptive/learning systems. The models learn to anticipate the occurrence of a conditioned response before the presence of a reinforcing stimulus when training is complete. Furthermore, each model can find the most nonredundant and earliest predictor of reinforcement. The behavior of the models accounts for several aspects of basic animal learning phenomena in Pavlovian conditioning beyond previous related models. Computer simulations show how well the models fit empirical data from various animal learning paradigms.

  3. Dissociation of neural regions associated with anticipatory versus consummatory phases of incentive processing.

    Science.gov (United States)

    Dillon, Daniel G; Holmes, Avram J; Jahn, Allison L; Bogdan, Ryan; Wald, Lawrence L; Pizzagalli, Diego A

    2008-01-01

    Incentive delay tasks implicate the striatum and medial frontal cortex in reward processing. However, prior studies delivered more rewards than penalties, possibly leading to unwanted differences in signal-to-noise ratio. Also, whether particular brain regions are specifically involved in anticipation or consumption is unclear. We used a task featuring balanced incentive delivery and an analytic strategy designed to identify activity specific to anticipation or consumption. Reaction time data in two independent samples (n=13 and n=8) confirmed motivated responding. Functional magnetic resonance imaging revealed regions activated by anticipation (anterior cingulate) versus consumption (orbital and medial frontal cortex). Ventral striatum was active during reward anticipation but not significantly more so than during consumption. Although the study features several methodological improvements and helps clarify the neural basis of incentive processing, replications in larger samples are needed.

  4. A spectrum of skeletal anomalies associated with pulmonary agenesis: Possible neural crest injuries

    Energy Technology Data Exchange (ETDEWEB)

    Osborne, J.; Masel, J.; McCredie, J.

    1989-07-01

    Six cases of unilateral pulmonary agenesis with skeletal and other deformities have been diagnosed in our hospitals. The various pulmonary, spinal, rib and limb anomalies with their possible interrelationships were examined and described in detail and comparison with previously reported cases was made. It became apparent that the limb abnormalities which most constantly involved hypoplasia of the phalanges of a thumb with varying metacarpal and radial anomalies, were ipsilateral to the pulmonary agenesis in all cases. The spinal deformities involved degrees of failure of segementation of T1-T3 with other vertebrae randomly involved. Rib abnormalities also varied and did not necessarily correspond to the same side as the pulmonary agenesis. The concept of the anomalies all being part of a group of neural crest injuries was then explored. (orig.).

  5. Dissociation of neural regions associated with anticipatory versus consummatory phases of incentive processing

    Science.gov (United States)

    Dillon, Daniel G.; Holmes, Avram J.; Jahn, Allison L.; Bogdan, Ryan; Wald, Lawrence L.; Pizzagalli, Diego A.

    2007-01-01

    Incentive delay tasks implicate the striatum and medial frontal cortex in reward processing. However, prior studies delivered more rewards than penalties, possibly leading to unwanted differences in signal-to-noise ratio. Also, whether particular brain regions are specifically involved in anticipation or consumption is unclear. We used a task featuring balanced incentive delivery and an analytic strategy designed to identify activity specific to anticipation or consumption. RT data in two independent samples (n=13 and n=8) confirmed motivated responding. FMRI revealed regions activated by anticipation (anterior cingulate) vs. consumption (orbital and medial frontal cortex). Ventral striatum was active during reward anticipation but not significantly more so than during consumption. While the study features several methodological improvements and helps clarify the neural basis of incentive processing, replications in larger samples are needed. PMID:17850241

  6. Neural changes associated with semantic processing in healthy aging despite intact behavioral performance.

    Science.gov (United States)

    Lacombe, Jacinthe; Jolicoeur, Pierre; Grimault, Stephan; Pineault, Jessica; Joubert, Sven

    2015-10-01

    Semantic memory recruits an extensive neural network including the left inferior prefrontal cortex (IPC) and the left temporoparietal region, which are involved in semantic control processes, as well as the anterior temporal lobe region (ATL) which is considered to be involved in processing semantic information at a central level. However, little is known about the underlying neuronal integrity of the semantic network in normal aging. Young and older healthy adults carried out a semantic judgment task while their cortical activity was recorded using magnetoencephalography (MEG). Despite equivalent behavioral performance, young adults activated the left IPC to a greater extent than older adults, while the latter group recruited the temporoparietal region bilaterally and the left ATL to a greater extent than younger adults. Results indicate that significant neuronal changes occur in normal aging, mainly in regions underlying semantic control processes, despite an apparent stability in performance at the behavioral level.

  7. Common and dissociable neural correlates associated with component processes of inductive reasoning.

    Science.gov (United States)

    Jia, Xiuqin; Liang, Peipeng; Lu, Jie; Yang, Yanhui; Zhong, Ning; Li, Kuncheng

    2011-06-15

    The ability to draw numerical inductive reasoning requires two key cognitive processes, identification and extrapolation. This study aimed to identify the neural correlates of both component processes of numerical inductive reasoning using event-related fMRI. Three kinds of tasks: rule induction (RI), rule induction and application (RIA), and perceptual judgment (Jud) were solved by twenty right-handed adults. Our results found that the left superior parietal lobule (SPL) extending into the precuneus and left dorsolateral prefrontal cortex (DLPFC) were commonly recruited in the two components. It was also observed that the fronto-parietal network was more specific to identification, whereas the striatal-thalamic network was more specific to extrapolation. The findings suggest that numerical inductive reasoning is mediated by the coordination of multiple brain areas including the prefrontal, parietal, and subcortical regions, of which some are more specific to demands on only one of these two component processes, whereas others are sensitive to both.

  8. An fMRI comparison of neural activity associated with recognition of familiar melodies in younger and older adults

    Directory of Open Access Journals (Sweden)

    Ritu eSikka

    2015-10-01

    Full Text Available Several studies of semantic memory in non-musical domains involving recognition of items from long-term memory have shown an age-related shift from the medial temporal lobe structures to the frontal lobe. However, the effects of aging on musical semantic memory remain unexamined. We compared activation associated with recognition of familiar melodies in younger and older adults. Recognition follows successful retrieval from the musical lexicon that comprises a lifetime of learned musical phrases. We used the sparse-sampling technique in fMRI to determine the neural correlates of melody recognition by comparing activation when listening to familiar versus unfamiliar melodies, and to identify age differences. Recognition-related cortical activation was detected in the right superior temporal, bilateral inferior and superior frontal, left middle orbitofrontal, bilateral precentral, and left supramarginal gyri. Region-of-interest analysis showed greater activation for younger adults in the left superior temporal gyrus and for older adults in the left superior frontal, left angular, and bilateral superior parietal regions. Our study provides powerful evidence for these musical memory networks due to a large sample (N = 40 that includes older adults. This study is the first to investigate the neural basis of melody recognition in older adults and to compare the findings to younger adults.

  9. Associating fuzzy logic, neural networks and multivariable statistic methodologies in the automatic identification of oil reservoir lithologies through well logs

    Energy Technology Data Exchange (ETDEWEB)

    Carrasquilla, Abel [Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF), Macae, RJ (Brazil). Lab. de Engenharia e Exploracao de Petroleo]. E-mail: abel@lenep.uenf.br; Silva, Jadir da [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Dept. de Geologia; Flexa, Roosevelt [Baker Hughes do Brasil Ltda, Macae, RJ (Brazil)

    2008-07-01

    In this article, we present a new approach to the automatic identification of lithologies using only well log data, which associates fuzzy logic, neural networks and multivariable statistic methods. Firstly, we chose well log data that represents lithological types, as gamma rays (GR) and density (RHOB), and, immediately, we applied a fuzzy logic algorithm to determine optimal number of clusters. In the following step, a competitive neural network is developed, based on Kohonen's learning rule, where the input layer is composed of two neurons, which represent the same number of used logs. On the other hand, the competitive layer is composed by several neurons, which have the same number of clusters as determined by the fuzzy logic algorithm. Finally, some data bank elements of the lithological types are selected at random to be the discriminate variables, which correspond to the input data of the multigroup discriminate analysis program. In this form, with the application of this methodology, the lithological types were automatically identified throughout the a well of the Namorado Oil Field, Campos Basin, which presented some difficulty in the results, mainly because of geological complexity of this field. (author)

  10. Neural correlates of adolescents' viewing of parents' and peers' emotions: Associations with risk-taking behavior and risky peer affiliations.

    Science.gov (United States)

    Saxbe, Darby; Del Piero, Larissa; Immordino-Yang, Mary Helen; Kaplan, Jonas; Margolin, Gayla

    2015-01-01

    Social reorientation from parents to same-age peers is normative in adolescence, but the neural correlates of youths' socioemotional processing of parents and peers have not been explored. In the current study, 22 adolescents (average age 16.98) underwent neuroimaging (functional magnetic resonance imaging) while viewing and rating emotions shown in brief video clips featuring themselves, their parents, or an unfamiliar peer. Viewing self vs. other and parents vs. the peer activated regions in the medial prefrontal cortex, replicating prior findings that this area responds to self-relevant stimuli, including familiar and not just similar others. Viewing the peer compared with parents elicited activation in posterior 'mentalizing' structures, the precuneus, posterior cingulate cortex (PCC), bilateral posterior superior temporal sulcus and right temporoparietal junction, as well as the ventral striatum and bilateral amygdala and hippocampus. Relative activations in the PCC and precuneus to the peer vs. the parent were related both to reported risk-taking behavior and to affiliations with more risk-taking peers. The results suggest neural correlates of the adolescent social reorientation toward peers and away from parents that may be associated with adolescents' real-life risk-taking behaviors and social relationships.

  11. Damage detection in carbon composite material typical of wind turbine blades using auto-associative neural networks

    Science.gov (United States)

    Dervilis, N.; Barthorpe, R. J.; Antoniadou, I.; Staszewski, W. J.; Worden, K.

    2012-04-01

    The structure of a wind turbine blade plays a vital role in the mechanical and structural operation of the turbine. As new generations of offshore wind turbines are trying to achieve a leading role in the energy market, key challenges such as a reliable Structural Health Monitoring (SHM) of the blades is significant for the economic and structural efficiency of the wind energy. Fault diagnosis of wind turbine blades is a "grand challenge" due to their composite nature, weight and length. The damage detection procedure involves additional difficulties focused on aerodynamic loads, environmental conditions and gravitational loads. It will be shown that vibration dynamic response data combined with AANNs is a robust and powerful tool, offering on-line and real time damage prediction. In this study the features used for SHM are Frequency Response Functions (FRFs) acquired via experimental methods based on an LMS system by which identification of mode shapes and natural frequencies is accomplished. The methods used are statistical outlier analysis which allows a diagnosis of deviation from normality and an Auto-Associative Neural Network (AANN). Both of these techniques are trained by adopting the FRF data for normal and damage condition. The AANN is a method which has not yet been widely used in the condition monitoring of composite materials of blades. This paper is trying to introduce a new scheme for damage detection, localisation and severity assessment by adopting simple measurements such as FRFs and exploiting multilayer neural networks and outlier novelty detection.

  12. Neural Induction, Neural Fate Stabilization, and Neural Stem Cells

    Directory of Open Access Journals (Sweden)

    Sally A. Moody

    2002-01-01

    Full Text Available The promise of stem cell therapy is expected to greatly benefit the treatment of neurodegenerative diseases. An underlying biological reason for the progressive functional losses associated with these diseases is the extremely low natural rate of self-repair in the nervous system. Although the mature CNS harbors a limited number of self-renewing stem cells, these make a significant contribution to only a few areas of brain. Therefore, it is particularly important to understand how to manipulate embryonic stem cells and adult neural stem cells so their descendants can repopulate and functionally repair damaged brain regions. A large knowledge base has been gathered about the normal processes of neural development. The time has come for this information to be applied to the problems of obtaining sufficient, neurally committed stem cells for clinical use. In this article we review the process of neural induction, by which the embryonic ectodermal cells are directed to form the neural plate, and the process of neural�fate stabilization, by which neural plate cells expand in number and consolidate their neural fate. We will present the current knowledge of the transcription factors and signaling molecules that are known to be involved in these processes. We will discuss how these factors may be relevant to manipulating embryonic stem cells to express a neural fate and to produce large numbers of neurally committed, yet undifferentiated, stem cells for transplantation therapies.

  13. The spatial decision-supporting system combination of RBR & CBR based on artificial neural network and association rules

    Science.gov (United States)

    Tian, Yangge; Bian, Fuling

    2007-06-01

    The technology of artificial intelligence should be imported on the basis of the geographic information system to bring up the spatial decision-supporting system (SDSS). The paper discusses the structure of SDSS, after comparing the characteristics of RBR and CBR, the paper brings up the frame of a spatial decisional system that combines RBR and CBR, which has combined the advantages of them both. And the paper discusses the CBR in agriculture spatial decisions, the application of ANN (Artificial Neural Network) in CBR, and enriching the inference rule base based on association rules, etc. And the paper tests and verifies the design of this system with the examples of the evaluation of the crops' adaptability.

  14. Fold-Hopf bifurcation in a simplified four-neuron BAM (bidirectional associative memory) neural network with two delays

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    The bidirectional associative memory (BAM) neural network with four neurons and two delays is considered in the present paper.A linear stability analysis for the trivial equilibrium is firstly employed to provide a possible critical point at which a zero and a pair of pure imaginary eigenvalues occur in the corresponding characteristic equation.A fold-Hopf bifurcation is proved to happen at this critical point by the nonlinear analysis.The coupling strength and the delay are considered as bifurcation parameters to investigate the dynamical behaviors derived from the fold-Hopf bifurcation.Various dynamical behaviours are qualitatively classified in the neighbourhood of the fold-Hopf bifurcation point by using the center manifold reduction (CMR) together with the normal form.The bifurcating periodic solutions are expressed analytically in an approximate form.The validity of the results is shown by their consistency with the numerical simulation.

  15. The neural underpinnings of associative learning in health and psychosis: how can performance be preserved when brain responses are abnormal?

    Science.gov (United States)

    Murray, Graham K; Corlett, Philip R; Fletcher, Paul C

    2010-05-01

    Associative learning experiments in schizophrenia and other psychoses reveal subtle abnormalities in patients' brain responses. These are sometimes accompanied by intact task performance. An important question arises: How can learning occur if the brain system is not functioning normally? Here, we examine a series of possible explanations for this apparent discrepancy: (1) standard brain activation patterns may be present in psychosis but partially obscured by greater noise, (2) brain signals may be more sensitive to real group differences than behavioral measures, and (3) patients may achieve comparable levels of performance to control subjects by employing alternative or compensatory neural strategies. We consider these explanations in relation to data from causal- and reward-learning imaging experiments in first-episode psychosis patients. The findings suggest that a combination of these factors may resolve the question of why performance is sometimes preserved when brain patterns are disrupted.

  16. Comparative analysis of the expression of neural stem cell-associated genes during neocortex and retina development in human.

    Science.gov (United States)

    Verdiev, B I; Milyushina, L A; Podgornyi, O V; Poltavtseva, R A; Zinov'eva, R D; Sukhikh, G T; Aleksandrova, M A

    2013-02-01

    We compared the expression of Sox2, Oct4, Nanog, Pax6, Prox1 genes associated with plasticity of neural stem and progenitor cells during human neocortex and retina development and in cell cultures. At the analyzed stages of neurogenesis, Pax6 gene is expressed in the neocortex and retina at constant levels, the expression is by one order of magnitude higher in the retina. The dynamics of Sox2 and Pax6 expression in the neocortex was similar. The expression of Oct4 and Nanog genes during neurogenesis in the neocortex and human fetal retina reflects the existence of a high-plasticity cell pool. The dynamics of βIII-tubulin expression indicates that the retina develops more rapidly than the neocortex. Our experiments showed that genetically determined cell potencies typical of native cells are realized in primary cultures without specific stimulation.

  17. Compassion-based emotion regulation up-regulates experienced positive affect and associated neural networks.

    Science.gov (United States)

    Engen, Haakon G; Singer, Tania

    2015-09-01

    Emotion regulation research has primarily focused on techniques that attenuate or modulate the impact of emotional stimuli. Recent evidence suggests that this mode regulation can be problematic in the context of regulation of emotion elicited by the suffering of others, resulting in reduced emotional connectedness. Here, we investigated the effects of an alternative emotion regulation technique based on the up-regulation of positive affect via Compassion-meditation on experiential and neural affective responses to depictions of individuals in distress, and compared these with the established emotion regulation strategy of Reappraisal. Using fMRI, we scanned 15 expert practitioners of Compassion-meditation either passively viewing, or using Compassion-meditation or Reappraisal to modulate their emotional reactions to film clips depicting people in distress. Both strategies effectively, but differentially regulated experienced affect, with Compassion primarily increasing positive and Reappraisal primarily decreasing negative affect. Imaging results showed that Compassion, relative to both passive-viewing and Reappraisal increased activation in regions involved in affiliation, positive affect and reward processing including ventral striatum and medial orbitfrontal cortex. This network was shown to be active prior to stimulus presentation, suggesting that the regulatory mechanism of Compassion is the stimulus-independent endogenous generation of positive affect.

  18. Selection of abnormal neural oscillation patterns associated with sentence-level language disorder in Schizophrenia.

    Science.gov (United States)

    Xu, Tingting; Stephane, Massoud; Parhi, Keshab K

    2012-01-01

    Language disorder is one of the core symptoms in schizophrenia. We propose a new framework based on machine intelligence techniques to investigate abnormal neural oscillations related to this impairment. Schizophrenia patients and healthy control subjects were instructed to discriminate semantically and syntactically correct sentences from syntactically correct but semantically incorrect sentences presented visually, and 248-channel MEG signals were recorded with a whole head machine during the task performance. Oscillation patterns were extracted from the MEG recordings in 8 frequency sub-bands throughout sentence processing, which form a large feature set. A two-step feature selection algorithm combining F-score filtering and Support Vector Machine recursive feature elimination (SVM-RFE) was designed to pick out a small subset of features which could discriminate patients and controls with high accuracy. We achieved a 90.48% prediction accuracy based on the selected top features, following the leave-one-out cross validation procedure. These top features provide interpretable spectral, spatial, and temporal information about the electrophysiological basis of sentence processing abnormality in schizophrenia which may help understand the underlying mechanism of this disease.

  19. Neural mechanisms of reward processing associated with depression-related personality traits.

    Science.gov (United States)

    Umemoto, Akina; Holroyd, Clay B

    2017-07-01

    Although impaired reward processing in depression has been well-documented, the exact nature of that deficit remains poorly understood. To investigate the link between depression and the neural mechanisms of reward processing, we examined individual differences in personality. We recorded the electroencephalogram from healthy college students engaged in a probabilistic reinforcement learning task. Participants also completed several personality questionnaires that assessed traits related to reward sensitivity, motivation, and depression. We examined whether behavioral measures of reward learning and event-related potential components related to outcome processing and reward anticipation-namely, the cue and feedback-related reward positivity (RewP) and the stimulus preceding negativity (SPN)-would link these personality traits to depression. Participants who scored high in reward sensitivity produced a relatively larger feedback-RewP. By contrast, participants who scored high in depression learned the contingencies for infrequently rewarded cue-response combinations relatively poorly, exhibited a larger SPN, and produced a smaller feedback-RewP, especially to outcomes following cue-response combinations that were frequently rewarded. These results point to a primary deficit in reward valuation in individuals who score high in depression, with secondary consequences that impact reward learning and anticipation. Despite recent evidence arguing for an anticipatory deficit in depression, impaired reward valuation as a primary deficit should be further examined in clinical samples. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

  20. Cognitive function predicts neural activity associated with pre-attentive temporal processing.

    Science.gov (United States)

    Foster, Shannon M; Kisley, Michael A; Davis, Hasker P; Diede, Nathaniel T; Campbell, Alana M; Davalos, Deana B

    2013-01-01

    Temporal processing, or processing time-related information, appears to play a significant role in a variety of vital psychological functions. One of the main confounds to assessing the neural underpinnings and cognitive correlates of temporal processing is that behavioral measures of timing are generally confounded by other supporting cognitive processes, such as attention. Further, much theorizing in this field has relied on findings from clinical populations (e.g., individuals with schizophrenia) known to have temporal processing deficits. In this study, we attempted to avoid these difficulties by comparing temporal processing assessed by a pre-attentive event-related brain potential (ERP) waveform, the mismatch negativity (MMN) elicited by time-based stimulus features, to a number of cognitive functions within a non-clinical sample. We studied healthy older adults (without dementia), as this population inherently ensures more prominent variability in cognitive function than a younger adult sample, allowing for the detection of significant relationships between variables. Using hierarchical regression analyses, we found that verbal memory and executive functions (i.e., planning and conditional inhibition, but not set-shifting) uniquely predicted variance in temporal processing beyond that predicted by the demographic variables of age, gender, and hearing loss. These findings are consistent with a frontotemporal model of MMN waveform generation in response to changes in the temporal features of auditory stimuli.

  1. Neural activity in the macaque putamen associated with saccades and behavioral outcome.

    Directory of Open Access Journals (Sweden)

    Jessica M Phillips

    Full Text Available It is now widely accepted that the basal ganglia nuclei form segregated, parallel loops with neocortical areas. The prevalent view is that the putamen is part of the motor loop, which receives inputs from sensorimotor areas, whereas the caudate, which receives inputs from frontal cortical eye fields and projects via the substantia nigra pars reticulata to the superior colliculus, belongs to the oculomotor loop. Tracer studies in monkeys and functional neuroimaging studies in human subjects, however, also suggest a potential role for the putamen in oculomotor control. To investigate the role of the putamen in saccadic eye movements, we recorded single neuron activity in the caudal putamen of two rhesus monkeys while they alternated between short blocks of pro- and anti-saccades. In each trial, the instruction cue was provided after the onset of the peripheral stimulus, thus the monkeys could either generate an immediate response to the stimulus based on the internal representation of the rule from the previous trial, or alternatively, could await the visual rule-instruction cue to guide their saccadic response. We found that a subset of putamen neurons showed saccade-related activity, that the preparatory mode (internally- versus externally-cued influenced the expression of task-selectivity in roughly one third of the task-modulated neurons, and further that a large proportion of neurons encoded the outcome of the saccade. These results suggest that the caudal putamen may be part of the neural network for goal-directed saccades, wherein the monitoring of saccadic eye movements, context and performance feedback may be processed together to ensure optimal behavioural performance and outcomes are achieved during ongoing behaviour.

  2. Multiscale Modeling of Gene-Behavior Associations in an Artificial Neural Network Model of Cognitive Development

    Science.gov (United States)

    Thomas, Michael S. C.; Forrester, Neil A.; Ronald, Angelica

    2016-01-01

    In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such…

  3. Neural correlates of verbal creativity: differences in resting-state functional connectivity associated with expertise in creative writing

    Science.gov (United States)

    Lotze, Martin; Erhard, Katharina; Neumann, Nicola; Eickhoff, Simon B.; Langner, Robert

    2014-01-01

    Neural characteristics of verbal creativity as assessed by word generation tasks have been recently identified, but differences in resting-state functional connectivity (rFC) between experts and non-experts in creative writing have not been reported yet. Previous electroencephalography (EEG) coherence measures during rest demonstrated a decreased cooperation between brain areas in association with creative thinking ability. Here, we used resting-state functional magnetic resonance imaging to compare 20 experts in creative writing and 23 age-matched non-experts with respect to rFC strengths within a brain network previously found to be associated with creative writing. Decreased rFC for experts was found between areas 44 of both hemispheres. Increased rFC for experts was observed between right hemispheric caudate and intraparietal sulcus. Correlation analysis of verbal creativity indices (VCIs) with rFC values in the expert group revealed predominantly negative associations, particularly of rFC between left area 44 and left temporal pole. Overall, our data support previous findings of reduced connectivity between interhemispheric areas and increased right-hemispheric connectivity during rest in highly verbally creative individuals. PMID:25076885

  4. The COMT Val/Met polymorphism is associated with reading-related skills and consistent patterns of functional neural activation.

    Science.gov (United States)

    Landi, Nicole; Frost, Stephen J; Mencl, W Einar; Preston, Jonathan L; Jacobsen, Leslie K; Lee, Maria; Yrigollen, Carolyn; Pugh, Kenneth R; Grigorenko, Elena L

    2013-01-01

    In both children and adults there is large variability in reading skill, with approximately 5-10% of individuals characterized as having reading disability; these individuals struggle to learn to read despite adequate intelligence and opportunity. Although it is well established that a substantial portion of this variability is attributed to the genetic differences between individuals, specifics of the connections between reading and the genome are not understood. This article presents data that suggest that variation in the COMT gene, which has previously been associated with variation in higher-order cognition, is associated with reading and reading-related skills, at the level of both brain and behavior. In particular, we found that the COMT Val/Met polymorphism at rs4680, which results in the substitution of the ancestral Valine (Val) by Methionine (Met), was associated with better performance on a number of critical reading measures and with patterns of functional neural activation that have been linked to better readers. We argue that this polymorphism, known for its broad effects on cognition, may modulate (likely through frontal lobe function) reading skill. © 2012 Blackwell Publishing Ltd.

  5. Neural correlates of verbal creativity: differences in resting-state functional connectivity associated with expertise in creative writing.

    Science.gov (United States)

    Lotze, Martin; Erhard, Katharina; Neumann, Nicola; Eickhoff, Simon B; Langner, Robert

    2014-01-01

    Neural characteristics of verbal creativity as assessed by word generation tasks have been recently identified, but differences in resting-state functional connectivity (rFC) between experts and non-experts in creative writing have not been reported yet. Previous electroencephalography (EEG) coherence measures during rest demonstrated a decreased cooperation between brain areas in association with creative thinking ability. Here, we used resting-state functional magnetic resonance imaging to compare 20 experts in creative writing and 23 age-matched non-experts with respect to rFC strengths within a brain network previously found to be associated with creative writing. Decreased rFC for experts was found between areas 44 of both hemispheres. Increased rFC for experts was observed between right hemispheric caudate and intraparietal sulcus. Correlation analysis of verbal creativity indices (VCIs) with rFC values in the expert group revealed predominantly negative associations, particularly of rFC between left area 44 and left temporal pole. Overall, our data support previous findings of reduced connectivity between interhemispheric areas and increased right-hemispheric connectivity during rest in highly verbally creative individuals.

  6. Neural correlates of verbal creativity: Differences in resting-state functional connectivity associated with expertise in creative writing

    Directory of Open Access Journals (Sweden)

    Martin eLotze

    2014-07-01

    Full Text Available Neural characteristics of verbal creativity as assessed by word generation tasks have been recently identified, but differences in resting-state functional connectivity (rFC between experts and non-experts in creative writing have not been reported yet. Previous electroencephalography (EEG coherence measures during rest demonstrated a decreased cooperation between brain areas in association with creative thinking ability. Here, we used resting-state functional magnetic resonance imaging to compare 20 experts in creative writing and 23 age-matched non-experts with respect to rFC strengths within a brain network previously found to be associated with creative writing. Decreased rFC for experts was found between areas 44 of both hemispheres. Increased rFC for experts was observed between right hemispheric caudate and intraparietal sulcus. Correlation analysis of verbal creativity indices with rFC values in the expert group revealed predominantly negative associations, particularly of rFC between left area 44 and left temporal pole. Overall, our data support previous findings on reduced connectivity between interhemispheric areas and increased right-hemispheric connectivity during rest in highly verbally creative individuals.

  7. Neural network modelling and dynamical system theory: are they relevant to study the governing dynamics of association football players?

    Science.gov (United States)

    Dutt-Mazumder, Aviroop; Button, Chris; Robins, Anthony; Bartlett, Roger

    2011-12-01

    Recent studies have explored the organization of player movements in team sports using a range of statistical tools. However, the factors that best explain the performance of association football teams remain elusive. Arguably, this is due to the high-dimensional behavioural outputs that illustrate the complex, evolving configurations typical of team games. According to dynamical system analysts, movement patterns in team sports exhibit nonlinear self-organizing features. Nonlinear processing tools (i.e. Artificial Neural Networks; ANNs) are becoming increasingly popular to investigate the coordination of participants in sports competitions. ANNs are well suited to describing high-dimensional data sets with nonlinear attributes, however, limited information concerning the processes required to apply ANNs exists. This review investigates the relative value of various ANN learning approaches used in sports performance analysis of team sports focusing on potential applications for association football. Sixty-two research sources were summarized and reviewed from electronic literature search engines such as SPORTDiscus, Google Scholar, IEEE Xplore, Scirus, ScienceDirect and Elsevier. Typical ANN learning algorithms can be adapted to perform pattern recognition and pattern classification. Particularly, dimensionality reduction by a Kohonen feature map (KFM) can compress chaotic high-dimensional datasets into low-dimensional relevant information. Such information would be useful for developing effective training drills that should enhance self-organizing coordination among players. We conclude that ANN-based qualitative analysis is a promising approach to understand the dynamical attributes of association football players.

  8. The neural substrates associated with attentional resources and difficulty of concurrent processing of the two verbal tasks.

    Science.gov (United States)

    Mizuno, Kei; Tanaka, Masaaki; Tanabe, Hiroki C; Sadato, Norihiro; Watanabe, Yasuyoshi

    2012-07-01

    The kana pick-out test has been widely used in Japan to evaluate the ability to divide attention in both adult and pediatric patients. However, the neural substrates underlying the ability to divide attention using the kana pick-out test, which requires participants to pick out individual letters (vowels) in a story while also reading for comprehension, thus requiring simultaneous allocation of attention to both activities, are still unclear. Moreover, outside of the clinical area, neuroimaging studies focused on the mechanisms of divided attention during complex story comprehension are rare. Thus, the purpose of the present study, to clarify the neural substrates of kana pick-out test, improves our current understanding of the basic neural mechanisms of dual task performance in verbal memory function. We compared patterns of activation in the brain obtained during performance of the individual tasks of vowel identification and story comprehension, to levels of activation when participants performed the two tasks simultaneously during the kana pick-out test. We found that activations of the left dorsal inferior frontal gyrus and superior parietal lobule increase in functional connectivity to a greater extent during the dual task condition compared to the two single task conditions. In contrast, activations of the left fusiform gyrus and middle temporal gyrus, which are significantly involved in picking out letters and complex sentences during story comprehension, respectively, were reduced in the dual task condition compared to during the two single task conditions. These results suggest that increased activations of the dorsal inferior frontal gyrus and superior parietal lobule during dual task performance may be associated with the capacity for attentional resources, and reduced activations of the left fusiform gyrus and middle temporal gyrus may reflect the difficulty of concurrent processing of the two tasks. In addition, the increase in synchronization between

  9. Dance type and flight parameters are associated with different mushroom body neural activities in worker honeybee brains.

    Directory of Open Access Journals (Sweden)

    Taketoshi Kiya

    Full Text Available BACKGROUND: Honeybee foragers can transmit the information concerning the location of food sources to their nestmates using dance communication. We previously used a novel immediate early gene, termed kakusei, to demonstrate that the neural activity of a specific mushroom body (MB neuron subtype is preferentially enhanced in the forager brain. The sensory information related to this MB neuron activity, however, remained unclear. METHODOLOGY/PRINCIPAL FINDINGS: Here, we used kakusei to analyze the relationship between MB neuron activity and types of foraging behavior. The number of kakusei-positive MB neurons was higher in the round dancers that had flown a short distance than in the waggle dancers that had flown a long distance. Furthermore, the amount of kakusei transcript in the MBs inversely related to the waggle-phase duration of the waggle dance, which correlates with the flight distance. Using a narrow tunnel whose inside was vertically or axially lined, we manipulated the pattern of visual input, which is received by the foragers during flight, and analysed kakusei expression. The amount of kakusei transcript in the MBs was related to the foraging frequency but not to the tunnel pattern. In contrast, the number of kakusei-positive MB neurons was affected by the tunnel patterns, but not related to foraging frequency. CONCLUSIONS/SIGNIFICANCE: These results suggest that the MB neuron activity depends on the foraging frequency, whereas the number of active MB neurons is related to the pattern of visual input received during foraging flight. Our results suggest that the foraging frequency and visual experience during foraging are associated with different MB neural activities.

  10. Mice with Tak1 deficiency in neural crest lineage exhibit cleft palate associated with abnormal tongue development.

    Science.gov (United States)

    Song, Zhongchen; Liu, Chao; Iwata, Junichi; Gu, Shuping; Suzuki, Akiko; Sun, Cheng; He, Wei; Shu, Rong; Li, Lu; Chai, Yang; Chen, YiPing

    2013-04-12

    Cleft palate represents one of the most common congenital birth defects in humans. TGFβ signaling, which is mediated by Smad-dependent and Smad-independent pathways, plays a crucial role in regulating craniofacial development and patterning, particularly in palate development. However, it remains largely unknown whether the Smad-independent pathway contributes to TGFβ signaling function during palatogenesis. In this study, we investigated the function of TGFβ activated kinase 1 (Tak1), a key regulator of Smad-independent TGFβ signaling in palate development. We show that Tak1 protein is expressed in both the epithelium and mesenchyme of the developing palatal shelves. Whereas deletion of Tak1 in the palatal epithelium or mesenchyme did not give rise to a cleft palate defect, inactivation of Tak1 in the neural crest lineage using the Wnt1-Cre transgenic allele resulted in failed palate elevation and subsequently the cleft palate formation. The failure in palate elevation in Wnt1-Cre;Tak1(F/F) mice results from a malformed tongue and micrognathia, resembling human Pierre Robin sequence cleft of the secondary palate. We found that the abnormal tongue development is associated with Fgf10 overexpression in the neural crest-derived tongue tissue. The failed palate elevation and cleft palate were recapitulated in an Fgf10-overexpressing mouse model. The repressive effect of the Tak1-mediated noncanonical TGFβ signaling on Fgf10 expression was further confirmed by inhibition of p38, a downstream kinase of Tak1, in the primary cell culture of developing tongue. Tak1 thus functions to regulate tongue development by controlling Fgf10 expression and could represent a candidate gene for mutation in human PRS clefting.

  11. Information theoretical performance measure for associative memories and its application to neural networks.

    Science.gov (United States)

    Schlüter, M; Kerschhaggl, O; Wagner, F

    1999-08-01

    We present a general performance measure (information loss) for associative memories based on information theoretical concepts. This performance measure can be estimated, provided that mean values of observables have been determined for the associative memory. Then the estimation guarantees a minimal association quality. The formalism allows the application of the performance measure to complex systems where the relation between input and output of the associative memory is not explicitly known. Here we apply our formalism to the Hopfield model and estimate the storage capacity alpha(c) from the numerically determined information loss. In contrast to other numerical methods the whole overlap distribution is taken into account. Our numerical value alpha(c)=0.1379(4) for the storage capacity in the Hopfield model is below numerical values obtained previously. This indicates that the consideration of small remnant overlaps lowers the storage capacity of the Hopfield model.

  12. Association between MTHFD1 G1958A polymorphism and neural tube defects susceptibility: a meta-analysis.

    Directory of Open Access Journals (Sweden)

    Jianxin Jiang

    Full Text Available OBJECTIVES: The methylenetetrahydrofolate dehydrogenase (MTHFD1 gene, as one of the key genes involved in the folate pathway, has been reported to play a critical role in the pathogenesis of neural tube defects (NTDs. However, the results of published studies are contradictory and inconclusive. Thus, this meta-analysis aimed to evaluate the effect of the common polymorphism in the MTHFD1 gene, the G1958A (R653Q, dbSNP ID: rs2236225 variant, on the risk of NTDs in all eligible studies. METHODS: Relevant literature published before January 3, 2014 was retrieved from the MEDLINE, EMBASE, Cochrane Library, and CBM databases. Pooled crude odds ratios (ORs and their corresponding 95% confidence intervals (CIs were calculated to evaluate the association between the MTHFD1 G1958A polymorphism and NTDs risk. RESULTS: We performed a meta-analysis of nine studies with a total of 4,302 NTDs patients and 4,238 healthy controls. Our results demonstrated a significant correlation between the MTHFD1 G1958A polymorphism and NTDs in an overall meta-analysis. For family-based studies, the study subjects were classified as NTD cases, mothers with NTDs offspring, and fathers with NTDs offspring. We found no association between any of the fathers' genotypes and NTDs, whereas there was a clear excess of the 1958A allele in the mothers of children with NTDs compared with controls individuals. CONCLUSIONS: In summary, our meta-analysis strongly suggests that the MTHFD1 G1958A polymorphism might be associated with maternal risk for NTDs in Caucasian populations. However, the evidence of this association should be interpreted with caution due to the selective nature of publication of genetic association studies.

  13. Transcription-associated processes cause DNA double-strand breaks and translocations in neural stem/progenitor cells.

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    Schwer, Bjoern; Wei, Pei-Chi; Chang, Amelia N; Kao, Jennifer; Du, Zhou; Meyers, Robin M; Alt, Frederick W

    2016-02-23

    High-throughput, genome-wide translocation sequencing (HTGTS) studies of activated B cells have revealed that DNA double-strand breaks (DSBs) capable of translocating to defined bait DSBs are enriched around the transcription start sites (TSSs) of active genes. We used the HTGTS approach to investigate whether a similar phenomenon occurs in primary neural stem/progenitor cells (NSPCs). We report that breakpoint junctions indeed are enriched around TSSs that were determined to be active by global run-on sequencing analyses of NSPCs. Comparative analyses of transcription profiles in NSPCs and B cells revealed that the great majority of TSS-proximal junctions occurred in genes commonly expressed in both cell types, possibly because this common set has higher transcription levels on average than genes transcribed in only one or the other cell type. In the latter context, among all actively transcribed genes containing translocation junctions in NSPCs, those with junctions located within 2 kb of the TSS show a significantly higher transcription rate on average than genes with junctions in the gene body located at distances greater than 2 kb from the TSS. Finally, analysis of repair junction signatures of TSS-associated translocations in wild-type versus classical nonhomologous end-joining (C-NHEJ)-deficient NSPCs reveals that both C-NHEJ and alternative end-joining pathways can generate translocations by joining TSS-proximal DSBs to DSBs on other chromosomes. Our studies show that the generation of transcription-associated DSBs is conserved across divergent cell types.

  14. Neural Correlates of Biased Responses: The Negative Method Effect in the Rosenberg Self-Esteem Scale Is Associated with Right Amygdala Volume.

    Science.gov (United States)

    Wang, Yinan; Kong, Feng; Huang, Lijie; Liu, Jia

    2016-10-01

    Self-esteem is a widely studied construct in psychology that is typically measured by the Rosenberg Self-Esteem Scale (RSES). However, a series of cross-sectional and longitudinal studies have suggested that a simple and widely used unidimensional factor model does not provide an adequate explanation of RSES responses due to method effects. To identify the neural correlates of the method effect, we sought to determine whether and how method effects were associated with the RSES and investigate the neural basis of these effects. Two hundred and eighty Chinese college students (130 males; mean age = 22.64 years) completed the RSES and underwent magnetic resonance imaging (MRI). Behaviorally, method effects were linked to both positively and negatively worded items in the RSES. Neurally, the right amygdala volume negatively correlated with the negative method factor, while the hippocampal volume positively correlated with the general self-esteem factor in the RSES. The neural dissociation between the general self-esteem factor and negative method factor suggests that there are different neural mechanisms underlying them. The amygdala is involved in modulating negative affectivity; therefore, the current study sheds light on the nature of method effects that are related to self-report with a mix of positively and negatively worded items. © 2015 Wiley Periodicals, Inc.

  15. Variation in the oxytocin receptor gene is associated with behavioral and neural correlates of empathic accuracy

    DEFF Research Database (Denmark)

    Laursen, Helle Ruff; Siebner, Hartwig Roman; Haren, Tina

    2014-01-01

    , but not the SLC6A4 5-HTTLPR, were associated with significant differences in empathic accuracy, with CC- and AA-carriers, respectively, displaying higher empathic accuracy. For OXTR rs2268498 there was also a genotype difference in the correlation between empathic accuracy and activity in the superior temporal...

  16. Neural tube defects and associated anomalies in a fetal and perinatal autopsy series

    DEFF Research Database (Denmark)

    Nielsen, Ljudmilla A G; Maroun, Lisa Leth; Broholm, Helle

    2006-01-01

    morphological anomalies, and organ weights. Organ weights were evaluated according to new fetal autopsy standards and grouped as low, normal or high. Ninety-seven NTD cases were found (4.9%): Spina bifida (38 cases), cephalocele (17 cases) and anencephaly (42 cases). 63% of NTD cases had associated morphologic...

