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Sample records for multiple neural functions

  1. Neural Plasticity in Multiple Sclerosis: The Functional and Molecular Background

    Directory of Open Access Journals (Sweden)

    Dominika Justyna Ksiazek-Winiarek

    2015-01-01

    Full Text Available Multiple sclerosis is an autoimmune neurodegenerative disorder resulting in motor dysfunction and cognitive decline. The inflammatory and neurodegenerative changes seen in the brains of MS patients lead to progressive disability and increasing brain atrophy. The most common type of MS is characterized by episodes of clinical exacerbations and remissions. This suggests the presence of compensating mechanisms for accumulating damage. Apart from the widely known repair mechanisms like remyelination, another important phenomenon is neuronal plasticity. Initially, neuroplasticity was connected with the developmental stages of life; however, there is now growing evidence confirming that structural and functional reorganization occurs throughout our lifetime. Several functional studies, utilizing such techniques as fMRI, TBS, or MRS, have provided valuable data about the presence of neuronal plasticity in MS patients. CNS ability to compensate for neuronal damage is most evident in RR-MS; however it has been shown that brain plasticity is also preserved in patients with substantial brain damage. Regardless of the numerous studies, the molecular background of neuronal plasticity in MS is still not well understood. Several factors, like IL-1β, BDNF, PDGF, or CB1Rs, have been implicated in functional recovery from the acute phase of MS and are thus considered as potential therapeutic targets.

  2. Dynamical Behaviors of Multiple Equilibria in Competitive Neural Networks With Discontinuous Nonmonotonic Piecewise Linear Activation Functions.

    Science.gov (United States)

    Nie, Xiaobing; Zheng, Wei Xing

    2016-03-01

    This paper addresses the problem of coexistence and dynamical behaviors of multiple equilibria for competitive neural networks. First, a general class of discontinuous nonmonotonic piecewise linear activation functions is introduced for competitive neural networks. Then based on the fixed point theorem and theory of strict diagonal dominance matrix, it is shown that under some conditions, such n -neuron competitive neural networks can have 5(n) equilibria, among which 3(n) equilibria are locally stable and the others are unstable. More importantly, it is revealed that the neural networks with the discontinuous activation functions introduced in this paper can have both more total equilibria and locally stable equilibria than the ones with other activation functions, such as the continuous Mexican-hat-type activation function and discontinuous two-level activation function. Furthermore, the 3(n) locally stable equilibria given in this paper are located in not only saturated regions, but also unsaturated regions, which is different from the existing results on multistability of neural networks with multiple level activation functions. A simulation example is provided to illustrate and validate the theoretical findings.

  3. Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment.

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    Yuichi Yamashita

    2008-11-01

    Full Text Available It is generally thought that skilled behavior in human beings results from a functional hierarchy of the motor control system, within which reusable motor primitives are flexibly integrated into various sensori-motor sequence patterns. The underlying neural mechanisms governing the way in which continuous sensori-motor flows are segmented into primitives and the way in which series of primitives are integrated into various behavior sequences have, however, not yet been clarified. In earlier studies, this functional hierarchy has been realized through the use of explicit hierarchical structure, with local modules representing motor primitives in the lower level and a higher module representing sequences of primitives switched via additional mechanisms such as gate-selecting. When sequences contain similarities and overlap, however, a conflict arises in such earlier models between generalization and segmentation, induced by this separated modular structure. To address this issue, we propose a different type of neural network model. The current model neither makes use of separate local modules to represent primitives nor introduces explicit hierarchical structure. Rather than forcing architectural hierarchy onto the system, functional hierarchy emerges through a form of self-organization that is based on two distinct types of neurons, each with different time properties ("multiple timescales". Through the introduction of multiple timescales, continuous sequences of behavior are segmented into reusable primitives, and the primitives, in turn, are flexibly integrated into novel sequences. In experiments, the proposed network model, coordinating the physical body of a humanoid robot through high-dimensional sensori-motor control, also successfully situated itself within a physical environment. Our results suggest that it is not only the spatial connections between neurons but also the timescales of neural activity that act as important mechanisms

  4. Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment.

    Science.gov (United States)

    Yamashita, Yuichi; Tani, Jun

    2008-11-01

    It is generally thought that skilled behavior in human beings results from a functional hierarchy of the motor control system, within which reusable motor primitives are flexibly integrated into various sensori-motor sequence patterns. The underlying neural mechanisms governing the way in which continuous sensori-motor flows are segmented into primitives and the way in which series of primitives are integrated into various behavior sequences have, however, not yet been clarified. In earlier studies, this functional hierarchy has been realized through the use of explicit hierarchical structure, with local modules representing motor primitives in the lower level and a higher module representing sequences of primitives switched via additional mechanisms such as gate-selecting. When sequences contain similarities and overlap, however, a conflict arises in such earlier models between generalization and segmentation, induced by this separated modular structure. To address this issue, we propose a different type of neural network model. The current model neither makes use of separate local modules to represent primitives nor introduces explicit hierarchical structure. Rather than forcing architectural hierarchy onto the system, functional hierarchy emerges through a form of self-organization that is based on two distinct types of neurons, each with different time properties ("multiple timescales"). Through the introduction of multiple timescales, continuous sequences of behavior are segmented into reusable primitives, and the primitives, in turn, are flexibly integrated into novel sequences. In experiments, the proposed network model, coordinating the physical body of a humanoid robot through high-dimensional sensori-motor control, also successfully situated itself within a physical environment. Our results suggest that it is not only the spatial connections between neurons but also the timescales of neural activity that act as important mechanisms leading to functional

  5. The multiple neural networks of familiarity: A meta-analysis of functional imaging studies.

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    Horn, Mathilde; Jardri, Renaud; D'Hondt, Fabien; Vaiva, Guillaume; Thomas, Pierre; Pins, Delphine

    2016-02-01

    Recent research has demonstrated the critical role of the feeling of familiarity in recognition memory. Various neuroimaging paradigms have been developed to identify the brain regions that sustain the processing of familiarity; however, there is still considerable controversy about the functional significance of each brain region implicated in familiarity-based retrieval. Here, we focused on the differences between paradigms that assess familiarity, with or without the encoding phase. We used the activation likelihood estimation (ALE) algorithm to conduct a whole-brain meta-analysis of neuroimaging studies that involved a familiarity task. Sixty-nine studies, performed in healthy subjects to determine the specific functions of the identified regions in familiarity processing, were finally selected. Distinct subanalyses were performed according to the experimental procedures used in the original studies. The ALE clusters that were highlighted revealed common activations for paradigms with and without encoding in the prefrontal cortex and in the parietal cortex. Additionally, supplementary activations related to specific familiarity (i.e., without the encoding phase) were observed in the limbic system (i.e., the amygdala, hippocampus, cingulate cortex, and insula) and in the associative sensory areas. The differences in the reported findings for different procedures are possibly due to differences in the concept of familiarity. To aid the exploration of the neural correlates of familiarity in future studies, the strengths and weaknesses of these experimental procedures are critically discussed.

  6. Myelin repair and functional recovery mediated by neural cell transplantation in a mouse model of multiple sclerosis

    Institute of Scientific and Technical Information of China (English)

    Lianhua Bai; Jordan Hecker; Amber Kerstetter; Robert H.Miller

    2013-01-01

    Cellular therapies are becoming a major focus for the treatment of demyelinating diseases such as multiple sclerosis (MS),therefore it is important to identify the most effective cell types that promote myelin repair.Several components contribute to the relative benefits of specific cell types including the overall efficacy of the cell therapy,the reproducibility of treatment,the mechanisms of action of distinct cell types and the ease of isolation and generation of therapeutic populations.A range of distinct cell populations promote functional recovery in animal models of MS including neural stem cells and mesenchymal stem cells derived from different tissues.Each of these cell populations has advantages and disadvantages and likely works through distinct mechanisms.The relevance of such mechanisms to myelin repair in the adult central nervous system is unclear since the therapeutic cells are generally derived from developing animals.Here we describe the isolation and characterization of a population of neural cells from the adult spinal cord that are characterized by the expression of the cell surface glycoprotein NG2.In functional studies,injection of adult NG2+ cells into mice with ongoing MOG35-55-induced experimental autoimmune encephalomyelitis (EAE) enhanced remyelination in the CNS while the number of CD3+ T cells in areas of spinal cord demyelination was reduced approximately three-fold.In vivo studies indicated that in EAE,NG2+ cells stimulated endogenous repair while in vitro they responded to signals in areas of induced inflammation by differentiating into oligodendrocytes.These results suggested that adult NG2+ cells represent a useful cell population for promoting neural repair in a variety of different conditions including demyelinating diseases such as MS.

  7. Coexistence and local μ-stability of multiple equilibrium points for memristive neural networks with nonmonotonic piecewise linear activation functions and unbounded time-varying delays.

    Science.gov (United States)

    Nie, Xiaobing; Zheng, Wei Xing; Cao, Jinde

    2016-12-01

    In this paper, the coexistence and dynamical behaviors of multiple equilibrium points are discussed for a class of memristive neural networks (MNNs) with unbounded time-varying delays and nonmonotonic piecewise linear activation functions. By means of the fixed point theorem, nonsmooth analysis theory and rigorous mathematical analysis, it is proven that under some conditions, such n-neuron MNNs can have 5(n) equilibrium points located in ℜ(n), and 3(n) of them are locally μ-stable. As a direct application, some criteria are also obtained on the multiple exponential stability, multiple power stability, multiple log-stability and multiple log-log-stability. All these results reveal that the addressed neural networks with activation functions introduced in this paper can generate greater storage capacity than the ones with Mexican-hat-type activation function. Numerical simulations are presented to substantiate the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Spiking neural P systems with multiple channels.

    Science.gov (United States)

    Peng, Hong; Yang, Jinyu; Wang, Jun; Wang, Tao; Sun, Zhang; Song, Xiaoxiao; Luo, Xiaohui; Huang, Xiangnian

    2017-11-01

    Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. The labeled systems of multiple neural networks.

    Science.gov (United States)

    Nemissi, M; Seridi, H; Akdag, H

    2008-08-01

    This paper proposes an implementation scheme of K-class classification problem using systems of multiple neural networks. Usually, a multi-class problem is decomposed into simple sub-problems solved independently using similar single neural networks. For the reason that these sub-problems are not equivalent in their complexity, we propose a system that includes reinforced networks destined to solve complicated parts of the entire problem. Our approach is inspired from principles of the multi-classifiers systems and the labeled classification, which aims to improve performances of the networks trained by the Back-Propagation algorithm. We propose two implementation schemes based on both OAO (one-against-all) and OAA (one-against-one). The proposed models are evaluated using iris and human thigh databases.

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

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    Vemana, Vinith

    2017-01-01

    Neuromodulators are endogenous neurochemicals that regulate biophysical and biochemical processes, which control brain function and behaviour, and are often the targets of neuropharmacological drugs. Neuromodulator effects are generally complex partly owing to the involvement of broad innervation, co-release of neuromodulators, complex intra- and extrasynaptic mechanism, existence of multiple receptor subtypes and high interconnectivity within the brain. In this work, we propose an efficient yet sufficiently realistic computational neural modelling framework to study some of these complex behaviours. Specifically, we propose a novel dynamical neural circuit model that integrates the effective neuromodulator-induced currents based on various experimental data (e.g. electrophysiology, neuropharmacology and voltammetry). The model can incorporate multiple interacting brain regions, including neuromodulator sources, simulate efficiently and easily extendable to large-scale brain models, e.g. for neuroimaging purposes. As an example, we model a network of mutually interacting neural populations in the lateral hypothalamus, dorsal raphe nucleus and locus coeruleus, which are major sources of neuromodulator orexin/hypocretin, serotonin and norepinephrine/noradrenaline, respectively, and which play significant roles in regulating many physiological functions. We demonstrate that such a model can provide predictions of systemic drug effects of the popular antidepressants (e.g. reuptake inhibitors), neuromodulator antagonists or their combinations. Finally, we developed user-friendly graphical user interface software for model simulation and visualization for both fundamental sciences and pharmacological studies. PMID:28100828

  11. Neural network pruning with Tukey-Kramer multiple comparison procedure.

    Science.gov (United States)

    Duckro, Donald E; Quinn, Dennis W; Gardner, Samuel J

    2002-05-01

    Reducing a neural network's complexity improves the ability of the network to generalize future examples. Like an overfitted regression function, neural networks may miss their target because of the excessive degrees of freedom stored up in unnecessary parameters. Over the past decade, the subject of pruning networks produced nonstatistical algorithms like Skeletonization, Optimal Brain Damage, and Optimal Brain Surgeon as methods to remove connections with the least salience. The method proposed here uses the bootstrap algorithm to estimate the distribution of the model parameter saliences. Statistical multiple comparison procedures are then used to make pruning decisions. We show this method compares well with Optimal Brain Surgeon in terms of ability to prune and the resulting network performance.

  12. Multiple neural representations of elementary logical connectives.

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    Baggio, Giosuè; Cherubini, Paolo; Pischedda, Doris; Blumenthal, Anna; Haynes, John-Dylan; Reverberi, Carlo

    2016-07-15

    A defining trait of human cognition is the capacity to form compounds out of simple thoughts. This ability relies on the logical connectives AND, OR and IF. Simple propositions, e.g., 'There is a fork' and 'There is a knife', can be combined in alternative ways using logical connectives: e.g., 'There is a fork AND there is a knife', 'There is a fork OR there is a knife', 'IF there is a fork, there is a knife'. How does the brain represent compounds based on different logical connectives, and how are compounds evaluated in relation to new facts? In the present study, participants had to maintain and evaluate conjunctive (AND), disjunctive (OR) or conditional (IF) compounds while undergoing functional MRI. Our results suggest that, during maintenance, the left posterior inferior frontal gyrus (pIFG, BA44, or Broca's area) represents the surface form of compounds. During evaluation, the left pIFG switches to processing the full logical meaning of compounds, and two additional areas are recruited: the left anterior inferior frontal gyrus (aIFG, BA47) and the left intraparietal sulcus (IPS, BA40). The aIFG shows a pattern of activation similar to pIFG, and compatible with processing the full logical meaning of compounds, whereas activations in IPS differ with alternative interpretations of conditionals: logical vs conjunctive. These results uncover the functions of a basic cortical network underlying human compositional thought, and provide a shared neural foundation for the cognitive science of language and reasoning.

  13. Bayes multiple decision functions.

    Science.gov (United States)

    Wu, Wensong; Peña, Edsel A

    2013-01-01

    This paper deals with the problem of simultaneously making many (M) binary decisions based on one realization of a random data matrix X. M is typically large and X will usually have M rows associated with each of the M decisions to make, but for each row the data may be low dimensional. Such problems arise in many practical areas such as the biological and medical sciences, where the available dataset is from microarrays or other high-throughput technology and with the goal being to decide which among of many genes are relevant with respect to some phenotype of interest; in the engineering and reliability sciences; in astronomy; in education; and in business. A Bayesian decision-theoretic approach to this problem is implemented with the overall loss function being a cost-weighted linear combination of Type I and Type II loss functions. The class of loss functions considered allows for use of the false discovery rate (FDR), false nondiscovery rate (FNR), and missed discovery rate (MDR) in assessing the quality of decision. Through this Bayesian paradigm, the Bayes multiple decision function (BMDF) is derived and an efficient algorithm to obtain the optimal Bayes action is described. In contrast to many works in the literature where the rows of the matrix X are assumed to be stochastically independent, we allow a dependent data structure with the associations obtained through a class of frailty-induced Archimedean copulas. In particular, non-Gaussian dependent data structure, which is typical with failure-time data, can be entertained. The numerical implementation of the determination of the Bayes optimal action is facilitated through sequential Monte Carlo techniques. The theory developed could also be extended to the problem of multiple hypotheses testing, multiple classification and prediction, and high-dimensional variable selection. The proposed procedure is illustrated for the simple versus simple hypotheses setting and for the composite hypotheses setting

  14. Bayes Multiple Decision Functions

    CERN Document Server

    Wu, Wensong

    2011-01-01

    This paper deals with the problem of simultaneously making many (M) binary decisions based on one realization of a random data matrix X. M is typically large and X will usually have M rows associated with each of the M decisions to make, but for each row the data may be low dimensional. A Bayesian decision-theoretic approach for this problem is implemented with the overall loss function being a cost-weighted linear combination of Type I and Type II loss functions. The class of loss functions considered allows for the use of the false discovery rate (FDR), false nondiscovery rate (FNR), and missed discovery rate (MDR) in assessing the decision. Through this Bayesian paradigm, the Bayes multiple decision function (BMDF) is derived and an efficient algorithm to obtain the optimal Bayes action is described. In contrast to many works in the literature where the rows of the matrix X are assumed to be stochastically independent, we allow in this paper a dependent data structure with the associations obtained through...

  15. Multiple image sensor data fusion through artificial neural networks

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    With multisensor data fusion technology, the data from multiple sensors are fused in order to make a more accurate estimation of the environment through measurement, processing and analysis. Artificial neural networks are the computational models that mimic biological neural networks. With high per...

  16. Symbolic functions from neural computation.

    Science.gov (United States)

    Smolensky, Paul

    2012-07-28

    Is thought computation over ideas? Turing, and many cognitive scientists since, have assumed so, and formulated computational systems in which meaningful concepts are encoded by symbols which are the objects of computation. Cognition has been carved into parts, each a function defined over such symbols. This paper reports on a research program aimed at computing these symbolic functions without computing over the symbols. Symbols are encoded as patterns of numerical activation over multiple abstract neurons, each neuron simultaneously contributing to the encoding of multiple symbols. Computation is carried out over the numerical activation values of such neurons, which individually have no conceptual meaning. This is massively parallel numerical computation operating within a continuous computational medium. The paper presents an axiomatic framework for such a computational account of cognition, including a number of formal results. Within the framework, a class of recursive symbolic functions can be computed. Formal languages defined by symbolic rewrite rules can also be specified, the subsymbolic computations producing symbolic outputs that simultaneously display central properties of both facets of human language: universal symbolic grammatical competence and statistical, imperfect performance.

  17. Identification of functionally related neural assemblies.

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    Gerstein, G L; Perkel, D H; Subramanian, K N

    1978-01-20

    Present-day techniques of multiple-electrode together with computer-aided separation of impulses arising from different neurons permit the simultaneous recording of nerve-impulse timings in sets of neurons exceeding 20 in number. This in turn makes it feasible to search for functional groups of neurons, defined as subsets that tend to fire in near simultaneity significantly more often than would independent neurons at corresponding mean rates. A statistical technique is described that permits the detection and identification of such functional groups. The method is accretional, based on identification of associated neurons through interative application of a significance test on multiple coincidences of neuronal firings within an observational window. Examples of the operation of the method and indications as to its sensitivity are furnished through computer simulations of neural networks. The entire algorithm may be used as a screening technique to select smaller groups of neurons for cross-correlational and related finer-grained temporal analyses, or it may be used in its own right to detect and characterize functional groups that are not distinguishable by other statistical procedures.

  18. Neural fuzzy digital filtering: multivariate identifier filters involving multiple inputs and multiple outputs (MIMO

    Directory of Open Access Journals (Sweden)

    Juan Carlos García Infante

    2011-01-01

    Full Text Available  Multivariate identifier filters (multiple inputs and multiple outputs - MIMO are adaptive digital systems having a loop in accordance with an objective function to adjust matrix parameter convergence to observable reference system dynamics. One way of complying with this condition is to use fuzzy logic inference mechanisms which interpret and select the best matrix parameter from a knowledge base. Such selection mechanisms with neural networks can provide a response from the best operational level for each change in state (Shannon, 1948. This paper considers the MIMO digital filter model using neuro fuzzy digital filtering to find an adaptive  parameter matrix which is integrated into the Kalman filter by the transition matrix. The filter uses the neural network as back-propagation into the fuzzy mechanism to do this, interpreting its variables and its respective levels and selecting the best values for automatically adjusting transition matrix values. The Matlab simulation describes the neural fuzzy digital filter giving an approximation of exponential convergence seen in functional error. 

  19. Exploiting deep neural networks and head movements for binaural localisation of multiple speakers in reverberant conditions

    DEFF Research Database (Denmark)

    Ma, Ning; Brown, Guy J.; May, Tobias

    2015-01-01

    This paper presents a novel machine-hearing system that exploits deep neural networks (DNNs) and head movements for binaural localisation of multiple speakers in reverberant conditions. DNNs are used to map binaural features, consisting of the complete crosscorrelation function (CCF) and interaural...... acoustic scenarios in which multiple speakers and room reverberation are present....

  20. Robustness analysis of uncertain dynamical neural networks with multiple time delays.

    Science.gov (United States)

    Senan, Sibel

    2015-10-01

    This paper studies the problem of global robust asymptotic stability of the equilibrium point for the class of dynamical neural networks with multiple time delays with respect to the class of slope-bounded activation functions and in the presence of the uncertainties of system parameters of the considered neural network model. By using an appropriate Lyapunov functional and exploiting the properties of the homeomorphism mapping theorem, we derive a new sufficient condition for the existence, uniqueness and global robust asymptotic stability of the equilibrium point for the class of neural networks with multiple time delays. The obtained stability condition basically relies on testing some relationships imposed on the interconnection matrices of the neural system, which can be easily verified by using some certain properties of matrices. An instructive numerical example is also given to illustrate the applicability of our result and show the advantages of this new condition over the previously reported corresponding results.

  1. Additive-Multiplicative Fuzzy Neural Network and Its Performance

    Institute of Scientific and Technical Information of China (English)

    翟东海; 靳蕃

    2003-01-01

    In view of the main weaknesses of current fuzzy neural networks such as low reasoning precision and long training time, an Additive-Multiplicative Fuzzy Neural Network (AMFNN) model and its architecture are presented. AMFNN combines additive inference and multiplicative inference into an integral whole, reasonably makes use of their advantages of inference and effectively overcomes their weaknesses when they are used for inference separately. Here, an error back propagation algorithm for AMFNN is presented based on the gradient descent method. Comparisons between the AMFNN and six representative fuzzy inference methods shows that the AMFNN is characterized by higher reasoning precision, wider application scope, stronger generalization capability and easier implementation.

  2. Neural modeling of prefrontal executive function

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    Levine, D.S. [Univ. of Texas, Arlington, TX (United States)

    1996-12-31

    Brain executive function is based in a distributed system whereby prefrontal cortex is interconnected with other cortical. and subcortical loci. Executive function is divided roughly into three interacting parts: affective guidance of responses; linkage among working memory representations; and forming complex behavioral schemata. Neural network models of each of these parts are reviewed and fit into a preliminary theoretical framework.

  3. Asymptotical stability of stochastic neural networks with multiple time-varying delays

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    Zhou, Xianghui; Zhou, Wuneng; Dai, Anding; Yang, Jun; Xie, Lili

    2015-03-01

    The stochastic neural networks can be considered as an expansion of cellular neural networks and Hopfield neural networks. In real world, the neural networks are prone to be instable due to time delay and external disturbance. In this paper, we consider the asymptotic stability for the stochastic neural networks with multiple time-varying delays. By employing a Lyapunov-Krasovskii function, a sufficient condition which guarantees the asymptotic stability of the state trajectory in the mean square is obtained. The criteria proposed can be verified readily by utilising the linear matrix inequality toolbox in Matlab, and no parameters to be tuned. In the end, two numerical examples are provided to demonstrate the effectiveness of the proposed method.

  4. Global asymptotic stability of cellular neural networks with multiple delays

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Global asymptotic stability (GAS) is discussed for cellular neural networks (CNN) with multiple time delays. Several criteria are proposed to ascertain the uniqueness and global asymptotic stability of the equilibrium point for the CNN with delays. These criteria can eliminate the difference between the neuronal excitatory and inhibitory effects. Two examples are presented to demonstrate the effectiveness of the criteria.

  5. Prefrontal cortex and neural mechanisms of executive function.

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    Funahashi, Shintaro; Andreau, Jorge Mario

    2013-12-01

    Executive function is a product of the coordinated operation of multiple neural systems and an essential prerequisite for a variety of cognitive functions. The prefrontal cortex is known to be a key structure for the performance of executive functions. To accomplish the coordinated operations of multiple neural systems, the prefrontal cortex must monitor the activities in other cortical and subcortical structures and control and supervise their operations by sending command signals, which is called top-down signaling. Although neurophysiological and neuroimaging studies have provided evidence that the prefrontal cortex sends top-down signals to the posterior cortices to control information processing, the neural correlate of these top-down signals is not yet known. Through use of the paired association task, it has been demonstrated that top-down signals are used to retrieve specific information stored in long-term memory. Therefore, we used a paired association task to examine the neural correlates of top-down signals in the prefrontal cortex. The preliminary results indicate that 32% of visual neurons exhibit pair-selectivity, which is similar to the characteristics of pair-coding activities in temporal neurons. The latency of visual responses in prefrontal neurons was longer than bottom-up signals but faster than top-down signals in inferior temporal neurons. These results suggest that pair-selective visual responses may be top-down signals that the prefrontal cortex provides to the temporal cortex, although further studies are needed to elucidate the neural correlates of top-down signals and their characteristics to understand the neural mechanism of executive control by the prefrontal cortex.

  6. Neural Cross-Frequency Coupling Functions

    Directory of Open Access Journals (Sweden)

    Tomislav Stankovski

    2017-06-01

    Full Text Available Although neural interactions are usually characterized only by their coupling strength and directionality, there is often a need to go beyond this by establishing the functional mechanisms of the interaction. We introduce the use of dynamical Bayesian inference for estimation of the coupling functions of neural oscillations in the presence of noise. By grouping the partial functional contributions, the coupling is decomposed into its functional components and its most important characteristics—strength and form—are quantified. The method is applied to characterize the δ-to-α phase-to-phase neural coupling functions from electroencephalographic (EEG data of the human resting state, and the differences that arise when the eyes are either open (EO or closed (EC are evaluated. The δ-to-α phase-to-phase coupling functions were reconstructed, quantified, compared, and followed as they evolved in time. Using phase-shuffled surrogates to test for significance, we show how the strength of the direct coupling, and the similarity and variability of the coupling functions, characterize the EO and EC states for different regions of the brain. We confirm an earlier observation that the direct coupling is stronger during EC, and we show for the first time that the coupling function is significantly less variable. Given the current understanding of the effects of e.g., aging and dementia on δ-waves, as well as the effect of cognitive and emotional tasks on α-waves, one may expect that new insights into the neural mechanisms underlying certain diseases will be obtained from studies of coupling functions. In principle, any pair of coupled oscillations could be studied in the same way as those shown here.

  7. Neural Cross-Frequency Coupling Functions

    Science.gov (United States)

    Stankovski, Tomislav; Ticcinelli, Valentina; McClintock, Peter V. E.; Stefanovska, Aneta

    2017-01-01

    Although neural interactions are usually characterized only by their coupling strength and directionality, there is often a need to go beyond this by establishing the functional mechanisms of the interaction. We introduce the use of dynamical Bayesian inference for estimation of the coupling functions of neural oscillations in the presence of noise. By grouping the partial functional contributions, the coupling is decomposed into its functional components and its most important characteristics—strength and form—are quantified. The method is applied to characterize the δ-to-α phase-to-phase neural coupling functions from electroencephalographic (EEG) data of the human resting state, and the differences that arise when the eyes are either open (EO) or closed (EC) are evaluated. The δ-to-α phase-to-phase coupling functions were reconstructed, quantified, compared, and followed as they evolved in time. Using phase-shuffled surrogates to test for significance, we show how the strength of the direct coupling, and the similarity and variability of the coupling functions, characterize the EO and EC states for different regions of the brain. We confirm an earlier observation that the direct coupling is stronger during EC, and we show for the first time that the coupling function is significantly less variable. Given the current understanding of the effects of e.g., aging and dementia on δ-waves, as well as the effect of cognitive and emotional tasks on α-waves, one may expect that new insights into the neural mechanisms underlying certain diseases will be obtained from studies of coupling functions. In principle, any pair of coupled oscillations could be studied in the same way as those shown here. PMID:28663726

  8. Adaptive Neurotechnology for Making Neural Circuits Functional .

    Science.gov (United States)

    Jung, Ranu

    2008-03-01

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

  9. 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.

  10. 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.

  11. Multiple neural mechanisms for coloring words in synesthesia.

    Science.gov (United States)

    Yokoyama, Takemasa; Noguchi, Yasuki; Koga, Hiroki; Tachibana, Ryosuke; Saiki, Jun; Kakigi, Ryusuke; Kita, Shinichi

    2014-07-01

    Grapheme-color synesthesia is a phenomenon in which achromatic letters/digits automatically induce particular colors. When multiple letters are integrated into a word, some synesthetes perceive that all those letters are changed into the same color, reporting lexical color to that word. Previous psychological studies found several "rules" that determine those lexical colors. The colors to most words are determined by the first letters of the words, while some words in ordinal sequences have their specific colors. Recent studies further reported the third case where lexical colors might be influenced by semantic information of words. Although neural mechanisms determining those lexical colors remained unknown, here we identified three separate neural systems in the synesthete's brain underlying three rules for illusory coloring of words. In addition to the occipito-temporal and parietal regions previously found to be associated with the grapheme-color synesthesia, neural systems for lexical coloring extended to linguistic areas in the left inferior frontal and anterior temporal regions that were engaged in semantic analyses of words. Those results indicate an involvement of wider and higher neural networks than previously assumed in a production of synesthetic colors to visual stimuli and further showed a multiplicity of synesthetic mechanisms represented in the single brain. Copyright © 2014 Elsevier Inc. All rights reserved.

  12. Multiple sine, multiple elliptic gamma functions and rational cones

    CERN Document Server

    Tizzano, Luigi

    2015-01-01

    We define generalizations of the multiple elliptic gamma functions and the multiple sine functions, labelled by rational cones in $\\mathbb{R}^r$. For $r=2,3$ we prove that the generalized multiple elliptic gamma functions enjoy a modular property determined by the cone. This generalizes the modular properties of the elliptic gamma function studied by Felder and Varchenko. The generalized multiple sine enjoy a related infinite product representation, generalizing the results of Narukawa for the ordinary multiple sine functions.

  13. Dynamical analysis of uncertain neural networks with multiple time delays

    Science.gov (United States)

    Arik, Sabri

    2016-02-01

    This paper investigates the robust stability problem for dynamical neural networks in the presence of time delays and norm-bounded parameter uncertainties with respect to the class of non-decreasing, non-linear activation functions. By employing the Lyapunov stability and homeomorphism mapping theorems together, a new delay-independent sufficient condition is obtained for the existence, uniqueness and global asymptotic stability of the equilibrium point for the delayed uncertain neural networks. The condition obtained for robust stability establishes a matrix-norm relationship between the network parameters of the neural system, which can be easily verified by using properties of the class of the positive definite matrices. Some constructive numerical examples are presented to show the applicability of the obtained result and its advantages over the previously published corresponding literature results.

  14. New Computer Simulations of Macular Neural Functioning

    Science.gov (United States)

    Ross, Muriel D.; Doshay, D.; Linton, S.; Parnas, B.; Montgomery, K.; Chimento, T.

    1994-01-01

    We use high performance graphics workstations and supercomputers to study the functional significance of the three-dimensional (3-D) organization of gravity sensors. These sensors have a prototypic architecture foreshadowing more complex systems. Scaled-down simulations run on a Silicon Graphics workstation and scaled-up, 3-D versions run on a Cray Y-MP supercomputer. A semi-automated method of reconstruction of neural tissue from serial sections studied in a transmission electron microscope has been developed to eliminate tedious conventional photography. The reconstructions use a mesh as a step in generating a neural surface for visualization. Two meshes are required to model calyx surfaces. The meshes are connected and the resulting prisms represent the cytoplasm and the bounding membranes. A finite volume analysis method is employed to simulate voltage changes along the calyx in response to synapse activation on the calyx or on calyceal processes. The finite volume method insures that charge is conserved at the calyx-process junction. These and other models indicate that efferent processes act as voltage followers, and that the morphology of some afferent processes affects their functioning. In a final application, morphological information is symbolically represented in three dimensions in a computer. The possible functioning of the connectivities is tested using mathematical interpretations of physiological parameters taken from the literature. Symbolic, 3-D simulations are in progress to probe the functional significance of the connectivities. This research is expected to advance computer-based studies of macular functioning and of synaptic plasticity.

  15. Density functional and neural network analysis

    DEFF Research Database (Denmark)

    Jalkanen, K. J.; Bohr, Henrik

    1997-01-01

    Density functional theory (DFT) calculations have been carried out for hydrated L-alanine, L-alanyl-L-alanine and N-acetyl L-alanine N'-methylamide and examined with respect to the effect of water on the structure, the vibrational frequencies, vibrational absorption (VA) and vibrational circular...... dichroism (VCD) intensities. The large changes due to hydration on the structures, relative stability of conformers, and in the VA and VCD spectra observed experimentally are reproduced by the DFT calculations. Furthermore a neural network was constructed for reproducing the inverse scattering data (infer...

  16. Multiple μ-stability of neural networks with unbounded time-varying delays.

    Science.gov (United States)

    Wang, Lili; Chen, Tianping

    2014-05-01

    In this paper, we are concerned with a class of recurrent neural networks with unbounded time-varying delays. Based on the geometrical configuration of activation functions, the phase space R(n) can be divided into several Φη-type subsets. Accordingly, a new set of regions Ωη are proposed, and rigorous mathematical analysis is provided to derive the existence of equilibrium point and its local μ-stability in each Ωη. It concludes that the n-dimensional neural networks can exhibit at least 3(n) equilibrium points and 2(n) of them are μ-stable. Furthermore, due to the compatible property, a set of new conditions are presented to address the dynamics in the remaining 3(n)-2(n) subset regions. As direct applications of these results, we can get some criteria on the multiple exponential stability, multiple power stability, multiple log-stability, multiple log-log-stability and so on. In addition, the approach and results can also be extended to the neural networks with K-level nonlinear activation functions and unbounded time-varying delays, in which there can store (2K+1)(n) equilibrium points, (K+1)(n) of them are locally μ-stable. Numerical examples are given to illustrate the effectiveness of our results.

  17. Pax3 and Zic1 trigger the early neural crest gene regulatory network by the direct activation of multiple key neural crest specifiers.

    Science.gov (United States)

    Plouhinec, Jean-Louis; Roche, Daniel D; Pegoraro, Caterina; Figueiredo, Ana Leonor; Maczkowiak, Frédérique; Brunet, Lisa J; Milet, Cécile; Vert, Jean-Philippe; Pollet, Nicolas; Harland, Richard M; Monsoro-Burq, Anne H

    2014-02-15

    Neural crest development is orchestrated by a complex and still poorly understood gene regulatory network. Premigratory neural crest is induced at the lateral border of the neural plate by the combined action of signaling molecules and transcription factors such as AP2, Gbx2, Pax3 and Zic1. Among them, Pax3 and Zic1 are both necessary and sufficient to trigger a complete neural crest developmental program. However, their gene targets in the neural crest regulatory network remain unknown. Here, through a transcriptome analysis of frog microdissected neural border, we identified an extended gene signature for the premigratory neural crest, and we defined novel potential members of the regulatory network. This signature includes 34 novel genes, as well as 44 known genes expressed at the neural border. Using another microarray analysis which combined Pax3 and Zic1 gain-of-function and protein translation blockade, we uncovered 25 Pax3 and Zic1 direct targets within this signature. We demonstrated that the neural border specifiers Pax3 and Zic1 are direct upstream regulators of neural crest specifiers Snail1/2, Foxd3, Twist1, and Tfap2b. In addition, they may modulate the transcriptional output of multiple signaling pathways involved in neural crest development (Wnt, Retinoic Acid) through the induction of key pathway regulators (Axin2 and Cyp26c1). We also found that Pax3 could maintain its own expression through a positive autoregulatory feedback loop. These hierarchical inductions, feedback loops, and pathway modulations provide novel tools to understand the neural crest induction network.

  18. Neural network and multiple linear regression to predict school children dimensions for ergonomic school furniture design.

    Science.gov (United States)

    Agha, Salah R; Alnahhal, Mohammed J

    2012-11-01

    The current study investigates the possibility of obtaining the anthropometric dimensions, critical to school furniture design, without measuring all of them. The study first selects some anthropometric dimensions that are easy to measure. Two methods are then used to check if these easy-to-measure dimensions can predict the dimensions critical to the furniture design. These methods are multiple linear regression and neural networks. Each dimension that is deemed necessary to ergonomically design school furniture is expressed as a function of some other measured anthropometric dimensions. Results show that out of the five dimensions needed for chair design, four can be related to other dimensions that can be measured while children are standing. Therefore, the method suggested here would definitely save time and effort and avoid the difficulty of dealing with students while measuring these dimensions. In general, it was found that neural networks perform better than multiple linear regression in the current study.

  19. Analysis of neural networks through base functions

    NARCIS (Netherlands)

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

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

  20. Analysis of Neural Networks through Base Functions

    NARCIS (Netherlands)

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

    2002-01-01

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

  1. FRICTION MODELING OF Al-Mg ALLOY SHEETS BASED ON MULTIPLE REGRESSION ANALYSIS AND NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Hirpa G. Lemu

    2017-03-01

    Full Text Available This article reports a proposed approach to a frictional resistance description in sheet metal forming processes that enables determination of the friction coefficient value under a wide range of friction conditions without performing time-consuming experiments. The motivation for this proposal is the fact that there exists a considerable amount of factors affect the friction coefficient value and as a result building analytical friction model for specified process conditions is practically impossible. In this proposed approach, a mathematical model of friction behaviour is created using multiple regression analysis and artificial neural networks. The regression analysis was performed using a subroutine in MATLAB programming code and STATISTICA Neural Networks was utilized to build an artificial neural networks model. The effect of different training strategies on the quality of neural networks was studied. As input variables for regression model and training of radial basis function networks, generalized regression neural networks and multilayer networks the results of strip drawing friction test were utilized. Four kinds of Al-Mg alloy sheets were used as a test material.

  2. Models of neural networks with fuzzy activation functions

    Science.gov (United States)

    Nguyen, A. T.; Korikov, A. M.

    2017-02-01

    This paper investigates the application of a new form of neuron activation functions that are based on the fuzzy membership functions derived from the theory of fuzzy systems. On the basis of the results regarding neuron models with fuzzy activation functions, we created the models of fuzzy-neural networks. These fuzzy-neural network models differ from conventional networks that employ the fuzzy inference systems using the methods of neural networks. While conventional fuzzy-neural networks belong to the first type, fuzzy-neural networks proposed here are defined as the second-type models. The simulation results show that the proposed second-type model can successfully solve the problem of the property prediction for time – dependent signals. Neural networks with fuzzy impulse activation functions can be widely applied in many fields of science, technology and mechanical engineering to solve the problems of classification, prediction, approximation, etc.

  3. Activation of endogenous neural stem cells for multiple sclerosis therapy

    Directory of Open Access Journals (Sweden)

    Iliana eMichailidou

    2015-01-01

    Full Text Available Multiple sclerosis (MS is a chronic inflammatory disorder of the central nervous system, leading to severe neurological deficits. Current MS treatment regimens, consist of immunomodulatory agents aiming to reduce the rate of relapses. However, these agents are usually insufficient to treat chronic neurological disability.A promising perspective for future therapy of MS is the regeneration of lesions with replacement of the damaged oligodendrocytes or neurons. Therapies targeting to the enhancement of endogenous remyelination, aim to promote the activation of either the parenchymal oligodendrocyte progenitor cells or the subventricular zone-derived neural stem cells (NSCs. Less studied but highly potent, is the strategy of neuronal regeneration with endogenous NSCs that although being linked to numerous limitations, is anticipated to ameliorate cognitive disability in MS. Focusing on the forebrain, this review highlights the role of NSCs in the regeneration of MS lesions.

  4. Neural networks for function approximation in nonlinear control

    Science.gov (United States)

    Linse, Dennis J.; Stengel, Robert F.

    1990-01-01

    Two neural network architectures are compared with a classical spline interpolation technique for the approximation of functions useful in a nonlinear control system. A standard back-propagation feedforward neural network and a cerebellar model articulation controller (CMAC) neural network are presented, and their results are compared with a B-spline interpolation procedure that is updated using recursive least-squares parameter identification. Each method is able to accurately represent a one-dimensional test function. Tradeoffs between size requirements, speed of operation, and speed of learning indicate that neural networks may be practical for identification and adaptation in a nonlinear control environment.

  5. Multiple elliptic gamma functions associated to cones

    CERN Document Server

    Winding, Jacob

    2016-01-01

    We define generalizations of the multiple elliptic gamma functions and the multiple sine functions, associated to good rational cones. We explain how good cones are related to collections of $SL_r(\\mathbb{Z})$-elements and prove that the generalized multiple sine and multiple elliptic gamma functions enjoy infinite product representations and modular properties determined by the cone. This generalizes the modular properties of the elliptic gamma function studied by Felder and Varchenko, and the results about the usual multiple sine and elliptic gamma functions found by Narukawa.

  6. Speech comprehension aided by multiple modalities: behavioural and neural interactions

    Science.gov (United States)

    McGettigan, Carolyn; Faulkner, Andrew; Altarelli, Irene; Obleser, Jonas; Baverstock, Harriet; Scott, Sophie K.

    2014-01-01

    Speech comprehension is a complex human skill, the performance of which requires the perceiver to combine information from several sources – e.g. voice, face, gesture, linguistic context – to achieve an intelligible and interpretable percept. We describe a functional imaging investigation of how auditory, visual and linguistic information interact to facilitate comprehension. Our specific aims were to investigate the neural responses to these different information sources, alone and in interaction, and further to use behavioural speech comprehension scores to address sites of intelligibility-related activation in multifactorial speech comprehension. In fMRI, participants passively watched videos of spoken sentences, in which we varied Auditory Clarity (with noise-vocoding), Visual Clarity (with Gaussian blurring) and Linguistic Predictability. Main effects of enhanced signal with increased auditory and visual clarity were observed in overlapping regions of posterior STS. Two-way interactions of the factors (auditory × visual, auditory × predictability) in the neural data were observed outside temporal cortex, where positive signal change in response to clearer facial information and greater semantic predictability was greatest at intermediate levels of auditory clarity. Overall changes in stimulus intelligibility by condition (as determined using an independent behavioural experiment) were reflected in the neural data by increased activation predominantly in bilateral dorsolateral temporal cortex, as well as inferior frontal cortex and left fusiform gyrus. Specific investigation of intelligibility changes at intermediate auditory clarity revealed a set of regions, including posterior STS and fusiform gyrus, showing enhanced responses to both visual and linguistic information. Finally, an individual differences analysis showed that greater comprehension performance in the scanning participants (measured in a post-scan behavioural test) were associated with

  7. Statistical technique for analysing functional connectivity of multiple spike trains.

    Science.gov (United States)

    Masud, Mohammad Shahed; Borisyuk, Roman

    2011-03-15

    A new statistical technique, the Cox method, used for analysing functional connectivity of simultaneously recorded multiple spike trains is presented. This method is based on the theory of modulated renewal processes and it estimates a vector of influence strengths from multiple spike trains (called reference trains) to the selected (target) spike train. Selecting another target spike train and repeating the calculation of the influence strengths from the reference spike trains enables researchers to find all functional connections among multiple spike trains. In order to study functional connectivity an "influence function" is identified. This function recognises the specificity of neuronal interactions and reflects the dynamics of postsynaptic potential. In comparison to existing techniques, the Cox method has the following advantages: it does not use bins (binless method); it is applicable to cases where the sample size is small; it is sufficiently sensitive such that it estimates weak influences; it supports the simultaneous analysis of multiple influences; it is able to identify a correct connectivity scheme in difficult cases of "common source" or "indirect" connectivity. The Cox method has been thoroughly tested using multiple sets of data generated by the neural network model of the leaky integrate and fire neurons with a prescribed architecture of connections. The results suggest that this method is highly successful for analysing functional connectivity of simultaneously recorded multiple spike trains.

  8. Should I stay or should I go? Cadherin function and regulation in the neural crest.

    Science.gov (United States)

    Taneyhill, Lisa A; Schiffmacher, Andrew T

    2017-03-02

    Our increasing comprehension of neural crest cell development has reciprocally advanced our understanding of cadherin expression, regulation, and function. As a transient population of multipotent stem cells that significantly contribute to the vertebrate body plan, neural crest cells undergo a variety of transformative processes and exhibit many cellular behaviors, including epithelial-to-mesenchymal transition (EMT), motility, collective cell migration, and differentiation. Multiple studies have elucidated regulatory and mechanistic details of specific cadherins during neural crest cell development in a highly contextual manner. Collectively, these results reveal that gradual changes within neural crest cells are accompanied by often times subtle, yet important, alterations in cadherin expression and function. The primary focus of this review is to coalesce recent data on cadherins in neural crest cells, from their specification to their emergence as motile cells soon after EMT, and to highlight the complexities of cadherin expression beyond our current perceptions, including the hypothesis that the neural crest EMT is a transition involving a predominantly singular cadherin switch. Further advancements in genetic approaches and molecular techniques will provide greater opportunities to integrate data from various model systems in order to distinguish unique or overlapping functions of cadherins expressed at any point throughout the ontogeny of the neural crest.

  9. Testing Multiple Psychological Processes for Common Neural Mechanisms Using EEG and Independent Component Analysis.

    Science.gov (United States)

    Wessel, Jan R

    2016-03-08

    Temporal independent component analysis (ICA) is applied to an electrophysiological signal mixture (such as an EEG recording) to disentangle the independent neural source signals-independent components-underlying said signal mixture. When applied to scalp EEG, ICA is most commonly used either as a pre-processing step (e.g., to isolate physiological processes from non-physiological artifacts), or as a data-reduction step (i.e., to focus on one specific neural process with increased signal-to-noise ratio). However, ICA can be used in an even more powerful way that fundamentally expands the inferential utility of scalp EEG. The core assumption of EEG-ICA-namely, that individual independent components represent separable neural processes-can be leveraged to derive the following inferential logic: If a specific independent component shows activity related to multiple psychological processes within the same dataset (e.g., elicited by different experimental events), it follows that those psychological processes involve a common, non-separable neural mechanism. As such, this logic allows testing a class of hypotheses that is beyond the reach of regular EEG analyses techniques, thereby crucially increasing the inferential utility of the EEG. In the current article, this logic will be referred to as the 'common independent process identification' (CIPI) approach. This article aims to provide a tutorial into the application of this powerful approach, targeted at researchers that have a basic understanding of standard EEG analysis. Furthermore, the article aims to exemplify the usage of CIPI by outlining recent studies that successfully applied this approach to test neural theories of mental functions.

  10. Vertically Aligned Carbon Nanofiber as Nano-Neuron Interface for Monitoring Neural Function

    OpenAIRE

    Yu, Zhe; McKnight, Timothy E.; Ericson, M. Nance; Melechko, Anatoli V.; Simpson, Michael L.; Morrison, Barclay

    2012-01-01

    Neural chips, which are capable of simultaneous, multi-site neural recording and stimulation, have been used to detect and modulate neural activity for almost 30 years. As a neural interface, neural chips provide dynamic functional information for neural decoding and neural control. By improving sensitivity and spatial resolution, nano-scale electrodes may revolutionize neural detection and modulation at cellular and molecular levels as nano-neuron interfaces. We developed a carbon-nanofiber ...

  11. Multistability and Instability of Neural Networks With Discontinuous Nonmonotonic Piecewise Linear Activation Functions.

    Science.gov (United States)

    Nie, Xiaobing; Zheng, Wei Xing

    2015-11-01

    In this paper, we discuss the coexistence and dynamical behaviors of multiple equilibrium points for recurrent neural networks with a class of discontinuous nonmonotonic piecewise linear activation functions. It is proved that under some conditions, such n -neuron neural networks can have at least 5(n) equilibrium points, 3(n) of which are locally stable and the others are unstable, based on the contraction mapping theorem and the theory of strict diagonal dominance matrix. The investigation shows that the neural networks with the discontinuous activation functions introduced in this paper can have both more total equilibrium points and more locally stable equilibrium points than the ones with continuous Mexican-hat-type activation function or discontinuous two-level activation functions. An illustrative example with computer simulations is presented to verify the theoretical analysis.

  12. Multiple functions of photosystem II

    NARCIS (Netherlands)

    Rensen, van J.J.S.; Curwiel, V.B.

    2000-01-01

    The most important function of photosystem II (PSII) is its action as a water-plastoquinone oxido-reductase. At the expense of light energy, water is split, and oxygen and plastoquinol are formed. In addition to this most important activity, PSII has additional functions, especially in the

  13. Functional expansion representations of artificial neural networks

    Science.gov (United States)

    Gray, W. Steven

    1992-01-01

    In the past few years, significant interest has developed in using artificial neural networks to model and control nonlinear dynamical systems. While there exists many proposed schemes for accomplishing this and a wealth of supporting empirical results, most approaches to date tend to be ad hoc in nature and rely mainly on heuristic justifications. The purpose of this project was to further develop some analytical tools for representing nonlinear discrete-time input-output systems, which when applied to neural networks would give insight on architecture selection, pruning strategies, and learning algorithms. A long term goal is to determine in what sense, if any, a neural network can be used as a universal approximator for nonliner input-output maps with memory (i.e., realized by a dynamical system). This property is well known for the case of static or memoryless input-output maps. The general architecture under consideration in this project was a single-input, single-output recurrent feedforward network.

  14. Adult neural stem cells-Functional potential and therapeutic applications

    Institute of Scientific and Technical Information of China (English)

    YANG Lin; ZHU Jianhong

    2004-01-01

    The adult brain has been thought traditionally as a structure with a very limited regenerative capacity. It is now evident that neurogenesis in adult mammalian brain is a prevailing phenomenon. Neural stem cells with the ability to self-renew, differentiate into neurons, astrocytes and oligodendrocytes reside in some regions of the adult brain. Adult neurogenesis can be stimulated by many physiological factors including pregnancy. More strikingly, newborn neurons in hippocampus integrally function with local neurons, thus neural stem cells might play important roles in memory and learning function. It seems that neural stem cells could transdifferentiate into other tissues, such as blood cells and muscles. Although there are some impediments in this field, some attempts have been made to employ adult neural stem cells in the cell replacement therapy for traumatic and ischemic brain injuries.

  15. Neural progenitor cells regulate microglia functions and activity.

    Science.gov (United States)

    Mosher, Kira I; Andres, Robert H; Fukuhara, Takeshi; Bieri, Gregor; Hasegawa-Moriyama, Maiko; He, Yingbo; Guzman, Raphael; Wyss-Coray, Tony

    2012-11-01

    We found mouse neural progenitor cells (NPCs) to have a secretory protein profile distinct from other brain cells and to modulate microglial activation, proliferation and phagocytosis. NPC-derived vascular endothelial growth factor was necessary and sufficient to exert at least some of these effects in mice. Thus, neural precursor cells may not only be shaped by microglia, but also regulate microglia functions and activity.

  16. Impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach.

    Science.gov (United States)

    Chandrasekar, A; Rakkiyappan, R; Cao, Jinde

    2015-10-01

    This paper studies the impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach. The array of neural networks are coupled in a random fashion which is governed by Bernoulli random variable. The aim of this paper is to obtain the synchronization criteria, which is suitable for both exactly known and partly unknown transition probabilities such that the coupled neural network is synchronized with mixed time-delay. The considered impulsive effects can be synchronized at partly unknown transition probabilities. Besides, a multiple integral approach is also proposed to strengthen the Markovian jumping randomly coupled neural networks with partly unknown transition probabilities. By making use of Kronecker product and some useful integral inequalities, a novel Lyapunov-Krasovskii functional was designed for handling the coupled neural network with mixed delay and then impulsive synchronization criteria are solvable in a set of linear matrix inequalities. Finally, numerical examples are presented to illustrate the effectiveness and advantages of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

  17. Representation of Functional Data in Neural Networks

    CERN Document Server

    Rossi, Fabrice; Conan-Guez, Brieuc; Verleysen, Michel

    2005-01-01

    Functional Data Analysis (FDA) is an extension of traditional data analysis to functional data, for example spectra, temporal series, spatio-temporal images, gesture recognition data, etc. Functional data are rarely known in practice; usually a regular or irregular sampling is known. For this reason, some processing is needed in order to benefit from the smooth character of functional data in the analysis methods. This paper shows how to extend the Radial-Basis Function Networks (RBFN) and Multi-Layer Perceptron (MLP) models to functional data inputs, in particular when the latter are known through lists of input-output pairs. Various possibilities for functional processing are discussed, including the projection on smooth bases, Functional Principal Component Analysis, functional centering and reduction, and the use of differential operators. It is shown how to incorporate these functional processing into the RBFN and MLP models. The functional approach is illustrated on a benchmark of spectrometric data ana...

  18. Distinct enhancers at the Pax3 locus can function redundantly to regulate neural tube and neural crest expressions.

    Science.gov (United States)

    Degenhardt, Karl R; Milewski, Rita C; Padmanabhan, Arun; Miller, Mayumi; Singh, Manvendra K; Lang, Deborah; Engleka, Kurt A; Wu, Meilin; Li, Jun; Zhou, Diane; Antonucci, Nicole; Li, Li; Epstein, Jonathan A

    2010-03-15

    Pax3 is a transcription factor expressed in somitic mesoderm, dorsal neural tube and pre-migratory neural crest during embryonic development. We have previously identified cis-acting enhancer elements within the proximal upstream genomic region of Pax3 that are sufficient to direct functional expression of Pax3 in neural crest. These elements direct expression of a reporter gene to pre-migratory neural crest in transgenic mice, and transgenic expression of a Pax3 cDNA using these elements is sufficient to rescue neural crest development in mice otherwise lacking endogenous Pax3. We show here that deletion of these enhancer sequences by homologous recombination is insufficient to abrogate neural crest expression of Pax3 and results in viable mice. We identify a distinct enhancer in the fourth intron that is also capable of mediating neural crest expression in transgenic mice and zebrafish. Our analysis suggests the existence of functionally redundant neural crest enhancer modules for Pax3.

  19. Representations of Multiple-Valued Logic Functions

    CERN Document Server

    Stankovic, Radomir S

    2012-01-01

    Compared to binary switching functions, multiple-valued functions offer more compact representations of the information content of signals modeled by logic functions and, therefore, their use fits very well in the general settings of data compression attempts and approaches. The first task in dealing with such signals is to provide mathematical methods for their representation in a way that will make their application in practice feasible.Representation of Multiple-Valued Logic Functions is aimed at providing an accessible introduction to these mathematical techniques that are necessary for ap

  20. Characteristic functions and process identification by neural networks

    CERN Document Server

    Dente, J A

    1997-01-01

    Principal component analysis (PCA) algorithms use neural networks to extract the eigenvectors of the correlation matrix from the data. However, if the process is non-Gaussian, PCA algorithms or their higher order generalisations provide only incomplete or misleading information on the statistical properties of the data. To handle such situations we propose neural network algorithms, with an hybrid (supervised and unsupervised) learning scheme, which constructs the characteristic function of the probability distribution and the transition functions of the stochastic process. Illustrative examples are presented, which include Cauchy and Levy-type processes

  1. Human neural progenitors express functional lysophospholipid receptors that regulate cell growth and morphology

    Directory of Open Access Journals (Sweden)

    Callihan Phillip

    2008-12-01

    Full Text Available Abstract Background Lysophospholipids regulate the morphology and growth of neurons, neural cell lines, and neural progenitors. A stable human neural progenitor cell line is not currently available in which to study the role of lysophospholipids in human neural development. We recently established a stable, adherent human embryonic stem cell-derived neuroepithelial (hES-NEP cell line which recapitulates morphological and phenotypic features of neural progenitor cells isolated from fetal tissue. The goal of this study was to determine if hES-NEP cells express functional lysophospholipid receptors, and if activation of these receptors mediates cellular responses critical for neural development. Results Our results demonstrate that Lysophosphatidic Acid (LPA and Sphingosine-1-phosphate (S1P receptors are functionally expressed in hES-NEP cells and are coupled to multiple cellular signaling pathways. We have shown that transcript levels for S1P1 receptor increased significantly in the transition from embryonic stem cell to hES-NEP. hES-NEP cells express LPA and S1P receptors coupled to Gi/o G-proteins that inhibit adenylyl cyclase and to Gq-like phospholipase C activity. LPA and S1P also induce p44/42 ERK MAP kinase phosphorylation in these cells and stimulate cell proliferation via Gi/o coupled receptors in an Epidermal Growth Factor Receptor (EGFR- and ERK-dependent pathway. In contrast, LPA and S1P stimulate transient cell rounding and aggregation that is independent of EGFR and ERK, but dependent on the Rho effector p160 ROCK. Conclusion Thus, lysophospholipids regulate neural progenitor growth and morphology through distinct mechanisms. These findings establish human ES cell-derived NEP cells as a model system for studying the role of lysophospholipids in neural progenitors.

  2. Potential Mechanisms and Functions of Intermittent Neural Synchronization

    Directory of Open Access Journals (Sweden)

    Sungwoo Ahn

    2017-05-01

    Full Text Available Neural synchronization is believed to play an important role in different brain functions. Synchrony in cortical and subcortical circuits is frequently variable in time and not perfect. Few long intervals of desynchronized dynamics may be functionally different from many short desynchronized intervals although the average synchrony may be the same. Recent analysis of imperfect synchrony in different neural systems reported one common feature: neural oscillations may go out of synchrony frequently, but primarily for a short time interval. This study explores potential mechanisms and functional advantages of this short desynchronizations dynamics using computational neuroscience techniques. We show that short desynchronizations are exhibited in coupled neurons if their delayed rectifier potassium current has relatively large values of the voltage-dependent activation time-constant. The delayed activation of potassium current is associated with generation of quickly-rising action potential. This “spikiness” is a very general property of neurons. This may explain why very different neural systems exhibit short desynchronization dynamics. We also show how the distribution of desynchronization durations may be independent of the synchronization strength. Finally, we show that short desynchronization dynamics requires weaker synaptic input to reach a pre-set synchrony level. Thus, this dynamics allows for efficient regulation of synchrony and may promote efficient formation of synchronous neural assemblies.

  3. The Specialization of Function: Cognitive and Neural Perspectives

    Science.gov (United States)

    Mahon, Bradford Z.; Cantlon, Jessica F.

    2014-01-01

    A unifying theme that cuts across all research areas and techniques in the cognitive and brain sciences is whether there is specialization of function at levels of processing that are ‘abstracted away’ from sensory inputs and motor outputs. Any theory that articulates claims about specialization of function in the mind/brain confronts the following types of interrelated questions, each of which carries with it certain theoretical commitments. What methods are appropriate for decomposing complex cognitive and neural processes into their constituent parts? How do cognitive processes map onto neural processes, and at what resolution are they related? What types of conclusions can be drawn about the structure of mind from dissociations observed at the neural level, and vice versa? The contributions that form this Special Issue of Cognitive Neuropsychology represent recent reflections on these and other issues from leading researchers in different areas of the cognitive and brain sciences. PMID:22185234

  4. Learning ambiguous functions by neural networks

    CERN Document Server

    Ligeiro, Rui

    2013-01-01

    It is not, in general, possible to have access to all variables that determine the behavior of a system. Having identified a number of variables whose values can be accessed, there may still be hidden variables which influence the dynamics of the system. The result is model ambiguity in the sense that, for the same (or very similar) input values, different objective outputs should have been obtained. In addition, the degree of ambiguity may vary widely across the whole range of input values. Thus, to evaluate the accuracy of a model it is of utmost importance to create a method to obtain the degree of reliability of each output result. In this paper we present such a scheme composed of two coupled artificial neural networks: the first one being responsible for outputting the predicted value, whereas the other evaluates the reliability of the output, which is learned from the error values of the first one. As an illustration, the scheme is applied to a model for tracking slopes in a straw chamber and to a cred...

  5. REAL-TIME MOTION PLANNING METHOD BASED ON NEURAL NETWORKS FOR MULTIPLE MOBILE ROBOTS

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The motion planning of multiple mobile robots undertaking individual tasks in the same environment is studied. A motion planning method based on neural networks is proposed. By storing fuzzy rules in neural networks the method can fully make use of the association ability and high processing speed of neural networks to make robots avoid collisions with other objects in real time.Compared with rules method,the method can not only avoid building a large and complex rules base but also make robots avoid collisions and conflicts at higher speed and with higher intelligence.

  6. Apraxia: neural mechanisms and functional recovery.

    Science.gov (United States)

    Foundas, Anne L

    2013-01-01

    Apraxia is a cognitive-motor disorder that impacts the performance of learned, skilled movements. Limb apraxia, which is the topic of this chapter, is specific to disordered movements of the upper limb that cannot be explained by weakness, sensory loss, abnormalities of posture/tone/movement, or a lack of understanding/cooperation. Patients with limb apraxia have deficits in the control or programming of the spatial-temporal organization and sequencing of goal-directed movements. People with limb apraxia can have difficulty manipulating and using tools including cutting with scissors or making a cup of coffee. Two praxis systems have been identified including a production system (action plan and production) and a conceptual system (action knowledge). Dysfunction of the former produces ideomotor apraxia (e.g., difficulty using scissors), and dysfunction of the latter induces ideational apraxia (e.g., difficulty making a cup of coffee). Neural mechanisms, including how to evaluate apraxia, will be presented in the context of these two praxis systems. Information about these praxis systems, including the nature of the disordered limb movement, is important for rehabilitation clinicians to understand for several reasons. First, limb apraxia is a common disorder. It is common in patients who have had a stroke, in neurodegenerative disorders like Alzheimer disease, in traumatic brain injury, and in developmental disorders. Second, limb apraxia has real world consequences. Patients with limb apraxia have difficulty managing activities of daily living. This factor impacts healthcare costs and contributes to increased caregiver burden. Unfortunately, very few treatments have been systematically studied in large numbers of patients with limb apraxia. This overview of limb apraxia should help rehabilitation clinicians to educate patients and caregivers about this debilitating problem, and should facilitate the development of better treatments that could benefit many people in

  7. Optimal multiple-information integration inherent in a ring neural network

    Science.gov (United States)

    Takiyama, Ken

    2017-02-01

    Although several behavioral experiments have suggested that our neural system integrates multiple sources of information based on the certainty of each type of information in the manner of maximum-likelihood estimation, it is unclear how the maximum-likelihood estimation is implemented in our neural system. Here, I investigate the relationship between maximum-likelihood estimation and a widely used ring-type neural network model that is used as a model of visual, motor, or prefrontal cortices. Without any approximation or ansatz, I analytically demonstrate that the equilibrium of an order parameter in the neural network model exactly corresponds to the maximum-likelihood estimation when the strength of the symmetrical recurrent synaptic connectivity within a neural population is appropriately stronger than that of asymmetrical connectivity, that of local and external inputs, and that of symmetrical or asymmetrical connectivity between different neural populations. In this case, strengths of local and external inputs or those of symmetrical connectivity between different neural populations exactly correspond to the input certainty in maximum-likelihood estimation. Thus, my analysis suggests appropriately strong symmetrical recurrent connectivity as a possible candidate for implementing the maximum-likelihood estimation within our neural system.

  8. Neural Basis of Tics: A Functional MRI Study

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2006-09-01

    Full Text Available Event-related functional MRI (fMRI was used to study the neural basis of spontaneous motor and vocal tics in 10 patients with Tourette syndrome, at the National Institute of Neurological Disorders and Stroke, Bethesda, MD.

  9. Neural network-based control using Lyapunov functions

    Science.gov (United States)

    Luxemburg, Leon A.

    1993-01-01

    We have successfully demonstrated how the problem of stabilization of plants can be reduced to a problem of approximation of functions. Neural networks have been shown to have approximating and interpolating properties. This approach is good for linear and nonlinear plants. Software has been generated to demonstrate this approach.

