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Sample records for neural complexity hypothesis

  1. Learning-Related Changes in Adolescents' Neural Networks during Hypothesis-Generating and Hypothesis-Understanding Training

    Science.gov (United States)

    Lee, Jun-Ki; Kwon, Yongju

    2012-01-01

    Fourteen science high school students participated in this study, which investigated neural-network plasticity associated with hypothesis-generating and hypothesis-understanding in learning. The students were divided into two groups and participated in either hypothesis-generating or hypothesis-understanding type learning programs, which were…

  2. Neural complexity, dissociation, and schizophrenia

    Czech Academy of Sciences Publication Activity Database

    Bob, P.; Šusta, M.; Chládek, Jan; Glaslová, K.; Fedor-Ferybergh, P.

    2007-01-01

    Roč. 13, č. 10 (2007), HY1-5 ISSN 1234-1010 Institutional research plan: CEZ:AV0Z20650511 Keywords : neural complexity * dissociation * schizophrenia Subject RIV: FH - Neurology Impact factor: 1.607, year: 2007

  3. Complex-Valued Neural Networks

    CERN Document Server

    Hirose, Akira

    2012-01-01

    This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published in 2006, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields. In the second edition the recent trends in CVNNs research are included, resulting in e.g. almost a doubled number of references. The parametron invented in 1954 is also referred to with discussion on analogy and disparity. Also various additional arguments on the advantages of the complex-valued neural networks enhancing the difference to real-valued neural networks are given in various sections. The book is useful for those beginning their studies, for instance, in adaptive signal processing for highly functional sensing and imaging, control in unknown and changing environment, robotics inspired by human neural systems, and brain-like information processing, as well as interdisciplina...

  4. A neural hypothesis for stress-induced headache.

    Science.gov (United States)

    Cathcart, Stuart

    2009-12-01

    The mechanisms by which stress contributes to CTH are not clearly understood. The commonly accepted notion of muscle hyper-reactivity to stress in CTH sufferers is not supported in the research data. We propose a neural model whereby stress acts supra-spinally to aggravate already increased pain sensitivity in CTH sufferers. Indirect support for the model comes from emerging research elucidating complex supra-spinal networks through which psychological stress may contribute to and even cause pain. Similarly, emerging research demonstrates supra-spinal pain processing abnormalities in CTH sufferers. While research with CTH sufferers offering direct support for the model is lacking at present, initial work by our group is consistent with the models predictions, particularly, that stress aggravates already increased pain sensitivity in CTH sufferers.

  5. Aging and motor variability: a test of the neural noise hypothesis.

    Science.gov (United States)

    Sosnoff, Jacob J; Newell, Karl M

    2011-07-01

    Experimental tests of the neural noise hypothesis of aging, which holds that aging-related increments in motor variability are due to increases in white noise in the perceptual-motor system, were conducted. Young (20-29 years old) and old (60-69 and 70-79 years old) adults performed several perceptual-motor tasks. Older adults were progressively more variable in their performance outcome, but there was no age-related difference in white noise in the motor output. Older adults had a greater frequency-dependent structure in their motor variability that was associated with performance decrements. The findings challenge the main tenet of the neural noise hypothesis of aging in that the increased variability of older adults was due to a decreased ability to adapt to the constraints of the task rather than an increment of neural noise per se.

  6. Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream.

    Science.gov (United States)

    Güçlü, Umut; van Gerven, Marcel A J

    2015-07-08

    Converging evidence suggests that the primate ventral visual pathway encodes increasingly complex stimulus features in downstream areas. We quantitatively show that there indeed exists an explicit gradient for feature complexity in the ventral pathway of the human brain. This was achieved by mapping thousands of stimulus features of increasing complexity across the cortical sheet using a deep neural network. Our approach also revealed a fine-grained functional specialization of downstream areas of the ventral stream. Furthermore, it allowed decoding of representations from human brain activity at an unsurpassed degree of accuracy, confirming the quality of the developed approach. Stimulus features that successfully explained neural responses indicate that population receptive fields were explicitly tuned for object categorization. This provides strong support for the hypothesis that object categorization is a guiding principle in the functional organization of the primate ventral stream. Copyright © 2015 the authors 0270-6474/15/3510005-10$15.00/0.

  7. Neural complexity: A graph theoretic interpretation

    Science.gov (United States)

    Barnett, L.; Buckley, C. L.; Bullock, S.

    2011-04-01

    One of the central challenges facing modern neuroscience is to explain the ability of the nervous system to coherently integrate information across distinct functional modules in the absence of a central executive. To this end, Tononi [Proc. Natl. Acad. Sci. USA.PNASA60027-842410.1073/pnas.91.11.5033 91, 5033 (1994)] proposed a measure of neural complexity that purports to capture this property based on mutual information between complementary subsets of a system. Neural complexity, so defined, is one of a family of information theoretic metrics developed to measure the balance between the segregation and integration of a system’s dynamics. One key question arising for such measures involves understanding how they are influenced by network topology. Sporns [Cereb. Cortex53OPAV1047-321110.1093/cercor/10.2.127 10, 127 (2000)] employed numerical models in order to determine the dependence of neural complexity on the topological features of a network. However, a complete picture has yet to be established. While De Lucia [Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.71.016114 71, 016114 (2005)] made the first attempts at an analytical account of this relationship, their work utilized a formulation of neural complexity that, we argue, did not reflect the intuitions of the original work. In this paper we start by describing weighted connection matrices formed by applying a random continuous weight distribution to binary adjacency matrices. This allows us to derive an approximation for neural complexity in terms of the moments of the weight distribution and elementary graph motifs. In particular, we explicitly establish a dependency of neural complexity on cyclic graph motifs.

  8. Energy Complexity of Recurrent Neural Networks

    Czech Academy of Sciences Publication Activity Database

    Šíma, Jiří

    2014-01-01

    Roč. 26, č. 5 (2014), s. 953-973 ISSN 0899-7667 R&D Projects: GA ČR GAP202/10/1333 Institutional support: RVO:67985807 Keywords : neural network * finite automaton * energy complexity * optimal size Subject RIV: IN - Informatics, Computer Science Impact factor: 2.207, year: 2014

  9. Mirror neurons and the social nature of language: the neural exploitation hypothesis.

    Science.gov (United States)

    Gallese, Vittorio

    2008-01-01

    This paper discusses the relevance of the discovery of mirror neurons in monkeys and of the mirror neuron system in humans to a neuroscientific account of primates' social cognition and its evolution. It is proposed that mirror neurons and the functional mechanism they underpin, embodied simulation, can ground within a unitary neurophysiological explanatory framework important aspects of human social cognition. In particular, the main focus is on language, here conceived according to a neurophenomenological perspective, grounding meaning on the social experience of action. A neurophysiological hypothesis--the "neural exploitation hypothesis"--is introduced to explain how key aspects of human social cognition are underpinned by brain mechanisms originally evolved for sensorimotor integration. It is proposed that these mechanisms were later on adapted as new neurofunctional architecture for thought and language, while retaining their original functions as well. By neural exploitation, social cognition and language can be linked to the experiential domain of action.

  10. Why would musical training benefit the neural encoding of speech? The OPERA hypothesis.

    Directory of Open Access Journals (Sweden)

    Aniruddh D. Patel

    2011-06-01

    Full Text Available Mounting evidence suggests that musical training benefits the neural encoding of speech. This paper offers a hypothesis specifying why such benefits occur. The OPERA hypothesis proposes that such benefits are driven by adaptive plasticity in speech-processing networks, and that this plasticity occurs when five conditions are met. These are: 1 Overlap: there is anatomical overlap in the brain networks that process an acoustic feature used in both music and speech (e.g., waveform periodicity, amplitude envelope, 2 Precision: music places higher demands on these shared networks than does speech, in terms of the precision of processing, 3 Emotion: the musical activities that engage this network elicit strong positive emotion, 4 Repetition: the musical activities that engage this network are frequently repeated, and 5 Attention: the musical activities that engage this network are associated with focused attention. According to the OPERA hypothesis, when these conditions are met neural plasticity drives the networks in question to function with higher precision than needed for ordinary speech communication. Yet since speech shares these networks with music, speech processing benefits. The OPERA hypothesis is used to account for the observed superior subcortical encoding of speech in musically trained individuals, and to suggest mechanisms by which musical training might improve linguistic reading abilities.

  11. Why would Musical Training Benefit the Neural Encoding of Speech? The OPERA Hypothesis.

    Science.gov (United States)

    Patel, Aniruddh D

    2011-01-01

    Mounting evidence suggests that musical training benefits the neural encoding of speech. This paper offers a hypothesis specifying why such benefits occur. The "OPERA" hypothesis proposes that such benefits are driven by adaptive plasticity in speech-processing networks, and that this plasticity occurs when five conditions are met. These are: (1) Overlap: there is anatomical overlap in the brain networks that process an acoustic feature used in both music and speech (e.g., waveform periodicity, amplitude envelope), (2) Precision: music places higher demands on these shared networks than does speech, in terms of the precision of processing, (3) Emotion: the musical activities that engage this network elicit strong positive emotion, (4) Repetition: the musical activities that engage this network are frequently repeated, and (5) Attention: the musical activities that engage this network are associated with focused attention. According to the OPERA hypothesis, when these conditions are met neural plasticity drives the networks in question to function with higher precision than needed for ordinary speech communication. Yet since speech shares these networks with music, speech processing benefits. The OPERA hypothesis is used to account for the observed superior subcortical encoding of speech in musically trained individuals, and to suggest mechanisms by which musical training might improve linguistic reading abilities.

  12. Suppressed neural complexity during ketamine- and propofol-induced unconsciousness.

    Science.gov (United States)

    Wang, Jisung; Noh, Gyu-Jeong; Choi, Byung-Moon; Ku, Seung-Woo; Joo, Pangyu; Jung, Woo-Sung; Kim, Seunghwan; Lee, Heonsoo

    2017-07-13

    Ketamine and propofol have distinctively different molecular mechanisms of action and neurophysiological features, although both induce loss of consciousness. Therefore, identifying a common feature of ketamine- and propofol-induced unconsciousness would provide insight into the underlying mechanism of losing consciousness. In this study we search for a common feature by applying the concept of type-II complexity, and argue that neural complexity is essential for a brain to maintain consciousness. To test this hypothesis, we show that complexity is suppressed during loss of consciousness induced by ketamine or propofol. We analyzed the randomness (type-I complexity) and complexity (type-II complexity) of electroencephalogram (EEG) signals before and after bolus injection of ketamine or propofol. For the analysis, we use Mean Information Gain (MIG) and Fluctuation Complexity (FC), which are information-theory-based measures that quantify disorder and complexity of dynamics respectively. Both ketamine and propofol reduced the complexity of the EEG signal, but ketamine increased the randomness of the signal and propofol decreased it. The finding supports our claim and suggests EEG complexity as a candidate for a consciousness indicator. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. Analysis of complex systems using neural networks

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1992-01-01

    The application of neural networks, alone or in conjunction with other advanced technologies (expert systems, fuzzy logic, and/or genetic algorithms), to some of the problems of complex engineering systems has the potential to enhance the safety, reliability, and operability of these systems. Typically, the measured variables from the systems are analog variables that must be sampled and normalized to expected peak values before they are introduced into neural networks. Often data must be processed to put it into a form more acceptable to the neural network (e.g., a fast Fourier transformation of the time-series data to produce a spectral plot of the data). Specific applications described include: (1) Diagnostics: State of the Plant (2) Hybrid System for Transient Identification, (3) Sensor Validation, (4) Plant-Wide Monitoring, (5) Monitoring of Performance and Efficiency, and (6) Analysis of Vibrations. Although specific examples described deal with nuclear power plants or their subsystems, the techniques described can be applied to a wide variety of complex engineering systems

  14. Weaving and neural complexity in symmetric quantum states

    Science.gov (United States)

    Susa, Cristian E.; Girolami, Davide

    2018-04-01

    We study the behaviour of two different measures of the complexity of multipartite correlation patterns, weaving and neural complexity, for symmetric quantum states. Weaving is the weighted sum of genuine multipartite correlations of any order, where the weights are proportional to the correlation order. The neural complexity, originally introduced to characterize correlation patterns in classical neural networks, is here extended to the quantum scenario. We derive closed formulas of the two quantities for GHZ states mixed with white noise.

  15. An adaptive workspace hypothesis about the neural correlates of consciousness: insights from neuroscience and meditation studies.

    Science.gov (United States)

    Raffone, Antonino; Srinivasan, Narayanan

    2009-01-01

    While enormous progress has been made to identify neural correlates of consciousness (NCC), crucial NCC aspects are still very controversial. A major hurdle is the lack of an adequate definition and characterization of different aspects of conscious experience and also its relationship to attention and metacognitive processes like monitoring. In this paper, we therefore attempt to develop a unitary theoretical framework for NCC, with an interdependent characterization of endogenous attention, access consciousness, phenomenal awareness, metacognitive consciousness, and a non-referential form of unified consciousness. We advance an adaptive workspace hypothesis about the NCC based on the global workspace model emphasizing transient resonant neurodynamics and prefrontal cortex function, as well as meditation-related characterizations of conscious experiences. In this hypothesis, transient dynamic links within an adaptive coding net in prefrontal cortex, especially in anterior prefrontal cortex, and between it and the rest of the brain, in terms of ongoing intrinsic and long-range signal exchanges, flexibly regulate the interplay between endogenous attention, access consciousness, phenomenal awareness, and metacognitive consciousness processes. Such processes are established in terms of complementary aspects of an ongoing transition between context-sensitive global workspace assemblies, modulated moment-to-moment by body and environment states. Brain regions associated to momentary interoceptive and exteroceptive self-awareness, or first-person experiential perspective as emphasized in open monitoring meditation, play an important modulatory role in adaptive workspace transitions.

  16. Classes of feedforward neural networks and their circuit complexity

    NARCIS (Netherlands)

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

    1992-01-01

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

  17. Complexity VIII. Ontology of closure in complex systems: The C* hypothesis and the O° notation

    Science.gov (United States)

    Chandler, Jerry LR

    1999-03-01

    Closure is a common characteristic of mathematical, natural and socio-cultural systems. Whether one is describing a graph, a molecule, a cell, a human, or a nation state, closure is implicitly understood. An objective of this paper is to continue a construction of a systematic framework for closure which is sufficient for future quantitative transdisciplinary investigations. A further objective is to extend the Birkhoff-von Neumann criterion for quantum systems to complex natural objects. The C* hypothesis is being constructed to be consistent with algebraic category theory (Ehresmann and Vanbremeersch, 1987, 1997, Chandler, 1990, 1991, Chandler, Ehresmann and Vanbremeersch, 1996). Five aspects of closure will be used to construct a framework for categories of complex systems: 1. Truth functions in mathematics and the natural sciences 2. Systematic descriptions in the mks and O° notations 3. Organizational structures in hierarchical scientific languages 4. Transitive organizational pathways in the causal structures of complex behaviors 5. Composing additive, multiplicative and exponential operations in complex systems Truth functions can be formal or objective or subjective, depending on the complexity of the system and on our capability to represent the fine structure of the system symbolically, observationally or descriptively. "Complete" material representations of the fine structure of a system may allow truth functions to be created over sets of one to one correspondences. Less complete descriptions can support less stringent truth functions based on coherence or subjective judgments. The role of human values in creating and perpetuating truth functions can be placed in context of the degree of fine structure in the system's description. The organization of complex systems are hypothesized to be categorizable into degrees relative to one another, thereby creating an ordering relationship. This ordering relationship is denoted by the symbols: O°1, O°2,O°3

  18. Complex-valued neural networks advances and applications

    CERN Document Server

    Hirose, Akira

    2013-01-01

    Presents the latest advances in complex-valued neural networks by demonstrating the theory in a wide range of applications Complex-valued neural networks is a rapidly developing neural network framework that utilizes complex arithmetic, exhibiting specific characteristics in its learning, self-organizing, and processing dynamics. They are highly suitable for processing complex amplitude, composed of amplitude and phase, which is one of the core concepts in physical systems to deal with electromagnetic, light, sonic/ultrasonic waves as well as quantum waves, namely, electron and

  19. Neural mechanisms of peristalsis in the isolated rabbit distal colon: a neuromechanical loop hypothesis.

    Science.gov (United States)

    Dinning, Phil G; Wiklendt, Lukasz; Omari, Taher; Arkwright, John W; Spencer, Nick J; Brookes, Simon J H; Costa, Marcello

    2014-01-01

    Propulsive contractions of circular muscle are largely responsible for the movements of content along the digestive tract. Mechanical and electrophysiological recordings of isolated colonic circular muscle have demonstrated that localized distension activates ascending and descending interneuronal pathways, evoking contraction orally and relaxation anally. These polarized enteric reflex pathways can theoretically be sequentially activated by the mechanical stimulation of the advancing contents. Here, we test the hypothesis that initiation and propagation of peristaltic contractions involves a neuromechanical loop; that is an initial gut distension activates local and oral reflex contraction and anal reflex relaxation, the subsequent movement of content then acts as new mechanical stimulus triggering sequentially reflex contractions/relaxations at each point of the gut resulting in a propulsive peristaltic contraction. In fluid filled isolated rabbit distal colon, we combined spatiotemporal mapping of gut diameter and intraluminal pressure with a new analytical method, allowing us to identify when and where active (neurally-driven) contraction or relaxation occurs. Our data indicate that gut dilation is associated with propagating peristaltic contractions, and that the associated level of dilation is greater than that preceding non-propagating contractions (2.7 ± 1.4 mm vs. 1.6 ± 1.2 mm; P polarized enteric circuits. These produce propulsion of the bolus which activates further anally, polarized enteric circuits by distension, thus closing the neuromechanical loop.

  20. Studying the mechanisms of the Somatic Marker Hypothesis in Spiking Neural Networks (SNN

    Directory of Open Access Journals (Sweden)

    Manuel GONZÁLEZ

    2013-07-01

    Full Text Available Normal 0 21 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} In this paper, a mechanism of emotional bias in decision making is studied using Spiking Neural Networks to simulate the associative and recurrent networks involved. The results obtained are along the lines of those proposed by A. Damasio as part of the Somatic Marker Hypothesis, in particular, that, in absence of emotional input, the decision making is driven by the rational input alone. Appropriate representations for the Objective and Emotional Values are also suggested, provided a spike representation (code of the information.

  1. Studying the mechanisms of the Somatic Marker Hypothesis in Spiking Neural Networks (SNN

    Directory of Open Access Journals (Sweden)

    Alejandro JIMÉNEZ-RODRÍGUEZ

    2012-09-01

    Full Text Available Normal 0 21 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} In this paper, a mechanism of emotional bias in decision making is studied using Spiking Neural Networks to simulate the associative and recurrent networks involved. The results obtained are along the lines of those proposed by A. Damasio as part of the Somatic Marker Hypothesis, in particular, that, in absence of emotional input, the decision making is driven by the rational input alone. Appropriate representations for the Objective and Emotional Values are also suggested, provided a spike representation (code of the information.

  2. Supervised Learning with Complex-valued Neural Networks

    CERN Document Server

    Suresh, Sundaram; Savitha, Ramasamy

    2013-01-01

    Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks.  Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computati...

  3. Suprahyoid Muscle Complex: A Reliable Neural Assessment Tool For Dysphagia?

    DEFF Research Database (Denmark)

    Kothari, Mohit; Stubbs, Peter William; Pedersen, Asger Roer

    be a non-invasive reliable neural assessment tool for patients with dysphagia. Objective: To investigate the possibility of using the suprahyoid muscle complex (SMC) using surface electromyography (sEMG) to assess changes to neural pathways by determining the reliability of measurements in healthy...

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

    CERN Document Server

    Wang, Zhanshan; Zheng, Chengde

    2016-01-01

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

  5. Use of neural networks in the analysis of complex systems

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1992-01-01

    The application of neural networks, alone or in conjunction with other advanced technologies (expert systems, fuzzy logic, and/or genetic algorithms) to some of the problems of complex engineering systems has the potential to enhance the safety reliability and operability of these systems. The work described here deals with complex systems or parts of such systems that can be isolated from the total system. Typically, the measured variables from the systems are analog variables that must be sampled and normalized to expected peak values before they are introduced into neural networks. Often data must be processed to put it into a form more acceptable to the neural network. The neural networks are usually simulated on modern high-speed computers that carry out the calculations serially. However, it is possible to implement neural networks using specially designed microchips where the network calculations are truly carried out in parallel, thereby providing virtually instantaneous outputs for each set of inputs. Specific applications described include: Diagnostics: State of the Plant; Hybrid System for Transient Identification; Detection of Change of Mode in Complex Systems; Sensor Validation; Plant-Wide Monitoring; Monitoring of Performance and Efficiency; and Analysis of Vibrations. Although the specific examples described deal with nuclear power plants or their subsystems, the techniques described can be applied to a wide variety of complex engineering systems

  6. [Dinitrosyl iron complexes are endogenous signaling agents in animal and human cells and tissues (a hypothesis)].

    Science.gov (United States)

    Vanin, A F

    2004-01-01

    The hypothesis was advanced that dinitrosyl iron complexes generated in animal and human cells and tissues producing nitric oxide can function as endogenous universal regulators of biochemical and physiological processes. This function is realized by the ability of dinitrosyl iron complexes to act as donors of free nitric oxide molecules interacting with the heme groups of proteins, nitrosonium ions, or Fe+(NO+)2 interacting with the thiol groups of proteins. The effect of dinitrosyl iron complexes on the activity of some enzymes and the expression of the genome at the translation and transcription levels was considered.

  7. Distributed Recurrent Neural Forward Models with Neural Control for Complex Locomotion in Walking Robots

    DEFF Research Database (Denmark)

    Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin

    2015-01-01

    here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated......Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental...... conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain...

  8. Systemic activation of the immune system in HIV infection: The role of the immune complexes (hypothesis).

    Science.gov (United States)

    Korolevskaya, Larisa B; Shmagel, Konstantin V; Shmagel, Nadezhda G; Saidakova, Evgeniya V

    2016-03-01

    Currently, immune activation is proven to be the basis for the HIV infection pathogenesis and a strong predictor of the disease progression. Among the causes of systemic immune activation the virus and its products, related infectious agents, pro-inflammatory cytokines, and regulatory CD4+ T cells' decrease are considered. Recently microbial translocation (bacterial products yield into the bloodstream as a result of the gastrointestinal tract mucosal barrier integrity damage) became the most popular hypothesis. Previously, we have found an association between immune complexes present in the bloodstream of HIV infected patients and the T cell activation. On this basis, we propose a significantly modified hypothesis of immune activation in HIV infection. It is based on the immune complexes' participation in the immunocompetent cells' activation. Immune complexes are continuously formed in the chronic phase of the infection. Together with TLR-ligands (viral antigens, bacterial products coming from the damaged gut) present in the bloodstream they interact with macrophages. As a result macrophages are transformed into the type II activated forms. These macrophages block IL-12 production and start synthesizing IL-10. High level of this cytokine slows down the development of the full-scale Th1-response. The anti-viral reactions are shifted towards the serogenesis. Newly synthesized antibodies' binding to viral antigens leads to continuous formation of the immune complexes capable of interacting with antigen-presenting cells. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. Decomposition of Rotor Hopfield Neural Networks Using Complex Numbers.

    Science.gov (United States)

    Kobayashi, Masaki

    2018-04-01

    A complex-valued Hopfield neural network (CHNN) is a multistate model of a Hopfield neural network. It has the disadvantage of low noise tolerance. Meanwhile, a symmetric CHNN (SCHNN) is a modification of a CHNN that improves noise tolerance. Furthermore, a rotor Hopfield neural network (RHNN) is an extension of a CHNN. It has twice the storage capacity of CHNNs and SCHNNs, and much better noise tolerance than CHNNs, although it requires twice many connection parameters. In this brief, we investigate the relations between CHNN, SCHNN, and RHNN; an RHNN is uniquely decomposed into a CHNN and SCHNN. In addition, the Hebbian learning rule for RHNNs is decomposed into those for CHNNs and SCHNNs.

  10. The Complexity of Dynamics in Small Neural Circuits.

    Directory of Open Access Journals (Sweden)

    Diego Fasoli

    2016-08-01

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

  11. Complexity and competition in appetitive and aversive neural circuits

    Directory of Open Access Journals (Sweden)

    Crista L. Barberini

    2012-11-01

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

  12. Hypothesis: the chaos and complexity theory may help our understanding of fibromyalgia and similar maladies.

    Science.gov (United States)

    Martinez-Lavin, Manuel; Infante, Oscar; Lerma, Claudia

    2008-02-01

    Modern clinicians are often frustrated by their inability to understand fibromyalgia and similar maladies since these illnesses cannot be explained by the prevailing linear-reductionist medical paradigm. This article proposes that new concepts derived from the Complexity Theory may help understand the pathogenesis of fibromyalgia, chronic fatigue syndrome, and Gulf War syndrome. This hypothesis is based on the recent recognition of chaos fractals and complex systems in human physiology. These nonlinear dynamics concepts offer a different perspective to the notion of homeostasis and disease. They propose that the essence of disease is dysfunction and not structural damage. Studies using novel nonlinear instruments have shown that fibromyalgia and similar maladies may be caused by the degraded performance of our main complex adaptive system. This dysfunction explains the multifaceted manifestations of these entities. To understand and alleviate the suffering associated with these complex illnesses, a paradigm shift from reductionism to holism based on the Complexity Theory is suggested. This shift perceives health as resilient adaptation and some chronic illnesses as rigid dysfunction.

  13. Sex differences in the neural mechanisms mediating addiction: a new synthesis and hypothesis

    Directory of Open Access Journals (Sweden)

    Becker Jill B

    2012-06-01

    Full Text Available Abstract In this review we propose that there are sex differences in how men and women enter onto the path that can lead to addiction. Males are more likely than females to engage in risky behaviors that include experimenting with drugs of abuse, and in susceptible individuals, they are drawn into the spiral that can eventually lead to addiction. Women and girls are more likely to begin taking drugs as self-medication to reduce stress or alleviate depression. For this reason women enter into the downward spiral further along the path to addiction, and so transition to addiction more rapidly. We propose that this sex difference is due, at least in part, to sex differences in the organization of the neural systems responsible for motivation and addiction. Additionally, we suggest that sex differences in these systems and their functioning are accentuated with addiction. In the current review we discuss historical, cultural, social and biological bases for sex differences in addiction with an emphasis on sex differences in the neurotransmitter systems that are implicated.

  14. Decision-making conflict and the neural efficiency hypothesis of intelligence: a functional near-infrared spectroscopy investigation.

    Science.gov (United States)

    Di Domenico, Stefano I; Rodrigo, Achala H; Ayaz, Hasan; Fournier, Marc A; Ruocco, Anthony C

    2015-04-01

    Research on the neural efficiency hypothesis of intelligence (NEH) has revealed that the brains of more intelligent individuals consume less energy when performing easy cognitive tasks but more energy when engaged in difficult mental operations. However, previous studies testing the NEH have relied on cognitive tasks that closely resemble psychometric tests of intelligence, potentially confounding efficiency during intelligence-test performance with neural efficiency per se. The present study sought to provide a novel test of the NEH by examining patterns of prefrontal activity while participants completed an experimental paradigm that is qualitatively distinct from the contents of psychometric tests of intelligence. Specifically, participants completed a personal decision-making task (e.g., which occupation would you prefer, dancer or chemist?) in which they made a series of forced choices according to their subjective preferences. The degree of decisional conflict (i.e., choice difficulty) between the available response options was manipulated on the basis of participants' unique preference ratings for the target stimuli, which were obtained prior to scanning. Evoked oxygenation of the prefrontal cortex was measured using 16-channel continuous-wave functional near-infrared spectroscopy. Consistent with the NEH, intelligence predicted decreased activation of the right inferior frontal gyrus (IFG) during low-conflict situations and increased activation of the right-IFG during high-conflict situations. This pattern of right-IFG activity among more intelligent individuals was complemented by faster reaction times in high-conflict situations. These results provide new support for the NEH and suggest that the neural efficiency of more intelligent individuals generalizes to the performance of cognitive tasks that are distinct from intelligence tests. Copyright © 2015 Elsevier Inc. All rights reserved.

  15. Simple neural substrate predicts complex rhythmic structure in duetting birds

    Science.gov (United States)

    Amador, Ana; Trevisan, M. A.; Mindlin, G. B.

    2005-09-01

    Horneros (Furnarius Rufus) are South American birds well known for their oven-looking nests and their ability to sing in couples. Previous work has analyzed the rhythmic organization of the duets, unveiling a mathematical structure behind the songs. In this work we analyze in detail an extended database of duets. The rhythms of the songs are compatible with the dynamics presented by a wide class of dynamical systems: forced excitable systems. Compatible with this nonlinear rule, we build a biologically inspired model for how the neural and the anatomical elements may interact to produce the observed rhythmic patterns. This model allows us to synthesize songs presenting the acoustic and rhythmic features observed in real songs. We also make testable predictions in order to support our hypothesis.

  16. Novel Behavioral and Neural Evidences for Age-Related changes in Force complexity.

    Science.gov (United States)

    Chen, Yi-Ching; Lin, Linda L; Hwang, Ing-Shiou

    2018-02-17

    This study investigated age-related changes in behavioral and neural complexity for a polyrhythmic movement, which appeared to be an exception to the loss of complexity hypothesis. Young (n = 15; age = 24.2 years) and older (15; 68.1 years) adults performed low-level force-tracking with isometric index abduction to couple a compound sinusoidal target. Multi-scale entropy (MSE) of tracking force and inter-spike interval (ISI) of motor unit (MU) in the first dorsal interosseus muscle were assessed. The MSE area of tracking force at shorter time scales of older adults was greater (more complex) than that of young adults, whereas an opposite trend (less complex for the elders) was noted at longer time scales. The MSE area of force fluctuations (the stochastic component of the tracking force) were generally smaller (less complex) for older adults. Along with greater mean and coefficient of ISI, the MSE area of the cumulative discharge rate of elders tended to be lower (less complex) than that of young adults. In conclusion, age-related complexity changes in polyrhythmic force-tracking depended on the time scale. The adaptive behavioral consequences could be multi-factorial origins of the age-related impairment in rate coding, increased discharge noises, and lower discharge complexity of pooled MUs.

  17. A One-Layer Recurrent Neural Network for Constrained Complex-Variable Convex Optimization.

    Science.gov (United States)

    Qin, Sitian; Feng, Jiqiang; Song, Jiahui; Wen, Xingnan; Xu, Chen

    2018-03-01

    In this paper, based on calculus and penalty method, a one-layer recurrent neural network is proposed for solving constrained complex-variable convex optimization. It is proved that for any initial point from a given domain, the state of the proposed neural network reaches the feasible region in finite time and converges to an optimal solution of the constrained complex-variable convex optimization finally. In contrast to existing neural networks for complex-variable convex optimization, the proposed neural network has a lower model complexity and better convergence. Some numerical examples and application are presented to substantiate the effectiveness of the proposed neural network.

  18. Convergence of Batch Split-Complex Backpropagation Algorithm for Complex-Valued Neural Networks

    Directory of Open Access Journals (Sweden)

    Huisheng Zhang

    2009-01-01

    Full Text Available The batch split-complex backpropagation (BSCBP algorithm for training complex-valued neural networks is considered. For constant learning rate, it is proved that the error function of BSCBP algorithm is monotone during the training iteration process, and the gradient of the error function tends to zero. By adding a moderate condition, the weights sequence itself is also proved to be convergent. A numerical example is given to support the theoretical analysis.

  19. Natural lecithin promotes neural network complexity and activity

    Science.gov (United States)

    Latifi, Shahrzad; Tamayol, Ali; Habibey, Rouhollah; Sabzevari, Reza; Kahn, Cyril; Geny, David; Eftekharpour, Eftekhar; Annabi, Nasim; Blau, Axel; Linder, Michel; Arab-Tehrany, Elmira

    2016-01-01

    Phospholipids in the brain cell membranes contain different polyunsaturated fatty acids (PUFAs), which are critical to nervous system function and structure. In particular, brain function critically depends on the uptake of the so-called “essential” fatty acids such as omega-3 (n-3) and omega-6 (n-6) PUFAs that cannot be readily synthesized by the human body. We extracted natural lecithin rich in various PUFAs from a marine source and transformed it into nanoliposomes. These nanoliposomes increased neurite outgrowth, network complexity and neural activity of cortical rat neurons in vitro. We also observed an upregulation of synapsin I (SYN1), which supports the positive role of lecithin in synaptogenesis, synaptic development and maturation. These findings suggest that lecithin nanoliposomes enhance neuronal development, which may have an impact on devising new lecithin delivery strategies for therapeutic applications. PMID:27228907

  20. Natural lecithin promotes neural network complexity and activity.

    Science.gov (United States)

    Latifi, Shahrzad; Tamayol, Ali; Habibey, Rouhollah; Sabzevari, Reza; Kahn, Cyril; Geny, David; Eftekharpour, Eftekhar; Annabi, Nasim; Blau, Axel; Linder, Michel; Arab-Tehrany, Elmira

    2016-05-27

    Phospholipids in the brain cell membranes contain different polyunsaturated fatty acids (PUFAs), which are critical to nervous system function and structure. In particular, brain function critically depends on the uptake of the so-called "essential" fatty acids such as omega-3 (n-3) and omega-6 (n-6) PUFAs that cannot be readily synthesized by the human body. We extracted natural lecithin rich in various PUFAs from a marine source and transformed it into nanoliposomes. These nanoliposomes increased neurite outgrowth, network complexity and neural activity of cortical rat neurons in vitro. We also observed an upregulation of synapsin I (SYN1), which supports the positive role of lecithin in synaptogenesis, synaptic development and maturation. These findings suggest that lecithin nanoliposomes enhance neuronal development, which may have an impact on devising new lecithin delivery strategies for therapeutic applications.

  1. Influence of the neural tube/notochord complex on MyoD expression and cellular proliferation in chicken embryos

    Directory of Open Access Journals (Sweden)

    H.J. Alves

    2003-02-01

    Full Text Available Important advances have been made in understanding the genetic processes that control skeletal muscle formation. Studies conducted on quails detected a delay in the myogenic program of animals selected for high growth rates. These studies have led to the hypothesis that a delay in myogenesis would allow somitic cells to proliferate longer and consequently increase the number of embryonic myoblasts. To test this hypothesis, recently segmented somites and part of the unsegmented paraxial mesoderm were separated from the neural tube/notochord complex in HH12 chicken embryos. In situ hybridization and competitive RT-PCR revealed that MyoD transcripts, which are responsible for myoblast determination, were absent in somites separated from neural tube/notochord (1.06 and 0.06 10-3 attomol MyoD/1 attomol ß-actin for control and separated somites, respectively; P<0.01. However, reapproximation of these structures allowed MyoD to be expressed in somites. Cellular proliferation was analyzed by immunohistochemical detection of incorporated BrdU, a thymidine analogue. A smaller but not significant (P = 0.27 number of proliferating cells was observed in somites that had been separated from neural tube/notochord (27 and 18 for control and separated somites, respectively. These results confirm the influence of the axial structures on MyoD activation but do not support the hypothesis that in the absence of MyoD transcripts the cellular proliferation would be maintained for a longer period of time.

  2. Complex Environmental Data Modelling Using Adaptive General Regression Neural Networks

    Science.gov (United States)

    Kanevski, Mikhail

    2015-04-01

    The research deals with an adaptation and application of Adaptive General Regression Neural Networks (GRNN) to high dimensional environmental data. GRNN [1,2,3] are efficient modelling tools both for spatial and temporal data and are based on nonparametric kernel methods closely related to classical Nadaraya-Watson estimator. Adaptive GRNN, using anisotropic kernels, can be also applied for features selection tasks when working with high dimensional data [1,3]. In the present research Adaptive GRNN are used to study geospatial data predictability and relevant feature selection using both simulated and real data case studies. The original raw data were either three dimensional monthly precipitation data or monthly wind speeds embedded into 13 dimensional space constructed by geographical coordinates and geo-features calculated from digital elevation model. GRNN were applied in two different ways: 1) adaptive GRNN with the resulting list of features ordered according to their relevancy; and 2) adaptive GRNN applied to evaluate all possible models N [in case of wind fields N=(2^13 -1)=8191] and rank them according to the cross-validation error. In both cases training were carried out applying leave-one-out procedure. An important result of the study is that the set of the most relevant features depends on the month (strong seasonal effect) and year. The predictabilities of precipitation and wind field patterns, estimated using the cross-validation and testing errors of raw and shuffled data, were studied in detail. The results of both approaches were qualitatively and quantitatively compared. In conclusion, Adaptive GRNN with their ability to select features and efficient modelling of complex high dimensional data can be widely used in automatic/on-line mapping and as an integrated part of environmental decision support systems. 1. Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning for Spatial Environmental Data. Theory, applications and software. EPFL Press

  3. Complexity, Compassion and Self-Organisation: Human Evolution and the Vulnerable Ape Hypothesis

    Directory of Open Access Journals (Sweden)

    Nick P. Winder

    2015-06-01

    Full Text Available Humans are agents capable of helping others, learning new behaviours and forgetting old ones. The evolutionary approach to archaeological systems has therefore been hampered by the 'modern synthesis' - a gene-centred model of evolution as a process that eliminates those that cannot handle stress. The result has been a form of environmental determinism that explains human evolution in terms of heroic struggles and selective winnowing. Biologists committed to the modern synthesis have either dismissed agency as a delusion wrought in our bodies by natural selection, or imposed a sharp, Cartesian split between 'natural' and 'artificial' ecologies. We revisit the seminal literature of evolutionary biology and show that the paradigmatic fault lines of 21st century anthropology can be traced back to the 19th century and beyond. Lamarck had developed a two-factor evolutionary theory - one factor an endogenous tendency to become more advanced and complex, the other an exogenous constraint that drove organisms into conformity with environment. Darwin tried to eliminate the progressive tendency and imposed linearity constraints on evolution that Thomas Henry Huxley rejected. When experimental evidence falsified Darwin's linear hypothesis, the race began to develop a new, gene-centred model of evolution. This became the modern synthesis. The modern synthesis is now under pressure from the evidence of anthropology, sociology, palaeontology, ecology and genetics. An 'extended synthesis' is emerging. If evolution is adequately summarised by the aphorism survival of the fittest, then 'fitness' cannot always be defined in the heroic sense of 'better able to compete and reproduce'. The fittest organisms are often those that evade selective winnowing, even when their ability to compete and reproduce has been compromised by their genes. Characteristically human traits like language, abstraction, compassion and altruism may have arisen as coping strategies that

  4. Complex social waves of giant honeybees provoked by a dummy wasp support the special-agent hypothesis.

    Science.gov (United States)

    Kastberger, Gerald; Weihmann, Frank; Hoetzl, Thomas

    2010-03-01

    The social waves in giant honeybees termed as shimmering are more complex than mexican waves. it has been demonstrated1 that shimmering is triggered by special agents at the nest surface. in this paper, we have used a nest that originated by amalgamation of two previously separated nests and stimulated waves by a dummy wasp moved on a miniature cable car. we illustrate the plausibility of the special-agent hypothesis1 also for complex shimmering processes.

  5. Complex social waves of giant honeybees provoked by a dummy wasp support the special-agent hypothesis

    OpenAIRE

    Kastberger, Gerald; Weihmann, Frank; Hoetzl, Thomas

    2010-01-01

    The social waves in giant honeybees termed as shimmering are more complex than mexican waves. it has been demonstrated1 that shimmering is triggered by special agents at the nest surface. in this paper, we have used a nest that originated by amalgamation of two previously separated nests and stimulated waves by a dummy wasp moved on a miniature cable car. we illustrate the plausibility of the special-agent hypothesis1 also for complex shimmering processes.

  6. Drosophila olfactory memory: single genes to complex neural circuits.

    Science.gov (United States)

    Keene, Alex C; Waddell, Scott

    2007-05-01

    A central goal of neuroscience is to understand how neural circuits encode memory and guide behaviour. Studying simple, genetically tractable organisms, such as Drosophila melanogaster, can illuminate principles of neural circuit organization and function. Early genetic dissection of D. melanogaster olfactory memory focused on individual genes and molecules. These molecular tags subsequently revealed key neural circuits for memory. Recent advances in genetic technology have allowed us to manipulate and observe activity in these circuits, and even individual neurons, in live animals. The studies have transformed D. melanogaster from a useful organism for gene discovery to an ideal model to understand neural circuit function in memory.

  7. Statistical mechanics of complex neural systems and high dimensional data

    International Nuclear Information System (INIS)

    Advani, Madhu; Lahiri, Subhaneil; Ganguli, Surya

    2013-01-01

    Recent experimental advances in neuroscience have opened new vistas into the immense complexity of neuronal networks. This proliferation of data challenges us on two parallel fronts. First, how can we form adequate theoretical frameworks for understanding how dynamical network processes cooperate across widely disparate spatiotemporal scales to solve important computational problems? Second, how can we extract meaningful models of neuronal systems from high dimensional datasets? To aid in these challenges, we give a pedagogical review of a collection of ideas and theoretical methods arising at the intersection of statistical physics, computer science and neurobiology. We introduce the interrelated replica and cavity methods, which originated in statistical physics as powerful ways to quantitatively analyze large highly heterogeneous systems of many interacting degrees of freedom. We also introduce the closely related notion of message passing in graphical models, which originated in computer science as a distributed algorithm capable of solving large inference and optimization problems involving many coupled variables. We then show how both the statistical physics and computer science perspectives can be applied in a wide diversity of contexts to problems arising in theoretical neuroscience and data analysis. Along the way we discuss spin glasses, learning theory, illusions of structure in noise, random matrices, dimensionality reduction and compressed sensing, all within the unified formalism of the replica method. Moreover, we review recent conceptual connections between message passing in graphical models, and neural computation and learning. Overall, these ideas illustrate how statistical physics and computer science might provide a lens through which we can uncover emergent computational functions buried deep within the dynamical complexities of neuronal networks. (paper)

  8. Application of Artificial Neural Networks to Complex Groundwater Management Problems

    International Nuclear Information System (INIS)

    Coppola, Emery; Poulton, Mary; Charles, Emmanuel; Dustman, John; Szidarovszky, Ferenc

    2003-01-01

    As water quantity and quality problems become increasingly severe, accurate prediction and effective management of scarcer water resources will become critical. In this paper, the successful application of artificial neural network (ANN) technology is described for three types of groundwater prediction and management problems. In the first example, an ANN was trained with simulation data from a physically based numerical model to predict head (groundwater elevation) at locations of interest under variable pumping and climate conditions. The ANN achieved a high degree of predictive accuracy, and its derived state-transition equations were embedded into a multiobjective optimization formulation and solved to generate a trade-off curve depicting water supply in relation to contamination risk. In the second and third examples, ANNs were developed with real-world hydrologic and climate data for different hydrogeologic environments. For the second problem, an ANN was developed using data collected for a 5-year, 8-month period to predict heads in a multilayered surficial and limestone aquifer system under variable pumping, state, and climate conditions. Using weekly stress periods, the ANN substantially outperformed a well-calibrated numerical flow model for the 71-day validation period, and provided insights into the effects of climate and pumping on water levels. For the third problem, an ANN was developed with data collected automatically over a 6-week period to predict hourly heads in 11 high-capacity public supply wells tapping a semiconfined bedrock aquifer and subject to large well-interference effects. Using hourly stress periods, the ANN accurately predicted heads for 24-hour periods in all public supply wells. These test cases demonstrate that the ANN technology can solve a variety of complex groundwater management problems and overcome many of the problems and limitations associated with traditional physically based flow models

  9. An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks

    Science.gov (United States)

    Cabessa, Jérémie; Villa, Alessandro E. P.

    2014-01-01

    We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of -automata, and then translating the most refined classification of -automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits. PMID:24727866

  10. Temporal neural networks and transient analysis of complex engineering systems

    Science.gov (United States)

    Uluyol, Onder

    A theory is introduced for a multi-layered Local Output Gamma Feedback (LOGF) neural network within the paradigm of Locally-Recurrent Globally-Feedforward neural networks. It is developed for the identification, prediction, and control tasks of spatio-temporal systems and allows for the presentation of different time scales through incorporation of a gamma memory. It is initially applied to the tasks of sunspot and Mackey-Glass series prediction as benchmarks, then it is extended to the task of power level control of a nuclear reactor at different fuel cycle conditions. The developed LOGF neuron model can also be viewed as a Transformed Input and State (TIS) Gamma memory for neural network architectures for temporal processing. The novel LOGF neuron model extends the static neuron model by incorporating into it a short-term memory structure in the form of a digital gamma filter. A feedforward neural network made up of LOGF neurons can thus be used to model dynamic systems. A learning algorithm based upon the Backpropagation-Through-Time (BTT) approach is derived. It is applicable for training a general L-layer LOGF neural network. The spatial and temporal weights and parameters of the network are iteratively optimized for a given problem using the derived learning algorithm.

  11. The Oedipal Complex and Child Sexual Abuse Research: A Re-examination of Freud's Hypothesis.

    Science.gov (United States)

    Kendall-Tackett, Kathleen A.

    In 1896, Sigmund Freud stated that early childhood seduction caused hysteria in his female patients. He later recanted his original finding and claimed that the reports of abuse he heard from his patients were not descriptions of real events, but his patients' expressions of unconscious childhood wishes. The theory of the Oedipal complex gave…

  12. Increasing Provasculature Complexity in the Arabidopsis Embryo May Increase Total Iron Content in Seeds: A Hypothesis

    Directory of Open Access Journals (Sweden)

    Hannetz Roschzttardtz

    2017-06-01

    Full Text Available Anemia due to iron deficiency is a worldwide issue, affecting mainly children and women. Seed iron is a major source of this micronutrient for feeding, however, in most crops these levels are too low to meet daily needs. Thus, increasing iron allocation and its storage in seeds can represent an important step to enhance iron provision for humans and animals. Our knowledge on seed iron homeostasis is mainly based on studies performed in the model plant Arabidopsis thaliana, where iron accumulates in endodermis cells surrounding the embryo provasculature. It has been reported that cotyledon provasculature pattern complexity can be modified, thus we hypothesize that changes in the complexity of embryo vein patterns may affect total iron content in Arabidopsis seeds. This approach could be used as basis to develop strategies aimed to biofortify seeds.

  13. Chromatin Remodeling BAF (SWI/SNF Complexes in Neural Development and Disorders

    Directory of Open Access Journals (Sweden)

    Godwin Sokpor

    2017-08-01

    Full Text Available The ATP-dependent BRG1/BRM associated factor (BAF chromatin remodeling complexes are crucial in regulating gene expression by controlling chromatin dynamics. Over the last decade, it has become increasingly clear that during neural development in mammals, distinct ontogenetic stage-specific BAF complexes derived from combinatorial assembly of their subunits are formed in neural progenitors and post-mitotic neural cells. Proper functioning of the BAF complexes plays critical roles in neural development, including the establishment and maintenance of neural fates and functionality. Indeed, recent human exome sequencing and genome-wide association studies have revealed that mutations in BAF complex subunits are linked to neurodevelopmental disorders such as Coffin-Siris syndrome, Nicolaides-Baraitser syndrome, Kleefstra's syndrome spectrum, Hirschsprung's disease, autism spectrum disorder, and schizophrenia. In this review, we focus on the latest insights into the functions of BAF complexes during neural development and the plausible mechanistic basis of how mutations in known BAF subunits are associated with certain neurodevelopmental disorders.

  14. Chromatin Remodeling BAF (SWI/SNF) Complexes in Neural Development and Disorders

    Science.gov (United States)

    Sokpor, Godwin; Xie, Yuanbin; Rosenbusch, Joachim; Tuoc, Tran

    2017-01-01

    The ATP-dependent BRG1/BRM associated factor (BAF) chromatin remodeling complexes are crucial in regulating gene expression by controlling chromatin dynamics. Over the last decade, it has become increasingly clear that during neural development in mammals, distinct ontogenetic stage-specific BAF complexes derived from combinatorial assembly of their subunits are formed in neural progenitors and post-mitotic neural cells. Proper functioning of the BAF complexes plays critical roles in neural development, including the establishment and maintenance of neural fates and functionality. Indeed, recent human exome sequencing and genome-wide association studies have revealed that mutations in BAF complex subunits are linked to neurodevelopmental disorders such as Coffin-Siris syndrome, Nicolaides-Baraitser syndrome, Kleefstra's syndrome spectrum, Hirschsprung's disease, autism spectrum disorder, and schizophrenia. In this review, we focus on the latest insights into the functions of BAF complexes during neural development and the plausible mechanistic basis of how mutations in known BAF subunits are associated with certain neurodevelopmental disorders. PMID:28824374

  15. Chromatin Remodeling BAF (SWI/SNF) Complexes in Neural Development and Disorders.

    Science.gov (United States)

    Sokpor, Godwin; Xie, Yuanbin; Rosenbusch, Joachim; Tuoc, Tran

    2017-01-01

    The ATP-dependent BRG1/BRM associated factor (BAF) chromatin remodeling complexes are crucial in regulating gene expression by controlling chromatin dynamics. Over the last decade, it has become increasingly clear that during neural development in mammals, distinct ontogenetic stage-specific BAF complexes derived from combinatorial assembly of their subunits are formed in neural progenitors and post-mitotic neural cells. Proper functioning of the BAF complexes plays critical roles in neural development, including the establishment and maintenance of neural fates and functionality. Indeed, recent human exome sequencing and genome-wide association studies have revealed that mutations in BAF complex subunits are linked to neurodevelopmental disorders such as Coffin-Siris syndrome, Nicolaides-Baraitser syndrome, Kleefstra's syndrome spectrum, Hirschsprung's disease, autism spectrum disorder, and schizophrenia. In this review, we focus on the latest insights into the functions of BAF complexes during neural development and the plausible mechanistic basis of how mutations in known BAF subunits are associated with certain neurodevelopmental disorders.

  16. 2,3-DPG-Hb complex: a hypothesis for an asymmetric binding.

    Science.gov (United States)

    Pomponi, M; Bertonati, C; Fuglei, E; Wiig, O; Derocher, A E

    2000-05-15

    This study was undertaken to test the symmetry of 2,3-diphosphoglycerate (2,3-DPG) binding site in hemoglobin (Hb). From Arnone's study [A. Arnone, Nature (London) 237 (1972) 146] the 2,3-DPG binding site is located at the top of the cavity, that runs through the center of the deoxy-Hb molecule. However, it is possible that this symmetry reported by Arnone, for crystals of 2,3-DPG-Hb complex, might not be conserved in solution. In this paper, we report the 31P nuclear magnetic resonances of the 2,3-DPG interaction with Hb. The 2,3-DPG chemical shifts of the P2 and P3 resonance are both pH- and hemoglobin-dependent [protein from man, polar bear (Ursus maritimus), Arctic fox (Alopex lagopus) and bovine]. 2,3-DPG binds tightly to deoxyhemoglobin and weakly, nevertheless significantly, to oxyhemoglobin. In particular, our results suggest similar spatial position of the binding site of 2,3-DPG in both forms of Hb in solutions. However, the most unexpected result was the apparent loss of symmetry in the binding site, which might correlate with the ability of the hemoglobin to modulate its functional behavior. The different interactions of the phosphate groups indicate small differences in the quaternary structure of the different deoxy forms of hemoglobin. Given the above structural perturbation an asymmetric binding in the complex could justify, at least in part, different physiological properties of Hb. Regardless, functionally relevant effects of 2,3-DPG seem to be measured and best elucidated through solution studies.

  17. Not a simple case - A first comprehensive phylogenetic hypothesis for the Midas cichlid complex in Nicaragua (Teleostei: Cichlidae: Amphilophus).

    Science.gov (United States)

    Geiger, Matthias F; McCrary, Jeffrey K; Schliewen, Ulrich K

    2010-09-01

    Nicaraguan Midas cichlids from crater lakes have recently attracted attention as potential model systems for speciation research, but no attempt has been made to comprehensively reconstruct phylogenetic relationships of this highly diverse and recently evolved species complex. We present a first AFLP (2793 loci) and mtDNA based phylogenetic hypothesis including all described and several undescribed species from six crater lakes (Apoyeque, Apoyo, Asososca Leon, Masaya, Tiscapa and Xiloá), the two great Lakes Managua and Nicaragua and the San Juan River. Our analyses demonstrate that the relationships between the Midas cichlid members are complex, and that phylogenetic information from different markers and methods do not always yield congruent results. Nevertheless, monophyly support for crater lake assemblages from Lakes Apoyeque, Apoyo, A. Leon is high as compared to those from L. Xiloá indicating occurrence of sympatric speciation. Further, we demonstrate that a 'three species' concept for the Midas cichlid complex is inapplicable and consequently that an individualized and voucher based approach in speciation research of the Midas cichlid complex is necessary at least as long as there is no comprehensive revision of the species complex available. Copyright 2010 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

    Li, Shuai; Li, Yangming

    2013-10-28

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

  19. Emotion processing in words: a test of the neural re-use hypothesis using surface and intracranial EEG.

    Science.gov (United States)

    Ponz, Aurélie; Montant, Marie; Liegeois-Chauvel, Catherine; Silva, Catarina; Braun, Mario; Jacobs, Arthur M; Ziegler, Johannes C

    2014-05-01

    This study investigates the spatiotemporal brain dynamics of emotional information processing during reading using a combination of surface and intracranial electroencephalography (EEG). Two different theoretical views were opposed. According to the standard psycholinguistic perspective, emotional responses to words are generated within the reading network itself subsequent to semantic activation. According to the neural re-use perspective, brain regions that are involved in processing emotional information contained in other stimuli (faces, pictures, smells) might be in charge of the processing of emotional information in words as well. We focused on a specific emotion-disgust-which has a clear locus in the brain, the anterior insula. Surface EEG showed differences between disgust and neutral words as early as 200 ms. Source localization suggested a cortical generator of the emotion effect in the left anterior insula. These findings were corroborated through the intracranial recordings of two epileptic patients with depth electrodes in insular and orbitofrontal areas. Both electrodes showed effects of disgust in reading as early as 200 ms. The early emotion effect in a brain region (insula) that responds to specific emotions in a variety of situations and stimuli clearly challenges classic sequential theories of reading in favor of the neural re-use perspective.

  20. A Hypothesis for Using Pathway Genetic Load Analysis for Understanding Complex Outcomes in Bilirubin Encephalopathy

    Science.gov (United States)

    Riordan, Sean M.; Bittel, Douglas C.; Le Pichon, Jean-Baptiste; Gazzin, Silvia; Tiribelli, Claudio; Watchko, Jon F.; Wennberg, Richard P.; Shapiro, Steven M.

    2016-01-01

    Genetic-based susceptibility to bilirubin neurotoxicity and chronic bilirubin encephalopathy (kernicterus) is still poorly understood. Neonatal jaundice affects 60–80% of newborns, and considerable effort goes into preventing this relatively benign condition from escalating into the development of kernicterus making the incidence of this potentially devastating condition very rare in more developed countries. The current understanding of the genetic background of kernicterus is largely comprised of mutations related to alterations of bilirubin production, elimination, or both. Less is known about mutations that may predispose or protect against CNS bilirubin neurotoxicity. The lack of a monogenetic source for this risk of bilirubin neurotoxicity suggests that disease progression is dependent upon an overall decrease in the functionality of one or more essential genetically controlled metabolic pathways. In other words, a “load” is placed on key pathways in the form of multiple genetic variants that combine to create a vulnerable phenotype. The idea of epistatic interactions creating a pathway genetic load (PGL) that affects the response to a specific insult has been previously reported as a PGL score. We hypothesize that the PGL score can be used to investigate whether increased susceptibility to bilirubin-induced CNS damage in neonates is due to a mutational load being placed on key genetic pathways important to the central nervous system's response to bilirubin neurotoxicity. We propose a modification of the PGL score method that replaces the use of a canonical pathway with custom gene lists organized into three tiers with descending levels of evidence combined with the utilization of single nucleotide polymorphism (SNP) causality prediction methods. The PGL score has the potential to explain the genetic background of complex bilirubin induced neurological disorders (BIND) such as kernicterus and could be the key to understanding ranges of outcome severity

  1. The role of BAF (mSWI/SNF) complexes in mammalian neural development.

    Science.gov (United States)

    Son, Esther Y; Crabtree, Gerald R

    2014-09-01

    The BAF (mammalian SWI/SNF) complexes are a family of multi-subunit ATP-dependent chromatin remodelers that use ATP hydrolysis to alter chromatin structure. Distinct BAF complex compositions are possible through combinatorial assembly of homologous subunit families and can serve non-redundant functions. In mammalian neural development, developmental stage-specific BAF assemblies are found in embryonic stem cells, neural progenitors and postmitotic neurons. In particular, the neural progenitor-specific BAF complexes are essential for controlling the kinetics and mode of neural progenitor cell division, while neuronal BAF function is necessary for the maturation of postmitotic neuronal phenotypes as well as long-term memory formation. The microRNA-mediated mechanism for transitioning from npBAF to nBAF complexes is instructive for the neuronal fate and can even convert fibroblasts into neurons. The high frequency of BAF subunit mutations in neurological disorders underscores the rate-determining role of BAF complexes in neural development, homeostasis, and plasticity. © 2014 Wiley Periodicals, Inc.