  17. Variation in the Oxytocin Receptor Gene Is Associated with Face Recognition and its Neural Correlates

    Science.gov (United States)

    Westberg, Lars; Henningsson, Susanne; Zettergren, Anna; Svärd, Joakim; Hovey, Daniel; Lin, Tian; Ebner, Natalie C.; Fischer, Håkan

    2016-01-01

    The ability to recognize faces is crucial for daily social interactions. Recent studies suggest that intranasal oxytocin administration improves social recognition in humans. Oxytocin signaling in the amygdala plays an essential role for social recognition in mice, and oxytocin administration has been shown to influence amygdala activity in humans. It is therefore possible that the effects of oxytocin on human social recognition depend on mechanisms that take place in the amygdala—a central region for memory processing also in humans. Variation in the gene encoding the oxytocin receptor (OXTR) has been associated with several aspects of social behavior. The present study examined the potential associations between nine OXTR polymorphisms, distributed across the gene, and the ability to recognize faces, as well as face-elicited amygdala activity measured by functional magnetic resonance imaging (fMRI) during incidental encoding of faces. The OXTR 3′ polymorphism rs7632287, previously related to social bonding behavior and autism risk, was associated with participants’ ability to recognize faces. Carriers of the GA genotype, associated with enhanced memory, displayed higher amygdala activity during face encoding compared to carriers of the GG genotype. In line with work in rodents, these findings suggest that, in humans, naturally occurring endogenous modulation of OXTR function affects social recognition through an amygdala-dependent mechanism. These findings contribute to the understanding of how oxytocin regulates human social behaviors. PMID:27713694

  18. Variation in the Oxytocin Receptor Gene is associated with Face Recognition and its neural correlates

    Directory of Open Access Journals (Sweden)

    Lars Westberg

    2016-09-01

    Full Text Available The ability to recognize faces is crucial for daily social interactions. Recent studies suggest that intranasal oxytocin administration improves social recognition in humans. Oxytocin signaling in the amygdala plays an essential role for social recognition in mice, and oxytocin administration has been shown to influence amygdala activity in humans. It is therefore possible that the effects of oxytocin on human social recognition depend on mechanisms that take place in the amygdala – a central region for memory processing also in humans. Variation in the gene encoding the oxytocin receptor (OXTR has been associated with several aspects of social behavior. The present study examined the potential associations between nine OXTR polymorphisms, distributed across the gene, and the ability to recognize faces, as well as face-elicited amygdala activity measured by functional magnetic resonance imaging during incidental encoding of faces. The OXTR 3’ polymorphism rs7632287, previously related to social bonding behavior and autism risk, was associated with participants’ ability to recognize faces. Carriers of the GA genotype, associated with enhanced memory, displayed higher amygdala activity during face encoding compared to carriers of the GG genotype. In line with work in rodents, these findings suggest that, in humans, naturally occurring endogenous modulation of OXTR function affects social recognition through an amygdala-dependent mechanism. These findings contribute to the understanding of how oxytocin regulates human social behaviors.

  19. Single nucleotide polymorphisms of the maternal Msx2 gene and their association with fetal neural tube defects in Han ethnic group in Shanxi Province, China

    Institute of Scientific and Technical Information of China (English)

    GUO Li; ZHAO Hong; PEI Yu-heng; HE Quan-ren; LI Wan-I; ZHANG Ting; ZHENG Xiao-ying; ZHOU Ran; XIE Jun

    2011-01-01

    Background Neural tube defects are the most common human birth defects. The causes are multifactorial with complex genetic and environmental factors, although the exact genetic causes are unknown. This research was conducted to study the frequency of Msx2 gene polymorphisms in 59 women with a history of pregnancy with a neural tube defect and in 73 healthy controls. We aimed to determine the effect of this genetic polymorphism on the incidence of neural tube defects in the Han Chinese population.Methods We studied 59 mothers with at least one previous child with a neural tube defect (the case group) and 73case-control subjects during the same period, from Shanxi Province, China. We analyzed the genotypic distributions and allele frequencies of Msx2 C386T poiymorphisms in DNA samples from the case and control groups. A three-dimensional protein model was predicted using Swiss-Pdb Viewer software version 4.0. Disease association was analyzed using chi-square tests.Results Significant differences were observed in the genotypes and allele frequencies of the Msx2 C386T allele between the case and control groups (CT: 32% vs. 15%, P=0.0073 and TT 15% vs. 4%, P=0.013, respectively). Logistic regression analysis showed that the C386T mutation is a potential risk factor for neural tube defects (P <0.05; OR: 3.466;95%CI: 1.831-6.560). Three-dimensional structure prediction revealed that the Msx2 C386T mutation results in a threonine substitution for methionine at position 129 of exon 2, which might lead to structural mutations or dysfunctions in the protein encoded by Msx2.Conclusion Maternal Msx2 C386T gene polymorphisms were associated with fetal neural tube defects in Han Chinese women in Shanxi Province.

  20. Neural Correlates of Moral Sensitivity and Moral Judgment Associated with Brain Circuitries of Selfhood: A Meta-Analysis

    Science.gov (United States)

    Han, Hyemin

    2017-01-01

    The present study meta-analyzed 45 experiments with 959 subjects and 463 activation foci reported in 43 published articles that investigated the neural mechanism of moral functions by comparing neural activity between the moral task conditions and non-moral task conditions with the Activation Likelihood Estimation method. The present study…

  1. Neurodevelopmental Outcomes and Neural Mechanisms Associated with Non-right Handedness in Children Born Very Preterm.

    Science.gov (United States)

    Pascoe, Leona; Scratch, Shannon E; Burnett, Alice C; Thompson, Deanne K; Lee, Katherine J; Doyle, Lex W; Cheong, Jeanie L Y; Inder, Terrie E; Anderson, Peter J

    2015-09-01

    Non-right handedness (NRH) is reportedly more common in very preterm (VPT; <32 weeks' gestation) children compared with term-born peers, but it is unclear whether neonatal brain injury or altered brain morphology and microstructure underpins NRH in this population. Given that NRH has been inconsistently reported to be associated with cognitive and motor difficulties, this study aimed to examine associations between handedness and neurodevelopmental outcomes in VPT 7-year-olds. Furthermore, the relationship between neonatal brain injury and integrity of motor tracts (corpus callosum and corticospinal tract) with handedness at age 7 years in VPT children was explored. One hundred seventy-five VPT and 69 term-born children completed neuropsychological and motor assessments and a measure of handedness at 7 years' corrected age. At term-equivalent age, brain injury on MRI was assessed and diffusion tensor measures were obtained for the corpus callosum and posterior limb of the internal capsule. There was little evidence of stronger NRH in the VPT group compared with term controls (regression coefficient [b] -1.95, 95% confidence interval [-5.67, 1.77]). Poorer academic and working memory outcomes were associated with stronger NRH in the VPT group. While there was little evidence that neonatal unilateral brain injury was associated with stronger NRH, increased area and fractional anisotropy of the corpus callosum splenium were predictive of stronger NRH in the VPT group. VPT birth may alter the relationship between handedness and academic outcomes, and neonatal corpus callosum integrity predicts hand preference in VPT children at school age. (JINS, 2015, 21, 610-621).

  2. Variation in the oxytocin receptor gene is associated with behavioral and neural correlates of empathic accuracy

    Directory of Open Access Journals (Sweden)

    Helle Ruff Laursen

    2014-12-01

    Full Text Available The neuromodulators oxytocin and serotonin have been implicated in regulating affective processes underlying empathy. Understanding this dependency, however, has been limited by a lack of objective metrics for measuring empathic performance. Here we employ a novel psychophysical method for measuring empathic performance that quantitatively measures the ability of subjects to decode the experience of another person’s pain. In 50 female subjects, we acquired functional magnetic resonance imaging data as they were exposed to a target subject experiencing variable degrees of pain, whilst performing an irrelevant attention-demanding task. We investigated the effect of variation in the oxytocin receptor gene (OXTR and the serotonin transporter gene (SLC6A4 on the psychophysical and neurometric variability associated with empathic performance. The OXTR rs2268498 and rs53576 polymorphisms, but not the SLC6A4 5-HTTLPR, were associated with significant differences in empathic accuracy, with CC- and AA-carriers, respectively, displaying higher empathic accuracy. For OXTR rs2268498 there was also a genotype difference in the correlation between empathic accuracy and activity in the superior temporal sulcus (STS. In OXTR rs2268498 CC-carriers, high empathic accuracy was associated with stronger responsiveness of the right STS to the observed pain. Together, the results show that genetic variation in the OXTR has significant influence on empathic accuracy and that this may be linked to variable responsivity of the STS.

  3. Defeitos de fechamento do tubo neural e fatores associados em recém-nascidos vivos e natimortos Neural tube defects and associated factors among liveborn and stillborn infants

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    Marcos J.B. Aguiar

    2003-04-01

    evaluate the prevalence and factors associated to neural tube defects in liveborn and stillborn infants delivered at the Hospital das Clínicas, UFMG, from January 8, 1999 to July 31, 2000. METHODS: this is a descriptive study, based on a database, according to the Latin-American Collaborative Study of Congenital Malformation (ECLAMC rules. Reports on liveborn and stillborn infants with congenital anomalies were prepared including information about morphological description, necropsy results, complementary exams, family, social and pregnancy histories and other clinical data. Each malformed liveborn infant originated a control of the same sex, without malformations. The liveborn and stillborn infants with neural tube defects delivered during that period were classified according to their defect and the presence or absence of associated defects. The liveborn and stillborn infants with neural tube defects were compared to newborns without neural tube defects according to their weight and sex and their mother's age and parity. Epi-Info 6.0 Program was used for the statistical analysis of the results. RESULTS: the prevalence of neural tube defects was 4.73 to 1,000 deliveries (89:18,807; it was significantly higher among stillborn infants (23.7:1,000 than among liveborn infants (4.16:1,000, p < 0.001. Neural tube defects were more often found among low weight liveborn infants (< 2,500 g, p < 0.001 and less frequently among women who had had more than three gestations, p = 0.007. No association was found regarding newborn's sex or maternal age. There was no association with newborn's sex and weight, maternal parity or age among stillborn infants. The most common neural tube defects were myelomeningocele (47.2%, anencephaly (26.9% and encephalocele (16.9%. The defects were found as isolated anomalies in 71.1% of the liveborn and 38.5% of the stillborn infants; they were part of a syndrome in 9.2% (liveborn and 7.7% (stillborn. CONCLUSIONS: the neural tube defect prevalence found

  4. Neural correlates of object-associated choice behavior in the perirhinal cortex of rats.

    Science.gov (United States)

    Ahn, Jae-Rong; Lee, Inah

    2015-01-28

    The perirhinal cortex (PRC) is reportedly important for object recognition memory, with supporting physiological evidence obtained largely from primate studies. Whether neurons in the rodent PRC also exhibit similar physiological correlates of object recognition, however, remains to be determined. We recorded single units from the PRC in a PRC-dependent, object-cued spatial choice task in which, when cued by an object image, the rat chose the associated spatial target from two identical discs appearing on a touchscreen monitor. The firing rates of PRC neurons were significantly modulated by critical events in the task, such as object sampling and choice response. Neuronal firing in the PRC was correlated primarily with the conjunctive relationships between an object and its associated choice response, although some neurons also responded to the choice response alone. However, we rarely observed a PRC neuron that represented a specific object exclusively regardless of spatial response in rats, although the neurons were influenced by the perceptual ambiguity of the object at the population level. Some PRC neurons fired maximally after a choice response, and this post-choice feedback signal significantly enhanced the neuronal specificity for the choice response in the subsequent trial. Our findings suggest that neurons in the rat PRC may not participate exclusively in object recognition memory but that their activity may be more dynamically modulated in conjunction with other variables, such as choice response and its outcomes.

  5. Neuroadaptive Changes Associated with Smoking: Structural and Functional Neural Changes in Nicotine Dependence

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    Chantal Martin-Soelch

    2013-02-01

    Full Text Available Tobacco smoking is the most frequent form of substance abuse. We provide a review of the neuroadaptive changes evidenced in human smokers with regard to the current neurobiological models of addiction. Addiction is thought to result from an interplay between positive and negative reinforcement. Positive reinforcing effects of the drugs are mediated by striatal dopamine release, while negative reinforcement involves the relief of withdrawal symptoms and neurobiological stress systems. In addition, drug-related stimuli are attributed with excessive motivational value and are thought to exert a control on the behavior. This mechanism plays a central role in drug maintenance and relapse. Further neuroadaptive changes associated with chronic use of the drug consist of reduced responses to natural rewards and in the activation of an antireward system, related to neurobiological stress systems. Reduced inhibitory cognitive control is believed to support the development and the maintenance of addiction. The findings observed in human nicotine dependence are generally in line with these models. The current state of the research indicates specific neuroadaptive changes associated with nicotine addiction that need to be further elucidated with regard to their role in the treatment of nicotine dependence.

  6. The neural network associated with lexical-semantic knowledge about social groups.

    Science.gov (United States)

    Piretti, Luca; Carnaghi, Andrea; Campanella, Fabio; Ambron, Elisabetta; Skrap, Miran; Rumiati, Raffaella I

    2015-09-01

    A person can be appraised as an individual or as a member of a social group. In the present study we tested whether the knowledge about social groups is represented independently of the living and non-living things. Patients with frontal and temporal lobe tumors involving either the left or the right hemisphere performed three tasks--picture naming, word-to-picture matching and picture sorting--tapping the lexical semantic knowledge of living things, non-living things and social groups. Both behavioral and voxel-based lesion-symptom mapping (VLSM) analyses suggested that social groups might be represented differently from other categories. VLSM analysis carried out on naming errors revealed that left-lateralized lesions in the inferior frontal gyrus, amygdala, insula and basal ganglia were associated with the lexical-semantic processing of social groups. These findings indicate that the social group representation may rely on areas associated with affective processing. Copyright © 2015 Elsevier Ltd. All rights reserved.

  7. Spike frequency adaptation is a possible mechanism for control of attractor preference in auto-associative neural networks

    Science.gov (United States)

    Roach, James; Sander, Leonard; Zochowski, Michal

    Auto-associative memory is the ability to retrieve a pattern from a small fraction of the pattern and is an important function of neural networks. Within this context, memories that are stored within the synaptic strengths of networks act as dynamical attractors for network firing patterns. In networks with many encoded memories, some attractors will be stronger than others. This presents the problem of how networks switch between attractors depending on the situation. We suggest that regulation of neuronal spike-frequency adaptation (SFA) provides a universal mechanism for network-wide attractor selectivity. Here we demonstrate in a Hopfield type attractor network that neurons minimal SFA will reliably activate in the pattern corresponding to a local attractor and that a moderate increase in SFA leads to the network to converge to the strongest attractor state. Furthermore, we show that on long time scales SFA allows for temporal sequences of activation to emerge. Finally, using a model of cholinergic modulation within the cortex we argue that dynamic regulation of attractor preference by SFA could be critical for the role of acetylcholine in attention or for arousal states in general. This work was supported by: NSF Graduate Research Fellowship Program under Grant No. DGE 1256260 (JPR), NSF CMMI 1029388 (MRZ) and NSF PoLS 1058034 (MRZ & LMS).

  8. Wechsler Memory Scale Revised Edition: neural correlates of the visual paired associates subtest adapted for fMRI.

    Science.gov (United States)

    Neuner, Irene; Stöcker, Tony; Kellermann, Thilo; Kircher, Tilo; Zilles, Karl; Schneider, Frank; Shah, N Jon

    2007-10-26

    Memory deficits in neurological and psychiatric patients are evaluated by neuropsychological tests such as the Wechsler Memory Scale Revised Edition (WMS-R). Neuropsychological data from patients with circumscribed lesions point to single elements of the underlying neural network but fail to identify its whole extent. We report the fMRI adaptation of a subtest of the WMS-R, the Visual Paired Associates. Fifteen healthy, right-handed male volunteers were studied using a 1.5T MRI scanner. The encoding of the combination between a shape and a colour, the assessment of the retrieval of this combination immediately after encoding took place, and the underlying network employed during retrieval a second time after approximately 25 min were investigated. The results show a fronto-parieto-occipital network with left frontal accentuation for encoding and a fronto-parieto-occipital network for immediate and delayed retrieval. Noteworthy is the specific role of the thalamus. During immediate retrieval, the thalamus showed significant bilateral activation; during delayed retrieval, there was no significant activation. The thalami are part of an extended hippocampal-diencephalic system which is critical for efficient encoding and normal retrieval of new episodic information. We describe the probability of thalamocortical connections during retrieval based on the Thalamus Connectivity Atlas. The cerebellum showed significant activation in all conditions; its part in higher cognitive functions such as memory was thereby confirmed.

  9. Social-network complexity in humans is associated with the neural response to social information.

    Science.gov (United States)

    Dziura, Sarah L; Thompson, James C

    2014-11-01

    Humans have evolved to thrive in large and complex social groups, and it is likely that this increase in group complexity has come with a greater need to decode and respond to complex and uncertain communicatory signals. In this functional MRI study, we examined whether complexity of social networks in humans is related to the functioning of brain regions key to the perception of basic, nonverbal social stimuli. Greater activation to biological than to scrambled motion in the right posterior superior temporal sulcus (pSTS) and right amygdala were positively correlated with the diversity of social-network roles. In the pSTS, in particular, this association was not due to a relationship between network diversity and network size. These findings suggest that increased functioning of brain regions involved in decoding social signals might facilitate the detection and decoding of subtle signals encountered in varied social settings.

  10. Associations between Proprioceptive Neural Pathway Structural Connectivity and Balance in People with Multiple Sclerosis.

    Science.gov (United States)

    Fling, Brett W; Dutta, Geetanjali Gera; Schlueter, Heather; Cameron, Michelle H; Horak, Fay B

    2014-01-01

    Mobility and balance impairments are a hallmark of multiple sclerosis (MS), affecting nearly half of patients at presentation and resulting in decreased activity and participation, falls, injuries, and reduced quality of life. A growing body of work suggests that balance impairments in people with mild MS are primarily the result of deficits in proprioception, the ability to determine body position in space in the absence of vision. A better understanding of the pathophysiology of balance disturbances in MS is needed to develop evidence-based rehabilitation approaches. The purpose of the current study was to (1) map the cortical proprioceptive pathway in vivo using diffusion-weighted imaging and (2) assess associations between proprioceptive pathway white matter microstructural integrity and performance on clinical and behavioral balance tasks. We hypothesized that people with MS (PwMS) would have reduced integrity of cerebral proprioceptive pathways, and that reduced white matter microstructure within these tracts would be strongly related to proprioceptive-based balance deficits. We found poorer balance control on proprioceptive-based tasks and reduced white matter microstructural integrity of the cortical proprioceptive tracts in PwMS compared with age-matched healthy controls (HC). Microstructural integrity of this pathway in the right hemisphere was also strongly associated with proprioceptive-based balance control in PwMS and controls. Conversely, while white matter integrity of the right hemisphere's proprioceptive pathway was significantly correlated with overall balance performance in HC, there was no such relationship in PwMS. These results augment existing literature suggesting that balance control in PwMS may become more dependent upon (1) cerebellar-regulated proprioceptive control, (2) the vestibular system, and/or (3) the visual system.

  11. Associations between proprioceptive neural pathway structural connectivity and balance in people with multiple sclerosis

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    Brett W Fling

    2014-10-01

    Full Text Available Mobility and balance impairments are a hallmark of multiple sclerosis (MS, affecting nearly half of patients at presentation and resulting in decreased activity and participation, falls, injuries, and reduced quality of life. A growing body of work suggests that balance impairments in people with mild MS are primarily the result of deficits in proprioception, the ability to determine body position in space in the absence of vision. A better understanding of the pathophysiology of balance disturbances in MS is needed to develop evidence-based rehabilitation approaches. The purpose of the current study was to 1 map the cortical proprioceptive pathway in-vivo using diffusion weighted imaging and 2 assess associations between proprioceptive pathway white matter microstructural integrity and performance on clinical and behavioral balance tasks. We hypothesized that people with MS (PwMS would have reduced integrity of cerebral proprioceptive pathways, and that reduced white matter microstructure within these tracts would be strongly related to proprioceptive-based balance deficits. We found poorer balance control on proprioceptive-based tasks and reduced white matter microstructural integrity of the cortical proprioceptive tracts in PwMS compared with age-matched healthy controls. Microstructural integrity of this pathway in the right hemisphere was also strongly associated with proprioceptive-based balance control in PwMS and controls. Conversely, while white matter integrity of the right hemisphere’s proprioceptive pathway was significantly correlated with overall balance performance in healthy controls, there was no such relationship in PwMS. These results augment existing literature suggesting that balance control in PwMS may become more dependent upon 1 cerebellar-regulated proprioceptive control, 2 the vestibular system, and/or 3 the visual system.

  12. Disostose espôndilo-costal associada a defeitos de fechamento do tubo neural Spondylocostal dysostosis associated with neural tube defects

    Directory of Open Access Journals (Sweden)

    Rafael Fabiano M. Rosa

    2009-09-01

    Full Text Available OBJETIVO: Salientar a relação dos defeitos de fechamento do tubo neural com a disostose espôndilo-costal (DEC por meio da descrição de três pacientes. DESCRIÇÃO DOS CASOS: Paciente 1: menina branca, 22 meses, nascida com mielomeningocele lombar. Na avaliação, apresentava hipotonia, baixa estatura, dolicocefalia, fendas palpebrais oblíquas para cima, pregas epicânticas e tronco curto com tórax assimétrico. A avaliação radiográfica revelou hemivértebras múltiplas, vértebras em borboleta e fusão e ausência de algumas costelas. Paciente 2: menina branca, 22 meses, com moderado atraso do desenvolvimento neuropsicomotor, baixa estatura, olhos profundos, pregas epicânticas, pescoço e tronco curtos com assimetria do tórax, abdome protruso, hemangioma plano na altura da transição lombossacra e fosseta sacral profunda no dorso. A avaliação radiográfica identificou hemivértebras, fusão incompleta de vértebras e vértebras em borboleta, malformações de costelas e espinha bífida oculta em L5/S1. Paciente 3: menina branca, 9 dias de vida, com fendas palpebrais oblíquas para cima, ponte nasal alargada, orelhas baixo implantadas e rotadas posteriormente, tronco curto, tórax assimétrico e meningocele tóraco-lombar. A avaliação radiográfica evidenciou hemivértebras, malformação e ausência de algumas costelas e agenesia diafragmática à esquerda. A tomografia computadorizada de encéfalo mostrou estenose de aqueduto. COMENTÁRIOS: Vários defeitos de fechamento do tubo neural, de espinha bífida oculta a grandes mielomeningoceles, são observados em pacientes com DEC, indicando que tais pacientes devem ser cuidadosamente avaliados quanto à possível presença desses defeitos.OBJECTIVE: To highlight the relationship between neural tube defects and spondylocostal dysostosis (SCD through the description of three patients. CASES DESCRIPTION: Patient 1: white girl, 22 months old, born with a lumbar meningomyelocele. At

  13. Prostaglandin E1 alleviates neuropathic pain and neural dysfunction from entrapment neuropathy associated with diabetes mellitus.

    Science.gov (United States)

    Natsume, Tadahiro; Iwatsuki, Katsuyuki; Nishizuka, Takanobu; Arai, Tetsuya; Yamamoto, Michiro; Hirata, Hitoshi

    2014-10-01

    In this report, we present the results of investigation of the effects of prostaglandin E1 (PGE1) on entrapment neuropathy using a diabetic rat. A total of 60 male Sprague-Dawley rats were used in the study. The model of tibial nerve entrapment neuropathy associated with diabetes mellitus was created by streptozotocin-induced diabetic rats reared in cages with wire grid flooring. Rats were assigned to four groups: nondiabetic (n = 15), untreated diabetic (n = 15), diabetic treated with 30 μg/kg PGE1 (n = 15), and diabetic treated with 100 μg/kg PGE1 (n = 15). Pain tests and electrophysiological tests were performed at 0, 2, and 4 weeks, and assessments of gait, histology, and mRNA expression levels were performed at 4 weeks after initiating the PGE1 administration. In the 30 and 100 μg groups, the mechanical withdrawal thresholds measured by pain tests at 4 weeks (36.2 ± 16.4 g and 31.7 ± 15.3 g, respectively) and the motor conduction velocity (24.0 ± 0.2 m/s and 24.4 ± 0.3 m/s, respectively) were significantly higher than the untreated diabetic group (all P < 0.05) and lower than the nondiabetic group (all P < 0.001). In the gait analysis, the mean intensities in the 30 and 100 μg group (128.0 ± 20.1 a.u. and 109.0 ± 27.8 a.u., respectively) were significantly higher than the untreated diabetic (P < 0.01) and were not significantly different from the nondiabetic group (P = 0.81). Fiber density (P = 0.46) and fiber diameter (P = 0.15) did not show any significant differences. PGE1 significantly decreased nerve growth factor (NGF) mRNA and increased vascular endothelial growth factor (VEGF) mRNA in the tibial nerve (both P < 0.01). In conclusion, neurological deteriorations of diabetic rats were alleviated with PGE1, which is associated with inhibition of NGF and enhancement of VEGF at the entrapment site.

  14. Lesser Neural Pattern Similarity across Repeated Tests Is Associated with Better Long-Term Memory Retention.

    Science.gov (United States)

    Karlsson Wirebring, Linnea; Wiklund-Hörnqvist, Carola; Eriksson, Johan; Andersson, Micael; Jonsson, Bert; Nyberg, Lars

    2015-07-01

    Encoding and retrieval processes enhance long-term memory performance. The efficiency of encoding processes has recently been linked to representational consistency: the reactivation of a representation that gets more specific each time an item is further studied. Here we examined the complementary hypothesis of whether the efficiency of retrieval processes also is linked to representational consistency. Alternatively, recurrent retrieval might foster representational variability--the altering or adding of underlying memory representations. Human participants studied 60 Swahili-Swedish word pairs before being scanned with fMRI the same day and 1 week later. On Day 1, participants were tested three times on each word pair, and on Day 7 each pair was tested once. A BOLD signal change in right superior parietal cortex was associated with subsequent memory on Day 1 and with successful long-term retention on Day 7. A representational similarity analysis in this parietal region revealed that beneficial recurrent retrieval was associated with representational variability, such that the pattern similarity on Day 1 was lower for retrieved words subsequently remembered compared with those subsequently forgotten. This was mirrored by a monotonically decreased BOLD signal change in dorsolateral prefrontal cortex on Day 1 as a function of repeated successful retrieval for words subsequently remembered, but not for words subsequently forgotten. This reduction in prefrontal response could reflect reduced demands on cognitive control. Collectively, the results offer novel insights into why memory retention benefits from repeated retrieval, and they suggest fundamental differences between repeated study and repeated testing. Repeated testing is known to produce superior long-term retention of the to-be-learned material compared with repeated encoding and other learning techniques, much because it fosters repeated memory retrieval. This study demonstrates that repeated memory

  15. Rumination in major depressive disorder is associated with impaired neural activation during conflict monitoring.

    Science.gov (United States)

    Alderman, Brandon L; Olson, Ryan L; Bates, Marsha E; Selby, Edward A; Buckman, Jennifer F; Brush, Christopher J; Panza, Emily A; Kranzler, Amy; Eddie, David; Shors, Tracey J

    2015-01-01

    Individuals with major depressive disorder (MDD) often ruminate about past experiences, especially those with negative content. These repetitive thoughts may interfere with cognitive processes related to attention and conflict monitoring. However, the temporal nature of these processes as reflected in event-related potentials (ERPs) has not been well-described. We examined behavioral and ERP indices of conflict monitoring during a modified flanker task and the allocation of attention during an attentional blink (AB) task in 33 individuals with MDD and 36 healthy controls, and whether their behavioral performance and ERPs varied with level of rumination. N2 amplitude elicited by the flanker task was significantly reduced in participants with MDD compared to healthy controls. Level of self-reported rumination was also correlated with N2 amplitude. In contrast, P3 amplitude during the AB task was not significantly different between groups, nor was it correlated with rumination. No significant differences were found in behavioral task performance measures between groups or by rumination levels. These findings suggest that rumination in MDD is associated with select deficits in cognitive control, particularly related to conflict monitoring.

  16. Neural effects of the CSMD1 genome-wide associated schizophrenia risk variant rs10503253.

    LENUS (Irish Health Repository)

    Rose, Emma J

    2013-09-01

    The single nucleotide polymorphism rs10503253 within the CUB and Sushi multiple domains-1 (CSMD1) gene on 8p23.2 has been identified as genome-wide significant for schizophrenia (SZ). This gene is of unknown function but has been implicated in multiple neurodevelopmental disorders that impact upon cognition, leading us to hypothesize that an effect on brain structure and function underlying cognitive processes may be part of the mechanism by which CMSD1 increases illness risk. To test this hypothesis, we investigated this CSMD1 variant in vivo in healthy participants in a magnetic resonance imaging (MRI) study comprised of both fMRI of spatial working memory (N = 50) and a voxel-based morphometry investigation of grey and white matter (WM) volume (N = 150). Analyses of these data indicated that the risk "A" allele was associated with comparatively reduced cortical activations in BA18, that is, middle occipital gyrus and cuneus; posterior brain regions that support maintenance processes during performance of a spatial working memory task. Conversely, there was an absence of significant structural differences in brain volume (i.e., grey or WM). In accordance with previous evidence, these data suggest that CSMD1 may mediate brain function related to cognitive processes (i.e., executive function); with the relatively deleterious effects of the identified "A" risk allele on brain activity possibly constituting part of the mechanism by which CSMD1 increases schizophrenia risk.

  17. Associative learning shapes the neural code for stimulus magnitude in primary auditory cortex.

    Science.gov (United States)

    Polley, Daniel B; Heiser, Marc A; Blake, David T; Schreiner, Christoph E; Merzenich, Michael M

    2004-11-16

    Since the dawn of experimental psychology, researchers have sought an understanding of the fundamental relationship between the amplitude of sensory stimuli and the magnitudes of their perceptual representations. Contemporary theories support the view that magnitude is encoded by a linear increase in firing rate established in the primary afferent pathways. In the present study, we have investigated sound intensity coding in the rat primary auditory cortex (AI) and describe its plasticity by following paired stimulus reinforcement and instrumental conditioning paradigms. In trained animals, population-response strengths in AI became more strongly nonlinear with increasing stimulus intensity. Individual AI responses became selective to more restricted ranges of sound intensities and, as a population, represented a broader range of preferred sound levels. These experiments demonstrate that the representation of stimulus magnitude can be powerfully reshaped by associative learning processes and suggest that the code for sound intensity within AI can be derived from intensity-tuned neurons that change, rather than simply increase, their firing rates in proportion to increases in sound intensity.

  18. Rumination in major depressive disorder is associated with impaired neural activation during conflict monitoring

    Directory of Open Access Journals (Sweden)

    Brandon L Alderman

    2015-05-01

    Full Text Available Individuals with major depressive disorder (MDD often ruminate about past experiences, especially those with negative content. These repetitive thoughts may interfere with cognitive processes related to attention and conflict monitoring. However, the temporal nature of these processes as reflected in event-related potentials (ERPs has not been well described. We examined behavioral and ERP indices of conflict monitoring during a modified flanker task and the allocation of attention during an attentional blink (AB task in 33 individuals with MDD and 36 healthy controls, and whether their behavioral performance and ERPs varied with level of rumination. N2 amplitude elicited by the flanker task was significantly reduced in participants with MDD compared to healthy controls. Level of self-reported rumination was also correlated with N2 amplitude. In contrast, P3 amplitude during the AB task was not significantly different between groups, nor was it correlated with rumination. No significant differences were found in behavioral task performance measures between groups or by rumination levels. These findings suggest that rumination in MDD is associated with select deficits in cognitive control, particularly related to conflict monitoring.

  19. Age related-changes in the neural basis of self-generation in verbal paired associate learning.

    Science.gov (United States)

    Vannest, Jennifer; Maloney, Thomas; Kay, Benjamin; Siegel, Miriam; Allendorfer, Jane B; Banks, Christi; Altaye, Mekibib; Szaflarski, Jerzy P

    2015-01-01

    Verbal information is better retained when it is self-generated rather than when it is received passively. The application of self-generation procedures has been found to improve memory in healthy elderly and in individuals with impaired cognition. Overall, the available studies support the notion that active participation in verbal encoding engages memory mechanisms that supplement those used during passive observation. Thus, the objective of this study was to investigate the age-related changes in the neural mechanisms involved in the encoding of paired-associates using a self-generation method that has been shown to improve memory performance across the lifespan. Subjects were 113 healthy right-handed adults (Edinburgh Handedness Inventory >50; 67 females) ages 18-76, native speakers of English with no history of neurological or psychiatric disorders. Subjects underwent fMRI at 3 T while performing didactic learning ("read") or self-generation learning ("generate") of 30 word pairs per condition. After fMRI, recognition memory for the second word in each pair was evaluated outside of the scanner. On the post-fMRI testing more "generate" words were correctly recognized than "read" words (p adults recognizing the "generated" words less accurately (p age, but the benefit from self-generation remained consistently significant across ages. Independent component analysis of the neuroimaging data revealed an extensive set of components engaged in self-generation learning compared with didactic learning, and identified areas that were associated with age-related changes independent of performance.

  20. Delayed rectifier and A-type potassium channels associated with Kv 2.1 and Kv 4.3 expression in embryonic rat neural progenitor cells.

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    Dean O Smith

    Full Text Available BACKGROUND: Because of the importance of voltage-activated K(+ channels during embryonic development and in cell proliferation, we present here the first description of these channels in E15 rat embryonic neural progenitor cells derived from the subventricular zone (SVZ. Activation, inactivation, and single-channel conductance properties of recorded progenitor cells were compared with those obtained by others when these Kv gene products were expressed in oocytes. METHODOLOGY/PRINCIPAL FINDINGS: Neural progenitor cells derived from the subventricular zone of E15 embryonic rats were cultured under conditions that did not promote differentiation. Immunocytochemical and Western blot assays for nestin expression indicated that almost all of the cells available for recording expressed this intermediate filament protein, which is generally accepted as a marker for uncommitted embryonic neural progenitor cells. However, a very small numbers of the cells expressed GFAP, a marker for astrocytes, O4, a marker for immature oligodendrocytes, and betaIII-tubulin, a marker for neurons. Using immunocytochemistry and Western blots, we detected consistently the expression of Kv2.1, and 4.3. In whole-cell mode, we recorded two outward currents, a delayed rectifier and an A-type current. CONCLUSIONS/SIGNIFICANCE: We conclude that Kv2.1, and 4.3 are expressed in E15 SVZ neural progenitor cells, and we propose that they may be associated with the delayed-rectifier and the A-type currents, respectively, that we recorded. These results demonstrate the early expression of delayed rectifier and A-type K(+ currents and channels in embryonic neural progenitor cells prior to the differentiation of these cells.

  1. Age related-changes in the neural basis of self-generation in verbal paired associate learning

    Directory of Open Access Journals (Sweden)

    Jennifer Vannest

    2015-01-01

    Full Text Available Verbal information is better retained when it is self-generated rather than when it is received passively. The application of self-generation procedures has been found to improve memory in healthy elderly and in individuals with impaired cognition. Overall, the available studies support the notion that active participation in verbal encoding engages memory mechanisms that supplement those used during passive observation. Thus, the objective of this study was to investigate the age-related changes in the neural mechanisms involved in the encoding of paired-associates using a self-generation method that has been shown to improve memory performance across the lifespan. Subjects were 113 healthy right-handed adults (Edinburgh Handedness Inventory >50; 67 females ages 18–76, native speakers of English with no history of neurological or psychiatric disorders. Subjects underwent fMRI at 3 T while performing didactic learning (“read” or self-generation learning (“generate” of 30 word pairs per condition. After fMRI, recognition memory for the second word in each pair was evaluated outside of the scanner. On the post-fMRI testing more “generate” words were correctly recognized than “read” words (p < 0.001 with older adults recognizing the “generated” words less accurately (p < 0.05. Independent component analysis of fMRI data identified task-related brain networks. Several components were positively correlated with the task reflecting multiple cognitive processes involved in self-generated encoding; other components correlated negatively with the task, including components of the default-mode network. Overall, memory performance on generated words decreased with age, but the benefit from self-generation remained consistently significant across ages. Independent component analysis of the neuroimaging data revealed an extensive set of components engaged in self-generation learning compared with didactic learning, and identified

  2. Revealing the neural networks associated with processing of natural social interaction and the related effects of actor-orientation and face-visibility.

    Science.gov (United States)

    Saggar, Manish; Shelly, Elizabeth Walter; Lepage, Jean-Francois; Hoeft, Fumiko; Reiss, Allan L

    2014-01-01

    Understanding the intentions and desires of those around us is vital for adapting to a dynamic social environment. In this paper, a novel event-related functional Magnetic Resonance Imaging (fMRI) paradigm with dynamic and natural stimuli (2s video clips) was developed to directly examine the neural networks associated with processing of gestures with social intent as compared to nonsocial intent. When comparing social to nonsocial gestures, increased activation in both the mentalizing (or theory of mind) and amygdala networks was found. As a secondary aim, a factor of actor-orientation was included in the paradigm to examine how the neural mechanisms differ with respect to personal engagement during a social interaction versus passively observing an interaction. Activity in the lateral occipital cortex and precentral gyrus was found sensitive to actor-orientation during social interactions. Lastly, by manipulating face-visibility we tested whether facial information alone is the primary driver of neural activation differences observed between social and nonsocial gestures. We discovered that activity in the posterior superior temporal sulcus (pSTS) and fusiform gyrus (FFG) was partially driven by observing facial expressions during social gestures. Altogether, using multiple factors associated with processing of natural social interaction, we conceptually advance our understanding of how social stimuli is processed in the brain and discuss the application of this paradigm to clinical populations where atypical social cognition is manifested as a key symptom. © 2013.

  3. Cell Junction Pathology of Neural Stem Cells Is Associated With Ventricular Zone Disruption, Hydrocephalus, and Abnormal Neurogenesis

    NARCIS (Netherlands)

    Montserrat Guerra, Maria; Henzi, Roberto; Ortloff, Alexander; Lichtin, Nicole; Vio, Karin; Jimenez, Antonio J.; Dolores Dominguez-Pinos, Maria; Gonzalez, Cesar; Clara Jara, Maria; Hinostroza, Fernando; Rodriguez, Sara; Jara, Maryoris; Ortega, Eduardo; Guerra, Francisco; Sival, Deborah A.; den Dunnen, Wilfred F. A.; Perez-Figares, Jose M.; McAllister, James P.; Johanson, Conrad E.; Rodriguez, Esteban M.

    2015-01-01

    Fetal-onset hydrocephalus affects 1 to 3 per 1,000 live births. It is not only a disorder of cerebrospinal fluid dynamics but also a brain disorder that corrective surgery does not ameliorate. We hypothesized that cell junction abnormalities of neural stem cells (NSCs) lead to the inseparable phenom

  4. A Smarter Brain Is Associated with Stronger Neural Interaction in Healthy Young Females: A Resting EEG Coherence Study

    Science.gov (United States)

    Lee, Tien-Wen; Wu, Yu-Te; Yu, Younger W.-Y.; Wu, Hung-Chi; Chen, Tai-Jui

    2012-01-01

    General intelligence, the "g" factor, is a major issue in psychology and neuroscience. However, the neural mechanism of the "g" factor is still not clear. It is suggested that the "g" factor should be non-modular (a property across the brain) and show good colinearity with various cognitive tests. This study examines…

  5. Molecular Study on Differentiation-Associated Genes Involved in Both Malignant Progression of Glioma and Differentiation of Human Fetal Neural Stem Cells

    Institute of Scientific and Technical Information of China (English)

    Jun Dong; Yinyan Wu; Qiang Huang; Fei Wang; Aidong Wang; Qing Lan

    2006-01-01

    OBJECTIVE It is unclear whether differentiation disturbances or deregulation of neural stem cells (NSCs) are the early key steps for gliomagenesis and tumor development. Furthermore, relevant molecular changes and gene-regulation pathways are unknown. This study focused on screening and validating differentiation-associated genes from both human NSCs and glioma cells with malignant progression, for the purpose of offering an experimental basis for the cellular origin of gilomas and molecular pathology of gliomagenesis.METHODS The differential-gene expression profiles of malignant progression of gliomas were established, then the differentiation related genes were screened out with a bioinformatics analysis. Expression levels of these genes was further analyzed in cultured human fetal NSCs undergoing differentiation processes with a semi-quantitative RT-PCR assay.RESULTS Eight genes were screened out from the gene-expression profiling of which the expression levels were associated with the differentiation processes of NSCs, namely CXCR4, TN-C, GLT1, IL1-RI, EGFR8, CDC2, Ndr3 and MAPKK4. Three of them, ie., GLT1, CDC2 and MAPKK4, were further analyzed, showing that expression levels decreased with the differentiation processes of NSCs, and increased with the malignant progression of ganglioglioma.CONCLUSION Three differentiation associated genes were found negatively associated with NSCs differentiation and positively associated with malignant progression of gliomas, suggesting that differentiation disturbances of neural stem ceils may be involved in oncogenesis, and that further studies on their roles in gliomagenesis should be conducted.

  6. Genome-wide association mapping in dogs enables identification of the homeobox gene, NKX2-8, as a genetic component of neural tube defects in humans.