  10. Bayesian Regularization in a Neural Network Model to Estimate Lines of Code Using Function Points

    Directory of Open Access Journals (Sweden)

    K. K. Aggarwal

    2005-01-01

    Full Text Available It is a well known fact that at the beginning of any project, the software industry needs to know, how much will it cost to develop and what would be the time required ? . This paper examines the potential of using a neural network model for estimating the lines of code, once the functional requirements are known. Using the International Software Benchmarking Standards Group (ISBSG Repository Data (release 9 for the experiment, this paper examines the performance of back propagation feed forward neural network to estimate the Source Lines of Code. Multiple training algorithms are used in the experiments. Results demonstrate that the neural network models trained using Bayesian Regularization provide the best results and are suitable for this purpose.

  11. Nonlinear Decoupling PID Control Using Neural Networks and Multiple Models

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential (PID) controller is proposed, which consists of a traditional PID controller, a decoupling compensator and a feedforward compensator for the unmodeled dynamics. The parameters of such controller is selected based on the generalized minimum variance control law. The unmodeled dynamics is estimated and compensated by neural networks, a switching mechanism is introduced to improve tracking performance, then a nonlinear decoupling PID control algorithm is proposed. All signals in such switching system are globally bounded and the tracking error is convergent. Simulations show effectiveness of the algorithm.

  12. A functional clustering algorithm for the analysis of neural relationships

    CERN Document Server

    Feldt, S; Hetrick, V L; Berke, J D; Zochowski, M

    2008-01-01

    We formulate a novel technique for the detection of functional clusters in neural data. In contrast to prior network clustering algorithms, our procedure progressively combines spike trains and derives the optimal clustering cutoff in a simple and intuitive manner. To demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both simulated data and real neural data obtained from the mouse hippocampus during exploration and slow-wave sleep. We observe state-dependent clustering patterns consistent with known neurophysiological processes involved in memory consolidation.

  13. Impact and point prediction using a neural extended Kalman filter with multiple sensors

    Science.gov (United States)

    Stubberud, Stephen C.; Kramer, Kathleen A.

    2007-04-01

    The neural extended Kalman filter is an adaptive estimation technique that has been shown to learn on-line the maneuver model of the trajectory of a target. This improved motion model can be used to better predict the location of a target at given point in time, especially when the target, such as a mortar shell, has limited maneuvering capabilities. In this paper, the neural extended Kalman filter is used to predict, with multiple-sensor-systems provided measurement reports, impact point and impact time of a ballistic-like projectile when the drag on the shell was not accurately modeled in the motion model. In previous work, the neural extended Kalman filter was shown to work well with a single sensor with a uniform sample rate. Multiple sensors can incorporate two major differences into the problem. The first difference is that of the multiple aspect angles and uncertainty that are used in the model adaptation. The second difference is that of a non-uniform update rate of the measurements to the tracking system. While most tracking systems can easily handle this difference, the adaptation of the neural network training parameters can be deleteriously affected by these variations. The first of these two differences, potential concerns to the neural extended Kalman filter's implementation, is investigated in this effort. In this effort, performance of this adaptive and predictive scheme with multiple sensors in a three dimensional space is shown to provide a quality impact estimate.

  14. Neutron spectrum unfolding using radial basis function neural networks.

    Science.gov (United States)

    Alvar, Amin Asgharzadeh; Deevband, Mohammad Reza; Ashtiyani, Meghdad

    2017-07-26

    Neutron energy spectrum unfolding has been the subject of research for several years. The Bayesian theory, Monte Carlo simulation, and iterative methods are some of the methods that have been used for neutron spectrum unfolding. In this study, the radial basis function (RBF), multilayer perceptron, and artificial neural networks (ANNs) were used for the unfolding of neutron spectrum, and a comparison was made between the networks' results. Both neural network architectures were trained and tested using the same data set for neutron spectrum unfolding from the response of LiI detectors with Eu impurity. Advantages of each ANN method in the unfolding of neutron energy spectrum were investigated, and the performance of the networks was compared. The results obtained showed that RBF neural network can be applied as an effective method for unfolding neutron spectrum, especially when the main target is the neutron dosimetry. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. The functional role of neural oscillations in non-verbal emotional communication

    Directory of Open Access Journals (Sweden)

    Ashley E Symons

    2016-05-01

    Full Text Available Effective interpersonal communication depends on the ability to perceive and interpret nonverbal emotional expressions from multiple sensory modalities. Current theoretical models propose that visual and auditory emotion perception involves a network of brain regions including the primary sensory cortices, the superior temporal sulcus (STS, and orbitofrontal cortex (OFC. However, relatively little is known about how the dynamic interplay between these regions gives rise to the perception of emotions. In recent years, there has been increasing recognition of the importance of neural oscillations in mediating neural communication within and between functional neural networks. Here we review studies investigating changes in oscillatory activity during the perception of visual, auditory, and audiovisual emotional expressions, and aim to characterise the functional role of neural oscillations in nonverbal emotion perception. Findings from the reviewed literature suggest that theta band oscillations most consistently differentiate between emotional and neutral expressions. While early theta synchronisation appears to reflect the initial encoding of emotionally salient sensory information, later fronto-central theta synchronisation may reflect the further integration of sensory information with internal representations. Additionally, gamma synchronisation reflects facilitated sensory binding of emotional expressions within regions such as the OFC, STS, and, potentially, the amygdala. However, the evidence is more ambiguous when it comes to the role of oscillations within the alpha and beta frequencies, which vary as a function of modality (or modalities, presence or absence of predictive information, and attentional or task demands. Thus, the synchronisation of neural oscillations within specific frequency bands mediates the rapid detection, integration, and evaluation of emotional expressions. Moreover, the functional coupling of oscillatory activity

  16. The Functional Role of Neural Oscillations in Non-Verbal Emotional Communication.

    Science.gov (United States)

    Symons, Ashley E; El-Deredy, Wael; Schwartze, Michael; Kotz, Sonja A

    2016-01-01

    Effective interpersonal communication depends on the ability to perceive and interpret nonverbal emotional expressions from multiple sensory modalities. Current theoretical models propose that visual and auditory emotion perception involves a network of brain regions including the primary sensory cortices, the superior temporal sulcus (STS), and orbitofrontal cortex (OFC). However, relatively little is known about how the dynamic interplay between these regions gives rise to the perception of emotions. In recent years, there has been increasing recognition of the importance of neural oscillations in mediating neural communication within and between functional neural networks. Here we review studies investigating changes in oscillatory activity during the perception of visual, auditory, and audiovisual emotional expressions, and aim to characterize the functional role of neural oscillations in nonverbal emotion perception. Findings from the reviewed literature suggest that theta band oscillations most consistently differentiate between emotional and neutral expressions. While early theta synchronization appears to reflect the initial encoding of emotionally salient sensory information, later fronto-central theta synchronization may reflect the further integration of sensory information with internal representations. Additionally, gamma synchronization reflects facilitated sensory binding of emotional expressions within regions such as the OFC, STS, and, potentially, the amygdala. However, the evidence is more ambiguous when it comes to the role of oscillations within the alpha and beta frequencies, which vary as a function of modality (or modalities), presence or absence of predictive information, and attentional or task demands. Thus, the synchronization of neural oscillations within specific frequency bands mediates the rapid detection, integration, and evaluation of emotional expressions. Moreover, the functional coupling of oscillatory activity across multiples

  17. The Fractional Differential Polynomial Neural Network for Approximation of Functions

    Directory of Open Access Journals (Sweden)

    Rabha W. Ibrahim

    2013-09-01

    Full Text Available In this work, we introduce a generalization of the differential polynomial neural network utilizing fractional calculus. Fractional calculus is taken in the sense of the Caputo differential operator. It approximates a multi-parametric function with particular polynomials characterizing its functional output as a generalization of input patterns. This method can be employed on data to describe modelling of complex systems. Furthermore, the total information is calculated by using the fractional Poisson process.

  18. Density functional and neural network analysis

    DEFF Research Database (Denmark)

    Jalkanen, K. J.; Bohr, Henrik

    1997-01-01

    Density functional theory (DFT) calculations have been carried out for hydrated L-alanine, L-alanyl-L-alanine and N-acetyl L-alanine N'-methylamide and examined with respect to the effect of water on the structure, the vibrational frequencies, vibrational absorption (VA) and vibrational circular...

  19. Building a functional multiple intelligences theory to advance educational neuroscience.

    Science.gov (United States)

    Cerruti, Carlo

    2013-01-01

    A key goal of educational neuroscience is to conduct constrained experimental research that is theory-driven and yet also clearly related to educators' complex set of questions and concerns. However, the fields of education, cognitive psychology, and neuroscience use different levels of description to characterize human ability. An important advance in research in educational neuroscience would be the identification of a cognitive and neurocognitive framework at a level of description relatively intuitive to educators. I argue that the theory of multiple intelligences (MI; Gardner, 1983), a conception of the mind that motivated a past generation of teachers, may provide such an opportunity. I criticize MI for doing little to clarify for teachers a core misunderstanding, specifically that MI was only an anatomical map of the mind but not a functional theory that detailed how the mind actually processes information. In an attempt to build a "functional MI" theory, I integrate into MI basic principles of cognitive and neural functioning, namely interregional neural facilitation and inhibition. In so doing I hope to forge a path toward constrained experimental research that bears upon teachers' concerns about teaching and learning.

  20. Response variance in functional maps: neural darwinism revisited.

    Directory of Open Access Journals (Sweden)

    Hirokazu Takahashi

    Full Text Available The mechanisms by which functional maps and map plasticity contribute to cortical computation remain controversial. Recent studies have revisited the theory of neural Darwinism to interpret the learning-induced map plasticity and neuronal heterogeneity observed in the cortex. Here, we hypothesize that the Darwinian principle provides a substrate to explain the relationship between neuron heterogeneity and cortical functional maps. We demonstrate in the rat auditory cortex that the degree of response variance is closely correlated with the size of its representational area. Further, we show that the response variance within a given population is altered through training. These results suggest that larger representational areas may help to accommodate heterogeneous populations of neurons. Thus, functional maps and map plasticity are likely to play essential roles in Darwinian computation, serving as effective, but not absolutely necessary, structures to generate diverse response properties within a neural population.

  1. A Novel Learning Scheme for Chebyshev Functional Link Neural Networks

    Directory of Open Access Journals (Sweden)

    Satchidananda Dehuri

    2011-01-01

    dimensional-space where linear separability is possible. Moreover, the proposed HCFLNN combines the best attribute of particle swarm optimization (PSO, back propagation learning (BP learning, and functional link neural networks (FLNNs. The proposed method eliminates the need of hidden layer by expanding the input patterns using Chebyshev orthogonal polynomials. We have shown its effectiveness of classifying the unknown pattern using the publicly available datasets obtained from UCI repository. The computational results are then compared with functional link neural network (FLNN with a generic basis functions, PSO-based FLNN, and EFLN. From the comparative study, we observed that the performance of the HCFLNN outperforms FLNN, PSO-based FLNN, and EFLN in terms of classification accuracy.

  2. Response variance in functional maps: neural darwinism revisited.

    Science.gov (United States)

    Takahashi, Hirokazu; Yokota, Ryo; Kanzaki, Ryohei

    2013-01-01

    The mechanisms by which functional maps and map plasticity contribute to cortical computation remain controversial. Recent studies have revisited the theory of neural Darwinism to interpret the learning-induced map plasticity and neuronal heterogeneity observed in the cortex. Here, we hypothesize that the Darwinian principle provides a substrate to explain the relationship between neuron heterogeneity and cortical functional maps. We demonstrate in the rat auditory cortex that the degree of response variance is closely correlated with the size of its representational area. Further, we show that the response variance within a given population is altered through training. These results suggest that larger representational areas may help to accommodate heterogeneous populations of neurons. Thus, functional maps and map plasticity are likely to play essential roles in Darwinian computation, serving as effective, but not absolutely necessary, structures to generate diverse response properties within a neural population.

  3. Neural substrate expansion for the restoration of brain function

    Directory of Open Access Journals (Sweden)

    Han-Chiao Isaac Chen

    2016-01-01

    Full Text Available Restoring neurological and cognitive function in individuals who have suffered brain damage is one of the principal objectives of modern translational neuroscience. Electrical stimulation approaches, such as deep-brain stimulation, have achieved the most clinical success, but they ultimately may be limited by the computational capacity of the residual cerebral circuitry. An alternative strategy is brain substrate expansion, in which the computational capacity of the brain is augmented through the addition of new processing units and the reconstitution of network connectivity. This latter approach has been explored to some degree using both biological and electronic means but thus far has not demonstrated the ability to reestablish the function of large-scale neuronal networks. In this review, we contend that fulfilling the potential of brain substrate expansion will require a significant shift from current methods that emphasize direct manipulations of the brain (e.g., injections of cellular suspensions and the implantation of multi-electrode arrays to the generation of more sophisticated neural tissues and neural-electric hybrids in vitro that are subsequently transplanted into the brain. Drawing from neural tissue engineering, stem cell biology, and neural interface technologies, this strategy makes greater use of the manifold techniques available in the laboratory to create biocompatible constructs that recapitulate brain architecture and thus are more easily recognized and utilized by brain networks.

  4. Activation of endogenous neural stem cells for multiple sclerosis therapy

    NARCIS (Netherlands)

    Michailidou, I.; de Vries, H.E.; Hol, E.M.; van Strien, M.E.

    2014-01-01

    Multiple sclerosis (MS) is a chronic inflammatory disorder of the central nervous system, leading to severe neurological deficits. Current MS treatment regimens, consist of immunomodulatory agents aiming to reduce the rate of relapses. However, these agents are usually insufficient to treat chronic

  5. Activation of endogenous neural stem cells for multiple sclerosis therapy

    NARCIS (Netherlands)

    Michailidou, Iliana; de Vries, Helga E.; Hol, Elly M.; van Strien, Miriam E.

    2015-01-01

    Multiple sclerosis (MS) is a chronic inflammatory disorder of the central nervous system, leading to severe neurological deficits. Current MS treatment regimens, consist of immunomodulatory agents aiming to reduce the rate of relapses. However, these agents are usually insufficient to treat chronic

  6. Quantitative differentiation of multiple virus in blood using nanoporous silicon oxide immunosensor and artificial neural network.

    Science.gov (United States)

    Chakraborty, W; Ray, R; Samanta, N; RoyChaudhuri, C

    2017-12-15

    In spite of the rapid developments in various nanosensor technologies, it still remains challenging to realize a reliable ultrasensitive electrical biosensing platform which will be able to detect multiple viruses in blood simultaneously with a fairly high reproducibility without using secondary labels. In this paper, we have reported quantitative differentiation of Hep-B and Hep-C viruses in blood using nanoporous silicon oxide immunosensor array and artificial neural network (ANN). The peak frequency output (fp) from the steady state sensitivity characteristics and the first cut off frequency (fc) from the transient characteristics have been considered as inputs to the multilayer ANN. Implementation of several classifier blocks in the ANN architecture and coupling them with both the sensor chips, functionalized with Hep-B and Hep-C antibodies have enabled the quantification of the viruses with an accuracy of around 95% in the range of 0.04fM-1pM and with an accuracy of around 90% beyond 1pM and within 25nM in blood serum. This is the most sensitive report on multiple virus quantification using label free method. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Derivatives of Meromorphic functions with multiple zeros and elliptic functions

    CERN Document Server

    Yang, Pai; Pang, Xuecheng

    2011-01-01

    Let f be a nonconstant meromorphic function in the plane and h be a nonconstant elliptic function. We show that if all zeros of f are multiple exept finitely many and T(r,h)=o{T(r,f)} as r tends to infinity, then f'=h has infinitely many solutions (including poles).

  8. Integrating multiple sensory systems to modulate neural networks controlling posture.

    Science.gov (United States)

    Lavrov, I; Gerasimenko, Y; Burdick, J; Zhong, H; Roy, R R; Edgerton, V R

    2015-12-01

    In this study we investigated the ability of sensory input to produce tonic responses in hindlimb muscles to facilitate standing in adult spinal rats and tested two hypotheses: 1) whether the spinal neural networks below a complete spinal cord transection can produce tonic reactions by activating different sensory inputs and 2) whether facilitation of tonic and rhythmic responses via activation of afferents and with spinal cord stimulation could engage similar neuronal mechanisms. We used a dynamically controlled platform to generate vibration during weight bearing, epidural stimulation (at spinal cord level S1), and/or tail pinching to determine the postural control responses that can be generated by the lumbosacral spinal cord. We observed that a combination of platform displacement, epidural stimulation, and tail pinching produces a cumulative effect that progressively enhances tonic responses in the hindlimbs. Tonic responses produced by epidural stimulation alone during standing were represented mainly by monosynaptic responses, whereas the combination of epidural stimulation and tail pinching during standing or epidural stimulation during stepping on a treadmill facilitated bilaterally both monosynaptic and polysynaptic responses. The results demonstrate that tonic muscle activity after complete spinal cord injury can be facilitated by activation of specific combinations of afferent inputs associated with load-bearing proprioception and cutaneous input in the presence of epidural stimulation and indicate that whether activation of tonic or rhythmic responses is generated depends on the specific combinations of sources and types of afferents activated in the hindlimb muscles.

  9. Multiple deep convolutional neural networks averaging for face alignment

    Science.gov (United States)

    Zhang, Shaohua; Yang, Hua; Yin, Zhouping

    2015-05-01

    Face alignment is critical for face recognition, and the deep learning-based method shows promise for solving such issues, given that competitive results are achieved on benchmarks with additional benefits, such as dispensing with handcrafted features and initial shape. However, most existing deep learning-based approaches are complicated and quite time-consuming during training. We propose a compact face alignment method for fast training without decreasing its accuracy. Rectified linear unit is employed, which allows all networks approximately five times faster convergence than a tanh neuron. An eight learnable layer deep convolutional neural network (DCNN) based on local response normalization and a padding convolutional layer (PCL) is designed to provide reliable initial values during prediction. A model combination scheme is presented to further reduce errors, while showing that only two network architectures and hyperparameter selection procedures are required in our approach. A three-level cascaded system is ultimately built based on the DCNNs and model combination mode. Extensive experiments validate the effectiveness of our method and demonstrate comparable accuracy with state-of-the-art methods on BioID, labeled face parts in the wild, and Helen datasets.

  10. Prediction of flow characteristics using multiple regression and neural networks: A case study in Zimbabwe

    NARCIS (Netherlands)

    Mazvimavi, D.; Meijerink, A.M.J.; Savenije, H.H.G.; Stein, A.

    2005-01-01

    The feasibility of predicting flow characteristics from basin descriptors using multiple regression and neural networks has been investigated on 52 basins in Zimbabwe. Flow characteristics considered were average annual runoff, base flow index, flow duration curve, and average monthly runoff . Mean

  11. A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation.

    Science.gov (United States)

    Vuković, Najdan; Miljković, Zoran

    2013-10-01

    Radial basis function (RBF) neural network is constructed of certain number of RBF neurons, and these networks are among the most used neural networks for modeling of various nonlinear problems in engineering. Conventional RBF neuron is usually based on Gaussian type of activation function with single width for each activation function. This feature restricts neuron performance for modeling the complex nonlinear problems. To accommodate limitation of a single scale, this paper presents neural network with similar but yet different activation function-hyper basis function (HBF). The HBF allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The HBF is based on generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. Compared to the RBF, the HBF neuron has more parameters to optimize, but HBF neural network needs less number of HBF neurons to memorize relationship between input and output sets in order to achieve good generalization property. However, recent research results of HBF neural network performance have shown that optimal way of constructing this type of neural network is needed; this paper addresses this issue and modifies sequential learning algorithm for HBF neural network that exploits the concept of neuron's significance and allows growing and pruning of HBF neuron during learning process. Extensive experimental study shows that HBF neural network, trained with developed learning algorithm, achieves lower prediction error and more compact neural network. Copyright © 2013 Elsevier Ltd. All rights reserved.

  12. The mean square of weighted multiplicities function

    OpenAIRE

    Vladimir, Lukianov

    2005-01-01

    We define a weighted multiplicity function for closed geodesics of given length on a finite area Riemann surface. These weighted multiplicities appear naturally in the Selberg trace formula, and in particular their mean square plays an important role in the study of statistics of the eigenvalues of the Laplacian on the surface. In the case of the modular domain, E. Bogomolny, F. Leyvraz and C. Schmit gave a formula for the mean square, which was rigorously proved by M. Peter. In this paper we...

  13. Zeta Regularized Product Expressions for Multiple Trigonometric Functions

    OpenAIRE

    Kurokawa, Nobushige; Wakayama, Masato

    2004-01-01

    We introduce a multiple analogue of the gamma function which differs from the one defined by Barnes [B]. Using this function, we give expressions of the multiple sine and cosine functions in terms of zeta regularized products. The expression of the multiple sine function can be interpreted as a reflection formula of this new multiple analogue of the gamma function.

  14. Nuclear charge radii: Density functional theory meets Bayesian neural networks

    CERN Document Server

    Utama, Raditya; Piekarewicz, Jorge

    2016-01-01

    The distribution of electric charge in atomic nuclei is fundamental to our understanding of the complex nuclear dynamics and a quintessential observable to validate nuclear structure models. We explore a novel approach that combines sophisticated models of nuclear structure with Bayesian neural networks (BNN) to generate predictions for the charge radii of thousands of nuclei throughout the nuclear chart. A class of relativistic energy density functionals is used to provide robust predictions for nuclear charge radii. In turn, these predictions are refined through Bayesian learning for a neural network that is trained using residuals between theoretical predictions and the experimental data. Although predictions obtained with density functional theory provide a fairly good description of experiment, our results show significant improvement (better than 40%) after BNN refinement. Moreover, these improved results for nuclear charge radii are supplemented with theoretical error bars. We have successfully demonst...

  15. Aberrant regional neural fluctuations and functional connectivity in generalized anxiety disorder revealed by resting-state functional magnetic resonance imaging.

    Science.gov (United States)

    Wang, Wei; Hou, Jingming; Qian, Shaowen; Liu, Kai; Li, Bo; Li, Min; Peng, Zhaohui; Xin, Kuolin; Sun, Gang

    2016-06-15

    The purpose of this study was to investigate the neural activity and functional connectivity in generalized anxiety disorder (GAD) during resting state, and how these alterations correlate to patients' symptoms. Twenty-eight GAD patients and 28 matched healthy controls underwent resting-state functional magnetic resonance (fMRI) scans. Amplitude of low-frequency fluctuation (ALFF) and seed-based resting-state functional connectivity (RSFC) were computed to explore regional activity and functional integration, and were compared between the two groups using the voxel-based two-sample t test. Pearson's correlation analyses were performed to examine the neural relationships with demographics and clinical symptoms scores. Compared to controls, GAD patients showed functional abnormalities: higher ALFF in the bilateral dorsomedial prefrontal cortex, bilateral dorsolateral prefrontal cortex and left precuneus/posterior cingulate cortex; lower connectivity in prefrontal gyrus; lower in prefrontal-limbic and cingulate RSFC and higher prefrontal-hippocampus RSFC were correlated with clinical symptoms severity, but these associations were unable to withstand correction for multiple testing. These findings may help facilitate further understanding of the potential neural substrate of GAD.

  16. Convergence and multiplicities for the Lempert function

    Science.gov (United States)

    Thomas, Pascal J.; Trao, Nguyen Van

    2009-04-01

    Given a domain Ω⊂ℂ n , the Lempert function is a functional on the space text{Hol}(mathbb{D},Ω) of analytic disks with values in Ω, depending on a set of poles in Ω. We generalize its definition to the case where poles have multiplicities given by local indicators (in the sense of Rashkovskii) to obtain a function which still dominates the corresponding Green function, behaves relatively well under limits, and is monotonic with respect to the local indicators. In particular, this is an improvement over the previous generalization used by the same authors to find an example of a set of poles in the bidisk so that the (usual) Green and Lempert functions differ.

  17. Recursive formulae for the multiplicative partition function

    Directory of Open Access Journals (Sweden)

    Jun Kyo Kim

    1999-01-01

    Full Text Available For a positive integer n, let f(n be the number of essentially different ways of writing n as a product of factors greater than 1, where two factorizations of a positive integer are said to be essentially the same if they differ only in the order of the factors. This paper gives a recursive formula for the multiplicative partition function f(n.

  18. Partial information decomposition as a unified approach to the specification of neural goal functions.

    Science.gov (United States)

    Wibral, Michael; Priesemann, Viola; Kay, Jim W; Lizier, Joseph T; Phillips, William A

    2017-03-01

    In many neural systems anatomical motifs are present repeatedly, but despite their structural similarity they can serve very different tasks. A prime example for such a motif is the canonical microcircuit of six-layered neo-cortex, which is repeated across cortical areas, and is involved in a number of different tasks (e.g. sensory, cognitive, or motor tasks). This observation has spawned interest in finding a common underlying principle, a 'goal function', of information processing implemented in this structure. By definition such a goal function, if universal, cannot be cast in processing-domain specific language (e.g. 'edge filtering', 'working memory'). Thus, to formulate such a principle, we have to use a domain-independent framework. Information theory offers such a framework. However, while the classical framework of information theory focuses on the relation between one input and one output (Shannon's mutual information), we argue that neural information processing crucially depends on the combination of multiple inputs to create the output of a processor. To account for this, we use a very recent extension of Shannon Information theory, called partial information decomposition (PID). PID allows to quantify the information that several inputs provide individually (unique information), redundantly (shared information) or only jointly (synergistic information) about the output. First, we review the framework of PID. Then we apply it to reevaluate and analyze several earlier proposals of information theoretic neural goal functions (predictive coding, infomax and coherent infomax, efficient coding). We find that PID allows to compare these goal functions in a common framework, and also provides a versatile approach to design new goal functions from first principles. Building on this, we design and analyze a novel goal function, called 'coding with synergy', which builds on combining external input and prior knowledge in a synergistic manner. We suggest that

  19. The neural bases of the multiplication problem-size effect across countries

    Directory of Open Access Journals (Sweden)

    Jérôme ePrado

    2013-05-01

    Full Text Available Multiplication problems involving large numbers (e.g., 9x8 are more difficult to solve than problems involving small numbers (e.g., 2x3. Behavioral research indicates that this problem-size effect might be due to different factors across countries and educational systems. However, there is no neuroimaging evidence supporting this hypothesis. Here, we compared the neural correlates of the multiplication problem-size effect in adults educated in China and the United States. We found a greater neural problem-size effect in Chinese than American participants in bilateral superior temporal regions associated with phonological processing. However, we found a greater neural problem-size effect in American than Chinese participants in right intra-parietal sulcus associated with calculation procedures. Therefore, while the multiplication problem-size effect might be a verbal retrieval effect in Chinese as compared to American participants, it may instead stem from the use of calculation procedures in American as compared to Chinese participants. Our results indicate that differences in educational practices might affect the neural bases of symbolic arithmetic.

  20. Neural network design for J function approximation in dynamic programming

    CERN Document Server

    Pang, X

    1998-01-01

    This paper shows that a new type of artificial neural network (ANN) -- the Simultaneous Recurrent Network (SRN) -- can, if properly trained, solve a difficult function approximation problem which conventional ANNs -- either feedforward or Hebbian -- cannot. This problem, the problem of generalized maze navigation, is typical of problems which arise in building true intelligent control systems using neural networks. (Such systems are discussed in the chapter by Werbos in K.Pribram, Brain and Values, Erlbaum 1998.) The paper provides a general review of other types of recurrent networks and alternative training techniques, including a flowchart of the Error Critic training design, arguable the only plausible approach to explain how the brain adapts time-lagged recurrent systems in real-time. The C code of the test is appended. As in the first tests of backprop, the training here was slow, but there are ways to do better after more experience using this type of network.

  1. Exploring the function of neural oscillations in early sensory systems

    Directory of Open Access Journals (Sweden)

    Kilian Koepsell

    2010-05-01

    Full Text Available Neuronal oscillations appear throughout the nervous system, in structures as diverse as the cerebral cortex, hippocampus, subcortical nuclei and sense organs. Whether or not neural rhythms contribute to normal function, are merely epiphenomena, or even interfere with physiological processing are topics of vigorous debate. Sensory pathways are ideal for investigation of oscillatory activity because their inputs can be defined. Thus, we will focus on sensory systems as we ask how neural oscillations arise and how they might encode information about the stimulus. We will highlight recent work in the early visual pathway that shows how oscillations can multiplex different types of signals to increase the amount of information that spike trains encode and transmit. Last, we will describe oscillation-based models of visual processing and explore how they might guide further research.

  2. Dimensionality reduction in conic section function neural network

    Indian Academy of Sciences (India)

    Tulay Yildirim; Lale Ozyilmaz

    2002-12-01

    This paper details how dimensionality can be reduced in conic section function neural networks (CSFNN). This is particularly important for hardware implementation of networks. One of the main problems to be solved when considering the hardware design is the high connectivity requirement. If the effect that each of the network inputs has on the network output after training a neural network is known, then some inputs can be removed from the network. Consequently, the dimensionality of the network, and hence, the connectivity and the training time can be reduced. Sensitivity analysis, which extracts the cause and effect relationship between the inputs and outputs of the network, has been proposed as a method to achieve this and is investigated for Iris plant, thyroid disease and ionosphere databases. Simulations demonstrate the validity of the method used.

  3. Radial Basis Function Neural Network-based PID model for functional electrical stimulation system control.

    Science.gov (United States)

    Cheng, Longlong; Zhang, Guangju; Wan, Baikun; Hao, Linlin; Qi, Hongzhi; Ming, Dong

    2009-01-01

    Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model.

  4. Using a Large-scale Neural Model of Cortical Object Processing to Investigate the Neural Substrate for Managing Multiple Items in Short-term Memory.

    Science.gov (United States)

    Liu, Qin; Ulloa, Antonio; Horwitz, Barry

    2017-07-07

    Many cognitive and computational models have been proposed to help understand working memory. In this article, we present a simulation study of cortical processing of visual objects during several working memory tasks using an extended version of a previously constructed large-scale neural model [Tagamets, M. A., & Horwitz, B. Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study. Cerebral Cortex, 8, 310-320, 1998]. The original model consisted of arrays of Wilson-Cowan type of neuronal populations representing primary and secondary visual cortices, inferotemporal (IT) cortex, and pFC. We added a module representing entorhinal cortex, which functions as a gating module. We successfully implemented multiple working memory tasks using the same model and produced neuronal patterns in visual cortex, IT cortex, and pFC that match experimental findings. These working memory tasks can include distractor stimuli or can require that multiple items be retained in mind during a delay period (Sternberg's task). Besides electrophysiology data and behavioral data, we also generated fMRI BOLD time series from our simulation. Our results support the involvement of IT cortex in working memory maintenance and suggest the cortical architecture underlying the neural mechanisms mediating particular working memory tasks. Furthermore, we noticed that, during simulations of memorizing a list of objects, the first and last items in the sequence were recalled best, which may implicate the neural mechanism behind this important psychological effect (i.e., the primacy and recency effect).

  5. Vertically aligned carbon nanofiber as nano-neuron interface for monitoring neural function

    Energy Technology Data Exchange (ETDEWEB)

    Ericson, Milton Nance [ORNL; McKnight, Timothy E [ORNL; Melechko, Anatoli Vasilievich [ORNL; Simpson, Michael L [ORNL; Morrison, Barclay [ORNL; Yu, Zhe [Columbia University

    2012-01-01

    Neural chips, which are capable of simultaneous, multi-site neural recording and stimulation, have been used to detect and modulate neural activity for almost 30 years. As a neural interface, neural chips provide dynamic functional information for neural decoding and neural control. By improving sensitivity and spatial resolution, nano-scale electrodes may revolutionize neural detection and modulation at cellular and molecular levels as nano-neuron interfaces. We developed a carbon-nanofiber neural chip with lithographically defined arrays of vertically aligned carbon nanofiber electrodes and demonstrated its capability of both stimulating and monitoring electrophysiological signals from brain tissues in vitro and monitoring dynamic information of neuroplasticity. This novel nano-neuron interface can potentially serve as a precise, informative, biocompatible, and dual-mode neural interface for monitoring of both neuroelectrical and neurochemical activity at the single cell level and even inside the cell.

  6. Rock property estimates using multiple seismic attributes and neural networks; Pegasus Field, West Texas

    Energy Technology Data Exchange (ETDEWEB)

    Schuelke, J.S.; Quirein, J.A.; Sarg, J.F.

    1998-12-31

    This case study shows the benefit of using multiple seismic trace attributes and the pattern recognition capabilities of neural networks to predict reservoir architecture and porosity distribution in the Pegasus Field, West Texas. The study used the power of neural networks to integrate geologic, borehole and seismic data. Illustrated are the improvements between the new neural network approach and the more traditional method of seismic trace inversion for porosity estimation. Comprehensive statistical methods and interpretational/subjective measures are used in the prediction of porosity from seismic attributes. A 3-D volume of seismic derived porosity estimates for the Devonian reservoir provide a very detailed estimate of porosity, both spatially and vertically, for the field. The additional reservoir porosity detail provided, between the well control, allows for optimal placement of horizontal wells and improved field development. 6 refs., 2 figs.

  7. Stochastic simulation and spatial estimation with multiple data types using artificial neural networks

    Science.gov (United States)

    Besaw, Lance E.; Rizzo, Donna M.

    2007-11-01

    A novel data-driven artificial neural network (ANN) that quantitatively combines large numbers of multiple types of soft data is presented for performing stochastic simulation and/or spatial estimation. A counterpropagation ANN is extended with a radial basis function to estimate parameter fields that reproduce the spatial structure exhibited in autocorrelated parameters. Applications involve using three geophysical properties measured on a slab of Berea sandstone and the delineation of landfill leachate at a site in the Netherlands using electrical formation conductivity as our primary variable and six types of secondary data (e.g., hydrochemistry, archaea, and bacteria). The ANN estimation fields are statistically similar to geostatistical methods (indicator simulation and cokriging) and reference fields (when available). The method is a nonparametric clustering/classification algorithm that can assimilate significant amounts of disparate data types with both continuous and categorical responses without the computational burden associated with the construction of positive definite covariance and cross-covariance matrices. The combination of simplicity and computational speed makes the method ideally suited for environmental subsurface characterization and other Earth science applications with spatially autocorrelated variables.

  8. Fatigue in multiple sclerosis: neural correlates and the role of non-invasive brain stimulation

    Directory of Open Access Journals (Sweden)

    Moussa A. Chalah

    2015-11-01

    Full Text Available Multiple sclerosis (MS is a chronic progressive inflammatory disease of the central nervous system and the major cause of non-traumatic disability in young adults. Fatigue is a frequent symptom reported by the majority of MS patients during their disease course and drastically af-fects their quality of life. Despite its significant prevalence and impact, the underlying patho-physiological mechanisms are not well elucidated. MS fatigue is still considered the result of multifactorial and complex constellations, and is commonly classified into primary fatigue related to the pathological changes of the disease itself, and secondary fatigue attributed to mimicking symptoms, comorbid sleep and mood disorders, and medications side effects. Data from neuroimaging, neurophysiology, neuroendocrine and neuroimmune studies have raised hypotheses regarding the origin of this symptom, some of which have succeeded in identifying an association between MS fatigue and structural or functional abnormalities within various brain networks. Hence, the aim of this work is to reappraise the neural correlates of MS fatigue and to discuss the rationale for the emergent use of noninvasive brain stimulation (NIBS techniques as potential treatments. This will include a presentation of the various NIBS modalities and a proposition of their potential mechanisms of action in this context. Specific issues related to the value of transcranial direct current stimulation will be addressed.

  9. Neural Network Emulation of the Integral Equation Model with Multiple Scattering

    Directory of Open Access Journals (Sweden)

    Luca Pulvirenti

    2009-10-01

    Full Text Available The Integral Equation Model with multiple scattering (IEMM represents a well-established method that provides a theoretical framework for the scattering of electromagnetic waves from rough surfaces. A critical aspect is the long computational time required to run such a complex model. To deal with this problem, a neural network technique is proposed in this work. In particular, we have adopted neural networks to reproduce the backscattering coefficients predicted by IEMM at L- and C-bands, thus making reference to presently operative satellite radar sensors, i.e., that aboard ERS-2, ASAR on board ENVISAT (C-band, and PALSAR aboard ALOS (L-band. The neural network-based model has been designed for radar observations of both flat and tilted surfaces, in order to make it applicable for hilly terrains too. The assessment of the proposed approach has been carried out by comparing neural network-derived backscattering coefficients with IEMM-derived ones. Different databases with respect to those employed to train the networks have been used for this purpose. The outcomes seem to prove the feasibility of relying on a neural network approach to efficiently and reliably approximate an electromagnetic model of surface scattering.

  10. Manipulation of orthogonal neural systems together in electrophysiological recordings: the MONSTER approach to simultaneous assessment of multiple neurocognitive dimensions.

    Science.gov (United States)

    Kappenman, Emily S; Luck, Steven J

    2012-01-01

    Event-related potentials (ERPs) are a powerful tool in understanding and evaluating cognitive, affective, motor, and sensory processing in both healthy and pathological samples. A typical ERP recording session takes considerable time but is designed to isolate only 1-2 components. Although this is appropriate for most basic science purposes, it is an inefficient approach for measuring the broad set of neurocognitive functions that may be disrupted in a neurological or psychiatric disease. The present study provides a framework for more efficiently evaluating multiple neural processes in a single experimental paradigm through the manipulation of functionally orthogonal dimensions. We describe the general MONSTER (Manipulation of Orthogonal Neural Systems Together in Electrophysiological Recordings) approach and explain how it can be adapted to investigate a variety of neurocognitive domains, ERP components, and neural processes of interest. We also demonstrate how this approach can be used to assess group differences by providing data from an implementation of the MONSTER approach in younger (18-30 y of age) and older (65-85 y of age) adult samples. This specific implementation of the MONSTER framework assesses 4 separate neural processes in the visual domain: (1) early sensory processing, using the C1 wave; (2) shifts of covert attention, with the N2pc component; (3) categorization, with the P3 component; and (4) self-monitoring, with the error-related negativity. Although the MONSTER approach is primarily described in the context of ERP experiments, it could also be adapted easily for use with functional magnetic resonance imaging.

  11. Neuronal spike sorting based on radial basis function neural networks

    Directory of Open Access Journals (Sweden)

    Taghavi Kani M

    2011-02-01

    Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.

  12. Functional Brain Network Changes Associated with Maintenance of Cognitive Function in Multiple Sclerosis

    Directory of Open Access Journals (Sweden)

    Santosh A Helekar

    2010-11-01

    Full Text Available In multiple sclerosis (MS functional changes in connectivity due to cortical reorganization could lead to cognitive impairment (CI, or reflect a re-adjustment to reduce the clinical effects of widespread tissue damage. Such alterations in connectivity could result in changes in neural activation as assayed by executive function tasks. We examined cognitive function in MS patients with mild to moderate cognitive impairment and age-matched controls. We evaluated brain activity using functional magnetic resonance imaging (fMRI during the successful performance of the Wisconsin-card sorting (WCS task by MS patients, showing compensatory maintenance of normal function, as measured by response latency and error rate. To assess changes in functional connectivity throughout the brain, we performed a global functional brain network analysis by computing voxel by voxel correlations on the fMRI time series data and carrying out a hierarchical cluster analysis. We found that during the WCS task there is a significant reduction in the number of smaller size brain functional networks, and a change in the brain areas representing the nodes of these networks in MS patients compared to age-matched controls. There is also a concomitant increase in the strength of functional connections between brain loci separated at intermediate scale distances in these patients. These functional alterations might reflect compensatory neuroplastic reorganization underlying maintenance of relatively normal cognitive function in the face of white matter lesions and cortical atrophy produced by MS.

  13. Nonlinear Time Series Forecast Using Radial Basis Function Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZHENGXin; CHENTian-Lun

    2003-01-01

    In the research of using Radial Basis Function Neural Network (RBF NN) forecasting nonlinear time series, we investigate how the different clusterings affect the process of learning and forecasting. We find that k-means clustering is very suitable. In order to increase the precision we introduce a nonlinear feedback term to escape from the local minima of energy, then we use the model to forecast the nonlinear time series which are produced by Mackey-Glass equation and stocks. By selecting the k-means clustering and the suitable feedback term, much better forecasting results are obtained.

  14. Nonlinear Time Series Forecast Using Radial Basis Function Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZHENG Xin; CHEN Tian-Lun

    2003-01-01

    In the research of using Radial Basis Function Neural Network (RBF NN) forecasting nonlinear timeseries, we investigate how the different clusterings affect the process of learning and forecasting. We find that k-meansclustering is very suitable. In order to increase the precision we introduce a nonlinear feedback term to escape from thelocal minima of energy, then we use the model to forecast the nonlinear time series which are produced by Mackey-Glassequation and stocks. By selecting the k-means clustering and the suitable feedback term, much better forecasting resultsare obtained.

  15. Synchronization of chaos using radial basis functions neural networks

    Institute of Scientific and Technical Information of China (English)

    Ren Haipeng; Liu Ding

    2007-01-01

    The Radial Basis Functions Neural Network (RBFNN) is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response system can be implemented by employing the RBFNN model and state feedback control. In this case, the exact mathematical model, which is the precondition for the conventional method, is unnecessary for implementing synchronization. The effect of the model error is investigated and a corresponding theorem is developed. The effect of the parameter perturbations and the measurement noise is investigated through simulations. The simulation results under different conditions show the effectiveness of the method.

  16. Exponential stability of cellular neural networks with multiple time delays and impulsive effects

    Institute of Scientific and Technical Information of China (English)

    Li Dong; Wang Hui; Yang Dan; Zhang Xiao-Hong; Wang Shi-Long

    2008-01-01

    In this work,the stability issues of the equilibrium points of the cellular neural networks with multiple time delays and impulsive effects are investigated.Based on the stability theory of Lyapunov-Krasovskii,the method of linear matrix inequality (LMI) and parametrized first-order model transformation,several novel conditions guaranteeing the delaydependent and the delay-independent exponential stabilities are obtained.A numerical example is given to illustrate the effectiveness of our results.

  17. Estimation of spatiotemporal neural activity using radial basis function networks.

    Science.gov (United States)

    Anderson, R W; Das, S; Keller, E L

    1998-12-01

    We report a method using radial basis function (RBF) networks to estimate the time evolution of population activity in topologically organized neural structures from single-neuron recordings. This is an important problem in neuroscience research, as such estimates may provide insights into systems-level function of these structures. Since single-unit neural data tends to be unevenly sampled and highly variable under similar behavioral conditions, obtaining such estimates is a difficult task. In particular, a class of cells in the superior colliculus called buildup neurons can have very narrow regions of saccade vectors for which they discharge at high rates but very large surround regions over which they discharge at low, but not zero, levels. Estimating the dynamic movement fields for these cells for two spatial dimensions at closely spaced timed intervals is a difficult problem, and no general method has been described that can be applied to all buildup cells. Estimation of individual collicular cells' spatiotemporal movement fields is a prerequisite for obtaining reliable two-dimensional estimates of the population activity on the collicular motor map during saccades. Therefore, we have developed several computational-geometry-based algorithms that regularize the data before computing a surface estimation using RBF networks. The method is then expanded to the problem of estimating simultaneous spatiotemporal activity occurring across the superior colliculus during a single movement (the inverse problem). In principle, this methodology could be applied to any neural structure with a regular, two-dimensional organization, provided a sufficient spatial distribution of sampled neurons is available.

  18. A multiple circular path convolution neural network system for detection of mammographic masses.

    Science.gov (United States)

    Lo, Shih-Chung B; Li, Huai; Wang, Yue; Kinnard, Lisa; Freedman, Matthew T

    2002-02-01

    A multiple circular path convolution neural network (MCPCNN) architecture specifically designed for the analysis of tumor and tumor-like structures has been constructed. We first divided each suspected tumor area into sectors and computed the defined mass features for each sector independently. These sector features were used on the input layer and were coordinated by convolution kernels of different sizes that propagated signals to the second layer in the neural network system. The convolution kernels were trained, as required, by presenting the training cases to the neural network. In this study, randomly selected mammograms were processed by a dual morphological enhancement technique. Radiodense areas were isolated and were delineated using a region growing algorithm. The boundary of each region of interest was then divided into 36 sectors using 36 equi-angular dividers radiated from the center of the region. A total of 144 Breast Imaging-Reporting and Data System-based features (i.e., four features per sector for 36 sectors) were computed as input values for the evaluation of this newly invented neural network system. The overall performance was 0.78-0.80 for the areas (Az) under the receiver operating characteristic curves using the conventional feed-forward neural network in the detection of mammographic masses. The performance was markedly improved with Az values ranging from 0.84 to 0.89 using the MCPCNN. This paper does not intend to claim the best mass detection system. Instead it reports a potentially better neural network structure for analyzing a set of the mass features defined by an investigator.

  19. Filtrado digital neuronal difuso: caso MIMO Neural fuzzy digital filtering: multivariate identifier filters involving multiple inputs and multiple outputs (MIMO

    Directory of Open Access Journals (Sweden)

    Medel Juárez José de J.

    2011-05-01

    Full Text Available  

    Los filtros identificadores multivariables (MIMO son sistemas digitales adaptivos que cuentan con retroalimentación para que, de acuerdo a una función objetivo, ajusten su matriz de parámetros con la que se aproximan a la di-námica observable del sistema de referencia. Una forma de que un identificador cumpla con esas condiciones, es la de la lógica difusa por medio de sus mecanismos de in-ferencia que interpretan y seleccionan en una base de co-nocimiento la mejor matriz de parámetros. Estos mecanismos de selección mediante las redes neuronales permiten encontrar la respuesta con el mejor nivel de operación para cada cambio de estado (Shannon, 1948. En este artículo se considera en el modelo MIMO del filtrado digital, el proceso neuronal difuso para la estimación matricial de parámetros adaptiva, que se integra en el filtro de Kalman a través de la matriz de transición. Para ello se utilizó la red neuronal del tipo retropropagación en el mecanismo difuso, interpretando sus variables y sus respectivos niveles, seleccionando los mejores valores para ajustar automáticamente los valores de la matriz de transición. La simulación en Matlab presenta al filtrado digital neuronal difuso dando el seguimiento, observándose un funcional de error decreciente exponencialmente.

     

     

    Multivariate identifier filters (multiple inputs and multiple outputs - MIMO are adaptive digital systems having a loop in accordance with an objective function to adjust matrix parameter

  20. Strategy over operation: neural activation in subtraction and multiplication during fact retrieval and procedural strategy use in children.

    Science.gov (United States)

    Polspoel, Brecht; Peters, Lien; Vandermosten, Maaike; De Smedt, Bert

    2017-09-01

    Arithmetic development is characterized by strategy shifts between procedural strategy use and fact retrieval. This study is the first to explicitly investigate children's neural activation associated with the use of these different strategies. Participants were 26 typically developing 4th graders (9- to 10-year-olds), who, in a behavioral session, were asked to verbally report on a trial-by-trial basis how they had solved 100 subtraction and multiplication items. These items were subsequently presented during functional magnetic resonance imaging. An event-related design allowed us to analyze the brain responses during retrieval and procedural trials, based on the children's verbal reports. During procedural strategy use, and more specifically for the decomposition of operands strategy, activation increases were observed in the inferior and superior parietal lobes (intraparietal sulci), inferior to superior frontal gyri, bilateral areas in the occipital lobe, and insular cortex. For retrieval, in comparison to procedural strategy use, we observed increased activity in the bilateral angular and supramarginal gyri, left middle to inferior temporal gyrus, right superior temporal gyrus, and superior medial frontal gyrus. No neural differences were found between the two operations under study. These results are the first in children to provide direct evidence for alternate neural activation when different arithmetic strategies are used and further unravel that previously found effects of operation on brain activity reflect differences in arithmetic strategy use. Hum Brain Mapp 38:4657-4670, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  1. Nuclear charge radii: density functional theory meets Bayesian neural networks

    Science.gov (United States)

    Utama, R.; Chen, Wei-Chia; Piekarewicz, J.

    2016-11-01

    The distribution of electric charge in atomic nuclei is fundamental to our understanding of the complex nuclear dynamics and a quintessential observable to validate nuclear structure models. The aim of this study is to explore a novel approach that combines sophisticated models of nuclear structure with Bayesian neural networks (BNN) to generate predictions for the charge radii of thousands of nuclei throughout the nuclear chart. A class of relativistic energy density functionals is used to provide robust predictions for nuclear charge radii. In turn, these predictions are refined through Bayesian learning for a neural network that is trained using residuals between theoretical predictions and the experimental data. Although predictions obtained with density functional theory provide a fairly good description of experiment, our results show significant improvement (better than 40%) after BNN refinement. Moreover, these improved results for nuclear charge radii are supplemented with theoretical error bars. We have successfully demonstrated the ability of the BNN approach to significantly increase the accuracy of nuclear models in the predictions of nuclear charge radii. However, as many before us, we failed to uncover the underlying physics behind the intriguing behavior of charge radii along the calcium isotopic chain.

  2. Nanofibers implant functionalized by neural growth factor as a strategy to innervate a bioengineered tooth.

    Science.gov (United States)

    Eap, Sandy; Bécavin, Thibault; Keller, Laetitia; Kökten, Tunay; Fioretti, Florence; Weickert, Jean-Luc; Deveaux, Etienne; Benkirane-Jessel, Nadia; Kuchler-Bopp, Sabine

    2014-03-01

    Current strategies for jaw reconstruction require multiple procedures, to repair the bone defect, to offer sufficient support, and to place the tooth implant. The entire procedure can be painful and time-consuming, and the desired functional repair can be achieved only when both steps are successful. The ability to engineer combined tooth and bone constructs, which would grow in a coordinated fashion with the surrounding tissues, could potentially improve the clinical outcomes and also reduce patient suffering. A unique nanofibrous and active implant for bone-tooth unit regeneration and also the innervation of this bioengineered tooth are demonstrated. A nanofibrous polycaprolactone membrane is functionalized with neural growth factor, along with dental germ, and tooth innervation follows. Such innervation allows complete functionality and tissue homeostasis of the tooth, such as dentinal sensitivity, odontoblast function, masticatory forces, and blood flow.

  3. Optogenetic Demonstration of Functional Innervation of Mouse Colon by Neurons Derived From Transplanted Neural Cells.

    Science.gov (United States)

    Stamp, Lincon A; Gwynne, Rachel M; Foong, Jaime P P; Lomax, Alan E; Hao, Marlene M; Kaplan, David I; Reid, Christopher A; Petrou, Steven; Allen, Andrew M; Bornstein, Joel C; Young, Heather M

    2017-05-01

    Cell therapy offers the potential to treat gastrointestinal motility disorders caused by diseased or absent enteric neurons. We examined whether neurons generated from transplanted enteric neural cells provide a functional innervation of bowel smooth muscle in mice. Enteric neural cells expressing the light-sensitive ion channel, channelrhodopsin, were isolated from the fetal or postnatal mouse bowel and transplanted into the distal colon of 3- to 4-week-old wild-type recipient mice. Intracellular electrophysiological recordings of responses to light stimulation of the transplanted cells were made from colonic smooth muscle cells in recipient mice. Electrical stimulation of endogenous enteric neurons was used as a control. The axons of graft-derived neurons formed a plexus in the circular muscle layer. Selective stimulation of graft-derived cells by light resulted in excitatory and inhibitory junction potentials, the electrical events underlying contraction and relaxation, respectively, in colonic muscle cells. Graft-derived excitatory and inhibitory motor neurons released the same neurotransmitters as endogenous motor neurons-acetylcholine and a combination of adenosine triphosphate and nitric oxide, respectively. Graft-derived neurons also included interneurons that provided synaptic inputs to motor neurons, but the pharmacologic properties of interneurons varied with the age of the donors from which enteric neural cells were obtained. Enteric neural cells transplanted into the bowel give rise to multiple functional types of neurons that integrate and provide a functional innervation of the smooth muscle of the bowel wall. Circuits composed of both motor neurons and interneurons were established, but the age at which cells are isolated influences the neurotransmitter phenotype of interneurons that are generated. Copyright © 2017 AGA Institute. Published by Elsevier Inc. All rights reserved.

  4. Layered neural networks with non-monotonic transfer functions

    Science.gov (United States)

    Katayama, Katsuki; Sakata, Yasuo; Horiguchi, Tsuyoshi

    2003-01-01

    We investigate storage capacity and generalization ability for two types of fully connected layered neural networks with non-monotonic transfer functions; random patterns are embedded into the networks by a Hebbian learning rule. One of them is a layered network in which a non-monotonic transfer function of even layers is different from that of odd layers. The other is a layered network with intra-layer connections, in which the non-monotonic transfer function of inter-layer is different from that of intra-layer, and inter-layered neurons and intra-layered neurons are updated alternately. We derive recursion relations for order parameters for those layered networks by the signal-to-noise ratio method. We clarify that the storage capacity and the generalization ability for those layered networks are enhanced in comparison with those with a conventional monotonic transfer function when non-monotonicity of the transfer functions is selected optimally. We also point out that some chaotic behavior appears in the order parameters for the layered networks when non-monotonicity of the transfer functions increases.

  5. Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading.

    Science.gov (United States)

    Li, Siqi; Jiang, Huiyan; Pang, Wenbo

    2017-05-01

    Accurate cell grading of cancerous tissue pathological image is of great importance in medical diagnosis and treatment. This paper proposes a joint multiple fully connected convolutional neural network with extreme learning machine (MFC-CNN-ELM) architecture for hepatocellular carcinoma (HCC) nuclei grading. First, in preprocessing stage, each grayscale image patch with the fixed size is obtained using center-proliferation segmentation (CPS) method and the corresponding labels are marked under the guidance of three pathologists. Next, a multiple fully connected convolutional neural network (MFC-CNN) is designed to extract the multi-form feature vectors of each input image automatically, which considers multi-scale contextual information of deep layer maps sufficiently. After that, a convolutional neural network extreme learning machine (CNN-ELM) model is proposed to grade HCC nuclei. Finally, a back propagation (BP) algorithm, which contains a new up-sample method, is utilized to train MFC-CNN-ELM architecture. The experiment comparison results demonstrate that our proposed MFC-CNN-ELM has superior performance compared with related works for HCC nuclei grading. Meanwhile, external validation using ICPR 2014 HEp-2 cell dataset shows the good generalization of our MFC-CNN-ELM architecture. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Altered thalamic functional connectivity in multiple sclerosis

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Yaou; Liang, Peipeng; Duan, Yunyun; Huang, Jing; Ren, Zhuoqiong; Jia, Xiuqin [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Dong, Huiqing; Ye, Jing [Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Shi, Fu-Dong [Department of Neurology and Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin 300052 (China); Butzkueven, Helmut [Department of Medicine, University of Melbourne, Parkville 3010 (Australia); Li, Kuncheng, E-mail: kunchengli55@gmail.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China)

    2015-04-15

    Highlights: •We demonstrated decreased connectivity between thalamus and cortical regions in MS. •Increased intra- and inter-thalamic connectivity was also observed in MS. •The increased functional connectivity is attenuated by increasing disease duration. -- Abstract: Objective: To compare thalamic functional connectivity (FC) in patients with multiple sclerosis (MS) and healthy controls (HC), and correlate these connectivity measures with other MRI and clinical variables. Methods: We employed resting-state functional MRI (fMRI) to examine changes in thalamic connectivity by comparing thirty-five patients with MS and 35 age- and sex-matched HC. Thalamic FC was investigated by correlating low frequency fMRI signal fluctuations in thalamic voxels with voxels in all other brain regions. Additionally thalamic volume fraction (TF), T2 lesion volume (T2LV), EDSS and disease duration were recorded and correlated with the FC changes. Results: MS patients were found to have a significantly lower TF than HC in bilateral thalami. Compared to HC, the MS group showed significantly decreased FC between thalamus and several brain regions including right middle frontal and parahippocampal gyri, and the left inferior parietal lobule. Increased intra- and inter-thalamic FC was observed in the MS group compared to HC. These FC alterations were not correlated with T2LV, thalamic volume or lesions. In the MS group, however, there was a negative correlation between disease duration and inter-thalamic connectivity (r = −0.59, p < 0.001). Conclusion: We demonstrated decreased FC between thalamus and several cortical regions, while increased intra- and inter-thalamic connectivity in MS patients. These complex functional changes reflect impairments and/or adaptations that are independent of T2LV, thalamic volume or presence of thalamic lesions. The negative correlation between disease duration and inter-thalamic connectivity could indicate an adaptive role of thalamus that is

  7. Mindfulness and dynamic functional neural connectivity in children and adolescents.

    Science.gov (United States)

    Marusak, Hilary A; Elrahal, Farrah; Peters, Craig A; Kundu, Prantik; Lombardo, Michael V; Calhoun, Vince D; Goldberg, Elimelech K; Cohen, Cindy; Taub, Jeffrey W; Rabinak, Christine A

    2017-09-05

    Interventions that promote mindfulness consistently show salutary effects on cognition and emotional wellbeing in adults, and more recently, in children and adolescents. However, we lack understanding of the neurobiological mechanisms underlying mindfulness in youth that should allow for more judicious application of these interventions in clinical and educational settings. Using multi-echo multi-band fMRI, we examined dynamic (i.e., time-varying) and conventional static resting-state connectivity between core neurocognitive networks (i.e., salience/emotion, default mode, central executive) in 42 children and adolescents (ages 6-17). We found that trait mindfulness in youth relates to dynamic but not static resting-state connectivity. Specifically, more mindful youth transitioned more between brain states over the course of the scan, spent overall less time in a certain connectivity state, and showed a state-specific reduction in connectivity between salience/emotion and central executive networks. The number of state transitions mediated the link between higher mindfulness and lower anxiety, providing new insights into potential neural mechanisms underlying benefits of mindfulness on psychological health in youth. Our results provide new evidence that mindfulness in youth relates to functional neural dynamics and interactions between neurocognitive networks, over time. Copyright © 2017. Published by Elsevier B.V.

  8. The Neural Basis of Typewriting: A Functional MRI Study.

    Directory of Open Access Journals (Sweden)

    Yuichi Higashiyama

    Full Text Available To investigate the neural substrate of typewriting Japanese words and to detect the difference between the neural substrate of typewriting and handwriting, we conducted a functional magnetic resonance imaging (fMRI study in 16 healthy volunteers. All subjects were skillful touch typists and performed five tasks: a typing task, a writing task, a reading task, and two control tasks. Three brain regions were activated during both the typing and the writing tasks: the left superior parietal lobule, the left supramarginal gyrus, and the left premotor cortex close to Exner's area. Although typing and writing involved common brain regions, direct comparison between the typing and the writing task revealed greater left posteromedial intraparietal cortex activation in the typing task. In addition, activity in the left premotor cortex was more rostral in the typing task than in the writing task. These findings suggest that, although the brain circuits involved in Japanese typewriting are almost the same as those involved in handwriting, there are brain regions that are specific for typewriting.

  9. Analysis of Neural Networks in Terms of Domain Functions

    NARCIS (Netherlands)

    Zwaag, van der Berend Jan; Slump, Cees; Spaanenburg, Lambert

    2002-01-01

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

  10. Multiplicative Inhibitory Velocity Detector and Multi-Velocity Motion Detection Neural Network Model

    Institute of Scientific and Technical Information of China (English)

    1998-01-01

    Motion perception is one of the most important aspects of the biological visual system,from which people get a lot of information of the natural world.In this paper,trying to simulate the neurons in MT(motion area in visual cortex)which respond selectively both in direction and speed,the authors propose a novel multiplicative inhibitory velocity detector(MIVD)model,whose spatiotemporal joint parameter K determines its optimal velocity.Based on the Response Amplitude Disparity(RAD) property of MIVD,two multi-velocity fusion neural networks(a simple one and an active one)are built to detect the velocity of 1-Dimension motion.The experiments show that the active MIVD Neural Network with a feedback fusion method has a relatively better result.

  11. Multiple Artificial Neural Networks with Interaction Noise for Estimation of Spatial Categorical Variables

    Directory of Open Access Journals (Sweden)

    Xiang Huang

    2016-08-01

    Full Text Available This paper presents a multiple artificial neural networks (MANN method with interaction noise for estimating the occurrence probabilities of different classes at any site in space. The MANN consists of several independent artificial neural networks, the number of which is determined by the neighbors around the target location. In the proposed algorithm, the conditional or pre-posterior (multi-point probabilities are viewed as output nodes, which can be estimated by weighted combinations of input nodes: two-point transition probabilities. The occurrence probability of a certain class at a certain location can be easily computed by the product of output probabilities using Bayes’ theorem. Spatial interaction or redundancy information can be measured in the form of interaction noises. Prediction results show that the method of MANN with interaction noise has a higher classification accuracy than the traditional Markov chain random fields (MCRF model and can successfully preserve small-scale features.

  12. The neural basis of theory of mind and its relationship to social functioning and social anhedonia in individuals with schizophrenia

    Directory of Open Access Journals (Sweden)

    David Dodell-Feder

    2014-01-01

    Full Text Available Theory of mind (ToM, the ability to attribute and reason about the mental states of others, is a strong determinant of social functioning among individuals with schizophrenia. Identifying the neural bases of ToM and their relationship to social functioning may elucidate functionally relevant neurobiological targets for intervention. ToM ability may additionally account for other social phenomena that affect social functioning, such as social anhedonia (SocAnh. Given recent research in schizophrenia demonstrating improved neural functioning in response to increased use of cognitive skills, it is possible that SocAnh, which decreases one's opportunity to engage in ToM, could compromise social functioning through its deleterious effect on ToM-related neural circuitry. Here, twenty individuals with schizophrenia and 18 healthy controls underwent fMRI while performing the False-Belief Task. Aspects of social functioning were assessed using multiple methods including self-report (Interpersonal Reactivity Index, Social Adjustment Scale, clinician-ratings (Global Functioning Social Scale, and performance-based tasks (MSCEIT—Managing Emotions. SocAnh was measured with the Revised Social Anhedonia Scale. Region-of-interest and whole-brain analyses revealed reduced recruitment of medial prefrontal cortex (MPFC for ToM in individuals with schizophrenia. Across all participants, activity in this region correlated with most social variables. Mediation analysis revealed that neural activity for ToM in MPFC accounted for the relationship between SocAnh and social functioning. These findings demonstrate that reduced recruitment of MPFC for ToM is an important neurobiological determinant of social functioning. Furthermore, SocAhn may affect social functioning through its impact on ToM-related neural circuitry. Together, these findings suggest ToM ability as an important locus for intervention.

  13. The neural basis of theory of mind and its relationship to social functioning and social anhedonia in individuals with schizophrenia.

    Science.gov (United States)

    Dodell-Feder, David; Tully, Laura M; Lincoln, Sarah Hope; Hooker, Christine I

    2014-01-01

    Theory of mind (ToM), the ability to attribute and reason about the mental states of others, is a strong determinant of social functioning among individuals with schizophrenia. Identifying the neural bases of ToM and their relationship to social functioning may elucidate functionally relevant neurobiological targets for intervention. ToM ability may additionally account for other social phenomena that affect social functioning, such as social anhedonia (SocAnh). Given recent research in schizophrenia demonstrating improved neural functioning in response to increased use of cognitive skills, it is possible that SocAnh, which decreases one's opportunity to engage in ToM, could compromise social functioning through its deleterious effect on ToM-related neural circuitry. Here, twenty individuals with schizophrenia and 18 healthy controls underwent fMRI while performing the False-Belief Task. Aspects of social functioning were assessed using multiple methods including self-report (Interpersonal Reactivity Index, Social Adjustment Scale), clinician-ratings (Global Functioning Social Scale), and performance-based tasks (MSCEIT-Managing Emotions). SocAnh was measured with the Revised Social Anhedonia Scale. Region-of-interest and whole-brain analyses revealed reduced recruitment of medial prefrontal cortex (MPFC) for ToM in individuals with schizophrenia. Across all participants, activity in this region correlated with most social variables. Mediation analysis revealed that neural activity for ToM in MPFC accounted for the relationship between SocAnh and social functioning. These findings demonstrate that reduced recruitment of MPFC for ToM is an important neurobiological determinant of social functioning. Furthermore, SocAhn may affect social functioning through its impact on ToM-related neural circuitry. Together, these findings suggest ToM ability as an important locus for intervention.