  2. An Improved Recurrent Neural Network for Complex-Valued Systems of Linear Equation and Its Application to Robotic Motion Tracking.

    Science.gov (United States)

    Ding, Lei; Xiao, Lin; Liao, Bolin; Lu, Rongbo; Peng, Hua

    2017-01-01

    To obtain the online solution of complex-valued systems of linear equation in complex domain with higher precision and higher convergence rate, a new neural network based on Zhang neural network (ZNN) is investigated in this paper. First, this new neural network for complex-valued systems of linear equation in complex domain is proposed and theoretically proved to be convergent within finite time. Then, the illustrative results show that the new neural network model has the higher precision and the higher convergence rate, as compared with the gradient neural network (GNN) model and the ZNN model. Finally, the application for controlling the robot using the proposed method for the complex-valued systems of linear equation is realized, and the simulation results verify the effectiveness and superiorness of the new neural network for the complex-valued systems of linear equation.

  3. A new hypothesis on the simultaneous direct and indirect proton pump mechanisms in NADH-quinone oxidoreductase (complex I).

    Science.gov (United States)

    Ohnishi, Tomoko; Nakamaru-Ogiso, Eiko; Ohnishi, S Tsuyoshi

    2010-10-08

    Recently, Sazanov's group reported the X-ray structure of whole complex I [Nature, 465, 441 (2010)], which presented a strong clue for a "piston-like" structure as a key element in an "indirect" proton pump. We have studied the NuoL subunit which has a high sequence similarity to Na(+)/H(+) antiporters, as do the NuoM and N subunits. We constructed 27 site-directed NuoL mutants. Our data suggest that the H(+)/e(-) stoichiometry seems to have decreased from (4H(+)/2e(-)) in the wild-type to approximately (3H(+)/2e(-)) in NuoL mutants. We propose a revised hypothesis that each of the "direct" and the "indirect" proton pumps transports 2H(+) per 2e(-). Copyright © 2010 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

  4. On the complexity of neural network classifiers: a comparison between shallow and deep architectures.

    Science.gov (United States)

    Bianchini, Monica; Scarselli, Franco

    2014-08-01

    Recently, researchers in the artificial neural network field have focused their attention on connectionist models composed by several hidden layers. In fact, experimental results and heuristic considerations suggest that deep architectures are more suitable than shallow ones for modern applications, facing very complex problems, e.g., vision and human language understanding. However, the actual theoretical results supporting such a claim are still few and incomplete. In this paper, we propose a new approach to study how the depth of feedforward neural networks impacts on their ability in implementing high complexity functions. First, a new measure based on topological concepts is introduced, aimed at evaluating the complexity of the function implemented by a neural network, used for classification purposes. Then, deep and shallow neural architectures with common sigmoidal activation functions are compared, by deriving upper and lower bounds on their complexity, and studying how the complexity depends on the number of hidden units and the used activation function. The obtained results seem to support the idea that deep networks actually implements functions of higher complexity, so that they are able, with the same number of resources, to address more difficult problems.

  5. Cooperation of deterministic dynamics and random noise in production of complex syntactical avian song sequences: a neural network model

    Directory of Open Access Journals (Sweden)

    Yuichi eYamashita

    2011-04-01

    Full Text Available How the brain learns and generates temporal sequences is a fundamental issue in neuroscience. The production of birdsongs, a process which involves complex learned sequences, provides researchers with an excellent biological model for this topic. The Bengalese finch in particular learns a highly complex song with syntactical structure. The nucleus HVC (HVC, a premotor nucleus within the avian song system, plays a key role in generating the temporal structures of their songs. From lesion studies, the nucleus interfacialis (NIf projecting to the HVC is considered one of the essential regions that contribute to the complexity of their songs. However, the types of interaction between the HVC and the NIf that can produce complex syntactical songs remain unclear. In order to investigate the function of interactions between the HVC and NIf, we have proposed a neural network model based on previous biological evidence. The HVC is modeled by a recurrent neural network (RNN that learns to generate temporal patterns of songs. The NIf is modeled as a mechanism that provides auditory feedback to the HVC and generates random noise that feeds into the HVC. The model showed that complex syntactical songs can be replicated by simple interactions between deterministic dynamics of the RNN and random noise. In the current study, the plausibility of the model is tested by the comparison between the changes in the songs of actual birds induced by pharmacological inhibition of the NIf and the changes in the songs produced by the model resulting from modification of parameters representing NIf functions. The efficacy of the model demonstrates that the changes of songs induced by pharmacological inhibition of the NIf can be interpreted as a trade-off between the effects of noise and the effects of feedback on the dynamics of the RNN of the HVC. These facts suggest that the current model provides a convincing hypothesis for the functional role of NIf-HVC interaction.

  6. Identification of Complex Dynamical Systems with Neural Networks (2/2)

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  7. Identification of Complex Dynamical Systems with Neural Networks (1/2)

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  8. Quasi-projective synchronization of fractional-order complex-valued recurrent neural networks.

    Science.gov (United States)

    Yang, Shuai; Yu, Juan; Hu, Cheng; Jiang, Haijun

    2018-08-01

    In this paper, without separating the complex-valued neural networks into two real-valued systems, the quasi-projective synchronization of fractional-order complex-valued neural networks is investigated. First, two new fractional-order inequalities are established by using the theory of complex functions, Laplace transform and Mittag-Leffler functions, which generalize traditional inequalities with the first-order derivative in the real domain. Additionally, different from hybrid control schemes given in the previous work concerning the projective synchronization, a simple and linear control strategy is designed in this paper and several criteria are derived to ensure quasi-projective synchronization of the complex-valued neural networks with fractional-order based on the established fractional-order inequalities and the theory of complex functions. Moreover, the error bounds of quasi-projective synchronization are estimated. Especially, some conditions are also presented for the Mittag-Leffler synchronization of the addressed neural networks. Finally, some numerical examples with simulations are provided to show the effectiveness of the derived theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

  9. Environmental layout complexity affects neural activity during navigation in humans.

    Science.gov (United States)

    Slone, Edward; Burles, Ford; Iaria, Giuseppe

    2016-05-01

    Navigating large-scale surroundings is a fundamental ability. In humans, it is commonly assumed that navigational performance is affected by individual differences, such as age, sex, and cognitive strategies adopted for orientation. We recently showed that the layout of the environment itself also influences how well people are able to find their way within it, yet it remains unclear whether differences in environmental complexity are associated with changes in brain activity during navigation. We used functional magnetic resonance imaging to investigate how the brain responds to a change in environmental complexity by asking participants to perform a navigation task in two large-scale virtual environments that differed solely in interconnection density, a measure of complexity defined as the average number of directional choices at decision points. The results showed that navigation in the simpler, less interconnected environment was faster and more accurate relative to the complex environment, and such performance was associated with increased activity in a number of brain areas (i.e. precuneus, retrosplenial cortex, and hippocampus) known to be involved in mental imagery, navigation, and memory. These findings provide novel evidence that environmental complexity not only affects navigational behaviour, but also modulates activity in brain regions that are important for successful orientation and navigation. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  10. Neural responses to complex auditory rhythms: the role of attending

    Directory of Open Access Journals (Sweden)

    Heather L Chapin

    2010-12-01

    Full Text Available The aim of this study was to explore the role of attention in pulse and meter perception using complex rhythms. We used a selective attention paradigm in which participants attended to either a complex auditory rhythm or a visually presented word list. Performance on a reproduction task was used to gauge whether participants were attending to the appropriate stimulus. We hypothesized that attention to complex rhythms – which contain no energy at the pulse frequency – would lead to activations in motor areas involved in pulse perception. Moreover, because multiple repetitions of a complex rhythm are needed to perceive a pulse, activations in pulse related areas would be seen only after sufficient time had elapsed for pulse perception to develop. Selective attention was also expected to modulate activity in sensory areas specific to the modality. We found that selective attention to rhythms led to increased BOLD responses in basal ganglia, and basal ganglia activity was observed only after the rhythms had cycled enough times for a stable pulse percept to develop. These observations suggest that attention is needed to recruit motor activations associated with the perception of pulse in complex rhythms. Moreover, attention to the auditory stimulus enhanced activity in an attentional sensory network including primary auditory, insula, anterior cingulate, and prefrontal cortex, and suppressed activity in sensory areas associated with attending to the visual stimulus.

  11. Pre-maxillary complex morphology in bilateral cleft and hypothesis on laterality of deviated pre-maxilla

    Directory of Open Access Journals (Sweden)

    Jyotsna Murthy

    2016-01-01

    Full Text Available Introduction: Pre-maxillary complex (pre-maxilla [PMX] + vomer morphology in bilateral complete cleft of primary and secondary palate (BCLCP is very complex and less reviewed in literature. Materials and Methods: In this retrospective cross-sectional study, 200 consecutive BCLCP patients were selected. Their pre-operative clinical photographs and dental casts were evaluated by a single investigator at two different points of time, to study the morphology of PMX and vomer with special emphasis on deviation of vomer and rotation of PMX. Results: It is found that in above 70% of patients, PMX and vomer both displaced or deviated towards left side in horizontal plane and PMX rotated anticlockwise at PMX vomerine suture (PVS. In 10% of cases, both PMX and vomer are displaced towards the right side, PMX rotated clockwise at PVS. In 11% of cases, vomer is displaced towards the left side, but PMX rotated clockwise at PVS. In 5% of cases, vomer is displaced towards the right side, but PMX rotated anticlockwise at PVS. Both PMX and vomer are in midline in 4% of cases. Conclusion: Specific morphological deviation of vomer and PMX has been studied. We put forward the probable hypothesis to explain the deviation and rotation of PMX.

  12. Application of complex discrete wavelet transform in classification of Doppler signals using complex-valued artificial neural network.

    Science.gov (United States)

    Ceylan, Murat; Ceylan, Rahime; Ozbay, Yüksel; Kara, Sadik

    2008-09-01

    In biomedical signal classification, due to the huge amount of data, to compress the biomedical waveform data is vital. This paper presents two different structures formed using feature extraction algorithms to decrease size of feature set in training and test data. The proposed structures, named as wavelet transform-complex-valued artificial neural network (WT-CVANN) and complex wavelet transform-complex-valued artificial neural network (CWT-CVANN), use real and complex discrete wavelet transform for feature extraction. The aim of using wavelet transform is to compress data and to reduce training time of network without decreasing accuracy rate. In this study, the presented structures were applied to the problem of classification in carotid arterial Doppler ultrasound signals. Carotid arterial Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group included 22 males and 16 females with an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal (lower extremity) angiographies (mean age, 59 years; range, 48-72 years). Healthy volunteers were young non-smokers who seem to not bear any risk of atherosclerosis, including 28 males and 12 females (mean age, 23 years; range, 19-27 years). Sensitivity, specificity and average detection rate were calculated for comparison, after training and test phases of all structures finished. These parameters have demonstrated that training times of CVANN and real-valued artificial neural network (RVANN) were reduced using feature extraction algorithms without decreasing accuracy rate in accordance to our aim.

  13. Environmental influences on neural systems of relational complexity

    Directory of Open Access Journals (Sweden)

    Layne eKalbfleisch

    2013-09-01

    Full Text Available Constructivist learning theory contends that we construct knowledge by experience and that environmental context influences learning. To explore this principle, we examined the cognitive process relational complexity (RC, defined as the number of visual dimensions considered during problem solving on a matrix reasoning task and a well-documented measure of mature reasoning capacity. We sought to determine how the visual environment influences RC by examining the influence of color and visual contrast on RC in a neuroimaging task. To specify the contributions of sensory demand and relational integration to reasoning, our participants performed a non-verbal matrix task comprised of color, no-color line, or black-white visual contrast conditions parametrically varied by complexity (relations 0, 1, 2. The use of matrix reasoning is ecologically valid for its psychometric relevance and for its potential to link the processing of psychophysically specific visual properties with various levels of relational complexity during reasoning. The role of these elements is important because matrix tests assess intellectual aptitude based on these seemingly context-less exercises. This experiment is a first step toward examining the psychophysical underpinnings of performance on these types of problems. The importance of this is increased in light of recent evidence that intelligence can be linked to visual discrimination. We submit three main findings. First, color and black-white visual contrast add demand at a basic sensory level, but contributions from color and from black-white visual contrast are dissociable in cortex such that color engages a reasoning heuristic and black-white visual contrast engages a sensory heuristic. Second, color supports contextual sense-making by boosting salience resulting in faster problem solving. Lastly, when visual complexity reaches 2-relations, color and visual contrast relinquish salience to other dimensions of problem

  14. Artificial neural networks using complex numbers and phase encoded weights.

    Science.gov (United States)

    Michel, Howard E; Awwal, Abdul Ahad S

    2010-04-01

    The model of a simple perceptron using phase-encoded inputs and complex-valued weights is proposed. The aggregation function, activation function, and learning rule for the proposed neuron are derived and applied to Boolean logic functions and simple computer vision tasks. The complex-valued neuron (CVN) is shown to be superior to traditional perceptrons. An improvement of 135% over the theoretical maximum of 104 linearly separable problems (of three variables) solvable by conventional perceptrons is achieved without additional logic, neuron stages, or higher order terms such as those required in polynomial logic gates. The application of CVN in distortion invariant character recognition and image segmentation is demonstrated. Implementation details are discussed, and the CVN is shown to be very attractive for optical implementation since optical computations are naturally complex. The cost of the CVN is less in all cases than the traditional neuron when implemented optically. Therefore, all the benefits of the CVN can be obtained without additional cost. However, on those implementations dependent on standard serial computers, CVN will be more cost effective only in those applications where its increased power can offset the requirement for additional neurons.

  15. Brain morphology of the threespine stickleback (Gasterosteus aculeatus) varies inconsistently with respect to habitat complexity: A test of the Clever Foraging Hypothesis.

    Science.gov (United States)

    Ahmed, Newaz I; Thompson, Cole; Bolnick, Daniel I; Stuart, Yoel E

    2017-05-01

    The Clever Foraging Hypothesis asserts that organisms living in a more spatially complex environment will have a greater neurological capacity for cognitive processes related to spatial memory, navigation, and foraging. Because the telencephalon is often associated with spatial memory and navigation tasks, this hypothesis predicts a positive association between telencephalon size and environmental complexity. The association between habitat complexity and brain size has been supported by comparative studies across multiple species but has not been widely studied at the within-species level. We tested for covariation between environmental complexity and neuroanatomy of threespine stickleback ( Gasterosteus aculeatus ) collected from 15 pairs of lakes and their parapatric streams on Vancouver Island. In most pairs, neuroanatomy differed between the adjoining lake and stream populations. However, the magnitude and direction of this difference were inconsistent between watersheds and did not covary strongly with measures of within-site environmental heterogeneity. Overall, we find weak support for the Clever Foraging Hypothesis in our study.

  16. Fitness landscape complexity and the emergence of modularity in neural networks

    Science.gov (United States)

    Lowell, Jessica

    Previous research has shown that the shape of the fitness landscape can affect the evolution of modularity. We evolved neural networks to solve different tasks with different fitness landscapes, using NEAT, a popular neuroevolution algorithm that quantifies similarity between genomes in order to divide them into species. We used this speciation mechanism as a means to examine fitness landscape complexity, and to examine connections between fitness landscape complexity and the emergence of modularity.

  17. Neuron's eye view: Inferring features of complex stimuli from neural responses.

    Directory of Open Access Journals (Sweden)

    Xin Chen

    2017-08-01

    Full Text Available Experiments that study neural encoding of stimuli at the level of individual neurons typically choose a small set of features present in the world-contrast and luminance for vision, pitch and intensity for sound-and assemble a stimulus set that systematically varies along these dimensions. Subsequent analysis of neural responses to these stimuli typically focuses on regression models, with experimenter-controlled features as predictors and spike counts or firing rates as responses. Unfortunately, this approach requires knowledge in advance about the relevant features coded by a given population of neurons. For domains as complex as social interaction or natural movement, however, the relevant feature space is poorly understood, and an arbitrary a priori choice of features may give rise to confirmation bias. Here, we present a Bayesian model for exploratory data analysis that is capable of automatically identifying the features present in unstructured stimuli based solely on neuronal responses. Our approach is unique within the class of latent state space models of neural activity in that it assumes that firing rates of neurons are sensitive to multiple discrete time-varying features tied to the stimulus, each of which has Markov (or semi-Markov dynamics. That is, we are modeling neural activity as driven by multiple simultaneous stimulus features rather than intrinsic neural dynamics. We derive a fast variational Bayesian inference algorithm and show that it correctly recovers hidden features in synthetic data, as well as ground-truth stimulus features in a prototypical neural dataset. To demonstrate the utility of the algorithm, we also apply it to cluster neural responses and demonstrate successful recovery of features corresponding to monkeys and faces in the image set.

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

    Directory of Open Access Journals (Sweden)

    Delong Zhang

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

  19. THE FRACTAL MARKET HYPOTHESIS

    OpenAIRE

    FELICIA RAMONA BIRAU

    2012-01-01

    In this article, the concept of capital market is analysed using Fractal Market Hypothesis which is a modern, complex and unconventional alternative to classical finance methods. Fractal Market Hypothesis is in sharp opposition to Efficient Market Hypothesis and it explores the application of chaos theory and fractal geometry to finance. Fractal Market Hypothesis is based on certain assumption. Thus, it is emphasized that investors did not react immediately to the information they receive and...

  20. Synchronization stability of memristor-based complex-valued neural networks with time delays.

    Science.gov (United States)

    Liu, Dan; Zhu, Song; Ye, Er

    2017-12-01

    This paper focuses on the dynamical property of a class of memristor-based complex-valued neural networks (MCVNNs) with time delays. By constructing the appropriate Lyapunov functional and utilizing the inequality technique, sufficient conditions are proposed to guarantee exponential synchronization of the coupled systems based on drive-response concept. The proposed results are very easy to verify, and they also extend some previous related works on memristor-based real-valued neural networks. Meanwhile, the obtained sufficient conditions of this paper may be conducive to qualitative analysis of some complex-valued nonlinear delayed systems. A numerical example is given to demonstrate the effectiveness of our theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Analysis of Power Laws, Shape Collapses, and Neural Complexity: New Techniques and MATLAB Support via the NCC Toolbox.

    Science.gov (United States)

    Marshall, Najja; Timme, Nicholas M; Bennett, Nicholas; Ripp, Monica; Lautzenhiser, Edward; Beggs, John M

    2016-01-01

    Neural systems include interactions that occur across many scales. Two divergent methods for characterizing such interactions have drawn on the physical analysis of critical phenomena and the mathematical study of information. Inferring criticality in neural systems has traditionally rested on fitting power laws to the property distributions of "neural avalanches" (contiguous bursts of activity), but the fractal nature of avalanche shapes has recently emerged as another signature of criticality. On the other hand, neural complexity, an information theoretic measure, has been used to capture the interplay between the functional localization of brain regions and their integration for higher cognitive functions. Unfortunately, treatments of all three methods-power-law fitting, avalanche shape collapse, and neural complexity-have suffered from shortcomings. Empirical data often contain biases that introduce deviations from true power law in the tail and head of the distribution, but deviations in the tail have often been unconsidered; avalanche shape collapse has required manual parameter tuning; and the estimation of neural complexity has relied on small data sets or statistical assumptions for the sake of computational efficiency. In this paper we present technical advancements in the analysis of criticality and complexity in neural systems. We use maximum-likelihood estimation to automatically fit power laws with left and right cutoffs, present the first automated shape collapse algorithm, and describe new techniques to account for large numbers of neural variables and small data sets in the calculation of neural complexity. In order to facilitate future research in criticality and complexity, we have made the software utilized in this analysis freely available online in the MATLAB NCC (Neural Complexity and Criticality) Toolbox.

  2. Fault detection and diagnosis for complex multivariable processes using neural networks

    International Nuclear Information System (INIS)

    Weerasinghe, M.

    1998-06-01

    Development of a reliable fault diagnosis method for large-scale industrial plants is laborious and often difficult to achieve due to the complexity of the targeted systems. The main objective of this thesis is to investigate the application of neural networks to the diagnosis of non-catastrophic faults in an industrial nuclear fuel processing plant. The proposed methods were initially developed by application to a simulated chemical process prior to further validation on real industrial data. The diagnosis of faults at a single operating point is first investigated. Statistical data conditioning methods of data scaling and principal component analysis are investigated to facilitate fault classification and reduce the complexity of neural networks. Successful fault diagnosis was achieved with significantly smaller networks than using all process variables as network inputs. Industrial processes often manufacture at various operating points, but demonstrated applications of neural networks for fault diagnosis usually only consider a single (primary) operating point. Developing a standard neural network scheme for fault diagnosis at all operating points would be usually impractical due to the unavailability of suitable training data for less frequently used (secondary) operating points. To overcome this problem, the application of a single neural network for the diagnosis of faults operating at different points is investigated. The data conditioning followed the same techniques as used for the fault diagnosis of a single operating point. The results showed that a single neural network could be successfully used to diagnose faults at operating points other than that it is trained for, and the data conditioning significantly improved the classification. Artificial neural networks have been shown to be an effective tool for process fault diagnosis. However, a main criticism is that details of the procedures taken to reach the fault diagnosis decisions are embedded in

  3. On the Keyhole Hypothesis

    DEFF Research Database (Denmark)

    Mikkelsen, Kaare B.; Kidmose, Preben; Hansen, Lars Kai

    2017-01-01

    simultaneously recorded scalp EEG. A cross-validation procedure was employed to ensure unbiased estimates. We present several pieces of evidence in support of the keyhole hypothesis: There is a high mutual information between data acquired at scalp electrodes and through the ear-EEG "keyhole," furthermore we......We propose and test the keyhole hypothesis that measurements from low dimensional EEG, such as ear-EEG reflect a broadly distributed set of neural processes. We formulate the keyhole hypothesis in information theoretical terms. The experimental investigation is based on legacy data consisting of 10...

  4. CRIM1 complexes with ß-catenin and cadherins, stabilizes cell-cell junctions and is critical for neural morphogenesis.

    Directory of Open Access Journals (Sweden)

    Virgilio G Ponferrada

    Full Text Available In multicellular organisms, morphogenesis is a highly coordinated process that requires dynamically regulated adhesion between cells. An excellent example of cellular morphogenesis is the formation of the neural tube from the flattened epithelium of the neural plate. Cysteine-rich motor neuron protein 1 (CRIM1 is a single-pass (type 1 transmembrane protein that is expressed in neural structures beginning at the neural plate stage. In the frog Xenopus laevis, loss of function studies using CRIM1 antisense morpholino oligonucleotides resulted in a failure of neural development. The CRIM1 knockdown phenotype was, in some cases, mild and resulted in perturbed neural fold morphogenesis. In severely affected embryos there was a dramatic failure of cell adhesion in the neural plate and complete absence of neural structures subsequently. Investigation of the mechanism of CRIM1 function revealed that it can form complexes with ß-catenin and cadherins, albeit indirectly, via the cytosolic domain. Consistent with this, CRIM1 knockdown resulted in diminished levels of cadherins and ß-catenin in junctional complexes in the neural plate. We conclude that CRIM1 is critical for cell-cell adhesion during neural development because it is required for the function of cadherin-dependent junctions.

  5. Complex Dynamical Network Control for Trajectory Tracking Using Delayed Recurrent Neural Networks

    Directory of Open Access Journals (Sweden)

    Jose P. Perez

    2014-01-01

    Full Text Available In this paper, the problem of trajectory tracking is studied. Based on the V-stability and Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a complex dynamical network is obtained. To illustrate the analytic results, we present a tracking simulation of a dynamical network with each node being just one Lorenz’s dynamical system and three identical Chen’s dynamical systems.

  6. Neural Correlates of Temporal Complexity and Synchrony during Audiovisual Correspondence Detection.

    Science.gov (United States)

    Baumann, Oliver; Vromen, Joyce M G; Cheung, Allen; McFadyen, Jessica; Ren, Yudan; Guo, Christine C

    2018-01-01

    We often perceive real-life objects as multisensory cues through space and time. A key challenge for audiovisual integration is to match neural signals that not only originate from different sensory modalities but also that typically reach the observer at slightly different times. In humans, complex, unpredictable audiovisual streams lead to higher levels of perceptual coherence than predictable, rhythmic streams. In addition, perceptual coherence for complex signals seems less affected by increased asynchrony between visual and auditory modalities than for simple signals. Here, we used functional magnetic resonance imaging to determine the human neural correlates of audiovisual signals with different levels of temporal complexity and synchrony. Our study demonstrated that greater perceptual asynchrony and lower signal complexity impaired performance in an audiovisual coherence-matching task. Differences in asynchrony and complexity were also underpinned by a partially different set of brain regions. In particular, our results suggest that, while regions in the dorsolateral prefrontal cortex (DLPFC) were modulated by differences in memory load due to stimulus asynchrony, areas traditionally thought to be involved in speech production and recognition, such as the inferior frontal and superior temporal cortex, were modulated by the temporal complexity of the audiovisual signals. Our results, therefore, indicate specific processing roles for different subregions of the fronto-temporal cortex during audiovisual coherence detection.

  7. A neural population model incorporating dopaminergic neurotransmission during complex voluntary behaviors.

    Directory of Open Access Journals (Sweden)

    Stefan Fürtinger

    2014-11-01

    Full Text Available Assessing brain activity during complex voluntary motor behaviors that require the recruitment of multiple neural sites is a field of active research. Our current knowledge is primarily based on human brain imaging studies that have clear limitations in terms of temporal and spatial resolution. We developed a physiologically informed non-linear multi-compartment stochastic neural model to simulate functional brain activity coupled with neurotransmitter release during complex voluntary behavior, such as speech production. Due to its state-dependent modulation of neural firing, dopaminergic neurotransmission plays a key role in the organization of functional brain circuits controlling speech and language and thus has been incorporated in our neural population model. A rigorous mathematical proof establishing existence and uniqueness of solutions to the proposed model as well as a computationally efficient strategy to numerically approximate these solutions are presented. Simulated brain activity during the resting state and sentence production was analyzed using functional network connectivity, and graph theoretical techniques were employed to highlight differences between the two conditions. We demonstrate that our model successfully reproduces characteristic changes seen in empirical data between the resting state and speech production, and dopaminergic neurotransmission evokes pronounced changes in modeled functional connectivity by acting on the underlying biological stochastic neural model. Specifically, model and data networks in both speech and rest conditions share task-specific network features: both the simulated and empirical functional connectivity networks show an increase in nodal influence and segregation in speech over the resting state. These commonalities confirm that dopamine is a key neuromodulator of the functional connectome of speech control. Based on reproducible characteristic aspects of empirical data, we suggest a number

  8. Determining the Effects of Cognitive Style, Problem Complexity, and Hypothesis Generation on the Problem Solving Ability of School-Based Agricultural Education Students

    Science.gov (United States)

    Blackburn, J. Joey; Robinson, J. Shane

    2016-01-01

    The purpose of this experimental study was to assess the effects of cognitive style, problem complexity, and hypothesis generation on the problem solving ability of school-based agricultural education students. Problem solving ability was defined as time to solution. Kirton's Adaption-Innovation Inventory was employed to assess students' cognitive…

  9. THE FRACTAL MARKET HYPOTHESIS

    Directory of Open Access Journals (Sweden)

    FELICIA RAMONA BIRAU

    2012-05-01

    Full Text Available In this article, the concept of capital market is analysed using Fractal Market Hypothesis which is a modern, complex and unconventional alternative to classical finance methods. Fractal Market Hypothesis is in sharp opposition to Efficient Market Hypothesis and it explores the application of chaos theory and fractal geometry to finance. Fractal Market Hypothesis is based on certain assumption. Thus, it is emphasized that investors did not react immediately to the information they receive and of course, the manner in which they interpret that information may be different. Also, Fractal Market Hypothesis refers to the way that liquidity and investment horizons influence the behaviour of financial investors.

  10. Identification of complex systems by artificial neural networks. Applications to mechanical frictions

    International Nuclear Information System (INIS)

    Dominguez, Manuel

    1998-01-01

    In the frame of complex systems modelization, we describe in this report the contribution of neural networks to mechanical friction modelization. This thesis is divided in three parts, each one corresponding to every stage of the realized work. The first part takes stock of the properties of neural networks by replacing them in the statistic frame of learning theory (particularly: non-linear and non-parametric regression models) and by showing the existing links with other more 'classic' techniques from automatics. We show then how identification models can be integrated in the neural networks description as a larger nonlinear model class. A methodology of neural networks use have been developed. We focused on validation techniques using correlation functions for non-linear systems, and on the use of regularization methods. The second part deals with the problematic of friction in mechanical systems. Particularly, we present the main current identified physical phenomena, which are integrated in advanced friction modelization. Characterization of these phenomena allows us to state a priori knowledge to be used in the identification stage. We expose some of the most well-known friction models: Dahl's model, Reset Integrator and Canuda's dynamical model, which are then used in simulation studies. The last part links the former one by illustrating a real-world application: an electric jack from SFIM-Industries, used in the Very Large Telescope (VLT) control scheme. This part begins with physical system presentation. The results are compared with more 'classic' methods. We finish using neural networks compensation scheme in closed-loop control. (author) [fr

  11. The neural correlates of emotional prosody comprehension: disentangling simple from complex emotion.

    Directory of Open Access Journals (Sweden)

    Lucy Alba-Ferrara

    Full Text Available BACKGROUND: Emotional prosody comprehension (EPC, the ability to interpret another person's feelings by listening to their tone of voice, is crucial for effective social communication. Previous studies assessing the neural correlates of EPC have found inconsistent results, particularly regarding the involvement of the medial prefrontal cortex (mPFC. It remained unclear whether the involvement of the mPFC is linked to an increased demand in socio-cognitive components of EPC such as mental state attribution and if basic perceptual processing of EPC can be performed without the contribution of this region. METHODS: fMRI was used to delineate neural activity during the perception of prosodic stimuli conveying simple and complex emotion. Emotional trials in general, as compared to neutral ones, activated a network comprising temporal and lateral frontal brain regions, while complex emotion trials specifically showed an additional involvement of the mPFC, premotor cortex, frontal operculum and left insula. CONCLUSION: These results indicate that the mPFC and premotor areas might be associated, but are not crucial to EPC. However, the mPFC supports socio-cognitive skills necessary to interpret complex emotion such as inferring mental states. Additionally, the premotor cortex involvement may reflect the participation of the mirror neuron system for prosody processing particularly of complex emotion.

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

    International Nuclear Information System (INIS)

    Huang Yu-Jiao; Hu Hai-Gen

    2015-01-01

    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. (paper)

  13. Non-neural Muscle Weakness Has Limited Influence on Complexity of Motor Control during Gait

    Directory of Open Access Journals (Sweden)

    Marije Goudriaan

    2018-01-01

    Full Text Available Cerebral palsy (CP and Duchenne muscular dystrophy (DMD are neuromuscular disorders characterized by muscle weakness. Weakness in CP has neural and non-neural components, whereas in DMD, weakness can be considered as a predominantly non-neural problem. Despite the different underlying causes, weakness is a constraint for the central nervous system when controlling gait. CP demonstrates decreased complexity of motor control during gait from muscle synergy analysis, which is reflected by a higher total variance accounted for by one synergy (tVAF1. However, it remains unclear if weakness directly contributes to higher tVAF1 in CP, or whether altered tVAF1 reflects mainly neural impairments. If muscle weakness directly contributes to higher tVAF1, then tVAF1 should also be increased in DMD. To examine the etiology of increased tVAF1, muscle activity data of gluteus medius, rectus femoris, medial hamstrings, medial gastrocnemius, and tibialis anterior were measured at self-selected walking speed, and strength data from knee extensors, knee flexors, dorsiflexors and plantar flexors, were analyzed in 15 children with CP [median (IQR age: 8.9 (2.2], 15 boys with DMD [8.7 (3.1], and 15 typical developing (TD children [8.6 (2.7]. We computed tVAF1 from 10 concatenated steps with non-negative matrix factorization, and compared tVAF1 between the three groups with a Mann-Whiney U-test. Spearman's rank correlation coefficients were used to determine if weakness in specific muscle groups contributed to altered tVAF1. No significant differences in tVAF1 were found between DMD [tVAF1: 0.60 (0.07] and TD children [0.65 (0.07], while tVAF1 was significantly higher in CP [(0.74 (0.09] than in the other groups (both p < 0.005. In CP, weakness in the plantar flexors was related to higher tVAF1 (r = −0.72. In DMD, knee extensor weakness related to increased tVAF1 (r = −0.50. These results suggest that the non-neural weakness in DMD had limited influence on

  14. Equivariant bifurcation in a coupled complex-valued neural network rings

    International Nuclear Information System (INIS)

    Zhang, Chunrui; Sui, Zhenzhang; Li, Hongpeng

    2017-01-01

    Highlights: • Complex value Hopfield-type network with Z4 × Z2 symmetry is discussed. • The spatio-temporal patterns of bifurcating periodic oscillations are obtained. • The oscillations can be in phase or anti-phase depending on the parameters and delay. - Abstract: Network with interacting loops and time delays are common in physiological systems. In the past few years, the dynamic behaviors of coupled interacting loops neural networks have been widely studied due to their extensive applications in classification of pattern recognition, signal processing, image processing, engineering optimization and animal locomotion, and other areas, see the references therein. In a large amount of applications, complex signals often occur and the complex-valued recurrent neural networks are preferable. In this paper, we study a complex value Hopfield-type network that consists of a pair of one-way rings each with four neurons and two-way coupling between each ring. We discuss the spatio-temporal patterns of bifurcating periodic oscillations by using the symmetric bifurcation theory of delay differential equations combined with representation theory of Lie groups. The existence of multiple branches of bifurcating periodic solution is obtained. We also found that the spatio-temporal patterns of bifurcating periodic oscillations alternate according to the change of the propagation time delay in the coupling, i.e., different ranges of delays correspond to different patterns of neural network oscillators. The oscillations of corresponding neurons in the two loops can be in phase or anti-phase depending on the parameters and delay. Some numerical simulations support our analysis results.

  15. Automatic QRS complex detection using two-level convolutional neural network.

    Science.gov (United States)

    Xiang, Yande; Lin, Zhitao; Meng, Jianyi

    2018-01-29

    The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and parameters, which may introduce significant computational complexity, especially in the transform domains. In addition, fixed features and parameters are not suitable for detecting various kinds of QRS complexes under different circumstances. In this study, based on 1-D convolutional neural network (CNN), an accurate method for QRS complex detection is proposed. The CNN consists of object-level and part-level CNNs for extracting different grained ECG morphological features automatically. All the extracted morphological features are used by multi-layer perceptron (MLP) for QRS complex detection. Additionally, a simple ECG signal preprocessing technique which only contains difference operation in temporal domain is adopted. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed detection method achieves overall sensitivity Sen = 99.77%, positive predictivity rate PPR = 99.91%, and detection error rate DER = 0.32%. In addition, the performance variation is performed according to different signal-to-noise ratio (SNR) values. An automatic QRS detection method using two-level 1-D CNN and simple signal preprocessing technique is proposed for QRS complex detection. Compared with the state-of-the-art QRS complex detection approaches, experimental results show that the proposed method acquires comparable accuracy.

  16. Short-term Music Training Enhances Complex, Distributed Neural Communication during Music and Linguistic Tasks.

    Science.gov (United States)

    Carpentier, Sarah M; Moreno, Sylvain; McIntosh, Anthony R

    2016-10-01

    Musical training is frequently associated with benefits to linguistic abilities, and recent focus has been placed on possible benefits of bilingualism to lifelong executive functions; however, the neural mechanisms for such effects are unclear. The aim of this study was to gain better understanding of the whole-brain functional effects of music and second-language training that could support such previously observed cognitive transfer effects. We conducted a 28-day longitudinal study of monolingual English-speaking 4- to 6-year-old children randomly selected to receive daily music or French language training, excluding weekends. Children completed passive EEG music note and French vowel auditory oddball detection tasks before and after training. Brain signal complexity was measured on source waveforms at multiple temporal scales as an index of neural information processing and network communication load. Comparing pretraining with posttraining, musical training was associated with increased EEG complexity at coarse temporal scales during the music and French vowel tasks in widely distributed cortical regions. Conversely, very minimal decreases in complexity at fine scales and trends toward coarse-scale increases were displayed after French training during the tasks. Spectral analysis failed to distinguish between training types and found overall theta (3.5-7.5 Hz) power increases after all training forms, with spatially fewer decreases in power at higher frequencies (>10 Hz). These findings demonstrate that musical training increased diversity of brain network states to support domain-specific music skill acquisition and music-to-language transfer effects.

  17. An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network.

    Science.gov (United States)

    Sun, Weifang; Yao, Bin; Zeng, Nianyin; Chen, Binqiang; He, Yuchao; Cao, Xincheng; He, Wangpeng

    2017-07-12

    As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical faults. Among these faults, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via vibration signals, the fault signatures are always submerged in overwhelming interfering contents. Therefore, identifying the critical fault's characteristic signal is far from an easy task. In order to improve the recognition accuracy of a fault's characteristic signal, a novel intelligent fault diagnosis method is presented. In this method, a dual-tree complex wavelet transform (DTCWT) is employed to acquire the multiscale signal's features. In addition, a convolutional neural network (CNN) approach is utilized to automatically recognise a fault feature from the multiscale signal features. The experiment results of the recognition for gear faults show the feasibility and effectiveness of the proposed method, especially in the gear's weak fault features.

  18. An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network

    Science.gov (United States)

    Sun, Weifang; Yao, Bin; Zeng, Nianyin; He, Yuchao; Cao, Xincheng; He, Wangpeng

    2017-01-01

    As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical faults. Among these faults, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via vibration signals, the fault signatures are always submerged in overwhelming interfering contents. Therefore, identifying the critical fault’s characteristic signal is far from an easy task. In order to improve the recognition accuracy of a fault’s characteristic signal, a novel intelligent fault diagnosis method is presented. In this method, a dual-tree complex wavelet transform (DTCWT) is employed to acquire the multiscale signal’s features. In addition, a convolutional neural network (CNN) approach is utilized to automatically recognise a fault feature from the multiscale signal features. The experiment results of the recognition for gear faults show the feasibility and effectiveness of the proposed method, especially in the gear’s weak fault features. PMID:28773148

  19. Master-slave exponential synchronization of delayed complex-valued memristor-based neural networks via impulsive control.

    Science.gov (United States)

    Li, Xiaofan; Fang, Jian-An; Li, Huiyuan

    2017-09-01

    This paper investigates master-slave exponential synchronization for a class of complex-valued memristor-based neural networks with time-varying delays via discontinuous impulsive control. Firstly, the master and slave complex-valued memristor-based neural networks with time-varying delays are translated to two real-valued memristor-based neural networks. Secondly, an impulsive control law is constructed and utilized to guarantee master-slave exponential synchronization of the neural networks. Thirdly, the master-slave synchronization problems are transformed into the stability problems of the master-slave error system. By employing linear matrix inequality (LMI) technique and constructing an appropriate Lyapunov-Krasovskii functional, some sufficient synchronization criteria are derived. Finally, a numerical simulation is provided to illustrate the effectiveness of the obtained theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Reservoir-based Online Adaptive Forward Models with Neural Control for Complex Locomotion in a Hexapod Robot

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Dasgupta, Sakyasingha; Goldschmidt, Dennis

    2014-01-01

    Walking animals show fascinating locomotor abilities and complex behaviors. Biological study has revealed that such complex behaviors is a result of a combination of biomechanics and neural mechanisms. While biomechanics allows for flexibility and a variety of movements, neural mechanisms generate...... locomotion, make predictions, and provide adaptation. Inspired by this finding, we present here an artificial bio-inspired walking system which combines biomechanics (in terms of its body and leg structures) and neural mechanisms. The neural mechanisms consist of 1) central pattern generator-based control...... for generating basic rhythmic patterns and coordinated movements, 2) reservoir-based adaptive forward models with efference copies for sensory prediction as well as state estimation, and 3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental...

  1. The Study of Learners' Preference for Visual Complexity on Small Screens of Mobile Computers Using Neural Networks

    Science.gov (United States)

    Wang, Lan-Ting; Lee, Kun-Chou

    2014-01-01

    The vision plays an important role in educational technologies because it can produce and communicate quite important functions in teaching and learning. In this paper, learners' preference for the visual complexity on small screens of mobile computers is studied by neural networks. The visual complexity in this study is divided into five…

  2. Neuroautonomic evaluation of patients with unexplained syncope: incidence of complex neurally mediated diagnoses in the elderly

    Directory of Open Access Journals (Sweden)

    Rafanelli M

    2014-02-01

    Full Text Available Martina Rafanelli, Alessandro Morrione, Annalisa Landi, Emilia Ruffolo, Valentina M Chisciotti, Maria A Brunetti, Niccolò Marchionni, Andrea Ungar Syncope Unit, Cardiology and Geriatric Medicine, University of Florence and Azienda Ospedaliero-Universitaria Careggi, Florence, Italy Background: The incidence of syncope increases in individuals over the age of 70 years, but data about this condition in the elderly are limited. Little is known about tilt testing (TT, carotid sinus massage (CSM, or supine and upright blood pressure measurement related to age or about patients with complex diagnoses, for example, those with a double diagnosis, ie, positivity in two of these three tests. Methods: A total of 873 consecutive patients of mean age 66.5±18 years underwent TT, CSM, and blood pressure measurement in the supine and upright positions according to the European Society of Cardiology guidelines on syncope.1 Neuroautonomic evaluation was performed if the first-line evaluation (clinical history, physical examination, electrocardiogram was suggestive of neurally mediated syncope, or if the first-line evaluation was suggestive of cardiac syncope but this diagnosis was excluded after specific diagnostic tests according to European Society of Cardiology guidelines on syncope, or if certain or suspected diagnostic criteria were not present after the first-line evaluation. Results: A diagnosis was reached in 64.3% of cases. TT was diagnostic in 50.4% of cases, CSM was diagnostic in 11.8% of cases, and orthostatic hypotension was present in 19.9% of cases. Predictors of a positive tilt test were prodromal symptoms and typical situational syncope. Increased age and a pathologic electrocardiogram were predictors of carotid sinus syndrome. Varicose veins and alpha-receptor blockers, nitrates, and benzodiazepines were associated with orthostatic hypotension. Twenty-three percent of the patients had a complex diagnosis. The most frequent association was

  3. The neural basis of precise visual short-term memory for complex recognisable objects.

    Science.gov (United States)

    Veldsman, Michele; Mitchell, Daniel J; Cusack, Rhodri

    2017-10-01

    Recent evidence suggests that visual short-term memory (VSTM) capacity estimated using simple objects, such as colours and oriented bars, may not generalise well to more naturalistic stimuli. More visual detail can be stored in VSTM when complex, recognisable objects are maintained compared to simple objects. It is not yet known if it is recognisability that enhances memory precision, nor whether maintenance of recognisable objects is achieved with the same network of brain regions supporting maintenance of simple objects. We used a novel stimulus generation method to parametrically warp photographic images along a continuum, allowing separate estimation of the precision of memory representations and the number of items retained. The stimulus generation method was also designed to create unrecognisable, though perceptually matched, stimuli, to investigate the impact of recognisability on VSTM. We adapted the widely-used change detection and continuous report paradigms for use with complex, photographic images. Across three functional magnetic resonance imaging (fMRI) experiments, we demonstrated greater precision for recognisable objects in VSTM compared to unrecognisable objects. This clear behavioural advantage was not the result of recruitment of additional brain regions, or of stronger mean activity within the core network. Representational similarity analysis revealed greater variability across item repetitions in the representations of recognisable, compared to unrecognisable complex objects. We therefore propose that a richer range of neural representations support VSTM for complex recognisable objects. Copyright © 2017 Elsevier Inc. All rights reserved.

  4. The neural exploitation hypothesis and its implications for an embodied approach to language and cognition: Insights from the study of action verbs processing and motor disorders in Parkinson's disease.

    Science.gov (United States)

    Gallese, Vittorio; Cuccio, Valentina

    2018-03-01

    As it is widely known, Parkinson's disease is clinically characterized by motor disorders such as the loss of voluntary movement control, including resting tremor, postural instability, and bradykinesia (Bocanegra et al., 2015; Helmich, Hallett, Deuschl, Toni, & Bloem, 2012; Liu et al., 2006; Rosin, Topka, & Dichgans, 1997). In the last years, many empirical studies (e.g., Bocanegra et al., 2015; Spadacenta et al., 2012) have also shown that the processing of action verbs is selectively impaired in patients affected by this neurodegenerative disorder. In the light of these findings, it has been suggested that Parkinson disorder can be interpreted within an embodied cognition framework (e.g., Bocanegra et al., 2015). The central tenet of any embodied approach to language and cognition is that high order cognitive functions are grounded in the sensory-motor system. With regard to this point, Gallese (2008) proposed the neural exploitation hypothesis to account for, at the phylogenetic level, how key aspects of human language are underpinned by brain mechanisms originally evolved for sensory-motor integration. Glenberg and Gallese (2012) also applied the neural exploitation hypothesis to the ontogenetic level. On the basis of these premises, they developed a theory of language acquisition according to which, sensory-motor mechanisms provide a neurofunctional architecture for the acquisition of language, while retaining their original functions as well. The neural exploitation hypothesis is here applied to interpret the profile of patients affected by Parkinson's disease. It is suggested that action semantic impairments directly tap onto motor disorders. Finally, a discussion of what theory of language is needed to account for the interactions between language and movement disorders is presented. Copyright © 2018 Elsevier Ltd. All rights reserved.

  5. Exploring the practicing-connections hypothesis: using gesture to support coordination of ideas in understanding a complex statistical concept.

    Science.gov (United States)

    Son, Ji Y; Ramos, Priscilla; DeWolf, Melissa; Loftus, William; Stigler, James W

    2018-01-01

    In this article, we begin to lay out a framework and approach for studying how students come to understand complex concepts in rich domains. Grounded in theories of embodied cognition, we advance the view that understanding of complex concepts requires students to practice, over time, the coordination of multiple concepts, and the connection of this system of concepts to situations in the world. Specifically, we explore the role that a teacher's gesture might play in supporting students' coordination of two concepts central to understanding in the domain of statistics: mean and standard deviation. In Study 1 we show that university students who have just taken a statistics course nevertheless have difficulty taking both mean and standard deviation into account when thinking about a statistical scenario. In Study 2 we show that presenting the same scenario with an accompanying gesture to represent variation significantly impacts students' interpretation of the scenario. Finally, in Study 3 we present evidence that instructional videos on the internet fail to leverage gesture as a means of facilitating understanding of complex concepts. Taken together, these studies illustrate an approach to translating current theories of cognition into principles that can guide instructional design.

  6. Robust fixed-time synchronization for uncertain complex-valued neural networks with discontinuous activation functions.

    Science.gov (United States)

    Ding, Xiaoshuai; Cao, Jinde; Alsaedi, Ahmed; Alsaadi, Fuad E; Hayat, Tasawar

    2017-06-01

    This paper is concerned with the fixed-time synchronization for a class of complex-valued neural networks in the presence of discontinuous activation functions and parameter uncertainties. Fixed-time synchronization not only claims that the considered master-slave system realizes synchronization within a finite time segment, but also requires a uniform upper bound for such time intervals for all initial synchronization errors. To accomplish the target of fixed-time synchronization, a novel feedback control procedure is designed for the slave neural networks. By means of the Filippov discontinuity theories and Lyapunov stability theories, some sufficient conditions are established for the selection of control parameters to guarantee synchronization within a fixed time, while an upper bound of the settling time is acquired as well, which allows to be modulated to predefined values independently on initial conditions. Additionally, criteria of modified controller for assurance of fixed-time anti-synchronization are also derived for the same system. An example is included to illustrate the proposed methodologies. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Effects of maturation and acidosis on the chaos-like complexity of the neural respiratory output in the isolated brainstem of the tadpole, Rana esculenta

    Science.gov (United States)

    Samara, Ziyad; Fiamma, Marie-Noëlle; Bautin, Nathalie; Ranohavimparany, Anja; Le Coz, Patrick; Golmard, Jean-Louis; Darré, Pierre; Zelter, Marc; Poon, Chi-Sang; Similowski, Thomas

    2011-01-01

    Human ventilation at rest exhibits mathematical chaos-like complexity that can be described as long-term unpredictability mediated (in whole or in part) by some low-dimensional nonlinear deterministic process. Although various physiological and pathological situations can affect respiratory complexity, the underlying mechanisms remain incompletely elucidated. If such chaos-like complexity is an intrinsic property of central respiratory generators, it should appear or increase when these structures mature or are stimulated. To test this hypothesis, we employed the isolated tadpole brainstem model [Rana (Pelophylax) esculenta] and recorded the neural respiratory output (buccal and lung rhythms) of pre- (n = 8) and postmetamorphic tadpoles (n = 8), at physiologic (7.8) and acidic pH (7.4). We analyzed the root mean square of the cranial nerve V or VII neurograms. Development and acidosis had no effect on buccal period. Lung frequency increased with development (P Chaos-like complexity, assessed through the noise limit, increased from pH 7.8 to pH 7.4 (P chaos-like complexity, especially in the postmetamorphic stage and at low pH. According to the ventilatory generators homology theory, this may also be the case in mammals. PMID:21325645

  8. Boundedness and global robust stability analysis of delayed complex-valued neural networks with interval parameter uncertainties.

    Science.gov (United States)

    Song, Qiankun; Yu, Qinqin; Zhao, Zhenjiang; Liu, Yurong; Alsaadi, Fuad E

    2018-07-01

    In this paper, the boundedness and robust stability for a class of delayed complex-valued neural networks with interval parameter uncertainties are investigated. By using Homomorphic mapping theorem, Lyapunov method and inequality techniques, sufficient condition to guarantee the boundedness of networks and the existence, uniqueness and global robust stability of equilibrium point is derived for the considered uncertain neural networks. The obtained robust stability criterion is expressed in complex-valued LMI, which can be calculated numerically using YALMIP with solver of SDPT3 in MATLAB. An example with simulations is supplied to show the applicability and advantages of the acquired result. Copyright © 2018 Elsevier Ltd. All rights reserved.

  9. Optimal system size for complex dynamics in random neural networks near criticality

    Energy Technology Data Exchange (ETDEWEB)

    Wainrib, Gilles, E-mail: wainrib@math.univ-paris13.fr [Laboratoire Analyse Géométrie et Applications, Université Paris XIII, Villetaneuse (France); García del Molino, Luis Carlos, E-mail: garciadelmolino@ijm.univ-paris-diderot.fr [Institute Jacques Monod, Université Paris VII, Paris (France)

    2013-12-15

    In this article, we consider a model of dynamical agents coupled through a random connectivity matrix, as introduced by Sompolinsky et al. [Phys. Rev. Lett. 61(3), 259–262 (1988)] in the context of random neural networks. When system size is infinite, it is known that increasing the disorder parameter induces a phase transition leading to chaotic dynamics. We observe and investigate here a novel phenomenon in the sub-critical regime for finite size systems: the probability of observing complex dynamics is maximal for an intermediate system size when the disorder is close enough to criticality. We give a more general explanation of this type of system size resonance in the framework of extreme values theory for eigenvalues of random matrices.

  10. Optimal system size for complex dynamics in random neural networks near criticality

    International Nuclear Information System (INIS)

    Wainrib, Gilles; García del Molino, Luis Carlos

    2013-01-01

    In this article, we consider a model of dynamical agents coupled through a random connectivity matrix, as introduced by Sompolinsky et al. [Phys. Rev. Lett. 61(3), 259–262 (1988)] in the context of random neural networks. When system size is infinite, it is known that increasing the disorder parameter induces a phase transition leading to chaotic dynamics. We observe and investigate here a novel phenomenon in the sub-critical regime for finite size systems: the probability of observing complex dynamics is maximal for an intermediate system size when the disorder is close enough to criticality. We give a more general explanation of this type of system size resonance in the framework of extreme values theory for eigenvalues of random matrices

  11. The SWI/SNF BAF-A complex is essential for neural crest development.

    Science.gov (United States)

    Chandler, Ronald L; Magnuson, Terry

    2016-03-01

    Growing evidence indicates that chromatin remodeler mutations underlie the pathogenesis of human neurocristopathies or disorders that affect neural crest cells (NCCs). However, causal relationships among chromatin remodeler subunit mutations and NCC defects remain poorly understood. Here we show that homozygous loss of ARID1A-containing, SWI/SNF chromatin remodeling complexes (BAF-A) in NCCs results in embryonic lethality in mice, with mutant embryos succumbing to heart defects. Strikingly, monoallelic loss of ARID1A in NCCs led to craniofacial defects in adult mice, including shortened snouts and low set ears, and these defects were more pronounced following homozygous loss of ARID1A, with the ventral cranial bones being greatly reduced in size. Early NCC specification and expression of the BRG1 NCC target gene, PLEXINA2, occurred normally in the absence of ARID1A. Nonetheless, mutant embryos displayed incomplete conotruncal septation of the cardiac outflow tract and defects in the posterior pharyngeal arteries, culminating in persistent truncus arteriosus and agenesis of the ductus arteriosus. Consistent with this, migrating cardiac NCCs underwent apoptosis within the circumpharyngeal ridge. Our data support the notion that multiple, distinct chromatin remodeling complexes govern genetically separable events in NCC development and highlight a potential pathogenic role for NCCs in the human BAF complex disorder, Coffin-Siris Syndrome. Copyright © 2016 Elsevier Inc. All rights reserved.

  12. Effects of maturation and acidosis on the chaos-like complexity of the neural respiratory output in the isolated brainstem of the tadpole, Rana esculenta.

    Science.gov (United States)

    Straus, Christian; Samara, Ziyad; Fiamma, Marie-Noëlle; Bautin, Nathalie; Ranohavimparany, Anja; Le Coz, Patrick; Golmard, Jean-Louis; Darré, Pierre; Zelter, Marc; Poon, Chi-Sang; Similowski, Thomas

    2011-05-01

    Human ventilation at rest exhibits mathematical chaos-like complexity that can be described as long-term unpredictability mediated (in whole or in part) by some low-dimensional nonlinear deterministic process. Although various physiological and pathological situations can affect respiratory complexity, the underlying mechanisms remain incompletely elucidated. If such chaos-like complexity is an intrinsic property of central respiratory generators, it should appear or increase when these structures mature or are stimulated. To test this hypothesis, we employed the isolated tadpole brainstem model [Rana (Pelophylax) esculenta] and recorded the neural respiratory output (buccal and lung rhythms) of pre- (n = 8) and postmetamorphic tadpoles (n = 8), at physiologic (7.8) and acidic pH (7.4). We analyzed the root mean square of the cranial nerve V or VII neurograms. Development and acidosis had no effect on buccal period. Lung frequency increased with development (P acidosis, but in postmetamorphic tadpoles only (P respiratory central rhythm generator accounts for ventilatory chaos-like complexity, especially in the postmetamorphic stage and at low pH. According to the ventilatory generators homology theory, this may also be the case in mammals.

  13. Obesity-specific neural cost of maintaining gait performance under complex conditions in community-dwelling older adults.

    Science.gov (United States)

    Osofundiya, Olufunmilola; Benden, Mark E; Dowdy, Diane; Mehta, Ranjana K

    2016-06-01

    Recent evidence of obesity-related changes in the prefrontal cortex during cognitive and seated motor activities has surfaced; however, the impact of obesity on neural activity during ambulation remains unclear. The purpose of this study was to determine obesity-specific neural cost of simple and complex ambulation in older adults. Twenty non-obese and obese individuals, 65years and older, performed three tasks varying in the types of complexity of ambulation (simple walking, walking+cognitive dual-task, and precision walking). Maximum oxygenated hemoglobin, a measure of neural activity, was measured bilaterally using a portable functional near infrared spectroscopy system, and gait speed and performance on the complex tasks were also obtained. Complex ambulatory tasks were associated with ~2-3.5 times greater cerebral oxygenation levels and ~30-40% slower gait speeds when compared to the simple walking task. Additionally, obesity was associated with three times greater oxygenation levels, particularly during the precision gait task, despite obese adults demonstrating similar gait speeds and performances on the complex gait tasks as non-obese adults. Compared to existing studies that focus solely on biomechanical outcomes, the present study is one of the first to examine obesity-related differences in neural activity during ambulation in older adults. In order to maintain gait performance, obesity was associated with higher neural costs, and this was augmented during ambulatory tasks requiring greater precision control. These preliminary findings have clinical implications in identifying individuals who are at greater risk of mobility limitations, particularly when performing complex ambulatory tasks. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Dangerous mating systems: signal complexity, signal content and neural capacity in spiders.