    Directory of Open Access Journals (Sweden)

    Noa Safra

    Full Text Available Neural tube defects (NTDs is a general term for central nervous system malformations secondary to a failure of closure or development of the neural tube. The resulting pathologies may involve the brain, spinal cord and/or vertebral column, in addition to associated structures such as soft tissue or skin. The condition is reported among the more common birth defects in humans, leading to significant infant morbidity and mortality. The etiology remains poorly understood but genetic, nutritional, environmental factors, or a combination of these, are known to play a role in the development of NTDs. The variable conditions associated with NTDs occur naturally in dogs, and have been previously reported in the Weimaraner breed. Taking advantage of the strong linkage-disequilibrium within dog breeds we performed genome-wide association analysis and mapped a genomic region for spinal dysraphism, a presumed NTD, using 4 affected and 96 unaffected Weimaraners. The associated region on canine chromosome 8 (pgenome  =3.0 × 10(-5, after 100,000 permutations, encodes 18 genes, including NKX2-8, a homeobox gene which is expressed in the developing neural tube. Sequencing NKX2-8 in affected Weimaraners revealed a G to AA frameshift mutation within exon 2 of the gene, resulting in a premature stop codon that is predicted to produce a truncated protein. The exons of NKX2-8 were sequenced in human patients with spina bifida and rare variants (rs61755040 and rs10135525 were found to be significantly over-represented (p=0.036. This is the first documentation of a potential role for NKX2-8 in the etiology of NTDs, made possible by investigating the molecular basis of naturally occurring mutations in dogs.

  7. Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns.

    Directory of Open Access Journals (Sweden)

    Hiroyuki Akama

    Full Text Available In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF. This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk and co-occurrence adjustment (degree balance and distribution. We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

  8. Impact of the Autism-Associated Long Noncoding RNA MSNP1AS on Neuronal Architecture and Gene Expression in Human Neural Progenitor Cells

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    Jessica J. DeWitt

    2016-09-01

    Full Text Available We previously identified the long noncoding RNA (lncRNA MSNP1AS (moesin pseudogene 1, antisense as a functional element revealed by genome wide significant association with autism spectrum disorder (ASD. MSNP1AS expression was increased in the postmortem cerebral cortex of individuals with ASD and particularly in individuals with the ASD-associated genetic markers on chromosome 5p14.1. Here, we mimicked the overexpression of MSNP1AS observed in postmortem ASD cerebral cortex in human neural progenitor cell lines to determine the impact on neurite complexity and gene expression. ReNcell CX and SK-N-SH were transfected with an overexpression vector containing full-length MSNP1AS. Neuronal complexity was determined by the number and length of neuronal processes. Gene expression was determined by strand-specific RNA sequencing. MSNP1AS overexpression decreased neurite number and neurite length in both human neural progenitor cell lines. RNA sequencing revealed changes in gene expression in proteins involved in two biological processes: protein synthesis and chromatin remodeling. These data indicate that overexpression of the ASD-associated lncRNA MSNP1AS alters the number and length of neuronal processes. The mechanisms by which MSNP1AS overexpression impacts neuronal differentiation may involve protein synthesis and chromatin structure. These same biological processes are also implicated by rare mutations associated with ASD, suggesting convergent mechanisms.

  9. Human bone marrow harbors cells with neural crest-associated characteristics like human adipose and dermis tissues.

    Science.gov (United States)

    Coste, Cécile; Neirinckx, Virginie; Sharma, Anil; Agirman, Gulistan; Rogister, Bernard; Foguenne, Jacques; Lallemend, François; Gothot, André; Wislet, Sabine

    2017-01-01

    Adult neural crest stem-derived cells (NCSC) are of extraordinary high plasticity and promising candidates for use in regenerative medicine. Several locations such as skin, adipose tissue, dental pulp or bone marrow have been described in rodent, as sources of NCSC. However, very little information is available concerning their correspondence in human tissues, and more precisely for human bone marrow. The main objective of this study was therefore to characterize NCSC from adult human bone marrow. In this purpose, we compared human bone marrow stromal cells to human adipose tissue and dermis, already described for containing NCSC. We performed comparative analyses in terms of gene and protein expression as well as functional characterizations. It appeared that human bone marrow, similarly to adipose tissue and dermis, contains NESTIN+ / SOX9+ / TWIST+ / SLUG+ / P75NTR+ / BRN3A+/ MSI1+/ SNAIL1+ cells and were able to differentiate into melanocytes, Schwann cells and neurons. Moreover, when injected into chicken embryos, all those cells were able to migrate and follow endogenous neural crest migration pathways. Altogether, the phenotypic characterization and migration abilities strongly suggest the presence of neural crest-derived cells in human adult bone marrow.

  10. Variation in TREK1 gene linked to depression-resistant phenotype is associated with potentiated neural responses to rewards in humans

    Science.gov (United States)

    Dillon, Daniel G.; Bogdan, Ryan; Fagerness, Jesen; Holmes, Avram J.; Perlis, Roy H.; Pizzagalli, Diego A.

    2010-01-01

    The TREK1 gene has been linked to a depression-resistant phenotype in rodents and antidepressant response in humans, but the neural mechanisms underlying these links are unclear. Because TREK1 is expressed in reward-related basal ganglia regions, it has been hypothesized that TREK1 genetic variation may be associated with anhedonic symptoms of depression. To investigate whether TREK1 genetic variation influences reward processing, we genotyped healthy individuals (n = 31) who completed a monetary incentive delay task during functional magnetic resonance imaging (fMRI). Three genotypes previously linked to positive antidepressant response were associated with potentiated basal ganglia activity to gains, but did not influence responses to penalties or no change feedback. TREK1 genetic variations did not affect basal ganglia volume, and fMRI group differences were confirmed when accounting for self-report measures of anhedonia. In addition, the total number of “protective” TREK1 alleles was associated with stronger responses to gains in several other reward-related regions, including the dorsal anterior cingulate cortex, orbitofrontal cortex, and mesial prefrontal cortex. In control analyses, associations between basal ganglia responses to gains and functional polymorphisms in the dopamine transporter (DAT1) and catechol-O-methyltransferase (COMT) genes were also explored. Results revealed that TREK1 and DAT/COMT genotypes were independently related to basal ganglia responses to gains. These findings indicate that TREK1 genotypes are associated with individual differences in reward-related brain activity. Future studies in depressed samples should evaluate whether variation in neural responses to rewards may contribute to the association between TREK1 and antidepressant response in humans. PMID:19621370

  11. Maladaptive Sexual Behavior Following Concurrent Methamphetamine and Sexual Experience in Male Rats is Associated with Altered Neural Activity in Frontal Cortex.

    Science.gov (United States)

    Kuiper, Lindsey B; Frohmader, Karla S; Coolen, Lique M

    2017-09-01

    The use of psychostimulants is often associated with hypersexuality, and psychostimulant users have identified the effects of drug on sexual behavior as a reason for further use. It was previously demonstrated in male rats that methamphetamine (Meth), when administered concurrently with sexual behavior results in impairment of inhibition of sexual behavior in a conditioned sex aversion (CSA) paradigm where mating is paired with illness. This is indicative of maladaptive sex behavior following Meth and sex experience. The present study examined the neural pathways activated during inhibition of sexual behavior in male rats and the effects of concurrent Meth and sexual behavior on neural activity, using ERK phosphorylation (pERK). First, exposure to conditioned aversive stimuli in males trained to inhibit sexual behavior in the CSA paradigm increased pERK expression in medial prefrontal (mPFC), orbitofrontal cortex (OFC) and areas in striatum and amygdala. Second, effects of concurrent Meth and sex experience were tested in males that were exposed to four daily sessions of concurrent Meth (1 mg/kg) or saline and mating and subsequently exposed to CSA one week after last treatment. Meth and mating-treated males showed significant impairment of inhibition of mating, higher pERK expression under baseline conditions, and disrupted pERK induction by exposure to the conditioned aversive stimuli in mPFC and OFC. These alterations of pERK occurred in CaMKII-expressing neurons, suggesting changes in efferent projections of these areas. Altogether, these data show that concurrent Meth and mating experience causes maladapative sexual behavior that is associated with alterations in neural activation in mPFC and OFC.

  12. Extracellular HMGB1 Modulates Glutamate Metabolism Associated with Kainic Acid-Induced Epilepsy-Like Hyperactivity in Primary Rat Neural Cells

    Directory of Open Access Journals (Sweden)

    Yuji Kaneko

    2017-02-01

    Full Text Available Background/Aims: Neuroinflammatory processes have been implicated in the pathophysiology of seizure/epilepsy. High mobility group box 1 (HMGB1, a non-histone DNA binding protein, behaves like an inflammatory cytokine in response to epileptogenic insults. Kainic acid (KA is an excitotoxic reagent commonly used to induce epilepsy in rodents. However, the molecular mechanism by which KA-induced HMGB1 affords the initiation of epilepsy, especially the role of extracellular HMGB1 in neurotransmitter expression, remains to be elucidated. Methods: Experimental early stage of epilepsy-related hyperexcitability was induced in primary rat neural cells (PRNCs by KA administration. We measured the localization of HMGB1, cell viability, mitochondrial activity, and expression level of glutamate metabolism-associated enzymes. Results: KA induced the translocation of HMGB1 from nucleus to cytosol, and its release from the neural cells. The translocation is associated with post-translational modifications. An increase in extracellular HMGB1 decreased PRNC cell viability and mitochondrial activity, downregulated expression of glutamate decarboxylase67 (GAD67 and glutamate dehydrogenase (GLUD1/2, and increased intracellular glutamate concentration and major histocompatibility complex II (MHC II level. Conclusions: That a surge in extracellular HMGB1 approximated seizure initiation suggests a key pathophysiological contribution of HMGB1 to the onset of epilepsy-related hyperexcitability.

  13. Neural reactivity to monetary rewards and losses in childhood: longitudinal and concurrent associations with observed and self-reported positive emotionality.

    Science.gov (United States)

    Kujawa, Autumn; Proudfit, Greg Hajcak; Kessel, Ellen M; Dyson, Margaret; Olino, Thomas; Klein, Daniel N

    2015-01-01

    Reward reactivity and positive emotion are key components of a theoretical, early-emerging approach motivational system, yet few studies have examined associations between positive emotion and neural reactivity to reward across development. In this multi-method prospective study, we examined the association of laboratory observations of positive emotionality (PE) at age 3 and self-reported positive affect (PA) at age 9 with an event-related potential component sensitive to the relative response to winning vs. losing money, the feedback negativity (ΔFN), at age 9 (N=381). Males had a larger ΔFN than females, and both greater observed PE at age 3 and self-reported PA at age 9 significantly, but modestly, predicted an enhanced ΔFN at age 9. Negative emotionality and behavioral inhibition did not predict ΔFN. Results contribute to understanding the neural correlates of PE and suggest that the FN and PE may be related to the same biobehavioral approach system.

  14. Neural processing of emotional facial and semantic expressions in euthymic bipolar disorder (BD and its association with theory of mind (ToM.

    Directory of Open Access Journals (Sweden)

    Agustin Ibanez

    Full Text Available BACKGROUND: Adults with bipolar disorder (BD have cognitive impairments that affect face processing and social cognition. However, it remains unknown whether these deficits in euthymic BD have impaired brain markers of emotional processing. METHODOLOGY/PRINCIPAL FINDINGS: We recruited twenty six participants, 13 controls subjects with an equal number of euthymic BD participants. We used an event-related potential (ERP assessment of a dual valence task (DVT, in which faces (angry and happy, words (pleasant and unpleasant, and face-word simultaneous combinations are presented to test the effects of the stimulus type (face vs word and valence (positive vs. negative. All participants received clinical, neuropsychological and social cognition evaluations. ERP analysis revealed that both groups showed N170 modulation of stimulus type effects (face > word. BD patients exhibited reduced and enhanced N170 to facial and semantic valence, respectively. The neural source estimation of N170 was a posterior section of the fusiform gyrus (FG, including the face fusiform area (FFA. Neural generators of N170 for faces (FG and FFA were reduced in BD. In these patients, N170 modulation was associated with social cognition (theory of mind. CONCLUSIONS/SIGNIFICANCE: This is the first report of euthymic BD exhibiting abnormal N170 emotional discrimination associated with theory of mind impairments.

  15. Enhanced food anticipatory activity associated with enhanced activation of extrahypothalamic neural pathways in serotonin2C receptor null mutant mice.

    Directory of Open Access Journals (Sweden)

    Jennifer L Hsu

    Full Text Available The ability to entrain circadian rhythms to food availability is important for survival. Food-entrained circadian rhythms are characterized by increased locomotor activity in anticipation of food availability (food anticipatory activity. However, the molecular components and neural circuitry underlying the regulation of food anticipatory activity remain unclear. Here we show that serotonin(2C receptor (5-HT2CR null mutant mice subjected to a daytime restricted feeding schedule exhibit enhanced food anticipatory activity compared to wild-type littermates, without phenotypic differences in the impact of restricted feeding on food consumption, body weight loss, or blood glucose levels. Moreover, we show that the enhanced food anticipatory activity in 5-HT2CR null mutant mice develops independent of external light cues and persists during two days of total food deprivation, indicating that food anticipatory activity in 5-HT2CR null mutant mice reflects the locomotor output of a food-entrainable oscillator. Whereas restricted feeding induces c-fos expression to a similar extent in hypothalamic nuclei of wild-type and null mutant animals, it produces enhanced expression in the nucleus accumbens and other extrahypothalamic regions of null mutant mice relative to wild-type subjects. These data suggest that 5-HT2CRs gate food anticipatory activity through mechanisms involving extrahypothalamic neural pathways.

  16. 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.

  17. Neural correlates of memory encoding and recognition for own-race and other-race faces in an associative-memory task.

    Science.gov (United States)

    Herzmann, Grit; Minor, Greta; Adkins, Makenzie

    2017-01-15

    The ability to recognize faces of family members, friends, and acquaintances plays an important role in our daily interactions. The other-race effect is the reduced ability to recognize other-race faces as compared to own-race faces. Previous studies showed different patterns of event-related potentials (ERPs) associated with recollection and familiarity during memory encoding (i.e., Dm) and recognition (i.e., parietal old/new effect) for own-race and other-race faces in a subjective-recollection task (remember-know judgments). The present study investigated the same neural correlates of the other-race effect in an associative-memory task, in which Caucasian and East Asian participants learned and recognized own-race and other-race faces along with background colors. Participants made more false alarms for other-race faces indicating lower memory performance. During the study phase, subsequently recognized other-race faces (with and without correct background information) elicited more positive mean amplitudes than own-race faces, suggesting increased neural activation during encoding of other-race faces. During the test phase, recollection-related old/new effects dissociated between own-race and other-race faces. Old/new effects were significant only for own-race but not for other-race faces, indicating that recognition only of own-race faces was supported by recollection and led to more detailed memory retrieval. Most of these results replicated previous studies that used a subjective-recollection task. Our study also showed that the increased demand on memory encoding during an associative-memory task led to Dm patterns that indicated similarly deep memory encoding for own-race and other-race faces.

  18. 恐惧联结的习得及其脑机制研究%Formation of Associative Learning of Fear and Its Neural Mechanisms

    Institute of Scientific and Technical Information of China (English)

    刘宏艳; 王倩; 胡治国

    2011-01-01

    Associative learning of fear is crucial for human survival and adaptation. In this review, we first introduced the three ways that leads to the development of associative learning of fear, including self-experiencing, social observation and verbal instruction. Secondly, we showed that the acquisition of associative learning of fear had important influence on the cognitive and social behaviors. The extant studies on the neural basis of associative learning of fear, including lesion studies, neuroimaging studies and studies on nucleus neurons in certain areas, demonstrated the crucial role of the amygdala and hippocampus in the associative learning of fear.%恐惧联结学习是保证有机体生存和适应的重要手段,主要表现为以下三种方式:亲身体验、社会观察和言语指导,习得之后会对认知和社会功能产生重要影响.脑损伤、脑功能成像及神经核团水平的研究均表明,恐惧联结的习得主要与杏仁核和海马等脑区有关.

  19. Asociación inversa entre asma y defectos del tubo neural: estudio ecológico binacional Inverse association between asthma and neural tube defects: a binational ecological study

    Directory of Open Access Journals (Sweden)

    Mario H Vargas

    2012-08-01

    Full Text Available OBJETIVO: Los donadores de metilo como el ácido fólico previenen defectos del tubo neural (DTN, pero estudios recientes sugieren que también favorecen el desarrollo de asma. En este trabajo exploramos una posible asociación ecológica entre DTN y asma. MATERIAL Y MÉTODOS: Se consultaron bases de datos de México y EUA para obtener información sobre distribución geográfica (por estado y tendencia temporal (por año de DTN y asma. RESULTADOS: Los estados con menor frecuencia de DTN tuvieron mayor frecuencia de asma, tanto en México (rS=-0.48, p=0.005 como en EUA (rS=-0.39, p=0.005. Las tendencias temporales también mostraron correlación inversa en México (1997-2007, rS=-0.73, p=0.01 y EUA (1979-1998, rS=-0.91, pOBJECTIVE: Dietary intake of methyl donors such as folic acid prevents neural tube defects (NTD, but recent studies showed that it might also favor the development of asthma. In this work a possible ecological association between NTD and asthma was explored. MATERIAL AND METHODS: Data bases from Mexico and the United States (US were reviewed to obtain information about geographical distribution (by state and temporal trends (by year of NTD and asthma. RESULTS: Those states with the lowest frequency of NTD had the highest frequency of asthma, both in Mexico (rS=-0.48, p=0.005 and US (rS=-0.39, p=0.005. Temporal trends also showed an inverse correlation in Mexico (1997-2007, rS=-0.73, p=0.01 and US (1979-1998, rS=-0.91, p<0.001. CONCLUSIONS: In both countries the frequency of asthma inversely correlated with the frequency of NTD, both in geographical distribution and annual trends, giving support to the possibility that methyl donors intake in diet or supplements is influencing the asthma frequency.

  20. Changes in neural circuitry associated with depression at pre-clinical, pre-motor and early motor phases of Parkinson's disease.

    Science.gov (United States)

    Borgonovo, Janina; Allende-Castro, Camilo; Laliena, Almudena; Guerrero, Néstor; Silva, Hernán; Concha, Miguel L

    2017-02-01

    Although Parkinson's Disease (PD) is mostly considered a motor disorder, it can present at early stages as a non-motor pathology. Among the non-motor clinical manifestations, depression shows a high prevalence and can be one of the first clinical signs to appear, even a decade before the onset of motor symptoms. Here, we review the evidence of early dysfunction in neural circuitry associated with depression in the context of PD, focusing on pre-clinical, pre-motor and early motor phases of the disease. In the pre-clinical phase, structural and functional changes in the substantia nigra, basal ganglia and limbic structures are already observed. Some of these changes are linked to motor compensation mechanisms while others correspond to pathological processes common to PD and depression and thus could underlie the appearance of depressive symptoms during the pre-motor phase. Studies of the early motor phase (less than five years post diagnosis) reveal an association between the extent of damage in different monoaminergic systems and the appearance of emotional disorders. We propose that the limbic loop of the basal ganglia and the lateral habenula play key roles in the early genesis of depression in PD. Alterations in the neural circuitry linked with emotional control might be sensitive markers of the ongoing neurodegenerative process and thus may serve to facilitate an early diagnosis of this disease. To take advantage of this, we need to improve the clinical criteria and develop biomarkers to identify depression, which could be used to determine individuals at risk to develop PD.

  1. Mutations in the COPII vesicle component gene SEC24B are associated with human neural tube defects.

    Science.gov (United States)

    Yang, Xue-Yan; Zhou, Xiang-Yu; Wang, Qing Qing; Li, Hong; Chen, Ying; Lei, Yun-Ping; Ma, Xiao-Hang; Kong, Pan; Shi, Yan; Jin, Li; Zhang, Ting; Wang, Hong-Yan

    2013-08-01

    Neural tube defects (NTDs) are severe birth malformations that affect one in 1,000 live births. Recently, mutations in the planar cell polarity (PCP) pathway genes had been implicated in the pathogenesis of NTDs in both the mouse model and in human cohorts. Mouse models indicate that the homozygous disruption of Sec24b, which mediates the ER-to-Golgi transportation of the core PCP gene Vangl2 as a component of the COPII vesicle, will result in craniorachischisis. In this study, we found four rare missense heterozygous SEC24B mutations (p.Phe227Ser, p.Phe682Leu, p.Arg1248Gln, and p.Ala1251Gly) in NTDs cases that were absent in all controls. Among them, p.Phe227Ser and p.Phe682Leu affected its protein stability and physical interaction with VANGL2. Three variants (p.Phe227Ser, p.Arg1248Gln, and p.Ala1251Gly) were demonstrated to affect VANGL2 subcellular localization in cultured cells. Further functional analysis in the zebrafish including overexpression and dosage-dependent rescue study suggested that these four mutations all displayed loss-of-function effects compared with wild-type SEC24B. Our study demonstrated that functional mutations in SEC24B might contribute to the etiology of a subset of human NTDs and further expanded our knowledge of the role of PCP pathway-related genes in the pathogenesis of human NTDs.

  2. Dehydroepiandrosterone (DHEA) inhibition of monocyte binding by vascular endothelium is associated with sialylation of neural cell adhesion molecule.

    Science.gov (United States)

    Curatola, Anna-Maria; Huang, Kui; Naftolin, Frederick

    2012-01-01

    Adhesion of monocytes to vascular endothelium is necessary for atheroma formation. This adhesion requires binding of endothelial neural cell adhesion molecule (NCAM) to monocyte NCAM. NCAM:NCAM binding is blocked by sialylation of NCAM (polysialylated NCAM; PSA-NCAM). Since estradiol (E2) and dihydrotestosterone (DHT) induced PSA-NCAM and decreased monocyte adhesion, in consideration of possible clinical applications we tested whether their prohormone dehydroepiandrosterone (DHEA) has similar effects. (1) DHEA was administered to cultured human coronary artery endothelial cells (HCAECs) from men and women. Monocyte binding was assessed using fluorescence-labeled monocytes. (2) HCEACs were incubated with E2, DHT, DHEA alone, or with trilostane, fulvestrant or flutamide. Expression of PSA-NCAM was assessed by immunohistochemistry and Western blotting. Dehydroepiandrosterone inhibited monocyte adhesion to HCAECs by ≥50% (P DHEA's inhibition of monocyte binding appeared to be gender dependent. The DHEA-induced expression of PSA-NCAM was completely blocked by trilostane. In these preliminary in vitro studies, DHEA increased PSA-NCAM expression and inhibited monocyte binding in an estrogen- and androgen receptor-dependent manner. Dehydroepiandrosteroneappears to act via its end metabolites, E2 and DHT. Dehydroepiandrosterone could furnish clinical prevention against atherogenesis and arteriosclerosis.

  3. Neural basis of understanding communicative actions: Changes associated with knowing the actor's intention and the meanings of the actions.

    Science.gov (United States)

    Möttönen, Riikka; Farmer, Harry; Watkins, Kate E

    2016-01-29

    People can communicate by using hand actions, e.g., signs. Understanding communicative actions requires that the observer knows that the actor has an intention to communicate and the meanings of the actions. Here, we investigated how this prior knowledge affects processing of observed actions. We used functional MRI to determine changes in action processing when non-signers were told that the observed actions are communicative (i.e., signs) and learned the meanings of half of the actions. Processing of hand actions activated the left and right inferior frontal gyrus (IFG, BA 44 and 45) when the communicative intention of the actor was known, even when the meanings of the actions remained unknown. These regions were not active when the observers did not know about the communicative nature of the hand actions. These findings suggest that the left and right IFG play a role in understanding the intention of the actor, but do not process visuospatial features of the communicative actions. Knowing the meanings of the hand actions further enhanced activity in the anterior part of the IFG (BA 45), the inferior parietal lobule and posterior inferior and middle temporal gyri in the left hemisphere. These left-hemisphere language regions could provide a link between meanings and observed actions. In sum, the findings provide evidence for the segregation of the networks involved in the neural processing of visuospatial features of communicative hand actions and those involved in understanding the actor's intention and the meanings of the actions.

  4. Convolutional neural network approach for enhanced capture of breast parenchymal complexity patterns associated with breast cancer risk

    Science.gov (United States)

    Oustimov, Andrew; Gastounioti, Aimilia; Hsieh, Meng-Kang; Pantalone, Lauren; Conant, Emily F.; Kontos, Despina

    2017-03-01

    We assess the feasibility of a parenchymal texture feature fusion approach, utilizing a convolutional neural network (ConvNet) architecture, to benefit breast cancer risk assessment. Hypothesizing that by capturing sparse, subtle interactions between localized motifs present in two-dimensional texture feature maps derived from mammographic images, a multitude of texture feature descriptors can be optimally reduced to five meta-features capable of serving as a basis on which a linear classifier, such as logistic regression, can efficiently assess breast cancer risk. We combine this methodology with our previously validated lattice-based strategy for parenchymal texture analysis and we evaluate the feasibility of this approach in a case-control study with 424 digital mammograms. In a randomized split-sample setting, we optimize our framework in training/validation sets (N=300) and evaluate its descriminatory performance in an independent test set (N=124). The discriminatory capacity is assessed in terms of the the area under the curve (AUC) of the receiver operator characteristic (ROC). The resulting meta-features exhibited strong classification capability in the test dataset (AUC = 0.90), outperforming conventional, non-fused, texture analysis which previously resulted in an AUC=0.85 on the same case-control dataset. Our results suggest that informative interactions between localized motifs exist and can be extracted and summarized via a fairly simple ConvNet architecture.

  5. ADM1-based modeling of methane production from acidified sweet sorghum extractin a two stage process

    DEFF Research Database (Denmark)

    Antonopoulou, Georgia; Gavala, Hariklia N.; Skiadas, Ioannis

    2012-01-01

    The present study focused on the application of the Anaerobic Digestion Model 1 οn the methane production from acidified sorghum extract generated from a hydrogen producing bioreactor in a two-stage anaerobic process. The kinetic parameters for hydrogen and volatile fatty acids consumption were...... estimated through fitting of the model equations to the data obtained from batch experiments. The simulation of the continuous reactor performance at all HRTs tested (20, 15 and 10d) was very satisfactory. Specifically, the largest deviation of the theoretical predictions against the experimental data...... was 12% for the methane production rate at the HRT of 20d while the deviation values for the 15 and 10 d HRT were 1.9% and 1.1%, respectively. The model predictions regarding pH, methane percentage in the gas phase and COD removal were in very good agreement with the experimental data with a deviation...

  6. ADM1-based modeling of methane production from acidified sweet sorghum extractin a two stage process

    DEFF Research Database (Denmark)

    Antonopoulou, Georgia; Gavala, Hariklia N.; Skiadas, Ioannis

    2012-01-01

    The present study focused on the application of the Anaerobic Digestion Model 1 οn the methane production from acidified sorghum extract generated from a hydrogen producing bioreactor in a two-stage anaerobic process. The kinetic parameters for hydrogen and volatile fatty acids consumption were...

  7. Neural networks with chaotic recursive nodes: techniques for the design of associative memories, contrast with Hopfield architectures, and extensions for time-dependent inputs.

    Science.gov (United States)

    Del-Moral-Hernandez, Emilio

    2003-01-01

    This paper addresses the coding and storage of information in neural architectures with bifurcating recursive nodes that exhibit chaotic dynamics. It describes architectures of coupled recursive processing elements (RPEs) used to store binary strings, discusses the choices of network parameters related to the coding of zeros and ones, and analyzes several aspects of the network operation in implementing associative memories through populations of logistic maps. Experiments for the performance evaluation of these memories are described, and results addressing the operation under digital noise (flipped bits) and analog noise added to the prompting pattern are presented and analyzed. Quantitative aspects related to the representation of binary strings through cyclic states are equated, and then related to the planning and analysis of several experiments. A simple pre-processing procedure useful in situations of prompting conditions with analog noise is proposed, and the resultant increase in recovery performance presented. The performance of the RPEs associative networks is contrasted with the performance of Hopfield associative memories, and the situations where the RPEs networks present significant superiority are identified. An extended version of the proposed architecture, which allows to address the issues of time-dependent inputs and analog inputs, is analyzed in detail. Experimental results are presented, and the role of this extended architecture in providing mechanisms for modular RPEs architectures is pointed out.

  8. Using a multi-port architecture of neural-net associative memory based on the equivalency paradigm for parallel cluster image analysis and self-learning

    Science.gov (United States)

    Krasilenko, Vladimir G.; Lazarev, Alexander A.; Grabovlyak, Sveta K.; Nikitovich, Diana V.

    2013-01-01

    We consider equivalency models, including matrix-matrix and matrix-tensor and with the dual adaptive-weighted correlation, multi-port neural-net auto-associative and hetero-associative memory (MP NN AAM and HAP), which are equivalency paradigm and the theoretical basis of our work. We make a brief overview of the possible implementations of the MP NN AAM and of their architectures proposed and investigated earlier by us. The main base unit of such architectures is a matrix-matrix or matrix-tensor equivalentor. We show that the MP NN AAM based on the equivalency paradigm and optoelectronic architectures with space-time integration and parallel-serial 2D images processing have advantages such as increased memory capacity (more than ten times of the number of neurons!), high performance in different modes (1010 - 1012 connections per second!) And the ability to process, store and associatively recognize highly correlated images. Next, we show that with minor modifications, such MP NN AAM can be successfully used for highperformance parallel clustering processing of images. We show simulation results of using these modifications for clustering and learning models and algorithms for cluster analysis of specific images and divide them into categories of the array. Show example of a cluster division of 32 images (40x32 pixels) letters and graphics for 12 clusters with simultaneous formation of the output-weighted space allocated images for each cluster. We discuss algorithms for learning and self-learning in such structures and their comparative evaluations based on Mathcad simulations are made. It is shown that, unlike the traditional Kohonen self-organizing maps, time of learning in the proposed structures of multi-port neuronet classifier/clusterizer (MP NN C) on the basis of equivalency paradigm, due to their multi-port, decreases by orders and can be, in some cases, just a few epochs. Estimates show that in the test clustering of 32 1280- element images into 12

  9. Automatic welding quality classification for the spot welding based on the Hopfield associative memory neural network and Chernoff face description of the electrode displacement signal features

    Science.gov (United States)

    Zhang, Hongjie; Hou, Yanyan; Zhao, Jian; Wang, Lijing; Xi, Tao; Li, Yafeng

    2017-02-01

    To develop an automatic welding quality classification method for the spot welding based on the Chernoff face image created by the electrode displacement signal features, an effective pattern feature extraction method was proposed by which the Chernoff face images were converted to binary ones, and each binary image could be characterized by a binary matrix. According to expression categories on the Chernoff face images, welding quality was classified into five levels and each level just corresponded to a kind of expression. The Hopfield associative memory neural network was used to build a welding quality classifier in which the pattern feature matrices of some weld samples with different welding quality levels were remembered as the stable states. When the pattern feature matrix of a test weld is input into the classifier, it can be converged to the most similar stable state through associative memory, thus, welding quality corresponding to this finally locked stable state can represent the welding quality of the test weld. The classification performance test results show that the proposed method significantly improves the applicability and efficiency of the Chernoff faces technique for spot welding quality evaluation and it is feasible, effective and reliable.

  10. Probing the neural correlates of associative memory formation : a parametrically analyzed event-related functional MRI study

    NARCIS (Netherlands)

    Tendolkar, I.; Arnold, J.F.; Petersson, K.M.; Weis, S.; Eijndhoven, P. van; Buitelaar, J.K.; Fernandez, G.

    2007-01-01

    The medial temporal lobe (MTL) is crucial for declarative memory formation, but the function of its subcomponents in associative memory formation remains controversial. Most functional imaging studies on this topic are based on a stepwise approach comparing a condition with and one without

  11. High expression of adenylate cyclase-associated protein 1 accelerates the proliferation, migration and invasion of neural glioma cells.

    Science.gov (United States)

    Bao, Zhen; Qiu, Xiaojun; Wang, Donglin; Ban, Na; Fan, Shaochen; Chen, Wenjuan; Sun, Jie; Xing, Weikang; Wang, Yunfeng; Cui, Gang

    2016-04-01

    Adenylate cyclase-associated protein 1 (CAP1), a conserved member of cyclase-associated proteins was reported to be associated with the proliferation, migration or invasion of the tumors of pancreas, breast and liver, and was involved in astrocyte proliferation after acute Traumatic Brain Injury (TBI). In this study, we sought to investigate the character of CAP1 in the pathological process of human glioma by detecting human glioma specimens and cell lines. 43 of 100 specimens showed high expression of CAP1 via immunohistochemistry. With statistics analysis, we found out the expression level of CAP1 was correlated with the WHO grades of human glioma and was great positively related to Ki-67 (p<0.01). In vitro, silencing CAP1 in U251 and U87MG, the glioma cell lines with the relatively higher expression of CAP1, induced the proliferation of the cells significantly retarded, migration and invasion as well. Obviously, our results indicated that CAP1 participated in the molecular pathological process of glioma indeed, and in a certain sense, CAP1 might be a potential and promising molecular target for glioma diagnosis and therapies in the future.

  12. HOPFIELD NEURAL NETWORK ASSOCIATIVE MEMORY ALGORITHM BASED ON MAPREDUCE MODEL%基于MapReduce模型的Hopfield神经网络联想记忆算法

    Institute of Scientific and Technical Information of China (English)

    曾俊

    2013-01-01

    Hopfield network is a widely used neural network for its excellent performance in associative memory and fault tolerant property.However,on cloud computing platform,it is not able to store high-dimensional mode in a single computer and to acquire good performance when come across massive data.Besides,the data storage in traditional associative memory networks is distributed,this enables the MapReduce structure can well solve the parallelisation and distribution problems.According to the principle above,we put forward an algorithm of MRHAM (MapReduce-based Hopfield Network for Associative Memory) which uses MapReduce architecture to implement largescale parallelised processing on traditional Hopfield associative memory algorithm.It is verified through experiment that the performance of MRHAM algorithm acquired in massive amount of data is better than that of the traditional Hopfield associative memory algorithm; this has important significance to massive data for content-based storage and associative memory.%Hopfield神经网络以良好的联想记忆功能、容错性而得到广泛的应用.然而,云计算平台下,面对海量数据时它并不能在单机上存储高维度模式以及获得良好的性能.另外,传统的联想记忆网络数据分布存储,使得MapReduce结构可以很好地解决并行化和分布性的问题.根据以上原理,提出一种MRHAM(MapReduce-based Hopfield Network for Association Memory)算法,对传统的Hopfield联想记忆算法采用MapReduce架构实现大规模并行化处理.通过实验验证在大规模数据量下获得比传统Hopfield联想记忆算法更好的性能,对于海量数据的基于内容存储、联想记忆有重要意义.

  13. Neural Networks

    Directory of Open Access Journals (Sweden)

    Schwindling Jerome

    2010-04-01

    Full Text Available This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p. corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.

  14. Serotonin, neural markers, and memory

    OpenAIRE

    Alfredo eMeneses

    2015-01-01

    Diverse neuropsychiatric disorders present dysfunctional memory and no effective treatment exits for them; likely as result of the absence of neural markers associated to memory. Neurotransmitter systems and signaling pathways have been implicated in memory and dysfunctional memory; however, their role is poorly understood. Hence, neural markers and cerebral functions and dysfunctions are revised. To our knowledge no previous systematic works have been published addressing these issues. The i...

  15. The maternal ITPK1 gene polymorphism is associated with neural tube defects in a high-risk Chinese population.

    Directory of Open Access Journals (Sweden)

    Zhen Guan

    Full Text Available BACKGROUND: Epidemiological surveys and animal studies have revealed that inositol metabolism is associated with NTDs, but the mechanisms are not clear. Inositol 1,3,4-trisphosphate 5/6-kinase (ITPK1 is a pivotal regulatory enzyme in inositol metabolic pathway. The objective was to assess the potential impact of the maternal ITPK1 genotypes on the inositol parameter and on the NTD risk in a NTD high-risk area in China. METHODOLOGY/RESULTS: A case-control study of pregnant women affected with NTDs (n = 200 and controls (n = 320 was carried out. 13 tag SNPs of ITPK1 were selected and genotyped by the Sequenom MassArray system. We found that 4 tag SNPs were statistically significant in spina bifida group (P<0.05. MACH was used to impute the un-genotyped SNPs in ITPK1 locus and showed that 3 meaningful SNPs in the non-coding regions were significant. We also predicted the binding capacity of transcription factors in the positive SNPs using the bioinformatics method and found that only rs3783903 was located in the conserved sequence of activator protein-1 (AP-1. To further study the association between biochemical values and genotypes, maternal plasma inositol hexakisphosphate (IP6 levels were also assessed using LC-MS. The maternal plasma IP6 concentrations in the spina bifida subgroup were 7.1% lower than control (136.67 vs. 147.05 ng mL(-1, P<0.05, and significantly lower in rs3783903 GG genotype than others (P<0.05. EMSA showed a different allelic binding capacity of AP-1 in rs3783903, which was affected by an A→G exchange. The RT-PCR suggested the ITPK1 expression was decreased significantly in mutant-type of rs3783903 compared with wild-type in the 60 healthy pregnancies (P<0.05. CONCLUSIONS/SIGNIFICANCE: These results suggested that the maternal rs3783903 of ITPK1 might be associated with spina bifida, and the allele G of rs3783903 might affect the binding of AP-1 and the decrease of maternal plasma IP6 concentration in this Chinese

  16. Association of maternal homocysteine and vitamins status with the risk of neural tube defects in Tunisia: A case-control study.

    Science.gov (United States)

    Nasri, Kaouther; Ben Fradj, Mohamed Kacem; Touati, Asma; Aloui, Mariem; Ben Jemaa, Nadia; Masmoudi, Aida; Elmay, Michèle Véronique; Omar, Souhail; Feki, Moncef; Kaabechi, Naziha; Marrakchi, Raja; Gaigi, Soumeya Siala

    2015-12-01

    This study was conducted to determine whether low folate and vitamin B12 levels, as well as high homocysteine levels in pregnant women are associated with neural tube defects (NTDs) in Tunisia. A total of 75 NTDs pregnancies and 75 matched controls were included in the study. Their vitamin B12, folate, and red blood cell folate concentrations were measured using a radio-immunoassay kit and total homocysteine concentrations were determined using a fluorescent polarization immunoassay. Vitamin B12 and folate concentrations were lower in NTD-affected pregnant women than in controls (respectively, p = 0.009 and p < 0.001). Total homocysteine concentration was significantly higher in the NTDs group than in controls (p = 0.008). In the case group, the folate levels were positively related with vitamin B12 levels (r = 0.54; p < 0.001) and negatively correlated with total homocysteine levels (r = -0.19; p = 0.04). Besides, red blood cell folate levels were positively correlated with folate levels (r = 0.24; p = 0.02) and negatively correlated with total homocysteine levels (r = -0.37; p = 0.001). Lower concentrations of folate and vitamin B12 are related to the increased risk of NTDs. Both folate and vitamin B12 intake insufficiency could contribute to the increased risk of NTDs. A dietary supplement, combining folate and vitamin B12, might be an effective measure to decrease the NTDs incidence in Tunisia. © 2015 Wiley Periodicals, Inc.

  17. FOXN1 homozygous mutation associated with anencephaly and severe neural tube defect in human athymic Nude/SCID fetus.

    Science.gov (United States)

    Amorosi, S; D'Armiento, M; Calcagno, G; Russo, I; Adriani, M; Christiano, A M; Weiner, L; Brissette, J L; Pignata, C

    2008-04-01

    The forkhead, Fox, gene family comprises a diverse group of 'winged-helix' transcription factors that play important roles in development, metabolism, cancer and aging. Recently, several forkhead genes have been demonstrated to play critical roles in lymphocyte development and effector functions. Alterations of the FOXN1 gene in both mice and humans result in a severe combined immunodeficiency caused by an intrinsic defect of the thymus associated with congenital alopecia (Nude/severe combined immunodeficiency phenotype). FOXN1 is a member of the class of proteins involved in the development and differentiation of the central nervous system. We identified a human fetus homozygous for a mutation in FOXN1 gene who lacked the thymus and also had abnormal skin, anencephaly and spina bifida. Moreover, we found that FOXN1 gene is expressed in mouse developing choroid plexus. These observations suggest that FOXN1 may be involved in neurulation in humans.

  18. Effect of altering neural, muscular and tendinous factors associated with aging on balance recovery using the ankle strategy: a simulation study.

