WorldWideScience

Sample records for exuberant neural connectivity

  1. Application of Partially Connected Neural Network

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    This paper focuses mainly on application of Partially Connected Backpropagation Neural Network (PCBP) instead of typical Fully Connected Neural Network (FCBP). The initial neural network is fully connected, after training with sample data using cross-entropy as error function, a clustering method is employed to cluster weights between inputs to hidden layer and from hidden to output layer, and connections that are relatively unnecessary are deleted, thus the initial network becomes a PCBP network.Then PCBP can be used in prediction or data mining by training PCBP with data that comes from database. At the end of this paper, several experiments are conducted to illustrate the effects of PCBP using Iris data set.

  2. Reaction to Reading The Age of Exuberance

    Institute of Scientific and Technical Information of China (English)

    杨士杰

    2013-01-01

    Up till now only a few papers have been found to analyze The Age of Exuberance. It is extremely interesting for us to read The Age of Exuberance: backgrounds to eighteenth-century English literature by Donald Greene, who tries to see the eigh⁃teenth century from a different perspective, observing it as an exuberant, luxuriant age full of variety. It is the eighteenth century that witnesses the emergence of many news things.

  3. Knowledge synthesis with maps of neural connectivity

    Directory of Open Access Journals (Sweden)

    Marcelo eTallis

    2011-11-01

    Full Text Available This paper describes software for neuroanatomical knowledge synthesis based on high-quality neural connectivity data. This software supports a mature neuroanatomical methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macroconnections using the Swanson 3rd edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the neuroanatomical data mapping components within a unified web-application. As a step towards developing an accurate sub-regional account of neural connectivity, we provide navigational access between the neuroanatomical data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called ’Knowledge Engineering from Experimental Design’ (KEfED model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web application that allows anatomical data sets to be described within a standard experimental context and thus incorporated with non-spatial data sets.

  4. Multiple neural representations of elementary logical connectives.

    Science.gov (United States)

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

    2016-07-15

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

  5. A morpho-density approach to estimating neural connectivity.

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    Michael P McAssey

    Full Text Available Neuronal signal integration and information processing in cortical neuronal networks critically depend on the organization of synaptic connectivity. Because of the challenges involved in measuring a large number of neurons, synaptic connectivity is difficult to determine experimentally. Current computational methods for estimating connectivity typically rely on the juxtaposition of experimentally available neurons and applying mathematical techniques to compute estimates of neural connectivity. However, since the number of available neurons is very limited, these connectivity estimates may be subject to large uncertainties. We use a morpho-density field approach applied to a vast ensemble of model-generated neurons. A morpho-density field (MDF describes the distribution of neural mass in the space around the neural soma. The estimated axonal and dendritic MDFs are derived from 100,000 model neurons that are generated by a stochastic phenomenological model of neurite outgrowth. These MDFs are then used to estimate the connectivity between pairs of neurons as a function of their inter-soma displacement. Compared with other density-field methods, our approach to estimating synaptic connectivity uses fewer restricting assumptions and produces connectivity estimates with a lower standard deviation. An important requirement is that the model-generated neurons reflect accurately the morphology and variation in morphology of the experimental neurons used for optimizing the model parameters. As such, the method remains subject to the uncertainties caused by the limited number of neurons in the experimental data set and by the quality of the model and the assumptions used in creating the MDFs and in calculating estimating connectivity. In summary, MDFs are a powerful tool for visualizing the spatial distribution of axonal and dendritic densities, for estimating the number of potential synapses between neurons with low standard deviation, and for obtaining

  6. Estimation of Effectivty Connectivity via Data-Driven Neural Modeling

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    Dean Robert Freestone

    2014-11-01

    Full Text Available This research introduces a new method for functional brain imaging via a process of model inversion. By estimating parameters of a computational model, we are able to track effective connectivity and mean membrane potential dynamics that cannot be directly measured using electrophysiological measurements alone. The ability to track the hidden aspects of neurophysiology will have a profound impact on the way we understand and treat epilepsy. For example, under the assumption the model captures the key features of the cortical circuits of interest, the framework will provide insights into seizure initiation and termination on a patient-specific basis. It will enable investigation into the effect a particular drug has on specific neural populations and connectivity structures using minimally invasive measurements. The method is based on approximating brain networks using an interconnected neural population model. The neural population model is based on a neural mass model that describes the functional activity of the brain, capturing the mesoscopic biophysics and anatomical structure. The model is made subject-specific by estimating the strength of intra-cortical connections within a region and inter-cortical connections between regions using a novel Kalman filtering method. We demonstrate through simulation how the framework can be used the track the mechanisms involved in seizure initiation and termination.

  7. Neural network connectivity and response latency modelled by stochastic processes

    DEFF Research Database (Denmark)

    Tamborrino, Massimiliano

    is connected to thousands of other neurons. The rst question is: how to model neural networks through stochastic processes? A multivariate Ornstein-Uhlenbeck process, obtained as a diffusion approximation of a jump process, is the proposed answer. Obviously, dependencies between neurons imply dependencies......Stochastic processes and their rst passage times have been widely used to describe the membrane potential dynamics of single neurons and to reproduce neuronal spikes, respectively.However, cerebral cortex in human brains is estimated to contain 10-20 billions of neurons and each of them...... between their spike times. Therefore, the second question is: how to detect neural network connectivity from simultaneously recorded spike trains? Answering this question corresponds to investigate the joint distribution of sequences of rst passage times. A non-parametric method based on copulas...

  8. Neural network connectivity and response latency modelled by stochastic processes

    DEFF Research Database (Denmark)

    Tamborrino, Massimiliano

    is connected to thousands of other neurons. The rst question is: how to model neural networks through stochastic processes? A multivariate Ornstein-Uhlenbeck process, obtained as a diffusion approximation of a jump process, is the proposed answer. Obviously, dependencies between neurons imply dependencies...... between their spike times. Therefore, the second question is: how to detect neural network connectivity from simultaneously recorded spike trains? Answering this question corresponds to investigate the joint distribution of sequences of rst passage times. A non-parametric method based on copulas...... generation of pikes. When a stimulus is applied to the network, the spontaneous rings may prevail and hamper detection of the effects of the stimulus. Therefore, the spontaneous rings cannot be ignored and the response latency has to be detected on top of a background signal. Everything becomes more dicult...

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

    Science.gov (United States)

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

    2017-09-05

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

  10. Alterations in neural connectivity in preterm children at school age.

    Science.gov (United States)

    Gozzo, Yeisid; Vohr, Betty; Lacadie, Cheryl; Hampson, Michelle; Katz, Karol H; Maller-Kesselman, Jill; Schneider, Karen C; Peterson, Bradley S; Rajeevan, Nallakkandi; Makuch, Robert W; Constable, R Todd; Ment, Laura R

    2009-11-01

    Converging data suggest recovery from injury in the preterm brain. We used functional magnetic resonance imaging (fMRI) to test the hypothesis that cerebral connectivity involving Wernicke's area and other important cortical language regions would differ between preterm (PT) and term (T) control school age children during performance of an auditory language task. Fifty-four PT children (600-1250 g birth weight) and 24 T controls were evaluated using an fMRI passive language task and neurodevelopmental assessments including: the Wechsler Intelligence Scale for Children - III (WISC-III), the Peabody Individual Achievement Test - Revised (PIAT-R) and the Peabody Picture Vocabulary Test - Revised (PPVT-R) at 8 years of age. Neural activity was assessed for language processing and the data were evaluated for connectivity and correlations to cognitive outcomes. We found that PT subjects scored significantly lower on all components of the WISC-III (p<0.009), the PIAT-R Reading Comprehension test (p=0.013), and the PPVT-R (p=0.001) compared to term subjects. Connectivity analyses revealed significantly stronger neural circuits in PT children between Wernicke's area and the right inferior frontal gyrus (R IFG, Broca's area homologue) and both the left and the right supramarginal gyri (SMG) components of the inferior parietal lobules (pneural systems for auditory language function at school age differently than T controls; these alterations may represent a delay in maturation of neural networks or the engagement of alternate circuits for language processing.

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

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    Jesper Mogensen

    2017-04-01

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

  12. Neural network connectivity differences in children who stutter.

    Science.gov (United States)

    Chang, Soo-Eun; Zhu, David C

    2013-12-01

    Affecting 1% of the general population, stuttering impairs the normally effortless process of speech production, which requires precise coordination of sequential movement occurring among the articulatory, respiratory, and resonance systems, all within millisecond time scales. Those afflicted experience frequent disfluencies during ongoing speech, often leading to negative psychosocial consequences. The aetiology of stuttering remains unclear; compared to other neurodevelopmental disorders, few studies to date have examined the neural bases of childhood stuttering. Here we report, for the first time, results from functional (resting state functional magnetic resonance imaging) and structural connectivity analyses (probabilistic tractography) of multimodal neuroimaging data examining neural networks in children who stutter. We examined how synchronized brain activity occurring among brain areas associated with speech production, and white matter tracts that interconnect them, differ in young children who stutter (aged 3-9 years) compared with age-matched peers. Results showed that children who stutter have attenuated connectivity in neural networks that support timing of self-paced movement control. The results suggest that auditory-motor and basal ganglia-thalamocortical networks develop differently in stuttering children, which may in turn affect speech planning and execution processes needed to achieve fluent speech motor control. These results provide important initial evidence of neurological differences in the early phases of symptom onset in children who stutter.

  13. Neural connections of the posteromedial cortex in the macaque

    Science.gov (United States)

    Parvizi, Josef; Van Hoesen, Gary W.; Buckwalter, Joseph; Damasio, Antonio

    2006-01-01

    The posterior cingulate and the medial parietal cortices constitute an ensemble known as the posteromedial cortex (PMC), which consists of Brodmann areas 23, 29, 30, 31, and 7m. To understand the neural relationship of the PMC with the rest of the brain, we injected its component areas with four different anterograde and retrograde tracers in the cynomolgus monkey and found that all PMC areas are interconnected with each other and with the anterior cingulate, the mid-dorsolateral prefrontal, the lateral parietal cortices, and area TPO, as well as the thalamus, where projections from some of the PMC areas traverse in an uninterrupted bar-like manner, the dorsum of this structure from the posteriormost nuclei to its rostralmost tip. All PMC regions also receive projections from the claustrum and the basal forebrain and project to the caudate, the basis pontis, and the zona incerta. Moreover, the posterior cingulate areas are interconnected with the parahippocampal regions, whereas the medial parietal cortex projects only sparsely to the presubiculum. Although local interconnections and shared remote connections of all PMC components suggest a functional relationship among them, the distinct connections of each area with different neural structures suggests that distinct functional modules may be operating within the PMC. Our study provides a large-scale map of the PMC connections with the rest of the brain, which may serve as a useful tool for future studies of this cortical region and may contribute to elucidating its intriguing pattern of activity seen in recent functional imaging studies. PMID:16432221

  14. Identification of neural connectivity signatures of autism using machine learning

    Directory of Open Access Journals (Sweden)

    Gopikrishna eDeshpande

    2013-10-01

    Full Text Available Alterations in neural connectivity have been suggested as a signature of the pathobiology of autism. Although disrupted correlation between cortical regions observed from functional MRI is considered to be an explanatory model for autism, the directional causal influence between brain regions is a vital link missing in these studies. The current study focuses on addressing this in an fMRI study of Theory-of-Mind in 15 high-functioning adolescents and adults with autism (ASD and 15 typically developing (TD controls. Participants viewed a series of comic strip vignettes in the MRI scanner and were asked to choose the most logical end to the story from three alternatives, separately for trials involving physical and intentional causality. Causal brain connectivity obtained from a multivariate autoregressive model, along with assessment scores, functional connectivity values, and fractional anisotropy obtained from DTI data for each participant, were submitted to a recursive cluster elimination based support vector machine classifier to determine the accuracy with which the classifier can predict a novel participant’s group membership (ASD or TD. We found a maximum classification accuracy of 95.9 % with 19 features which had the highest discriminative ability between the groups. All of the 19 features were effective connectivity paths, indicating that causal information may be critical in discriminating between ASD and TD groups. These effective connectivity paths were also found to be significantly greater in controls as compared to ASD participants and consisted predominantly of outputs from the fusiform face area and middle temporal gyrus indicating impaired connectivity in ASD participants, particularly in the social brain areas. These findings collectively point towards the fact that alterations in causal brain connectivity in individuals with ASD could serve as a potential non-invasive neuroimaging signature for autism

  15. Adolescent nicotine induces persisting changes in development of neural connectivity.

    Science.gov (United States)

    Smith, Robert F; McDonald, Craig G; Bergstrom, Hadley C; Ehlinger, Daniel G; Brielmaier, Jennifer M

    2015-08-01

    Adolescent nicotine induces persisting changes in development of neural connectivity. A large number of brain changes occur during adolescence as the CNS matures. These changes suggest that the adolescent brain may still be susceptible to developmental alterations by substances which impact its growth. Here we review recent studies on adolescent nicotine which show that the adolescent brain is differentially sensitive to nicotine-induced alterations in dendritic elaboration, in several brain areas associated with processing reinforcement and emotion, specifically including nucleus accumbens, medial prefrontal cortex, basolateral amygdala, bed nucleus of the stria terminalis, and dentate gyrus. Both sensitivity to nicotine, and specific areas responding to nicotine, differ between adolescent and adult rats, and dendritic changes in response to adolescent nicotine persist into adulthood. Areas sensitive to, and not sensitive to, structural remodeling induced by adolescent nicotine suggest that the remodeling generally corresponds to the extended amygdala. Evidence suggests that dendritic remodeling is accompanied by persisting changes in synaptic connectivity. Modeling, electrophysiological, neurochemical, and behavioral data are consistent with the implication of our anatomical studies showing that adolescent nicotine induces persisting changes in neural connectivity. Emerging data thus suggest that early adolescence is a period when nicotine consumption, presumably mediated by nicotine-elicited changes in patterns of synaptic activity, can sculpt late brain development, with consequent effects on synaptic interconnection patterns and behavior regulation. Adolescent nicotine may induce a more addiction-prone phenotype, and the structures altered by nicotine also subserve some emotional and cognitive functions, which may also be altered. We suggest that dendritic elaboration and associated changes are mediated by activity-dependent synaptogenesis, acting in part

  16. Activity-dependent modulation of neural circuit synaptic connectivity

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    Charles R Tessier

    2009-07-01

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

  17. Interpreting the effects of altered brain anatomical connectivity on fMRI functional connectivity: a role for computational neural modeling.

    Science.gov (United States)

    Horwitz, Barry; Hwang, Chuhern; Alstott, Jeff

    2013-01-01

    Recently, there have been a large number of studies using resting state fMRI to characterize abnormal brain connectivity in patients with a variety of neurological, psychiatric, and developmental disorders. However, interpreting what the differences in resting state fMRI functional connectivity (rsfMRI-FC) actually reflect in terms of the underlying neural pathology has proved to be elusive because of the complexity of brain anatomical connectivity. The same is the case for task-based fMRI studies. In the last few years, several groups have used large-scale neural modeling to help provide some insight into the relationship between brain anatomical connectivity and the corresponding patterns of fMRI-FC. In this paper we review several efforts at using large-scale neural modeling to investigate the relationship between structural connectivity and functional/effective connectivity to determine how alterations in structural connectivity are manifested in altered patterns of functional/effective connectivity. Because the alterations made in the anatomical connectivity between specific brain regions in the model are known in detail, one can use the results of these simulations to determine the corresponding alterations in rsfMRI-FC. Many of these simulation studies found that structural connectivity changes do not necessarily result in matching changes in functional/effective connectivity in the areas of structural modification. Often, it was observed that increases in functional/effective connectivity in the altered brain did not necessarily correspond to increases in the strength of the anatomical connection weights. Note that increases in rsfMRI-FC in patients have been interpreted in some cases as resulting from neural plasticity. These results suggest that this interpretation can be mistaken. The relevance of these simulation findings to the use of functional/effective fMRI connectivity as biomarkers for brain disorders is also discussed.

  18. Neural connections foster social connections: a diffusion-weighted imaging study of social networks.

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    Hampton, William H; Unger, Ashley; Von Der Heide, Rebecca J; Olson, Ingrid R

    2016-05-01

    Although we know the transition from childhood to adulthood is marked by important social and neural development, little is known about how social network size might affect neurocognitive development or vice versa. Neuroimaging research has identified several brain regions, such as the amygdala, as key to this affiliative behavior. However, white matter connectivity among these regions, and its behavioral correlates, remain unclear. Here we tested two hypotheses: that an amygdalocentric structural white matter network governs social affiliative behavior and that this network changes during adolescence and young adulthood. We measured social network size behaviorally, and white matter microstructure using probabilistic diffusion tensor imaging in a sample of neurologically normal adolescents and young adults. Our results suggest amygdala white matter microstructure is key to understanding individual differences in social network size, with connectivity to other social brain regions such as the orbitofrontal cortex and anterior temporal lobe predicting much variation. In addition, participant age correlated with both network size and white matter variation in this network. These findings suggest the transition to adulthood may constitute a critical period for the optimization of structural brain networks underlying affiliative behavior.

  19. Connecting Neural Coding to Number Cognition: A Computational Account

    Science.gov (United States)

    Prather, Richard W.

    2012-01-01

    The current study presents a series of computational simulations that demonstrate how the neural coding of numerical magnitude may influence number cognition and development. This includes behavioral phenomena cataloged in cognitive literature such as the development of numerical estimation and operational momentum. Though neural research has…

  20. Towards Confirming Neural Circuit Inference from Population Calcium Imaging. NIPS Workshop on Connectivity Inference in Neuroimaging

    OpenAIRE

    NeuroData; Mishchenko, Y.; AM, Packer; TA, Machado; Yuste, R.; Paninski, L

    2015-01-01

    Vogelstein JT, Mishchenko Y, Packer AM, Machado TA, Yuste R, Paninski L. Towards Confirming Neural Circuit Inference from Population Calcium Imaging. NIPS Workshop on Connectivity Inference in Neuroimaging, 2009

  1. Age-Related Increases in Long-Range Connectivity in Fetal Functional Neural Connectivity Networks In Utero

    Science.gov (United States)

    Thomason, Moriah E.; Grove, Lauren E.; Lozon, Tim A.; Vila, Angela M.; Ye, Yongquan; Nye, Matthew J.; Manning, Janessa H.; Pappas, Athina; Hernandez-Andrade, Edgar; Yeo, Lami; Mody, Swati; Berman, Susan; Hassan, Sonia S.; Romero, Roberto

    2015-01-01

    Formation of operational neural networks is one of the most significant accomplishments of human fetal brain growth. Recent advances in functional magnetic resonance imaging (fMRI) have made it possible to obtain information about brain function during fetal development. Specifically, resting-state fMRI and novel signal covariation approaches have opened up a new avenue for non-invasive assessment of neural functional connectivity (FC) before birth. Early studies in this area have unearthed new insights about principles of prenatal brain function. However, very little is known about the emergence and maturation of neural networks during fetal life. Here, we obtained cross-sectional rs-fMRI data from 39 fetuses between 24 and 38 weeks postconceptual age to examine patterns of connectivity across ten neural FC networks. We identified primitive forms of motor, visual, default mode, thalamic, and temporal networks in the human fetal brain. We discovered the first evidence of increased long-range, cerebral-cerebellar, cortical-subcortical, and intra-hemispheric FC with advancing fetal age. Continued aggregation of data about fundamental neural connectivity systems in utero is essential to establishing principles of connectomics at the beginning of human life. Normative data provides a vital context against which to compare instances of abnormal neurobiological development. PMID:25284273

  2. Age-related increases in long-range connectivity in fetal functional neural connectivity networks in utero

    Directory of Open Access Journals (Sweden)

    Moriah E. Thomason

    2015-02-01

    Full Text Available Formation of operational neural networks is one of the most significant accomplishments of human fetal brain growth. Recent advances in functional magnetic resonance imaging (fMRI have made it possible to obtain information about brain function during fetal development. Specifically, resting-state fMRI and novel signal covariation approaches have opened up a new avenue for non-invasive assessment of neural functional connectivity (FC before birth. Early studies in this area have unearthed new insights about principles of prenatal brain function. However, very little is known about the emergence and maturation of neural networks during fetal life. Here, we obtained cross-sectional rs-fMRI data from 39 fetuses between 24 and 38 weeks postconceptual age to examine patterns of connectivity across ten neural FC networks. We identified primitive forms of motor, visual, default mode, thalamic, and temporal networks in the human fetal brain. We discovered the first evidence of increased long-range, cerebral-cerebellar, cortical-subcortical, and intra-hemispheric FC with advancing fetal age. Continued aggregation of data about fundamental neural connectivity systems in utero is essential to establishing principles of connectomics at the beginning of human life. Normative data provides a vital context against which to compare instances of abnormal neurobiological development.