    Science.gov (United States)

    Herberstein, M E; Wignall, A E; Hebets, E A; Schneider, J M

    2014-10-01

    Spiders are highly efficient predators in possession of exquisite sensory capacities for ambushing prey, combined with machinery for launching rapid and determined attacks. As a consequence, any sexually motivated approach carries a risk of ending up as prey rather than as a mate. Sexual selection has shaped courtship to effectively communicate the presence, identity, motivation and/or quality of potential mates, which help ameliorate these risks. Spiders communicate this information via several sensory channels, including mechanical (e.g. vibrational), visual and/or chemical, with examples of multimodal signalling beginning to emerge in the literature. The diverse environments that spiders inhabit have further shaped courtship content and form. While our understanding of spider neurobiology remains in its infancy, recent studies are highlighting the unique and considerable capacities of spiders to process and respond to complex sexual signals. As a result, the dangerous mating systems of spiders are providing important insights into how ecology shapes the evolution of communication systems, with future work offering the potential to link this complex communication with its neural processes. Copyright © 2014 Elsevier Ltd. All rights reserved.

  15. Low-complexity object detection with deep convolutional neural network for embedded systems

    Science.gov (United States)

    Tripathi, Subarna; Kang, Byeongkeun; Dane, Gokce; Nguyen, Truong

    2017-09-01

    We investigate low-complexity convolutional neural networks (CNNs) for object detection for embedded vision applications. It is well-known that consolidation of an embedded system for CNN-based object detection is more challenging due to computation and memory requirement comparing with problems like image classification. To achieve these requirements, we design and develop an end-to-end TensorFlow (TF)-based fully-convolutional deep neural network for generic object detection task inspired by one of the fastest framework, YOLO.1 The proposed network predicts the localization of every object by regressing the coordinates of the corresponding bounding box as in YOLO. Hence, the network is able to detect any objects without any limitations in the size of the objects. However, unlike YOLO, all the layers in the proposed network is fully-convolutional. Thus, it is able to take input images of any size. We pick face detection as an use case. We evaluate the proposed model for face detection on FDDB dataset and Widerface dataset. As another use case of generic object detection, we evaluate its performance on PASCAL VOC dataset. The experimental results demonstrate that the proposed network can predict object instances of different sizes and poses in a single frame. Moreover, the results show that the proposed method achieves comparative accuracy comparing with the state-of-the-art CNN-based object detection methods while reducing the model size by 3× and memory-BW by 3 - 4× comparing with one of the best real-time CNN-based object detectors, YOLO. Our 8-bit fixed-point TF-model provides additional 4× memory reduction while keeping the accuracy nearly as good as the floating-point model. Moreover, the fixed- point model is capable of achieving 20× faster inference speed comparing with the floating-point model. Thus, the proposed method is promising for embedded implementations.

  16. Examining neural correlates of skill acquisition in a complex videogame training program.

    Science.gov (United States)

    Prakash, Ruchika S; De Leon, Angeline A; Mourany, Lyla; Lee, Hyunkyu; Voss, Michelle W; Boot, Walter R; Basak, Chandramallika; Fabiani, Monica; Gratton, Gabriele; Kramer, Arthur F

    2012-01-01

    Acquisition of complex skills is a universal feature of human behavior that has been conceptualized as a process that starts with intense resource dependency, requires effortful cognitive control, and ends in relative automaticity on the multi-faceted task. The present study examined the effects of different theoretically based training strategies on cortical recruitment during acquisition of complex video game skills. Seventy-five participants were recruited and assigned to one of three training groups: (1) Fixed Emphasis Training (FET), in which participants practiced the game, (2) Hybrid Variable-Priority Training (HVT), in which participants practiced using a combination of part-task training and variable priority training, or (3) a Control group that received limited game play. After 30 h of training, game data indicated a significant advantage for the two training groups relative to the control group. The HVT group demonstrated enhanced benefits of training, as indexed by an improvement in overall game score and a reduction in cortical recruitment post-training. Specifically, while both groups demonstrated a significant reduction of activation in attentional control areas, namely the right middle frontal gyrus, right superior frontal gyrus, and the ventral medial prefrontal cortex, participants in the control group continued to engage these areas post-training, suggesting a sustained reliance on attentional regions during challenging task demands. The HVT group showed a further reduction in neural resources post-training compared to the FET group in these cognitive control regions, along with reduced activation in the motor and sensory cortices and the posteromedial cortex. Findings suggest that training, specifically one that emphasizes cognitive flexibility can reduce the attentional demands of a complex cognitive task, along with reduced reliance on the motor network.

  17. Examining neural correlates of skill acquisition in a complex videogame training program

    Directory of Open Access Journals (Sweden)

    Ruchika S Prakash

    2012-05-01

    Full Text Available Acquisition of complex skills is a universal feature of human behavior that has been conceptualized as a process that starts with intense resource dependency, requires effortful cognitive control, and ends in relative automaticity on the multi-faceted task. The present study examined the effects of different theoretically-based training strategies on cortical recruitment during acquisition of complex videogame skills. Seventy-five participants were recruited and assigned to one of three training groups: Fixed Emphasis Training (FET, in which participants practiced the game, Hybrid Variable Priority Training (HVT, in which participants practiced using a combination of part-task training and variable priority training, or a Control group that received limited game play. After 30 hours of training, game data indicated a significant advantage for the two training groups relative to the control group. The HVT group demonstrated enhanced benefits of training, as indexed by an improvement in overall game score and a reduction in cortical recruitment post-training. Specifically, while both groups demonstrated a significant reduction of activation in attentional control areas, namely the right middle frontal gyrus, right superior frontal gyrus, and the ventral medial prefrontal cortex, participants in the control group continued to engage these areas post-training, suggesting a sustained reliance on attentional regions during challenging task demands. The HVT group showed a further reduction in neural resources post-training compared to the FET group in these cognitive control regions, along with reduced activation in the motor and sensory cortices and the posteromedial cortex. Findings suggest that training, specifically one that emphasizes cognitive flexibility can reduce the attentional demands of a complex cognitive task, along with reduced reliance on the motor network.

  18. A novel neural-wavelet approach for process diagnostics and complex system modeling

    Science.gov (United States)

    Gao, Rong

    Neural networks have been effective in several engineering applications because of their learning abilities and robustness. However certain shortcomings, such as slow convergence and local minima, are always associated with neural networks, especially neural networks applied to highly nonlinear and non-stationary problems. These problems can be effectively alleviated by integrating a new powerful tool, wavelets, into conventional neural networks. The multi-resolution analysis and feature localization capabilities of the wavelet transform offer neural networks new possibilities for learning. A neural wavelet network approach developed in this thesis enjoys fast convergence rate with little possibility to be caught at a local minimum. It combines the localization properties of wavelets with the learning abilities of neural networks. Two different testbeds are used for testing the efficiency of the new approach. The first is magnetic flowmeter-based process diagnostics: here we extend previous work, which has demonstrated that wavelet groups contain process information, to more general process diagnostics. A loop at Applied Intelligent Systems Lab (AISL) is used for collecting and analyzing data through the neural-wavelet approach. The research is important for thermal-hydraulic processes in nuclear and other engineering fields. The neural-wavelet approach developed is also tested with data from the electric power grid. More specifically, the neural-wavelet approach is used for performing short-term and mid-term prediction of power load demand. In addition, the feasibility of determining the type of load using the proposed neural wavelet approach is also examined. The notion of cross scale product has been developed as an expedient yet reliable discriminator of loads. Theoretical issues involved in the integration of wavelets and neural networks are discussed and future work outlined.

  19. Characterization of K-complexes and slow wave activity in a neural mass model.

    Directory of Open Access Journals (Sweden)

    Arne Weigenand

    2014-11-01

    Full Text Available NREM sleep is characterized by two hallmarks, namely K-complexes (KCs during sleep stage N2 and cortical slow oscillations (SOs during sleep stage N3. While the underlying dynamics on the neuronal level is well known and can be easily measured, the resulting behavior on the macroscopic population level remains unclear. On the basis of an extended neural mass model of the cortex, we suggest a new interpretation of the mechanisms responsible for the generation of KCs and SOs. As the cortex transitions from wake to deep sleep, in our model it approaches an oscillatory regime via a Hopf bifurcation. Importantly, there is a canard phenomenon arising from a homoclinic bifurcation, whose orbit determines the shape of large amplitude SOs. A KC corresponds to a single excursion along the homoclinic orbit, while SOs are noise-driven oscillations around a stable focus. The model generates both time series and spectra that strikingly resemble real electroencephalogram data and points out possible differences between the different stages of natural sleep.

  20. Disrupting neural activity related to awake-state sharp wave-ripple complexes prevents hippocampal learning.

    Science.gov (United States)

    Nokia, Miriam S; Mikkonen, Jarno E; Penttonen, Markku; Wikgren, Jan

    2012-01-01

    Oscillations in hippocampal local-field potentials (LFPs) reflect the crucial involvement of the hippocampus in memory trace formation: theta (4-8 Hz) oscillations and ripples (~200 Hz) occurring during sharp waves are thought to mediate encoding and consolidation, respectively. During sharp wave-ripple complexes (SPW-Rs), hippocampal cell firing closely follows the pattern that took place during the initial experience, most likely reflecting replay of that event. Disrupting hippocampal ripples using electrical stimulation either during training in awake animals or during sleep after training retards spatial learning. Here, adult rabbits were trained in trace eyeblink conditioning, a hippocampus-dependent associative learning task. A bright light was presented to the animals during the inter-trial interval (ITI), when awake, either during SPW-Rs or irrespective of their neural state. Learning was particularly poor when the light was presented following SPW-Rs. While the light did not disrupt the ripple itself, it elicited a theta-band oscillation, a state that does not usually coincide with SPW-Rs. Thus, it seems that consolidation depends on neuronal activity within and beyond the hippocampus taking place immediately after, but by no means limited to, hippocampal SPW-Rs.

  1. Distributed Recurrent Neural Forward Models with Synaptic Adaptation and CPG-based control for Complex Behaviors of Walking Robots

    Directory of Open Access Journals (Sweden)

    Sakyasingha eDasgupta

    2015-09-01

    Full Text Available Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures with the underlying neural mechanisms. The neural mechanisms consist of 1 central pattern generator based control for generating basic rhythmic patterns and coordinated movements, 2 distributed (at each leg recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and 3 searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps as well as climbing over high obstacles. Furthermore we demonstrate that the newly developed recurrent network based approach to sensorimotor prediction outperforms the previous state of the art adaptive neuron

  2. Fractal Hypothesis of the Pelagic Microbial Ecosystem—Can Simple Ecological Principles Lead to Self-Similar Complexity in the Pelagic Microbial Food Web?

    Science.gov (United States)

    Våge, Selina; Thingstad, T. Frede

    2015-01-01

    Trophic interactions are highly complex and modern sequencing techniques reveal enormous biodiversity across multiple scales in marine microbial communities. Within the chemically and physically relatively homogeneous pelagic environment, this calls for an explanation beyond spatial and temporal heterogeneity. Based on observations of simple parasite-host and predator-prey interactions occurring at different trophic levels and levels of phylogenetic resolution, we present a theoretical perspective on this enormous biodiversity, discussing in particular self-similar aspects of pelagic microbial food web organization. Fractal methods have been used to describe a variety of natural phenomena, with studies of habitat structures being an application in ecology. In contrast to mathematical fractals where pattern generating rules are readily known, however, identifying mechanisms that lead to natural fractals is not straight-forward. Here we put forward the hypothesis that trophic interactions between pelagic microbes may be organized in a fractal-like manner, with the emergent network resembling the structure of the Sierpinski triangle. We discuss a mechanism that could be underlying the formation of repeated patterns at different trophic levels and discuss how this may help understand characteristic biomass size-spectra that hint at scale-invariant properties of the pelagic environment. If the idea of simple underlying principles leading to a fractal-like organization of the pelagic food web could be formalized, this would extend an ecologists mindset on how biological complexity could be accounted for. It may furthermore benefit ecosystem modeling by facilitating adequate model resolution across multiple scales. PMID:26648929

  3. Matching of Remote Sensing Images with Complex Background Variations via Siamese Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Haiqing He

    2018-02-01

    Full Text Available Feature-based matching methods have been widely used in remote sensing image matching given their capability to achieve excellent performance despite image geometric and radiometric distortions. However, most of the feature-based methods are unreliable for complex background variations, because the gradient or other image grayscale information used to construct the feature descriptor is sensitive to image background variations. Recently, deep learning-based methods have been proven suitable for high-level feature representation and comparison in image matching. Inspired by the progresses made in deep learning, a new technical framework for remote sensing image matching based on the Siamese convolutional neural network is presented in this paper. First, a Siamese-type network architecture is designed to simultaneously learn the features and the corresponding similarity metric from labeled training examples of matching and non-matching true-color patch pairs. In the proposed network, two streams of convolutional and pooling layers sharing identical weights are arranged without the manually designed features. The number of convolutional layers is determined based on the factors that affect image matching. The sigmoid function is employed to compute the matching and non-matching probabilities in the output layer. Second, a gridding sub-pixel Harris algorithm is used to obtain the accurate localization of candidate matches. Third, a Gaussian pyramid coupling quadtree is adopted to gradually narrow down the searching space of the candidate matches, and multiscale patches are compared synchronously. Subsequently, a similarity measure based on the output of the sigmoid is adopted to find the initial matches. Finally, the random sample consensus algorithm and the whole-to-local quadratic polynomial constraints are used to remove false matches. In the experiments, different types of satellite datasets, such as ZY3, GF1, IKONOS, and Google Earth images

  4. Determination of Elastic and Dissipative Properties of Material Using Combination of FEM and Complex Artificial Neural Networks

    Science.gov (United States)

    Soloviev, A. N.; Giang, N. D. T.; Chang, S.-H.

    This paper describes the application of complex artificial neural networks (CANN) in the inverse identification problem of the elastic and dissipative properties of solids. Additional information for the inverse problem serves the components of the displacement vector measured on the body boundary, which performs harmonic oscillations at the first resonant frequency. The process of displacement measurement in this paper is simulated using calculation of finite element (FE) software ANSYS. In the shown numerical example, we focus on the accurate identification of elastic modulus and quality of material depending on the number of measurement points and their locations as well as on the architecture of neural network and time of the training process, which is conducted by using algorithms RProp, QuickProp.

  5. Vocal production complexity correlates with neural instructions in the oyster toadfish (Opsanus tau)

    DEFF Research Database (Denmark)

    Elemans, C. P. H.; Mensinger, A. F.; Rome, L. C.

    2014-01-01

    frequencies are determined directly by the firing rate of a vocal-acoustic neural network that drives the contraction frequency of superfast swimbladder muscles. The oyster toadfish boatwhistle call starts with an irregular sound waveform that could be an emergent property of the peripheral nonlinear sound...

  6. Neural Responses to Peer Rejection in Anxious Adolescents: Contributions from the Amygdala-Hippocampal Complex

    Science.gov (United States)

    Lau, Jennifer Y. F.; Guyer, Amanda E.; Tone, Erin B.; Jenness, Jessica; Parrish, Jessica M.; Pine, Daniel S.; Nelson, Eric E.

    2012-01-01

    Peer rejection powerfully predicts adolescent anxiety. While cognitive differences influence anxious responses to social feedback, little is known about neural contributions. Twelve anxious and twelve age-, gender- and IQ-matched, psychiatrically healthy adolescents received "not interested" and "interested" feedback from unknown peers during a…

  7. Sequence conservation and combinatorial complexity of Drosophila neural precursor cell enhancers

    Directory of Open Access Journals (Sweden)

    Kuzin Alexander

    2008-08-01

    Full Text Available Abstract Background The presence of highly conserved sequences within cis-regulatory regions can serve as a valuable starting point for elucidating the basis of enhancer function. This study focuses on regulation of gene expression during the early events of Drosophila neural development. We describe the use of EvoPrinter and cis-Decoder, a suite of interrelated phylogenetic footprinting and alignment programs, to characterize highly conserved sequences that are shared among co-regulating enhancers. Results Analysis of in vivo characterized enhancers that drive neural precursor gene expression has revealed that they contain clusters of highly conserved sequence blocks (CSBs made up of shorter shared sequence elements which are present in different combinations and orientations within the different co-regulating enhancers; these elements contain either known consensus transcription factor binding sites or consist of novel sequences that have not been functionally characterized. The CSBs of co-regulated enhancers share a large number of sequence elements, suggesting that a diverse repertoire of transcription factors may interact in a highly combinatorial fashion to coordinately regulate gene expression. We have used information gained from our comparative analysis to discover an enhancer that directs expression of the nervy gene in neural precursor cells of the CNS and PNS. Conclusion The combined use EvoPrinter and cis-Decoder has yielded important insights into the combinatorial appearance of fundamental sequence elements required for neural enhancer function. Each of the 30 enhancers examined conformed to a pattern of highly conserved blocks of sequences containing shared constituent elements. These data establish a basis for further analysis and understanding of neural enhancer function.

  8. Prediction of Increasing Production Activities using Combination of Query Aggregation on Complex Events Processing and Neural Network

    Directory of Open Access Journals (Sweden)

    Achmad Arwan

    2016-07-01

    Full Text Available AbstrakProduksi, order, penjualan, dan pengiriman adalah serangkaian event yang saling terkait dalam industri manufaktur. Selanjutnya hasil dari event tersebut dicatat dalam event log. Complex Event Processing adalah metode yang digunakan untuk menganalisis apakah terdapat pola kombinasi peristiwa tertentu (peluang/ancaman yang terjadi pada sebuah sistem, sehingga dapat ditangani secara cepat dan tepat. Jaringan saraf tiruan adalah metode yang digunakan untuk mengklasifikasi data peningkatan proses produksi. Hasil pencatatan rangkaian proses yang menyebabkan peningkatan produksi digunakan sebagai data latih untuk mendapatkan fungsi aktivasi dari jaringan saraf tiruan. Penjumlahan hasil catatan event log dimasukkan ke input jaringan saraf tiruan untuk perhitungan nilai aktivasi. Ketika nilai aktivasi lebih dari batas yang ditentukan, maka sistem mengeluarkan sinyal untuk meningkatkan produksi, jika tidak, sistem tetap memantau kejadian. Hasil percobaan menunjukkan bahwa akurasi dari metode ini adalah 77% dari 39 rangkaian aliran event.Kata kunci: complex event processing, event, jaringan saraf tiruan, prediksi peningkatan produksi, proses. AbstractProductions, orders, sales, and shipments are series of interrelated events within manufacturing industry. Further these events were recorded in the event log. Complex event processing is a method that used to analyze whether there are patterns of combinations of certain events (opportunities / threats that occur in a system, so it can be addressed quickly and appropriately. Artificial neural network is a method that we used to classify production increase activities. The series of events that cause the increase of the production used as a dataset to train the weight of neural network which result activation value. An aggregate stream of events inserted into the neural network input to compute the value of activation. When the value is over a certain threshold (the activation value results

  9. Recognition of neural brain activity patterns correlated with complex motor activity

    Science.gov (United States)

    Kurkin, Semen; Musatov, Vyacheslav Yu.; Runnova, Anastasia E.; Grubov, Vadim V.; Efremova, Tatyana Yu.; Zhuravlev, Maxim O.

    2018-04-01

    In this paper, based on the apparatus of artificial neural networks, a technique for recognizing and classifying patterns corresponding to imaginary movements on electroencephalograms (EEGs) obtained from a group of untrained subjects was developed. The works on the selection of the optimal type, topology, training algorithms and neural network parameters were carried out from the point of view of the most accurate and fast recognition and classification of patterns on multi-channel EEGs associated with the imagination of movements. The influence of the number and choice of the analyzed channels of a multichannel EEG on the quality of recognition of imaginary movements was also studied, and optimal configurations of electrode arrangements were obtained. The effect of pre-processing of EEG signals is analyzed from the point of view of improving the accuracy of recognition of imaginary movements.

  10. Cut loose and run: The complex role of ADAM proteases during neural crest cell development.

    Science.gov (United States)

    Alfandari, Dominique; Taneyhill, Lisa A

    2018-02-24

    ADAM metalloproteases have been shown to play critical roles during development. In this review, we will describe functional evidence that implicates ADAM proteins during the genesis, migration and differentiation of neural crest cells. We will restrict our analysis to the transmembrane ADAMs as other reviews have addressed the role of extracellular metalloproteases (Christian et al. [2013] Critical Reviews in Biochemistry and Molecular Biology 48:544-560). This review will describe advances that have been obtained mainly through the use of two vertebrate model systems, the frog, and avian embryos. The role of the principal substrates of ADAMs, the cadherins, has been extensively described in other reviews, most recently in (Cousin [1997] Mechanisms of Development 148:79-88; Taneyhill and Schiffmacher [2017] Genesis, 55). The function of ADAMs in the migration of other cell types, including the immune system, wound healing and cancer has been described previously in (Dreymueller et al. [2017] Mediators of Inflammation 2017: 9621724). Our goal is to illustrate both the importance of ADAMs in controlling neural crest behavior and how neural crest cells have helped us understand the molecular interactions, substrates, and functions of ADAM proteins in vivo. © 2018 Wiley Periodicals, Inc.

  11. Effect of task complexity on intelligence and neural efficiency in children: an event-related potential study.

    Science.gov (United States)

    Zhang, Qiong; Shi, Jiannong; Luo, Yuejia; Liu, Sainan; Yang, Jie; Shen, Mowei

    2007-10-08

    The present study investigates the effects of task complexity, intelligence and neural efficiency on children's performance on an Elementary Cognitive Task. Twenty-three children were divided into two groups on the basis of their Raven Progressive Matrix scores and were then asked to complete a choice reaction task with two test conditions. We recorded the electroencephalogram and calculated the peak latencies and amplitudes for anteriorly distributed P225, N380 and late positive component. Our results suggested shorter late positive component latencies in brighter children, possibly reflecting a higher processing speed in these individuals. Increased P225 amplitude and increased N380 amplitudes for brighter children may indicate a more efficient allocation of attention for brighter children. No moderating effect of task complexity on brain-intelligence relationship was found.

  12. Automatic sleep stage classification of single-channel EEG by using complex-valued convolutional neural network.

    Science.gov (United States)

    Zhang, Junming; Wu, Yan

    2018-03-28

    Many systems are developed for automatic sleep stage classification. However, nearly all models are based on handcrafted features. Because of the large feature space, there are so many features that feature selection should be used. Meanwhile, designing handcrafted features is a difficult and time-consuming task because the feature designing needs domain knowledge of experienced experts. Results vary when different sets of features are chosen to identify sleep stages. Additionally, many features that we may be unaware of exist. However, these features may be important for sleep stage classification. Therefore, a new sleep stage classification system, which is based on the complex-valued convolutional neural network (CCNN), is proposed in this study. Unlike the existing sleep stage methods, our method can automatically extract features from raw electroencephalography data and then classify sleep stage based on the learned features. Additionally, we also prove that the decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron intersect orthogonally. The classification performances of handcrafted features are compared with those of learned features via CCNN. Experimental results show that the proposed method is comparable to the existing methods. CCNN obtains a better classification performance and considerably faster convergence speed than convolutional neural network. Experimental results also show that the proposed method is a useful decision-support tool for automatic sleep stage classification.

  13. Lagrange α-exponential stability and α-exponential convergence for fractional-order complex-valued neural networks.

    Science.gov (United States)

    Jian, Jigui; Wan, Peng

    2017-07-01

    This paper deals with the problem on Lagrange α-exponential stability and α-exponential convergence for a class of fractional-order complex-valued neural networks. To this end, some new fractional-order differential inequalities are established, which improve and generalize previously known criteria. By using the new inequalities and coupling with the Lyapunov method, some effective criteria are derived to guarantee Lagrange α-exponential stability and α-exponential convergence of the addressed network. Moreover, the framework of the α-exponential convergence ball is also given, where the convergence rate is related to the parameters and the order of differential of the system. These results here, which the existence and uniqueness of the equilibrium points need not to be considered, generalize and improve the earlier publications and can be applied to monostable and multistable fractional-order complex-valued neural networks. Finally, one example with numerical simulations is given to show the effectiveness of the obtained results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Neural Correlates in the Processing of Phoneme-Level Complexity in Vowel Production

    Science.gov (United States)

    Park, Haeil; Iverson, Gregory K.; Park, Hae-Jeong

    2011-01-01

    We investigated how articulatory complexity at the phoneme level is manifested neurobiologically in an overt production task. fMRI images were acquired from young Korean-speaking adults as they pronounced bisyllabic pseudowords in which we manipulated phonological complexity defined in terms of vowel duration and instability (viz., COMPLEX:…

  15. 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.

  16. The Technology of Suppressing Harmonics with Complex Neural Network is Applied to Microgrid

    Science.gov (United States)

    Zhang, Jing; Li, Zhan-Ying; Wang, Yan-ping; Li, Yang; Zong, Ke-yong

    2018-03-01

    According to the traits of harmonics in microgrid, a new CANN controller which combines BP and RBF neural network is proposed to control APF to detect and suppress harmonics. This controller has the function of current prediction. By simulation in Matlab / Simulink, this design can shorten the delay time nearly 0.02s (a power supply current cycle) in comparison with the traditional controller based on ip-iq method. The new controller also has higher compensation accuracy and better dynamic tracking traits, it can greatly suppress the harmonics and improve the power quality.

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

    Science.gov (United States)

    Muralidharan, Pooja; Sarmah, Swapnalee; Zhou, Feng C; Marrs, James A

    2013-06-19

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

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

    Directory of Open Access Journals (Sweden)

    James A. Marrs

    2013-06-01

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

  19. Topological probability and connection strength induced activity in complex neural networks

    International Nuclear Information System (INIS)

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

    2010-01-01

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

  20. The neural dynamics of stimulus and response conflict processing as a function of response complexity and task demands

    Science.gov (United States)

    Donohue, Sarah E.; Appelbaum, Lawrence G.; McKay, Cameron C.; Woldorff, Marty G.

    2016-01-01

    Both stimulus and response conflict can disrupt behavior by slowing response times and decreasing accuracy. Although several neural activations have been associated with conflict processing, it is unclear how specific any of these are to the type of stimulus conflict or the amount of response conflict. Here, we recorded electrical brain activity, while manipulating the type of stimulus conflict in the task (spatial [Flanker] versus semantic [Stroop]) and the amount of response conflict (two versus four response choices). Behaviorally, responses were slower to incongruent versus congruent stimuli across all task and response types, along with overall slowing for higher response-mapping complexity. The earliest incongruency-related neural effect was a short-duration frontally-distributed negativity at ~200 ms that was only present in the Flanker spatial-conflict task. At longer latencies, the classic fronto-central incongruency-related negativity ‘Ninc’ was observed for all conditions, which was larger and ~100 ms longer in duration with more response options. Further, the onset of the motor-related lateralized readiness potential (LRP) was earlier for the two vs. four response sets, indicating that smaller response sets enabled faster motor-response preparation. The late positive complex (LPC) was present in all conditions except the two-response Stroop task, suggesting this late conflict-related activity is not specifically related to task type or response-mapping complexity. Importantly, across tasks and conditions, the LRP onset at or before the conflict-related Ninc, indicating that motor preparation is a rapid, automatic process that interacts with the conflict-detection processes after it has begun. Together, these data highlight how different conflict-related processes operate in parallel and depend on both the cognitive demands of the task and the number of response options. PMID:26827917

  1. Opaque for the Reader but Transparent for the Brain: Neural Signatures of Morphological Complexity

    Science.gov (United States)

    Meinzer, Marcus; Lahiri, Aditi; Flaisch, Tobias; Hannemann, Ronny; Eulitz, Carsten

    2009-01-01

    Within linguistics, words with a complex internal structure are commonly assumed to be decomposed into their constituent morphemes (e.g., un-help-ful). Nevertheless, an ongoing debate concerns the brain structures that subserve this process. Using functional magnetic resonance imaging, the present study varied the internal complexity of derived…

  2. A Rhodium(III) Complex as an Inhibitor of Neural Precursor Cell Expressed, Developmentally Down-Regulated 8-Activating Enzyme with in Vivo Activity against Inflammatory Bowel Disease.

    Science.gov (United States)

    Zhong, Hai-Jing; Wang, Wanhe; Kang, Tian-Shu; Yan, Hui; Yang, Yali; Xu, Lipeng; Wang, Yuqiang; Ma, Dik-Lung; Leung, Chung-Hang

    2017-01-12

    We report herein the identification of the rhodium(III) complex [Rh(phq) 2 (MOPIP)] + (1) as a potent and selective ATP-competitive neural precursor cell expressed, developmentally down-regulated 8 (NEDD8)-activating enzyme (NAE) inhibitor. Structure-activity relationship analysis indicated that the overall organometallic design of complex 1 was important for anti-inflammatory activity. Complex 1 showed promising anti-inflammatory activity in vivo for the potential treatment of inflammatory bowel disease.

  3. Arc Requires PSD95 for Assembly into Postsynaptic Complexes Involved with Neural Dysfunction and Intelligence

    Directory of Open Access Journals (Sweden)

    Esperanza Fernández

    2017-10-01

    Full Text Available Arc is an activity-regulated neuronal protein, but little is known about its interactions, assembly into multiprotein complexes, and role in human disease and cognition. We applied an integrated proteomic and genetic strategy by targeting a tandem affinity purification (TAP tag and Venus fluorescent protein into the endogenous Arc gene in mice. This allowed biochemical and proteomic characterization of native complexes in wild-type and knockout mice. We identified many Arc-interacting proteins, of which PSD95 was the most abundant. PSD95 was essential for Arc assembly into 1.5-MDa complexes and activity-dependent recruitment to excitatory synapses. Integrating human genetic data with proteomic data showed that Arc-PSD95 complexes are enriched in schizophrenia, intellectual disability, autism, and epilepsy mutations and normal variants in intelligence. We propose that Arc-PSD95 postsynaptic complexes potentially affect human cognitive function.

  4. Component Neural Systems for the Creation of Emotional Memories during Free Viewing of a Complex, Real-World Event.

    Science.gov (United States)

    Botzung, Anne; Labar, Kevin S; Kragel, Philip; Miles, Amanda; Rubin, David C

    2010-01-01

    To investigate the neural systems that contribute to the formation of complex, self-relevant emotional memories, dedicated fans of rival college basketball teams watched a competitive game while undergoing functional magnetic resonance imaging (fMRI). During a subsequent recognition memory task, participants were shown video clips depicting plays of the game, stemming either from previously-viewed game segments (targets) or from non-viewed portions of the same game (foils). After an old-new judgment, participants provided emotional valence and intensity ratings of the clips. A data driven approach was first used to decompose the fMRI signal acquired during free viewing of the game into spatially independent components. Correlations were then calculated between the identified components and post-scanning emotion ratings for successfully encoded targets. Two components were correlated with intensity ratings, including temporal lobe regions implicated in memory and emotional functions, such as the hippocampus and amygdala, as well as a midline fronto-cingulo-parietal network implicated in social cognition and self-relevant processing. These data were supported by a general linear model analysis, which revealed additional valence effects in fronto-striatal-insular regions when plays were divided into positive and negative events according to the fan's perspective. Overall, these findings contribute to our understanding of how emotional factors impact distributed neural systems to successfully encode dynamic, personally-relevant event sequences.

  5. GDNF/GFRα1 Complex Abrogates Self-Renewing Activity of Cortical Neural Precursors Inducing Their Differentiation

    Directory of Open Access Journals (Sweden)

    Antonela Bonafina

    2018-03-01

    Full Text Available Summary: The balance between factors leading to proliferation and differentiation of cortical neural precursors (CNPs determines the correct cortical development. In this work, we show that GDNF and its receptor GFRα1 are expressed in the neocortex during the period of cortical neurogenesis. We show that the GDNF/GFRα1 complex inhibits the self-renewal capacity of mouse CNP cells induced by fibroblast growth factor 2 (FGF2, promoting neuronal differentiation. While GDNF leads to decreased proliferation of cultured cortical precursor cells, ablation of GFRα1 in glutamatergic cortical precursors enhances its proliferation. We show that GDNF treatment of CNPs promoted morphological differentiation even in the presence of the self-renewal-promoting factor, FGF2. Analysis of GFRα1-deficient mice shows an increase in the number of cycling cells during cortical development and a reduction in dendrite development of cortical GFRα1-expressing neurons. Together, these results indicate that GDNF/GFRα1 signaling plays an essential role in regulating the proliferative condition and the differentiation of cortical progenitors. : In this article, Ledda and colleagues show that GDNF acting through its receptor GFRα1 plays a critical role in the maturation of cortical progenitors by counteracting FGF2 self-renewal activity on neural stem cells and promoting neuronal differentiation. Keywords: GDNF, GFRα1, cortical precursors, proliferation, postmitotic neurons, neuronal differentiation

  6. Component neural systems for the creation of emotional memories during free viewing of a complex, real-world event

    Directory of Open Access Journals (Sweden)

    Anne Botzung

    2010-05-01

    Full Text Available To investigate the neural systems that contribute to the formation of complex, self-relevant emotional memories, dedicated fans of rival college basketball teams watched a competitive game while undergoing functional magnetic resonance imaging (fMRI. During a subsequent recognition memory task, participants were shown video clips depicting plays of the game, stemming either from previously-viewed game segments (targets or from non-viewed portions of the same game (foils. After an old-new judgment, participants provided emotional valence and intensity ratings of the clips. A data driven approach was first used to decompose the fMRI signal acquired during free viewing of the game into spatially independent components. Correlations were then calculated between the identified components and post-scanning emotion ratings for successfully encoded targets. Two components were correlated with intensity ratings, including temporal lobe regions implicated in memory and emotional functions, such as the hippocampus and amygdala, as well as a midline fronto-cingulo-parietal network implicated in social cognition and self-relevant processing. These data were supported by a general linear model analysis, which revealed additional valence effects in fronto-striatal-insular regions when plays were divided into positive and negative events according to the fan’s perspective. Overall, these findings contribute to our understanding of how emotional factors impact distributed neural systems to successfully encode dynamic, personally-relevant event sequences.

  7. Working memory activation of neural networks in the elderly as a function of information processing phase and task complexity.

    Science.gov (United States)

    Charroud, Céline; Steffener, Jason; Le Bars, Emmanuelle; Deverdun, Jérémy; Bonafe, Alain; Abdennour, Meriem; Portet, Florence; Molino, François; Stern, Yaakov; Ritchie, Karen; Menjot de Champfleur, Nicolas; Akbaraly, Tasnime N

    2015-11-01

    Changes in working memory are sensitive indicators of both normal and pathological brain aging and associated disability. The present study aims to further understanding of working memory in normal aging using a large cohort of healthy elderly in order to examine three separate phases of information processing in relation to changes in task load activation. Using covariance analysis, increasing and decreasing neural activation was observed on fMRI in response to a delayed item recognition task in 337 cognitively healthy elderly persons as part of the CRESCENDO (Cognitive REServe and Clinical ENDOphenotypes) study. During three phases of the task (stimulation, retention, probe), increased activation was observed with increasing task load in bilateral regions of the prefrontal cortex, parietal lobule, cingulate gyrus, insula and in deep gray matter nuclei, suggesting an involvement of central executive and salience networks. Decreased activation associated with increasing task load was observed during the stimulation phase, in bilateral temporal cortex, parietal lobule, cingulate gyrus and prefrontal cortex. This spatial distribution of decreased activation is suggestive of the default mode network. These findings support the hypothesis of an increased activation in salience and central executive networks and a decreased activation in default mode network concomitant to increasing task load. Copyright © 2015 Elsevier Inc. All rights reserved.

  8. Complex dynamics of a delayed discrete neural network of two nonidentical neurons

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Yuanlong [Mathematics Department, GuangDong University of Finance, Guangzhou 510521 (China); Huang, Tingwen [Mathematics Department, Texas A and M University at Qatar, P. O. Box 23874, Doha (Qatar); Huang, Yu, E-mail: stshyu@mail.sysu.edu.cn [Mathematics Department, Sun Yat-Sen University, Guangzhou 510275, People' s Republic China (China)

    2014-03-15

    In this paper, we discover that a delayed discrete Hopfield neural network of two nonidentical neurons with self-connections and no self-connections can demonstrate chaotic behaviors. To this end, we first transform the model, by a novel way, into an equivalent system which has some interesting properties. Then, we identify the chaotic invariant set for this system and show that the dynamics of this system within this set is topologically conjugate to the dynamics of the full shift map with two symbols. This confirms chaos in the sense of Devaney. Our main results generalize the relevant results of Huang and Zou [J. Nonlinear Sci. 15, 291–303 (2005)], Kaslik and Balint [J. Nonlinear Sci. 18, 415–432 (2008)] and Chen et al. [Sci. China Math. 56(9), 1869–1878 (2013)]. We also give some numeric simulations to verify our theoretical results.

  9. Complex dynamics of a delayed discrete neural network of two nonidentical neurons

    International Nuclear Information System (INIS)

    Chen, Yuanlong; Huang, Tingwen; Huang, Yu

    2014-01-01

    In this paper, we discover that a delayed discrete Hopfield neural network of two nonidentical neurons with self-connections and no self-connections can demonstrate chaotic behaviors. To this end, we first transform the model, by a novel way, into an equivalent system which has some interesting properties. Then, we identify the chaotic invariant set for this system and show that the dynamics of this system within this set is topologically conjugate to the dynamics of the full shift map with two symbols. This confirms chaos in the sense of Devaney. Our main results generalize the relevant results of Huang and Zou [J. Nonlinear Sci. 15, 291–303 (2005)], Kaslik and Balint [J. Nonlinear Sci. 18, 415–432 (2008)] and Chen et al. [Sci. China Math. 56(9), 1869–1878 (2013)]. We also give some numeric simulations to verify our theoretical results

  10. Complex dynamics of a delayed discrete neural network of two nonidentical neurons.

    Science.gov (United States)

    Chen, Yuanlong; Huang, Tingwen; Huang, Yu

    2014-03-01

    In this paper, we discover that a delayed discrete Hopfield neural network of two nonidentical neurons with self-connections and no self-connections can demonstrate chaotic behaviors. To this end, we first transform the model, by a novel way, into an equivalent system which has some interesting properties. Then, we identify the chaotic invariant set for this system and show that the dynamics of this system within this set is topologically conjugate to the dynamics of the full shift map with two symbols. This confirms chaos in the sense of Devaney. Our main results generalize the relevant results of Huang and Zou [J. Nonlinear Sci. 15, 291-303 (2005)], Kaslik and Balint [J. Nonlinear Sci. 18, 415-432 (2008)] and Chen et al. [Sci. China Math. 56(9), 1869-1878 (2013)]. We also give some numeric simulations to verify our theoretical results.

  11. Examining neural correlates of skill acquisition in a complex videogame training program

    OpenAIRE

    Prakash, Ruchika S.; De Leon, Angeline A.; Mourany, Lyla; Lee, Hyunkyu; Voss, Michelle W.; Boot, Walter R.; Basak, Chandramallika; Fabiani, Monica; Gratton, Gabriele; Kramer, Arthur F.

    2012-01-01

    Acquisition of complex skills is a universal feature of human behavior that has been conceptualized as a process that starts with intense resource dependency, requires effortful cognitive control, and ends in relative automaticity on the multi-faceted task. The present study examined the effects of different theoretically based training strategies on cortical recruitment during acquisition of complex video game skills. Seventy-five participants were recruited and assigned to one of three trai...

  12. Gap-filling eddy-covariance data using a complex system of neural networks

    Science.gov (United States)

    Dúbrava, Matúš; Rebok, Tomáš; Havránková, Kateřina; Pavelka, Marian

    2014-05-01

    The eddy-covariance technique measures the flux of matter and energy between various ecosystems and the atmosphere. The fluxes characterize an interaction of the ecosystems with their surroundings and provide valuable knowledge to Global Climate Change issues. Among the main assets of the method belongs the possible evaluation of the carbon balance, expressed as the Net Ecosystem carbon Exchange (NEE) parameter. However, when unfavorable micro-meteorological conditions (e.g., stable stratification and low turbulent mixing) happen, measured fluxes are inaccurate and need to be corrected and/or gap-filled. Thus, there is a long-term challenge for many research teams from the flux community to develop the most accurate gap-filling method -- many statistical as well as empirical approaches have been proposed so far (e.g., mean replacement, interpolation, extrapolation, regression analysis, methods based on plant physiology depending on meteorological variables, etc.), each of them having its strengths and weaknesses. The artificial neural networks (ANNs) -- purely empirical non-linear regression models generally able to solve any fitness approximation and pattern recognition problem -- were proven as a promising approach and one of the most precise method for gap-filling the eddy-covariance data. However, even though providing encouraging results when considering a prediction error throughout the whole dataset, they considerably fail in fitting inherently present spikes in the NEE values. This drawback results from the nature of ANNs, since their ability to fit spikes is partly in contrast with their ability to reliably approximate previously unseen data -- while the spike fitting can be improved by an increasing number of training epochs, this often leads to ANNs over-fitting and thus losing their generalization ability, resulting in higher overall prediction error. Since the proper generalization ability has greater impact on the precision of the results, current

  13. On the Reduction of Computational Complexity of Deep Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Partha Maji

    2018-04-01

    Full Text Available Deep convolutional neural networks (ConvNets, which are at the heart of many new emerging applications, achieve remarkable performance in audio and visual recognition tasks. Unfortunately, achieving accuracy often implies significant computational costs, limiting deployability. In modern ConvNets it is typical for the convolution layers to consume the vast majority of computational resources during inference. This has made the acceleration of these layers an important research area in academia and industry. In this paper, we examine the effects of co-optimizing the internal structures of the convolutional layers and underlying implementation of fundamental convolution operation. We demonstrate that a combination of these methods can have a big impact on the overall speedup of a ConvNet, achieving a ten-fold increase over baseline. We also introduce a new class of fast one-dimensional (1D convolutions for ConvNets using the Toom–Cook algorithm. We show that our proposed scheme is mathematically well-grounded, robust, and does not require any time-consuming retraining, while still achieving speedups solely from convolutional layers with no loss in baseline accuracy.

  14. Artificial neural network combined with principal component analysis for resolution of complex pharmaceutical formulations.

    Science.gov (United States)

    Ioele, Giuseppina; De Luca, Michele; Dinç, Erdal; Oliverio, Filomena; Ragno, Gaetano

    2011-01-01

    A chemometric approach based on the combined use of the principal component analysis (PCA) and artificial neural network (ANN) was developed for the multicomponent determination of caffeine (CAF), mepyramine (MEP), phenylpropanolamine (PPA) and pheniramine (PNA) in their pharmaceutical preparations without any chemical separation. The predictive ability of the ANN method was compared with the classical linear regression method Partial Least Squares 2 (PLS2). The UV spectral data between 220 and 300 nm of a training set of sixteen quaternary mixtures were processed by PCA to reduce the dimensions of input data and eliminate the noise coming from instrumentation. Several spectral ranges and different numbers of principal components (PCs) were tested to find the PCA-ANN and PLS2 models reaching the best determination results. A two layer ANN, using the first four PCs, was used with log-sigmoid transfer function in first hidden layer and linear transfer function in output layer. Standard error of prediction (SEP) was adopted to assess the predictive accuracy of the models when subjected to external validation. PCA-ANN showed better prediction ability in the determination of PPA and PNA in synthetic samples with added excipients and pharmaceutical formulations. Since both components are characterized by low absorptivity, the better performance of PCA-ANN was ascribed to the ability in considering all non-linear information from noise or interfering excipients.

  15. CRIM1 Complexes with ß-catenin and Cadherins, Stabilizes Cell-Cell Junctions and Is Critical for Neural Morphogenesis

    OpenAIRE

    Ponferrada, Virgilio G.; Fan, Jieqing; Vallance, Jefferson E.; Hu, Shengyong; Mamedova, Aygun; Rankin, Scott A.; Kofron, Matthew; Zorn, Aaron M.; Hegde, Rashmi S.; Lang, Richard A.

    2012-01-01

    In multicellular organisms, morphogenesis is a highly coordinated process that requires dynamically regulated adhesion between cells. An excellent example of cellular morphogenesis is the formation of the neural tube from the flattened epithelium of the neural plate. Cysteine-rich motor neuron protein 1 (CRIM1) is a single-pass (type 1) transmembrane protein that is expressed in neural structures beginning at the neural plate stage. In the frog Xenopus laevis, loss of function studies using C...

  16. Robust artificial neural network for reliability and sensitivity analyses of complex non-linear systems.

    Science.gov (United States)

    Oparaji, Uchenna; Sheu, Rong-Jiun; Bankhead, Mark; Austin, Jonathan; Patelli, Edoardo

    2017-12-01

    Artificial Neural Networks (ANNs) are commonly used in place of expensive models to reduce the computational burden required for uncertainty quantification, reliability and sensitivity analyses. ANN with selected architecture is trained with the back-propagation algorithm from few data representatives of the input/output relationship of the underlying model of interest. However, different performing ANNs might be obtained with the same training data as a result of the random initialization of the weight parameters in each of the network, leading to an uncertainty in selecting the best performing ANN. On the other hand, using cross-validation to select the best performing ANN based on the ANN with the highest R 2 value can lead to biassing in the prediction. This is as a result of the fact that the use of R 2 cannot determine if the prediction made by ANN is biased. Additionally, R 2 does not indicate if a model is adequate, as it is possible to have a low R 2 for a good model and a high R 2 for a bad model. Hence, in this paper, we propose an approach to improve the robustness of a prediction made by ANN. The approach is based on a systematic combination of identical trained ANNs, by coupling the Bayesian framework and model averaging. Additionally, the uncertainties of the robust prediction derived from the approach are quantified in terms of confidence intervals. To demonstrate the applicability of the proposed approach, two synthetic numerical examples are presented. Finally, the proposed approach is used to perform a reliability and sensitivity analyses on a process simulation model of a UK nuclear effluent treatment plant developed by National Nuclear Laboratory (NNL) and treated in this study as a black-box employing a set of training data as a test case. This model has been extensively validated against plant and experimental data and used to support the UK effluent discharge strategy. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Energy optimization and prediction of complex petrochemical industries using an improved artificial neural network approach integrating data envelopment analysis

    International Nuclear Information System (INIS)

    Han, Yong-Ming; Geng, Zhi-Qiang; Zhu, Qun-Xiong

    2016-01-01

    Graphical abstract: This paper proposed an energy optimization and prediction of complex petrochemical industries based on a DEA-integrated ANN approach (DEA-ANN). The proposed approach utilizes the DEA model with slack variables for sensitivity analysis to determine the effective decision making units (DMUs) and indicate the optimized direction of the ineffective DMUs. Compared with the traditional ANN approach, the DEA-ANN prediction model is effectively verified by executing a linear comparison between all DMUs and the effective DMUs through the standard data source from the UCI (University of California at Irvine) repository. Finally, the proposed model is validated through an application in a complex ethylene production system of China petrochemical industry. Meanwhile, the optimization result and the prediction value are obtained to reduce energy consumption of the ethylene production system, guide ethylene production and improve energy efficiency. - Highlights: • The DEA-integrated ANN approach is proposed. • The DEA-ANN prediction model is effectively verified through the standard data source from the UCI repository. • The energy optimization and prediction framework of complex petrochemical industries based on the proposed method is obtained. • The proposed method is valid and efficient in improvement of energy efficiency in complex petrochemical plants. - Abstract: Since the complex petrochemical data have characteristics of multi-dimension, uncertainty and noise, it is difficult to accurately optimize and predict the energy usage of complex petrochemical systems. Therefore, this paper proposes a data envelopment analysis (DEA) integrated artificial neural network (ANN) approach (DEA-ANN). The proposed approach utilizes the DEA model with slack variables for sensitivity analysis to determine the effective decision making units (DMUs) and indicate the optimized direction of the ineffective DMUs. Compared with the traditional ANN approach, the DEA

  18. General-Purpose Computation with Neural Networks: A Survey of Complexity Theoretic Results

    Czech Academy of Sciences Publication Activity Database

    Šíma, Jiří; Orponen, P.

    2003-01-01

    Roč. 15, č. 12 (2003), s. 2727-2778 ISSN 0899-7667 R&D Projects: GA AV ČR IAB2030007; GA ČR GA201/02/1456 Institutional research plan: AV0Z1030915 Keywords : computational power * computational complexity * perceptrons * radial basis functions * spiking neurons * feedforward networks * reccurent networks * probabilistic computation * analog computation Subject RIV: BA - General Mathematics Impact factor: 2.747, year: 2003

  19. Tutorial at CNS 2013: Managing complex workflows in neural simulation and data analysis

    OpenAIRE

    Grün, Sonja; Davison, Andrew

    2013-01-01

    In our attempts to uncover the mechanisms that govern brain processing on the level of interacting neurons, neuroscientists have taken on the challenge of tackling the sheer complexity exhibited by neuronal networks. Neuronal simulations are nowadays performed with a high degree of detail, covering large, heterogeneous networks. Experimentally, electrophysiologists can simultaneously record from hundreds of neurons in complicated behavioral paradigms. The data streams of simulation and experi...

  20. Complex Membrane Channel Blockade: A Unifying Hypothesis for the Prodromal and Acute Neuropsychiatric Sequelae Resulting from Exposure to the Antimalarial Drug Mefloquine

    Directory of Open Access Journals (Sweden)

    Jane C. Quinn

    2015-01-01

    Full Text Available The alkaloid toxin quinine and its derivative compounds have been used for many centuries as effective medications for the prevention and treatment of malaria. More recently, synthetic derivatives, such as the quinoline derivative mefloquine (bis(trifluoromethyl-(2-piperidyl-4-quinolinemethanol, have been widely used to combat disease caused by chloroquine-resistant strains of the malaria parasite, Plasmodium falciparum. However, the parent compound quinine, as well as its more recent counterparts, suffers from an incidence of adverse neuropsychiatric side effects ranging from mild mood disturbances and anxiety to hallucinations, seizures, and psychosis. This review considers how the pharmacology, cellular neurobiology, and membrane channel kinetics of mefloquine could lead to the significant and sometimes life-threatening neurotoxicity associated with mefloquine exposure. A key role for mefloquine blockade of ATP-sensitive potassium channels and connexins in the substantia nigra is considered as a unifying hypothesis for the pathogenesis of severe neuropsychiatric events after mefloquine exposure in humans.

  1. Polyphenol-Protein Complexes and Their Consequences for the Redox Activity, Structure and Function of Honey. A Current View and New Hypothesis – a Review

    Directory of Open Access Journals (Sweden)

    Brudzynski Katrina

    2015-06-01

    Full Text Available There is increasing evidence that protein complexation by honey polyphenols is changing honey structure and function. This relatively less investigated filed of honey research is presented in a context of known mechanism of formation of the stable polyphenol-protein complexes in other foods. At a core of these interactions lies the ability of polyphenols to form non-covalent and covalent bonds with proteins leading to transient and/or irreversible complexes, respectively. Honey storage and thermal processing induces non-enzymatic oxidation of polyphenols to reactive quinones and enables them to form covalent bonds with proteins. In this short review, we present data from our laboratory on previously unrecognized types of protein-polyphenol complexes that differed in size, stoichiometry, and antioxidant capacities, and the implications they have to honey antioxidant and antibacterial activities. Our intent is to provide a current understanding of protein-polyphenol complexation in honey and also some new thoughts /hypotheses that can be useful in directing future research.

  2. Phase dynamics of complex-valued neural networks and its application to traffic signal control.

    Science.gov (United States)

    Nishikawa, Ikuko; Iritani, Takeshi; Sakakibara, Kazutoshi; Kuroe, Yasuaki

    2005-01-01

    Complex-valued Hopfield networks which possess the energy function are analyzed. The dynamics of the network with certain forms of an activation function is de-composable into the dynamics of the amplitude and phase of each neuron. Then the phase dynamics is described as a coupled system of phase oscillators with a pair-wise sinusoidal interaction. Therefore its phase synchronization mechanism is useful for the area-wide offset control of the traffic signals. The computer simulations show the effectiveness under the various traffic conditions.

  3. Neural bases of event knowledge and syntax integration in comprehension of complex sentences.

    Science.gov (United States)

    Malaia, Evie; Newman, Sharlene

    2015-01-01

    Comprehension of complex sentences is necessarily supported by both syntactic and semantic knowledge, but what linguistic factors trigger a readers' reliance on a specific system? This functional neuroimaging study orthogonally manipulated argument plausibility and verb event type to investigate cortical bases of the semantic effect on argument comprehension during reading. The data suggest that telic verbs facilitate online processing by means of consolidating the event schemas in episodic memory and by easing the computation of syntactico-thematic hierarchies in the left inferior frontal gyrus. The results demonstrate that syntax-semantics integration relies on trade-offs among a distributed network of regions for maximum comprehension efficiency.

  4. Variability: A Pernicious Hypothesis.

    Science.gov (United States)

    Noddings, Nel

    1992-01-01

    The hypothesis of greater male variability in test results is discussed in its historical context, and reasons feminists have objected to the hypothesis are considered. The hypothesis acquires political importance if it is considered that variability results from biological, rather than cultural, differences. (SLD)

  5. Neural substrates of decision-making.

    Science.gov (United States)

    Broche-Pérez, Y; Herrera Jiménez, L F; Omar-Martínez, E

    2016-06-01

    Decision-making is the process of selecting a course of action from among 2 or more alternatives by considering the potential outcomes of selecting each option and estimating its consequences in the short, medium and long term. The prefrontal cortex (PFC) has traditionally been considered the key neural structure in decision-making process. However, new studies support the hypothesis that describes a complex neural network including both cortical and subcortical structures. The aim of this review is to summarise evidence on the anatomical structures underlying the decision-making process, considering new findings that support the existence of a complex neural network that gives rise to this complex neuropsychological process. Current evidence shows that the cortical structures involved in decision-making include the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), and dorsolateral prefrontal cortex (DLPFC). This process is assisted by subcortical structures including the amygdala, thalamus, and cerebellum. Findings to date show that both cortical and subcortical brain regions contribute to the decision-making process. The neural basis of decision-making is a complex neural network of cortico-cortical and cortico-subcortical connections which includes subareas of the PFC, limbic structures, and the cerebellum. Copyright © 2014 Sociedad Española de Neurología. Published by Elsevier España, S.L.U. All rights reserved.

  6. Questioning the social intelligence hypothesis.

    Science.gov (United States)

    Holekamp, Kay E

    2007-02-01

    The social intelligence hypothesis posits that complex cognition and enlarged "executive brains" evolved in response to challenges that are associated with social complexity. This hypothesis has been well supported, but some recent data are inconsistent with its predictions. It is becoming increasingly clear that multiple selective agents, and non-selective constraints, must have acted to shape cognitive abilities in humans and other animals. The task now is to develop a larger theoretical framework that takes into account both inter-specific differences and similarities in cognition. This new framework should facilitate consideration of how selection pressures that are associated with sociality interact with those that are imposed by non-social forms of environmental complexity, and how both types of functional demands interact with phylogenetic and developmental constraints.

  7. Diagnosis of Alzheimer’s Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network

    Directory of Open Access Journals (Sweden)

    Debesh Jha

    2017-01-01

    Full Text Available Background. Error-free diagnosis of Alzheimer’s disease (AD from healthy control (HC patients at an early stage of the disease is a major concern, because information about the condition’s severity and developmental risks present allows AD sufferer to take precautionary measures before irreversible brain damage occurs. Recently, there has been great interest in computer-aided diagnosis in magnetic resonance image (MRI classification. However, distinguishing between Alzheimer’s brain data and healthy brain data in older adults (age > 60 is challenging because of their highly similar brain patterns and image intensities. Recently, cutting-edge feature extraction technologies have found extensive application in numerous fields, including medical image analysis. Here, we propose a dual-tree complex wavelet transform (DTCWT for extracting features from an image. The dimensionality of feature vector is reduced by using principal component analysis (PCA. The reduced feature vector is sent to feed-forward neural network (FNN to distinguish AD and HC from the input MR images. These proposed and implemented pipelines, which demonstrate improvements in classification output when compared to that of recent studies, resulted in high and reproducible accuracy rates of 90.06 ± 0.01% with a sensitivity of 92.00 ± 0.04%, a specificity of 87.78 ± 0.04%, and a precision of 89.6 ± 0.03% with 10-fold cross-validation.

  8. Elucidating the 3D structures of Al(iii)-Aβ complexes: a template free strategy based on the pre-organization hypothesis.