    Science.gov (United States)

    Barrett, R S; Lichtwark, G A

    2008-10-07

    Aging is associated with declines in neuromuscular function and reduced ability to recover balance from an imbalance episode. However, little is known about the relations amongst these factors. The purpose of this study was to determine the relative influence of age-related changes in neural, muscular and tendinous properties on the ability to recovery balance from a forward leaning position using the ankle strategy. A computer simulation was developed which consisted of an inverted pendulum with one rotational degree of freedom controlled by two muscles representing the ankle joint plantar flexor (PF) and dorsi flexor (DF) muscle groups. Model parameter values were adjusted so that the isometric torque-angle relation was in agreement with experimental ankle joint torque-angle curves from the literature. Muscle excitation was adjusted to match an experimentally determined maximum recoverable lean angle (MRLA) of 7.2 degrees (baseline condition). The effect of 20% alterations to maximum isometric force, optimum muscle fibre length, maximum shortening velocity, tendon stiffness, reaction time delay (RTD), activation time constant and the maximum excitation of the PF muscles, and maximum excitation of the DF muscles (co-activation) on MRLA was then assessed. The parameters that had the greatest influence on MRLA were maximum isometric force, the maximum excitation of the ankle joint PFs and RTD, which, respectively, resulted in 19.0%, 17.8% and 4.6% reductions in MRLA. Individual changes to other parameters influenced MRLA by less than 1.9%. When selected parameter values were adjusted in accordance with age-related changes reported in the literature, MRLA was reduced to 5.3 degrees , a value in relative agreement with experimental values reported in the literature (4.6+/-1.8 degrees ). In general, these results suggest that MRLA is most sensitive to PF muscle mass and the ability to maximally activate the PFs, and that the combined effect of multiple changes in

  19. FGF Signaling Transforms Non-neural Ectoderm into Neural Crest

    OpenAIRE

    Yardley, Nathan; García-Castro, Martín I.

    2012-01-01

    The neural crest arises at the border between the neural plate and the adjacent non-neural ectoderm. It has been suggested that both neural and non-neural ectoderm can contribute to the neural crest. Several studies have examined the molecular mechanisms that regulate neural crest induction in neuralized tissues or the neural plate border. Here, using the chick as a model system, we address the molecular mechanisms by which non-neural ectoderm generates neural crest. We report that in respons...

  20. Altered Neural Correlates of Emotion Associated Pain Processing in Persistent Somatoform Pain Disorder: An fMRI Study.

    Science.gov (United States)

    Luo, Yanli; Yan, Chao; Huang, Tianming; Fan, Mingxia; Liu, Liang; Zhao, Zhiyong; Ni, Kaiji; Jiang, Hong; Huang, Xiao; Lu, Zheng; Wu, Wenyuan; Zhang, Mingyuan; Fan, Xiaoduo

    2016-09-19

    Patients with persistent somatoform pain disorder (PSPD) suffer from long-term pain and emotional conflicts. Recently, accumulating evidence indicated that emotion has a significant role in pain perception of somatoform pain disorder. To further understand the association between emotion and pain-related brain activities, functional activities of patients with PSPD fulfilling ICD-10 criteria and healthy controls were assessed using functional magnetic resonance imaging technology, while participants viewed a series of positive, neutral, or negative pictures with or without pinprick pain stimulation. Results showed that patients with PSPD had altered brain activities in the parietal gyrus, temporal gyrus, posterior cingulate cortex, prefrontal cortex, and parahippocampus in response to pinprick pain stimuli during different emotions compared with the healthy control group. Moreover, patients with PSPD consistently showed hyperactivities in the prefrontal, the fusiform gyrus and the insula in response to negative stimuli under pinprick pain vs. non-pain condition. The current findings provide some insights into the underlying relationship between emotion and pain-related brain activity in patients with PSPD, which is of both theoretical and clinical importance.

  1. Association between Toll-Like Receptor 4 Expression and Neural Stem Cell Proliferation in the Hippocampus Following Traumatic Brain Injury in Mice

    Directory of Open Access Journals (Sweden)

    Yuqin Ye

    2014-07-01

    Full Text Available Whether or how neural stem cells (NSCs respond to toll-like receptor 4 (TLR4 in an inflammatory environment caused by traumatic brain injury (TBI has not been understood. In the present study, association between TLR4 expression and NSCs proliferation in the hippocampus was investigated in a mouse model of TBI using controlled cortical impact (CCI. Hippocampal proliferating cells were labeled with the thymidine analog 5-bromo-2-deoxyuridine (BrdU. In order to identify NSCs, the proliferating cells were further co-labeled with BrdU/sex determination region of Y chromosome related high mobility group box gene 2 (SOX2. Morphological observation on the expression of BrdU, SOX2, and TLR4 in the hippocampus was performed by inmmunofluorescence (IF. Relative quantification of TLR4 expression at the protein and mRNA level was performed using Western blotting and real-time polymerase chain reaction (PCR. It was observed that BrdU+/SOX2+cells accounted for 95.80% ± 7.91% among BrdU+ cells; several BrdU+ cells and SOX2+ cells in the hippocampus were also TLR4-positive post injury, and that BrdU+ cell numbers, together with TLR4 expression at either protein or mRNA level, increased significantly in TBI mice over 1, 3, 7, 14, and 21 days survivals and changed in a similar temporal pattern with a peak at 3 day post-injury. These results indicate that hippocampal proliferating cells (suggestive of NSCs expressed TLR4, and that there was a potential association between increased expression of TLR4 and the proliferation of NSCs post TBI. It is concluded that hippocampal TLR4 may play a potential role in endogenous neurogenesis after TBI.

  2. Neural Cell Adhesion Molecule-Associated Polysialic Acid Regulates Synaptic Plasticity and Learning by Restraining the Signaling through GluN2B-Containing NMDA Receptors

    Science.gov (United States)

    Kochlamazashvili, Gaga; Senkov, Oleg; Grebenyuk, Sergei; Robinson, Catrina; Xiao, Mei-Fang; Stummeyer, Katharina; Gerardy-Schahn, Rita; Engel, Andreas K.; Feig, Larry; Semyanov, Alexey; Suppiramaniam, Vishnu; Schachner, Melitta; Dityatev, Alexander

    2017-01-01

    The neural cell adhesion molecule (NCAM) is the predominant carrier of α2,8 polysialic acid (PSA) in the mammalian brain. Abnormalities in PSA and NCAM expression are associated with schizophrenia in humans and cause deficits in hippocampal synaptic plasticity and contextual fear conditioning in mice. Here, we show that PSA inhibits opening of recombinant NMDA receptors composed of GluN1/2B (NR1/NR2B) or GluN1/2A/2B (NR1/NR2A/NR2B) but not of GluN1/2A (NR1/NR2A) subunits. Deficits in NCAM/PSA increase GluN2B-mediated transmission and Ca2+ transients in the CA1 region of the hippocampus. In line with elevation of GluN2B-mediated transmission, defects in long-term potentiation in the CA1 region and contextual fear memory in NCAM/PSA-deficient mice are abrogated by application of a GluN2B-selective antagonist. Furthermore, treatment with the glutamate scavenger glutamic-pyruvic transaminase, ablation of Ras-GRF1 (a mediator of GluN2B signaling to p38 MAPK), or direct inhibition of hyperactive p38 MAPK can restore impaired synaptic plasticity in brain slices lacking PSA/NCAM. Thus, PSA carried by NCAM regulates plasticity and learning by inhibition of the GluN2B-Ras-GRF1-p38 MAPK signaling pathway. These findings implicate carbohydrates carried by adhesion molecules in modulating NMDA receptor signaling in the brain and demonstrate reversibility of cognitive deficits associated with ablation of a schizophrenia-related adhesion molecule. PMID:20237287

  3. Analysis and design of associative memories based on recurrent neural networks with linear saturation activation functions and time-varying delays.

    Science.gov (United States)

    Zeng, Zhigang; Wang, Jun

    2007-08-01

    In this letter, some sufficient conditions are obtained to guarantee recurrent neural networks with linear saturation activation functions, and time-varying delays have multiequilibria located in the saturation region and the boundaries of the saturation region. These results on pattern characterization are used to analyze and design autoassociative memories, which are directly based on the parameters of the neural networks. Moreover, a formula for the numbers of spurious equilibria is also derived. Four design procedures for recurrent neural networks with linear saturation activation functions and time-varying delays are developed based on stability results. Two of these procedures allow the neural network to be capable of learning and forgetting. Finally, simulation results demonstrate the validity and characteristics of the proposed approach.

  4. Neural Network Applications

    NARCIS (Netherlands)

    Vonk, E.; Jain, L.C.; Veelenturf, L.P.J.

    1995-01-01

    Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas

  5. Quantum Neural Networks

    CERN Document Server

    Gupta, S; Gupta, Sanjay

    2002-01-01

    This paper initiates the study of quantum computing within the constraints of using a polylogarithmic ($O(\\log^k n), k\\geq 1$) number of qubits and a polylogarithmic number of computation steps. The current research in the literature has focussed on using a polynomial number of qubits. A new mathematical model of computation called \\emph{Quantum Neural Networks (QNNs)} is defined, building on Deutsch's model of quantum computational network. The model introduces a nonlinear and irreversible gate, similar to the speculative operator defined by Abrams and Lloyd. The precise dynamics of this operator are defined and while giving examples in which nonlinear Schr\\"{o}dinger's equations are applied, we speculate on its possible implementation. The many practical problems associated with the current model of quantum computing are alleviated in the new model. It is shown that QNNs of logarithmic size and constant depth have the same computational power as threshold circuits, which are used for modeling neural network...

  6. Image Recalling Based on Multidimensional Associative Memory Neural Network%基于多维联想记忆神经网络的图像回忆

    Institute of Scientific and Technical Information of China (English)

    杨家稳; 孙合明

    2012-01-01

    多维联想记忆神经网络在高噪声情况下图像回忆效果差.针对该问题,将图像矩阵垂直分成4个同型小矩阵,依次将4个小矩阵垂向聚合成一个新矩阵,以新矩阵的列向量作为库向量.数值实验结果表明,相比2个列向量构成的库向量,以4个列向量构成的库向量进行回忆的灰度图像更清晰且效率更高.%Multidimensional associative memory neural networks can be used for image recalling. When the image is corrupted by high noise, the recalling image is not clear using the traditional method. In order to make the recalling image clearer, one library vector made up of four column vectors is used in the recalling image to take the place of the other traditional library vectors made up of two column vectors. That is, a new matrix is formed by vertically dividing the mage matrix into four small matrices of the same order and vertically concatenating the four matrices in order. A column vector of the new matrix is regarded as a library vector. Numerical examples show that the restored image is clearer and the recalling process spends less time when the former library vector is used.

  7. PACAP Protects Adult Neural Stem Cells from the Neurotoxic Effect of Ketamine Associated with Decreased Apoptosis, ER Stress and mTOR Pathway Activation.

    Science.gov (United States)

    Mansouri, Shiva; Agartz, Ingrid; Ögren, Sven-Ove; Patrone, Cesare; Lundberg, Mathias

    2017-01-01

    Ketamine administration is a well-established approach to mimic experimentally some aspects of schizophrenia. Adult neurogenesis dysregulation is associated with psychiatric disorders, including schizophrenia. The potential role of neurogenesis in the ketamine-induced phenotype is largely unknown. Recent results from human genetic studies have shown the pituitary adenylate cyclase-activating polypeptide (PACAP) gene is a risk factor for schizophrenia. Its potential role on the regulation of neurogenesis in experimental model of schizophrenia remains to be investigated. We aimed to determine whether ketamine affects the viability of adult neural stem cells (NSC). We also investigated whether the detrimental effect mediated by ketamine could be counteracted by PACAP. NSCs were isolated from the subventricular zone of the mouse and exposed to ketamine with/without PACAP. After 24 hours, cell viability, potential involvement of apoptosis, endoplasmic reticulum (ER) stress, mTOR and AMPA pathway activation were assessed by quantitative RT-PCR and Western blot analysis. We show that ketamine impairs NSC viability in correlation with increased apoptosis, ER stress and mTOR activation. The results also suggest that the effect of ketamine occurs via AMPA receptor activation. Finally, we show that PACAP counteracted the decreased NSC viability induced by ketamine via the specific activation of the PAC-1 receptor subtype. Our study shows that the NSC viability may be negatively affected by ketamine with putative importance for the development of a schizophrenia phenotype in the ketamine induced animal model of schizophrenia. The neuroprotective effect via PAC-1 activation suggests a potentially novel pharmacological target for the treatment of schizophrenia, via neurogenesis normalization.

  8. When a good taste turns bad: Neural mechanisms underlying the emergence of negative affect and associated natural reward devaluation by cocaine.

    Science.gov (United States)

    Carelli, Regina M; West, Elizabeth A

    2014-01-01

    infusion. Collectively, our findings suggest that cocaine-conditioned cues elicit a cocaine-need state that is aversive, is encoded by a distinct subset of NAc neurons and rapid dopamine signaling, and promotes cocaine-seeking behavior. Finally, we present data showing that experimentally induced abstinence (30 days) exacerbates this natural reward devaluation by cocaine, and this effect is correlated with a greater motivation to lever press during extinction. Dissecting the neural mechanisms underlying these detrimental consequences of addiction is critical since it may lead to novel treatments that ameliorate negative affective states associated with drug use and decrease the drive (craving) for the drug. This article is part of a Special Issue entitled 'NIDA 40th Anniversary Issue'.

  9. The holographic neural network: Performance comparison with other neural networks

    Science.gov (United States)

    Klepko, Robert

    1991-10-01

    The artificial neural network shows promise for use in recognition of high resolution radar images of ships. The holographic neural network (HNN) promises a very large data storage capacity and excellent generalization capability, both of which can be achieved with only a few learning trials, unlike most neural networks which require on the order of thousands of learning trials. The HNN is specially designed for pattern association storage, and mathematically realizes the storage and retrieval mechanisms of holograms. The pattern recognition capability of the HNN was studied, and its performance was compared with five other commonly used neural networks: the Adaline, Hamming, bidirectional associative memory, recirculation, and back propagation networks. The patterns used for testing represented artificial high resolution radar images of ships, and appear as a two dimensional topology of peaks with various amplitudes. The performance comparisons showed that the HNN does not perform as well as the other neural networks when using the same test data. However, modification of the data to make it appear more Gaussian distributed, improved the performance of the network. The HNN performs best if the data is completely Gaussian distributed.

  10. The DNA glycosylases OGG1 and NEIL3 influence differentiation potential, proliferation, and senescence-associated signs in neural stem cells

    Energy Technology Data Exchange (ETDEWEB)

    Reis, Amilcar [Linnaeus Center in Developmental Biology for Regenerative Medicine (DBRM), Department of Neuroscience, Karolinska Institutet, SE 17177 Stockholm (Sweden); Hermanson, Ola, E-mail: ola.hermanson@ki.se [Linnaeus Center in Developmental Biology for Regenerative Medicine (DBRM), Department of Neuroscience, Karolinska Institutet, SE 17177 Stockholm (Sweden)

    2012-07-13

    Highlights: Black-Right-Pointing-Pointer DNA glycosylases OGG1 and NEIL3 are required for neural stem cell state. Black-Right-Pointing-Pointer No effect on cell viability by OGG1 or NEIL3 knockdown in neural stem cells. Black-Right-Pointing-Pointer OGG1 or NEIL3 RNA knockdown result in decreased proliferation and differentiation. Black-Right-Pointing-Pointer Increased HP1{gamma} immunoreactivity after NEIL3 knockdown suggests premature senescence. -- Abstract: Embryonic neural stem cells (NSCs) exhibit self-renewal and multipotency as intrinsic characteristics that are key parameters for proper brain development. When cells are challenged by oxidative stress agents the resulting DNA lesions are repaired by DNA glycosylases through the base excision repair (BER) pathway as a means to maintain the fidelity of the genome, and thus, proper cellular characteristics. The functional roles for DNA glycosylases in NSCs have however remained largely unexplored. Here we demonstrate that RNA knockdown of the DNA glycosylases OGG1 and NEIL3 decreased NSC differentiation ability and resulted in decreased expression of both neuronal and astrocytic genes after mitogen withdrawal, as well as the stem cell marker Musashi-1. Furthermore, while cell survival remained unaffected, NEIL3 deficient cells displayed decreased cell proliferation rates along with an increase in HP1{gamma} immunoreactivity, a sign of premature senescence. Our results suggest that DNA glycosylases play multiple roles in governing essential neural stem cell characteristics.

  11. Neural inhibition enables selection during language processing.

    Science.gov (United States)

    Snyder, Hannah R; Hutchison, Natalie; Nyhus, Erika; Curran, Tim; Banich, Marie T; O'Reilly, Randall C; Munakata, Yuko

    2010-09-21

    Whether grocery shopping or choosing words to express a thought, selecting between options can be challenging, especially for people with anxiety. We investigate the neural mechanisms supporting selection during language processing and its breakdown in anxiety. Our neural network simulations demonstrate a critical role for competitive, inhibitory dynamics supported by GABAergic interneurons. As predicted by our model, we find that anxiety (associated with reduced neural inhibition) impairs selection among options and associated prefrontal cortical activity, even in a simple, nonaffective verb-generation task, and the GABA agonist midazolam (which increases neural inhibition) improves selection, whereas retrieval from semantic memory is unaffected when selection demands are low. Neural inhibition is key to choosing our words.

  12. Dynamic Object Identification with SOM-based neural networks

    Directory of Open Access Journals (Sweden)

    Aleksey Averkin

    2014-03-01

    Full Text Available In this article a number of neural networks based on self-organizing maps, that can be successfully used for dynamic object identification, is described. Unique SOM-based modular neural networks with vector quantized associative memory and recurrent self-organizing maps as modules are presented. The structured algorithms of learning and operation of such SOM-based neural networks are described in details, also some experimental results and comparison with some other neural networks are given.

  13. Spin glasses and neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Parga, N. (Comision Nacional de Energia Atomica, San Carlos de Bariloche (Argentina). Centro Atomico Bariloche; Universidad Nacional de Cuyo, San Carlos de Bariloche (Argentina). Inst. Balseiro)

    1989-07-01

    The mean-field theory of spin glass models has been used as a prototype of systems with frustration and disorder. One of the most interesting related systems are models of associative memories. In these lectures we review the main concepts developed to solve the Sherrington-Kirkpatrick model and its application to neural networks. (orig.).

  14. Memory Storage and Neural Systems.

    Science.gov (United States)

    Alkon, Daniel L.

    1989-01-01

    Investigates memory storage and molecular nature of associative-memory formation by analyzing Pavlovian conditioning in marine snails and rabbits. Presented is the design of a computer-based memory system (neural networks) using the rules acquired in the investigation. Reports that the artificial network recognized patterns well. (YP)

  15. Neural networks and statistical learning

    CERN Document Server

    Du, Ke-Lin

    2014-01-01

    Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...

  16. Implementation of artificial neural networks with optics

    Science.gov (United States)

    Yu, Francis T. S.

    1999-04-01

    Optical implementation of artificial neural nets (ANNs) with electronically addressable liquid crystal televisions (LCTVs) are presented. The major advantages of the proposed ANNs must be the low cost and the flexibility to operate. To test the performance, several artificial neural net models have been implemented in the LCTV ANNs. These models include the Hopfield, Interpattern Association, Hetero-association, and Unsupervised ANNs. System design considerations and experimental demonstrates are provided.

  17. Morphological neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)

    1996-12-31

    The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

  18. Neural Tube Defects

    Science.gov (United States)

    Neural tube defects are birth defects of the brain, spine, or spinal cord. They happen in the ... that she is pregnant. The two most common neural tube defects are spina bifida and anencephaly. In ...

  19. Artificial Neural Networks

    OpenAIRE

    Chung-Ming Kuan

    2006-01-01

    Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods.

  20. A Neural Model of Mind Wandering.

    Science.gov (United States)

    Mittner, Matthias; Hawkins, Guy E; Boekel, Wouter; Forstmann, Birte U

    2016-08-01

    The role of the default-mode network (DMN) in the emergence of mind wandering and task-unrelated thought has been studied extensively. In parallel work, mind wandering has been associated with neuromodulation via the locus coeruleus (LC) norepinephrine (LC-NE) system. Here we propose a neural model that links the two systems in an integrative framework. The model attempts to explain how dynamic changes in brain systems give rise to the subjective experience of mind wandering. The model implies a neural and conceptual distinction between an off-focus state and an active mind-wandering state and provides a potential neural grounding for well-known cognitive theories of mind wandering. Finally, the proposed neural model of mind wandering generates precise, testable predictions at neural and behavioral levels.

  1. Constructive neural network learning

    OpenAIRE

    Lin, Shaobo; Zeng, Jinshan; Zhang, Xiaoqin

    2016-01-01

    In this paper, we aim at developing scalable neural network-type learning systems. Motivated by the idea of "constructive neural networks" in approximation theory, we focus on "constructing" rather than "training" feed-forward neural networks (FNNs) for learning, and propose a novel FNNs learning system called the constructive feed-forward neural network (CFN). Theoretically, we prove that the proposed method not only overcomes the classical saturation problem for FNN approximation, but also ...

  2. Neural activation in stress-related exhaustion

    DEFF Research Database (Denmark)

    Gavelin, Hanna Malmberg; Neely, Anna Stigsdotter; Andersson, Micael

    2017-01-01

    The primary purpose of this study was to investigate the association between burnout and neural activation during working memory processing in patients with stress-related exhaustion. Additionally, we investigated the neural effects of cognitive training as part of stress rehabilitation. Fifty......, burnout level was positively associated with neural activation in the rostral prefrontal cortex, the posterior parietal cortex and the striatum, primarily in the 2-back condition. Following stress rehabilitation, the striatal activity decreased as a function of improved levels of burnout. No significant...... association between burnout level and working memory performance was found, however, our findings indicate that frontostriatal neural responses related to working memory were modulated by burnout severity. We suggest that patients with high levels of burnout need to recruit additional cognitive resources...

  3. READING A NEURAL CODE

    NARCIS (Netherlands)

    BIALEK, W; RIEKE, F; VANSTEVENINCK, RRD; WARLAND, D

    1991-01-01

    Traditional approaches to neural coding characterize the encoding of known stimuli in average neural responses. Organisms face nearly the opposite task - extracting information about an unknown time-dependent stimulus from short segments of a spike train. Here the neural code was characterized from

  4. Generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2013-03-01

    In this work a new radial basis function based classification neural network named as generalized classifier neural network, is proposed. The proposed generalized classifier neural network has five layers, unlike other radial basis function based neural networks such as generalized regression neural network and probabilistic neural network. They are input, pattern, summation, normalization and output layers. In addition to topological difference, the proposed neural network has gradient descent based optimization of smoothing parameter approach and diverge effect term added calculation improvements. Diverge effect term is an improvement on summation layer calculation to supply additional separation ability and flexibility. Performance of generalized classifier neural network is compared with that of the probabilistic neural network, multilayer perceptron algorithm and radial basis function neural network on 9 different data sets and with that of generalized regression neural network on 3 different data sets include only two classes in MATLAB environment. Better classification performance up to %89 is observed. Improved classification performances proved the effectivity of the proposed neural network.

  5. Neural Processes Associated with Vocabulary and Vowel-Length Differences in a Dialect: An ERP Study in Pre-literate Children.

    Science.gov (United States)

    Bühler, Jessica C; Waßmann, Franziska; Buser, Daniela; Zumberi, Flutra; Maurer, Urs

    2017-04-17

    Although familiarity with a language impacts how phonology and semantics are processed at the neural level, little is known how these processes are affected by familiarity with a dialect. By measuring event-related potentials (ERPs) in kindergarten children we investigated neural processing related to familiarity with dialect-specific pronunciation and lexicality of spoken words before literacy acquisition in school. Children speaking one of two German dialects were presented with spoken word-picture pairings, in which congruity (or the lack thereof) was defined by dialect familiarity with pronunciation or vocabulary. In a dialect-independent control contrast, congruity was defined by audio-visual semantic (mis)match. Congruity effects and congruity-by-dialect group interactions in the ERPs were tested by data-driven topographic analyses of variance (TANOVA) and theory-driven focal analyses. Converging results revealed similar congruity effects in the N400 and late-positive-complex (LPC) in the control contrast for both dialect groups. In the dialect-specific vocabulary contrast, topographies of the N400- and LPC-effects were reversed depending on familiarity with the presented dialect words. In the dialect-specific pronunciation contrast, again a topography reversal was found depending on dialect familiarity, however, only for the LPC. Our data suggest that neural processing of unfamiliar words, but not pronunciation variants, is characterized by semantic processing (increased N400-effect). However, both unfamiliar words and pronunciation variants seem to engage congruity judgment, as indicated by the LPC-effect. Thus, semantic processing of pronunciation in dialect words seems to be rather robust against slight alterations in pronunciation, like changes in vowel duration, while such alterations may still trigger subsequent control processes.

  6. Transfer Effects to a Multimodal Dual-Task after Working Memory Training and Associated Neural Correlates in Older Adults – A Pilot Study

    Science.gov (United States)

    Heinzel, Stephan; Rimpel, Jérôme; Stelzel, Christine; Rapp, Michael A.

    2017-01-01

    Working memory (WM) performance declines with age. However, several studies have shown that WM training may lead to performance increases not only in the trained task, but also in untrained cognitive transfer tasks. It has been suggested that transfer effects occur if training task and transfer task share specific processing components that are supposedly processed in the same brain areas. In the current study, we investigated whether single-task WM training and training-related alterations in neural activity might support performance in a dual-task setting, thus assessing transfer effects to higher-order control processes in the context of dual-task coordination. A sample of older adults (age 60–72) was assigned to either a training or control group. The training group participated in 12 sessions of an adaptive n-back training. At pre and post-measurement, a multimodal dual-task was performed in all participants to assess transfer effects. This task consisted of two simultaneous delayed match to sample WM tasks using two different stimulus modalities (visual and auditory) that were performed either in isolation (single-task) or in conjunction (dual-task). A subgroup also participated in functional magnetic resonance imaging (fMRI) during the performance of the n-back task before and after training. While no transfer to single-task performance was found, dual-task costs in both the visual modality (p < 0.05) and the auditory modality (p < 0.05) decreased at post-measurement in the training but not in the control group. In the fMRI subgroup of the training participants, neural activity changes in left dorsolateral prefrontal cortex (DLPFC) during one-back predicted post-training auditory dual-task costs, while neural activity changes in right DLPFC during three-back predicted visual dual-task costs. Results might indicate an improvement in central executive processing that could facilitate both WM and dual-task coordination. PMID:28286477

  7. Nonlinear system identification and control based on modular neural networks.

    Science.gov (United States)

    Puscasu, Gheorghe; Codres, Bogdan

    2011-08-01

    A new approach for nonlinear system identification and control based on modular neural networks (MNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This is obtained using a partitioning algorithm. Each local nonlinear model is associated with a nonlinear controller. These are also implemented by neural networks. The switching between the neural controllers is done by a dynamical switcher, also implemented by neural networks, that tracks the different operating points. The proposed multiple modelling and control strategy has been successfully tested on simulated laboratory scale liquid-level system.

  8. Neonatal lethality of neural crest cell-specific Rest knockout mice is associated with gastrointestinal distension caused by aberrations of myenteric plexus.

    Science.gov (United States)

    Aoki, Hitomi; Hara, Akira; Oomori, Yoshiyuki; Shimizu, Yasutake; Yamada, Yasuhiro; Kunisada, Takahiro

    2014-10-01

    RE1-silencing transcription factor (REST), also known as NRSF (neuron-restrictive silencer factor), is a well-known transcriptional repressor of neural genes. Rest null mice have embryonic lethality which prevents further investigations of the functions of the Rest gene in vivo. We studied neonatal but not embryonic lethality that was characterized by gastrointestinal tract dilation in the neural crest cell (NCC)-specific Rest conditional knockout (CKO) mice. While no histological abnormalities except the thinning of the digestive tract as a consequence of the gas accumulation were found in the digestive tract of the mutant mice, they do not have proper gastric retention after oral dye administration and the reduction of acetylcholinesterase (AChE) activity in NCC-derived myenteric plexus in the stomach was detected. High CO2 concentration in the dilated digestive tract of the Rest CKO mice indicates a failure of gut function by underdeveloped cholinergic transmission in the enteric nervous system. The observed gastrointestinal distension phenotype provides a model for understanding the genetic and molecular basis of NCC defects in humans.

  9. Nonequilibrium landscape theory of neural networks.

    Science.gov (United States)

    Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin

    2013-11-05

    The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape-flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments.

  10. Nonequilibrium landscape theory of neural networks

    Science.gov (United States)

    Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin

    2013-01-01

    The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape–flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments. PMID:24145451

  11. Neural induction and factors that stabilize a neural fate

    OpenAIRE

    Rogers, Crystal; Moody, Sally A.; Casey, Elena

    2009-01-01

    The neural ectoderm of vertebrates forms when the BMP signaling pathway is suppressed. Herein we review the molecules that directly antagonize extracellular BMP and the signaling pathways that further contribute to reduce BMP activity in the neural ectoderm. Downstream of neural induction, a large number of “neural fate stabilizing” (NFS) transcription factors are expressed in the presumptive neural ectoderm, developing neural tube, and ultimately in neural stem cells. Herein we review what i...

  12. Consciousness and neural plasticity

    DEFF Research Database (Denmark)

    In contemporary consciousness studies the phenomenon of neural plasticity has received little attention despite the fact that neural plasticity is of still increased interest in neuroscience. We will, however, argue that neural plasticity could be of great importance to consciousness studies....... If consciousness is related to neural processes it seems, at least prima facie, that the ability of the neural structures to change should be reflected in a theory of this relationship "Neural plasticity" refers to the fact that the brain can change due to its own activity. The brain is not static but rather...... the relation between consciousness and brain functions. If consciousness is connected to specific brain structures (as a function or in identity) what happens to consciousness when those specific underlying structures change? It is therefore possible that the understanding and theories of neural plasticity can...

  13. Neural Darwinism and consciousness.

    Science.gov (United States)

    Seth, Anil K; Baars, Bernard J

    2005-03-01

    Neural Darwinism (ND) is a large scale selectionist theory of brain development and function that has been hypothesized to relate to consciousness. According to ND, consciousness is entailed by reentrant interactions among neuronal populations in the thalamocortical system (the 'dynamic core'). These interactions, which permit high-order discriminations among possible core states, confer selective advantages on organisms possessing them by linking current perceptual events to a past history of value-dependent learning. Here, we assess the consistency of ND with 16 widely recognized properties of consciousness, both physiological (for example, consciousness is associated with widespread, relatively fast, low amplitude interactions in the thalamocortical system), and phenomenal (for example, consciousness involves the existence of a private flow of events available only to the experiencing subject). While no theory accounts fully for all of these properties at present, we find that ND and its recent extensions fare well.

  14. A new formulation for feedforward neural networks.

    Science.gov (United States)

    Razavi, Saman; Tolson, Bryan A

    2011-10-01

    Feedforward neural network is one of the most commonly used function approximation techniques and has been applied to a wide variety of problems arising from various disciplines. However, neural networks are black-box models having multiple challenges/difficulties associated with training and generalization. This paper initially looks into the internal behavior of neural networks and develops a detailed interpretation of the neural network functional geometry. Based on this geometrical interpretation, a new set of variables describing neural networks is proposed as a more effective and geometrically interpretable alternative to the traditional set of network weights and biases. Then, this paper develops a new formulation for neural networks with respect to the newly defined variables; this reformulated neural network (ReNN) is equivalent to the common feedforward neural network but has a less complex error response surface. To demonstrate the learning ability of ReNN, in this paper, two training methods involving a derivative-based (a variation of backpropagation) and a derivative-free optimization algorithms are employed. Moreover, a new measure of regularization on the basis of the developed geometrical interpretation is proposed to evaluate and improve the generalization ability of neural networks. The value of the proposed geometrical interpretation, the ReNN approach, and the new regularization measure are demonstrated across multiple test problems. Results show that ReNN can be trained more effectively and efficiently compared to the common neural networks and the proposed regularization measure is an effective indicator of how a network would perform in terms of generalization.

  15. Chaotic diagonal recurrent neural network

    Institute of Scientific and Technical Information of China (English)

    Wang Xing-Yuan; Zhang Yi

    2012-01-01

    We propose a novel neural network based on a diagonal recurrent neural network and chaos,and its structure andlearning algorithm are designed.The multilayer feedforward neural network,diagonal recurrent neural network,and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map.The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks.

  16. Neural Excitability and Singular Bifurcations.

    Science.gov (United States)

    De Maesschalck, Peter; Wechselberger, Martin

    2015-12-01

    We discuss the notion of excitability in 2D slow/fast neural models from a geometric singular perturbation theory point of view. We focus on the inherent singular nature of slow/fast neural models and define excitability via singular bifurcations. In particular, we show that type I excitability is associated with a novel singular Bogdanov-Takens/SNIC bifurcation while type II excitability is associated with a singular Andronov-Hopf bifurcation. In both cases, canards play an important role in the understanding of the unfolding of these singular bifurcation structures. We also explain the transition between the two excitability types and highlight all bifurcations involved, thus providing a complete analysis of excitability based on geometric singular perturbation theory.

  17. The neural ring: an algebraic tool for analyzing the intrinsic structure of neural codes.

    Science.gov (United States)

    Curto, Carina; Itskov, Vladimir; Veliz-Cuba, Alan; Youngs, Nora

    2013-09-01

    Neurons in the brain represent external stimuli via neural codes. These codes often arise from stereotyped stimulus-response maps, associating to each neuron a convex receptive field. An important problem confronted by the brain is to infer properties of a represented stimulus space without knowledge of the receptive fields, using only the intrinsic structure of the neural code. How does the brain do this? To address this question, it is important to determine what stimulus space features can--in principle--be extracted from neural codes. This motivates us to define the neural ring and a related neural ideal, algebraic objects that encode the full combinatorial data of a neural code. Our main finding is that these objects can be expressed in a "canonical form" that directly translates to a minimal description of the receptive field structure intrinsic to the code. We also find connections to Stanley-Reisner rings, and use ideas similar to those in the theory of monomial ideals to obtain an algorithm for computing the primary decomposition of pseudo-monomial ideals. This allows us to algorithmically extract the canonical form associated to any neural code, providing the groundwork for inferring stimulus space features from neural activity alone.

  18. Serotonin, neural markers and memory

    Directory of Open Access Journals (Sweden)

    Alfredo eMeneses

    2015-07-01

    Full Text Available Diverse neuropsychiatric disorders present dysfunctional memory and no effective treatment exits for them; likely as result of the absence of neural markers associated to memory. Neurotransmitter systems and signaling pathways have been implicated in memory and dysfunctional memory; however, their role is poorly understood. Hence, neural markers and cerebral functions and dysfunctions are revised. To our knowledge no previous systematic works have been published addressing these issues. The interactions among behavioral tasks, control groups and molecular changes and/or pharmacological effects are mentioned. Neurotransmitter receptors and signaling pathways, during normal and abnormally functioning memory with an emphasis on the behavioral aspects of memory are revised. With focus on serotonin, since as it is a well characterized neurotransmitter, with multiple pharmacological tools, and well characterized downstream signaling in mammals’ species. 5-HT1A, 5-HT4, 5-HT5, 5-HT6 and 5-HT7 receptors as well as SERT (serotonin transporter seem to be useful neural markers and/or therapeutic targets. Certainly, if the mentioned evidence is replicated, then the translatability from preclinical and clinical studies to neural changes might be confirmed. Hypothesis and theories might provide appropriate limits and perspectives of evidence

  19. Serotonin, neural markers, and memory.

    Science.gov (United States)

    Meneses, Alfredo

    2015-01-01

    Diverse neuropsychiatric disorders present dysfunctional memory and no effective treatment exits for them; likely as result of the absence of neural markers associated to memory. Neurotransmitter systems and signaling pathways have been implicated in memory and dysfunctional memory; however, their role is poorly understood. Hence, neural markers and cerebral functions and dysfunctions are revised. To our knowledge no previous systematic works have been published addressing these issues. The interactions among behavioral tasks, control groups and molecular changes and/or pharmacological effects are mentioned. Neurotransmitter receptors and signaling pathways, during normal and abnormally functioning memory with an emphasis on the behavioral aspects of memory are revised. With focus on serotonin, since as it is a well characterized neurotransmitter, with multiple pharmacological tools, and well characterized downstream signaling in mammals' species. 5-HT1A, 5-HT4, 5-HT5, 5-HT6, and 5-HT7 receptors as well as SERT (serotonin transporter) seem to be useful neural markers and/or therapeutic targets. Certainly, if the mentioned evidence is replicated, then the translatability from preclinical and clinical studies to neural changes might be confirmed. Hypothesis and theories might provide appropriate limits and perspectives of evidence.

  20. Envisaging the Regulation of Alkaloid Biosynthesis and Associated Growth Kinetics in Hairy Roots of Vinca minor Through the Function of Artificial Neural Network.

    Science.gov (United States)

    Verma, Priyanka; Anjum, Shahin; Khan, Shamshad Ahmad; Roy, Sudeep; Odstrcilik, Jan; Mathur, Ajay Kumar

    2016-03-01

    Artificial neural network based modeling is a generic approach to understand and correlate different complex parameters of biological systems for improving the desired output. In addition, some new inferences can also be predicted in a shorter time with less cost and labor. As terpenoid indole alkaloid pathway in Vinca minor is very less investigated or elucidated, a strategy of elicitation with hydroxylase and acetyltransferase along with incorporation of various precursors from primary shikimate and secoiridoid pools via simultaneous employment of cyclooxygenase inhibitor was performed in the hairy roots of V. minor. This led to the increment in biomass accumulation, total alkaloid concentration, and vincamine production in selected treatments. The resultant experimental values were correlated with algorithm approaches of artificial neural network that assisted in finding the yield of vincamine, alkaloids, and growth kinetics using number of elicits. The inputs were the hydroxylase/acetyltransferase elicitors and cyclooxygenase inhibitor along with various precursors from shikimate and secoiridoid pools and the outputs were growth index (GI), alkaloids, and vincamine. The approach incorporates two MATLAB codes; GRNN and FFBPNN. Growth kinetic studies revealed that shikimate and tryptophan supplementation triggers biomass accumulation (GI = 440.2 to 540.5); while maximum alkaloid (3.7 % dry wt.) and vincamine production (0.017 ± 0.001 % dry wt.) was obtained on supplementation of secologanin along with tryptophan, naproxen, hydrogen peroxide, and acetic anhydride. The study shows that experimental and predicted values strongly correlate each other. The correlation coefficient for growth index (GI), alkaloids, and vincamine was found to be 0.9997, 0.9980, 0.9511 in GRNN and 0.9725, 0.9444, 0.9422 in FFBPNN, respectively. GRNN provided greater similarity between the target and predicted dataset in comparison to FFBPNN. The findings can provide future

  1. 基于神经网络的机动多目标数据关联算法%The Maneuvering Multi-Target Data Association Algorithm Based on Neural Net

    Institute of Scientific and Technical Information of China (English)

    范跃华; 徐永红; 辛大欣

    2000-01-01

    Though the Joint of Probabilistic Data Association (JPDA) algorithm has been previouslyreported to be suitable for the problem of tracking multiple targets in the presence of clutter, thecomplexity of this algorithm increases rapidly with the number of targets and returns. A neural algorithmhas been suggested after studying how the Hopfield neural net resolved the traveling salesman problem%虽然JPDA被公认为是杂波多目标环境下跟踪效果最理想的数据关联算法之一,但在密集回波情况下其计算量易出现组合爆炸现象,难于实时处理。通过对Hopfield网络解决TSP问题的研究,探讨用神经网络解决联合概率数据关联(JPDA)中数据运算的组合爆炸问题的办法

  2. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

    The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

  3. Neural networks and applications tutorial

    Science.gov (United States)

    Guyon, I.

    1991-09-01

    The importance of neural networks has grown dramatically during this decade. While only a few years ago they were primarily of academic interest, now dozens of companies and many universities are investigating the potential use of these systems and products are beginning to appear. The idea of building a machine whose architecture is inspired by that of the brain has roots which go far back in history. Nowadays, technological advances of computers and the availability of custom integrated circuits, permit simulations of hundreds or even thousands of neurons. In conjunction, the growing interest in learning machines, non-linear dynamics and parallel computation spurred renewed attention in artificial neural networks. Many tentative applications have been proposed, including decision systems (associative memories, classifiers, data compressors and optimizers), or parametric models for signal processing purposes (system identification, automatic control, noise canceling, etc.). While they do not always outperform standard methods, neural network approaches are already used in some real world applications for pattern recognition and signal processing tasks. The tutorial is divided into six lectures, that where presented at the Third Graduate Summer Course on Computational Physics (September 3-7, 1990) on Parallel Architectures and Applications, organized by the European Physical Society: (1) Introduction: machine learning and biological computation. (2) Adaptive artificial neurons (perceptron, ADALINE, sigmoid units, etc.): learning rules and implementations. (3) Neural network systems: architectures, learning algorithms. (4) Applications: pattern recognition, signal processing, etc. (5) Elements of learning theory: how to build networks which generalize. (6) A case study: a neural network for on-line recognition of handwritten alphanumeric characters.

  4. Stability analysis of discrete-time BAM neural networks based on standard neural network models

    Institute of Scientific and Technical Information of China (English)

    ZHANG Sen-lin; LIU Mei-qin

    2005-01-01

    To facilitate stability analysis of discrete-time bidirectional associative memory (BAM) neural networks, they were converted into novel neural network models, termed standard neural network models (SNNMs), which interconnect linear dynamic systems and bounded static nonlinear operators. By combining a number of different Lyapunov functionals with S-procedure, some useful criteria of global asymptotic stability and global exponential stability of the equilibrium points of SNNMs were derived. These stability conditions were formulated as linear matrix inequalities (LMIs). So global stability of the discrete-time BAM neural networks could be analyzed by using the stability results of the SNNMs. Compared to the existing stability analysis methods, the proposed approach is easy to implement, less conservative, and is applicable to other recurrent neural networks.