  3. Age-related increases in long-range connectivity in fetal functional neural connectivity networks in utero.

    Science.gov (United States)

    Thomason, Moriah E; Grove, Lauren E; Lozon, Tim A; Vila, Angela M; Ye, Yongquan; Nye, Matthew J; Manning, Janessa H; Pappas, Athina; Hernandez-Andrade, Edgar; Yeo, Lami; Mody, Swati; Berman, Susan; Hassan, Sonia S; Romero, Roberto

    2015-02-01

    Formation of operational neural networks is one of the most significant accomplishments of human fetal brain growth. Recent advances in functional magnetic resonance imaging (fMRI) have made it possible to obtain information about brain function during fetal development. Specifically, resting-state fMRI and novel signal covariation approaches have opened up a new avenue for non-invasive assessment of neural functional connectivity (FC) before birth. Early studies in this area have unearthed new insights about principles of prenatal brain function. However, very little is known about the emergence and maturation of neural networks during fetal life. Here, we obtained cross-sectional rs-fMRI data from 39 fetuses between 24 and 38 weeks postconceptual age to examine patterns of connectivity across ten neural FC networks. We identified primitive forms of motor, visual, default mode, thalamic, and temporal networks in the human fetal brain. We discovered the first evidence of increased long-range, cerebral-cerebellar, cortical-subcortical, and intra-hemispheric FC with advancing fetal age. Continued aggregation of data about fundamental neural connectivity systems in utero is essential to establishing principles of connectomics at the beginning of human life. Normative data provides a vital context against which to compare instances of abnormal neurobiological development. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

  4. Neural systems language: a formal modeling language for the systematic description, unambiguous communication, and automated digital curation of neural connectivity.

    Science.gov (United States)

    Brown, Ramsay A; Swanson, Larry W

    2013-09-01

    Systematic description and the unambiguous communication of findings and models remain among the unresolved fundamental challenges in systems neuroscience. No common descriptive frameworks exist to describe systematically the connective architecture of the nervous system, even at the grossest level of observation. Furthermore, the accelerating volume of novel data generated on neural connectivity outpaces the rate at which this data is curated into neuroinformatics databases to synthesize digitally systems-level insights from disjointed reports and observations. To help address these challenges, we propose the Neural Systems Language (NSyL). NSyL is a modeling language to be used by investigators to encode and communicate systematically reports of neural connectivity from neuroanatomy and brain imaging. NSyL engenders systematic description and communication of connectivity irrespective of the animal taxon described, experimental or observational technique implemented, or nomenclature referenced. As a language, NSyL is internally consistent, concise, and comprehensible to both humans and computers. NSyL is a promising development for systematizing the representation of neural architecture, effectively managing the increasing volume of data on neural connectivity and streamlining systems neuroscience research. Here we present similar precedent systems, how NSyL extends existing frameworks, and the reasoning behind NSyL's development. We explore NSyL's potential for balancing robustness and consistency in representation by encoding previously reported assertions of connectivity from the literature as examples. Finally, we propose and discuss the implications of a framework for how NSyL will be digitally implemented in the future to streamline curation of experimental results and bridge the gaps among anatomists, imagers, and neuroinformatics databases.

  5. The necessity of connection structures in neural models of variable binding

    NARCIS (Netherlands)

    Velde, van der Frank; Kamps, de Marc

    2015-01-01

    In his review of neural binding problems, Feldman (Cogn Neurodyn 7:1–11, 2013) addressed two types of models as solutions of (novel) variable binding. The one type uses labels such as phase synchrony of activation. The other (‘connectivity based’) type uses dedicated connections structures to achiev

  6. Visually-salient contour detection using a V1 neural model with horizontal connections

    CERN Document Server

    Loxley, P N

    2011-01-01

    A convolution model which accounts for neural activity dynamics in the primary visual cortex is derived and used to detect visually salient contours in images. Image inputs to the model are modulated by long-range horizontal connections, allowing contextual effects in the image to determine visual saliency, i.e. line segments arranged in a closed contour elicit a larger neural response than line segments forming background clutter. The model is tested on 3 types of contour, including a line, a circular closed contour, and a non-circular closed contour. Using a modified association field to describe horizontal connections the model is found to perform well for different parameter values. For each type of contour a different facilitation mechanism is found. Operating as a feed-forward network, the model assigns saliency by increasing the neural activity of line segments facilitated by the horizontal connections. Alternatively, operating as a feedback network, the model can achieve further improvement over sever...

  7. Analytical estimates of efficiency of attractor neural networks with inborn connections

    Directory of Open Access Journals (Sweden)

    Solovyeva Ksenia

    2016-01-01

    Full Text Available The analysis is restricted to the features of neural networks endowed to the latter by the inborn (not learned connections. We study attractor neural networks in which for almost all operation time the activity resides in close vicinity of a relatively small number of attractor states. The number of the latter, M, is proportional to the number of neurons in the neural network, N, while the total number of the states in it is 2N. The unified procedure of growth/fabrication of neural networks with sets of all attractor states with dimensionality d=0 and d=1, based on model molecular markers, is studied in detail. The specificity of the networks (d=0 or d=1 depends on topology (i.e., the set of distances between elements which can be provided to the set of molecular markers by their physical nature. The neural networks parameters estimates and trade-offs for them in attractor neural networks are calculated analytically. The proposed mechanisms reveal simple and efficient ways of implementation in artificial as well as in natural neural networks of multiplexity, i.e. of using activity of single neurons in representation of multiple values of the variables, which are operated by the neural systems. It is discussed how the neuronal multiplexity provides efficient and reliable ways of performing functional operations in the neural systems.

  8. Synaptic organizations and dynamical properties of weakly connected neural oscillators. I. Analysis of a canonical model.

    Science.gov (United States)

    Hoppensteadt, F C; Izhikevich, E M

    1996-08-01

    We study weakly connected networks of neural oscillators near multiple Andronov-Hopf bifurcation points. We analyze relationships between synaptic organizations (anatomy) of the networks and their dynamical properties (function). Our principal assumptions are: (1) Each neural oscillator comprises two populations of neurons; excitatory and inhibitory ones; (2) activity of each population of neurons is described by a scalar (one-dimensional) variable; (3) each neural oscillator is near a nondegenerate supercritical Andronov-Hopf bifurcation point; (4) the synaptic connections between the neural oscillators are weak. All neural networks satisfying these hypotheses are governed by the same dynamical system, which we call the canonical model. Studying the canonical model shows that: (1) A neural oscillator can communicate only with those oscillators which have roughly the same natural frequency. That is, synaptic connections between a pair of oscillators having different natural frequencies are functionally insignificant. (2) Two neural oscillators having the same natural frequencies might not communicate if the connections between them are from among a class of pathological synaptic configurations. In both cases the anatomical presence of synaptic connections between neural oscillators does not necessarily guarantee that the connections are functionally significant. (3) There can be substantial phase differences (time delays) between the neural oscillators, which result from the synaptic organization of the network, not from the transmission delays. Using the canonical model we can illustrate self-ignition and autonomous quiescence (oscillator death) phenomena. That is, a network of passive elements can exhibit active properties and vice versa. We also study how Dale's principle affects dynamics of the networks, in particular, the phase differences that the network can reproduce. We present a complete classification of all possible synaptic organizations from this

  9. Neural Connectivity in Epilepsy as Measured by Granger Causality

    Science.gov (United States)

    Coben, Robert; Mohammad-Rezazadeh, Iman

    2015-01-01

    Epilepsy is a chronic neurological disorder characterized by repeated seizures or excessive electrical discharges in a group of brain cells. Prevalence rates include about 50 million people worldwide and 10% of all people have at least one seizure at one time in their lives. Connectivity models of epilepsy serve to provide a deeper understanding of the processes that control and regulate seizure activity. These models have received initial support and have included measures of EEG, MEG, and MRI connectivity. Preliminary findings have shown regions of increased connectivity in the immediate regions surrounding the seizure foci and associated low connectivity in nearby regions and pathways. There is also early evidence to suggest that these patterns change during ictal events and that these changes may even by related to the occurrence or triggering of seizure events. We present data showing how Granger causality can be used with EEG data to measure connectivity across brain regions involved in ictal events and their resolution. We have provided two case examples as a demonstration of how to obtain and interpret such data. EEG data of ictal events are processed, converted to independent components and their dipole localizations, and these are used to measure causality and connectivity between these locations. Both examples have shown hypercoupling near the seizure foci and low causality across nearby and associated neuronal pathways. This technique also allows us to track how these measures change over time and during the ictal and post-ictal periods. Areas for further research into this technique, its application to epilepsy, and the formation of more effective therapeutic interventions are recommended. PMID:26236211

  10. Effective connectivity of hippocampal neural network and its alteration in Mg2+-free epilepsy model.

    Science.gov (United States)

    Gong, Xin-Wei; Li, Jing-Bo; Lu, Qin-Chi; Liang, Pei-Ji; Zhang, Pu-Ming

    2014-01-01

    Understanding the connectivity of the brain neural network and its evolution in epileptiform discharges is meaningful in the epilepsy researches and treatments. In the present study, epileptiform discharges were induced in rat hippocampal slices perfused with Mg2+-free artificial cerebrospinal fluid. The effective connectivity of the hippocampal neural network was studied by comparing the normal and epileptiform discharges recorded by a microelectrode array. The neural network connectivity was constructed by using partial directed coherence and analyzed by graph theory. The transition of the hippocampal network topology from control to epileptiform discharges was demonstrated. Firstly, differences existed in both the averaged in- and out-degree between nodes in the pyramidal cell layer and the granule cell layer, which indicated an information flow from the pyramidal cell layer to the granule cell layer during epileptiform discharges, whereas no consistent information flow was observed in control. Secondly, the neural network showed different small-worldness in the early, middle and late stages of the epileptiform discharges, whereas the control network did not show the small-world property. Thirdly, the network connectivity began to change earlier than the appearance of epileptiform discharges and lasted several seconds after the epileptiform discharges disappeared. These results revealed the important network bases underlying the transition from normal to epileptiform discharges in hippocampal slices. Additionally, this work indicated that the network analysis might provide a useful tool to evaluate the neural network and help to improve the prediction of seizures.

  11. Modeling analysis of the relationship between EEG rhythms and connectivity among different neural populations.

    Science.gov (United States)

    Ursino, Mauro; Zavaglia, Melissa

    2007-12-01

    In our research, a neural mass model consisting of four interconnected neural groups (pyramidal neurons, excitatory interneurons, inhibitory interneurons with slow synaptic kinetics, and inhibitory interneurons with fast synaptic kinetics) is used to investigate the mechanisms which cause the appearance of multiple rhythms in EEG spectra, and to assess how these rhythms can be affected by connectivity among different populations. First, we showed that a single neural population, stimulated with white noise, can oscillate with its intrinsic rhythm, and that the position of this rhythm can be finely tuned (in the alpha, beta or gamma frequency ranges), acting on the population synaptic kinetics. Subsequently, we analyzed more complex circuits, composed of two or three interconnected populations, each with a different synaptic kinetics between neural groups within a population (hence, with a different intrinsic rhythm). The results demonstrates apex that a single population can exhibit many different simultaneous rhythms, provided that some of these come from external sources (for instance, from remote regions). The analysis of coherence, and of the position of the peaks in power spectral density, reveals important information on the possible connections among populations, and is especially useful to follow temporal changes in connectivity. In perspective, the results may be of value for a deeper comprehension of the mechanisms causing EEGs rhythms, for the study of connectivity among different neural populations and for the test of neurophysiological hypotheses.

  12. Network burst dynamics under heterogeneous cholinergic modulation of neural firing properties and heterogeneous synaptic connectivity.

    Science.gov (United States)

    Knudstrup, Scott; Zochowski, Michal; Booth, Victoria

    2016-05-01

    The characteristics of neural network activity depend on intrinsic neural properties and synaptic connectivity in the network. In brain networks, both of these properties are critically affected by the type and levels of neuromodulators present. The expression of many of the most powerful neuromodulators, including acetylcholine (ACh), varies tonically and phasically with behavioural state, leading to dynamic, heterogeneous changes in intrinsic neural properties and synaptic connectivity properties. Namely, ACh significantly alters neural firing properties as measured by the phase response curve in a manner that has been shown to alter the propensity for network synchronization. The aim of this simulation study was to build an understanding of how heterogeneity in cholinergic modulation of neural firing properties and heterogeneity in synaptic connectivity affect the initiation and maintenance of synchronous network bursting in excitatory networks. We show that cells that display different levels of ACh modulation have differential roles in generating network activity: weakly modulated cells are necessary for burst initiation and provide synchronizing drive to the rest of the network, whereas strongly modulated cells provide the overall activity level necessary to sustain burst firing. By applying several quantitative measures of network activity, we further show that the existence of network bursting and its characteristics, such as burst duration and intraburst synchrony, are dependent on the fraction of cell types providing the synaptic connections in the network. These results suggest mechanisms underlying ACh modulation of brain oscillations and the modulation of seizure activity during sleep states.

  13. Changes in Resting Neural Connectivity during Propofol Sedation

    NARCIS (Netherlands)

    Stamatakis, Emmanuel A.; Adapa, Ram M.; Absalom, Anthony R.; Menon, David K.

    2010-01-01

    Background: The default mode network consists of a set of functionally connected brain regions (posterior cingulate, medial prefrontal cortex and bilateral parietal cortex) maximally active in functional imaging studies under "no task" conditions. It has been argued that the posterior cingulate is

  14. Changes in resting neural connectivity during propofol sedation.

    Directory of Open Access Journals (Sweden)

    Emmanuel A Stamatakis

    Full Text Available The default mode network consists of a set of functionally connected brain regions (posterior cingulate, medial prefrontal cortex and bilateral parietal cortex maximally active in functional imaging studies under "no task" conditions. It has been argued that the posterior cingulate is important in consciousness/awareness, but previous investigations of resting interactions between the posterior cingulate cortex and other brain regions during sedation and anesthesia have produced inconsistent results.We examined the connectivity of the posterior cingulate at different levels of consciousness. "No task" fMRI (BOLD data were collected from healthy volunteers while awake and at low and moderate levels of sedation, induced by the anesthetic agent propofol. Our data show that connectivity of the posterior cingulate changes during sedation to include areas that are not traditionally considered to be part of the default mode network, such as the motor/somatosensory cortices, the anterior thalamic nuclei, and the reticular activating system.This neuroanatomical signature resembles that of non-REM sleep, and may be evidence for a system that reduces its discriminable states and switches into more stereotypic patterns of firing under sedation.

  15. The biocultural paradigm: the neural connection between science and mysticism.

    Science.gov (United States)

    de Nicolas, A T

    1998-01-01

    New discoveries in perceptual psychology, brain chemistry, brain evolution, brain development, ethology, cultural anthropology, the more recent work of MacLean on the structure of the brains and the discovery by Gazzaniga of the role of the, so-called, "interpreter module," are the foundations of a new paradigm on human cortical information processing, called by its discoverer, Dr. M. Colavito, the "biocultural paradigm." This paradigm shows that biology and culture act on one another as the conditioning parameters of neurocultural information. Through mutual interaction biology in humans becomes culture, and vice versa, culture opens and stimulates the neural passages of the brains, accounting thus for the varieties of brains in humans, and for cultural diversity. Culture conditions and stimulates biology, while biology conditions and makes culture possible. Cultures and brains may be distinguished from one another through identification with certain functions or combination of functions that are exercised habitually, or become neural hard-wire through repetition or habit. This new model has replaced older and simpler models of the nature/ nurture controversy, such as the unextended rational substance of Descartes, the tabula rasa of Locke, the associated-matrix of Hume, the passive, reinforcement-driven animal of Skinner, and the genetically hard-wired robot of the sociobiologists. However, elements of these earlier models are included in the new one, but the conversation about human experience has changed, and, therefore, the human images of ourselves. This change was forced on scientists by the importance of the conditionality of the experience of "I" and "not-I" as described by Alex Comfort in his book I and That, and was introduced in the conversations some of us already had with each other. This article focuses on the "I" and "not-I" experiences with a description of the "not-I" or "oceanic" or "mystical" experience to clarify the new paradigm of

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

    Science.gov (United States)

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

    2016-08-01

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

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

    Science.gov (United States)

    Eriksson, David

    2016-01-01

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

  18. Exuberant oral myiasis caused by Musca domestica (Housefly

    Directory of Open Access Journals (Sweden)

    Rajkumar N Parwani

    2014-01-01

    Full Text Available Tissues of oral cavity, when invaded by the parasitic larvae of houseflies, the condition is called as oral myiasis. It is a rare disease that is most common in developing countries and is associated with conditions leading to persistent mouth opening along with poor oral hygiene, suppurative lesions, severe halitosis and maxillofacial trauma. A case of exuberant oral myiasis in a 42-year-old female patient is described here. She reported with swelling, pain, mobility of teeth and foul odor. Diagnosis was based primarily on history and clinical features. Management included use of turpentine oil, mechanical removal of larvae followed by extraction of mobile teeth and curettage along with supportive antibiotic and analgesic therapy. Supportive nutritional supplements and timely institution of treatment encompassing removal of the offending larvae and carious teeth with proper education and motivation of the patient including oral hygiene instructions led to the resolution of these lesions.

  19. Exuberant callus formation misdiagnosed as osteosarcoma: a case report

    Institute of Scientific and Technical Information of China (English)

    Fariba Binesh; Mohammad Sobhan; Reza Nafisi Moghadam; Ali Akhavan

    2013-01-01

    Reactive lesions of bone and soft tissue can appear alarming on histologic examination because they are often cellular and have atypical cytologic features, such as distinct nucleoli, mild hyperchromasia, and mitotic activity. Reactive lesions of bone and periosteum also produce bone and cartilage matrix, resulting in confusion with osteosarcoma or chon-drosarcoma. Careful attention to key cytomorphological features such as the pattern of bone formation, uniform appearance of cells, and absence of atypical mitoses should help identify the reactive nature of a lesion. Correlation with clinical and radiological findings is also imperative to avoid misclassification of the tumor because reactive lesions often arise at sites where osteosarcoma and chondrosarcoma are rare (eg, the hand) and lack aggressive radiological features. Here we pres-ent a case of exuberant callus formation after avulsion fracture of tibia in a three year-old Iranian girl which misdiagnosed as osteosarcoma.

  20. Exuberant Oral Myiasis Caused by Musca domestica (Housefly).

    Science.gov (United States)

    Parwani, Rajkumar N; Patidar, Kalpana A; Parwani, Simran R; Wanjari, Sangeeta P

    2014-01-01

    Tissues of oral cavity, when invaded by the parasitic larvae of houseflies, the condition is called as oral myiasis. It is a rare disease that is most common in developing countries and is associated with conditions leading to persistent mouth opening along with poor oral hygiene, suppurative lesions, severe halitosis and maxillofacial trauma. A case of exuberant oral myiasis in a 42-year-old female patient is described here. She reported with swelling, pain, mobility of teeth and foul odor. Diagnosis was based primarily on history and clinical features. Management included use of turpentine oil, mechanical removal of larvae followed by extraction of mobile teeth and curettage along with supportive antibiotic and analgesic therapy. Supportive nutritional supplements and timely institution of treatment encompassing removal of the offending larvae and carious teeth with proper education and motivation of the patient including oral hygiene instructions led to the resolution of these lesions.

  1. Nonnegative spline regression of incomplete tracing data reveals high resolution neural connectivity

    CERN Document Server

    Harris, Kameron Decker; Shea-Brown, Eric

    2016-01-01

    Whole-brain neural connectivity data are now available from viral tracing experiments, which reveal the connections between a source injection site and elsewhere in the brain. These hold the promise of revealing spatial patterns of connectivity throughout the mammalian brain. To achieve this goal, we seek to fit a weighted, nonnegative adjacency matrix among 100 {\\mu}m brain "voxels" using viral tracer data. Despite a multi-year experimental effort, the problem remains severely underdetermined: Injection sites provide incomplete coverage, and the number of voxels is orders of magnitude larger than the number of injections. Furthermore, projection data are missing within the injection site because local connections there are not separable from the injection signal. We use a novel machine-learning algorithm to meet these challenges and develop a spatially explicit, voxel-scale connectivity map of the mouse visual system. Our method combines three features: a matrix completion loss for missing data, a smoothing ...