  14. The neural basis of theory of mind and its relationship to social functioning and social anhedonia in individuals with schizophrenia☆

    Science.gov (United States)

    Dodell-Feder, David; Tully, Laura M.; Lincoln, Sarah Hope; Hooker, Christine I.

    2013-01-01

    Theory of mind (ToM), the ability to attribute and reason about the mental states of others, is a strong determinant of social functioning among individuals with schizophrenia. Identifying the neural bases of ToM and their relationship to social functioning may elucidate functionally relevant neurobiological targets for intervention. ToM ability may additionally account for other social phenomena that affect social functioning, such as social anhedonia (SocAnh). Given recent research in schizophrenia demonstrating improved neural functioning in response to increased use of cognitive skills, it is possible that SocAnh, which decreases one's opportunity to engage in ToM, could compromise social functioning through its deleterious effect on ToM-related neural circuitry. Here, twenty individuals with schizophrenia and 18 healthy controls underwent fMRI while performing the False-Belief Task. Aspects of social functioning were assessed using multiple methods including self-report (Interpersonal Reactivity Index, Social Adjustment Scale), clinician-ratings (Global Functioning Social Scale), and performance-based tasks (MSCEIT—Managing Emotions). SocAnh was measured with the Revised Social Anhedonia Scale. Region-of-interest and whole-brain analyses revealed reduced recruitment of medial prefrontal cortex (MPFC) for ToM in individuals with schizophrenia. Across all participants, activity in this region correlated with most social variables. Mediation analysis revealed that neural activity for ToM in MPFC accounted for the relationship between SocAnh and social functioning. These findings demonstrate that reduced recruitment of MPFC for ToM is an important neurobiological determinant of social functioning. Furthermore, SocAhn may affect social functioning through its impact on ToM-related neural circuitry. Together, these findings suggest ToM ability as an important locus for intervention. PMID:24371798

  15. Intraoperative Neural Response Telemetry and Neural Recovery Function: a Comparative Study between Adults and Children

    Science.gov (United States)

    Carvalho, Bettina; Hamerschmidt, Rogerio; Wiemes, Gislaine

    2014-01-01

    Introduction Neural response telemetry (NRT) is a method of capturing the action potential of the distal portion of the auditory nerve in cochlear implant (CI) users, using the CI itself to elicit and record the answers. In addition, it can also measure the recovery function of the auditory nerve (REC), that is, the refractory properties of the nerve. It is not clear in the literature whether the responses from adults are the same as those from children. Objective To compare the results of NRT and REC between adults and children undergoing CI surgery. Methods Cross-sectional, descriptive, and retrospective study of the results of NRT and REC for patients undergoing IC at our service. The NRT is assessed by the level of amplitude (microvolts) and REC as a function of three parameters: A (saturation level, in microvolts), t0 (absolute refractory period, in seconds), and tau (curve of the model function), measured in three electrodes (apical, medial, and basal). Results Fifty-two patients were evaluated with intraoperative NRT (26 adults and 26 children), and 24 with REC (12 adults and 12 children). No statistically significant difference was found between intraoperative responses of adults and children for NRT or for REC's three parameters, except for parameter A of the basal electrode. Conclusion The results of intraoperative NRT and REC were not different between adults and children, except for parameter A of the basal electrode. PMID:25992145

  16. Intraoperative Neural Response Telemetry and Neural Recovery Function: a Comparative Study between Adults and Children

    Directory of Open Access Journals (Sweden)

    Carvalho, Bettina

    2014-04-01

    Full Text Available Introduction Neural response telemetry (NRT is a method of capturing the action potential of the distal portion of the auditory nerve in cochlear implant (CI users, using the CI itself to elicit and record the answers. In addition, it can also measure the recovery function of the auditory nerve (REC, that is, the refractory properties of the nerve. It is not clear in the literature whether the responses from adults are the same as those from children. Objective To compare the results of NRT and REC between adults and children undergoing CI surgery. Methods Cross-sectional, descriptive, and retrospective study of the results of NRT and REC for patients undergoing IC at our service. The NRT is assessed by the level of amplitude (microvolts and REC as a function of three parameters: A (saturation level, in microvolts, t0 (absolute refractory period, in seconds, and tau (curve of the model function, measured in three electrodes (apical, medial, and basal. Results Fifty-two patients were evaluated with intraoperative NRT (26 adults and 26 children, and 24 with REC (12 adults and 12 children. No statistically significant difference was found between intraoperative responses of adults and children for NRT or for REC's three parameters, except for parameter A of the basal electrode. Conclusion The results of intraoperative NRT and REC were not different between adults and children, except for parameter A of the basal electrode.

  17. Chaos in neural networks with a nonmonotonic transfer function

    CERN Document Server

    Caroppo, D; Nardulli, Giuseppe; Stramaglia, S

    1999-01-01

    Time evolution of diluted neural networks with a nonmonotonic transfer function is analitically described by flow equations for macroscopic variables. The macroscopic dynamics shows a rich variety of behaviours: fixed-point, periodicity and chaos. We examine in detail the structure of the strange attractor and in particular we study the main features of the stable and unstable manifolds, the hyperbolicity of the attractor and the existence of homoclinic intersections. We also discuss the problem of the robustness of the chaos and we prove that in the present model chaotic behaviour is fragile (chaotic regions are densely intercalated with periodicity windows), according to a recently discussed conjecture. Finally we perform an analysis of the microscopic behaviour and in particular we examine the occurrence of damage spreading by studying the time evolution of two almost identical initial configurations. We show that for any choice of the parameters the two initial states remain microscopically distinct.

  18. Hydrogen sulfide improves neural function in rats following cardiopulmonary resuscitation

    Science.gov (United States)

    LIN, JI-YAN; ZHANG, MIN-WEI; WANG, JIN-GAO; LI, HUI; WEI, HONG-YAN; LIU, RONG; DAI, GANG; LIAO, XIAO-XING

    2016-01-01

    The alleviation of brain injury is a key issue following cardiopulmonary resuscitation (CPR). Hydrogen sulfide (H2S) is hypothesized to be involved in the pathophysiological process of ischemia-reperfusion injury, and exerts a protective effect on neurons. The aim of the present study was to investigate the effects of H2S on neural functions following cardiac arrest (CA) in rats. A total of 60 rats were allocated at random into three groups. CA was induced to establish the model and CPR was performed after 6 min. Subsequently, sodium hydrosulfide (NaHS), hydroxylamine or saline was administered to the rats. Serum levels of H2S, neuron-specific enolase (NSE) and S100β were determined following CPR. In addition, neurological deficit scoring (NDS), the beam walking test (BWT), prehensile traction test and Morris water maze experiment were conducted. Neuronal apoptosis rates were detected in the hippocampal region following sacrifice. After CPR, as the H2S levels increased or decreased, the serum NSE and S100β concentrations decreased or increased, respectively (P<0.0w. The NDS results of the NaHS group were improved compared with those of the hydroxylamine group at 24 h after CPR (P<0.05). In the Morris water maze experiment, BWT and prehensile traction test the animals in the NaHS group performed best and rats in the hydroxylamine group performed worst. At day 7, the apoptotic index and the expression of caspase-3 were reduced in the hippocampal CA1 region, while the expression of Bcl-2 increased in the NaHS group; and results of the hydroxylamine group were in contrast. Therefore, the results of the present study indicate that H2S is able to improve neural function in rats following CPR. PMID:26893650

  19. Neural Network Hydrological Modelling: Linear Output Activation Functions?

    Science.gov (United States)

    Abrahart, R. J.; Dawson, C. W.

    2005-12-01

    The power to represent non-linear hydrological processes is of paramount importance in neural network hydrological modelling operations. The accepted wisdom requires non-polynomial activation functions to be incorporated in the hidden units such that a single tier of hidden units can thereafter be used to provide a 'universal approximation' to whatever particular hydrological mechanism or function is of interest to the modeller. The user can select from a set of default activation functions, or in certain software packages, is able to define their own function - the most popular options being logistic, sigmoid and hyperbolic tangent. If a unit does not transform its inputs it is said to possess a 'linear activation function' and a combination of linear activation functions will produce a linear solution; whereas the use of non-linear activation functions will produce non-linear solutions in which the principle of superposition does not hold. For hidden units, speed of learning and network complexities are important issues. For the output units, it is desirable to select an activation function that is suited to the distribution of the target values: e.g. binary targets (logistic); categorical targets (softmax); continuous-valued targets with a bounded range (logistic / tanh); positive target values with no known upper bound (exponential; but beware of overflow); continuous-valued targets with no known bounds (linear). It is also standard practice in most hydrological applications to use the default software settings and to insert a set of identical non-linear activation functions in the hidden layer and output layer processing units. Mixed combinations have nevertheless been reported in several hydrological modelling papers and the full ramifications of such activities requires further investigation and assessment i.e. non-linear activation functions in the hidden units connected to linear or clipped-linear activation functions in the output unit. There are two

  20. A Complex-Valued Projection Neural Network for Constrained Optimization of Real Functions in Complex Variables.

    Science.gov (United States)

    Zhang, Songchuan; Xia, Youshen; Wang, Jun

    2015-12-01

    In this paper, we present a complex-valued projection neural network for solving constrained convex optimization problems of real functions with complex variables, as an extension of real-valued projection neural networks. Theoretically, by developing results on complex-valued optimization techniques, we prove that the complex-valued projection neural network is globally stable and convergent to the optimal solution. Obtained results are completely established in the complex domain and thus significantly generalize existing results of the real-valued projection neural networks. Numerical simulations are presented to confirm the obtained results and effectiveness of the proposed complex-valued projection neural network.

  1. 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.

  2. Parametrization of analytic interatomic potential functions using neural networks.

    Science.gov (United States)

    Malshe, M; Narulkar, R; Raff, L M; Hagan, M; Bukkapatnam, S; Komanduri, R

    2008-07-28

    A generalized method that permits the parameters of an arbitrary empirical potential to be efficiently and accurately fitted to a database is presented. The method permits the values of a subset of the potential parameters to be considered as general functions of the internal coordinates that define the instantaneous configuration of the system. The parameters in this subset are computed by a generalized neural network (NN) with one or more hidden layers and an input vector with at least 3n-6 elements, where n is the number of atoms in the system. The Levenberg-Marquardt algorithm is employed to efficiently affect the optimization of the weights and biases of the NN as well as all other potential parameters being treated as constants rather than as functions of the input coordinates. In order to effect this minimization, the usual Jacobian employed in NN operations is modified to include the Jacobian of the computed errors with respect to the parameters of the potential function. The total Jacobian employed in each epoch of minimization is the concatenation of two Jacobians, one containing derivatives of the errors with respect to the weights and biases of the network, and the other with respect to the constant parameters of the potential function. The method provides three principal advantages. First, it obviates the problem of selecting the form of the functional dependence of the parameters upon the system's coordinates by employing a NN. If this network contains a sufficient number of neurons, it will automatically find something close to the best functional form. This is the case since Hornik et al., [Neural Networks 2, 359 (1989)] have shown that two-layer NNs with sigmoid transfer functions in the first hidden layer and linear functions in the output layer are universal approximators for analytic functions. Second, the entire fitting procedure is automated so that excellent fits are obtained rapidly with little human effort. Third, the method provides a

  3. Vibrational and Stochastic Resonance in the FitzHugh-Nagumo Neural Model with Multiplicative and Additive Noise

    Institute of Scientific and Technical Information of China (English)

    HE Zheng-You; ZHOU Yu-Rong

    2011-01-01

    The vibrational resonance and stochastic resonance phenomena in the FitzHugh-Nagumo (FHN) neural model,driven by a high-frequency (HF) signal and a low-frequency (LF) signal and by coupled multiplicative and additive noises,is investigated.For the case that the frequency of the HF signal is much higher than that of the LF signal,under the adiabatic approximation condition,the expression of the signal-to-noise ratio (SNR) with respect to the LF signal is obtained.It is shown that the SNR is a non-monotonous function of the amplitude and frequency of the HF signal In addition,the SNR varies non-monotonically with the increasing intensities of the multiplicative and additive noise as well as with the increasing system parameters of the FHN model The influence of the coupling strength between the multiplicative and additive noises on the SNR is discussed.Stochastic resonance (SR) describes the phenomenon where an appropriate amount of noise is of constructive use in the sense that a weak signal becomes amplified upon harvesting the ambient noise in nonlinear systems.[1] Since its first discovery in the early eighties,SR has been observed in a great variety of systems pertaining to different disciplines such as physics,chemistry,engineering,biology and biomedical sciences.[1-4] The phenomenon vibrational resonance (VR) was named by Landa and McClintock.[5]%The vibrational resonance and stochastic resonance phenomena in the FitzHugh-Nagumo (FHN) neural model, driven by a high-frequency (HF) signal and a low-frequency (LF) signal and by coupled multiplicative and additive noises, is investigated. For the case that the frequency of the HF signal is much higher than that of the LF signal, under the adiabatic approximation condition, the expression of the signal-to-noise ratio (SNR) with respect to the LF signal is obtained. It is shown that the SNR is a non-monotonous function of the amplitude and frequency of the HF signal. In addition, the SNR varies non

  4. 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.

  5. Multistability of delayed complex-valued recurrent neural networks with discontinuous real-imaginary-type activation functions

    Science.gov (United States)

    Huang, Yu-Jiao; Hu, Hai-Gen

    2015-12-01

    In this paper, the multistability issue is discussed for delayed complex-valued recurrent neural networks with discontinuous real-imaginary-type activation functions. Based on a fixed theorem and stability definition, sufficient criteria are established for the existence and stability of multiple equilibria of complex-valued recurrent neural networks. The number of stable equilibria is larger than that of real-valued recurrent neural networks, which can be used to achieve high-capacity associative memories. 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. 61374094 and 61503338) and the Natural Science Foundation of Zhejiang Province, China (Grant No. LQ15F030005).

  6. Multistability of memristive Cohen-Grossberg neural networks with non-monotonic piecewise linear activation functions and time-varying delays.

    Science.gov (United States)

    Nie, Xiaobing; Zheng, Wei Xing; Cao, Jinde

    2015-11-01

    The problem of coexistence and dynamical behaviors of multiple equilibrium points is addressed for a class of memristive Cohen-Grossberg neural networks with non-monotonic piecewise linear activation functions and time-varying delays. By virtue of the fixed point theorem, nonsmooth analysis theory and other analytical tools, some sufficient conditions are established to guarantee that such n-dimensional memristive Cohen-Grossberg neural networks can have 5(n) equilibrium points, among which 3(n) equilibrium points are locally exponentially stable. It is shown that greater storage capacity can be achieved by neural networks with the non-monotonic activation functions introduced herein than the ones with Mexican-hat-type activation function. In addition, unlike most existing multistability results of neural networks with monotonic activation functions, those obtained 3(n) locally stable equilibrium points are located both in saturated regions and unsaturated regions. The theoretical findings are verified by an illustrative example with computer simulations.

  7. Neurogenic and non neurogenic functions of endogenous neural stem cells.

    Directory of Open Access Journals (Sweden)

    Erica eButti

    2014-04-01

    Full Text Available Adult neurogenesis is a lifelong process that occurs in two main neurogenic niches of the brain, namely in the subventricular zone (SVZ of the lateral ventricles and in the subgranular zone (SGZ of the dentate gyrus (DG in the hippocampus. In the 1960s, studies on adult neurogenesis have been hampered by the lack of established phenotypic markers. The precise tracing of neural stem/progenitor cells (NPCs was therefore, not properly feasible. After the (partial identification of those markers, it was the lack of specific tools that hindered a proper experimental elimination and tracing of those cells to demonstrate their terminal fate and commitment. Nowadays, irradia-tion, cytotoxic drugs as well as genetic tracing/ablation procedures have moved the field forward and increased our understanding of neurogenesis processes in both physiological and pathological conditions. Newly formed NPC progeny from the SVZ can replace granule cells in the olfactory bulbs of rodents, thus contributing to orchestrate sophisticated odour behaviour. SGZ-derived new granule cells, instead, integrate within the DG where they play an essential role in memory functions. Furthermore, converging evidence claim that endogenous NPCs not only exert neurogenic functions, but might also have non-neurogenic homeostatic functions by the release of different types of neuroprotective molecules. Remarkably, these non-neurogenic homeostatic functions seem to be necessary, both in healthy and diseased conditions, for example for preventing or limiting tissue damage. In this review, we will discuss the neurogenic and the non-neurogenic functions of adult NPCs both in physiological and pathological conditions.

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

    CERN Document Server

    Liu, Jinkun

    2013-01-01

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

  9. Global exponential stability analysis of cellular neural networks with multiple time delays

    Institute of Scientific and Technical Information of China (English)

    Zhanshan WANG; Huaguang ZHANG

    2007-01-01

    Global exponential stability problems are investigated for cellular neural networks (CNN) with multiple time-varying delays. Several new criteria in linear matrix inequality form or in algebraic form are presented to ascertain the uniqueness and global exponential stability of the equilibrium point for CNN with multiple time-varying delays and with constant time delays. The proposed method has the advantage of considering the difference of neuronal excitatory and inhibitory effects, which is also computationally efficient as it can be solved numerically using the recently developed interior-point algorithm or be checked using simple algebraic calculation. In addition, the proposed results generalize and improve upon some previous works. Two numerical examples are used to show the effectiveness of the obtained results.

  10. Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks

    Institute of Scientific and Technical Information of China (English)

    Ghiasi Majid; Askarnejad Nematollah; Dindarloo Saeid R.; Shamsoddini Hamed

    2016-01-01

    The most important objective of blasting in open pit mines is rock fragmentation. Prediction of produced boulders (oversized crushed rocks) is a key parameter in designing blast patterns. In this study, the amount of boulder produced in blasting operations of Golegohar iron ore open pit mine, Iran was pre-dicted via multiple regression method and artificial neural networks. Results of 33 blasts in the mine were collected for modeling. Input variables were: joints spacing, density and uniaxial compressive strength of the intact rock, burden, spacing, stemming, bench height to burden ratio, and specific charge. The dependent variable was ratio of boulder volume to pattern volume. Both techniques were successful in predicting the ratio. In this study, the multiple regression method was superior with coefficient of determination and root mean squared error values of 0.89 and 0.19, respectively.

  11. Fuzzy Neural Network-Based Interacting Multiple Model for Multi-Node Target Tracking Algorithm

    Directory of Open Access Journals (Sweden)

    Baoliang Sun

    2016-11-01

    Full Text Available An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN to solve the multi-node target tracking problem of wireless sensor networks (WSNs. Measured error variance was adaptively adjusted during the multiple model interacting output stage using the difference between the theoretical and estimated values of the measured error covariance matrix. The FNN fusion system was established during multi-node fusion to integrate with the target state estimated data from different nodes and consequently obtain network target state estimation. The feasibility of the algorithm was verified based on a network of nine detection nodes. Experimental results indicated that the proposed algorithm could trace the maneuvering target effectively under sensor failure and unknown system measurement errors. The proposed algorithm exhibited great practicability in the multi-node target tracking of WSNs.

  12. REPRESENTATION OF SYMMETRIC SUPER-MARTINGALE MULTIPLICATIVE FUNCTIONALS

    Institute of Scientific and Technical Information of China (English)

    金蒙为; 应坚刚

    2002-01-01

    The authors introduce concepts of even and odd additive functionals and prove that an even martingale continuous additive functional of a symmetric Markov process vanishes identically.A representation for symmetric super-martingale multiplicative functionals are also given.

  13. Determination of Activation Functions in A Feedforward Neural Network by using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Oğuz ÜSTÜN

    2009-03-01

    Full Text Available In this study, activation functions of all layers of the multilayered feedforward neural network have been determined by using genetic algorithm. The main criteria that show the efficiency of the neural network is to approximate to the desired output with the same number nodes and connection weights. One of the important parameter to determine this performance is to choose a proper activation function. In the classical neural network designing, a network is designed by choosing one of the generally known activation function. In the presented study, a table has been generated for the activation functions. The ideal activation function for each node has been chosen from this table by using the genetic algorithm. Two dimensional regression problem clusters has been used to compare the performance of the classical static neural network and the genetic algorithm based neural network. Test results reveal that the proposed method has a high level approximation capacity.

  14. Uniqueness and stability of traveling waves for cellular neural networks with multiple delays

    Science.gov (United States)

    Yu, Zhi-Xian; Mei, Ming

    2016-01-01

    In this paper, we investigate the properties of traveling waves to a class of lattice differential equations for cellular neural networks with multiple delays. Following the previous study [38] on the existence of the traveling waves, here we focus on the uniqueness and the stability of these traveling waves. First of all, by establishing the a priori asymptotic behavior of traveling waves and applying Ikehara's theorem, we prove the uniqueness (up to translation) of traveling waves ϕ (n - ct) with c ≤c* for the cellular neural networks with multiple delays, where c* < 0 is the critical wave speed. Then, by the weighted energy method together with the squeezing technique, we further show the global stability of all non-critical traveling waves for this model, that is, for all monotone waves with the speed c

  15. Functional integration of human neural precursor cells in mouse cortex.

    Directory of Open Access Journals (Sweden)

    Fu-Wen Zhou

    Full Text Available This study investigates the electrophysiological properties and functional integration of different phenotypes of transplanted human neural precursor cells (hNPCs in immunodeficient NSG mice. Postnatal day 2 mice received unilateral injections of 100,000 GFP+ hNPCs into the right parietal cortex. Eight weeks after transplantation, 1.21% of transplanted hNPCs survived. In these hNPCs, parvalbumin (PV-, calretinin (CR-, somatostatin (SS-positive inhibitory interneurons and excitatory pyramidal neurons were confirmed electrophysiologically and histologically. All GFP+ hNPCs were immunoreactive with anti-human specific nuclear protein. The proportions of PV-, CR-, and SS-positive cells among GFP+ cells were 35.5%, 15.7%, and 17.1%, respectively; around 15% of GFP+ cells were identified as pyramidal neurons. Those electrophysiologically and histological identified GFP+ hNPCs were shown to fire action potentials with the appropriate firing patterns for different classes of neurons and to display spontaneous excitatory and inhibitory postsynaptic currents (sEPSCs and sIPSCs. The amplitude, frequency and kinetic properties of sEPSCs and sIPSCs in different types of hNPCs were comparable to host cells of the same type. In conclusion, GFP+ hNPCs produce neurons that are competent to integrate functionally into host neocortical neuronal networks. This provides promising data on the potential for hNPCs to serve as therapeutic agents in neurological diseases with abnormal neuronal circuitry such as epilepsy.

  16. Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic Functionalities Through Domain Wall Motion in Ferromagnets.

    Science.gov (United States)

    Sengupta, Abhronil; Shim, Yong; Roy, Kaushik

    2016-12-01

    Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking the neuron, or the synapse functionality. While memristive devices have been proposed to emulate biological synapses, spintronic devices have proved to be efficient at performing the thresholding operation of the neuron at ultra-low currents. In this work, we propose an All-Spin Artificial Neural Network where a single spintronic device acts as the basic building block of the system. The device offers a direct mapping to synapse and neuron functionalities in the brain while inter-layer network communication is accomplished via CMOS transistors. To the best of our knowledge, this is the first demonstration of a neural architecture where a single nanoelectronic device is able to mimic both neurons and synapses. The ultra-low voltage operation of low resistance magneto-metallic neurons enables the low-voltage operation of the array of spintronic synapses, thereby leading to ultra-low power neural architectures. Device-level simulations, calibrated to experimental results, was used to drive the circuit and system level simulations of the neural network for a standard pattern recognition problem. Simulation studies indicate energy savings by  ∼  100× in comparison to a corresponding digital/analog CMOS neuron implementation.

  17. Functional plasticity before the cradle: a review of neural functional imaging in the human fetus.

    Science.gov (United States)

    Anderson, Amy L; Thomason, Moriah E

    2013-11-01

    The organization of the brain is highly plastic in fetal life. Establishment of healthy neural functional systems during the fetal period is essential to normal growth and development. Across the last several decades, remarkable progress has been made in understanding the development of human fetal functional brain systems. This is largely due to advances in imaging methodologies. Fetal neuroimaging began in the 1950-1970's with fetal electroencephalography (EEG) applied during labor. Later, in the 1980's, magnetoencephalography (MEG) emerged as an effective approach for investigating fetal brain function. Most recently, functional magnetic resonance imaging (fMRI) has arisen as an additional powerful approach for examining fetal brain function. This review will discuss major developmental findings from fetal imaging studies such as the maturation of prenatal sensory system functions, functional hemispheric asymmetry, and sensory-driven neurodevelopment. We describe how with improved imaging and analysis techniques, functional imaging of the fetus has the potential to assess the earliest point of neural maturation and provide insight into the patterning and sequence of normal and abnormal brain development. Copyright © 2013 Elsevier Ltd. All rights reserved.

  18. Language Learning Enhanced by Massive Multiple Online Role-Playing Games (MMORPGs) and the Underlying Behavioral and Neural Mechanisms.

    Science.gov (United States)

    Zhang, Yongjun; Song, Hongwen; Liu, Xiaoming; Tang, Dinghong; Chen, Yue-E; Zhang, Xiaochu

    2017-01-01

    Massive Multiple Online Role-Playing Games (MMORPGs) have increased in popularity among children, juveniles, and adults since MMORPGs' appearance in this digital age. MMORPGs can be applied to enhancing language learning, which is drawing researchers' attention from different fields and many studies have validated MMORPGs' positive effect on language learning. However, there are few studies on the underlying behavioral or neural mechanism of such effect. This paper reviews the educational application of the MMORPGs based on relevant macroscopic and microscopic studies, showing that gamers' overall language proficiency or some specific language skills can be enhanced by real-time online interaction with peers and game narratives or instructions embedded in the MMORPGs. Mechanisms underlying the educational assistant role of MMORPGs in second language learning are discussed from both behavioral and neural perspectives. We suggest that attentional bias makes gamers/learners allocate more cognitive resources toward task-related stimuli in a controlled or an automatic way. Moreover, with a moderating role played by activation of reward circuit, playing the MMORPGs may strengthen or increase functional connectivity from seed regions such as left anterior insular/frontal operculum (AI/FO) and visual word form area to other language-related brain areas.

  19. Language Learning Enhanced by Massive Multiple Online Role-Playing Games (MMORPGs) and the Underlying Behavioral and Neural Mechanisms

    Science.gov (United States)

    Zhang, Yongjun; Song, Hongwen; Liu, Xiaoming; Tang, Dinghong; Chen, Yue-e; Zhang, Xiaochu

    2017-01-01

    Massive Multiple Online Role-Playing Games (MMORPGs) have increased in popularity among children, juveniles, and adults since MMORPGs’ appearance in this digital age. MMORPGs can be applied to enhancing language learning, which is drawing researchers’ attention from different fields and many studies have validated MMORPGs’ positive effect on language learning. However, there are few studies on the underlying behavioral or neural mechanism of such effect. This paper reviews the educational application of the MMORPGs based on relevant macroscopic and microscopic studies, showing that gamers’ overall language proficiency or some specific language skills can be enhanced by real-time online interaction with peers and game narratives or instructions embedded in the MMORPGs. Mechanisms underlying the educational assistant role of MMORPGs in second language learning are discussed from both behavioral and neural perspectives. We suggest that attentional bias makes gamers/learners allocate more cognitive resources toward task-related stimuli in a controlled or an automatic way. Moreover, with a moderating role played by activation of reward circuit, playing the MMORPGs may strengthen or increase functional connectivity from seed regions such as left anterior insular/frontal operculum (AI/FO) and visual word form area to other language-related brain areas. PMID:28303097

  20. Neural Substrates of Dopamine D2 Receptor Modulated Executive Functions in the Monkey Prefrontal Cortex.

    Science.gov (United States)

    Puig, M Victoria; Miller, Earl K

    2015-09-01

    Dopamine D2 receptors (D2R) play a major role in cognition, mood and motor movements. Their blockade by antipsychotic drugs reduces hallucinatory and delusional behaviors in schizophrenia, but often fails to alleviate affective and cognitive dysfunctions. The prefrontal cortex (PFC) expresses D2R and is altered in schizophrenia. We investigated how D2R modulate behavior and PFC function in monkeys. Two monkeys learned new and performed highly familiar visuomotor associations, where each cue was associated with a saccade to a right or left target. We recorded neural spikes and local field potentials from multiple electrodes while injecting the D2R antagonist eticlopride in the lateral PFC. Blocking prefrontal D2R impaired associative learning and cognitive flexibility, reduced motivation, but left the performance of familiar associations intact. Eticlopride reduced saccade-direction selectivity of prefrontal neurons, leading to a decrease in neural information about the associations, and an increase in alpha oscillations. These results, together with our recent study using a D1R antagonist, suggest that D1R and D2R in the primate lateral PFC cooperate to modulate several executive functions. Our findings help to gain insight into why antipsychotic drugs, with strong antagonistic actions on D2R, fail to ameliorate cognitive and emotional deficits in schizophrenia.

  1. Reconfigurable Flight Control Design using a Robust Servo LQR and Radial Basis Function Neural Networks

    Science.gov (United States)

    Burken, John J.

    2005-01-01

    This viewgraph presentation reviews the use of a Robust Servo Linear Quadratic Regulator (LQR) and a Radial Basis Function (RBF) Neural Network in reconfigurable flight control designs in adaptation to a aircraft part failure. The method uses a robust LQR servomechanism design with model Reference adaptive control, and RBF neural networks. During the failure the LQR servomechanism behaved well, and using the neural networks improved the tracking.

  2. A Robust Intelligent Framework for Multiple Response Statistical Optimization Problems Based on Artificial Neural Network and Taguchi Method

    Directory of Open Access Journals (Sweden)

    Ali Salmasnia

    2012-01-01

    Full Text Available An important problem encountered in product or process design is the setting of process variables to meet a required specification of quality characteristics (response variables, called a multiple response optimization (MRO problem. Common optimization approaches often begin with estimating the relationship between the response variable with the process variables. Among these methods, response surface methodology (RSM, due to simplicity, has attracted most attention in recent years. However, in many manufacturing cases, on one hand, the relationship between the response variables with respect to the process variables is far too complex to be efficiently estimated; on the other hand, solving such an optimization problem with accurate techniques is associated with problem. Alternative approach presented in this paper is to use artificial neural network to estimate response functions and meet heuristic algorithms in process optimization. In addition, the proposed approach uses the Taguchi robust parameter design to overcome the common limitation of the existing multiple response approaches, which typically ignore the dispersion effect of the responses. The paper presents a case study to illustrate the effectiveness of the proposed intelligent framework for tackling multiple response optimization problems.

  3. Neural networks with multiple general neuron models: a hybrid computational intelligence approach using Genetic Programming.

    Science.gov (United States)

    Barton, Alan J; Valdés, Julio J; Orchard, Robert

    2009-01-01

    Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of neural network (NN-GP) is presented in which: (i) each neuron may have its own neuron model in the form of a general function, (ii) any layout (i.e network interconnection) is possible, and (iii) no bias nodes or weights are associated to the connections, neurons or layers. The general functions associated to a neuron are learned by searching a function space. They are not provided a priori, but are rather built as part of an Evolutionary Computation process based on Genetic Programming. The resulting network solutions are evaluated based on a fitness measure, which may, for example, be based on classification or regression errors. Two real-world examples are presented to illustrate the promising behaviour on classification problems via construction of a low-dimensional representation of a high-dimensional parameter space associated to the set of all network solutions.

  4. Invertebrate diversity classification using self-organizing map neural network: with some special topological functions

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2014-06-01

    Full Text Available In present study we used self-organizing map (SOM neural network to conduct the non-supervisory clustering of invertebrate orders in rice field. Four topological functions, i.e., cossintopf, sincostopf, acossintopf, and expsintopf, established on the template in toolbox of Matlab, were used in SOM neural network learning. Results showed that clusters were different when using different topological functions because different topological functions will generate different spatial structure of neurons in neural network. We may chose these functions and results based on comparison with the practical situation.

  5. Gastrointestinal parasites and the neural control of gut functions

    Directory of Open Access Journals (Sweden)

    Marie Christiane Halliez

    2015-11-01

    Full Text Available Gastrointestinal motility and transport of water and electrolytes play key roles in the pathophysiology of diarrhea upon exposure to enteric parasites. These processes are actively modulated by the enteric nervous system (ENS, which includes efferent, and afferent neurons, as well as interneurons. ENS integrity is essential to the maintenance of homeostatic gut responses. A number of gastrointestinal parasites are known to cause disease by altering the enteric nervous system. The mechanisms remain incompletely understood. Cryptosporidium parvum, Giardia duodenalis (syn. G. intestinalis, G. lamblia, Trypanosoma cruzi, Schistosoma sp and others alter gastrointestinal motility, absorption, or secretion at least in part via effects on the ENS. Recent findings also implicate enteric parasites such as Cryptosporidium parvum and Giardia duodenalis in the development of post-infectious complications such as irritable bowel syndrome, which further underscores their effects on the gut-brain axis. This article critically reviews recent advances and the current state of knowledge on the impact of enteric parasitism on the neural control of gut functions, and provides insights into mechanisms underlying these abnormalities.

  6. Neudesin as a unique secreted protein with multi-functional roles in neural functions, energy metabolism, and tumorigenesis

    Directory of Open Access Journals (Sweden)

    Hiroya eOhta

    2015-05-01

    Full Text Available Neudesin was originally identified as a secreted protein with neurotrophic activity, and, thereafter, was also termed neuron-derived neurotrophic factor (NENF or the candidate oncogene GIG47. Neudesin with a conserved cytochrome 5-like heme/steroid-binding domain activates intracellular signaling pathways possibly through the activation of G protein-coupled receptors. In the brain, hypothalamic Neudesin decreases food intake. Neudesin knockout mice also exhibit anxiety-like behavior, indicating its roles in the hippocampal anxiety circuitry. Neudesin is also expressed in various peripheral tissues. Neudesin knockout mice are strongly resistant to high-fat diet-induced obesity due to elevated systemic sympathetic activity, heat production, and adipocytic lipolysis. Neudesin, which is over-expressed or induced by DNA hypomethylation in multiple human cancers, also stimulates tumorigenesis. These findings indicate that Neudesin plays roles in neural functions, energy metabolism, and tumorigenesis and is expected to be a novel target for obesity and anti-cancer treatments.

  7. Multistability of neural networks with discontinuous non-monotonic piecewise linear activation functions and time-varying delays.

    Science.gov (United States)

    Nie, Xiaobing; Zheng, Wei Xing

    2015-05-01

    This paper is concerned with the problem of coexistence and dynamical behaviors of multiple equilibrium points for neural networks with discontinuous non-monotonic piecewise linear activation functions and time-varying delays. The fixed point theorem and other analytical tools are used to develop certain sufficient conditions that ensure that the n-dimensional discontinuous neural networks with time-varying delays can have at least 5(n) equilibrium points, 3(n) of which are locally stable and the others are unstable. The importance of the derived results is that it reveals that the discontinuous neural networks can have greater storage capacity than the continuous ones. Moreover, different from the existing results on multistability of neural networks with discontinuous activation functions, the 3(n) locally stable equilibrium points obtained in this paper are located in not only saturated regions, but also unsaturated regions, due to the non-monotonic structure of discontinuous activation functions. A numerical simulation study is conducted to illustrate and support the derived theoretical results.

  8. Clustered Protocadherins Are Required for Building Functional Neural Circuits

    Directory of Open Access Journals (Sweden)

    Takeshi Yagi

    2017-04-01

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

  9. Application of neural networks to channel assignment for cellular CDMA networks with multiple services and mobile base stations

    Science.gov (United States)

    Hortos, William S.

    1996-03-01

    of channels to each cell such that these constraints are satisfied. This study applies and extends the Hopfield-Tank neural network models to the channel assignment problem for both uniform and non-homogeneous cellular CDMA network topologies. These models are shown to be applicable to future networks that provide multiple types of service, dynamic demand, and mobile base stations. The derived algorithms minimize energy functions representing interference constraints and traffic demand based on local data at the cell sites. The primary objectives of the approach are to increase the forward and reverse link capacities and to distribute selected management tasks at the Mobile Telecommunications Switching Office to the cell sites. The structure of the resulting neural network algorithms have the advantage of inherent parallelism and the potential for extension to a wide range of interference criteria. Two cases are considered. In the first case, traffic demands are uniform over the radio cells, while the radio cells are assumed to be a fixed hexagonal pattern. The second case corresponds to an urban cellular radio environment, where the location of the radio cells are not homogeneous and the spatial distribution of traffic demand are non-uniform.

  10. High-Dimensional Function Approximation With Neural Networks for Large Volumes of Data.

    Science.gov (United States)

    Andras, Peter

    2017-01-25

    Approximation of high-dimensional functions is a challenge for neural networks due to the curse of dimensionality. Often the data for which the approximated function is defined resides on a low-dimensional manifold and in principle the approximation of the function over this manifold should improve the approximation performance. It has been show that projecting the data manifold into a lower dimensional space, followed by the neural network approximation of the function over this space, provides a more precise approximation of the function than the approximation of the function with neural networks in the original data space. However, if the data volume is very large, the projection into the low-dimensional space has to be based on a limited sample of the data. Here, we investigate the nature of the approximation error of neural networks trained over the projection space. We show that such neural networks should have better approximation performance than neural networks trained on high-dimensional data even if the projection is based on a relatively sparse sample of the data manifold. We also find that it is preferable to use a uniformly distributed sparse sample of the data for the purpose of the generation of the low-dimensional projection. We illustrate these results considering the practical neural network approximation of a set of functions defined on high-dimensional data including real world data as well.

  11. Radial basis function neural network for power system load-flow

    Energy Technology Data Exchange (ETDEWEB)

    Karami, A.; Mohammadi, M.S. [Faculty of Engineering, The University of Guilan, P.O. Box 41635-3756, Rasht (Iran)

    2008-01-15

    This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)

  12. Validation and Error in Multiplicative Utility Functions

    Science.gov (United States)

    1978-12-01

    decision analysis with multiple objectives: the Mexico City Airport. Bell Jo’irnal of Economics and Management Science, 1973,4, 101-117...94135 Department of Government and Politica Univeraity of Maryland Attantion: Dr. Davia B. Bobrow College Park, MD 20747 Department of Paychology

  13. Radial Basis Function Neural Networks Based QSPR for the Prediction of log P

    Institute of Scientific and Technical Information of China (English)

    姚小军; 范波涛; 等

    2002-01-01

    Quantitative structure-property relationship(QSPR) method is used to study the correlation models between the structures of a set of diverse organic compounds and their log P.Molecular descriptors calculated from strucure alone are used to describe the molecular structures.A subset of the calcualted descriptors,selected using forward stepwise regression,is used in the QSPR models development.Multiple linear regression (MLR) and radial basis function neural networks (RBFNNs) are utilied to construct the linear and non-linear correlation model,respectively,The optimal QSPR model developed is based on a 7-17-1 RBFNNs architecture using sever calculated molecular descriptors .The root mean square errors in predictions for the training,predicting and overall data sets are 0.284,0.327 and 0.291 log P units respectively.

  14. Handwritten Multiscript Pin Code Recognition System having Multiple hidden layers using Back Propagation Neural Network

    Directory of Open Access Journals (Sweden)

    Stuti Asthana Rakesh K Bhujade Niresh Sharma Rajdeep Singh

    2011-10-01

    Full Text Available India is a country where multiple languages are spoken depending upon the place where people live as well as their mother tongue. For example ,a person living in tamilnadu is more familiar in writing/speaking Tamil as compared to Hindi or any other language. Usually due to this problem , sometimes people write pincode in the combination of two different numeric scripting, mainly the local language (which depend on the location or the mother tongue and the official language (which may be national language hindi or international language english.In this paper, we are concentrating on this problem of recognizing multiscript number recognition using artificial neural network on postcard, keeping accuracy as a chief criteria. This work has been tested on five different popular Indian scripts namely Hindi, Urdu, Tamil ,English and Telugu. Experiments were performed on samples by using two hidden layers having 250 neurons each and the results revealed that with the use of proper combination of number of neurons and number of layers in the neural network, accuracy upto 96% can be achieved under ideal condition.

  15. Functional alterations in neural substrates of geometric reasoning in adults with high-functioning autism.

    Directory of Open Access Journals (Sweden)

    Takashi Yamada

    Full Text Available Individuals with autism spectrum condition (ASC are known to excel in some perceptual cognitive tasks, but such developed functions have been often regarded as "islets of abilities" that do not significantly contribute to broader intellectual capacities. However, recent behavioral studies have reported that individuals with ASC have advantages for performing Raven's (Standard Progressive Matrices (RPM/RSPM, a standard neuropsychological test for general fluid intelligence, raising the possibility that ASC's cognitive strength can be utilized for more general purposes like novel problem solving. Here, the brain activity of 25 adults with high-functioning ASC and 26 matched normal controls (NC was measured using functional magnetic resonance imaging (fMRI to examine neural substrates of geometric reasoning during the engagement of a modified version of the RSPM test. Among the frontal and parietal brain regions involved in fluid intelligence, ASC showed larger activation in the left lateral occipitotemporal cortex (LOTC during an analytic condition with moderate difficulty than NC. Activation in the left LOTC and ventrolateral prefrontal cortex (VLPFC increased with task difficulty in NC, whereas such modulation of activity was absent in ASC. Furthermore, functional connectivity analysis revealed a significant reduction of activation coupling between the left inferior parietal cortex and the right anterior prefrontal cortex during both figural and analytic conditions in ASC. These results indicate altered pattern of functional specialization and integration in the neural system for geometric reasoning in ASC, which may explain its atypical cognitive pattern, including performance on the Raven's Matrices test.

  16. 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.

  17. Neural drive increases following resistance training in patients with multiple sclerosis

    DEFF Research Database (Denmark)

    Dalgas, Ulrik; Stenager, Egon; Lund, Charlote Caroline

    2013-01-01

    The present study tested the hypothesis that lower body progressive resistance training (PRT) increases the neural drive expressed as surface electromyographical (EMG) activity in patients with multiple sclerosis (MS). The study was a randomised controlled trial (RCT) including a 12-week follow up...... intervention period, the exercise group were encouraged to continue training on their own for a 12-week follow up period, while the control group completed the 12-week supervised PRT program. Surface EMG was recorded from vastus lateralis, rectus femoris and semitendinosus during maximal isometric knee...... compared to the post values, all effects, except for AI during knee flexion, were maintained at follow up in the exercise group. When the control group was exposed to PRT, a similar pattern of improvements were found, albeit not all improvements were significant. In conclusion twelve weeks of intense PRT...

  18. Multiple Stability of a Sparsely Encoded Attractor Neural Network Model for the Inferior Temporal Cortex

    Science.gov (United States)

    Kimoto, Tomoyuki; Uezu, Tatsuya; Okada, Masato

    2008-12-01

    We study a neural network model for the inferior temporal cortex, in terms of finite memory loading and sparse coding. We show that an uncorrelated Hopfield-type attractor and some correlated attractors have multiple stability, and examine the retrieval dynamics for these attractors when the initial state is set to a noise-degraded memory pattern. Then, we show that there is a critical initial overlap: that is, the system converges to the correlated attractor when the noise level is large, and otherwise to the Hopfield-type attractor. Furthermore, we study the time course of the correlation between the correlated attractors in the retrieval dynamics. On the basis of these theoretical results, we resolve the controversy regarding previous physiologic experimental findings regarding neuron properties in the inferior temporal cortex and propose a new experimental paradigm.

  19. Application of functional-link neural network in evaluation of sublayer suspension based on FWD test

    Institute of Scientific and Technical Information of China (English)

    陈瑜; 张起森

    2004-01-01

    Several methods for evaluating the sublayer suspension beneath old pavement with falling weight deflectormeter(FWD), were summarized and the respective advantages and disadvantages were analyzed. Based on these methods, the evaluation principles were improved and a new type of the neural network, functional-link neural network was proposed to evaluate the sublayer suspension with FWD test results. The concept of function link, learning method of functional-link neural network and the establishment process of neural network model were studied in detail. Based on the old pavement over-repairing engineering of Kaiping section, Guangdong Province in G325 National Highway, the application of functional-link neural network in evaluation of sublayer suspension beneath old pavement based on FWD test data on the spot was investigated. When learning rate is 0.1 and training cycles are 405, the functional-link network error is less than 0.0001, while the optimum chosen 4-8-1 BP needs over 10000 training cycles to reach the same accuracy with less precise evaluation results. Therefore, in contrast to common BP neural network,the functional-link neural network adopts single layer structure to learn and calculate, which simplifies the network, accelerates the convergence speed and improves the accuracy. Moreover the trained functional-link neural network can be adopted to directly evaluate the sublayer suspension based on FWD test data on the site. Engineering practice indicates that the functional-link neural model gains very excellent results and effectively guides the pavement over-repairing construction.

  20. Construction of plateaued functions satisfying multiple criteria

    Institute of Scientific and Technical Information of China (English)

    Zhang Weiguo

    2005-01-01

    A class of plateaued functions has been got by using the Maiorana-McFarland construction. A variety of desirable criteria for functions with cryptographic application could be satisfied: balancedness, high nonlinearity, correlation immunity of reasonably high order, strict avalanche criterion, non-existence of non-zero linear structures, good global avalanche characteristics, etc.

  1. Mechanical and neural function of triceps surae in elite racewalking.

    Science.gov (United States)

    Cronin, Neil J; Hanley, Brian; Bissas, Athanassios

    2016-07-01

    Racewalking is a unique event combining mechanical elements of walking with speeds associated with running. It is currently unclear how racewalking technique impacts lower limb muscle-tendon function despite the relevance of this to muscle economy and overall performance. The present study examined triceps surae neuromechanics in 11 internationally competitive racewalkers (age 25 ± 11 yr) walking and running on a treadmill at speeds between 4.5 and 13.8 km/h while triceps surae fascicle lengths, electromyography, and kinematic data were recorded. Cumulative muscle activity required to traverse a unit distance (CMAPD) was calculated for each muscle. Medial gastrocnemius (MG) and soleus fascicle lengths/velocities were determined using an automated tracking algorithm, and muscle-tendon unit lengths were determined. Running was associated with net shortening of muscle fascicles during stance, combined with substantial lengthening of the muscle-tendon unit, implying energy storage in the Achilles tendon. When the same participants racewalked at the same speed, the fascicles shortened (soleus) or lengthened (MG), coinciding with rapid shortening followed by a relatively small increase in muscle-tendon length during stance. Consequently, compared with running at the same speed, racewalking decreased the energy-saving role of the Achilles tendon. Moreover, CMAPD was generally highest in racewalking, implying that in individual muscles, the energy cost of racewalking was higher than running. Together these results suggest that racewalking is neurally and mechanically costly relative to running at a given speed. As racewalking events are typically between 10 and 50 km, neuromechanical inefficiencies that occur with each stride likely result in substantial energetic penalties.

  2. Stability Switches and Hopf Bifurcation in a Coupled FitzHugh-Nagumo Neural System with Multiple Delays

    Directory of Open Access Journals (Sweden)

    Shengwei Yao

    2014-01-01

    Full Text Available A FitzHugh-Nagumo (FHN neural system with multiple delays has been proposed. The number of equilibrium point is analyzed. It implies that the neural system exhibits a unique equilibrium and three ones for the different values of coupling weight by employing the saddle-node bifurcation of nontrivial equilibrium point and transcritical bifurcation of trivial one. Further, the stability of equilibrium point is studied by analyzing the corresponding characteristic equation. Some stability criteria involving the multiple delays and coupling weight are obtained. The results show that the neural system exhibits the delay-independence and delay-dependence stability. Increasing delay induces the stability switching between resting state and periodic activity in some parameter regions of coupling weight. Finally, numerical simulations are taken to support the theoretical results.

  3. Satisfiability of logic programming based on radial basis function neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)

    2014-07-10

    In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.

  4. Satisfiability of logic programming based on radial basis function neural networks

    Science.gov (United States)

    Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong

    2014-07-01

    In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.

  5. Angiogenic microspheres promote neural regeneration and motor function recovery after spinal cord injury in rats

    National Research Council Canada - National Science Library

    Yu, Shukui; Yao, Shenglian; Wen, Yujun; Wang, Ying; Wang, Hao; Xu, Qunyuan

    2016-01-01

    ... (bFGF) encapsulated in angiogenic microspheres. These spheres were delivered to sites of spinal cord contusion injury in rats, and their ability to induce vessel formation, neural regeneration and improve hindlimb motor function was assessed...

  6. Inversion of snow parameters from passive microwave remote sensing measurements by a neural network trained with a multiple scattering model

    Science.gov (United States)

    Tsang, Leung; Chen, Zhengxiao; Oh, Seho; Marks, Robert J., II; Chang, A. T. C.

    1992-01-01

    Simultaneous inversion of the three parameters was performed which included mean-grain size of ice particles in snow, snow density, and snow temperatures from five brightness temperatures. Good results for the inversion of parameters were obtained using the neural network based on the simulated data computed from the dense media radiative transfer equation that takes into account the effects of multiple scattering.

  7. The Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks

    CERN Document Server

    Yao, Kun

    2015-01-01

    We demonstrate a convolutional neural network trained to reproduce the Kohn-Sham kinetic energy of hydrocarbons from electron density. The output of the network is used as a non-local correction to the conventional local and semi-local kinetic functionals. We show that this approximation qualitatively reproduces Kohn-Sham potential energy surfaces when used with conventional exchange correlation functionals. Numerical noise inherited from the non-linearity of the neural network is identified as the major challenge for the model. Finally we examine the features in the density learned by the neural network to anticipate the prospects of generalizing these models.

  8. A fast learning algorithm of neural network with tunable activation function

    Institute of Scientific and Technical Information of China (English)

    SHEN Yanjun; WANG Bingwen

    2004-01-01

    This paper presents a modified structure of a neural network with tunable activation function and provides a new learning algorithm for the neural network training. Simulation results of XOR problem, Feigenbaum function, and Henon map show that the new algorithm has better performance than BP (back propagation) algorithm in terms of shorter convergence time and higher convergence accuracy. Further modifications of the structure of the neural network with the faster learning algorithm demonstrate simpler structure with even faster convergence speed and better convergence accuracy.

  9. The Ridge Function Representation of Polynomials and an Application to Neural Networks

    Institute of Scientific and Technical Information of China (English)

    Ting Fan XIE; Fei Long CAO

    2011-01-01

    The first goal of this paper is to establish some properties of the ridge function representation for multivariate polynomials,and the second one is to apply these results to the problem of approximation by neural networks.We find that for continuous functions,the rate of approximation obtained by a neural network with one hidden layer is no slower than that of an algebraic polynomial.

  10. Shaping Early Reorganization of Neural Networks Promotes Motor Function after Stroke.

    OpenAIRE

    2016-01-01

    Neural plasticity is a major factor driving cortical reorganization after stroke. We here tested whether repetitively enhancing motor cortex plasticity by means of intermittent theta-burst stimulation (iTBS) prior to physiotherapy might promote recovery of function early after stroke. Functional magnetic resonance imaging (fMRI) was used to elucidate underlying neural mechanisms. Twenty-six hospitalized, first-ever stroke patients (time since stroke: 1–16 days) with hand motor deficits were e...

  11. Multiple Soft Limits of Cosmological Correlation Functions

    CERN Document Server

    Joyce, Austin; Simonović, Marko

    2015-01-01

    We derive novel identities satisfied by inflationary correlation functions in the limit where two external momenta are taken to be small. We derive these statements in two ways: using background-wave arguments and as Ward identities following from the fixed-time path integral. Interestingly, these identities allow us to constrain some of the O(q^2) components of the soft limit, in contrast to their single-soft analogues. We provide several nontrivial checks of our identities both in the context of resonant non-Gaussianities and in small sound speed models. Additionally, we extend the relation at lowest order in external momenta to arbitrarily many soft legs, and comment on the many-soft extension at higher orders in the soft momentum. Finally, we consider how higher soft limits lead to identities satisfied by correlation functions in large-scale structure.

  12. Multiple soft limits of cosmological correlation functions

    Energy Technology Data Exchange (ETDEWEB)

    Joyce, Austin [Enrico Fermi Institute and Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637 (United States); Khoury, Justin [Center for Particle Cosmology, Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104 (United States); Simonović, Marko, E-mail: ajoy@uchicago.edu, E-mail: jkhoury@sas.upenn.edu, E-mail: msimonov@sissa.it [SISSA, via Bonomea 265, 34136, Trieste (Italy)

    2015-01-01

    We derive novel identities satisfied by inflationary correlation functions in the limit where two external momenta are taken to be small. We derive these statements in two ways: using background-wave arguments and as Ward identities following from the fixed-time path integral. Interestingly, these identities allow us to constrain some of the O(q{sup 2}) components of the soft limit, in contrast to their single-soft analogues. We provide several nontrivial checks of our identities both in the context of resonant non-Gaussianities and in small sound speed models. Additionally, we extend the relation at lowest order in external momenta to arbitrarily many soft legs, and comment on the many-soft extension at higher orders in the soft momentum. Finally, we consider how higher soft limits lead to identities satisfied by correlation functions in large-scale structure.

  13. The Rule of Four, Executive Function and Neural Exercises

    Directory of Open Access Journals (Sweden)

    Russell Jay Hendel

    2015-08-01

    Full Text Available Deborah Hughes-Hallet has made many significant contributions to Calculus pedagogy. Among the tools she has introduced is the rule of four, which requires successful pedagogy to simultaneously address four approaches to each course concept, verbal, graphical, algebraic and numeric. We explore examples of this rule of X approach in other disciplines: i Literary analysis is enhanced through the rule of two, a simultaneous approach of grammar and literary analysis; ii Actuarial mathematics requires a rule of six, a simultaneous approach of verbal, graphical, algebraic, calculator, modules, and English conventions; (iii Masters of Tic-Tac-Toe and Chess use a rule of two, simultaneously approaching the game positionally and combinatorically. We offer a unified and deep analysis of the rule of X approach by relating it to executive function, the area of the brain responsible for organizing and synthesizing multiple brain areas. We conclude the paper with an illustration of classroom activities that strengthen executive function and improve pedagogy. Our results are content independent, depending exclusively on paths of information flow, and consequently, our analysis is cybernetic in flavor [1]. [1] American Society of Cybernetics, www.asc-cybernetics.org/

  14. Identification of genes required for neural-specific glycosylation using functional genomics.

    Directory of Open Access Journals (Sweden)

    Miki Yamamoto-Hino

    Full Text Available Glycosylation plays crucial regulatory roles in various biological processes such as development, immunity, and neural functions. For example, α1,3-fucosylation, the addition of a fucose moiety abundant in Drosophila neural cells, is essential for neural development, function, and behavior. However, it remains largely unknown how neural-specific α1,3-fucosylation is regulated. In the present study, we searched for genes involved in the glycosylation of a neural-specific protein using a Drosophila RNAi library. We obtained 109 genes affecting glycosylation that clustered into nine functional groups. Among them, members of the RNA regulation group were enriched by a secondary screen that identified genes specifically regulating α1,3-fucosylation. Further analyses revealed that an RNA-binding protein, second mitotic wave missing (Swm, upregulates expression of the neural-specific glycosyltransferase FucTA and facilitates its mRNA export from the nucleus. This first large-scale genetic screen for glycosylation-related genes has revealed novel regulation of fucTA mRNA in neural cells.

  15. Adaptive Channel Equalization Using Multiplicative Neural Network for Rayleigh Faded Channel

    Directory of Open Access Journals (Sweden)

    P. Sivakumar

    2011-01-01

    Full Text Available Problem statement: Digital transmission over band-limited communication channel largely suffers from ISIS and various noise sources. The presence of ISI and noise causes bit errors in the received signal. Equalization is necessary at the receiver to overcome these channel impairment to recover the original transmitted sequence. Traditionally equalization is considered as equivalent to inverse filtering and implemented using linear-perform under severe distortion conditions when Signal to Noise Ratio (SNR is poor. Equalization can be considered as a non-linear classification problem and optimum solution is given by Bayesian solution. Non-linear techniques like Artificial Neutral Networks (ANN are very good choice for non-linear classification problems. Several non-lines are equalizers have been implemented using ANN which outperformed LTE and solved the problem of equalization to the varying degree of sources. Approach: Forward neural network architecture with optimum number of nodes was used to achieve adaptive channel equalization. Summation at each node was replaced by multiplications which result in powerful mapping. The equalizer was tested on Rayleigh fading channel with a BPSK signal. Results: Results showed that proposed equalizer provides simplified architecture and improvement in the bit error rate at various levels of signal to noise ratio for Rayleigh faded channel. Conclusion: A high order feed forward network equalizer with multiplicative neuron is proposed in this study. Use of Multiplication allows direct computing of polynomial inputs and approximation with fewer nodes. Performance comparison in terms of network architecture and BER performance suggest the better classification capability of the proposed MNN equalizer over RRBF.

  16. Upset Prediction in Friction Welding Using Radial Basis Function Neural Network

    Directory of Open Access Journals (Sweden)

    Wei Liu

    2013-01-01

    Full Text Available This paper addresses the upset prediction problem of friction welded joints. Based on finite element simulations of inertia friction welding (IFW, a radial basis function (RBF neural network was developed initially to predict the final upset for a number of welding parameters. The predicted joint upset by the RBF neural network was compared to validated finite element simulations, producing an error of less than 8.16% which is reasonable. Furthermore, the effects of initial rotational speed and axial pressure on the upset were investigated in relation to energy conversion with the RBF neural network. The developed RBF neural network was also applied to linear friction welding (LFW and continuous drive friction welding (CDFW. The correlation coefficients of RBF prediction for LFW and CDFW were 0.963 and 0.998, respectively, which further suggest that an RBF neural network is an effective method for upset prediction of friction welded joints.

  17. A multiple-levels-of-analysis perspective on resilience: implications for the developing brain, neural plasticity, and preventive interventions.

    Science.gov (United States)

    Cicchetti, Dante; Blender, Jennifer A

    2006-12-01

    Resilient functioning, the attainment of unexpected competence despite significant adversity, is among the most intriguing and adaptive phenomena of human development. Although growing attention has been paid to discovering the processes through which individuals at high risk do not develop maladaptively, the empirical study of resilience has focused predominantly on detecting the psychosocial determinants of the phenomenon. For the field of resilience to grow in ways that are commensurate with the complexity inherent to the construct, efforts to understand underlying processes will be facilitated by the increased implementation of interdisciplinary research designed within a developmental psychopathology framework. Research of this nature would entail a consideration of psychological, biological, and environmental-contextual processes from which pathways to resilience might eventuate (known as equifinality), as well as those that result in diverse outcomes among individuals who have achieved resilient functioning (know as multifinality). The possible relation between the mechanisms of neural plasticity and resilience and specific suggestions concerning research questions needed to examine this association are discussed. Examples from developmental neuroscience and molecular genetics are provided to illustrate the potential of incorporating biology into the study of resilience. The importance of adopting a multiple-levels-of-analysis perspective for designing and evaluating interventions aimed at fostering resilient outcomes in persons facing significant adversity is emphasized.

  18. Cerebrospinal fluid derived from progressive multiple sclerosis patients promotes neuronal and oligodendroglial differentiation of human neural precursor cells in vitro.

    Science.gov (United States)

    Cristofanilli, M; Cymring, B; Lu, A; Rosenthal, H; Sadiq, S A

    2013-10-10

    In the adult CNS, tissue-specific germinal niches, such as the subventricular zone of the lateral ventricles and the subgranular zone of the dentate gyrus of the hippocampus, contain multipotent neural precursor cells (NPCs) with the capacity to self-renew and differentiate into functional brain cells (i.e. neurons, astrocytes or oligodendrocytes). Due to their intrinsic plasticity, NPCs can be considered an essential part of the cellular mechanism(s) by which the CNS tries to repair itself after an injury. In inflammatory CNS disorders, such as multiple sclerosis (MS), neurogenesis and gliogenesis occur as part of an 'intrinsic' self-repair process. However, full and long-lasting repair in progressive MS is not achieved. Recent data suggest that endogenous NPCs, while trying to repair the damaged CNS in MS, may become the target of the disease itself. It is possible that factors produced during MS, like CNS-infiltrating blood-borne inflammatory mononuclear cells, reactive CNS-resident cells, and humoral mediators, can alter the physiological properties of NPCs, ultimately impairing their ability to promote neural regeneration. Here, we investigate the effect of cerebrospinal fluid (CSF) derived from primary progressive (PPMS) and secondary progressive (SPMS) MS patients (CSF-MS) on the survival, proliferation, and differentiation of commercially available human embryonic-derived NPCs named ENStem-A. We found that PPMS derived CSF markedly reduced the proliferation of ENStem-A and increased their differentiation toward neuronal and oligodendroglial cells, compared to control CSF. Similar but less striking results were seen when ENstem-A were treated with SPMS derived CSF. Our findings suggest that in both SPMS and PPMS the CNS milieu, as determined by extrapolation from CSF findings, may stimulate the endogenous pool of NPCs to differentiate into neurons and oligodendrocytes.

  19. Multiple Functions of Sterols in Yeast Endocytosis

    Science.gov (United States)

    Heese-Peck, Antje; Pichler, Harald; Zanolari, Bettina; Watanabe, Reika; Daum, Günther; Riezman, Howard

    2002-01-01

    Sterols are essential factors for endocytosis in animals and yeast. To investigate the sterol structural requirements for yeast endocytosis, we created a variety of ergΔ mutants, each accumulating a distinct set of sterols different from ergosterol. Mutant erg2Δerg6Δ and erg3Δerg6Δ cells exhibit a strong internalization defect of the α-factor receptor (Ste2p). Specific sterol structures are necessary for pheromone-dependent receptor hyperphosphorylation, a prerequisite for internalization. The lack of phosphorylation is not due to a defect in Ste2p localization or in ligand–receptor interaction. Contrary to most known endocytic factors, sterols seem to function in internalization independently of actin. Furthermore, sterol structures are required at a postinternalization step of endocytosis. ergΔ cells were able to take up the membrane marker FM4-64, but exhibited defects in FM4-64 movement through endosomal compartments to the vacuole. Therefore, there are at least two roles for sterols in endocytosis. Based on sterol analysis, the sterol structural requirements for these two processes were different, suggesting that sterols may have distinct functions at different places in the endocytic pathway. Interestingly, sterol structures unable to support endocytosis allowed transport of the glycosylphosphatidylinositol-anchored protein Gas1p from the endoplasmic reticulum to Golgi compartment. PMID:12181337

  20. Microelectronic neural bridge for signal regeneration and function rebuilding over two separate nerves

    Energy Technology Data Exchange (ETDEWEB)

    Shen Xiaoyan; Wang Zhigong; Xie Shushan; Huang Zonghao [Institute of RF- and OE-ICs, Southeast University, Nanjing 210096 (China); Lue Xiaoying, E-mail: zgwang@seu.edu.cn [Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096 (China)

    2011-06-15

    According to the feature of neural signals, a micro-electronic neural bridge (MENB) has been designed. It consists of two electrode arrays for neural signal detection and functional electrical stimulation (FES), and a microelectronic circuit for signal amplifying, processing, and FES driving. The core of the system is realized in 0.5-{mu}m CMOS technology and used in animal experiments. A special experimental strategy has been designed to demonstrate the feasibility of the system. With the help of the MENB, the withdrawal reflex function of the left/right leg of one spinal toad has been rebuilt in the corresponding leg of another spinal toad. According to the coherence analysis between the source and regenerated neural signals, the controlled spinal toad's sciatic nerve signal is delayed by 0.72 ms in relation to the sciatic nerve signal of the source spinal toad and the cross-correlation function reaches a value of 0.73. This shows that the regenerated signal is correlated with the source sciatic signal significantly and the neural activities involved in reflex function have been regenerated. The experiment demonstrates that the MENB is useful in rebuilding the neural function between nerves of different bodies. (semiconductor integrated circuits)

  1. Microelectronic neural bridge for signal regeneration and function rebuilding over two separate nerves*

    Institute of Scientific and Technical Information of China (English)

    Shen Xiaoyan; Wang Zhigong; Lü Xiaoying; Xie Shushan; Huang Zonghao

    2011-01-01

    According to the feature of neural signals, a micro-electronic neural bridge (MENB) has been designed.It consists of two electrode arrays for neural signal detection and functional electrical stimulation (FES), and a microelectronic circuit for signal amplifying, processing, and FES driving. The core of the system is realized in 0.5-μm CMOS technology and used in animal experiments. A special experimental strategy has been designed to demonstrate the feasibility of the system. With the help of the MENB, the withdrawal reflex function of the left/right leg of one spinal toad has been rebuilt in the corresponding leg of another spinal toad. According to the coherence analysis between the source and regenerated neural signals, the controlled spinal toad's sciatic nerve signal is delayed by 0.72 ms in relation to the sciatic nerve signal of the source spinal toad and the cross-correlation function reaches a value of 0.73. This shows that the regenerated signal is correlated with the source sciatic signal significantly and the neural activities involved in reflex function have been regenerated. The experiment demonstrates that the MENB is useful in rebuilding the neural function between nerves of different bodies.