    Science.gov (United States)

    Mujika, Jon I; Rodríguez-Guerra Pedregal, Jaime; Lopez, Xabier; Ugalde, Jesus M; Rodríguez-Santiago, Luis; Sodupe, Mariona; Maréchal, Jean-Didier

    2017-07-01

    Senile plaques are extracellular deposits found in patients with Alzheimer's Disease (AD) and are mainly formed by insoluble fibrils of β-amyloid (Aβ) peptides. The mechanistic details about how AD develops are not fully understood yet, but metals such as Cu, Zn, or Fe are proposed to have a non-innocent role. Many studies have also linked the non biological metal aluminum with AD, a species whose concentration in the environment and food has been constantly increasing since the industrial revolution. Gaining a molecular picture of how Al(iii) interacts with an Aβ peptide is of fundamental interest to improve understanding of the many variables in the evolution of AD. So far, no consensus has been reached on how this metal interacts with Aβ, partially due to the experimental complexity of detecting and quantifying the resulting Al(iii)-Aβ complexes. Computational chemistry arises as a powerful alternative to investigate how Al(iii) can interact with Aβ peptides, as suitable strategies could shed light on the metal-peptide description at the molecular level. However, the absence of any reliable template that could be used for the modeling of the metallopeptide structure makes computational insight extremely difficult. Here, we present a novel strategy to generate accurate 3D models of the Al(iii)-Aβ complexes, which still circumvents first principles simulations of metal binding to peptides of Aβ. The key to this approach lies in the identification of experimental structures of the isolated peptide that are favourably pre-organized for the binding of a given metal in configurations of the first coordination sphere that were previously identified as the most stable with amino acid models. This approach solves the problem of the absence of clear structural templates for novel metallopeptide constructs. The posterior refinement of the structures via QM/MM and MD calculations allows us to provide, for the first time, physically sound models for Al

  9. Physiopathological Hypothesis of Cellulite

    Science.gov (United States)

    de Godoy, José Maria Pereira; de Godoy, Maria de Fátima Guerreiro

    2009-01-01

    A series of questions are asked concerning this condition including as regards to its name, the consensus about the histopathological findings, physiological hypothesis and treatment of the disease. We established a hypothesis for cellulite and confirmed that the clinical response is compatible with this hypothesis. Hence this novel approach brings a modern physiological concept with physiopathologic basis and clinical proof of the hypothesis. We emphasize that the choice of patient, correct diagnosis of cellulite and the technique employed are fundamental to success. PMID:19756187

  10. Hypothesis test for synchronization: twin surrogates revisited.

    Science.gov (United States)

    Romano, M Carmen; Thiel, Marco; Kurths, Jürgen; Mergenthaler, Konstantin; Engbert, Ralf

    2009-03-01

    The method of twin surrogates has been introduced to test for phase synchronization of complex systems in the case of passive experiments. In this paper we derive new analytical expressions for the number of twins depending on the size of the neighborhood, as well as on the length of the trajectory. This allows us to determine the optimal parameters for the generation of twin surrogates. Furthermore, we determine the quality of the twin surrogates with respect to several linear and nonlinear statistics depending on the parameters of the method. In the second part of the paper we perform a hypothesis test for phase synchronization in the case of experimental data from fixational eye movements. These miniature eye movements have been shown to play a central role in neural information processing underlying the perception of static visual scenes. The high number of data sets (21 subjects and 30 trials per person) allows us to compare the generated twin surrogates with the "natural" surrogates that correspond to the different trials. We show that the generated twin surrogates reproduce very well all linear and nonlinear characteristics of the underlying experimental system. The synchronization analysis of fixational eye movements by means of twin surrogates reveals that the synchronization between the left and right eye is significant, indicating that either the centers in the brain stem generating fixational eye movements are closely linked, or, alternatively that there is only one center controlling both eyes.

  11. Life Origination Hydrate Hypothesis (LOH-Hypothesis

    Directory of Open Access Journals (Sweden)

    Victor Ostrovskii

    2012-01-01

    Full Text Available The paper develops the Life Origination Hydrate Hypothesis (LOH-hypothesis, according to which living-matter simplest elements (LMSEs, which are N-bases, riboses, nucleosides, nucleotides, DNA- and RNA-like molecules, amino-acids, and proto-cells repeatedly originated on the basis of thermodynamically controlled, natural, and inevitable processes governed by universal physical and chemical laws from CH4, niters, and phosphates under the Earth's surface or seabed within the crystal cavities of the honeycomb methane-hydrate structure at low temperatures; the chemical processes passed slowly through all successive chemical steps in the direction that is determined by a gradual decrease in the Gibbs free energy of reacting systems. The hypothesis formulation method is based on the thermodynamic directedness of natural movement and consists ofan attempt to mentally backtrack on the progression of nature and thus reveal principal milestones alongits route. The changes in Gibbs free energy are estimated for different steps of the living-matter origination process; special attention is paid to the processes of proto-cell formation. Just the occurrence of the gas-hydrate periodic honeycomb matrix filled with LMSEs almost completely in its final state accounts for size limitation in the DNA functional groups and the nonrandom location of N-bases in the DNA chains. The slowness of the low-temperature chemical transformations and their “thermodynamic front” guide the gross process of living matter origination and its successive steps. It is shown that the hypothesis is thermodynamically justified and testable and that many observed natural phenomena count in its favor.

  12. Equilibrium-point control hypothesis examined by measured arm stiffness during multijoint movement.

    Science.gov (United States)

    Gomi, H; Kawato

    1996-04-05

    For the last 20 years, it has been hypothesized that well-coordinated, multijoint movements are executed without complex computation by the brain, with the use of springlike muscle properties and peripheral neural feedback loops. However, it has been technically and conceptually difficult to examine this "equilibrium-point control" hypothesis directly in physiological or behavioral experiments. A high-performance manipulandum was developed and used here to measure human arm stiffness, the magnitude of which during multijoint movement is important for this hypothesis. Here, the equilibrium-point trajectory was estimated from the measured stiffness, the actual trajectory, and the generated torque. Its velocity profile differed from that of the actual trajectory. These results argue against the hypothesis that the brain sends as a motor command only an equilibrium-point trajectory similar to the actual trajectory.

  13. Hypothesis analysis methods, hypothesis analysis devices, and articles of manufacture

    Science.gov (United States)

    Sanfilippo, Antonio P [Richland, WA; Cowell, Andrew J [Kennewick, WA; Gregory, Michelle L [Richland, WA; Baddeley, Robert L [Richland, WA; Paulson, Patrick R [Pasco, WA; Tratz, Stephen C [Richland, WA; Hohimer, Ryan E [West Richland, WA

    2012-03-20

    Hypothesis analysis methods, hypothesis analysis devices, and articles of manufacture are described according to some aspects. In one aspect, a hypothesis analysis method includes providing a hypothesis, providing an indicator which at least one of supports and refutes the hypothesis, using the indicator, associating evidence with the hypothesis, weighting the association of the evidence with the hypothesis, and using the weighting, providing information regarding the accuracy of the hypothesis.

  14. Neural Networks

    International Nuclear Information System (INIS)

    Smith, Patrick I.

    2003-01-01

    Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

  15. The modulation of neural gain facilitates a transition between functional segregation and integration in the brain.

    Science.gov (United States)

    Shine, James M; Aburn, Matthew J; Breakspear, Michael; Poldrack, Russell A

    2018-01-29

    Cognitive function relies on a dynamic, context-sensitive balance between functional integration and segregation in the brain. Previous work has proposed that this balance is mediated by global fluctuations in neural gain by projections from ascending neuromodulatory nuclei. To test this hypothesis in silico, we studied the effects of neural gain on network dynamics in a model of large-scale neuronal dynamics. We found that increases in neural gain directed the network through an abrupt dynamical transition, leading to an integrated network topology that was maximal in frontoparietal 'rich club' regions. This gain-mediated transition was also associated with increased topological complexity, as well as increased variability in time-resolved topological structure, further highlighting the potential computational benefits of the gain-mediated network transition. These results support the hypothesis that neural gain modulation has the computational capacity to mediate the balance between integration and segregation in the brain. © 2018, Shine et al.

  16. The Qualitative Expectations Hypothesis

    DEFF Research Database (Denmark)

    Frydman, Roman; Johansen, Søren; Rahbek, Anders

    2017-01-01

    We introduce the Qualitative Expectations Hypothesis (QEH) as a new approach to modeling macroeconomic and financial outcomes. Building on John Muth's seminal insight underpinning the Rational Expectations Hypothesis (REH), QEH represents the market's forecasts to be consistent with the predictions...... of an economistís model. However, by assuming that outcomes lie within stochastic intervals, QEH, unlike REH, recognizes the ambiguity faced by an economist and market participants alike. Moreover, QEH leaves the model open to ambiguity by not specifying a mechanism determining specific values that outcomes take...

  17. The Qualitative Expectations Hypothesis

    DEFF Research Database (Denmark)

    Frydman, Roman; Johansen, Søren; Rahbek, Anders

    We introduce the Qualitative Expectations Hypothesis (QEH) as a new approach to modeling macroeconomic and financial outcomes. Building on John Muth's seminal insight underpinning the Rational Expectations Hypothesis (REH), QEH represents the market's forecasts to be consistent with the predictions...... of an economist's model. However, by assuming that outcomes lie within stochastic intervals, QEH, unlike REH, recognizes the ambiguity faced by an economist and market participants alike. Moreover, QEH leaves the model open to ambiguity by not specifying a mechanism determining specific values that outcomes take...

  18. Revisiting the Dutch hypothesis

    NARCIS (Netherlands)

    Postma, Dirkje S.; Weiss, Scott T.; van den Berge, Maarten; Kerstjens, Huib A. M.; Koppelman, Gerard H.

    The Dutch hypothesis was first articulated in 1961, when many novel and advanced scientific techniques were not available, such as genomics techniques for pinpointing genes, gene expression, lipid and protein profiles, and the microbiome. In addition, computed tomographic scans and advanced analysis

  19. The Lehman Sisters Hypothesis

    NARCIS (Netherlands)

    I.P. van Staveren (Irene)

    2014-01-01

    markdownabstract__Abstract__ This article explores the Lehman Sisters Hypothesis. It reviews empirical literature about gender differences in behavioral, experimental, and neuro-economics as well as in other fields of behavioral research. It discusses gender differences along three dimensions of

  20. Understanding the Functional Plasticity in Neural Networks of the Basal Ganglia in Cocaine Use Disorder: A Role for Allosteric Receptor-Receptor Interactions in A2A-D2 Heteroreceptor Complexes

    Directory of Open Access Journals (Sweden)

    Dasiel O. Borroto-Escuela

    2016-01-01

    Full Text Available Our hypothesis is that allosteric receptor-receptor interactions in homo- and heteroreceptor complexes may form the molecular basis of learning and memory. This principle is illustrated by showing how cocaine abuse can alter the adenosine A2AR-dopamine D2R heterocomplexes and their receptor-receptor interactions and hereby induce neural plasticity in the basal ganglia. Studies with A2AR ligands using cocaine self-administration procedures indicate that antagonistic allosteric A2AR-D2R heterocomplexes of the ventral striatopallidal GABA antireward pathway play a significant role in reducing cocaine induced reward, motivation, and cocaine seeking. Anticocaine actions of A2AR agonists can also be produced at A2AR homocomplexes in these antireward neurons, actions in which are independent of D2R signaling. At the A2AR-D2R heterocomplex, they are dependent on the strength of the antagonistic allosteric A2AR-D2R interaction and the number of A2AR-D2R and A2AR-D2R-sigma1R heterocomplexes present in the ventral striatopallidal GABA neurons. It involves a differential cocaine-induced increase in sigma1Rs in the ventral versus the dorsal striatum. In contrast, the allosteric brake on the D2R protomer signaling in the A2AR-D2R heterocomplex of the dorsal striatopallidal GABA neurons is lost upon cocaine self-administration. This is potentially due to differences in composition and allosteric plasticity of these complexes versus those in the ventral striatopallidal neurons.

  1. Bayesian Hypothesis Testing

    Energy Technology Data Exchange (ETDEWEB)

    Andrews, Stephen A. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Sigeti, David E. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-11-15

    These are a set of slides about Bayesian hypothesis testing, where many hypotheses are tested. The conclusions are the following: The value of the Bayes factor obtained when using the median of the posterior marginal is almost the minimum value of the Bayes factor. The value of τ2 which minimizes the Bayes factor is a reasonable choice for this parameter. This allows a likelihood ratio to be computed with is the least favorable to H0.

  2. The Drift Burst Hypothesis

    OpenAIRE

    Christensen, Kim; Oomen, Roel; Renò, Roberto

    2016-01-01

    The Drift Burst Hypothesis postulates the existence of short-lived locally explosive trends in the price paths of financial assets. The recent US equity and Treasury flash crashes can be viewed as two high profile manifestations of such dynamics, but we argue that drift bursts of varying magnitude are an expected and regular occurrence in financial markets that can arise through established mechanisms such as feedback trading. At a theoretical level, we show how to build drift bursts into the...

  3. Hypothesis in research

    Directory of Open Access Journals (Sweden)

    Eudaldo Enrique Espinoza Freire

    2018-01-01

    Full Text Available It is intended with this work to have a material with the fundamental contents, which enable the university professor to formulate the hypothesis, for the development of an investigation, taking into account the problem to be solved. For its elaboration, the search of information in primary documents was carried out, such as thesis of degree and reports of research results, selected on the basis of its relevance with the analyzed subject, current and reliability, secondary documents, as scientific articles published in journals of recognized prestige, the selection was made with the same terms as in the previous documents. It presents a conceptualization of the updated hypothesis, its characterization and an analysis of the structure of the hypothesis in which the determination of the variables is deepened. The involvement of the university professor in the teaching-research process currently faces some difficulties, which are manifested, among other aspects, in an unstable balance between teaching and research, which leads to a separation between them.

  4. The evolution of cerebellum structure correlates with nest complexity.

    Science.gov (United States)

    Hall, Zachary J; Street, Sally E; Healy, Susan D

    2013-01-01

    Across the brains of different bird species, the cerebellum varies greatly in the amount of surface folding (foliation). The degree of cerebellar foliation is thought to correlate positively with the processing capacity of the cerebellum, supporting complex motor abilities, particularly manipulative skills. Here, we tested this hypothesis by investigating the relationship between cerebellar foliation and species-typical nest structure in birds. Increasing complexity of nest structure is a measure of a bird's ability to manipulate nesting material into the required shape. Consistent with our hypothesis, avian cerebellar foliation increases as the complexity of the nest built increases, setting the scene for the exploration of nest building at the neural level.

  5. Neural chips, neural computers and application in high and superhigh energy physics experiments

    International Nuclear Information System (INIS)

    Nikityuk, N.M.; )

    2001-01-01

    Architecture peculiarity and characteristics of series of neural chips and neural computes used in scientific instruments are considered. Tendency of development and use of them in high energy and superhigh energy physics experiments are described. Comparative data which characterize the efficient use of neural chips for useful event selection, classification elementary particles, reconstruction of tracks of charged particles and for search of hypothesis Higgs particles are given. The characteristics of native neural chips and accelerated neural boards are considered [ru

  6. Invariant recognition drives neural representations of action sequences.

    Directory of Open Access Journals (Sweden)

    Andrea Tacchetti

    2017-12-01

    Full Text Available Recognizing the actions of others from visual stimuli is a crucial aspect of human perception that allows individuals to respond to social cues. Humans are able to discriminate between similar actions despite transformations, like changes in viewpoint or actor, that substantially alter the visual appearance of a scene. This ability to generalize across complex transformations is a hallmark of human visual intelligence. Advances in understanding action recognition at the neural level have not always translated into precise accounts of the computational principles underlying what representations of action sequences are constructed by human visual cortex. Here we test the hypothesis that invariant action discrimination might fill this gap. Recently, the study of artificial systems for static object perception has produced models, Convolutional Neural Networks (CNNs, that achieve human level performance in complex discriminative tasks. Within this class, architectures that better support invariant object recognition also produce image representations that better match those implied by human and primate neural data. However, whether these models produce representations of action sequences that support recognition across complex transformations and closely follow neural representations of actions remains unknown. Here we show that spatiotemporal CNNs accurately categorize video stimuli into action classes, and that deliberate model modifications that improve performance on an invariant action recognition task lead to data representations that better match human neural recordings. Our results support our hypothesis that performance on invariant discrimination dictates the neural representations of actions computed in the brain. These results broaden the scope of the invariant recognition framework for understanding visual intelligence from perception of inanimate objects and faces in static images to the study of human perception of action sequences.

  7. The Stoichiometric Divisome: A Hypothesis

    Directory of Open Access Journals (Sweden)

    Waldemar eVollmer

    2015-05-01

    Full Text Available Dividing Escherichia coli cells simultaneously constrict the inner membrane, peptidoglycan layer and outer membrane to synthesize the new poles of the daughter cells. For this, more than 30 proteins localize to mid-cell where they form a large, ring-like assembly, the divisome, facilitating division. Although the precise function of most divisome proteins is unknown, it became apparent in recent years that dynamic protein-protein interactions are essential for divisome assembly and function. However, little is known about the nature of the interactions involved and the stoichiometry of the proteins within the divisome. A recent study (Li et al., 2014 used ribosome profiling to measure the absolute protein synthesis rates in E. coli. Interestingly, they observed that most proteins which participate in known multiprotein complexes are synthesized proportional to their stoichiometry. Based on this principle we present a hypothesis for the stoichiometry of the core of the divisome, taking into account known protein-protein interactions. From this hypothesis we infer a possible mechanism for PG synthesis during division.

  8. Neural correlates of conflict between gestures and words: A domain-specific role for a temporal-parietal complex.

    Science.gov (United States)

    Noah, J Adam; Dravida, Swethasri; Zhang, Xian; Yahil, Shaul; Hirsch, Joy

    2017-01-01

    The interpretation of social cues is a fundamental function of human social behavior, and resolution of inconsistencies between spoken and gestural cues plays an important role in successful interactions. To gain insight into these underlying neural processes, we compared neural responses in a traditional color/word conflict task and to a gesture/word conflict task to test hypotheses of domain-general and domain-specific conflict resolution. In the gesture task, recorded spoken words ("yes" and "no") were presented simultaneously with video recordings of actors performing one of the following affirmative or negative gestures: thumbs up, thumbs down, head nodding (up and down), or head shaking (side-to-side), thereby generating congruent and incongruent communication stimuli between gesture and words. Participants identified the communicative intent of the gestures as either positive or negative. In the color task, participants were presented the words "red" and "green" in either red or green font and were asked to identify the color of the letters. We observed a classic "Stroop" behavioral interference effect, with participants showing increased response time for incongruent trials relative to congruent ones for both the gesture and color tasks. Hemodynamic signals acquired using functional near-infrared spectroscopy (fNIRS) were increased in the right dorsolateral prefrontal cortex (DLPFC) for incongruent trials relative to congruent trials for both tasks consistent with a common, domain-general mechanism for detecting conflict. However, activity in the left DLPFC and frontal eye fields and the right temporal-parietal junction (TPJ), superior temporal gyrus (STG), supramarginal gyrus (SMG), and primary and auditory association cortices was greater for the gesture task than the color task. Thus, in addition to domain-general conflict processing mechanisms, as suggested by common engagement of right DLPFC, socially specialized neural modules localized to the left

  9. Neural correlates of conflict between gestures and words: A domain-specific role for a temporal-parietal complex.

    Directory of Open Access Journals (Sweden)

    J Adam Noah

    Full Text Available The interpretation of social cues is a fundamental function of human social behavior, and resolution of inconsistencies between spoken and gestural cues plays an important role in successful interactions. To gain insight into these underlying neural processes, we compared neural responses in a traditional color/word conflict task and to a gesture/word conflict task to test hypotheses of domain-general and domain-specific conflict resolution. In the gesture task, recorded spoken words ("yes" and "no" were presented simultaneously with video recordings of actors performing one of the following affirmative or negative gestures: thumbs up, thumbs down, head nodding (up and down, or head shaking (side-to-side, thereby generating congruent and incongruent communication stimuli between gesture and words. Participants identified the communicative intent of the gestures as either positive or negative. In the color task, participants were presented the words "red" and "green" in either red or green font and were asked to identify the color of the letters. We observed a classic "Stroop" behavioral interference effect, with participants showing increased response time for incongruent trials relative to congruent ones for both the gesture and color tasks. Hemodynamic signals acquired using functional near-infrared spectroscopy (fNIRS were increased in the right dorsolateral prefrontal cortex (DLPFC for incongruent trials relative to congruent trials for both tasks consistent with a common, domain-general mechanism for detecting conflict. However, activity in the left DLPFC and frontal eye fields and the right temporal-parietal junction (TPJ, superior temporal gyrus (STG, supramarginal gyrus (SMG, and primary and auditory association cortices was greater for the gesture task than the color task. Thus, in addition to domain-general conflict processing mechanisms, as suggested by common engagement of right DLPFC, socially specialized neural modules localized to

  10. Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering

    Science.gov (United States)

    Elangasinghe, M. A.; Singhal, N.; Dirks, K. N.; Salmond, J. A.; Samarasinghe, S.

    2014-09-01

    This paper uses artificial neural networks (ANN), combined with k-means clustering, to understand the complex time series of PM10 and PM2.5 concentrations at a coastal location of New Zealand based on data from a single site. Out of available meteorological parameters from the network (wind speed, wind direction, solar radiation, temperature, relative humidity), key factors governing the pattern of the time series concentrations were identified through input sensitivity analysis performed on the trained neural network model. The transport pathways of particulate matter under these key meteorological parameters were further analysed through bivariate concentration polar plots and k-means clustering techniques. The analysis shows that the external sources such as marine aerosols and local sources such as traffic and biomass burning contribute equally to the particulate matter concentrations at the study site. These results are in agreement with the results of receptor modelling by the Auckland Council based on Positive Matrix Factorization (PMF). Our findings also show that contrasting concentration-wind speed relationships exist between marine aerosols and local traffic sources resulting in very noisy and seemingly large random PM10 concentrations. The inclusion of cluster rankings as an input parameter to the ANN model showed a statistically significant (p advanced air dispersion models.

  11. [Dilemma of null hypothesis in ecological hypothesis's experiment test.

    Science.gov (United States)

    Li, Ji

    2016-06-01

    Experimental test is one of the major test methods of ecological hypothesis, though there are many arguments due to null hypothesis. Quinn and Dunham (1983) analyzed the hypothesis deduction model from Platt (1964) and thus stated that there is no null hypothesis in ecology that can be strictly tested by experiments. Fisher's falsificationism and Neyman-Pearson (N-P)'s non-decisivity inhibit statistical null hypothesis from being strictly tested. Moreover, since the null hypothesis H 0 (α=1, β=0) and alternative hypothesis H 1 '(α'=1, β'=0) in ecological progresses are diffe-rent from classic physics, the ecological null hypothesis can neither be strictly tested experimentally. These dilemmas of null hypothesis could be relieved via the reduction of P value, careful selection of null hypothesis, non-centralization of non-null hypothesis, and two-tailed test. However, the statistical null hypothesis significance testing (NHST) should not to be equivalent to the causality logistical test in ecological hypothesis. Hence, the findings and conclusions about methodological studies and experimental tests based on NHST are not always logically reliable.

  12. Cytoarchitecture and ultrastructure of neural stem cell niches and neurogenic complexes maintaining adult neurogenesis in the olfactory midbrain of spiny lobsters, Panulirus argus.

    Science.gov (United States)

    Schmidt, Manfred; Derby, Charles D

    2011-08-15

    New interneurons are continuously generated in small proliferation zones within neuronal somata clusters in the olfactory deutocerebrum of adult decapod crustaceans. Each proliferation zone is connected to a clump of cells containing one neural stem cell (i.e., adult neuroblast), thus forming a "neurogenic complex." Here we provide a detailed analysis of the cytoarchitecture of neurogenic complexes in adult spiny lobsters, Panulirus argus, based on transmission electron microscopy and labeling with cell-type-selective markers. The clump of cells is composed of unique bipolar clump-forming cells that collectively completely envelop the adult neuroblast and are themselves ensheathed by a layer of processes of multipolar cell body glia. An arteriole is attached to the clump of cells, but dye perfusion experiments show that hemolymph has no access to the interior of the clump of cells. Thus, the clump of cells fulfills morphological criteria of a protective stem cell niche, with clump-forming cells constituting the adult neuroblast's microenvironment together with the cell body glia processes separating it from other tissue components. Bromodeoxyuridine pulse-chase experiments with short survival times suggest that adult neuroblasts are not quiescent but rather cycle actively during daytime. We propose a cell lineage model in which an asymmetrically dividing adult neuroblast repopulates the pool of neuronal progenitor cells in the associated proliferation zone. In conclusion, as in mammalian brains, adult neurogenesis in crustacean brains is fueled by neural stem cells that are maintained by stem cell niches that preserve elements of the embryonic microenvironment and contain glial and vascular elements. Copyright © 2011 Wiley-Liss, Inc.

  13. The Bergschrund Hypothesis Revisited

    Science.gov (United States)

    Sanders, J. W.; Cuffey, K. M.; MacGregor, K. R.

    2009-12-01

    After Willard Johnson descended into the Lyell Glacier bergschrund nearly 140 years ago, he proposed that the presence of the bergschrund modulated daily air temperature fluctuations and enhanced freeze-thaw processes. He posited that glaciers, through their ability to birth bergschrunds, are thus able to induce rapid cirque headwall retreat. In subsequent years, many researchers challenged the bergschrund hypothesis on grounds that freeze-thaw events did not occur at depth in bergschrunds. We propose a modified version of Johnson’s original hypothesis: that bergschrunds maintain subfreezing temperatures at values that encourage rock fracture via ice lensing because they act as a cold air trap in areas that would otherwise be held near zero by temperate glacial ice. In support of this claim we investigated three sections of the bergschrund at the West Washmawapta Glacier, British Columbia, Canada, which sits in an east-facing cirque. During our bergschrund reconnaissance we installed temperature sensors at multiple elevations, light sensors at depth in 2 of the 3 locations and painted two 1 m2 sections of the headwall. We first emphasize bergschrunds are not wanting for ice: verglas covers significant fractions of the headwall and icicles dangle from the base of bödens or overhanging rocks. If temperature, rather than water availability, is the limiting factor governing ice-lensing rates, our temperature records demonstrate that the bergschrund provides a suitable environment for considerable rock fracture. At the three sites (north, west, and south walls), the average temperature at depth from 9/3/2006 to 8/6/2007 was -3.6, -3.6, and -2.0 °C, respectively. During spring, when we observed vast amounts of snow melt trickle in to the bergschrund, temperatures averaged -3.7, -3.8, and -2.2 °C, respectively. Winter temperatures are even lower: -8.5, -7.3, and -2.4 °C, respectively. Values during the following year were similar. During the fall, diurnal

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

    Science.gov (United States)

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

    2017-03-01

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

  15. The venom optimization hypothesis revisited.

    Science.gov (United States)

    Morgenstern, David; King, Glenn F

    2013-03-01

    Animal venoms are complex chemical mixtures that typically contain hundreds of proteins and non-proteinaceous compounds, resulting in a potent weapon for prey immobilization and predator deterrence. However, because venoms are protein-rich, they come with a high metabolic price tag. The metabolic cost of venom is sufficiently high to result in secondary loss of venom whenever its use becomes non-essential to survival of the animal. The high metabolic cost of venom leads to the prediction that venomous animals may have evolved strategies for minimizing venom expenditure. Indeed, various behaviors have been identified that appear consistent with frugality of venom use. This has led to formulation of the "venom optimization hypothesis" (Wigger et al. (2002) Toxicon 40, 749-752), also known as "venom metering", which postulates that venom is metabolically expensive and therefore used frugally through behavioral control. Here, we review the available data concerning economy of venom use by animals with either ancient or more recently evolved venom systems. We conclude that the convergent nature of the evidence in multiple taxa strongly suggests the existence of evolutionary pressures favoring frugal use of venom. However, there remains an unresolved dichotomy between this economy of venom use and the lavish biochemical complexity of venom, which includes a high degree of functional redundancy. We discuss the evidence for biochemical optimization of venom as a means of resolving this conundrum. Copyright © 2012 Elsevier Ltd. All rights reserved.

  16. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

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

  17. Understanding the Implications of Neural Population Activity on Behavior

    Science.gov (United States)

    Briguglio, John

    Learning how neural activity in the brain leads to the behavior we exhibit is one of the fundamental questions in Neuroscience. In this dissertation, several lines of work are presented to that use principles of neural coding to understand behavior. In one line of work, we formulate the efficient coding hypothesis in a non-traditional manner in order to test human perceptual sensitivity to complex visual textures. We find a striking agreement between how variable a particular texture signal is and how sensitive humans are to its presence. This reveals that the efficient coding hypothesis is still a guiding principle for neural organization beyond the sensory periphery, and that the nature of cortical constraints differs from the peripheral counterpart. In another line of work, we relate frequency discrimination acuity to neural responses from auditory cortex in mice. It has been previously observed that optogenetic manipulation of auditory cortex, in addition to changing neural responses, evokes changes in behavioral frequency discrimination. We are able to account for changes in frequency discrimination acuity on an individual basis by examining the Fisher information from the neural population with and without optogenetic manipulation. In the third line of work, we address the question of what a neural population should encode given that its inputs are responses from another group of neurons. Drawing inspiration from techniques in machine learning, we train Deep Belief Networks on fake retinal data and show the emergence of Garbor-like filters, reminiscent of responses in primary visual cortex. In the last line of work, we model the state of a cortical excitatory-inhibitory network during complex adaptive stimuli. Using a rate model with Wilson-Cowan dynamics, we demonstrate that simple non-linearities in the signal transferred from inhibitory to excitatory neurons can account for real neural recordings taken from auditory cortex. This work establishes and tests

  18. Olfactory learning without the mushroom bodies: Spiking neural network models of the honeybee lateral antennal lobe tract reveal its capacities in odour memory tasks of varied complexities.

    Science.gov (United States)

    MaBouDi, HaDi; Shimazaki, Hideaki; Giurfa, Martin; Chittka, Lars

    2017-06-01

    The honeybee olfactory system is a well-established model for understanding functional mechanisms of learning and memory. Olfactory stimuli are first processed in the antennal lobe, and then transferred to the mushroom body and lateral horn through dual pathways termed medial and lateral antennal lobe tracts (m-ALT and l-ALT). Recent studies reported that honeybees can perform elemental learning by associating an odour with a reward signal even after lesions in m-ALT or blocking the mushroom bodies. To test the hypothesis that the lateral pathway (l-ALT) is sufficient for elemental learning, we modelled local computation within glomeruli in antennal lobes with axons of projection neurons connecting to a decision neuron (LHN) in the lateral horn. We show that inhibitory spike-timing dependent plasticity (modelling non-associative plasticity by exposure to different stimuli) in the synapses from local neurons to projection neurons decorrelates the projection neurons' outputs. The strength of the decorrelations is regulated by global inhibitory feedback within antennal lobes to the projection neurons. By additionally modelling octopaminergic modification of synaptic plasticity among local neurons in the antennal lobes and projection neurons to LHN connections, the model can discriminate and generalize olfactory stimuli. Although positive patterning can be accounted for by the l-ALT model, negative patterning requires further processing and mushroom body circuits. Thus, our model explains several-but not all-types of associative olfactory learning and generalization by a few neural layers of odour processing in the l-ALT. As an outcome of the combination between non-associative and associative learning, the modelling approach allows us to link changes in structural organization of honeybees' antennal lobes with their behavioural performances over the course of their life.

  19. Non-SMC condensin I complex proteins control chromosome segregation and survival of proliferating cells in the zebrafish neural retina

    Directory of Open Access Journals (Sweden)

    Harris William A

    2009-07-01

    Full Text Available Abstract Background The condensation of chromosomes and correct sister chromatid segregation during cell division is an essential feature of all proliferative cells. Structural maintenance of chromosomes (SMC and non-SMC proteins form the condensin I complex and regulate chromosome condensation and segregation during mitosis. However, due to the lack of appropriate mutants, the function of the condensin I complex during vertebrate development has not been described. Results Here, we report the positional cloning and detailed characterization of retinal phenotypes of a zebrafish mutation at the cap-g locus. High resolution live imaging reveals that the progression of mitosis between prometa- to telophase is delayed and that sister chromatid segregation is impaired upon loss of CAP-G. CAP-G associates with chromosomes between prometa- and telophase of the cell cycle. Loss of the interaction partners CAP-H and CAP-D2 causes cytoplasmic mislocalization of CAP-G throughout mitosis. DNA content analysis reveals increased genomic imbalances upon loss of non-SMC condensin I subunits. Within the retina, loss of condensin I function causes increased rates of apoptosis among cells within the proliferative ciliary marginal zone (CMZ whereas postmitotic retinal cells are viable. Inhibition of p53-mediated apoptosis partially rescues cell numbers in cap-g mutant retinae and allows normal layering of retinal cell types without alleviating their aberrant nuclear sizes. Conclusion Our findings indicate that the condensin I complex is particularly important within rapidly amplifying progenitor cell populations to ensure faithful chromosome segregation. In contrast, differentiation of postmitotic retinal cells is not impaired upon polyploidization.

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

    Science.gov (United States)

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

    2015-12-01

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

  1. Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

    Directory of Open Access Journals (Sweden)

    Andrea Maesani

    2015-11-01

    Full Text Available The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior.

  2. The oxidative hypothesis of senescence

    Directory of Open Access Journals (Sweden)

    Gilca M

    2007-01-01

    Full Text Available The oxidative hypothesis of senescence, since its origin in 1956, has garnered significant evidence and growing support among scientists for the notion that free radicals play an important role in ageing, either as "damaging" molecules or as signaling molecules. Age-increasing oxidative injuries induced by free radicals, higher susceptibility to oxidative stress in short-lived organisms, genetic manipulations that alter both oxidative resistance and longevity and the anti-ageing effect of caloric restriction and intermittent fasting are a few examples of accepted scientific facts that support the oxidative theory of senescence. Though not completely understood due to the complex "network" of redox regulatory systems, the implication of oxidative stress in the ageing process is now well documented. Moreover, it is compatible with other current ageing theories (e.g., those implicating the mitochondrial damage/mitochondrial-lysosomal axis, stress-induced premature senescence, biological "garbage" accumulation, etc. This review is intended to summarize and critically discuss the redox mechanisms involved during the ageing process: sources of oxidant agents in ageing (mitochondrial -electron transport chain, nitric oxide synthase reaction- and non-mitochondrial- Fenton reaction, microsomal cytochrome P450 enzymes, peroxisomal β -oxidation and respiratory burst of phagocytic cells, antioxidant changes in ageing (enzymatic- superoxide dismutase, glutathione-reductase, glutathion peroxidase, catalase- and non-enzymatic glutathione, ascorbate, urate, bilirubine, melatonin, tocopherols, carotenoids, ubiquinol, alteration of oxidative damage repairing mechanisms and the role of free radicals as signaling molecules in ageing.

  3. Is the Aluminum Hypothesis Dead?

    Science.gov (United States)

    2014-01-01

    The Aluminum Hypothesis, the idea that aluminum exposure is involved in the etiology of Alzheimer disease, dates back to a 1965 demonstration that aluminum causes neurofibrillary tangles in the brains of rabbits. Initially the focus of intensive research, the Aluminum Hypothesis has gradually been abandoned by most researchers. Yet, despite this current indifference, the Aluminum Hypothesis continues to attract the attention of a small group of scientists and aluminum continues to be viewed with concern by some of the public. This review article discusses reasons that mainstream science has largely abandoned the Aluminum Hypothesis and explores a possible reason for some in the general public continuing to view aluminum with mistrust. PMID:24806729

  4. A cholinergic hypothesis of the unconscious in affective disorders.

    Directory of Open Access Journals (Sweden)

    Costa eVakalopoulos

    2013-11-01

    Full Text Available The interactions between distinct pharmacological systems are proposed as a key dynamic in the formation of unconscious memories underlying rumination and mood disorder, but also reflect the plastic capacity of neural networks that can aid recovery. An inverse and reciprocal relationship is postulated between cholinergic and monoaminergic receptor subtypes. M1-type muscarinic receptor transduction facilitates encoding of unconscious, prepotent behavioural repertoires at the core of affective disorders and ADHD. Behavioural adaptation to new contingencies is mediated by the classic prototype receptor: 5-HT1A (Gi/o and its modulation of m1-plasticity. Reversal of learning is dependent on increased phasic activation of midbrain monoaminergic nuclei and is a function of hippocampal theta. Acquired hippocampal dysfunction due to abnormal activation of the hypothalamic-pituitary-adrenal (HPA axis predicts deficits in hippocampal-dependent memory and executive function and further impairments to cognitive inhibition. Encoding of explicit memories is mediated by Gq/11 and Gs signalling of monoamines only. A role is proposed for the phasic activation of the basal forebrain cholinergic nucleus by cortical projections from the complex consisting of the insula and claustrum. Although controversial. recent studies suggest a common ontogenetic origin of the two structures and a functional coupling. Lesions of the region result in loss of motivational behaviour and familiarity based judgements. A major hypothesis of the paper is that these lost faculties result indirectly, from reduced cholinergic tone.

  5. Memory in astrocytes: a hypothesis

    Directory of Open Access Journals (Sweden)

    Caudle Robert M

    2006-01-01

    Full Text Available Abstract Background Recent work has indicated an increasingly complex role for astrocytes in the central nervous system. Astrocytes are now known to exchange information with neurons at synaptic junctions and to alter the information processing capabilities of the neurons. As an extension of this trend a hypothesis was proposed that astrocytes function to store information. To explore this idea the ion channels in biological membranes were compared to models known as cellular automata. These comparisons were made to test the hypothesis that ion channels in the membranes of astrocytes form a dynamic information storage device. Results Two dimensional cellular automata were found to behave similarly to ion channels in a membrane when they function at the boundary between order and chaos. The length of time information is stored in this class of cellular automata is exponentially related to the number of units. Therefore the length of time biological ion channels store information was plotted versus the estimated number of ion channels in the tissue. This analysis indicates that there is an exponential relationship between memory and the number of ion channels. Extrapolation of this relationship to the estimated number of ion channels in the astrocytes of a human brain indicates that memory can be stored in this system for an entire life span. Interestingly, this information is not affixed to any physical structure, but is stored as an organization of the activity of the ion channels. Further analysis of two dimensional cellular automata also demonstrates that these systems have both associative and temporal memory capabilities. Conclusion It is concluded that astrocytes may serve as a dynamic information sink for neurons. The memory in the astrocytes is stored by organizing the activity of ion channels and is not associated with a physical location such as a synapse. In order for this form of memory to be of significant duration it is necessary

  6. Modulation of the major histocompatibility complex by neural stem cell-derived neurotrophic factors used for regenerative therapy in a rat model of stroke

    Directory of Open Access Journals (Sweden)

    Sun Chongran

    2010-08-01

    Full Text Available Abstract Background The relationship between functional improvements in ischemic rats given a neural stem cell (NSC transplant and the modulation of the class I major histocompatibility complex (MHC mediated by NSC-derived neurotrophins was investigated. Methods The levels of gene expression of nerve growth factor (NGF, brain-derived neurotropic factor (BDNF and neurotrophin-3 (NT-3 were assayed from cultures of cortical NSC from Sprague-Dawley rat E16 embryos. The levels of translated NGF in spent culture media from NSC cultures and the cerebral spinal fluid (CSF of rats with and without NGF injection or NSC transplant were also measured. Results We found a significant increase of NGF, BDNF and NT-3 transcripts and NGF proteins in both the NSC cultures and the CSF of the rats. The immunochemical staining for MHC in brain sections and the enzyme-linked immunosorbent assay of CSF were carried out in sham-operated rats and rats with surgically induced focal cerebral ischemia. These groups were further divided into animals that did and did not receive NGF administration or NSC transplant into the cisterna magna. Our results show an up-regulation of class I MHC in the ischemic rats with NGF and NSC administration. The extent of caspase-III immunoreactivity was comparable among three arms in the ischemic rats. Conclusion Readouts of somatosensory evoked potential and the trap channel test illustrated improvements in the neurological function of ischemic rats treated with NGF administration and NSC transplant.

  7. Application of neural networks in coastal engineering

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.

    the neural network attractive. A neural network is an information processing system modeled on the structure of the dynamic process. It can solve the complex/nonlinear problems quickly once trained by operating on problems using an interconnected number...

  8. Neural adaptations to electrical stimulation strength training

    NARCIS (Netherlands)

    Hortobagyi, Tibor; Maffiuletti, Nicola A.

    2011-01-01

    This review provides evidence for the hypothesis that electrostimulation strength training (EST) increases the force of a maximal voluntary contraction (MVC) through neural adaptations in healthy skeletal muscle. Although electrical stimulation and voluntary effort activate muscle differently, there

  9. Hypothesis Designs for Three-Hypothesis Test Problems

    OpenAIRE

    Yan Li; Xiaolong Pu

    2010-01-01

    As a helpful guide for applications, the alternative hypotheses of the three-hypothesis test problems are designed under the required error probabilities and average sample number in this paper. The asymptotic formulas and the proposed numerical quadrature formulas are adopted, respectively, to obtain the hypothesis designs and the corresponding sequential test schemes under the Koopman-Darmois distributions. The example of the normal mean test shows that our methods are qu...

  10. The equilibrium-point hypothesis--past, present and future.

    Science.gov (United States)

    Feldman, Anatol G; Levin, Mindy F

    2009-01-01

    This chapter is a brief account of fundamentals of the equilibrium-point hypothesis or more adequately called the threshold control theory (TCT). It also compares the TCT with other approaches to motor control. The basic notions of the TCT are reviewed with a major focus on solutions to the problems of multi-muscle and multi-degrees of freedom redundancy. The TCT incorporates cognitive aspects by explaining how neurons recognize that internal (neural) and external (environmental) events match each other. These aspects as well as how motor learning occurs are subjects of further development of the TCT hypothesis.

  11. Bioprinting for Neural Tissue Engineering.

    Science.gov (United States)

    Knowlton, Stephanie; Anand, Shivesh; Shah, Twisha; Tasoglu, Savas

    2018-01-01

    Bioprinting is a method by which a cell-encapsulating bioink is patterned to create complex tissue architectures. Given the potential impact of this technology on neural research, we review the current state-of-the-art approaches for bioprinting neural tissues. While 2D neural cultures are ubiquitous for studying neural cells, 3D cultures can more accurately replicate the microenvironment of neural tissues. By bioprinting neuronal constructs, one can precisely control the microenvironment by specifically formulating the bioink for neural tissues, and by spatially patterning cell types and scaffold properties in three dimensions. We review a range of bioprinted neural tissue models and discuss how they can be used to observe how neurons behave, understand disease processes, develop new therapies and, ultimately, design replacement tissues. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Tests of the lunar hypothesis

    Science.gov (United States)

    Taylor, S. R.

    1984-01-01

    The concept that the Moon was fissioned from the Earth after core separation is the most readily testable hypothesis of lunar origin, since direct comparisons of lunar and terrestrial compositions can be made. Differences found in such comparisons introduce so many ad hoc adjustments to the fission hypothesis that it becomes untestable. Further constraints may be obtained from attempting to date the volatile-refractory element fractionation. The combination of chemical and isotopic problems suggests that the fission hypothesis is no longer viable, and separate terrestrial and lunar accretion from a population of fractionated precursor planetesimals provides a more reasonable explanation.

  13. Proneness to social anxiety modulates neural complexity in the absence of exposure: A resting state fMRI study using Hurst exponent.

    Science.gov (United States)

    Gentili, Claudio; Vanello, Nicola; Cristea, Ioana; David, Daniel; Ricciardi, Emiliano; Pietrini, Pietro

    2015-05-30

    To test the hypothesis that brain activity is modulated by trait social anxiety, we measured the Hurst Exponent (HE), an index of complexity in time series, in healthy individuals at rest in the absence of any social trigger. Functional magnetic resonance imaging (fMRI) time series were recorded in 36 subjects at rest. All volunteers were healthy without any psychiatric, medical or neurological disorder. Subjects completed the Liebowitz Social Anxiety Scale (LSAS) and the Brief Fear of Negative Evaluation (BFNE) to assess social anxiety and thoughts in social contexts. We also obtained the fractional Amplitude of Low Frequency Fluctuations (fALFF) of the BOLD signal as an independent control measure for HE data. BFNE scores correlated positively with HE in the posterior cingulate/precuneus, while LSAS scores correlated positively with HE in the precuneus, in the inferior parietal sulci and in the parahippocamus. Results from fALFF were highly consistent with those obtained using LSAS and BFNE to predict HE. Overall our data indicate that spontaneous brain activity is influenced by the degree of social anxiety, on a continuum and in the absence of social stimuli. These findings suggest that social anxiety is a trait characteristic that shapes brain activity and predisposes to different reactions in social contexts. Copyright © 2015. Published by Elsevier Ireland Ltd.

  14. Evaluating the Stage Learning Hypothesis.

    Science.gov (United States)

    Thomas, Hoben

    1980-01-01

    A procedure for evaluating the Genevan stage learning hypothesis is illustrated by analyzing Inhelder, Sinclair, and Bovet's guided learning experiments (in "Learning and the Development of Cognition." Cambridge: Harvard University Press, 1974). (Author/MP)

  15. The Purchasing Power Parity Hypothesis:

    African Journals Online (AJOL)

    2011-10-02

    Oct 2, 2011 ... reject the unit root hypothesis in real exchange rates may simply be due to the shortness ..... Violations of Purchasing Power Parity and Their Implications for Efficient ... Official Intervention in the Foreign Exchange Market:.

  16. Debates—Hypothesis testing in hydrology: Introduction

    Science.gov (United States)

    Blöschl, Günter

    2017-03-01

    This paper introduces the papers in the "Debates—Hypothesis testing in hydrology" series. The four articles in the series discuss whether and how the process of testing hypotheses leads to progress in hydrology. Repeated experiments with controlled boundary conditions are rarely feasible in hydrology. Research is therefore not easily aligned with the classical scientific method of testing hypotheses. Hypotheses in hydrology are often enshrined in computer models which are tested against observed data. Testability may be limited due to model complexity and data uncertainty. All four articles suggest that hypothesis testing has contributed to progress in hydrology and is needed in the future. However, the procedure is usually not as systematic as the philosophy of science suggests. A greater emphasis on a creative reasoning process on the basis of clues and explorative analyses is therefore needed.

  17. Multi-scale, multi-modal analysis uncovers complex relationship at the brain tissue-implant neural interface: new emphasis on the biological interface

    Science.gov (United States)

    Michelson, Nicholas J.; Vazquez, Alberto L.; Eles, James R.; Salatino, Joseph W.; Purcell, Erin K.; Williams, Jordan J.; Cui, X. Tracy; Kozai, Takashi D. Y.

    2018-06-01

    Objective. Implantable neural electrode devices are important tools for neuroscience research and have an increasing range of clinical applications. However, the intricacies of the biological response after implantation, and their ultimate impact on recording performance, remain challenging to elucidate. Establishing a relationship between the neurobiology and chronic recording performance is confounded by technical challenges related to traditional electrophysiological, material, and histological limitations. This can greatly impact the interpretations of results pertaining to device performance and tissue health surrounding the implant. Approach. In this work, electrophysiological activity and immunohistological analysis are compared after controlling for motion artifacts, quiescent neuronal activity, and material failure of devices in order to better understand the relationship between histology and electrophysiological outcomes. Main results. Even after carefully accounting for these factors, the presence of viable neurons and lack of glial scarring does not convey single unit recording performance. Significance. To better understand the biological factors influencing neural activity, detailed cellular and molecular tissue responses were examined. Decreases in neural activity and blood oxygenation in the tissue surrounding the implant, shift in expression levels of vesicular transporter proteins and ion channels, axon and myelin injury, and interrupted blood flow in nearby capillaries can impact neural activity around implanted neural interfaces. Combined, these tissue changes highlight the need for more comprehensive, basic science research to elucidate the relationship between biology and chronic electrophysiology performance in order to advance neural technologies.

  18. The atomic hypothesis: physical consequences

    International Nuclear Information System (INIS)

    Rivas, Martin

    2008-01-01

    The hypothesis that matter is made of some ultimate and indivisible objects, together with the restricted relativity principle, establishes a constraint on the kind of variables we are allowed to use for the variational description of elementary particles. We consider that the atomic hypothesis not only states the indivisibility of elementary particles, but also that these ultimate objects, if not annihilated, cannot be modified by any interaction so that all allowed states of an elementary particle are only kinematical modifications of any one of them. Therefore, an elementary particle cannot have excited states. In this way, the kinematical group of spacetime symmetries not only defines the symmetries of the system, but also the variables in terms of which the mathematical description of the elementary particles can be expressed in either the classical or the quantum mechanical description. When considering the interaction of two Dirac particles, the atomic hypothesis restricts the interaction Lagrangian to a kind of minimal coupling interaction

  19. Multiple sclerosis: a geographical hypothesis.

    Science.gov (United States)

    Carlyle, I P

    1997-12-01

    Multiple sclerosis remains a rare neurological disease of unknown aetiology, with a unique distribution, both geographically and historically. Rare in equatorial regions, it becomes increasingly common in higher latitudes; historically, it was first clinically recognized in the early nineteenth century. A hypothesis, based on geographical reasoning, is here proposed: that the disease is the result of a specific vitamin deficiency. Different individuals suffer the deficiency in separate and often unique ways. Evidence to support the hypothesis exists in cultural considerations, in the global distribution of the disease, and in its historical prevalence.

  20. Discussion of the Porter hypothesis

    International Nuclear Information System (INIS)

    1999-11-01

    In the reaction to the long-range vision of RMNO, published in 1996, The Dutch government posed the question whether a far-going and progressive modernization policy will lead to competitive advantages of high-quality products on partly new markets. Such a question is connected to the so-called Porter hypothesis: 'By stimulating innovation, strict environmental regulations can actually enhance competitiveness', from which statement it can be concluded that environment and economy can work together quite well. A literature study has been carried out in order to determine under which conditions that hypothesis is endorsed in the scientific literature and policy documents. Recommendations are given for further studies. refs

  1. The thrifty phenotype hypothesis revisited

    DEFF Research Database (Denmark)

    Vaag, A A; Grunnet, L G; Arora, G P

    2012-01-01

    Twenty years ago, Hales and Barker along with their co-workers published some of their pioneering papers proposing the 'thrifty phenotype hypothesis' in Diabetologia (4;35:595-601 and 3;36:62-67). Their postulate that fetal programming could represent an important player in the origin of type 2...... of the underlying molecular mechanisms. Type 2 diabetes is a multiple-organ disease, and developmental programming, with its idea of organ plasticity, is a plausible hypothesis for a common basis for the widespread organ dysfunctions in type 2 diabetes and the metabolic syndrome. Only two among the 45 known type 2...

  2. RANDOM WALK HYPOTHESIS IN FINANCIAL MARKETS

    Directory of Open Access Journals (Sweden)

    Nicolae-Marius JULA

    2017-05-01

    Full Text Available Random walk hypothesis states that the stock market prices do not follow a predictable trajectory, but are simply random. If you are trying to predict a random set of data, one should test for randomness, because, despite the power and complexity of the used models, the results cannot be trustworthy. There are several methods for testing these hypotheses and the use of computational power provided by the R environment makes the work of the researcher easier and with a cost-effective approach. The increasing power of computing and the continuous development of econometric tests should give the potential investors new tools in selecting commodities and investing in efficient markets.

  3. Neural networks

    International Nuclear Information System (INIS)

    Denby, Bruce; Lindsey, Clark; Lyons, Louis

    1992-01-01

    The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive

  4. A New Artificial Neural Network Enhanced by the Shuffled Complex Evolution Optimization with Principal Component Analysis (SP-UCI) for Water Resources Management

    Science.gov (United States)

    Hayatbini, N.; Faridzad, M.; Yang, T.; Akbari Asanjan, A.; Gao, X.; Sorooshian, S.

    2016-12-01

    The Artificial Neural Networks (ANNs) are useful in many fields, including water resources engineering and management. However, due to the non-linear and chaotic characteristics associated with natural processes and human decision making, the use of ANNs in real-world applications is still limited, and its performance needs to be further improved for a broader practical use. The commonly used Back-Propagation (BP) scheme and gradient-based optimization in training the ANNs have already found to be problematic in some cases. The BP scheme and gradient-based optimization methods are associated with the risk of premature convergence, stuck in local optimums, and the searching is highly dependent on initial conditions. Therefore, as an alternative to BP and gradient-based searching scheme, we propose an effective and efficient global searching method, termed the Shuffled Complex Evolutionary Global optimization algorithm with Principal Component Analysis (SP-UCI), to train the ANN connectivity weights. Large number of real-world datasets are tested with the SP-UCI-based ANN, as well as various popular Evolutionary Algorithms (EAs)-enhanced ANNs, i.e., Particle Swarm Optimization (PSO)-, Genetic Algorithm (GA)-, Simulated Annealing (SA)-, and Differential Evolution (DE)-enhanced ANNs. Results show that SP-UCI-enhanced ANN is generally superior over other EA-enhanced ANNs with regard to the convergence and computational performance. In addition, we carried out a case study for hydropower scheduling in the Trinity Lake in the western U.S. In this case study, multiple climate indices are used as predictors for the SP-UCI-enhanced ANN. The reservoir inflows and hydropower releases are predicted up to sub-seasonal to seasonal scale. Results show that SP-UCI-enhanced ANN is able to achieve better statistics than other EAs-based ANN, which implies the usefulness and powerfulness of proposed SP-UCI-enhanced ANN for reservoir operation, water resources engineering and management

  5. The Stress Acceleration Hypothesis of Nightmares

    Directory of Open Access Journals (Sweden)

    Tore Nielsen

    2017-06-01

    Full Text Available Adverse childhood experiences can deleteriously affect future physical and mental health, increasing risk for many illnesses, including psychiatric problems, sleep disorders, and, according to the present hypothesis, idiopathic nightmares. Much like post-traumatic nightmares, which are triggered by trauma and lead to recurrent emotional dreaming about the trauma, idiopathic nightmares are hypothesized to originate in early adverse experiences that lead in later life to the expression of early memories and emotions in dream content. Accordingly, the objectives of this paper are to (1 review existing literature on sleep, dreaming and nightmares in relation to early adverse experiences, drawing upon both empirical studies of dreaming and nightmares and books and chapters by recognized nightmare experts and (2 propose a new approach to explaining nightmares that is based upon the Stress Acceleration Hypothesis of mental illness. The latter stipulates that susceptibility to mental illness is increased by adversity occurring during a developmentally sensitive window for emotional maturation—the infantile amnesia period—that ends around age 3½. Early adversity accelerates the neural and behavioral maturation of emotional systems governing the expression, learning, and extinction of fear memories and may afford short-term adaptive value. But it also engenders long-term dysfunctional consequences including an increased risk for nightmares. Two mechanisms are proposed: (1 disruption of infantile amnesia allows normally forgotten early childhood memories to influence later emotions, cognitions and behavior, including the common expression of threats in nightmares; (2 alterations of normal emotion regulation processes of both waking and sleep lead to increased fear sensitivity and less effective fear extinction. These changes influence an affect network previously hypothesized to regulate fear extinction during REM sleep, disruption of which leads to

  6. Neural crest does not contribute to the neck and shoulder in the axolotl (Ambystoma mexicanum).

    Science.gov (United States)

    Epperlein, Hans-Henning; Khattak, Shahryar; Knapp, Dunja; Tanaka, Elly M; Malashichev, Yegor B

    2012-01-01

    A major step during the evolution of tetrapods was their transition from water to land. This process involved the reduction or complete loss of the dermal bones that made up connections to the skull and a concomitant enlargement of the endochondral shoulder girdle. In the mouse the latter is derived from three separate embryonic sources: lateral plate mesoderm, somites, and neural crest. The neural crest was suggested to sustain the muscle attachments. How this complex composition of the endochondral shoulder girdle arose during evolution and whether it is shared by all tetrapods is unknown. Salamanders that lack dermal bone within their shoulder girdle were of special interest for a possible contribution of the neural crest to the endochondral elements and muscle attachment sites, and we therefore studied them in this context. We grafted neural crest from GFP+ fluorescent transgenic axolotl (Ambystoma mexicanum) donor embryos into white (d/d) axolotl hosts and followed the presence of neural crest cells within the cartilage of the shoulder girdle and the connective tissue of muscle attachment sites of the neck-shoulder region. Strikingly, neural crest cells did not contribute to any part of the endochondral shoulder girdle or to the connective tissue at muscle attachment sites in axolotl. Our results in axolotl suggest that neural crest does not serve a general function in vertebrate shoulder muscle attachment sites as predicted by the "muscle scaffold theory," and that it is not necessary to maintain connectivity of the endochondral shoulder girdle to the skull. Our data support the possibility that the contribution of the neural crest to the endochondral shoulder girdle, which is observed in the mouse, arose de novo in mammals as a developmental basis for their skeletal synapomorphies. This further supports the hypothesis of an increased neural crest diversification during vertebrate evolution.

  7. Neural crest does not contribute to the neck and shoulder in the axolotl (Ambystoma mexicanum.

    Directory of Open Access Journals (Sweden)

    Hans-Henning Epperlein

    Full Text Available BACKGROUND: A major step during the evolution of tetrapods was their transition from water to land. This process involved the reduction or complete loss of the dermal bones that made up connections to the skull and a concomitant enlargement of the endochondral shoulder girdle. In the mouse the latter is derived from three separate embryonic sources: lateral plate mesoderm, somites, and neural crest. The neural crest was suggested to sustain the muscle attachments. How this complex composition of the endochondral shoulder girdle arose during evolution and whether it is shared by all tetrapods is unknown. Salamanders that lack dermal bone within their shoulder girdle were of special interest for a possible contribution of the neural crest to the endochondral elements and muscle attachment sites, and we therefore studied them in this context. RESULTS: We grafted neural crest from GFP+ fluorescent transgenic axolotl (Ambystoma mexicanum donor embryos into white (d/d axolotl hosts and followed the presence of neural crest cells within the cartilage of the shoulder girdle and the connective tissue of muscle attachment sites of the neck-shoulder region. Strikingly, neural crest cells did not contribute to any part of the endochondral shoulder girdle or to the connective tissue at muscle attachment sites in axolotl. CONCLUSIONS: Our results in axolotl suggest that neural crest does not serve a general function in vertebrate shoulder muscle attachment sites as predicted by the "muscle scaffold theory," and that it is not necessary to maintain connectivity of the endochondral shoulder girdle to the skull. Our data support the possibility that the contribution of the neural crest to the endochondral shoulder girdle, which is observed in the mouse, arose de novo in mammals as a developmental basis for their skeletal synapomorphies. This further supports the hypothesis of an increased neural crest diversification during vertebrate evolution.