  2. What Is Lost During Dreamless Sleep: The Relationship Between Neural Connectivity Patterns and Consciousness

    Directory of Open Access Journals (Sweden)

    Michaela Klimova

    2014-09-01

    Full Text Available Non-rapid eye movement (NREM sleep is characterised by reduced consciousness; thus, studying its neural characteristics acts as a useful indication of what is needed for conscious experience. The integrated information theory (Tononi, 2008 states that the ability of different thalamocortical regions to interact is crucial for consciousness, thereby motivating research concerning connectivity changes in the thalamocortical system that accompany changing consciousness levels. This review aims to discuss investigations of functional connectivity of resting-state and large-scale brain networks, applying correlational approaches to neuroimaging data as well as studies that used brain stimulation to investigate effective connectivity. Most findings suggest a reorganisation of functional brain networks where inter-region connectivity is reduced and intra-region connectivity is stronger in deep sleep than wakefulness.

  3. Binary synaptic connections based on memory switching in a-Si:H for artificial neural networks

    Science.gov (United States)

    Thakoor, A. P.; Lamb, J. L.; Moopenn, A.; Khanna, S. K.

    1987-01-01

    A scheme for nonvolatile associative electronic memory storage with high information storage density is proposed which is based on neural network models and which uses a matrix of two-terminal passive interconnections (synapses). It is noted that the massive parallelism in the architecture would require the ON state of a synaptic connection to be unusually weak (highly resistive). Memory switching using a-Si:H along with ballast resistors patterned from amorphous Ge-metal alloys is investigated for a binary programmable read only memory matrix. The fabrication of a 1600 synapse test array of uniform connection strengths and a-Si:H switching elements is discussed.

  4. Estimate the effective connectivity in multi-coupled neural mass model using particle swarm optimization

    Science.gov (United States)

    Shan, Bonan; Wang, Jiang; Deng, Bin; Zhang, Zhen; Wei, Xile

    2017-03-01

    Assessment of the effective connectivity among different brain regions during seizure is a crucial problem in neuroscience today. As a consequence, a new model inversion framework of brain function imaging is introduced in this manuscript. This framework is based on approximating brain networks using a multi-coupled neural mass model (NMM). NMM describes the excitatory and inhibitory neural interactions, capturing the mechanisms involved in seizure initiation, evolution and termination. Particle swarm optimization method is used to estimate the effective connectivity variation (the parameters of NMM) and the epileptiform dynamics (the states of NMM) that cannot be directly measured using electrophysiological measurement alone. The estimated effective connectivity includes both the local connectivity parameters within a single region NMM and the remote connectivity parameters between multi-coupled NMMs. When the epileptiform activities are estimated, a proportional-integral controller outputs control signal so that the epileptiform spikes can be inhibited immediately. Numerical simulations are carried out to illustrate the effectiveness of the proposed framework. The framework and the results have a profound impact on the way we detect and treat epilepsy.

  5. Analyzing the scaling of connectivity in neuromorphic hardware and in models of neural networks.

    Science.gov (United States)

    Partzsch, Johannes; Schüffny, René

    2011-06-01

    In recent years, neuromorphic hardware systems have significantly grown in size. With more and more neurons and synapses integrated in such systems, the neural connectivity and its configurability have become crucial design constraints. To tackle this problem, we introduce a generic extended graph description of connection topologies that allows a systematical analysis of connectivity in both neuromorphic hardware and neural network models. The unifying nature of our approach enables a close exchange between hardware and models. For an existing hardware system, the optimally matched network model can be extracted. Inversely, a hardware architecture may be fitted to a particular model network topology with our description method. As a further strength, the extended graph can be used to quantify the amount of configurability for a certain network topology. This is a hardware design variable that has widely been neglected, mainly because of a missing analysis method. To condense our analysis results, we develop a classification for the scaling complexity of network models and neuromorphic hardware, based on the total number of connections and the configurability. We find a gap between several models and existing hardware, making these hardware systems either impossible or inefficient to use for scaled-up network models. In this respect, our analysis results suggest models with locality in their connections as promising approach for tackling this scaling gap.

  6. Multi-Connection Pattern Analysis: Decoding the representational content of neural communication.

    Science.gov (United States)

    Li, Yuanning; Richardson, Robert Mark; Ghuman, Avniel Singh

    2017-08-13

    The lack of multivariate methods for decoding the representational content of interregional neural communication has left it difficult to know what information is represented in distributed brain circuit interactions. Here we present Multi-Connection Pattern Analysis (MCPA), which works by learning mappings between the activity patterns of the populations as a factor of the information being processed. These maps are used to predict the activity from one neural population based on the activity from the other population. Successful MCPA-based decoding indicates the involvement of distributed computational processing and provides a framework for probing the representational structure of the interaction. Simulations demonstrate the efficacy of MCPA in realistic circumstances. In addition, we demonstrate that MCPA can be applied to different signal modalities to evaluate a variety of hypothesis associated with information coding in neural communications. We apply MCPA to fMRI and human intracranial electrophysiological data to provide a proof-of-concept of the utility of this method for decoding individual natural images and faces in functional connectivity data. We further use a MCPA-based representational similarity analysis to illustrate how MCPA may be used to test computational models of information transfer among regions of the visual processing stream. Thus, MCPA can be used to assess the information represented in the coupled activity of interacting neural circuits and probe the underlying principles of information transformation between regions. Copyright © 2017 Elsevier Inc. All rights reserved.

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

    Institute of Scientific and Technical Information of China (English)

    CHENG Chun-ling; ZHOU Jie

    2004-01-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

  9. An optimally evolved connective ratio of neural networks that maximizes the occurrence of synchronized bursting behavior

    Science.gov (United States)

    2012-01-01

    Background Synchronized bursting activity (SBA) is a remarkable dynamical behavior in both ex vivo and in vivo neural networks. Investigations of the underlying structural characteristics associated with SBA are crucial to understanding the system-level regulatory mechanism of neural network behaviors. Results In this study, artificial pulsed neural networks were established using spike response models to capture fundamental dynamics of large scale ex vivo cortical networks. Network simulations with synaptic parameter perturbations showed the following two findings. (i) In a network with an excitatory ratio (ER) of 80-90%, its connective ratio (CR) was within a range of 10-30% when the occurrence of SBA reached the highest expectation. This result was consistent with the experimental observation in ex vivo neuronal networks, which were reported to possess a matured inhibitory synaptic ratio of 10-20% and a CR of 10-30%. (ii) No SBA occurred when a network does not contain any all-positive-interaction feedback loop (APFL) motif. In a neural network containing APFLs, the number of APFLs presented an optimal range corresponding to the maximal occurrence of SBA, which was very similar to the optimal CR. Conclusions In a neural network, the evolutionarily selected CR (10-30%) optimizes the occurrence of SBA, and APFL serves a pivotal network motif required to maximize the occurrence of SBA. PMID:22462685

  10. An optimally evolved connective ratio of neural networks that maximizes the occurrence of synchronized bursting behavior

    Directory of Open Access Journals (Sweden)

    Dong Chao-Yi

    2012-03-01

    Full Text Available Abstract Background Synchronized bursting activity (SBA is a remarkable dynamical behavior in both ex vivo and in vivo neural networks. Investigations of the underlying structural characteristics associated with SBA are crucial to understanding the system-level regulatory mechanism of neural network behaviors. Results In this study, artificial pulsed neural networks were established using spike response models to capture fundamental dynamics of large scale ex vivo cortical networks. Network simulations with synaptic parameter perturbations showed the following two findings. (i In a network with an excitatory ratio (ER of 80-90%, its connective ratio (CR was within a range of 10-30% when the occurrence of SBA reached the highest expectation. This result was consistent with the experimental observation in ex vivo neuronal networks, which were reported to possess a matured inhibitory synaptic ratio of 10-20% and a CR of 10-30%. (ii No SBA occurred when a network does not contain any all-positive-interaction feedback loop (APFL motif. In a neural network containing APFLs, the number of APFLs presented an optimal range corresponding to the maximal occurrence of SBA, which was very similar to the optimal CR. Conclusions In a neural network, the evolutionarily selected CR (10-30% optimizes the occurrence of SBA, and APFL serves a pivotal network motif required to maximize the occurrence of SBA.

  11. Knowledge engineering tools for reasoning with scientific observations and interpretations: a neural connectivity use case

    Directory of Open Access Journals (Sweden)

    Bota Mihail

    2011-08-01

    Full Text Available Abstract Background We address the goal of curating observations from published experiments in a generalizable form; reasoning over these observations to generate interpretations and then querying this interpreted knowledge to supply the supporting evidence. We present web-application software as part of the 'BioScholar' project (R01-GM083871 that fully instantiates this process for a well-defined domain: using tract-tracing experiments to study the neural connectivity of the rat brain. Results The main contribution of this work is to provide the first instantiation of a knowledge representation for experimental observations called 'Knowledge Engineering from Experimental Design' (KEfED based on experimental variables and their interdependencies. The software has three parts: (a the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c a 'neural connection matrix' interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. BioScholar is built in Flex 3.5. It uses Persevere (a noSQL database as a flexible data store and PowerLoom® (a mature First Order Logic reasoning system to execute queries using spatial reasoning over the BAMS neuroanatomical ontology. Conclusions We first introduce the KEfED approach as a general approach and describe its possible role as a way of introducing structured reasoning into models of argumentation within new models of scientific publication. We then describe the design and implementation of our example application: the BioScholar software. This is presented as a possible biocuration interface and supplementary reasoning toolkit for a larger

  12. Initialization and self-organized optimization of recurrent neural network connectivity.

    Science.gov (United States)

    Boedecker, Joschka; Obst, Oliver; Mayer, N Michael; Asada, Minoru

    2009-10-01

    Reservoir computing (RC) is a recent paradigm in the field of recurrent neural networks. Networks in RC have a sparsely and randomly connected fixed hidden layer, and only output connections are trained. RC networks have recently received increased attention as a mathematical model for generic neural microcircuits to investigate and explain computations in neocortical columns. Applied to specific tasks, their fixed random connectivity, however, leads to significant variation in performance. Few problem-specific optimization procedures are known, which would be important for engineering applications, but also in order to understand how networks in biology are shaped to be optimally adapted to requirements of their environment. We study a general network initialization method using permutation matrices and derive a new unsupervised learning rule based on intrinsic plasticity (IP). The IP-based learning uses only local learning, and its aim is to improve network performance in a self-organized way. Using three different benchmarks, we show that networks with permutation matrices for the reservoir connectivity have much more persistent memory than the other methods but are also able to perform highly nonlinear mappings. We also show that IP-based on sigmoid transfer functions is limited concerning the output distributions that can be achieved.

  13. Connectivity strategies for higher-order neural networks applied to pattern recognition

    Science.gov (United States)

    Spirkovska, Lilly; Reid, Max B.

    1990-01-01

    Different strategies for non-fully connected HONNs (higher-order neural networks) are discussed, showing that by using such strategies an input field of 128 x 128 pixels can be attained while still achieving in-plane rotation and translation-invariant recognition. These techniques allow HONNs to be used with the larger input scenes required for practical pattern-recognition applications. The number of interconnections that must be stored has been reduced by a factor of approximately 200,000 in a T/C case and about 2000 in a Space Shuttle/F-18 case by using regional connectivity. Third-order networks have been simulated using several connection strategies. The method found to work best is regional connectivity. The main advantages of this strategy are the following: (1) it considers features of various scales within the image and thus gets a better sample of what the image looks like; (2) it is invariant to shape-preserving geometric transformations, such as translation and rotation; (3) the connections are predetermined so that no extra computations are necessary during run time; and (4) it does not require any extra storage for recording which connections were formed.

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

    Science.gov (United States)

    Li, Siqi; Jiang, Huiyan; Pang, Wenbo

    2017-05-01

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

  15. On the connection between level of education and the neural circuitry of emotion perception

    Directory of Open Access Journals (Sweden)

    Liliana Ramona Demenescu

    2014-10-01

    Full Text Available Through education, a social group transmits accumulated knowledge, skills, customs, and values to its members. So far, to the best of our knowledge, the association between educational attainment and neural correlates of emotion processing has been left unexplored. In a retrospective analysis of the NESDA fMRI study, we compared two groups of fourteen healthy volunteers with intermediate and high educational attainment, matched for age and gender. The data concerned event-related functional magnetic resonance imaging of brain activation during perception of facial emotional expressions. The region of interest analysis showed stronger right amygdala activation to facial expressions in participants with lower relative to higher educational attainment. The psychophysiological interaction analysis revealed that participants with higher educational attainment exhibited stronger right amygdala – right insula connectivity during perception of emotional and neutral facial expressions. This exploratory study suggests the relevance of educational attainment on the neural mechanism of facial expression processing.

  16. Small-World Connections to Induce Firing Activity and Phase Synchronization in Neural Networks

    Institute of Scientific and Technical Information of China (English)

    QIN Ying-Hua; LUO Xiao-Shu

    2009-01-01

    We investigate how the firing activity and the subsequent phase synchronization of neural networks with small-world topological connections depend on the probability p of adding-links. Network elements are described by two-dimensional map neurons (2DMNs) in a quiescent original state. Neurons burst for a given coupling strength when the topological randomness p increases, which is absent in a regular-lattice neural network. The bursting activity becomes frequent and synchronization of neurons emerges as topological randomness further increases.The maximal firing frequency and phase synchronization appear at a particular value of p. However, if the randomness p further increases, the firing frequency decreases and synchronization is apparently destroyed.

  17. Mutual information and self-control of a fully-connected low-activity neural network

    Science.gov (United States)

    Bollé, D.; Carreta, D. Dominguez

    2000-11-01

    A self-control mechanism for the dynamics of a three-state fully connected neural network is studied through the introduction of a time-dependent threshold. The self-adapting threshold is a function of both the neural and the pattern activity in the network. The time evolution of the order parameters is obtained on the basis of a recently developed dynamical recursive scheme. In the limit of low activity the mutual information is shown to be the relevant parameter in order to determine the retrieval quality. Due to self-control an improvement of this mutual information content as well as an increase of the storage capacity and an enlargement of the basins of attraction are found. These results are compared with numerical simulations.

  18. On the connection between level of education and the neural circuitry of emotion perception.

    Science.gov (United States)

    Demenescu, Liliana R; Stan, Adrian; Kortekaas, Rudie; van der Wee, Nic J A; Veltman, Dick J; Aleman, André

    2014-01-01

    Through education, a social group transmits accumulated knowledge, skills, customs, and values to its members. So far, to the best of our knowledge, the association between educational attainment and neural correlates of emotion processing has been left unexplored. In a retrospective analysis of The Netherlands Study of Depression and Anxiety (NESDA) functional magnetic resonance imaging (fMRI) study, we compared two groups of fourteen healthy volunteers with intermediate and high educational attainment, matched for age and gender. The data concerned event-related fMRI of brain activation during perception of facial emotional expressions. The region of interest (ROI) analysis showed stronger right amygdala activation to facial expressions in participants with lower relative to higher educational attainment (HE). The psychophysiological interaction analysis revealed that participants with HE exhibited stronger right amygdala-right insula connectivity during perception of emotional and neutral facial expressions. This exploratory study suggests the relevance of educational attainment on the neural mechanism of facial expressions processing.

  19. Reciprocal inhibitory connections within a neural network for rotational optic-flow processing

    Directory of Open Access Journals (Sweden)

    Juergen Haag

    2007-10-01

    Full Text Available Neurons in the visual system of the blowfly have large receptive fields that are selective for specific optic flow fields. Here, we studied the neural mechanisms underlying flow-field selectivity in proximal Vertical System (VS-cells, a particular subset of tangential cells in the fly. These cells have local preferred directions that are distributed such as to match the flow field occurring during a rotation of the fly. However, the neural circuitry leading to this selectivity is not fully understood. Through dual intracellular recordings from proximal VS cells and other tangential cells, we characterized the specific wiring between VS cells themselves and between proximal VS cells and horizontal sensitive tangential cells. We discovered a spiking neuron (Vi involved in this circuitry that has not been described before. This neuron turned out to be connected to proximal VS cells via gap junctions and, in addition, it was found to be inhibitory onto VS1.

  20. Genetic dyslexia risk variant is related to neural connectivity patterns underlying phonological awareness in children.

    Science.gov (United States)

    Skeide, Michael A; Kirsten, Holger; Kraft, Indra; Schaadt, Gesa; Müller, Bent; Neef, Nicole; Brauer, Jens; Wilcke, Arndt; Emmrich, Frank; Boltze, Johannes; Friederici, Angela D

    2015-09-01

    Phonological awareness is the best-validated predictor of reading and spelling skill and therefore highly relevant for developmental dyslexia. Prior imaging genetics studies link several dyslexia risk genes to either brain-functional or brain-structural factors of phonological deficits. However, coherent evidence for genetic associations with both functional and structural neural phenotypes underlying variation in phonological awareness has not yet been provided. Here we demonstrate that rs11100040, a reported modifier of SLC2A3, is related to the functional connectivity of left fronto-temporal phonological processing areas at resting state in a sample of 9- to 12-year-old children. Furthermore, we provide evidence that rs11100040 is related to the fractional anisotropy of the arcuate fasciculus, which forms the structural connection between these areas. This structural connectivity phenotype is associated with phonological awareness, which is in turn associated with the individual retrospective risk scores in an early dyslexia screening as well as to spelling. These results suggest a link between a dyslexia risk genotype and a functional as well as a structural neural phenotype, which is associated with a phonological awareness phenotype. The present study goes beyond previous work by integrating genetic, brain-functional and brain-structural aspects of phonological awareness within a single approach. These combined findings might be another step towards a multimodal biomarker for developmental dyslexia.

  1. Directed neural connectivity changes in robot-assisted gait training: a partial Granger causality analysis.

    Science.gov (United States)

    Youssofzadeh, Vahab; Zanotto, Damiano; Stegall, Paul; Naeem, Muhammad; Wong-Lin, KongFatt; Agrawal, Sunil K; Prasad, Girijesh

    2014-01-01

    Now-a-days robotic exoskeletons are often used to help in gait training of stroke patients. However, such robotic systems have so far yielded only mixed results in benefiting the clinical population. Therefore, there is a need to investigate how gait learning and de-learning get characterised in brain signals and thus determine neural substrate to focus attention on, possibly, through an appropriate brain-computer interface (BCI). To this end, this paper reports the analysis of EEG data acquired from six healthy individuals undergoing robot-assisted gait training of a new gait pattern. Time-domain partial Granger causality (PGC) method was applied to estimate directed neural connectivity among relevant brain regions. To validate the results, a power spectral density (PSD) analysis was also performed. Results showed a strong causal interaction between lateral motor cortical areas. A frontoparietal connection was found in all robot-assisted training sessions. Following training, a causal "top-down" cognitive control was evidenced, which may indicate plasticity in the connectivity in the respective brain regions.

  2. Longitudinal stability of temperamental exuberance and social-emotional outcomes in early childhood.

    Science.gov (United States)

    Degnan, Kathryn A; Hane, Amie Ashley; Henderson, Heather A; Moas, Olga Lydia; Reeb-Sutherland, Bethany C; Fox, Nathan A

    2011-05-01

    The goals of the current study were to investigate the stability of temperamental exuberance across infancy and toddlerhood and to examine the associations between exuberance and social-emotional outcomes in early childhood. The sample consisted of 291 4-month-olds followed at 9, 24, and 36 months and again at 5 years of age. Behavioral measures of exuberance were collected at 9, 24, and 36 months. At 36 months, frontal electroencephalogram (EEG) asymmetry was assessed. At 5 years, maternal reports of temperament and behavior problems were collected, as were observational measures of social behavior during an interaction with an unfamiliar peer in the laboratory. Latent profile analysis revealed a high, stable exuberance profile that was associated with greater ratings of 5-year externalizing behavior and surgency, as well as observed disruptive behavior and social competence with unfamiliar peers. These associations were particularly true for children who displayed left frontal EEG asymmetry. Multiple factors supported an approach bias for exuberant temperament but did not differentiate between adaptive and maladaptive social-emotional outcomes at 5 years of age.

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

    Science.gov (United States)

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

    2017-01-01

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

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

    Science.gov (United States)

    Corty, Megan M; Freeman, Marc R

    2013-11-11

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

  5. Transient dynamics of sparsely connected Hopfield neural networks with arbitrary degree distributions

    Science.gov (United States)

    Zhang, Pan; Chen, Yong

    2008-02-01

    Using probabilistic approach, the transient dynamics of sparsely connected Hopfield neural networks is studied for arbitrary degree distributions. A recursive scheme is developed to determine the time evolution of overlap parameters. As illustrative examples, the explicit calculations of dynamics for networks with binomial, power-law, and uniform degree distribution are performed. The results are good agreement with the extensive numerical simulations. It indicates that with the same average degree, there is a gradual improvement of network performance with increasing sharpness of its degree distribution, and the most efficient degree distribution for global storage of patterns is the delta function.

  6. Parallel dynamics of the fully connected Blume-Emery-Griffiths neural network

    Science.gov (United States)

    Bollé, D.; Blanco, J. Busquets; Shim, G. M.