  5. A neural flow estimator

    DEFF Research Database (Denmark)

    Jørgensen, Ivan Harald Holger; Bogason, Gudmundur; Bruun, Erik

    1995-01-01

    This paper proposes a new way to estimate the flow in a micromechanical flow channel. A neural network is used to estimate the delay of random temperature fluctuations induced in a fluid. The design and implementation of a hardware efficient neural flow estimator is described. The system...... is implemented using switched-current technique and is capable of estimating flow in the μl/s range. The neural estimator is built around a multiplierless neural network, containing 96 synaptic weights which are updated using the LMS1-algorithm. An experimental chip has been designed that operates at 5 V...

  6. Neural Systems Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...

  7. SPECTRUM OF NEURAL-TUBE DEFECTS IN 34 INFANTS PRENATALLY EXPOSED TO ANTIEPILEPTIC DRUGS

    NARCIS (Netherlands)

    LINDHOUT, D; OMTZIGT, JGC; CORNEL, MC

    We analyzed the spectrum of neural-tube defects associated with maternal exposure to antiepileptic drugs (AEDs) and the possible contribution of familial and genetic factors to epilepsy or neural-tube defects. No specific association with maternal family history of neural-tube defects or epilepsy

  8. SPECTRUM OF NEURAL-TUBE DEFECTS IN 34 INFANTS PRENATALLY EXPOSED TO ANTIEPILEPTIC DRUGS

    NARCIS (Netherlands)

    LINDHOUT, D; OMTZIGT, JGC; CORNEL, MC

    1992-01-01

    We analyzed the spectrum of neural-tube defects associated with maternal exposure to antiepileptic drugs (AEDs) and the possible contribution of familial and genetic factors to epilepsy or neural-tube defects. No specific association with maternal family history of neural-tube defects or epilepsy wa

  9. Association of neural tube defects in children of mothers with MTHFR 677TT genotype and abnormal carbohydrate metabolism risk: a case-control study.

    Science.gov (United States)

    Cadenas-Benitez, N M; Yanes-Sosa, F; Gonzalez-Meneses, A; Cerrillos, L; Acosta, D; Praena-Fernandez, J M; Neth, O; Gomez de Terreros, I; Ybot-González, P

    2014-03-26

    Abnormalities in maternal folate and carbohydrate metabolism have both been shown to induce neural tube defects (NTD) in humans and animal models. However, the relationship between these two factors in the development of NTDs remains unclear. Data from mothers of children with spina bifida seen at the Unidad de Espina Bífida del Hospital Infantil Virgen del Rocío (case group) were compared to mothers of healthy children with no NTD (control group) who were randomly selected from patients seen at the outpatient ward in the same hospital. There were 25 individuals in the case group and 41 in the control group. Analysis of genotypes for the methylenetetrahydrofolate reductase (MTHFR) 677CT polymorphism in women with or without risk factors for abnormal carbohydrate metabolism revealed that mothers who were homozygous for the MTHFR 677TT polymorphism and at risk of abnormal carbohydrate metabolism were more likely to have offspring with spina bifida and high levels of homocysteine, compared to the control group. The increased incidence of NTDs in mothers homozygous for the MTHFR 677TT polymorphism and at risk of abnormal carbohydrate metabolism stresses the need for careful metabolic screening in pregnant women, and, if necessary, determination of the MTHFR 677CT genotype in those mothers at risk of developing abnormal carbohydrate metabolism.

  10. The neural pathway underlying a numerical working memory task in abacus-trained children and associated functional connectivity in the resting brain.

    Science.gov (United States)

    Li, Yongxin; Hu, Yuzheng; Zhao, Ming; Wang, Yunqi; Huang, Jian; Chen, Feiyan

    2013-11-20

    Training can induce significant changes in brain functioning and behavioral performance. One consequence of training is changing the pattern of brain activation. Abacus training is of interest because abacus experts gain the ability to handle digits with unusual speed and accuracy. However, the neural correlates of numerical memory in abacus-trained children remain unknown. In the current study, we aimed to detect a training effect of abacus-based mental calculations on numerical working memory in children. We measured brain functional magnetic resonance imaging (fMRI) activation patterns in 17 abacus-trained children and 17 control children as they performed two numerical working memory tasks (digits and beads). Functional MRI results revealed higher activation in abacus-trained children than in the controls in the right posterior superior parietal lobule/superior occipital gyrus (PSPL/SOG) and the right supplementary motor area (SMA) in both tasks. When these regions were used as seeds in a functional connectivity analysis of the resting brain, the abacus-trained children showed significantly enhanced integration between the right SMA and the right inferior frontal gyrus (IFG). The IFG is considered to be the key region for the control of attention. These findings demonstrate that extensive engagement of the fronto-parietal network occurs during numerical memory tasks in the abacus-trained group. Furthermore, abacus training may increase the functional integration of visuospatial-attention circuitry, which and thus enhances high-level cognitive process.

  11. Recognition of Telugu characters using neural networks.

    Science.gov (United States)

    Sukhaswami, M B; Seetharamulu, P; Pujari, A K

    1995-09-01

    The aim of the present work is to recognize printed and handwritten Telugu characters using artificial neural networks (ANNs). Earlier work on recognition of Telugu characters has been done using conventional pattern recognition techniques. We make an initial attempt here of using neural networks for recognition with the aim of improving upon earlier methods which do not perform effectively in the presence of noise and distortion in the characters. The Hopfield model of neural network working as an associative memory is chosen for recognition purposes initially. Due to limitation in the capacity of the Hopfield neural network, we propose a new scheme named here as the Multiple Neural Network Associative Memory (MNNAM). The limitation in storage capacity has been overcome by combining multiple neural networks which work in parallel. It is also demonstrated that the Hopfield network is suitable for recognizing noisy printed characters as well as handwritten characters written by different "hands" in a variety of styles. Detailed experiments have been carried out using several learning strategies and results are reported. It is shown here that satisfactory recognition is possible using the proposed strategy. A detailed preprocessing scheme of the Telugu characters from digitized documents is also described.

  12. Neural Networks: Implementations and Applications

    NARCIS (Netherlands)

    Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.

    1996-01-01

    Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas

  13. Neural Networks: Implementations and Applications

    NARCIS (Netherlands)

    Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.

    1996-01-01

    Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas

  14. What Are Neural Tube Defects?

    Science.gov (United States)

    ... NICHD Research Information Clinical Trials Resources and Publications Neural Tube Defects (NTDs): Condition Information Skip sharing on social media links Share this: Page Content What are neural tube defects? Neural (pronounced NOOR-uhl ) tube defects are ...

  15. Optical implementation of neural networks

    Science.gov (United States)

    Yu, Francis T. S.; Guo, Ruyan

    2002-12-01

    An adaptive optical neuro-computing (ONC) using inexpensive pocket size liquid crystal televisions (LCTVs) had been developed by the graduate students in the Electro-Optics Laboratory at The Pennsylvania State University. Although this neuro-computing has only 8×8=64 neurons, it can be easily extended to 16×20=320 neurons. The major advantages of this LCTV architecture as compared with other reported ONCs, are low cost and the flexibility to operate. To test the performance, several neural net models are used. These models are Interpattern Association, Hetero-association and unsupervised learning algorithms. The system design considerations and experimental demonstrations are also included.

  16. The Physics of Neural Networks

    Science.gov (United States)

    Gutfreund, Hanoch; Toulouse, Gerard

    The following sections are included: * Introduction * Historical Perspective * Why Statistical Physics? * Purpose and Outline of the Paper * Basic Elements of Neural Network Models * The Biological Neuron * From the Biological to the Formal Neuron * The Formal Neuron * Network Architecture * Network Dynamics * Basic Functions of Neural Network Models * Associative Memory * Learning * Categorization * Generalization * Optimization * The Hopfield Model * Solution of the Model * The Merit of the Hopfield Model * Beyond the Standard Model * The Gardner Approach * A Microcanonical Formulation * The Case of Biased Patterns * A Canonical Formulation * Constraints on the Synaptic Weights * Learning with Errors * Learning with Noise * Hierarchically Correlated Data and Categorization * Hierarchical Data Structures * Storage of Hierarchical Data Structures * Categorization * Generalization * Learning a Classification Task * The Reference Perceptron Problem * The Contiguity Problem * Discussion - Issues of Relevance * The Notion of Attractors and Modes of Computation * The Nature of Attractors * Temporal versus Spatial Coding * Acknowledgements * References

  17. Childhood social inequalities influences neural processes in young adult caregiving.

    Science.gov (United States)

    Kim, Pilyoung; Ho, Shaun S; Evans, Gary W; Liberzon, Israel; Swain, James E

    2015-12-01

    Childhood poverty is associated with harsh parenting with a risk of transmission to the next generation. This prospective study examined the relations between childhood poverty and non-parent adults' neural responses to infant cry sounds. While no main effects of poverty were revealed in contrasts of infant cry versus acoustically matched white noise, a gender by childhood poverty interaction emerged. In females, childhood poverty was associated with increased neural activations in the posterior insula, striatum, calcarine sulcus, hippocampus, and fusiform gyrus, while, in males, childhood poverty was associated with reduced levels of neural responses to infant cry in the same regions. Irrespective of gender, neural activation in these regions was associated with higher levels of annoyance with the cry sound and reduced desire to approach the crying infant. The findings suggest gender differences in neural and emotional responses to infant cry sounds among young adults growing up in poverty.

  18. Kunstige neurale net

    DEFF Research Database (Denmark)

    Hørning, Annette

    1994-01-01

    Artiklen beskæftiger sig med muligheden for at anvende kunstige neurale net i forbindelse med datamatisk procession af naturligt sprog, specielt automatisk talegenkendelse.......Artiklen beskæftiger sig med muligheden for at anvende kunstige neurale net i forbindelse med datamatisk procession af naturligt sprog, specielt automatisk talegenkendelse....

  19. Critical Branching Neural Networks

    Science.gov (United States)

    Kello, Christopher T.

    2013-01-01

    It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…

  20. Consciousness and neural plasticity

    DEFF Research Database (Denmark)

    changes or to abandon the strong identity thesis altogether. Were one to pursue a theory according to which consciousness is not an epiphenomenon to brain processes, consciousness may in fact affect its own neural basis. The neural correlate of consciousness is often seen as a stable structure, that is...

  1. Critical Branching Neural Networks

    Science.gov (United States)

    Kello, Christopher T.

    2013-01-01

    It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…

  2. Functional connectivity associated with hand shape generation: Imitating novel hand postures and pantomiming tool grips challenge different nodes of a shared neural network.

    Science.gov (United States)

    Vingerhoets, Guy; Clauwaert, Amanda

    2015-09-01

    Clinical research suggests that imitating meaningless hand postures and pantomiming tool-related hand shapes rely on different neuroanatomical substrates. We investigated the BOLD responses to different tasks of hand posture generation in 14 right handed volunteers. Conjunction and contrast analyses were applied to select regions that were either common or sensitive to imitation and/or pantomime tasks. The selection included bilateral areas of medial and lateral extrastriate cortex, superior and inferior regions of the lateral and medial parietal lobe, primary motor and somatosensory cortex, and left dorsolateral prefrontal, and ventral and dorsal premotor cortices. Functional connectivity analysis revealed that during hand shape generation the BOLD-response of every region correlated significantly with every other area regardless of the hand posture task performed, although some regions were more involved in some hand postures tasks than others. Based on between-task differences in functional connectivity we predict that imitation of novel hand postures would suffer most from left superior parietal disruption and that pantomiming hand postures for tools would be impaired following left frontal damage, whereas both tasks would be sensitive to inferior parietal dysfunction. We also unveiled that posterior temporal cortex is committed to pantomiming tool grips, but that the involvement of this region to the execution of hand postures in general appears limited. We conclude that the generation of hand postures is subserved by a highly interconnected task-general neural network. Depending on task requirements some nodes/connections will be more engaged than others and these task-sensitive findings are in general agreement with recent lesion studies.

  3. Is neural Darwinism Darwinism?

    Science.gov (United States)

    van Belle, T

    1997-01-01

    Neural Darwinism is a theory of cognition developed by Gerald Edelman along with George Reeke and Olaf Sporns at Rockefeller University. As its name suggests, neural Darwinism is modeled after biological Darwinism, and its authors assert that the two processes are strongly analogous. both operate on variation in a population, amplifying the more adaptive individuals. However, from a computational perspective, neural Darwinism is quite different from other models of natural selection, such as genetic algorithms. The individuals of neural Darwinism do not replicate, thus robbing the process of the capacity to explore new solutions over time and ultimately reducing it to a random search. Because neural Darwinism does not have the computational power of a truly Darwinian process, it is misleading to label it as such. to illustrate this disparity in adaptive power, one of Edelman's early computer experiments, Darwin I, is revisited, and it is shown that adding replication greatly improves the adaptive power of the system.

  4. 混沌神经网络在分离叠加模式和多对多联想记忆中的应用%A Chaotic Neural Network and its Applications in Separation of Superimposed Pattern and Many-to-Many Associative Memory

    Institute of Scientific and Technical Information of China (English)

    刘光远; 段书凯

    2003-01-01

    In this paper, we propose a modified chaotic associative memory neural network(MCAM). It has two im-portant features :it can recall stored patterns from superimposed input; (2)it can deal with many-to-many associativememory. The computer simulations show the effectiveness of the proposed model.

  5. Threshold control of chaotic neural network.

    Science.gov (United States)

    He, Guoguang; Shrimali, Manish Dev; Aihara, Kazuyuki

    2008-01-01

    The chaotic neural network constructed with chaotic neurons exhibits rich dynamic behaviour with a nonperiodic associative memory. In the chaotic neural network, however, it is difficult to distinguish the stored patterns in the output patterns because of the chaotic state of the network. In order to apply the nonperiodic associative memory into information search, pattern recognition etc. it is necessary to control chaos in the chaotic neural network. We have studied the chaotic neural network with threshold activated coupling, which provides a controlled network with associative memory dynamics. The network converges to one of its stored patterns or/and reverse patterns which has the smallest Hamming distance from the initial state of the network. The range of the threshold applied to control the neurons in the network depends on the noise level in the initial pattern and decreases with the increase of noise. The chaos control in the chaotic neural network by threshold activated coupling at varying time interval provides controlled output patterns with different temporal periods which depend upon the control parameters.

  6. Dynamics of neural cryptography.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  7. Methodology of Neural Design: Applications in Microwave Engineering

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2006-06-01

    Full Text Available In the paper, an original methodology for the automatic creation of neural models of microwave structures is proposed and verified. Following the methodology, neural models of the prescribed accuracy are built within the minimum CPU time. Validity of the proposed methodology is verified by developing neural models of selected microwave structures. Functionality of neural models is verified in a design - a neural model is joined with a genetic algorithm to find a global minimum of a formulated objective function. The objective function is minimized using different versions of genetic algorithms, and their mutual combinations. The verified methodology of the automated creation of accurate neural models of microwave structures, and their association with global optimization routines are the most important original features of the paper.

  8. Neural fibrolipoma in pharyngeal mucosal space: A rare occurrence

    Directory of Open Access Journals (Sweden)

    Nishith Kumar

    2012-01-01

    Full Text Available Neural fibrolipoma is a rare lesion presenting in early childhood, as a slow-growing fusiform swelling of a nerve, usually in the forearm or wrist (median nerve, associated with symptoms of compression neuropathy. There are only few case reports of neural fibrolipoma in neck and no such case has been reported in pharyngeal mucosal space.

  9. Neural Systems for Speech and Song in Autism

    Science.gov (United States)

    Lai, Grace; Pantazatos, Spiro P.; Schneider, Harry; Hirsch, Joy

    2012-01-01

    Despite language disabilities in autism, music abilities are frequently preserved. Paradoxically, brain regions associated with these functions typically overlap, enabling investigation of neural organization supporting speech and song in autism. Neural systems sensitive to speech and song were compared in low-functioning autistic and age-matched…

  10. ANT Advanced Neural Tool

    Energy Technology Data Exchange (ETDEWEB)

    Labrador, I.; Carrasco, R.; Martinez, L.

    1996-07-01

    This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs.

  11. AUV fuzzy neural BDI

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    The typical BDI (belief desire intention) model of agent is not efficiently computable and the strict logic expression is not easily applicable to the AUV (autonomous underwater vehicle) domain with uncertainties. In this paper, an AUV fuzzy neural BDI model is proposed. The model is a fuzzy neural network composed of five layers: input ( beliefs and desires) , fuzzification, commitment, fuzzy intention, and defuzzification layer. In the model, the fuzzy commitment rules and neural network are combined to form intentions from beliefs and desires. The model is demonstrated by solving PEG (pursuit-evasion game), and the simulation result is satisfactory.

  12. LMI-based approach for global asymptotic stability analysis of continuous BAM neural networks

    Institute of Scientific and Technical Information of China (English)

    ZHANG Sen-lin; LIU Mei-qin

    2005-01-01

    Studies on the stability of the equilibrium points of continuous bidirectional associative memory (BAM) neural network have yielded many useful results. A novel neural network model called standard neural network model (SNNM) is advanced. By using state affine transformation, the BAM neural networks were converted to SNNMs. Some sufficient conditions for the global asymptotic stability of continuous BAM neural networks were derived from studies on the SNNMs' stability. These conditions were formulated as easily verifiable linear matrix inequalities (LMIs), whose conservativeness is relatively low. The approach proposed extends the known stability results, and can also be applied to other forms of recurrent neural networks (RNNs).

  13. Unavoidable Errors: A Spatio-Temporal Analysis of Time-Course and Neural Sources of Evoked Potentials Associated with Error Processing in a Speeded Task

    Science.gov (United States)

    Vocat, Roland; Pourtois, Gilles; Vuilleumier, Patrik

    2008-01-01

    The detection of errors is known to be associated with two successive neurophysiological components in EEG, with an early time-course following motor execution: the error-related negativity (ERN/Ne) and late positivity (Pe). The exact cognitive and physiological processes contributing to these two EEG components, as well as their functional…

  14. Linear matrix inequality approach to exponential synchronization of a class of chaotic neural networks with time-varying delays

    Institute of Scientific and Technical Information of China (English)

    Wu Wei; Cui Bao-Tong

    2007-01-01

    In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented.This class of chaotic neural networks covers several well-known neural network, such a Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.

  15. European Seminar on Neural Computing

    Science.gov (United States)

    1988-08-31

    when McCulloch and Pitts (1943) showed (auto-associator) or a relationship between remembered that networks of neuronlike elements were general com...idea of modeling neurons by threshold automa- ta can be traced back to work done by McCulloch and Artificial Neural Systems Pitts in 1942, according...34The U-Interpreter," IEEE Computer, McCulloch , W.S. and W. Pitts , "A Logical Calculus of the Ideas Immi- Vol. 15, No.2, (February 1982), 42-49. nent in

  16. Critical branching neural networks.

    Science.gov (United States)

    Kello, Christopher T

    2013-01-01

    It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical branching and, in doing so, simulates observed scaling laws as pervasive to neural and behavioral activity. These scaling laws are related to neural and cognitive functions, in that critical branching is shown to yield spiking activity with maximal memory and encoding capacities when analyzed using reservoir computing techniques. The model is also shown to account for findings of pervasive 1/f scaling in speech and cued response behaviors that are difficult to explain by isolable causes. Issues and questions raised by the model and its results are discussed from the perspectives of physics, neuroscience, computer and information sciences, and psychological and cognitive sciences.

  17. Hidden neural networks

    DEFF Research Database (Denmark)

    Krogh, Anders Stærmose; Riis, Søren Kamaric

    1999-01-01

    A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability...... parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum...... likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear...

  18. Neural Oscillators Programming Simplified

    Directory of Open Access Journals (Sweden)

    Patrick McDowell

    2012-01-01

    Full Text Available The neurological mechanism used for generating rhythmic patterns for functions such as swallowing, walking, and chewing has been modeled computationally by the neural oscillator. It has been widely studied by biologists to model various aspects of organisms and by computer scientists and robotics engineers as a method for controlling and coordinating the gaits of walking robots. Although there has been significant study in this area, it is difficult to find basic guidelines for programming neural oscillators. In this paper, the authors approach neural oscillators from a programmer’s point of view, providing background and examples for developing neural oscillators to generate rhythmic patterns that can be used in biological modeling and robotics applications.

  19. Folate receptors and neural tube closure.

    Science.gov (United States)

    Saitsu, Hirotomo

    2017-02-28

    Neural tube defects (NTD) are among the most common human congenital malformations, affecting 0.5-8/1000 of live births. Human clinical trials have shown that periconceptional folate supplementation significantly decreases the occurrence of NTD in offspring. However, the mechanism by which folate acts on NTD remains largely unknown. Folate receptor (Folr) is one of the three membrane proteins that mediate cellular uptake of folates. Recent studies suggest that mouse Folr1 (formerly referred to as Fbp1) is essential for neural tube closure. Therefore, we examined spatial and temporal expression patterns of Folr1 in developing mouse embryos, showing a close association between Folr1 and anterior neural tube closure. Transient transgenic analysis was performed using lacZ as a reporter; we identified a 1.1-kb enhancer that directs lacZ expression in the neural tube and optic vesicle in a manner that is similar to endogenous Folr1. The 1.1-kb enhancer sequences were highly conserved between humans and mice, suggesting that human FOLR1 is associated with anterior neural tube closure in humans. Several experimental studies in mice and human epidemiological and genetics studies have suggested that folate receptor abnormalities are involved in a portion of human NTDs, although the solo defect of FOLR1 did not cause NTD.

  20. Neural networks and graph theory

    Institute of Scientific and Technical Information of China (English)

    许进; 保铮

    2002-01-01

    The relationships between artificial neural networks and graph theory are considered in detail. The applications of artificial neural networks to many difficult problems of graph theory, especially NP-complete problems, and the applications of graph theory to artificial neural networks are discussed. For example graph theory is used to study the pattern classification problem on the discrete type feedforward neural networks, and the stability analysis of feedback artificial neural networks etc.

  1. Building a Neural Computer

    OpenAIRE

    Carreira, Paulo J.F.; Rosa, Miguel A.; Neto, João Pedro; Costa, José Félix

    1998-01-01

    In the work of [Siegelmann 95] it was showed that Artificial Recursive Neural Networks have the same computing power as Turing machines. A Turing machine can be programmed in a proper high-level language - the language of partial recursive functions. In this paper we present the implementation of a compiler that directly translates high-level Turing machine programs to Artificial Recursive Neural Networks. The application contains a simulator that can be used to test the resulting networks. W...

  2. Neural cryptography with feedback

    Science.gov (United States)

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

    2004-04-01

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

  3. Imaging the Neural Symphony.

    Science.gov (United States)

    Svoboda, Karel

    2016-01-01

    Since the start of the new millennium, a method called two-photon microscopy has allowed scientists to peer farther into the brain than ever before. Our author, one of the pioneers in the development of this new technology, writes that "directly observing the dynamics of neural networks in an intact brain has become one of the holy grails of brain research." His article describes the advances that led to this remarkable breakthrough-one that is helping neuroscientists better understand neural networks.

  4. Building a Neural Computer

    OpenAIRE

    1998-01-01

    In the work of [Siegelmann 95] it was showed that Artificial Recursive Neural Networks have the same computing power as Turing machines. A Turing machine can be programmed in a proper high-level language - the language of partial recursive functions. In this paper we present the implementation of a compiler that directly translates high-level Turing machine programs to Artificial Recursive Neural Networks. The application contains a simulator that can be used to test the resulting networks. W...

  5. Neural cryptography with feedback.

    Science.gov (United States)

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

    2004-04-01

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

  6. Correction of Hirschsprung-Associated Mutations in Human Induced Pluripotent Stem Cells Via Clustered Regularly Interspaced Short Palindromic Repeats/Cas9, Restores Neural Crest Cell Function.

    Science.gov (United States)

    Lai, Frank Pui-Ling; Lau, Sin-Ting; Wong, John Kwong-Leong; Gui, Hongsheng; Wang, Reeson Xu; Zhou, Tingwen; Lai, Wing Hon; Tse, Hung-Fat; Tam, Paul Kwong-Hang; Garcia-Barcelo, Maria-Mercedes; Ngan, Elly Sau-Wai

    2017-07-01

    Hirschsprung disease is caused by failure of enteric neural crest cells (ENCCs) to fully colonize the bowel, leading to bowel obstruction and megacolon. Heterozygous mutations in the coding region of the RET gene cause a severe form of Hirschsprung disease (total colonic aganglionosis). However, 80% of HSCR patients have short-segment Hirschsprung disease (S-HSCR), which has not been associated with genetic factors. We sought to identify mutations associated with S-HSCR, and used the clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 gene editing system to determine how mutations affect ENCC function. We created induced pluripotent stem cell (iPSC) lines from 1 patient with total colonic aganglionosis (with the G731del mutation in RET) and from 2 patients with S-HSCR (without a RET mutation), as well as RET(+/-) and RET(-/-) iPSCs. IMR90-iPSC cells were used as the control cell line. Migration and differentiation capacities of iPSC-derived ENCCs were analyzed in differentiation and migration assays. We searched for mutation(s) associated with S-HSCR by combining genetic and transcriptome data from patient blood- and iPSC-derived ENCCs, respectively. Mutations in the iPSCs were corrected using the CRISPR/Cas9 system. ENCCs derived from all iPSC lines, but not control iPSCs, had defects in migration and neuronal lineage differentiation. RET mutations were associated with differentiation and migration defects of ENCCs in vitro. Genetic and transcriptome analyses associated a mutation in the vinculin gene (VCL M209L) with S-HSCR. CRISPR/Cas9 correction of the RET G731del and VCL M209L mutations in iPSCs restored the differentiation and migration capacities of ENCCs. We identified mutations in VCL associated with S-HSCR. Correction of this mutation in iPSC using CRISPR/Cas9 editing, as well as the RET G731del mutation that causes Hirschsprung disease with total colonic aganglionosis, restored ENCC function. Our study demonstrates how human i

  7. Malformation/dysplasia syndrome (neural tube defect, hypospadias neuroblastoma) associated with an extra dicentric marker chromosome 15 ({open_quotes}inversion duplication 15{close_quotes})

    Energy Technology Data Exchange (ETDEWEB)

    Reitnauer, P.J.; Rao, K.W.; Tepperberg, J.H. [Univ. of North Carolina, Chapel Hill, NC (United States)

    1994-09-01

    Extra dicentric 15 marker chromosomes are associated with variable degrees of mental retardation but not major structural birth defects. We have studied a unique patient, a male infant who was prenatally diagnosed with lumbar meningomyelocele and an extra pseudodicentric marker chromosome: 47,XY,+psu dic(15)t(15;15)(?q12,?q12)mat. Hairy ears and a coronal hypospadias were noted at birth. At three months of age, a stage I thoracic neuroblastoma was primarily resected. Tumor cells, skin fibroblasts and peripheral blood lymphocytes contained the dicentric 15. The mother is mosaic for the marker chromosome. Fluorescence in situ hybridization (FISH) studies using a classic 15 satellite probe (D15Z1 [Oncor]) confirmed the presence of 2 number 15 centromeres in the marker. The marker is felt to be the result of a translocation rather than an inverted duplication because the G-band morphology of the short arm/satellite complexes differ from one another, implying that the arms originate from 2 different number 15s. FISH analysis using cosmid probes for the Prader-Willi/Angelman critical region (D15S11 and GABRB3 [Oncor]) revealed 2 copies of this region, indicating that these loci are duplicated in the marker. Although some features of the patient`s phenotype such as developmental delay and hypotonia have been associated with dicentric chromosome 15 markers, this is the first malformation/dysplasia syndrome or neuroblastoma reported to our knowledge. The association of neuroblastoma with chromosome 15 aberrations in this case provides speculation as to the role of chromosome 15 loci in cell division control.

  8. Neural repair in the adult brain [version 1; referees: 3 approved

    OpenAIRE

    Sebastian Jessberger

    2016-01-01

    Acute or chronic injury to the adult brain often results in substantial loss of neural tissue and subsequent permanent functional impairment. Over the last two decades, a number of approaches have been developed to harness the regenerative potential of neural stem cells and the existing fate plasticity of neural cells in the nervous system to prevent tissue loss or to enhance structural and functional regeneration upon injury. Here, we review recent advances of stem cell-associated neural rep...

  9. Artificial Neural Networks and Instructional Technology.

    Science.gov (United States)

    Carlson, Patricia A.

    1991-01-01

    Artificial neural networks (ANN), part of artificial intelligence, are discussed. Such networks are fed sample cases (training sets), learn how to recognize patterns in the sample data, and use this experience in handling new cases. Two cognitive roles for ANNs (intelligent filters and spreading, associative memories) are examined. Prototypes…

  10. Estimating Conditional Distributions by Neural Networks

    DEFF Research Database (Denmark)

    Kulczycki, P.; Schiøler, Henrik

    1998-01-01

    Neural Networks for estimating conditionaldistributions and their associated quantiles are investigated in this paper. A basic network structure is developed on the basis of kernel estimation theory, and consistency property is considered from a mild set of assumptions. A number of applications...

  11. Neural Network for Estimating Conditional Distribution

    DEFF Research Database (Denmark)

    Schiøler, Henrik; Kulczycki, P.

    Neural networks for estimating conditional distributions and their associated quantiles are investigated in this paper. A basic network structure is developed on the basis of kernel estimation theory, and consistency is proved from a mild set of assumptions. A number of applications within...... statistcs, decision theory and signal processing are suggested, and a numerical example illustrating the capabilities of the elaborated network is given...

  12. Artificial Neural Networks and Instructional Technology.

    Science.gov (United States)

    Carlson, Patricia A.

    1991-01-01

    Artificial neural networks (ANN), part of artificial intelligence, are discussed. Such networks are fed sample cases (training sets), learn how to recognize patterns in the sample data, and use this experience in handling new cases. Two cognitive roles for ANNs (intelligent filters and spreading, associative memories) are examined. Prototypes…

  13. Fructose:glucose ratios--a study of sugar self-administration and associated neural and physiological responses in the rat.

    Science.gov (United States)

    Levy, AnneMarie; Marshall, Paul; Zhou, Yan; Kreek, Mary Jeanne; Kent, Katrina; Daniels, Stephen; Shore, Ari; Downs, Tiana; Fernandes, Maria Fernanda; Mutch, David M; Leri, Francesco

    2015-05-22

    This study explored whether different ratios of fructose (F) and glucose (G) in sugar can engender significant differences in self-administration and associated neurobiological and physiological responses in male Sprague-Dawley rats. In Experiment 1, animals self-administered pellets containing 55% F + 45% G or 30% F + 70% G, and Fos immunoreactivity was assessed in hypothalamic regions regulating food intake and reward. In Experiment 2, rats self-administered solutions of 55% F + 42% G (high fructose corn syrup (HFCS)), 50% F + 50% G (sucrose) or saccharin, and mRNA of the dopamine 2 (D2R) and mu-opioid (MOR) receptor genes were assessed in striatal regions involved in addictive behaviors. Finally, in Experiment 3, rats self-administered HFCS and sucrose in their home cages, and hepatic fatty acids were quantified. It was found that higher fructose ratios engendered lower self-administration, lower Fos expression in the lateral hypothalamus/arcuate nucleus, reduced D2R and increased MOR mRNA in the dorsal striatum and nucleus accumbens core, respectively, as well as elevated omega-6 polyunsaturated fatty acids in the liver. These data indicate that a higher ratio of fructose may enhance the reinforcing effects of sugar and possibly lead to neurobiological and physiological alterations associated with addictive and metabolic disorders.

  14. Fructose:Glucose Ratios—A Study of Sugar Self-Administration and Associated Neural and Physiological Responses in the Rat

    Directory of Open Access Journals (Sweden)

    AnneMarie Levy

    2015-05-01

    Full Text Available This study explored whether different ratios of fructose (F and glucose (G in sugar can engender significant differences in self-administration and associated neurobiological and physiological responses in male Sprague-Dawley rats. In Experiment 1, animals self-administered pellets containing 55% F + 45% G or 30% F + 70% G, and Fos immunoreactivity was assessed in hypothalamic regions regulating food intake and reward. In Experiment 2, rats self-administered solutions of 55% F + 42% G (high fructose corn syrup (HFCS, 50% F + 50% G (sucrose or saccharin, and mRNA of the dopamine 2 (D2R and mu-opioid (MOR receptor genes were assessed in striatal regions involved in addictive behaviors. Finally, in Experiment 3, rats self-administered HFCS and sucrose in their home cages, and hepatic fatty acids were quantified. It was found that higher fructose ratios engendered lower self-administration, lower Fos expression in the lateral hypothalamus/arcuate nucleus, reduced D2R and increased MOR mRNA in the dorsal striatum and nucleus accumbens core, respectively, as well as elevated omega-6 polyunsaturated fatty acids in the liver. These data indicate that a higher ratio of fructose may enhance the reinforcing effects of sugar and possibly lead to neurobiological and physiological alterations associated with addictive and metabolic disorders.

  15. Pax7 lineage contributions to the mammalian neural crest.

    Directory of Open Access Journals (Sweden)

    Barbara Murdoch

    Full Text Available Neural crest cells are vertebrate-specific multipotent cells that contribute to a variety of tissues including the peripheral nervous system, melanocytes, and craniofacial bones and cartilage. Abnormal development of the neural crest is associated with several human maladies including cleft/lip palate, aggressive cancers such as melanoma and neuroblastoma, and rare syndromes, like Waardenburg syndrome, a complex disorder involving hearing loss and pigment defects. We previously identified the transcription factor Pax7 as an early marker, and required component for neural crest development in chick embryos. In mammals, Pax7 is also thought to play a role in neural crest development, yet the precise contribution of Pax7 progenitors to the neural crest lineage has not been determined.Here we use Cre/loxP technology in double transgenic mice to fate map the Pax7 lineage in neural crest derivates. We find that Pax7 descendants contribute to multiple tissues including the cranial, cardiac and trunk neural crest, which in the cranial cartilage form a distinct regional pattern. The Pax7 lineage, like the Pax3 lineage, is additionally detected in some non-neural crest tissues, including a subset of the epithelial cells in specific organs.These results demonstrate a previously unappreciated widespread distribution of Pax7 descendants within and beyond the neural crest. They shed light regarding the regionally distinct phenotypes observed in Pax3 and Pax7 mutants, and provide a unique perspective into the potential roles of Pax7 during disease and development.

  16. Apoptosis is not required for mammalian neural tube closure.

    Science.gov (United States)

    Massa, Valentina; Savery, Dawn; Ybot-Gonzalez, Patricia; Ferraro, Elisabetta; Rongvaux, Anthony; Cecconi, Francesco; Flavell, Richard; Greene, Nicholas D E; Copp, Andrew J

    2009-05-19

    Apoptotic cell death occurs in many tissues during embryonic development and appears to be essential for processes including digit formation and cardiac outflow tract remodeling. Studies in the chick suggest a requirement for apoptosis during neurulation, because inhibition of caspase activity was found to prevent neural tube closure. In mice, excessive apoptosis occurs in association with failure of neural tube closure in several genetic mutants, but whether regulated apoptosis is also necessary for neural tube closure in mammals is unknown. Here we investigate the possible role of apoptotic cell death during mouse neural tube closure. We confirm the presence of apoptosis in the neural tube before and during closure, and identify a correlation with 3 main events: bending and fusion of the neural folds, postfusion remodeling of the dorsal neural tube and surface ectoderm, and emigration of neural crest cells. Both Casp3 and Apaf1 null embryos exhibit severely reduced apoptosis, yet neurulation proceeds normally in the forebrain and spine. In contrast, the mutant embryos fail to complete neural tube closure in the midbrain and hindbrain. Application of the apoptosis inhibitors z-Vad-fmk and pifithrin-alpha to neurulation-stage embryos in culture suppresses apoptosis but does not prevent initiation or progression of neural tube closure along the entire neuraxis, including the midbrain and hindbrain. Remodeling of the surface ectoderm to cover the closed tube, as well as delamination and migration of neural crest cells, also appear to be normal in the apoptosis-suppressed embryos. We conclude that apoptosis is not required for neural tube closure in the mouse embryo.

  17. Neural-metabolic coupling in the central visual pathway.

    Science.gov (United States)

    Freeman, Ralph D; Li, Baowang

    2016-10-05

    Studies are described which are intended to improve our understanding of the primary measurements made in non-invasive neural imaging. The blood oxygenation level-dependent signal used in functional magnetic resonance imaging (fMRI) reflects changes in deoxygenated haemoglobin. Tissue oxygen concentration, along with blood flow, changes during neural activation. Therefore, measurements of tissue oxygen together with the use of a neural sensor can provide direct estimates of neural-metabolic interactions. We have used this relationship in a series of studies in which a neural microelectrode is combined with an oxygen micro-sensor to make simultaneous co-localized measurements in the central visual pathway. Oxygen responses are typically biphasic with small initial dips followed by large secondary peaks during neural activation. By the use of established visual response characteristics, we have determined that the oxygen initial dip provides a better estimate of local neural function than the positive peak. This contrasts sharply with fMRI for which the initial dip is unreliable. To extend these studies, we have examined the relationship between the primary metabolic agents, glucose and lactate, and associated neural activity. For this work, we also use a Doppler technique to measure cerebral blood flow (CBF) together with neural activity. Results show consistent synchronously timed changes such that increases in neural activity are accompanied by decreases in glucose and simultaneous increases in lactate. Measurements of CBF show clear delays with respect to neural response. This is consistent with a slight delay in blood flow with respect to oxygen delivery during neural activation.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'.

  18. Dysfunction of Rapid Neural Adaptation in Dyslexia.

    Science.gov (United States)

    Perrachione, Tyler K; Del Tufo, Stephanie N; Winter, Rebecca; Murtagh, Jack; Cyr, Abigail; Chang, Patricia; Halverson, Kelly; Ghosh, Satrajit S; Christodoulou, Joanna A; Gabrieli, John D E

    2016-12-21

    Identification of specific neurophysiological dysfunctions resulting in selective reading difficulty (dyslexia) has remained elusive. In addition to impaired reading development, individuals with dyslexia frequently exhibit behavioral deficits in perceptual adaptation. Here, we assessed neurophysiological adaptation to stimulus repetition in adults and children with dyslexia for a wide variety of stimuli, spoken words, written words, visual objects, and faces. For every stimulus type, individuals with dyslexia exhibited significantly diminished neural adaptation compared to controls in stimulus-specific cortical areas. Better reading skills in adults and children with dyslexia were associated with greater repetition-induced neural adaptation. These results highlight a dysfunction of rapid neural adaptation as a core neurophysiological difference in dyslexia that may underlie impaired reading development. Reduced neurophysiological adaptation may relate to prior reports of reduced behavioral adaptation in dyslexia and may reveal a difference in brain functions that ultimately results in a specific reading impairment. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. Discrete Neural Signatures of Basic Emotions.

    Science.gov (United States)

    Saarimäki, Heini; Gotsopoulos, Athanasios; Jääskeläinen, Iiro P; Lampinen, Jouko; Vuilleumier, Patrik; Hari, Riitta; Sams, Mikko; Nummenmaa, Lauri

    2016-06-01

    Categorical models of emotions posit neurally and physiologically distinct human basic emotions. We tested this assumption by using multivariate pattern analysis (MVPA) to classify brain activity patterns of 6 basic emotions (disgust, fear, happiness, sadness, anger, and surprise) in 3 experiments. Emotions were induced with short movies or mental imagery during functional magnetic resonance imaging. MVPA accurately classified emotions induced by both methods, and the classification generalized from one induction condition to another and across individuals. Brain regions contributing most to the classification accuracy included medial and inferior lateral prefrontal cortices, frontal pole, precentral and postcentral gyri, precuneus, and posterior cingulate cortex. Thus, specific neural signatures across these regions hold representations of different emotional states in multimodal fashion, independently of how the emotions are induced. Similarity of subjective experiences between emotions was associated with similarity of neural patterns for the same emotions, suggesting a direct link between activity in these brain regions and the subjective emotional experience.

  20. Neural net approach to predictive vector quantization

    Science.gov (United States)

    Mohsenian, Nader; Nasrabadi, Nasser M.

    1992-11-01

    A new predictive vector quantization (PVQ) technique, capable of exploring the nonlinear dependencies in addition to the linear dependencies that exist between adjacent blocks of pixels, is introduced. Two different classes of neural nets form the components of the PVQ scheme. A multi-layer perceptron is embedded in the predictive component of the compression system. This neural network, using the non-linearity condition associated with its processing units, can perform as a non-linear vector predictor. The second component of the PVQ scheme vector quantizes (VQ) the residual vector that is formed by subtracting the output of the perceptron from the original wave-pattern. Kohonen Self-Organizing Feature Map (KSOFM) was utilized as a neural network clustering algorithm to design the codebook for the VQ technique. Coding results are presented for monochrome 'still' images.