  8. Whiplash and the compensation hypothesis.

    Science.gov (United States)

    Spearing, Natalie M; Connelly, Luke B

    2011-12-01

    Review article. To explain why the evidence that compensation-related factors lead to worse health outcomes is not compelling, either in general, or in the specific case of whiplash. There is a common view that compensation-related factors lead to worse health outcomes ("the compensation hypothesis"), despite the presence of important, and unresolved sources of bias. The empirical evidence on this question has ramifications for the design of compensation schemes. Using studies on whiplash, this article outlines the methodological problems that impede attempts to confirm or refute the compensation hypothesis. Compensation studies are prone to measurement bias, reverse causation bias, and selection bias. Errors in measurement are largely due to the latent nature of whiplash injuries and health itself, a lack of clarity over the unit of measurement (specific factors, or "compensation"), and a lack of appreciation for the heterogeneous qualities of compensation-related factors and schemes. There has been a failure to acknowledge and empirically address reverse causation bias, or the likelihood that poor health influences the decision to pursue compensation: it is unclear if compensation is a cause or a consequence of poor health, or both. Finally, unresolved selection bias (and hence, confounding) is evident in longitudinal studies and natural experiments. In both cases, between-group differences have not been addressed convincingly. The nature of the relationship between compensation-related factors and health is unclear. Current approaches to testing the compensation hypothesis are prone to several important sources of bias, which compromise the validity of their results. Methods that explicitly test the hypothesis and establish whether or not a causal relationship exists between compensation factors and prolonged whiplash symptoms are needed in future studies.

  9. Capture of fixation by rotational flow; a deterministic hypothesis regarding scaling and stochasticity in fixational eye movements

    Directory of Open Access Journals (Sweden)

    Nicholas Mansel Wilkinson

    2014-02-01

    Full Text Available Visual scan paths exhibit complex, stochastic dynamics. Even during visual fixation, the eye is in constant motion. Fixational drift and tremor are thought to reflect fluctuations in the persistent neural activity of neural integrators in the oculomotor brainstem, which integrate sequences of transient saccadic velocity signals into a short term memory of eye position. Despite intensive research and much progress, the precise mechanisms by which oculomotor posture is maintained remain elusive. Drift exhibits a stochastic statistical profile which has been modelled using random walk formalisms. Tremor is widely dismissed as noise. Here we focus on the dynamical profile of fixational tremor, and argue that tremor may be a signal which usefully reflects the workings of the oculomotor postural control. We identify signatures reminiscent of a certain flavour of transient neurodynamics; toric travelling waves which rotate around a central phase singularity. Spiral waves play an organisational role in dynamical systems at many scales throughout nature, though their potential functional role in brain activity remains a matter of educated speculation. Spiral waves have a repertoire of functionally interesting dynamical properties, including persistence, which suggest that they could in theory contribute to persistent neural activity in the oculomotor postural control system. Whilst speculative, the singularity hypothesis of oculomotor postural control implies testable predictions, and could provide the beginnings of an integrated dynamical framework for eye movements across scales.

  10. A Molecular–Structure Hypothesis

    Directory of Open Access Journals (Sweden)

    Jan C. A. Boeyens

    2010-11-01

    Full Text Available The self-similar symmetry that occurs between atomic nuclei, biological growth structures, the solar system, globular clusters and spiral galaxies suggests that a similar pattern should characterize atomic and molecular structures. This possibility is explored in terms of the current molecular structure-hypothesis and its extension into four-dimensional space-time. It is concluded that a quantum molecule only has structure in four dimensions and that classical (Newtonian structure, which occurs in three dimensions, cannot be simulated by quantum-chemical computation.

  11. Antiaging therapy: a prospective hypothesis

    Directory of Open Access Journals (Sweden)

    Shahidi Bonjar MR

    2015-01-01

    Full Text Available Mohammad Rashid Shahidi Bonjar,1 Leyla Shahidi Bonjar2 1School of Dentistry, Kerman University of Medical Sciences, Kerman Iran; 2Department of Pharmacology, College of Pharmacy, Kerman University of Medical Sciences, Kerman, Iran Abstract: This hypothesis proposes a new prospective approach to slow the aging process in older humans. The hypothesis could lead to developing new treatments for age-related illnesses and help humans to live longer. This hypothesis has no previous documentation in scientific media and has no protocol. Scientists have presented evidence that systemic aging is influenced by peculiar molecules in the blood. Researchers at Albert Einstein College of Medicine, New York, and Harvard University in Cambridge discovered elevated titer of aging-related molecules (ARMs in blood, which trigger cascade of aging process in mice; they also indicated that the process can be reduced or even reversed. By inhibiting the production of ARMs, they could reduce age-related cognitive and physical declines. The present hypothesis offers a new approach to translate these findings into medical treatment: extracorporeal adjustment of ARMs would lead to slower rates of aging. A prospective “antiaging blood filtration column” (AABFC is a nanotechnological device that would fulfill the central role in this approach. An AABFC would set a near-youth homeostatic titer of ARMs in the blood. In this regard, the AABFC immobilizes ARMs from the blood while blood passes through the column. The AABFC harbors antibodies against ARMs. ARM antibodies would be conjugated irreversibly to ARMs on contact surfaces of the reaction platforms inside the AABFC till near-youth homeostasis is attained. The treatment is performed with the aid of a blood-circulating pump. Similar to a renal dialysis machine, blood would circulate from the body to the AABFC and from there back to the body in a closed circuit until ARMs were sufficiently depleted from the blood. The

  12. Criticality and avalanches in neural networks

    International Nuclear Information System (INIS)

    Zare, Marzieh; Grigolini, Paolo

    2013-01-01

    Highlights: • Temporal criticality is used as criticality indicator. • The Mittag–Leffler function is proposed as a proper form of temporal complexity. • The distribution of avalanche size becomes scale free in the supercritical state. • The scale-free distribution of avalanche sizes is an epileptic manifestation. -- Abstract: Experimental work, both in vitro and in vivo, reveals the occurrence of neural avalanches with an inverse power law distribution in size and time duration. These properties are interpreted as an evident manifestation of criticality, thereby suggesting that the brain is an operating near criticality complex system: an attractive theoretical perspective that according to Gerhard Werner may help to shed light on the origin of consciousness. However, a recent experimental observation shows no clear evidence for power-law scaling in awake and sleeping brain of mammals, casting doubts on the assumption that the brain works at criticality. This article rests on a model proposed by our group in earlier publications to generate neural avalanches with the time duration and size distribution matching the experimental results on neural networks. We now refine the analysis of the time distance between consecutive firing bursts and observe the deviation of the corresponding distribution from the Poisson statistics, as the system moves from the non-cooperative to the cooperative regime. In other words, we make the assumption that the genuine signature of criticality may emerge from temporal complexity rather than from the size and time duration of avalanches. We argue that the Mittag–Leffler (ML) exponential function is a satisfactory indicator of temporal complexity, namely of the occurrence of non-Poisson and renewal events. The assumption that the onset of criticality corresponds to the birth of renewal non-Poisson events establishes a neat distinction between the ML function and the power law avalanches generating regime. We find that

  13. Updating the lamellar hypothesis of hippocampal organization

    Directory of Open Access Journals (Sweden)

    Robert S Sloviter

    2012-12-01

    Full Text Available In 1971, Andersen and colleagues proposed that excitatory activity in the entorhinal cortex propagates topographically to the dentate gyrus, and on through a trisynaptic circuit lying within transverse hippocampal slices or lamellae [Andersen, Bliss, and Skrede. 1971. Lamellar organization of hippocampal pathways. Exp Brain Res 13, 222-238]. In this way, a relatively simple structure might mediate complex functions in a manner analogous to the way independent piano keys can produce a nearly infinite variety of unique outputs. The lamellar hypothesis derives primary support from the lamellar distribution of dentate granule cell axons (the mossy fibers, which innervate dentate hilar neurons and area CA3 pyramidal cells and interneurons within the confines of a thin transverse hippocampal segment. Following the initial formulation of the lamellar hypothesis, anatomical studies revealed that unlike granule cells, hilar mossy cells, CA3 pyramidal cells, and Layer II entorhinal cells all form axonal projections that are more divergent along the longitudinal axis than the clearly lamellar mossy fiber pathway. The existence of pathways with translamellar distribution patterns has been interpreted, incorrectly in our view, as justifying outright rejection of the lamellar hypothesis [Amaral and Witter. 1989. The three-dimensional organization of the hippocampal formation: a review of anatomical data. Neuroscience 31, 571-591]. We suggest that the functional implications of longitudinally-projecting axons depend not on whether they exist, but on what they do. The observation that focal granule cell layer discharges normally inhibit, rather than excite, distant granule cells suggests that longitudinal axons in the dentate gyrus may mediate "lateral" inhibition and define lamellar function, rather than undermine it. In this review, we attempt a reconsideration of the evidence that most directly impacts the physiological concept of hippocampal lamellar

  14. Is PMI the Hypothesis or the Null Hypothesis?

    Science.gov (United States)

    Tarone, Aaron M; Sanford, Michelle R

    2017-09-01

    Over the past several decades, there have been several strident exchanges regarding whether forensic entomologists estimate the postmortem interval (PMI), minimum PMI, or something else. During that time, there has been a proliferation of terminology reflecting this concern regarding "what we do." This has been a frustrating conversation for some in the community because much of this debate appears to be centered on what assumptions are acknowledged directly and which are embedded within a list of assumptions (or ignored altogether) in the literature and in case reports. An additional component of the conversation centers on a concern that moving away from the use of certain terminology like PMI acknowledges limitations and problems that would make the application of entomology appear less useful in court-a problem for lawyers, but one that should not be problematic for scientists in the forensic entomology community, as uncertainty is part of science that should and can be presented effectively in the courtroom (e.g., population genetic concepts in forensics). Unfortunately, a consequence of the way this conversation is conducted is that even as all involved in the debate acknowledge the concerns of their colleagues, parties continue to talk past one another advocating their preferred terminology. Progress will not be made until the community recognizes that all of the terms under consideration take the form of null hypothesis statements and that thinking about "what we do" as a null hypothesis has useful legal and scientific ramifications that transcend arguments over the usage of preferred terminology. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  15. Case report 332: A complex anomaly of the craniovertebral junction representing a regressive malformation with agenesis of the neural arch of C-2, hypomorphogenesis at C5-C6 and instability of the upper cervical spine

    International Nuclear Information System (INIS)

    Bernini, F.P.; Muras, I.

    1985-01-01

    In summary, a very complex anomaly, including agenesis of the neural arch of the axis, hypomorphogenesis of the cervical spine at the C5-C6 level with a partial ''blocked'' vertebra and disability of the upper cervical spine, is reported in a 31-year-old man. The anomalies associated with these changes are described in detail in the text and illustrated radiologically. The relationship of the embryological alterations in contrast with the normal is described and emphasized, particularly in the upper cervical area. It is stressed that narrowing of the space from the back of the odontoid (or the posterior lip of the foramen magnum) is a direct result of the complex anomalies described in this case, producing compression of the medulla and/or the upper cervical spinal cord. The literature on this subject is reviewed in depth. (orig.)

  16. Inoculation stress hypothesis of environmental enrichment.

    Science.gov (United States)

    Crofton, Elizabeth J; Zhang, Yafang; Green, Thomas A

    2015-02-01

    One hallmark of psychiatric conditions is the vast continuum of individual differences in susceptibility vs. resilience resulting from the interaction of genetic and environmental factors. The environmental enrichment paradigm is an animal model that is useful for studying a range of psychiatric conditions, including protective phenotypes in addiction and depression models. The major question is how environmental enrichment, a non-drug and non-surgical manipulation, can produce such robust individual differences in such a wide range of behaviors. This paper draws from a variety of published sources to outline a coherent hypothesis of inoculation stress as a factor producing the protective enrichment phenotypes. The basic tenet suggests that chronic mild stress from living in a complex environment and interacting non-aggressively with conspecifics can inoculate enriched rats against subsequent stressors and/or drugs of abuse. This paper reviews the enrichment phenotypes, mulls the fundamental nature of environmental enrichment vs. isolation, discusses the most appropriate control for environmental enrichment, and challenges the idea that cortisol/corticosterone equals stress. The intent of the inoculation stress hypothesis of environmental enrichment is to provide a scaffold with which to build testable hypotheses for the elucidation of the molecular mechanisms underlying these protective phenotypes and thus provide new therapeutic targets to treat psychiatric/neurological conditions. Copyright © 2014 Elsevier Ltd. All rights reserved.

  17. Evolvable synthetic neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2009-01-01

    An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

  18. Sigmund Freud and the Crick-Koch hypothesis. A footnote to the history of consciousness studies.

    Science.gov (United States)

    Smith, D L

    1999-06-01

    The author describes Crick and Koch's recently developed theory of the neurophysiological basis of consciousness as synchronised neural oscillations. The thesis that neural oscillations provide the neurophysiological basis for consciousness was anticipated by Sigmund Freud in his 1895 'Project for a scientific psychology'. Freud attempted to solve his neuropsychological 'problem of quality' by means of the hypothesis that information concerning conscious sensory qualities is transmitted through the mental apparatus by means of neural 'periods'. Freud believed that information carried by neural oscillations would proliferate across 'contact-barriers' (synapses) without inhibition. Freud's theory thus appears to imply that synchronised neural oscillations are an important component of the neurophysiological basis of consciousness. It is possible that Freud's thesis was developed in response to the experimental research of the American neuroscientist M. M. Garver.

  19. Novel High-Viscosity Polyacrylamidated Chitosan for Neural Tissue Engineering: Fabrication of Anisotropic Neurodurable Scaffold via Molecular Disposition of Persulfate-Mediated Polymer Slicing and Complexation

    Directory of Open Access Journals (Sweden)

    Viness Pillay

    2012-10-01

    Full Text Available Macroporous polyacrylamide-grafted-chitosan scaffolds for neural tissue engineering were fabricated with varied synthetic and viscosity profiles. A novel approach and mechanism was utilized for polyacrylamide grafting onto chitosan using potassium persulfate (KPS mediated degradation of both polymers under a thermally controlled environment. Commercially available high molecular mass polyacrylamide was used instead of the acrylamide monomer for graft copolymerization. This grafting strategy yielded an enhanced grafting efficiency (GE = 92%, grafting ratio (GR = 263%, intrinsic viscosity (IV = 5.231 dL/g and viscometric average molecular mass (MW = 1.63 × 106 Da compared with known acrylamide that has a GE = 83%, GR = 178%, IV = 3.901 dL/g and MW = 1.22 × 106 Da. Image processing analysis of SEM images of the newly grafted neurodurable scaffold was undertaken based on the polymer-pore threshold. Attenuated Total Reflectance-FTIR spectral analyses in conjugation with DSC were used for the characterization and comparison of the newly grafted copolymers. Static Lattice Atomistic Simulations were employed to investigate and elucidate the copolymeric assembly and reaction mechanism by exploring the spatial disposition of chitosan and polyacrylamide with respect to the reactional profile of potassium persulfate. Interestingly, potassium persulfate, a peroxide, was found to play a dual role initially degrading the polymers—“polymer slicing”—thereby initiating the formation of free radicals and subsequently leading to synthesis of the high molecular mass polyacrylamide-grafted-chitosan (PAAm-g-CHT—“polymer complexation”. Furthermore, the applicability of the uniquely grafted scaffold for neural tissue engineering was evaluated via PC12 neuronal cell seeding. The novel PAAm-g-CHT exhibited superior neurocompatibility in terms of cell infiltration owing to the anisotropic porous architecture, high molecular mass mediated robustness

  20. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  1. Multiple roles for executive control in belief-desire reasoning: distinct neural networks are recruited for self perspective inhibition and complexity of reasoning.

    Science.gov (United States)

    Hartwright, Charlotte E; Apperly, Ian A; Hansen, Peter C

    2012-07-16

    Belief-desire reasoning is a core component of 'Theory of Mind' (ToM), which can be used to explain and predict the behaviour of agents. Neuroimaging studies reliably identify a network of brain regions comprising a 'standard' network for ToM, including temporoparietal junction and medial prefrontal cortex. Whilst considerable experimental evidence suggests that executive control (EC) may support a functioning ToM, co-ordination of neural systems for ToM and EC is poorly understood. We report here use of a novel task in which psychologically relevant ToM parameters (true versus false belief; approach versus avoidance desire) were manipulated orthogonally. The valence of these parameters not only modulated brain activity in the 'standard' ToM network but also in EC regions. Varying the valence of both beliefs and desires recruits anterior cingulate cortex, suggesting a shared inhibitory component associated with negatively valenced mental state concepts. Varying the valence of beliefs additionally draws on ventrolateral prefrontal cortex, reflecting the need to inhibit self perspective. These data provide the first evidence that separate functional and neural systems for EC may be recruited in the service of different aspects of ToM. Copyright © 2012 Elsevier Inc. All rights reserved.

  2. Robust and distributed hypothesis testing

    CERN Document Server

    Gül, Gökhan

    2017-01-01

    This book generalizes and extends the available theory in robust and decentralized hypothesis testing. In particular, it presents a robust test for modeling errors which is independent from the assumptions that a sufficiently large number of samples is available, and that the distance is the KL-divergence. Here, the distance can be chosen from a much general model, which includes the KL-divergence as a very special case. This is then extended by various means. A minimax robust test that is robust against both outliers as well as modeling errors is presented. Minimax robustness properties of the given tests are also explicitly proven for fixed sample size and sequential probability ratio tests. The theory of robust detection is extended to robust estimation and the theory of robust distributed detection is extended to classes of distributions, which are not necessarily stochastically bounded. It is shown that the quantization functions for the decision rules can also be chosen as non-monotone. Finally, the boo...

  3. Alien abduction: a medical hypothesis.

    Science.gov (United States)

    Forrest, David V

    2008-01-01

    In response to a new psychological study of persons who believe they have been abducted by space aliens that found that sleep paralysis, a history of being hypnotized, and preoccupation with the paranormal and extraterrestrial were predisposing experiences, I noted that many of the frequently reported particulars of the abduction experience bear more than a passing resemblance to medical-surgical procedures and propose that experience with these may also be contributory. There is the altered state of consciousness, uniformly colored figures with prominent eyes, in a high-tech room under a round bright saucerlike object; there is nakedness, pain and a loss of control while the body's boundaries are being probed; and yet the figures are thought benevolent. No medical-surgical history was apparently taken in the above mentioned study, but psychological laboratory work evaluated false memory formation. I discuss problems in assessing intraoperative awareness and ways in which the medical hypothesis could be elaborated and tested. If physicians are causing this syndrome in a percentage of patients, we should know about it; and persons who feel they have been abducted should be encouraged to inform their surgeons and anesthesiologists without challenging their beliefs.

  4. Introduction to neural networks

    International Nuclear Information System (INIS)

    Pavlopoulos, P.

    1996-01-01

    This lecture is a presentation of today's research in neural computation. Neural computation is inspired by knowledge from neuro-science. It draws its methods in large degree from statistical physics and its potential applications lie mainly in computer science and engineering. Neural networks models are algorithms for cognitive tasks, such as learning and optimization, which are based on concepts derived from research into the nature of the brain. The lecture first gives an historical presentation of neural networks development and interest in performing complex tasks. Then, an exhaustive overview of data management and networks computation methods is given: the supervised learning and the associative memory problem, the capacity of networks, the Perceptron networks, the functional link networks, the Madaline (Multiple Adalines) networks, the back-propagation networks, the reduced coulomb energy (RCE) networks, the unsupervised learning and the competitive learning and vector quantization. An example of application in high energy physics is given with the trigger systems and track recognition system (track parametrization, event selection and particle identification) developed for the CPLEAR experiment detectors from the LEAR at CERN. (J.S.). 56 refs., 20 figs., 1 tab., 1 appendix

  5. The Neural Border: Induction, Specification and Maturation of the territory that generates Neural Crest cells.

    Science.gov (United States)

    Pla, Patrick; Monsoro-Burq, Anne H

    2018-05-28

    The neural crest is induced at the edge between the neural plate and the nonneural ectoderm, in an area called the neural (plate) border, during gastrulation and neurulation. In recent years, many studies have explored how this domain is patterned, and how the neural crest is induced within this territory, that also participates to the prospective dorsal neural tube, the dorsalmost nonneural ectoderm, as well as placode derivatives in the anterior area. This review highlights the tissue interactions, the cell-cell signaling and the molecular mechanisms involved in this dynamic spatiotemporal patterning, resulting in the induction of the premigratory neural crest. Collectively, these studies allow building a complex neural border and early neural crest gene regulatory network, mostly composed by transcriptional regulations but also, more recently, including novel signaling interactions. Copyright © 2018. Published by Elsevier Inc.

  6. Mode Choice Modeling Using Artificial Neural Networks

    OpenAIRE

    Edara, Praveen Kumar

    2003-01-01

    Artificial intelligence techniques have produced excellent results in many diverse fields of engineering. Techniques such as neural networks and fuzzy systems have found their way into transportation engineering. In recent years, neural networks are being used instead of regression techniques for travel demand forecasting purposes. The basic reason lies in the fact that neural networks are able to capture complex relationships and learn from examples and also able to adapt when new data becom...

  7. Adaptive optimization and control using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.

    1993-10-22

    Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.

  8. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...

  9. Memristor-based neural networks

    International Nuclear Information System (INIS)

    Thomas, Andy

    2013-01-01

    The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them. (topical review)

  10. Interpretable neural networks with BP-SOM

    NARCIS (Netherlands)

    Weijters, A.J.M.M.; Bosch, van den A.P.J.; Pobil, del A.P.; Mira, J.; Ali, M.

    1998-01-01

    Artificial Neural Networks (ANNS) are used successfully in industry and commerce. This is not surprising since neural networks are especially competitive for complex tasks for which insufficient domain-specific knowledge is available. However, interpretation of models induced by ANNS is often

  11. Shared neural coding for social hierarchy and reward value in primate amygdala.

    Science.gov (United States)

    Munuera, Jérôme; Rigotti, Mattia; Salzman, C Daniel

    2018-03-01

    The social brain hypothesis posits that dedicated neural systems process social information. In support of this, neurophysiological data have shown that some brain regions are specialized for representing faces. It remains unknown, however, whether distinct anatomical substrates also represent more complex social variables, such as the hierarchical rank of individuals within a social group. Here we show that the primate amygdala encodes the hierarchical rank of individuals in the same neuronal ensembles that encode the rewards associated with nonsocial stimuli. By contrast, orbitofrontal and anterior cingulate cortices lack strong representations of hierarchical rank while still representing reward values. These results challenge the conventional view that dedicated neural systems process social information. Instead, information about hierarchical rank-which contributes to the assessment of the social value of individuals within a group-is linked in the amygdala to representations of rewards associated with nonsocial stimuli.

  12. Neural Networks

    Directory of Open Access Journals (Sweden)

    Schwindling Jerome

    2010-04-01

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

  13. Common neural substrates for visual working memory and attention.

    Science.gov (United States)

    Mayer, Jutta S; Bittner, Robert A; Nikolić, Danko; Bledowski, Christoph; Goebel, Rainer; Linden, David E J

    2007-06-01

    Humans are severely limited in their ability to memorize visual information over short periods of time. Selective attention has been implicated as a limiting factor. Here we used functional magnetic resonance imaging to test the hypothesis that this limitation is due to common neural resources shared by visual working memory (WM) and selective attention. We combined visual search and delayed discrimination of complex objects and independently modulated the demands on selective attention and WM encoding. Participants were presented with a search array and performed easy or difficult visual search in order to encode one or three complex objects into visual WM. Overlapping activation for attention-demanding visual search and WM encoding was observed in distributed posterior and frontal regions. In the right prefrontal cortex and bilateral insula blood oxygen-level-dependent activation additively increased with increased WM load and attentional demand. Conversely, several visual, parietal and premotor areas showed overlapping activation for the two task components and were severely reduced in their WM load response under the condition with high attentional demand. Regions in the left prefrontal cortex were selectively responsive to WM load. Areas selectively responsive to high attentional demand were found within the right prefrontal and bilateral occipital cortex. These results indicate that encoding into visual WM and visual selective attention require to a high degree access to common neural resources. We propose that competition for resources shared by visual attention and WM encoding can limit processing capabilities in distributed posterior brain regions.

  14. Statistical inferences under the Null hypothesis: Common mistakes and pitfalls in neuroimaging studies.

    Directory of Open Access Journals (Sweden)

    Jean-Michel eHupé

    2015-02-01

    Full Text Available Published studies using functional and structural MRI include many errors in the way data are analyzed and conclusions reported. This was observed when working on a comprehensive review of the neural bases of synesthesia, but these errors are probably endemic to neuroimaging studies. All studies reviewed had based their conclusions using Null Hypothesis Significance Tests (NHST. NHST have yet been criticized since their inception because they are more appropriate for taking decisions related to a Null hypothesis (like in manufacturing than for making inferences about behavioral and neuronal processes. Here I focus on a few key problems of NHST related to brain imaging techniques, and explain why or when we should not rely on significance tests. I also observed that, often, the ill-posed logic of NHST was even not correctly applied, and describe what I identified as common mistakes or at least problematic practices in published papers, in light of what could be considered as the very basics of statistical inference. MRI statistics also involve much more complex issues than standard statistical inference. Analysis pipelines vary a lot between studies, even for those using the same software, and there is no consensus which pipeline is the best. I propose a synthetic view of the logic behind the possible methodological choices, and warn against the usage and interpretation of two statistical methods popular in brain imaging studies, the false discovery rate (FDR procedure and permutation tests. I suggest that current models for the analysis of brain imaging data suffer from serious limitations and call for a revision taking into account the new statistics (confidence intervals logic.

  15. DAMPs, ageing, and cancer: The 'DAMP Hypothesis'.

    Science.gov (United States)

    Huang, Jin; Xie, Yangchun; Sun, Xiaofang; Zeh, Herbert J; Kang, Rui; Lotze, Michael T; Tang, Daolin

    2015-11-01

    Ageing is a complex and multifactorial process characterized by the accumulation of many forms of damage at the molecular, cellular, and tissue level with advancing age. Ageing increases the risk of the onset of chronic inflammation-associated diseases such as cancer, diabetes, stroke, and neurodegenerative disease. In particular, ageing and cancer share some common origins and hallmarks such as genomic instability, epigenetic alteration, aberrant telomeres, inflammation and immune injury, reprogrammed metabolism, and degradation system impairment (including within the ubiquitin-proteasome system and the autophagic machinery). Recent advances indicate that damage-associated molecular pattern molecules (DAMPs) such as high mobility group box 1, histones, S100, and heat shock proteins play location-dependent roles inside and outside the cell. These provide interaction platforms at molecular levels linked to common hallmarks of ageing and cancer. They can act as inducers, sensors, and mediators of stress through individual plasma membrane receptors, intracellular recognition receptors (e.g., advanced glycosylation end product-specific receptors, AIM2-like receptors, RIG-I-like receptors, and NOD1-like receptors, and toll-like receptors), or following endocytic uptake. Thus, the DAMP Hypothesis is novel and complements other theories that explain the features of ageing. DAMPs represent ideal biomarkers of ageing and provide an attractive target for interventions in ageing and age-associated diseases. Copyright © 2014 Elsevier B.V. All rights reserved.

  16. Prediction based chaos control via a new neural network

    International Nuclear Information System (INIS)

    Shen Liqun; Wang Mao; Liu Wanyu; Sun Guanghui

    2008-01-01

    In this Letter, a new chaos control scheme based on chaos prediction is proposed. To perform chaos prediction, a new neural network architecture for complex nonlinear approximation is proposed. And the difficulty in building and training the neural network is also reduced. Simulation results of Logistic map and Lorenz system show the effectiveness of the proposed chaos control scheme and the proposed neural network

  17. Validity of Linder Hypothesis in Bric Countries

    Directory of Open Access Journals (Sweden)

    Rana Atabay

    2016-03-01

    Full Text Available In this study, the theory of similarity in preferences (Linder hypothesis has been introduced and trade in BRIC countries has been examined whether the trade between these countries was valid for this hypothesis. Using the data for the period 1996 – 2010, the study applies to panel data analysis in order to provide evidence regarding the empirical validity of the Linder hypothesis for BRIC countries’ international trade. Empirical findings show that the trade between BRIC countries is in support of Linder hypothesis.

  18. Why Does REM Sleep Occur? A Wake-up Hypothesis

    OpenAIRE

    Dr. W. R. eKlemm

    2011-01-01

    Brain activity differs in the various sleep stages and in conscious wakefulness. Awakening from sleep requires restoration of the complex nerve impulse patterns in neuronal network assemblies necessary to re-create and sustain conscious wakefulness. Herein I propose that the brain uses REM to help wake itself up after it has had a sufficient amount of sleep. Evidence suggesting this hypothesis includes the facts that, 1) when first going to sleep, the brain plunges into Stage N3 (formerly ca...

  19. Hypothesis Testing in the Real World

    Science.gov (United States)

    Miller, Jeff

    2017-01-01

    Critics of null hypothesis significance testing suggest that (a) its basic logic is invalid and (b) it addresses a question that is of no interest. In contrast to (a), I argue that the underlying logic of hypothesis testing is actually extremely straightforward and compelling. To substantiate that, I present examples showing that hypothesis…

  20. Error probabilities in default Bayesian hypothesis testing

    NARCIS (Netherlands)

    Gu, Xin; Hoijtink, Herbert; Mulder, J,

    2016-01-01

    This paper investigates the classical type I and type II error probabilities of default Bayes factors for a Bayesian t test. Default Bayes factors quantify the relative evidence between the null hypothesis and the unrestricted alternative hypothesis without needing to specify prior distributions for

  1. Reassessing the Trade-off Hypothesis

    DEFF Research Database (Denmark)

    Rosas, Guillermo; Manzetti, Luigi

    2015-01-01

    Do economic conditions drive voters to punish politicians that tolerate corruption? Previous scholarly work contends that citizens in young democracies support corrupt governments that are capable of promoting good economic outcomes, the so-called trade-off hypothesis. We test this hypothesis based...

  2. Mastery Learning and the Decreasing Variability Hypothesis.

    Science.gov (United States)

    Livingston, Jennifer A.; Gentile, J. Ronald

    1996-01-01

    This report results from studies that tested two variations of Bloom's decreasing variability hypothesis using performance on successive units of achievement in four graduate classrooms that used mastery learning procedures. Data do not support the decreasing variability hypothesis; rather, they show no change over time. (SM)

  3. Optimization behavior of brainstem respiratory neurons. A cerebral neural network model.

    Science.gov (United States)

    Poon, C S

    1991-01-01

    A recent model of respiratory control suggested that the steady-state respiratory responses to CO2 and exercise may be governed by an optimal control law in the brainstem respiratory neurons. It was not certain, however, whether such complex optimization behavior could be accomplished by a realistic biological neural network. To test this hypothesis, we developed a hybrid computer-neural model in which the dynamics of the lung, brain and other tissue compartments were simulated on a digital computer. Mimicking the "controller" was a human subject who pedalled on a bicycle with varying speed (analog of ventilatory output) with a view to minimize an analog signal of the total cost of breathing (chemical and mechanical) which was computed interactively and displayed on an oscilloscope. In this manner, the visuomotor cortex served as a proxy (homolog) of the brainstem respiratory neurons in the model. Results in 4 subjects showed a linear steady-state ventilatory CO2 response to arterial PCO2 during simulated CO2 inhalation and a nearly isocapnic steady-state response during simulated exercise. Thus, neural optimization is a plausible mechanism for respiratory control during exercise and can be achieved by a neural network with cognitive computational ability without the need for an exercise stimulus.

  4. From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

    Science.gov (United States)

    Ross, Muriel D.

    1991-01-01

    The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

  5. Serotonin, neural markers and memory

    Directory of Open Access Journals (Sweden)

    Alfredo eMeneses

    2015-07-01

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

  6. New Markov-Shannon Entropy models to assess connectivity quality in complex networks: from molecular to cellular pathway, Parasite-Host, Neural, Industry, and Legal-Social networks.

    Science.gov (United States)

    Riera-Fernández, Pablo; Munteanu, Cristian R; Escobar, Manuel; Prado-Prado, Francisco; Martín-Romalde, Raquel; Pereira, David; Villalba, Karen; Duardo-Sánchez, Aliuska; González-Díaz, Humberto

    2012-01-21

    Graph and Complex Network theory is expanding its application to different levels of matter organization such as molecular, biological, technological, and social networks. A network is a set of items, usually called nodes, with connections between them, which are called links or edges. There are many different experimental and/or theoretical methods to assign node-node links depending on the type of network we want to construct. Unfortunately, the use of a method for experimental reevaluation of the entire network is very expensive in terms of time and resources; thus the development of cheaper theoretical methods is of major importance. In addition, different methods to link nodes in the same type of network are not totally accurate in such a way that they do not always coincide. In this sense, the development of computational methods useful to evaluate connectivity quality in complex networks (a posteriori of network assemble) is a goal of major interest. In this work, we report for the first time a new method to calculate numerical quality scores S(L(ij)) for network links L(ij) (connectivity) based on the Markov-Shannon Entropy indices of order k-th (θ(k)) for network nodes. The algorithm may be summarized as follows: (i) first, the θ(k)(j) values are calculated for all j-th nodes in a complex network already constructed; (ii) A Linear Discriminant Analysis (LDA) is used to seek a linear equation that discriminates connected or linked (L(ij)=1) pairs of nodes experimentally confirmed from non-linked ones (L(ij)=0); (iii) the new model is validated with external series of pairs of nodes; (iv) the equation obtained is used to re-evaluate the connectivity quality of the network, connecting/disconnecting nodes based on the quality scores calculated with the new connectivity function. This method was used to study different types of large networks. The linear models obtained produced the following results in terms of overall accuracy for network reconstruction

  7. The neural cell adhesion molecule

    DEFF Research Database (Denmark)

    Berezin, V; Bock, E; Poulsen, F M

    2000-01-01

    During the past year, the understanding of the structure and function of neural cell adhesion has advanced considerably. The three-dimensional structures of several of the individual modules of the neural cell adhesion molecule (NCAM) have been determined, as well as the structure of the complex...... between two identical fragments of the NCAM. Also during the past year, a link between homophilic cell adhesion and several signal transduction pathways has been proposed, connecting the event of cell surface adhesion to cellular responses such as neurite outgrowth. Finally, the stimulation of neurite...

  8. Cross-cultural differences in cognitive performance and Spearman's hypothesis : g or c?

    NARCIS (Netherlands)

    Helms-Lorenz, M; Van de Vijver, FJR; Poortinga, YH

    2003-01-01

    Common tests of Spearman's hypothesis, according to which performance differences between cultural groups on cognitive tests increase with their g loadings, confound cognitive complexity and verbal-cultural aspects. The present study attempts to disentangle these components. Two intelligence

  9. Synchronization and phonological skills: precise auditory timing hypothesis (PATH

    Directory of Open Access Journals (Sweden)

    Adam eTierney

    2014-11-01

    Full Text Available Phonological skills are enhanced by music training, but the mechanisms enabling this cross-domain enhancement remain unknown. To explain this cross-domain transfer, we propose a precise auditory timing hypothesis (PATH whereby entrainment practice is the core mechanism underlying enhanced phonological abilities in musicians. Both rhythmic synchronization and language skills such as consonant discrimination, detection of word and phrase boundaries, and conversational turn-taking rely on the perception of extremely fine-grained timing details in sound. Auditory-motor timing is an acoustic feature which meets all five of the pre-conditions necessary for cross-domain enhancement to occur (Patel 2011, 2012, 2014. There is overlap between the neural networks that process timing in the context of both music and language. Entrainment to music demands more precise timing sensitivity than does language processing. Moreover, auditory-motor timing integration captures the emotion of the trainee, is repeatedly practiced, and demands focused attention. The precise auditory timing hypothesis predicts that musical training emphasizing entrainment will be particularly effective in enhancing phonological skills.

  10. Diprosopia revisited in light of the recognized role of neural crest cells in facial development.

    Science.gov (United States)

    Carles, D; Weichhold, W; Alberti, E M; Léger, F; Pigeau, F; Horovitz, J

    1995-01-01

    The aim of this study is to compare the theory of embryogenesis of the face with human diprosopia. This peculiar form of conjoined twinning is of great interest because 1) only the facial structures are duplicated and 2) almost all cases have a rather monomorphic pattern. The hypothesis is that an initial duplication of the notochord leads to two neural plates and subsequently duplicated neural crests. In those conditions, derivatives of the neural crests will be partially or totally duplicated; therefore, in diprosopia, the duplicated facial structures would be considered to be neural crest derivatives. If these structures are identical to those that are experimentally demonstrated to be neural crest derivatives in animals, these findings are an argument to apply this theory of facial embryogenesis in man. Serial horizontal sections of the face of two diprosopic fetuses (11 and 21 weeks gestation) were studied macro- and microscopically to determine the external and internal structures that are duplicated. Complete postmortem examination was performed in search for additional malformations. The face of both fetuses showed a very similar morphologic pattern with duplication of ocular, nasal, and buccal structures. The nasal fossae and the anterior part of the tongue were also duplicated, albeit the posterior part and the pharyngolaryngeal structures were unique. Additional facial clefts were present in both fetuses. Extrafacial anomalies were represented by a craniorachischisis, two fused vertebral columns and, in the older fetus, by a complex cardiac malformation morphologically identical to malformations induced by removal or grafting of additional cardiac neural crest cells in animals. These pathological findings could identify the facial structures that are neural crest derivatives in man. They are similar to those experimentally demonstrated to be neural crest derivatives in animals. In this respect, diprosopia could be considered as the end of a spectrum

  11. Development and application of artificial neural network models to estimate values of a complex human thermal comfort index associated with urban heat and cool island patterns using air temperature data from a standard meteorological station.

    Science.gov (United States)

    Moustris, Konstantinos; Tsiros, Ioannis X; Tseliou, Areti; Nastos, Panagiotis

    2018-04-11

    The present study deals with the development and application of artificial neural network models (ANNs) to estimate the values of a complex human thermal comfort-discomfort index associated with urban heat and cool island conditions inside various urban clusters using as only inputs air temperature data from a standard meteorological station. The index used in the study is the Physiologically Equivalent Temperature (PET) index which requires as inputs, among others, air temperature, relative humidity, wind speed, and radiation (short- and long-wave components). For the estimation of PET hourly values, ANN models were developed, appropriately trained, and tested. Model results are compared to values calculated by the PET index based on field monitoring data for various urban clusters (street, square, park, courtyard, and gallery) in the city of Athens (Greece) during an extreme hot weather summer period. For the evaluation of the predictive ability of the developed ANN models, several statistical evaluation indices were applied: the mean bias error, the root mean square error, the index of agreement, the coefficient of determination, the true predictive rate, the false alarm rate, and the Success Index. According to the results, it seems that ANNs present a remarkable ability to estimate hourly PET values within various urban clusters using only hourly values of air temperature. This is very important in cases where the human thermal comfort-discomfort conditions have to be analyzed and the only available parameter is air temperature.

  12. Development and application of artificial neural network models to estimate values of a complex human thermal comfort index associated with urban heat and cool island patterns using air temperature data from a standard meteorological station

    Science.gov (United States)

    Moustris, Konstantinos; Tsiros, Ioannis X.; Tseliou, Areti; Nastos, Panagiotis

    2018-04-01

    The present study deals with the development and application of artificial neural network models (ANNs) to estimate the values of a complex human thermal comfort-discomfort index associated with urban heat and cool island conditions inside various urban clusters using as only inputs air temperature data from a standard meteorological station. The index used in the study is the Physiologically Equivalent Temperature (PET) index which requires as inputs, among others, air temperature, relative humidity, wind speed, and radiation (short- and long-wave components). For the estimation of PET hourly values, ANN models were developed, appropriately trained, and tested. Model results are compared to values calculated by the PET index based on field monitoring data for various urban clusters (street, square, park, courtyard, and gallery) in the city of Athens (Greece) during an extreme hot weather summer period. For the evaluation of the predictive ability of the developed ANN models, several statistical evaluation indices were applied: the mean bias error, the root mean square error, the index of agreement, the coefficient of determination, the true predictive rate, the false alarm rate, and the Success Index. According to the results, it seems that ANNs present a remarkable ability to estimate hourly PET values within various urban clusters using only hourly values of air temperature. This is very important in cases where the human thermal comfort-discomfort conditions have to be analyzed and the only available parameter is air temperature.

  13. Implications of the Bohm-Aharonov hypothesis

    International Nuclear Information System (INIS)

    Ghirardi, G.C.; Rimini, A.; Weber, T.

    1976-01-01

    It is proved that the Bohm-Aharonov hypothesis concerning largerly separated subsystems of composite quantum systems implies that it is impossible to express the dynamical evolution in terms of the density operator

  14. Multi-agent sequential hypothesis testing

    KAUST Repository

    Kim, Kwang-Ki K.; Shamma, Jeff S.

    2014-01-01

    incorporate costs of taking private/public measurements, costs of time-difference and disagreement in actions of agents, and costs of false declaration/choices in the sequential hypothesis testing. The corresponding sequential decision processes have well

  15. The (not so) Immortal Strand Hypothesis

    OpenAIRE

    Tomasetti, Cristian; Bozic, Ivana

    2015-01-01

    Background: Non-random segregation of DNA strands during stem cell replication has been proposed as a mechanism to minimize accumulated genetic errors in stem cells of rapidly dividing tissues. According to this hypothesis, an “immortal” DNA strand is passed to the stem cell daughter and not the more differentiated cell, keeping the stem cell lineage replication error-free. After it was introduced, experimental evidence both in favor and against the hypothesis has been presented. Principal...

  16. Linking neural and symbolic representation and processing of conceptual structures

    NARCIS (Netherlands)

    van der Velde, Frank; Forth, Jamie; Nazareth, Deniece S.; Wiggins, Geraint A.

    2017-01-01

    We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like) structures. First is the Neural Blackboard Architecture (NBA), which aims to account for representation and processing of complex and combinatorial conceptual

  17. Optics in neural computation

    Science.gov (United States)

    Levene, Michael John

    In all attempts to emulate the considerable powers of the brain, one is struck by both its immense size, parallelism, and complexity. While the fields of neural networks, artificial intelligence, and neuromorphic engineering have all attempted oversimplifications on the considerable complexity, all three can benefit from the inherent scalability and parallelism of optics. This thesis looks at specific aspects of three modes in which optics, and particularly volume holography, can play a part in neural computation. First, holography serves as the basis of highly-parallel correlators, which are the foundation of optical neural networks. The huge input capability of optical neural networks make them most useful for image processing and image recognition and tracking. These tasks benefit from the shift invariance of optical correlators. In this thesis, I analyze the capacity of correlators, and then present several techniques for controlling the amount of shift invariance. Of particular interest is the Fresnel correlator, in which the hologram is displaced from the Fourier plane. In this case, the amount of shift invariance is limited not just by the thickness of the hologram, but by the distance of the hologram from the Fourier plane. Second, volume holography can provide the huge storage capacity and high speed, parallel read-out necessary to support large artificial intelligence systems. However, previous methods for storing data in volume holograms have relied on awkward beam-steering or on as-yet non- existent cheap, wide-bandwidth, tunable laser sources. This thesis presents a new technique, shift multiplexing, which is capable of very high densities, but which has the advantage of a very simple implementation. In shift multiplexing, the reference wave consists of a focused spot a few millimeters in front of the hologram. Multiplexing is achieved by simply translating the hologram a few tens of microns or less. This thesis describes the theory for how shift

  18. Visual Complexity: A Review

    Science.gov (United States)

    Donderi, Don C.

    2006-01-01

    The idea of visual complexity, the history of its measurement, and its implications for behavior are reviewed, starting with structuralism and Gestalt psychology at the beginning of the 20th century and ending with visual complexity theory, perceptual learning theory, and neural circuit theory at the beginning of the 21st. Evidence is drawn from…

  19. Dynamic decomposition of spatiotemporal neural signals.

    Directory of Open Access Journals (Sweden)

    Luca Ambrogioni

    2017-05-01

    Full Text Available Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals.

  20. Decentralized neural control application to robotics

    CERN Document Server

    Garcia-Hernandez, Ramon; Sanchez, Edgar N; Alanis, Alma y; Ruz-Hernandez, Jose A

    2017-01-01

    This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors. This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF). The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural i...

  1. Intelligent neural network diagnostic system

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

    Recently, artificial neural network (ANN) has made a significant mark in the domain of diagnostic applications. Neural networks are used to implement complex non-linear mappings (functions) using simple elementary units interrelated through connections with adaptive weights. The performance of the ANN is mainly depending on their topology structure and weights. Some systems have been developed using genetic algorithm (GA) to optimize the topology of the ANN. But, they suffer from some limitations. They are : (1) The computation time requires for training the ANN several time reaching for the average weight required, (2) Slowness of GA for optimization process and (3) Fitness noise appeared in the optimization of ANN. This research suggests new issues to overcome these limitations for finding optimal neural network architectures to learn particular problems. This proposed methodology is used to develop a diagnostic neural network system. It has been applied for a 600 MW turbo-generator as a case of real complex systems. The proposed system has proved its significant performance compared to two common methods used in the diagnostic applications.

  2. Changes in EEG complexity with electroconvulsive therapy in a patient with autism spectrum disorders: a multiscale entropy approach

    Directory of Open Access Journals (Sweden)

    Ryoko eOkazaki

    2015-02-01

    Full Text Available Autism spectrum disorders (ASD are heterogeneous neurodevelopmental disorders that are reportedly characterized by aberrant neural networks. Recently developed multiscale entropy analysis (MSE can characterize the complexity inherent in EEG dynamics over multiple temporal scales in the dynamics of neural networks. We encountered an 18-year-old man with ASD whose refractory catatonic obsessive–compulsive symptoms were improved dramatically after electroconvulsive therapy (ECT. In this clinical case study, we strove to clarify the neurophysiological mechanism of ECT in ASD by assessing EEG complexity using MSE. Along with ECT, the frontocentral region showed decreased EEG complexity at higher temporal scales, whereas the occipital region expressed an increase at lower temporal scales. Furthermore, these changes were associated with clinical improvement associated with the elevation of brain-derived neurotrophic factor, which is a molecular hypothesis of ECT, playing key roles in ASD pathogenesis. Changes in EEG complexity in a region-specific and temporal scale-specific manner we found might reflect atypical EEG dynamics in ASD. Although MSE is not a direct approach to measuring neural connectivity and the results are from only a single case, they might reflect specific aberrant neural network activity and the therapeutic neurophysiological mechanism of ECT in ASD.

  3. Multiple hypothesis tracking for the cyber domain

    Science.gov (United States)

    Schwoegler, Stefan; Blackman, Sam; Holsopple, Jared; Hirsch, Michael J.

    2011-09-01

    This paper discusses how methods used for conventional multiple hypothesis tracking (MHT) can be extended to domain-agnostic tracking of entities from non-kinematic constraints such as those imposed by cyber attacks in a potentially dense false alarm background. MHT is widely recognized as the premier method to avoid corrupting tracks with spurious data in the kinematic domain but it has not been extensively applied to other problem domains. The traditional approach is to tightly couple track maintenance (prediction, gating, filtering, probabilistic pruning, and target confirmation) with hypothesis management (clustering, incompatibility maintenance, hypothesis formation, and Nassociation pruning). However, by separating the domain specific track maintenance portion from the domain agnostic hypothesis management piece, we can begin to apply the wealth of knowledge gained from ground and air tracking solutions to the cyber (and other) domains. These realizations led to the creation of Raytheon's Multiple Hypothesis Extensible Tracking Architecture (MHETA). In this paper, we showcase MHETA for the cyber domain, plugging in a well established method, CUBRC's INFormation Engine for Real-time Decision making, (INFERD), for the association portion of the MHT. The result is a CyberMHT. We demonstrate the power of MHETA-INFERD using simulated data. Using metrics from both the tracking and cyber domains, we show that while no tracker is perfect, by applying MHETA-INFERD, advanced nonkinematic tracks can be captured in an automated way, perform better than non-MHT approaches, and decrease analyst response time to cyber threats.

  4. Aminoglycoside antibiotics and autism: a speculative hypothesis

    Directory of Open Access Journals (Sweden)

    Manev Hari

    2001-10-01

    Full Text Available Abstract Background Recently, it has been suspected that there is a relationship between therapy with some antibiotics and the onset of autism; but even more curious, some children benefited transiently from a subsequent treatment with a different antibiotic. Here, we speculate how aminoglycoside antibiotics might be associated with autism. Presentation We hypothesize that aminoglycoside antibiotics could a trigger the autism syndrome in susceptible infants by causing the stop codon readthrough, i.e., a misreading of the genetic code of a hypothetical critical gene, and/or b improve autism symptoms by correcting the premature stop codon mutation in a hypothetical polymorphic gene linked to autism. Testing Investigate, retrospectively, whether a link exists between aminoglycoside use (which is not extensive in children and the onset of autism symptoms (hypothesis "a", or between amino glycoside use and improvement of these symptoms (hypothesis "b". Whereas a prospective study to test hypothesis "a" is not ethically justifiable, a study could be designed to test hypothesis "b". Implications It should be stressed that at this stage no direct evidence supports our speculative hypothesis and that its main purpose is to initiate development of new ideas that, eventually, would improve our understanding of the pathobiology of autism.

  5. The equilibrium point hypothesis and its application to speech motor control.

    Science.gov (United States)

    Perrier, P; Ostry, D J; Laboissière, R

    1996-04-01

    In this paper, we address a number of issues in speech research in the context of the equilibrium point hypothesis of motor control. The hypothesis suggests that movements arise from shifts in the equilibrium position of the limb or the speech articulator. The equilibrium is a consequence of the interaction of central neural commands, reflex mechanisms, muscle properties, and external loads, but it is under the control of central neural commands. These commands act to shift the equilibrium via centrally specified signals acting at the level of the motoneurone (MN) pool. In the context of a model of sagittal plane jaw and hyoid motion based on the lambda version of the equilibrium point hypothesis, we consider the implications of this hypothesis for the notion of articulatory targets. We suggest that simple linear control signals may underlie smooth articulatory trajectories. We explore as well the phenomenon of intraarticulator coarticulation in jaw movement. We suggest that even when no account is taken of upcoming context, that apparent anticipatory changes in movement amplitude and duration may arise due to dynamics. We also present a number of simulations that show in different ways how variability in measured kinematics can arise in spite of constant magnitude speech control signals.

  6. Neural Synchronization and Cryptography

    Science.gov (United States)

    Ruttor, Andreas

    2007-11-01

    Neural networks can synchronize by learning from each other. In the case of discrete weights full synchronization is achieved in a finite number of steps. Additional networks can be trained by using the inputs and outputs generated during this process as examples. Several learning rules for both tasks are presented and analyzed. In the case of Tree Parity Machines synchronization is much faster than learning. Scaling laws for the number of steps needed for full synchronization and successful learning are derived using analytical models. They indicate that the difference between both processes can be controlled by changing the synaptic depth. In the case of bidirectional interaction the synchronization time increases proportional to the square of this parameter, but it grows exponentially, if information is transmitted in one direction only. Because of this effect neural synchronization can be used to construct a cryptographic key-exchange protocol. Here the partners benefit from mutual interaction, so that a passive attacker is usually unable to learn the generated key in time. The success probabilities of different attack methods are determined by numerical simulations and scaling laws are derived from the data. They show that the partners can reach any desired level of security by just increasing the synaptic depth. Then the complexity of a successful attack grows exponentially, but there is only a polynomial increase of the effort needed to generate a key. Further improvements of security are possible by replacing the random inputs with queries generated by the partners.

  7. Testing competing forms of the Milankovitch hypothesis

    DEFF Research Database (Denmark)

    Kaufmann, Robert K.; Juselius, Katarina

    2016-01-01

    We test competing forms of the Milankovitch hypothesis by estimating the coefficients and diagnostic statistics for a cointegrated vector autoregressive model that includes 10 climate variables and four exogenous variables for solar insolation. The estimates are consistent with the physical...... ice volume and solar insolation. The estimated adjustment dynamics show that solar insolation affects an array of climate variables other than ice volume, each at a unique rate. This implies that previous efforts to test the strong form of the Milankovitch hypothesis by examining the relationship...... that the latter is consistent with a weak form of the Milankovitch hypothesis and that it should be restated as follows: Internal climate dynamics impose perturbations on glacial cycles that are driven by solar insolation. Our results show that these perturbations are likely caused by slow adjustment between land...

  8. Rejecting the equilibrium-point hypothesis.

    Science.gov (United States)

    Gottlieb, G L

    1998-01-01

    The lambda version of the equilibrium-point (EP) hypothesis as developed by Feldman and colleagues has been widely used and cited with insufficient critical understanding. This article offers a small antidote to that lack. First, the hypothesis implicitly, unrealistically assumes identical transformations of lambda into muscle tension for antagonist muscles. Without that assumption, its definitions of command variables R, C, and lambda are incompatible and an EP is not defined exclusively by R nor is it unaffected by C. Second, the model assumes unrealistic and unphysiological parameters for the damping properties of the muscles and reflexes. Finally, the theory lacks rules for two of its three command variables. A theory of movement should offer insight into why we make movements the way we do and why we activate muscles in particular patterns. The EP hypothesis offers no unique ideas that are helpful in addressing either of these questions.

  9. The linear hypothesis and radiation carcinogenesis

    International Nuclear Information System (INIS)

    Roberts, P.B.

    1981-10-01

    An assumption central to most estimations of the carcinogenic potential of low levels of ionising radiation is that the risk always increases in direct proportion to the dose received. This assumption (the linear hypothesis) has been both strongly defended and attacked on several counts. It appears unlikely that conclusive, direct evidence on the validity of the hypothesis will be forthcoming. We review the major indirect arguments used in the debate. All of them are subject to objections that can seriously weaken their case. In the present situation, retention of the linear hypothesis as the basis of extrapolations from high to low dose levels can lead to excessive fears, over-regulation and unnecessarily expensive protection measures. To offset these possibilities, support is given to suggestions urging a cut-off dose, probably some fraction of natural background, below which risks can be deemed acceptable

  10. Rayleigh's hypothesis and the geometrical optics limit.

    Science.gov (United States)

    Elfouhaily, Tanos; Hahn, Thomas

    2006-09-22

    The Rayleigh hypothesis (RH) is often invoked in the theoretical and numerical treatment of rough surface scattering in order to decouple the analytical form of the scattered field. The hypothesis stipulates that the scattered field away from the surface can be extended down onto the rough surface even though it is formed by solely up-going waves. Traditionally this hypothesis is systematically used to derive the Volterra series under the small perturbation method which is equivalent to the low-frequency limit. In this Letter we demonstrate that the RH also carries the high-frequency or the geometrical optics limit, at least to first order. This finding has never been explicitly derived in the literature. Our result comforts the idea that the RH might be an exact solution under some constraints in the general case of random rough surfaces and not only in the case of small-slope deterministic periodic gratings.

  11. Computational modeling of neural plasticity for self-organization of neural networks.

    Science.gov (United States)

    Chrol-Cannon, Joseph; Jin, Yaochu

    2014-11-01

    Self-organization in biological nervous systems during the lifetime is known to largely occur through a process of plasticity that is dependent upon the spike-timing activity in connected neurons. In the field of computational neuroscience, much effort has been dedicated to building up computational models of neural plasticity to replicate experimental data. Most recently, increasing attention has been paid to understanding the role of neural plasticity in functional and structural neural self-organization, as well as its influence on the learning performance of neural networks for accomplishing machine learning tasks such as classification and regression. Although many ideas and hypothesis have been suggested, the relationship between the structure, dynamics and learning performance of neural networks remains elusive. The purpose of this article is to review the most important computational models for neural plasticity and discuss various ideas about neural plasticity's role. Finally, we suggest a few promising research directions, in particular those along the line that combines findings in computational neuroscience and systems biology, and their synergetic roles in understanding learning, memory and cognition, thereby bridging the gap between computational neuroscience, systems biology and computational intelligence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  12. 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.

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

    Science.gov (United States)

    Gisiger, Thomas; Kerszberg, Michel

    2006-04-01

    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.