    2003-02-01

    The parallel dynamics of the fully connected Blume-Emery-Griffiths neural network model is studied at zero temperature using a probabilistic approach. A recursive scheme is found determining the complete time evolution of the order parameters, taking into account all feedback correlations. It is based upon the evolution of the distribution of the local field, the structure of which is determined in detail. As an illustrative example explicit analytic formula are given for the first few time steps of the dynamics. Furthermore, equilibrium fixed-point equations are derived and compared with the thermodynamic approach. The analytic results find excellent confirmation in extensive numerical simulations.

  7. Application of feedback connection artificial neural network to seismic data filtering

    CERN Document Server

    Djarfour, Noureddine; Baddari, Kamel; Mihoubi, Abdelhafid; Ferahtia, Jalal; 10.1016/j.crte.2008.03.003

    2008-01-01

    The Elman artificial neural network (ANN) (feedback connection) was used for seismic data filtering. The recurrent connection that characterizes this network offers the advantage of storing values from the previous time step to be used in the current time step. The proposed structure has the advantage of training simplicity by a back-propagation algorithm (steepest descent). Several trials were addressed on synthetic (with 10% and 50% of random and Gaussian noise) and real seismic data using respectively 10 to 30 neurons and a minimum of 60 neurons in the hidden layer. Both an iteration number up to 4000 and arrest criteria were used to obtain satisfactory performances. Application of such networks on real data shows that the filtered seismic section was efficient. Adequate cross-validation test is done to ensure the performance of network on new data sets.

  8. Dynamic Changes in Amygdala Psychophysiological Connectivity Reveal Distinct Neural Networks for Facial Expressions of Basic Emotions

    Science.gov (United States)

    Diano, Matteo; Tamietto, Marco; Celeghin, Alessia; Weiskrantz, Lawrence; Tatu, Mona-Karina; Bagnis, Arianna; Duca, Sergio; Geminiani, Giuliano; Cauda, Franco; Costa, Tommaso

    2017-01-01

    The quest to characterize the neural signature distinctive of different basic emotions has recently come under renewed scrutiny. Here we investigated whether facial expressions of different basic emotions modulate the functional connectivity of the amygdala with the rest of the brain. To this end, we presented seventeen healthy participants (8 females) with facial expressions of anger, disgust, fear, happiness, sadness and emotional neutrality and analyzed amygdala’s psychophysiological interaction (PPI). In fact, PPI can reveal how inter-regional amygdala communications change dynamically depending on perception of various emotional expressions to recruit different brain networks, compared to the functional interactions it entertains during perception of neutral expressions. We found that for each emotion the amygdala recruited a distinctive and spatially distributed set of structures to interact with. These changes in amygdala connectional patters characterize the dynamic signature prototypical of individual emotion processing, and seemingly represent a neural mechanism that serves to implement the distinctive influence that each emotion exerts on perceptual, cognitive, and motor responses. Besides these differences, all emotions enhanced amygdala functional integration with premotor cortices compared to neutral faces. The present findings thus concur to reconceptualise the structure-function relation between brain-emotion from the traditional one-to-one mapping toward a network-based and dynamic perspective. PMID:28345642

  9. Functional connectivity and information flow of the respiratory neural network in chronic obstructive pulmonary disease.

    Science.gov (United States)

    Yu, Lianchun; De Mazancourt, Marine; Hess, Agathe; Ashadi, Fakhrul R; Klein, Isabelle; Mal, Hervé; Courbage, Maurice; Mangin, Laurence

    2016-08-01

    Breathing involves a complex interplay between the brainstem automatic network and cortical voluntary command. How these brain regions communicate at rest or during inspiratory loading is unknown. This issue is crucial for several reasons: (i) increased respiratory loading is a major feature of several respiratory diseases, (ii) failure of the voluntary motor and cortical sensory processing drives is among the mechanisms that precede acute respiratory failure, (iii) several cerebral structures involved in responding to inspiratory loading participate in the perception of dyspnea, a distressing symptom in many disease. We studied functional connectivity and Granger causality of the respiratory network in controls and patients with chronic obstructive pulmonary disease (COPD), at rest and during inspiratory loading. Compared with those of controls, the motor cortex area of patients exhibited decreased connectivity with their contralateral counterparts and no connectivity with the brainstem. In the patients, the information flow was reversed at rest with the source of the network shifted from the medulla towards the motor cortex. During inspiratory loading, the system was overwhelmed and the motor cortex became the sink of the network. This major finding may help to understand why some patients with COPD are prone to acute respiratory failure. Network connectivity and causality were related to lung function and illness severity. We validated our connectivity and causality results with a mathematical model of neural network. Our findings suggest a new therapeutic strategy involving the modulation of brain activity to increase motor cortex functional connectivity and improve respiratory muscles performance in patients. Hum Brain Mapp 37:2736-2754, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

  10. A Novel Neural Network Vector Control for Single-Phase Grid-Connected Converters with L, LC and LCL Filters

    OpenAIRE

    Xingang Fu; Shuhui Li

    2016-01-01

    This paper investigates a novel recurrent neural network (NN)-based vector control approach for single-phase grid-connected converters (GCCs) with L (inductor), LC (inductor-capacitor) and LCL (inductor-capacitor-inductor) filters and provides their comparison study with the conventional standard vector control method. A single neural network controller replaces two current-loop PI controllers, and the NN training approximates the optimal control for the single-phase GCC system. The Levenberg...

  11. Backward renormalization-group inference of cortical dipole sources and neural connectivity efficacy.

    Science.gov (United States)

    Amaral, Selene da Rocha; Baccalá, Luiz A; Barbosa, Leonardo S; Caticha, Nestor

    2017-06-01

    Proper neural connectivity inference has become essential for understanding cognitive processes associated with human brain function. Its efficacy is often hampered by the curse of dimensionality. In the electroencephalogram case, which is a noninvasive electrophysiological monitoring technique to record electrical activity of the brain, a possible way around this is to replace multichannel electrode information with dipole reconstructed data. We use a method based on maximum entropy and the renormalization group to infer the position of the sources, whose success hinges on transmitting information from low- to high-resolution representations of the cortex. The performance of this method compares favorably to other available source inference algorithms, which are ranked here in terms of their performance with respect to directed connectivity inference by using artificially generated dynamic data. We examine some representative scenarios comprising different numbers of dynamically connected dipoles over distinct cortical surface positions and under different sensor noise impairment levels. The overall conclusion is that inverse problem solutions do not affect the correct inference of the direction of the flow of information as long as the equivalent dipole sources are correctly found.

  12. Linear IgA bullous dermatosis: report of an exuberant case.

    Science.gov (United States)

    Souza, Beatriz Cavalcanti de; Fregonesi, Nádire Cristina Freire Pontes; Tebcherani, Antônio José; Sanchez, Ana Paula Galli; Aoki, Valéria; Fernandes, Juliana Christien

    2013-01-01

    Linear immunoglobulin A dermatosis is a rare autoimmune bullous disease, but the most common autoimmune bullous dermatosis in children. We report a typical exuberant case of linear IgA dermatosis in a ten-month old child, who showed good response to treatment with corticosteroids and dapsone.

  13. Linear IgA bullous dermatosis: report of an exuberant case*

    OpenAIRE

    Souza,Beatriz Cavalcanti de; Fregonesi, Nádire Cristina Freire Pontes; Tebcherani,Antônio José; Sanchez,Ana Paula Galli; AOKI, Valéria; Fernandes, Juliana Christien

    2013-01-01

    Linear immunoglobulin A dermatosis is a rare autoimmune bullous disease, but the most common autoimmune bullous dermatosis in children. We report a typical exuberant case of linear IgA dermatosis in a ten-month old child, who showed good response to treatment with corticosteroids and dapsone.

  14. Investigation of micropatterning and micromechanical forces towards engineering neural networks with defined connectivity

    Science.gov (United States)

    de Silva, Mauris Nishanga

    2005-07-01

    Previously, microfabrication technology has been used to control the growth of dissociated neurons in culture by surface micropatterning. However, such systems did not provide control over synaptic connectivity between neurons. In addition, mechanical tension exerted by the growth cone plays an important role during neurite outgrowth, and mechanical force can be used as a stimulus for eliciting a neurite from a neuron. Therefore, one could, in principle, pattern neurons on adhesive islands with non-permissive intervening regions that prevent spontaneous outgrowth and formation of synaptic connections, and then form connections on demand with the desired directionality and specificity by eliciting neurites using mechanical force. In order to investigate the possibility of creating such a neural network, a novel microsystem was developed having an array of glass microposts that can be used to micromechanically stimulate multiple neurons simultaneously in vitro. Traditional approaches to micropatterning of cells require photolithography, which typically requires functionalizing of surfaces with one molecule type that promotes cell adhesion and another molecule type that inhibits cell adhesion, and which is a complex, multi-step process that is time consuming and difficult to reproduce consistently. To simplify the micropatterning process, we developed a novel method of microcontact printing on polydimethylsiloxane (PDMS) substrates, a direct PDMS-PDMS stamping method that eliminated the need for adhesion-inhibiting molecules to achieve cellular patterns. However, direct PDMS-PDMS stamping is difficult to implement due to the complexity of the photolithography involved in stamp fabrication, and due to the inability to change patterns rapidly. Therefore, a novel precision spraying (PS) method was developed to micropattern cells in two steps, that is low cost, enables the facile changing of patterns for rapid prototyping, and has the ability to achieve patterns on non

  15. Visual working memory load-related changes in neural activity and functional connectivity.

    Directory of Open Access Journals (Sweden)

    Ling Li

    Full Text Available BACKGROUND: Visual working memory (VWM helps us store visual information to prepare for subsequent behavior. The neuronal mechanisms for sustaining coherent visual information and the mechanisms for limited VWM capacity have remained uncharacterized. Although numerous studies have utilized behavioral accuracy, neural activity, and connectivity to explore the mechanism of VWM retention, little is known about the load-related changes in functional connectivity for hemi-field VWM retention. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we recorded electroencephalography (EEG from 14 normal young adults while they performed a bilateral visual field memory task. Subjects had more rapid and accurate responses to the left visual field (LVF memory condition. The difference in mean amplitude between the ipsilateral and contralateral event-related potential (ERP at parietal-occipital electrodes in retention interval period was obtained with six different memory loads. Functional connectivity between 128 scalp regions was measured by EEG phase synchronization in the theta- (4-8 Hz, alpha- (8-12 Hz, beta- (12-32 Hz, and gamma- (32-40 Hz frequency bands. The resulting matrices were converted to graphs, and mean degree, clustering coefficient and shortest path length was computed as a function of memory load. The results showed that brain networks of theta-, alpha-, beta-, and gamma- frequency bands were load-dependent and visual-field dependent. The networks of theta- and alpha- bands phase synchrony were most predominant in retention period for right visual field (RVF WM than for LVF WM. Furthermore, only for RVF memory condition, brain network density of theta-band during the retention interval were linked to the delay of behavior reaction time, and the topological property of alpha-band network was negative correlation with behavior accuracy. CONCLUSIONS/SIGNIFICANCE: We suggest that the differences in theta- and alpha- bands between LVF and RVF

  16. Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks.

    Science.gov (United States)

    Lin, Lan; Jin, Cong; Fu, Zhenrong; Zhang, Baiwen; Bin, Guangyu; Wu, Shuicai

    2016-03-01

    Brain ageing is followed by changes of the connectivity of white matter (WM) and changes of the grey matter (GM) concentration. Neurodegenerative disease is more vulnerable to an accelerated brain ageing, which is associated with prospective cognitive decline and disease severity. Accurate detection of accelerated ageing based on brain network analysis has a great potential for early interventions designed to hinder atypical brain changes. To capture the brain ageing, we proposed a novel computational approach for modeling the 112 normal older subjects (aged 50-79 years) brain age by connectivity analyses of networks of the brain. Our proposed method applied principal component analysis (PCA) to reduce the redundancy in network topological parameters. Back propagation artificial neural network (BPANN) improved by hybrid genetic algorithm (GA) and Levenberg-Marquardt (LM) algorithm is established to model the relation among principal components (PCs) and brain age. The predicted brain age is strongly correlated with chronological age (r=0.8). The model has mean absolute error (MAE) of 4.29 years. Therefore, we believe the method can provide a possible way to quantitatively describe the typical and atypical network organization of human brain and serve as a biomarker for presymptomatic detection of neurodegenerative diseases in the future.

  17. Adults with high social anhedonia have altered neural connectivity with ventral lateral prefrontal cortex when processing positive social signals

    Directory of Open Access Journals (Sweden)

    Hong eYin

    2015-08-01

    Full Text Available Social anhedonia (SA is a debilitating characteristic of schizophrenia and a vulnerability for developing schizophrenia among people at risk. Prior work (Hooker et al, 2014 has revealed neural deficits in ventral lateral prefrontal cortex (VLPFC during processing of positive emotion in a community sample of people with high social anhedonia. Deficits in VLPFC neural activity are related to worse self-reported schizophrenia-spectrum symptoms and worse mood and behavior after social stress. In the current study, psychophysiological interaction (PPI analysis was applied to investigate the neural mechanisms mediated by VLPFC during emotion processing. PPI analysis revealed that, compared to low SA controls, participants with high SA displayed reduced VLPFC integration, specifically reduced connectivity between VLPFC and premotor cortex, inferior parietal and posterior temporal regions when viewing positive relative to neutral emotion. Across all participants, connectivity between VLPFC and inferior parietal region when viewing positive (versus neutral emotion was significantly correlated with measures of emotion management and attentional control. Additionally connectivity between VLPFC and superior temporal sulcus was related to reward and pleasure anticipation, and connectivity between VLPFC and inferior temporal sulcus correlated with attentional control measure. Our results suggest that impairments to VLPFC mediated neural circuitry underlie the cognitive and emotional deficits.

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

    Science.gov (United States)

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

    2013-01-01

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

  19. On the Nature of the Intrinsic Connectivity of the Cat Motor Cortex: Evidence for a Recurrent Neural Network Topology

    DEFF Research Database (Denmark)

    Capaday, Charles; Ethier, C; Brizzi, L

    2009-01-01

    Capaday C, Ethier C, Brizzi L, Sik A, van Vreeswijk C, Gingras D. On the nature of the intrinsic connectivity of the cat motor cortex: evidence for a recurrent neural network topology. J Neurophysiol 102: 2131-2141, 2009. First published July 22, 2009; doi: 10.1152/jn.91319.2008. The details...... and functional significance of the intrinsic horizontal connections between neurons in the motor cortex (MCx) remain to be clarified. To further elucidate the nature of this intracortical connectivity pattern, experiments were done on the MCx of three cats. The anterograde tracer biocytin was ejected...

  20. In search of neural mechanisms of mirror neuron dysfunction in schizophrenia: resting state functional connectivity approach.

    Science.gov (United States)

    Zaytseva, Yuliya; Bendova, Marie; Garakh, Zhanna; Tintera, Jaroslav; Rydlo, Jan; Spaniel, Filip; Horacek, Jiri

    2015-09-01

    It has been repeatedly shown that schizophrenia patients have immense alterations in goal-directed behaviour, social cognition, and social interactions, cognitive abilities that are presumably driven by the mirror neurons system (MNS). However, the neural bases of these deficits still remain unclear. Along with the task-related fMRI and EEG research tapping into the mirror neuron system, the characteristics of the resting state activity in the particular areas that encompass mirror neurons might be of interest as they obviously determine the baseline of the neuronal activity. Using resting state fMRI, we investigated resting state functional connectivity (FC) in four predefined brain structures, ROIs (inferior frontal gyrus, superior parietal lobule, premotor cortex and superior temporal gyrus), known for their mirror neurons activity, in 12 patients with first psychotic episode and 12 matched healthy individuals. As a specific hypothesis, based on the knowledge of the anatomical inputs of thalamus to all preselected ROIs, we have investigated the FC between thalamus and the ROIs. Of all ROIs included, seed-to-voxel connectivity analysis revealed significantly decreased FC only in left posterior superior temporal gyrus (STG) and the areas in visual cortex and cerebellum in patients as compared to controls. Using ROI-to-ROI analysis (thalamus and selected ROIs), we have found an increased FC of STG and bilateral thalamus whereas the FC of these areas was decreased in controls. Our results suggest that: (1) schizophrenia patients exhibit FC of STG which corresponds to the previously reported changes of superior temporal gyrus in schizophrenia and might contribute to the disturbances of specific functions, such as emotional processing or spatial awareness; (2) as the thalamus plays a pivotal role in the sensory gating, providing the filtering of the redundant stimulation, the observed hyperconnectivity between the thalami and the STGs in patients with schizophrenia

  1. Dishevelled is essential for neural connectivity and planar cell polarity in planarians.

    Science.gov (United States)

    Almuedo-Castillo, Maria; Saló, Emili; Adell, Teresa

    2011-02-15

    The Wingless/Integrated (Wnt) signaling pathway controls multiple events during development and homeostasis. It comprises multiple branches, mainly classified according to their dependence on β-catenin activation. The Wnt/β-catenin branch is essential for the establishment of the embryonic anteroposterior (AP) body axis throughout the phylogenetic tree. It is also required for AP axis establishment during planarian regeneration. Wnt/β-catenin-independent signaling encompasses several different pathways, of which the most extensively studied is the planar cell polarity (PCP) pathway, which is responsible for planar polarization of cell structures within an epithelial sheet. Dishevelled (Dvl) is the hub of Wnt signaling because it regulates and channels the Wnt signal into every branch. Here, we analyze the role of Schmidtea mediterranea Dvl homologs (Smed-dvl-1 and Smed-dvl-2) using gene silencing. We demonstrate that in addition to a role in AP axis specification, planarian Dvls are involved in at least two different β-catenin-independent processes. First, they are essential for neural connectivity through Smed-wnt5 signaling. Second, Smed-dvl-2, together with the S. mediterranea homologs of Van-Gogh (Vang) and Diversin (Div), is required for apical positioning of the basal bodies of epithelial cells. These data represent evidence not only of the function of the PCP network in lophotrocozoans but of the involvement of the PCP core elements Vang and Div in apical positioning of the cilia.

  2. Llgl1 Connects Cell Polarity with Cell-Cell Adhesion in Embryonic Neural Stem Cells.

    Science.gov (United States)

    Jossin, Yves; Lee, Minhui; Klezovitch, Olga; Kon, Elif; Cossard, Alexia; Lien, Wen-Hui; Fernandez, Tania E; Cooper, Jonathan A; Vasioukhin, Valera

    2017-06-05

    Malformations of the cerebral cortex (MCCs) are devastating developmental disorders. We report here that mice with embryonic neural stem-cell-specific deletion of Llgl1 (Nestin-Cre/Llgl1(fl/fl)), a mammalian ortholog of the Drosophila cell polarity gene lgl, exhibit MCCs resembling severe periventricular heterotopia (PH). Immunohistochemical analyses and live cortical imaging of PH formation revealed that disruption of apical junctional complexes (AJCs) was responsible for PH in Nestin-Cre/Llgl1(fl/fl) brains. While it is well known that cell polarity proteins govern the formation of AJCs, the exact mechanisms remain unclear. We show that LLGL1 directly binds to and promotes internalization of N-cadherin, and N-cadherin/LLGL1 interaction is inhibited by atypical protein kinase C-mediated phosphorylation of LLGL1, restricting the accumulation of AJCs to the basolateral-apical boundary. Disruption of the N-cadherin-LLGL1 interaction during cortical development in vivo is sufficient for PH. These findings reveal a mechanism responsible for the physical and functional connection between cell polarity and cell-cell adhesion machineries in mammalian cells. Copyright © 2017 Elsevier Inc. All rights reserved.

  3. The effect of connectivity on EEG rhythms, power spectral density and coherence among coupled neural populations: analysis with a neural mass model.

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    Zavaglia, Melissa; Astolfi, Laura; Babiloni, Fabio; Ursino, Mauro

    2008-01-01

    In the present work, a neural mass model consisting of four interconnected neural groups (pyramidal neurons, excitatory interneurons, inhibitory interneurons with slow synaptic kinetics, and inhibitory interneurons with fast synaptic kinetics) is used to investigate the mechanisms which cause the appearance of multiple rhythms in EEG spectra, and to assess how these rhythms can be affected by connectivity among different populations. In particular, we analyze a circuit, composed of three interconnected populations, each with a different synaptic kinetics (hence, with a different intrinsic rhythm). Results demonstrate that a single population can exhibit many different simultaneous rhythms, provided that some of these come from external sources (for instance, from remote regions). Analysis of coherence, and of the position of peaks in power spectral density, reveals important information on the possible connections among populations, especially useful to follow temporal changes in connectivity. Subsequently, the model is validated by comparing the power spectral density simulated in one population with that computed in the controlateral cingulated cortex (a region involved in motion preparation) during a right foot movement task in four normal subjects. The model is able to simulate real spectra quite well with only moderate parameter changes within the subject. In perspective, the results may be of value for a deeper comprehension of mechanism causing EEGs rhythms, for the study of brain connectivity and for the test of neurophysiological hypotheses.