  1. Neural networks in seismic discrimination

    Energy Technology Data Exchange (ETDEWEB)

    Dowla, F.U.

    1995-01-01

    Neural networks are powerful and elegant computational tools that can be used in the analysis of geophysical signals. At Lawrence Livermore National Laboratory, we have developed neural networks to solve problems in seismic discrimination, event classification, and seismic and hydrodynamic yield estimation. Other researchers have used neural networks for seismic phase identification. We are currently developing neural networks to estimate depths of seismic events using regional seismograms. In this paper different types of network architecture and representation techniques are discussed. We address the important problem of designing neural networks with good generalization capabilities. Examples of neural networks for treaty verification applications are also described.

  2. 1991 IEEE International Joint Conference on Neural Networks, Singapore, Nov. 18-21, 1991, Proceedings. Vols. 1-3

    Energy Technology Data Exchange (ETDEWEB)

    1991-01-01

    The present conference the application of neural networks to associative memories, neurorecognition, hybrid systems, supervised and unsupervised learning, image processing, neurophysiology, sensation and perception, electrical neurocomputers, optimization, robotics, machine vision, sensorimotor control systems, and neurodynamics. Attention is given to such topics as optimal associative mappings in recurrent networks, self-improving associative neural network models, fuzzy activation functions, adaptive pattern recognition with sparse associative networks, efficient question-answering in a hybrid system, the use of abstractions by neural networks, remote-sensing pattern classification, speech recognition with guided propagation, inverse-step competitive learning, and rotational quadratic function neural networks. Also discussed are electrical load forecasting, evolutionarily stable and unstable strategies, the capacity of recurrent networks, neural net vs control theory, perceptrons for image recognition, storage capacity of bidirectional associative memories, associative random optimization for control, automatic synthesis of digital neural architectures, self-learning robot vision, and the associative dynamics of chaotic neural networks.

  3. Neural codes of seeing architectural styles.

    Science.gov (United States)

    Choo, Heeyoung; Nasar, Jack L; Nikrahei, Bardia; Walther, Dirk B

    2017-01-10

    Images of iconic buildings, such as the CN Tower, instantly transport us to specific places, such as Toronto. Despite the substantial impact of architectural design on people's visual experience of built environments, we know little about its neural representation in the human brain. In the present study, we have found patterns of neural activity associated with specific architectural styles in several high-level visual brain regions, but not in primary visual cortex (V1). This finding suggests that the neural correlates of the visual perception of architectural styles stem from style-specific complex visual structure beyond the simple features computed in V1. Surprisingly, the network of brain regions representing architectural styles included the fusiform face area (FFA) in addition to several scene-selective regions. Hierarchical clustering of error patterns further revealed that the FFA participated to a much larger extent in the neural encoding of architectural styles than entry-level scene categories. We conclude that the FFA is involved in fine-grained neural encoding of scenes at a subordinate-level, in our case, architectural styles of buildings. This study for the first time shows how the human visual system encodes visual aspects of architecture, one of the predominant and longest-lasting artefacts of human culture.

  4. Bilirubin-induced neural impairment: a special focus on myelination, age-related windows of susceptibility and associated co-morbidities.

    Science.gov (United States)

    Brites, Dora; Fernandes, Adelaide

    2015-02-01

    Bilirubin-induced neurologic dysfunction (BIND) and classical kernicterus are clinical manifestations of moderate to severe hyperbilirubinemia whenever bilirubin levels exceed the capacity of the brain defensive mechanisms in preventing its entrance and cytotoxicity. In such circumstances and depending on the associated co-morbidities, bilirubin accumulation may lead to short- or long-term neurodevelopmental disabilities, which may include deficits in auditory, cognitive, and motor processing. Neuronal cell death, astrocytic reactivity, and microglia activation are part of the bilirubin-induced pathogenesis. Less understood is how abnormal growth and maturation of oligodendrocytes may impact on brain development, affecting the formation of myelin tracts. Based on in-vitro and in-vivo models, as well as in clinical cases presented here, we propose the existence of impaired myelination by bilirubin with long-term sequelae, mainly in pre-term infants. Sensitive time-windows are highlighted and centered on the different developmental-dependent impairments determined by bilirubin, and the influence of sepsis and hypoxia is reviewed.

  5. Disruptions in neural connectivity associated with reduced susceptibility to a depth inversion illusion in youth at ultra high risk for psychosis

    Directory of Open Access Journals (Sweden)

    Tina Gupta

    2016-01-01

    Full Text Available Patients with psychosis exhibit a reduced susceptibility to depth inversion illusions (DII in which a physically concave surface is perceived as convex (e.g., the hollow mask illusion. Here, we examined the extent to which lessened susceptibility to DII characterized youth at ultra high risk (UHR for psychosis. In this study, 44 UHR participants and 29 healthy controls judged the apparent convexity of face-like human masks, two of which were concave and the other convex. One of the concave masks was painted with realistic texture to enhance the illusion; the other was shown without such texture. Networks involved with top-down and bottom-up processing were evaluated with resting state functional connectivity magnetic resonance imaging (fcMRI. We examined regions associated with the fronto-parietal network and the visual system and their relations with susceptibility to DII. Consistent with prior studies, the UHR group was less susceptible to DII (i.e., they were characterized by more veridical perception of the stimuli than the healthy control group. Veridical responses were related to weaker connectivity within the fronto-parietal network, and this relationship was stronger in the UHR group, suggesting possible abnormalities of top-down modulation of sensory signals. This could serve as a vulnerability marker and a further clue to the pathogenesis of psychosis.

  6. DIFFERENCE FEATURE NEURAL NETWORK IN RECOGNITION OF HUMAN FACES

    Institute of Scientific and Technical Information of China (English)

    Chen Gang; Qi Feihu

    2001-01-01

    This article discusses vision recognition process and finds out that human recognizes objects not by their isolated features, but by their main difference features which people get by contrasting them. According to the resolving character of difference features for vision recognition, the difference feature neural network(DFNN) which is the improved auto-associative neural network is proposed.Using ORL database, the comparative experiment for face recognition with face images and the ones added Gaussian noise is performed, and the result shows that DFNN is better than the auto-associative neural network and it proves DFNN is more efficient.

  7. The Neural Cell Adhesion Molecule (NCAM) Promotes Clustering and Activation of EphA3 Receptors in GABAergic Interneurons to Induce Ras Homolog Gene Family, Member A (RhoA)/Rho-associated protein kinase (ROCK)-mediated Growth Cone Collapse.

    Science.gov (United States)

    Sullivan, Chelsea S; Kümper, Maike; Temple, Brenda S; Maness, Patricia F

    2016-12-16

    Establishment of a proper balance of excitatory and inhibitory connectivity is achieved during development of cortical networks and adjusted through synaptic plasticity. The neural cell adhesion molecule (NCAM) and the receptor tyrosine kinase EphA3 regulate the perisomatic synapse density of inhibitory GABAergic interneurons in the mouse frontal cortex through ephrin-A5-induced growth cone collapse. In this study, it was demonstrated that binding of NCAM and EphA3 occurred between the NCAM Ig2 domain and EphA3 cysteine-rich domain (CRD). The binding interface was further refined through molecular modeling and mutagenesis and shown to be comprised of complementary charged residues in the NCAM Ig2 domain (Arg-156 and Lys-162) and the EphA3 CRD (Glu-248 and Glu-264). Ephrin-A5 induced co-clustering of surface-bound NCAM and EphA3 in GABAergic cortical interneurons in culture. Receptor clustering was impaired by a charge reversal mutation that disrupted NCAM/EphA3 association, emphasizing the importance of the NCAM/EphA3 binding interface for cluster formation. NCAM enhanced ephrin-A5-induced EphA3 autophosphorylation and activation of RhoA GTPase, indicating a role for NCAM in activating EphA3 signaling through clustering. NCAM-mediated clustering of EphA3 was essential for ephrin-A5-induced growth cone collapse in cortical GABAergic interneurons, and RhoA and a principal effector, Rho-associated protein kinase, mediated the collapse response. This study delineates a mechanism in which NCAM promotes ephrin-A5-dependent clustering of EphA3 through interaction of the NCAM Ig2 domain and the EphA3 CRD, stimulating EphA3 autophosphorylation and RhoA signaling necessary for growth cone repulsion in GABAergic interneurons in vitro, which may extend to remodeling of axonal terminals of interneurons in vivo.

  8. Combination of counterpropagation artificial neural networks and antioxidant activities for comprehensive evaluation of associated-extraction efficiency of various cyclodextrins in the traditional Chinese formula Xue-Zhi-Ning.

    Science.gov (United States)

    Sun, Lili; Yang, Jianwen; Wang, Meng; Zhang, Huijie; Liu, Yanan; Ren, Xiaoliang; Qi, Aidi

    2015-11-10

    Xue-Zhi-Ning (XZN) is a widely used traditional Chinese medicine formula to treat hyperlipidemia. Recently, cyclodextrins (CDs) have been extensively used to minimize problems relative to medicine bioavailability, such as low solubility and poor stability. The objective of this study was to determine the associated-extraction efficiency of various CDs in XZN. Three various type CDs were evaluated, including native CDs (α-CD, β-CD), hydrophilic CD derivatives (HP-β-CD and Me-β-CD), and ionic CD derivatives (SBE-β-CD and CM-β-CD). An ultra high-performance liquid chromatography (UHPLC) fingerprint was applied to determine the components in CD extracts and original aqueous extract (OAE). A counterpropagation artificial neural network (CP-ANN) was used to analyze the components in different extracts and compare the selective extraction of various CDs. Extraction efficiencies of the various CDs in terms of extracted components follow the ranking, ionic CD derivatives>hydrophilic CD derivatives>native CDs>OAE. Besides, different types of CDs have their own selective extraction and ionic CD derivatives present the strongest associated-extraction efficiency. Antioxidant potentials of various extracts were evaluated by determining the inhibition of spontaneous, H2O2-induced, CCl4-induced and Fe(2+)/ascorbic acid-induced lipid peroxidation (LPO) and analyzing the scavenging capacity for DPPH and hydroxyl radicals. The order of extraction efficiencies of the various CDs relative to antioxidant activities is as follows: SBE-β-CD>CM-β-CD>HP-β-CD>Me-β-CD>β-CD>α-CD. It can be demonstrated that all of the CDs studied increase the extraction efficiency and that ionic CD derivatives (SBE-β-CD and CM-β-CD) present the highest extraction capability in terms of amount extracted and antioxidant activities of extracts.

  9. Cotton genotypes selection through artificial neural networks.

    Science.gov (United States)

    Júnior, E G Silva; Cardoso, D B O; Reis, M C; Nascimento, A F O; Bortolin, D I; Martins, M R; Sousa, L B

    2017-09-27

    Breeding programs currently use statistical analysis to assist in the identification of superior genotypes at various stages of a cultivar's development. Differently from these analyses, the computational intelligence approach has been little explored in genetic improvement of cotton. Thus, this study was carried out with the objective of presenting the use of artificial neural networks as auxiliary tools in the improvement of the cotton to improve fiber quality. To demonstrate the applicability of this approach, this research was carried out using the evaluation data of 40 genotypes. In order to classify the genotypes for fiber quality, the artificial neural networks were trained with replicate data of 20 genotypes of cotton evaluated in the harvests of 2013/14 and 2014/15, regarding fiber length, uniformity of length, fiber strength, micronaire index, elongation, short fiber index, maturity index, reflectance degree, and fiber quality index. This quality index was estimated by means of a weighted average on the determined score (1 to 5) of each characteristic of the HVI evaluated, according to its industry standards. The artificial neural networks presented a high capacity of correct classification of the 20 selected genotypes based on the fiber quality index, so that when using fiber length associated with the short fiber index, fiber maturation, and micronaire index, the artificial neural networks presented better results than using only fiber length and previous associations. It was also observed that to submit data of means of new genotypes to the neural networks trained with data of repetition, provides better results of classification of the genotypes. When observing the results obtained in the present study, it was verified that the artificial neural networks present great potential to be used in the different stages of a genetic improvement program of the cotton, aiming at the improvement of the fiber quality of the future cultivars.

  10. “I Know that You Know that I Know”: Neural Substrates Associated with Social Cognition Deficits in DM1 Patients

    Science.gov (United States)

    Serra, Laura; Cercignani, Mara; Bruschini, Michela; Cipolotti, Lisa; Mancini, Matteo; Silvestri, Gabriella; Petrucci, Antonio; Bucci, Elisabetta; Antonini, Giovanni; Licchelli, Loretta; Spanò, Barbara; Giacanelli, Manlio; Caltagirone, Carlo; Meola, Giovanni; Bozzali, Marco

    2016-01-01

    Myotonic dystrophy type-1 (DM1) is a genetic multi-systemic disorder involving several organs including the brain. Despite the heterogeneity of this condition, some patients with non-congenital DM1 can present with minimal cognitive impairment on formal testing but with severe difficulties in daily-living activities including social interactions. One explanation for this paradoxical mismatch can be found in patients’ dysfunctional social cognition, which can be assessed in the framework of the Theory of Mind (ToM). We hypothesize here that specific disease driven abnormalities in DM1 brains may result in ToM impairments. We recruited 20 DM1 patients who underwent the “Reading the Mind in the Eyes” and the ToM-story tests. These patients, together with 18 healthy controls, also underwent resting-state functional MRI. A composite Theory of Mind score was computed for all recruited patients and correlated with their brain functional connectivity. This analysis provided the patients’ “Theory of Mind-network”, which was compared, for its topological properties, with that of healthy controls. We found that DM1 patients showed deficits in both tests assessing ToM. These deficits were associated with specific patterns of abnormal connectivity between the left inferior temporal and fronto-cerebellar nodes in DM1 brains. The results confirm the previous suggestions of ToM dysfunctions in patients with DM1 and support the hypothesis that difficulties in social interactions and personal relationships are a direct consequence of brain abnormalities, and not a reaction symptom. This is relevant not only for a better pathophysiological comprehension of DM1, but also for non-pharmacological interventions to improve clinical aspects and impact on patients’ success in life. PMID:27258100

  11. Rule Extraction:Using Neural Networks or for Neural Networks?

    Institute of Scientific and Technical Information of China (English)

    Zhi-Hua Zhou

    2004-01-01

    In the research of rule extraction from neural networks, fidelity describes how well the rules mimic the behavior of a neural network while accuracy describes how well the rules can be generalized. This paper identifies the fidelity-accuracy dilemma. It argues to distinguish rule extraction using neural networks and rule extraction for neural networks according to their different goals, where fidelity and accuracy should be excluded from the rule quality evaluation framework, respectively.

  12. Fuzzy Multiresolution Neural Networks

    Science.gov (United States)

    Ying, Li; Qigang, Shang; Na, Lei

    A fuzzy multi-resolution neural network (FMRANN) based on particle swarm algorithm is proposed to approximate arbitrary nonlinear function. The active function of the FMRANN consists of not only the wavelet functions, but also the scaling functions, whose translation parameters and dilation parameters are adjustable. A set of fuzzy rules are involved in the FMRANN. Each rule either corresponding to a subset consists of scaling functions, or corresponding to a sub-wavelet neural network consists of wavelets with same dilation parameters. Incorporating the time-frequency localization and multi-resolution properties of wavelets with the ability of self-learning of fuzzy neural network, the approximation ability of FMRANN can be remarkable improved. A particle swarm algorithm is adopted to learn the translation and dilation parameters of the wavelets and adjusting the shape of membership functions. Simulation examples are presented to validate the effectiveness of FMRANN.

  13. Neural mechanisms underlying breathing complexity.

    Directory of Open Access Journals (Sweden)

    Agathe Hess

    Full Text Available Breathing is maintained and controlled by a network of automatic neurons in the brainstem that generate respiratory rhythm and receive regulatory inputs. Breathing complexity therefore arises from respiratory central pattern generators modulated by peripheral and supra-spinal inputs. Very little is known on the brainstem neural substrates underlying breathing complexity in humans. We used both experimental and theoretical approaches to decipher these mechanisms in healthy humans and patients with chronic obstructive pulmonary disease (COPD. COPD is the most frequent chronic lung disease in the general population mainly due to tobacco smoke. In patients, airflow obstruction associated with hyperinflation and respiratory muscles weakness are key factors contributing to load-capacity imbalance and hence increased respiratory drive. Unexpectedly, we found that the patients breathed with a higher level of complexity during inspiration and expiration than controls. Using functional magnetic resonance imaging (fMRI, we scanned the brain of the participants to analyze the activity of two small regions involved in respiratory rhythmogenesis, the rostral ventro-lateral (VL medulla (pre-Bötzinger complex and the caudal VL pons (parafacial group. fMRI revealed in controls higher activity of the VL medulla suggesting active inspiration, while in patients higher activity of the VL pons suggesting active expiration. COPD patients reactivate the parafacial to sustain ventilation. These findings may be involved in the onset of respiratory failure when the neural network becomes overwhelmed by respiratory overload We show that central neural activity correlates with airflow complexity in healthy subjects and COPD patients, at rest and during inspiratory loading. We finally used a theoretical approach of respiratory rhythmogenesis that reproduces the kernel activity of neurons involved in the automatic breathing. The model reveals how a chaotic activity in

  14. Introduction to Artificial Neural Networks

    DEFF Research Database (Denmark)

    Larsen, Jan

    1999-01-01

    The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

  15. 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.

  16. A Possible Neural Representation of Mathematical Group Structures.

    Science.gov (United States)

    Pomi, Andrés

    2016-09-01

    Every cognitive activity has a neural representation in the brain. When humans deal with abstract mathematical structures, for instance finite groups, certain patterns of activity are occurring in the brain that constitute their neural representation. A formal neurocognitive theory must account for all the activities developed by our brain and provide a possible neural representation for them. Associative memories are neural network models that have a good chance of achieving a universal representation of cognitive phenomena. In this work, we present a possible neural representation of mathematical group structures based on associative memory models that store finite groups through their Cayley graphs. A context-dependent associative memory stores the transitions between elements of the group when multiplied by each generator of a given presentation of the group. Under a convenient election of the vector basis mapping the elements of the group in the neural activity, the input of a vector corresponding to a generator of the group collapses the context-dependent rectangular matrix into a virtual square permutation matrix that is the matrix representation of the generator. This neural representation corresponds to the regular representation of the group, in which to each element is assigned a permutation matrix. This action of the generator on the memory matrix can also be seen as the dissection of the corresponding monochromatic subgraph of the Cayley graph of the group, and the adjacency matrix of this subgraph is the permutation matrix corresponding to the generator.

  17. Improvement of the detection response time of gas sensors using the association of artificial neural networks with pattern recognition technique; Amelioration de la reponse temporelle de capteurs de gaz par reconnaissance de forme a l'aide de reseaux de neurones

    Energy Technology Data Exchange (ETDEWEB)

    Bordieu, Ch.; Rebiere, D. [Bordeaux-1 Univ., Lab. IXL, UMR CNRS 5818, 33 (France); Pistre, J.; Planata, R. [Centre d' Etudes du Bouchet, 91 - Vert-le-Petit (France)

    1999-07-01

    The association of artificial neural networks (multilayer perceptrons) with a real time pattern recognition technique (shifting windows) allowed the development of systems for the detection and the quantification of gases. Shifting window technique is presented and offers an interesting way to improve the detection response time. The partial detector characterization with regard to its parameters was realized. Applications dealing with the detection of gas compounds using surface acoustic sensors permit to show the shifting window technique feasibility. (author)

  18. Generalized Adaptive Artificial Neural Networks

    Science.gov (United States)

    Tawel, Raoul

    1993-01-01

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

  19. Neural Network Ensembles

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Salamon, Peter

    1990-01-01

    We propose several means for improving the performance an training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining generalization error can be reduced by invoking ensembles of similar...... networks....

  20. Interval probabilistic neural network.

    Science.gov (United States)

    Kowalski, Piotr A; Kulczycki, Piotr

    2017-01-01

    Automated classification systems have allowed for the rapid development of exploratory data analysis. Such systems increase the independence of human intervention in obtaining the analysis results, especially when inaccurate information is under consideration. The aim of this paper is to present a novel approach, a neural networking, for use in classifying interval information. As presented, neural methodology is a generalization of probabilistic neural network for interval data processing. The simple structure of this neural classification algorithm makes it applicable for research purposes. The procedure is based on the Bayes approach, ensuring minimal potential losses with regard to that which comes about through classification errors. In this article, the topological structure of the network and the learning process are described in detail. Of note, the correctness of the procedure proposed here has been verified by way of numerical tests. These tests include examples of both synthetic data, as well as benchmark instances. The results of numerical verification, carried out for different shapes of data sets, as well as a comparative analysis with other methods of similar conditioning, have validated both the concept presented here and its positive features.

  1. Automating parallel implementation of neural learning algorithms.

    Science.gov (United States)

    Rana, O F

    2000-06-01

    Neural learning algorithms generally involve a number of identical processing units, which are fully or partially connected, and involve an update function, such as a ramp, a sigmoid or a Gaussian function for instance. Some variations also exist, where units can be heterogeneous, or where an alternative update technique is employed, such as a pulse stream generator. Associated with connections are numerical values that must be adjusted using a learning rule, and and dictated by parameters that are learning rule specific, such as momentum, a learning rate, a temperature, amongst others. Usually, neural learning algorithms involve local updates, and a global interaction between units is often discouraged, except in instances where units are fully connected, or involve synchronous updates. In all of these instances, concurrency within a neural algorithm cannot be fully exploited without a suitable implementation strategy. A design scheme is described for translating a neural learning algorithm from inception to implementation on a parallel machine using PVM or MPI libraries, or onto programmable logic such as FPGAs. A designer must first describe the algorithm using a specialised Neural Language, from which a Petri net (PN) model is constructed automatically for verification, and building a performance model. The PN model can be used to study issues such as synchronisation points, resource sharing and concurrency within a learning rule. Specialised constructs are provided to enable a designer to express various aspects of a learning rule, such as the number and connectivity of neural nodes, the interconnection strategies, and information flows required by the learning algorithm. A scheduling and mapping strategy is then used to translate this PN model onto a multiprocessor template. We demonstrate our technique using a Kohonen and backpropagation learning rules, implemented on a loosely coupled workstation cluster, and a dedicated parallel machine, with PVM libraries.

  2. Sonic hedgehog elevates N-myc gene expression in neural stem cells.

    Science.gov (United States)

    Liu, Dongsheng; Wang, Shouyu; Cui, Yan; Shen, Lun; Du, Yanping; Li, Guilin; Zhang, Bo; Wang, Renzhi

    2012-08-05

    Proliferation of neural stem cells is regulated by the secreted signaling molecule sonic hedgehog. In this study, neural stem cells were infected with recombinant adeno-associated virus expressing sonic hedgehog-N-enhanced green fluorescent protein. The results showed that overexpression of sonic hedgehog in neural stem cells induced the increased expression of Gli1 and N-myc, a target gene of sonic hedgehog. These findings suggest that N-myc is a direct downstream target of the sonic hedgehog signal pathway in neural stem cells. Sonic hedgehog and N-myc are important mediators of sonic hedgehog-induced proliferation of neural stem cells.

  3. Neural networks for predicting mass transfer parameters in supercritical extraction

    Directory of Open Access Journals (Sweden)

    A.P. Fonseca

    2000-12-01

    Full Text Available Neural networks have been investigated for predicting mass transfer coefficients from supercritical Carbon Dioxide/Ethanol/Water system. To avoid the difficulties associated with reduce experimental data set available for supercritical extraction in question, it was chosen to use a technique to generate new semi-empirical data. It combines experimental mass transfer coefficient with those obtained from correlation available in literature, producing an extended data set enough for efficient neural network identification. With respect to available experimental data, the results obtained to benefit neural networks in comparing with empirical correlations for predicting mass transfer parameters.

  4. Optimizing Neural Network Architectures Using Generalization Error Estimators

    DEFF Research Database (Denmark)

    Larsen, Jan

    1994-01-01

    This paper addresses the optimization of neural network architectures. It is suggested to optimize the architecture by selecting the model with minimal estimated averaged generalization error. We consider a least-squares (LS) criterion for estimating neural network models, i.e., the associated...... neural network applications, it is impossible to suggest a perfect model, and consequently the ability to handle incomplete models is urgent. A concise derivation of the GEN-estimator is provided, and its qualities are demonstrated by comparative numerical studies...

  5. Optimizing Neural Network Architectures Using Generalization Error Estimators

    DEFF Research Database (Denmark)

    Larsen, Jan

    1994-01-01

    This paper addresses the optimization of neural network architectures. It is suggested to optimize the architecture by selecting the model with minimal estimated averaged generalization error. We consider a least-squares (LS) criterion for estimating neural network models, i.e., the associated...... neural network applications, it is impossible to suggest a perfect model, and consequently the ability to handle incomplete models is urgent. A concise derivation of the GEN-estimator is provided, and its qualities are demonstrated by comparative numerical studies...

  6. Salience-Affected Neural Networks

    CERN Document Server

    Remmelzwaal, Leendert A; Ellis, George F R

    2010-01-01

    We present a simple neural network model which combines a locally-connected feedforward structure, as is traditionally used to model inter-neuron connectivity, with a layer of undifferentiated connections which model the diffuse projections from the human limbic system to the cortex. This new layer makes it possible to model global effects such as salience, at the same time as the local network processes task-specific or local information. This simple combination network displays interactions between salience and regular processing which correspond to known effects in the developing brain, such as enhanced learning as a result of heightened affect. The cortex biases neuronal responses to affect both learning and memory, through the use of diffuse projections from the limbic system to the cortex. Standard ANNs do not model this non-local flow of information represented by the ascending systems, which are a significant feature of the structure of the brain, and although they do allow associational learning with...

  7. Neural dynamics based on the recognition of neural fingerprints

    Directory of Open Access Journals (Sweden)

    José Luis eCarrillo-Medina

    2015-03-01

    Full Text Available Experimental evidence has revealed the existence of characteristic spiking features in different neural signals, e.g. individual neural signatures identifying the emitter or functional signatures characterizing specific tasks. These neural fingerprints may play a critical role in neural information processing, since they allow receptors to discriminate or contextualize incoming stimuli. This could be a powerful strategy for neural systems that greatly enhances the encoding and processing capacity of these networks. Nevertheless, the study of information processing based on the identification of specific neural fingerprints has attracted little attention. In this work, we study (i the emerging collective dynamics of a network of neurons that communicate with each other by exchange of neural fingerprints and (ii the influence of the network topology on the self-organizing properties within the network. Complex collective dynamics emerge in the network in the presence of stimuli. Predefined inputs, i.e. specific neural fingerprints, are detected and encoded into coexisting patterns of activity that propagate throughout the network with different spatial organization. The patterns evoked by a stimulus can survive after the stimulation is over, which provides memory mechanisms to the network. The results presented in this paper suggest that neural information processing based on neural fingerprints can be a plausible, flexible and powerful strategy.

  8. Identifying the neural substrates of intrinsic motivation during task performance.

    Science.gov (United States)

    Lee, Woogul; Reeve, Johnmarshall

    2017-06-21

    Intrinsic motivation is the inherent tendency to seek out novelty and challenge, to explore and investigate, and to stretch and extend one's capacities. When people imagine performing intrinsically motivating tasks, they show heightened anterior insular cortex (AIC) activity. To fully explain the neural system of intrinsic motivation, however, requires assessing neural activity while people actually perform intrinsically motivating tasks (i.e., while answering curiosity-inducing questions or solving competence-enabling anagrams). Using event-related functional magnetic resonance imaging, we found that the neural system of intrinsic motivation involves not only AIC activity, but also striatum activity and, further, AIC-striatum functional interactions. These findings suggest that subjective feelings of intrinsic satisfaction (associated with AIC activations), reward processing (associated with striatum activations), and their interactions underlie the actual experience of intrinsic motivation. These neural findings are consistent with the conceptualization of intrinsic motivation as the pursuit and satisfaction of subjective feelings (interest and enjoyment) as intrinsic rewards.

  9. Contextual behavior and neural circuits

    Directory of Open Access Journals (Sweden)

    Inah eLee

    2013-05-01

    Full Text Available Animals including humans engage in goal-directed behavior flexibly in response to items and their background, which is called contextual behavior in this review. Although the concept of context has long been studied, there are differences among researchers in defining and experimenting with the concept. The current review aims to provide a categorical framework within which not only the neural mechanisms of contextual information processing but also the contextual behavior can be studied in more concrete ways. For this purpose, we categorize contextual behavior into three subcategories as follows by considering the types of interactions among context, item, and response: contextual response selection, contextual item selection, and contextual item-response selection. Contextual response selection refers to the animal emitting different types of responses to the same item depending on the context in the background. Contextual item selection occurs when there are multiple items that need to be chosen in a contextual manner. Finally, when multiple items and multiple contexts are involved, contextual item-response selection takes place whereby the animal either choose an item or inhibit such a response depending on item-context paired association. The literature suggests that the rhinal cortical regions and the hippocampal formation play key roles in mnemonically categorizing and recognizing contextual representations and the associated items. In addition, it appears that the fronto-striatal cortical loops in connection with the contextual information-processing areas critically control the flexible deployment of adaptive action sets and motor responses for maximizing goals. We suggest that contextual information processing should be investigated in experimental settings where contextual stimuli and resulting behaviors are clearly defined and measurable, considering the dynamic top-down and bottom-up interactions among the neural systems for

  10. Contextual behavior and neural circuits

    Science.gov (United States)

    Lee, Inah; Lee, Choong-Hee

    2013-01-01

    Animals including humans engage in goal-directed behavior flexibly in response to items and their background, which is called contextual behavior in this review. Although the concept of context has long been studied, there are differences among researchers in defining and experimenting with the concept. The current review aims to provide a categorical framework within which not only the neural mechanisms of contextual information processing but also the contextual behavior can be studied in more concrete ways. For this purpose, we categorize contextual behavior into three subcategories as follows by considering the types of interactions among context, item, and response: contextual response selection, contextual item selection, and contextual item–response selection. Contextual response selection refers to the animal emitting different types of responses to the same item depending on the context in the background. Contextual item selection occurs when there are multiple items that need to be chosen in a contextual manner. Finally, when multiple items and multiple contexts are involved, contextual item–response selection takes place whereby the animal either chooses an item or inhibits such a response depending on item–context paired association. The literature suggests that the rhinal cortical regions and the hippocampal formation play key roles in mnemonically categorizing and recognizing contextual representations and the associated items. In addition, it appears that the fronto-striatal cortical loops in connection with the contextual information-processing areas critically control the flexible deployment of adaptive action sets and motor responses for maximizing goals. We suggest that contextual information processing should be investigated in experimental settings where contextual stimuli and resulting behaviors are clearly defined and measurable, considering the dynamic top-down and bottom-up interactions among the neural systems for contextual behavior

  11. Applying Artificial Neural Networks for Face Recognition

    Directory of Open Access Journals (Sweden)

    Thai Hoang Le

    2011-01-01

    Full Text Available This paper introduces some novel models for all steps of a face recognition system. In the step of face detection, we propose a hybrid model combining AdaBoost and Artificial Neural Network (ABANN to solve the process efficiently. In the next step, labeled faces detected by ABANN will be aligned by Active Shape Model and Multi Layer Perceptron. In this alignment step, we propose a new 2D local texture model based on Multi Layer Perceptron. The classifier of the model significantly improves the accuracy and the robustness of local searching on faces with expression variation and ambiguous contours. In the feature extraction step, we describe a methodology for improving the efficiency by the association of two methods: geometric feature based method and Independent Component Analysis method. In the face matching step, we apply a model combining many Neural Networks for matching geometric features of human face. The model links many Neural Networks together, so we call it Multi Artificial Neural Network. MIT + CMU database is used for evaluating our proposed methods for face detection and alignment. Finally, the experimental results of all steps on CallTech database show the feasibility of our proposed model.

  12. Artificial neural networks in neutron dosimetry

    Energy Technology Data Exchange (ETDEWEB)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Mercado, G.A.; Perales M, W.A.; Robles R, J.A. [Unidades Academicas de Estudios Nucleares, UAZ, A.P. 336, 98000 Zacatecas (Mexico); Gallego, E.; Lorente, A. [Depto. de Ingenieria Nuclear, Universidad Politecnica de Madrid, (Spain)

    2005-07-01

    An artificial neural network has been designed to obtain the neutron doses using only the Bonner spheres spectrometer's count rates. Ambient, personal and effective neutron doses were included. 187 neutron spectra were utilized to calculate the Bonner count rates and the neutron doses. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. Re-binned spectra, UTA4 response matrix and fluence-to-dose coefficients were used to calculate the count rates in Bonner spheres spectrometer and the doses. Count rates were used as input and the respective doses were used as output during neural network training. Training and testing was carried out in Mat lab environment. The artificial neural network performance was evaluated using the {chi}{sup 2}- test, where the original and calculated doses were compared. The use of Artificial Neural Networks in neutron dosimetry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

  13. Neutron spectrometry with artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Rodriguez, J.M.; Mercado S, G.A. [Universidad Autonoma de Zacatecas, A.P. 336, 98000 Zacatecas (Mexico); Iniguez de la Torre Bayo, M.P. [Universidad de Valladolid, Valladolid (Spain); Barquero, R. [Hospital Universitario Rio Hortega, Valladolid (Spain); Arteaga A, T. [Envases de Zacatecas, S.A. de C.V., Zacatecas (Mexico)]. e-mail: rvega@cantera.reduaz.mx

    2005-07-01

    An artificial neural network has been designed to obtain the neutron spectra from the Bonner spheres spectrometer's count rates. The neural network was trained using 129 neutron spectra. These include isotopic neutron sources; reference and operational spectra from accelerators and nuclear reactors, spectra from mathematical functions as well as few energy groups and monoenergetic spectra. The spectra were transformed from lethargy to energy distribution and were re-bin ned to 31 energy groups using the MCNP 4C code. Re-binned spectra and UTA4 response matrix were used to calculate the expected count rates in Bonner spheres spectrometer. These count rates were used as input and the respective spectrum was used as output during neural network training. After training the network was tested with the Bonner spheres count rates produced by a set of neutron spectra. This set contains data used during network training as well as data not used. Training and testing was carried out in the Mat lab program. To verify the network unfolding performance the original and unfolded spectra were compared using the {chi}{sup 2}-test and the total fluence ratios. The use of Artificial Neural Networks to unfold neutron spectra in neutron spectrometry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

  14. Neural tube defects

    Directory of Open Access Journals (Sweden)

    M.E. Marshall

    1981-09-01

    Full Text Available Neural tube defects refer to any defect in the morphogenesis of the neural tube, the most common types being spina bifida and anencephaly. Spina bifida has been recognised in skeletons found in north-eastern Morocco and estimated to have an age of almost 12 000 years. It was also known to the ancient Greek and Arabian physicians who thought that the bony defect was due to the tumour. The term spina bifida was first used by Professor Nicolai Tulp of Amsterdam in 1652. Many other terms have been used to describe this defect, but spina bifida remains the most useful general term, as it describes the separation of the vertebral elements in the midline.

  15. Neural tissue-spheres

    DEFF Research Database (Denmark)

    Andersen, Rikke K; Johansen, Mathias; Blaabjerg, Morten

    2007-01-01

    maintained their neurogenic potential throughout 77 days of propagation, while the ability of anterior NTS to generate neurons severely declined from day 40. The present procedure describes isolation and long-term expansion of forebrain SVZ tissue with potential preservation of the endogenous cellular......By combining new and established protocols we have developed a procedure for isolation and propagation of neural precursor cells from the forebrain subventricular zone (SVZ) of newborn rats. Small tissue blocks of the SVZ were dissected and propagated en bloc as free-floating neural tissue......-spheres (NTS) in EGF and FGF2 containing medium. The spheres were cut into quarters when passaged every 10-15th day, avoiding mechanical or enzymatic dissociation in order to minimize cellular trauma and preserve intercellular contacts. For analysis of regional differences within the forebrain SVZ, NTS were...

  16. 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.

  17. Image watermarking capacity analysis based on Hopfield neural network

    Institute of Scientific and Technical Information of China (English)

    Fan Zhang(张帆); Hongbin Zhang(张鸿宾)

    2004-01-01

    In watermarking schemes, watermarking can be viewed as a form of communication problems. Almost all of previous works on image watermarking capacity are based on information theory, using Shannon formula to calculate the capacity of watermarking. In this paper, we present a blind watermarking algorithm using Hopfield neural network, and analyze watermarking capacity based on neural network. In our watermarking algorithm, watermarking capacity is decided by attraction basin of associative memory.

  18. Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Kapil Nahar

    2012-12-01

    Full Text Available An artificial neural network is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information.The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons working in unison to solve specific problems.Ann’s, like people, learn by example.

  19. Neural networks for triggering

    Energy Technology Data Exchange (ETDEWEB)

    Denby, B. (Fermi National Accelerator Lab., Batavia, IL (USA)); Campbell, M. (Michigan Univ., Ann Arbor, MI (USA)); Bedeschi, F. (Istituto Nazionale di Fisica Nucleare, Pisa (Italy)); Chriss, N.; Bowers, C. (Chicago Univ., IL (USA)); Nesti, F. (Scuola Normale Superiore, Pisa (Italy))

    1990-01-01

    Two types of neural network beauty trigger architectures, based on identification of electrons in jets and recognition of secondary vertices, have been simulated in the environment of the Fermilab CDF experiment. The efficiencies for B's and rejection of background obtained are encouraging. If hardware tests are successful, the electron identification architecture will be tested in the 1991 run of CDF. 10 refs., 5 figs., 1 tab.

  20. Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Kapil Nahar

    2012-12-01

    Full Text Available An artificial neural network is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons working in unison to solve specific problems. Ann’s, like people, learn by example.

  1. Compressing Convolutional Neural Networks

    OpenAIRE

    Chen, Wenlin; Wilson, James T.; Tyree, Stephen; Weinberger, Kilian Q.; Chen, Yixin

    2015-01-01

    Convolutional neural networks (CNN) are increasingly used in many areas of computer vision. They are particularly attractive because of their ability to "absorb" great quantities of labeled data through millions of parameters. However, as model sizes increase, so do the storage and memory requirements of the classifiers. We present a novel network architecture, Frequency-Sensitive Hashed Nets (FreshNets), which exploits inherent redundancy in both convolutional layers and fully-connected laye...

  2. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  3. Genome-wide association study identifies 74 loci associated with educational attainment

    DEFF Research Database (Denmark)

    Okbay, Aysu; P. Beauchamp, Jonathan; Alan Fontana, Mark;

    2016-01-01

    -nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural...

  4. Continual Learning through Evolvable Neural Turing Machines

    DEFF Research Database (Denmark)

    Lüders, Benno; Schläger, Mikkel; Risi, Sebastian

    2016-01-01

    Continual learning, i.e. the ability to sequentially learn tasks without catastrophic forgetting of previously learned ones, is an important open challenge in machine learning. In this paper we take a step in this direction by showing that the recently proposed Evolving Neural Turing Machine (ENTM......) approach is able to perform one-shot learning in a reinforcement learning task without catastrophic forgetting of previously stored associations....

  5. Neural Network-Based Hyperspectral Algorithms

    Science.gov (United States)

    2016-06-07

    Neural Network-Based Hyperspectral Algorithms Walter F. Smith, Jr. and Juanita Sandidge Naval Research Laboratory Code 7340, Bldg 1105 Stennis Space...our effort is development of robust numerical inversion algorithms , which will retrieve inherent optical properties of the water column as well as...validate the resulting inversion algorithms with in-situ data and provide estimates of the error bounds associated with the inversion algorithm . APPROACH

  6. Diagnosing process faults using neural network models

    Energy Technology Data Exchange (ETDEWEB)

    Buescher, K.L.; Jones, R.D.; Messina, M.J.

    1993-11-01

    In order to be of use for realistic problems, a fault diagnosis method should have the following three features. First, it should apply to nonlinear processes. Second, it should not rely on extensive amounts of data regarding previous faults. Lastly, it should detect faults promptly. The authors present such a scheme for static (i.e., non-dynamic) systems. It involves using a neural network to create an associative memory whose fixed points represent the normal behavior of the system.

  7. Progress in neural plasticity

    Institute of Scientific and Technical Information of China (English)

    POO; Mu-Ming

    2010-01-01

    One of the properties of the nervous system is the use-dependent plasticity of neural circuits.The structure and function of neural circuits are susceptible to changes induced by prior neuronal activity,as reflected by short-and long-term modifications of synaptic efficacy and neuronal excitability.Regarded as the most attractive cellular mechanism underlying higher cognitive functions such as learning and memory,activity-dependent synaptic plasticity has been in the spotlight of modern neuroscience since 1973 when activity-induced long-term potentiation(LTP) of hippocampal synapses was first discovered.Over the last 10 years,Chinese neuroscientists have made notable contributions to the study of the cellular and molecular mechanisms of synaptic plasticity,as well as of the plasticity beyond synapses,including activity-dependent changes in intrinsic neuronal excitability,dendritic integration functions,neuron-glia signaling,and neural network activity.This work highlight some of these significant findings.