  2. Functional recordings from awake, behaving rodents through a microchannel based regenerative neural interface

    Science.gov (United States)

    Gore, Russell K.; Choi, Yoonsu; Bellamkonda, Ravi; English, Arthur

    2015-02-01

    Objective. Neural interface technologies could provide controlling connections between the nervous system and external technologies, such as limb prosthetics. The recording of efferent, motor potentials is a critical requirement for a peripheral neural interface, as these signals represent the user-generated neural output intended to drive external devices. Our objective was to evaluate structural and functional neural regeneration through a microchannel neural interface and to characterize potentials recorded from electrodes placed within the microchannels in awake and behaving animals. Approach. Female rats were implanted with muscle EMG electrodes and, following unilateral sciatic nerve transection, the cut nerve was repaired either across a microchannel neural interface or with end-to-end surgical repair. During a 13 week recovery period, direct muscle responses to nerve stimulation proximal to the transection were monitored weekly. In two rats repaired with the neural interface, four wire electrodes were embedded in the microchannels and recordings were obtained within microchannels during proximal stimulation experiments and treadmill locomotion. Main results. In these proof-of-principle experiments, we found that axons from cut nerves were capable of functional reinnervation of distal muscle targets, whether regenerating through a microchannel device or after direct end-to-end repair. Discrete stimulation-evoked and volitional potentials were recorded within interface microchannels in a small group of awake and behaving animals and their firing patterns correlated directly with intramuscular recordings during locomotion. Of 38 potentials extracted, 19 were identified as motor axons reinnervating tibialis anterior or soleus muscles using spike triggered averaging. Significance. These results are evidence for motor axon regeneration through microchannels and are the first report of in vivo recordings from regenerated motor axons within microchannels in a small

  3. Multiple sclerosis impairs regional functional connectivity in the cerebellum

    DEFF Research Database (Denmark)

    Dogonowski, Anne-Marie; Andersen, Kasper Winther; Madsen, Kristoffer Hougaard

    2013-01-01

    Resting-state functional magnetic resonance imaging (rs-fMRI) has been used to study changes in long-range functional brain connectivity in multiple sclerosis (MS). Yet little is known about how MS affects functional brain connectivity at the local level. Here we studied 42 patients with MS and 30...

  4. Logarithmic r-θ mapping for hybrid optical neural network filter for multiple objects recognition within cluttered scenes

    Science.gov (United States)

    Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.; Birch, Phil M.

    2009-04-01

    θThe window unit in the design of the complex logarithmic r-θ mapping for hybrid optical neural network filter can allow multiple objects of the same class to be detected within the input image. Additionally, the architecture of the neural network unit of the complex logarithmic r-θ mapping for hybrid optical neural network filter becomes attractive for accommodating the recognition of multiple objects of different classes within the input image by modifying the output layer of the unit. We test the overall filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. Logarithmic r-θ mapping for hybrid optical neural network filter is shown to exhibit with a single pass over the input data simultaneously in-plane rotation, out-of-plane rotation, scale, log r-θ map translation and shift invariance, and good clutter tolerance by recognizing correctly the different objects within the cluttered scenes. We record in our results additional extracted information from the cluttered scenes about the objects' relative position, scale and in-plane rotation.

  5. Effect of Multiple Testing Adjustment in Differential Item Functioning Detection

    Science.gov (United States)

    Kim, Jihye; Oshima, T. C.

    2013-01-01

    In a typical differential item functioning (DIF) analysis, a significance test is conducted for each item. As a test consists of multiple items, such multiple testing may increase the possibility of making a Type I error at least once. The goal of this study was to investigate how to control a Type I error rate and power using adjustment…

  6. The Chebyshev-polynomials-based unified model neural networks for function approximation.

    Science.gov (United States)

    Lee, T T; Jeng, J T

    1998-01-01

    In this paper, we propose the approximate transformable technique, which includes the direct transformation and indirect transformation, to obtain a Chebyshev-Polynomials-Based (CPB) unified model neural networks for feedforward/recurrent neural networks via Chebyshev polynomials approximation. Based on this approximate transformable technique, we have derived the relationship between the single-layer neural networks and multilayer perceptron neural networks. It is shown that the CPB unified model neural networks can be represented as a functional link networks that are based on Chebyshev polynomials, and those networks use the recursive least square method with forgetting factor as learning algorithm. It turns out that the CPB unified model neural networks not only has the same capability of universal approximator, but also has faster learning speed than conventional feedforward/recurrent neural networks. Furthermore, we have also derived the condition such that the unified model generating by Chebyshev polynomials is optimal in the sense of error least square approximation in the single variable ease. Computer simulations show that the proposed method does have the capability of universal approximator in some functional approximation with considerable reduction in learning time.

  7. Application of neural networks with orthogonal activation functions in control of dynamical systems

    Science.gov (United States)

    Nikolić, Saša S.; Antić, Dragan S.; Milojković, Marko T.; Milovanović, Miroslav B.; Perić, Staniša Lj.; Mitić, Darko B.

    2016-04-01

    In this article, we present a new method for the synthesis of almost and quasi-orthogonal polynomials of arbitrary order. Filters designed on the bases of these functions are generators of generalised quasi-orthogonal signals for which we derived and presented necessary mathematical background. Based on theoretical results, we designed and practically implemented generalised first-order (k = 1) quasi-orthogonal filter and proved its quasi-orthogonality via performed experiments. Designed filters can be applied in many scientific areas. In this article, generated functions were successfully implemented in Nonlinear Auto Regressive eXogenous (NARX) neural network as activation functions. One practical application of the designed orthogonal neural network is demonstrated through the example of control of the complex technical non-linear system - laboratory magnetic levitation system. Obtained results were compared with neural networks with standard activation functions and orthogonal functions of trigonometric shape. The proposed network demonstrated superiority over existing solutions in the sense of system performances.

  8. Multiple bases of human intelligence revealed by cortical thickness and neural activation.

    Science.gov (United States)

    Choi, Yu Yong; Shamosh, Noah A; Cho, Sun Hee; DeYoung, Colin G; Lee, Min Joo; Lee, Jong-Min; Kim, Sun I; Cho, Zang-Hee; Kim, Kyungjin; Gray, Jeremy R; Lee, Kun Ho

    2008-10-08

    We hypothesized that individual differences in intelligence (Spearman's g) are supported by multiple brain regions, and in particular that fluid (gF) and crystallized (gC) components of intelligence are related to brain function and structure with a distinct profile of association across brain regions. In 225 healthy young adults scanned with structural and functional magnetic resonance imaging sequences, regions of interest (ROIs) were defined on the basis of a correlation between g and either brain structure or brain function. In these ROIs, gC was more strongly related to structure (cortical thickness) than function, whereas gF was more strongly related to function (blood oxygenation level-dependent signal during reasoning) than structure. We further validated this finding by generating a neurometric prediction model of intelligence quotient (IQ) that explained 50% of variance in IQ in an independent sample. The data compel a nuanced view of the neurobiology of intelligence, providing the most persuasive evidence to date for theories emphasizing multiple distributed brain regions differing in function.

  9. Human Neural Precursor Cells Promote Neurologic Recovery in a Viral Model of Multiple Sclerosis

    Directory of Open Access Journals (Sweden)

    Lu Chen

    2014-06-01

    Full Text Available Using a viral model of the demyelinating disease multiple sclerosis (MS, we show that intraspinal transplantation of human embryonic stem cell-derived neural precursor cells (hNPCs results in sustained clinical recovery, although hNPCs were not detectable beyond day 8 posttransplantation. Improved motor skills were associated with a reduction in neuroinflammation, decreased demyelination, and enhanced remyelination. Evidence indicates that the reduced neuroinflammation is correlated with an increased number of CD4+CD25+FOXP3+ regulatory T cells (Tregs within the spinal cords. Coculture of hNPCs with activated T cells resulted in reduced T cell proliferation and increased Treg numbers. The hNPCs acted, in part, through secretion of TGF-β1 and TGF-β2. These findings indicate that the transient presence of hNPCs transplanted in an animal model of MS has powerful immunomodulatory effects and mediates recovery. Further investigation of the restorative effects of hNPC transplantation may aid in the development of clinically relevant MS treatments.

  10. Ear Detection under Uncontrolled Conditions with Multiple Scale Faster Region-Based Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Yi Zhang

    2017-04-01

    Full Text Available Ear detection is an important step in ear recognition approaches. Most existing ear detection techniques are based on manually designing features or shallow learning algorithms. However, researchers found that the pose variation, occlusion, and imaging conditions provide a great challenge to the traditional ear detection methods under uncontrolled conditions. This paper proposes an efficient technique involving Multiple Scale Faster Region-based Convolutional Neural Networks (Faster R-CNN to detect ears from 2D profile images in natural images automatically. Firstly, three regions of different scales are detected to infer the information about the ear location context within the image. Then an ear region filtering approach is proposed to extract the correct ear region and eliminate the false positives automatically. In an experiment with a test set of 200 web images (with variable photographic conditions, 98% of ears were accurately detected. Experiments were likewise conducted on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2 and University of Beira Interior Ear dataset (UBEAR, which contain large occlusion, scale, and pose variations. Detection rates of 100% and 98.22%, respectively, demonstrate the effectiveness of the proposed approach.

  11. Neural-adaptive control of single-master-multiple-slaves teleoperation for coordinated multiple mobile manipulators with time-varying communication delays and input uncertainties.

    Science.gov (United States)

    Li, Zhijun; Su, Chun-Yi

    2013-09-01

    In this paper, adaptive neural network control is investigated for single-master-multiple-slaves teleoperation in consideration of time delays and input dead-zone uncertainties for multiple mobile manipulators carrying a common object in a cooperative manner. Firstly, concise dynamics of teleoperation systems consisting of a single master robot, multiple coordinated slave robots, and the object are developed in the task space. To handle asymmetric time-varying delays in communication channels and unknown asymmetric input dead zones, the nonlinear dynamics of the teleoperation system are transformed into two subsystems through feedback linearization: local master or slave dynamics including the unknown input dead zones and delayed dynamics for the purpose of synchronization. Then, a model reference neural network control strategy based on linear matrix inequalities (LMI) and adaptive techniques is proposed. The developed control approach ensures that the defined tracking errors converge to zero whereas the coordination internal force errors remain bounded and can be made arbitrarily small. Throughout this paper, stability analysis is performed via explicit Lyapunov techniques under specific LMI conditions. The proposed adaptive neural network control scheme is robust against motion disturbances, parametric uncertainties, time-varying delays, and input dead zones, which is validated by simulation studies.

  12. Multiple resting state network functional connectivity abnormalities in mild traumatic brain injury.

    Science.gov (United States)

    Stevens, Michael C; Lovejoy, David; Kim, Jinsuh; Oakes, Howard; Kureshi, Inam; Witt, Suzanne T

    2012-06-01

    Several reports show that traumatic brain injury (TBI) results in abnormalities in the coordinated activation among brain regions. Because most previous studies examined moderate/severe TBI, the extensiveness of functional connectivity abnormalities and their relationship to postconcussive complaints or white matter microstructural damage are unclear in mild TBI. This study characterized widespread injury effects on multiple integrated neural networks typically observed during a task-unconstrained "resting state" in mild TBI patients. Whole brain functional connectivity for twelve separate networks was identified using independent component analysis (ICA) of fMRI data collected from thirty mild TBI patients mostly free of macroscopic intracerebral injury and thirty demographically-matched healthy control participants. Voxelwise group comparisons found abnormal mild TBI functional connectivity in every brain network identified by ICA, including visual processing, motor, limbic, and numerous circuits believed to underlie executive cognition. Abnormalities not only included functional connectivity deficits, but also enhancements possibly reflecting compensatory neural processes. Postconcussive symptom severity was linked to abnormal regional connectivity within nearly every brain network identified, particularly anterior cingulate. A recently developed multivariate technique that identifies links between whole brain profiles of functional and anatomical connectivity identified several novel mild TBI abnormalities, and represents a potentially important new tool in the study of the complex neurobiological sequelae of TBI.

  13. Neuroprotective effect of transplanted human embryonic stem cell-derived neural precursors in an animal model of multiple sclerosis.

    Directory of Open Access Journals (Sweden)

    Michal Aharonowiz

    Full Text Available BACKGROUND: Multiple sclerosis (MS is an immune mediated demyelinating disease of the central nervous system (CNS. A potential new therapeutic approach for MS is cell transplantation which may promote remyelination and suppress the inflammatory process. METHODS: We transplanted human embryonic stem cells (hESC-derived early multipotent neural precursors (NPs into the brain ventricles of mice induced with experimental autoimmune encephalomyelitis (EAE, the animal model of MS. We studied the effect of the transplanted NPs on the functional and pathological manifestations of the disease. RESULTS: Transplanted hESC-derived NPs significantly reduced the clinical signs of EAE. Histological examination showed migration of the transplanted NPs to the host white matter, however, differentiation to mature oligodendrocytes and remyelination were negligible. Time course analysis of the evolution and progression of CNS inflammation and tissue injury showed an attenuation of the inflammatory process in transplanted animals, which was correlated with the reduction of both axonal damage and demyelination. Co-culture experiments showed that hESC-derived NPs inhibited the activation and proliferation of lymph node-derived T cells in response to nonspecific polyclonal stimuli. CONCLUSIONS: The therapeutic effect of transplantation was not related to graft or host remyelination but was mediated by an immunosuppressive neuroprotective mechanism. The attenuation of EAE by hESC-derived NPs, demonstrated here, may serve as the first step towards further developments of hESC for cell therapy in MS.

  14. The functional genetic link of NLGN4X knockdown and neurodevelopment in neural stem cells.

    Science.gov (United States)

    Shi, Lingling; Chang, Xiao; Zhang, Peilin; Coba, Marcelo P; Lu, Wange; Wang, Kai

    2013-09-15

    Genetic mutations in NLGN4X (neuroligin 4), including point mutations and copy number variants (CNVs), have been associated with susceptibility to autism spectrum disorders (ASDs). However, it is unclear how mutations in NLGN4X result in neurodevelopmental defects. Here, we used neural stem cells (NSCs) as in vitro models to explore the impacts of NLGN4X knockdown on neurodevelopment. Using two shRNAmir-based vectors targeting NLGN4X and one control shRNAmir vector, we modulated NLGN4X expression and differentiated these NSCs into mature neurons. We monitored the neurodevelopmental process at Weeks 0, 0.5, 1, 2, 4 and 6, based on morphological analysis and whole-genome gene expression profiling. At the cellular level, in NSCs with NLGN4X knockdown, we observed increasingly delayed neuronal development and compromised neurite formation, starting from Week 2 through Week 6 post differentiation. At the molecular level, we identified multiple pathways, such as neurogenesis, neuron differentiation and muscle development, which are increasingly disturbed in cells with NLGN4X knockdown. Notably, several postsynaptic genes, including DLG4, NLGN1 and NLGN3, also have decreased expression. Based on in vitro models, NLGN4X knockdown directly impacts neurodevelopmental process during the formation of neurons and their connections. Our functional genomics study highlights the utility of NSCs models in understanding the functional roles of CNVs in affecting neurodevelopment and conferring susceptibility to neurodevelopmental diseases.

  15. Synchronous neural interactions assessed by magnetoencephalography: a functional biomarker for brain disorders

    Science.gov (United States)

    Georgopoulos, Apostolos P.; Karageorgiou, Elissaios; Leuthold, Arthur C.; Lewis, Scott M.; Lynch, Joshua K.; Alonso, Aurelio A.; Aslam, Zaheer; Carpenter, Adam F.; Georgopoulos, Angeliki; Hemmy, Laura S.; Koutlas, Ioannis G.; Langheim, Frederick J. P.; Riley McCarten, J.; McPherson, Susan E.; Pardo, José V.; Pardo, Patricia J.; Parry, Gareth J.; Rottunda, Susan J.; Segal, Barbara M.; Sponheim, Scott R.; Stanwyck, John J.; Stephane, Massoud; Westermeyer, Joseph J.

    2007-12-01

    We report on a test to assess the dynamic brain function at high temporal resolution using magnetoencephalography (MEG). The essence of the test is the measurement of the dynamic synchronous neural interactions, an essential aspect of the brain function. MEG signals were recorded from 248 axial gradiometers while 142 human subjects fixated a spot of light for 45-60 s. After fitting an autoregressive integrative moving average (ARIMA) model and taking the stationary residuals, all pairwise, zero-lag, partial cross-correlations (PCCij0) and their z-transforms (zij0) between i and j sensors were calculated, providing estimates of the strength and sign (positive, negative) of direct synchronous coupling at 1 ms temporal resolution. We found that subsets of zij0 successfully classified individual subjects to their respective groups (multiple sclerosis, Alzheimer's disease, schizophrenia, Sjögren's syndrome, chronic alcoholism, facial pain, healthy controls) and gave excellent external cross-validation results. Contribution by the authors: Designed research (APG); acquired data (AAA, IGK, FJPL, ACL, SML, JJS); analyzed data (APG, EK, ACL, JKL); wrote the paper (APG, EK, ACL, SML); contributed subjects (AAA, ZA, AFC, AG, LSH, IGK, FJPL, SML, JRM, SEM, JVP, PJP, GJP, SJR, BMS, SRS, MS, JJS, JJW); discussed results (All); contributed equally (ZA, AFC, AG, LSH, FJPL, JRM, SEM, JVP, PJP, GJP, SJR, BMS, SRS, MS, JJS, JJW).

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

    Science.gov (United States)

    Mu, Li; Yigang, He

    2016-12-01

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

  17. A neuromorphic implementation of multiple spike-timing synaptic plasticity rules for large-scale neural networks

    Science.gov (United States)

    Wang, Runchun M.; Hamilton, Tara J.; Tapson, Jonathan C.; van Schaik, André

    2015-01-01

    We present a neuromorphic implementation of multiple synaptic plasticity learning rules, which include both Spike Timing Dependent Plasticity (STDP) and Spike Timing Dependent Delay Plasticity (STDDP). We present a fully digital implementation as well as a mixed-signal implementation, both of which use a novel dynamic-assignment time-multiplexing approach and support up to 226 (64M) synaptic plasticity elements. Rather than implementing dedicated synapses for particular types of synaptic plasticity, we implemented a more generic synaptic plasticity adaptor array that is separate from the neurons in the neural network. Each adaptor performs synaptic plasticity according to the arrival times of the pre- and post-synaptic spikes assigned to it, and sends out a weighted or delayed pre-synaptic spike to the post-synaptic neuron in the neural network. This strategy provides great flexibility for building complex large-scale neural networks, as a neural network can be configured for multiple synaptic plasticity rules without changing its structure. We validate the proposed neuromorphic implementations with measurement results and illustrate that the circuits are capable of performing both STDP and STDDP. We argue that it is practical to scale the work presented here up to 236 (64G) synaptic adaptors on a current high-end FPGA platform. PMID:26041985

  18. A neuromorphic implementation of multiple spike-timing synaptic plasticity rules for large-scale neural networks.

    Science.gov (United States)

    Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan C; van Schaik, André

    2015-01-01

    We present a neuromorphic implementation of multiple synaptic plasticity learning rules, which include both Spike Timing Dependent Plasticity (STDP) and Spike Timing Dependent Delay Plasticity (STDDP). We present a fully digital implementation as well as a mixed-signal implementation, both of which use a novel dynamic-assignment time-multiplexing approach and support up to 2(26) (64M) synaptic plasticity elements. Rather than implementing dedicated synapses for particular types of synaptic plasticity, we implemented a more generic synaptic plasticity adaptor array that is separate from the neurons in the neural network. Each adaptor performs synaptic plasticity according to the arrival times of the pre- and post-synaptic spikes assigned to it, and sends out a weighted or delayed pre-synaptic spike to the post-synaptic neuron in the neural network. This strategy provides great flexibility for building complex large-scale neural networks, as a neural network can be configured for multiple synaptic plasticity rules without changing its structure. We validate the proposed neuromorphic implementations with measurement results and illustrate that the circuits are capable of performing both STDP and STDDP. We argue that it is practical to scale the work presented here up to 2(36) (64G) synaptic adaptors on a current high-end FPGA platform.

  19. A neuromorphic implementation of multiple spike-timing synaptic plasticity rules for large-scale neural networks

    Directory of Open Access Journals (Sweden)

    Runchun Mark Wang

    2015-05-01

    Full Text Available We present a neuromorphic implementation of multiple synaptic plasticity learning rules, which include both Spike Timing Dependent Plasticity (STDP and Spike Timing Dependent Delay Plasticity (STDDP. We present a fully digital implementation as well as a mixed-signal implementation, both of which use a novel dynamic-assignment time-multiplexing approach and support up to 2^26 (64M synaptic plasticity elements. Rather than implementing dedicated synapses for particular types of synaptic plasticity, we implemented a more generic synaptic plasticity adaptor array that is separate from the neurons in the neural network. Each adaptor performs synaptic plasticity according to the arrival times of the pre- and post-synaptic spikes assigned to it, and sends out a weighted and/or delayed pre-synaptic spike to the target synapse in the neural network. This strategy provides great flexibility for building complex large-scale neural networks, as a neural network can be configured for multiple synaptic plasticity rules without changing its structure. We validate the proposed neuromorphic implementations with measurement results and illustrate that the circuits are capable of performing both STDP and STDDP. We argue that it is practical to scale the work presented here up to 2^36 (64G synaptic adaptors on a current high-end FPGA platform.

  20. Niche-dependent development of functional neuronal networks from embryonic stem cell-derived neural populations

    Directory of Open Access Journals (Sweden)

    Siebler Mario

    2009-08-01

    Full Text Available Abstract Background The present work was performed to investigate the ability of two different embryonic stem (ES cell-derived neural precursor populations to generate functional neuronal networks in vitro. The first ES cell-derived neural precursor population was cultivated as free-floating neural aggregates which are known to form a developmental niche comprising different types of neural cells, including neural precursor cells (NPCs, progenitor cells and even further matured cells. This niche provides by itself a variety of different growth factors and extracellular matrix proteins that influence the proliferation and differentiation of neural precursor and progenitor cells. The second population was cultivated adherently in monolayer cultures to control most stringently the extracellular environment. This population comprises highly homogeneous NPCs which are supposed to represent an attractive way to provide well-defined neuronal progeny. However, the ability of these different ES cell-derived immature neural cell populations to generate functional neuronal networks has not been assessed so far. Results While both precursor populations were shown to differentiate into sufficient quantities of mature NeuN+ neurons that also express GABA or vesicular-glutamate-transporter-2 (vGlut2, only aggregate-derived neuronal populations exhibited a synchronously oscillating network activity 2–4 weeks after initiating the differentiation as detected by the microelectrode array technology. Neurons derived from homogeneous NPCs within monolayer cultures did merely show uncorrelated spiking activity even when differentiated for up to 12 weeks. We demonstrated that these neurons exhibited sparsely ramified neurites and an embryonic vGlut2 distribution suggesting an inhibited terminal neuronal maturation. In comparison, neurons derived from heterogeneous populations within neural aggregates appeared as fully mature with a dense neurite network and punctuated

  1. Filtered-X Radial Basis Function Neural Networks for Active Noise Control

    Directory of Open Access Journals (Sweden)

    Bambang Riyanto

    2004-05-01

    Full Text Available This paper presents active control of acoustic noise using radial basis function (RBF networks and its digital signal processor (DSP real-time implementation. The neural control system consists of two stages: first, identification (modeling of secondary path of the active noise control using RBF networks and its learning algorithm, and secondly neural control of primary path based on neural model obtained in the first stage. A tapped delay line is introduced in front of controller neural, and another tapped delay line is inserted between controller neural networks and model neural networks. A new algorithm referred to as Filtered X-RBF is proposed to account for secondary path effects of the control system arising in active noise control. The resulting algorithm turns out to be the filtered-X version of the standard RBF learning algorithm. We address centralized and decentralized controller configurations and their DSP implementation is carried out. Effectiveness of the neural controller is demonstrated by applying the algorithm to active noise control within a 3 dimension enclosure to generate quiet zones around error microphones. Results of the real-time experiments show that 10-23 dB noise attenuation is produced with moderate transient response.

  2. Using Multiple Representations to Teach Composition of Functions

    Science.gov (United States)

    Steketee, Scott; Scher, Daniel

    2012-01-01

    Composition of functions is one of the five big ideas identified in NCTM's "Developing Essential Understanding of Functions, Grades 9-12" (Cooney, Beckmann, and Lloyd 2010). Through multiple representations (another big idea) and the use of The Geometer's Sketchpad[R] (GSP), students can directly manipulate variables and thus see dynamic visual…

  3. Evaluating Functional Decline in Patients with Multiple Sclerosis

    Science.gov (United States)

    Rosenblum, Sara; Weiss, Patrice L.

    2010-01-01

    Multiple Sclerosis (MS) is a disease with a wide-ranging impact on functional status. The aim of the study was to examine the added value of simultaneously evaluating fatigue, personal ADL and handwriting performance as indicators for functional decline among patients with MS. Participants were 50 outpatients with MS and 26 matched healthy…

  4. Using piecewise sinusoidal basis functions to blanket multiple wire segments

    CSIR Research Space (South Africa)

    Lysko, AA

    2009-06-01

    Full Text Available This paper discusses application of the piecewise sinusoidal (PWS) basis function (BF) over a chain of several wire segments, for example as a multiple domain basis function. The usage of PWS BF is compared to results based on the piecewise linear...

  5. Using Multiple Representations to Teach Composition of Functions

    Science.gov (United States)

    Steketee, Scott; Scher, Daniel

    2012-01-01

    Composition of functions is one of the five big ideas identified in NCTM's "Developing Essential Understanding of Functions, Grades 9-12" (Cooney, Beckmann, and Lloyd 2010). Through multiple representations (another big idea) and the use of The Geometer's Sketchpad[R] (GSP), students can directly manipulate variables and thus see dynamic visual…

  6. Puzzle Pieces: Neural Structure and Function in Prader-Willi Syndrome

    Directory of Open Access Journals (Sweden)

    Katherine E. Manning

    2015-12-01

    Full Text Available Prader-Willi syndrome (PWS is a neurodevelopmental disorder of genomic imprinting, presenting with a behavioural phenotype encompassing hyperphagia, intellectual disability, social and behavioural difficulties, and propensity to psychiatric illness. Research has tended to focus on the cognitive and behavioural investigation of these features, and, with the exception of eating behaviour, the neural physiology is currently less well understood. A systematic review was undertaken to explore findings relating to neural structure and function in PWS, using search terms designed to encompass all published articles concerning both in vivo and post-mortem studies of neural structure and function in PWS. This supported the general paucity of research in this area, with many articles reporting case studies and qualitative descriptions or focusing solely on the overeating behaviour, although a number of systematic investigations were also identified. Research to date implicates a combination of subcortical and higher order structures in PWS, including those involved in processing reward, motivation, affect and higher order cognitive functions, with both anatomical and functional investigations indicating abnormalities. It appears likely that PWS involves aberrant activity across distributed neural networks. The characterisation of neural structure and function warrants both replication and further systematic study.

  7. Identification of Sparse Neural Functional Connectivity using Penalized Likelihood Estimation and Basis Functions

    Science.gov (United States)

    Song, Dong; Wang, Haonan; Tu, Catherine Y.; Marmarelis, Vasilis Z.; Hampson, Robert E.; Deadwyler, Sam A.; Berger, Theodore W.

    2013-01-01

    One key problem in computational neuroscience and neural engineering is the identification and modeling of functional connectivity in the brain using spike train data. To reduce model complexity, alleviate overfitting, and thus facilitate model interpretation, sparse representation and estimation of functional connectivity is needed. Sparsities include global sparsity, which captures the sparse connectivities between neurons, and local sparsity, which reflects the active temporal ranges of the input-output dynamical interactions. In this paper, we formulate a generalized functional additive model (GFAM) and develop the associated penalized likelihood estimation methods for such a modeling problem. A GFAM consists of a set of basis functions convolving the input signals, and a link function generating the firing probability of the output neuron from the summation of the convolutions weighted by the sought model coefficients. Model sparsities are achieved by using various penalized likelihood estimations and basis functions. Specifically, we introduce two variations of the GFAM using a global basis (e.g., Laguerre basis) and group LASSO estimation, and a local basis (e.g., B-spline basis) and group bridge estimation, respectively. We further develop an optimization method based on quadratic approximation of the likelihood function for the estimation of these models. Simulation and experimental results show that both group-LASSO-Laguerre and group-bridge-B-spline can capture faithfully the global sparsities, while the latter can replicate accurately and simultaneously both global and local sparsities. The sparse models outperform the full models estimated with the standard maximum likelihood method in out-of-sample predictions. PMID:23674048

  8. A Stable Control of a Cstr with Input Multiplicities Using Artificial Neural Network Based Narma-l2

    Directory of Open Access Journals (Sweden)

    Prabhaker Reddy Ginuga

    2014-01-01

    Full Text Available In this paper, the Neural network based NARMA-L2 controller is analyzed to an isothermal continuous stirred tank reactor (CSTR which exhibits input multiplicities in space velocity on product of B. i.e., two values of space velocity will give the same value of product B. The Performance of Neural network based NARMA-L2 controller and conventional PI controller have been evaluated through simulation studies. As the NARMA-L2 controller provides always the two values of space velocity for control action and by selecting the value nearer to the operating point, it is found to give stable and better responses than conventional PI controller. The PI controller results in unstable condition or switch over from initial lower input space velocity to higher input space velocity vice versa. Thus, NARMA-L2 controller is found to overcome the control problems of PI controller due to the input multiplicities.

  9. Experimental design and multiple response optimization. Using the desirability function in analytical methods development.

    Science.gov (United States)

    Candioti, Luciana Vera; De Zan, María M; Cámara, María S; Goicoechea, Héctor C

    2014-06-01

    A review about the application of response surface methodology (RSM) when several responses have to be simultaneously optimized in the field of analytical methods development is presented. Several critical issues like response transformation, multiple response optimization and modeling with least squares and artificial neural networks are discussed. Most recent analytical applications are presented in the context of analytLaboratorio de Control de Calidad de Medicamentos (LCCM), Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, C.C. 242, S3000ZAA Santa Fe, ArgentinaLaboratorio de Control de Calidad de Medicamentos (LCCM), Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, C.C. 242, S3000ZAA Santa Fe, Argentinaical methods development, especially in multiple response optimization procedures using the desirability function.

  10. Using neural networks to predict the functionality of reconfigurable nano-material networks

    NARCIS (Netherlands)

    Greff, Klaus; Damme, van Ruud; Koutnik, Jan; Broersma, Hajo; Mikhal, Julia; Lawrence, Celestine; Wiel, van der Wilfred; Schmidhuber, Jürgen

    2017-01-01

    This paper demonstrates how neural networks can be applied to model and predict the functional behaviour of disordered nano-particle and nano-tube networks. In recently published experimental work, nano-particle and nano-tube networks show promising functionality for future reconfigurable devices, w

  11. Neural Correlates of Visual Perceptual Expertise: Evidence from Cognitive Neuroscience Using Functional Neuroimaging

    Science.gov (United States)

    Gegenfurtner, Andreas; Kok, Ellen M.; van Geel, Koos; de Bruin, Anique B. H.; Sorger, Bettina

    2017-01-01

    Functional neuroimaging is a useful approach to study the neural correlates of visual perceptual expertise. The purpose of this paper is to review the functional-neuroimaging methods that have been implemented in previous research in this context. First, we will discuss research questions typically addressed in visual expertise research. Second,…

  12. Reorganization of the Connectivity between Elementary Functions – A Model Relating Conscious States to Neural Connections

    Directory of Open Access Journals (Sweden)

    Jesper Mogensen

    2017-04-01

    Full Text Available In the present paper it is argued that the “neural correlate of consciousness” (NCC does not appear to be a separate “module” – but an aspect of information processing within the neural substrate of various cognitive processes. Consequently, NCC can only be addressed adequately within frameworks that model the general relationship between neural processes and mental states – and take into account the dynamic connectivity of the brain. We presently offer the REFGEN (general reorganization of elementary functions model as such a framework. This model builds upon and expands the REF (reorganization of elementary functions and REFCON (of elementary functions and consciousness models. All three models integrate the relationship between the neural and mental layers of description via the construction of an intermediate level dealing with computational states. The importance of experience based organization of neural and cognitive processes is stressed. The models assume that the mechanisms of consciousness are in principle the same as the basic mechanisms of all aspects of cognition – when information is processed to a sufficiently “high level” it becomes available to conscious experience. The NCC is within the REFGEN model seen as aspects of the dynamic and experience driven reorganizations of the synaptic connectivity between the neurocognitive “building blocks” of the model – the elementary functions.

  13. Multiple neural states of representation in short-term memory? It's a matter of attention.

    Science.gov (United States)

    Larocque, Joshua J; Lewis-Peacock, Jarrod A; Postle, Bradley R

    2014-01-01

    Short-term memory (STM) refers to the capacity-limited retention of information over a brief period of time, and working memory (WM) refers to the manipulation and use of that information to guide behavior. In recent years it has become apparent that STM and WM interact and overlap with other cognitive processes, including attention (the selection of a subset of information for further processing) and long-term memory (LTM-the encoding and retention of an effectively unlimited amount of information for a much longer period of time). Broadly speaking, there have been two classes of memory models: systems models, which posit distinct stores for STM and LTM (Atkinson and Shiffrin, 1968; Baddeley and Hitch, 1974); and state-based models, which posit a common store with different activation states corresponding to STM and LTM (Cowan, 1995; McElree, 1996; Oberauer, 2002). In this paper, we will focus on state-based accounts of STM. First, we will consider several theoretical models that postulate, based on considerable behavioral evidence, that information in STM can exist in multiple representational states. We will then consider how neural data from recent studies of STM can inform and constrain these theoretical models. In the process we will highlight the inferential advantage of multivariate, information-based analyses of neuroimaging data (fMRI and electroencephalography (EEG)) over conventional activation-based analysis approaches (Postle, in press). We will conclude by addressing lingering questions regarding the fractionation of STM, highlighting differences between the attention to information vs. the retention of information during brief memory delays.

  14. Multiple neural states of representation in short-term memory? It's a matter of attention

    Directory of Open Access Journals (Sweden)

    Joshua J Larocque

    2014-01-01

    Full Text Available Short-term memory (STM refers to the capacity-limited retention of information over a brief period of time, and working memory (WM refers to the manipulation and use of that information to guide behavior. In recent years it has become apparent that STM and WM interact and overlap with other cognitive processes, including attention (the selection of a subset of information for further processing and long-term memory (LTM – the encoding and retention of an effectively unlimited amount of information for a much longer period of time. Broadly speaking, there have been two classes of memory models: systems models, which posit distinct stores for STM and LTM (Atkinson & Shiffrin, 1968; Baddeley & Hitch, 1974; and state-based models, which posit a common store with different activation states corresponding to STM and LTM (Cowan, 1995; McElree, 1996; Oberauer, 2002. In this paper, we will focus on state-based accounts of STM. First, we will consider several theoretical models that postulate, based on considerable behavioral evidence, that information in STM can exist in multiple representational states. We will then consider how neural data from recent studies of STM can inform and constrain these theoretical models. In the process we will highlight the inferential advantage of multivariate, information-based analyses of neuroimaging data (fMRI and EEG over conventional activation-based analysis approaches (Postle, in press. We will conclude by addressing lingering questions regarding the fractionation of STM, highlighting differences between the attention to information vs. the retention of information during brief memory delays.

  15. A canonical correlation neural network for multicollinearity and functional data.

    Science.gov (United States)

    Gou, Zhenkun; Fyfe, Colin

    2004-03-01

    We review a recent neural implementation of Canonical Correlation Analysis and show, using ideas suggested by Ridge Regression, how to make the algorithm robust. The network is shown to operate on data sets which exhibit multicollinearity. We develop a second model which not only performs as well on multicollinear data but also on general data sets. This model allows us to vary a single parameter so that the network is capable of performing Partial Least Squares regression (at one extreme) to Canonical Correlation Analysis (at the other)and every intermediate operation between the two. On multicollinear data, the parameter setting is shown to be important but on more general data no particular parameter setting is required. Finally, we develop a second penalty term which acts on such data as a smoother in that the resulting weight vectors are much smoother and more interpretable than the weights without the robustification term. We illustrate our algorithms on both artificial and real data.

  16. Data Fusion Using Different Activation Functions in Artificial Neural Networks for Vehicular Navigation

    Directory of Open Access Journals (Sweden)

    MALLESWARAN M,

    2010-12-01

    Full Text Available Global positioning System (GPS and Inertial Navigation System (INS data can be integrated together to provide a reliable navigation. GPS/INS data integration provides reliable navigation solutions by overcoming each of their shortcomings, including signal blockage for GPS and increase in position errors with time for INS. This paper aims to provide GPS/INS data integration utilizing Artificial Neural Network (ANN architecture. This architecture is based on Feed Forward Neural Networks, which generally includes Radial Basis Function (RBF neural network and Back Propagation neural network (BPN. These are systematic methods for training multi-layer artificial networks. The BPN-ANN and RBF-ANN modules are trained to predict the INS position error and provide accurate positioning of the moving vehicle. This paper also compares performance of theGPS/INS data integration system by using different activation function like Bipolar Sigmoidal Function (BPSF, Binary Sigmoidal Function (BISF, Hyperbolic Tangential Function (HTF and Gaussian Function (GF in BPN-ANN and using Gaussian function in RBF-ANN.

  17. An application of multilayer neural network on hepatitis disease diagnosis using approximations of sigmoid activation function

    Directory of Open Access Journals (Sweden)

    Onursal Çetin

    2015-06-01

    Full Text Available Objective: Implementation of multilayer neural network (MLNN with sigmoid activation function for the diagnosis of hepatitis disease. Methods: Artificial neural networks (ANNs are efficient tools currently in common use for medical diagnosis. In hardware based architectures activation functions play an important role in ANN behavior. Sigmoid function is the most frequently used activation function because of its smooth response. Thus, sigmoid function and its close approximations were implemented as activation function. The dataset is taken from the UCI machine learning database. Results: For the diagnosis of hepatitis disease, MLNN structure was implemented and Levenberg Morquardt (LM algorithm was used for learning. Our method of classifying hepatitis disease produced an accuracy of 91.9% to 93.8% via 10 fold cross validation. Conclusion: When compared to previous work that diagnosed hepatitis disease using artificial neural networks and the identical data set, our results are promising in order to reduce the size and cost of neural network based hardware. Thus, hardware based diagnosis systems can be developed effectively by using approximations of sigmoid function.

  18. A case for spiking neural network simulation based on configurable multiple-FPGA systems.

    Science.gov (United States)

    Yang, Shufan; Wu, Qiang; Li, Renfa

    2011-09-01

    Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.

  19. Speaking in multiple languages: neural correlates of language proficiency in multilingual word production.

    Science.gov (United States)

    Videsott, Gerda; Herrnberger, Bärbel; Hoenig, Klaus; Schilly, Edgar; Grothe, Jo; Wiater, Werner; Spitzer, Manfred; Kiefer, Markus

    2010-06-01

    The human brain has the fascinating ability to represent and to process several languages. Although the first and further languages activate partially different brain networks, the linguistic factors underlying these differences in language processing have to be further specified. We investigated the neural correlates of language proficiency in a homogeneous sample of multilingual native Ladin speakers from a mountain valley in South Tyrol, Italy, who speak Italian as second language at a high level, and English at an intermediate level. In a constrained word production task under functional magnetic resonance imaging (fMRI), participants had to name pictures of objects in Ladin, Italian and English in separate blocks. Overall, multilingual word production activated a common set of brain areas dedicated to known subcomponents of picture naming. In comparison to English, the fluently spoken languages Ladin and Italian were associated with enhanced right prefrontal activity. In addition, the MR signal in right prefrontal cortex correlated with naming accuracy as a measure of language proficiency. Our results demonstrate the significance of right prefrontal areas for language proficiency. Based on the role of these areas for cognitive control, our findings suggest that right prefrontal cortex supports language proficiency by effectively supervising word retrieval.

  20. One-way hash function based on hyper-chaotic cellular neural network

    Institute of Scientific and Technical Information of China (English)

    Yang Qun-Ting; Gao Tie-Gang

    2008-01-01

    The design of an efficient one-way hash function with good performance is a hot spot in modern cryptography researches. In this paper, a hash function construction method based on cell neural network with hyper-chaos characteristics is proposed. First, the chaos sequence is gotten by iterating cellular neural network with Runge-Kutta algorithm, and then the chaos sequence is iterated with the message. The hash code is obtained through the corresponding transform of the latter chaos sequence. Simulation and analysis demonstrate that the new method has the merit of convenience, high sensitivity to initial values, good hash performance, especially the strong stability.

  1. Effects of Interactive Function Forms in a Self-Organized Critical Model Based on Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZHAO Xiao-Wei; ZHOU Li-Ming; CHEN Tian-Lun

    2003-01-01

    Based on the standard self-organizing map neural network model and an integrate-and-fire mechanism, we introduce a kind of coupled map lattice system to investigate scale-invariance behavior in the activity of model neural populations. We let the parameter β, which together with α represents the interactive strength between neurons, have different function forms, and we find the function forms and their parameters are very important to our model's avalanche dynamical behaviors, especially to the emergence of different avalanche behaviors in different areas of our system.

  2. Effects of Interactive Function Forms in a Self-Organized Critical Model Based on Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZHAOXiao-Wei; ZHOULi-Ming; CHENTian-Lun

    2003-01-01

    Based on the standard self-organizing map neural network model and an integrate-and-fire mechanism, we introduce a kind of coupled map lattice system to investigate scale-invariance behavior in the activity of model neural populations. We let the parameter β, which together with α represents the interactive strength between neurons, have different function forms, and we find the function forms and their parameters are very important to our model''s avalanche dynamical behaviors, especially to the emergence of different avalanche behaviors in different areas of our system.

  3. ASYMPTOTICAL STABILITY OFNON-AUTONOMOUS DISCRETE-TIME NEURAL NETWORKS WITH GENERALIZED INPUT-OUTPUT FUNCTION

    Institute of Scientific and Technical Information of China (English)

    阮炯; 王军平; 郭德典

    2004-01-01

    In this paper, we first introduce the model of discrete-time neural networks with generalized input-output function and present a proof of the existence of a fixed point by Schauder fixed-point principle. Secondly, we study the uniformly asymptotical stability of equilibrium in non-autonomous discrete-time neural networks and give some sufficient conditions that guarantee the stability of it by using the converse theorem of Lyapunov function. Finally, several examples and numerical simulations are given to illustrate and reinforce our theories.

  4. Different acceptance functions for Multiple Try Metropolis schemes

    CERN Document Server

    Martino, Luca

    2012-01-01

    The Multiple Try Metropolis method is an extension of the classical Metropolis algorithm in which the next state of the chain is chosen among a set of samples, according to normalized weights. In literature, a generalization of this technique has been proposed in Pandolfi et al. (2010) where the weight functions are not analytically specified. In this work, we propose different Multiple Try Metropolis schemes where the analytic form of weight functions can be chosen arbitrarily. The resulting algorithms fulfill the detailed balance condition.

  5. Optimizing rTMS treatment of a balance disorder with EEG neural synchrony and functional connectivity.

    Science.gov (United States)

    Guofa Shou; Han Yuan; Urbano, Diamond; Yoon-Hee Cha; Lei Ding

    2016-08-01

    Repetitive transcranial magnetic stimulation (rTMS) has been increasingly used for its potential treatment effects across diverse mental disorders. However, the treatment effect is elusive and the rate of positive responders is not high, which make it in great demand of optimizing rTMS protocols to improve the treatment effects and the rate. In this regard, neural activity guided optimization has indicated great potential in several neuroimaging studies. In this paper, we present our ongoing work on optimizing rTMS treatment of a balance disorder, i.e., Mal de Debarquement syndrome (MdDS), by investigating treatment-related EEG neural synchrony and functional connectivity changes. Motivated by our previous pilot study of rTMS on MdDS, we firstly applied a bilateral dorsolateral prefrontal cortex (DLPFC) rTMS protocol to evaluate its efficacy and the treatment-related neural responses via an independent component analysis (ICA)-based framework. Thereafter, guided by identified EEG neural synchrony and functional connectivity patterns, we proposed three potential stimulation targets covering posterior nodes of the default mode network (DMN), and implemented a new rTMS protocol by stimulating the target with the great symptoms relief. The preliminary clinical response data has indicated that the new rTMS protocol significantly increase the rate of positive responders and the degrees of the improvement. The present study demonstrates that it is promising to integrate EEG neural synchrony and functional connectivity into the optimization of rTMS protocols for different mental disorders.

  6. Functional imaging of structures involved in neural control of the lower urinary tract.

    Science.gov (United States)

    Griffiths, Derek

    2015-01-01

    Recent functional brain imaging studies, building on earlier observations, suggest a working model of brain control of the lower urinary tract. It comprises a few cerebral neural circuits that, during the storage phase, act on the midbrain periaqueductal gray to inhibit the long-loop, spinobulbospinal voiding reflex, thus promoting continence. Circuit 1, centered on the medial prefrontal cortex, appears to be concerned with conscious control of both continence and voiding. Circuit 2, centered on the dorsal anterior cingulate (midcingulate) and supplementary motor area, is concerned with emotional aspects of bladder control: desire to void or urgency with concomitant urethral sphincter activation to delay leakage. A subcortical circuit 3 has been less well studied. Circuit 1 is bilateral with a right-sided preference. Scattered studies of the connectivity of the control network suggest that white-matter damage may contribute to urinary incontinence. A few studies confirm that isolated cerebral lesions, if in the medial prefrontal cortex or its connecting pathways, may lead to incontinence. Lower urinary tract dysfunction in other neurologic diseases (normal-pressure hydrocephalus, Parkinson's disease, and multiple systems atrophy) appears consistent with the working model, and even spinal or peripheral lesions have central effects. However, this model omits the contributions of brain regions already observed in some imaging studies and therefore is certainly oversimplified.

  7. Multiple Stressors and the Functioning of Coral Reefs

    Science.gov (United States)

    Harborne, Alastair R.; Rogers, Alice; Bozec, Yves-Marie; Mumby, Peter J.

    2017-01-01

    Coral reefs provide critical services to coastal communities, and these services rely on ecosystem functions threatened by stressors. By summarizing the threats to the functioning of reefs from fishing, climate change, and decreasing water quality, we highlight that these stressors have multiple, conflicting effects on functionally similar groups of species and their interactions, and that the overall effects are often uncertain because of a lack of data or variability among taxa. The direct effects of stressors on links among functional groups, such as predator-prey interactions, are particularly uncertain. Using qualitative modeling, we demonstrate that this uncertainty of stressor impacts on functional groups (whether they are positive, negative, or neutral) can have significant effects on models of ecosystem stability, and reducing uncertainty is vital for understanding changes to reef functioning. This review also provides guidance for future models of reef functioning, which should include interactions among functional groups and the cumulative effect of stressors.

  8. A comparison of neural network models, fuzzy logic, and multiple linear regression for prediction of hatchability.

    Science.gov (United States)

    Mehri, M

    2013-04-01

    Application of appropriate models to approximate the performance function warrants more precise prediction and helps to make the best decisions in the poultry industry. This study reevaluated the factors affecting hatchability in laying hens from 29 to 56 wk of age. Twenty-eight data lines representing 4 inputs consisting of egg weight, eggshell thickness, egg sphericity, and yolk/albumin ratio and 1 output, hatchability, were obtained from the literature and used to train an artificial neural network (ANN). The prediction ability of ANN was compared with that of fuzzy logic to evaluate the fitness of these 2 methods. The models were compared using R(2), mean absolute deviation (MAD), mean squared error (MSE), mean absolute percentage error (MAPE), and bias. The developed model was used to assess the relative importance of each variable on the hatchability by calculating the variable sensitivity ratio. The statistical evaluations showed that the ANN-based model predicted hatchability more accurately than fuzzy logic. The ANN-based model had a higher determination of coefficient (R(2) = 0.99) and lower residual distribution (MAD = 0.005; MSE = 0.00004; MAPE = 0.732; bias = 0.0012) than fuzzy logic (R(2) = 0.87; MAD = 0.014; MSE = 0.0004; MAPE = 2.095; bias = 0.0046). The sensitivity analysis revealed that the most important variable in the ANN-based model of hatchability was egg weight (variable sensitivity ratio, VSR = 283.11), followed by yolk/albumin ratio (VSR = 113.16), eggshell thickness (VSR = 16.23), and egg sphericity (VSR = 3.63). The results of this research showed that the universal approximation capability of ANN made it a powerful tool to approximate complex functions such as hatchability in the incubation process.

  9. On the use of back propagation and radial basis function neural networks in surface roughness prediction

    Science.gov (United States)

    Markopoulos, Angelos P.; Georgiopoulos, Sotirios; Manolakos, Dimitrios E.

    2016-03-01

    Various artificial neural networks types are examined and compared for the prediction of surface roughness in manufacturing technology. The aim of the study is to evaluate different kinds of neural networks and observe their performance and applicability on the same problem. More specifically, feed-forward artificial neural networks are trained with three different back propagation algorithms, namely the adaptive back propagation algorithm of the steepest descent with the use of momentum term, the back propagation Levenberg-Marquardt algorithm and the back propagation Bayesian algorithm. Moreover, radial basis function neural networks are examined. All the aforementioned algorithms are used for the prediction of surface roughness in milling, trained with the same input parameters and output data so that they can be compared. The advantages and disadvantages, in terms of the quality of the results, computational cost and time are identified. An algorithm for the selection of the spread constant is applied and tests are performed for the determination of the neural network with the best performance. The finally selected neural networks can satisfactorily predict the quality of the manufacturing process performed, through simulation and input-output surfaces for combinations of the input data, which correspond to milling cutting conditions.

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

    Science.gov (United States)

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

    2014-09-01

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

  11. Building a functional neurocognitive theory of the multiple intelligences anatomical framework

    Directory of Open Access Journals (Sweden)

    Carlo eCerruti

    2013-12-01

    Full Text Available A key goal of educational neuroscience is to conduct constrained experimental research that is theory-driven and yet also clearly related to educators’ complex set of questions and concerns. However, the fields of education, cognitive psychology and neuroscience use different levels of description to characterize human ability. An important advance in research in educational neuroscience would be the identification of a cognitive and neurocognitive framework at a level of description relatively intuitive to educators. I argue that the theory of multiple intelligences (Gardner, 1983, a conception of the mind that motivated a past generation of teachers, may provide such an opportunity. I criticize MI for doing little to clarify for teachers a core misunderstanding, specifically that MI was only an anatomical map of the mind but not a functional theory that detailed how the mind actually processes information. In an attempt to build a functional MI theory, I integrate into MI basic principles of cognitive and neural functioning, namely interregional neural facilitation and inhibition. In so doing I hope to forge a path towards constrained experimental research that bears upon teachers’ concerns about teaching and learning.

  12. Constructive feedforward neural networks using hermite polynomial activation functions.

    Science.gov (United States)

    Ma, Liying; Khorasani, K

    2005-07-01

    In this paper, a constructive one-hidden-layer network is introduced where each hidden unit employs a polynomial function for its activation function that is different from other units. Specifically, both a structure level as well as a function level adaptation methodologies are utilized in constructing the network. The functional level adaptation scheme ensures that the "growing" or constructive network has different activation functions for each neuron such that the network may be able to capture the underlying input-output map more effectively. The activation functions considered consist of orthonormal Hermite polynomials. It is shown through extensive simulations that the proposed network yields improved performance when compared to networks having identical sigmoidal activation functions.

  13. Efficient Digital Implementation of The Sigmoidal Function For Artificial Neural Network

    Science.gov (United States)

    Pratap, Rana; Subadra, M.

    2011-10-01

    An efficient piecewise linear approximation of a nonlinear function (PLAN) is proposed. This uses simulink environment design to perform a direct transformation from X to Y, where X is the input and Y is the approximated sigmoidal output. This PLAN is then used within the outputs of an artificial neural network to perform the nonlinear approximation. In This paper, is proposed a method to implement in FPGA (Field Programmable Gate Array) circuits different approximation of the sigmoid function.. The major benefit of the proposed method resides in the possibility to design neural networks by means of predefined block systems created in System Generator environment and the possibility to create a higher level design tools used to implement neural networks in logical circuits.

  14. Low Density Lipoprotein Receptor Related Proteins as Regulators of Neural Stem and Progenitor Cell Function

    Directory of Open Access Journals (Sweden)

    Loic Auderset

    2016-01-01

    Full Text Available The central nervous system (CNS is a highly organised structure. Many signalling systems work in concert to ensure that neural stem cells are appropriately directed to generate progenitor cells, which in turn mature into functional cell types including projection neurons, interneurons, astrocytes, and oligodendrocytes. Herein we explore the role of the low density lipoprotein (LDL receptor family, in particular family members LRP1 and LRP2, in regulating the behaviour of neural stem and progenitor cells during development and adulthood. The ability of LRP1 and LRP2 to bind a diverse and extensive range of ligands, regulate ligand endocytosis, recruit nonreceptor tyrosine kinases for direct signal transduction and signal in conjunction with other receptors, enables them to modulate many crucial neural cell functions.

  15. Navigation of autonomous mobile robot using different activation functions of wavelet neural network

    Directory of Open Access Journals (Sweden)

    Panigrahi Pratap Kumar

    2015-03-01

    Full Text Available An autonomous mobile robot is a robot which can move and act autonomously without the help of human assistance. Navigation problem of mobile robot in unknown environment is an interesting research area. This is a problem of deducing a path for the robot from its initial position to a given goal position without collision with the obstacles. Different methods such as fuzzy logic, neural networks etc. are used to find collision free path for mobile robot. This paper examines behavior of path planning of mobile robot using three activation functions of wavelet neural network i.e. Mexican Hat, Gaussian and Morlet wavelet functions by MATLAB. The simulation result shows that WNN has faster learning speed with respect to traditional artificial neural network.

  16. Complete stability of delayed recurrent neural networks with Gaussian activation functions.

    Science.gov (United States)

    Liu, Peng; Zeng, Zhigang; Wang, Jun

    2017-01-01

    This paper addresses the complete stability of delayed recurrent neural networks with Gaussian activation functions. By means of the geometrical properties of Gaussian function and algebraic properties of nonsingular M-matrix, some sufficient conditions are obtained to ensure that for an n-neuron neural network, there are exactly 3(k) equilibrium points with 0≤k≤n, among which 2(k) and 3(k)-2(k) equilibrium points are locally exponentially stable and unstable, respectively. Moreover, it concludes that all the states converge to one of the equilibrium points; i.e., the neural networks are completely stable. The derived conditions herein can be easily tested. Finally, a numerical example is given to illustrate the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. Neural network model for the efficient calculation of Green's functions in layered media

    CERN Document Server

    Soliman, E A; El-Gamal, M A; 10.1002/mmce.10066

    2003-01-01

    In this paper, neural networks are employed for fast and efficient calculation of Green's functions in a layered medium. Radial basis function networks (RBFNs) are effectively trained to estimate the coefficients and the exponents that represent a Green's function in the discrete complex image method (DCIM). Results show very good agreement with the DCIM, and the trained RBFNs are very fast compared with the corresponding DCIM. (23 refs).

  18. Neural Activations of Guided Imagery and Music in Negative Emotional Processing: A Functional MRI Study.

    Science.gov (United States)

    Lee, Sang Eun; Han, Yeji; Park, HyunWook

    2016-01-01

    The Bonny Method of Guided Imagery and Music uses music and imagery to access and explore personal emotions associated with episodic memories. Understanding the neural mechanism of guided imagery and music (GIM) as combined stimuli for emotional processing informs clinical application. We performed functional magnetic resonance imaging (fMRI) to demonstrate neural mechanisms of GIM for negative emotional processing when personal episodic memory is recalled and re-experienced through GIM processes. Twenty-four healthy volunteers participated in the study, which used classical music and verbal instruction stimuli to evoke negative emotions. To analyze the neural mechanism, activated regions associated with negative emotional and episodic memory processing were extracted by conducting volume analyses for the contrast between GIM and guided imagery (GI) or music (M). The GIM stimuli showed increased activation over the M-only stimuli in five neural regions associated with negative emotional and episodic memory processing, including the left amygdala, left anterior cingulate gyrus, left insula, bilateral culmen, and left angular gyrus (AG). Compared with GI alone, GIM showed increased activation in three regions associated with episodic memory processing in the emotional context, including the right posterior cingulate gyrus, bilateral parahippocampal gyrus, and AG. No neural regions related to negative emotional and episodic memory processing showed more activation for M and GI than for GIM. As a combined multimodal stimulus, GIM may increase neural activations related to negative emotions and episodic memory processing. Findings suggest a neural basis for GIM with personal episodic memories affecting cortical and subcortical structures and functions. © the American Music Therapy Association 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  19. Comparing the Selected Transfer Functions and Local Optimization Methods for Neural Network Flood Runoff Forecast

    Directory of Open Access Journals (Sweden)

    Petr Maca

    2014-01-01

    Full Text Available The presented paper aims to analyze the influence of the selection of transfer function and training algorithms on neural network flood runoff forecast. Nine of the most significant flood events, caused by the extreme rainfall, were selected from 10 years of measurement on small headwater catchment in the Czech Republic, and flood runoff forecast was investigated using the extensive set of multilayer perceptrons with one hidden layer of neurons. The analyzed artificial neural network models with 11 different activation functions in hidden layer were trained using 7 local optimization algorithms. The results show that the Levenberg-Marquardt algorithm was superior compared to the remaining tested local optimization methods. When comparing the 11 nonlinear transfer functions, used in hidden layer neurons, the RootSig function was superior compared to the rest of analyzed activation functions.

  20. Central-peripheral neural network interactions evoked by vagus nerve stimulation: functional consequences on control of cardiac function.

    Science.gov (United States)

    Ardell, Jeffrey L; Rajendran, Pradeep S; Nier, Heath A; KenKnight, Bruce H; Armour, J Andrew

    2015-11-15

    Using vagus nerve stimulation (VNS), we sought to determine the contribution of vagal afferents to efferent control of cardiac function. In anesthetized dogs, the right and left cervical vagosympathetic trunks were stimulated in the intact state, following ipsilateral or contralateral vagus nerve transection (VNTx), and then following bilateral VNTx. Stimulations were performed at currents from 0.25 to 4.0 mA, frequencies from 2 to 30 Hz, and a 500-μs pulse width. Right or left VNS evoked significantly greater current- and frequency-dependent suppression of chronotropic, inotropic, and lusitropic function subsequent to sequential VNTx. Bradycardia threshold was defined as the current first required for a 5% decrease in heart rate. The threshold for the right vs. left vagus-induced bradycardia in the intact state (2.91 ± 0.18 and 3.47 ± 0.20 mA, respectively) decreased significantly with right VNTx (1.69 ± 0.17 mA for right and 3.04 ± 0.27 mA for left) and decreased further following bilateral VNTx (1.29 ± 0.16 mA for right and 1.74 ± 0.19 mA for left). Similar effects were observed following left VNTx. The thresholds for afferent-mediated effects on cardiac parameters were 0.62 ± 0.04 and 0.65 ± 0.06 mA with right and left VNS, respectively, and were reflected primarily as augmentation. Afferent-mediated tachycardias were maintained following β-blockade but were eliminated by VNTx. The increased effectiveness and decrease in bradycardia threshold with sequential VNTx suggest that 1) vagal afferents inhibit centrally mediated parasympathetic efferent outflow and 2) the ipsilateral and contralateral vagi exert a substantial buffering capacity. The intact threshold reflects the interaction between multiple levels of the cardiac neural hierarchy.

  1. Multiplication free neural network for cancer stem cell detection in H-and-E stained liver images

    Science.gov (United States)

    Badawi, Diaa; Akhan, Ece; Mallah, Ma'en; Üner, Ayşegül; ćetin-Atalay, Rengül; ćetin, A. Enis

    2017-05-01

    Markers such as CD13 and CD133 have been used to identify Cancer Stem Cells (CSC) in various tissue images. It is highly likely that CSC nuclei appear as brown in CD13 stained liver tissue images. We observe that there is a high correlation between the ratio of brown to blue colored nuclei in CD13 images and the ratio between the dark blue to blue colored nuclei in H&E stained liver images. Therefore, we recommend that a pathologist observing many dark blue nuclei in an H&E stained tissue image may also order CD13 staining to estimate the CSC ratio. In this paper, we describe a computer vision method based on a neural network estimating the ratio of dark blue to blue colored nuclei in an H&E stained liver tissue image. The neural network structure is based on a multiplication free operator using only additions and sign operations. Experimental results are presented.

  2. Dissecting Repulsive Guidance Molecule/Neogenin function and signaling during neural development

    NARCIS (Netherlands)

    van den Heuvel, D.M.A.

    2013-01-01

    During neural development a series of precisely ordered cellular processes acts to establish a functional brain comprising millions of neurons and many more neuronal connections. Neogenin and its repulsive guidance molecule (RGM) ligands contribute to neuronal network formation by inducing axon repu

  3. Functional dissociations in top-down control dependent neural repetition priming.

    NARCIS (Netherlands)

    Klaver, P.; Schnaidt, M.; Fell, J.; Ruhlmann, J.; Elger, C.E.; Fernandez, G.

    2007-01-01

    Little is known about the neural mechanisms underlying top-down control of repetition priming. Here, we use functional brain imaging to investigate these mechanisms. Study and repetition tasks used a natural/man-made forced choice task. In the study phase subjects were required to respond to either

  4. Zero Cost Function Training Algorithms for Three-Layered Feedforward Neural Networks

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    In this paper, two theorems are proved for zero cost function (or precise I/O map ping) training algorithms about three-layered feedforward neural networks. Two training algorithms based on Moore-Penrose pseudoinverse (MPPI) matrix together with corresponding structure design guidelines are also proposed.