  4. Connecting teratogen-induced congenital heart defects to neural crest cells and their effect on cardiac function.

    Science.gov (United States)

    Karunamuni, Ganga H; Ma, Pei; Gu, Shi; Rollins, Andrew M; Jenkins, Michael W; Watanabe, Michiko

    2014-09-01

    Neural crest cells play many key roles in embryonic development, as demonstrated by the abnormalities that result from their specific absence or dysfunction. Unfortunately, these key cells are particularly sensitive to abnormalities in various intrinsic and extrinsic factors, such as genetic deletions or ethanol-exposure that lead to morbidity and mortality for organisms. This review discusses the role identified for a segment of neural crest in regulating the morphogenesis of the heart and associated great vessels. The paradox is that their derivatives constitute a small proportion of cells to the cardiovascular system. Findings supporting that these cells impact early cardiac function raises the interesting possibility that they indirectly control cardiovascular development at least partially through regulating function. Making connections between insults to the neural crest, cardiac function, and morphogenesis is more approachable with technological advances. Expanding our understanding of early functional consequences could be useful in improving diagnosis and testing therapies.

  5. A Novel Neural Network Vector Control for Single-Phase Grid-Connected Converters with L, LC and LCL Filters

    Directory of Open Access Journals (Sweden)

    Xingang Fu

    2016-04-01

    Full Text Available This paper investigates a novel recurrent neural network (NN-based vector control approach for single-phase grid-connected converters (GCCs with L (inductor, LC (inductor-capacitor and LCL (inductor-capacitor-inductor filters and provides their comparison study with the conventional standard vector control method. A single neural network controller replaces two current-loop PI controllers, and the NN training approximates the optimal control for the single-phase GCC system. The Levenberg–Marquardt (LM algorithm was used to train the NN controller based on the complete system equations without any decoupling policies. The proposed NN approach can solve the decoupling problem associated with the conventional vector control methods for L, LC and LCL-filter-based single-phase GCCs. Both simulation study and hardware experiments demonstrate that the neural network vector controller shows much more improved performance than that of conventional vector controllers, including faster response speed and lower overshoot. Especially, NN vector control could achieve very good performance using low switch frequency. More importantly, the neural network vector controller is a damping free controller, which is generally required by a conventional vector controller for an LCL-filter-based single-phase grid-connected converter and, therefore, can overcome the inefficiency problem caused by damping policies.

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

    Science.gov (United States)

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

    2016-06-15

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

  7. A two-level on-line learning algorithm of Artificial Neural Network with forward connections

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    Stanislaw Placzek

    2014-12-01

    Full Text Available An Artificial Neural Network with cross-connection is one of the most popular network structures. The structure contains: an input layer, at least one hidden layer and an output layer. Analysing and describing an ANN structure, one usually finds that the first parameter is the number of ANN’s layers. A hierarchical structure is a default and accepted way of describing the network. Using this assumption, the network structure can be described from a different point of view. A set of concepts and models can be used to describe the complexity of ANN’s structure in addition to using a two-level learning algorithm. Implementing the hierarchical structure to the learning algorithm, an ANN structure is divided into sub-networks. Every sub-network is responsible for finding the optimal value of its weight coefficients using a local target function to minimise the learning error. The second coordination level of the learning algorithm is responsible for coordinating the local solutions and finding the minimum of the global target function. In the article a special emphasis is placed on the coordinator’s role in the learning algorithm and its target function. In each iteration the coordinator has to send coordination parameters into the first level of subnetworks. Using the input X and the teaching Z vectors, the local procedures are working and finding their weight coefficients. At the same step the feedback information is calculated and sent to the coordinator. The process is being repeated until the minimum of local target functions is achieved. As an example, a two-level learning algorithm is used to implement an ANN in the underwriting process for classifying the category of health in a life insurance company.

  8. Connectivity in the yeast cell cycle transcription network: inferences from neural networks.

    Directory of Open Access Journals (Sweden)

    Christopher E Hart

    2006-12-01

    Full Text Available A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN models can effectively discover gene network structure by integrating global in vivo protein:DNA interaction data (ChIP/Array with genome-wide microarray RNA data. We test this on the yeast cell cycle transcription network, which is composed of several hundred genes with phase-specific RNA outputs. These ANNs were robust to noise in data and to a variety of perturbations. They reliably identified and ranked 10 of 12 known major cell cycle factors at the top of a set of 204, based on a sum-of-squared weights metric. Comparative analysis of motif occurrences among multiple yeast species independently confirmed relationships inferred from ANN weights analysis. ANN models can capitalize on properties of biological gene networks that other kinds of models do not. ANNs naturally take advantage of patterns of absence, as well as presence, of factor binding associated with specific expression output; they are easily subjected to in silico "mutation" to uncover biological redundancies; and they can use the full range of factor binding values. A prominent feature of cell cycle ANNs suggested an analogous property might exist in the biological network. This postulated that "network-local discrimination" occurs when regulatory connections (here between MBF and target genes are explicitly disfavored in one network module (G2, relative to others and to the class of genes outside the mitotic network. If correct, this predicts that MBF motifs will be significantly depleted from the discriminated class and that the discrimination will persist through evolution. Analysis of distantly related Schizosaccharomyces pombe confirmed this, suggesting that network-local discrimination is real and complements well-known enrichment of

  9. Hypermineralization and High Osteocyte Lacunar Density in Osteogenesis Imperfecta Type V Bone Indicate Exuberant Primary Bone Formation.

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    Blouin, Stéphane; Fratzl-Zelman, Nadja; Glorieux, Francis H; Roschger, Paul; Klaushofer, Klaus; Marini, Joan C; Rauch, Frank

    2017-09-01

    In contrast to "classical" forms of osteogenesis imperfecta (OI) types I to IV, caused by a mutation in COL1A1/A2, OI type V is due to a gain-of-function mutation in the IFITM5 gene, encoding the interferon-induced transmembrane protein 5, or bone-restricted interferon-inducible transmembrane (IFITM)-like protein (BRIL). Its phenotype distinctly differs from OI types I to IV by absence of blue sclerae and dentinogenesis imperfecta, by the occurrence of ossification disorders such as hyperplastic callus and forearm interosseous membrane ossification. Little is known about the impact of the mutation on bone tissue/material level in untreated and bisphosphonate-treated patients. Therefore, investigations of transiliac bone biopsy samples from a cohort of OI type V children (n = 15, 8.7 ± 4 years old) untreated at baseline and a subset (n = 8) after pamidronate treatment (2.6 years in average) were performed. Quantitative backscattered electron imaging (qBEI) was used to determine bone mineralization density distribution (BMDD) as well as osteocyte lacunar density. The BMDD of type V OI bone was distinctly shifted toward a higher degree of mineralization. The most frequently occurring calcium concentration (CaPeak) in cortical (Ct) and cancellous (Cn) bone was markedly increased (+11.5%, +10.4%, respectively, p V Ct and Cn bone (+171%, p V patients is hypermineralized, similar to other forms of OI. The elevated osteocyte lacunar density in connection with lack of regular bone lamellation points to an exuberant primary bone formation and an alteration of the bone remodeling process in OI type V. © 2017 American Society for Bone and Mineral Research. © 2017 American Society for Bone and Mineral Research.

  10. Functional connectivity among spike trains in neural assemblies during rat working memory task.

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    Xie, Jiacun; Bai, Wenwen; Liu, Tiaotiao; Tian, Xin

    2014-11-01

    Working memory refers to a brain system that provides temporary storage to manipulate information for complex cognitive tasks. As the brain is a more complex, dynamic and interwoven network of connections and interactions, the questions raised here: how to investigate the mechanism of working memory from the view of functional connectivity in brain network? How to present most characteristic features of functional connectivity in a low-dimensional network? To address these questions, we recorded the spike trains in prefrontal cortex with multi-electrodes when rats performed a working memory task in Y-maze. The functional connectivity matrix among spike trains was calculated via maximum likelihood estimation (MLE). The average connectivity value Cc, mean of the matrix, was calculated and used to describe connectivity strength quantitatively. The spike network was constructed by the functional connectivity matrix. The information transfer efficiency Eglob was calculated and used to present the features of the network. In order to establish a low-dimensional spike network, the active neurons with higher firing rates than average rate were selected based on sparse coding. The results show that the connectivity Cc and the network transfer efficiency Eglob vaired with time during the task. The maximum values of Cc and Eglob were prior to the working memory behavior reference point. Comparing with the results in the original network, the feature network could present more characteristic features of functional connectivity.

  11. A realistic neural mass model of the cortex with laminar-specific connections and synaptic plasticity - evaluation with auditory habituation.

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    Peng Wang

    Full Text Available In this work we propose a biologically realistic local cortical circuit model (LCCM, based on neural masses, that incorporates important aspects of the functional organization of the brain that have not been covered by previous models: (1 activity dependent plasticity of excitatory synaptic couplings via depleting and recycling of neurotransmitters and (2 realistic inter-laminar dynamics via laminar-specific distribution of and connections between neural populations. The potential of the LCCM was demonstrated by accounting for the process of auditory habituation. The model parameters were specified using Bayesian inference. It was found that: (1 besides the major serial excitatory information pathway (layer 4 to layer 2/3 to layer 5/6, there exists a parallel "short-cut" pathway (layer 4 to layer 5/6, (2 the excitatory signal flow from the pyramidal cells to the inhibitory interneurons seems to be more intra-laminar while, in contrast, the inhibitory signal flow from inhibitory interneurons to the pyramidal cells seems to be both intra- and inter-laminar, and (3 the habituation rates of the connections are unsymmetrical: forward connections (from layer 4 to layer 2/3 are more strongly habituated than backward connections (from Layer 5/6 to layer 4. Our evaluation demonstrates that the novel features of the LCCM are of crucial importance for mechanistic explanations of brain function. The incorporation of these features into a mass model makes them applicable to modeling based on macroscopic data (like EEG or MEG, which are usually available in human experiments. Our LCCM is therefore a valuable building block for future realistic models of human cognitive function.

  12. Large-scale neural model validation of partial correlation analysis for effective connectivity investigation in functional MRI.

    Science.gov (United States)

    Marrelec, G; Kim, J; Doyon, J; Horwitz, B

    2009-03-01

    Recent studies of functional connectivity based upon blood oxygen level dependent functional magnetic resonance imaging have shown that this technique allows one to investigate large-scale functional brain networks. In a previous study, we advocated that data-driven measures of effective connectivity should be developed to bridge the gap between functional and effective connectivity. To attain this goal, we proposed a novel approach based on the partial correlation matrix. In this study, we further validate the use of partial correlation analysis by employing a large-scale, neurobiologically realistic neural network model to generate simulated data that we analyze with both structural equation modeling (SEM) and the partial correlation approach. Unlike real experimental data, where the interregional anatomical links are not necessarily known, the links between the nodes of the network model are fully specified, and thus provide a standard against which to judge the results of SEM and partial correlation analyses. Our results show that partial correlation analysis from the data alone exhibits patterns of effective connectivity that are similar to those found using SEM, and both are in agreement with respect to the underlying neuroarchitecture. Our findings thus provide a strong validation for the partial correlation method.

  13. On the connection between level of education and the neural circuitry of emotion perception

    NARCIS (Netherlands)

    Demenescu, Liliana R.; Stan, Adrian; Kortekaas, Rudie; van der Wee, Nic J. A.; Veltman, Dick J.; Aleman, Andre

    2014-01-01

    Through education, a social group transmits accumulated knowledge, skills, customs, and values to its members. So far, to the best of our knowledge, the association between educational attainment and neural correlates of emotion processing has been left unexplored. In a retrospective analysis of The

  14. On the connection between level of education and the neural circuitry of emotion perception

    NARCIS (Netherlands)

    Demenescu, Liliana R.; Stan, Adrian; Kortekaas, Rudie; van der Wee, Nic J. A.; Veltman, Dick J.; Aleman, Andre

    2014-01-01

    Through education, a social group transmits accumulated knowledge, skills, customs, and values to its members. So far, to the best of our knowledge, the association between educational attainment and neural correlates of emotion processing has been left unexplored. In a retrospective analysis of The

  15. Speed hysteresis and noise shaping of traveling fronts in neural fields: role of local circuitry and nonlocal connectivity

    Science.gov (United States)

    Capone, Cristiano; Mattia, Maurizio

    2017-01-01

    Neural field models are powerful tools to investigate the richness of spatiotemporal activity patterns like waves and bumps, emerging from the cerebral cortex. Understanding how spontaneous and evoked activity is related to the structure of underlying networks is of central interest to unfold how information is processed by these systems. Here we focus on the interplay between local properties like input-output gain function and recurrent synaptic self-excitation of cortical modules, and nonlocal intermodular synaptic couplings yielding to define a multiscale neural field. In this framework, we work out analytic expressions for the wave speed and the stochastic diffusion of propagating fronts uncovering the existence of an optimal balance between local and nonlocal connectivity which minimizes the fluctuations of the activation front propagation. Incorporating an activity-dependent adaptation of local excitability further highlights the independent role that local and nonlocal connectivity play in modulating the speed of propagation of the activation and silencing wavefronts, respectively. Inhomogeneities in space of local excitability give raise to a novel hysteresis phenomenon such that the speed of waves traveling in opposite directions display different velocities in the same location. Taken together these results provide insights on the multiscale organization of brain slow-waves measured during deep sleep and anesthesia.

  16. Mode of Effective Connectivity within a Putative Neural Network Differentiates Moral Cognitions Related to Care and Justice Ethics

    Science.gov (United States)

    Cáceda, Ricardo; James, G. Andrew; Ely, Timothy D.; Snarey, John; Kilts, Clinton D.

    2011-01-01

    Background Moral sensitivity refers to the interpretive awareness of moral conflict and can be justice or care oriented. Justice ethics is associated primarily with human rights and the application of moral rules, whereas care ethics is related to human needs and a situational approach involving social emotions. Among the core brain regions involved in moral issue processing are: medial prefrontal cortex, anterior (ACC) and posterior (PCC) cingulate cortex, posterior superior temporal sulcus (pSTS), insula and amygdala. This study sought to inform the long standing debate of whether care and justice moral ethics represent one or two different forms of cognition. Methodology/Principal Findings Model-free and model-based connectivity analysis were used to identify functional neural networks underlying care and justice ethics for a moral sensitivity task. In addition to modest differences in patterns of associated neural activity, distinct modes of functional and effective connectivity were observed for moral sensitivity for care and justice issues that were modulated by individual variation in moral ability. Conclusions/Significance These results support a neurobiological differentiation between care and justice ethics and suggest that human moral behavior reflects the outcome of integrating opposing rule-based, self-other perspectives, and emotional responses. PMID:21364916

  17. Modeling, control, and simulation of grid connected intelligent hybrid battery/photovoltaic system using new hybrid fuzzy-neural method.

    Science.gov (United States)

    Rezvani, Alireza; Khalili, Abbas; Mazareie, Alireza; Gandomkar, Majid

    2016-07-01

    Nowadays, photovoltaic (PV) generation is growing increasingly fast as a renewable energy source. Nevertheless, the drawback of the PV system is its dependence on weather conditions. Therefore, battery energy storage (BES) can be considered to assist for a stable and reliable output from PV generation system for loads and improve the dynamic performance of the whole generation system in grid connected mode. In this paper, a novel topology of intelligent hybrid generation systems with PV and BES in a DC-coupled structure is presented. Each photovoltaic cell has a specific point named maximum power point on its operational curve (i.e. current-voltage or power-voltage curve) in which it can generate maximum power. Irradiance and temperature changes affect these operational curves. Therefore, the nonlinear characteristic of maximum power point to environment has caused to development of different maximum power point tracking techniques. In order to capture the maximum power point (MPP), a hybrid fuzzy-neural maximum power point tracking (MPPT) method is applied in the PV system. Obtained results represent the effectiveness and superiority of the proposed method, and the average tracking efficiency of the hybrid fuzzy-neural is incremented by approximately two percentage points in comparison to the conventional methods. It has the advantages of robustness, fast response and good performance. A detailed mathematical model and a control approach of a three-phase grid-connected intelligent hybrid system have been proposed using Matlab/Simulink.

  18. Speed hysteresis and noise shaping of traveling fronts in neural fields: role of local circuitry and nonlocal connectivity

    Science.gov (United States)

    Capone, Cristiano; Mattia, Maurizio

    2017-01-01

    Neural field models are powerful tools to investigate the richness of spatiotemporal activity patterns like waves and bumps, emerging from the cerebral cortex. Understanding how spontaneous and evoked activity is related to the structure of underlying networks is of central interest to unfold how information is processed by these systems. Here we focus on the interplay between local properties like input-output gain function and recurrent synaptic self-excitation of cortical modules, and nonlocal intermodular synaptic couplings yielding to define a multiscale neural field. In this framework, we work out analytic expressions for the wave speed and the stochastic diffusion of propagating fronts uncovering the existence of an optimal balance between local and nonlocal connectivity which minimizes the fluctuations of the activation front propagation. Incorporating an activity-dependent adaptation of local excitability further highlights the independent role that local and nonlocal connectivity play in modulating the speed of propagation of the activation and silencing wavefronts, respectively. Inhomogeneities in space of local excitability give raise to a novel hysteresis phenomenon such that the speed of waves traveling in opposite directions display different velocities in the same location. Taken together these results provide insights on the multiscale organization of brain slow-waves measured during deep sleep and anesthesia. PMID:28045036

  19. Mode of effective connectivity within a putative neural network differentiates moral cognitions related to care and justice ethics.

    Directory of Open Access Journals (Sweden)

    Ricardo Cáceda

    Full Text Available BACKGROUND: Moral sensitivity refers to the interpretive awareness of moral conflict and can be justice or care oriented. Justice ethics is associated primarily with human rights and the application of moral rules, whereas care ethics is related to human needs and a situational approach involving social emotions. Among the core brain regions involved in moral issue processing are: medial prefrontal cortex, anterior (ACC and posterior (PCC cingulate cortex, posterior superior temporal sulcus (pSTS, insula and amygdala. This study sought to inform the long standing debate of whether care and justice moral ethics represent one or two different forms of cognition. METHODOLOGY/PRINCIPAL FINDINGS: Model-free and model-based connectivity analysis were used to identify functional neural networks underlying care and justice ethics for a moral sensitivity task. In addition to modest differences in patterns of associated neural activity, distinct modes of functional and effective connectivity were observed for moral sensitivity for care and justice issues that were modulated by individual variation in moral ability. CONCLUSIONS/SIGNIFICANCE: These results support a neurobiological differentiation between care and justice ethics and suggest that human moral behavior reflects the outcome of integrating opposing rule-based, self-other perspectives, and emotional responses.

  20. Elimination of spiral waves in a locally connected chaotic neural network by a dynamic phase space constraint.

    Science.gov (United States)

    Li, Yang; Oku, Makito; He, Guoguang; Aihara, Kazuyuki

    2017-01-16

    In this study, a method is proposed that eliminates spiral waves in a locally connected chaotic neural network (CNN) under some simplified conditions, using a dynamic phase space constraint (DPSC) as a control method. In this method, a control signal is constructed from the feedback internal states of the neurons to detect phase singularities based on their amplitude reduction, before modulating a threshold value to truncate the refractory internal states of the neurons and terminate the spirals. Simulations showed that with appropriate parameter settings, the network was directed from a spiral wave state into either a plane wave (PW) state or a synchronized oscillation (SO) state, where the control vanished automatically and left the original CNN model unaltered. Each type of state had a characteristic oscillation frequency, where spiral wave states had the highest, and the intra-control dynamics was dominated by low-frequency components, thereby indicating slow adjustments to the state variables. In addition, the PW-inducing and SO-inducing control processes were distinct, where the former generally had longer durations but smaller average proportions of affected neurons in the network. Furthermore, variations in the control parameter allowed partial selectivity of the control results, which were accompanied by modulation of the control processes. The results of this study broaden the applicability of DPSC to chaos control and they may also facilitate the utilization of locally connected CNNs in memory retrieval and the exploration of traveling wave dynamics in biological neural networks.