  8. Neural Changes after Phonological Treatment for Anomia: An fMRI Study

    Science.gov (United States)

    Rochon, Elizabeth; Leonard, Carol; Burianova, Hana; Laird, Laura; Soros, Peter; Graham, Simon; Grady, Cheryl

    2010-01-01

    Functional magnetic resonance imaging (fMRI) was used to investigate the neural processing characteristics associated with word retrieval abilities after a phonologically-based treatment for anomia in two stroke patients with aphasia. Neural activity associated with a phonological and a semantic task was compared before and after treatment with…

  9. Neural Changes after Phonological Treatment for Anomia: An fMRI Study

    Science.gov (United States)

    Rochon, Elizabeth; Leonard, Carol; Burianova, Hana; Laird, Laura; Soros, Peter; Graham, Simon; Grady, Cheryl

    2010-01-01

    Functional magnetic resonance imaging (fMRI) was used to investigate the neural processing characteristics associated with word retrieval abilities after a phonologically-based treatment for anomia in two stroke patients with aphasia. Neural activity associated with a phonological and a semantic task was compared before and after treatment with…

  10. Neural correlates of creative thinking and schizotypy.

    Science.gov (United States)

    Park, Haeme R P; Kirk, Ian J; Waldie, Karen E

    2015-07-01

    Empirical studies indicate a link between creativity and schizotypal personality traits, where individuals who score highly on schizotypy measures also display greater levels of creative behaviour. However, the exact nature of this relationship is not yet clear, with only a few studies examining this association using neuroimaging methods. In the present study, the neural substrates of creative thinking were assessed with a drawing task paradigm in healthy individuals using fMRI. These regions were then statistically correlated with the participants' level of schizotypy as measured by the Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE), which is a questionnaire consisting of four dimensions. Neural activations associated with the creativity task were observed in bilateral inferior temporal gyri, left insula, left parietal lobule, right angular gyrus, as well as regions in the prefrontal cortex. This widespread pattern of activation suggests that creative thinking utilises multiple neurocognitive networks, with creative production being the result of collaboration between these regions. Furthermore, the correlational analyses found the Unusual Experiences factor of the O-LIFE to be the most common dimension associated with these areas, followed by the Impulsive Nonconformity dimension. These correlations were negative, indicating that individuals who scored the highest in these factors displayed the least amount of activation when performing the creative task. This is in line with the idea that 'less is more' for creativity, where the deactivation of specific cortical areas may facilitate creativity. Thus, these findings contribute to the evidence of a common neural basis between creativity and schizotypy.

  11. Trimaran Resistance Artificial Neural Network

    Science.gov (United States)

    2011-01-01

    11th International Conference on Fast Sea Transportation FAST 2011, Honolulu, Hawaii, USA, September 2011 Trimaran Resistance Artificial Neural Network Richard...Trimaran Resistance Artificial Neural Network 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e... Artificial Neural Network and is restricted to the center and side-hull configurations tested. The value in the parametric model is that it is able to

  12. Neural networks for nuclear spectroscopy

    Energy Technology Data Exchange (ETDEWEB)

    Keller, P.E.; Kangas, L.J.; Hashem, S.; Kouzes, R.T. [Pacific Northwest Lab., Richland, WA (United States)] [and others

    1995-12-31

    In this paper two applications of artificial neural networks (ANNs) in nuclear spectroscopy analysis are discussed. In the first application, an ANN assigns quality coefficients to alpha particle energy spectra. These spectra are used to detect plutonium contamination in the work environment. The quality coefficients represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with quality coefficients by an expert and used to train the ANN expert system. Our investigation shows that the expert knowledge of spectral quality can be transferred to an ANN system. The second application combines a portable gamma-ray spectrometer with an ANN. In this system the ANN is used to automatically identify, radioactive isotopes in real-time from their gamma-ray spectra. Two neural network paradigms are examined: the linear perception and the optimal linear associative memory (OLAM). A comparison of the two paradigms shows that OLAM is superior to linear perception for this application. Both networks have a linear response and are useful in determining the composition of an unknown sample when the spectrum of the unknown is a linear superposition of known spectra. One feature of this technique is that it uses the whole spectrum in the identification process instead of only the individual photo-peaks. For this reason, it is potentially more useful for processing data from lower resolution gamma-ray spectrometers. This approach has been tested with data generated by Monte Carlo simulations and with field data from sodium iodide and Germanium detectors. With the ANN approach, the intense computation takes place during the training process. Once the network is trained, normal operation consists of propagating the data through the network, which results in rapid identification of samples. This approach is useful in situations that require fast response where precise quantification is less important.

  13. [Artificial neural networks in Neurosciences].

    Science.gov (United States)

    Porras Chavarino, Carmen; Salinas Martínez de Lecea, José María

    2011-11-01

    This article shows that artificial neural networks are used for confirming the relationships between physiological and cognitive changes. Specifically, we explore the influence of a decrease of neurotransmitters on the behaviour of old people in recognition tasks. This artificial neural network recognizes learned patterns. When we change the threshold of activation in some units, the artificial neural network simulates the experimental results of old people in recognition tasks. However, the main contributions of this paper are the design of an artificial neural network and its operation inspired by the nervous system and the way the inputs are coded and the process of orthogonalization of patterns.

  14. Embedding responses in spontaneous neural activity shaped through sequential learning.

    Directory of Open Access Journals (Sweden)

    Tomoki Kurikawa

    Full Text Available Recent experimental measurements have demonstrated that spontaneous neural activity in the absence of explicit external stimuli has remarkable spatiotemporal structure. This spontaneous activity has also been shown to play a key role in the response to external stimuli. To better understand this role, we proposed a viewpoint, "memories-as-bifurcations," that differs from the traditional "memories-as-attractors" viewpoint. Memory recall from the memories-as-bifurcations viewpoint occurs when the spontaneous neural activity is changed to an appropriate output activity upon application of an input, known as a bifurcation in dynamical systems theory, wherein the input modifies the flow structure of the neural dynamics. Learning, then, is a process that helps create neural dynamical systems such that a target output pattern is generated as an attractor upon a given input. Based on this novel viewpoint, we introduce in this paper an associative memory model with a sequential learning process. Using a simple hebbian-type learning, the model is able to memorize a large number of input/output mappings. The neural dynamics shaped through the learning exhibit different bifurcations to make the requested targets stable upon an increase in the input, and the neural activity in the absence of input shows chaotic dynamics with occasional approaches to the memorized target patterns. These results suggest that these dynamics facilitate the bifurcations to each target attractor upon application of the corresponding input, which thus increases the capacity for learning. This theoretical finding about the behavior of the spontaneous neural activity is consistent with recent experimental observations in which the neural activity without stimuli wanders among patterns evoked by previously applied signals. In addition, the neural networks shaped by learning properly reflect the correlations of input and target-output patterns in a similar manner to those designed in

  15. On the neural mechanisms subserving consciousness and attention

    Directory of Open Access Journals (Sweden)

    Catherine eTallon-Baudry

    2012-01-01

    Full Text Available Consciousness, as described in the experimental literature, is a multi-faceted phenomenon, that impinges on other well-studied concepts such as attention and control. Do consciousness and attention refer to different aspects of the same core phenomenon, or do they correspond to distinct functions? One possibility to address this question is to examine the neural mechanisms underlying consciousness and attention. If consciousness and attention pertain to the same concept, they should rely on shared neural mechanisms. Conversely, if their underlying mechanisms are distinct, then consciousness and attention should be considered as distinct entities. This paper therefore reviews neurophysiological facts arguing in favor or against a tight relationship between consciousness and attention. Three neural mechanisms that have been associated with both attention and consciousness are examined (neural amplification, involvement of the fronto-parietal network, and oscillatory synchrony, to conclude that the commonalities between attention and consciousness at the neural level may have been overestimated. Last but not least, experiments in which both attention and consciousness were probed at the neural level point toward a dissociation between the two concepts. It therefore appears from this review that consciousness and attention rely on distinct neural properties, although they can interact at the behavioral level. It is proposed that a "cumulative influence model", in which attention and consciousness correspond to distinct neural mechanisms feeding a single decisional process leading to behavior, fits best with available neural and behavioral data. In this view, consciousness should not be considered as a top-level executive function but should rather be defined by its experiential properties.

  16. High Accuracy Human Activity Monitoring using Neural network

    CERN Document Server

    Sharma, Annapurna; Chung, Wan-Young

    2011-01-01

    This paper presents the designing of a neural network for the classification of Human activity. A Triaxial accelerometer sensor, housed in a chest worn sensor unit, has been used for capturing the acceleration of the movements associated. All the three axis acceleration data were collected at a base station PC via a CC2420 2.4GHz ISM band radio (zigbee wireless compliant), processed and classified using MATLAB. A neural network approach for classification was used with an eye on theoretical and empirical facts. The work shows a detailed description of the designing steps for the classification of human body acceleration data. A 4-layer back propagation neural network, with Levenberg-marquardt algorithm for training, showed best performance among the other neural network training algorithms.

  17. GSK-3 is a master regulator of neural progenitor homeostasis

    Science.gov (United States)

    Kim, Woo-Yang; Wang, Xinshuo; Wu, Yaohong; Doble, Bradley W; Patel, Satish; Woodgett, James R; Snider, William D

    2016-01-01

    The development of the brain requires the exquisite coordination of progenitor proliferation and differentiation to achieve complex circuit assembly. It has been suggested that glycogen synthase kinase 3 (GSK-3) acts as an integrating molecule for multiple proliferation and differentiation signals because of its essential role in the RTK, Wnt and Shh signaling pathways. We created conditional mutations that deleted both the α and β forms of GSK-3 in mouse neural progenitors. GSK-3 deletion resulted in massive hyperproliferation of neural progenitors along the entire neuraxis. Generation of both intermediate neural progenitors and postmitotic neurons was markedly suppressed. These effects were associated with the dysregulation of β-catenin, Sonic Hedgehog, Notch and fibroblast growth factor signaling. Our results indicate that GSK-3 signaling is an essential mediator of homeostatic controls that regulate neural progenitors during mammalian brain development. PMID:19801986

  18. Discriminating lysosomal membrane protein types using dynamic neural network.

    Science.gov (United States)

    Tripathi, Vijay; Gupta, Dwijendra Kumar

    2014-01-01

    This work presents a dynamic artificial neural network methodology, which classifies the proteins into their classes from their sequences alone: the lysosomal membrane protein classes and the various other membranes protein classes. In this paper, neural networks-based lysosomal-associated membrane protein type prediction system is proposed. Different protein sequence representations are fused to extract the features of a protein sequence, which includes seven feature sets; amino acid (AA) composition, sequence length, hydrophobic group, electronic group, sum of hydrophobicity, R-group, and dipeptide composition. To reduce the dimensionality of the large feature vector, we applied the principal component analysis. The probabilistic neural network, generalized regression neural network, and Elman regression neural network (RNN) are used as classifiers and compared with layer recurrent network (LRN), a dynamic network. The dynamic networks have memory, i.e. its output depends not only on the input but the previous outputs also. Thus, the accuracy of LRN classifier among all other artificial neural networks comes out to be the highest. The overall accuracy of jackknife cross-validation is 93.2% for the data-set. These predicted results suggest that the method can be effectively applied to discriminate lysosomal associated membrane proteins from other membrane proteins (Type-I, Outer membrane proteins, GPI-Anchored) and Globular proteins, and it also indicates that the protein sequence representation can better reflect the core feature of membrane proteins than the classical AA composition.

  19. via dynamic neural networks

    Directory of Open Access Journals (Sweden)

    J. Reyes-Reyes

    2000-01-01

    Full Text Available In this paper, an adaptive technique is suggested to provide the passivity property for a class of partially known SISO nonlinear systems. A simple Dynamic Neural Network (DNN, containing only two neurons and without any hidden-layers, is used to identify the unknown nonlinear system. By means of a Lyapunov-like analysis the new learning law for this DNN, guarantying both successful identification and passivation effects, is derived. Based on this adaptive DNN model, an adaptive feedback controller, serving for wide class of nonlinear systems with an a priori incomplete model description, is designed. Two typical examples illustrate the effectiveness of the suggested approach.

  20. Neural substrates of sublexical processing for spelling.

    Science.gov (United States)

    DeMarco, Andrew T; Wilson, Stephen M; Rising, Kindle; Rapcsak, Steven Z; Beeson, Pélagie M

    2017-01-01

    We used fMRI to examine the neural substrates of sublexical phoneme-grapheme conversion during spelling in a group of healthy young adults. Participants performed a writing-to-dictation task involving irregular words (e.g., choir), plausible nonwords (e.g., kroid), and a control task of drawing familiar geometric shapes (e.g., squares). Written production of both irregular words and nonwords engaged a left-hemisphere perisylvian network associated with reading/spelling and phonological processing skills. Effects of lexicality, manifested by increased activation during nonword relative to irregular word spelling, were noted in anterior perisylvian regions (posterior inferior frontal gyrus/operculum/precentral gyrus/insula), and in left ventral occipito-temporal cortex. In addition to enhanced neural responses within domain-specific components of the language network, the increased cognitive demands associated with spelling nonwords engaged domain-general frontoparietal cortical networks involved in selective attention and executive control. These results elucidate the neural substrates of sublexical processing during written language production and complement lesion-deficit correlation studies of phonological agraphia. Copyright © 2016 Elsevier Inc. All rights reserved.

  1. Neural network correction of astrometric chromaticity

    CERN Document Server

    Gai, M

    2005-01-01

    In this paper we deal with the problem of chromaticity, i.e. apparent position variation of stellar images with their spectral distribution, using neural networks to analyse and process astronomical images. The goal is to remove this relevant source of systematic error in the data reduction of high precision astrometric experiments, like Gaia. This task can be accomplished thanks to the capability of neural networks to solve a nonlinear approximation problem, i.e. to construct an hypersurface that approximates a given set of scattered data couples. Images are encoded associating each of them with conveniently chosen moments, evaluated along the y axis. The technique proposed, in the current framework, reduces the initial chromaticity of few milliarcseconds to values of few microarcseconds.

  2. Sparse neural networks with large learning diversity

    CERN Document Server

    Gripon, Vincent

    2011-01-01

    Coded recurrent neural networks with three levels of sparsity are introduced. The first level is related to the size of messages, much smaller than the number of available neurons. The second one is provided by a particular coding rule, acting as a local constraint in the neural activity. The third one is a characteristic of the low final connection density of the network after the learning phase. Though the proposed network is very simple since it is based on binary neurons and binary connections, it is able to learn a large number of messages and recall them, even in presence of strong erasures. The performance of the network is assessed as a classifier and as an associative memory.

  3. Sonic hedgehog elevates N-myc gene expression in neural stem cells★

    OpenAIRE

    Liu, Dongsheng; Wang, Shouyu; Cui, Yan; Shen, Lun; Du, Yanping; Li, Guilin; Zhang, Bo; Wang, Renzhi

    2012-01-01

    Proliferation of neural stem cells is regulated by the secreted signaling molecule sonic hedgehog. In this study, neural stem cells were infected with recombinant adeno-associated virus expressing sonic hedgehog-N-enhanced green fluorescent protein. The results showed that overexpression of sonic hedgehog in neural stem cells induced the increased expression of Gli1 and N-myc, a target gene of sonic hedgehog. These findings suggest that N-myc is a direct downstream target of the sonic hedgeho...

  4. Implementation of a kinematic fit of single top-quark production in association with a W boson and its application in a neural-network-based analysis in ATLAS

    Energy Technology Data Exchange (ETDEWEB)

    Loddenkoetter, Thomas

    2012-08-15

    In order to provide discrimination between the Wt-channel signal and its backgrounds for analyses that try to measure single top-quark production in the Wt-channel, a kinematic fit to the lepton+jets decay mode of the Wt-channel has been implemented using the KLFitter package. The fit has been validated by studying its performance in terms of the efficiency of the fit to correctly assign the final-state quarks of the fit model to the measured jets as a function of various parameters, as well as the improvement of the energy resolutions of the fitted particles due to the fit. By combining the output variables of the kinematic fitter using neural networks, it has been shown that the fit results are suitable to identify the decay mode of the top quark in Wt events and to identify whether the kinematic fit succeeded in correctly assigning the final-state quarks to the measured jets. In order to demonstrate the value of the kinematic fit for analysis, another neural network - again using strictly results of the kinematic fit as input - has been trained to separate to the Wt-channel signal from its backgrounds. A separation power comparable to a conventional neural-network-based Wt-channel analysis has been achieved.

  5. Development of a multi-sensor system associated with a neural network signal processing device for the analysis of air quality; Implantation materielle d'un systeme multi-capteurs et d'une structure neuronale en vue de l'analyse de la qualite de l'air

    Energy Technology Data Exchange (ETDEWEB)

    Taright, Y.; Hubin, M. [Institut National des Sciences Appliquees (INSA), Lab. Perception Systemes Information, 76 - Rouen (France)

    1999-07-01

    The chronic or accidental atmospheric pollution follow up creates a lot of reflections and various technological approaches. In order to answer the needs specified in the new law on 'air quality', we have envisaged the development of an autonomous cost effective micro-system specially dedicated to real-time air analysis. This system is based upon the electronic nose concept: that is to say the use of gas chemical micro-sensors associated with a neural network signal processing device implemented in a FPGA circuit, designed with VHDL language. (authors)

  6. Neural networks in astronomy.

    Science.gov (United States)

    Tagliaferri, Roberto; Longo, Giuseppe; Milano, Leopoldo; Acernese, Fausto; Barone, Fabrizio; Ciaramella, Angelo; De Rosa, Rosario; Donalek, Ciro; Eleuteri, Antonio; Raiconi, Giancarlo; Sessa, Salvatore; Staiano, Antonino; Volpicelli, Alfredo

    2003-01-01

    In the last decade, the use of neural networks (NN) and of other soft computing methods has begun to spread also in the astronomical community which, due to the required accuracy of the measurements, is usually reluctant to use automatic tools to perform even the most common tasks of data reduction and data mining. The federation of heterogeneous large astronomical databases which is foreseen in the framework of the astrophysical virtual observatory and national virtual observatory projects, is, however, posing unprecedented data mining and visualization problems which will find a rather natural and user friendly answer in artificial intelligence tools based on NNs, fuzzy sets or genetic algorithms. This review is aimed to both astronomers (who often have little knowledge of the methodological background) and computer scientists (who often know little about potentially interesting applications), and therefore will be structured as follows: after giving a short introduction to the subject, we shall summarize the methodological background and focus our attention on some of the most interesting fields of application, namely: object extraction and classification, time series analysis, noise identification, and data mining. Most of the original work described in the paper has been performed in the framework of the AstroNeural collaboration (Napoli-Salerno).

  7. 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...

  8. Neural relativity principle

    Science.gov (United States)

    Koulakov, Alexei

    Olfaction is the final frontier of our senses - the one that is still almost completely mysterious to us. Despite extensive genetic and perceptual data, and a strong push to solve the neural coding problem, fundamental questions about the sense of smell remain unresolved. Unlike vision and hearing, where relatively straightforward relationships between stimulus features and neural responses have been foundational to our understanding sensory processing, it has been difficult to quantify the properties of odorant molecules that lead to olfactory percepts. In a sense, we do not have olfactory analogs of ``red'', ``green'' and ``blue''. The seminal work of Linda Buck and Richard Axel identified a diverse family of about 1000 receptor molecules that serve as odorant sensors in the nose. However, the properties of smells that these receptors detect remain a mystery. I will review our current understanding of the molecular properties important to the olfactory system. I will also describe a theory that explains how odorant identity can be preserved despite substantial changes in the odorant concentration.

  9. The Neural Web of War

    NARCIS (Netherlands)

    Kennis, M.

    2016-01-01

    The aim of this thesis was to gain more insight in the neural network alterations that may underlie PTSD and trauma-focused therapy outcome. To investigate TheNeural Web of War brain scans of healthy civilians (n=26), and veterans with (n=58) and without (n=29) PTSD were assessed. Structural and fun

  10. The Neural Support Vector Machine

    NARCIS (Netherlands)

    Wiering, Marco; van der Ree, Michiel; Embrechts, Mark; Stollenga, Marijn; Meijster, Arnold; Nolte, A; Schomaker, Lambertus

    2013-01-01

    This paper describes a new machine learning algorithm for regression and dimensionality reduction tasks. The Neural Support Vector Machine (NSVM) is a hybrid learning algorithm consisting of neural networks and support vector machines (SVMs). The output of the NSVM is given by SVMs that take a

  11. Neural Networks for Optimal Control

    DEFF Research Database (Denmark)

    Sørensen, O.

    1995-01-01

    Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....

  12. The Neural Support Vector Machine

    NARCIS (Netherlands)

    Wiering, Marco; van der Ree, Michiel; Embrechts, Mark; Stollenga, Marijn; Meijster, Arnold; Nolte, A; Schomaker, Lambertus

    2013-01-01

    This paper describes a new machine learning algorithm for regression and dimensionality reduction tasks. The Neural Support Vector Machine (NSVM) is a hybrid learning algorithm consisting of neural networks and support vector machines (SVMs). The output of the NSVM is given by SVMs that take a centr

  13. Neural Networks for Optimal Control

    DEFF Research Database (Denmark)

    Sørensen, O.

    1995-01-01

    Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....

  14. VOLTAGE COMPENSATION USING ARTIFICIAL NEURAL NETWORK

    African Journals Online (AJOL)

    VOLTAGE COMPENSATION USING ARTIFICIAL NEURAL NETWORK: A CASE STUDY OF RUMUOLA DISTRIBUTION NETWORK. ... The artificial neural networks controller engaged to controlling the dynamic voltage ... Article Metrics.

  15. Medical diagnosis using neural network

    CERN Document Server

    Kamruzzaman, S M; Siddiquee, Abu Bakar; Mazumder, Md Ehsanul Hoque

    2010-01-01

    This research is to search for alternatives to the resolution of complex medical diagnosis where human knowledge should be apprehended in a general fashion. Successful application examples show that human diagnostic capabilities are significantly worse than the neural diagnostic system. This paper describes a modified feedforward neural network constructive algorithm (MFNNCA), a new algorithm for medical diagnosis. The new constructive algorithm with backpropagation; offer an approach for the incremental construction of near-minimal neural network architectures for pattern classification. The algorithm starts with minimal number of hidden units in the single hidden layer; additional units are added to the hidden layer one at a time to improve the accuracy of the network and to get an optimal size of a neural network. The MFNNCA was tested on several benchmarking classification problems including the cancer, heart disease and diabetes. Experimental results show that the MFNNCA can produce optimal neural networ...

  16. Neural fields theory and applications

    CERN Document Server

    Graben, Peter; Potthast, Roland; Wright, James

    2014-01-01

    With this book, the editors present the first comprehensive collection in neural field studies, authored by leading scientists in the field - among them are two of the founding-fathers of neural field theory. Up to now, research results in the field have been disseminated across a number of distinct journals from mathematics, computational neuroscience, biophysics, cognitive science and others. Starting with a tutorial for novices in neural field studies, the book comprises chapters on emergent patterns, their phase transitions and evolution, on stochastic approaches, cortical development, cognition, robotics and computation, large-scale numerical simulations, the coupling of neural fields to the electroencephalogram and phase transitions in anesthesia. The intended readership are students and scientists in applied mathematics, theoretical physics, theoretical biology, and computational neuroscience. Neural field theory and its applications have a long-standing tradition in the mathematical and computational ...

  17. Iris sector heterochromia as a marker for neural crest disease.

    Science.gov (United States)

    Brazel, S M; Sullivan, T J; Thorner, P S; Clarke, M P; Hunter, W S; Morin, J D

    1992-02-01

    A 6-month-old female infant with biopsy-proved Hirschsprung's disease had associated sector heterochromia of the irides. The association between sector heterochromia and Hirschsprung's disease has been previously reported and both conditions have been ascribed to neural crest defects. Histologic characteristics of the ocular involvement have not previously been reported, to our knowledge. Histopathologic examination of the globes revealed decreased iris stroma, decreased pigmentation in the anterior stroma, and reduced numbers of pigment-producing cells in the affected areas. Both the ocular and gastrointestinal findings reflect abnormalities in tissues of neural crest origin.

  18. Neural basis of social status hierarchy across species.

    Science.gov (United States)

    Chiao, Joan Y

    2010-12-01

    Social status hierarchy is a ubiquitous principle of social organization across the animal kingdom. Recent findings in social neuroscience reveal distinct neural networks associated with the recognition and experience of social hierarchy in humans, as well as modulation of these networks by personality and culture. Additionally, allelic variation in the serotonin transporter gene is associated with prevalence of social hierarchy across species and cultures, suggesting the importance of the study of genetic factors underlying social hierarchy. Future studies are needed to determine how genetic and environmental factors shape neural systems involved in the production and maintenance of social hierarchy across ontogeny and phylogeny. Copyright © 2010 Elsevier Ltd. All rights reserved.

  19. Neural correlates of consciousness.

    Science.gov (United States)

    Negrao, B L; Viljoen, M

    2009-11-01

    A basic understanding of consciousness and its neural correlates is of major importance for all clinicians, especially those involved with patients with altered states of consciousness. In this paper it is shown that consciousness is dependent on the brainstem and thalamus for arousal; that basic cognition is supported by recurrent electrical activity between the cortex and the thalamus at gamma band frequencies; aand that some kind of working memory must, at least fleetingly, be present for awareness to occur. The problem of cognitive binding and the role of attention are briefly addressed and it shown that consciousness depends on a multitude of subconscious processes. Although these processes do not represent consciousness, consciousness cannot exist without them.

  20. Prospective use of skin-derived precursors in neural regeneration

    Institute of Scientific and Technical Information of China (English)

    LU Xiao-cheng; TAO Yi; LI Li-xin

    2012-01-01

    Objective To review recent studies concerning the origins of skin-derived precursors (SKPs),their differentiation characteristics,and their potential application in neural regenerative medicine.Data sources Data were retrieved from studies reported in PubMed published between April,1974 and June,2012.The search terms used were "skin-derived precursors","stem cells",and "neural diseases".Study selection Articles were included in the review if they were relevant to SKPs as stem cells,as well as their applications in neural regenerative medicine,such as in the treatment of spinal cord injury,Parkinson's disease,spinal muscular atrophy and Shah-Waardenburg syndrome.Results SKPs are a novel population of neural crest-derived precursors that arise during embryogenesis and persist into adulthood.They can generate both neural cells and mesodermal lineage cells (including smooth muscle cells and adipocytes).Compared with other stem cells,SKPs are abundant in adult skin,can differentiate easily into neural cells,and are not associated with any ethical controversies.Conclusion SKPs may provide an alternative source of stem cells to embryonic stem cells for transplantation therapy for neurological diseases.

  1. High level cognitive information processing in neural networks

    Science.gov (United States)

    Barnden, John A.; Fields, Christopher A.

    1992-01-01

    Two related research efforts were addressed: (1) high-level connectionist cognitive modeling; and (2) local neural circuit modeling. The goals of the first effort were to develop connectionist models of high-level cognitive processes such as problem solving or natural language understanding, and to understand the computational requirements of such models. The goals of the second effort were to develop biologically-realistic model of local neural circuits, and to understand the computational behavior of such models. In keeping with the nature of NASA's Innovative Research Program, all the work conducted under the grant was highly innovative. For instance, the following ideas, all summarized, are contributions to the study of connectionist/neural networks: (1) the temporal-winner-take-all, relative-position encoding, and pattern-similarity association techniques; (2) the importation of logical combinators into connection; (3) the use of analogy-based reasoning as a bridge across the gap between the traditional symbolic paradigm and the connectionist paradigm; and (4) the application of connectionism to the domain of belief representation/reasoning. The work on local neural circuit modeling also departs significantly from the work of related researchers. In particular, its concentration on low-level neural phenomena that could support high-level cognitive processing is unusual within the area of biological local circuit modeling, and also serves to expand the horizons of the artificial neural net field.

  2. [Folic acid: Primary prevention of neural tube defects. Literature Review].

    Science.gov (United States)

    Llamas Centeno, M J; Miguélez Lago, C

    2016-03-01

    Neural tube defects (NTD) are the most common congenital malformations of the nervous system, they have a multifactorial etiology, are caused by exposure to chemical, physical or biological toxic agents, factors deficiency, diabetes, obesity, hyperthermia, genetic alterations and unknown causes. Some of these factors are associated with malnutrition by interfering with the folic acid metabolic pathway, the vitamin responsible for neural tube closure. Its deficit produce anomalies that can cause abortions, stillbirths or newborn serious injuries that cause disability, impaired quality of life and require expensive treatments to try to alleviate in some way the alterations produced in the embryo. Folic acid deficiency is considered the ultimate cause of the production of neural tube defects, it is clear the reduction in the incidence of Espina Bifida after administration of folic acid before conception, this leads us to want to further study the action of folic acid and its application in the primary prevention of neural tube defects. More than 40 countries have made the fortification of flour with folate, achieving encouraging data of decrease in the prevalence of neural tube defects. This paper attempts to make a literature review, which clarify the current situation and future of the prevention of neural tube defects.

  3. Neural repair in the adult brain [version 1; referees: 3 approved

    Directory of Open Access Journals (Sweden)

    Sebastian Jessberger

    2016-02-01

    Full Text Available Acute or chronic injury to the adult brain often results in substantial loss of neural tissue and subsequent permanent functional impairment. Over the last two decades, a number of approaches have been developed to harness the regenerative potential of neural stem cells and the existing fate plasticity of neural cells in the nervous system to prevent tissue loss or to enhance structural and functional regeneration upon injury. Here, we review recent advances of stem cell-associated neural repair in the adult brain, discuss current challenges and limitations, and suggest potential directions to foster the translation of experimental stem cell therapies into the clinic.

  4. The study of fuzzy chaotic neural network based on chaotic method

    Institute of Scientific and Technical Information of China (English)

    WANG Ke-jun; TANG Mo; ZHANG Yan

    2006-01-01

    This paper proposes a type of Fuzzy Chaotic Neural Network (FCNN). Firstly, the model of recurrent fuzzy neural network (RFNN) is considered, which adds a feedback in the second layer to realize dynamic map. Then, the Logistic map is introduced into the recurrent fuzzy neural network, so as to build a Fuzzy Chaotic Neural Network (FCNN). Its chaotic character is analyzed, and then the training algorithm and associate memory ability are studied subsequently. And then, a chaotic system is approximated using FCNN; the simulation results indicate that FCNN could approach dynamic system preferably. And owing to the introducing of chaotic map, the chaotic recollect capacity of FCNN is increased.

  5. Functional model of biological neural networks.

    Science.gov (United States)

    Lo, James Ting-Ho

    2010-12-01

    A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

  6. Neural Tube Defects, Folic Acid and Methylation

    Directory of Open Access Journals (Sweden)

    Henk J. Blom

    2013-09-01

    Full Text Available Neural tube defects (NTDs are common complex congenital malformations resulting from failure of the neural tube closure during embryogenesis. It is established that folic acid supplementation decreases the prevalence of NTDs, which has led to national public health policies regarding folic acid. To date, animal studies have not provided sufficient information to establish the metabolic and/or genomic mechanism(s underlying human folic acid responsiveness in NTDs. However, several lines of evidence suggest that not only folates but also choline, B12 and methylation metabolisms are involved in NTDs. Decreased B12 vitamin and increased total choline or homocysteine in maternal blood have been shown to be associated with increased NTDs risk. Several polymorphisms of genes involved in these pathways have also been implicated in risk of development of NTDs. This raises the question whether supplementation with B12 vitamin, betaine or other methylation donors in addition to folic acid periconceptional supplementation will further reduce NTD risk. The objective of this article is to review the role of methylation metabolism in the onset of neural tube defects.

  7. Memory Consolidation and Neural Substrate of Reward

    Directory of Open Access Journals (Sweden)

    Redolar-Ripoll, Diego

    2012-08-01

    Full Text Available The aim of this report is to analyze the relationships between reward and learning and memory processes. Different studies have described how information about rewards influences behavior and how the brain uses this reward information to control learning and memory processes. Reward nature seems to be processed in different ways by neurons in different brain structures, ranging from the detection and perception of rewards to the use of information about predicted rewards for the control of goal-directed behavior. The neural substrate underling this processing of reward information is a reliable way of improving learning and memory processes. Evidence from several studies indicates that this neural system can facilitate memory consolidation in a wide variety of learning tasks. From a molecular perspective, certain cardinal features of reward have been described as forms of memory. Studies of human addicts and studies in animal models of addiction show that chronic drug exposure produces stable changes in the brain at the cellular and molecular levels that underlie the long-lasting behavioral plasticity associated with addiction. These molecular and cellular adaptations involved in addiction are also implicated in learning and memory processes. Dopamine seems to be a critical common signal to activate different genetic mechanisms that ultimately remodel synapses and circuits. Despite memory is an active and complex process mediated by different brain areas, the neural substrate of reward is able to improve memory consolidation in a several paradigms. We believe that there are many equivalent traits between reward and learning and memory processes.

  8. Neural Correlates of Visuomotor Learning in Autism.

    Science.gov (United States)

    Sharer, Elizabeth; Crocetti, Deana; Muschelli, John; Barber, Anita D; Nebel, Mary Beth; Caffo, Brian S; Pekar, Jim J; Mostofsky, Stewart H

    2015-12-01

    Motor impairments are prevalent in children with autism spectrum disorder. The Serial Reaction Time Task, a well-established visuomotor sequence learning probe, has produced inconsistent behavioral findings in individuals with autism. Moreover, it remains unclear how underlying neural processes for visuomotor learning in children with autism compare to processes for typically developing children. Neural activity differences were assessed using functional magnetic resonance imaging during a modified version of the Serial Reaction Time Task in children with and without autism. Though there was no group difference in visuomotor sequence learning, underlying patterns of neural activation significantly differed when comparing sequence (i.e., learning) to random (i.e., nonlearning) blocks. Children with autism demonstrated decreased activity in brain regions implicated in visuomotor sequence learning: superior temporal sulcus and posterior cingulate cortex. The findings implicate differences in brain mechanisms that support initial sequence learning in autism and can help explain behavioral observations of autism-associated impairments in skill development (motor, social, communicative) reliant on visuomotor integration.

  9. Prevenção de defeitos do tubo neural: prevalência do uso da suplementação de ácido fólico e fatores associados em gestantes na cidade de Pelotas, Rio Grande do Sul, Brasil Prevention of neural tube defects: prevalence of folic acid supplementation during pregnancy and associated factors in Pelotas, Rio Grande do Sul State, Brazil

    Directory of Open Access Journals (Sweden)

    Cíntia Leal Sclowitz Mezzomo

    2007-11-01

    Full Text Available Com o objetivo de determinar a prevalência do uso do ácido fólico e fatores associados na gestação e no período periconcepcional, realizou-se um estudo transversal de base populacional nas cinco maternidades da cidade de Pelotas, Rio Grande do Sul, Brasil. A coleta de dados ocorreu no período de 1º de abril a 15 de agosto de 2006, com 1.450 mulheres. As entrevistas foram realizadas em nível hospitalar por questionário padronizado. A análise estatística se realizou por regressão de Poisson. A prevalência do uso de ácido fólico na gestação foi de 31,8%, e no período periconcepcional, foi de 4,3%. Os fatores associados ao uso de ácido fólico foram: cor branca, escolaridade acima de nove anos, renda acima de 600 Reais, idade acima de trinta anos, gestação planejada, sete ou mais consultas de pré-natal, consultas na rede privada de saúde e conhecimento sobre o ácido fólico. Para diminuir a prevalência de defeitos do tubo neural, é importante promover-se o uso do ácido fólico nas mulheres em idade fértil, nas mulheres sócio-economicamente menos favorecidas e torná-lo disponível na rede pública de saúde.To determine folic acid use and associated factors, a cross-sectional population-based study was conducted in all five maternity hospitals in Pelotas, Rio Grande do Sul State, Brazil. Data were collected from April 1 to August 15, 2006 (n = 1,450 women. A standard questionnaire was applied in the hospitals. Statistical analysis used Poisson regression. Prevalence of folic acid consumption during pregnancy was 31.8%, and periconceptional use was 4.3%. The following were associated with folic acid use: white skin color, schooling > 9 years, family income > R$600, age > 30 years, planned pregnancy, > 7 prenatal visits, knowledge on folic acid, and prenatal care in the private health system. In order to prevent neural tube defects, it is important to promote folic acid use among childbearing-age women and to supply folic

  10. A study of reactor monitoring method with neural network

    Energy Technology Data Exchange (ETDEWEB)

    Nabeshima, Kunihiko [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

    2001-03-01

    The purpose of this study is to investigate the methodology of Nuclear Power Plant (NPP) monitoring with neural networks, which create the plant models by the learning of the past normal operation patterns. The concept of this method is to detect the symptom of small anomalies by monitoring the deviations between the process signals measured from an actual plant and corresponding output signals from the neural network model, which might not be equal if the abnormal operational patterns are presented to the input of the neural network. Auto-associative network, which has same output as inputs, can detect an kind of anomaly condition by using normal operation data only. The monitoring tests of the feedforward neural network with adaptive learning were performed using the PWR plant simulator by which many kinds of anomaly conditions can be easily simulated. The adaptively trained feedforward network could follow the actual plant dynamics and the changes of plant condition, and then find most of the anomalies much earlier than the conventional alarm system during steady state and transient operations. Then the off-line and on-line test results during one year operation at the actual NPP (PWR) showed that the neural network could detect several small anomalies which the operators or the conventional alarm system didn't noticed. Furthermore, the sensitivity analysis suggests that the plant models by neural networks are appropriate. Finally, the simulation results show that the recurrent neural network with feedback connections could successfully model the slow behavior of the reactor dynamics without adaptive learning. Therefore, the recurrent neural network with adaptive learning will be the best choice for the actual reactor monitoring system. (author)

  11. Thyroid hormone and retinoic acid interact to regulate zebrafish craniofacial neural crest development.

    Science.gov (United States)

    Bohnsack, Brenda L; Kahana, Alon

    2013-01-15

    Craniofacial and ocular morphogenesis require proper regulation of cranial neural crest migration, proliferation, survival and differentiation. Although alterations in maternal thyroid hormone (TH) are associated with congenital craniofacial anomalies, the role of TH on the neural crest has not been previously described. Using zebrafish, we demonstrate that pharmacologic and genetic alterations in TH signaling disrupt cranial neural crest migration, proliferation, and survival, leading to craniofacial, extraocular muscle, and ocular developmental abnormalities. In the rostral cranial neural crest that gives rise to the periocular mesenchyme and the frontonasal process, retinoic acid (RA) rescued migratory defects induced by decreased TH signaling. In the caudal cranial neural crest, TH and RA had reciprocal effects on anterior and posterior pharyngeal arch development. The interactions between TH and RA signaling were partially mediated by the retinoid X receptor. We conclude that TH regulates both rostral and caudal cranial neural crest. Further, coordinated interactions of TH and RA are required for proper craniofacial and ocular development.

  12. Exponential synchronization of general chaotic delayed neural networks via hybrid feedback

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    This paper investigates the exponential synchronization problem of some chaotic delayed neural networks based on the proposed general neural network model, which is the interconnection of a linear delayed dynamic system and a bounded static nonlinear operator, and covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks (CNNs), bidirectional associative memory (BAM) networks, recurrent multilayer perceptrons (RMLPs). By virtue of LyapunovKrasovskii stability theory and linear matrix inequality (LMI) technique, some exponential synchronization criteria are derived.Using the drive-response concept, hybrid feedback controllers are designed to synchronize two identical chaotic neural networks based on those synchronization criteria. Finally, detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws.

  13. Pathological personality traits modulate neural interactions.

    Science.gov (United States)

    James, Lisa M; Engdahl, Brian E; Leuthold, Arthur C; Krueger, Robert F; Georgopoulos, Apostolos P

    2015-12-01

    The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), includes an empirically supported dimensional model of personality pathology that is assessed via the Personality Inventory for the DSM-5 (PID-5). Here we used magnetoencephalography (MEG; 248 sensors) to evaluate resting-state neural network properties associated with the five primary DSM-5 maladaptive personality domains (negative affect, detachment, antagonism, disinhibition, and psychoticism) in 150 healthy veterans ("control" group) and 179 veterans with various psychiatric disorders ("psychopathology" group). Since a fundamental network property is the strength of functional connectivity among network elements, we used the absolute value of the pairwise correlation coefficient (aCC) between prewhitened MEG sensor time series as a measure of neural functional connectivity and assessed its relations to the quantitative PID-5 scores in a linear regression model, where the log-transformed aCC was the dependent variable and individual PID scores, age, and gender were the independent variables. The partial regression coefficient (pRC) for a specific PID-5 score in that model provided information concerning the direction (positive, negative) and size (absolute value) of the PID effect on the strength of neural correlations. We found that, overall, PID domains had a negative effect (i.e., negative pRC; decorrelation) on aCC in the control group, but a positive one (i.e., positive pRC; hyper-correlation) in the psychopathology group. This dissociation of PID effects on aCC was especially pronounced for disinhibition, psychoticism, and negative affect. These results document for the first time a fundamental difference in neural-PID relations between control and psychopathology groups.