  5. Functional and topographic concordance of right atrial neural structures inducing sinus tachycardia.

    Science.gov (United States)

    Eickholt, Christian; Mischke, Karl; Schimpf, Thomas; Knackstedt, Christian; Scherer, Kira; Pauza, Danius; Marx, Nikolaus; Shin, Dong-In; Kelm, Malte; Meyer, Christian

    2013-01-01

    Cardiorespiratory autonomic control is in tight interaction with an intracardiac neural network modulating sinus node function. To gain novel mechanistical insights and to investigate possible novel targets concerning the treatment of inadequate sinus tachycardia, we aimed to characterize functionally and topographically the right atrial neural network modulating sinus node function. In 16 sheep 3-dimensional electro-anatomical mapping of the right atrium was performed. In five animals additionally magnetically steered remote navigation was used. Selective stimulation of nerve fibers was conducted by applying high frequency (200 Hz) electrical impulses within the atrial refractory period. Histological analysis of whole heart preparations by acetylcholinesterase staining was performed and compared to the acquired neuroanatomical mapping.We found that neural stimulation in the cranial part of the right atrium, within a perimeter around the sinus node area, elicited predominantly shortening of the sinus cycle length of -20.3 ± 10.1 % (n = 80, P < 0.05). Along the course of the crista terminalis atrial premature beats (n = 117) and atrial fibrillation (n = 123) could be induced. Catheter stability was excellent during remote catheter navigation. Histological work-up (n = 4) was in accord with the distribution of neurostimulation sites. Ganglions were mainly innervated by the dorsal right-atrial subplexus, with substantial additional input from the ventral right atrial subplexus. In conclusion, our findings suggest a functional and topographic concordance of right atrial neural structures inducing sinus tachycardia. This might open up new avenues in the treatment of heart rate related disorders.

  6. Comparison of Multiple Linear Regressions and Neural Networks based QSAR models for the design of new antitubercular compounds.

    Science.gov (United States)

    Ventura, Cristina; Latino, Diogo A R S; Martins, Filomena

    2013-01-01

    The performance of two QSAR methodologies, namely Multiple Linear Regressions (MLR) and Neural Networks (NN), towards the modeling and prediction of antitubercular activity was evaluated and compared. A data set of 173 potentially active compounds belonging to the hydrazide family and represented by 96 descriptors was analyzed. Models were built with Multiple Linear Regressions (MLR), single Feed-Forward Neural Networks (FFNNs), ensembles of FFNNs and Associative Neural Networks (AsNNs) using four different data sets and different types of descriptors. The predictive ability of the different techniques used were assessed and discussed on the basis of different validation criteria and results show in general a better performance of AsNNs in terms of learning ability and prediction of antitubercular behaviors when compared with all other methods. MLR have, however, the advantage of pinpointing the most relevant molecular characteristics responsible for the behavior of these compounds against Mycobacterium tuberculosis. The best results for the larger data set (94 compounds in training set and 18 in test set) were obtained with AsNNs using seven descriptors (R(2) of 0.874 and RMSE of 0.437 against R(2) of 0.845 and RMSE of 0.472 in MLRs, for test set). Counter-Propagation Neural Networks (CPNNs) were trained with the same data sets and descriptors. From the scrutiny of the weight levels in each CPNN and the information retrieved from MLRs, a rational design of potentially active compounds was attempted. Two new compounds were synthesized and tested against M. tuberculosis showing an activity close to that predicted by the majority of the models.

  7. DENSENESS OF RADIAL-BASIS FUNCTIONS IN L2(Rn) AND ITS APPLICATIONS IN NEURAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    CHENTIANPING; CHENHONG

    1996-01-01

    The authors discuss problems of approximation to functions in L2 (Rn)and operators from L2(Rn1)to L2(Rn2)by Radial-Basis Functions. The results obtained solve the parblem of capability of RBF neural networks,a basic problem in neural networks.

  8. Multiplicative LSTM for sequence modelling

    OpenAIRE

    Krause, Ben; Lu, Liang; Murray, Iain; Renals, Steve

    2016-01-01

    This paper introduces multiplicative LSTM, a novel hybrid recurrent neural network architecture for sequence modelling that combines the long short-term memory (LSTM) and multiplicative recurrent neural network architectures. Multiplicative LSTM is motivated by its flexibility to have very different recurrent transition functions for each possible input, which we argue helps make it more expressive in autoregressive density estimation. We show empirically that multiplicative LSTM outperforms ...

  9. Improving the accuracy of density-functional theory calculation: the genetic algorithm and neural network approach.

    Science.gov (United States)

    Li, Hui; Shi, LiLi; Zhang, Min; Su, Zhongmin; Wang, XiuJun; Hu, LiHong; Chen, GuanHua

    2007-04-14

    The combination of genetic algorithm and neural network approach (GANN) has been developed to improve the calculation accuracy of density functional theory. As a demonstration, this combined quantum mechanical calculation and GANN correction approach has been applied to evaluate the optical absorption energies of 150 organic molecules. The neural network approach reduces the root-mean-square (rms) deviation of the calculated absorption energies of 150 organic molecules from 0.47 to 0.22 eV for the TDDFTB3LYP6-31G(d) calculation, and the newly developed GANN correction approach reduces the rms deviation to 0.16 eV.

  10. VARIABILITY OF NEURONAL RESPONSES: TYPES AND FUNCTIONAL SIGNIFICANCE IN NEUROPLASTICITY AND NEURAL DARWINISM

    Directory of Open Access Journals (Sweden)

    Alexander Chervyakov

    2016-11-01

    Full Text Available In many cases, neural signals detected under the same external experimental conditions significantly change from trial to trial. The variability phenomenon, which complicates extraction of reproducible results and is ignored in many studies by averaging, has attracted attention of researchers in recent years. In this paper, we classify possible types of variability based on its functional significance and describe features of each type. We describe the key adaptive significance of variability at the neural network level and the degeneracy phenomenon that may be important for learning processes in connection with the principle of neuronal group selection.

  11. Protein distance constraints predicted by neural networks and probability density functions

    DEFF Research Database (Denmark)

    Lund, Ole; Frimand, Kenneth; Gorodkin, Jan

    1997-01-01

    We predict interatomic C-α distances by two independent data driven methods. The first method uses statistically derived probability distributions of the pairwise distance between two amino acids, whilst the latter method consists of a neural network prediction approach equipped with windows taking....... The predictions are based on a data set derived using a new threshold similarity. We show that distances in proteins are predicted more accurately by neural networks than by probability density functions. We show that the accuracy of the predictions can be further increased by using sequence profiles. A threading...

  12. Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

    Science.gov (United States)

    Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

    2000-01-01

    Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

  13. Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks.

    Science.gov (United States)

    Yao, Kun; Parkhill, John

    2016-03-01

    We demonstrate a convolutional neural network trained to reproduce the Kohn-Sham kinetic energy of hydrocarbons from an input electron density. The output of the network is used as a nonlocal correction to conventional local and semilocal kinetic functionals. We show that this approximation qualitatively reproduces Kohn-Sham potential energy surfaces when used with conventional exchange correlation functionals. The density which minimizes the total energy given by the functional is examined in detail. We identify several avenues to improve on this exploratory work, by reducing numerical noise and changing the structure of our functional. Finally we examine the features in the density learned by the neural network to anticipate the prospects of generalizing these models.

  14. Neural-net based unstable machine identification using individual energy functions. [Transient disturbances in power systems

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M. (Institut Nikola Tesla, Belgrade (Yugoslavia)); Sobajic, D.J.; Yohhan Pao (Case Western Reserve Univ., Cleveland, OH (United States))

    1991-10-01

    The identification of the mode of instability plays an essential role in generating principal energy boundary hypersurfaces. We present a new method for unstable machine identification based on the use of supervised learning neural-net technology, and the adaptive pattern recognition concept. It is shown that using individual energy functions as pattern features, appropriately trained neural-nets can retrieve the reliable characterization of the transient process including critical clearing time parameter, mode of instability and energy margins. Generalization capabilities of the neural-net processing allow for these assessments to be made independently of load levels. The results obtained from computer simulations are presented using the New England power system, as an example. (author).

  15. Amphioxus SARM involved in neural development may function as a suppressor of TLR signaling.

    Science.gov (United States)

    Yuan, Shaochun; Wu, Kui; Yang, Manyi; Xu, Liqun; Huang, Ling; Liu, Huiling; Tao, Xin; Huang, Shengfeng; Xu, Anlong

    2010-06-15

    Among five Toll/IL-1R resistance adaptors, sterile alpha and Toll/IL-1R resistance motif containing protein (SARM) is the only one conserved from Caenorhabditis elegans to human. However, its physiologic roles are hardly understood, and its involvement in TLR signaling remains debatable. In this study, we first demonstrated a predominant expression of amphioxus SARM (Branchiostoma belcheri tsingtauense SARM) in neural cells during embryogenesis and its predominant expression in the digestive system from larva to adult, suggesting its primitive role in neural development and a potential physiologic role in immunity. We further found that B. belcheri tsingtauense SARM was localized in mitochondria and could attenuate the TLR signaling via interacting with amphioxus MyD88 and tumor necrosis receptor associated factor 6. Thus, amphioxus SARM appears unique in that it may play dual functions in neural development and innate immunity by targeting amphioxus TLR signaling.

  16. Compressor performance prediction using a novel feed-forward neural network based on Gaussian kernel function

    Directory of Open Access Journals (Sweden)

    Jingzhou Fei

    2016-01-01

    Full Text Available In this article, a novel artificial neural network integrating feed-forward back-propagation neural network with Gaussian kernel function is proposed for the prediction of compressor performance map. To demonstrate the potential capability of the proposed approach for the typical interpolated and extrapolated predictions, other two classical data-driven modeling methods including feed-forward back-propagation neural network and support vector machine are compared. An assessment is performed and discussed on the sensitivity of different models to the number of training samples (48 training samples, 32 training samples, and 18 training samples. All the results indicate that the proposed neural network in this article has superior prediction performance to the existing feed-forward back-propagation neural network and support vector machine, especially for the extrapolation with small samples. Furthermore, this study can be utilized in refining the existing performance-based modeling for improved simulation analysis, condition monitoring, and fault diagnosis of gas turbine compressor.

  17. [Hyperspectral remote sensing image classification based on radical basis function neural network].

    Science.gov (United States)

    Tan, Kun; Du, Pei-jun

    2008-09-01

    Based on the radial basis function neural network (RBFNN) theory and the specialty of hyperspectral remote sensing data, the effective feature extraction model was designed, and those extracted features were connected to the input layer of RBFNN, finally the classifier based on radial basis function neural network was constructed. The hyperspectral image with 64 bands of OMIS II made by Chinese was experimented, and the case study area was zhongguancun in Beijing. Minimum noise fraction (MNF) was conducted, and the former 20 components were extracted for further processing. The original data (20 dimension) of extraction by MNF, the texture transformation data (20 dimension) extracted from the former 20 components after MNF, and the principal component analysis data (20 dimension) of extraction were combined to 60 dimension. For classification by RBFNN, the sizes of training samples were less than 6.13% of the whole image. That classifier has a simple structure and fast convergence capacity, and can be easily trained. The classification precision of radial basis function neural network classifier is up to 69.27% in contrast with the 51.20% of back propagation neural network (BPNN) and 40. 88% of traditional minimum distance classification (MDC), so RBFNN classifier performs better than the other three classifiers. It proves that RBFNN is of validity in hyperspectral remote sensing classification.

  18. Computing single step operators of logic programming in radial basis function neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)

    2014-07-10

    Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.

  19. Computing single step operators of logic programming in radial basis function neural networks

    Science.gov (United States)

    Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong

    2014-07-01

    Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (Tp:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.

  20. Nitric Oxide and Lutein: Function, Performance, and Protection of Neural Tissue

    Directory of Open Access Journals (Sweden)

    James M. Stringham

    2015-11-01

    Full Text Available The soluble gas neurotransmitter nitric oxide (NO serves many important metabolic and neuroregulatory functions in the retina and brain. Although it is necessary for normal neural function, NO can play a significant role in neurotoxicity. This is often seen in disease states that involve oxidative stress and inflammation of neural tissues, such as age-related macular degeneration and Alzheimer’s disease. The dietary xanthophyll carotenoid lutein (L is a potent antioxidant and anti-inflammatory agent that, if consumed in sufficient amounts, is deposited in neural tissues that require substantial metabolic demand. Some of these specific tissues, such as the central retina and frontal lobes of the brain, are impacted by age-related diseases such as those noted above. The conspicuous correspondence between metabolic demand, NO, and L is suggestive of a homeostatic relationship that serves to facilitate normal function, enhance performance, and protect vulnerable neural tissues. The purpose of this paper is to review the literature on these points.

  1. Fitting VFC's Output Using Functionally Connected High-Order Neural Networks

    Institute of Scientific and Technical Information of China (English)

    CHENG Chun-ling; ZHOU Jie

    2004-01-01

    A new method is presented in this paper for fitting Voltage-to-Frequency Converter (VFC's) output functions by using Functionally Connected High-order Neural Networks (FCHNN). The nonlinear estimation is implemented when the VFC110 is used at a full-scale output frequency of 4 MHz. Two kinds of on-line dynamic calibrating circuits are designed to improve the sampling precision. This method can also be applied to different industrial areas.

  2. Genetic control of astrocyte function in neural circuits

    Directory of Open Access Journals (Sweden)

    Hannah Maria Jahn

    2015-08-01

    Full Text Available During the last two decades numerous genetic approaches affecting cell function in vivo have been developed. Current state-of-the-art technology permits the selective switching of gene function in distinct cell populations within the complex organization of a given tissue parenchyma. The tamoxifen-inducible Cre/loxP gene recombination and the doxycycline-dependent modulation of gene expression are probably the most popular genetic paradigms.Here, we will review applications of these two strategies while focussing on the interactions of astrocytes and neurons in the central nervous system (CNS and their impact for the whole organism. Abolishing glial sensing of neuronal activity by selective deletion of glial transmitter receptors demonstrated the impact of astrocytes for higher cognitive functions such as learning and memory, or the more basic body control of muscle coordination. Interestingly, also interfering with glial output, i.e. the release of gliotransmitters can drastically change animal’s physiology like sleeping behavior. Furthermore, such genetic approaches have also been used to restore astrocyte function. In these studies two alternatives were employed to achieve proper genetic targeting of astrocytes: transgenes using the promoter of the human glial fibrillary acidic protein (GFAP or homologous recombination into the glutamate-aspartate transporter (GLAST locus. We will highlight their specific properties that could be relevant for their use.

  3. Characterizing the Spatial Density Functions of Neural Arbors

    Science.gov (United States)

    Teeter, Corinne Michelle

    Recently, it has been proposed that a universal function describes the way in which all arbors (axons and dendrites) spread their branches over space. Data from fish retinal ganglion cells as well as cortical and hippocampal arbors from mouse, rat, cat, monkey and human provide evidence that all arbor density functions (adf) can be described by a Gaussian function truncated at approximately two standard deviations. A Gaussian density function implies that there is a minimal set of parameters needed to describe an adf: two or three standard deviations (depending on the dimensionality of the arbor) and an amplitude. However, the parameters needed to completely describe an adf could be further constrained by a scaling law found between the product of the standard deviations and the amplitude of the function. In the following document, I examine the scaling law relationship in order to determine the minimal set of parameters needed to describe an adf. First, I find that the at, two-dimensional arbors of fish retinal ganglion cells require only two out of the three fundamental parameters to completely describe their density functions. Second, the three-dimensional, volume filling, cortical arbors require four fundamental parameters: three standard deviations and the total length of an arbor (which corresponds to the amplitude of the function). Next, I characterize the shape of arbors in the context of the fundamental parameters. I show that the parameter distributions of the fish retinal ganglion cells are largely homogenous. In general, axons are bigger and less dense than dendrites; however, they are similarly shaped. The parameter distributions of these two arbor types overlap and, therefore, can only be differentiated from one another probabilistically based on their adfs. Despite artifacts in the cortical arbor data, different types of arbors (apical dendrites, non-apical dendrites, and axons) can generally be differentiated based on their adfs. In addition, within

  4. Effect of puerarin on neural function and histopathological damages after transient spinal cord ischemia in rabbits

    Institute of Scientific and Technical Information of China (English)

    桑韩飞; 梅其炳; 徐礼鲜; 王强; 程虹; 熊利泽

    2004-01-01

    Objective: To investigate the effect of Puerarin on the neural function and the histopathological changes after ischemic spinal cord injury in rabbits.Methods: Thirty male New Zealand white rabbits were randomly divided into three groups as follows:puerarin group ( n= 10) receiving intravenous infusion of 30 mg/kg puerarin for 10 minutes, control group ( n = 10)receiving intravenous infusion of the same volume of normal saline as puerarin for 10 minutes, and sham operation group ( n= 10) undergoing only the surgical exposure of the abdominal aorta. Temporary spinal cord ischemia was induced by infrarenal aortic occlusion for 20 minutes and followed by reperfusion. The neural status was scored with the Tarlov criteria at 8, 12, 24 and 48 hours after reperfusion. All the animals were killed at 48 hours after reperfusion and the spinal cords (Ls were removed immediately for histopathological study.Results: The neural function scores at 8, 12, 24 and 48 hours after reperfusion were higher in the puerarin group and sham operation group than those in the control group (P < 0.05 ). More normal motor neurons in the anterior horn of spinal cord were present in the puerarin group and sham operation group than those in the control group (P < 0.01 ). There was a strong correlation between the final neural function scores and the number of normal motor neurons in the anterior horn of spinal cord ( r =0.839, P <0.01).Conclusions: Puerarin can significantly ameliorate the neural function and the histopathological damages after transient spinal cord ischemia in rabbits.

  5. Assessment of the expected construction company’s net profit using neural network and multiple regression models

    Directory of Open Access Journals (Sweden)

    H.H. Mohamad

    2013-09-01

    This research aims to develop a mathematical model for assessing the expected net profit of any construction company. To achieve the research objective, four steps were performed. First, the main factors affecting firms’ net profit were identified. Second, pertinent data regarding the net profit factors were collected. Third, two different net profit models were developed using the Multiple Regression (MR and the Neural Network (NN techniques. The validity of the proposed models was also investigated. Finally, the results of both MR and NN models were compared to investigate the predictive capabilities of the two models.

  6. Functional neural correlates of reduced physiological falls risk

    Directory of Open Access Journals (Sweden)

    Hsu Chun

    2011-08-01

    Full Text Available Abstract Background It is currently unclear whether the function of brain regions associated with executive cognitive processing are independently associated with reduced physiological falls risk. If these are related, it would suggest that the development of interventions targeted at improving executive neurocognitive function would be an effective new approach for reducing physiological falls risk in seniors. Methods We performed a secondary analysis of 73 community-dwelling senior women aged 65 to 75 years old who participated in a 12-month randomized controlled trial of resistance training. Functional MRI data were acquired while participants performed a modified Eriksen Flanker Task - a task of selective attention and conflict resolution. Brain volumes were obtained using MRI. Falls risk was assessed using the Physiological Profile Assessment (PPA. Results After accounting for baseline age, experimental group, baseline PPA score, and total baseline white matter brain volume, baseline activation in the left frontal orbital cortex extending towards the insula was negatively associated with reduced physiological falls risk over the 12-month period. In contrast, baseline activation in the paracingulate gyrus extending towards the anterior cingulate gyrus was positively associated with reduced physiological falls risk. Conclusions Baseline activation levels of brain regions underlying response inhibition and selective attention were independently associated with reduced physiological falls risk. This suggests that falls prevention strategies may be facilitated by incorporating intervention components - such as aerobic exercise - that are specifically designed to induce neurocognitive plasticity. Trial Registration ClinicalTrials.gov Identifier: NCT00426881

  7. Pinning Synchronization of Directed Networks With Switching Topologies: A Multiple Lyapunov Functions Approach.

    Science.gov (United States)

    Wen, Guanghui; Yu, Wenwu; Hu, Guoqiang; Cao, Jinde; Yu, Xinghuo

    2015-12-01

    This paper studies the global pinning synchronization problem for a class of complex networks with switching directed topologies. The common assumption in the existing related literature that each possible network topology contains a directed spanning tree is removed in this paper. Using tools from M -matrix theory and stability analysis of the switched nonlinear systems, a new kind of network topology-dependent multiple Lyapunov functions is proposed for analyzing the synchronization behavior of the whole network. It is theoretically shown that the global pinning synchronization in switched complex networks can be ensured if some nodes are appropriately pinned and the coupling is carefully selected. Interesting issues of how many and which nodes should be pinned for possibly realizing global synchronization are further addressed. Finally, some numerical simulations on coupled neural networks are provided to verify the theoretical results.

  8. Expanded functional coupling of subcortical nuclei with the motor resting-state network in multiple sclerosis.

    Science.gov (United States)

    Dogonowski, Anne-Marie; Siebner, Hartwig R; Sørensen, Per Soelberg; Wu, Xingchen; Biswal, Bharat; Paulson, Olaf B; Dyrby, Tim B; Skimminge, Arnold; Blinkenberg, Morten; Madsen, Kristoffer H

    2013-04-01

    Multiple sclerosis (MS) impairs signal transmission along cortico-cortical and cortico-subcortical connections, affecting functional integration within the motor network. Functional magnetic resonance imaging (fMRI) during motor tasks has revealed altered functional connectivity in MS, but it is unclear how much motor disability contributed to these abnormal functional interaction patterns. To avoid any influence of impaired task performance, we examined disease-related changes in functional motor connectivity in MS at rest. A total of 42 patients with MS and 30 matched controls underwent a 20-minute resting-state fMRI session at 3 Tesla. Independent component analysis was applied to the fMRI data to identify disease-related changes in motor resting-state connectivity. Patients with MS showed a spatial expansion of motor resting-state connectivity in deep subcortical nuclei but not at the cortical level. The anterior and middle parts of the putamen, adjacent globus pallidus, anterior and posterior thalamus and the subthalamic region showed stronger functional connectivity with the motor network in the MS group compared with controls. MS is characterised by more widespread motor connectivity in the basal ganglia while cortical motor resting-state connectivity is preserved. The expansion of subcortical motor resting-state connectivity in MS indicates less efficient funnelling of neural processing in the executive motor cortico-basal ganglia-thalamo-cortical loops.

  9. Adaptive Weibull Multiplicative Model and Multilayer Perceptron Neural Networks for Dark-Spot Detection from SAR Imagery

    Directory of Open Access Journals (Sweden)

    Alireza Taravat

    2014-12-01

    Full Text Available Oil spills represent a major threat to ocean ecosystems and their environmental status. Previous studies have shown that Synthetic Aperture Radar (SAR, as its recording is independent of clouds and weather, can be effectively used for the detection and classification of oil spills. Dark formation detection is the first and critical stage in oil-spill detection procedures. In this paper, a novel approach for automated dark-spot detection in SAR imagery is presented. A new approach from the combination of adaptive Weibull Multiplicative Model (WMM and MultiLayer Perceptron (MLP neural networks is proposed to differentiate between dark spots and the background. The results have been compared with the results of a model combining non-adaptive WMM and pulse coupled neural networks. The presented approach overcomes the non-adaptive WMM filter setting parameters by developing an adaptive WMM model which is a step ahead towards a full automatic dark spot detection. The proposed approach was tested on 60 ENVISAT and ERS2 images which contained dark spots. For the overall dataset, an average accuracy of 94.65% was obtained. Our experimental results demonstrate that the proposed approach is very robust and effective where the non-adaptive WMM & pulse coupled neural network (PCNN model generates poor accuracies.

  10. EEG frequency tagging dissociates between neural processing of motion synchrony and human quality of multiple point-light dancers

    Science.gov (United States)

    Alp, Nihan; Nikolaev, Andrey R.; Wagemans, Johan; Kogo, Naoki

    2017-01-01

    Do we perceive a group of dancers moving in synchrony differently from a group of drones flying in-sync? The brain has dedicated networks for perception of coherent motion and interacting human bodies. However, it is unclear to what extent the underlying neural mechanisms overlap. Here we delineate these mechanisms by independently manipulating the degree of motion synchrony and the humanoid quality of multiple point-light displays (PLDs). Four PLDs moving within a group were changing contrast in cycles of fixed frequencies, which permits the identification of the neural processes that are tagged by these frequencies. In the frequency spectrum of the steady-state EEG we found two emergent frequency components, which signified distinct levels of interactions between PLDs. The first component was associated with motion synchrony, the second with the human quality of the moving items. These findings indicate that visual processing of synchronously moving dancers involves two distinct neural mechanisms: one for the perception of a group of items moving in synchrony and one for the perception of a group of moving items with human quality. We propose that these mechanisms underlie high-level perception of social interactions. PMID:28272421

  11. FUZZY-DISTANCE FUNCTION APPROACH FOR MULTIPLE CRITERIA DECISION MAKING

    Directory of Open Access Journals (Sweden)

    Mayank Kumar

    2012-06-01

    Full Text Available In this paper, a method for decision making using fuzzy integral and distance function is presented. Case studies of multiple-response process with correlated responses are used to illustrate the effective application of the proposed approach. The efficacy of this method is compared with the existing methods of MCDM like TOPSIS and GRA. The proposed method is robust, requires less information and less complex as compared to many existing methods.

  12. Crystallization induced by multiple seeds: dynamical density functional approach.

    Science.gov (United States)

    Neuhaus, T; Schmiedeberg, M; Löwen, H

    2013-12-01

    Using microscopic dynamical density functional theory, we calculate the dynamical formation of polycrystals by following the crystal growth around multiple crystalline seeds imposed to an undercooled fluid. Depending on the undercooling and the size ratio as well as the relative crystal orientation of two neighboring seeds, three possibilities of the final state emerge, namely no crystallization at all, formation of a monocrystal, or two crystallites separated by a curved grain boundary. Our results, which are obtained for two-dimensional hard disk systems using a fundamental-measure density functional, shed new light on the particle-resolved structure and growth of polycrystalline material in general.

  13. Neural compensation, muscle load distribution and muscle function in control of biped models

    Science.gov (United States)

    Bavarian, B.

    Three aspects of the neuromuscular control of muscle actuators in biped movements were studied: neural compensation, muscle load distribution, and muscle function. A block diagram of a neural control circuit model of the control nervous system is presented. Based on this block diagram a circuit comprised of a dynamic compensator, an inverse plant, and pre-programmed reference trajectory generators is proposed for control of a general n-link biped model. This circuit is used to study the postural stability and point-to-point voluntary movement of a two-link planar biped with two pairs of muscle models. The muscle load distribution, relevant to functional electrical stimulation of paraplegic patients for restoration of limited motor function, is considered. A quantitative analysis of the local controllability of a two-link planar biped model incorporating six major muscles of the lower extremities is presented. A model of the muscle for the lower extremities is presented.

  14. Stability analysis of memristor-based fractional-order neural networks with different memductance functions.

    Science.gov (United States)

    Rakkiyappan, R; Velmurugan, G; Cao, Jinde

    2015-04-01

    In this paper, the problem of the existence, uniqueness and uniform stability of memristor-based fractional-order neural networks (MFNNs) with two different types of memductance functions is extensively investigated. Moreover, we formulate the complex-valued memristor-based fractional-order neural networks (CVMFNNs) with two different types of memductance functions and analyze the existence, uniqueness and uniform stability of such networks. By using Banach contraction principle and analysis technique, some sufficient conditions are obtained to ensure the existence, uniqueness and uniform stability of the considered MFNNs and CVMFNNs with two different types of memductance functions. The analysis results establish from the theory of fractional-order differential equations with discontinuous right-hand sides. Finally, four numerical examples are presented to show the effectiveness of our theoretical results.

  15. A new method for the re-implementation of threshold logic functions with cellular neural networks.

    Science.gov (United States)

    Bénédic, Y; Wira, P; Mercklé, J

    2008-08-01

    A new strategy is presented for the implementation of threshold logic functions with binary-output Cellular Neural Networks (CNNs). The objective is to optimize the CNNs weights to develop a robust implementation. Hence, the concept of generative set is introduced as a convenient representation of any linearly separable Boolean function. Our analysis of threshold logic functions leads to a complete algorithm that automatically provides an optimized generative set. New weights are deduced and a more robust CNN template assuming the same function can thus be implemented. The strategy is illustrated by a detailed example.

  16. Application of radial basis function neural network to predict soil sorption partition coefficient using topological descriptors.

    Science.gov (United States)

    Sabour, Mohammad Reza; Moftakhari Anasori Movahed, Saman

    2017-02-01

    The soil sorption partition coefficient logKoc is an indispensable parameter that can be used in assessing the environmental risk of organic chemicals. In order to predict soil sorption partition coefficient for different and even unknown compounds in a fast and accurate manner, a radial basis function neural network (RBFNN) model was developed. Eight topological descriptors of 800 organic compounds were used as inputs of the model. These 800 organic compounds were chosen from a large and very diverse data set. Generalized Regression Neural Network (GRNN) was utilized as the function in this neural network model due to its capability to adapt very quickly. Hence, it can be used to predict logKoc for new chemicals, as well. Out of total data set, 560 organic compounds were used for training and 240 to test efficiency of the model. The obtained results indicate that the model performance is very well. The correlation coefficients (R2) for training and test sets were 0.995 and 0.933, respectively. The root-mean square errors (RMSE) were 0.2321 for training set and 0.413 for test set. As the results for both training and test set are extremely satisfactory, the proposed neural network model can be employed not only to predict logKoc of known compounds, but also to be adaptive for prediction of this value precisely for new products that enter the market each year. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. Towards Integration of Biological and Physiological Functions at Multiple Levels

    Directory of Open Access Journals (Sweden)

    Taishin eNomura

    2010-12-01

    Full Text Available An aim of systems physiology today can be stated as to establish logical and quantitative bridges between phenomenological attributes of physiological entities such as cells and organs and physical attributes of biological entities, i.e., biological molecules, allowing us to describe and better understand physiological functions in terms of underlying biological functions. This article illustrates possible schema that can be used for promoting systems physiology by integrating quantitative knowledge of biological and physiological functions at multiple levels of time and space with the use of information technology infrastructure. Emphasis will be made for systematic, modular, hierarchical, and standardized descriptions of mathematical models of the functions and advantages for the use of them.

  18. NNScore 2.0: A Neural-Network Receptor–Ligand Scoring Function

    Science.gov (United States)

    2011-01-01

    NNScore is a neural-network-based scoring function designed to aid the computational identification of small-molecule ligands. While the test cases included in the original NNScore article demonstrated the utility of the program, the application examples were limited. The purpose of the current work is to further confirm that neural-network scoring functions are effective, even when compared to the scoring functions of state-of-the-art docking programs, such as AutoDock, the most commonly cited program, and AutoDock Vina, thought to be two orders of magnitude faster. Aside from providing additional validation of the original NNScore function, we here present a second neural-network scoring function, NNScore 2.0. NNScore 2.0 considers many more binding characteristics when predicting affinity than does the original NNScore. The network output of NNScore 2.0 also differs from that of NNScore 1.0; rather than a binary classification of ligand potency, NNScore 2.0 provides a single estimate of the pKd. To facilitate use, NNScore 2.0 has been implemented as an open-source python script. A copy can be obtained from http://www.nbcr.net/software/nnscore/. PMID:22017367

  19. PERSPECTIVE: Translational neural engineering: multiple perspectives on bringing benchtop research into the clinical domain

    Science.gov (United States)

    Rousche, Patrick; Schneeweis, David M.; Perreault, Eric J.; Jensen, Winnie

    2008-03-01

    A half-day forum to address a wide range of issues related to translational neural engineering was conducted at the annual meeting of the Biomedical Engineering Society. Successful practitioners of translational neural engineering from academics, clinical medicine and industry were invited to share a diversity of perspectives and experiences on the translational process. The forum was targeted towards traditional academic researchers who may be interested in the expanded funding opportunities available for translational research that emphasizes product commercialization and clinical implementation. The seminar was funded by the NIH with support from the Rehabilitation Institute of Chicago. We report here a summary of the speaker viewpoints with particular focus on extracting successful strategies for engaging in or conducting translational neural engineering research. Daryl Kipke, PhD, (Department of Biomedical Engineering at the University of Michigan) and Molly Shoichet, PhD, (Department of Chemical Engineering at the University of Toronto) gave details of their extensive experience with product commercialization while holding primary appointments in academic departments. They both encouraged strong clinical input at very early stages of research. Neurosurgeon Fady Charbel, MD, (Department of Neurosurgery at the University of Illinois at Chicago) discussed his role in product commercialization as a clinician. Todd Kuiken, MD, PhD, (Director of the Neural Engineering for Artificial Limbs at the Rehabilitation Institute of Chicago, affiliated with Northwestern University) also a clinician, described a model of translational engineering that emphasized the development of clinically relevant technology, without a strong commercialization imperative. The clinicians emphasized the importance of communicating effectively with engineers. Representing commercial neural engineering was Doug Sheffield, PhD, (Director of New Technology at Vertis Neuroscience, Inc.) who

  20. Translational neural engineering: multiple perspectives on bringing benchtop research into the clinical domain.

    Science.gov (United States)

    Rousche, Patrick; Schneeweis, David M; Perreault, Eric J; Jensen, Winnie

    2008-03-01

    A half-day forum to address a wide range of issues related to translational neural engineering was conducted at the annual meeting of the Biomedical Engineering Society. Successful practitioners of translational neural engineering from academics, clinical medicine and industry were invited to share a diversity of perspectives and experiences on the translational process. The forum was targeted towards traditional academic researchers who may be interested in the expanded funding opportunities available for translational research that emphasizes product commercialization and clinical implementation. The seminar was funded by the NIH with support from the Rehabilitation Institute of Chicago. We report here a summary of the speaker viewpoints with particular focus on extracting successful strategies for engaging in or conducting translational neural engineering research. Daryl Kipke, PhD, (Department of Biomedical Engineering at the University of Michigan) and Molly Shoichet, PhD, (Department of Chemical Engineering at the University of Toronto) gave details of their extensive experience with product commercialization while holding primary appointments in academic departments. They both encouraged strong clinical input at very early stages of research. Neurosurgeon Fady Charbel, MD, (Department of Neurosurgery at the University of Illinois at Chicago) discussed his role in product commercialization as a clinician. Todd Kuiken, MD, PhD, (Director of the Neural Engineering for Artificial Limbs at the Rehabilitation Institute of Chicago, affiliated with Northwestern University) also a clinician, described a model of translational engineering that emphasized the development of clinically relevant technology, without a strong commercialization imperative. The clinicians emphasized the importance of communicating effectively with engineers. Representing commercial neural engineering was Doug Sheffield, PhD, (Director of New Technology at Vertis Neuroscience, Inc.) who

  1. Robust stability analysis for Markovian jumping stochastic neural networks with mode-dependent time-varying interval delay and multiplicative noise

    Institute of Scientific and Technical Information of China (English)

    Zhang Hua-Guang; Fu Jie; Ma Tie-Dong; Tong Shao-Cheng

    2009-01-01

    This paper is concerned with the problem of robust stability for a class of Markovian jumping stochastic neural networks (MJSNNs) subject to mode-dependent time-varying interval delay and state-multiplicative noise.Based on the Lyapunov-Krasovskii functional and a stochastic analysis approach,some new delay-dependent sufficient conditions are obtained in the linear matrix inequality (LMI) format such that delayed MJSNNs are globally asymptotically stable in the mean-square sense for all admissible uncertainties.An important feature of the results is that the stability criteria are dependent on not only the lower bound and upper bound of delay for all modes but also the covariance matrix consisting of the correlation coefficient.Numerical examples are given to illustrate the effectiveness.

  2. An improved robust stability result for uncertain neural networks with multiple time delays.

    Science.gov (United States)

    Arik, Sabri

    2014-06-01

    This paper proposes a new alternative sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point for the class of delayed neural networks under the parameter uncertainties of the neural system. The existence and uniqueness of the equilibrium point is proved by using the Homomorphic mapping theorem. The asymptotic stability of the equilibrium point is established by employing the Lyapunov stability theorems. The obtained robust stability condition establishes a new relationship between the network parameters of the system. We compare our stability result with the previous corresponding robust stability results derived in the past literature. Some comparative numerical examples together with some simulation results are also given to show the applicability and advantages of our result.

  3. Reversible neural stem cell niche dysfunction in a model of multiple sclerosis

    DEFF Research Database (Denmark)

    Rasmussen, Stine; Imitola, Jaime; Ayuso-Sacido, Angel

    2011-01-01

    OBJECTIVE: The subventricular zone (SVZ) of the brain constitutes a niche for neural stem and progenitor cells that can initiate repair after central nervous system (CNS) injury. In a relapsing-remitting model of experimental autoimmune encephalomyelitis (EAE), the neural stem cells (NSCs) become...... activated and initiate regeneration during acute disease, but lose this ability during the chronic phases of disease. We hypothesized that chronic microglia activation contributes to the failure of the NSC repair potential in the SVZ. METHODS: Using bromodeoxyuridine injections at different time points...... during EAE, we quantified the number of proliferating and differentiating progenitors, and evaluated the structure of the SVZ by electron microscopy. In vivo minocycline treatment during EAE was used to address the effect of microglia inactivation on SVZ dysfunction. RESULTS: In vivo treatment...

  4. Multiple Linear Regression Model Based on Neural Network and Its Application in the MBR Simulation

    Directory of Open Access Journals (Sweden)

    Chunqing Li

    2012-01-01

    Full Text Available The computer simulation of the membrane bioreactor MBR has become the research focus of the MBR simulation. In order to compensate for the defects, for example, long test period, high cost, invisible equipment seal, and so forth, on the basis of conducting in-depth study of the mathematical model of the MBR, combining with neural network theory, this paper proposed a three-dimensional simulation system for MBR wastewater treatment, with fast speed, high efficiency, and good visualization. The system is researched and developed with the hybrid programming of VC++ programming language and OpenGL, with a multifactor linear regression model of affecting MBR membrane fluxes based on neural network, applying modeling method of integer instead of float and quad tree recursion. The experiments show that the three-dimensional simulation system, using the above models and methods, has the inspiration and reference for the future research and application of the MBR simulation technology.

  5. MaNN: Multiple Artificial Neural Networks for modelling the Interstellar Medium

    CERN Document Server

    Grassi, T; Piovan, L; Buonomo, U; Chiosi, C

    2011-01-01

    Modelling the complex physics of the Interstellar Medium (ISM) in the context of large-scale numerical simulations is a challenging task. A number of methods have been proposed to embed a description of the ISM into different codes. We propose a new way to achieve this task: Artificial Neural Networks (ANNs). The ANN has been trained on a pre-compiled model database, and its predictions have been compared to the expected theoretical ones, finding good agreement both in static and in dynamical tests run using the Padova Tree-SPH code \\textsc{EvoL}. A neural network can reproduce the details of the interstellar gas evolution, requiring limited computational resources. We suggest that such an algorithm can replace a real-time calculation of mass elements chemical evolution in hydrodynamical codes.

  6. Estimating Fast Neural Input Using Anatomical and Functional Connectivity.

    Science.gov (United States)

    Eriksson, David

    2016-01-01

    In the last 20 years there has been an increased interest in estimating signals that are sent between neurons and brain areas. During this time many new methods have appeared for measuring those signals. Here we review a wide range of methods for which connected neurons can be identified anatomically, by tracing axons that run between the cells, or functionally, by detecting if the activity of two neurons are correlated with a short lag. The signals that are sent between the neurons are represented by the activity in the neurons that are connected to the target population or by the activity at the corresponding synapses. The different methods not only differ in the accuracy of the signal measurement but they also differ in the type of signal being measured. For example, unselective recording of all neurons in the source population encompasses more indirect pathways to the target population than if one selectively record from the neurons that project to the target population. Infact, this degree of selectivity is similar to that of optogenetic perturbations; one can perturb selectively or unselectively. Thus it becomes possible to match a given signal measurement method with a signal perturbation method, something that allows for an exact input control to any neuronal population.

  7. Relaxed fault-tolerant hardware implementation of neural networks in the presence of multiple transient errors.

    Science.gov (United States)

    Mahdiani, Hamid Reza; Fakhraie, Sied Mehdi; Lucas, Caro

    2012-08-01

    Reliability should be identified as the most important challenge in future nano-scale very large scale integration (VLSI) implementation technologies for the development of complex integrated systems. Normally, fault tolerance (FT) in a conventional system is achieved by increasing its redundancy, which also implies higher implementation costs and lower performance that sometimes makes it even infeasible. In contrast to custom approaches, a new class of applications is categorized in this paper, which is inherently capable of absorbing some degrees of vulnerability and providing FT based on their natural properties. Neural networks are good indicators of imprecision-tolerant applications. We have also proposed a new class of FT techniques called relaxed fault-tolerant (RFT) techniques which are developed for VLSI implementation of imprecision-tolerant applications. The main advantage of RFT techniques with respect to traditional FT solutions is that they exploit inherent FT of different applications to reduce their implementation costs while improving their performance. To show the applicability as well as the efficiency of the RFT method, the experimental results for implementation of a face-recognition computationally intensive neural network and its corresponding RFT realization are presented in this paper. The results demonstrate promising higher performance of artificial neural network VLSI solutions for complex applications in faulty nano-scale implementation environments.

  8. Transcriptional and Genomic Targets of Neural Stem Cells for Functional Recovery after Hemorrhagic Stroke

    Directory of Open Access Journals (Sweden)

    Le Zhang

    2017-01-01

    Full Text Available Hemorrhagic stroke is a life-threatening disease characterized by a sudden rupture of cerebral blood vessels, and it is widely believed that neural cell death occurs after exposure to blood metabolites or subsequently damaged cells. Neural stem cells (NSCs, which maintain neurogenesis and are found in subgranular zone and subventricular zone, are thought to be an endogenous neuroprotective mechanism for these brain injuries. However, due to the complexity of NSCs and their microenvironment, current strategies cannot satisfactorily enhance functional recovery after hemorrhagic stroke. It is well known that transcriptional and genomic pathways play important roles in ensuring the normal functions of NSCs, including proliferation, migration, differentiation, and neural reconnection. Recently, emerging evidence from the use of new technologies such as next-generation sequencing and transcriptome profiling has provided insight into our understanding of genomic function and regulation of NSCs. In the present article, we summarize and present the current data on the control of NSCs at both the transcriptional and genomic levels. Using bioinformatics methods, we sought to predict novel therapeutic targets of endogenous neurogenesis and exogenous NSC transplantation for functional recovery after hemorrhagic stroke, which could also advance our understanding of its pathophysiology.

  9. Altered Neural Substrates of Cognitive Control in Childhood ADHD: Evidence From Functional Magnetic Resonance Imaging

    Science.gov (United States)

    Vaidya, Chandan J.; Bunge, Silvia A.; Dudukovic, Nicole M.; Zalecki, Christine A.; Elliott, Glen R.; Gabrieli, John D.E.

    2015-01-01

    Objective The study compared the neural bases of two cognitive control operations, interference suppression and response inhibition, between children with and children without attention deficit hyperactivity disorder (ADHD). Method Ten children (7–11 years of age) with combined-type ADHD and 10 comparison subjects matched for age and gender underwent rapid event-related functional magnetic resonance imaging (fMRI) during performance of a modified flanker task. Functional maps were generated through group averaging and performance-based correlational analyses. Results Interference suppression in ADHD subjects was characterized by reduced engagement of a frontal-striatal-temporal-parietal network that subserved healthy performance. In contrast, response inhibition performance relied upon different regions in the two groups, frontal-striatal in comparison subjects but right superior temporal in ADHD children. Conclusions Alteration in the neural basis of two cognitive control operations in childhood ADHD was characterized by distinct, rather than unitary, patterns of functional abnormality. Greater between-group overlap in the neural network activated for interference suppression than in response inhibition suggests that components of cognitive control are differentially sensitive to ADHD. The ADHD children's inability to activate the caudate nucleus constitutes a core abnormality in ADHD. Observed functional abnormalities did not result from prolonged stimulant exposure, since most children were medication naive. PMID:16135618

  10. Exploring the mechanism of neural-function reconstruction by reinnervated nerves in targeted muscles

    Institute of Scientific and Technical Information of China (English)

    Hui ZHOU; Lin YANG; Feng-xia WU; Jian-ping HUANG; Liang-qing ZHANG; Ying-jian YANG; Guang-lin LI

    2014-01-01

    A lack of myoelectric sources after limb amputation is a critical challenge in the control of multifunctional motorized prostheses. To reconstruct myoelectric sources physiologically related to lost limbs, a newly proposed neural-function construc-tion method, targeted muscle reinnervation (TMR), appears promising. Recent advances in the TMR technique suggest that TMR could provide additional motor command information for the control of multifunctional myoelectric prostheses. However, little is known about the nature of the physiological functional recovery of the reinnervated muscles. More understanding of the under-lying mechanism of TMR could help us fine tune the technique to maximize its capability to achieve a much higher performance in the control of multifunctional prostheses. In this study, rats were used as an animal model for TMR surgery involving transferring a median nerve into the pectoralis major, which served as the target muscle. Intramuscular myoelectric signals reconstructed following TMR were recorded by implanted wire electrodes and analyzed to explore the nature of the neural-function recon-struction achieved by reinnervation of targeted muscles. Our results showed that the active myoelectric signal reconstructed in the targeted muscle was acquired one week after TMR surgery, and its amplitude gradually became stronger over time. These pre-liminary results from rats may serve as a basis for exploring the mechanism of neural-function reconstruction by the TMR tech-nique in human subjects.

  11. Random number generation deficits in patients with multiple sclerosis: Characteristics and neural correlates.

    Science.gov (United States)

    Geisseler, Olivia; Pflugshaupt, Tobias; Buchmann, Andreas; Bezzola, Ladina; Reuter, Katja; Schuknecht, Bernhard; Weller, David; Linnebank, Michael; Brugger, Peter

    2016-09-01

    Human subjects typically deviate systematically from randomness when attempting to produce a sequence of random numbers. Despite an increasing number of behavioral and functional neuroimaging studies on random number generation (RNG), its structural correlates have never been investigated. We set out to fill this gap in 44 patients with multiple sclerosis (MS), a disease whose impact on RNG has never been studied. The RNG task required the paced (1 Hz) generation of the numbers from 1 to 6 in a sequence as random as possible. The same task was administered in 39 matched healthy controls. To assess neuroanatomical correlates such as cortical thickness, lesion load and third ventricle width, all subjects underwent high-resolution structural MRI. Compared to controls, MS patients exhibited an enhanced tendency to arrange consecutive numbers in an ascending order ("forward counting"). Furthermore, patients showed a higher susceptibility to rule breaks (producing out-of-category digits like 7) and to skip beats of the metronome. Clinico-anatomical correlation analyses revealed two main findings: First, increased counting in MS patients was associated with higher cortical lesion load. Second, increased number of skipped beats was related to widespread cortical thinning. In conclusion, our test results illustrate a loss of behavioral complexity in the course of MS, while the imaging results suggest an association between this loss and cortical pathology.

  12. Multiple neural tube defects: a rare combination of limited dorsal myeloschisis, diplomyelia with dorsal bony spur, sacral meningocoele, syringohydromyelia, and tethered cord.

    Science.gov (United States)

    Shashank R, Ramdurg; Shubhi, Dubey; Vishal, Kadeli

    2017-04-01

    Multiple neural tube defects are relatively rare. They account for less than 1% reported neural tube defects. Cases of limited dorsal myeloschisis (LDM) and diplomyelia (two cords in single sac without intervening bony or fibrous septae) with dorsal bony spur are also a rare event. Here, the authors report a rare case of neonate with thoracic LDM, diplomyelia with dorsal bony spur, sacral meningocoele with syringohydromyelia, and low-lying tethered cord. The child also had a ventricular septal defect (VSD) and bilateral rocker bottom feet. Various environmental factors and genetic mutations in transmembrane proteins have been studied in animal models explaining the origin of neural tube defects. To the best of author's knowledge, this is the first case of varied multiple neural tube defects with diplomyelia reported in world literature.

  13. Body composition and physical function in women with multiple sclerosis

    Directory of Open Access Journals (Sweden)

    Christie L. Ward, MS

    2013-11-01

    Full Text Available Persons with multiple sclerosis (MS have reduced physical activity (PA and lower-limb physical function and potentially disordered body composition compared with their peers without MS. The aim of this study was to determine whether PA and body composition were differentially associated with lower-limb physical function in persons with MS compared with controls. Females with MS and age- and body mass index-matched female controls (n = 51; average age 48.1 +/– 9.7 yr were measured for PA with daily step counts, relative fat mass (%Fat, and leg lean mass (LM-LEG via dual energy X-ray absorptiometry and for lower-limb physical function with objective performance tests. Persons with MS had 12.5% to 53% poorer lower-limb physical function than controls (all p < 0.05. PA, %Fat, and LM-LEG to body mass ratio (LM-LEG/BM were associated with lower-limb physical function in both persons with MS and controls (all p < 0.05. Based on median splits, higher %Fat, lower LM-LEG/BM, and MS conferred poorer lower-limb physical function (all p < 0.05. PA, %Fat, and LM-LEG/BM were associated with lower-limb physical function, suggesting that body composition, specifically reducing adiposity and increasing lean mass and/or increasing PA levels, may be a potential target for MS interventions.

  14. Psychological functioning in primary progressive versus secondary progressive multiple sclerosis

    DEFF Research Database (Denmark)

    Vleugels, L; Pfennings, L E; Pouwer, F

    1998-01-01

    (SD 11.1). Patients completed questionnaires measuring among others the following aspects of psychological functioning: depression (BDI, SCL-90), anxiety (STAI, SCL-90), agoraphobia (SCL-90), somatic complaints (SCL-90), hostility (SCL-90) and attitude towards handicap (GHAS). Patients with a PP......Psychological functioning in two types of multiple sclerosis (MS) patients is assessed: primary progressive (PP) and secondary progressive (SP) patients. On the basis of differences in clinical course and underlying pathology we hypothesized that primary progressive patients and secondary...... progressive patients might have different psychological functioning. Seventy patients treated in an MS centre were examined cross-sectionally. Forty had an SP course of MS and 30 a PP course. The 33 male and 37 female patients had a mean age of 48.4 years (SD 11.2) and mean age of onset of MS of 30.7 years...

  15. A hybrid Evolutionary Functional Link Artificial Neural Network for Data mining and Classification

    Directory of Open Access Journals (Sweden)

    Faissal MILI

    2012-08-01

    Full Text Available This paper presents a specific structure of neural network as the functional link artificial neural network (FLANN. This technique has been employed for classification tasks of data mining. In fact, there are a few studies that used this tool for solving classification problems. In this present research, we propose a hybrid FLANN (HFLANN model, where the optimization process is performed using 3 known population based techniques such as genetic algorithms, particle swarm and differential evolution. This model will be empirically compared to FLANN based back-propagation algorithm and to others classifiers as decision tree, multilayer perceptron based back-propagation algorithm, radical basic function, support vector machine, and K-nearest Neighbor. Our results proved that the proposed model outperforms the other single model. (Abstract

  16. Assigning Function to Adult-Born Neurons: A Theoretical Framework for Characterizing Neural Manipulation of Learning

    Directory of Open Access Journals (Sweden)

    Sarah eHersman

    2016-01-01

    Full Text Available Neuroscientists are concerned with neural processes or computations, but these may not be directly observable. In the field of learning, a behavioral procedure is observed to lead to performance outcomes, but differing inferences on underlying internal processes can lead to difficulties in interpreting conflicting results. An example of this challenge is how many functions have been attributed to adult-born granule cells in the dentate gyrus. Some of these functions were suggested by computational models of the properties of these neurons, while others were hypothesized after manipulations of adult-born neurons resulted in changes to behavioral metrics. This review seeks to provide a framework, based in learning theory classification of behavioral procedures, of the processes that may be underlying behavioral results after manipulating procedure and observing performance. We propose that this framework can serve to clarify experimental findings on adult-born neurons as well as other classes of neural manipulations and their effects on behavior.

  17. Stability Analysis of Distributed Delay Neural Networks Based on Relaxed Lyapunov-Krasovskii Functionals.

    Science.gov (United States)

    Zhang, Baoyong; Lam, James; Xu, Shengyuan

    2015-07-01

    This paper revisits the problem of asymptotic stability analysis for neural networks with distributed delays. The distributed delays are assumed to be constant and prescribed. Since a positive-definite quadratic functional does not necessarily require all the involved symmetric matrices to be positive definite, it is important for constructing relaxed Lyapunov-Krasovskii functionals, which generally lead to less conservative stability criteria. Based on this fact and using two kinds of integral inequalities, a new delay-dependent condition is obtained, which ensures that the distributed delay neural network under consideration is globally asymptotically stable. This stability criterion is then improved by applying the delay partitioning technique. Two numerical examples are provided to demonstrate the advantage of the presented stability criteria.

  18. Neural functional organization of hallucinations in schizophrenia: multisensory dissolution of pathological emergence in consciousness.

    Science.gov (United States)

    Jardri, Renaud; Pins, Delphine; Bubrovszky, Maxime; Lucas, Bernard; Lethuc, Vianney; Delmaire, Christine; Vantyghem, Vincent; Despretz, Pascal; Thomas, Pierre

    2009-06-01

    Although complex hallucinations are extremely vivid, painful symptoms in schizophrenia, little is known about the underlying mechanisms of multisensory integration in such a phenomenon. We investigated the neural basis of these altered states of consciousness in a patient with schizophrenia, by combining state of the art neuroscientific exploratory methods like functional MRI, diffusion tensor imaging, cortical thickness analysis, electrical source reconstruction and trans-cranial magnetic stimulation. The results shed light on the functional architecture of the hallucinatory processes, in which unimodal information from different modalities is strongly functionally connected to higher-order integrative areas.

  19. Anger under control: neural correlates of frustration as a function of trait aggression.

    Directory of Open Access Journals (Sweden)

    Christina M Pawliczek

    Full Text Available Antisocial behavior and aggression are prominent symptoms in several psychiatric disorders including antisocial personality disorder. An established precursor to aggression is a frustrating event, which can elicit anger or exasperation, thereby prompting aggressive responses. While some studies have investigated the neural correlates of frustration and aggression, examination of their relation to trait aggression in healthy populations are rare. Based on a screening of 550 males, we formed two extreme groups, one including individuals reporting high (n=21 and one reporting low (n=18 trait aggression. Using functional magnetic resonance imaging (fMRI at 3T, all participants were put through a frustration task comprising unsolvable anagrams of German nouns. Despite similar behavioral performance, males with high trait aggression reported higher ratings of negative affect and anger after the frustration task. Moreover, they showed relatively decreased activation in the frontal brain regions and the dorsal anterior cingulate cortex (dACC as well as relatively less amygdala activation in response to frustration. Our findings indicate distinct frontal and limbic processing mechanisms following frustration modulated by trait aggression. In response to a frustrating event, HA individuals show some of the personality characteristics and neural processing patterns observed in abnormally aggressive populations. Highlighting the impact of aggressive traits on the behavioral and neural responses to frustration in non-psychiatric extreme groups can facilitate further characterization of neural dysfunctions underlying psychiatric disorders that involve abnormal frustration processing and aggression.

  20. Development and function of human cerebral cortex neural networks from pluripotent stem cells in vitro.

    Science.gov (United States)

    Kirwan, Peter; Turner-Bridger, Benita; Peter, Manuel; Momoh, Ayiba; Arambepola, Devika; Robinson, Hugh P C; Livesey, Frederick J

    2015-09-15

    A key aspect of nervous system development, including that of the cerebral cortex, is the formation of higher-order neural networks. Developing neural networks undergo several phases with distinct activity patterns in vivo, which are thought to prune and fine-tune network connectivity. We report here that human pluripotent stem cell (hPSC)-derived cerebral cortex neurons form large-scale networks that reflect those found in the developing cerebral cortex in vivo. Synchronised oscillatory networks develop in a highly stereotyped pattern over several weeks in culture. An initial phase of increasing frequency of oscillations is followed by a phase of decreasing frequency, before giving rise to non-synchronous, ordered activity patterns. hPSC-derived cortical neural networks are excitatory, driven by activation of AMPA- and NMDA-type glutamate receptors, and can undergo NMDA-receptor-mediated plasticity. Investigating single neuron connectivity within PSC-derived cultures, using rabies-based trans-synaptic tracing, we found two broad classes of neuronal connectivity: most neurons have small numbers (40). These data demonstrate that the formation of hPSC-derived cortical networks mimics in vivo cortical network development and function, demonstrating the utility of in vitro systems for mechanistic studies of human forebrain neural network biology. © 2015. Published by The Company of Biologists Ltd.

  1. Neural correlates underlying mental calculation in abacus experts: a functional magnetic resonance imaging study.

    Science.gov (United States)

    Hanakawa, Takashi; Honda, Manabu; Okada, Tomohisa; Fukuyama, Hidenao; Shibasaki, Hiroshi

    2003-06-01

    Experts of abacus operation demonstrate extraordinary ability in mental calculation. There is psychological evidence that abacus experts utilize a mental image of an abacus to remember and manipulate large numbers in solving problems; however, the neural correlates underlying this expertise are unknown. Using functional magnetic resonance imaging, we compared the neural correlates associated with three mental-operation tasks (numeral, spatial, verbal) among six experts in abacus operations and eight nonexperts. In general, there was more involvement of neural correlates for visuospatial processing (e.g., right premotor and parietal areas) for abacus experts during the numeral mental-operation task. Activity of these areas and the fusiform cortex was correlated with the size of numerals used in the numeral mental-operation task. Particularly, the posterior superior parietal cortex revealed significantly enhanced activity for experts compared with controls during the numeral mental-operation task. Comparison with the other mental-operation tasks indicated that activity in the posterior superior parietal cortex was relatively specific to computation in 2-dimensional space. In conclusion, mental calculation of abacus experts is likely associated with enhanced involvement of the neural resources for visuospatial information processing in 2-dimensional space.

  2. Magnesium regulates neural stem cell proliferation in the mouse hippocampus by altering mitochondrial function.

    Science.gov (United States)

    Jia, Shanshan; Mou, Chengzhi; Ma, Yihe; Han, Ruijie; Li, Xue

    2016-04-01

    In the adult brain, neural stem cells from the subgranular zone (SGZ) of the hippocampus and the subventricular zone (SVZ) of the cortex progress through the following five developmental stages: radial glia-like cells, neural progenitor cells, neuroblasts, immature neurons, and mature neurons. These developmental stages are linked to both neuronal microenvironments and energy metabolism. Neurogenesis is restricted and has been demonstrated to arise from tissue microenvironments. We determined that magnesium, a key nutrient in cellular energy metabolism, affects neural stem cell (NSC) proliferation in cells derived from the embryonic hippocampus by influencing mitochondrial function. Densities of proliferating cells and NSCs both showed their highest values at 0.8 mM [Mg(2+) ]o , whereas lower proliferation rates were observed at 0.4 and 1.4 mM [Mg(2+) ]o . The numbers and sizes of the neurospheres reached the maximum at 0.8 mM [Mg(2+) ]o and were weaker under both low (0.4 mM) and high (1.4 mM) concentrations of magnesium. In vitro experimental evidence demonstrates that extracellular magnesium regulates the number of cultured hippocampal NSCs, affecting both magnesium homeostasis and mitochondrial function. Our findings indicate that the effect of [Mg(2+) ]o on NSC proliferation may lie downstream of alterations in mitochondrial function because mitochondrial membrane potential was highest in the NSCs in the moderate [Mg(2+) ]o (0.8 mM) group and lower in both the low (0.4 mM) and high (1.4 mM) [Mg(2+) ]o groups. Overall, these findings demonstrate a new function for magnesium in the brain in the regulation of hippocampal neural stem cells: affecting their cellular energy metabolism.

  3. Neural correlates of post-error slowing during a stop signal task: a functional magnetic resonance imaging study.

    Science.gov (United States)

    Li, Chiang-shan Ray; Huang, Cong; Yan, Peisi; Paliwal, Prashni; Constable, Robert Todd; Sinha, Rajita

    2008-06-01

    The ability to detect errors and adjust behavior accordingly is essential for maneuvering in an uncertain environment. Errors are particularly prone to occur when multiple, conflicting responses are registered in a situation that requires flexible behavioral outputs; for instance, when a go signal requires a response and a stop signal requires inhibition of the response during a stop signal task (SST). Previous studies employing the SST have provided ample evidence indicating the importance of the medial cortical brain regions in conflict/error processing. Other studies have also related these regional activations to postconflict/error behavioral adjustment. However, very few studies have directly explored the neural correlates of postconflict/error behavioral adjustment. Here we employed an SST to elicit errors in approximately half of the stop trials despite constant behavioral adjustment of the observers. Using functional magnetic resonance imaging, we showed that prefrontal loci including the ventrolateral prefrontal cortex are involved in post-error slowing in reaction time. These results delineate the neural circuitry specifically involved in error-associated behavioral modifications.

  4. Multi-sensor integration for on-line tool wear estimation through radial basis function networks and fuzzy neural network.

    Science.gov (United States)

    Kuo, R J.; Cohen, P H.

    1999-03-01

    On-line tool wear estimation plays a very critical role in industry automation for higher productivity and product quality. In addition, appropriate and timely decision for tool change is significantly required in the machining systems. Thus, this paper is dedicated to develop an estimation system through integration of two promising technologies, artificial neural networks (ANN) and fuzzy logic. An on-line estimation system consisting of five components: (1) data collection; (2) feature extraction; (3) pattern recognition; (4) multi-sensor integration; and (5) tool/work distance compensation for tool flank wear, is proposed herein. For each sensor, a radial basis function (RBF) network is employed to recognize the extracted features. Thereafter, the decisions from multiple sensors are integrated through a proposed fuzzy neural network (FNN) model. Such a model is self-organizing and self-adjusting, and is able to learn from the experience. Physical experiments for the metal cutting process are implemented to evaluate the proposed system. The results show that the proposed system can significantly increase the accuracy of the product profile.

  5. 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.

  6. Relating functional connectivity in V1 neural circuits and 3D natural scenes using Boltzmann machines

    Science.gov (United States)

    Zhang, Yimeng; Li, Xiong; Samonds, Jason M.

    2015-01-01

    Bayesian theory has provided a compelling conceptualization for perceptual inference in the brain. Central to Bayesian inference is the notion of statistical priors. To understand the neural mechanisms of Bayesian inference, we need to understand the neural representation of statistical regularities in the natural environment. In this paper, we investigated empirically how statistical regularities in natural 3D scenes are represented in the functional connectivity of disparity-tuned neurons in the primary visual cortex of primates. We applied a Boltzmann machine model to learn from 3D natural scenes, and found that the units in the model exhibited cooperative and competitive interactions, forming a “disparity association field”, analogous to the contour association field. The cooperative and competitive interactions in the disparity association field are consistent with constraints of computational models for stereo matching. In addition, we simulated neurophysiological experiments on the model, and found the results to be consistent with neurophysiological data in terms of the functional connectivity measurements between disparity-tuned neurons in the macaque primary visual cortex. These findings demonstrate that there is a relationship between the functional connectivity observed in the visual cortex and the statistics of natural scenes. They also suggest that the Boltzmann machine can be a viable model for conceptualizing computations in the visual cortex and, as such, can be used to predict neural circuits in the visual cortex from natural scene statistics. PMID:26712581

  7. An investigation of the relationship between activation of a social cognitive neural network and social functioning.

    Science.gov (United States)

    Pinkham, Amy E; Hopfinger, Joseph B; Ruparel, Kosha; Penn, David L

    2008-07-01

    Previous work examining the neurobiological substrates of social cognition in healthy individuals has reported modulation of a social cognitive network such that increased activation of the amygdala, fusiform gyrus, and superior temporal sulcus are evident when individuals judge a face to be untrustworthy as compared with trustworthy. We examined whether this pattern would be present in individuals with schizophrenia who are known to show reduced activation within these same neural regions when processing faces. Additionally, we sought to determine how modulation of this social cognitive network may relate to social functioning. Neural activation was measured using functional magnetic resonance imaging with blood oxygenation level dependent contrast in 3 groups of individuals--nonparanoid individuals with schizophrenia, paranoid individuals with schizophrenia, and healthy controls--while they rated faces as either trustworthy or untrustworthy. Analyses of mean percent signal change extracted from a priori regions of interest demonstrated that both controls and nonparanoid individuals with schizophrenia showed greater activation of this social cognitive network when they rated a face as untrustworthy relative to trustworthy. In contrast, paranoid individuals did not show a significant difference in levels of activation based on how they rated faces. Further, greater activation of this social cognitive network to untrustworthy faces was significantly and positively correlated with social functioning. These findings indicate that impaired modulation of neural activity while processing social stimuli may underlie deficits in social cognition and social dysfunction in schizophrenia.