  14. Language and emotions: emotional Sapir-Whorf hypothesis.

    Science.gov (United States)

    Perlovsky, Leonid

    2009-01-01

    An emotional version of Sapir-Whorf hypothesis suggests that differences in language emotionalities influence differences among cultures no less than conceptual differences. Conceptual contents of languages and cultures to significant extent are determined by words and their semantic differences; these could be borrowed among languages and exchanged among cultures. Emotional differences, as suggested in the paper, are related to grammar and mostly cannot be borrowed. The paper considers conceptual and emotional mechanisms of language along with their role in the mind and cultural evolution. Language evolution from primordial undifferentiated animal cries is discussed: while conceptual contents increase, emotional reduced. Neural mechanisms of these processes are suggested as well as their mathematical models: the knowledge instinct, the dual model connecting language and cognition, neural modeling fields. Mathematical results are related to cognitive science, linguistics, and psychology. Experimental evidence and theoretical arguments are discussed. Dynamics of the hierarchy-heterarchy of human minds and cultures is formulated using mean-field approach and approximate equations are obtained. The knowledge instinct operating in the mind heterarchy leads to mechanisms of differentiation and synthesis determining ontological development and cultural evolution. These mathematical models identify three types of cultures: "conceptual" pragmatic cultures in which emotionality of language is reduced and differentiation overtakes synthesis resulting in fast evolution at the price of uncertainty of values, self doubts, and internal crises; "traditional-emotional" cultures where differentiation lags behind synthesis, resulting in cultural stability at the price of stagnation; and "multi-cultural" societies combining fast cultural evolution and stability. Unsolved problems and future theoretical and experimental directions are discussed.

  15. Explosive synchronization transitions in complex neural networks

    Science.gov (United States)

    Chen, Hanshuang; He, Gang; Huang, Feng; Shen, Chuansheng; Hou, Zhonghuai

    2013-09-01

    It has been recently reported that explosive synchronization transitions can take place in networks of phase oscillators [Gómez-Gardeñes et al. Phys. Rev. Lett. 106, 128701 (2011)] and chaotic oscillators [Leyva et al. Phys. Rev. Lett. 108, 168702 (2012)]. Here, we investigate the effect of a microscopic correlation between the dynamics and the interacting topology of coupled FitzHugh-Nagumo oscillators on phase synchronization transition in Barabási-Albert (BA) scale-free networks and Erdös-Rényi (ER) random networks. We show that, if natural frequencies of the oscillations are positively correlated with node degrees and the width of the frequency distribution is larger than a threshold value, a strong hysteresis loop arises in the synchronization diagram of BA networks, indicating the evidence of an explosive transition towards synchronization of relaxation oscillators system. In contrast to the results in BA networks, in more homogeneous ER networks, the synchronization transition is always of continuous type regardless of the width of the frequency distribution. Moreover, we consider the effect of degree-mixing patterns on the nature of the synchronization transition, and find that the degree assortativity is unfavorable for the occurrence of such an explosive transition.

  16. On the generalized gravi-magnetic hypothesis

    International Nuclear Information System (INIS)

    Massa, C.

    1989-01-01

    According to a generalization of the gravi-magnetic hypothesis (GMH) any neutral mass moving in a curvilinear path with respect to an inertial frame creates a magnetic field, dependent on the curvature radius of the path. A simple astrophysical consequence of the generalized GMH is suggested considering the special cases of binary pulsars and binary neutron stars

  17. Remarks about the hypothesis of limiting fragmentation

    International Nuclear Information System (INIS)

    Chou, T.T.; Yang, C.N.

    1987-01-01

    Remarks are made about the hypothesis of limiting fragmentation. In particular, the concept of favored and disfavored fragment distribution is introduced. Also, a sum rule is proved leading to a useful quantity called energy-fragmentation fraction. (author). 11 refs, 1 fig., 2 tabs

  18. Multiple hypothesis clustering in radar plot extraction

    NARCIS (Netherlands)

    Huizing, A.G.; Theil, A.; Dorp, Ph. van; Ligthart, L.P.

    1995-01-01

    False plots and plots with inaccurate range and Doppler estimates may severely degrade the performance of tracking algorithms in radar systems. This paper describes how a multiple hypothesis clustering technique can be applied to mitigate the problems involved in plot extraction. The measures of

  19. The (not so immortal strand hypothesis

    Directory of Open Access Journals (Sweden)

    Cristian Tomasetti

    2015-03-01

    Significance: Utilizing an approach that is fundamentally different from previous efforts to confirm or refute the immortal strand hypothesis, we provide evidence against non-random segregation of DNA during stem cell replication. Our results strongly suggest that parental DNA is passed randomly to stem cell daughters and provides new insight into the mechanism of DNA replication in stem cells.

  20. A Developmental Study of the Infrahumanization Hypothesis

    Science.gov (United States)

    Martin, John; Bennett, Mark; Murray, Wayne S.

    2008-01-01

    Intergroup attitudes in children were examined based on Leyen's "infrahumanization hypothesis". This suggests that some uniquely human emotions, such as shame and guilt (secondary emotions), are reserved for the in-group, whilst other emotions that are not uniquely human and shared with animals, such as anger and pleasure (primary…

  1. Morbidity and Infant Development: A Hypothesis.

    Science.gov (United States)

    Pollitt, Ernesto

    1983-01-01

    Results of a study conducted in 14 villages of Sui Lin Township, Taiwan, suggest the hypothesis that, under conditions of extreme economic impoverishment and among children within populations where energy protein malnutrition is endemic, there is an inverse relationship between incidence of morbidity in infancy and measures of motor and mental…

  2. Diagnostic Hypothesis Generation and Human Judgment

    Science.gov (United States)

    Thomas, Rick P.; Dougherty, Michael R.; Sprenger, Amber M.; Harbison, J. Isaiah

    2008-01-01

    Diagnostic hypothesis-generation processes are ubiquitous in human reasoning. For example, clinicians generate disease hypotheses to explain symptoms and help guide treatment, auditors generate hypotheses for identifying sources of accounting errors, and laypeople generate hypotheses to explain patterns of information (i.e., data) in the…

  3. Multi-hypothesis distributed stereo video coding

    DEFF Research Database (Denmark)

    Salmistraro, Matteo; Zamarin, Marco; Forchhammer, Søren

    2013-01-01

    for stereo sequences, exploiting an interpolated intra-view SI and two inter-view SIs. The quality of the SI has a major impact on the DVC Rate-Distortion (RD) performance. As the inter-view SIs individually present lower RD performance compared with the intra-view SI, we propose multi-hypothesis decoding...

  4. [Resonance hypothesis of heart rate variability origin].

    Science.gov (United States)

    Sheĭkh-Zade, Iu R; Mukhambetaliev, G Kh; Cherednik, I L

    2009-09-01

    A hypothesis is advanced of the heart rate variability being subjected to beat-to-beat regulation of cardiac cycle duration in order to ensure the resonance interaction between respiratory and own fluctuation of the arterial system volume for minimization of power expenses of cardiorespiratory system. Myogenic, parasympathetic and sympathetic machanisms of heart rate variability are described.

  5. In Defense of Chi's Ontological Incompatibility Hypothesis

    Science.gov (United States)

    Slotta, James D.

    2011-01-01

    This article responds to an article by A. Gupta, D. Hammer, and E. F. Redish (2010) that asserts that M. T. H. Chi's (1992, 2005) hypothesis of an "ontological commitment" in conceptual development is fundamentally flawed. In this article, I argue that Chi's theoretical perspective is still very much intact and that the critique offered by Gupta…

  6. Vacuum counterexamples to the cosmic censorship hypothesis

    International Nuclear Information System (INIS)

    Miller, B.D.

    1981-01-01

    In cylindrically symmetric vacuum spacetimes it is possible to specify nonsingular initial conditions such that timelike singularities will (necessarily) evolve from these conditions. Examples are given; the spacetimes are somewhat analogous to one of the spherically symmetric counterexamples to the cosmic censorship hypothesis

  7. Trade-off between multiple constraints enables simultaneous formation of modules and hubs in neural systems.

    Directory of Open Access Journals (Sweden)

    Yuhan Chen

    Full Text Available The formation of the complex network architecture of neural systems is subject to multiple structural and functional constraints. Two obvious but apparently contradictory constraints are low wiring cost and high processing efficiency, characterized by short overall wiring length and a small average number of processing steps, respectively. Growing evidence shows that neural networks are results from a trade-off between physical cost and functional value of the topology. However, the relationship between these competing constraints and complex topology is not well understood quantitatively. We explored this relationship systematically by reconstructing two known neural networks, Macaque cortical connectivity and C. elegans neuronal connections, from combinatory optimization of wiring cost and processing efficiency constraints, using a control parameter α, and comparing the reconstructed networks to the real networks. We found that in both neural systems, the reconstructed networks derived from the two constraints can reveal some important relations between the spatial layout of nodes and the topological connectivity, and match several properties of the real networks. The reconstructed and real networks had a similar modular organization in a broad range of α, resulting from spatial clustering of network nodes. Hubs emerged due to the competition of the two constraints, and their positions were close to, and partly coincided, with the real hubs in a range of α values. The degree of nodes was correlated with the density of nodes in their spatial neighborhood in both reconstructed and real networks. Generally, the rebuilt network matched a significant portion of real links, especially short-distant ones. These findings provide clear evidence to support the hypothesis of trade-off between multiple constraints on brain networks. The two constraints of wiring cost and processing efficiency, however, cannot explain all salient features in the real

  8. Neuronal avalanches in complex networks

    Directory of Open Access Journals (Sweden)

    Victor Hernandez-Urbina

    2016-12-01

    Full Text Available Brain networks are neither regular nor random. Their structure allows for optimal information processing and transmission across the entire neural substrate of an organism. However, for topological features to be appropriately harnessed, brain networks should implement a dynamical regime which prevents phase-locked and chaotic behaviour. Critical neural dynamics refer to a dynamical regime in which the system is poised at the boundary between regularity and randomness. It has been reported that neural systems poised at this boundary achieve maximum computational power. In this paper, we review recent results regarding critical neural dynamics that emerge from systems whose underlying structure exhibits complex network properties.

  9. A novel hypothesis splitting method implementation for multi-hypothesis filters

    DEFF Research Database (Denmark)

    Bayramoglu, Enis; Ravn, Ole; Andersen, Nils Axel

    2013-01-01

    The paper presents a multi-hypothesis filter library featuring a novel method for splitting Gaussians into ones with smaller variances. The library is written in C++ for high performance and the source code is open and free1. The multi-hypothesis filters commonly approximate the distribution tran...

  10. The Income Inequality Hypothesis Revisited : Assessing the Hypothesis Using Four Methodological Approaches

    NARCIS (Netherlands)

    Kragten, N.; Rözer, J.

    The income inequality hypothesis states that income inequality has a negative effect on individual’s health, partially because it reduces social trust. This article aims to critically assess the income inequality hypothesis by comparing several analytical strategies, namely OLS regression,

  11. Einstein's Revolutionary Light-Quantum Hypothesis

    Science.gov (United States)

    Stuewer, Roger H.

    2005-05-01

    The paper in which Albert Einstein proposed his light-quantum hypothesis was the only one of his great papers of 1905 that he himself termed ``revolutionary.'' Contrary to widespread belief, Einstein did not propose his light-quantum hypothesis ``to explain the photoelectric effect.'' Instead, he based his argument for light quanta on the statistical interpretation of the second law of thermodynamics, with the photoelectric effect being only one of three phenomena that he offered as possible experimental support for it. I will discuss Einstein's light-quantum hypothesis of 1905 and his introduction of the wave-particle duality in 1909 and then turn to the reception of his work on light quanta by his contemporaries. We will examine the reasons that prominent physicists advanced to reject Einstein's light-quantum hypothesis in succeeding years. Those physicists included Robert A. Millikan, even though he provided convincing experimental proof of the validity of Einstein's equation of the photoelectric effect in 1915. The turning point came after Arthur Holly Compton discovered the Compton effect in late 1922, but even then Compton's discovery was contested both on experimental and on theoretical grounds. Niels Bohr, in particular, had never accepted the reality of light quanta and now, in 1924, proposed a theory, the Bohr-Kramers-Slater theory, which assumed that energy and momentum were conserved only statistically in microscopic interactions. Only after that theory was disproved experimentally in 1925 was Einstein's revolutionary light-quantum hypothesis generally accepted by physicists---a full two decades after Einstein had proposed it.

  12. A Dopamine Hypothesis of Autism Spectrum Disorder.

    Science.gov (United States)

    Pavăl, Denis

    2017-01-01

    Autism spectrum disorder (ASD) comprises a group of neurodevelopmental disorders characterized by social deficits and stereotyped behaviors. While several theories have emerged, the pathogenesis of ASD remains unknown. Although studies report dopamine signaling abnormalities in autistic patients, a coherent dopamine hypothesis which could link neurobiology to behavior in ASD is currently lacking. In this paper, we present such a hypothesis by proposing that autistic behavior arises from dysfunctions in the midbrain dopaminergic system. We hypothesize that a dysfunction of the mesocorticolimbic circuit leads to social deficits, while a dysfunction of the nigrostriatal circuit leads to stereotyped behaviors. Furthermore, we discuss 2 key predictions of our hypothesis, with emphasis on clinical and therapeutic aspects. First, we argue that dopaminergic dysfunctions in the same circuits should associate with autistic-like behavior in nonautistic subjects. Concerning this, we discuss the case of PANDAS (pediatric autoimmune neuropsychiatric disorder associated with streptococcal infections) which displays behaviors similar to those of ASD, presumed to arise from dopaminergic dysfunctions. Second, we argue that providing dopamine modulators to autistic subjects should lead to a behavioral improvement. Regarding this, we present clinical studies of dopamine antagonists which seem to have improving effects on autistic behavior. Furthermore, we explore the means of testing our hypothesis by using neuroreceptor imaging, which could provide comprehensive evidence for dopamine signaling dysfunctions in autistic subjects. Lastly, we discuss the limitations of our hypothesis. Along these lines, we aim to provide a dopaminergic model of ASD which might lead to a better understanding of the ASD pathogenesis. © 2017 S. Karger AG, Basel.

  13. Response variability in Attention-Deficit/Hyperactivity Disorder: a neuronal and glial energetics hypothesis.

    Science.gov (United States)

    Russell, Vivienne A; Oades, Robert D; Tannock, Rosemary; Killeen, Peter R; Auerbach, Judith G; Johansen, Espen B; Sagvolden, Terje

    2006-08-23

    conditions that vary in duration, complexity, speed, and reinforcement; 2) Use of sensitive neuroimaging techniques such as diffusion tensor imaging, magnetic resonance spectroscopy, electroencephalography or magnetoencephalopathy to quantify developmental changes in myelination in ADHD as a potential basis for the delayed maturation of brain function and coordination, and 3) Investigation of the prevalence of genetic markers for factors that regulate energy metabolism (lactate, glutamate, glucose transporters, glycogen synthase, glycogen phosphorylase, glycolytic enzymes), release of glutamate from synaptic terminals and glutamate-stimulated lactate production (SNAP25, glutamate receptors, adenosine receptors, neurexins, intracellular Ca2+), as well as astrocyte function (alpha1, alpha2 and beta-adrenoceptors, dopamine D1 receptors) and myelin synthesis (lactate transporter, Lingo-1, Quaking homolog, leukemia inhibitory factor, and Transferrin). The hypothesis extends existing theories of ADHD by proposing a physiological basis for specific aspects of the ADHD phenotype - namely frequent, transient and impairing fluctuations in functioning, particularly during performance of speeded, effortful tasks. The immediate effects of deficient ATP production and slow restoration of ionic gradients across membranes of rapidly firing neurons have implications for daily functioning: For individuals with ADHD, performance efficacy would be enhanced if repetitive and lengthy effortful tasks were segmented to reduce concurrent demands for speed and accuracy of response (introduction of breaks into lengthy/effortful activities such as examinations, motorway driving, assembly-line production). Also, variations in task or modality and the use of self- rather than system-paced schedules would be helpful. This would enable energetic demands to be distributed to alternate neural resources, and energy reserves to be re-established. Longer-term effects may manifest as reduction in regional brain

  14. Morphological neural networks

    Energy Technology Data Exchange (ETDEWEB)

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

    1996-12-31

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

  15. Neural Tube Defects

    Science.gov (United States)

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

  16. Visual perception and imagery: a new molecular hypothesis.

    Science.gov (United States)

    Bókkon, I

    2009-05-01

    Here, we put forward a redox molecular hypothesis about the natural biophysical substrate of visual perception and visual imagery. This hypothesis is based on the redox and bioluminescent processes of neuronal cells in retinotopically organized cytochrome oxidase-rich visual areas. Our hypothesis is in line with the functional roles of reactive oxygen and nitrogen species in living cells that are not part of haphazard process, but rather a very strict mechanism used in signaling pathways. We point out that there is a direct relationship between neuronal activity and the biophoton emission process in the brain. Electrical and biochemical processes in the brain represent sensory information from the external world. During encoding or retrieval of information, electrical signals of neurons can be converted into synchronized biophoton signals by bioluminescent radical and non-radical processes. Therefore, information in the brain appears not only as an electrical (chemical) signal but also as a regulated biophoton (weak optical) signal inside neurons. During visual perception, the topological distribution of photon stimuli on the retina is represented by electrical neuronal activity in retinotopically organized visual areas. These retinotopic electrical signals in visual neurons can be converted into synchronized biophoton signals by radical and non-radical processes in retinotopically organized mitochondria-rich areas. As a result, regulated bioluminescent biophotons can create intrinsic pictures (depictive representation) in retinotopically organized cytochrome oxidase-rich visual areas during visual imagery and visual perception. The long-term visual memory is interpreted as epigenetic information regulated by free radicals and redox processes. This hypothesis does not claim to solve the secret of consciousness, but proposes that the evolution of higher levels of complexity made the intrinsic picture representation of the external visual world possible by regulated

  17. Antenna analysis using neural networks

    Science.gov (United States)

    Smith, William T.

    1992-01-01

    shaping. The interesting thing about D-C synthesis is that the side lobes have the same amplitude. Five-element arrays were used. Again, 41 pattern samples were used for the input. Nine actual D-C patterns ranging from -10 dB to -30 dB side lobe levels were used to train the network. A comparison between simulated and actual D-C techniques for a pattern with -22 dB side lobe level is shown. The goal for this research was to evaluate the performance of neural network computing with antennas. Future applications will employ the backpropagation training algorithm to drastically reduce the computational complexity involved in performing EM compensation for surface errors in large space reflector antennas.

  18. The neural signature of emotional memories in serial crimes.

    Science.gov (United States)

    Chassy, Philippe

    2017-10-01

    Neural plasticity is the process whereby semantic information and emotional responses are stored in neural networks. It is hypothesized that the neural networks built over time to encode the sexual fantasies that motivate serial killers to act should display a unique, detectable activation pattern. The pathological neural watermark hypothesis posits that such networks comprise activation of brain sites that reflect four cognitive components: autobiographical memory, sexual arousal, aggression, and control over aggression. The neural sites performing these cognitive functions have been successfully identified by previous research. The key findings are reviewed to hypothesise the typical pattern of activity that serial killers should display. Through the integration of biological findings into one framework, the neural approach proposed in this paper is in stark contrast with the many theories accounting for serial killers that offer non-medical taxonomies. The pathological neural watermark hypothesis offers a new framework to understand and detect deviant individuals. The technical and legal issues are briefly discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Two social brains: neural mechanisms of intersubjectivity.

    Science.gov (United States)

    Vogeley, Kai

    2017-08-19

    It is the aim of this article to present an empirically justified hypothesis about the functional roles of the two social neural systems, namely the so-called 'mirror neuron system' (MNS) and the 'mentalizing system' (MENT, also 'theory of mind network' or 'social neural network'). Both systems are recruited during cognitive processes that are either related to interaction or communication with other conspecifics, thereby constituting intersubjectivity. The hypothesis is developed in the following steps: first, the fundamental distinction that we make between persons and things is introduced; second, communication is presented as the key process that allows us to interact with others; third, the capacity to 'mentalize' or to understand the inner experience of others is emphasized as the fundamental cognitive capacity required to establish successful communication. On this background, it is proposed that MNS serves comparably early stages of social information processing related to the 'detection' of spatial or bodily signals, whereas MENT is recruited during comparably late stages of social information processing related to the 'evaluation' of emotional and psychological states of others. This hypothesis of MNS as a social detection system and MENT as a social evaluation system is illustrated by findings in the field of psychopathology. Finally, new research questions that can be derived from this hypothesis are discussed.This article is part of the themed issue 'Physiological determinants of social behaviour in animals'. © 2017 The Author(s).

  20. Neural mechanism of gastric motility regulation by electroacupuncture at RN12 and BL21: A paraventricular hypothalamic nucleus-dorsal vagal complex-vagus nerve-gastric channel pathway

    Science.gov (United States)

    Wang, Hao; Liu, Wen-Jian; Shen, Guo-Ming; Zhang, Meng-Ting; Huang, Shun; He, Ying

    2015-01-01

    AIM: To study the neural mechanism by which electroacupuncture (EA) at RN12 (Zhongwan) and BL21 (Weishu) regulates gastric motility. METHODS: One hundred and forty-four adult Sprague Dawley rats were studied in four separate experiments. Intragastric pressure was measured using custom-made rubber balloons, and extracellular neuron firing activity, which is sensitive to gastric distention in the dorsal vagal complex (DVC), was recorded by an electrophysiological technique. The expression levels of c-fos, motilin (MTL) and gastrin (GAS) in the paraventricular hypothalamic nucleus (PVN) were assayed by immunohistochemistry, and the expression levels of motilin receptor (MTL-R) and gastrin receptor (GAS-R) in both the PVN and the gastric antrum were assayed by western blotting. RESULTS: EA at RN12 + BL21 (gastric Shu and Mu points), BL21 (gastric Back-Shu point), RN12 (gastric Front-Mu point), resulted in increased neuron-activating frequency in the DVC (2.08 ± 0.050, 1.17 ± 0.023, 1.55 ± 0.079 vs 0.75 ± 0.046, P < 0.001) compared with a model group. The expression of c-fos (36.24 ± 1.67, 29.41 ± 2.55, 31.79 ± 3.00 vs 5.73 ± 2.18, P < 0.001), MTL (22.48 ± 2.66, 20.76 ± 2.41, 19.17 ± 1.71 vs 11.68 ± 2.52, P < 0.001), GAS (24.99 ± 2.95, 21.69 ± 3.24, 23.03 ± 3.09 vs 12.53 ± 2.15, P < 0.001), MTL-R (1.39 ± 0.05, 1.22 ± 0.05, 1.17 ± 0.12 vs 0.84 ± 0.06, P < 0.001), and GAS-R (1.07 ± 0.07, 0.91 ± 0.06, 0.78 ± 0.05 vs 0.45 ± 0.04, P < 0.001) increased in the PVN after EA compared with the model group. The expression of MTL-R (1.46 ± 0.14, 1.26 ± 0.11, 0.99 ± 0.07 vs 0.65 ± 0.03, P < 0.001), and GAS-R (1.63 ± 0.11, 1.26 ± 0.16, 1.13 ± 0.02 vs 0.80 ± 0.11, P < 0.001) increased in the gastric antrum after EA compared with the model group. Damaging the PVN resulted in reduced intragastric pressure (13.67 ± 3.72 vs 4.27 ± 1.48, P < 0.001). These data demonstrate that the signals induced by EA stimulation of acupoints RN12 and BL21 are detectable

  1. Neurodevelopmental Hypothesis about the Etiology of Autism Spectrum Disorders

    Directory of Open Access Journals (Sweden)

    Toshio Inui

    2017-07-01

    Full Text Available Previous models or hypotheses of autism spectral disorder (ASD failed to take into full consideration the chronological and causal developmental trajectory, leading to the emergence of diverse phenotypes through a complex interaction between individual etiologies and environmental factors. Those phenotypes include persistent deficits in social communication and social interaction (criteria A in DSM-5, and restricted, repetitive patterns of behavior, interests, or activities (criteria B in DSM-5. In this article, we proposed a domain-general model that can explain criteria in DSM-5 based on the assumption that the same etiological mechanism would trigger the various phenotypes observed in different individuals with ASD. In the model, we assumed the following joint causes as the etiology of autism: (1 Hypoplasia of the pons in the brainstem, occurring immediately following neural tube closure; and (2 Deficiency in the GABA (γ-aminobutyric acid developmental switch during the perinatal period. Microstructural abnormalities of the pons directly affect both the structural and functional development of the brain areas strongly connected to it, especially amygdala. The impairment of GABA switch could not only lead to the deterioration of inhibitory processing in the neural network, but could also cause abnormal cytoarchitecture. We introduced a perspective that atypical development in both brain structure and function can give full explanation of diverse phenotypes and pathogenetic mechanism of ASD. Finally, we discussed about neural mechanisms underlying the phenotypic characteristics of ASD that are not described in DSM-5 but should be considered as important foundation: sleep, global precedence, categorical perception, intelligence, interoception and motor control.

  2. Neural tissue-spheres

    DEFF Research Database (Denmark)

    Andersen, Rikke K; Johansen, Mathias; Blaabjerg, Morten

    2007-01-01

    By combining new and established protocols we have developed a procedure for isolation and propagation of neural precursor cells from the forebrain subventricular zone (SVZ) of newborn rats. Small tissue blocks of the SVZ were dissected and propagated en bloc as free-floating neural tissue...... content, thus allowing experimental studies of neural precursor cells and their niche...

  3. How organisms do the right thing: The attractor hypothesis

    Science.gov (United States)

    Emlen, J.M.; Freeman, D.C.; Mills, A.; Graham, J.H.

    1998-01-01

    Neo-Darwinian theory is highly successful at explaining the emergence of adaptive traits over successive generations. However, there are reasons to doubt its efficacy in explaining the observed, impressively detailed adaptive responses of organisms to day-to-day changes in their surroundings. Also, the theory lacks a clear mechanism to account for both plasticity and canalization. In effect, there is a growing sentiment that the neo-Darwinian paradigm is incomplete, that something more than genetic structure, mutation, genetic drift, and the action of natural selection is required to explain organismal behavior. In this paper we extend the view of organisms as complex self-organizing entities by arguing that basic physical laws, coupled with the acquisitive nature of organisms, makes adaptation all but tautological. That is, much adaptation is an unavoidable emergent property of organisms' complexity and, to some a significant degree, occurs quite independently of genomic changes wrought by natural selection. For reasons that will become obvious, we refer to this assertion as the attractor hypothesis. The arguments also clarify the concept of "adaptation." Adaptation across generations, by natural selection, equates to the (game theoretic) maximization of fitness (the success with which one individual produces more individuals), while self-organizing based adaptation, within generations, equates to energetic efficiency and the matching of intake and biosynthesis to need. Finally, we discuss implications of the attractor hypothesis for a wide variety of genetical and physiological phenomena, including genetic architecture, directed mutation, genetic imprinting, paramutation, hormesis, plasticity, optimality theory, genotype-phenotype linkage and puncuated equilibrium, and present suggestions for tests of the hypothesis. ?? 1998 American Institute of Physics.

  4. Spike Neural Models Part II: Abstract Neural Models

    Directory of Open Access Journals (Sweden)

    Johnson, Melissa G.

    2018-02-01

    Full Text Available Neurons are complex cells that require a lot of time and resources to model completely. In spiking neural networks (SNN though, not all that complexity is required. Therefore simple, abstract models are often used. These models save time, use less computer resources, and are easier to understand. This tutorial presents two such models: Izhikevich's model, which is biologically realistic in the resulting spike trains but not in the parameters, and the Leaky Integrate and Fire (LIF model which is not biologically realistic but does quickly and easily integrate input to produce spikes. Izhikevich's model is based on Hodgkin-Huxley's model but simplified such that it uses only two differentiation equations and four parameters to produce various realistic spike patterns. LIF is based on a standard electrical circuit and contains one equation. Either of these two models, or any of the many other models in literature can be used in a SNN. Choosing a neural model is an important task that depends on the goal of the research and the resources available. Once a model is chosen, network decisions such as connectivity, delay, and sparseness, need to be made. Understanding neural models and how they are incorporated into the network is the first step in creating a SNN.

  5. Tests of the Giant Impact Hypothesis

    Science.gov (United States)

    Jones, J. H.

    1998-01-01

    The giant impact hypothesis has gained popularity as a means of explaining a volatile-depleted Moon that still has a chemical affinity to the Earth. As Taylor's Axiom decrees, the best models of lunar origin are testable, but this is difficult with the giant impact model. The energy associated with the impact would be sufficient to totally melt and partially vaporize the Earth. And this means that there should he no geological vestige of Barber times. Accordingly, it is important to devise tests that may be used to evaluate the giant impact hypothesis. Three such tests are discussed here. None of these is supportive of the giant impact model, but neither do they disprove it.

  6. The discovered preference hypothesis - an empirical test

    DEFF Research Database (Denmark)

    Lundhede, Thomas; Ladenburg, Jacob; Olsen, Søren Bøye

    Using stated preference methods for valuation of non-market goods is known to be vulnerable to a range of biases. Some authors claim that these so-called anomalies in effect render the methods useless for the purpose. However, the Discovered Preference Hypothesis, as put forth by Plott [31], offers...... an nterpretation and explanation of biases which entails that the stated preference methods need not to be completely written off. In this paper we conduct a test for the validity and relevance of the DPH interpretation of biases. In a choice experiment concerning preferences for protection of Danish nature areas...... as respondents evaluate more and more choice sets. This finding supports the Discovered Preference Hypothesis interpretation and explanation of starting point bias....

  7. The Hypothesis-Driven Physical Examination.

    Science.gov (United States)

    Garibaldi, Brian T; Olson, Andrew P J

    2018-05-01

    The physical examination remains a vital part of the clinical encounter. However, physical examination skills have declined in recent years, in part because of decreased time at the bedside. Many clinicians question the relevance of physical examinations in the age of technology. A hypothesis-driven approach to teaching and practicing the physical examination emphasizes the performance of maneuvers that can alter the likelihood of disease. Likelihood ratios are diagnostic weights that allow clinicians to estimate the post-probability of disease. This hypothesis-driven approach to the physical examination increases its value and efficiency, while preserving its cultural role in the patient-physician relationship. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. MOLIERE: Automatic Biomedical Hypothesis Generation System.

    Science.gov (United States)

    Sybrandt, Justin; Shtutman, Michael; Safro, Ilya

    2017-08-01

    Hypothesis generation is becoming a crucial time-saving technique which allows biomedical researchers to quickly discover implicit connections between important concepts. Typically, these systems operate on domain-specific fractions of public medical data. MOLIERE, in contrast, utilizes information from over 24.5 million documents. At the heart of our approach lies a multi-modal and multi-relational network of biomedical objects extracted from several heterogeneous datasets from the National Center for Biotechnology Information (NCBI). These objects include but are not limited to scientific papers, keywords, genes, proteins, diseases, and diagnoses. We model hypotheses using Latent Dirichlet Allocation applied on abstracts found near shortest paths discovered within this network, and demonstrate the effectiveness of MOLIERE by performing hypothesis generation on historical data. Our network, implementation, and resulting data are all publicly available for the broad scientific community.

  9. The Method of Hypothesis in Plato's Philosophy

    Directory of Open Access Journals (Sweden)

    Malihe Aboie Mehrizi

    2016-09-01

    Full Text Available The article deals with the examination of method of hypothesis in Plato's philosophy. This method, respectively, will be examined in three dialogues of Meno, Phaedon and Republic in which it is explicitly indicated. It will be shown the process of change of Plato’s attitude towards the position and usage of the method of hypothesis in his realm of philosophy. In Meno, considering the geometry, Plato attempts to introduce a method that can be used in the realm of philosophy. But, ultimately in Republic, Plato’s special attention to the method and its importance in the philosophical investigations, leads him to revise it. Here, finally Plato introduces the particular method of philosophy, i.e., the dialectic

  10. Multi-agent sequential hypothesis testing

    KAUST Repository

    Kim, Kwang-Ki K.

    2014-12-15

    This paper considers multi-agent sequential hypothesis testing and presents a framework for strategic learning in sequential games with explicit consideration of both temporal and spatial coordination. The associated Bayes risk functions explicitly incorporate costs of taking private/public measurements, costs of time-difference and disagreement in actions of agents, and costs of false declaration/choices in the sequential hypothesis testing. The corresponding sequential decision processes have well-defined value functions with respect to (a) the belief states for the case of conditional independent private noisy measurements that are also assumed to be independent identically distributed over time, and (b) the information states for the case of correlated private noisy measurements. A sequential investment game of strategic coordination and delay is also discussed as an application of the proposed strategic learning rules.

  11. Hypothesis testing of scientific Monte Carlo calculations

    Science.gov (United States)

    Wallerberger, Markus; Gull, Emanuel

    2017-11-01

    The steadily increasing size of scientific Monte Carlo simulations and the desire for robust, correct, and reproducible results necessitates rigorous testing procedures for scientific simulations in order to detect numerical problems and programming bugs. However, the testing paradigms developed for deterministic algorithms have proven to be ill suited for stochastic algorithms. In this paper we demonstrate explicitly how the technique of statistical hypothesis testing, which is in wide use in other fields of science, can be used to devise automatic and reliable tests for Monte Carlo methods, and we show that these tests are able to detect some of the common problems encountered in stochastic scientific simulations. We argue that hypothesis testing should become part of the standard testing toolkit for scientific simulations.

  12. Reverse hypothesis machine learning a practitioner's perspective

    CERN Document Server

    Kulkarni, Parag

    2017-01-01

    This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning. All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same—the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as ...

  13. Exploring heterogeneous market hypothesis using realized volatility

    Science.gov (United States)

    Chin, Wen Cheong; Isa, Zaidi; Mohd Nor, Abu Hassan Shaari

    2013-04-01

    This study investigates the heterogeneous market hypothesis using high frequency data. The cascaded heterogeneous trading activities with different time durations are modelled by the heterogeneous autoregressive framework. The empirical study indicated the presence of long memory behaviour and predictability elements in the financial time series which supported heterogeneous market hypothesis. Besides the common sum-of-square intraday realized volatility, we also advocated two power variation realized volatilities in forecast evaluation and risk measurement in order to overcome the possible abrupt jumps during the credit crisis. Finally, the empirical results are used in determining the market risk using the value-at-risk approach. The findings of this study have implications for informationally market efficiency analysis, portfolio strategies and risk managements.

  14. An endocannabinoid hypothesis of drug reward and drug addiction.

    Science.gov (United States)

    Onaivi, Emmanuel S

    2008-10-01

    Pharmacologic treatment of drug and alcohol dependency has largely been disappointing, and new therapeutic targets and hypotheses are needed. There is accumulating evidence indicating a central role for the previously unknown but ubiquitous endocannabinoid physiological control system (EPCS) in the regulation of the rewarding effects of abused substances. Thus an endocannabinoid hypothesis of drug reward is postulated. Endocannabinoids mediate retrograde signaling in neuronal tissues and are involved in the regulation of synaptic transmission to suppress neurotransmitter release by the presynaptic cannabinoid receptors (CB-Rs). This powerful modulatory action on synaptic transmission has significant functional implications and interactions with the effects of abused substances. Our data, along with those from other investigators, provide strong new evidence for a role for EPCS modulation in the effects of drugs of abuse, and specifically for involvement of cannabinoid receptors in the neural basis of addiction. Cannabinoids and endocannabinoids appear to be involved in adding to the rewarding effects of addictive substances, including, nicotine, opiates, alcohol, cocaine, and BDZs. The results suggest that the EPCS may be an important natural regulatory mechanism for drug reward and a target for the treatment of addictive disorders.

  15. Water Taxation and the Double Dividend Hypothesis

    OpenAIRE

    Nicholas Kilimani

    2014-01-01

    The double dividend hypothesis contends that environmental taxes have the potential to yield multiple benefits for the economy. However, empirical evidence of the potential impacts of environmental taxation in developing countries is still limited. This paper seeks to contribute to the literature by exploring the impact of a water tax in a developing country context, with Uganda as a case study. Policy makers in Uganda are exploring ways of raising revenue by taxing environmental goods such a...

  16. [Working memory, phonological awareness and spelling hypothesis].

    Science.gov (United States)

    Gindri, Gigiane; Keske-Soares, Márcia; Mota, Helena Bolli

    2007-01-01

    Working memory, phonological awareness and spelling hypothesis. To verify the relationship between working memory, phonological awareness and spelling hypothesis in pre-school children and first graders. Participants of this study were 90 students, belonging to state schools, who presented typical linguistic development. Forty students were preschoolers, with the average age of six and 50 students were first graders, with the average age of seven. Participants were submitted to an evaluation of the working memory abilities based on the Working Memory Model (Baddeley, 2000), involving phonological loop. Phonological loop was evaluated using the Auditory Sequential Test, subtest 5 of Illinois Test of Psycholinguistic Abilities (ITPA), Brazilian version (Bogossian & Santos, 1977), and the Meaningless Words Memory Test (Kessler, 1997). Phonological awareness abilities were investigated using the Phonological Awareness: Instrument of Sequential Assessment (CONFIAS - Moojen et al., 2003), involving syllabic and phonemic awareness tasks. Writing was characterized according to Ferreiro & Teberosky (1999). Preschoolers presented the ability of repeating sequences of 4.80 digits and 4.30 syllables. Regarding phonological awareness, the performance in the syllabic level was of 19.68 and in the phonemic level was of 8.58. Most of the preschoolers demonstrated to have a pre-syllabic writing hypothesis. First graders repeated, in average, sequences of 5.06 digits and 4.56 syllables. These children presented a phonological awareness of 31.12 in the syllabic level and of 16.18 in the phonemic level, and demonstrated to have an alphabetic writing hypothesis. The performance of working memory, phonological awareness and spelling level are inter-related, as well as being related to chronological age, development and scholarity.

  17. Privacy on Hypothesis Testing in Smart Grids

    OpenAIRE

    Li, Zuxing; Oechtering, Tobias

    2015-01-01

    In this paper, we study the problem of privacy information leakage in a smart grid. The privacy risk is assumed to be caused by an unauthorized binary hypothesis testing of the consumer's behaviour based on the smart meter readings of energy supplies from the energy provider. Another energy supplies are produced by an alternative energy source. A controller equipped with an energy storage device manages the energy inflows to satisfy the energy demand of the consumer. We study the optimal ener...

  18. Box-particle probability hypothesis density filtering

    OpenAIRE

    Schikora, M.; Gning, A.; Mihaylova, L.; Cremers, D.; Koch, W.

    2014-01-01

    This paper develops a novel approach for multitarget tracking, called box-particle probability hypothesis density filter (box-PHD filter). The approach is able to track multiple targets and estimates the unknown number of targets. Furthermore, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic, and data association uncertainty. The box-PHD filter reduces the number of particles significantly, which improves the runtime considerably. The small number of box-p...

  19. Quantum effects and hypothesis of cosmic censorship

    International Nuclear Information System (INIS)

    Parnovskij, S.L.

    1989-01-01

    It is shown that filamentary characteristics with linear mass of less than 10 25 g/cm distort slightly the space-time at distances, exceeding Planck ones. Their formation doesn't change vacuum energy and doesn't lead to strong quantum radiation. Therefore, the problem of their occurrence can be considered within the framework of classical collapse. Quantum effects can be ignored when considering the problem of validity of cosmic censorship hypothesis

  20. The (not so) immortal strand hypothesis.

    Science.gov (United States)

    Tomasetti, Cristian; Bozic, Ivana

    2015-03-01

    Non-random segregation of DNA strands during stem cell replication has been proposed as a mechanism to minimize accumulated genetic errors in stem cells of rapidly dividing tissues. According to this hypothesis, an "immortal" DNA strand is passed to the stem cell daughter and not the more differentiated cell, keeping the stem cell lineage replication error-free. After it was introduced, experimental evidence both in favor and against the hypothesis has been presented. Using a novel methodology that utilizes cancer sequencing data we are able to estimate the rate of accumulation of mutations in healthy stem cells of the colon, blood and head and neck tissues. We find that in these tissues mutations in stem cells accumulate at rates strikingly similar to those expected without the protection from the immortal strand mechanism. Utilizing an approach that is fundamentally different from previous efforts to confirm or refute the immortal strand hypothesis, we provide evidence against non-random segregation of DNA during stem cell replication. Our results strongly suggest that parental DNA is passed randomly to stem cell daughters and provides new insight into the mechanism of DNA replication in stem cells. Copyright © 2015. Published by Elsevier B.V.

  1. A test of the orthographic recoding hypothesis

    Science.gov (United States)

    Gaygen, Daniel E.

    2003-04-01

    The Orthographic Recoding Hypothesis [D. E. Gaygen and P. A. Luce, Percept. Psychophys. 60, 465-483 (1998)] was tested. According to this hypothesis, listeners recognize spoken words heard for the first time by mapping them onto stored representations of the orthographic forms of the words. Listeners have a stable orthographic representation of words, but no phonological representation, when those words have been read frequently but never heard or spoken. Such may be the case for low frequency words such as jargon. Three experiments using visually and auditorily presented nonword stimuli tested this hypothesis. The first two experiments were explicit tests of memory (old-new tests) for words presented visually. In the first experiment, the recognition of auditorily presented nonwords was facilitated when they previously appeared on a visually presented list. The second experiment was similar, but included a concurrent articulation task during a visual word list presentation, thus preventing covert rehearsal of the nonwords. The results were similar to the first experiment. The third experiment was an indirect test of memory (auditory lexical decision task) for visually presented nonwords. Auditorily presented nonwords were identified as nonwords significantly more slowly if they had previously appeared on the visually presented list accompanied by a concurrent articulation task.

  2. Consumer health information seeking as hypothesis testing.

    Science.gov (United States)

    Keselman, Alla; Browne, Allen C; Kaufman, David R

    2008-01-01

    Despite the proliferation of consumer health sites, lay individuals often experience difficulty finding health information online. The present study attempts to understand users' information seeking difficulties by drawing on a hypothesis testing explanatory framework. It also addresses the role of user competencies and their interaction with internet resources. Twenty participants were interviewed about their understanding of a hypothetical scenario about a family member suffering from stable angina and then searched MedlinePlus consumer health information portal for information on the problem presented in the scenario. Participants' understanding of heart disease was analyzed via semantic analysis. Thematic coding was used to describe information seeking trajectories in terms of three key strategies: verification of the primary hypothesis, narrowing search within the general hypothesis area and bottom-up search. Compared to an expert model, participants' understanding of heart disease involved different key concepts, which were also differently grouped and defined. This understanding provided the framework for search-guiding hypotheses and results interpretation. Incorrect or imprecise domain knowledge led individuals to search for information on irrelevant sites, often seeking out data to confirm their incorrect initial hypotheses. Online search skills enhanced search efficiency, but did not eliminate these difficulties. Regardless of their web experience and general search skills, lay individuals may experience difficulty with health information searches. These difficulties may be related to formulating and evaluating hypotheses that are rooted in their domain knowledge. Informatics can provide support at the levels of health information portals, individual websites, and consumer education tools.

  3. Neural electrical activity and neural network growth.

    Science.gov (United States)

    Gafarov, F M

    2018-05-01

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

  4. Hardware implementation of stochastic spiking neural networks.

    Science.gov (United States)

    Rosselló, Josep L; Canals, Vincent; Morro, Antoni; Oliver, Antoni

    2012-08-01

    Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field Programmable Gate Arrays. The high computational capabilities of the proposed model are demonstrated by the study of both feed-forward and recurrent networks that are able to implement high-speed signal filtering and to solve complex systems of linear equations.

  5. Genetic attack on neural cryptography.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Naeh, Rivka; Kanter, Ido

    2006-03-01

    Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the limit of infinite synaptic depth. This method is improved by adding a genetic algorithm, which selects the fittest neural networks. The probability of a successful genetic attack is calculated for different model parameters using numerical simulations. The results show that scaling laws observed in the case of other attacks hold for the improved algorithm, too. The number of networks needed for an effective attack grows exponentially with increasing synaptic depth. In addition, finite-size effects caused by Hebbian and anti-Hebbian learning are analyzed. These learning rules converge to the random walk rule if the synaptic depth is small compared to the square root of the system size.

  6. Genetic attack on neural cryptography

    International Nuclear Information System (INIS)

    Ruttor, Andreas; Kinzel, Wolfgang; Naeh, Rivka; Kanter, Ido

    2006-01-01

    Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the limit of infinite synaptic depth. This method is improved by adding a genetic algorithm, which selects the fittest neural networks. The probability of a successful genetic attack is calculated for different model parameters using numerical simulations. The results show that scaling laws observed in the case of other attacks hold for the improved algorithm, too. The number of networks needed for an effective attack grows exponentially with increasing synaptic depth. In addition, finite-size effects caused by Hebbian and anti-Hebbian learning are analyzed. These learning rules converge to the random walk rule if the synaptic depth is small compared to the square root of the system size

  7. Genetic attack on neural cryptography

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Naeh, Rivka; Kanter, Ido

    2006-03-01

    Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the limit of infinite synaptic depth. This method is improved by adding a genetic algorithm, which selects the fittest neural networks. The probability of a successful genetic attack is calculated for different model parameters using numerical simulations. The results show that scaling laws observed in the case of other attacks hold for the improved algorithm, too. The number of networks needed for an effective attack grows exponentially with increasing synaptic depth. In addition, finite-size effects caused by Hebbian and anti-Hebbian learning are analyzed. These learning rules converge to the random walk rule if the synaptic depth is small compared to the square root of the system size.

  8. Melatonin receptors: Current status, facts, and hypothesis

    International Nuclear Information System (INIS)

    Stankov, B.; Reiter, R.J.

    1990-01-01

    Great progress has been made in the identification of melatonin binding sites, commonly identified as melatonin receptors by many authors, in recent years. The bulk of these studies have investigated the sites using either autoradiographic and biochemical techniques with the majority of the experiments being done on the rat, Djungarian and Syrian hamster, and sheep, although human tissue has also been employed. Many of the studies have identified melatonin binding in the central nervous system with either tritium- or iodine-labelled ligands. The latter ligand seems to provide the most reproducible and consistent data. Of the central neural tissues examined, the suprachiasmatic nuclei are most frequently mentioned as a location for melatonin binding sites although binding seems to be widespread in the brain. The other tissue that has been prominently mentioned as a site for melatonin binding is the pars tuberalis of the anterior pituitary gland. There may be time-dependent variations in melatonin binding densities in both neural and pituitary gland tissue. Very few attempts have been made to identify melatonin binding outside of the central nervous system despite the widespread actions of melatonin. Preliminary experiments have been carried out on the intracellular second messengers which mediate the actions of melatonin

  9. [Evolutionary process unveiled by the maximum genetic diversity hypothesis].

    Science.gov (United States)

    Huang, Yi-Min; Xia, Meng-Ying; Huang, Shi

    2013-05-01

    As two major popular theories to explain evolutionary facts, the neutral theory and Neo-Darwinism, despite their proven virtues in certain areas, still fail to offer comprehensive explanations to such fundamental evolutionary phenomena as the genetic equidistance result, abundant overlap sites, increase in complexity over time, incomplete understanding of genetic diversity, and inconsistencies with fossil and archaeological records. Maximum genetic diversity hypothesis (MGD), however, constructs a more complete evolutionary genetics theory that incorporates all of the proven virtues of existing theories and adds to them the novel concept of a maximum or optimum limit on genetic distance or diversity. It has yet to meet a contradiction and explained for the first time the half-century old Genetic Equidistance phenomenon as well as most other major evolutionary facts. It provides practical and quantitative ways of studying complexity. Molecular interpretation using MGD-based methods reveal novel insights on the origins of humans and other primates that are consistent with fossil evidence and common sense, and reestablished the important role of China in the evolution of humans. MGD theory has also uncovered an important genetic mechanism in the construction of complex traits and the pathogenesis of complex diseases. We here made a series of sequence comparisons among yeasts, fishes and primates to illustrate the concept of limit on genetic distance. The idea of limit or optimum is in line with the yin-yang paradigm in the traditional Chinese view of the universal creative law in nature.

  10. Modelling the permeability of polymers: a neural network approach

    NARCIS (Netherlands)

    Wessling, Matthias; Mulder, M.H.V.; Bos, A.; Bos, A.; van der Linden, M.K.T.; Bos, M.; van der Linden, W.E.

    1994-01-01

    In this short communication, the prediction of the permeability of carbon dioxide through different polymers using a neural network is studied. A neural network is a numeric-mathematical construction that can model complex non-linear relationships. Here it is used to correlate the IR spectrum of a

  11. Chaotic diagonal recurrent neural network

    International Nuclear Information System (INIS)

    Wang Xing-Yuan; Zhang Yi

    2012-01-01

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

  12. Hypothesis Testing as an Act of Rationality

    Science.gov (United States)

    Nearing, Grey

    2017-04-01

    Statistical hypothesis testing is ad hoc in two ways. First, setting probabilistic rejection criteria is, as Neyman (1957) put it, an act of will rather than an act of rationality. Second, physical theories like conservation laws do not inherently admit probabilistic predictions, and so we must use what are called epistemic bridge principles to connect model predictions with the actual methods of hypothesis testing. In practice, these bridge principles are likelihood functions, error functions, or performance metrics. I propose that the reason we are faced with these problems is because we have historically failed to account for a fundamental component of basic logic - namely the portion of logic that explains how epistemic states evolve in the presence of empirical data. This component of Cox' (1946) calculitic logic is called information theory (Knuth, 2005), and adding information theory our hypothetico-deductive account of science yields straightforward solutions to both of the above problems. This also yields a straightforward method for dealing with Popper's (1963) problem of verisimilitude by facilitating a quantitative approach to measuring process isomorphism. In practice, this involves data assimilation. Finally, information theory allows us to reliably bound measures of epistemic uncertainty, thereby avoiding the problem of Bayesian incoherency under misspecified priors (Grünwald, 2006). I therefore propose solutions to four of the fundamental problems inherent in both hypothetico-deductive and/or Bayesian hypothesis testing. - Neyman (1957) Inductive Behavior as a Basic Concept of Philosophy of Science. - Cox (1946) Probability, Frequency and Reasonable Expectation. - Knuth (2005) Lattice Duality: The Origin of Probability and Entropy. - Grünwald (2006). Bayesian Inconsistency under Misspecification. - Popper (1963) Conjectures and Refutations: The Growth of Scientific Knowledge.

  13. The conscious access hypothesis: Explaining the consciousness.

    Science.gov (United States)

    Prakash, Ravi

    2008-01-01

    The phenomenon of conscious awareness or consciousness is complicated but fascinating. Although this concept has intrigued the mankind since antiquity, exploration of consciousness from scientific perspectives is not very old. Among myriad of theories regarding nature, functions and mechanism of consciousness, off late, cognitive theories have received wider acceptance. One of the most exciting hypotheses in recent times has been the "conscious access hypotheses" based on the "global workspace model of consciousness". It underscores an important property of consciousness, the global access of information in cerebral cortex. Present article reviews the "conscious access hypothesis" in terms of its theoretical underpinnings as well as experimental supports it has received.

  14. Interstellar colonization and the zoo hypothesis

    International Nuclear Information System (INIS)

    Jones, E.M.

    1978-01-01

    Michael Hart and others have pointed out that current estimates of the number of technological civilizations arisen in the Galaxy since its formation is in fundamental conflict with the expectation that such a civilization could colonize and utilize the entire Galaxy in 10 to 20 million years. This dilemma can be called Hart's paradox. Resolution of the paradox requires that one or more of the following are true: we are the Galaxy's first technical civilization; interstellar travel is immensely impractical or simply impossible; technological civilizations are very short-lived; or we inhabit a wildnerness preserve. The latter is the zoo hypothesis

  15. Confluence Model or Resource Dilution Hypothesis?

    DEFF Research Database (Denmark)

    Jæger, Mads

    have a negative effect on educational attainment most studies cannot distinguish empirically between the CM and the RDH. In this paper, I use the different theoretical predictions in the CM and the RDH on the role of cognitive ability as a partial or complete mediator of the sibship size effect......Studies on family background often explain the negative effect of sibship size on educational attainment by one of two theories: the Confluence Model (CM) or the Resource Dilution Hypothesis (RDH). However, as both theories – for substantively different reasons – predict that sibship size should...

  16. Set theory and the continuum hypothesis

    CERN Document Server

    Cohen, Paul J

    2008-01-01

    This exploration of a notorious mathematical problem is the work of the man who discovered the solution. The independence of the continuum hypothesis is the focus of this study by Paul J. Cohen. It presents not only an accessible technical explanation of the author's landmark proof but also a fine introduction to mathematical logic. An emeritus professor of mathematics at Stanford University, Dr. Cohen won two of the most prestigious awards in mathematics: in 1964, he was awarded the American Mathematical Society's Bôcher Prize for analysis; and in 1966, he received the Fields Medal for Logic.

  17. Statistical hypothesis testing with SAS and R

    CERN Document Server

    Taeger, Dirk

    2014-01-01

    A comprehensive guide to statistical hypothesis testing with examples in SAS and R When analyzing datasets the following questions often arise:Is there a short hand procedure for a statistical test available in SAS or R?If so, how do I use it?If not, how do I program the test myself? This book answers these questions and provides an overview of the most commonstatistical test problems in a comprehensive way, making it easy to find and performan appropriate statistical test. A general summary of statistical test theory is presented, along with a basicdescription for each test, including the

  18. Sex differences in the neural mechanisms mediating addiction: a new synthesis and hypothesis

    OpenAIRE

    Becker, Jill B; Perry, Adam N; Westenbroek, Christel

    2012-01-01

    Abstract In this review we propose that there are sex differences in how men and women enter onto the path that can lead to addiction. Males are more likely than females to engage in risky behaviors that include experimenting with drugs of abuse, and in susceptible individuals, they are drawn into the spiral that can eventually lead to addiction. Women and girls are more likely to begin taking drugs as self-medication to reduce stress or alleviate depression. For this reason women enter into ...

  19. Modeling Broadband Microwave Structures by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    V. Otevrel

    2004-06-01

    Full Text Available The paper describes the exploitation of feed-forward neural networksand recurrent neural networks for replacing full-wave numerical modelsof microwave structures in complex microwave design tools. Building aneural model, attention is turned to the modeling accuracy and to theefficiency of building a model. Dealing with the accuracy, we describea method of increasing it by successive completing a training set.Neural models are mutually compared in order to highlight theiradvantages and disadvantages. As a reference model for comparisons,approximations based on standard cubic splines are used. Neural modelsare used to replace both the time-domain numeric models and thefrequency-domain ones.

  20. Artificial Neural Networks For Hadron Hadron Cross-sections

    International Nuclear Information System (INIS)

    ELMashad, M.; ELBakry, M.Y.; Tantawy, M.; Habashy, D.M.

    2011-01-01

    In recent years artificial neural networks (ANN ) have emerged as a mature and viable framework with many applications in various areas. Artificial neural networks theory is sometimes used to refer to a branch of computational science that uses neural networks as models to either simulate or analyze complex phenomena and/or study the principles of operation of neural networks analytically. In this work a model of hadron- hadron collision using the ANN technique is present, the hadron- hadron based ANN model calculates the cross sections of hadron- hadron collision. The results amply demonstrate the feasibility of such new technique in extracting the collision features and prove its effectiveness

  1. Why Does REM Sleep Occur? A Wake-up Hypothesis

    Directory of Open Access Journals (Sweden)

    Dr. W. R. eKlemm

    2011-09-01

    Full Text Available Brain activity differs in the various sleep stages and in conscious wakefulness. Awakening from sleep requires restoration of the complex nerve impulse patterns in neuronal network assemblies necessary to re-create and sustain conscious wakefulness. Herein I propose that the brain uses REM to help wake itself up after it has had a sufficient amount of sleep. Evidence suggesting this hypothesis includes the facts that, 1 when first going to sleep, the brain plunges into Stage N3 (formerly called Stage IV, a deep abyss of sleep, and, as the night progresses, the sleep is punctuated by episodes of REM that become longer and more frequent toward morning, 2 conscious-like dreams are a reliable component of the REM state in which the dreamer is an active mental observer or agent in the dream, 3 the last awakening during a night’s sleep usually occurs in a REM episode during or at the end of a dream, 4 both REM and awake consciousness seem to arise out of a similar brainstem ascending arousal system 5 N3 is a functionally perturbed state that eventually must be corrected so that embodied brain can direct adaptive behavior, and 6 corticofugal projections to brainstem arousal areas provide a way to trigger increased cortical activity in REM to progressively raise the sleeping brain to the threshold required for wakefulness. This paper shows how the hypothesis conforms to common experience and has substantial predictive and explanatory power regarding the phenomenology of sleep in terms of ontogeny, aging, phylogeny, abnormal/disease states, cognition, and behavioral physiology. That broad range of consistency is not matched by competing theories, which are summarized herein. Specific ways to test this wake-up hypothesis are suggested. Such research could lead to a better understanding of awake consciousness.