  14. Artificial Neural Network Analysis System

    Science.gov (United States)

    2007-11-02

    Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis

  15. Cooperating attackers in neural cryptography.

    Science.gov (United States)

    Shacham, Lanir N; Klein, Einat; Mislovaty, Rachel; Kanter, Ido; Kinzel, Wolfgang

    2004-06-01

    A successful attack strategy in neural cryptography is presented. The neural cryptosystem, based on synchronization of neural networks by mutual learning, has been recently shown to be secure under different attack strategies. The success of the advanced attacker presented here, called the "majority-flipping attacker," does not decay with the parameters of the model. This attacker's outstanding success is due to its using a group of attackers which cooperate throughout the synchronization process, unlike any other attack strategy known. An analytical description of this attack is also presented, and fits the results of simulations.

  16. Learning control of inverted pendulum system by neural network driven fuzzy reasoning: The learning function of NN-driven fuzzy reasoning under changes of reasoning environment

    Science.gov (United States)

    Hayashi, Isao; Nomura, Hiroyoshi; Wakami, Noboru

    1991-01-01

    Whereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm.

  17. Morphological associative memories.

    Science.gov (United States)

    Ritter, G X; Sussner, P; Diza-de-Leon, J L

    1998-01-01

    The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. A nonlinear activation function usually follows the linear operation in order to provide for nonlinearity of the network and set the next state of the neuron. In this paper we introduce a novel class of artificial neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before possible application of a nonlinear activation function. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. The main emphasis of the research presented here is on morphological associative memories. We examine the computing and storage capabilities of morphological associative memories and discuss differences between morphological models and traditional semilinear models such as the Hopfield net.

  18. The neural basis of body form and body action agnosia.

    Science.gov (United States)

    Moro, Valentina; Urgesi, Cosimo; Pernigo, Simone; Lanteri, Paola; Pazzaglia, Mariella; Aglioti, Salvatore Maria

    2008-10-23

    Visual analysis of faces and nonfacial body stimuli brings about neural activity in different cortical areas. Moreover, processing body form and body action relies on distinct neural substrates. Although brain lesion studies show specific face processing deficits, neuropsychological evidence for defective recognition of nonfacial body parts is lacking. By combining psychophysics studies with lesion-mapping techniques, we found that lesions of ventromedial, occipitotemporal areas induce face and body recognition deficits while lesions involving extrastriate body area seem causatively associated with impaired recognition of body but not of face and object stimuli. We also found that body form and body action recognition deficits can be double dissociated and are causatively associated with lesions to extrastriate body area and ventral premotor cortex, respectively. Our study reports two category-specific visual deficits, called body form and body action agnosia, and highlights their neural underpinnings.

  19. EDITORIAL: Why we need a new journal in neural engineering

    Science.gov (United States)

    Durand, Dominique M.

    2004-03-01

    revolutionary change in the quality of life of persons with sensory and/or motor deficits. Microelectrode technology represents the initial step towards this goal and has already improved the quality of life of many patients, as is evident from the success of auditory prostheses. The cost to society of neurological disorders such as stroke, Parkinson's disease, Alzheimer's disease and epilepsy is staggering. Stroke, which is the third leading cause of death in North America, runs up costs of 40 billion to society per year for its treatment. Costs associated with brain disorders are estimated at 285 billion. Breakthroughs in this field will have a significant impact on the market for enabling technologies. The market for neurological medical devices totaled 2 billion in 1999 and is projected to grow at a rate of 20 to 30% in the next ten years, far outpacing the market for cardiac devices. Although we have all recognized the importance of interdisciplinary research (see the NIH Road map at http://nihroadmap.nih.gov/), the fields of neuroscience and engineering have remained compartmentalized. Collaboration is still difficult since the language of these disciplines is different. Moreover, the scientific journals in these fields are also clearly separate. Researchers involved in neural engineering have a choice of publishing their research in either neuroscience-oriented journals such as Journal of Neuroscience, Journal of Neurophysiology and Brain Research or in engineering journals such as IEEE Transactions on Biomedical Engineering, IEEE Transactions on Neural Systems and Rehabilitation and Annals of Biomedical Engineering. There is no journal currently available focusing on the interdisciplinary field of neural engineering. In order to capitalize on the potential of neural engineering to investigate neural function and to solve problems related to neural disorders, it is necessary to break down the traditional barriers between neuroscientists and engineers not just in the

  20. Morphogenetic movements in the neural plate and neural tube: mouse.

    Science.gov (United States)

    Massarwa, R'ada; Ray, Heather J; Niswander, Lee

    2014-01-01

    The neural tube (NT), the embryonic precursor of the vertebrate brain and spinal cord, is generated by a complex and highly dynamic morphological process. In mammals, the initially flat neural plate bends and lifts bilaterally to generate the neural folds followed by fusion of the folds at the midline during the process of neural tube closure (NTC). Failures in any step of this process can lead to neural tube defects (NTDs), a common class of birth defects that occur in approximately 1 in 1000 live births. These severe birth abnormalities include spina bifida, a failure of closure at the spinal level; craniorachischisis, a failure of NTC along the entire body axis; and exencephaly, a failure of the cranial neural folds to close which leads to degeneration of the exposed brain tissue termed anencephaly. The mouse embryo presents excellent opportunities to explore the genetic basis of NTC in mammals; however, its in utero development has also presented great challenges in generating a deeper understanding of how gene function regulates the cell and tissue behaviors that drive this highly dynamic process. Recent technological advances are now allowing researchers to address these questions through visualization of NTC dynamics in the mouse embryo in real time, thus offering new insights into the morphogenesis of mammalian NTC.

  1. Racial bias in neural empathic responses to pain.

    Science.gov (United States)

    Contreras-Huerta, Luis Sebastian; Baker, Katharine S; Reynolds, Katherine J; Batalha, Luisa; Cunnington, Ross

    2013-01-01

    Recent studies have shown that perceiving the pain of others activates brain regions in the observer associated with both somatosensory and affective-motivational aspects of pain, principally involving regions of the anterior cingulate and anterior insula cortex. The degree of these empathic neural responses is modulated by racial bias, such that stronger neural activation is elicited by observing pain in people of the same racial group compared with people of another racial group. The aim of the present study was to examine whether a more general social group category, other than race, could similarly modulate neural empathic responses and perhaps account for the apparent racial bias reported in previous studies. Using a minimal group paradigm, we assigned participants to one of two mixed-race teams. We use the term race to refer to the Chinese or Caucasian appearance of faces and whether the ethnic group represented was the same or different from the appearance of the participant' own face. Using fMRI, we measured neural empathic responses as participants observed members of their own group or other group, and members of their own race or other race, receiving either painful or non-painful touch. Participants showed clear group biases, with no significant effect of race, on behavioral measures of implicit (affective priming) and explicit group identification. Neural responses to observed pain in the anterior cingulate cortex, insula cortex, and somatosensory areas showed significantly greater activation when observing pain in own-race compared with other-race individuals, with no significant effect of minimal groups. These results suggest that racial bias in neural empathic responses is not influenced by minimal forms of group categorization, despite the clear association participants showed with in-group more than out-group members. We suggest that race may be an automatic and unconscious mechanism that drives the initial neural responses to observed pain in

  2. Racial bias in neural empathic responses to pain.

    Directory of Open Access Journals (Sweden)

    Luis Sebastian Contreras-Huerta

    Full Text Available Recent studies have shown that perceiving the pain of others activates brain regions in the observer associated with both somatosensory and affective-motivational aspects of pain, principally involving regions of the anterior cingulate and anterior insula cortex. The degree of these empathic neural responses is modulated by racial bias, such that stronger neural activation is elicited by observing pain in people of the same racial group compared with people of another racial group. The aim of the present study was to examine whether a more general social group category, other than race, could similarly modulate neural empathic responses and perhaps account for the apparent racial bias reported in previous studies. Using a minimal group paradigm, we assigned participants to one of two mixed-race teams. We use the term race to refer to the Chinese or Caucasian appearance of faces and whether the ethnic group represented was the same or different from the appearance of the participant' own face. Using fMRI, we measured neural empathic responses as participants observed members of their own group or other group, and members of their own race or other race, receiving either painful or non-painful touch. Participants showed clear group biases, with no significant effect of race, on behavioral measures of implicit (affective priming and explicit group identification. Neural responses to observed pain in the anterior cingulate cortex, insula cortex, and somatosensory areas showed significantly greater activation when observing pain in own-race compared with other-race individuals, with no significant effect of minimal groups. These results suggest that racial bias in neural empathic responses is not influenced by minimal forms of group categorization, despite the clear association participants showed with in-group more than out-group members. We suggest that race may be an automatic and unconscious mechanism that drives the initial neural responses to

  3. Global stability analysis on a class of cellular neural networks

    Institute of Scientific and Technical Information of China (English)

    ZHANG; Yi

    2001-01-01

    stability of delay Hopfield Neural networks, International J. Sys. Sci., 1996, (9): 895.[17]Liao, X., Yu, J., Qualitative analysis of bidirection associative memory networks with time delays, Int. J. Of Circuit Theory and Applications, 1998, (3): 219.[18]Takahashi, N., Chua, L. O., A new sufficient condition for nonsymmetric CNN's to have a stable equilibrium point, IEEE Trans. CAS-I, 1998, (12): 1092.[19]Zhang Yi, Qualitative analysis of bidirectional associative memory neural networks with delays, Journal of Computer Research and Development, 1999, 36(2): 150.

  4. Complex-Valued Neural Networks

    CERN Document Server

    Hirose, Akira

    2012-01-01

    This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published in 2006, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields. In the second edition the recent trends in CVNNs research are included, resulting in e.g. almost a doubled number of references. The parametron invented in 1954 is also referred to with discussion on analogy and disparity. Also various additional arguments on the advantages of the complex-valued neural networks enhancing the difference to real-valued neural networks are given in various sections. The book is useful for those beginning their studies, for instance, in adaptive signal processing for highly functional sensing and imaging, control in unknown and changing environment, robotics inspired by human neural systems, and brain-like information processing, as well as interdisciplina...

  5. Artificial intelligence: Deep neural reasoning

    Science.gov (United States)

    Jaeger, Herbert

    2016-10-01

    The human brain can solve highly abstract reasoning problems using a neural network that is entirely physical. The underlying mechanisms are only partially understood, but an artificial network provides valuable insight. See Article p.471

  6. Logic Mining Using Neural Networks

    CERN Document Server

    Sathasivam, Saratha

    2008-01-01

    Knowledge could be gained from experts, specialists in the area of interest, or it can be gained by induction from sets of data. Automatic induction of knowledge from data sets, usually stored in large databases, is called data mining. Data mining methods are important in the management of complex systems. There are many technologies available to data mining practitioners, including Artificial Neural Networks, Regression, and Decision Trees. Neural networks have been successfully applied in wide range of supervised and unsupervised learning applications. Neural network methods are not commonly used for data mining tasks, because they often produce incomprehensible models, and require long training times. One way in which the collective properties of a neural network may be used to implement a computational task is by way of the concept of energy minimization. The Hopfield network is well-known example of such an approach. The Hopfield network is useful as content addressable memory or an analog computer for s...

  7. Neural correlates of consciousness reconsidered.

    Science.gov (United States)

    Neisser, Joseph

    2012-06-01

    It is widely accepted among philosophers that neuroscientists are conducting a search for the neural correlates of consciousness, or NCC. Chalmers (2000) conceptualized this research program as the attempt to correlate the contents of conscious experience with the contents of representations in specific neural populations. A notable claim on behalf of this interpretation is that the neutral language of "correlates" frees us from philosophical disputes over the mind/body relation, allowing the science to move independently. But the experimental paradigms and explanatory canons of neuroscience are not neutral about the mechanical relation between consciousness and the brain. I argue that NCC research is best characterized as an attempt to locate a causally relevant neural mechanism and not as an effort to identify a discrete neural representation, the content of which correlates with some actual experience. It might be said that the first C in "NCC" should stand for "causes" rather than "correlates."

  8. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...

  9. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    examined, and it appears that considering 'normal' neural network models with, say, 500 samples, the problem of over-fitting is neglible, and therefore it is not taken into consideration afterwards. Numerous model types, often met in control applications, are implemented as neural network models....... - Control concepts including parameter estimation - Control concepts including inverse modelling - Control concepts including optimal control For each of the three groups, different control concepts and specific training methods are detailed described.Further, all control concepts are tested on the same......The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...

  10. Neural communication in posttraumatic growth.

    Science.gov (United States)

    Anders, Samantha L; Peterson, Carly K; James, Lisa M; Engdahl, Brian; Leuthold, Arthur C; Georgopoulos, Apostolos P

    2015-07-01

    Posttraumatic growth (PTG), or positive psychological changes following exposure to traumatic events, is commonly reported among trauma survivors. In the present study, we examined neural correlates of PTG in 106 veterans with PTSD and 193 veteran controls using task-free magnetoencephalography (MEG), diagnostic interviews and measures of PTG, and traumatic event exposure. Global synchronous neural interactions (SNIs) were significantly modulated downward with increasing PTG scores in controls (p = .005), but not in veterans with PTSD (p = .601). This effect was primarily characterized by negative slopes in local neural networks, was strongest in the medial prefrontal cortex, and was much stronger and more extensive in the control than the PTSD group. The present study complements previous research highlighting the role of neural adaptation in healthy functioning.

  11. Neural components of altruistic punishment

    Directory of Open Access Journals (Sweden)

    Emily eDu

    2015-02-01

    Full Text Available Altruistic punishment, which occurs when an individual incurs a cost to punish in response to unfairness or a norm violation, may play a role in perpetuating cooperation. The neural correlates underlying costly punishment have only recently begun to be explored. Here we review the current state of research on the neural basis of altruism from the perspectives of costly punishment, emphasizing the importance of characterizing elementary neural processes underlying a decision to punish. In particular, we emphasize three cognitive processes that contribute to the decision to altruistically punish in most scenarios: inequity aversion, cost-benefit calculation, and social reference frame to distinguish self from others. Overall, we argue for the importance of understanding the neural correlates of altruistic punishment with respect to the core computations necessary to achieve a decision to punish.

  12. Acetaminophen reduces social pain: behavioral and neural evidence.

    Science.gov (United States)

    Dewall, C Nathan; Macdonald, Geoff; Webster, Gregory D; Masten, Carrie L; Baumeister, Roy F; Powell, Caitlin; Combs, David; Schurtz, David R; Stillman, Tyler F; Tice, Dianne M; Eisenberger, Naomi I

    2010-07-01

    Pain, whether caused by physical injury or social rejection, is an inevitable part of life. These two types of pain-physical and social-may rely on some of the same behavioral and neural mechanisms that register pain-related affect. To the extent that these pain processes overlap, acetaminophen, a physical pain suppressant that acts through central (rather than peripheral) neural mechanisms, may also reduce behavioral and neural responses to social rejection. In two experiments, participants took acetaminophen or placebo daily for 3 weeks. Doses of acetaminophen reduced reports of social pain on a daily basis (Experiment 1). We used functional magnetic resonance imaging to measure participants' brain activity (Experiment 2), and found that acetaminophen reduced neural responses to social rejection in brain regions previously associated with distress caused by social pain and the affective component of physical pain (dorsal anterior cingulate cortex, anterior insula). Thus, acetaminophen reduces behavioral and neural responses associated with the pain of social rejection, demonstrating substantial overlap between social and physical pain.

  13. Realistic thermodynamic and statistical-mechanical measures for neural synchronization.

    Science.gov (United States)

    Kim, Sang-Yoon; Lim, Woochang

    2014-04-15

    Synchronized brain rhythms, associated with diverse cognitive functions, have been observed in electrical recordings of brain activity. Neural synchronization may be well described by using the population-averaged global potential VG in computational neuroscience. The time-averaged fluctuation of VG plays the role of a "thermodynamic" order parameter O used for describing the synchrony-asynchrony transition in neural systems. Population spike synchronization may be well visualized in the raster plot of neural spikes. The degree of neural synchronization seen in the raster plot is well measured in terms of a "statistical-mechanical" spike-based measure Ms introduced by considering the occupation and the pacing patterns of spikes. The global potential VG is also used to give a reference global cycle for the calculation of Ms. Hence, VG becomes an important collective quantity because it is associated with calculation of both O and Ms. However, it is practically difficult to directly get VG in real experiments. To overcome this difficulty, instead of VG, we employ the instantaneous population spike rate (IPSR) which can be obtained in experiments, and develop realistic thermodynamic and statistical-mechanical measures, based on IPSR, to make practical characterization of the neural synchronization in both computational and experimental neuroscience. Particularly, more accurate characterization of weak sparse spike synchronization can be achieved in terms of realistic statistical-mechanical IPSR-based measure, in comparison with the conventional measure based on VG. Copyright © 2014. Published by Elsevier B.V.

  14. Social status modulates neural activity in the mentalizing network.

    Science.gov (United States)

    Muscatell, Keely A; Morelli, Sylvia A; Falk, Emily B; Way, Baldwin M; Pfeifer, Jennifer H; Galinsky, Adam D; Lieberman, Matthew D; Dapretto, Mirella; Eisenberger, Naomi I

    2012-04-15

    The current research explored the neural mechanisms linking social status to perceptions of the social world. Two fMRI studies provide converging evidence that individuals lower in social status are more likely to engage neural circuitry often involved in 'mentalizing' or thinking about others' thoughts and feelings. Study 1 found that college students' perception of their social status in the university community was related to neural activity in the mentalizing network (e.g., DMPFC, MPFC, precuneus/PCC) while encoding social information, with lower social status predicting greater neural activity in this network. Study 2 demonstrated that socioeconomic status, an objective indicator of global standing, predicted adolescents' neural activity during the processing of threatening faces, with individuals lower in social status displaying greater activity in the DMPFC, previously associated with mentalizing, and the amygdala, previously associated with emotion/salience processing. These studies demonstrate that social status is fundamentally and neurocognitively linked to how people process and navigate their social worlds. Copyright © 2012 Elsevier Inc. All rights reserved.

  15. Modular, Hierarchical Learning By Artificial Neural Networks

    Science.gov (United States)

    Baldi, Pierre F.; Toomarian, Nikzad

    1996-01-01

    Modular and hierarchical approach to supervised learning by artificial neural networks leads to neural networks more structured than neural networks in which all neurons fully interconnected. These networks utilize general feedforward flow of information and sparse recurrent connections to achieve dynamical effects. The modular organization, sparsity of modular units and connections, and fact that learning is much more circumscribed are all attractive features for designing neural-network hardware. Learning streamlined by imitating some aspects of biological neural networks.

  16. Neural Network Communications Signal Processing

    Science.gov (United States)

    1994-08-01

    Technical Information Report for the Neural Network Communications Signal Processing Program, CDRL A003, 31 March 1993. Software Development Plan for...track changing jamming conditions to provide the decoder with the best log- likelihood ratio metrics at a given time. As part of our development plan we...Artificial Neural Networks (ICANN-91) Volume 2, June 24-28, 1991, pp. 1677-1680. Kohonen, Teuvo, Raivio, Kimmo, Simula, Oli, Venta , 011i, Henriksson

  17. What are artificial neural networks?

    DEFF Research Database (Denmark)

    Krogh, Anders

    2008-01-01

    Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb......Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb...

  18. Simulating the neural correlates of stuttering.

    Science.gov (United States)

    den Ouden, Dirk-Bart; Montgomery, Allen; Adams, Charley

    2014-08-01

    For functional neuroimaging studies of stuttering, two challenges are (1) the elicitation of naturally stuttered versus fluent speech and (2) the separation of activation associated with abnormal motor execution from activation that reflects the cognitive substrates of stuttering. This paper reports on a proof-of-concept study, in which a single-subject approach was applied to address these two issues. A stuttering speaker used his insight into his own stuttering behavior to create a list of stutter-prone words versus a list of "fluent" words. He was then matched to a non-stuttering speaker, who imitated the specific articulatory and orofacial motor pattern of the stuttering speaker. Both study participants performed a functional MRI experiment of single word reading, each being presented with the same lexical items. Results suggest that the generally observed right-hemisphere lateralization appears to reflect a true neural correlate of stuttering. Some of the classically reported activation associated with stuttering appears to be driven more by nonspecific motor patterns than by cognitive substrates of stuttering, while anterior cingulate activation may reflect awareness of (upcoming) dysfluencies. This study shows that it is feasible to match stuttering speakers' utterances more closely to simulated stutters for the investigation of neural correlates of real stuttering. Significant main effects and contrast effects were obtained for the differences between fluent and stuttered speech, and right-hemisphere lateralization associated with real stuttered speech was shown in a single subject.

  19. Flexibility of neural stem cells

    Directory of Open Access Journals (Sweden)

    Eumorphia eRemboutsika

    2011-04-01

    Full Text Available Embryonic cortical neural stem cells are self-renewing progenitors that can differentiate into neurons and glia. We generated neurospheres from the developing cerebral cortex using a mouse genetic model that allows for lineage selection and found that the self-renewing neural stem cells are restricted to Sox2 expressing cells. Under normal conditions, embryonic cortical neurospheres are heterogeneous with regard to Sox2 expression and contain astrocytes, neural stem cells and neural progenitor cells sufficiently plastic to give rise to neural crest cells when transplanted into the hindbrain of E1.5 chick and E8 mouse embryos. However, when neurospheres are maintained under lineage selection, such that all cells express Sox2, neural stem cells maintain their Pax6+ cortical radial glia identity and exhibit a more restricted fate in vitro and after transplantation. These data demonstrate that Sox2 preserves the cortical identity and regulates the plasticity of self-renewing Pax6+ radial glia cells.

  20. Fuzzy associative memories

    Science.gov (United States)

    Kosko, Bart

    1991-01-01

    Mappings between fuzzy cubes are discussed. This level of abstraction provides a surprising and fruitful alternative to the propositional and predicate-calculas reasoning techniques used in expert systems. It allows one to reason with sets instead of propositions. Discussed here are fuzzy and neural function estimators, neural vs. fuzzy representation of structured knowledge, fuzzy vector-matrix multiplication, and fuzzy associative memory (FAM) system architecture.

  1. 平面细胞极性通路核心基因与神经管畸形的相关性%Association between core genes in planar cell polarity pathway and neural tube defects

    Institute of Scientific and Technical Information of China (English)

    杨章民; 王丽娜; 杨雪艳; 王红艳

    2013-01-01

    The roles, the basic structure and the rare mutations in human neural tube defects (NTDs) were invesitaged of the six core proteins in the planar cell polarity (PCP) pathway (Frizzled, Flamingo, Vangl, Dishevelled, Prickle and Diego). It has been revealed that upon stimulation from Wnt signaling pathway, these six proteins formed a membrane complex with asymmetric localization and determined the planar cell polarity of neuron cells via downstream Rho/Rac signaling pathway during neural tube closure. Up to now, several specific missense mutations have been identified in Frizzled, Vangl, Flamingo and Prickle genes, SNP site alterations have also been found in Dishevelled gene, but mutations in Diego gene is still unknown. In the future, great efforts should be made to elucidate how these core genes interact with environment factors in PCP pathway,and how SNP sites or mutation of core genes influence their protein functions which may then participate in neural tube defects.%对平面细胞极性(planar cell polarity,PCP)信号通路中6种核心蛋白质(Frizzled、Flamingo、Vangl、Dishevelled、Prickle及Diego)的基本结构、在神经管畸形发生过程中的作用以及在人类神经管畸形患者中发现的相关突变位点的研究现状进行了综述.研究表明,接受Wnt信号通路刺激后这6个蛋白结合形成不对称性分布的膜复合物,经下游Rho/Rac信号通路来共同决定神经元的平面细胞极性及神经管的闭合.目前已在Frizzled、Vangl、Flamingo及Prickle 4个基因中发现了多个特异性错义突变,在Dishevelled基因中发现有SNP位点改变,Diego基因在神经管畸形中的突变不明.未来研究应在阐明核心基因与环境因素如何互作、核心基因SNP位点或突变如何影响其蛋白功能,从而参与神经管畸形发生方面进行突破.

  2. Recurrent neural collective classification.

    Science.gov (United States)

    Monner, Derek D; Reggia, James A

    2013-12-01

    With the recent surge in availability of data sets containing not only individual attributes but also relationships, classification techniques that take advantage of predictive relationship information have gained in popularity. The most popular existing collective classification techniques have a number of limitations-some of them generate arbitrary and potentially lossy summaries of the relationship data, whereas others ignore directionality and strength of relationships. Popular existing techniques make use of only direct neighbor relationships when classifying a given entity, ignoring potentially useful information contained in expanded neighborhoods of radius greater than one. We present a new technique that we call recurrent neural collective classification (RNCC), which avoids arbitrary summarization, uses information about relationship directionality and strength, and through recursive encoding, learns to leverage larger relational neighborhoods around each entity. Experiments with synthetic data sets show that RNCC can make effective use of relationship data for both direct and expanded neighborhoods. Further experiments demonstrate that our technique outperforms previously published results of several collective classification methods on a number of real-world data sets.

  3. Understanding Neural Networks for Machine Learning using Microsoft Neural Network Algorithm

    National Research Council Canada - National Science Library

    Nagesh Ramprasad

    2016-01-01

    .... In this research, focus is on the Microsoft Neural System Algorithm. The Microsoft Neural System Algorithm is a simple implementation of the adaptable and popular neural networks that are used in the machine learning...

  4. Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks.

    Science.gov (United States)

    Miconi, Thomas

    2017-02-23

    Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.

  5. Neural Correlates of Machiavellian Strategies in a Social Dilemma Task

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    Bereczkei, Tamas; Deak, Anita; Papp, Peter; Perlaki, Gabor; Orsi, Gergely

    2013-01-01

    In spite of having deficits in various areas of social cognition, especially in mindreading, Machiavellian individuals are typically very successful in different tasks, including solving social dilemmas. We assume that a profound examination of neural structures associated with decision-making processes is needed to learn more about…

  6. Neural Alterations in Acquired Age-Related Hearing Loss

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    Raksha Anand Mudar

    2016-06-01

    Full Text Available Hearing loss is one of the most prevalent chronic health conditions in older adults. Growing evidence suggests that hearing loss is associated with reduced cognitive functioning and incident dementia. In this mini-review, we briefly examine literature on anatomical and functional alterations in the brains of adults with acquired age-associated hearing loss, which may underlie the cognitive consequences observed in this population, focusing on studies that have used structural and functional magnetic resonance imaging, diffusion tensor imaging, and event-related electroencephalography. We discuss structural and functional alterations observed in the temporal and frontal cortices and the limbic system. These neural alterations are discussed in the context of common cause, information-degradation, and sensory-deprivation hypotheses, and we suggest possible rehabilitation strategies. Although we are beginning to learn more about changes in neural architecture and functionality related to age-associated hearing loss, much work remains to be done. Understanding the neural alterations will provide objective markers for early identification of neural consequences of age-associated hearing loss and for evaluating benefits of intervention approaches.

  7. Neural Circuitry and Plasticity Mechanisms Underlying Delay Eyeblink Conditioning

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    Freeman, John H.; Steinmetz, Adam B.

    2011-01-01

    Pavlovian eyeblink conditioning has been used extensively as a model system for examining the neural mechanisms underlying associative learning. Delay eyeblink conditioning depends on the intermediate cerebellum ipsilateral to the conditioned eye. Evidence favors a two-site plasticity model within the cerebellum with long-term depression of…

  8. Age and the neural network of personal familiarity.

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    Markus Donix

    Full Text Available BACKGROUND: Accessing information that defines personally familiar context in real-world situations is essential for the social interactions and the independent functioning of an individual. Personal familiarity is associated with the availability of semantic and episodic information as well as the emotional meaningfulness surrounding a stimulus. These features are known to be associated with neural activity in distinct brain regions across different stimulus conditions (e.g., when perceiving faces, voices, places, objects, which may reflect a shared neural basis. Although perceiving context-rich personal familiarity may appear unchanged in aging on the behavioral level, it has not yet been studied whether this can be supported by neuroimaging data. METHODOLOGY/PRINCIPAL FINDINGS: We used functional magnetic resonance imaging to investigate the neural network associated with personal familiarity during the perception of personally familiar faces and places. Twelve young and twelve elderly cognitively healthy subjects participated in the study. Both age groups showed a similar activation pattern underlying personal familiarity, predominantly in anterior cingulate and posterior cingulate cortices, irrespective of the stimulus type. The young subjects, but not the elderly subjects demonstrated an additional anterior cingulate deactivation when perceiving unfamiliar stimuli. CONCLUSIONS/SIGNIFICANCE: Although we found evidence for an age-dependent reduction in frontal cortical deactivation, our data show that there is a stimulus-independent neural network associated with personal familiarity of faces and places, which is less susceptible to aging-related changes.

  9. Leader emergence through interpersonal neural synchronization.

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    Jiang, Jing; Chen, Chuansheng; Dai, Bohan; Shi, Guang; Ding, Guosheng; Liu, Li; Lu, Chunming

    2015-04-07

    The neural mechanism of leader emergence is not well understood. This study investigated (i) whether interpersonal neural synchronization (INS) plays an important role in leader emergence, and (ii) whether INS and leader emergence are associated with the frequency or the quality of communications. Eleven three-member groups were asked to perform a leaderless group discussion (LGD) task, and their brain activities were recorded via functional near infrared spectroscopy (fNIRS)-based hyperscanning. Video recordings of the discussions were coded for leadership and communication. Results showed that the INS for the leader-follower (LF) pairs was higher than that for the follower-follower (FF) pairs in the left temporo-parietal junction (TPJ), an area important for social mentalizing. Although communication frequency was higher for the LF pairs than for the FF pairs, the frequency of leader-initiated and follower-initiated communication did not differ significantly. Moreover, INS for the LF pairs was significantly higher during leader-initiated communication than during follower-initiated communications. In addition, INS for the LF pairs during leader-initiated communication was significantly correlated with the leaders' communication skills and competence, but not their communication frequency. Finally, leadership could be successfully predicted based on INS as well as communication frequency early during the LGD (before half a minute into the task). In sum, this study found that leader emergence was characterized by high-level neural synchronization between the leader and followers and that the quality, rather than the frequency, of communications was associated with synchronization. These results suggest that leaders emerge because they are able to say the right things at the right time.

  10. Neural stem cells could serve as a therapeutic material for age-related neurodegenerative diseases.

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    Suksuphew, Sarawut; Noisa, Parinya

    2015-03-26

    Progressively loss of neural and glial cells is the key event that leads to nervous system dysfunctions and diseases. Several neurodegenerative diseases, for instance Alzheimer's disease, Parkinson's disease, and Huntington's disease, are associated to aging and suggested to be a consequence of deficiency of neural stem cell pool in the affected brain regions. Endogenous neural stem cells exist throughout life and are found in specific niches of human brain. These neural stem cells are responsible for the regeneration of new neurons to restore, in the normal circumstance, the functions of the brain. Endogenous neural stem cells can be isolated, propagated, and, notably, differentiated to most cell types of the brain. On the other hand, other types of stem cells, such as mesenchymal stem cells, embryonic stem cells, and induced pluripotent stem cells can also serve as a source for neural stem cell production, that hold a great promise for regeneration of the brain. The replacement of neural stem cells, either endogenous or stem cell-derived neural stem cells, into impaired brain is highly expected as a possible therapeutic mean for neurodegenerative diseases. In this review, clinical features and current routinely treatments of age-related neurodegenerative diseases are documented. Noteworthy, we presented the promising evidence of neural stem cells and their derivatives in curing such diseases, together with the remaining challenges to achieve the best outcome for patients.

  11. Neurometabolic coupling between neural activity, glucose, and lactate in activated visual cortex.

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    Li, Baowang; Freeman, Ralph D

    2015-11-01

    Neural activity is closely coupled with energy metabolism but details of the association remain to be identified. One basic area involves the relationships between neural activity and the main supportive substrates of glucose and lactate. This is of fundamental significance for the interpretation of non-invasive neural imaging. Here, we use microelectrodes with high spatial and temporal resolution to determine simultaneous co-localized changes in glucose, lactate, and neural activity during visual activation of the cerebral cortex in the cat. Tissue glucose and lactate concentration levels are measured with electrochemical microelectrodes while neural spiking activity and local field potentials are sampled by a microelectrode. These measurements are performed simultaneously while neurons are activated by visual stimuli of different contrast levels, orientations, and sizes. We find immediate decreases in tissue glucose concentration and simultaneous increases in lactate during neural activation. Both glucose and lactate signals return to their baseline levels instantly as neurons cease firing. No sustained changes or initial dips in glucose or lactate signals are elicited by visual stimulation. However, co-localized measurements of cerebral blood flow and neural activity demonstrate a clear delay in the cerebral blood flow signal such that it does not correlate temporally with the neural response. These results provide direct real-time evidence regarding the coupling between co-localized energy metabolism and neural activity during physiological stimulation. They are also relevant to a current question regarding the role of lactate in energy metabolism in the brain during neural activation. Dynamic changes in energy metabolites can be measured directly with high spatial and temporal resolution by use of enzyme-based microelectrodes. Here, to examine neuro-metabolic coupling during brain activation, we use combined microelectrodes to simultaneously measure

  12. A novel neural-wavelet approach for process diagnostics and complex system modeling

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    Gao, Rong

    Neural networks have been effective in several engineering applications because of their learning abilities and robustness. However certain shortcomings, such as slow convergence and local minima, are always associated with neural networks, especially neural networks applied to highly nonlinear and non-stationary problems. These problems can be effectively alleviated by integrating a new powerful tool, wavelets, into conventional neural networks. The multi-resolution analysis and feature localization capabilities of the wavelet transform offer neural networks new possibilities for learning. A neural wavelet network approach developed in this thesis enjoys fast convergence rate with little possibility to be caught at a local minimum. It combines the localization properties of wavelets with the learning abilities of neural networks. Two different testbeds are used for testing the efficiency of the new approach. The first is magnetic flowmeter-based process diagnostics: here we extend previous work, which has demonstrated that wavelet groups contain process information, to more general process diagnostics. A loop at Applied Intelligent Systems Lab (AISL) is used for collecting and analyzing data through the neural-wavelet approach. The research is important for thermal-hydraulic processes in nuclear and other engineering fields. The neural-wavelet approach developed is also tested with data from the electric power grid. More specifically, the neural-wavelet approach is used for performing short-term and mid-term prediction of power load demand. In addition, the feasibility of determining the type of load using the proposed neural wavelet approach is also examined. The notion of cross scale product has been developed as an expedient yet reliable discriminator of loads. Theoretical issues involved in the integration of wavelets and neural networks are discussed and future work outlined.

  13. Morphological bidirectional associative memories.

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    Ritter, G X.; Diaz-de-Leon, J L.; Sussner, P

    1999-07-01

    The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we discuss a novel class of artificial neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different from those of traditional neural network models. The main emphasis of the research presented here is on morphological bidirectional associative memories (MBAMs). In particular, we establish a mathematical theory for MBAMs and provide conditions that guarantee perfect bidirectional recall for corrupted patterns. Some examples that illustrate performance differences between the morphological model and the traditional semilinear model are also given.

  14. Serum zinc levels in newborns with neural tube defects.

    Science.gov (United States)

    Golalipour, Mohammad Jafar; Mansourian, Azad Reza; Keshtkar, Abasali

    2006-09-01

    Neural tube defects (NTD) comprise of a group of congenital malformations that include spina bifida, anencephaly and encephalocele. Reports have implicated zinc deficiency as one of the causative factors of NTDs. We compared the serum zinc level of 23 newborns having neural tube defects with 35 healthy controls by spectrophotometery during 2003-2004. Zinc deficiency was documented in 43.5% of the cases and 8.6% of the controls (P = 0.002). Multivariate logistic regression analysis revealed a significant association between the presence of NTDs and zinc deficiency (OR = 8.2, 95% Cl: 1.9-34.7).

  15. An Optimal Implementation on FPGA of a Hopfield Neural Network

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    W. Mansour

    2011-01-01

    Full Text Available The associative Hopfield memory is a form of recurrent Artificial Neural Network (ANN that can be used in applications such as pattern recognition, noise removal, information retrieval, and combinatorial optimization problems. This paper presents the implementation of the Hopfield Neural Network (HNN parallel architecture on a SRAM-based FPGA. The main advantage of the proposed implementation is its high performance and cost effectiveness: it requires O(1 multiplications and O(log⁡ N additions, whereas most others require O(N multiplications and O(N additions.

  16. Effect of Cyclophosphamide on Neural Tube Development in Chick Embryos

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    SHABANA SULTANA

    2014-06-01

    Full Text Available Cyclophosphamide is a nitrogen mustard alkylating agent. CP has potent immunosuppressive properties and issued clinically in a number of autoimmune disorders like Wegener’s granulomatosis, rheumatoid arthritis, nephritic syndrome, systemic lupus erythematous and has also been used to prevent organ rejection after transplantation. In the present study fertilized eggs were administered with cyclophosphamide and the development of neural tube was studied after 21 days. The histological and gross features of neural tube were identified. Cyclophosphamide cytotoxicity results in depression of proliferation of cell activity associated with malformations and embryonic death. Injection of the drug causes depression of mitotic activity by day 2 which produces malformations.

  17. Neural correlates of eating disorders: translational potential

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    McAdams CJ

    2015-09-01

    Full Text Available Carrie J McAdams,1,2 Whitney Smith1 1University of Texas at Southwestern Medical Center, 2Department of Psychiatry, Texas Health Presbyterian Hospital of Dallas, Dallas, TX, USA Abstract: Eating disorders are complex and serious psychiatric illnesses whose etiology includes psychological, biological, and social factors. Treatment of eating disorders is challenging as there are few evidence-based treatments and limited understanding of the mechanisms that result in sustained recovery. In the last 20 years, we have begun to identify neural pathways that are altered in eating disorders. Consideration of how these pathways may contribute to an eating disorder can provide an understanding of expected responses to treatments. Eating disorder behaviors include restrictive eating, compulsive overeating, and purging behaviors after eating. Eating disorders are associated with changes in many neural systems. In this targeted review, we focus on three cognitive processes associated with neurocircuitry differences in subjects with eating disorders such as reward, decision-making, and social behavior. We briefly examine how each of these systems function in healthy people, using Neurosynth meta-analysis to identify key regions commonly implicated in these circuits. We review the evidence for disruptions of these regions and systems in eating disorders. Finally, we describe psychiatric and psychological treatments that are likely to function by impacting these regions. Keywords: anorexia nervosa, bulimia nervosa, social cognition, reward processing, decision-making

  18. An exclusively mesodermal origin of fin mesenchyme demonstrates that zebrafish trunk neural crest does not generate ectomesenchyme.

    Science.gov (United States)

    Lee, Raymond Teck Ho; Knapik, Ela W; Thiery, Jean Paul; Carney, Thomas J

    2013-07-01

    The neural crest is a multipotent stem cell population that arises from the dorsal aspect of the neural tube and generates both non-ectomesenchymal (melanocytes, peripheral neurons and glia) and ectomesenchymal (skeletogenic, odontogenic, cartilaginous and connective tissue) derivatives. In amniotes, only cranial neural crest generates both classes, with trunk neural crest restricted to non-ectomesenchyme. By contrast, it has been suggested that anamniotes might generate derivatives of both classes at all axial levels, with trunk neural crest generating fin osteoblasts, scale mineral-forming cells and connective tissue cells; however, this has not been fully tested. The cause and evolutionary significance of this cranial/trunk dichotomy, and its absence in anamniotes, are debated. Recent experiments have disputed the contribution of fish trunk neural crest to fin osteoblasts and scale mineral-forming cells. This prompted us to test the contribution of anamniote trunk neural crest to fin connective tissue cells. Using genetics-based lineage tracing in zebrafish, we find that these fin mesenchyme cells derive entirely from the mesoderm and that neural crest makes no contribution. Furthermore, contrary to previous suggestions, larval fin mesenchyme cells do not generate the skeletogenic cells of the adult fin, but persist to form fibroblasts associated with adult fin rays. Our data demonstrate that zebrafish trunk neural crest does not generate ectomesenchymal derivatives and challenge long-held ideas about trunk neural crest fate. These findings have important implications for the ontogeny and evolution of the neural crest.

  19. Neural mechanisms supporting the extraction of general knowledge across episodic memories

    NARCIS (Netherlands)

    Sweegers, C.C.G.; Takashima, A.; Fernández, G.; Talamini, L.M.

    2014-01-01

    General knowledge acquisition entails the extraction of statistical regularities from the environment. At high levels of complexity, this may involve the extraction, and consolidation, of associative regularities across event memories. The underlying neural mechanisms would likely involve a

  20. Neural mechanisms supporting the extraction of general knowledge across episodic memories

    NARCIS (Netherlands)

    Sweegers, C.C.; Takashima, A.; Fernandez, G.S.E.; Talamini, L.M.

    2014-01-01

    General knowledge acquisition entails the extraction of statistical regularities from the environment. At high levels of complexity, this may involve the extraction, and consolidation, of associative regularities across event memories. The underlying neural mechanisms would likely involve a hippocam