  8. Functional Magnetic Resonance Imaging for Imaging Neural Activity in the Human Brain: The Annual Progress

    Directory of Open Access Journals (Sweden)

    Shengyong Chen

    2012-01-01

    Full Text Available Functional magnetic resonance imaging (fMRI is recently developed and applied to measure the hemodynamic response related to neural activity. The fMRI can not only noninvasively record brain signals without risks of ionising radiation inherent in other scanning methods, such as CT or PET scans, but also record signal from all regions of the brain, unlike EEG/MEG which are biased towards the cortical surface. This paper introduces the fundamental principles and summarizes the research progress of the last year for imaging neural activity in the human brain. Aims of functional analysis of neural activity from fMRI include biological findings, functional connectivity, vision and hearing research, emotional research, neurosurgical planning, pain management, and many others. Besides formulations and basic processing methods, models and strategies of processing technology are introduced, including general linear model, nonlinear model, generative model, spatial pattern analysis, statistical analysis, correlation analysis, and multimodal combination. This paper provides readers the most recent representative contributions in the area.

  9. Comparative performance of some popular artificial neural network algorithms on benchmark and function approximation problems

    Indian Academy of Sciences (India)

    V K Dhar; A K Tickoo; R Koul; B P Dubey

    2010-02-01

    We report an inter-comparison of some popular algorithms within the artificial neural network domain (viz., local search algorithms, global search algorithms, higher-order algorithms and the hybrid algorithms) by applying them to the standard benchmarking problems like the IRIS data, XOR/N-bit parity and two-spiral problems. Apart from giving a brief description of these algorithms, the results obtained for the above benchmark problems are presented in the paper. The results suggest that while Levenberg–Marquardt algorithm yields the lowest RMS error for the N-bit parity and the two-spiral problems, higher-order neuron algorithm gives the best results for the IRIS data problem. The best results for the XOR problem are obtained with the neuro-fuzzy algo- rithm. The above algorithms were also applied for solving several regression problems such as cos() and a few special functions like the gamma function, the complimentary error function and the upper tail cumulative 2-distribution function. The results of these regression problems indicate that, among all the ANN algorithms used in the present study, Levenberg–Marquardt algorithm yields the best results. Keeping in view the highly non-linear behaviour and the wide dynamic range of these functions, it is suggested that these functions can also be considered as standard benchmark problems for function approximation using artificial neural networks.

  10. A generalized exchange-correlation functional: the Neural-Networks approach

    CERN Document Server

    Zheng, X; Wang, X J; Chen, G H; Zheng, Xiao; Hu, LiHong; Wang, XiuJun; Chen, GuanHua

    2003-01-01

    A Neural-Networks-based approach is proposed to construct a new type of exchange-correlation functional for density functional theory. It is applied to improve B3LYP functional by taking into account of high-order contributions to the exchange-correlation functional. The improved B3LYP functional is based on a neural network whose structure and synaptic weights are determined from 116 known experimental atomization energies, ionization potentials, proton affinities or total atomic energies which were used by Becke in his pioneer work on the hybrid functionals [J. Chem. Phys. ${\\bf 98}$, 5648 (1993)]. It leads to better agreement between the first-principles calculation results and these 116 experimental data. The new B3LYP functional is further tested by applying it to calculate the ionization potentials of 24 molecules of the G2 test set. The 6-311+G(3{\\it df},2{\\it p}) basis set is employed in the calculation, and the resulting root-mean-square error is reduced to 2.2 kcal$\\cdot$mol$^{-1}$ in comparison to ...

  11. Neural cell adhesion molecule induces intracellular signaling via multiple mechanisms of Ca2+ homeostasis

    DEFF Research Database (Denmark)

    Kiryushko, Darya; Korshunova, Irina; Berezin, Vladimir;

    2006-01-01

    The neural cell adhesion molecule (NCAM) plays a pivotal role in the development of the nervous system, promoting neuronal differentiation via homophilic (NCAM-NCAM) as well as heterophilic (NCAM-fibroblast growth factor receptor [FGFR]) interactions. NCAM-induced intracellular signaling has been....... The first pathway was associated with activation of FGFR, phospholipase Cgamma, and production of diacylglycerol, and the second pathway involved Src-family kinases. Moreover, NCAM-mediated Ca2+ entry required activation of nonselective cation and T-type voltage-gated Ca2+ channels. These channels, together...

  12. Cross-model convolutional neural network for multiple modality data representation

    OpenAIRE

    Wu, Yanbin; Wang, Li; Cui, Fan; Zhai, Hongbin; Dong, Baoming; Wang, Jim Jing-Yan

    2016-01-01

    A novel data representation method of convolutional neural net- work (CNN) is proposed in this paper to represent data of different modalities. We learn a CNN model for the data of each modality to map the data of differ- ent modalities to a common space, and regularize the new representations in the common space by a cross-model relevance matrix. We further impose that the class label of data points can also be predicted from the CNN representa- tions in the common space. The learning proble...

  13. Multiple-model-and-neural-network-based nonlinear multivariable adaptive control

    Institute of Scientific and Technical Information of China (English)

    Yue FU; Tianyou CHAI

    2007-01-01

    A multivariable adaptive controller feasible for implementation on distributed computer systems (DCS) is presented for a class of uncertain nonlinear multivariable discrete time systems. The adaptive controller is composed of a linear adaptive controller, a neural network nonlinear adaptive controller and a switching mechanism. The linear controller can provide boundedness of the input and output signals, and the nonlinear controller can improve the performance of the system. The purpose of using the switching mechanism is to obtain the improved system performance and stability simultaneously. Theory analysis and simulation results are presented to show the effectiveness of the proposed method.

  14. Research on image retrieval using deep convolutional neural network combining L1 regularization and PRelu activation function

    Science.gov (United States)

    QingJie, Wei; WenBin, Wang

    2017-06-01

    In this paper, the image retrieval using deep convolutional neural network combined with regularization and PRelu activation function is studied, and improves image retrieval accuracy. Deep convolutional neural network can not only simulate the process of human brain to receive and transmit information, but also contains a convolution operation, which is very suitable for processing images. Using deep convolutional neural network is better than direct extraction of image visual features for image retrieval. However, the structure of deep convolutional neural network is complex, and it is easy to over-fitting and reduces the accuracy of image retrieval. In this paper, we combine L1 regularization and PRelu activation function to construct a deep convolutional neural network to prevent over-fitting of the network and improve the accuracy of image retrieval

  15. A one-layer recurrent neural network with a discontinuous hard-limiting activation function for quadratic programming.

    Science.gov (United States)

    Liu, Q; Wang, J

    2008-04-01

    In this paper, a one-layer recurrent neural network with a discontinuous hard-limiting activation function is proposed for quadratic programming. This neural network is capable of solving a large class of quadratic programming problems. The state variables of the neural network are proven to be globally stable and the output variables are proven to be convergent to optimal solutions as long as the objective function is strictly convex on a set defined by the equality constraints. In addition, a sequential quadratic programming approach based on the proposed recurrent neural network is developed for general nonlinear programming. Simulation results on numerical examples and support vector machine (SVM) learning show the effectiveness and performance of the neural network.

  16. Neural network simulation of the atmospheric point spread function for the adjacency effect research

    Science.gov (United States)

    Ma, Xiaoshan; Wang, Haidong; Li, Ligang; Yang, Zhen; Meng, Xin

    2016-10-01

    Adjacency effect could be regarded as the convolution of the atmospheric point spread function (PSF) and the surface leaving radiance. Monte Carlo is a common method to simulate the atmospheric PSF. But it can't obtain analytic expression and the meaningful results can be only acquired by statistical analysis of millions of data. A backward Monte Carlo algorithm was employed to simulate photon emitting and propagating in the atmosphere under different conditions. The PSF was determined by recording the photon-receiving numbers in fixed bin at different position. A multilayer feed-forward neural network with a single hidden layer was designed to learn the relationship between the PSF's and the input condition parameters. The neural network used the back-propagation learning rule for training. Its input parameters involved atmosphere condition, spectrum range, observing geometry. The outputs of the network were photon-receiving numbers in the corresponding bin. Because the output units were too many to be allowed by neural network, the large network was divided into a collection of smaller ones. These small networks could be ran simultaneously on many workstations and/or PCs to speed up the training. It is important to note that the simulated PSF's by Monte Carlo technique in non-nadir viewing angles are more complicated than that in nadir conditions which brings difficulties in the design of the neural network. The results obtained show that the neural network approach could be very useful to compute the atmospheric PSF based on the simulated data generated by Monte Carlo method.

  17. A model for integrating elementary neural functions into delayed-response behavior.

    Directory of Open Access Journals (Sweden)

    Thomas Gisiger

    2006-04-01

    Full Text Available It is well established that various cortical regions can implement a wide array of neural processes, yet the mechanisms which integrate these processes into behavior-producing, brain-scale activity remain elusive. We propose that an important role in this respect might be played by executive structures controlling the traffic of information between the cortical regions involved. To illustrate this hypothesis, we present a neural network model comprising a set of interconnected structures harboring stimulus-related activity (visual representation, working memory, and planning, and a group of executive units with task-related activity patterns that manage the information flowing between them. The resulting dynamics allows the network to perform the dual task of either retaining an image during a delay (delayed-matching to sample task, or recalling from this image another one that has been associated with it during training (delayed-pair association task. The model reproduces behavioral and electrophysiological data gathered on the inferior temporal and prefrontal cortices of primates performing these same tasks. It also makes predictions on how neural activity coding for the recall of the image associated with the sample emerges and becomes prospective during the training phase. The network dynamics proves to be very stable against perturbations, and it exhibits signs of scale-invariant organization and cooperativity. The present network represents a possible neural implementation for active, top-down, prospective memory retrieval in primates. The model suggests that brain activity leading to performance of cognitive tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia.

  18. Electromyography function, disability degree, and pain in leprosy patients undergoing neural mobilization treatment

    Directory of Open Access Journals (Sweden)

    Larissa Sales Téles Véras

    2012-02-01

    Full Text Available INTRODUCTION: This study aimed to evaluate the effect of the neural mobilization technique on electromyography function, disability degree, and pain in patients with leprosy. METHODS: A sample of 56 individuals with leprosy was randomized into an experimental group, composed of 29 individuals undergoing treatment with neural mobilization, and a control group of 27 individuals who underwent conventional treatment. In both groups, the lesions in the lower limbs were treated. In the treatment with neural mobilization, the procedure used was mobilization of the lumbosacral roots and sciatic nerve biased to the peroneal nerve that innervates the anterior tibial muscle, which was evaluated in the electromyography. RESULTS: Analysis of the electromyography function showed a significant increase (p<0.05 in the experimental group in both the right (Δ%=22.1, p=0.013 and the left anterior tibial muscles (Δ%=27.7, p=0.009, compared with the control group pre- and post-test. Analysis of the strength both in the movement of horizontal extension (Δ%right=11.7, p=0.003/Δ%left=27.4, p=0.002 and in the movement of back flexion (Δ%right=31.1; p=0.000/Δ%left=34.7, p=0.000 showed a significant increase (p<0.05 in both the right and the left segments when comparing the experimental group pre- and post-test. The experimental group showed a significant reduction (p=0.000 in pain perception and disability degree when the pre- and post-test were compared and when compared with the control group in the post-test. CONCLUSIONS: Leprosy patients undergoing the technique of neural mobilization had an improvement in electromyography function and muscle strength, reducing disability degree and pain.

  19. Spatial working memory in neurofibromatosis 1: Altered neural activity and functional connectivity.

    Science.gov (United States)

    Ibrahim, Amira F A; Montojo, Caroline A; Haut, Kristen M; Karlsgodt, Katherine H; Hansen, Laura; Congdon, Eliza; Rosser, Tena; Bilder, Robert M; Silva, Alcino J; Bearden, Carrie E

    2017-01-01

    Neurofibromatosis Type 1 (NF1) is a genetic disorder that disrupts central nervous system development and neuronal function. Cognitively, NF1 is characterized by difficulties with executive control and visuospatial abilities. Little is known about the neural substrates underlying these deficits. The current study utilized Blood-Oxygen-Level-Dependent (BOLD) functional MRI (fMRI) to explore the neural correlates of spatial working memory (WM) deficits in patients with NF1. BOLD images were acquired from 23 adults with NF1 (age M = 32.69; 61% male) and 25 matched healthy controls (age M = 33.08; 64% male) during an in-scanner visuo-spatial WM task. Whole brain functional and psycho-physiological interaction analyses were utilized to investigate neural activity and functional connectivity, respectively, during visuo-spatial WM performance. Participants also completed behavioral measures of spatial reasoning and verbal WM. Relative to healthy controls, participants with NF1 showed reduced recruitment of key components of WM circuitry, the left dorsolateral prefrontal cortex and right parietal cortex. In addition, healthy controls exhibited greater simultaneous deactivation between the posterior cingulate cortex (PCC) and temporal regions than NF1 patients. In contrast, NF1 patients showed greater PCC and bilateral parietal connectivity with visual cortices as well as between the PCC and the cerebellum. In NF1 participants, increased functional coupling of the PCC with frontal and parietal regions was associated with better spatial reasoning and WM performance, respectively; these relationships were not observed in controls. Dysfunctional engagement of WM circuitry, and aberrant functional connectivity of 'task-negative' regions in NF1 patients may underlie spatial WM difficulties characteristic of the disorder.

  20. Neural Correlates of Symptom Dimensions in Pediatric Obsessive-Compulsive Disorder: A Functional Magnetic Resonance Imaging Study

    Science.gov (United States)

    Gilbert, Andrew R.; Akkal, Dalila; Almeida, Jorge R. C.; Mataix-Cols, David; Kalas, Catherine; Devlin, Bernie; Birmaher, Boris; Phillips, Mary L.

    2009-01-01

    The use of functional magnetic resonance imaging on a group of pediatric subjects with obsessive compulsive disorder reveals that this group has reduced activity in neural regions underlying emotional processing, cognitive processing, and motor performance as compared to control subjects.

  1. Neural Correlates of Symptom Dimensions in Pediatric Obsessive-Compulsive Disorder: A Functional Magnetic Resonance Imaging Study

    Science.gov (United States)

    Gilbert, Andrew R.; Akkal, Dalila; Almeida, Jorge R. C.; Mataix-Cols, David; Kalas, Catherine; Devlin, Bernie; Birmaher, Boris; Phillips, Mary L.

    2009-01-01

    The use of functional magnetic resonance imaging on a group of pediatric subjects with obsessive compulsive disorder reveals that this group has reduced activity in neural regions underlying emotional processing, cognitive processing, and motor performance as compared to control subjects.

  2. Loss of SUFU Function in Familial Multiple Meningioma

    Science.gov (United States)

    Aavikko, Mervi; Li, Song-Ping; Saarinen, Silva; Alhopuro, Pia; Kaasinen, Eevi; Morgunova, Ekaterina; Li, Yilong; Vesanen, Kari; Smith, Miriam J.; Evans, D. Gareth R.; Pöyhönen, Minna; Kiuru, Anne; Auvinen, Anssi; Aaltonen, Lauri A.; Taipale, Jussi; Vahteristo, Pia

    2012-01-01

    Meningiomas are the most common primary tumors of the CNS and account for up to 30% of all CNS tumors. An increased risk of meningiomas has been associated with certain tumor-susceptibility syndromes, especially neurofibromatosis type II, but no gene defects predisposing to isolated familial meningiomas have thus far been identified. Here, we report on a family of five meningioma-affected siblings, four of whom have multiple tumors. No NF2 mutations were identified in the germline or tumors. We combined genome-wide linkage analysis and exome sequencing, and we identified in suppressor of fused homolog (Drosophila), SUFU, a c.367C>T (p.Arg123Cys) mutation segregating with the meningiomas in the family. The variation was not present in healthy controls, and all seven meningiomas analyzed displayed loss of the wild-type allele according to the classic two-hit model for tumor-suppressor genes. In silico modeling predicted the variant to affect the tertiary structure of the protein, and functional analyses showed that the activity of the altered SUFU was significantly reduced and therefore led to dysregulated hedgehog (Hh) signaling. SUFU is a known tumor-suppressor gene previously associated with childhood medulloblastoma predisposition. Our genetic and functional analyses indicate that germline mutations in SUFU also predispose to meningiomas, particularly to multiple meningiomas. It is possible that other genic mutations resulting in aberrant activation of the Hh pathway might underlie meningioma predisposition in families with an unknown etiology. PMID:22958902

  3. Advances in two photon scanning and scanless microscopy technologies for functional neural circuit imaging.

    Science.gov (United States)

    Schultz, Simon R; Copeland, Caroline S; Foust, Amanda J; Quicke, Peter; Schuck, Renaud

    2017-01-01

    Recent years have seen substantial developments in technology for imaging neural circuits, raising the prospect of large scale imaging studies of neural populations involved in information processing, with the potential to lead to step changes in our understanding of brain function and dysfunction. In this article we will review some key recent advances: improved fluorophores for single cell resolution functional neuroimaging using a two photon microscope; improved approaches to the problem of scanning active circuits; and the prospect of scanless microscopes which overcome some of the bandwidth limitations of current imaging techniques. These advances in technology for experimental neuroscience have in themselves led to technical challenges, such as the need for the development of novel signal processing and data analysis tools in order to make the most of the new experimental tools. We review recent work in some active topics, such as region of interest segmentation algorithms capable of demixing overlapping signals, and new highly accurate algorithms for calcium transient detection. These advances motivate the development of new data analysis tools capable of dealing with spatial or spatiotemporal patterns of neural activity, that scale well with pattern size.

  4. Improved Radio Frequency Identification Indoor Localization Method via Radial Basis Function Neural Network

    Directory of Open Access Journals (Sweden)

    Dongliang Guo

    2014-01-01

    Full Text Available Indoor localization technique has received much attention in recent years. Many techniques have been developed to solve the problem. Among the recent proposed methods, radio frequency identification (RFID indoor localization technology has the advantages of low-cost, noncontact, non-line-of-sight, and high precision. This paper proposed two radial basis function (RBF neural network based indoor localization methods. The RBF neural networks are trained to learn the mapping relationship between received signal strength indication values and position of objects. Traditional method used the received signal strength directly as the input of neural network; we added another input channel by taking the difference of the received signal strength, thus improving the reliability and precision of positioning. Fuzzy clustering is used to determine the center of radial basis function. In order to reduce the impact of signal fading due to non-line-of-sight and multipath transmission in indoor environment, we improved the Gaussian filter to process received signal strength values. The experimental results show that the proposed method outperforms the existing methods as well as improves the reliability and precision of the RFID indoor positioning system.

  5. Neural mechanisms of subclinical depressive symptoms in women: a pilot functional brain imaging study

    Directory of Open Access Journals (Sweden)

    Felder Jennifer N

    2012-09-01

    Full Text Available Abstract Background Studies of individuals who do not meet criteria for major depressive disorder (MDD but with subclinical levels of depressive symptoms may aid in the identification of neurofunctional abnormalities that possibly precede and predict the development of MDD. The purpose of this study was to evaluate relations between subclinical levels of depressive symptoms and neural activation patterns during tasks previously shown to differentiate individuals with and without MDD. Methods Functional magnetic resonance imaging (fMRI was used to assess neural activations during active emotion regulation, a resting state scan, and reward processing. Participants were twelve females with a range of depressive symptoms who did not meet criteria for MDD. Results Increased depressive symptom severity predicted (1 decreased left midfrontal gyrus activation during reappraisal of sad stimuli; (2 increased right midfrontal gyrus activation during distraction from sad stimuli; (3 increased functional connectivity between a precuneus seed region and left orbitofrontal cortex during a resting state scan; and (4 increased paracingulate activation during non-win outcomes during a reward-processing task. Conclusions These pilot data shed light on relations between subclinical levels of depressive symptoms in the absence of a formal MDD diagnosis and neural activation patterns. Future studies will be needed to test the utility of these activation patterns for predicting MDD onset in at-risk samples.

  6. Complement emerges as a masterful regulator of CNS homeostasis, neural synaptic plasticity and cognitive function.

    Science.gov (United States)

    Mastellos, Dimitrios C

    2014-11-01

    Growing evidence points to a previously elusive role of complement-modulated pathways in CNS development, neurogenesis and synaptic plasticity. Distinct complement effectors appear to play a multifaceted role in brain homeostasis by regulating synaptic pruning in the retinogeniculate system and sculpting functional neural circuits both in the developing and adult mammalian brain. A recent study by Perez-Alcazar et al. (2014) provides novel insights into this intricate interplay between complement and the dynamically regulated brain synaptic circuitry, by reporting that mice deficient in C3 exhibit enhanced hippocampus-dependent spatial learning and cognitive performance. This behavioral pattern is associated with an impact of C3 on the functional capacity of glutamatergic synapses, supporting a crucial role for complement in excitatory synapse elimination in the hippocampus. These findings add a fresh twist to this rapidly evolving research field, suggesting that discrete complement components may differentially modulate synaptic connectivity by wiring up with diverse neural effectors in different regions of the brain. The emerging role of complement in synaptogenesis and neural network plasticity opens new conceptual avenues for considering complement interception as a potential therapeutic modality for ameliorating progressive cognitive impairment in age-related, debilitating brain diseases with a prominent inflammatory signature.

  7. Identification of tartrazine and sunset yellow by fluorescence spectroscopy combined with radial basis function neural network

    Institute of Scientific and Technical Information of China (English)

    Jun Wang; Guoqing Chen; Tuo Zhu; Shumei Gao; Bailin Wei; Linna Bi

    2009-01-01

    @@ The fluorescence spectra of synthetic food dyes of sunset yellow and tartrazine are analyzed.The fluorescence peak wavelengths of sunset yellow and tartrazine are 576 and 569 nm, respectively, while the fluorescence spectra widths are 480-750 and 500-750 nm induced by ultraviolet light between 310-400 nm.The fluorescence spectra of sunset yellow overlap heavily with those of tartrazine, so it is diffic ult to distinguish them.Based on the principle of radial basis function neural network, a neural network is obtained from the training of the 14 groups of experimental data.The results show that the species of sunset yellow and tartrazine could be recognized accurately.This method has potential applications in other synthetic food dyes detection and food safety inspection.

  8. fMRI Study Revealing Neural Mechanisms of the Functions of SOA in Spatial Orienting

    Institute of Scientific and Technical Information of China (English)

    Yin Tian; Qian Zhang; De-Zhong Yao

    2009-01-01

    It is well documented that orienting attention plays an important role in visual search. However, it remains unclear how the executive brain regions will act when two different stimulus onset asynchrony (SOA) are used in visual search. In this work, event-related fMRI was used to investigate neural mechanisms on the functions of SOA in endogenous and exogenous orienting. The results showed that in the endogenous orienting, long SOA versus short SOA resulted in widespread cortical activation mainly including right medial frontal gyrus and bilateral middle frontal gyri. Conversely, in exogenous orienting, long SOA compared to short SOA resulted in only activations in bilateral middle frontal gyri. These findings indicated that these two spatial orienting involved different brain areas and neural mechanisms.

  9. Prediction Study on PCI Failure of Reactor Fuel Based on a Radial Basis Function Neural Network

    Directory of Open Access Journals (Sweden)

    Xinyu Wei

    2016-01-01

    Full Text Available Pellet-clad interaction (PCI is one of the major issues in fuel rod design and reactor core operation in water cooled reactors. The prediction of fuel rod failure by PCI is studied in this paper by the method of radial basis function neural network (RBFNN. The neural network is built through the analysis of the existing experimental data. It is concluded that it is a suitable way to reduce the calculation complexity. A self-organized RBFNN is used in our study, which can vary its structure dynamically in order to maintain the prediction accuracy. For the purpose of the appropriate network complexity and overall computational efficiency, the hidden neurons in the RBFNN can be changed online based on the neuron activity and mutual information. The presented method is tested by the experimental data from the reference, and the results demonstrate its effectiveness.

  10. On-line Cutting Quality Recognition in Milling Using a Radical Basis Function Neural Network

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Tool wear, chatter vibration, chip breaking and built-up edge are main phenomena to be monitored in modern manufacturing processes, which are considered as important factors to the quality of products.They are closely related to the cutting parameters, which are to be selected in manufacturing process.However, it is very difficult to measure directly the cutting quality based on on-line monitoring.In this study, the relationship between the cutting parameters and cutting quality is analyzed.A Radical Basis Function (RBF) neural network based on-line quality recognition scheme is also presented, which monitors the level of surface roughness.The experimental results reveal that the RBF neural network has a high prediction success rate.

  11. An efficient learning algorithm for improving generalization performance of radial basis function neural networks.

    Science.gov (United States)

    Wang, Z O; Zhu, T

    2000-01-01

    This paper presents an efficient recursive learning algorithm for improving generalization performance of radial basis function (RBF) neural networks. The approach combines the rival penalized competitive learning (PRCL) [Xu, L., Kizyzak, A. & Oja, E. (1993). Rival penalized competitive learning for clustering analysis, RBF net and curve detection, IEEE Transactions on Neural Networks, 4, 636-649] and the regularized least squares (RLS) to provide an efficient and powerful procedure for constructing a minimal RBF network that generalizes very well. The RPCL selects the number of hidden units of network and adjusts centers, while the RLS constructs the parsimonious network and estimates the connection weights. In the RLS we derived a simple recursive algorithm, which needs no matrix calculation, and so largely reduces the computational cost. This combined algorithm significantly enhances the generalization performance and the real-time capability of the RBF networks. Simulation results of three different problems demonstrate much better generalization performance of the present algorithm over other existing similar algorithms.

  12. Possible scenarios of the influence of low-dose ionizing radiation on neural functioning.

    Science.gov (United States)

    Zakhvataev, Vladimir E

    2015-12-01

    Possible scenarios of the influence of ionizing radiation on neural functioning and the CNS are suggested. We argue that the radiation-induced bystander mechanisms associated with Ca(2+) flows, reactive nitrogen and oxygen species, and cytokines might lead to modulation of certain neuronal signaling pathways. The considered scenarios of conjugation of the bystander signaling and the neuronal signaling might result in modulation of certain synaptic receptors, neurogenesis, neurotransmission, channel conductance, synaptic signaling, different forms of neural plasticity, memory formation and storage, and learning. On this basis, corresponding new possible strategies for treating neurodegenerative deceases and mental disorders are proposed. The mechanisms considered might also be associated with neuronal survival and relevant to the treatment for brain injuries. At the same time, these mechanisms might be associated with detrimental effects and might facilitate the development of some neurological and psychiatric disorders. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. H∞ State Estimation for Discrete-Time Delayed Systems of the Neural Network Type With Multiple Missing Measurements.

    Science.gov (United States)

    Liu, Meiqin; Chen, Haiyang

    2015-12-01

    This paper investigates the H∞ state estimation problem for a class of discrete-time nonlinear systems of the neural network type with random time-varying delays and multiple missing measurements. These nonlinear systems include recurrent neural networks, complex network systems, Lur'e systems, and so on which can be described by a unified model consisting of a linear dynamic system and a static nonlinear operator. The missing phenomenon commonly existing in measurements is assumed to occur randomly by introducing mutually individual random variables satisfying certain kind of probability distribution. Throughout this paper, first a Luenberger-like estimator based on the imperfect output data is constructed to obtain the immeasurable system states. Then, by virtue of Lyapunov stability theory and stochastic method, the H∞ performance of the estimation error dynamical system (augmented system) is analyzed. Based on the analysis, the H∞ estimator gains are deduced such that the augmented system is globally mean square stable. In this paper, both the variation range and distribution probability of the time delay are incorporated into the control laws, which allows us to not only have more accurate models of the real physical systems, but also obtain less conservative results. Finally, three illustrative examples are provided to validate the proposed control laws.

  14. Establishing causality for dopamine in neural function and behavior with optogenetics.

    Science.gov (United States)

    Steinberg, Elizabeth E; Janak, Patricia H

    2013-05-20

    Dopamine (DA) is known to play essential roles in neural function and behavior. Accordingly, DA neurons have been the focus of intense experimental investigation that has led to many important advances in our understanding of how DA influences these processes. However, it is becoming increasingly appreciated that delineating the precise contributions of DA neurons to cellular, circuit, and systems-level phenomena will require more sophisticated control over their patterns of activity than conventional techniques can provide. Specifically, the roles played by DA neurons are likely to depend on their afferent and efferent connectivity, the timing and length of their neural activation, and the nature of the behavior under investigation. Recently developed optogenetic tools hold great promise for disentangling these complex issues. Here we discuss the use of light-sensitive microbial opsins in the context of outstanding questions in DA research. A major technical advance offered by these proteins is the ability to bidirectionally modulate DA neuron activity in in vitro and in vivo preparations on a time scale that more closely approximates those of neural, perceptual and behavioral events. In addition, continued advances in rodent genetics and viral-mediated gene delivery have contributed to the ability to selectively target DA neurons or their individual afferent and efferent connections. Further, these tools are suitable for use in experimental subjects engaged in complex behaviors. After reviewing the strengths and limitations of optogenetic methodologies, we conclude by describing early efforts in the application of this valuable new approach that demonstrate its potential to improve our understanding of the neural and behavioral functions of DA. This article is part of a Special Issue entitled Optogenetics (7th BRES).

  15. Adaptive Neural Control of Nonaffine Nonlinear Systems without Differential Condition for Nonaffine Function

    Directory of Open Access Journals (Sweden)

    Chaojiao Sun

    2016-01-01

    Full Text Available An adaptive neural control scheme is proposed for nonaffine nonlinear system without using the implicit function theorem or mean value theorem. The differential conditions on nonaffine nonlinear functions are removed. The control-gain function is modeled with the nonaffine function probably being indifferentiable. Furthermore, only a semibounded condition for nonaffine nonlinear function is required in the proposed method, and the basic idea of invariant set theory is then constructively introduced to cope with the difficulty in the control design for nonaffine nonlinear systems. It is rigorously proved that all the closed-loop signals are bounded and the tracking error converges to a small residual set asymptotically. Finally, simulation examples are provided to demonstrate the effectiveness of the designed method.

  16. Neural networks: further insights into error function, generalized weights and others.

    Science.gov (United States)

    Zhang, Zhongheng

    2016-08-01

    The article is a continuum of a previous one providing further insights into the structure of neural network (NN). Key concepts of NN including activation function, error function, learning rate and generalized weights are introduced. NN topology can be visualized with generic plot() function by passing a "nn" class object. Generalized weights assist interpretation of NN model with respect to the independent effect of individual input variables. A large variance of generalized weights for a covariate indicates non-linearity of its independent effect. If generalized weights of a covariate are approximately zero, the covariate is considered to have no effect on outcome. Finally, prediction of new observations can be performed using compute() function. Make sure that the feature variables passed to the compute() function are in the same order to that in the training NN.

  17. Protection of visual functions by human neural progenitors in a rat model of retinal disease.

    Directory of Open Access Journals (Sweden)

    David M Gamm

    Full Text Available BACKGROUND: A promising clinical application for stem and progenitor cell transplantation is in rescue therapy for degenerative diseases. This strategy seeks to preserve rather than restore host tissue function by taking advantage of unique properties often displayed by these versatile cells. In studies using different neurodegenerative disease models, transplanted human neural progenitor cells (hNPC protected dying host neurons within both the brain and spinal cord. Based on these reports, we explored the potential of hNPC transplantation to rescue visual function in an animal model of retinal degeneration, the Royal College of Surgeons rat. METHODOLOGY/PRINCIPAL FINDINGS: Animals received unilateral subretinal injections of hNPC or medium alone at an age preceding major photoreceptor loss. Principal outcomes were quantified using electroretinography, visual acuity measurements and luminance threshold recordings from the superior colliculus. At 90-100 days postnatal, a time point when untreated rats exhibit little or no retinal or visual function, hNPC-treated eyes retained substantial retinal electrical activity and visual field with near-normal visual acuity. Functional efficacy was further enhanced when hNPC were genetically engineered to secrete glial cell line-derived neurotrophic factor. Histological examination at 150 days postnatal showed hNPC had formed a nearly continuous pigmented layer between the neural retina and retinal pigment epithelium, as well as distributed within the inner retina. A concomitant preservation of host cone photoreceptors was also observed. CONCLUSIONS/SIGNIFICANCE: Wild type and genetically modified human neural progenitor cells survive for prolonged periods, migrate extensively, secrete growth factors and rescue visual functions following subretinal transplantation in the Royal College of Surgeons rat. These results underscore the potential therapeutic utility of hNPC in the treatment of retinal degenerative

  18. Time Series Analysis of Soil Radon Data Using Multiple Linear Regression and Artificial Neural Network in Seismic Precursory Studies

    Science.gov (United States)

    Singh, S.; Jaishi, H. P.; Tiwari, R. P.; Tiwari, R. C.

    2017-07-01

    This paper reports the analysis of soil radon data recorded in the seismic zone-V, located in the northeastern part of India (latitude 23.73N, longitude 92.73E). Continuous measurements of soil-gas emission along Chite fault in Mizoram (India) were carried out with the replacement of solid-state nuclear track detectors at weekly interval. The present study was done for the period from March 2013 to May 2015 using LR-115 Type II detectors, manufactured by Kodak Pathe, France. In order to reduce the influence of meteorological parameters, statistical analysis tools such as multiple linear regression and artificial neural network have been used. Decrease in radon concentration was recorded prior to some earthquakes that occurred during the observation period. Some false anomalies were also recorded which may be attributed to the ongoing crustal deformation which was not major enough to produce an earthquake.

  19. Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

    Science.gov (United States)

    Soares dos Santos, T.; Mendes, D.; Rodrigues Torres, R.

    2016-01-01

    Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANNs) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon; northeastern Brazil; and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model output and observed monthly precipitation. We used general circulation model (GCM) experiments for the 20th century (RCP historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANNs significantly outperform the MLR downscaling of monthly precipitation variability.

  20. Neural signal during immediate reward anticipation in schizophrenia: Relationship to real-world motivation and function

    Directory of Open Access Journals (Sweden)

    Karuna Subramaniam

    2015-01-01

    Full Text Available Amotivation in schizophrenia is a central predictor of poor functioning, and is thought to occur due to deficits in anticipating future rewards, suggesting that impairments in anticipating pleasure can contribute to functional disability in schizophrenia. In healthy comparison (HC participants, reward anticipation is associated with activity in frontal–striatal networks. By contrast, schizophrenia (SZ participants show hypoactivation within these frontal–striatal networks during this motivated anticipatory brain state. Here, we examined neural activation in SZ and HC participants during the anticipatory phase of stimuli that predicted immediate upcoming reward and punishment, and during the feedback/outcome phase, in relation to trait measures of hedonic pleasure and real-world functional capacity. SZ patients showed hypoactivation in ventral striatum during reward anticipation. Additionally, we found distinct differences between HC and SZ groups in their association between reward-related immediate anticipatory neural activity and their reported experience of pleasure. HC participants recruited reward-related regions in striatum that significantly correlated with subjective consummatory pleasure, while SZ patients revealed activation in attention-related regions, such as the IPL, which correlated with consummatory pleasure and functional capacity. These findings may suggest that SZ patients activate compensatory attention processes during anticipation of immediate upcoming rewards, which likely contribute to their functional capacity in daily life.

  1. Neural signal during immediate reward anticipation in schizophrenia: Relationship to real-world motivation and function.

    Science.gov (United States)

    Subramaniam, Karuna; Hooker, Christine I; Biagianti, Bruno; Fisher, Melissa; Nagarajan, Srikantan; Vinogradov, Sophia

    2015-01-01

    Amotivation in schizophrenia is a central predictor of poor functioning, and is thought to occur due to deficits in anticipating future rewards, suggesting that impairments in anticipating pleasure can contribute to functional disability in schizophrenia. In healthy comparison (HC) participants, reward anticipation is associated with activity in frontal-striatal networks. By contrast, schizophrenia (SZ) participants show hypoactivation within these frontal-striatal networks during this motivated anticipatory brain state. Here, we examined neural activation in SZ and HC participants during the anticipatory phase of stimuli that predicted immediate upcoming reward and punishment, and during the feedback/outcome phase, in relation to trait measures of hedonic pleasure and real-world functional capacity. SZ patients showed hypoactivation in ventral striatum during reward anticipation. Additionally, we found distinct differences between HC and SZ groups in their association between reward-related immediate anticipatory neural activity and their reported experience of pleasure. HC participants recruited reward-related regions in striatum that significantly correlated with subjective consummatory pleasure, while SZ patients revealed activation in attention-related regions, such as the IPL, which correlated with consummatory pleasure and functional capacity. These findings may suggest that SZ patients activate compensatory attention processes during anticipation of immediate upcoming rewards, which likely contribute to their functional capacity in daily life.

  2. Neural correlates of set-shifting: decomposing executive functions in schizophrenia

    Science.gov (United States)

    Wilmsmeier, Andreas; Ohrmann, Patricia; Suslow, Thomas; Siegmund, Ansgar; Koelkebeck, Katja; Rothermundt, Matthias; Kugel, Harald; Arolt, Volker; Bauer, Jochen; Pedersen, Anya

    2010-01-01

    Background Although there is considerable evidence that patients with schizophrenia have impaired executive functions, the neural mechanisms underlying these deficits are unclear. Generation and selection is one of the basic mechanisms of executive functioning. We investigated the neural correlates of this mechanism by means of functional magnetic resonance imaging (fMRI) in patients with schizophrenia and healthy controls. Methods We used the Wisconsin Card Sorting Test (WCST) in an event-related fMRI study to analyze neural activation patterns during the distinct components of the WCST in 36 patients with schizophrenia and 28 controls. We focused our analyses on the process of set-shifting. After participants received negative feedback, they had to generate and decide on a new sorting rule. Results A widespread activation pattern encompassing the inferior and middle frontal gyrus, parietal, temporal and occipital cortices, anterior cingulate cortex (ACC), supplementary motor area, insula, caudate, thalamus and brainstem was observed in patients with schizophrenia after negative versus positive feedback, whereas in healthy controls, significant activation clusters were more confined to the cortical areas. Significantly increased activation in the rostral ACC after negative feedback and in the dorsal ACC during matching after negative feedback were observed in schizophrenia patients compared with controls. Controls showed activation in the bilateral dorsolateral prefrontal cortex (Brodmann area 46), whereas schizophrenia patients showed activation in the right dorsolateral pre-frontal cortex only. Limitations All patients were taking neuroleptic medication, which has an impact on cognitive function as well as on dopaminergic and serotonergic prefrontal metabolism. Conclusion Our data suggest that, in patients with schizophrenia, set-shifting is associated with increased activation in the rostral and dorsal ACC, reflecting higher emotional and cognitive demands

  3. Neural bases of food perception: coordinate-based meta-analyses of neuroimaging studies in multiple modalities.

    Science.gov (United States)

    Huerta, Claudia I; Sarkar, Pooja R; Duong, Timothy Q; Laird, Angela R; Fox, Peter T

    2014-06-01

    The purpose of this study was to compare the results of the three food-cue paradigms most commonly used for functional neuroimaging studies to determine: i) commonalities and differences in the neural response patterns by paradigm and ii) the relative robustness and reliability of responses to each paradigm. Functional magnetic resonance imaging studies using standardized stereotactic coordinates to report brain responses to food cues were identified using online databases. Studies were grouped by food-cue modality as: i) tastes (8 studies); ii) odors (8 studies); and, iii) images (11 studies). Activation likelihood estimation was used to identify statistically reliable regional responses within each stimulation paradigm. Brain response distributions were distinctly different for the three stimulation modalities, corresponding to known differences in location of the respective primary and associative cortices. Visual stimulation induced the most robust and extensive responses. The left anterior insula was the only brain region reliably responding to all three stimulus categories. These findings suggest visual food-cue paradigm as promising candidate for imaging studies addressing the neural substrate of therapeutic interventions. Copyright © 2014 The Obesity Society.

  4. Multiple sclerosis impairs regional functional connectivity in the cerebellum

    Directory of Open Access Journals (Sweden)

    Anne-Marie Dogonowski

    2014-01-01

    Full Text Available Resting-state functional magnetic resonance imaging (rs-fMRI has been used to study changes in long-range functional brain connectivity in multiple sclerosis (MS. Yet little is known about how MS affects functional brain connectivity at the local level. Here we studied 42 patients with MS and 30 matched healthy controls with whole-brain rs-fMRI at 3 T to examine local functional connectivity. Using the Kendall's Coefficient of Concordance, regional homogeneity of blood-oxygen-level-dependent (BOLD-signal fluctuations was calculated for each voxel and used as a measure of local connectivity. Patients with MS showed a decrease in regional homogeneity in the upper left cerebellar hemisphere in lobules V and VI relative to healthy controls. Similar trend changes in regional homogeneity were present in the right cerebellar hemisphere. The results indicate a disintegration of regional processing in the cerebellum in MS. This might be caused by a functional disruption of cortico-ponto-cerebellar and spino-cerebellar inputs, since patients with higher lesion load in the left cerebellar peduncles showed a stronger reduction in cerebellar homogeneity. In patients, two clusters in the left posterior cerebellum expressed a reduction in regional homogeneity with increasing global disability as reflected by the Expanded Disability Status Scale (EDSS score or higher ataxia scores. The two clusters were mainly located in Crus I and extended into Crus II and the dentate nucleus but with little spatial overlap. These findings suggest a link between impaired regional integration in the cerebellum and general disability and ataxia.

  5. Probability of detection as a function of multiple influencing parameters

    Energy Technology Data Exchange (ETDEWEB)

    Pavlovic, Mato

    2014-10-15

    Non-destructive testing is subject to measurement uncertainties. In safety critical applications the reliability assessment of its capability to detect flaws is therefore necessary. In most applications, the flaw size is the single most important parameter that influences the probability of detection (POD) of the flaw. That is why the POD is typically calculated and expressed as a function of the flaw size. The capability of the inspection system to detect flaws is established by comparing the size of reliably detected flaw with the size of the flaw that is critical for the structural integrity. Applications where several factors have an important influence on the POD are investigated in this dissertation. To devise a reliable estimation of the NDT system capability it is necessary to express the POD as a function of all these factors. A multi-parameter POD model is developed. It enables POD to be calculated and expressed as a function of several influencing parameters. The model was tested on the data from the ultrasonic inspection of copper and cast iron components with artificial flaws. Also, a technique to spatially present POD data called the volume POD is developed. The fusion of the POD data coming from multiple inspections of the same component with different sensors is performed to reach the overall POD of the inspection system.

  6. Lung Function Measurement with Multiple-Breath-Helium Washout System

    CERN Document Server

    Wang, Jau-Yi; Owers-Bradley, John; Mellor, Chris

    2011-01-01

    Multiple-breath-washout (MBW) measurements are regarded as a sensitive technique which can reflect the ventilation inhomogeneity of respiratory airways. Typically nitrogen is used as the tracer gas and is washed out by pure oxygen in multi-breath-nitrogen (MBNW) washout tests. In this work, instead of using nitrogen, helium is used as the tracer gas and a multiple-helium-breath-washout (MBHW) system has been developed for the lung function study. A commercial quartz tuning fork with a resonance frequency of 32768 Hz has been used for detecting the change of the respiratory gas density. The resonance frequency of the tuning fork decreases linearly with increasing density of the surrounding gas. Knowing the CO2 concentration from the infrared carbon dioxide detector, the helium concentration can be determined. Results from 12 volunteers (3 mild asthmatics, 2 smokers, 1 with asthma history, 1 with COPD history, 5 normal) have shown that mild asthmatics have higher ventilation inhomogeneity in either conducting o...

  7. An improved method using radial basis function neural networks to speed up optimization algorithms

    CERN Document Server

    Bazan, M; Russenschuck, Stephan

    2002-01-01

    The paper presents a method using radial basis function (RBF) neural networks to speed up deterministic search algorithms used for the optimization of superconducting magnets for the LHC accelerator project at CERN. The optimization of the iron yoke of the main LHC dipoles requires a number of numerical field computations per trial solution as the field quality depends on the excitation and local iron saturation in the yoke. This results in computation times of about 30 min for each objective function evaluation (on DEC-Alpha 600 /333). In this paper, we present a method for constructing an RBF neural network for a local approximation of the objective function. The computational time required for such a construction is negligible compared to the deterministic function evaluation, and, thus, yields a speed-up of the overall search process. The effectiveness of this method is demonstrated by means of two- and three-parametric optimization examples. The achieved speed-up of the search routine is up to 30%. (12 r...

  8. Neural substrates of shared attention as social memory: A hyperscanning functional magnetic resonance imaging study.

    Science.gov (United States)

    Koike, Takahiko; Tanabe, Hiroki C; Okazaki, Shuntaro; Nakagawa, Eri; Sasaki, Akihiro T; Shimada, Koji; Sugawara, Sho K; Takahashi, Haruka K; Yoshihara, Kazufumi; Bosch-Bayard, Jorge; Sadato, Norihiro

    2016-01-15

    During a dyadic social interaction, two individuals can share visual attention through gaze, directed to each other (mutual gaze) or to a third person or an object (joint attention). Shared attention is fundamental to dyadic face-to-face interaction, but how attention is shared, retained, and neutrally represented in a pair-specific manner has not been well studied. Here, we conducted a two-day hyperscanning functional magnetic resonance imaging study in which pairs of participants performed a real-time mutual gaze task followed by a joint attention task on the first day, and mutual gaze tasks several days later. The joint attention task enhanced eye-blink synchronization, which is believed to be a behavioral index of shared attention. When the same participant pairs underwent mutual gaze without joint attention on the second day, enhanced eye-blink synchronization persisted, and this was positively correlated with inter-individual neural synchronization within the right inferior frontal gyrus. Neural synchronization was also positively correlated with enhanced eye-blink synchronization during the previous joint attention task session. Consistent with the Hebbian association hypothesis, the right inferior frontal gyrus had been activated both by initiating and responding to joint attention. These results indicate that shared attention is represented and retained by pair-specific neural synchronization that cannot be reduced to the individual level.

  9. A functional neural network computing some eigenvalues and eigenvectors of a special real matrix.

    Science.gov (United States)

    Liu, Yiguang; You, Zhisheng; Cao, Liping

    2005-12-01

    How to quickly compute eigenvalues and eigenvectors of a matrix, especially, a general real matrix, is significant in engineering. Since neural network runs in asynchronous and concurrent manner, and can achieve high rapidity, this paper designs a concise functional neural network (FNN) to extract some eigenvalues and eigenvectors of a special real matrix. After equivalent transforming the FNN into a complex differential equation and obtaining the analytic solution, the convergence properties of the FNN are analyzed. If the eigenvalue whose imaginary part is nonzero and the largest of all eigenvalues is unique, the FNN will converge to the eigenvector corresponding to this special eigenvalue with general nonzero initial vector. If all eigenvalues are real numbers or there are more than one eigenvalue whose imaginary part equals the largest, the FNN will converge to zero point or fall into a cycle procedure. Comparing with other neural networks designed for the same domain, the restriction to matrix is very slack. At last, three examples are employed to illustrate the performance of the FNN.

  10. Dynamic neural network of insight: a functional magnetic resonance imaging study on solving Chinese 'chengyu' riddles.

    Directory of Open Access Journals (Sweden)

    Qingbai Zhao

    Full Text Available The key components of insight include breaking mental sets and forming the novel, task-related associations. The majority of researchers have agreed that the anterior cingulate cortex may mediate processes of breaking one's mental set, while the exact neural correlates of forming novel associations are still debatable. In the present study, we used a paradigm of answer selection to explore brain activations of insight by using event-related functional magnetic resonance imaging during solving Chinese 'chengyu' (in Chinese pinyin riddles. Based on the participant's choice, the trials were classified into the insight and non-insight conditions. Both stimulus-locked and response-locked analyses are conducted to detect the neural activity corresponding to the early and late periods of insight solution, respectively. Our data indicate that the early period of insight solution shows more activation in the middle temporal gyrus, the middle frontal gyrus and the anterior cingulate cortex. These activities might be associated to the extensive semantic processing, as well as detecting and resolving cognitive conflicts. In contrast, the late period of insight solution produced increased activities in the hippocampus and the amygdala, possibly reflecting the forming of novel association and the concomitant "Aha" feeling. Our study supports the key role of hippocampus in forming novel associations, and indicates a dynamic neural network during insight solution.

  11. Differential item functioning analysis by applying multiple comparison procedures.

    Science.gov (United States)

    Eusebi, Paolo; Kreiner, Svend

    2015-01-01

    Analysis within a Rasch measurement framework aims at development of valid and objective test score. One requirement of both validity and objectivity is that items do not show evidence of differential item functioning (DIF). A number of procedures exist for the assessment of DIF including those based on analysis of contingency tables by Mantel-Haenszel tests and partial gamma coefficients. The aim of this paper is to illustrate Multiple Comparison Procedures (MCP) for analysis of DIF relative to a variable defining a very large number of groups, with an unclear ordering with respect to the DIF effect. We propose a single step procedure controlling the false discovery rate for DIF detection. The procedure applies for both dichotomous and polytomous items. In addition to providing evidence against a hypothesis of no DIF, the procedure also provides information on subset of groups that are homogeneous with respect to the DIF effect. A stepwise MCP procedure for this purpose is also introduced.

  12. Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator.

    Science.gov (United States)

    Hoellinger, Thomas; Petieau, Mathieu; Duvinage, Matthieu; Castermans, Thierry; Seetharaman, Karthik; Cebolla, Ana-Maria; Bengoetxea, Ana; Ivanenko, Yuri; Dan, Bernard; Cheron, Guy

    2013-01-01

    The existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum, or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for addressing this central question. The coordination of the elevation of the 3 leg segments under a planar covariation rule (Borghese et al., 1996) was recently modeled (Barliya et al., 2009) by phase-adjusted simple oscillators shedding new light on the understanding of the central pattern generator (CPG) processing relevant oscillation signals. We describe the use of a dynamic recurrent neural network (DRNN) mimicking the natural oscillatory behavior of human locomotion for reproducing the planar covariation rule in both legs at different walking speeds. Neural network learning was based on sinusoid signals integrating frequency and amplitude features of the first three harmonics of the sagittal elevation angles of the thigh, shank, and foot of each lower limb. We verified the biological plausibility of the neural networks. Best results were obtained with oscillations extracted from the first three harmonics in comparison to oscillations outside the harmonic frequency peaks. Physiological replication steadily increased with the number of neuronal units from 1 to 80, where similarity index reached 0.99. Analysis of synaptic weighting showed that the proportion of inhibitory connections consistently increased with the number of neuronal units in the DRNN. This emerging property in the artificial neural networks resonates with recent advances in neurophysiology of inhibitory neurons that are involved in central nervous system oscillatory activities. The main message of this study is that this type of DRNN may offer a useful model of physiological central pattern generator for gaining insights in basic research and developing clinical applications.

  13. Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator

    Directory of Open Access Journals (Sweden)

    Thomas eHoellinger

    2013-05-01

    Full Text Available The existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for addressing this central question. The coordination of the elevation of the 3 leg segments under a planar covariation rule (Borghese et al., 1996 was recently modeled (Barliya et al., 2009 by phase-adjusted simple oscillators shedding new light on the understanding of the central pattern generator processing relevant oscillation signals. We describe the use of a dynamic recurrent neural network (DRNN mimicking the natural oscillatory behavior of human locomotion for reproducing the planar covariation rule in both legs at different walking speeds. Neural network learning was based on sinusoid signals integrating frequency and amplitude features of the first three harmonics of the sagittal elevation angles of the thigh, shank and foot of each lower limb. We verified the biological plausibility of the neural networks. Best results were obtained with oscillations extracted from the first three harmonics in comparison to oscillations outside the harmonic frequency peaks. Physiological replication steadily increased with the number of neuronal units from 1 to 80, where similarity index reached 0.99. Analysis of synaptic weighting showed that the proportion of inhibitory connections consistently increased with the number of neuronal units in the DRNN. This emerging property in the artificial neural networks resonates with recent advances in neurophysiology of inhibitory neurons that are involved in central nervous system oscillatory activities. The main message of this study is that this type of DRNN may offer a useful model of physiological central pattern generator for gaining insights in basic research and developing clinical applications.

  14. Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Muammar Sadrawi

    2015-01-01

    Full Text Available This study evaluated the depth of anesthesia (DoA index using artificial neural networks (ANN which is performed as the modeling technique. Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively. The empirical mode decomposition (EMD is utilized to purify between the electroencephalography (EEG signal and the noise. The filtered EEG signal is subsequently extracted to achieve a sample entropy index by every 5-second signal. Then, it is combined with other mean values of vital signs, that is, electromyography (EMG, heart rate (HR, pulse, systolic blood pressure (SBP, diastolic blood pressure (DBP, and signal quality index (SQI to evaluate the DoA index as the input. The 5 doctor scores are averaged to obtain an output index. The mean absolute error (MAE is utilized as the performance evaluation. 10-fold cross-validation is performed in order to generalize the model. The ANN model is compared with the bispectral index (BIS. The results show that the ANN is able to produce lower MAE than BIS. For the correlation coefficient, ANN also has higher value than BIS tested on the 46-patient testing data. Sensitivity analysis and cross-validation method are applied in advance. The results state that EMG has the most effecting parameter, significantly.

  15. Multiple modes of clearing one's mind of current thoughts: overlapping and distinct neural systems.

    Science.gov (United States)

    Banich, Marie T; Mackiewicz Seghete, Kristen L; Depue, Brendan E; Burgess, Gregory C

    2015-03-01

    This study used the power of neuroimaging to identify the neural systems that remove information from working memory, a thorny issue to examine because it is difficult to confirm that individuals have actually modified their thoughts. To overcome this problem, brain activation as measured via fMRI was assessed when individuals had to clear their mind of all thought (global clear), clear their mind of a particular thought (targeted clear), or replace the current thought (replace), relative to maintaining an item in working memory. The pattern of activity in posterior sensory regions across these conditions confirmed compliance with task demands. A hierarchy of brain regions involved in cognitive control, including parietal, dorsolateral prefrontal and frontopolar regions, were engaged to varying degrees depending on the manner in which information was removed from working memory. In addition, individuals with greater difficulty in controlling internal thoughts exhibited greater activity in prefrontal brain regions associated with cognitive control, as well as in left lateral prefrontal areas including Broca's area, which is associated with inner speech. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Congestion control for ATM multiplexers using neural networks:multiple sources/single buffer scenario

    Institute of Scientific and Technical Information of China (English)

    杜树新; 袁石勇

    2004-01-01

    A new neural network based method for solving the problem of congestion control arising at the user network interface (UNI) of ATM networks is proposed in this paper. Unlike the previous methods where the coding rate for all traffic sources as controller output signals is tuned in a body, the proposed method adjusts the coding rate for only a part of the traffic sources while the remainder sources send the cells in the previous coding rate in case of occurrence of congestion. The controller output signals include the source coding rate and the percentage of the sources that send cells at the corresponding coding rate. The control methods not only minimize the cell loss rate but also guarantee the quality of information (such as voice sources) fed into the multiplexer buffer. Simulations with 150 ADPCM voice sources fed into the multiplexer buffer showed that the proposed methods have advantage over the previous methods in the aspect of the performance indices such as cell loss rate (CLR) and voice quality.

  17. Congestion control for ATM multiplexers using neural networks:multiple sources/single buffer scenario

    Institute of Scientific and Technical Information of China (English)

    杜树新; 袁石勇

    2004-01-01

    A new neural network based method for solving the problem of congestion control arising at the user network interface (UNI) of ATM networks is proposed in this paper. Unlike the previous methods where the coding rate for all traffic sources as controller output signals is tuned in a body, the proposed method adjusts the coding rate for only a part of the traffic sources while the remainder sources send the cells in the previous coding rate in case of occurrence of congestion. The controller output signals include the source coding rate and the percentage of the sources that send cells at the corresponding coding rate. The control methods not only minimize the cell loss rate but also guarantee the quality of information (such as voice sources) fed into the multiplexer buffer. Simulations with 150 ADPCM voice sources fed into the multiplexer buffer showed that the proposed methods have advantage over the previous methods in the aspect of the performance indices such as cell loss rate (CLR) and voice quality.

  18. Expandable and Rapidly Differentiating Human Induced Neural Stem Cell Lines for Multiple Tissue Engineering Applications

    Directory of Open Access Journals (Sweden)

    Dana M. Cairns

    2016-09-01

    Full Text Available Limited availability of human neurons poses a significant barrier to progress in biological and preclinical studies of the human nervous system. Current stem cell-based approaches of neuron generation are still hindered by prolonged culture requirements, protocol complexity, and variability in neuronal differentiation. Here we establish stable human induced neural stem cell (hiNSC lines through the direct reprogramming of neonatal fibroblasts and adult adipose-derived stem cells. These hiNSCs can be passaged indefinitely and cryopreserved as colonies. Independently of media composition, hiNSCs robustly differentiate into TUJ1-positive neurons within 4 days, making them ideal for innervated co-cultures. In vivo, hiNSCs migrate, engraft, and contribute to both central and peripheral nervous systems. Lastly, we demonstrate utility of hiNSCs in a 3D human brain model. This method provides a valuable interdisciplinary tool that could be used to develop drug screening applications as well as patient-specific disease models related to disorders of innervation and the brain.

  19. A potential neural substrate for processing functional classes of complex acoustic signals.

    Directory of Open Access Journals (Sweden)

    Isabelle George

    Full Text Available Categorization is essential to all cognitive processes, but identifying the neural substrates underlying categorization processes is a real challenge. Among animals that have been shown to be able of categorization, songbirds are particularly interesting because they provide researchers with clear examples of categories of acoustic signals allowing different levels of recognition, and they possess a system of specialized brain structures found only in birds that learn to sing: the song system. Moreover, an avian brain nucleus that is analogous to the mammalian secondary auditory cortex (the caudo-medial nidopallium, or NCM has recently emerged as a plausible site for sensory representation of birdsong, and appears as a well positioned brain region for categorization of songs. Hence, we tested responses in this non-primary, associative area to clear and distinct classes of songs with different functions and social values, and for a possible correspondence between these responses and the functional aspects of songs, in a highly social songbird species: the European starling. Our results clearly show differential neuronal responses to the ethologically defined classes of songs, both in the number of neurons responding, and in the response magnitude of these neurons. Most importantly, these differential responses corresponded to the functional classes of songs, with increasing activation from non-specific to species-specific and from species-specific to individual-specific sounds. These data therefore suggest a potential neural substrate for sorting natural communication signals into categories, and for individual vocal recognition of same-species members. Given the many parallels that exist between birdsong and speech, these results may contribute to a better understanding of the neural bases of speech.

  20. Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response

    Energy Technology Data Exchange (ETDEWEB)

    Nikiforov, M P; Guo, S; Kalinin, S V; Jesse, S [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN 37831 (United States); Reukov, V V; Thompson, G L; Vertegel, A A, E-mail: sergei2@ornl.go [Department of Bioengineering, Clemson University, Clemson, SC 29634 (United States)

    2009-10-07

    Functional recognition imaging in scanning probe microscopy (SPM) using artificial neural network identification is demonstrated. This approach utilizes statistical analysis of complex SPM responses at a single spatial location to identify the target behavior, which is reminiscent of associative thinking in the human brain, obviating the need for analytical models. We demonstrate, as an example of recognition imaging, rapid identification of cellular organisms using the difference in electromechanical activity over a broad frequency range. Single-pixel identification of model Micrococcus lysodeikticus and Pseudomonas fluorescens bacteria is achieved, demonstrating the viability of the method.

  1. Radial basis function neural networks with sequential learning MRAN and its applications

    CERN Document Server

    Sundararajan, N; Wei Lu Ying

    1999-01-01

    This book presents in detail the newly developed sequential learning algorithm for radial basis function neural networks, which realizes a minimal network. This algorithm, created by the authors, is referred to as Minimal Resource Allocation Networks (MRAN). The book describes the application of MRAN in different areas, including pattern recognition, time series prediction, system identification, control, communication and signal processing. Benchmark problems from these areas have been studied, and MRAN is compared with other algorithms. In order to make the book self-contained, a review of t

  2. Emerging role of non-coding RNA in neural plasticity, cognitive function, and neuropsychiatric disorders

    Directory of Open Access Journals (Sweden)

    Paola eSpadaro

    2012-07-01

    Full Text Available Non-coding RNAs have emerged as critical regulators of transcription, epigenetic processes, and gene silencing, which make them ideal candidates for insight into molecular evolution and a better understanding of the molecular pathways of neuropsychiatric disease. Here we provide an overview of the current state of knowledge regarding various classes of ncRNAs and their role in neural plasticity and cognitive function, and highlight the potential contribution they may make to the development of a variety of neuropsychiatric disorders, including schizophrenia, addiction and fear-related anxiety disorders.

  3. Noise Reduction Technique for Images using Radial Basis Function Neural Networks

    Directory of Open Access Journals (Sweden)

    Sander Ali Khowaja

    2014-07-01

    Full Text Available This paper presents a NN (Neural Network based model for reducing the noise from images. This is a RBF (Radial Basis Function network which is used to reduce the effect of noise and blurring from the captured images. The proposed network calculates the mean MSE (Mean Square Error and PSNR (Peak Signal to Noise Ratio of the noisy images. The proposed network has also been successfully applied to medical images. The performance of the trained RBF network has been compared with the MLP (Multilayer Perceptron Network and it has been demonstrated that the performance of the RBF network is better than the MLP network.

  4. 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.

  5. Neural correlate of vernier acuity tasks assessed by functional MRI (FMRI).

    Science.gov (United States)

    Sheth, Kevin N; Walker, B Michael; Modestino, Edward J; Miki, Atsushi; Terhune, Kyla P; Francis, Ellie L; Haselgrove, John C; Liu, Grant T

    2007-01-01

    Vernier acuity refers to the ability to discern a small offset within a line. However, while Vernier acuity has been extensively studied psychophysically, its neural correlates are uncertain. Based upon previous psychophysical and electrophysiologic data, we hypothesized that extrastriate areas of the brain would be involved in Vernier acuity tasks, so we designed event-related functional MRI (fMRI) paradigms to identify cortical regions of the brain involved in this behavior. Normal subjects identified suprathreshold and subthreshold Vernier offsets. The results suggest a cortical network including frontal, parietal, occipital, and cerebellar regions subserves the observation, processing, interpretation, and acknowledgment of briefly presented Vernier offsets.