  2. Why does rem sleep occur? A wake-up hypothesis.

    Science.gov (United States)

    Klemm, W R

    2011-01-01

    Brain activity differs in the various sleep stages and in conscious wakefulness. Awakening from sleep requires restoration of the complex nerve impulse patterns in neuronal network assemblies necessary to re-create and sustain conscious wakefulness. Herein I propose that the brain uses rapid eye movement (REM) to help wake itself up after it has had a sufficient amount of sleep. Evidence suggesting this hypothesis includes the facts that, (1) when first going to sleep, the brain plunges into Stage N3 (formerly called Stage IV), a deep abyss of sleep, and, as the night progresses, the sleep is punctuated by episodes of REM that become longer and more frequent toward morning, (2) conscious-like dreams are a reliable component of the REM state in which the dreamer is an active mental observer or agent in the dream, (3) the last awakening during a night's sleep usually occurs in a REM episode during or at the end of a dream, (4) both REM and awake consciousness seem to arise out of a similar brainstem ascending arousal system (5) N3 is a functionally perturbed state that eventually must be corrected so that embodied brain can direct adaptive behavior, and (6) cortico-fugal projections to brainstem arousal areas provide a way to trigger increased cortical activity in REM to progressively raise the sleeping brain to the threshold required for wakefulness. This paper shows how the hypothesis conforms to common experience and has substantial predictive and explanatory power regarding the phenomenology of sleep in terms of ontogeny, aging, phylogeny, abnormal/disease states, cognition, and behavioral physiology. That broad range of consistency is not matched by competing theories, which are summarized herein. Specific ways to test this wake-up hypothesis are suggested. Such research could lead to a better understanding of awake consciousness.

  3. Hypothesis-driven physical examination curriculum.

    Science.gov (United States)

    Allen, Sharon; Olson, Andrew; Menk, Jeremiah; Nixon, James

    2017-12-01

    Medical students traditionally learn physical examination skills as a rote list of manoeuvres. Alternatives like hypothesis-driven physical examination (HDPE) may promote students' understanding of the contribution of physical examination to diagnostic reasoning. We sought to determine whether first-year medical students can effectively learn to perform a physical examination using an HDPE approach, and then tailor the examination to specific clinical scenarios. Medical students traditionally learn physical examination skills as a rote list of manoeuvres CONTEXT: First-year medical students at the University of Minnesota were taught both traditional and HDPE approaches during a required 17-week clinical skills course in their first semester. The end-of-course evaluation assessed HDPE skills: students were assigned one of two cardiopulmonary cases. Each case included two diagnostic hypotheses. During an interaction with a standardised patient, students were asked to select physical examination manoeuvres in order to make a final diagnosis. Items were weighted and selection order was recorded. First-year students with minimal pathophysiology performed well. All students selected the correct diagnosis. Importantly, students varied the order when selecting examination manoeuvres depending on the diagnoses under consideration, demonstrating early clinical decision-making skills. An early introduction to HDPE may reinforce physical examination skills for hypothesis generation and testing, and can foster early clinical decision-making skills. This has important implications for further research in physical examination instruction. © 2016 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

  4. A default Bayesian hypothesis test for mediation.

    Science.gov (United States)

    Nuijten, Michèle B; Wetzels, Ruud; Matzke, Dora; Dolan, Conor V; Wagenmakers, Eric-Jan

    2015-03-01

    In order to quantify the relationship between multiple variables, researchers often carry out a mediation analysis. In such an analysis, a mediator (e.g., knowledge of a healthy diet) transmits the effect from an independent variable (e.g., classroom instruction on a healthy diet) to a dependent variable (e.g., consumption of fruits and vegetables). Almost all mediation analyses in psychology use frequentist estimation and hypothesis-testing techniques. A recent exception is Yuan and MacKinnon (Psychological Methods, 14, 301-322, 2009), who outlined a Bayesian parameter estimation procedure for mediation analysis. Here we complete the Bayesian alternative to frequentist mediation analysis by specifying a default Bayesian hypothesis test based on the Jeffreys-Zellner-Siow approach. We further extend this default Bayesian test by allowing a comparison to directional or one-sided alternatives, using Markov chain Monte Carlo techniques implemented in JAGS. All Bayesian tests are implemented in the R package BayesMed (Nuijten, Wetzels, Matzke, Dolan, & Wagenmakers, 2014).

  5. Gaussian Hypothesis Testing and Quantum Illumination.

    Science.gov (United States)

    Wilde, Mark M; Tomamichel, Marco; Lloyd, Seth; Berta, Mario

    2017-09-22

    Quantum hypothesis testing is one of the most basic tasks in quantum information theory and has fundamental links with quantum communication and estimation theory. In this paper, we establish a formula that characterizes the decay rate of the minimal type-II error probability in a quantum hypothesis test of two Gaussian states given a fixed constraint on the type-I error probability. This formula is a direct function of the mean vectors and covariance matrices of the quantum Gaussian states in question. We give an application to quantum illumination, which is the task of determining whether there is a low-reflectivity object embedded in a target region with a bright thermal-noise bath. For the asymmetric-error setting, we find that a quantum illumination transmitter can achieve an error probability exponent stronger than a coherent-state transmitter of the same mean photon number, and furthermore, that it requires far fewer trials to do so. This occurs when the background thermal noise is either low or bright, which means that a quantum advantage is even easier to witness than in the symmetric-error setting because it occurs for a larger range of parameters. Going forward from here, we expect our formula to have applications in settings well beyond those considered in this paper, especially to quantum communication tasks involving quantum Gaussian channels.

  6. Athlete's Heart: Is the Morganroth Hypothesis Obsolete?

    Science.gov (United States)

    Haykowsky, Mark J; Samuel, T Jake; Nelson, Michael D; La Gerche, Andre

    2018-05-01

    In 1975, Morganroth and colleagues reported that the increased left ventricular (LV) mass in highly trained endurance athletes versus nonathletes was primarily due to increased end-diastolic volume while the increased LV mass in resistance trained athletes was solely due to an increased LV wall thickness. Based on the divergent remodelling patterns observed, Morganroth and colleagues hypothesised that the increased "volume" load during endurance exercise may be similar to that which occurs in patients with mitral or aortic regurgitation while the "pressure" load associated with performing a Valsalva manoeuvre (VM) during resistance exercise may mimic the stress imposed on the heart by systemic hypertension or aortic stenosis. Despite widespread acceptance of the four-decade old Morganroth hypothesis in sports cardiology, some investigators have questioned whether such a divergent "athlete's heart" phenotype exists. Given this uncertainty, the purpose of this brief review is to re-evaluate the Morganroth hypothesis regarding: i) the acute effects of resistance exercise performed with a brief VM on LV wall stress, and the patterns of LV remodelling in resistance-trained athletes; ii) the acute effects of endurance exercise on biventricular wall stress, and the time course and pattern of LV and right ventricular (RV) remodelling with endurance training; and iii) the value of comparing "loading" conditions between athletes and patients with cardiac pathology. Copyright © 2018. Published by Elsevier B.V.

  7. The Debt Overhang Hypothesis: Evidence from Pakistan

    Directory of Open Access Journals (Sweden)

    Shah Muhammad Imran

    2016-04-01

    Full Text Available This study investigates the debt overhang hypothesis for Pakistan in the period 1960-2007. The study examines empirically the dynamic behaviour of GDP, debt services, the employed labour force and investment using the time series concepts of unit roots, cointegration, error correlation and causality. Our findings suggest that debt-servicing has a negative impact on the productivity of both labour and capital, and that in turn has adversely affected economic growth. By severely constraining the ability of the country to service debt, this lends support to the debt-overhang hypothesis in Pakistan. The long run relation between debt services and economic growth implies that future increases in output will drain away in form of high debt service payments to lender country as external debt acts like a tax on output. More specifically, foreign creditors will benefit more from the rise in productivity than will domestic producers and labour. This suggests that domestic labour and capital are the ultimate losers from this heavy debt burden.

  8. Roots and Route of the Artification Hypothesis

    Directory of Open Access Journals (Sweden)

    Ellen Dissanayake

    2017-08-01

    Full Text Available Over four decades, my ideas about the arts in human evolution have themselves evolved, from an original notion of art as a human behaviour of “making special” to a full-fledged hypothesis of artification. A summary of the gradual developmental path (or route of the hypothesis, based on ethological principles and concepts, is given, and an argument presented in which artification is described as an exaptation whose roots lie in adaptive features of ancestral mother–infant interaction that contributed to infant survival and maternal reproductive success. I show how the interaction displays features of a ritualised behavior whose operations (formalization, repetition, exaggeration, and elaboration can be regarded as characteristic elements of human ritual ceremonies as well as of art (including song, dance, performance, literary language, altered surroundings, and other examples of making ordinary sounds, movement, language, environments, objects, and bodies extraordinary. Participation in these behaviours in ritual practices served adaptive ends in early Homo by coordinating brain and body states, and thereby emotionally bonding members of a group in common cause as well as reducing existential anxiety in individuals. A final section situates artification within contemporary philosophical and popular ideas of art, claiming that artifying is not a synonym for or definition of art but foundational to any evolutionary discussion of artistic/aesthetic behaviour.

  9. Hypothesis: does ochratoxin A cause testicular cancer?

    Science.gov (United States)

    Schwartz, Gary G

    2002-02-01

    Little is known about the etiology of testicular cancer, which is the most common cancer among young men. Epidemiologic data point to a carcinogenic exposure in early life or in utero, but the nature of the exposure is unknown. We hypothesize that the mycotoxin, ochratoxin A, is a cause of testicular cancer. Ochratoxin A is a naturally occurring contaminant of cereals, pigmeat, and other foods and is a known genotoxic carcinogen in animals. The major features of the descriptive epidemiology of testicular cancer (a high incidence in northern Europe, increasing incidence over time, and associations with high socioeconomic status, and with poor semen quality) are all associated with exposure to ochratoxin A. Exposure of animals to ochratoxin A via the diet or via in utero transfer induces adducts in testicular DNA. We hypothesize that consumption of foods contaminated with ochratoxin A during pregnancy and/or childhood induces lesions in testicular DNA and that puberty promotes these lesions to testicular cancer. We tested the ochratoxin A hypothesis using ecologic data on the per-capita consumption of cereals, coffee, and pigmeat, the principal dietary sources of ochratoxin A. Incidence rates for testicular cancer in 20 countries were significantly correlated with the per-capita consumption of coffee and pigmeat (r = 0.49 and 0.54, p = 0.03 and 0.01). The ochratoxin A hypothesis offers a coherent explanation for much of the descriptive epidemiology of testicular cancer and suggests new avenues for analytic research.

  10. Urbanization and the more-individuals hypothesis.

    Science.gov (United States)

    Chiari, Claudia; Dinetti, Marco; Licciardello, Cinzia; Licitra, Gaetano; Pautasso, Marco

    2010-03-01

    1. Urbanization is a landscape process affecting biodiversity world-wide. Despite many urban-rural studies of bird assemblages, it is still unclear whether more species-rich communities have more individuals, regardless of the level of urbanization. The more-individuals hypothesis assumes that species-rich communities have larger populations, thus reducing the chance of local extinctions. 2. Using newly collated avian distribution data for 1 km(2) grid cells across Florence, Italy, we show a significantly positive relationship between species richness and assemblage abundance for the whole urban area. This richness-abundance relationship persists for the 1 km(2) grid cells with less than 50% of urbanized territory, as well as for the remaining grid cells, with no significant difference in the slope of the relationship. These results support the more-individuals hypothesis as an explanation of patterns in species richness, also in human modified and fragmented habitats. 3. However, the intercept of the species richness-abundance relationship is significantly lower for highly urbanized grid cells. Our study confirms that urban communities have lower species richness but counters the common notion that assemblages in densely urbanized ecosystems have more individuals. In Florence, highly inhabited areas show fewer species and lower assemblage abundance. 4. Urbanized ecosystems are an ongoing large-scale natural experiment which can be used to test ecological theories empirically.

  11. The Younger Dryas impact hypothesis: A requiem

    Science.gov (United States)

    Pinter, Nicholas; Scott, Andrew C.; Daulton, Tyrone L.; Podoll, Andrew; Koeberl, Christian; Anderson, R. Scott; Ishman, Scott E.

    2011-06-01

    The Younger Dryas (YD) impact hypothesis is a recent theory that suggests that a cometary or meteoritic body or bodies hit and/or exploded over North America 12,900 years ago, causing the YD climate episode, extinction of Pleistocene megafauna, demise of the Clovis archeological culture, and a range of other effects. Since gaining widespread attention in 2007, substantial research has focused on testing the 12 main signatures presented as evidence of a catastrophic extraterrestrial event 12,900 years ago. Here we present a review of the impact hypothesis, including its evolution and current variants, and of efforts to test and corroborate the hypothesis. The physical evidence interpreted as signatures of an impact event can be separated into two groups. The first group consists of evidence that has been largely rejected by the scientific community and is no longer in widespread discussion, including: particle tracks in archeological chert; magnetic nodules in Pleistocene bones; impact origin of the Carolina Bays; and elevated concentrations of radioactivity, iridium, and fullerenes enriched in 3He. The second group consists of evidence that has been active in recent research and discussions: carbon spheres and elongates, magnetic grains and magnetic spherules, byproducts of catastrophic wildfire, and nanodiamonds. Over time, however, these signatures have also seen contrary evidence rather than support. Recent studies have shown that carbon spheres and elongates do not represent extraterrestrial carbon nor impact-induced megafires, but are indistinguishable from fungal sclerotia and arthropod fecal material that are a small but common component of many terrestrial deposits. Magnetic grains and spherules are heterogeneously distributed in sediments, but reported measurements of unique peaks in concentrations at the YD onset have yet to be reproduced. The magnetic grains are certainly just iron-rich detrital grains, whereas reported YD magnetic spherules are

  12. A hypothesis on a role of oxytocin in the social mechanisms of speech and vocal learning.

    Science.gov (United States)

    Theofanopoulou, Constantina; Boeckx, Cedric; Jarvis, Erich D

    2017-08-30

    Language acquisition in humans and song learning in songbirds naturally happen as a social learning experience, providing an excellent opportunity to reveal social motivation and reward mechanisms that boost sensorimotor learning. Our knowledge about the molecules and circuits that control these social mechanisms for vocal learning and language is limited. Here we propose a hypothesis of a role for oxytocin (OT) in the social motivation and evolution of vocal learning and language. Building upon existing evidence, we suggest specific neural pathways and mechanisms through which OT might modulate vocal learning circuits in specific developmental stages. © 2017 The Authors.

  13. The efficient market hypothesis: problems with interpretations of empirical tests

    Directory of Open Access Journals (Sweden)

    Denis Alajbeg

    2012-03-01

    Full Text Available Despite many “refutations” in empirical tests, the efficient market hypothesis (EMH remains the central concept of financial economics. The EMH’s resistance to the results of empirical testing emerges from the fact that the EMH is not a falsifiable theory. Its axiomatic definition shows how asset prices would behave under assumed conditions. Testing for this price behavior does not make much sense as the conditions in the financial markets are much more complex than the simplified conditions of perfect competition, zero transaction costs and free information used in the formulation of the EMH. Some recent developments within the tradition of the adaptive market hypothesis are promising regarding development of a falsifiable theory of price formation in financial markets, but are far from giving assurance that we are approaching a new formulation. The most that can be done in the meantime is to be very cautious while interpreting the empirical evidence that is presented as “testing” the EMH.

  14. A new glaucoma hypothesis: a role of glymphatic system dysfunction.

    Science.gov (United States)

    Wostyn, Peter; Van Dam, Debby; Audenaert, Kurt; Killer, Hanspeter Esriel; De Deyn, Peter Paul; De Groot, Veva

    2015-06-29

    In a recent review article titled "A new look at cerebrospinal fluid circulation", Brinker et al. comprehensively described novel insights from molecular and cellular biology as well as neuroimaging research, which indicate that cerebrospinal fluid (CSF) physiology is much more complex than previously believed. The glymphatic system is a recently defined brain-wide paravascular pathway for CSF and interstitial fluid exchange that facilitates efficient clearance of interstitial solutes, including amyloid-β, from the brain. Although further studies are needed to substantiate the functional significance of the glymphatic concept, one implication is that glymphatic pathway dysfunction may contribute to the deficient amyloid-β clearance in Alzheimer's disease. In this paper, we review several lines of evidence suggesting that the glymphatic system may also have potential clinical relevance for the understanding of glaucoma. As a clinically acceptable MRI-based approach to evaluate glymphatic pathway function in humans has recently been developed, a unique opportunity now exists to investigate whether suppression of the glymphatic system contributes to the development of glaucoma. The observation of a dysfunctional glymphatic system in patients with glaucoma would provide support for the hypothesis recently proposed by our group that CSF circulatory dysfunction may play a contributory role in the pathogenesis of glaucomatous damage. This would suggest a new hypothesis for glaucoma, which, just like Alzheimer's disease, might be considered then as an imbalance between production and clearance of neurotoxins, including amyloid-β.

  15. A Highest Order Hypothesis Compatibility Test for Monocular SLAM

    Directory of Open Access Journals (Sweden)

    Edmundo Guerra

    2013-08-01

    Full Text Available Simultaneous Location and Mapping (SLAM is a key problem to solve in order to build truly autonomous mobile robots. SLAM with a unique camera, or monocular SLAM, is probably one of the most complex SLAM variants, based entirely on a bearing-only sensor working over six DOF. The monocular SLAM method developed in this work is based on the Delayed Inverse-Depth (DI-D Feature Initialization, with the contribution of a new data association batch validation technique, the Highest Order Hypothesis Compatibility Test, HOHCT. The Delayed Inverse-Depth technique is used to initialize new features in the system and defines a single hypothesis for the initial depth of features with the use of a stochastic technique of triangulation. The introduced HOHCT method is based on the evaluation of statistically compatible hypotheses and a search algorithm designed to exploit the strengths of the Delayed Inverse-Depth technique to achieve good performance results. This work presents the HOHCT with a detailed formulation of the monocular DI-D SLAM problem. The performance of the proposed HOHCT is validated with experimental results, in both indoor and outdoor environments, while its costs are compared with other popular approaches.

  16. Harmony as Ideology: Questioning the Diversity-Stability Hypothesis.

    Science.gov (United States)

    Nikisianis, Nikos; Stamou, Georgios P

    2016-03-01

    The representation of a complex but stable, self-regulated and, finally, harmonious nature penetrates the whole history of Ecology, thus contradicting the core of the Darwinian evolution. Originated in the pre-Darwinian Natural History, this representation defined theoretically the various schools of early ecology and, in the context of the cybernetic synthesis of the 1950s, it assumed a typical mathematical form on account of α positive correlation between species diversity and community stability. After 1960, these two aforementioned concepts and their positive correlation were proposed as environmental management tools, in the face of the ecological crisis arising at the time. In the early 1970s, and particularly after May's evolutionary arguments, the consensus around this positive correlation collapsed for a while, only to be promptly restored for the purpose of attaching an ecological value on biodiversity. In this paper, we explore the history of the diversity-stability hypothesis and we review the successive terms that have been used to express community stability. We argue that this hypothesis has been motivated by the nodal ideological presuppositions of order and harmony and that the scientific developments in this field largely correspond to external social pressures. We conclude that the conflict about the diversity-stability relationship is in fact an ideological debate, referring mostly to the way we see nature and society rather than to an autonomous scientific question. From this point of view, we may understand why Ecology's concepts and perceptions may decline and return again and again, forming a pluralistic scientific history.

  17. Approaches to informed consent for hypothesis-testing and hypothesis-generating clinical genomics research.

    Science.gov (United States)

    Facio, Flavia M; Sapp, Julie C; Linn, Amy; Biesecker, Leslie G

    2012-10-10

    Massively-parallel sequencing (MPS) technologies create challenges for informed consent of research participants given the enormous scale of the data and the wide range of potential results. We propose that the consent process in these studies be based on whether they use MPS to test a hypothesis or to generate hypotheses. To demonstrate the differences in these approaches to informed consent, we describe the consent processes for two MPS studies. The purpose of our hypothesis-testing study is to elucidate the etiology of rare phenotypes using MPS. The purpose of our hypothesis-generating study is to test the feasibility of using MPS to generate clinical hypotheses, and to approach the return of results as an experimental manipulation. Issues to consider in both designs include: volume and nature of the potential results, primary versus secondary results, return of individual results, duty to warn, length of interaction, target population, and privacy and confidentiality. The categorization of MPS studies as hypothesis-testing versus hypothesis-generating can help to clarify the issue of so-called incidental or secondary results for the consent process, and aid the communication of the research goals to study participants.

  18. The ctenophore genome and the evolutionary origins of neural systems

    NARCIS (Netherlands)

    Moroz, Leonid L.; Kocot, Kevin M.; Citarella, Mathew R.; Dosung, Sohn; Norekian, Tigran P.; Povolotskaya, Inna S.; Grigorenko, Anastasia P.; Dailey, Christopher; Berezikov, Eugene; Buckley, Katherine M.; Ptitsyn, Andrey; Reshetov, Denis; Mukherjee, Krishanu; Moroz, Tatiana P.; Bobkova, Yelena; Yu, Fahong; Kapitonov, Vladimir V.; Jurka, Jerzy; Bobkov, Yuri V.; Swore, Joshua J.; Girardo, David O.; Fodor, Alexander; Gusev, Fedor; Sanford, Rachel; Bruders, Rebecca; Kittler, Ellen; Mills, Claudia E.; Rast, Jonathan P.; Derelle, Romain; Solovyev, Victor V.; Kondrashov, Fyodor A.; Swalla, Billie J.; Sweedler, Jonathan V.; Rogaev, Evgeny I.; Halanych, Kenneth M.; Kohn, Andrea B.

    2014-01-01

    The origins of neural systems remain unresolved. In contrast to other basal metazoans, ctenophores (comb jellies) have both complex nervous and mesoderm-derived muscular systems. These holoplanktonic predators also have sophisticated ciliated locomotion, behaviour and distinct development. Here we

  19. Neuroticism, intelligence, and intra-individual variability in elementary cognitive tasks: testing the mental noise hypothesis.

    Science.gov (United States)

    Colom, Roberto; Quiroga, Ma Angeles

    2009-08-01

    Some studies show positive correlations between intraindividual variability in elementary speed measures (reflecting processing efficiency) and individual differences in neuroticism (reflecting instability in behaviour). The so-called neural noise hypothesis assumes that higher levels of noise are related both to smaller indices of processing efficiency and greater levels of neuroticism. Here, we test this hypothesis measuring mental speed by means of three elementary cognitive tasks tapping similar basic processes but varying systematically their content (verbal, numerical, and spatial). Neuroticism and intelligence are also measured. The sample comprised 196 undergraduate psychology students. The results show that (1) processing efficiency is generally unrelated to individual differences in neuroticism, (2) processing speed and efficiency correlate with intelligence, and (3) only the efficiency index is genuinely related to intelligence when the colinearity between speed and efficiency is controlled.

  20. Evolutionary Algorithms For Neural Networks Binary And Real Data Classification

    Directory of Open Access Journals (Sweden)

    Dr. Hanan A.R. Akkar

    2015-08-01

    Full Text Available Artificial neural networks are complex networks emulating the way human rational neurons process data. They have been widely used generally in prediction clustering classification and association. The training algorithms that used to determine the network weights are almost the most important factor that influence the neural networks performance. Recently many meta-heuristic and Evolutionary algorithms are employed to optimize neural networks weights to achieve better neural performance. This paper aims to use recently proposed algorithms for optimizing neural networks weights comparing these algorithms performance with other classical meta-heuristic algorithms used for the same purpose. However to evaluate the performance of such algorithms for training neural networks we examine such algorithms to classify four opposite binary XOR clusters and classification of continuous real data sets such as Iris and Ecoli.

  1. Neural Ranking Models with Weak Supervision

    NARCIS (Netherlands)

    Dehghani, M.; Zamani, H.; Severyn, A.; Kamps, J.; Croft, W.B.

    2017-01-01

    Despite the impressive improvements achieved by unsupervised deep neural networks in computer vision and NLP tasks, such improvements have not yet been observed in ranking for information retrieval. The reason may be the complexity of the ranking problem, as it is not obvious how to learn from

  2. Based on BP Neural Network Stock Prediction

    Science.gov (United States)

    Liu, Xiangwei; Ma, Xin

    2012-01-01

    The stock market has a high profit and high risk features, on the stock market analysis and prediction research has been paid attention to by people. Stock price trend is a complex nonlinear function, so the price has certain predictability. This article mainly with improved BP neural network (BPNN) to set up the stock market prediction model, and…

  3. Neural modeling of prefrontal executive function

    Energy Technology Data Exchange (ETDEWEB)

    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.

  4. The immunology of the allergy epidemic and the hygiene hypothesis.

    Science.gov (United States)

    Lambrecht, Bart N; Hammad, Hamida

    2017-09-19

    The immunology of the hygiene hypothesis of allergy is complex and involves the loss of cellular and humoral immunoregulatory pathways as a result of the adoption of a Western lifestyle and the disappearance of chronic infectious diseases. The influence of diet and reduced microbiome diversity now forms the foundation of scientific thinking on how the allergy epidemic occurred, although clear mechanistic insights into the process in humans are still lacking. Here we propose that barrier epithelial cells are heavily influenced by environmental factors and by microbiome-derived danger signals and metabolites, and thus act as important rheostats for immunoregulation, particularly during early postnatal development. Preventive strategies based on this new knowledge could exploit the diversity of the microbial world and the way humans react to it, and possibly restore old symbiotic relationships that have been lost in recent times, without causing disease or requiring a return to an unhygienic life style.

  5. Ten Challenges of the Amyloid Hypothesis of Alzheimer's Disease.

    Science.gov (United States)

    Kepp, Kasper Planeta

    2017-01-01

    The inability to effectively halt or cure Alzheimer's disease (AD), exacerbated by the recent failures of high-profile clinical trials, emphasizes the urgent need to understand the complex biochemistry of this major neurodegenerative disease. In this paper, ten central, current challenges of the major paradigm in the field, the amyloid hypothesis, are sharply formulated. These challenges together show that new approaches are necessary that address data heterogeneity, increase focus on the proteome level, use available human patient data more actively, account for the aging phenotype as a background model of the disease, unify our understanding of the interplay between genetic and non-genetic risk factors, and combine into one framework both the familial and sporadic forms of the disease.

  6. Mirror neurons, birdsong, and human language: a hypothesis.

    Science.gov (United States)

    Levy, Florence

    2011-01-01

    THE MIRROR SYSTEM HYPOTHESIS AND INVESTIGATIONS OF BIRDSONG ARE REVIEWED IN RELATION TO THE SIGNIFICANCE FOR THE DEVELOPMENT OF HUMAN SYMBOLIC AND LANGUAGE CAPACITY, IN TERMS OF THREE FUNDAMENTAL FORMS OF COGNITIVE REFERENCE: iconic, indexical, and symbolic. Mirror systems are initially iconic but can progress to indexical reference when produced without the need for concurrent stimuli. Developmental stages in birdsong are also explored with reference to juvenile subsong vs complex stereotyped adult syllables, as an analogy with human language development. While birdsong remains at an indexical reference stage, human language benefits from the capacity for symbolic reference. During a pre-linguistic "babbling" stage, recognition of native phonemic categories is established, allowing further development of subsequent prefrontal and linguistic circuits for sequential language capacity.

  7. Mirror neurons, birdsong and human language: a hypothesis

    Directory of Open Access Journals (Sweden)

    Florence eLevy

    2012-01-01

    Full Text Available AbstractThe Mirror System Hypothesis (MSH and investigations of birdsong are reviewed in relation to the significance for the development of human symbolic and language capacity, in terms of three fundamental forms of cognitive reference: iconic, indexical, and symbolic. Mirror systems are initially iconic but can progress to indexal reference when produced without the need for concurrent stimuli. Developmental stages in birdsong are also explored with reference to juvenile subsong vs complex stereotyped adult syllables, as an analogy with human language development. While birdsong remains at an indexal reference stage, human language benefits from the capacity for symbolic reference. During a pre-linguistic ‘babbling’ stage, recognition of native phonemic categories is established, allowing further development of a subsequent prefrontal and linguistic circuits for sequential language capacity.

  8. Multitask Classification Hypothesis Space With Improved Generalization Bounds.

    Science.gov (United States)

    Li, Cong; Georgiopoulos, Michael; Anagnostopoulos, Georgios C

    2015-07-01

    This paper presents a pair of hypothesis spaces (HSs) of vector-valued functions intended to be used in the context of multitask classification. While both are parameterized on the elements of reproducing kernel Hilbert spaces and impose a feature mapping that is common to all tasks, one of them assumes this mapping as fixed, while the more general one learns the mapping via multiple kernel learning. For these new HSs, empirical Rademacher complexity-based generalization bounds are derived, and are shown to be tighter than the bound of a particular HS, which has appeared recently in the literature, leading to improved performance. As a matter of fact, the latter HS is shown to be a special case of ours. Based on an equivalence to Group-Lasso type HSs, the proposed HSs are utilized toward corresponding support vector machine-based formulations. Finally, experimental results on multitask learning problems underline the quality of the derived bounds and validate this paper's analysis.

  9. A neural flow estimator

    DEFF Research Database (Denmark)

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

    1995-01-01

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

  10. Neural Systems Laboratory

    Data.gov (United States)

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

  11. Modular representation of layered neural networks.

    Science.gov (United States)

    Watanabe, Chihiro; Hiramatsu, Kaoru; Kashino, Kunio

    2018-01-01

    Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood. In this paper, we propose a new method for extracting a global and simplified structure from a layered neural network. Based on network analysis, the proposed method detects communities or clusters of units with similar connection patterns. We show its effectiveness by applying it to three use cases. (1) Network decomposition: it can decompose a trained neural network into multiple small independent networks thus dividing the problem and reducing the computation time. (2) Training assessment: the appropriateness of a trained result with a given hyperparameter or randomly chosen initial parameters can be evaluated by using a modularity index. And (3) data analysis: in practical data it reveals the community structure in the input, hidden, and output layers, which serves as a clue for discovering knowledge from a trained neural network. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. The Laplacian spectrum of neural networks

    Science.gov (United States)

    de Lange, Siemon C.; de Reus, Marcel A.; van den Heuvel, Martijn P.

    2014-01-01

    The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. Many popular graph metrics, however, encode average properties of individual network elements. Complementing these “conventional” graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks. PMID:24454286

  13. Parallelization of Neural Network Training for NLP with Hogwild!

    Directory of Open Access Journals (Sweden)

    Deyringer Valentin

    2017-10-01

    Full Text Available Neural Networks are prevalent in todays NLP research. Despite their success for different tasks, training time is relatively long. We use Hogwild! to counteract this phenomenon and show that it is a suitable method to speed up training Neural Networks of different architectures and complexity. For POS tagging and translation we report considerable speedups of training, especially for the latter. We show that Hogwild! can be an important tool for training complex NLP architectures.

  14. Gear Fault Diagnosis Based on BP Neural Network

    Science.gov (United States)

    Huang, Yongsheng; Huang, Ruoshi

    2018-03-01

    Gear transmission is more complex, widely used in machinery fields, which form of fault has some nonlinear characteristics. This paper uses BP neural network to train the gear of four typical failure modes, and achieves satisfactory results. Tested by using test data, test results have an agreement with the actual results. The results show that the BP neural network can effectively solve the complex state of gear fault in the gear fault diagnosis.

  15. Nonthermal effects of therapeutic ultrasound: the frequency resonance hypothesis.

    Science.gov (United States)

    Johns, Lennart D

    2002-07-01

    To present the frequency resonance hypothesis, a possible mechanical mechanism by which treatment with non-thermal levels of ultrasound stimulates therapeutic effects. The review encompasses a 4-decade history but focuses on recent reports describing the effects of nonthermal therapeutic levels of ultrasound at the cellular and molecular levels. A search of MEDLINE from 1965 through 2000 using the terms ultrasound and therapeutic ultrasound. The literature provides a number of examples in which exposure of cells to therapeutic ultrasound under nonthermal conditions modified cellular functions. Nonthermal levels of ultrasound are reported to modulate membrane properties, alter cellular proliferation, and produce increases in proteins associated with inflammation and injury repair. Combined, these data suggest that nonthermal effects of therapeutic ultrasound can modify the inflammatory response. The concept of the absorption of ultrasonic energy by enzymatic proteins leading to changes in the enzymes activity is not novel. However, recent reports demonstrating that ultrasound affects enzyme activity and possibly gene regulation provide sufficient data to present a probable molecular mechanism of ultrasound's nonthermal therapeutic action. The frequency resonance hypothesis describes 2 possible biological mechanisms that may alter protein function as a result of the absorption of ultrasonic energy. First, absorption of mechanical energy by a protein may produce a transient conformational shift (modifying the 3-dimensional structure) and alter the protein's functional activity. Second, the resonance or shearing properties of the wave (or both) may dissociate a multimolecular complex, thereby disrupting the complex's function. This review focuses on recent studies that have reported cellular and molecular effects of therapeutic ultrasound and presents a mechanical mechanism that may lead to a better understanding of how the nonthermal effects of ultrasound may be

  16. Adaptive competitive learning neural networks

    Directory of Open Access Journals (Sweden)

    Ahmed R. Abas

    2013-11-01

    Full Text Available In this paper, the adaptive competitive learning (ACL neural network algorithm is proposed. This neural network not only groups similar input feature vectors together but also determines the appropriate number of groups of these vectors. This algorithm uses a new proposed criterion referred to as the ACL criterion. This criterion evaluates different clustering structures produced by the ACL neural network for an input data set. Then, it selects the best clustering structure and the corresponding network architecture for this data set. The selected structure is composed of the minimum number of clusters that are compact and balanced in their sizes. The selected network architecture is efficient, in terms of its complexity, as it contains the minimum number of neurons. Synaptic weight vectors of these neurons represent well-separated, compact and balanced clusters in the input data set. The performance of the ACL algorithm is evaluated and compared with the performance of a recently proposed algorithm in the literature in clustering an input data set and determining its number of clusters. Results show that the ACL algorithm is more accurate and robust in both determining the number of clusters and allocating input feature vectors into these clusters than the other algorithm especially with data sets that are sparsely distributed.

  17. Alternatives to the linear risk hypothesis

    International Nuclear Information System (INIS)

    Craig, A.G.

    1976-01-01

    A theoretical argument is presented which suggests that in using the linear hypothesis for all values of LET the low dose risk is overestimated for low LET but that it is underestimated for very high LET. The argument is based upon the idea that cell lesions which do not lead to cell death may in fact lead to a malignant cell. Expressions for the Surviving Fraction and the Cancer Risk based on this argument are given. An advantage of this very general approach is that is expresses cell survival and cancer risk entirely in terms of the cell lesions and avoids the rather contentious argument as to how the average number of lesions should be related to the dose. (U.K.)

  18. Large numbers hypothesis. II - Electromagnetic radiation

    Science.gov (United States)

    Adams, P. J.

    1983-01-01

    This paper develops the theory of electromagnetic radiation in the units covariant formalism incorporating Dirac's large numbers hypothesis (LNH). A direct field-to-particle technique is used to obtain the photon propagation equation which explicitly involves the photon replication rate. This replication rate is fixed uniquely by requiring that the form of a free-photon distribution function be preserved, as required by the 2.7 K cosmic radiation. One finds that with this particular photon replication rate the units covariant formalism developed in Paper I actually predicts that the ratio of photon number to proton number in the universe varies as t to the 1/4, precisely in accord with LNH. The cosmological red-shift law is also derived and it is shown to differ considerably from the standard form of (nu)(R) - const.

  19. Artistic talent in dyslexia--a hypothesis.

    Science.gov (United States)

    Chakravarty, Ambar

    2009-10-01

    The present article hints at a curious neurocognitive phenomenon of development of artistic talents in some children with dyslexia. The article also takes note of the phenomenon of creating in the midst of language disability as observed in the lives of such creative people like Leonardo da Vinci and Albert Einstein who were most probably affected with developmental learning disorders. It has been hypothesised that a developmental delay in the dominant hemisphere most likely 'disinhibits' the non-dominant parietal lobe to unmask talents, artistic or otherwise, in some such individuals. The present hypothesis follows the phenomenon of paradoxical functional facilitation described earlier. It has been suggested that children with learning disorders be encouraged to develop such hidden talents to full capacity, rather than be subjected to overemphasising on the correction of the disturbed coded symbol operations, in remedial training.

  20. Tissue misrepair hypothesis for radiation carcinogenesis

    International Nuclear Information System (INIS)

    Kondo, Sohei

    1991-01-01

    Dose-response curves for chronic leukemia in A-bomb survivors and liver tumors in patients given Thorotrast (colloidal thorium dioxide) show large threshold effects. The existence of these threshold effects can be explained by the following hypothesis. A high dose of radiation causes a persistent wound in a cellrenewable tissue. Disorder of the injured cell society partly frees the component cells from territorial restraints on their proliferation, enabling them to continue development of their cellular functions toward advanced autonomy. This progression might be achieved by continued epigenetic and genetic changes as a result of occasional errors in the otherwise concerted healing action of various endogeneous factors recruited for tissue repair. Carcinogenesis is not simply a single-cell problem but a cell-society problem. Therefore, it is not warranted to estimate risk at low doses by linear extrapolation from cancer data at high doses without knowledge of the mechanism of radiation carcinogenesis. (author) 57 refs

  1. Statistical hypothesis tests of some micrometeorological observations

    International Nuclear Information System (INIS)

    SethuRaman, S.; Tichler, J.

    1977-01-01

    Chi-square goodness-of-fit is used to test the hypothesis that the medium scale of turbulence in the atmospheric surface layer is normally distributed. Coefficients of skewness and excess are computed from the data. If the data are not normal, these coefficients are used in Edgeworth's asymptotic expansion of Gram-Charlier series to determine an altrnate probability density function. The observed data are then compared with the modified probability densities and the new chi-square values computed.Seventy percent of the data analyzed was either normal or approximatley normal. The coefficient of skewness g 1 has a good correlation with the chi-square values. Events with vertical-barg 1 vertical-bar 1 vertical-bar<0.43 were approximately normal. Intermittency associated with the formation and breaking of internal gravity waves in surface-based inversions over water is thought to be the reason for the non-normality

  2. The hexagon hypothesis: Six disruptive scenarios.

    Science.gov (United States)

    Burtles, Jim

    2015-01-01

    This paper aims to bring a simple but effective and comprehensive approach to the development, delivery and monitoring of business continuity solutions. To ensure that the arguments and principles apply across the board, the paper sticks to basic underlying concepts rather than sophisticated interpretations. First, the paper explores what exactly people are defending themselves against. Secondly, the paper looks at how defences should be set up. Disruptive events tend to unfold in phases, each of which invites a particular style of protection, ranging from risk management through to business continuity to insurance cover. Their impact upon any business operation will fall into one of six basic scenarios. The hexagon hypothesis suggests that everyone should be prepared to deal with each of these six disruptive scenarios and it provides them with a useful benchmark for business continuity.

  3. Novae, supernovae, and the island universe hypothesis

    International Nuclear Information System (INIS)

    Van Den Bergh, S.

    1988-01-01

    Arguments in Curtis's (1917) paper related to the island universe hypothesis and the existence of novae in spiral nebulae are considered. It is noted that the maximum magnitude versus rate-of-decline relation for novae may be the best tool presently available for the calibration of the extragalactic distance scale. Light curve observations of six novae are used to determine a distance of 18.6 + or - 3.5 MPc to the Virgo cluster. Results suggest that Type Ia supernovae cannot easily be used as standard candles, and that Type II supernovae are unsuitable as distance indicators. Factors other than precursor mass are probably responsible for determining the ultimate fate of evolving stars. 83 references

  4. Extra dimensions hypothesis in high energy physics

    Directory of Open Access Journals (Sweden)

    Volobuev Igor

    2017-01-01

    Full Text Available We discuss the history of the extra dimensions hypothesis and the physics and phenomenology of models with large extra dimensions with an emphasis on the Randall- Sundrum (RS model with two branes. We argue that the Standard Model extension based on the RS model with two branes is phenomenologically acceptable only if the inter-brane distance is stabilized. Within such an extension of the Standard Model, we study the influence of the infinite Kaluza-Klein (KK towers of the bulk fields on collider processes. In particular, we discuss the modification of the scalar sector of the theory, the Higgs-radion mixing due to the coupling of the Higgs boson to the radion and its KK tower, and the experimental restrictions on the mass of the radion-dominated states.

  5. Multiple model cardinalized probability hypothesis density filter

    Science.gov (United States)

    Georgescu, Ramona; Willett, Peter

    2011-09-01

    The Probability Hypothesis Density (PHD) filter propagates the first-moment approximation to the multi-target Bayesian posterior distribution while the Cardinalized PHD (CPHD) filter propagates both the posterior likelihood of (an unlabeled) target state and the posterior probability mass function of the number of targets. Extensions of the PHD filter to the multiple model (MM) framework have been published and were implemented either with a Sequential Monte Carlo or a Gaussian Mixture approach. In this work, we introduce the multiple model version of the more elaborate CPHD filter. We present the derivation of the prediction and update steps of the MMCPHD particularized for the case of two target motion models and proceed to show that in the case of a single model, the new MMCPHD equations reduce to the original CPHD equations.

  6. Differential patterns of cortical activation as a function of fluid reasoning complexity.

    Science.gov (United States)

    Perfetti, Bernardo; Saggino, Aristide; Ferretti, Antonio; Caulo, Massimo; Romani, Gian Luca; Onofrj, Marco

    2009-02-01

    Fluid intelligence (gf) refers to abstract reasoning and problem solving abilities. It is considered a human higher cognitive factor central to general intelligence (g). The regions of the cortex supporting gf have been revealed by recent bioimaging studies and valuable hypothesis on the neural correlates of individual differences have been proposed. However, little is known about the interaction between individual variability in gf and variation in cortical activity following task complexity increase. To further investigate this, two samples of participants (high-IQ, N = 8; low-IQ, N = 10) with significant differences in gf underwent two reasoning (moderate and complex) tasks and a control task adapted from the Raven progressive matrices. Functional magnetic resonance was used and the recorded signal analyzed between and within the groups. The present study revealed two opposite patterns of neural activity variation which were probably a reflection of the overall differences in cognitive resource modulation: when complexity increased, high-IQ subjects showed a signal enhancement in some frontal and parietal regions, whereas low-IQ subjects revealed a decreased activity in the same areas. Moreover, a direct comparison between the groups' activation patterns revealed a greater neural activity in the low-IQ sample when conducting moderate task, with a strong involvement of medial and lateral frontal regions thus suggesting that the recruitment of executive functioning might be different between the groups. This study provides evidence for neural differences in facing reasoning complexity among subjects with different gf level that are mediated by specific patterns of activation of the underlying fronto-parietal network.

  7. On the immunostimulatory hypothesis of cancer

    Directory of Open Access Journals (Sweden)

    Juan Bruzzo

    2011-12-01

    Full Text Available There is a rather generalized belief that the worst possible outcome for the application of immunological therapies against cancer is a null effect on tumor growth. However, a significant body of evidence summarized in the immunostimulatory hypothesis of cancer suggests that, upon certain circumstances, the growth of incipient and established tumors can be accelerated rather than inhibited by the immune response supposedly mounted to limit tumor growth. In order to provide more compelling evidence of this proposition, we have explored the growth behavior characteristics of twelve murine tumors -most of them of spontaneous origin- arisen in the colony of our laboratory, in putatively immunized and control mice. Using classical immunization procedures, 8 out of 12 tumors were actually stimulated in "immunized" mice while the remaining 4 were neither inhibited nor stimulated. Further, even these apparently non-antigenic tumors could reveal some antigenicity if more stringent than classical immunization procedures were used. This possibility was suggested by the results obtained with one of these four apparently non-antigenic tumors: the LB lymphoma. In effect, upon these stringent immunization pretreatments, LB was slightly inhibited or stimulated, depending on the titer of the immune reaction mounted against the tumor, with higher titers rendering inhibition and lower titers rendering tumor stimulation. All the above results are consistent with the immunostimulatory hypothesis that entails the important therapeutic implications -contrary to the orthodoxy- that, anti-tumor vaccines may run a real risk of doing harm if the vaccine-induced immunity is too weak to move the reaction into the inhibitory part of the immune response curve and that, a slight and prolonged immunodepression -rather than an immunostimulation- might interfere with the progression of some tumors and thus be an aid to cytotoxic therapies.

  8. Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank

    OpenAIRE

    Zhao, Liang; Liao, Siyu; Wang, Yanzhi; Li, Zhe; Tang, Jian; Pan, Victor; Yuan, Bo

    2017-01-01

    Recently low displacement rank (LDR) matrices, or so-called structured matrices, have been proposed to compress large-scale neural networks. Empirical results have shown that neural networks with weight matrices of LDR matrices, referred as LDR neural networks, can achieve significant reduction in space and computational complexity while retaining high accuracy. We formally study LDR matrices in deep learning. First, we prove the universal approximation property of LDR neural networks with a ...

  9. Neural control of magnetic suspension systems

    Science.gov (United States)

    Gray, W. Steven

    1993-01-01

    The purpose of this research program is to design, build and test (in cooperation with NASA personnel from the NASA Langley Research Center) neural controllers for two different small air-gap magnetic suspension systems. The general objective of the program is to study neural network architectures for the purpose of control in an experimental setting and to demonstrate the feasibility of the concept. The specific objectives of the research program are: (1) to demonstrate through simulation and experimentation the feasibility of using neural controllers to stabilize a nonlinear magnetic suspension system; (2) to investigate through simulation and experimentation the performance of neural controllers designs under various types of parametric and nonparametric uncertainty; (3) to investigate through simulation and experimentation various types of neural architectures for real-time control with respect to performance and complexity; and (4) to benchmark in an experimental setting the performance of neural controllers against other types of existing linear and nonlinear compensator designs. To date, the first one-dimensional, small air-gap magnetic suspension system has been built, tested and delivered to the NASA Langley Research Center. The device is currently being stabilized with a digital linear phase-lead controller. The neural controller hardware is under construction. Two different neural network paradigms are under consideration, one based on hidden layer feedforward networks trained via back propagation and one based on using Gaussian radial basis functions trained by analytical methods related to stability conditions. Some advanced nonlinear control algorithms using feedback linearization and sliding mode control are in simulation studies.

  10. Neural Networks: Implementations and Applications

    OpenAIRE

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

    1996-01-01

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

  11. Further details of a hypothesis for the initiation of genetic recombination from recognition sites

    Energy Technology Data Exchange (ETDEWEB)

    Markham, P [Queen Elizabeth College, London (G.B.)

    1982-01-01

    Consideration of the initiation of genetic recombination from fixed sites recognised by an initiation complex, has provided more details of the envisaged mechanism and implications of a recent hypothesis. It has been shown that the hypothesis allows for more than one recombinogenic-event to result from a single binding of the recombination initiation complex to a recognition site in a DNA duplex. This capacity can explain data from fungal systems which are apparently inconsistent with the Meselson-Radding model of genetic recombination with respect to the positional relationship between tracts of hybrid DNA and sites of crossing-over. A mechanism for conversion, involving hybrid DNA formation, but without mismatch correction has also been proposed on the basis of this capacity. It is suggested that the hypothesis may apply generally to genetic recombination, in prokaryotes as well as eukaryotes.

  12. Critical Branching Neural Networks

    Science.gov (United States)

    Kello, Christopher T.

    2013-01-01

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

  13. Consciousness and neural plasticity

    DEFF Research Database (Denmark)

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

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

    Science.gov (United States)

    Gafarov, F M; Gafarova, V R

    2016-09-01

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

  15. The Matter-Gravity Entanglement Hypothesis

    Science.gov (United States)

    Kay, Bernard S.

    2018-03-01

    I outline some of my work and results (some dating back to 1998, some more recent) on my matter-gravity entanglement hypothesis, according to which the entropy of a closed quantum gravitational system is equal to the system's matter-gravity entanglement entropy. The main arguments presented are: (1) that this hypothesis is capable of resolving what I call the second-law puzzle, i.e. the puzzle as to how the entropy increase of a closed system can be reconciled with the asssumption of unitary time-evolution; (2) that the black hole information loss puzzle may be regarded as a special case of this second law puzzle and that therefore the same resolution applies to it; (3) that the black hole thermal atmosphere puzzle (which I recall) can be resolved by adopting a radically different-from-usual description of quantum black hole equilibrium states, according to which they are total pure states, entangled between matter and gravity in such a way that the partial states of matter and gravity are each approximately thermal equilibrium states (at the Hawking temperature); (4) that the Susskind-Horowitz-Polchinski string-theoretic understanding of black hole entropy as the logarithm of the degeneracy of a long string (which is the weak string coupling limit of a black hole) cannot be quite correct but should be replaced by a modified understanding according to which it is the entanglement entropy between a long string and its stringy atmosphere, when in a total pure equilibrium state in a suitable box, which (in line with (3)) goes over, at strong-coupling, to a black hole in equilibrium with its thermal atmosphere. The modified understanding in (4) is based on a general result, which I also describe, which concerns the likely state of a quantum system when it is weakly coupled to an energy-bath and the total state is a random pure state with a given energy. This result generalizes Goldstein et al.'s `canonical typicality' result to systems which are not necessarily small.

  16. The Matter-Gravity Entanglement Hypothesis

    Science.gov (United States)

    Kay, Bernard S.

    2018-05-01

    I outline some of my work and results (some dating back to 1998, some more recent) on my matter-gravity entanglement hypothesis, according to which the entropy of a closed quantum gravitational system is equal to the system's matter-gravity entanglement entropy. The main arguments presented are: (1) that this hypothesis is capable of resolving what I call the second-law puzzle, i.e. the puzzle as to how the entropy increase of a closed system can be reconciled with the asssumption of unitary time-evolution; (2) that the black hole information loss puzzle may be regarded as a special case of this second law puzzle and that therefore the same resolution applies to it; (3) that the black hole thermal atmosphere puzzle (which I recall) can be resolved by adopting a radically different-from-usual description of quantum black hole equilibrium states, according to which they are total pure states, entangled between matter and gravity in such a way that the partial states of matter and gravity are each approximately thermal equilibrium states (at the Hawking temperature); (4) that the Susskind-Horowitz-Polchinski string-theoretic understanding of black hole entropy as the logarithm of the degeneracy of a long string (which is the weak string coupling limit of a black hole) cannot be quite correct but should be replaced by a modified understanding according to which it is the entanglement entropy between a long string and its stringy atmosphere, when in a total pure equilibrium state in a suitable box, which (in line with (3)) goes over, at strong-coupling, to a black hole in equilibrium with its thermal atmosphere. The modified understanding in (4) is based on a general result, which I also describe, which concerns the likely state of a quantum system when it is weakly coupled to an energy-bath and the total state is a random pure state with a given energy. This result generalizes Goldstein et al.'s `canonical typicality' result to systems which are not necessarily small.

  17. Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks.

    Science.gov (United States)

    Naveros, Francisco; Garrido, Jesus A; Carrillo, Richard R; Ros, Eduardo; Luque, Niceto R

    2017-01-01

    Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under

  18. Marginal contrasts and the Contrastivist Hypothesis

    Directory of Open Access Journals (Sweden)

    Daniel Currie Hall

    2016-12-01

    Full Text Available The Contrastivist Hypothesis (CH; Hall 2007; Dresher 2009 holds that the only features that can be phonologically active in any language are those that serve to distinguish phonemes, which presupposes that phonemic status is categorical. Many researchers, however, demonstrate the existence of gradient relations. For instance, Hall (2009 quantifies these using the information-theoretic measure of entropy (unpredictability of distribution and shows that a pair of sounds may have an entropy between 0 (totally predictable and 1 (totally unpredictable. We argue that the existence of such intermediate degrees of contrastiveness does not make the CH untenable, but rather offers insight into contrastive hierarchies. The existence of a continuum does not preclude categorical distinctions: a categorical line can be drawn between zero entropy (entirely predictable, and thus by the CH phonologically inactive and non-zero entropy (at least partially contrastive, and thus potentially phonologically active. But this does not mean that intermediate degrees of surface contrastiveness are entirely irrelevant to the CH; rather, we argue, they can shed light on how deeply ingrained a phonemic distinction is in the phonological system. As an example, we provide a case study from Pulaar [ATR] harmony, which has previously been claimed to be problematic for the CH.

  19. The Stem Cell Hypothesis of Aging

    Directory of Open Access Journals (Sweden)

    Anna Meiliana

    2010-04-01

    Full Text Available BACKGROUND: There is probably no single way to age. Indeed, so far there is no single accepted explanation or mechanisms of aging (although more than 300 theories have been proposed. There is an overall decline in tissue regenerative potential with age, and the question arises as to whether this is due to the intrinsic aging of stem cells or rather to the impairment of stem cell function in the aged tissue environment. CONTENT: Recent data suggest that we age, in part, because our self-renewing stem cells grow old as a result of heritable intrinsic events, such as DNA damage, as well as extrinsic forces, such as changes in their supporting niches. Mechanisms that suppress the development of cancer, such as senescence and apoptosis, which rely on telomere shortening and the activities of p53 and p16INK4a may also induce an unwanted consequence: a decline in the replicative function of certain stem cells types with advancing age. This decrease regenerative capacity appears to pointing to the stem cell hypothesis of aging. SUMMARY: Recent evidence suggested that we grow old partly because of our stem cells grow old as a result of mechanisms that suppress the development of cancer over a lifetime. We believe that a further, more precise mechanistic understanding of this process will be required before this knowledge can be translated into human anti-aging therapies. KEYWORDS: stem cells, senescence, telomere, DNA damage, epigenetic, aging.

  20. Confabulation: Developing the 'emotion dysregulation' hypothesis.

    Science.gov (United States)

    Turnbull, Oliver H; Salas, Christian E

    2017-02-01

    Confabulations offer unique opportunities for establishing the neurobiological basis of delusional thinking. As regards causal factors, a review of the confabulation literature suggests that neither amnesia nor executive impairment can be the sole (or perhaps even the primary) cause of all delusional beliefs - though they may act in concert with other factors. A key perspective in the modern literature is that many delusions have an emotionally positive or 'wishful' element, that may serve to modulate or manage emotional experience. Some authors have referred to this perspective as the 'emotion dysregulation' hypothesis. In this article we review the theoretical underpinnings of this approach, and develop the idea by suggesting that the positive aspects of confabulatory states may have a role in perpetuating the imbalance between cognitive control and emotion. We draw on existing evidence from fields outside neuropsychology, to argue for three main causal factors: that positive emotions are related to more global or schematic forms of cognitive processing; that positive emotions influence the accuracy of memory recollection; and that positive emotions make people more susceptible to false memories. These findings suggest that the emotions that we want to feel (or do not want to feel) can influence the way we reconstruct past experiences and generate a sense of self - a proposition that bears on a unified theory of delusional belief states. Crown Copyright © 2016. Published by Elsevier Ltd. All rights reserved.

  1. Evolutionary hypothesis for Chiari type I malformation.

    Science.gov (United States)

    Fernandes, Yvens Barbosa; Ramina, Ricardo; Campos-Herrera, Cynthia Resende; Borges, Guilherme

    2013-10-01

    Chiari I malformation (CM-I) is classically defined as a cerebellar tonsillar herniation (≥5 mm) through the foramen magnum. A decreased posterior fossa volume, mainly due to basioccipital hypoplasia and sometimes platybasia, leads to posterior fossa overcrowding and consequently cerebellar herniation. Regardless of radiological findings, embryological genetic hypothesis or any other postulations, the real cause behind this malformation is yet not well-elucidated and remains largely unknown. The aim of this paper is to approach CM-I under a broader and new perspective, conjoining anthropology, genetics and neurosurgery, with special focus on the substantial changes that have occurred in the posterior cranial base through human evolution. Important evolutionary allometric changes occurred during brain expansion and genetics studies of human evolution demonstrated an unexpected high rate of gene flow interchange and possibly interbreeding during this process. Based upon this review we hypothesize that CM-I may be the result of an evolutionary anthropological imprint, caused by evolving species populations that eventually met each other and mingled in the last 1.7 million years. Copyright © 2013 Elsevier Ltd. All rights reserved.

  2. Environmental Kuznets Curve Hypothesis. A Survey

    International Nuclear Information System (INIS)

    Dinda, Soumyananda

    2004-01-01

    The Environmental Kuznets Curve (EKC) hypothesis postulates an inverted-U-shaped relationship between different pollutants and per capita income, i.e., environmental pressure increases up to a certain level as income goes up; after that, it decreases. An EKC actually reveals how a technically specified measurement of environmental quality changes as the fortunes of a country change. A sizeable literature on EKC has grown in recent period. The common point of all the studies is the assertion that the environmental quality deteriorates at the early stages of economic development/growth and subsequently improves at the later stages. In other words, environmental pressure increases faster than income at early stages of development and slows down relative to GDP growth at higher income levels. This paper reviews some theoretical developments and empirical studies dealing with EKC phenomenon. Possible explanations for this EKC are seen in (1) the progress of economic development, from clean agrarian economy to polluting industrial economy to clean service economy; (2) tendency of people with higher income having higher preference for environmental quality, etc. Evidence of the existence of the EKC has been questioned from several corners. Only some air quality indicators, especially local pollutants, show the evidence of an EKC. However, an EKC is empirically observed, till there is no agreement in the literature on the income level at which environmental degradation starts declining. This paper provides an overview of the EKC literature, background history, conceptual insights, policy and the conceptual and methodological critique

  3. Identity of Particles and Continuum Hypothesis

    Science.gov (United States)

    Berezin, Alexander A.