  1. Global and regional functional connectivity maps of neural oscillations in focal epilepsy.

    Science.gov (United States)

    Englot, Dario J; Hinkley, Leighton B; Kort, Naomi S; Imber, Brandon S; Mizuiri, Danielle; Honma, Susanne M; Findlay, Anne M; Garrett, Coleman; Cheung, Paige L; Mantle, Mary; Tarapore, Phiroz E; Knowlton, Robert C; Chang, Edward F; Kirsch, Heidi E; Nagarajan, Srikantan S

    2015-08-01

    Intractable focal epilepsy is a devastating disorder with profound effects on cognition and quality of life. Epilepsy surgery can lead to seizure freedom in patients with focal epilepsy; however, sometimes it fails due to an incomplete delineation of the epileptogenic zone. Brain networks in epilepsy can be studied with resting-state functional connectivity analysis, yet previous investigations using functional magnetic resonance imaging or electrocorticography have produced inconsistent results. Magnetoencephalography allows non-invasive whole-brain recordings, and can be used to study both long-range network disturbances in focal epilepsy and regional connectivity at the epileptogenic zone. In magnetoencephalography recordings from presurgical epilepsy patients, we examined: (i) global functional connectivity maps in patients versus controls; and (ii) regional functional connectivity maps at the region of resection, compared to the homotopic non-epileptogenic region in the contralateral hemisphere. Sixty-one patients were studied, including 30 with mesial temporal lobe epilepsy and 31 with focal neocortical epilepsy. Compared with a group of 31 controls, patients with epilepsy had decreased resting-state functional connectivity in widespread regions, including perisylvian, posterior temporo-parietal, and orbitofrontal cortices (P regional connectivity within the resection site (n = 24) were more likely to achieve seizure postoperative seizure freedom (87.5% with Engel I outcome) than those with neutral (n = 15, 64.3% seizure free) or decreased (n = 23, 47.8% seizure free) regional connectivity (P < 0.02, chi-square). Widespread global decreases in functional connectivity are observed in patients with focal epilepsy, and may reflect deleterious long-term effects of recurrent seizures. Furthermore, enhanced regional functional connectivity at the area of resection may help predict seizure outcome and aid surgical planning.

  2. Navigating toward a novel environment from a route or survey perspective: neural correlates and context-dependent connectivity.

    Science.gov (United States)

    Boccia, Maddalena; Guariglia, C; Sabatini, U; Nemmi, F

    2016-05-01

    When we move toward a novel environment we may learn it in different ways, i.e., by walking around or studying a map. Both types of learning seem to be very effective in daily life navigation and correspond to two different types of mental representation of space: route and survey representation. In the present study, we investigated the neural basis of route and survey perspectives during learning and retrieval of novel environments. The study was carried out over 5 days, during which participants learned two paths from a different perspective (i.e., route learning and survey learning). Then participants had to retrieve these paths using a survey or route perspective during fMRI scans, on the first and fifth day. We found that the left inferior temporal lobe and right angular gyrus (AG) were activated more during recall of paths learned in a survey perspective than in a route perspective. We also found a session by perspective interaction effect on neural activity in brain areas classically involved in navigation such as the parahippocampal place area (PPA) and the retrosplenial cortex (RSC). A set of frontal, parietal and temporal areas showed different patterns of activity according to the type of retrieval perspective. We tested the context-dependent connectivity of right PPA, RSC and AG, finding that these areas showed different patterns of connectivity in relation to the learning and recalling perspective. Our results shed more light on the segregation of neural circuits involved in the acquisition of a novel environment and navigational strategies.

  3. Is Silence Golden? Elementary School Teachers' Strategies and Beliefs regarding Hypothetical Shy/Quiet and Exuberant/Talkative Children

    Science.gov (United States)

    Coplan, Robert J.; Hughes, Kathleen; Bosacki, Sandra; Rose-Krasnor, Linda

    2011-01-01

    The primary goal of the present study was to examine elementary teachers' strategies, attitudes, and beliefs regarding hypothetical shy (i.e., quiet), exuberant (i.e., overly talkative), and average (i.e., typical) children. We explored whether these strategies and beliefs varied as a function of the gender of the hypothetical child as well as…

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

    Directory of Open Access Journals (Sweden)

    Amira F.A. Ibrahim

    2017-01-01

    Conclusions: Dysfunctional engagement of WM circuitry, and aberrant functional connectivity of ‘task-negative’ regions in NF1 patients may underlie spatial WM difficulties characteristic of the disorder.

  5. Distinct Neural Signatures Detected for ADHD Subtypes After Controlling for Micro-Movements in Resting State Functional Connectivity MRI Data

    Directory of Open Access Journals (Sweden)

    Damien eFair

    2013-02-01

    Full Text Available In recent years, there has been growing enthusiasm that functional MRI could achieve clinical utility for a broad range of neuropsychiatric disorders. However, several barriers remain. For example, the acquisition of large-scale datasets capable of clarifying the marked heterogeneity that exists in psychiatric illnesses will need to be realized. In addition, there continues to be a need for the development of image processing and analysis methods capable of separating signal from artifact. As a prototypical hyperkinetic disorder, and movement related artifact being a significant confound in functional imaging studies, ADHD offers a unique challenge. As part of the ADHD-200 Global Competition and this special edition of Frontiers, the ADHD-200 Consortium demonstrates the utility of an aggregate dataset pooled across five institutions in addressing these challenges. The work aimed to A examine the impact of emerging techniques for controlling for micro-movements, and B provide novel insights into the neural correlates of ADHD subtypes. Using SVM based MVPA we show that functional connectivity patterns in individuals are capable of differentiating the two most prominent ADHD subtypes. The application of graph-theory revealed that the Combined (ADHD-C and Inattentive (ADHD-I subtypes demonstrated some overlapping (particularly sensorimotor systems, but unique patterns of atypical connectivity. For ADHD-C, atypical connectivity was prominent in midline default network components, as well as insular cortex; in contrast, the ADHD-I group exhibited atypical patterns within the dlPFC regions and cerebellum. Systematic motion-related artifact was noted, and highlighted the need for stringent motion correction. Findings reported were robust to the specific motion correction strategy employed. These data suggest that rs-fcMRI data can be used to characterize individual patients with ADHD and to identify neural distinctions underlying the clinical

  6. Design of a Neural Controller for Single Phase Inverter in Grid Connected Photovoltaic System

    Directory of Open Access Journals (Sweden)

    A. Ndiaye

    2014-02-01

    Full Text Available This study shows a neural network based control strategy of the current injected into a single-phase grid via an inverter. The inverter is supplied by a Photovoltaic Generator (PVG. The optimal control of PVG is ensured by an MPPT algorithm of type P and O (Perturbation-Observation. The synchronization of the inverter with the electrical grid is ensured by a Phase-Locked Loop (PLL device. The sizing and the modeling of the system components have been presented. A Neural Network Controller (NNC and a Proportional Integral (PI controller have been implemented and compared. Obtained results show that the NNC have faster response and lower THD without overshoots.

  7. Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks

    OpenAIRE

    2015-01-01

    The paper introduces a method which improves localization accuracy of the signal strength fingerprinting approach. According to the proposed method, entire localization area is divided into regions by clustering the fingerprint database. For each region a prototype of the received signal strength is determined and a dedicated artificial neural network (ANN) is trained by using only those fingerprints that belong to this region (cluster). Final estimation of the location is obtained by fusion ...

  8. Global and regional functional connectivity maps of neural oscillations in focal epilepsy

    Science.gov (United States)

    Englot, Dario J.; Hinkley, Leighton B.; Kort, Naomi S.; Imber, Brandon S.; Mizuiri, Danielle; Honma, Susanne M.; Findlay, Anne M.; Garrett, Coleman; Cheung, Paige L.; Mantle, Mary; Tarapore, Phiroz E.; Knowlton, Robert C.; Chang, Edward F.; Nagarajan, Srikantan S.

    2015-01-01

    Intractable focal epilepsy is a devastating disorder with profound effects on cognition and quality of life. Epilepsy surgery can lead to seizure freedom in patients with focal epilepsy; however, sometimes it fails due to an incomplete delineation of the epileptogenic zone. Brain networks in epilepsy can be studied with resting-state functional connectivity analysis, yet previous investigations using functional magnetic resonance imaging or electrocorticography have produced inconsistent results. Magnetoencephalography allows non-invasive whole-brain recordings, and can be used to study both long-range network disturbances in focal epilepsy and regional connectivity at the epileptogenic zone. In magnetoencephalography recordings from presurgical epilepsy patients, we examined: (i) global functional connectivity maps in patients versus controls; and (ii) regional functional connectivity maps at the region of resection, compared to the homotopic non-epileptogenic region in the contralateral hemisphere. Sixty-one patients were studied, including 30 with mesial temporal lobe epilepsy and 31 with focal neocortical epilepsy. Compared with a group of 31 controls, patients with epilepsy had decreased resting-state functional connectivity in widespread regions, including perisylvian, posterior temporo-parietal, and orbitofrontal cortices (P epilepsy and higher frequency of consciousness-impairing seizures (P seizure postoperative seizure freedom (87.5% with Engel I outcome) than those with neutral (n = 15, 64.3% seizure free) or decreased (n = 23, 47.8% seizure free) regional connectivity (P epilepsy, and may reflect deleterious long-term effects of recurrent seizures. Furthermore, enhanced regional functional connectivity at the area of resection may help predict seizure outcome and aid surgical planning. PMID:25981965

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

    Science.gov (United States)

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

    2014-01-01

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

  10. Non-homogenous neural networks with chaotic recursive nodes: connectivity and multi-assemblies structures in recursive processing elements architectures.

    Science.gov (United States)

    Del Moral Hernandez, Emilio

    2005-01-01

    This paper addresses recurrent neural architectures based on bifurcating nodes that exhibit chaotic dynamics, with local dynamics defined by first order parametric recursions. In the studied architectures, logistic recursive nodes interact through parametric coupling, they self organize, and the network evolves to global spatio-temporal period-2 attractors that encode stored patterns. The performance of associative memories arrangements is measured through the average error in pattern recovery, under several levels of prompting noise. The impact of the synaptic connections magnitude on architecture performance is analyzed in detail, through pattern recovery performance measures and basin of attraction characterization. The importance of a planned choice of the synaptic connections scale in RPEs architectures is shown. A strategy for minimizing pattern recovery degradation when the number of stored patterns increases is developed. Experimental results show the success of such strategy. Mechanisms for allowing the studied associative networks to deal with asynchronous changes in input patterns, and tools for the interconnection between different associative assemblies are developed. Finally, coupling in heterogeneous assemblies with diverse recursive maps is analyzed, and the associated synaptic connections are equated.

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

    Science.gov (United States)

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

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Martin eLotze

    2014-07-01

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

  13. Neural traces of stress: cortisol related sustained enhancement of amygdala-hippocampal functional connectivity

    Directory of Open Access Journals (Sweden)

    Sharon eVaisvaser

    2013-07-01

    Full Text Available Stressful experiences modulate neuro-circuitry function, and the temporal trajectory of these alterations, elapsing from early disturbances to late recovery, heavily influences resilience and vulnerability to stress. Such effects of stress may depend on processes that are engaged during resting-state, through active recollection of past experiences and anticipation of future events, all known to involve the default mode network (DMN. By inducing social stress and acquiring resting-state fMRI before stress, immediately following it, and two hours later, we expanded the time-window for examining the trajectory of the stress response. Throughout the study repeated cortisol samplings and self-reports of stress levels were obtained from 51 healthy young males. Post-stress alterations were investigated by whole brain resting-state functional connectivity of two central hubs of the DMN: the posterior cingulate cortex and hippocampus. Results indicate a 'recovery' pattern of DMN connectivity, in which all alterations, ascribed to the intervening stress, returned to pre-stress levels. The only exception to this pattern was a stress-induced rise in amygdala-hippocampal connectivity, which was sustained for as long as two hours following stress induction. Furthermore, this sustained enhancement of limbic connectivity was inversely correlated to individual stress-induced cortisol responsiveness (AUCi and characterized only the group lacking such increased cortisol (i.e., non-responders. Our observations provide evidence of a prolonged post-stress response profile, characterized by both the comprehensive balance of most DMN functional connections and the distinct time and cortisol dependent ascent of intra-limbic connectivity. These novel insights into neuro-endocrine relations are another milestone in the ongoing search for individual markers in stress-related psychopathologies.

  14. Effects of aging on neural connectivity underlying selective memory for emotional scenes.

    Science.gov (United States)

    Waring, Jill D; Addis, Donna Rose; Kensinger, Elizabeth A

    2013-02-01

    Older adults show age-related reductions in memory for neutral items within complex visual scenes, but just like young adults, older adults exhibit a memory advantage for emotional items within scenes compared with the background scene information. The present study examined young and older adults' encoding-stage effective connectivity for selective memory of emotional items versus memory for both the emotional item and its background. In a functional magnetic resonance imaging (fMRI) study, participants viewed scenes containing either positive or negative items within neutral backgrounds. Outside the scanner, participants completed a memory test for items and backgrounds. Irrespective of scene content being emotionally positive or negative, older adults had stronger positive connections among frontal regions and from frontal regions to medial temporal lobe structures than did young adults, especially when items and backgrounds were subsequently remembered. These results suggest there are differences between young and older adults' connectivity accompanying the encoding of emotional scenes. Older adults may require more frontal connectivity to encode all elements of a scene rather than just encoding the emotional item. Published by Elsevier Inc.

  15. Impact of acoustic coordinated reset neuromodulation on effective connectivity in a neural network of phantom sound.

    Science.gov (United States)

    Silchenko, Alexander N; Adamchic, Ilya; Hauptmann, Christian; Tass, Peter A

    2013-08-15

    Chronic subjective tinnitus is an auditory phantom phenomenon characterized by abnormal neuronal synchrony in the central auditory system. As recently shown in a proof of concept clinical trial, acoustic coordinated reset (CR) neuromodulation causes a significant relief of tinnitus symptoms combined with a significant decrease of pathological oscillatory activity in a network comprising auditory and non-auditory brain areas. The objective of the present study was to analyze whether CR therapy caused an alteration of the effective connectivity in a tinnitus related network of localized EEG brain sources. To determine which connections matter, in a first step, we considered a larger network of brain sources previously associated with tinnitus. To that network we applied a data-driven approach, combining empirical mode decomposition and partial directed coherence analysis, in patients with bilateral tinnitus before and after 12 weeks of CR therapy as well as in healthy controls. To increase the signal-to-noise ratio, we focused on the good responders, classified by a reliable-change-index (RCI). Prior to CR therapy and compared to the healthy controls, the good responders showed a significantly increased connectivity between the left primary cortex auditory cortex and the posterior cingulate cortex in the gamma and delta bands together with a significantly decreased effective connectivity between the right primary auditory cortex and the dorsolateral prefrontal cortex in the alpha band. Intriguingly, after 12 weeks of CR therapy most of the pathological interactions were gone, so that the connectivity patterns of good responders and healthy controls became statistically indistinguishable. In addition, we used dynamic causal modeling (DCM) to examine the types of interactions which were altered by CR therapy. Our DCM results show that CR therapy specifically counteracted the imbalance of excitation and inhibition. CR significantly weakened the excitatory connection

  16. Connecting Neurons to a Mobile Robot: An In Vitro Bidirectional Neural Interface

    Directory of Open Access Journals (Sweden)

    A. Novellino

    2007-01-01

    Full Text Available One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason x201C;embodiment” represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA, to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses.

  17. Cell-Cell Connection Enhances Proliferation and Neuronal Differentiation of Rat Embryonic Neural Stem/Progenitor Cells

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    Qian Jiao

    2017-07-01

    Full Text Available Cell-cell interaction as one of the niche signals plays an important role in the balance of stem cell quiescence and proliferation or differentiation. In order to address the effect and the possible mechanisms of cell-cell connection on neural stem/progenitor cells (NSCs/NPCs proliferation and differentiation, upon passaging, NSCs/NPCs were either dissociated into single cell as usual (named Group I or mechanically triturated into a mixture of single cell and small cell clusters containing direct cell-cell connections (named Group II. Then the biological behaviors including proliferation and differentiation of NSCs/NPCs were observed. Moreover, the expression of gap junction channel, neurotrophic factors and the phosphorylation status of MAPK signals were compared to investigate the possible mechanisms. Our results showed that, in comparison to the counterparts in Group I, NSCs/NPCs in Group II survived well with preferable neuronal differentiation. In coincidence with this, the expression of connexin 45 (Cx45, as well as brain derived neurotrophic factor (BDNF and neurotrophin 3 (NT-3 in Group II were significantly higher than those in Group I. Phosphorylation of ERK1/2 and JNK2 were significantly upregulated in Group II too, while no change was found about p38. Furthermore, the differences of NSCs/NPCs biological behaviors between Group I and II completely disappeared when ERK and JNK phosphorylation were inhibited. These results indicated that cell-cell connection in Group II enhanced NSCs/NPCs survival, proliferation and neuronal differentiation through upregulating the expression of gap junction and neurotrophic factors. MAPK signals- ERK and JNK might contribute to the enhancement. Efforts for maintaining the direct cell-cell connection are worth making to provide more favorable niches for NSCs/NPCs survival, proliferation and neuronal differentiation.

  18. Neural substrates underlying balanced time perspective: A combined voxel-based morphometry and resting-state functional connectivity study.

    Science.gov (United States)

    Guo, Yiqun; Chen, Zhiyi; Feng, Tingyong

    2017-08-14

    Balanced time perspective (BTP), which is defined as a mental ability to switch flexibly among different time perspectives Zimbardo and Boyd (1999), has been suggested to be a central component of positive psychology Boniwell and Zimbardo (2004). BTP reflects individual's cognitive flexibility towards different time frames, which leads to many positive outcomes, including positive mood, subjective wellbeing, emotional intelligence, fluid intelligence, and executive control. However, the neural basis of BTP is still unclear. To address this question, we quantified individual's deviation from the BTP (DBTP), and investigated the neural substrates of DBTP using both voxel-based morphometry (VBM) and resting-state functional connectivity (RSFC) methods VBM analysis found that DBTP scores were positively correlated with gray matter volume (GMV) in the ventral precuneus. We further found that DBTP scores were negatively associated with RSFCs between the ventral precuneus seed region and medial prefrontal cortex (mPFC), bilateral temporoparietal junction (TPJ), parahippocampa gyrus (PHG), and middle frontal gyrus (MFG). These brain regions found in both VBM and RSFC analyses are commonly considered as core nodes of the default mode network (DMN) that is known to be involved in many functions, including episodic and autobiographical memory, self-related processing, theory of mind, and imagining the future. These functions of the DMN are also essential to individuals with BTP. Taken together, we provide the first evidence for the structural and functional neural basis of BTP, and highlight the crucial role of the DMN in cultivating an individual's BTP. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Neural connectivity during reward expectation dissociates psychopathic criminals from non-criminal individuals with high impulsive/antisocial psychopathic traits.

    Science.gov (United States)

    Geurts, Dirk E M; von Borries, Katinka; Volman, Inge; Bulten, Berend Hendrik; Cools, Roshan; Verkes, Robbert-Jan

    2016-08-01

    Criminal behaviour poses a big challenge for society. A thorough understanding of the neurobiological mechanisms underlying criminality could optimize its prevention and management. Specifically,elucidating the neural mechanisms underpinning reward expectation might be pivotal to understanding criminal behaviour. So far no study has assessed reward expectation and its mechanisms in a criminal sample. To fill this gap, we assessed reward expectation in incarcerated, psychopathic criminals. We compared this group to two groups of non-criminal individuals: one with high levels and another with low levels of impulsive/antisocial traits. Functional magnetic resonance imaging was used to quantify neural responses to reward expectancy. Psychophysiological interaction analyses were performed to examine differences in functional connectivity patterns of reward-related regions. The data suggest that overt criminality is characterized, not by abnormal reward expectation per se, but rather by enhanced communication between reward-related striatal regions and frontal brain regions. We establish that incarcerated psychopathic criminals can be dissociated from non-criminal individuals with comparable impulsive/antisocial personality tendencies based on the degree to which reward-related brain regions interact with brain regions that control behaviour. The present results help us understand why some people act according to their impulsive/antisocial personality while others are able to behave adaptively despite reward-related urges.

  20. Theory of Mind and the Whole Brain Functional Connectivity: Behavioral and Neural Evidences with the Amsterdam Resting State Questionnaire.

    Science.gov (United States)

    Marchetti, Antonella; Baglio, Francesca; Costantini, Isa; Dipasquale, Ottavia; Savazzi, Federica; Nemni, Raffaello; Sangiuliano Intra, Francesca; Tagliabue, Semira; Valle, Annalisa; Massaro, Davide; Castelli, Ilaria

    2015-01-01

    A topic of common interest to psychologists and philosophers is the spontaneous flow of thoughts when the individual is awake but not involved in cognitive demands. This argument, classically referred to as the "stream of consciousness" of James, is now known in the psychological literature as "Mind-Wandering." Although of great interest, this construct has been scarcely investigated so far. Diaz et al. (2013) created the Amsterdam Resting State Questionnaire (ARSQ), composed of 27 items, distributed in seven factors: discontinuity of mind, theory of mind (ToM), self, planning, sleepiness, comfort, and somatic awareness. The present study aims at: testing psychometric properties of the ARSQ in a sample of 670 Italian subjects; exploring the neural correlates of a subsample of participants (N = 28) divided into two groups on the basis of the scores obtained in the ToM factor. Results show a satisfactory reliability of the original factional structure in the Italian sample. In the subjects with a high mean in the ToM factor compared to low mean subjects, functional MRI revealed: a network (48 nodes) with higher functional connectivity (FC) with a dominance of the left hemisphere; an increased within-lobe FC in frontal and insular lobes. In both neural and behavioral terms, our results support the idea that the mind, which does not rest even when explicitly asked to do so, has various and interesting mentalistic-like contents.