  6. Elevated body mass index and maintenance of cognitive function in late life: exploring underlying neural mechanisms.

    Science.gov (United States)

    Hsu, Chun Liang; Voss, Michelle W; Best, John R; Handy, Todd C; Madden, Kenneth; Bolandzadeh, Niousha; Liu-Ambrose, Teresa

    2015-01-01

    Obesity is associated with vascular risk factors that in turn, may increase dementia risk. However, higher body mass index (BMI) in late life may be neuroprotective. The possible neural mechanisms underlying the benefit of higher BMI on cognition in older adults are largely unknown. Thus, we used functional connectivity magnetic resonance imaging (fcMRI) to examine: (1) the relationship between BMI and functional brain connectivity; and (2) the mediating role of functional brain connectivity in the association between baseline BMI and change in cognitive function over a 12-month period. We conducted a 12-month, prospective study among 66 community-dwelling older adults, aged 70 to 80 years, who were categorized as: normal weight (BMI from 18.50 to 24.99); overweight (BMI from 25.00 to 29.99); and obese (BMI ≥ 30.00). At baseline, participants performed a finger-tapping task during fMRI scanning. Relevant neural networks were initially identified through independent component analysis (ICA) and subsequently examined through seed-based functional connectivity analysis. At baseline and 12-months, we measured three executive cognitive processes: (1) response inhibition; (2) set shifting; and (3) working memory. Obese individuals showed lower task-related functional connectivity during finger tapping in the default mode network (DMN) compared with their healthy weight counterparts (p BMI and working memory at 12-months (indirect effect: -0.155, 95% confidence interval [-0.313, -0.053]). These findings suggest that functional connectivity of the DMN may be an underlying mechanism by which higher BMI confers protective effects to cognition in late life.

  7. Mobilization of Neural Precursors in the Circulating Blood of Patients with Multiple Sclerosis

    Science.gov (United States)

    2013-09-01

    progressive forms. Demyelinated lesions or “plaques” are found randomly distributed in the MS CNS (Frohman et al., 2006) and evidence of spontaneous...MH, Prineas JW (2004) Relapsing and remitting multiple sclerosis: pathology of the newly forming lesion . Ann Neurol 55:458-468. Brannvall K...progenitor subclasses with a mild phenotype in Duchenne muscular dystrophy patients. PLoS One 3:e2218. Martinez HR, Gonzalez-Garza MT, Moreno-Cuevas JE

  8. Functional electrical stimulation-facilitated proliferation and regeneration of neural precursor cells in the brains of rats with cerebral infarction

    Institute of Scientific and Technical Information of China (English)

    Yun Xiang; Huihua Liu; Tiebin Yan; Zhiqiang Zhuang; Dongmei Jin; Yuan Peng

    2014-01-01

    Previous studies have shown that proliferation of endogenous neural precursor cells cannot alone compensate for the damage to neurons and axons. From the perspective of neural plastici-ty, we observed the effects of functional electrical stimulation treatment on endogenous neural precursor cell proliferation and expression of basic fibroblast growth factor and epidermal growth factor in the rat brain on the infarct side. Functional electrical stimulation was performed in rat models of acute middle cerebral artery occlusion. Simultaneously, we set up a placebo stimulation group and a sham-operated group. Immunohistochemical staining showed that, at 7 and 14 days, compared with the placebo group, the numbers of nestin (a neural precursor cell marker)-positive cells in the subgranular zone and subventricular zone were increased in the functional electrical stimulation treatment group. Western blot assays and reverse-transcription PCR showed that total protein levels and gene expression of epidermal growth factor and basic ifbroblast growth factor were also upregulated on the infarct side. Prehensile traction test results showed that, at 14 days, prehension function of rats in the functional electrical stimulation group was signiifcantly better than in the placebo group. These results suggest that functional electrical stimulation can promote endogenous neural precursor cell proliferation in the brains of acute cerebral infarction rats, enhance expression of basic fibroblast growth factor and epidermal growth factor, and improve the motor function of rats.

  9. Skin sensitization risk assessment model using artificial neural network analysis of data from multiple in vitro assays.

    Science.gov (United States)

    Tsujita-Inoue, Kyoko; Hirota, Morihiko; Ashikaga, Takao; Atobe, Tomomi; Kouzuki, Hirokazu; Aiba, Setsuya

    2014-06-01

    The sensitizing potential of chemicals is usually identified and characterized using in vivo methods such as the murine local lymph node assay (LLNA). Due to regulatory constraints and ethical concerns, alternatives to animal testing are needed to predict skin sensitization potential of chemicals. For this purpose, combined evaluation using multiple in vitro and in silico parameters that reflect different aspects of the sensitization process seems promising. We previously reported that LLNA thresholds could be well predicted by using an artificial neural network (ANN) model, designated iSENS ver.1 (integrating in vitro sensitization tests version 1), to analyze data obtained from two in vitro tests: the human Cell Line Activation Test (h-CLAT) and the SH test. Here, we present a more advanced ANN model, iSENS ver.2, which additionally utilizes the results of antioxidant response element (ARE) assay and the octanol-water partition coefficient (LogP, reflecting lipid solubility and skin absorption). We found a good correlation between predicted LLNA thresholds calculated by iSENS ver.2 and reported values. The predictive performance of iSENS ver.2 was superior to that of iSENS ver.1. We conclude that ANN analysis of data from multiple in vitro assays is a useful approach for risk assessment of chemicals for skin sensitization.

  10. Maximum likelihood cost functions for neural network models of air quality data

    Science.gov (United States)

    Dorling, Stephen R.; Foxall, Robert J.; Mandic, Danilo P.; Cawley, Gavin C.

    The prediction of episodes of poor air quality using artificial neural networks is investigated, concentrating on selection of the most appropriate cost function used in training. Different cost functions correspond to different distributional assumptions regarding the data, the appropriate choice depends on whether a forecast of absolute pollutant concentration or prediction of exceedence events is of principle importance. The cost functions investigated correspond to logistic regression, homoscedastic Gaussian (i.e. conventional sum-of-squares) regression and heteroscedastic Gaussian regression. Both linear and nonlinear neural network architectures are evaluated. While the results presented relate to a dataset describing the daily time-series of the concentration of surface level ozone (O 3) in urban Berlin, the methods applied are quite general and applicable to a wide range of pollutants and locations. The heteroscedastic Gaussian regression model outperforms the other nonlinear methods investigated; however, there is little improvement resulting from the use of nonlinear rather than linear models. Of greater significance is the flexibility afforded by the nonlinear heteroscedastic Gaussian regression model for a range of potential end-users, who may all have different answers to the question: "What is more important, correctly predicting exceedences or avoiding false alarms?".

  11. Shaping Early Reorganization of Neural Networks Promotes Motor Function after Stroke.

    Science.gov (United States)

    Volz, L J; Rehme, A K; Michely, J; Nettekoven, C; Eickhoff, S B; Fink, G R; Grefkes, C

    2016-06-01

    Neural plasticity is a major factor driving cortical reorganization after stroke. We here tested whether repetitively enhancing motor cortex plasticity by means of intermittent theta-burst stimulation (iTBS) prior to physiotherapy might promote recovery of function early after stroke. Functional magnetic resonance imaging (fMRI) was used to elucidate underlying neural mechanisms. Twenty-six hospitalized, first-ever stroke patients (time since stroke: 1-16 days) with hand motor deficits were enrolled in a sham-controlled design and pseudo-randomized into 2 groups. iTBS was administered prior to physiotherapy on 5 consecutive days either over ipsilesional primary motor cortex (M1-stimulation group) or parieto-occipital vertex (control-stimulation group). Hand motor function, cortical excitability, and resting-state fMRI were assessed 1 day prior to the first stimulation and 1 day after the last stimulation. Recovery of grip strength was significantly stronger in the M1-stimulation compared to the control-stimulation group. Higher levels of motor network connectivity were associated with better motor outcome. Consistently, control-stimulated patients featured a decrease in intra- and interhemispheric connectivity of the motor network, which was absent in the M1-stimulation group. Hence, adding iTBS to prime physiotherapy in recovering stroke patients seems to interfere with motor network degradation, possibly reflecting alleviation of post-stroke diaschisis.

  12. Transdiagnostic dimensions of anxiety: Neural mechanisms, executive functions, and new directions.

    Science.gov (United States)

    Sharp, Paul B; Miller, Gregory A; Heller, Wendy

    2015-11-01

    Converging neuroscientific and psychological evidence points to several transdiagnostic factors that cut across DSM-defined disorders, which both affect and are affected by executive dysfunction. Two of these factors, anxious apprehension and anxious arousal, have helped bridge the gap between psychological and neurobiological models of anxiety. The present integration of diverse findings advances an understanding of the relationships between these transdiagnostic anxiety dimensions, their interactions with each other and executive function, and their neural mechanisms. Additionally, a discussion is provided concerning how these constructs fit within the Research Domain Criteria (RDoC) matrix developed by the National Institutes of Mental Health and how they relate to other anxiety constructs studied with different methods and at other units of analysis. Suggestions for future research are offered, including how to (1) improve measurement and delineation of these constructs, (2) use new neuroimaging methods and theoretical approaches of how the brain functions to build neural mechanistic models of these constructs, and (3) advance understanding of the relationships of these constructs to diverse emotional phenomena and executive functions.

  13. Contact psychophysiological and neural functions with technical and tactical readiness volleyball

    Directory of Open Access Journals (Sweden)

    Glazyrin I.D.

    2013-06-01

    Full Text Available Set the level of neural development, psycho-physiological functions in highly skilled volleyball players. Defined technical and tactical preparedness highly skilled volleyball players in the competitive period of the annual cycle of training. The study involved six masters of sport and 8 candidates for the master of sports. That the quality of play activities and the successful execution of technical elements depend on functional mobility, strength and reactivity of nerve processes, associative thinking, memory and attention. The results, which may have a prognostic value. It is shown that the neurodynamic functions are genetically determined. It is recommended to use them for the initial recruitment and selection stages for sports improvement. The necessity influence the types of thinking, memory and attention in the training process of volleyball players.

  14. Multistability of complex-valued neural networks with discontinuous activation functions.

    Science.gov (United States)

    Liang, Jinling; Gong, Weiqiang; Huang, Tingwen

    2016-12-01

    In this paper, based on the geometrical properties of the discontinuous activation functions and the Brouwer's fixed point theory, the multistability issue is tackled for the complex-valued neural networks with discontinuous activation functions and time-varying delays. To address the network with discontinuous functions, Filippov solution of the system is defined. Through rigorous analysis, several sufficient criteria are obtained to assure the existence of 25(n) equilibrium points. Among them, 9(n) points are locally stable and 16(n)-9(n) equilibrium points are unstable. Furthermore, to enlarge the attraction basins of the 9(n) equilibrium points, some mild conditions are imposed. Finally, one numerical example is provided to illustrate the effectiveness of the obtained results.

  15. Multiple threshold tracking methods for improved observation of nociceptive function

    NARCIS (Netherlands)

    Doll, Robert; Meijer, Hil Gaétan Ellart; Buitenweg, Jan R.

    2013-01-01

    Estimating momentary perception thresholds cannot reveal dynamic properties of underlying mechanisms. However, continuously estimating multiple thresholds can. This talk focussed on the possibility of tracking multiple thresholds over time. A cold pressor model was used to activate descending

  16. Decreasing Multicollinearity: A Method for Models with Multiplicative Functions.

    Science.gov (United States)

    Smith, Kent W.; Sasaki, M. S.

    1979-01-01

    A method is proposed for overcoming the problem of multicollinearity in multiple regression equations where multiplicative independent terms are entered. The method is not a ridge regression solution. (JKS)

  17. Sensitive periods for the functional specialization of the neural system for human face processing.

    Science.gov (United States)

    Röder, Brigitte; Ley, Pia; Shenoy, Bhamy H; Kekunnaya, Ramesh; Bottari, Davide

    2013-10-15

    The aim of the study was to identify possible sensitive phases in the development of the processing system for human faces. We tested the neural processing of faces in 11 humans who had been blind from birth and had undergone cataract surgery between 2 mo and 14 y of age. Pictures of faces and houses, scrambled versions of these pictures, and pictures of butterflies were presented while event-related potentials were recorded. Participants had to respond to the pictures of butterflies (targets) only. All participants, even those who had been blind from birth for several years, were able to categorize the pictures and to detect the targets. In healthy controls and in a group of visually impaired individuals with a history of developmental or incomplete congenital cataracts, the well-known enhancement of the N170 (negative peak around 170 ms) event-related potential to faces emerged, but a face-sensitive response was not observed in humans with a history of congenital dense cataracts. By contrast, this group showed a similar N170 response to all visual stimuli, which was indistinguishable from the N170 response to faces in the controls. The face-sensitive N170 response has been associated with the structural encoding of faces. Therefore, these data provide evidence for the hypothesis that the functional differentiation of category-specific neural representations in humans, presumably involving the elaboration of inhibitory circuits, is dependent on experience and linked to a sensitive period. Such functional specialization of neural systems seems necessary to archive high processing proficiency.

  18. Rapid and Objective Assessment of Neural Function in Autism Spectrum Disorder Using Transient Visual Evoked Potentials

    Science.gov (United States)

    Siper, Paige M.; Zemon, Vance; Gordon, James; George-Jones, Julia; Lurie, Stacey; Zweifach, Jessica; Tavassoli, Teresa; Wang, A. Ting; Jamison, Jesslyn; Buxbaum, Joseph D.; Kolevzon, Alexander

    2016-01-01

    Objective There is a critical need to identify biomarkers and objective outcome measures that can be used to understand underlying neural mechanisms in autism spectrum disorder (ASD). Visual evoked potentials (VEPs) offer a noninvasive technique to evaluate the functional integrity of neural mechanisms, specifically visual pathways, while probing for disease pathophysiology. Methods Transient VEPs (tVEPs) were obtained from 96 unmedicated children, including 37 children with ASD, 36 typically developing (TD) children, and 23 unaffected siblings (SIBS). A conventional contrast-reversing checkerboard condition was compared to a novel short-duration condition, which was developed to enable objective data collection from severely affected populations who are often excluded from electroencephalographic (EEG) studies. Results Children with ASD showed significantly smaller amplitudes compared to TD children at two of the earliest critical VEP components, P60-N75 and N75-P100. SIBS showed intermediate responses relative to ASD and TD groups. There were no group differences in response latency. Frequency band analyses indicated significantly weaker responses for the ASD group in bands encompassing gamma-wave activity. Ninety-two percent of children with ASD were able to complete the short-duration condition compared to 68% for the standard condition. Conclusions The current study establishes the utility of a short-duration tVEP test for use in children at varying levels of functioning and describes neural abnormalities in children with idiopathic ASD. Implications for excitatory/inhibitory balance as well as the potential application of VEP for use in clinical trials are discussed. PMID:27716799

  19. Functional Magnetic Resonance Imaging with Concurrent Urodynamic Testing Identifies Brain Structures Involved in Micturition Cycle in Patients with Multiple Sclerosis.

    Science.gov (United States)

    Khavari, Rose; Karmonik, Christof; Shy, Michael; Fletcher, Sophie; Boone, Timothy

    2017-02-01

    Neurogenic lower urinary tract dysfunction, which is common in patients with multiple sclerosis, has a significant impact on quality of life. In this study we sought to determine brain activity processes during the micturition cycle in female patients with multiple sclerosis and neurogenic lower urinary tract dysfunction. We report brain activity on functional magnetic resonance imaging and simultaneous urodynamic testing in 23 ambulatory female patients with multiple sclerosis. Individual functional magnetic resonance imaging activation maps at strong desire to void and at initiation of voiding were calculated and averaged at Montreal Neuroimaging Institute. Areas of significant activation were identified in these average maps. Subgroup analysis was performed in patients with elicitable neurogenic detrusor overactivity or detrusor-sphincter dyssynergia. Group analysis of all patients at strong desire to void yielded areas of activation in regions associated with executive function (frontal gyrus), emotional regulation (cingulate gyrus) and motor control (putamen, cerebellum and precuneus). Comparison of the average change in activation between previously reported healthy controls and patients with multiple sclerosis showed predominantly stronger, more focal activation in the former and lower, more diffused activation in the latter. Patients with multiple sclerosis who had demonstrable neurogenic detrusor overactivity and detrusor-sphincter dyssynergia showed a trend toward distinct brain activation at full urge and at initiation of voiding respectively. We successfully studied brain activation during the entire micturition cycle in female patients with neurogenic lower urinary tract dysfunction and multiple sclerosis using a concurrent functional magnetic resonance imaging/urodynamic testing platform. Understanding the central neural processes involved in specific parts of micturition in patients with neurogenic lower urinary tract dysfunction may identify areas

  20. Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes

    Science.gov (United States)

    Mekanik, F.; Imteaz, M. A.; Gato-Trinidad, S.; Elmahdi, A.

    2013-10-01

    In this study, the application of Artificial Neural Networks (ANN) and Multiple regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors. The use of dual (combined lagged ENSO-IOD) input sets for calibrating and validating ANN and MR Models is proposed to investigate the simultaneous effect of past values of these two major climate modes on long-term spring rainfall prediction. The MR models that did not violate the limits of statistical significance and multicollinearity were selected for future spring rainfall forecast. The ANN was developed in the form of multilayer perceptron using Levenberg-Marquardt algorithm. Both MR and ANN modelling were assessed statistically using mean square error (MSE), mean absolute error (MAE), Pearson correlation (r) and Willmott index of agreement (d). The developed MR and ANN models were tested on out-of-sample test sets; the MR models showed very poor generalisation ability for east Victoria with correlation coefficients of -0.99 to -0.90 compared to ANN with correlation coefficients of 0.42-0.93; ANN models also showed better generalisation ability for central and west Victoria with correlation coefficients of 0.68-0.85 and 0.58-0.97 respectively. The ability of multiple regression models to forecast out-of-sample sets is compatible with ANN for Daylesford in central Victoria and Kaniva in west Victoria (r = 0.92 and 0.67 respectively). The errors of the testing sets for ANN models are generally lower compared to multiple regression models. The statistical analysis suggest the potential of ANN over MR models for rainfall forecasting using large scale climate modes.

  1. Neural Markers and Rehabilitation of Executive Functioning in Veterans with TBI and PTSD

    Science.gov (United States)

    2016-10-01

    multiple domains of living and reduce emotional dysregulation and disinhibition associated with the co-occurring disorders . 15 Specific...Posttraumatic Stress Disorder (PTSD). TBI and PTSD are both characterized by deficits in multiple cognitive domains, including attention, executive function...provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently

  2. Analysis of Abnormal Intra-QRS Potentials in Signal-Averaged Electrocardiograms Using a Radial Basis Function Neural Network

    Directory of Open Access Journals (Sweden)

    Chun-Cheng Lin

    2016-09-01

    Full Text Available Abnormal intra-QRS potentials (AIQPs are commonly observed in patients at high risk for ventricular tachycardia. We present a method for approximating a measured QRS complex using a non-linear neural network with all radial basis functions having the same smoothness. We extracted the high frequency, but low amplitude intra-QRS potentials using the approximation error to identify possible ventricular tachycardia. With a specified number of neurons, we performed an orthogonal least squares algorithm to determine the center of each Gaussian radial basis function. We found that the AIQP estimation error arising from part of the normal QRS complex could cause clinicians to misjudge patients with ventricular tachycardia. Our results also show that it is possible to correct this misjudgment by combining multiple AIQP parameters estimated using various spread parameters and numbers of neurons. Clinical trials demonstrate that higher AIQP-to-QRS ratios in the X, Y and Z leads are visible in patients with ventricular tachycardia than in normal subjects. A linear combination of 60 AIQP-to-QRS ratios can achieve 100% specificity, 90% sensitivity, and 95.8% total prediction accuracy for diagnosing ventricular tachycardia.

  3. Using zebrafish to assess the impact of drugs on neural development and function

    Science.gov (United States)

    Guo, Su

    2009-01-01

    Background Zebrafish is becoming an increasingly attractive model organism for understanding biology and developing therapeutics, because as a vertebrate, it shares considerable similarity with mammals in both genetic compositions and tissue/organ structures, and yet remains accessible to high throughput phenotype-based genetic and small molecule compound screening. Objective/method The focus of this review is on the nervous system, which is arguably the most complex organ and known to be afflicted by more than six hundred disorders in humans. I discuss the past, present, and future of using zebrafish to assess the impact of small molecule drugs on neural development and function, in light of understanding and treating neurodevelopmental disorders such as autism, neurodegenerative disorders including Alzheimer’s, Parkinson’s, and Hungtington’s disease, and neural system dysfunctions such as anxiety/depression and addiction. Conclusion These studies hold promise to reveal fundamental mechanisms governing nervous system development and function, and to facilitate small molecule drug discovery for the many types of neurological disorders. PMID:19774094

  4. Neural Correlates of Sevoflurane-induced Unconsciousness Identified by Simultaneous Functional Magnetic Resonance Imaging and Electroencephalography.

    Science.gov (United States)

    Ranft, Andreas; Golkowski, Daniel; Kiel, Tobias; Riedl, Valentin; Kohl, Philipp; Rohrer, Guido; Pientka, Joachim; Berger, Sebastian; Thul, Alexander; Maurer, Max; Preibisch, Christine; Zimmer, Claus; Mashour, George A; Kochs, Eberhard F; Jordan, Denis; Ilg, Rüdiger

    2016-11-01

    The neural correlates of anesthetic-induced unconsciousness have yet to be fully elucidated. Sedative and anesthetic states induced by propofol have been studied extensively, consistently revealing a decrease of frontoparietal and thalamocortical connectivity. There is, however, less understanding of the effects of halogenated ethers on functional brain networks. The authors recorded simultaneous resting-state functional magnetic resonance imaging and electroencephalography in 16 artificially ventilated volunteers during sevoflurane anesthesia at burst suppression and 3 and 2 vol% steady-state concentrations for 700 s each to assess functional connectivity changes compared to wakefulness. Electroencephalographic data were analyzed using symbolic transfer entropy (surrogate of information transfer) and permutation entropy (surrogate of cortical information processing). Functional magnetic resonance imaging data were analyzed by an independent component analysis and a region-of-interest-based analysis. Electroencephalographic analysis showed a significant reduction of anterior-to-posterior symbolic transfer entropy and global permutation entropy. At 2 vol% sevoflurane concentrations, frontal and thalamic networks identified by independent component analysis showed significantly reduced within-network connectivity. Primary sensory networks did not show a significant change. At burst suppression, all cortical networks showed significantly reduced functional connectivity. Region-of-interest-based thalamic connectivity at 2 vol% was significantly reduced to frontoparietal and posterior cingulate cortices but not to sensory areas. Sevoflurane decreased frontal and thalamocortical connectivity. The changes in blood oxygenation level dependent connectivity were consistent with reduced anterior-to-posterior directed connectivity and reduced cortical information processing. These data advance the understanding of sevoflurane-induced unconsciousness and contribute to a

  5. Functions and imaging of mast cell and neural axis of the gut.

    Science.gov (United States)

    Schemann, Michael; Camilleri, Michael

    2013-04-01

    Close association between nerves and mast cells in the gut wall provides the microanatomic basis for functional interactions between these elements, supporting the hypothesis that a mast cell-nerve axis influences gut functions in health and disease. Advanced morphology and imaging techniques are now available to assess structural and functional relationships of the mast cell-nerve axis in human gut tissues. Morphologic techniques including co-labeling of mast cells and nerves serve to evaluate changes in their densities and anatomic proximity. Calcium (Ca(++)) and potentiometric dye imaging provide novel insights into functions such as mast cell-nerve signaling in the human gut tissues. Such imaging promises to reveal new ionic or molecular targets to normalize nerve sensitization induced by mast cell hyperactivity or mast cell sensitization by neurogenic inflammatory pathways. These targets include proteinase-activated receptor (PAR) 1 or histamine receptors. In patients, optical imaging in the gut in vivo has the potential to identify neural structures and inflammation in vivo. The latter has some risks and potential of sampling error with a single biopsy. Techniques that image nerve fibers in the retina without the need for contrast agents (optical coherence tomography and full-field optical coherence microscopy) may be applied to study submucous neural plexus. Moreover, the combination of submucosal dissection, use of a fluorescent marker, and endoscopic confocal microscopy provides detailed imaging of myenteric neurons and smooth muscle cells in the muscularis propria. Studies of motility and functional gastrointestinal disorders would be feasible without the need for full-thickness biopsy.

  6. Connecting teratogen-induced congenital heart defects to neural crest cells and their effect on cardiac function.

    Science.gov (United States)

    Karunamuni, Ganga H; Ma, Pei; Gu, Shi; Rollins, Andrew M; Jenkins, Michael W; Watanabe, Michiko

    2014-09-01

    Neural crest cells play many key roles in embryonic development, as demonstrated by the abnormalities that result from their specific absence or dysfunction. Unfortunately, these key cells are particularly sensitive to abnormalities in various intrinsic and extrinsic factors, such as genetic deletions or ethanol-exposure that lead to morbidity and mortality for organisms. This review discusses the role identified for a segment of neural crest in regulating the morphogenesis of the heart and associated great vessels. The paradox is that their derivatives constitute a small proportion of cells to the cardiovascular system. Findings supporting that these cells impact early cardiac function raises the interesting possibility that they indirectly control cardiovascular development at least partially through regulating function. Making connections between insults to the neural crest, cardiac function, and morphogenesis is more approachable with technological advances. Expanding our understanding of early functional consequences could be useful in improving diagnosis and testing therapies.

  7. Neural correlates of own- and other-race face recognition in children: a functional near-infrared spectroscopy study.

    Science.gov (United States)

    Ding, Xiao Pan; Fu, Genyue; Lee, Kang

    2014-01-15

    The present study used the functional Near-infrared Spectroscopy (fNIRS) methodology to investigate the neural correlates of elementary school children's own- and other-race face processing. An old-new paradigm was used to assess children's recognition ability of own- and other-race faces. FNIRS data revealed that other-race faces elicited significantly greater [oxy-Hb] changes than own-race faces in the right middle frontal gyrus and inferior frontal gyrus regions (BA9) and the left cuneus (BA18). With increased age, the [oxy-Hb] activity differences between own- and other-race faces, or the neural other-race effect (NORE), underwent significant changes in these two cortical areas: at younger ages, the neural response to the other-race faces was modestly greater than that to the own-race faces, but with increased age, the neural response to the own-race faces became increasingly greater than that to the other-race faces. Moreover, these areas had strong regional functional connectivity with a swath of the cortical regions in terms of the neural other-race effect that also changed with increased age. We also found significant and positive correlations between the behavioral other-race effect (reaction time) and the neural other-race effect in the right middle frontal gyrus and inferior frontal gyrus regions (BA9). These results taken together suggest that children, like adults, devote different amounts of neural resources to processing own- and other-race faces, but the size and direction of the neural other-race effect and associated functional regional connectivity change with increased age.

  8. Functional Roles of Neural Preparatory Processes in a Cued Stroop Task Revealed by Linking Electrophysiology with Behavioral Performance.

    Directory of Open Access Journals (Sweden)

    Chao Wang

    Full Text Available It is well established that cuing facilitates behavioral performance and that different aspects of instructional cues evoke specific neural preparatory processes in cued task-switching paradigms. To deduce the functional role of these neural preparatory processes the majority of studies vary aspects of the experimental paradigm and describe how these variations alter markers of neural preparatory processes. Although these studies provide important insights, they also have notable limitations, particularly in terms of understanding the causal or functional relationship of neural markers to cognitive and behavioral processes. In this study, we sought to address these limitations and uncover the functional roles of neural processes by examining how variability in the amplitude of neural preparatory processes predicts behavioral performance to subsequent stimuli. To achieve this objective 16 young adults were recruited to perform a cued Stroop task while their brain activity was measured using high-density electroencephalography. Four temporally overlapping but functionally and topographically distinct cue-triggered event related potentials (ERPs were identified: 1 A left-frontotemporal negativity (250-700 ms that was positively associated with word-reading performance; 2 a midline-frontal negativity (450-800 ms that was positively associated with color-naming and incongruent performance; 3 a left-frontal negativity (450-800 ms that was positively associated with switch trial performance; and 4 a centroparietal positivity (450-800 ms that was positively associated with performance for almost all trial types. These results suggest that at least four dissociable cognitive processes are evoked by instructional cues in the present task, including: 1 domain-specific task facilitation; 2 switch-specific task-set reconfiguration; 3 preparation for response conflict; and 4 proactive attentional control. Examining the relationship between ERPs and behavioral

  9. Enhancing multiple-point geostatistical modeling: 2. Iterative simulation and multiple distance function

    Science.gov (United States)

    Tahmasebi, Pejman; Sahimi, Muhammad

    2016-03-01

    This series addresses a fundamental issue in multiple-point statistical (MPS) simulation for generation of realizations of large-scale porous media. Past methods suffer from the fact that they generate discontinuities and patchiness in the realizations that, in turn, affect their flow and transport properties. Part I of this series addressed certain aspects of this fundamental issue, and proposed two ways of improving of one such MPS method, namely, the cross correlation-based simulation (CCSIM) method that was proposed by the authors. In the present paper, a new algorithm is proposed to further improve the quality of the realizations. The method utilizes the realizations generated by the algorithm introduced in Part I, iteratively removes any possible remaining discontinuities in them, and addresses the problem with honoring hard (quantitative) data, using an error map. The map represents the differences between the patterns in the training image (TI) and the current iteration of a realization. The resulting iterative CCSIM—the iCCSIM algorithm—utilizes a random path and the error map to identify the locations in the current realization in the iteration process that need further "repairing;" that is, those locations at which discontinuities may still exist. The computational time of the new iterative algorithm is considerably lower than one in which every cell of the simulation grid is visited in order to repair the discontinuities. Furthermore, several efficient distance functions are introduced by which one extracts effectively key information from the TIs. To increase the quality of the realizations and extracting the maximum amount of information from the TIs, the distance functions can be used simultaneously. The performance of the iCCSIM algorithm is studied using very complex 2-D and 3-D examples, including those that are process-based. Comparison is made between the quality and accuracy of the results with those generated by the original CCSIM

  10. Functional studies of microRNAs in neural stem cells: problems and perspectives.

    Directory of Open Access Journals (Sweden)

    Malin eÅkerblom

    2012-02-01

    Full Text Available In adult mammals, neural stem cells (NSCs are found in two niches of the brain; the subventricular zone at the lateral ventricle and the subgranular zone of the dentate gyrus in the hippocampus. Neurogenesis is a complex process that is tightly controlled on a molecular level. Recently, microRNAs (miRNAs have been implicated to play a central role in the regulation of NCSs. miRNAs are small, endogenously expressed RNAs that regulate gene expression at the post-transcriptional level. However, functional studies of miRNAs are complicated due to current technical limitations. In this review we describe recent findings about miRNAs in NSCs looking closely at miR-124, miR-9 and let-7. We also highlight technical strategies used to investigate miRNA function, accentuating limitations and potentials.

  11. Design of Radial Basis Function Neural Networks for Software Effort Estimation

    Directory of Open Access Journals (Sweden)

    Ali Idri

    2010-07-01

    Full Text Available In spite of the several software effort estimation models developed over the last 30 years, providing accurate estimates of the software project under development is still unachievable goal. Therefore, many researchers are working on the development of new models and the improvement of the existing ones using artificial intelligence techniques such as: case-based reasoning, decision trees, genetic algorithms and neural networks. This paper is devoted to the design of Radial Basis Function Networks for software cost estimation. It shows the impact of the RBFN network structure, especially the number of neurons in the hidden layer and the widths of the basis function, on the accuracy of the produced estimates measured by means of MMRE and Pred indicators. The empirical study uses two different software project datasets namely, artificial COCOMO'81 and Tukutuku datasets.

  12. A Squeezed Artificial Neural Network for the Symbolic Network Reliability Functions of Binary-State Networks.

    Science.gov (United States)

    Yeh, Wei-Chang

    2016-08-18

    Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.

  13. Rescue of Brain Function Using Tunneling Nanotubes Between Neural Stem Cells and Brain Microvascular Endothelial Cells.

    Science.gov (United States)

    Wang, Xiaoqing; Yu, Xiaowen; Xie, Chong; Tan, Zijian; Tian, Qi; Zhu, Desheng; Liu, Mingyuan; Guan, Yangtai

    2016-05-01

    Evidence indicates that neural stem cells (NSCs) can ameliorate cerebral ischemia in animal models. In this study, we investigated the mechanism underlying one of the neuroprotective effects of NSCs: tunneling nanotube (TNT) formation. We addressed whether the control of cell-to-cell communication processes between NSCs and brain microvascular endothelial cells (BMECs) and, particularly, the control of TNT formation could influence the rescue function of stem cells. In an attempt to mimic the cellular microenvironment in vitro, a co-culture system consisting of terminally differentiated BMECs from mice in a distressed state and NSCs was constructed. Additionally, engraftment experiments with infarcted mouse brains revealed that control of TNT formation influenced the effects of stem cell transplantation in vivo. In conclusion, our findings provide the first evidence that TNTs exist between NSCs and BMECs and that regulation of TNT formation alters cell function.

  14. Functional topography of a distributed neural system for spatial and nonspatial information maintenance in working memory.

    Science.gov (United States)

    Sala, Joseph B; Rämä, Pia; Courtney, Susan M

    2003-01-01

    We investigated the degree to which the distributed and overlapping patterns of activity for working memory (WM) maintenance of objects and spatial locations are functionally dissociable. Previous studies of the neural system responsible for maintenance of different types of information in WM have reported seemingly contradictory results concerning the degree to which spatial and nonspatial information maintenance leads to distinct patterns of activation in prefrontal cortex. These inconsistent results may be partly attributable to the fact that different types of objects were used for the "object WM task" across studies. In the current study, we directly compared the patterns of response during WM tasks for face identity, house identity, and spatial location using functional magnetic resonance imaging (fMRI). Furthermore, independence of the neural resources available for spatial and object WM was tested behaviorally using a dual-task paradigm. Together, these results suggest that the mechanisms for the maintenance of house identity information are distributed and overlapping with those that maintain spatial location information, while the mechanisms for maintenance of face identity information are relatively more independent. There is, however, a consistent functional topography that results in superior prefrontal cortex producing the greatest response during spatial WM tasks, and middle and inferior prefrontal cortices producing their greatest responses during object WM tasks, independent of the object type. These results argue for a dorsal-ventral functional organization for spatial and nonspatial information. However, objects may contain both spatial and nonspatial information and, thus, have a distributed but not equipotent representation across both dorsal and ventral prefrontal cortex.

  15. A common functional neural network for overt production of speech and gesture.

    Science.gov (United States)

    Marstaller, L; Burianová, H

    2015-01-22

    The perception of co-speech gestures, i.e., hand movements that co-occur with speech, has been investigated by several studies. The results show that the perception of co-speech gestures engages a core set of frontal, temporal, and parietal areas. However, no study has yet investigated the neural processes underlying the production of co-speech gestures. Specifically, it remains an open question whether Broca's area is central to the coordination of speech and gestures as has been suggested previously. The objective of this study was to use functional magnetic resonance imaging to (i) investigate the regional activations underlying overt production of speech, gestures, and co-speech gestures, and (ii) examine functional connectivity with Broca's area. We hypothesized that co-speech gesture production would activate frontal, temporal, and parietal regions that are similar to areas previously found during co-speech gesture perception and that both speech and gesture as well as co-speech gesture production would engage a neural network connected to Broca's area. Whole-brain analysis confirmed our hypothesis and showed that co-speech gesturing did engage brain areas that form part of networks known to subserve language and gesture. Functional connectivity analysis further revealed a functional network connected to Broca's area that is common to speech, gesture, and co-speech gesture production. This network consists of brain areas that play essential roles in motor control, suggesting that the coordination of speech and gesture is mediated by a shared motor control network. Our findings thus lend support to the idea that speech can influence co-speech gesture production on a motoric level. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

  16. Estimation of vegetation biophysical parameters by remote sensing using radial basis function neural network

    Institute of Scientific and Technical Information of China (English)

    YANG Xiao-hua; HUANG Jing-feng; WANG Jian-wen; WANG Xiu-zhen; LIU Zhan-yu

    2007-01-01

    Hyperspectral reflectance (350-2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices (VIs) were used to predict the rice agronomic parameters including Leaf Area Index (LAI, m2 green leaf/m2 soil) and Green Leaf Chlorophyll Density (GLCD,mg chlorophyll/m2 soil) by the traditional regression models and Radial Basis Function Neural Network (RBF). RBF emerged as a variant of Artificial Neural Networks (ANNs) in the late 1980's. A large variety of training algorithms has been tested for training RBF networks. In this study, Original RBF (ORBF), Gradient Descent RBF (GDRBF), and Generalized Regression Neural Network (GRNN) were employed. Results showed that green waveband Normalized Difference Vegetation Index (NDVIgreen) and TCARI/OSAVI have the best prediction power for LAI by exponent model and ORBF respectively, and that TCARI/OSAVI has the best prediction power for GLCD by exponent model and GDRBF. The best performances of RBF are compared with the traditional models, showing that the relationship between VIs and agronomic variables are further improved when RBF is used.Compared with the best traditional models, ORBF using TCARI/OSAVI improves the prediction power for LAI by lowering the Root Mean Square Error (RMSE) for 0.1119, and GDRBF using TCARI/OSAVI improves the prediction power for GLCD by lowering the RMSE for 26.7853. It is concluded that RBF provides a useful exploratory and predictive tool when applied to the sensitive VIs.

  17. Chaos and asymptotical stability in discrete-time recurrent neural networks with generalized input-output function

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    We theoretically investigate the asymptotical stability, localbifurcations and chaos of discrete-time recurrent neural networks with the form ofwhere the input-output function is defined as a generalized sigmoid function, such as vi=tanh(μiui), etc. Numerical simulations are also provided to demonstrate the theoretical results.

  18. Neurovascular Recovery via Cotransplanted Neural and Vascular Progenitors Leads to Improved Functional Restoration after Ischemic Stroke in Rats

    Directory of Open Access Journals (Sweden)

    Jia Li

    2014-07-01

    Full Text Available The concept of the “neurovascular unit,” emphasizing the interactions between neural and vascular components in the brain, raised the notion that neural progenitor cell (NPC transplantation therapy aimed at neural repair may be insufficient for the treatment of ischemic stroke. Here, we demonstrate that enhanced neurovascular recovery via cotransplantation of NPCs and embryonic stem cell-derived vascular progenitor cells (VPCs in a rat stroke model is correlated with improved functional recovery after stroke. We found that cotransplantation promoted the survival, migration, differentiation, and maturation of neuronal and vascular cells derived from the cotransplanted progenitors. Furthermore, it triggered an increased generation of VEGF-, BDNF-, and IGF1-expressing neural cells derived from the grafted NPCs. Consistently, compared with transplantation of NPCs alone, cotransplantation more effectively improved the neurobehavioral deficits and attenuated the infarct volume. Thus, cotransplantation of NPCs and VPCs represents a more effective therapeutic strategy for the treatment of stroke than transplantation of NPCs alone.

  19. Neural bases of human mate choice: multiple value dimensions, sex difference, and self-assessment system.

    Science.gov (United States)

    Funayama, Risa; Sugiura, Motoaki; Sassa, Yuko; Jeong, Hyeonjeong; Wakusawa, Keisuke; Horie, Kaoru; Sato, Shigeru; Kawashima, Ryuta

    2012-01-01

    Mate choice is an example of sophisticated daily decision making supported by multiple componential processes. In mate-choice literature, different characteristics of the value dimensions, including the sex difference in the value dimensions, and the involvement of self-assessment due to the mutual nature of the choice, have been suggested. We examined whether the brain-activation pattern during virtual mate choice would be congruent with these characteristics in terms of stimulus selectivity and activated brain regions. In measuring brain activity, young men and women were shown two pictures of either faces or behaviors, and they indicated which person they would choose either as a spouse or as a friend. Activation selective to spouse choice was observed face-selectively in men's amygdala and behavior-selectively in women's motor system. During both partner-choice conditions, behavior-selective activation was observed in the temporoparietal regions. Taking the available knowledge of these regions into account, these results are congruent with the suggested characteristics of value dimensions for physical attractiveness, parenting resources, and beneficial personality traits for a long-lasting relationship, respectively. The medial prefrontal and posterior cingulate cortices were nonselectively activated during the partner choices, suggesting the involvement of a self-assessment process. The results thus provide neuroscientific support for the multi-component mate-choice mechanism.

  20. Distinct neural responses to chord violations: a multiple source analysis study.

    Science.gov (United States)

    Garza Villarreal, Eduardo A; Brattico, Elvira; Leino, Sakari; Ostergaard, Leif; Vuust, Peter

    2011-05-10

    The human brain is constantly predicting the auditory environment by representing sequential similarities and extracting temporal regularities. It has been proposed that simple auditory regularities are extracted at lower stations of the auditory cortex and more complex ones at other brain regions, such as the prefrontal cortex. Deviations from auditory regularities elicit a family of early negative electric potentials distributed over the frontal regions of the scalp. In this study, we wished to disentangle the brain processes associated with sequential vs. hierarchical auditory regularities in a musical context by studying the event-related potentials (ERPs), the behavioral responses to violations of these regularities, and the localization of the underlying ERP generators using two different source analysis algorithms. To this aim, participants listened to musical cadences constituted by seven chords, each containing either harmonically congruous chords, harmonically incongruous chords, or harmonically congruous but mistuned chords. EEG was recorded and multiple source analysis was performed. Incongruous chords violating the rules of harmony elicited a bilateral ERAN, whereas mistuned chords within chord sequences elicited a right-lateralized MMN. We found that the dominant cortical sources for the ERAN were localized around Broca's area and its right homolog, whereas the MMN generators were localized around the primary auditory cortex. These findings suggest a predominant role of the auditory cortices in detecting sequential scale regularities and the posterior prefrontal cortex in parsing hierarchical regularities in music. Copyright © 2011 Elsevier B.V. All rights reserved.

  1. Age-Related Increases in Long-Range Connectivity in Fetal Functional Neural Connectivity Networks In Utero

    Science.gov (United States)

    Thomason, Moriah E.; Grove, Lauren E.; Lozon, Tim A.; Vila, Angela M.; Ye, Yongquan; Nye, Matthew J.; Manning, Janessa H.; Pappas, Athina; Hernandez-Andrade, Edgar; Yeo, Lami; Mody, Swati; Berman, Susan; Hassan, Sonia S.; Romero, Roberto

    2015-01-01

    Formation of operational neural networks is one of the most significant accomplishments of human fetal brain growth. Recent advances in functional magnetic resonance imaging (fMRI) have made it possible to obtain information about brain function during fetal development. Specifically, resting-state fMRI and novel signal covariation approaches have opened up a new avenue for non-invasive assessment of neural functional connectivity (FC) before birth. Early studies in this area have unearthed new insights about principles of prenatal brain function. However, very little is known about the emergence and maturation of neural networks during fetal life. Here, we obtained cross-sectional rs-fMRI data from 39 fetuses between 24 and 38 weeks postconceptual age to examine patterns of connectivity across ten neural FC networks. We identified primitive forms of motor, visual, default mode, thalamic, and temporal networks in the human fetal brain. We discovered the first evidence of increased long-range, cerebral-cerebellar, cortical-subcortical, and intra-hemispheric FC with advancing fetal age. Continued aggregation of data about fundamental neural connectivity systems in utero is essential to establishing principles of connectomics at the beginning of human life. Normative data provides a vital context against which to compare instances of abnormal neurobiological development. PMID:25284273

  2. Age-related increases in long-range connectivity in fetal functional neural connectivity networks in utero

    Directory of Open Access Journals (Sweden)

    Moriah E. Thomason

    2015-02-01

    Full Text Available Formation of operational neural networks is one of the most significant accomplishments of human fetal brain growth. Recent advances in functional magnetic resonance imaging (fMRI have made it possible to obtain information about brain function during fetal development. Specifically, resting-state fMRI and novel signal covariation approaches have opened up a new avenue for non-invasive assessment of neural functional connectivity (FC before birth. Early studies in this area have unearthed new insights about principles of prenatal brain function. However, very little is known about the emergence and maturation of neural networks during fetal life. Here, we obtained cross-sectional rs-fMRI data from 39 fetuses between 24 and 38 weeks postconceptual age to examine patterns of connectivity across ten neural FC networks. We identified primitive forms of motor, visual, default mode, thalamic, and temporal networks in the human fetal brain. We discovered the first evidence of increased long-range, cerebral-cerebellar, cortical-subcortical, and intra-hemispheric FC with advancing fetal age. Continued aggregation of data about fundamental neural connectivity systems in utero is essential to establishing principles of connectomics at the beginning of human life. Normative data provides a vital context against which to compare instances of abnormal neurobiological development.

  3. Age-related increases in long-range connectivity in fetal functional neural connectivity networks in utero.

    Science.gov (United States)

    Thomason, Moriah E; Grove, Lauren E; Lozon, Tim A; Vila, Angela M; Ye, Yongquan; Nye, Matthew J; Manning, Janessa H; Pappas, Athina; Hernandez-Andrade, Edgar; Yeo, Lami; Mody, Swati; Berman, Susan; Hassan, Sonia S; Romero, Roberto

    2015-02-01

    Formation of operational neural networks is one of the most significant accomplishments of human fetal brain growth. Recent advances in functional magnetic resonance imaging (fMRI) have made it possible to obtain information about brain function during fetal development. Specifically, resting-state fMRI and novel signal covariation approaches have opened up a new avenue for non-invasive assessment of neural functional connectivity (FC) before birth. Early studies in this area have unearthed new insights about principles of prenatal brain function. However, very little is known about the emergence and maturation of neural networks during fetal life. Here, we obtained cross-sectional rs-fMRI data from 39 fetuses between 24 and 38 weeks postconceptual age to examine patterns of connectivity across ten neural FC networks. We identified primitive forms of motor, visual, default mode, thalamic, and temporal networks in the human fetal brain. We discovered the first evidence of increased long-range, cerebral-cerebellar, cortical-subcortical, and intra-hemispheric FC with advancing fetal age. Continued aggregation of data about fundamental neural connectivity systems in utero is essential to establishing principles of connectomics at the beginning of human life. Normative data provides a vital context against which to compare instances of abnormal neurobiological development. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

  4. Poly (3, 4-ethylenedioxythiophene)-ionic liquid coating improves neural recording and stimulation functionality of MEAs

    OpenAIRE

    Du, Zhanhong Jeff; Luo, Xiliang; Weaver, Cassandra; Cui, Xinyan Tracy

    2015-01-01

    In vivo multi-electrode arrays (MEAs) can sense electrical signals from a small set of neurons or modulate neural activity through micro-stimulation. Electrode's geometric surface area (GSA) and impedance are important for both unit recording and neural stimulation. Smaller GSA is preferred due to enhanced selectivity of neural signal, but it tends to increase electrode impedance. Higher impedance leads to increased electrical noise and signal loss in single unit neural recording. It also yie...

  5. Predicting in vitro rumen VFA production using CNCPS carbohydrate fractions with multiple linear models and artificial neural networks.

    Directory of Open Access Journals (Sweden)

    Ruilan Dong

    Full Text Available The objectives of this trial were to develop multiple linear regression (MLR models and three-layer Levenberg-Marquardt back propagation (BP3 neural network models using the Cornell Net Carbohydrate and Protein System (CNCPS carbohydrate fractions as dietary variables for predicting in vitro rumen volatile fatty acid (VFA production and further compare MLR and BP3 models. Two datasets were established for the trial, of which the first dataset containing 45 feed mixtures with concentrate/roughage ratios of 10∶90, 20∶80, 30∶70, 40∶60, and 50∶50 were used for establishing the models and the second dataset containing 10 feed mixtures with the same concentrate/roughage ratios with the first dataset were used for testing the models. The VFA production of feed samples was determined using an in vitro incubation technique. The CNCPS carbohydrate fractions (g, i.e. CA (sugars, CB1 (starch and pectin, CB2 (available cell wall of feed samples were calculated based on chemical analysis. The performance of MLR models and BP3 models were compared using a paired t-test, the determination coefficient (R2 and the root mean square prediction error (RMSPE between observed and predicted values. Statistical analysis indicated that VFA production (mmol was significantly correlated with CNCPS carbohydrate fractions (g CA, CB1, and CB2 in a multiple linear pattern. Compared with MLR models, BP3 models were more accurate in predicting acetate, propionate, and total VFA production while similar in predicting butyrate production. The trial indicated that both MLR and BP3 models were suitable for predicting in vitro rumen VFA production of feed mixtures using CNCPS carbohydrate fractions CA, CB1, and CB2 as input dietary variables while BP3 models showed greater accuracy for prediction.

  6. Prediction of Currency Volume Issued in Taiwan Using a Hybrid Artificial Neural Network and Multiple Regression Approach

    Directory of Open Access Journals (Sweden)

    Yuehjen E. Shao

    2013-01-01

    Full Text Available Because the volume of currency issued by a country always affects its interest rate, price index, income levels, and many other important macroeconomic variables, the prediction of currency volume issued has attracted considerable attention in recent years. In contrast to the typical single-stage forecast model, this study proposes a hybrid forecasting approach to predict the volume of currency issued in Taiwan. The proposed hybrid models consist of artificial neural network (ANN and multiple regression (MR components. The MR component of the hybrid models is established for a selection of fewer explanatory variables, wherein the selected variables are of higher importance. The ANN component is then designed to generate forecasts based on those important explanatory variables. Subsequently, the model is used to analyze a real dataset of Taiwan's currency from 1996 to 2011 and twenty associated explanatory variables. The prediction results reveal that the proposed hybrid scheme exhibits superior forecasting performance for predicting the volume of currency issued in Taiwan.

  7. Initiation to end point: the multiple roles of fibroblast growth factors in neural development.

    Science.gov (United States)

    Mason, Ivor

    2007-08-01

    From a wealth of experimental findings, derived from both in vitro and in vivo experiments, it is becoming clear that fibroblast growth factors regulate processes that are central to all aspects of nervous system development. Some of these functions are well known, whereas others, such as the roles of these proteins in axon guidance and synaptogenesis, have been established only recently. The emergent picture is one of remarkable economy, in which this family of ligands is deployed and redeployed at successive developmental stages to sculpt the nervous system.

  8. 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.

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

    Directory of Open Access Journals (Sweden)

    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.

  10. Multiple Bracket Function, Stirling Number, and Lah Number Identities

    OpenAIRE

    Coskun, Hasan

    2012-01-01

    The author has constructed multiple analogues of several families of combinatorial numbers in a recent article, including the bracket symbol, and the Stirling numbers of the first and second kind. In the present paper, a multiple analogue of another sequence, the Lah numbers, is developed, and certain associated identities and significant properties of all these sequences are constructed.

  11. Microfluidic production of multiple emulsions and functional microcapsules

    NARCIS (Netherlands)

    Lee, Tae Yong; Choi, Tae Min; Shim, Tae Soup; Frijns, Raoul A.M.; Kim, Shin Hyun

    2016-01-01

    Recent advances in microfluidics have enabled the controlled production of multiple-emulsion drops with onion-like topology. The multiple-emulsion drops possess an intrinsic core–shell geometry, which makes them useful as templates to create microcapsules with a solid membrane. High flexibility in t

  12. The neural basis of semantic and episodic forms of self-knowledge: insights from functional neuroimaging.

    Science.gov (United States)

    D'Argembeau, Arnaud; Salmon, Eric

    2012-01-01

    Throughout evolution, hominids have developed greater capacity to think about themselves in abstract and symbolic ways. This process has reached its apex in humans with the construction of a concept of self as a distinct entity with a personal history. This chapter provides a review of recent functional neuroimaging studies that have investigated the neural correlates of such "higher-level" aspects of the human self, focusing in particular on processes that allow individuals to consciously represent and reflect on their own personal attributes (semantic forms of self-knowledge) and experiences (episodic forms of self-knowledge). These studies point to the medial prefrontal cortex (MPFC) as a key neural structure for processing various kinds of self-referential information. We speculate that the MPFC may mediate dynamic processes that appraise and code the self-relatedness or self-relevance of information. This brain region may thus play a key role in creating the mental model of the self that is displayed in our mind at a given moment.

  13. Neural substrates of sarcasm: a functional magnetic-resonance imaging study.

    Science.gov (United States)

    Uchiyama, Hitoshi; Seki, Ayumi; Kageyama, Hiroko; Saito, Daisuke N; Koeda, Tatsuya; Ohno, Kousaku; Sadato, Norihiro

    2006-12-08

    The understanding of sarcasm reflects a complex process, which involves recognizing the beliefs of the speaker. There is a clear association between deficits in mentalizing, which is the ability to understand other people's behavior in terms of their mental state, and the understanding of sarcasm in individuals with autistic spectrum disorders. This suggests that mentalizing is important in pragmatic non-literal language comprehension. To highlight the neural substrates of sarcasm, 20 normal adult volunteers underwent functional magnetic-resonance imaging. We used scenario-reading tasks, in which sentences describing a certain situation were presented, followed by the protagonist's comments regarding that situation. Depending on the situation, the semantic content of the comments was classified as sarcastic, non-sarcastic, or contextually unconnected. As the combination of the first and second sentences represented discourse-level information that was not encoded in the individual sentences, sarcasm detection was represented as the differential activation induced by the second sentences. Sarcasm detection activated the left temporal pole, the superior temporal sulcus, the medial prefrontal cortex, and the inferior frontal gyrus (Brodmann's area [BA] 47). The left BA 47 was activated more prominently by sarcasm detection than by the first sentence. These findings indicate that the detection of sarcasm recruits the medial prefrontal cortex, which is part of the mentalizing system, as well as the neural substrates involved in reading sentences. The left BA 47 might therefore be where mentalizing and language processes interact during sarcasm detection.

  14. The Prediction of Global Electron Content with Radial Basis Function Neural Network

    Science.gov (United States)

    Xue, J.

    2016-12-01

    The global electron content (GEC) is a sensitive indicator representing the dynamics of solar activities, which can faithfully reflect the global ionospheric variation.For understanding the global ionospheric variation in real time, it is important to predict the GEC through the observations of recent past. Since the change of GEC is complex, non-linear and time-varing, it is difficult to obtain a desirable prediction using the ordinary linear model. In this study, the Radial Basis Function (RBF) neural network method was used to predict the GEC through training the time series of significant length. The statistics were obtained by comparing the predictions with the measurements. It was found that the mean squared error(MSE) for the single-step prediction was within 0.24 TECu ^ 2 , while that for the multi-step prediction was less than 2 TECu ^ 2 . In addition, the Auto-Regressive and Moving Average Model (ARMA) method was also used for the GEC prediction. Comparison of the statistics of the two methods revealed that the performance of RBF nerual network is better than that of ARMA. Our study shows that the RBF neural network approach is capable of predicting the GEC with a superior performance.Furthermore, this provides a more accurate approach to understand the trend of the GEC.

  15. Neural correlates of simple unimanual discrete and continuous movements: a functional imaging study at 3 T

    Energy Technology Data Exchange (ETDEWEB)

    Habas, Christophe; Cabanis, Emmanuel A. [Hopital des Quinze-Vingts, UPMC Paris 6, Service de NeuroImagerie, Paris (France)

    2008-04-15

    The cerebral and cerebellar network involved in unimanual continuous and discrete movements was studied in blood oxygenation level-dependent functional magnetic resonance imaging (fMRI) at 3 T. Seven healthy right-handed volunteers were scanned (1) while drawing a circle with the tip of the right index finger (continuous motor task), and (2) while drawing a triangle with the tip of the right index finger (discrete motor task). In both motor tasks, extensive activations were observed in the sensorimotor (M1/S1), parietal, prefrontal, insular, lateral occipital (LOC) and anterior cerebellar cortices. Subcortical activations within red, thalamic and lentiform nuclei were also detected. However, discrete movements were specifically followed by the recruitment of the left orbitofrontal cortex, right dentate nucleus and the second cerebellar homunculus (HVIII), and bilateral and stronger activation of the sensorimotor cortical areas, whereas continuous movements specifically activated the right prefrontal cortex and the lateral hemispherical part of the neocerebellum (crus 1). We confirm the findings of previous studies showing partly distinct neural networks involved in monitoring continuous and discrete movements, but we found new differential neural relays within the prefrontal, insular and neocerebellar cortices. (orig.)

  16. UNIVERSAL APPROXIMATION WITH NON-SIGMOID HIDDEN LAYER ACTIVATION FUNCTIONS BY USING ARTIFICIAL NEURAL NETWORK MODELING

    Directory of Open Access Journals (Sweden)

    R. Murugadoss

    2014-10-01

    Full Text Available Neural networks are modeled on the way the human brain. They are capable of learning and can automatically recognize by skillfully training and design complex relationships and hidden dependencies based on historical example patterns and use this information for forecasting. The main difference, and at the same time is biggest advantage of the model of neural networks over statistical techniques seen that the forecaster the exact functional structure between input and Output variables need not be specified, but this by the system with certain Learning algorithms is "learned" using a kind of threshold logic. Goal of the learning procedure is to define the training phase while those parameters of the network, with Help the network has one of those adequate for the problem behavior. Mathematically, the training phase is an iterative, converging towards a minimum error value process. They identify the processors of the network, minimize the "total error". The currently the most popular and most widely for business applications algorithm is the backpropagation algorithm. This paper opens the black box of Backpropagation networks and makes the optimization process in the network over time and locally comprehensible.

  17. Functional analysis of sirtuin genes in multiple Plasmodium falciparum strains.

    Directory of Open Access Journals (Sweden)

    Catherine J Merrick

    Full Text Available Plasmodium falciparum, the causative agent of severe human malaria, employs antigenic variation to avoid host immunity. Antigenic variation is achieved by transcriptional switching amongst polymorphic var genes, enforced by epigenetic modification of chromatin. The histone-modifying 'sirtuin' enzymes PfSir2a and PfSir2b have been implicated in this process. Disparate patterns of var expression have been reported in patient isolates as well as in cultured strains. We examined var expression in three commonly used laboratory strains (3D7, NF54 and FCR-3 in parallel. NF54 parasites express significantly lower levels of var genes compared to 3D7, despite the fact that 3D7 was originally a clone of the NF54 strain. To investigate whether this was linked to the expression of sirtuins, genetic disruption of both sirtuins was attempted in all three strains. No dramatic changes in var gene expression occurred in NF54 or FCR-3 following PfSir2b disruption, contrasting with previous observations in 3D7. In 3D7, complementation of the PfSir2a genetic disruption resulted in a significant decrease in previously-elevated var gene expression levels, but with the continued expression of multiple var genes. Finally, rearranged chromosomes were observed in the 3D7 PfSir2a knockout line. Our results focus on the potential for parasite genetic background to contribute to sirtuin function in regulating virulence gene expression and suggest a potential role for sirtuins in maintaining genome integrity.

  18. Functional analysis of sirtuin genes in multiple Plasmodium falciparum strains.

    Science.gov (United States)

    Merrick, Catherine J; Jiang, Rays H Y; Skillman, Kristen M; Samarakoon, Upeka; Moore, Rachel M; Dzikowski, Ron; Ferdig, Michael T; Duraisingh, Manoj T

    2015-01-01

    Plasmodium falciparum, the causative agent of severe human malaria, employs antigenic variation to avoid host immunity. Antigenic variation is achieved by transcriptional switching amongst polymorphic var genes, enforced by epigenetic modification of chromatin. The histone-modifying 'sirtuin' enzymes PfSir2a and PfSir2b have been implicated in this process. Disparate patterns of var expression have been reported in patient isolates as well as in cultured strains. We examined var expression in three commonly used laboratory strains (3D7, NF54 and FCR-3) in parallel. NF54 parasites express significantly lower levels of var genes compared to 3D7, despite the fact that 3D7 was originally a clone of the NF54 strain. To investigate whether this was linked to the expression of sirtuins, genetic disruption of both sirtuins was attempted in all three strains. No dramatic changes in var gene expression occurred in NF54 or FCR-3 following PfSir2b disruption, contrasting with previous observations in 3D7. In 3D7, complementation of the PfSir2a genetic disruption resulted in a significant decrease in previously-elevated var gene expression levels, but with the continued expression of multiple var genes. Finally, rearranged chromosomes were observed in the 3D7 PfSir2a knockout line. Our results focus on the potential for parasite genetic background to contribute to sirtuin function in regulating virulence gene expression and suggest a potential role for sirtuins in maintaining genome integrity.

  19. Walking function in clinical monitoring of multiple sclerosis by telemedicine.

    Science.gov (United States)

    Sola-Valls, Núria; Blanco, Yolanda; Sepúlveda, Maria; Llufriu, Sara; Martínez-Lapiscina, Elena H; La Puma, Delon; Graus, Francesc; Villoslada, Pablo; Saiz, Albert

    2015-07-01

    Walking limitation is a key component of disability in patients with multiple sclerosis (MS), but the information on daily walking activity and disability over time is limited. To determine, (1) the agreement between the standard measurements of MS-related disability [expanded disability status scale (EDSS), functional systems (FS) and ambulation index (AI)] obtained by conventional and remote evaluation using a multimedia platform; (2) the usefulness of monitoring 6-min walk test (6MWT) and average daily walking activity (aDWA) to better characterize patients disability. Twenty-five patients (EDSS score 1.0-6.5) were evaluated every 3 months for the first year, and aDWA repeated at year 2. Remote visits included the recording of a video with self-performed neurological examination and specific multimedia questionnaires. aDWA was measured by a triaxial accelerometer. All but two patients completed the study. Modest agreement between conventional and multimedia EDSS was found for EDSS ≤ 4.0 (kappa = 0.2) and good for EDSS ≥ 4.5 (kappa = 0.6). For the overall sample, pyramidal, cerebellar and brainstem FS showed the greatest agreement (kappa = 0.7). SR-AI showed a modest agreement for EDSS ≤ 4.0 and good for EDSS ≥ 4.5 (kappa = 0.3 and 0.6, respectively). There was a strong correlation between conventional and 6MWT measured by accelerometer (r = 0.76). The aDWA correlated strongly with the EDSS (r = -0.86) and a cut-off point of 3279.3 steps/day discriminated patients with ambulatory impairment. There was a significant decline in aDWA over 2 years in patients with ambulatory impairment that were not observed by standard measurements of disability. MS clinical monitoring by telemedicine is feasible, but the observed lower agreement in less disabled patients emphasizes the need to optimize the assessment methodology. Accelerometers capture changes that may indicate deterioration over time.

  20. Changes in bird functional diversity across multiple land uses: interpretations of functional redundancy depend on functional group identity.

    Science.gov (United States)

    Luck, Gary W; Carter, Andrew; Smallbone, Lisa

    2013-01-01

    Examinations of the impact of land-use change on functional diversity link changes in ecological community structure driven by land modification with the consequences for ecosystem function. Yet, most studies have been small-scale, experimental analyses and primarily focussed on plants. There is a lack of research on fauna communities and at large-scales across multiple land uses. We assessed changes in the functional diversity of bird communities across 24 land uses aligned along an intensification gradient. We tested the hypothesis that functional diversity is higher in less intensively used landscapes, documented changes in diversity using four diversity metrics, and examined how functional diversity varied with species richness to identify levels of functional redundancy. Functional diversity, measured using a dendogram-based metric, increased from high to low intensity land uses, but observed values did not differ significantly from randomly-generated expected values. Values for functional evenness and functional divergence did not vary consistently with land-use intensification, although higher than expected values were mostly recorded in high intensity land uses. A total of 16 land uses had lower than expected values for functional dispersion and these were mostly low intensity native vegetation sites. Relations between functional diversity and bird species richness yielded strikingly different patterns for the entire bird community vs. particular functional groups. For all birds and insectivores, functional evenness, divergence and dispersion showed a linear decline with increasing species richness suggesting substantial functional redundancy across communities. However, for nectarivores, frugivores and carnivores, there was a significant hump-shaped or non-significant positive linear relationship between these functional measures and species richness indicating less redundancy. Hump-shaped relationships signify that the most functionally diverse

  1. The Mind as Neural Software? Understanding Functionalism, Computationalism, and Computational Functionalism

    OpenAIRE

    Piccinini, Dr. Gualtiero

    2009-01-01

    Defending or attacking either functionalism or computationalism requires clarity on what they amount to and what evidence counts for or against them. My goal here is not to evaluate their plausibility. My goal is to formulate them and their relationship clearly enough that we can determine which type of evidence is relevant to them. I aim to dispel some sources of confusion that surround functionalism and computationalism, recruit recent philosophical work on mechanisms and computation to she...