    2001-04-01

    Why all electrons are the same? Unlike other objects, particles and atoms (same isotopes) are forbidden to have individuality or personal history (or reveal their hidden variables, even if they do have them). Or at least, what we commonly call physics so far was unable to disprove particle's sameness (Berezin and Nakhmanson, Physics Essays, 1990). Consider two opposing hypotheses: (A) particles are indeed absolutely same, or (B) they do have individuality, but it is beyond our capacity to demonstrate. This dilemma sounds akin to undecidability of Continuum Hypothesis of existence (or not) of intermediate cardinalities between integers and reals (P.Cohen). Both yes and no of it are true. Thus, (alleged) sameness of electrons and atoms may be a physical translation (embodiment) of this fundamental Goedelian undecidability. Experiments unlikely to help: even if we find that all electrons are same within 30 decimal digits, could their masses (or charges) still differ in100-th digit? Within (B) personalized informationally rich (infinitely rich?) digital tails (starting at, say, 100-th decimal) may carry individual record of each particle history. Within (A) parameters (m, q) are indeed exactly same in all digits and their sameness is based on some inherent (meta)physical principle akin to Platonism or Eddington-type numerology.

  4. Environmental Kuznets Curve Hypothesis. A Survey

    Energy Technology Data Exchange (ETDEWEB)

    Dinda, Soumyananda [Economic Research Unit, Indian Statistical Institute, 203, B.T. Road, Kolkata-108 (India)

    2004-08-01

    The Environmental Kuznets Curve (EKC) hypothesis postulates an inverted-U-shaped relationship between different pollutants and per capita income, i.e., environmental pressure increases up to a certain level as income goes up; after that, it decreases. An EKC actually reveals how a technically specified measurement of environmental quality changes as the fortunes of a country change. A sizeable literature on EKC has grown in recent period. The common point of all the studies is the assertion that the environmental quality deteriorates at the early stages of economic development/growth and subsequently improves at the later stages. In other words, environmental pressure increases faster than income at early stages of development and slows down relative to GDP growth at higher income levels. This paper reviews some theoretical developments and empirical studies dealing with EKC phenomenon. Possible explanations for this EKC are seen in (1) the progress of economic development, from clean agrarian economy to polluting industrial economy to clean service economy; (2) tendency of people with higher income having higher preference for environmental quality, etc. Evidence of the existence of the EKC has been questioned from several corners. Only some air quality indicators, especially local pollutants, show the evidence of an EKC. However, an EKC is empirically observed, till there is no agreement in the literature on the income level at which environmental degradation starts declining. This paper provides an overview of the EKC literature, background history, conceptual insights, policy and the conceptual and methodological critique.

  5. A NONPARAMETRIC HYPOTHESIS TEST VIA THE BOOTSTRAP RESAMPLING

    OpenAIRE

    Temel, Tugrul T.

    2001-01-01

    This paper adapts an already existing nonparametric hypothesis test to the bootstrap framework. The test utilizes the nonparametric kernel regression method to estimate a measure of distance between the models stated under the null hypothesis. The bootstraped version of the test allows to approximate errors involved in the asymptotic hypothesis test. The paper also develops a Mathematica Code for the test algorithm.

  6. Pax7 lineage contributions to the mammalian neural crest.

    Directory of Open Access Journals (Sweden)

    Barbara Murdoch

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

  7. Dynamics of neural cryptography.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

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

  8. Dynamics of neural cryptography

    International Nuclear Information System (INIS)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-01-01

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

  9. Dynamics of neural cryptography

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

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

  10. Updating the mild encephalitis hypothesis of schizophrenia.

    Science.gov (United States)

    Bechter, K

    2013-04-05

    Schizophrenia seems to be a heterogeneous disorder. Emerging evidence indicates that low level neuroinflammation (LLNI) may not occur infrequently. Many infectious agents with low overall pathogenicity are risk factors for psychoses including schizophrenia and for autoimmune disorders. According to the mild encephalitis (ME) hypothesis, LLNI represents the core pathogenetic mechanism in a schizophrenia subgroup that has syndromal overlap with other psychiatric disorders. ME may be triggered by infections, autoimmunity, toxicity, or trauma. A 'late hit' and gene-environment interaction are required to explain major findings about schizophrenia, and both aspects would be consistent with the ME hypothesis. Schizophrenia risk genes stay rather constant within populations despite a resulting low number of progeny; this may result from advantages associated with risk genes, e.g., an improved immune response, which may act protectively within changing environments, although they are associated with the disadvantage of increased susceptibility to psychotic disorders. Specific schizophrenic symptoms may arise with instances of LLNI when certain brain functional systems are involved, in addition to being shaped by pre-existing liability factors. Prodrome phase and the transition to a diseased status may be related to LLNI processes emerging and varying over time. The variability in the course of schizophrenia resembles the varying courses of autoimmune disorders, which result from three required factors: genes, the environment, and the immune system. Preliminary criteria for subgrouping neurodevelopmental, genetic, ME, and other types of schizophrenias are provided. A rare example of ME schizophrenia may be observed in Borna disease virus infection. Neurodevelopmental schizophrenia due to early infections has been estimated by others to explain approximately 30% of cases, but the underlying pathomechanisms of transition to disease remain in question. LLNI (e.g. from

  11. [Psychodynamic hypothesis about suicidality in elderly men].

    Science.gov (United States)

    Lindner, Reinhard

    2010-08-01

    Old men are overrepresented in the whole of all suicides. In contrast, only very few elderly men find their way to specialised treatment facilities. Elderly accept psychotherapy more rarely than younger persons. Therefore presentations on the psychodynamics of suicidality in old men are rare and mostly casuistical. By means of a stepwise reconstructable qualitative case comparison of five randomly chosen elderly suicidal men with ideal types of suicidal (younger) men concerning biography, suicidal symptoms and transference, psychodynamic hypothesis of suicidality in elderly men are developed. All patients came into psychotherapy in a specialised academic out-patient clinic for psychodynamic treatment of acute and chronic suicidality. The five elderly suicidal men predominantly were living in long-term, conflictuous sexual relationships and also had ambivalent relationships to their children. Suicidality in old age refers to lifelong existing intrapsychic conflicts, concerning (male) identity, self-esteem and a core conflict between fusion and separation wishes. The body gets a central role in suicidal experiences, being a defensive instance modified by age and/or physical illness, which brings up to consciousness aggressive and envious impulses, but also feelings of emptiness and insecurity, which have to be warded off again by projection into the body. In transference relationships there are on the one hand the regular transference, on the other hand an age specific turned around transference, with their counter transference reactions. The chosen methodological approach serves the systematic finding of hypotheses with a higher degree in evidence than hypotheses generated from single case studies. Georg Thieme Verlag KG Stuttgart - New York.

  12. Atopic dermatitis and the hygiene hypothesis revisited.

    Science.gov (United States)

    Flohr, Carsten; Yeo, Lindsey

    2011-01-01

    We published a systematic review on atopic dermatitis (AD) and the hygiene hypothesis in 2005. Since then, the body of literature has grown significantly. We therefore repeated our systematic review to examine the evidence from population-based studies for an association between AD risk and specific infections, childhood immunizations, the use of antibiotics and environmental exposures that lead to a change in microbial burden. Medline was searched from 1966 until June 2010 to identify relevant studies. We found an additional 49 papers suitable for inclusion. There is evidence to support an inverse relationship between AD and endotoxin, early day care, farm animal and dog exposure in early life. Cat exposure in the presence of skin barrier impairment is positively associated with AD. Helminth infection at least partially protects against AD. This is not the case for viral and bacterial infections, but consumption of unpasteurized farm milk seems protective. Routine childhood vaccinations have no effect on AD risk. The positive association between viral infections and AD found in some studies appears confounded by antibiotic prescription, which has been consistently associated with an increase in AD risk. There is convincing evidence for an inverse relationship between helminth infections and AD but no other pathogens. The protective effect seen with early day care, endotoxin, unpasteurized farm milk and animal exposure is likely to be due to a general increase in exposure to non-pathogenic microbes. This would also explain the risk increase associated with the use of broad-spectrum antibiotics. Future studies should assess skin barrier gene mutation carriage and phenotypic skin barrier impairment, as gene-environment interactions are likely to impact on AD risk. Copyright © 041_ S. Karger AG, Basel.

  13. Research progress on neural mechanisms of primary insomnia by MRI

    Directory of Open Access Journals (Sweden)

    Man WANG

    2018-04-01

    Full Text Available In recent years, more and more researches focused on the neural mechanism of primary insomnia (PI, especially with the development and application of MRI, and researches of brain structure and function related with primary insomnia were more and more in-depth. According to the hyperarousal hypothesis, there are abnormal structure, function and metabolism under certain brain regions of the cortex and subcortex of primary insomnia patients, including amygdala, hippocampus, cingulate gyrus, insular lobe, frontal lobe and parietal lobe. This paper reviewed the research progress of neural mechanisms of primary insomnia by using MRI. DOI: 10.3969/j.issn.1672-6731.2018.03.003

  14. ANT Advanced Neural Tool

    Energy Technology Data Exchange (ETDEWEB)

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

    1996-07-01

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

  15. ANT Advanced Neural Tool

    International Nuclear Information System (INIS)

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

    1996-01-01

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

  16. Why Traditional Expository Teaching-Learning Approaches May Founder? An Experimental Examination of Neural Networks in Biology Learning

    Science.gov (United States)

    Lee, Jun-Ki; Kwon, Yong-Ju

    2011-01-01

    Using functional magnetic resonance imaging (fMRI), this study investigates and discusses neurological explanations for, and the educational implications of, the neural network activations involved in hypothesis-generating and hypothesis-understanding for biology education. Two sets of task paradigms about biological phenomena were designed:…

  17. Microbial decomposition of keratin in nature—a new hypothesis of industrial relevance

    DEFF Research Database (Denmark)

    Lange, Lene; Huang, Yuhong; Kamp Busk, Peter

    2016-01-01

    with the keratinases to loosen the molecular structure, thus giving the enzymes access to their substrate, the protein structure. With such complexity, it is relevant to compare microbial keratin decomposition with the microbial decomposition of well-studied polymers such as cellulose and chitin. Interestingly...... enzymatic and boosting factors needed for keratin breakdown have been used to formulate a hypothesis for mode of action of the LPMOs in keratin decomposition and for a model for degradation of keratin in nature. Testing such hypotheses and models still needs to be done. Even now, the hypothesis can serve...

  18. Rekonsiliasi Perseteruan antara Efficient Market Hypothesis dan Behavioral Finance melalui Perspektif Neuroeconomics

    Directory of Open Access Journals (Sweden)

    Satia Nur Maharani

    2014-08-01

    Full Text Available Behavioral finance evaluation on Efficient Market Hypothesis causes debates among scientists supporting both theories. This article describes a comprehensive debate between rational behavior perspective on the Efficient Market Hypothesis with irrational behavior on behavioral finance, and how neuroeconomics shed some light on these two perspectives. This article gives a wider range of colors to represent investors behavior that is very complex, and encourage the growth of new generations of related theory of capital markets through interdisciplinary collaboration. Findings indicated that neuroeconomics perspective identified economic behavior through psychological functions.

  19. Upregulation of the Nr2f1-A830082K12Rik gene pair in murine neural crest cells results in a complex phenotype reminiscent of Waardenburg syndrome type 4.

    Science.gov (United States)

    Bergeron, Karl-F; Nguyen, Chloé M A; Cardinal, Tatiana; Charrier, Baptiste; Silversides, David W; Pilon, Nicolas

    2016-11-01

    Waardenburg syndrome is a neurocristopathy characterized by a combination of skin and hair depigmentation, and inner ear defects. In the type 4 form, these defects show comorbidity with Hirschsprung disease, a disorder marked by an absence of neural ganglia in the distal colon, triggering functional intestinal obstruction. Here, we report that the Spot mouse line - obtained through an insertional mutagenesis screen for genes involved in neural crest cell (NCC) development - is a model for Waardenburg syndrome type 4. We found that the Spot insertional mutation causes overexpression of an overlapping gene pair composed of the transcription-factor-encoding Nr2f1 and the antisense long non-coding RNA A830082K12Rik in NCCs through a mechanism involving relief of repression of these genes. Consistent with the previously described role of Nr2f1 in promoting gliogenesis in the central nervous system, we further found that NCC-derived progenitors of the enteric nervous system fail to fully colonize Spot embryonic guts owing to their premature differentiation in glial cells. Taken together, our data thus identify silencer elements of the Nr2f1-A830082K12Rik gene pair as new candidate loci for Waardenburg syndrome type 4. © 2016. Published by The Company of Biologists Ltd.

  20. Upregulation of the Nr2f1-A830082K12Rik gene pair in murine neural crest cells results in a complex phenotype reminiscent of Waardenburg syndrome type 4

    Directory of Open Access Journals (Sweden)

    Karl-F. Bergeron

    2016-11-01

    Full Text Available Waardenburg syndrome is a neurocristopathy characterized by a combination of skin and hair depigmentation, and inner ear defects. In the type 4 form, these defects show comorbidity with Hirschsprung disease, a disorder marked by an absence of neural ganglia in the distal colon, triggering functional intestinal obstruction. Here, we report that the Spot mouse line – obtained through an insertional mutagenesis screen for genes involved in neural crest cell (NCC development – is a model for Waardenburg syndrome type 4. We found that the Spot insertional mutation causes overexpression of an overlapping gene pair composed of the transcription-factor-encoding Nr2f1 and the antisense long non-coding RNA A830082K12Rik in NCCs through a mechanism involving relief of repression of these genes. Consistent with the previously described role of Nr2f1 in promoting gliogenesis in the central nervous system, we further found that NCC-derived progenitors of the enteric nervous system fail to fully colonize Spot embryonic guts owing to their premature differentiation in glial cells. Taken together, our data thus identify silencer elements of the Nr2f1-A830082K12Rik gene pair as new candidate loci for Waardenburg syndrome type 4.

  1. Weather forecasting based on hybrid neural model

    Science.gov (United States)

    Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

    2017-11-01

    Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

  2. Neural codes of seeing architectural styles.

    Science.gov (United States)

    Choo, Heeyoung; Nasar, Jack L; Nikrahei, Bardia; Walther, Dirk B

    2017-01-10

    Images of iconic buildings, such as the CN Tower, instantly transport us to specific places, such as Toronto. Despite the substantial impact of architectural design on people's visual experience of built environments, we know little about its neural representation in the human brain. In the present study, we have found patterns of neural activity associated with specific architectural styles in several high-level visual brain regions, but not in primary visual cortex (V1). This finding suggests that the neural correlates of the visual perception of architectural styles stem from style-specific complex visual structure beyond the simple features computed in V1. Surprisingly, the network of brain regions representing architectural styles included the fusiform face area (FFA) in addition to several scene-selective regions. Hierarchical clustering of error patterns further revealed that the FFA participated to a much larger extent in the neural encoding of architectural styles than entry-level scene categories. We conclude that the FFA is involved in fine-grained neural encoding of scenes at a subordinate-level, in our case, architectural styles of buildings. This study for the first time shows how the human visual system encodes visual aspects of architecture, one of the predominant and longest-lasting artefacts of human culture.

  3. Kernel Temporal Differences for Neural Decoding

    Science.gov (United States)

    Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

    2015-01-01

    We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

  4. The zinc dyshomeostasis hypothesis of Alzheimer's disease.

    Directory of Open Access Journals (Sweden)

    Travis J A Craddock

    Full Text Available Alzheimer's disease (AD is the most common form of dementia in the elderly. Hallmark AD neuropathology includes extracellular amyloid plaques composed largely of the amyloid-β protein (Aβ, intracellular neurofibrillary tangles (NFTs composed of hyper-phosphorylated microtubule-associated protein tau (MAP-tau, and microtubule destabilization. Early-onset autosomal dominant AD genes are associated with excessive Aβ accumulation, however cognitive impairment best correlates with NFTs and disrupted microtubules. The mechanisms linking Aβ and NFT pathologies in AD are unknown. Here, we propose that sequestration of zinc by Aβ-amyloid deposits (Aβ oligomers and plaques not only drives Aβ aggregation, but also disrupts zinc homeostasis in zinc-enriched brain regions important for memory and vulnerable to AD pathology, resulting in intra-neuronal zinc levels, which are either too low, or excessively high. To evaluate this hypothesis, we 1 used molecular modeling of zinc binding to the microtubule component protein tubulin, identifying specific, high-affinity zinc binding sites that influence side-to-side tubulin interaction, the sensitive link in microtubule polymerization and stability. We also 2 performed kinetic modeling showing zinc distribution in extra-neuronal Aβ deposits can reduce intra-neuronal zinc binding to microtubules, destabilizing microtubules. Finally, we 3 used metallomic imaging mass spectrometry (MIMS to show anatomically-localized and age-dependent zinc dyshomeostasis in specific brain regions of Tg2576 transgenic, mice, a model for AD. We found excess zinc in brain regions associated with memory processing and NFT pathology. Overall, we present a theoretical framework and support for a new theory of AD linking extra-neuronal Aβ amyloid to intra-neuronal NFTs and cognitive dysfunction. The connection, we propose, is based on β-amyloid-induced alterations in zinc ion concentration inside neurons affecting stability of

  5. The zinc dyshomeostasis hypothesis of Alzheimer's disease.

    Science.gov (United States)

    Craddock, Travis J A; Tuszynski, Jack A; Chopra, Deepak; Casey, Noel; Goldstein, Lee E; Hameroff, Stuart R; Tanzi, Rudolph E

    2012-01-01

    Alzheimer's disease (AD) is the most common form of dementia in the elderly. Hallmark AD neuropathology includes extracellular amyloid plaques composed largely of the amyloid-β protein (Aβ), intracellular neurofibrillary tangles (NFTs) composed of hyper-phosphorylated microtubule-associated protein tau (MAP-tau), and microtubule destabilization. Early-onset autosomal dominant AD genes are associated with excessive Aβ accumulation, however cognitive impairment best correlates with NFTs and disrupted microtubules. The mechanisms linking Aβ and NFT pathologies in AD are unknown. Here, we propose that sequestration of zinc by Aβ-amyloid deposits (Aβ oligomers and plaques) not only drives Aβ aggregation, but also disrupts zinc homeostasis in zinc-enriched brain regions important for memory and vulnerable to AD pathology, resulting in intra-neuronal zinc levels, which are either too low, or excessively high. To evaluate this hypothesis, we 1) used molecular modeling of zinc binding to the microtubule component protein tubulin, identifying specific, high-affinity zinc binding sites that influence side-to-side tubulin interaction, the sensitive link in microtubule polymerization and stability. We also 2) performed kinetic modeling showing zinc distribution in extra-neuronal Aβ deposits can reduce intra-neuronal zinc binding to microtubules, destabilizing microtubules. Finally, we 3) used metallomic imaging mass spectrometry (MIMS) to show anatomically-localized and age-dependent zinc dyshomeostasis in specific brain regions of Tg2576 transgenic, mice, a model for AD. We found excess zinc in brain regions associated with memory processing and NFT pathology. Overall, we present a theoretical framework and support for a new theory of AD linking extra-neuronal Aβ amyloid to intra-neuronal NFTs and cognitive dysfunction. The connection, we propose, is based on β-amyloid-induced alterations in zinc ion concentration inside neurons affecting stability of polymerized

  6. Hidden neural networks

    DEFF Research Database (Denmark)

    Krogh, Anders Stærmose; Riis, Søren Kamaric

    1999-01-01

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

  7. Neural networks for aircraft control

    Science.gov (United States)

    Linse, Dennis

    1990-01-01

    Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.

  8. Neural networks and their potential application in nuclear power plants

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1991-01-01

    A neural network is a data processing system consisting of a number of simple, highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. Neural networks have emerged in the past few years as an area of unusual opportunity for research, development and application to a variety of real world problems. Indeed, neural networks exhibit characteristics and capabilities not provided by any other technology. Examples include reading Japanese Kanji characters and human handwriting, reading a typewritten manuscript aloud, compensating for alignment errors in robots, interpreting very noise signals (e.g., electroencephalograms), modeling complex systems that cannot be modeled mathematically, and predicting whether proposed loans will be good or fail. This paper presents a brief tutorial on neural networks and describes research on the potential applications to nuclear power plants

  9. Active Neural Localization

    OpenAIRE

    Chaplot, Devendra Singh; Parisotto, Emilio; Salakhutdinov, Ruslan

    2018-01-01

    Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. We propose "Active Neural Localizer", a fully differentiable neural network that learns to localize accurately and efficiently. The proposed model incorporates ideas of tradition...

  10. Neural cryptography with feedback.

    Science.gov (United States)

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

    2004-04-01

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

  11. The neural correlates of beauty comparison.

    Science.gov (United States)

    Kedia, Gayannée; Mussweiler, Thomas; Mullins, Paul; Linden, David E J

    2014-05-01

    Beauty is in the eye of the beholder. How attractive someone is perceived to be depends on the individual or cultural standards to which this person is compared. But although comparisons play a central role in the way people judge the appearance of others, the brain processes underlying attractiveness comparisons remain unknown. In the present experiment, we tested the hypothesis that attractiveness comparisons rely on the same cognitive and neural mechanisms as comparisons of simple nonsocial magnitudes such as size. We recorded brain activity with functional magnetic resonance imaging (fMRI) while participants compared the beauty or height of two women or two dogs. Our data support the hypothesis of a common process underlying these different types of comparisons. First, we demonstrate that the distance effect characteristic of nonsocial comparisons also holds for attractiveness comparisons. Behavioral results indicated, for all our comparisons, longer response times for near than far distances. Second, the neural correlates of these distance effects overlapped in a frontoparietal network known for its involvement in processing simple nonsocial quantities. These results provide evidence for overlapping processes in the comparison of physical attractiveness and nonsocial magnitudes.

  12. Polystochastic Models for Complexity

    CERN Document Server

    Iordache, Octavian

    2010-01-01

    This book is devoted to complexity understanding and management, considered as the main source of efficiency and prosperity for the next decades. Divided into six chapters, the book begins with a presentation of basic concepts as complexity, emergence and closure. The second chapter looks to methods and introduces polystochastic models, the wave equation, possibilities and entropy. The third chapter focusing on physical and chemical systems analyzes flow-sheet synthesis, cyclic operations of separation, drug delivery systems and entropy production. Biomimetic systems represent the main objective of the fourth chapter. Case studies refer to bio-inspired calculation methods, to the role of artificial genetic codes, neural networks and neural codes for evolutionary calculus and for evolvable circuits as biomimetic devices. The fifth chapter, taking its inspiration from systems sciences and cognitive sciences looks to engineering design, case base reasoning methods, failure analysis, and multi-agent manufacturing...

  13. Investigating neural efficiency of elite karate athletes during a mental arithmetic task using EEG.

    Science.gov (United States)

    Duru, Adil Deniz; Assem, Moataz

    2018-02-01

    Neural efficiency is proposed as one of the neural mechanisms underlying elite athletic performances. Previous sports studies examined neural efficiency using tasks that involve motor functions. In this study we investigate the extent of neural efficiency beyond motor tasks by using a mental subtraction task. A group of elite karate athletes are compared to a matched group of non-athletes. Electroencephalogram is used to measure cognitive dynamics during resting and increased mental workload periods. Mainly posterior alpha band power of the karate players was found to be higher than control subjects under both tasks. Moreover, event related synchronization/desynchronization has been computed to investigate the neural efficiency hypothesis among subjects. Finally, this study is the first study to examine neural efficiency related to a cognitive task, not a motor task, in elite karate players using ERD/ERS analysis. The results suggest that the effect of neural efficiency in the brain is global rather than local and thus might be contributing to the elite athletic performances. Also the results are in line with the neural efficiency hypothesis tested for motor performance studies.

  14. Control of beam halo-chaos using neural network self-adaptation method

    International Nuclear Information System (INIS)

    Fang Jinqing; Huang Guoxian; Luo Xiaoshu

    2004-11-01

    Taking the advantages of neural network control method for nonlinear complex systems, control of beam halo-chaos in the periodic focusing channels (network) of high intensity accelerators is studied by feed-forward back-propagating neural network self-adaptation method. The envelope radius of high-intensity proton beam is reached to the matching beam radius by suitably selecting the control structure of neural network and the linear feedback coefficient, adjusted the right-coefficient of neural network. The beam halo-chaos is obviously suppressed and shaking size is much largely reduced after the neural network self-adaptation control is applied. (authors)

  15. Introduction to neural networks in high energy physics

    International Nuclear Information System (INIS)

    Therhaag, J.

    2013-01-01

    Artificial neural networks are a well established tool in high energy physics, playing an important role in both online and offline data analysis. Nevertheless they are often perceived as black boxes which perform obscure operations beyond the control of the user, resulting in a skepticism against any results that may be obtained using them. The situation is not helped by common explanations which try to draw analogies between artificial neural networks and the human brain, for the brain is an even more complex black box itself. In this introductory text, I will take a problem-oriented approach to neural network techniques, showing how the fundamental concepts arise naturally from the demand to solve classification tasks which are frequently encountered in high energy physics. Particular attention is devoted to the question how probability theory can be used to control the complexity of neural networks. (authors)

  16. Complex Networks in Psychological Models

    Science.gov (United States)

    Wedemann, R. S.; Carvalho, L. S. A. V. D.; Donangelo, R.

    We develop schematic, self-organizing, neural-network models to describe mechanisms associated with mental processes, by a neurocomputational substrate. These models are examples of real world complex networks with interesting general topological structures. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, we propose neural network models to explain development of cortical map structure and dynamics of memory access, and unify different mental processes into a single neurocomputational substrate. Based on our neural network models, neurotic behavior may be understood as an associative memory process in the brain, and the linguistic, symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neural network. The models are illustrated through computer simulations, where we varied dopaminergic modulation and observed the self-organizing emergent patterns at the resulting semantic map, interpreting them as different manifestations of mental functioning, from psychotic through to normal and neurotic behavior, and creativity.

  17. Imaging Posture Veils Neural Signals

    Directory of Open Access Journals (Sweden)

    Robert T Thibault

    2016-10-01

    Full Text Available Whereas modern brain imaging often demands holding body positions incongruent with everyday life, posture governs both neural activity and cognitive performance. Humans commonly perform while upright; yet, many neuroimaging methodologies require participants to remain motionless and adhere to non-ecological comportments within a confined space. This inconsistency between ecological postures and imaging constraints undermines the transferability and generalizability of many a neuroimaging assay.Here we highlight the influence of posture on brain function and behavior. Specifically, we challenge the tacit assumption that brain processes and cognitive performance are comparable across a spectrum of positions. We provide an integrative synthesis regarding the increasingly prominent influence of imaging postures on autonomic function, mental capacity, sensory thresholds, and neural activity. Arguing that neuroimagers and cognitive scientists could benefit from considering the influence posture wields on both general functioning and brain activity, we examine existing imaging technologies and the potential of portable and versatile imaging devices (e.g., functional near infrared spectroscopy. Finally, we discuss ways that accounting for posture may help unveil the complex brain processes of everyday cognition.

  18. Convergent evolution of germ granule nucleators: A hypothesis.

    Science.gov (United States)

    Kulkarni, Arpita; Extavour, Cassandra G

    2017-10-01

    Germ cells have been considered "the ultimate stem cell" because they alone, during normal development of sexually reproducing organisms, are able to give rise to all organismal cell types. Morphological descriptions of a specialized cytoplasm termed 'germ plasm' and associated electron dense ribonucleoprotein (RNP) structures called 'germ granules' within germ cells date back as early as the 1800s. Both germ plasm and germ granules are implicated in germ line specification across metazoans. However, at a molecular level, little is currently understood about the molecular mechanisms that assemble these entities in germ cells. The discovery that in some animals, the gene products of a small number of lineage-specific genes initiate the assembly (also termed nucleation) of germ granules and/or germ plasm is the first step towards facilitating a better understanding of these complex biological processes. Here, we draw on research spanning over 100years that supports the hypothesis that these nucleator genes may have evolved convergently, allowing them to perform analogous roles across animal lineages. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  19. Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    Yidong Tang

    2016-01-01

    Full Text Available The sparse representation based classifier (SRC and its kernel version (KSRC have been employed for hyperspectral image (HSI classification. However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given. Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH model is established in this paper. In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary. The background dictionary is composed of samples selected from the local dual concentric window centered at the query pixel. Thus, for each pixel the classification issue becomes an adaptive multiclass classification problem, where only the number of desired classes is required. Furthermore, the kernel method is employed to improve the interclass separability. In kernel space, the coding vector is obtained by using kernel-based orthogonal matching pursuit (KOMP algorithm. Then the query pixel can be labeled by the characteristics of the coding vectors. Instead of directly using the reconstruction residuals, the different impacts the background dictionary and union dictionary have on reconstruction are used for validation and classification. It enhances the discrimination and hence improves the performance.

  20. Proposal of a model of mammalian neural induction

    Science.gov (United States)

    Levine, Ariel J.; Brivanlou, Ali H.

    2009-01-01

    How does the vertebrate embryo make a nervous system? This complex question has been at the center of developmental biology for many years. The earliest step in this process – the induction of neural tissue – is intimately linked to patterning of the entire early embryo, and the molecular and embryological basis these processes are beginning to emerge. Here, we analyze classic and cutting-edge findings on neural induction in the mouse. We find that data from genetics, tissue explants, tissue grafting, and molecular marker expression support a coherent framework for mammalian neural induction. In this model, the gastrula organizer of the mouse embryo inhibits BMP signaling to allow neural tissue to form as a default fate – in the absence of instructive signals. The first neural tissue induced is anterior and subsequent neural tissue is posteriorized to form the midbrain, hindbrain, and spinal cord. The anterior visceral endoderm protects the pre-specified anterior neural fate from similar posteriorization, allowing formation of forebrain. This model is very similar to the default model of neural induction in the frog, thus bridging the evolutionary gap between amphibians and mammals. PMID:17585896

  1. Neural reactivation links unconscious thought to decision-making performance.

    Science.gov (United States)

    Creswell, John David; Bursley, James K; Satpute, Ajay B

    2013-12-01

    Brief periods of unconscious thought (UT) have been shown to improve decision making compared with making an immediate decision (ID). We reveal a neural mechanism for UT in decision making using blood oxygen level-dependent (BOLD) functional magnetic resonance imaging. Participants (N = 33) encoded information on a set of consumer products (e.g. 48 attributes describing four different cars), and we manipulated whether participants (i) consciously thought about this information (conscious thought), (ii) completed a difficult 2-back working memory task (UT) or (iii) made an immediate decision about the consumer products (ID) in a within-subjects blocked design. To differentiate UT neural activity from 2-back working memory neural activity, participants completed an independent 2-back task and this neural activity was subtracted from neural activity occurring during the UT 2-back task. Consistent with a neural reactivation account, we found that the same regions activated during the encoding of complex decision information (right dorsolateral prefrontal cortex and left intermediate visual cortex) continued to be activated during a subsequent 2-min UT period. Moreover, neural reactivation in these regions was predictive of subsequent behavioral decision-making performance after the UT period. These results provide initial evidence for post-encoding unconscious neural reactivation in facilitating decision making.

  2. Neural networks - Potential appplication in the nuclear industry

    International Nuclear Information System (INIS)

    Yiftah, S.

    1989-01-01

    Neural networks are an emerging technology which is perceived to have potential for solving complex computation problems which cannot be solved by standard computational methods. One such example is the inverse kinematics problem which is considered to be the most difficult problem in robotics. In 1986, only one neural network modelling tool was available, now there are about twenty offered commercially by various companies in North America

  3. An Efficient Implementation of Track-Oriented Multiple Hypothesis Tracker Using Graphical Model Approaches

    Directory of Open Access Journals (Sweden)

    Jinping Sun

    2017-01-01

    Full Text Available The multiple hypothesis tracker (MHT is currently the preferred method for addressing data association problem in multitarget tracking (MTT application. MHT seeks the most likely global hypothesis by enumerating all possible associations over time, which is equal to calculating maximum a posteriori (MAP estimate over the report data. Despite being a well-studied method, MHT remains challenging mostly because of the computational complexity of data association. In this paper, we describe an efficient method for solving the data association problem using graphical model approaches. The proposed method uses the graph representation to model the global hypothesis formation and subsequently applies an efficient message passing algorithm to obtain the MAP solution. Specifically, the graph representation of data association problem is formulated as a maximum weight independent set problem (MWISP, which translates the best global hypothesis formation into finding the maximum weight independent set on the graph. Then, a max-product belief propagation (MPBP inference algorithm is applied to seek the most likely global hypotheses with the purpose of avoiding a brute force hypothesis enumeration procedure. The simulation results show that the proposed MPBP-MHT method can achieve better tracking performance than other algorithms in challenging tracking situations.

  4. Implicitly Defined Neural Networks for Sequence Labeling

    Science.gov (United States)

    2017-07-31

    ularity has soared for the Long Short - Term Memory (LSTM) (Hochreiter and Schmidhuber, 1997) and vari- ants such as Gated Recurrent Unit (GRU) (Cho et...610. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short - term memory . Neural computation 9(8):1735– 1780. Zhiheng Huang, Wei Xu, and Kai Yu. 2015...network are coupled together, in order to improve perfor- mance on complex, long -range dependencies in either direction of a sequence. We contrast our

  5. "The seven sins" of the Hebbian synapse: can the hypothesis of synaptic plasticity explain long-term memory consolidation?

    Science.gov (United States)

    Arshavsky, Yuri I

    2006-10-01

    Memorizing new facts and events means that entering information produces specific physical changes within the brain. According to the commonly accepted view, traces of memory are stored through the structural modifications of synaptic connections, which result in changes of synaptic efficiency and, therefore, in formations of new patterns of neural activity (the hypothesis of synaptic plasticity). Most of the current knowledge on learning and initial stages of memory consolidation ("synaptic consolidation") is based on this hypothesis. However, the hypothesis of synaptic plasticity faces a number of conceptual and experimental difficulties when it deals with potentially permanent consolidation of declarative memory ("system consolidation"). These difficulties are rooted in the major intrinsic self-contradiction of the hypothesis: stable declarative memory is unlikely to be based on such a non-stable foundation as synaptic plasticity. Memory that can last throughout an entire lifespan should be "etched in stone." The only "stone-like" molecules within living cells are DNA molecules. Therefore, I advocate an alternative, genomic hypothesis of memory, which suggests that acquired information is persistently stored within individual neurons through modifications of DNA, and that these modifications serve as the carriers of elementary memory traces.

  6. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    are examined. The models are separated into three groups representing input/output descriptions as well as state space descriptions: - Models, where all in- and outputs are measurable (static networks). - Models, where some inputs are non-measurable (recurrent networks). - Models, where some in- and some...... outputs are non-measurable (recurrent networks with incomplete state information). The three groups are ordered in increasing complexity, and for each group it is shown how to solve the problems concerning training and application of the specific model type. Of particular interest are the model types...... Kalmann filter) representing state space description. The potentials of neural networks for control of non-linear processes are also examined, focusing on three different groups of control concepts, all considered as generalizations of known linear control concepts to handle also non-linear processes...

  7. Parallel consensual neural networks.

    Science.gov (United States)

    Benediktsson, J A; Sveinsson, J R; Ersoy, O K; Swain, P H

    1997-01-01

    A new type of a neural-network architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and another for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugate-gradient backpropagation neural network and conventional statistical methods in terms of overall classification accuracy of test data.

  8. Continuity and change in children's longitudinal neural responses to numbers.

    Science.gov (United States)

    Emerson, Robert W; Cantlon, Jessica F

    2015-03-01

    Human children possess the ability to approximate numerical quantity nonverbally from a young age. Over the course of early childhood, children develop increasingly precise representations of numerical values, including a symbolic number system that allows them to conceive of numerical information as Arabic numerals or number words. Functional brain imaging studies of adults report that activity in bilateral regions of the intraparietal sulcus (IPS) represents a key neural correlate of numerical cognition. Developmental neuroimaging studies indicate that the right IPS develops its number-related neural response profile more rapidly than the left IPS during early childhood. One prediction that can be derived from previous findings is that there is longitudinal continuity in the number-related neural responses of the right IPS over development while the development of the left IPS depends on the acquisition of numerical skills. We tested this hypothesis using fMRI in a longitudinal design with children ages 4 to 9. We found that neural responses in the right IPS are correlated over a 1-2-year period in young children whereas left IPS responses change systematically as a function of children's numerical discrimination acuity. The data are consistent with the hypothesis that functional properties of the right IPS in numerical processing are stable over early childhood whereas the functions of the left IPS are dynamically modulated by the development of numerical skills. © 2014 John Wiley & Sons Ltd.

  9. Men’s Perception of Raped Women: Test of the Sexually Transmitted Disease Hypothesis and the Cuckoldry Hypothesis

    Directory of Open Access Journals (Sweden)

    Prokop Pavol

    2016-06-01

    Full Text Available Rape is a recurrent adaptive problem of female humans and females of a number of non-human animals. Rape has various physiological and reproductive costs to the victim. The costs of rape are furthermore exaggerated by social rejection and blaming of a victim, particularly by men. The negative perception of raped women by men has received little attention from an evolutionary perspective. Across two independent studies, we investigated whether the risk of sexually transmitted diseases (the STD hypothesis, Hypothesis 1 or paternity uncertainty (the cuckoldry hypothesis, Hypothesis 2 influence the negative perception of raped women by men. Raped women received lower attractiveness score than non-raped women, especially in long-term mate attractiveness score. The perceived attractiveness of raped women was not influenced by the presence of experimentally manipulated STD cues on faces of putative rapists. Women raped by three men received lower attractiveness score than women raped by one man. These results provide stronger support for the cuckoldry hypothesis (Hypothesis 2 than for the STD hypothesis (Hypothesis 1. Single men perceived raped women as more attractive than men in a committed relationship (Hypothesis 3, suggesting that the mating opportunities mediate men’s perception of victims of rape. Overall, our results suggest that the risk of cuckoldry underlie the negative perception of victims of rape by men rather than the fear of disease transmission.

  10. Neural Architectures for Control

    Science.gov (United States)

    Peterson, James K.

    1991-01-01

    The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs.

  11. Why Does Rem Sleep Occur? A Wake-Up Hypothesis 1

    OpenAIRE

    Klemm, W. R.

    2011-01-01

    Brain activity differs in the various sleep stages and in conscious wakefulness. Awakening from sleep requires restoration of the complex nerve impulse patterns in neuronal network assemblies necessary to re-create and sustain conscious wakefulness. Herein I propose that the brain uses rapid eye movement (REM) to help wake itself up after it has had a sufficient amount of sleep. Evidence suggesting this hypothesis includes the facts that, (1) when first going to sleep, the brain plunges into ...

  12. [Distinguishing the voice of self from others: the self-monitoring hypothesis of auditory hallucination].

    Science.gov (United States)

    Asai, Tomohisa; Tanno, Yoshihiko

    2010-08-01

    Auditory hallucinations (AH), a psychopathological phenomenon where a person hears non-existent voices, commonly occur in schizophrenia. Recent cognitive and neuroscience studies suggest that AH may be the misattribution of one's own inner speech. Self-monitoring through neural feedback mechanisms allows individuals to distinguish between their own and others' actions, including speech. AH maybe the results of an individual's inability to discriminate between their own speech and that of others. The present paper tries to integrate the three theories (behavioral, brain, and model approaches) proposed to explain the self-monitoring hypothesis of AH. In addition, we investigate the lateralization of self-other representation in the brain, as suggested by recent studies, and discuss future research directions.

  13. New Hypothesis for SOFC Ceramic Oxygen Electrode Mechanisms

    DEFF Research Database (Denmark)

    Mogensen, Mogens Bjerg; Chatzichristodoulou, Christodoulos; Graves, Christopher R.

    2016-01-01

    A new hypothesis for the electrochemical reaction mechanism in solid oxide cell ceramic oxygen electrodes is proposed based on literature including our own results. The hypothesis postulates that the observed thin layers of SrO-La2O3 on top of ceramic perovskite and other Ruddlesden-Popper...

  14. Assess the Critical Period Hypothesis in Second Language Acquisition

    Science.gov (United States)

    Du, Lihong

    2010-01-01

    The Critical Period Hypothesis aims to investigate the reason for significant difference between first language acquisition and second language acquisition. Over the past few decades, researchers carried out a series of studies to test the validity of the hypothesis. Although there were certain limitations in these studies, most of their results…

  15. Dynamical agents' strategies and the fractal market hypothesis

    Czech Academy of Sciences Publication Activity Database

    Vácha, Lukáš; Vošvrda, Miloslav

    2005-01-01

    Roč. 14, č. 2 (2005), s. 172-179 ISSN 1210-0455 Grant - others:GA UK(CZ) 454/2004/A EK/FSV Institutional research plan: CEZ:AV0Z10750506 Keywords : efficient market hypothesis * fractal market hypothesis * agent's investment horizons Subject RIV: AH - Economics

  16. An Exercise for Illustrating the Logic of Hypothesis Testing

    Science.gov (United States)

    Lawton, Leigh

    2009-01-01

    Hypothesis testing is one of the more difficult concepts for students to master in a basic, undergraduate statistics course. Students often are puzzled as to why statisticians simply don't calculate the probability that a hypothesis is true. This article presents an exercise that forces students to lay out on their own a procedure for testing a…

  17. A default Bayesian hypothesis test for ANOVA designs

    NARCIS (Netherlands)

    Wetzels, R.; Grasman, R.P.P.P.; Wagenmakers, E.J.

    2012-01-01

    This article presents a Bayesian hypothesis test for analysis of variance (ANOVA) designs. The test is an application of standard Bayesian methods for variable selection in regression models. We illustrate the effect of various g-priors on the ANOVA hypothesis test. The Bayesian test for ANOVA

  18. Adaptation hypothesis of biological efficiency of ionizing radiation

    International Nuclear Information System (INIS)

    Kudritskij, Yu.K.; Georgievskij, A.B.; Karpov, V.I.

    1992-01-01

    Adaptation hypothesis of biological efficiency of ionizing radiation is based on acknowledgement of invariance of fundamental laws and principles of biology related to unity of biota and media, evolution and adaptation for radiobiology. The basic arguments for adaptation hypothesis validity, its correspondence to the requirements imposed on scientific hypothes are presented

  19. New methods of testing nonlinear hypothesis using iterative NLLS estimator

    Science.gov (United States)

    Mahaboob, B.; Venkateswarlu, B.; Mokeshrayalu, G.; Balasiddamuni, P.

    2017-11-01

    This research paper discusses the method of testing nonlinear hypothesis using iterative Nonlinear Least Squares (NLLS) estimator. Takeshi Amemiya [1] explained this method. However in the present research paper, a modified Wald test statistic due to Engle, Robert [6] is proposed to test the nonlinear hypothesis using iterative NLLS estimator. An alternative method for testing nonlinear hypothesis using iterative NLLS estimator based on nonlinear hypothesis using iterative NLLS estimator based on nonlinear studentized residuals has been proposed. In this research article an innovative method of testing nonlinear hypothesis using iterative restricted NLLS estimator is derived. Pesaran and Deaton [10] explained the methods of testing nonlinear hypothesis. This paper uses asymptotic properties of nonlinear least squares estimator proposed by Jenrich [8]. The main purpose of this paper is to provide very innovative methods of testing nonlinear hypothesis using iterative NLLS estimator, iterative NLLS estimator based on nonlinear studentized residuals and iterative restricted NLLS estimator. Eakambaram et al. [12] discussed least absolute deviation estimations versus nonlinear regression model with heteroscedastic errors and also they studied the problem of heteroscedasticity with reference to nonlinear regression models with suitable illustration. William Grene [13] examined the interaction effect in nonlinear models disused by Ai and Norton [14] and suggested ways to examine the effects that do not involve statistical testing. Peter [15] provided guidelines for identifying composite hypothesis and addressing the probability of false rejection for multiple hypotheses.

  20. The Younger Dryas impact hypothesis: A critical review

    NARCIS (Netherlands)

    van Hoesel, A.; Hoek, W.Z.; Pennock, G.M.; Drury, Martyn

    2014-01-01

    The Younger Dryas impact hypothesis suggests that multiple extraterrestrial airbursts or impacts resulted in the Younger Dryas cooling, extensive wildfires, megafaunal extinctions and changes in human population. After the hypothesis was first published in 2007, it gained much criticism, as the

  1. Sacred or Neural?

    DEFF Research Database (Denmark)

    Runehov, Anne Leona Cesarine

    Are religious spiritual experiences merely the product of the human nervous system? Anne L.C. Runehov investigates the potential of contemporary neuroscience to explain religious experiences. Following the footsteps of Michael Persinger, Andrew Newberg and Eugene d'Aquili she defines...... the terminological bounderies of "religious experiences" and explores the relevant criteria for the proper evaluation of scientific research, with a particular focus on the validity of reductionist models. Runehov's theis is that the perspectives looked at do not necessarily exclude each other but can be merged....... The question "sacred or neural?" becomes a statement "sacred and neural". The synergies thus produced provide manifold opportunities for interdisciplinary dialogue and research....

  2. Deconvolution using a neural network

    Energy Technology Data Exchange (ETDEWEB)

    Lehman, S.K.

    1990-11-15

    Viewing one dimensional deconvolution as a matrix inversion problem, we compare a neural network backpropagation matrix inverse with LMS, and pseudo-inverse. This is a largely an exercise in understanding how our neural network code works. 1 ref.

  3. Introduction to Artificial Neural Networks

    DEFF Research Database (Denmark)

    Larsen, Jan

    1999-01-01

    The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

  4. Artificial Astrocytes Improve Neural Network Performance

    Science.gov (United States)

    Porto-Pazos, Ana B.; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-01-01

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157

  5. Neural Control of the Lower Urinary Tract

    Science.gov (United States)

    de Groat, William C.; Griffiths, Derek; Yoshimura, Naoki

    2015-01-01

    This article summarizes anatomical, neurophysiological, pharmacological, and brain imaging studies in humans and animals that have provided insights into the neural circuitry and neurotransmitter mechanisms controlling the lower urinary tract. The functions of the lower urinary tract to store and periodically eliminate urine are regulated by a complex neural control system in the brain, spinal cord, and peripheral autonomic ganglia that coordinates the activity of smooth and striated muscles of the bladder and urethral outlet. The neural control of micturition is organized as a hierarchical system in which spinal storage mechanisms are in turn regulated by circuitry in the rostral brain stem that initiates reflex voiding. Input from the forebrain triggers voluntary voiding by modulating the brain stem circuitry. Many neural circuits controlling the lower urinary tract exhibit switch-like patterns of activity that turn on and off in an all-or-none manner. The major component of the micturition switching circuit is a spinobulbospinal parasympathetic reflex pathway that has essential connections in the periaqueductal gray and pontine micturition center. A computer model of this circuit that mimics the switching functions of the bladder and urethra at the onset of micturition is described. Micturition occurs involuntarily in infants and young children until the age of 3 to 5 years, after which it is regulated voluntarily. Diseases or injuries of the nervous system in adults can cause the re-emergence of involuntary micturition, leading to urinary incontinence. Neuroplasticity underlying these developmental and pathological changes in voiding function is discussed. PMID:25589273

  6. Artificial astrocytes improve neural network performance.

    Directory of Open Access Journals (Sweden)

    Ana B Porto-Pazos

    Full Text Available Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN and artificial neuron-glia networks (NGN to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

  7. Artificial astrocytes improve neural network performance.

    Science.gov (United States)

    Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-04-19

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

  8. Language Networks as Complex Systems

    Science.gov (United States)

    Lee, Max Kueiming; Ou, Sheue-Jen

    2008-01-01

    Starting in the late eighties, with a growing discontent with analytical methods in science and the growing power of computers, researchers began to study complex systems such as living organisms, evolution of genes, biological systems, brain neural networks, epidemics, ecology, economy, social networks, etc. In the early nineties, the research…

  9. Semanticized autobiographical memory and the default - executive coupling hypothesis of aging.

    Science.gov (United States)

    Spreng, R Nathan; Lockrow, Amber W; DuPre, Elizabeth; Setton, Roni; Spreng, Karen A P; Turner, Gary R

    2018-02-01

    As we age, the architecture of cognition undergoes a fundamental transition. Fluid intellectual abilities decline while crystalized abilities remain stable or increase. This shift has a profound impact across myriad cognitive and functional domains, yet the neural mechanisms remain under-specified. We have proposed that greater connectivity between the default network and executive control regions in lateral prefrontal cortex may underlie this shift, as older adults increasingly rely upon accumulated knowledge to support goal-directed behavior. Here we provide direct evidence for this mechanism within the domain of autobiographical memory. In a large sample of healthy adult participants (n = 103 Young; n = 80 Old) the strength of default - executive coupling reliably predicted more semanticized, or knowledge-based, recollection of autobiographical memories in the older adult cohort. The findings are consistent with the default - executive coupling hypothesis of aging and identify this shift in network dynamics as a candidate neural mechanism associated with crystalized cognition in later life that may signal adaptive capacity in the context of declining fluid cognitive abilities. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Neural Network Ensembles

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Salamon, Peter

    1990-01-01

    We propose several means for improving the performance an training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining generalization error can be reduced by invoking ensembles of similar...... networks....

  11. Neural correlates of consciousness

    African Journals Online (AJOL)

    neural cells.1 Under this approach, consciousness is believed to be a product of the ... possible only when the 40 Hz electrical hum is sustained among the brain circuits, ... expect the brain stem ascending reticular activating system. (ARAS) and the ... related synchrony of cortical neurons.11 Indeed, stimulation of brainstem ...

  12. Neural Networks and Micromechanics

    Science.gov (United States)

    Kussul, Ernst; Baidyk, Tatiana; Wunsch, Donald C.

    The title of the book, "Neural Networks and Micromechanics," seems artificial. However, the scientific and technological developments in recent decades demonstrate a very close connection between the two different areas of neural networks and micromechanics. The purpose of this book is to demonstrate this connection. Some artificial intelligence (AI) methods, including neural networks, could be used to improve automation system performance in manufacturing processes. However, the implementation of these AI methods within industry is rather slow because of the high cost of conducting experiments using conventional manufacturing and AI systems. To lower the cost, we have developed special micromechanical equipment that is similar to conventional mechanical equipment but of much smaller size and therefore of lower cost. This equipment could be used to evaluate different AI methods in an easy and inexpensive way. The proved methods could be transferred to industry through appropriate scaling. In this book, we describe the prototypes of low cost microequipment for manufacturing processes and the implementation of some AI methods to increase precision, such as computer vision systems based on neural networks for microdevice assembly and genetic algorithms for microequipment characterization and the increase of microequipment precision.

  13. Learning from neural control.

    Science.gov (United States)

    Wang, Cong; Hill, David J

    2006-01-01

    One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.

  14. Neural systems for control

    National Research Council Canada - National Science Library

    Omidvar, Omid; Elliott, David L

    1997-01-01

    ... is reprinted with permission from A. Barto, "Reinforcement Learning," Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed.. The MIT Press, Cambridge, MA, pp. 804-809, 1995. Chapter 4, Figures 4-5 and 7-9 and Tables 2-5, are reprinted with permission, from S. Cho, "Map Formation in Proprioceptive Cortex," International Jour...

  15. Neural underpinnings of music

    DEFF Research Database (Denmark)

    Vuust, Peter; Gebauer, Line K; Witek, Maria A G

    2014-01-01

    . According to this theory, perception and learning is manifested through the brain’s Bayesian minimization of the error between the input to the brain and the brain’s prior expectations. Fourth, empirical studies of neural and behavioral effects of syncopation, polyrhythm and groove will be reported, and we...

  16. The application of neural networks with artificial intelligence technique in the modeling of industrial processes

    International Nuclear Information System (INIS)

    Saini, K. K.; Saini, Sanju

    2008-01-01

    Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

  17. Environmental policy without costs? A review of the Porter hypothesis

    Energy Technology Data Exchange (ETDEWEB)

    Braennlund, Runar; Lundgren, Tommy. e-mail: runar.brannlund@econ.umu.se

    2009-03-15

    This paper reviews the theoretical and empirical literature connected to the so called Porter Hypothesis. That is, to review the literature connected to the discussion about the relation between environmental policy and competitiveness. According to the conventional wisdom environmental policy, aiming for improving the environment through for example emission reductions, do imply costs since scarce resources must be diverted from somewhere else. However, this conventional wisdom has been challenged and questioned recently through what has been denoted the 'Porter hypothesis'. Those in the forefront of the Porter hypothesis challenge the conventional wisdom basically on the ground that resources are used inefficiently in the absence of the right kind of environmental regulations, and that the conventional neo-classical view is too static to take inefficiencies into account. The conclusions that can be made from this review is (1) that the theoretical literature can identify the circumstances and mechanisms that must exist for a Porter effect to occur, (2) that these circumstances are rather non-general, hence rejecting the Porter hypothesis in general, (3) that the empirical literature give no general support for the Porter hypothesis. Furthermore, a closer look at the 'Swedish case' reveals no support for the Porter hypothesis in spite of the fact that Swedish environmental policy the last 15-20 years seems to be in line the prerequisites stated by the Porter hypothesis concerning environmental policy

  18. The linear hypothesis - an idea whose time has passed

    International Nuclear Information System (INIS)

    Tschaeche, A.N.

    1995-01-01

    The linear no-threshold hypothesis is the basis for radiation protection standards in the United States. In the words of the National Council on Radiation Protection and Measurements (NCRP), the hypothesis is: open-quotes In the interest of estimating effects in humans conservatively, it is not unreasonable to follow the assumption of a linear relationship between dose and effect in the low dose regions for which direct observational data are not available.close quotes The International Commission on Radiological Protection (ICRP) stated the hypothesis in a slightly different manner: open-quotes One such basic assumption ... is that ... there is ... a linear relationship without threshold between dose and the probability of an effect. The hypothesis was necessary 50 yr ago when it was first enunciated because the dose-effect curve for ionizing radiation for effects in humans was not known. The ICRP and NCRP needed a model to extrapolate high-dose effects to low-dose effects. So the linear no-threshold hypothesis was born. Certain details of the history of the development and use of the linear hypothesis are presented. In particular, use of the hypothesis by the U.S. regulatory agencies is examined. Over time, the sense of the hypothesis has been corrupted. The corruption of the hypothesis into the current paradigm of open-quote a little radiation, no matter how small, can and will harm youclose quotes is presented. The reasons the corruption occurred are proposed. The effects of the corruption are enumerated, specifically, the use of the corruption by the antinuclear forces in the United States and some of the huge costs to U.S. taxpayers due to the corruption. An alternative basis for radiation protection standards to assure public safety, based on the weight of scientific evidence on radiation health effects, is proposed

  19. Simulation of nonlinear random vibrations using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Paez, T.L.; Tucker, S.; O`Gorman, C.

    1997-02-01

    The simulation of mechanical system random vibrations is important in structural dynamics, but it is particularly difficult when the system under consideration is nonlinear. Artificial neural networks provide a useful tool for the modeling of nonlinear systems, however, such modeling may be inefficient or insufficiently accurate when the system under consideration is complex. This paper shows that there are several transformations that can be used to uncouple and simplify the components of motion of a complex nonlinear system, thereby making its modeling and random vibration simulation, via component modeling with artificial neural networks, a much simpler problem. A numerical example is presented.

  20. Biostatistics series module 2: Overview of hypothesis testing

    Directory of Open Access Journals (Sweden)

    Avijit Hazra

    2016-01-01

    Full Text Available Hypothesis testing (or statistical inference is one of the major applications of biostatistics. Much of medical research begins with a research question that can be framed as a hypothesis. Inferential statistics begins with a null hypothesis that reflects the conservative position of no change or no difference in comparison to baseline or between groups. Usually, the researcher has reason to believe that there is some effect or some difference which is the alternative hypothesis. The researcher therefore proceeds to study samples and measure outcomes in the hope of generating evidence strong enough for the statistician to be able to reject the null hypothesis. The concept of the P value is almost universally used in hypothesis testing. It denotes the probability of obtaining by chance a result at least as extreme as that observed, even when the null hypothesis is true and no real difference exists. Usually, if P is < 0.05 the null hypothesis is rejected and sample results are deemed statistically significant. With the increasing availability of computers and access to specialized statistical software, the drudgery involved in statistical calculations is now a thing of the past, once the learning curve of the software has been traversed. The life sciences researcher is therefore free to devote oneself to optimally designing the study, carefully selecting the hypothesis tests to be applied, and taking care in conducting the study well. Unfortunately, selecting the right test seems difficult initially. Thinking of the research hypothesis as addressing one of five generic research questions helps in selection of the right hypothesis test. In addition, it is important to be clear about the nature of the variables (e.g., numerical vs. categorical; parametric vs. nonparametric and the number of groups or data sets being compared (e.g., two or more than two at a time. The same research question may be explored by more than one type of hypothesis test