  1. THEORY OF MIND AND THE WHOLE BRAIN FUNCTIONAL CONNECTIVITY: BEHAVIORAL AND NEURAL EVIDENCES WITH THE AMSTERDAM RESTING STATE QUESTIONNAIRE

    Directory of Open Access Journals (Sweden)

    ANTONELLA eMARCHETTI

    2015-12-01

    Full Text Available A topic of common interest to psychologists and philosophers is the spontaneous flow of thoughts when the individual is awake but not involved in cognitive demands. This argument, classically referred to as the stream of consciousness of James, is now known in the psychological literature as Mind-Wandering. Although of great interest, this construct has been scarcely investigated so far. Diaz and colleagues (2013 created the Amsterdam Resting State Questionnaire (ARSQ, composed of 27 items, distributed in seven factors: discontinuity of mind, theory of mind (ToM, self, planning, sleepiness, comfort and somatic awareness. The present study aims at: testing psychometric properties of the ARSQ in a sample of 670 Italian subjects; exploring the neural correlates of a subsample of participants (N=28 divided into two groups on the basis of the scores obtained in the ToM factor. Results show a satisfactory reliability of the original factional structure in the Italian sample. In the subjects with a high mean in the ToM factor compared to low mean subjects, functional MRI revealed: a network (48 nodes with higher functional connectivity (FC with a dominance of the left hemisphere; an increased within-lobe FC in frontal and insular lobes. In both neural and behavioral terms, our results support the idea that the mind, which does not rest even when explicitly asked to do so, has various and interesting mentalistic-like contents.

  2. Resilience and cross-network connectivity: A neural model for post-trauma survival.

    Science.gov (United States)

    Brunetti, Marcella; Marzetti, Laura; Sepede, Gianna; Zappasodi, Filippo; Pizzella, Vittorio; Sarchione, Fabiola; Vellante, Federica; Martinotti, Giovanni; Di Giannantonio, Massimo

    2017-07-03

    Literature on the neurobiological bases of Post-Traumatic Stress Disorder (PTSD) considers medial Prefrontal cortex (mPFC), a core region of the Default Mode Network (DMN), as a region involved in response regulation to stressors. Disrupted functioning of the DMN has been recognized at the basis of the pathophysiology of a number of mental disorders. Furthermore, in the evaluation of the protective factors to trauma consequence, an important role has been assigned to resilience. Our aim was to investigate the specific relation of resilience and PTSD symptoms severity with resting state brain connectivity in a traumatized population using magnetoencephalography (MEG), a non-invasive imaging technique with high temporal resolution and documented advantages in clinical applications. Nineteen Trauma Exposed non-PTSD (TENP) and 19 PTSD patients participated to a resting state MEG session. MEG functional connectivity of mPFC seed to the whole brain was calculated. Correlation between mPFC functional connectivity and Clinician Administered PTSD Scale (CAPS) or Connor-Davidson Resilience Scale (CD-RISC) total score was also assessed. In the whole group, it has been evidenced that the higher was the resilience, the lower was the cross-network connectivity between DMN and Salience Network (SN) nodes. Contrarily, in the TENP group, the negative correlation between resilience and DMN-SN cross-interaction disappeared, suggesting a protective role of resilience for brain functioning. Regarding our findings as a continuum between healthy and pathological after trauma outcomes, we could suggest a link between resilience and the good dialogue between the networks needed to face a traumatic event and its long-term consequence on individuals' lives. Copyright © 2017 Elsevier Inc. All rights reserved.

  3. Central Thalamic Deep-Brain Stimulation Alters Striatal-Thalamic Connectivity in Cognitive Neural Behavior.

    Science.gov (United States)

    Lin, Hui-Ching; Pan, Han-Chi; Lin, Sheng-Huang; Lo, Yu-Chun; Shen, Elise Ting-Hsin; Liao, Lun-De; Liao, Pei-Han; Chien, Yi-Wei; Liao, Kuei-Da; Jaw, Fu-Shan; Chu, Kai-Wen; Lai, Hsin-Yi; Chen, You-Yin

    2015-01-01

    Central thalamic deep brain stimulation (CT-DBS) has been proposed as an experimental therapeutic approach to produce consistent sustained regulation of forebrain arousal for several neurological diseases. We investigated local field potentials (LFPs) induced by CT-DBS from the thalamic central lateral nuclei (CL) and the striatum as potential biomarkers for the enhancement of lever-pressing skill learning. LFPs were simultaneously recorded from multiple sites in the CL, ventral striatum (Vstr), and dorsal striatum (Dstr). LFP oscillation power and functional connectivity were assessed and compared between the CT-DBS and sham control groups. The theta and alpha LFP oscillations were significantly increased in the CL and striatum in the CT-DBS group. Furthermore, interhemispheric coherences between bilateral CL and striatum were increased in the theta band. Additionally, enhancement of c-Fos activity, dopamine D2 receptor (Drd2), and α4-nicotinic acetylcholine receptor (α4-nAChR) occurred after CT-DBS treatment in the striatum and hippocampus. CT-DBS strengthened thalamic-striatal functional connectivity, which demonstrates that the inter-regional connectivity enhancement might contribute to synaptic plasticity in the striatum. Altered dopaminergic and cholinergic receptors resulted in modulation of striatal synaptic plasticity's ability to regulate downstream signaling cascades for higher brain functions of lever-pressing skill learning.

  4. Central Thalamic Deep-Brain Stimulation Alters Striatal–Thalamic Connectivity in Cognitive Neural Behavior

    Directory of Open Access Journals (Sweden)

    Hui-Ching eLin

    2016-01-01

    Full Text Available Central thalamic deep brain stimulation (CT-DBS has been proposed as an experimental therapeutic approach to produce consistent sustained regulation of forebrain arousal for several neurological diseases. We investigated local field potentials (LFPs induced by CT-DBS from the thalamic central lateral nuclei (CL and the striatum as potential biomarkers for the enhancement of lever-pressing skill learning. LFPs were simultaneously recorded from multiple sites in the CL, ventral striatum (Vstr, and dorsal striatum (Dstr. LFP oscillation power and functional connectivity were assessed and compared between the CT-DBS and sham control groups. The theta and alpha LFP oscillations were significantly increased in the CL and striatum in the CT-DBS group. Furthermore, interhemispheric coherences between bilateral CL and striatum were increased in the theta band. Additionally, enhancement of c-Fos activity, dopamine D2 receptor (Drd2 and 4-nicotinic acetylcholine receptor (4-nAChR occurred after CT-DBS treatment in the striatum and hippocampus. CT-DBS strengthened thalamic-striatal functional connectivity, which demonstrates that the inter-regional connectivity enhancement might contribute to synaptic plasticity in the striatum. Altered dopaminergic and cholinergic receptors resulted in modulation of striatal synaptic plasticity’s ability to regulate downstream signaling cascades for higher brain functions of lever-pressing skill learning.

  5. Deep dreaming, aberrant salience and psychosis: Connecting the dots by artificial neural networks.

    Science.gov (United States)

    Keshavan, Matcheri S; Sudarshan, Mukund

    2017-01-24

    Why some individuals, when presented with unstructured sensory inputs, develop altered perceptions not based in reality, is not well understood. Machine learning approaches can potentially help us understand how the brain normally interprets sensory inputs. Artificial neural networks (ANN) progressively extract higher and higher-level features of sensory input and identify the nature of an object based on a priori information. However, some ANNs which use algorithms such as the "deep-dreaming" developed by Google, allow the network to over-emphasize some objects it "thinks" it recognizes in those areas, and iteratively enhance such outputs leading to representations that appear farther and farther from "reality". We suggest that such "deep dreaming" ANNs may model aberrant salience, a mechanism suggested for pathogenesis of psychosis. Such models can generate testable predictions for psychosis.

  6. Connectivity, Pharmacology, and Computation: Toward a Mechanistic Understanding of Neural System Dysfunction in Schizophrenia

    Science.gov (United States)

    Anticevic, Alan; Cole, Michael W.; Repovs, Grega; Savic, Aleksandar; Driesen, Naomi R.; Yang, Genevieve; Cho, Youngsun T.; Murray, John D.; Glahn, David C.; Wang, Xiao-Jing; Krystal, John H.

    2013-01-01

    Neuropsychiatric diseases such as schizophrenia and bipolar illness alter the structure and function of distributed neural networks. Functional neuroimaging tools have evolved sufficiently to reliably detect system-level disturbances in neural networks. This review focuses on recent findings in schizophrenia and bipolar illness using resting-state neuroimaging, an advantageous approach for biomarker development given its ease of data collection and lack of task-based confounds. These benefits notwithstanding, neuroimaging does not yet allow the evaluation of individual neurons within local circuits, where pharmacological treatments ultimately exert their effects. This limitation constitutes an important obstacle in translating findings from animal research to humans and from healthy humans to patient populations. Integrating new neuroscientific tools may help to bridge some of these gaps. We specifically discuss two complementary approaches. The first is pharmacological manipulations in healthy volunteers, which transiently mimic some cardinal features of psychiatric conditions. We specifically focus on recent neuroimaging studies using the NMDA receptor antagonist, ketamine, to probe glutamate synaptic dysfunction associated with schizophrenia. Second, we discuss the combination of human pharmacological imaging with biophysically informed computational models developed to guide the interpretation of functional imaging studies and to inform the development of pathophysiologic hypotheses. To illustrate this approach, we review clinical investigations in addition to recent findings of how computational modeling has guided inferences drawn from our studies involving ketamine administration to healthy subjects. Thus, this review asserts that linking experimental studies in humans with computational models will advance to effort to bridge cellular, systems, and clinical neuroscience approaches to psychiatric disorders. PMID:24399974

  7. Connectivity, Pharmacology and Computation: Towards a Mechanistic Understanding of Neural System Dysfunction in Schizophrenia

    Directory of Open Access Journals (Sweden)

    Alan eAnticevic

    2013-12-01

    Full Text Available Neuropsychiatric diseases such as schizophrenia and bipolar illness alter the structure and function of distributed neural networks. Functional neuroimaging tools have evolved sufficiently to reliably detect system-level disturbances in neural networks. This review focuses on recent findings in schizophrenia and bipolar illness using resting-state neuroimaging, an advantageous approach for biomarker development given its ease of data collection and lack of task-based confounds. These benefits notwithstanding, neuroimaging does not yet allow the evaluation of individual neurons within local circuits, where pharmacological treatments ultimately exert their effects. This limitation constitutes an important obstacle in translating findings from animal research to humans and from healthy humans to patient populations. Integrating new neuroscientific tools may help to bridge some of these gaps. We specifically discuss two complementary approaches. The first is pharmacological manipulations in healthy volunteers, which transiently mimic some cardinal features of psychiatric conditions. We specifically focus on recent neuroimaging studies using the NMDA receptor antagonist, ketamine, to probe glutamate synaptic dysfunction associated with schizophrenia. Second, we discuss the combination of human pharmacological imaging with biophysically-informed computational models developed to guide the interpretation of functional imaging studies and to inform the development of pathophysiologic hypotheses. To illustrate this approach, we review clinical investigations in addition to recent findings of how computational modeling has guided inferences drawn from our studies involving ketamine administration to healthy subjects. Thus, this review asserts that linking experimental studies in humans with computational models will advance to effort to bridge cellular, systems, and clinical neuroscience approaches to psychiatric disorders.

  8. Abnormal left-sided orbitomedial prefrontal cortical-amygdala connectivity during happy and fear face processing: a potential neural mechanism of female MDD

    Directory of Open Access Journals (Sweden)

    Jorge eAlmeida

    2011-12-01

    Full Text Available Background: Pathophysiologic processes supporting abnormal emotion regulation in major depressive disorder (MDD are poorly understood. We previously found abnormal inverse left-sided ventromedial prefrontal cortical- amygdala effective connectivity to happy faces in females with MDD. We aimed to replicate and expand this previous finding in an independent participant sample, using a more inclusive neural model, and a novel emotion-processing paradigm.Methods: Nineteen individuals with MDD in depressed episode (12 females, and nineteen healthy individuals, age and gender matched, performed an implicit emotion processing and automatic attentional control paradigm to examine abnormalities in prefrontal cortical-amygdala neural circuitry during happy, angry, fearful and sad face processing measured with functional magnetic resonance imaging in a 3Tesla scanner. Effective connectivity was estimated with Dynamic Causal Modelling in a trinodal neural model including two anatomically defined prefrontal cortical regions, ventromedial prefrontal cortex and subgenual cingulate cortex(sgACC, and the amygdala. Results: We replicated our previous finding of abnormal inverse left-sided inverse top-down ventromedial prefrontal cortical-amygdala connectivity to happy faces in females with MDD (p=.04, and also showed a similar pattern of abnormal inverse left-sided sgACC-amygdala connectivity to these stimuli (p=0.03. These findings were paralleled by abnormally reduced positive left-sided ventromedial prefrontal cortical-sgACC connectivity to happy faces in females with MDD (p=0.008, and abnormally increased positive left-sided sgACC-amygdala connectivity to fearful faces in females, and all individuals, with MDD (p=0.008;p=0.003.Conclusions: Different patterns of abnormal prefrontal cortical-amygdala connectivity to happy and fearful stimuli might represent neural mechanisms for the excessive self-reproach and comorbid anxiety that characterize female MDD.

  9. Chimeras in leaky integrate-and-fire neural networks: effects of reflecting connectivities

    Science.gov (United States)

    Tsigkri-DeSmedt, Nefeli Dimitra; Hizanidis, Johanne; Schöll, Eckehard; Hövel, Philipp; Provata, Astero

    2017-07-01

    The effects of attracting-nonlocal and reflecting connectivity are investigated in coupled Leaky Integrate-and-Fire (LIF) elements, which model the exchange of electrical signals between neurons. Earlier investigations have demonstrated that repulsive-nonlocal and hierarchical network connectivity can induce complex synchronization patterns and chimera states in systems of coupled oscillators. In the LIF system we show that if the elements are nonlocally linked with positive diffusive coupling on a ring network, the system splits into a number of alternating domains. Half of these domains contain elements whose potential stays near the threshold and they are interrupted by active domains where the elements perform regular LIF oscillations. The active domains travel along the ring with constant velocity, depending on the system parameters. When we introduce reflecting coupling in LIF networks unexpected complex spatio-temporal structures arise. For relatively extensive ranges of parameter values, the system splits into two coexisting domains: one where all elements stay near the threshold and one where incoherent states develop, characterized by multi-leveled mean phase velocity profiles.

  10. Happier People Show Greater Neural Connectivity during Negative Self-Referential Processing.

    Science.gov (United States)

    Kim, Eun Joo; Kyeong, Sunghyon; Cho, Sang Woo; Chun, Ji-Won; Park, Hae-Jeong; Kim, Jihye; Kim, Joohan; Dolan, Raymond J; Kim, Jae-Jin

    2016-01-01

    Life satisfaction is an essential component of subjective well-being and provides a fundamental resource for optimal everyday functioning. The goal of the present study was to examine how life satisfaction influences self-referential processing of emotionally valenced stimuli. Nineteen individuals with high life satisfaction (HLS) and 21 individuals with low life satisfaction (LLS) were scanned using functional MRI while performing a face-word relevance rating task, which consisted of 3 types of face stimuli (self, public other, and unfamiliar other) and 3 types of word stimuli (positive, negative, and neutral). We found a significant group x word valence interaction effect, most strikingly in the dorsal medial prefrontal cortex. In the positive word condition dorsal medial prefrontal cortex activity was significantly higher in the LLS group, whereas in the negative word condition it was significantly higher in the HLS group. The two groups showed distinct functional connectivity of the dorsal medial prefrontal cortex with emotional processing-related regions. The findings suggest that, in response to emotional stimuli, individuals with HLS may successfully recruit emotion regulation-related regions in contrast to individuals with LLS. The difference in functional connectivity during self-referential processing may lead to an influence of life satisfaction on responses to emotion-eliciting stimuli.

  11. Schizophrenia and periodontal disease: An oro-neural connection? A cross-sectional epidemiological study

    Directory of Open Access Journals (Sweden)

    Shreya Shetty

    2014-01-01

    Full Text Available Background: Schizophrenia is a psychosis characterized by delusions and hallucinations occurring in clear consciousness. Studies have shown that the cytokines may modulate dopaminergic metabolism and schizophrenic symptomatology in schizophrenia. Cytokine involvement in periodontal disease is also well documented. To date, however, there has been relatively little research assessing periodontal status of patients with schizophrenia. The present study was therefore mainly intended to understand the exact link, if any, between periodontal disease and schizophrenia. Materials and Methods: A total of 250 schizophrenic patients (140 males and 110 females, between 25 and 55 years of age, were selected from the out patient department of National Institute of Mental Health and Neural Sciences, Bangalore and their periodontal status was assessed as part of this cross-sectional epidemiological survey. Results: ANOVA showed that there was increased evidence of poor periodontal condition, as evidenced by gingival index and plaque index in patients who had been schizophrenic for a longer duration of time (P < 0.001. So also, higher probing pocket depths were found in schizophrenics suffering from a longer period of time than others (P < 0.001. Conclusions: Although oral neglect might be a cause of poor periodontal health in schizophrenics, the possible link between periodontal diseases giving rise to schizophrenia cannot be overlooked due to the presence of cytokine activity which is present both in schizophrenia and periodontal disease.

  12. Preterm birth results in alterations in neural connectivity at age 16 years.

    Science.gov (United States)

    Mullen, Katherine M; Vohr, Betty R; Katz, Karol H; Schneider, Karen C; Lacadie, Cheryl; Hampson, Michelle; Makuch, Robert W; Reiss, Allan L; Constable, R Todd; Ment, Laura R

    2011-02-14

    Very low birth weight preterm (PT) children are at high risk for brain injury. Employing diffusion tensor imaging (DTI), we tested the hypothesis that PT adolescents would demonstrate microstructural white matter disorganization relative to term controls at 16 years of age. Forty-four PT subjects (600-1250 g birth weight) without neonatal brain injury and 41 term controls were evaluated at age 16 years with DTI, the Wechsler Intelligence Scale for Children-III (WISC), the Peabody Picture Vocabulary Test-Revised (PPVT), and the Comprehensive Test of Phonological Processing (CTOPP). PT subjects scored lower than term subjects on WISC full scale (p=0.003), verbal (p=0.043), and performance IQ tests (p=0.001), as well as CTOPP phonological awareness (p=0.004), but scored comparably to term subjects on PPVT and CTOPP Rapid Naming tests. PT subjects had lower fractional anisotropy (FA) values in multiple regions including bilateral uncinate fasciculi (left: p=0.01; right: p=0.004), bilateral external capsules (left: p<0.001; right: p<0.001), the splenium of the corpus callosum (p=0.008), and white matter serving the inferior frontal gyrus bilaterally (left: p<0.001; right: p=0.011). FA values in both the left and right uncinate fasciculi correlated with PPVT scores (a semantic language task) in the PT subjects (left: r=0.314, p=0.038; right: r=0.336, p=0.026). FA values in the left and right arcuate fasciculi correlated with CTOPP Rapid Naming scores (a phonologic task) in the PT subjects (left: r=0.424, p=0.004; right: r=0.301, p=0.047). These data support for the first time that dual pathways underlying language function are present in PT adolescents. The striking bilateral dorsal correlations for the PT group suggest that prematurely born subjects rely more heavily on the right hemisphere than typically developing adults for performance of phonological language tasks. These findings may represent either a delay in maturation or the engagement of alternative neural

  13. Effect of intermodular connection on fast sparse synchronization in clustered small-world neural networks

    Science.gov (United States)

    Kim, Sang-Yoon; Lim, Woochang

    2015-11-01

    We consider a clustered network with small-world subnetworks of inhibitory fast spiking interneurons and investigate the effect of intermodular connection on the emergence of fast sparsely synchronized rhythms by varying both the intermodular coupling strength Jinter and the average number of intermodular links per interneuron Msyn(inter ). In contrast to the case of nonclustered networks, two kinds of sparsely synchronized states such as modular and global synchronization are found. For the case of modular sparse synchronization, the population behavior reveals the modular structure, because the intramodular dynamics of subnetworks make some mismatching. On the other hand, in the case of global sparse synchronization, the population behavior is globally identical, independently of the cluster structure, because the intramodular dynamics of subnetworks make perfect matching. We introduce a realistic cross-correlation modularity measure, representing the matching degree between the instantaneous subpopulation spike rates of the subnetworks, and examine whether the sparse synchronization is global or modular. Depending on its magnitude, the intermodular coupling strength Jinter seems to play "dual" roles for the pacing between spikes in each subnetwork. For large Jinter, due to strong inhibition it plays a destructive role to "spoil" the pacing between spikes, while for small Jinter it plays a constructive role to "favor" the pacing between spikes. Through competition between the constructive and the destructive roles of Jinter, there exists an intermediate optimal Jinter at which the pacing degree between spikes becomes maximal. In contrast, the average number of intermodular links per interneuron Msyn(inter ) seems to play a role just to favor the pacing between spikes. With increasing Msyn(inter ), the pacing degree between spikes increases monotonically thanks to the increase in the degree of effectiveness of global communication between spikes. Furthermore, we

  14. Neural Signatures of the Reading-Writing Connection: Greater Involvement of Writing in Chinese Reading than English Reading.