  2. Behavioral and Neural Correlates of Executive Function: Interplay between Inhibition and Updating Processes

    Directory of Open Access Journals (Sweden)

    Na Young Kim

    2017-06-01

    Full Text Available This study investigated the interaction between two executive function processes, inhibition and updating, through analyses of behavioral, neurophysiological, and effective connectivity metrics. Although, many studies have focused on behavioral effects of executive function processes individually, few studies have examined the dynamic causal interactions between these two functions. A total of twenty participants from a local university performed a dual task combing flanker and n-back experimental paradigms, and completed the Operation Span Task designed to measure working memory capacity. We found that both behavioral (accuracy and reaction time and neurophysiological (P300 amplitude and alpha band power metrics on the inhibition task (i.e., flanker task were influenced by the updating load (n-back level and modulated by working memory capacity. Using independent component analysis, source localization (DIPFIT, and Granger Causality analysis of the EEG time-series data, the present study demonstrated that manipulation of cognitive demand in a dual executive function task influenced the causal neural network. We compared connectivity across three updating loads (n-back levels and found that experimental manipulation of working memory load enhanced causal connectivity of a large-scale neurocognitive network. This network contains the prefrontal and parietal cortices, which are associated with inhibition and updating executive function processes. This study has potential applications in human performance modeling and assessment of mental workload, such as the design of training materials and interfaces for those performing complex multitasking under stress.

  3. Children with profound intellectual and multiple disabilities : the effects of functional movement activities

    NARCIS (Netherlands)

    van der Putten, A; Vlaskamp, C; Reynders, K; Nakken, H

    2005-01-01

    Objective: To determine the effect of functional movement activities within the MOVE ( Mobility Opportunities Via Education) curriculum on the independence of children with profound intellectual and multiple disabilities. Subjects: Forty-four children with profound intellectual and multiple disabili

  4. Diversity of neural signals mediated by multiple, burst-firing mechanisms in rat olfactory tubercle neurons.

    Science.gov (United States)

    Chiang, Elizabeth; Strowbridge, Ben W

    2007-11-01

    Olfactory information is processed by a diverse group of interconnected forebrain regions. Most efforts to define the cellular mechanisms involved in processing olfactory information have been focused on understanding the function of the olfactory bulb, the primary second-order olfactory region, and its principal target, the piriform cortex. However, the olfactory bulb also projects to other targets, including the rarely studied olfactory tubercle, a ventral brain region recently implicated in regulating cocaine-related reward behavior. We used whole cell patch-clamp recordings from rat tubercle slices to define the intrinsic properties of neurons in the dense and multiform cell layers. We find three common firing modes of tubercle neurons: regular-spiking, intermittent-discharging, and bursting. Regular-spiking neurons are typically spiny-dense-cell-layer cells with pyramidal-shaped, dendritic arborizations. Intermittently discharging and bursting neurons comprise the majority of the deeper multiform layer and share a common morphology: multipolar, sparsely spiny cells. Rather than generating all-or-none stereotyped discharges, as observed in many brain areas, bursting cells in the tubercle generate depolarizing plateau potentials that trigger graded but time-limited discharges. We find two distinct subclasses of bursting cells that respond similarly to step stimuli but differ in the role transmembrane Ca currents play in their intrinsic behavior. Calcium currents amplify depolarizing inputs and enhance excitability in regenerative bursting cells, whereas the primary action of Ca in nonregenerative bursting tubercle neurons appears to be to decrease excitability by triggering Ca-activated K currents. Nonregenerative bursting cells exhibit a prolonged refractory period after even short discharges suggesting that they may function to detect transient events.

  5. Shroom3 functions downstream of planar cell polarity to regulate myosin II distribution and cellular organization during neural tube closure

    Directory of Open Access Journals (Sweden)

    Erica M. McGreevy

    2015-01-01

    Full Text Available Neural tube closure is a critical developmental event that relies on actomyosin contractility to facilitate specific processes such as apical constriction, tissue bending, and directional cell rearrangements. These complicated processes require the coordinated activities of Rho-Kinase (Rock, to regulate cytoskeletal dynamics and actomyosin contractility, and the Planar Cell Polarity (PCP pathway, to direct the polarized cellular behaviors that drive convergent extension (CE movements. Here we investigate the role of Shroom3 as a direct linker between PCP and actomyosin contractility during mouse neural tube morphogenesis. In embryos, simultaneous depletion of Shroom3 and the PCP components Vangl2 or Wnt5a results in an increased liability to NTDs and CE failure. We further show that these pathways intersect at Dishevelled, as Shroom3 and Dishevelled 2 co-distribute and form a physical complex in cells. We observed that multiple components of the Shroom3 pathway are planar polarized along mediolateral cell junctions in the neural plate of E8.5 embryos in a Shroom3 and PCP-dependent manner. Finally, we demonstrate that Shroom3 mutant embryos exhibit defects in planar cell arrangement during neural tube closure, suggesting a role for Shroom3 activity in CE. These findings support a model in which the Shroom3 and PCP pathways interact to control CE and polarized bending of the neural plate and provide a clear illustration of the complex genetic basis of NTDs.

  6. Shroom3 functions downstream of planar cell polarity to regulate myosin II distribution and cellular organization during neural tube closure.

    Science.gov (United States)

    McGreevy, Erica M; Vijayraghavan, Deepthi; Davidson, Lance A; Hildebrand, Jeffrey D

    2015-01-16

    Neural tube closure is a critical developmental event that relies on actomyosin contractility to facilitate specific processes such as apical constriction, tissue bending, and directional cell rearrangements. These complicated processes require the coordinated activities of Rho-Kinase (Rock), to regulate cytoskeletal dynamics and actomyosin contractility, and the Planar Cell Polarity (PCP) pathway, to direct the polarized cellular behaviors that drive convergent extension (CE) movements. Here we investigate the role of Shroom3 as a direct linker between PCP and actomyosin contractility during mouse neural tube morphogenesis. In embryos, simultaneous depletion of Shroom3 and the PCP components Vangl2 or Wnt5a results in an increased liability to NTDs and CE failure. We further show that these pathways intersect at Dishevelled, as Shroom3 and Dishevelled 2 co-distribute and form a physical complex in cells. We observed that multiple components of the Shroom3 pathway are planar polarized along mediolateral cell junctions in the neural plate of E8.5 embryos in a Shroom3 and PCP-dependent manner. Finally, we demonstrate that Shroom3 mutant embryos exhibit defects in planar cell arrangement during neural tube closure, suggesting a role for Shroom3 activity in CE. These findings support a model in which the Shroom3 and PCP pathways interact to control CE and polarized bending of the neural plate and provide a clear illustration of the complex genetic basis of NTDs.

  7. The neural basis of deictic shifting in linguistic perspective-taking in high-functioning autism.

    Science.gov (United States)

    Mizuno, Akiko; Liu, Yanni; Williams, Diane L; Keller, Timothy A; Minshew, Nancy J; Just, Marcel Adam

    2011-08-01

    Personal pronouns, such as 'I' and 'you', require a speaker/listener to continuously re-map their reciprocal relation to their referent, depending on who is saying the pronoun. This process, called 'deictic shifting', may underlie the incorrect production of these pronouns, or 'pronoun reversals', such as referring to oneself with the pronoun 'you', which has been reported in children with autism. The underlying neural basis of deictic shifting, however, is not understood, nor has the processing of pronouns been studied in adults with autism. The present study compared the brain activation pattern and functional connectivity (synchronization of activation across brain areas) of adults with high-functioning autism and control participants using functional magnetic resonance imaging in a linguistic perspective-taking task that required deictic shifting. The results revealed significantly diminished frontal (right anterior insula) to posterior (precuneus) functional connectivity during deictic shifting in the autism group, as well as reliably slower and less accurate behavioural responses. A comparison of two types of deictic shifting revealed that the functional connectivity between the right anterior insula and precuneus was lower in autism while answering a question that contained the pronoun 'you', querying something about the participant's view, but not when answering a query about someone else's view. In addition to the functional connectivity between the right anterior insula and precuneus being lower in autism, activation in each region was atypical, suggesting over reliance on individual regions as a potential compensation for the lower level of collaborative interregional processing. These findings indicate that deictic shifting constitutes a challenge for adults with high-functioning autism, particularly when reference to one's self is involved, and that the functional collaboration of two critical nodes, right anterior insula and precuneus, may play a

  8. Reduced surface wave transmission function and neural networks for crack evaluation of concrete structures

    Science.gov (United States)

    Shin, Sung Woo; Yun, Chung Bang; Furuta, Hitoshi; Popovics, John S.

    2007-04-01

    Determination of crack depth in field using the self-calibrating surface wave transmission measurement and the cutting frequency in the transmission function (TRF) is very difficult due to variations of the measurement conditions. In this study, it is proposed to use the measured full TRF as a feature for crack depth assessment. A principal component analysis (PCA) is employed to generate a basis of the measured TRFs for various crack cases. The measured TRFs are represented by their projections onto the most significant principal components. Then artificial neural network (ANN) using the PCA-compressed TRFs is applied to assess the crack in concrete. Experimental study is carried out for five different crack cases to investigate the effectiveness of the proposed method. Results reveal that the proposed method can be effectively used for the crack depth assessment of concrete structures.

  9. Shape identification of electrocardiographic ST segment based on radial basis function neural network

    Institute of Scientific and Technical Information of China (English)

    LIU Hailong; TANG Jiling

    2007-01-01

    The types of myocardial ischemia can be revealed by electrocardiographic (ECG) ST segment.Effective measurement and electrocardiographic analysis of ST as well as calculation of displacement and shape change of ST segment can help doctors diagnose coronary heart disease and myocardial ischemia,especially for asymptomatic myocardial ischemia.Therefore,it is a very important subject in clinical practice to measure and classify the ECG ST segment.In this paper,we introduce a computerized automatic identification method of the electrocardiographic ST segment shape with radial basis function neural network based on adaptive fuzzy system,which has a better effect than other methods.It helps to analyze the reason of the ST segment change and confirm the position of myocardial ischemia,and is useful for doctor diagnosis.

  10. Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function.

    Science.gov (United States)

    He, Wei; Yin, Zhao; Sun, Changyin

    2016-05-11

    In this paper, we consider the trajectory tracking of a marine surface vessel in the presence of output constraints and uncertainties. An asymmetric barrier Lyapunov function is employed to cope with the output constraints. To handle the system uncertainties, we apply adaptive neural networks to approximate the unknown model parameters of a vessel. Both full state feedback control and output feedback control are proposed in this paper. The state feedback control law is designed by using the Moore-Penrose pseudoinverse in case that all states are known, and the output feedback control is designed using a high-gain observer. Under the proposed method the controller is able to achieve the constrained output. Meanwhile, the signals of the closed loop system are semiglobally uniformly bounded. Finally, numerical simulations are carried out to verify the feasibility of the proposed controller.

  11. Radial Basis Function Neural Network Based Super-Resolution Restoration for an Underspled Image

    Institute of Scientific and Technical Information of China (English)

    苏秉华; 金伟其; 牛丽红

    2004-01-01

    To achieve restoration of high frequency information for an underspled and degraded low-resolution image, a nonlinear and real-time processing method-the radial basis function (RBF) neural network based super-resolution method of restoration is proposed. The RBF network configuration and processing method is suitable for a high resolution restoration from an underspled low-resolution image. The soft-competition learning scheme based on the k-means algorithm is used, and can achieve higher mapping approximation accuracy without increase in the network size. Experiments showed that the proposed algorithm can achieve a super-resolution restored image from an underspled and degraded low-resolution image, and requires a shorter training time when compared with the multiplayer perception (MLP) network.

  12. Hybrid model decomposition of speech and noise in a radial basis function neural model framework

    DEFF Research Database (Denmark)

    Sørensen, Helge Bjarup Dissing; Hartmann, Uwe

    1994-01-01

    applied is based on a combination of the hidden Markov model (HMM) decomposition method, for speech recognition in noise, developed by Varga and Moore (1990) from DRA and the hybrid (HMM/RBF) recognizer containing hidden Markov models and radial basis function (RBF) neural networks, developed by Singer...... and Lippmann (1992) from MIT Lincoln Lab. The present authors modified the hybrid recognizer to fit into the decomposition method to achieve high performance speech recognition in noisy environments. The approach has been denoted the hybrid model decomposition method and it provides an optimal method...... for decomposition of speech and noise by using a set of speech pattern models and a noise model(s), each realized as an HMM/RBF pattern model...

  13. The Neural Substrate and Functional Integration of Uncertainty in Decision Making: An Information Theory Approach

    Science.gov (United States)

    Goñi, Joaquín; Aznárez-Sanado, Maite; Arrondo, Gonzalo; Fernández-Seara, María; Loayza, Francis R.; Heukamp, Franz H.; Pastor, María A.

    2011-01-01

    Decision making can be regarded as the outcome of cognitive processes leading to the selection of a course of action among several alternatives. Borrowing a central measurement from information theory, Shannon entropy, we quantified the uncertainties produced by decisions of participants within an economic decision task under different configurations of reward probability and time. These descriptors were used to obtain blood oxygen level-dependent (BOLD) signal correlates of uncertainty and two clusters codifying the Shannon entropy of task configurations were identified: a large cluster including parts of the right middle cingulate cortex (MCC) and left and right pre-supplementary motor areas (pre-SMA) and a small cluster at the left anterior thalamus. Subsequent functional connectivity analyses using the psycho-physiological interactions model identified areas involved in the functional integration of uncertainty. Results indicate that clusters mostly located at frontal and temporal cortices experienced an increased connectivity with the right MCC and left and right pre-SMA as the uncertainty was higher. Furthermore, pre-SMA was also functionally connected to a rich set of areas, most of them associative areas located at occipital and parietal lobes. This study provides a map of the human brain segregation and integration (i.e., neural substrate and functional connectivity respectively) of the uncertainty associated to an economic decision making paradigm. PMID:21408065

  14. 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.

  15. Adult Neurogenesis Leads to the Functional Reconstruction of a Telencephalic Neural Circuit

    Science.gov (United States)

    Macedo-Lima, Matheus; Miller, Kimberly E.; Brenowitz, Eliot A.

    2016-01-01

    Seasonally breeding songbirds exhibit pronounced annual changes in song behavior, and in the morphology and physiology of the telencephalic neural circuit underlying production of learned song. Each breeding season, new adult-born neurons are added to the pallial nucleus HVC in response to seasonal changes in steroid hormone levels, and send long axonal projections to their target nucleus, the robust nucleus of the arcopallium (RA). We investigated the role that adult neurogenesis plays in the seasonal reconstruction of this circuit. We labeled newborn HVC neurons with BrdU, and RA-projecting HVC neurons (HVCRA) with retrograde tracer injected in RA of adult male white-crowned sparrows (Zonotrichia leucophrys gambelii) in breeding or nonbreeding conditions. We found that there were many more HVCRA neurons in breeding than nonbreeding birds. Furthermore, we observed that more newborn HVC neurons were back-filled by the tracer in breeding animals. Behaviorally, song structure degraded as the HVC-RA circuit degenerated, and recovered as the circuit regenerated, in close correlation with the number of new HVCRA neurons. These results support the hypothesis that the HVC-RA circuit degenerates in nonbreeding birds, and that newborn neurons reconstruct the circuit in breeding birds, leading to functional recovery of song behavior. SIGNIFICANCE STATEMENT We investigated the role that adult neurogenesis plays in the seasonal reconstruction of a telencephalic neural circuit that controls song behavior in white-crowned sparrows. We showed that nonbreeding birds had a 36%–49% reduction in the number of projection neurons compared with breeding birds, and the regeneration of the circuit in the breeding season is due to the integration of adult-born projection neurons. Additionally, song structure degraded as the circuit degenerated and recovered as the circuit regenerated, in close correlation with new projection neuron number. This study demonstrates that steroid hormones

  16. Identification of time-varying neural dynamics from spike train data using multiwavelet basis functions.

    Science.gov (United States)

    Xu, Song; Li, Yang; Guo, Qi; Yang, Xiao-Feng; Chan, Rosa H M

    2017-02-15

    Tracking the changes of neural dynamics based on neuronal spiking activities is a critical step to understand the neurobiological basis of learning from behaving animals. These dynamical neurobiological processes associated with learning are also time-varying, which makes the modeling problem challenging. We developed a novel multiwavelet-based time-varying generalized Laguerre-Volterra (TVGLV) modeling framework to study the time-varying neural dynamical systems using natural spike train data. By projecting the time-varying parameters in the TVGLV model onto a finite sequence of multiwavelet basis functions, the time-varying identification problem is converted into a time invariant linear-in-the-parameters one. An effective forward orthogonal regression (FOR) algorithm aided by mutual information (MI) criterion is then applied for the selection of significant model regressors or terms and the refinement of model structure. A generalized linear model fit approach is finally employed for parameter estimation from spike train data. The proposed multiwavelet-based TVGLV approach is used to identify both synthetic input-output spike trains and spontaneous retinal spike train recordings. The proposed method gives excellent the performance of tracking either sharply or slowly changing parameters with high sensitivity and accuracy regardless of the a priori knowledge of spike trains, which these results indicate that the proposed method is shown to deal well with spike train data. The proposed multiwavelet-based TVGLV approach was compared with several state-of-art parametric estimation methods like the steepest descent point process filter (SDPPF) or Chebyshev polynomial expansion method. The conventional SDPPF algorithm, or SDPPF with B-splines wavelet expansion method was shown to have the poor performance of tracking the time-varying system changes with the synthetic spike train data due to the slow convergence of the adaptive filter methods. Although the Chebyshev

  17. Regional brain atrophy and functional connectivity changes related to fatigue in multiple sclerosis.

    Directory of Open Access Journals (Sweden)

    Álvaro Javier Cruz Gómez

    Full Text Available Fatigue is one of the most frequent symptoms in multiple sclerosis (MS, and recent studies have described a relationship between the sensorimotor cortex and its afferent and efferent pathways as a substrate of fatigue. The objectives of this study were to assess the neural correlates of fatigue in MS through gray matter (GM and white matter (WM atrophy, and resting state functional connectivity (rs-FC of the sensorimotor network (SMN. Eighteen healthy controls (HCs and 60 relapsing-remitting patients were assessed with the Fatigue Severity Scale (FSS. Patients were classified as fatigued (F or nonfatigued (NF. We investigated GM and WM atrophy using voxel-based morphometry, and rs-FC changes with a seed-based method and independent component analysis (ICA. F patients showed extended GM and WM atrophy focused on areas related to the SMN. High FSS scores were associated with reductions of WM in the supplementary motor area. Seed analysis of GM atrophy in the SMN showed that HCs presented increased rs-FC between the primary motor and somatosensory cortices while patients with high FSS scores were associated with decreased rs-FC between the supplementary motor area and associative somatosensory cortex. ICA results showed that NF patients presented higher rs-FC in the primary motor cortex compared to HCs and in the premotor cortex compared to F patients. Atrophy reduced functional connectivity in SMN pathways and MS patients consequently experienced high levels of fatigue. On the contrary, NF patients experienced high synchronization in this network that could be interpreted as a compensatory mechanism to reduce fatigue sensation.

  18. Cortical reorganization in patients with subcortical hemiparesis: neural mechanisms of functional recovery and prognostic implication.

    Science.gov (United States)

    Fujii, Yukihiko; Nakada, Tsutomu

    2003-01-01

    A systematic investigation on cortical reorganization in patients with hemiparesis of a subcortical origin, with special emphasis on functional correlates, was conducted using functional magnetic resonance (fMR) imaging performed on a 3-tesla system specifically optimized for fMR imaging investigation. The study group included 46 patients with hemiparesis (25 with right and 21 with left hemiparesis) and 30 age-matched healthy volunteers as controls. All study participants were originally right handed. The characteristics of the lesion were putaminal hemorrhage in 19 patients, thalamic hemorrhage in 10 patients, and striatocapsular bland infarction in 17 patients. Functional recovery in subcortical hemiparesis showed two distinct phases of the recovery process involving entirely different neural mechanisms. Phase I is characterized by the process of recovery and/or reorganization of the primary system. Successful recovery of this system is typically reached within 1 month after stroke onset. Its clinical correlate is a rapid recovery course and significant recovery of function within 1 month of stroke onset. Failure of recovery of the primary system shifts the recovery process to Phase II, during which reorganization involving the ipsilateral pathway takes place. The clinical correlate of Phase II is a slow recovery course with variable functional outcome. Effective functional organization of the ipsilateral pathway, as identified by linked activation of the ipsilateral primary sensorimotor cortex and contralateral anterior lobe of the cerebellum, is correlated with a good prognostic outcome for patients in the slow recovery group. A high degree of connectivity between supplementary motor areas, bilaterally, appears to influence functional recovery adversely.

  19. Functional Alterations in Neural Substrates of Geometric Reasoning in Adults with High-Functioning Autism

    OpenAIRE

    Takashi Yamada; Haruhisa Ohta; Hiromi Watanabe; Chieko Kanai; Masayuki Tani; Taisei Ohno; Yuko Takayama; Akira Iwanami; Nobumasa Kato; Ryuichiro Hashimoto

    2012-01-01

    Individuals with autism spectrum condition (ASC) are known to excel in some perceptual cognitive tasks, but such developed functions have been often regarded as "islets of abilities" that do not significantly contribute to broader intellectual capacities. However, recent behavioral studies have reported that individuals with ASC have advantages for performing Raven's (Standard) Progressive Matrices (RPM/RSPM), a standard neuropsychological test for general fluid intelligence, raising the poss...

  20. Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs).

    Science.gov (United States)

    Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold

    2014-12-01

    In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature.

  1. Controlling multi-function of biomaterials interfaces based on multiple and competing adsorption of functional proteins.

    Science.gov (United States)

    Guan, Zhen-Yu; Huang, Chao-Wei; Huang, Mei-Ching; Wu, Chih-Yu; Liu, Hui-Yu; Ding, Shih-Torng; Chen, Hsien-Yeh

    2017-01-01

    Multifunctional biomaterial surfaces can be created by controlling the competing adsorption of multiple proteins. To demonstrate this concept, bone morphogenetic protein 2 (BMP-2) and fibronectin were adsorbed to the hydrophobic surface of polychloro-para-xylylene. The resulting adsorption properties on the surface depended on the dimensional and steric characteristics of the selected protein molecule, the degree of denaturation of the adsorbed proteins, the associated adsorption of interphase water molecules within the protein layers, and the aggregation of proteins in a planar direction with respect to the adsorbent surface. Additionally, a defined surface composition was formed by the competing adsorption of multiple proteins, and this surface composition was directly linked to the composition of the protein mixture in the solution phase. Although the mechanism of this complex competing adsorption process is not fully understood, the adsorbed proteins were irreversibly adsorbed and were unaffected by the further adsorption of homologous or heterologous proteins. Moreover, synergistic biological activities, including cell osteogenesis and proliferation independently and specifically induced by BMP-2 or fibronectin, were observed on the modified surface, and these biological activities were positively correlated with the surface composition of the multiple adsorbed proteins. These results provide insights and important design parameters for prospective biomaterials and biointerfaces for (multi)functional modifications. The ability to control protein/interface properties will be beneficial for the processing of biomaterials for clinical applications and industrial products.

  2. Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression

    Directory of Open Access Journals (Sweden)

    Faezehossadat Khademi

    2016-12-01

    Full Text Available Compressive strength of concrete, recognized as one of the most significant mechanical properties of concrete, is identified as one of the most essential factors for the quality assurance of concrete. In the current study, three different data-driven models, i.e., Artificial Neural Network (ANN, Adaptive Neuro-Fuzzy Inference System (ANFIS, and Multiple Linear Regression (MLR were used to predict the 28 days compressive strength of recycled aggregate concrete (RAC. Recycled aggregate is the current need of the hour owing to its environmental pleasant aspect of re-using the wastes due to construction. 14 different input parameters, including both dimensional and non-dimensional parameters, were used in this study for predicting the 28 days compressive strength of concrete. The present study concluded that estimation of 28 days compressive strength of recycled aggregate concrete was performed better by ANN and ANFIS in comparison to MLR. In other words, comparing the test step of all the three models, it can be concluded that the MLR model is better to be utilized for preliminary mix design of concrete, and ANN and ANFIS models are suggested to be used in the mix design optimization and in the case of higher accuracy necessities. In addition, the performance of data-driven models with and without the non-dimensional parameters is explored. It was observed that the data-driven models show better accuracy when the non-dimensional parameters were used as additional input parameters. Furthermore, the effect of each non-dimensional parameter on the performance of each data-driven model is investigated. Finally, the effect of number of input parameters on 28 days compressive strength of concrete is examined.

  3. Network Events on Multiple Space and Time Scales in Cultured Neural Networks and in a Stochastic Rate Model.

    Directory of Open Access Journals (Sweden)

    Guido Gigante

    2015-11-01

    Full Text Available Cortical networks, in-vitro as well as in-vivo, can spontaneously generate a variety of collective dynamical events such as network spikes, UP and DOWN states, global oscillations, and avalanches. Though each of them has been variously recognized in previous works as expression of the excitability of the cortical tissue and the associated nonlinear dynamics, a unified picture of the determinant factors (dynamical and architectural is desirable and not yet available. Progress has also been partially hindered by the use of a variety of statistical measures to define the network events of interest. We propose here a common probabilistic definition of network events that, applied to the firing activity of cultured neural networks, highlights the co-occurrence of network spikes, power-law distributed avalanches, and exponentially distributed 'quasi-orbits', which offer a third type of collective behavior. A rate model, including synaptic excitation and inhibition with no imposed topology, synaptic short-term depression, and finite-size noise, accounts for all these different, coexisting phenomena. We find that their emergence is largely regulated by the proximity to an oscillatory instability of the dynamics, where the non-linear excitable behavior leads to a self-amplification of activity fluctuations over a wide range of scales in space and time. In this sense, the cultured network dynamics is compatible with an excitation-inhibition balance corresponding to a slightly sub-critical regime. Finally, we propose and test a method to infer the characteristic time of the fatigue process, from the observed time course of the network's firing rate. Unlike the model, possessing a single fatigue mechanism, the cultured network appears to show multiple time scales, signalling the possible coexistence of different fatigue mechanisms.

  4. Network Events on Multiple Space and Time Scales in Cultured Neural Networks and in a Stochastic Rate Model.

    Science.gov (United States)

    Gigante, Guido; Deco, Gustavo; Marom, Shimon; Del Giudice, Paolo

    2015-11-01

    Cortical networks, in-vitro as well as in-vivo, can spontaneously generate a variety of collective dynamical events such as network spikes, UP and DOWN states, global oscillations, and avalanches. Though each of them has been variously recognized in previous works as expression of the excitability of the cortical tissue and the associated nonlinear dynamics, a unified picture of the determinant factors (dynamical and architectural) is desirable and not yet available. Progress has also been partially hindered by the use of a variety of statistical measures to define the network events of interest. We propose here a common probabilistic definition of network events that, applied to the firing activity of cultured neural networks, highlights the co-occurrence of network spikes, power-law distributed avalanches, and exponentially distributed 'quasi-orbits', which offer a third type of collective behavior. A rate model, including synaptic excitation and inhibition with no imposed topology, synaptic short-term depression, and finite-size noise, accounts for all these different, coexisting phenomena. We find that their emergence is largely regulated by the proximity to an oscillatory instability of the dynamics, where the non-linear excitable behavior leads to a self-amplification of activity fluctuations over a wide range of scales in space and time. In this sense, the cultured network dynamics is compatible with an excitation-inhibition balance corresponding to a slightly sub-critical regime. Finally, we propose and test a method to infer the characteristic time of the fatigue process, from the observed time course of the network's firing rate. Unlike the model, possessing a single fatigue mechanism, the cultured network appears to show multiple time scales, signalling the possible coexistence of different fatigue mechanisms.

  5. Multiple neural states of representation in short-term memory? It’s a matter of attention

    Science.gov (United States)

    LaRocque, Joshua J.; Lewis-Peacock, Jarrod A.; Postle, Bradley R.

    2014-01-01

    Short-term memory (STM) refers to the capacity-limited retention of information over a brief period of time, and working memory (WM) refers to the manipulation and use of that information to guide behavior. In recent years it has become apparent that STM and WM interact and overlap with other cognitive processes, including attention (the selection of a subset of information for further processing) and long-term memory (LTM—the encoding and retention of an effectively unlimited amount of information for a much longer period of time). Broadly speaking, there have been two classes of memory models: systems models, which posit distinct stores for STM and LTM (Atkinson and Shiffrin, 1968; Baddeley and Hitch, 1974); and state-based models, which posit a common store with different activation states corresponding to STM and LTM (Cowan, 1995; McElree, 1996; Oberauer, 2002). In this paper, we will focus on state-based accounts of STM. First, we will consider several theoretical models that postulate, based on considerable behavioral evidence, that information in STM can exist in multiple representational states. We will then consider how neural data from recent studies of STM can inform and constrain these theoretical models. In the process we will highlight the inferential advantage of multivariate, information-based analyses of neuroimaging data (fMRI and electroencephalography (EEG)) over conventional activation-based analysis approaches (Postle, in press). We will conclude by addressing lingering questions regarding the fractionation of STM, highlighting differences between the attention to information vs. the retention of information during brief memory delays. PMID:24478671

  6. Molecular and Neural Functions of Rai1, the Causal Gene for Smith-Magenis Syndrome.

    Science.gov (United States)

    Huang, Wei-Hsiang; Guenthner, Casey J; Xu, Jin; Nguyen, Tiffany; Schwarz, Lindsay A; Wilkinson, Alex W; Gozani, Or; Chang, Howard Y; Shamloo, Mehrdad; Luo, Liqun

    2016-10-19

    Haploinsufficiency of Retinoic Acid Induced 1 (RAI1) causes Smith-Magenis syndrome (SMS), which is associated with diverse neurodevelopmental and behavioral symptoms as well as obesity. RAI1 encodes a nuclear protein but little is known about its molecular function or the cell types responsible for SMS symptoms. Using genetically engineered mice, we found that Rai1 preferentially occupies DNA regions near active promoters and promotes the expression of a group of genes involved in circuit assembly and neuronal communication. Behavioral analyses demonstrated that pan-neural loss of Rai1 causes deficits in motor function, learning, and food intake. These SMS-like phenotypes are produced by loss of Rai1 function in distinct neuronal types: Rai1 loss in inhibitory neurons or subcortical glutamatergic neurons causes learning deficits, while Rai1 loss in Sim1(+) or SF1(+) cells causes obesity. By integrating molecular and organismal analyses, our study suggests potential therapeutic avenues for a complex neurodevelopmental disorder.

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

    Science.gov (United States)

    Potjans, Wiebke; Morrison, Abigail; Diesmann, Markus

    2010-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Wiebke ePotjans

    2010-11-01

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

  9. Targeting Lumbar Spinal Neural Circuitry by Epidural Stimulation to Restore Motor Function After Spinal Cord Injury.

    Science.gov (United States)

    Minassian, Karen; McKay, W Barry; Binder, Heinrich; Hofstoetter, Ursula S

    2016-04-01

    Epidural spinal cord stimulation has a long history of application for improving motor control in spinal cord injury. This review focuses on its resurgence following the progress made in understanding the underlying neurophysiological mechanisms and on recent reports of its augmentative effects upon otherwise subfunctional volitional motor control. Early work revealed that the spinal circuitry involved in lower-limb motor control can be accessed by stimulating through electrodes placed epidurally over the posterior aspect of the lumbar spinal cord below a paralyzing injury. Current understanding is that such stimulation activates large-to-medium-diameter sensory fibers within the posterior roots. Those fibers then trans-synaptically activate various spinal reflex circuits and plurisegmentally organized interneuronal networks that control more complex contraction and relaxation patterns involving multiple muscles. The induced change in responsiveness of this spinal motor circuitry to any residual supraspinal input via clinically silent translesional neural connections that have survived the injury may be a likely explanation for rudimentary volitional control enabled by epidural stimulation in otherwise paralyzed muscles. Technological developments that allow dynamic control of stimulation parameters and the potential for activity-dependent beneficial plasticity may further unveil the remarkable capacity of spinal motor processing that remains even after severe spinal cord injuries.

  10. All-trans retinoic acid promotes neural lineage entry by pluripotent embryonic stem cells via multiple pathways

    Directory of Open Access Journals (Sweden)

    Fang Bo

    2009-07-01

    Full Text Available Abstract Background All-trans retinoic acid (RA is one of the most important morphogens with pleiotropic actions. Its embryonic distribution correlates with neural differentiation in the developing central nervous system. To explore the precise effects of RA on neural differentiation of mouse embryonic stem cells (ESCs, we detected expression of RA nuclear receptors and RA-metabolizing enzymes in mouse ESCs and investigated the roles of RA in adherent monolayer culture. Results Upon addition of RA, cell differentiation was directed rapidly and exclusively into the neural lineage. Conversely, pharmacological interference with RA signaling suppressed this neural differentiation. Inhibition of fibroblast growth factor (FGF signaling did not suppress significantly neural differentiation in RA-treated cultures. Pharmacological interference with extracellular signal-regulated kinase (ERK pathway or activation of Wnt pathway effectively blocked the RA-promoted neural specification. ERK phosphorylation was enhanced in RA-treated cultures at the early stage of differentiation. Conclusion RA can promote neural lineage entry by ESCs in adherent monolayer culture systems. This effect depends on RA signaling and its crosstalk with the ERK and Wnt pathways.

  11. An Intelligent Approach to Educational Data: Performance Comparison of the Multilayer Perceptron and the Radial Basis Function Artificial Neural Networks

    Science.gov (United States)

    Kayri, Murat

    2015-01-01

    The objective of this study is twofold: (1) to investigate the factors that affect the success of university students by employing two artificial neural network methods (i.e., multilayer perceptron [MLP] and radial basis function [RBF]); and (2) to compare the effects of these methods on educational data in terms of predictive ability. The…

  12. Effects of Interactive Function Forms and Refractoryperiod in a Self-Organized Critical Model Based on Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZHOULi-Ming; CHENTian-Lun

    2004-01-01

    Based on the standard self-organizing map neural network model and an integrate-and-fire mechanism, we investigate the effect of the nonlinear interactive function on the self-organized criticality in our model. Based on the sewe also investigate the effect of the refractoryperiod on the self-organized criticality of the system.

  13. Effects of Interactive Function Forms and Refractoryperiod in a Self-Organized Critical Model Based on Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZHOU Li-Ming; CHEN Tian-Lun

    2004-01-01

    Based on the standard self-organizing map neural network model and an integrate-and-tire mechanism, we investigate the effect of the nonlinear interactive function on the self-organized criticality in our model. Based on these we also investigate the effect of the refractoryperiod on the self-organized criticality of the system.

  14. 40LoVe and Samba are involved in Xenopus neural development and functionally distinct from hnRNP AB.

    Directory of Open Access Journals (Sweden)

    Maria Andreou

    Full Text Available Heterogeneous nuclear ribonucleoproteins (hnRNPs comprise a large group of modular RNA-binding proteins classified according to their conserved domains. This modular nature, coupled with a large choice of alternative splice variants generates functional diversity. Here, we investigate the biological differences between 40LoVe, its splice variant Samba and its pseudoallele hnRNP AB in neural development. Loss of function experiments lead to defects in neural development with reduction of eye size, which stem primarily from increased apoptosis and reduced proliferation in neural tissues. Despite very high homology between 40LoVe/Samba and hnRNP AB, these proteins display major differences in localization, which appear to be in part responsible for functional differences. Specifically, we show that the 40Love/Samba carboxy-terminal domain (GRD enables nucleocytoplasmic shuttling behavior. This domain is slightly different in hnRNP AB, leading to nuclear-restricted localization. Finally, we show that shuttling is required for 40LoVe/Samba function in neural development.

  15. Experiencing Past and Future Personal Events: Functional Neuroimaging Evidence on the Neural Bases of Mental Time Travel

    Science.gov (United States)

    Botzung, Anne; Denkova, Ekaterina; Manning, Lilianne

    2008-01-01

    Functional MRI was used in healthy subjects to investigate the existence of common neural structures supporting re-experiencing the past and pre-experiencing the future. Past and future events evocation appears to involve highly similar patterns of brain activation including, in particular, the medial prefrontal cortex, posterior regions and the…

  16. Neural correlates of the popular music phenomenon: evidence from functional MRI and PET imaging

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Qiaozhen [The Second Affiliated Hospital of Zhejiang University School of Medicine, Department of Psychiatry, Hangzhou (China); Zhejiang University Medical PET Center, Hangzhou (China); Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou (China); Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou (China); The Second Affiliated Hospital of Zhejiang University School of Medicine, Department of Nuclear Medicine, Hangzhou, Zhejiang (China); Zhang, Ying; Hou, Haifeng; Du, Fenglei; Wu, Shuang; Chen, Lin; Shen, Yehua; Chao, Fangfang; Zhang, Hong; Tian, Mei [Zhejiang University Medical PET Center, Hangzhou (China); Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou (China); Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou (China); The Second Affiliated Hospital of Zhejiang University School of Medicine, Department of Nuclear Medicine, Hangzhou, Zhejiang (China); Chung, June-key [Seoul National University Hospital, Department of Nuclear Medicine, Seoul (Korea, Republic of)

    2017-06-15

    Music can induce different emotions. However, its neural mechanism remains unknown. The aim of this study was to use functional magnetic resonance imaging (fMRI) and position emission tomography (PET) imaging for mapping of neural changes under the most popular music in healthy volunteers. Blood-oxygen-level-dependent (BOLD) fMRI and monoamine receptor PET imaging with {sup 11}C-N-methylspiperone ({sup 11}C-NMSP) were conducted under the popular music Gangnam Style and light music A Comme Amour in healthy subjects. PET and fMRI images were analyzed by using the Statistical Parametric Mapping software (SPM). Significantly increased fMRI BOLD signals were found in the bilateral superior temporal cortices, left cerebellum, left putamen and right thalamus cortex. Monoamine receptor availability was increased significantly in the left superior temporal gyrus and left putamen, but decreased in the bilateral superior occipital cortices under the Gangnam Style compared with the light music condition. Significant positive correlation was found between {sup 11}C-NMSP binding and fMRI BOLD signals in the left temporal cortex. Furthermore, increased {sup 11}C-NMSP binding in the left putamen was positively correlated with the mood arousal level score under the Gangnam Style condition. Popular music Gangnam Style can arouse pleasure experience and strong emotional response. The left putamen is positively correlated with the mood arousal level score under the Gangnam Style condition. Our results revealed characteristic patterns of brain activity associated with Gangnam Style, and may also provide more general insights into the music-induced emotional processing. (orig.)

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

    Science.gov (United States)

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

    2005-12-01

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

  18. Transfer functions for protein signal transduction: application to a model of striatal neural plasticity.

    Directory of Open Access Journals (Sweden)

    Gabriele Scheler

    Full Text Available We present a novel formulation for biochemical reaction networks in the context of protein signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of "source" species, which are interpreted as input signals. Signals are transmitted to all other species in the system (the "target" species with a specific delay and with a specific transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and even recalled to build dynamical models on the basis of state changes. By separating the temporal and the magnitudinal domain we can greatly simplify the computational model, circumventing typical problems of complex dynamical systems. The transfer function transformation of biochemical reaction systems can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant novel insights while remaining a fully testable and executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modularizations that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. Remarkably, we found that overall interconnectedness depends on the magnitude of inputs, with higher connectivity at low input concentrations and significant modularization at moderate to high input concentrations. This general result, which directly follows from the properties of

  19. Transfer functions for protein signal transduction: application to a model of striatal neural plasticity.

    Science.gov (United States)

    Scheler, Gabriele

    2013-01-01

    We present a novel formulation for biochemical reaction networks in the context of protein signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of "source" species, which are interpreted as input signals. Signals are transmitted to all other species in the system (the "target" species) with a specific delay and with a specific transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and even recalled to build dynamical models on the basis of state changes. By separating the temporal and the magnitudinal domain we can greatly simplify the computational model, circumventing typical problems of complex dynamical systems. The transfer function transformation of biochemical reaction systems can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant novel insights while remaining a fully testable and executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modularizations that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. Remarkably, we found that overall interconnectedness depends on the magnitude of inputs, with higher connectivity at low input concentrations and significant modularization at moderate to high input concentrations. This general result, which directly follows from the properties of individual transfer

  20. Combination of multiple neural crest migration assays to identify environmental toxicants from a proof-of-concept chemical library.

    Science.gov (United States)

    Nyffeler, Johanna; Dolde, Xenia; Krebs, Alice; Pinto-Gil, Kevin; Pastor, Manuel; Behl, Mamta; Waldmann, Tanja; Leist, Marcel

    2017-05-05

    Many in vitro tests have been developed to screen for potential neurotoxicity. However, only few cell function-based tests have been used for comparative screening, and thus experience is scarce on how to confirm and evaluate screening hits. We addressed these questions for the neural crest cell migration test (cMINC). After an initial screen, a hit follow-up strategy was devised. A library of 75 compounds plus internal controls (NTP80-list), assembled by the National Toxicology Program of the USA (NTP) was used. It contained some known classes of (developmental) neurotoxic compounds. The primary screen yielded 23 confirmed hits, which comprised ten flame retardants, seven pesticides and six drug-like compounds. Comparison of concentration-response curves for migration and viability showed that all hits were specific. The extent to which migration was inhibited was 25-90%, and two organochlorine pesticides (DDT, heptachlor) were most efficient. In the second part of this study, (1) the cMINC assay was repeated under conditions that prevent proliferation; (2) a transwell migration assay was used as a different type of migration assay; (3) cells were traced to assess cell speed. Some toxicants had largely varying effects between assays, but each hit was confirmed in at least one additional test. This comparative study allows an estimate on how confidently the primary hits from a cell function-based screen can be considered as toxicants disturbing a key neurodevelopmental process. Testing of the NTP80-list in more assays will be highly interesting to assemble a test battery and to build prediction models for developmental toxicity.

  1. Neural interface of mirror therapy in chronic stroke patients: a functional magnetic resonance imaging study.

    Science.gov (United States)

    Bhasin, Ashu; Padma Srivastava, M V; Kumaran, Senthil S; Bhatia, Rohit; Mohanty, Sujata

    2012-01-01

    Recovery in stroke is mediated by neural plasticity. Neuro-restorative therapies improve recovery after stroke by promoting repair and function. Mirror neuron system (MNS) has been studied widely in humans in stroke and phantom sensations. Study subjects included 20 patients with chronic stroke and 10 healthy controls. Patients had clinical disease-severity scores, functional magnetic resonance imaging (fMRI) and diffuse tensor imaging (DTI) at baseline, 8 and at 24 weeks. Block design with alternate baseline and activation cycles was used with a total of 90 whole brain echo planar imaging (EPI) measurements (timed repetition (TR) = 4520 ms, timed echo (TE) = 44 ms, slices = 31, slice thickness = 4 mm, EPI factor 127, matrix = 128 × 128, FOV = 230 mm). Whole brain T1-weighted images were acquired using 3D sequence (MPRage) with 120 contiguous slices of 1.0 mm thickness. The mirror therapy was aimed via laptop system integrated with web camera, mirroring the movement of the unaffected hand. This therapy was administered for 5 days in a week for 60-90 min for 8 weeks. All the patients showed statistical significant improvement in Fugl Meyer and modified Barthel Index (P stroke. Therapy induced cortical reorganization was also observed from our study.

  2. Adaptation to New Microphones Using Artificial Neural Networks With Trainable Activation Functions.

    Science.gov (United States)

    Siniscalchi, Sabato Marco; Salerno, Valerio Mario

    2016-04-14

    Model adaptation is a key technique that enables a modern automatic speech recognition (ASR) system to adjust its parameters, using a small amount of enrolment data, to the nuances in the speech spectrum due to microphone mismatch in the training and test data. In this brief, we investigate four different adaptation schemes for connectionist (also known as hybrid) ASR systems that learn microphone-specific hidden unit contributions, given some adaptation material. This solution is made possible adopting one of the following schemes: 1) the use of Hermite activation functions; 2) the introduction of bias and slope parameters in the sigmoid activation functions; 3) the injection of an amplitude parameter specific for each sigmoid unit; or 4) the combination of 2) and 3). Such a simple yet effective solution allows the adapted model to be stored in a small-sized storage space, a highly desirable property of adaptation algorithms for deep neural networks that are suitable for large-scale online deployment. Experimental results indicate that the investigated approaches reduce word error rates on the standard Spoke 6 task of the Wall Street Journal corpus compared with unadapted ASR systems. Moreover, the proposed adaptation schemes all perform better than simple multicondition training and comparable favorably against conventional linear regression-based approaches while using up to 15 orders of magnitude fewer parameters. The proposed adaptation strategies are also effective when a single adaptation sentence is available.

  3. Active Noise Control Using a Functional Link Artificial Neural Network with the Simultaneous Perturbation Learning Rule

    Directory of Open Access Journals (Sweden)

    Ya-li Zhou

    2009-01-01

    Full Text Available In practical active noise control (ANC systems, the primary path and the secondary path may be nonlinear and time-varying. It has been reported that the linear techniques used to control such ANC systems exhibit degradation in performance. In addition, the actuators of an ANC system very often have nonminimum-phase response. A linear controller under such situations yields poor performance. A novel functional link artificial neural network (FLANN-based simultaneous perturbation stochastic approximation (SPSA algorithm, which functions as a nonlinear mode-free (MF controller, is proposed in this paper. Computer simulations have been carried out to demonstrate that the proposed algorithm outperforms the standard filtered-x least mean square (FXLMS algorithm, and performs better than the recently proposed filtered-s least mean square (FSLMS algorithm when the secondary path is time-varying. This observation implies that the SPSA-based MF controller can eliminate the need of the modeling of the secondary path for the ANC system.

  4. Extracting functional components of neural dynamics with Independent Component Analysis and inverse Current Source Density.

    Science.gov (United States)

    Lęski, Szymon; Kublik, Ewa; Swiejkowski, Daniel A; Wróbel, Andrzej; Wójcik, Daniel K

    2010-12-01

    Local field potentials have good temporal resolution but are blurred due to the slow spatial decay of the electric field. For simultaneous recordings on regular grids one can reconstruct efficiently the current sources (CSD) using the inverse Current Source Density method (iCSD). It is possible to decompose the resultant spatiotemporal information about the current dynamics into functional components using Independent Component Analysis (ICA). We show on test data modeling recordings of evoked potentials on a grid of 4 × 5 × 7 points that meaningful results are obtained with spatial ICA decomposition of reconstructed CSD. The components obtained through decomposition of CSD are better defined and allow easier physiological interpretation than the results of similar analysis of corresponding evoked potentials in the thalamus. We show that spatiotemporal ICA decompositions can perform better for certain types of sources but it does not seem to be the case for the experimental data studied. Having found the appropriate approach to decomposing neural dynamics into functional components we use the technique to study the somatosensory evoked potentials recorded on a grid spanning a large part of the forebrain. We discuss two example components associated with the first waves of activation of the somatosensory thalamus. We show that the proposed method brings up new, more detailed information on the time and spatial location of specific activity conveyed through various parts of the somatosensory thalamus in the rat.

  5. Interval model updating using perturbation method and Radial Basis Function neural networks

    Science.gov (United States)

    Deng, Zhongmin; Guo, Zhaopu; Zhang, Xinjie

    2017-02-01

    In recent years, stochastic model updating techniques have been applied to the quantification of uncertainties inherently existing in real-world engineering structures. However in engineering practice, probability density functions of structural parameters are often unavailable due to insufficient information of a structural system. In this circumstance, interval analysis shows a significant advantage of handling uncertain problems since only the upper and lower bounds of inputs and outputs are defined. To this end, a new method for interval identification of structural parameters is proposed using the first-order perturbation method and Radial Basis Function (RBF) neural networks. By the perturbation method, each random variable is denoted as a perturbation around the mean value of the interval of each parameter and that those terms can be used in a two-step deterministic updating sense. Interval model updating equations are then developed on the basis of the perturbation technique. The two-step method is used for updating the mean values of the structural parameters and subsequently estimating the interval radii. The experimental and numerical case studies are given to illustrate and verify the proposed method in the interval identification of structural parameters.

  6. Neural convergence and divergence in the mammalian cerebral cortex: from experimental neuroanatomy to functional neuroimaging

    Science.gov (United States)

    Man, Kingson; Kaplan, Jonas; Damasio, Hanna; Damasio, Antonio

    2013-01-01

    A development essential for understanding the neural basis of complex behavior and cognition is the description, during the last quarter of the twentieth century, of detailed patterns of neuronal circuitry in the mammalian cerebral cortex. This effort established that sensory pathways exhibit successive levels of convergence, from the early sensory cortices to sensory-specific association cortices and to multisensory association cortices, culminating in maximally integrative regions; and that this convergence is reciprocated by successive levels of divergence, from the maximally integrative areas all the way back to the early sensory cortices. This article first provides a brief historical review of these neuroanatomical findings, which were relevant to the study of brain and mind-behavior relationships using a variety of approaches and to the proposal of heuristic anatomo-functional frameworks. In a second part, the article reviews new evidence that has accumulated from studies of functional neuroimaging, employing both univariate and multivariate analyses, as well as electrophysiology, in humans and other mammals, that the integration of information across the auditory, visual, and somatosensory-motor modalities proceeds in a content-rich manner. Behaviorally and cognitively relevant information is extracted from and conserved across the different modalities, both in higher-order association cortices and in early sensory cortices. Such stimulus-specific information is plausibly relayed along the neuroanatomical pathways alluded to above. The evidence reviewed here suggests the need for further in-depth exploration of the intricate connectivity of the mammalian cerebral cortex in experimental neuroanatomical studies. PMID:23840023

  7. Behavioral, Perceptual, and Neural Alterations in Sensory and Multisensory Function in Autism Spectrum Disorder

    Science.gov (United States)

    Baum, Sarah H.; Stevenson, Ryan A.; Wallace, Mark T.

    2015-01-01

    Although sensory processing challenges have been noted since the first clinical descriptions of autism, it has taken until the release of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) in 2013 for sensory problems to be included as part of the core symptoms of autism spectrum disorder (ASD) in the diagnostic profile. Because sensory information forms the building blocks for higher-order social and cognitive functions, we argue that sensory processing is not only an additional piece of the puzzle, but rather a critical cornerstone for characterizing and understanding ASD. In this review we discuss what is currently known about sensory processing in ASD, how sensory function fits within contemporary models of ASD, and what is understood about the differences in the underlying neural processing of sensory and social communication observed between individuals with and without ASD. In addition to highlighting the sensory features associated with ASD, we also emphasize the importance of multisensory processing in building perceptual and cognitive representations, and how deficits in multisensory integration may also be a core characteristic of ASD. PMID:26455789

  8. Analysis of Multiple Cracks in an Infinite Functionally Graded Plate

    Science.gov (United States)

    Shbeeb, N. I.; Binienda, W. K.; Kreider, K. L.

    1999-01-01

    A general methodology was constructed to develop the fundamental solution for a crack embedded in an infinite non-homogeneous material in which the shear modulus varies exponentially with the y coordinate. The fundamental solution was used to generate a solution to fully interactive multiple crack problems for stress intensity factors and strain energy release rates. Parametric studies were conducted for two crack configurations. The model displayed sensitivity to crack distance, relative angular orientation, and to the coefficient of nonhomogeneity.

  9. Visual working memory load-related changes in neural activity and functional connectivity.

    Directory of Open Access Journals (Sweden)

    Ling Li

    Full Text Available BACKGROUND: Visual working memory (VWM helps us store visual information to prepare for subsequent behavior. The neuronal mechanisms for sustaining coherent visual information and the mechanisms for limited VWM capacity have remained uncharacterized. Although numerous studies have utilized behavioral accuracy, neural activity, and connectivity to explore the mechanism of VWM retention, little is known about the load-related changes in functional connectivity for hemi-field VWM retention. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we recorded electroencephalography (EEG from 14 normal young adults while they performed a bilateral visual field memory task. Subjects had more rapid and accurate responses to the left visual field (LVF memory condition. The difference in mean amplitude between the ipsilateral and contralateral event-related potential (ERP at parietal-occipital electrodes in retention interval period was obtained with six different memory loads. Functional connectivity between 128 scalp regions was measured by EEG phase synchronization in the theta- (4-8 Hz, alpha- (8-12 Hz, beta- (12-32 Hz, and gamma- (32-40 Hz frequency bands. The resulting matrices were converted to graphs, and mean degree, clustering coefficient and shortest path length was computed as a function of memory load. The results showed that brain networks of theta-, alpha-, beta-, and gamma- frequency bands were load-dependent and visual-field dependent. The networks of theta- and alpha- bands phase synchrony were most predominant in retention period for right visual field (RVF WM than for LVF WM. Furthermore, only for RVF memory condition, brain network density of theta-band during the retention interval were linked to the delay of behavior reaction time, and the topological property of alpha-band network was negative correlation with behavior accuracy. CONCLUSIONS/SIGNIFICANCE: We suggest that the differences in theta- and alpha- bands between LVF and RVF

  10. Robot manipulator identification based on adaptive multiple-input and multiple-output neural model optimized by advanced differential evolution algorithm

    Directory of Open Access Journals (Sweden)

    Nguyen Ngoc Son

    2016-12-01

    Full Text Available This article proposes a novel advanced differential evolution method which combines the differential evolution with the modified back-propagation algorithm. This new proposed approach is applied to train an adaptive enhanced neural model for approximating the inverse model of the industrial robot arm. Experimental results demonstrate that the proposed modeling procedure using the new identification approach obtains better convergence and more precision than the traditional back-propagation method or the lonely differential evolution approach. Furthermore, the inverse model of the industrial robot arm using the adaptive enhanced neural model performs outstanding results.

  11. On the convergence of multiple Fourier series of functions of bounded partial generalized variation

    OpenAIRE

    Goginava, Ushangi; Sahakian, Artur

    2012-01-01

    The convergence of multiple Fourier series of functions of bounded partial $% \\Lambda$-variation is investigated. The sufficient and necessary conditions on the sequence $\\Lambda=\\{\\lambda_n\\}$ are found for the convergence of multiple Fourier series of functions of bounded partial $\\Lambda$-variation.

  12. Treatment of Pica through Multiple Analyses of Its Reinforcing Functions.

    Science.gov (United States)

    Piazza, Cathleen C.; Fisher, Wayne W.; Hanley, Gregory P.; LeBlanc, Linda A.; Worsdell, April S.; And Others

    1998-01-01

    A study conducted functional analyses of the pica of three young children. The pica of one participant was maintained by automatic reinforcement; that of the other two was multiply-controlled by social and automatic reinforcement. Preference and treatment analyses were used to address the automatic function of the pica. (Author/CR)

  13. Attenuation of multiple Nef functions in HIV-1 elite controllers

    Directory of Open Access Journals (Sweden)

    Mwimanzi Philip

    2013-01-01

    Full Text Available Abstract Background Impaired HIV-1 Gag, Pol, and Env function has been described in elite controllers (EC who spontaneously suppress plasma viremia to Results In general, EC Nef clones were functional; however, all five activities were significantly lower in EC compared to CP. Nef clones from HLA-B*57-expressing EC exhibited poorer CD4 down-regulation function compared to those from non-B*57 EC, and the number of EC-specific B*57-associated Nef polymorphisms correlated inversely with 4 of 5 Nef functions in these individuals. Conclusion Results indicate that decreased HIV-1 Nef function, due in part to host immune selection pressures, may be a hallmark of the EC phenotype.

  14. Multiple-time correlation functions for non-Markovian interaction: Beyond the Quantum Regression Theorem

    CERN Document Server

    Alonso, D; Alonso, Daniel; Vega, In\\'es de

    2004-01-01

    Multiple time correlation functions are found in the dynamical description of different phenomena. They encode and describe the fluctuations of the dynamical variables of a system. In this paper we formulate a theory of non-Markovian multiple-time correlation functions (MTCF) for a wide class of systems. We derive the dynamical equation of the {\\it reduced propagator}, an object that evolve state vectors of the system conditioned to the dynamics of its environment, which is not necessarily at the vacuum state at the initial time. Such reduced propagator is the essential piece to obtain multiple-time correlation functions. An average over the different environmental histories of the reduced propagator permits us to obtain the evolution equations of the multiple-time correlation functions. We also study the evolution of MTCF within the weak coupling limit and it is shown that the multiple-time correlation function of some observables satisfy the Quantum Regression Theorem (QRT), whereas other correlations do no...

  15. Multiple Crop Classification Using Various Support Vector Machine Kernel Functions

    Directory of Open Access Journals (Sweden)

    Rupali R. Surase

    2015-01-01

    Full Text Available This study was carried out with techniques of Remote Sensing (RS based crop discrimination and area estimation with single date approach. Several kernel functions are employed and compared in this study for mapping the input space with including linear, sigmoid, and polynomial and Radial Basis Function (RBF. The present study highlights the advantages of Remote Sensing (RS and Geographic Information System (GIS techniques for analyzing the land use/land cover mapping for Aurangabad region of Maharashtra, India. Single date, cloud free IRS-Resourcesat-1 LISS-III data was used for further classification on training set for supervised classification. ENVI 4.4 is used for image analysis and interpretation. The experimental tests show that system is achieved 94.82% using SVM with kernel functions including Polynomial kernel function compared with Radial Basis Function, Sigmoid and linear kernel. The Overall Accuracy (OA to up to 5.17% in comparison to using sigmoid kernel function, and up to 3.45% in comparison to a 3rd degree polynomial kernel function and RBF with 200 as a penalty parameter.

  16. Multiple multiresolution representation of functions and calculus for fast computation

    Energy Technology Data Exchange (ETDEWEB)

    Fann, George I [ORNL; Harrison, Robert J [ORNL; Hill, Judith C [ORNL; Jia, Jun [ORNL; Galindo, Diego A [ORNL

    2010-01-01

    We describe the mathematical representations, data structure and the implementation of the numerical calculus of functions in the software environment multiresolution analysis environment for scientific simulations, MADNESS. In MADNESS, each smooth function is represented using an adaptive pseudo-spectral expansion using the multiwavelet basis to a arbitrary but finite precision. This is an extension of the capabilities of most of the existing net, mesh and spectral based methods where the discretization is based on a single adaptive mesh, or expansions.

  17. Multiple healing in multi-functional polymer composites

    OpenAIRE

    Lafont, U.L.; Van Zeijl, H.; van der Zwaag, S.

    2013-01-01

    In this work, we investigate the formation of self-healing systems that are able to recover more than once more than one (mechanical or other physical) functionality. To this aim composites were created consisting of a polysulfide thermoset rubber matrix having intrinsic self-healing properties filled with thermally/electrically conductive particles. The cohesion, adhesion and thermal/electrical conduction recovery of these composites are investigated, monitored and quantified as function of ...

  18. Functional connectivity and information flow of the respiratory neural network in chronic obstructive pulmonary disease.

    Science.gov (United States)

    Yu, Lianchun; De Mazancourt, Marine; Hess, Agathe; Ashadi, Fakhrul R; Klein, Isabelle; Mal, Hervé; Courbage, Maurice; Mangin, Laurence

    2016-08-01

    Breathing involves a complex interplay between the brainstem automatic network and cortical voluntary command. How these brain regions communicate at rest or during inspiratory loading is unknown. This issue is crucial for several reasons: (i) increased respiratory loading is a major feature of several respiratory diseases, (ii) failure of the voluntary motor and cortical sensory processing drives is among the mechanisms that precede acute respiratory failure, (iii) several cerebral structures involved in responding to inspiratory loading participate in the perception of dyspnea, a distressing symptom in many disease. We studied functional connectivity and Granger causality of the respiratory network in controls and patients with chronic obstructive pulmonary disease (COPD), at rest and during inspiratory loading. Compared with those of controls, the motor cortex area of patients exhibited decreased connectivity with their contralateral counterparts and no connectivity with the brainstem. In the patients, the information flow was reversed at rest with the source of the network shifted from the medulla towards the motor cortex. During inspiratory loading, the system was overwhelmed and the motor cortex became the sink of the network. This major finding may help to understand why some patients with COPD are prone to acute respiratory failure. Network connectivity and causality were related to lung function and illness severity. We validated our connectivity and causality results with a mathematical model of neural network. Our findings suggest a new therapeutic strategy involving the modulation of brain activity to increase motor cortex functional connectivity and improve respiratory muscles performance in patients. Hum Brain Mapp 37:2736-2754, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

  19. Neural regulation of the kidney function in rats with cisplatin induced renal failure

    Directory of Open Access Journals (Sweden)

    Niamh E Goulding

    2015-06-01

    Full Text Available Aim: Chronic kidney disease (CKD is often associated with a disturbed cardiovascular homeostasis. This investigation explored the role of the renal innervation in mediating deranged baroreflex control of renal sympathetic nerve activity (RSNA and renal excretory function in cisplatin-induced renal failure.Methods: Rats were either intact or bilaterally renally denervated four days prior to receiving cisplatin (5mg/kg i.p. and entered a chronic metabolic study for 8 days. At day 8, other groups of rats were prepared for acute measurement of RSNA or renal function with either intact or denervated kidneys.Results: Following the cisplatin challenge, creatinine clearance was 50% lower while fractional sodium excretion and renal cortical and medullary TGF-β1 concentrations were 3-4 fold higher in both intact and renally denervated rats compared to control rats. In cisplatin-treated rats, the maximal gain of the high-pressure baroreflex curve was only 20% that of control rats, but not different from that of renally denervated control rats. Volume expansion reduced RSNA by 50% in control and in cisplatin-treated rats but only following bilateral renal denervation. The volume expansion mediated natriuresis/diuresis was absent in the cisplatin-treated rats but was normalised following renal denervation. Conclusions: Cisplatin-induced renal injury impaired renal function and caused a sympatho-excitation with blunting of high and low pressure baroreflex regulation of RSNA, which was dependent on the renal innervation. It is suggested that in man with CKD there is a dysregulation of the neural control of the kidney mediated by its sensory innervation.

  20. Mentalizing and motivation neural function during social interactions in autism spectrum disorders.

    Science.gov (United States)

    Assaf, Michal; Hyatt, Christopher J; Wong, Christina G; Johnson, Matthew R; Schultz, Robert T; Hendler, Talma; Pearlson, Godfrey D

    2013-01-01

    Autism Spectrum Disorders (ASDs) are characterized by core deficits in social functions. Two theories have been suggested to explain these deficits: mind-blindness theory posits impaired mentalizing processes (i.e. decreased ability for establishing a representation of others' state of mind), while social motivation theory proposes that diminished reward value for social information leads to reduced social attention, social interactions, and social learning. Mentalizing and motivation are integral to typical social interactions, and neuroimaging evidence points to independent brain networks that support these processes in healthy individuals. However, the simultaneous function of these networks has not been explored in individuals with ASDs. We used a social, interactive fMRI task, the Domino game, to explore mentalizing- and motivation-related brain activation during a well-defined interval where participants respond to rewards or punishments (i.e. motivation) and concurrently process information about their opponent's potential next actions (i.e. mentalizing). Thirteen individuals with high-functioning ASDs, ages 12-24, and 14 healthy controls played fMRI Domino games against a computer-opponent and separately, what they were led to believe was a human-opponent. Results showed that while individuals with ASDs understood the game rules and played similarly to controls, they showed diminished neural activity during the human-opponent runs only (i.e. in a social context) in bilateral middle temporal gyrus (MTG) during mentalizing and right Nucleus Accumbens (NAcc) during reward-related motivation (Pcluster motivation networks, respectively, activate normally in a