    Science.gov (United States)

    Cao, Fan; Perfetti, Charles A

    2016-01-01

    Research on cross-linguistic comparisons of the neural correlates of reading has consistently found that the left middle frontal gyrus (MFG) is more involved in Chinese than in English. However, there is a lack of consensus on the interpretation of the language difference. Because this region has been found to be involved in writing, we hypothesize that reading Chinese characters involves this writing region to a greater degree because Chinese speakers learn to read by repeatedly writing the characters. To test this hypothesis, we recruited English L1 learners of Chinese, who performed a reading task and a writing task in each language. The English L1 sample had learned some Chinese characters through character-writing and others through phonological learning, allowing a test of writing-on-reading effect. We found that the left MFG was more activated in Chinese than English regardless of task, and more activated in writing than in reading regardless of language. Furthermore, we found that this region was more activated for reading Chinese characters learned by character-writing than those learned by phonological learning. A major conclusion is that writing regions are also activated in reading, and that this reading-writing connection is modulated by the learning experience. We replicated the main findings in a group of native Chinese speakers, which excluded the possibility that the language differences observed in the English L1 participants were due to different language proficiency level.

  15. Training Recurrent Neural Networks With the Levenberg-Marquardt Algorithm for Optimal Control of a Grid-Connected Converter.

    Science.gov (United States)

    Fu, Xingang; Li, Shuhui; Fairbank, Michael; Wunsch, Donald C; Alonso, Eduardo

    2015-09-01

    This paper investigates how to train a recurrent neural network (RNN) using the Levenberg-Marquardt (LM) algorithm as well as how to implement optimal control of a grid-connected converter (GCC) using an RNN. To successfully and efficiently train an RNN using the LM algorithm, a new forward accumulation through time (FATT) algorithm is proposed to calculate the Jacobian matrix required by the LM algorithm. This paper explores how to incorporate FATT into the LM algorithm. The results show that the combination of the LM and FATT algorithms trains RNNs better than the conventional backpropagation through time algorithm. This paper presents an analytical study on the optimal control of GCCs, including theoretically ideal optimal and suboptimal controllers. To overcome the inapplicability of the optimal GCC controller under practical conditions, a new RNN controller with an improved input structure is proposed to approximate the ideal optimal controller. The performance of an ideal optimal controller and a well-trained RNN controller was compared in close to real-life power converter switching environments, demonstrating that the proposed RNN controller can achieve close to ideal optimal control performance even under low sampling rate conditions. The excellent performance of the proposed RNN controller under challenging and distorted system conditions further indicates the feasibility of using an RNN to approximate optimal control in practical applications.

  16. A Homotopy—Controlled Mean Field Annealing Partially—Connected Neural Equalizer for Pan—European GSM Syst5em

    Institute of Scientific and Technical Information of China (English)

    XueJianjun; YouXiaohu

    1997-01-01

    Channel equalization is essential in the Pan-European GSM mobile communication system.The maximum likelihood sequence estimation(MLSE) using the Viterbi algorithm(VA)iscommonly recommended for the dqualization,which can only accommodate the channels with limited time delay spread.In[1],we presented a mean field annealing(MFA)partially connected neural equalizer for the GSM system,in which the complexity is linearly proportional to the time delay spread and therefore relatively fast convergence speed is achieved.But the annealing coefficient of the MFA equalizer is fixed,which is not flexible in timing-varying circumstance such as mobile communications.To decrease the computation of MFA approach so as to make it more easy for practical use,the MFA approach is reated as a homotopy problem.The ordinary equations which the MFA approach should obey are derived.These equations can be used to reflect the deviation of the iteration result from the track of MFA approach.Based on this tesult,an adaptive annealing control algorithm is proposed,which can dynamically control the annealing coefficient according to the iteration deviation.Computer simulations show that our approach can provide a much higher convergence speed and performance improvement over 16-state and 32-state VA's which are usually suggested for practical applications.

  17. Change of Neural Connectivity of the Red Nucleus in Patients with Striatocapsular Hemorrhage: A Diffusion Tensor Tractography Study

    Directory of Open Access Journals (Sweden)

    Sung Ho Jang

    2015-01-01

    Full Text Available The red nucleus (RN is involved in motor control and it is known to have potential to compensate for injury of the corticospinal tract (CST. We investigated the change of connectivity of the RN (RNc and its relation to motor function in patients with striatocapsular hemorrhage. Thirty-five chronic patients with striatocapsular hemorrhage were recruited. Motricity Index (MI, Modified Brunnstrom Classification (MBC, and Functional Ambulation Category (FAC were measured for motor function. The probabilistic tractography method was used for evaluation of the RNc. Fractional anisotropy (FA, mean diffusivity (MD, and tract volume (TV of the RNc were measured. FA and TV ratios of the RNc in patients with discontinuation of the affected CST were significantly higher than those of patients with preserved integrity of the CST in the affected hemisphere (p<0.05. TV ratio of the RNc showed significant negative correlation with upper MI (weak correlation, r=-0.35, total MI (weak correlation, r=-0.34, and MBC (moderate correlation, r=-0.43, respectively (p<0.05. We found that the neural structure of the RNc was relatively increased in the unaffected hemisphere compared with the affected hemisphere in patients with more severe injury of the CST.

  18. Augmented Nonlinear Controller for Maximum Power-Point Tracking with Artificial Neural Network in Grid-Connected Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Suliang Ma

    2016-11-01

    Full Text Available Photovoltaic (PV systems have non-linear characteristics that generate maximum power at one particular operating point. Environmental factors such as irradiance and temperature variations greatly affect the maximum power point (MPP. Diverse offline and online techniques have been introduced for tracking the MPP. Here, to track the MPP, an augmented-state feedback linearized (AFL non-linear controller combined with an artificial neural network (ANN is proposed. This approach linearizes the non-linear characteristics in PV systems and DC/DC converters, for tracking and optimizing the PV system operation. It also reduces the dependency of the designed controller on linearized models, to provide global stability. A complete model of the PV system is simulated. The existing maximum power-point tracking (MPPT and DC/DC boost-converter controller techniques are compared with the proposed ANN method. Two case studies, which simulate realistic circumstances, are presented to demonstrate the effectiveness and superiority of the proposed method. The AFL with ANN controller can provide good dynamic operation, faster convergence speed, and fewer operating-point oscillations around the MPP. It also tracks the global maxima under different conditions, especially irradiance-mutating situations, more effectively than the conventional methods. Detailed mathematical models and a control approach for a three-phase grid-connected intelligent hybrid system are proposed using MATLAB/Simulink.

  19. The neural processing of second language comprehension modulated by the degree of proficiency: a listening connected speech FMRI study.

    Science.gov (United States)

    Hesling, Isabelle; Dilharreguy, Bixente; Bordessoules, Martine; Allard, Michèle

    2012-01-01

    While the neural network encompassing the processing of the mother tongue (L1) is well defined and has revealed the existence of a bilateral ventral pathway and a left dorsal pathway in which 3 loops have been defined, the question of the processing of a second language (L2) is still a matter of debate. Among variables accounting for the discrepancies in results, the degree of L2 proficiency appears to be one of the main factors. The present study aimed at assessing both pathways in L2, making it possible to determine the degree of mastery of the different speech components (prosody, phonology, semantics and syntax) that are intrinsically embedded within connected speech and that vary according to the degree of proficiency using high degrees of prosodic information. Two groups of high and moderate proficiency in L2 performed an fMRI comprehension task in L1 and L2. The modifications in brain activity observed within the dorsal and the ventral pathways according to L2 proficiency suggest that different processes of L2 are supported by differences in the integrated activity within distributed networks that included the left STSp, the left Spt and the left pars triangularis.

  20. Fetal neural tube stem cells from Pax3 mutant mice proliferate, differentiate, and form synaptic connections when stimulated with folic acid.

    Science.gov (United States)

    Ichi, Shunsuke; Nakazaki, Hiromichi; Boshnjaku, Vanda; Singh, Ravneet Monny; Mania-Farnell, Barbara; Xi, Guifa; McLone, David G; Tomita, Tadanori; Mayanil, Chandra Shekhar K

    2012-01-20

    Although maternal intake of folic acid (FA) prevents neural tube defects in 70% of the population, the exact mechanism of prevention has not been elucidated. We hypothesized that FA affects neural stem cell (NSC) proliferation and differentiation. This hypothesis was examined in a folate-responsive spina bifida mouse model, Splotch (Sp(-/-)), which has a homozygous loss-of-function mutation in the Pax3 gene. Neurospheres were generated with NSCs from the lower lumbar neural tube of E10.5 wild-type (WT) and Sp(-/-) embryos, in the presence and absence of FA. In the absence of FA, the number of neurospheres generated from Sp(-/-) embryos compared with WT was minimal (Pcell differentiation, FA-stimulated Sp(-/-) neurospheres were allowed to differentiate in the continued presence or absence of FA. Neurospheres from both conditions expressed multi-potent stem cell characteristics and the same differentiation potential as WT. Further, multiple neurospheres from both WT and FA-stimulated Sp(-/-) cell cultures formed extensive synaptic connections. On the whole, FA-mediated rescue of neural tube defects in Sp(-/-) embryos promotes NSC proliferation at an early embryonic stage. FA-stimulated Sp(-/-) neurospheres differentiate and form synaptic connections, comparable to WT.

  1. Self-consistent determination of the spike-train power spectrum in a neural network with sparse connectivity

    Directory of Open Access Journals (Sweden)

    Benjamin eDummer

    2014-09-01

    Full Text Available A major source of random variability in cortical networks is the quasi-random arrival of presynaptic action potentials from many other cells. In network studies as well as in the study of the response properties of single cells embedded in a network, synaptic background input is often approximated by Poissonian spike trains. However, the output statistics of the cells is in most cases far from being Poisson. This is inconsistent with the assumption of similar spike-train statistics for pre- and postsynaptic cells in a recurrent network. Here we tackle this problem for the popular class of integrate-and-fire neurons and study a self-consistent statistics of input and output spectra of neural spike trains. Instead of actually using a large network, we use an iterative scheme, in which we simulate a single neuron over several generations. In each of these generations, the neuron is stimulated with surrogate stochastic input that has a similar statistics as the output of the previous generation. For the surrogate input, we employ two distinct approximations: (i a superposition of renewal spike trains with the same interspike interval density as observed in the previous generation and (ii a Gaussian current with a power spectrum proportional to that observed in the previous generation. For input parameters that correspond to balanced input in the network, both the renewal and the Gaussian iteration procedure converge quickly and yield comparable results for the self-consistent spike-train power spectrum. We compare our results to large-scale simulations of a random sparsely connected network of leaky integrate-and-fire neurons (Brunel, J. Comp. Neurosci. 2000 and show that in the asynchronous regime close to a state of balanced synaptic input from the network, our iterative schemes provide excellent approximations to the autocorrelation of spike trains in the recurrent network.

  2. Self-consistent determination of the spike-train power spectrum in a neural network with sparse connectivity.

    Science.gov (United States)

    Dummer, Benjamin; Wieland, Stefan; Lindner, Benjamin

    2014-01-01

    A major source of random variability in cortical networks is the quasi-random arrival of presynaptic action potentials from many other cells. In network studies as well as in the study of the response properties of single cells embedded in a network, synaptic background input is often approximated by Poissonian spike trains. However, the output statistics of the cells is in most cases far from being Poisson. This is inconsistent with the assumption of similar spike-train statistics for pre- and postsynaptic cells in a recurrent network. Here we tackle this problem for the popular class of integrate-and-fire neurons and study a self-consistent statistics of input and output spectra of neural spike trains. Instead of actually using a large network, we use an iterative scheme, in which we simulate a single neuron over several generations. In each of these generations, the neuron is stimulated with surrogate stochastic input that has a similar statistics as the output of the previous generation. For the surrogate input, we employ two distinct approximations: (i) a superposition of renewal spike trains with the same interspike interval density as observed in the previous generation and (ii) a Gaussian current with a power spectrum proportional to that observed in the previous generation. For input parameters that correspond to balanced input in the network, both the renewal and the Gaussian iteration procedure converge quickly and yield comparable results for the self-consistent spike-train power spectrum. We compare our results to large-scale simulations of a random sparsely connected network of leaky integrate-and-fire neurons (Brunel, 2000) and show that in the asynchronous regime close to a state of balanced synaptic input from the network, our iterative schemes provide an excellent approximations to the autocorrelation of spike trains in the recurrent network.

  3. Comparative study on CO2 emissions from different types of alpine meadows during grass exuberance period

    Institute of Scientific and Technical Information of China (English)

    HUQiwu; CAOGuangmin; WUQin; LIDong; WANGYuesi

    2004-01-01

    Potentilla fruticosa scrub, Kobresia humilis meadow and Kobresia tibetica meadow are widely distributed on the Qinghai-Tibet Plateau. During the grass exuberance period from 3 July to 4 September, based on close chamber-GC method, a study on CO2 emissions from different treatments was conducted in these meadows at Haibei research station, CAS. Results indicated that mean CO2 emission rates from various treatments were 672.09±152.37 mgm-2h-1 for FC (grass treatment); 425.41±191.99 mgm-2h-1 for FJ (grass exclusion treatment); 280.36±174.83 mgm-2h-1 for FL (grass and roots exclusion treatment); 838.95±237.02 mgm-2h-1 for GG (scrub+grass treatment); 528.48±205.67 mgm-2h-1 for GC (grass treatment); 268.97±99.72 mgm-2h-1 for GL (grass and roots exclusion treatment); and 659.20±94.83 mgm-2h-1 for LC (grass treatment), respectively (FC, FJ, FL, GG, GC, GL, LC were the Chinese abbreviation for various treatments). Furthermore, Kobresia humilis meadow, Potentilla fruticosa scrub meadow and Kobresia tibetica meadow differed greatly in average CO2 emission rate of soil-plant system, in the order of GG>FC>LC>GC. Moreover, in Kobresia hurnilis meadow,heterotrophic and autotrophic respiration accounted for 42% and 58% of the total respiration of soil-plant system respectively, whereas, in Potentilla fruticosa scrub meadow, heterotrophic and autotrophic respiration accounted for 32% and 68% of total system respiration from GG; 49% and 51% from GC. In addition, root respiration from Kobresia humilis meadow approximated 145 mgCO2m-2h-1,contributed 34% to soil respiration. During the experiment period, Kobresia humilis meadow and Potentilla fruticosa scrub meadow had a net carbon fixation of 111.11 gm-2 and 243.89 gm-2 respectively. Results also showed that soil temperature was the main factor which influenced CO2 emission from alpine meadow ecosystem, significant correlations were found between soil temperature at 5 cm depth and CO2 emission from GG, GC, FC and FJ treatments

  4. Differential functional connectivity within an emotion regulation neural network among individuals resilient and susceptible to the depressogenic effects of early life stress.

    Science.gov (United States)

    Cisler, J M; James, G A; Tripathi, S; Mletzko, T; Heim, C; Hu, X P; Mayberg, H S; Nemeroff, C B; Kilts, C D

    2013-03-01

    Early life stress (ELS) is a significant risk factor for depression. The effects of ELS exposure on neural network organization have not been differentiated from the effect of depression. Furthermore, many individuals exposed to ELS do not develop depression, yet the network organization patterns differentiating resiliency versus susceptibility to the depressogenic effects of ELS are not clear. Women aged 18-44 years with either a history of ELS and no history of depression (n = 7), a history of ELS and current or past depression (n = 19), or a history of neither ELS nor depression (n = 12) underwent a resting-state 3-T functional magnetic resonance imaging (fMRI) scan. An emotion regulation brain network consisting of 21 nodes was described using graph analyses and compared between groups. Group differences in network topology involved decreased global connectivity and hub-like properties for the right ventrolateral prefrontal cortex (vlPFC) and decreased local network connectivity for the dorsal anterior cingulate cortex (dACC) among resilient individuals. Decreased local connectivity and increased hub-like properties of the left amygdala, decreased hub-like properties of the dACC and decreased local connectivity of the left vlPFC were observed among susceptible individuals. Regression analyses suggested that the severity of ELS (measured by self-report) correlated negatively with global connectivity and hub-like qualities for the left dorsolateral PFC (dlPFC). These preliminary results suggest functional neural connectivity patterns specific to ELS exposure and resiliency versus susceptibility to the depressogenic effects of ELS exposure.

  5. Connective-Tissue Growth Factor (CTGF/CCN2 Induces Astrogenesis and Fibronectin Expression of Embryonic Neural Cells In Vitro.

    Directory of Open Access Journals (Sweden)

    Fabio A Mendes

    Full Text Available Connective-tissue growth factor (CTGF is a modular secreted protein implicated in multiple cellular events such as chondrogenesis, skeletogenesis, angiogenesis and wound healing. CTGF contains four different structural modules. This modular organization is characteristic of members of the CCN family. The acronym was derived from the first three members discovered, cysteine-rich 61 (CYR61, CTGF and nephroblastoma overexpressed (NOV. CTGF is implicated as a mediator of important cell processes such as adhesion, migration, proliferation and differentiation. Extensive data have shown that CTGF interacts particularly with the TGFβ, WNT and MAPK signaling pathways. The capacity of CTGF to interact with different growth factors lends it an important role during early and late development, especially in the anterior region of the embryo. ctgf knockout mice have several cranio-facial defects, and the skeletal system is also greatly affected due to an impairment of the vascular-system development during chondrogenesis. This study, for the first time, indicated that CTGF is a potent inductor of gliogenesis during development. Our results showed that in vitro addition of recombinant CTGF protein to an embryonic mouse neural precursor cell culture increased the number of GFAP- and GFAP/Nestin-positive cells. Surprisingly, CTGF also increased the number of Sox2-positive cells. Moreover, this induction seemed not to involve cell proliferation. In addition, exogenous CTGF activated p44/42 but not p38 or JNK MAPK signaling, and increased the expression and deposition of the fibronectin extracellular matrix protein. Finally, CTGF was also able to induce GFAP as well as Nestin expression in a human malignant glioma stem cell line, suggesting a possible role in the differentiation process of gliomas. These results implicate ctgf as a key gene for astrogenesis during development, and suggest that its mechanism may involve activation of p44/42 MAPK signaling

  6. Neural connectivity during reward expectation dissociates psychopathic criminals from noncriminal individuals with high impulsive/antisocial psychopathic traits

    NARCIS (Netherlands)

    Geurts, D.E.M.; Borries, A.K.L. von; Volman, I.A.C.; Bulten, B.H.; Cools, R.; Verkes, R.J.

    2016-01-01

    Criminal behaviour poses a big challenge for society. A thorough understanding of neurobiological mechanisms underlying criminality could optimize its prevention and management. Recently, it has been proposed that neural mechanisms underpinning reward expectation might be pivotal to understanding cr

  7. Neural connectivity during reward expectation dissociates psychopathic criminals from non-criminal individuals with high impulsive/antisocial psychopathic traits

    NARCIS (Netherlands)

    Geurts, D.E.; Borries, K. von; Volman, I.; Bulten, B.H.; Cools, R.; Verkes, R.J.

    2016-01-01

    Criminal behaviour poses a big challenge for society. A thorough understanding of the neurobiological mechanisms underlying criminality could optimize its prevention and management. Specifically,elucidating the neural mechanisms underpinning reward expectation might be pivotal to understanding crimi

  8. Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe

    Directory of Open Access Journals (Sweden)

    Eli eShlizerman

    2014-08-01

    Full Text Available The antennal lobe (AL, olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units, and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (i design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (ii characterize scent recognition, i.e., decision-making based on olfactory signals and (iii infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns.

  9. Regional disturbances in blood flow and metabolism in equine limb wound healing with formation of exuberant granulation tissue

    DEFF Research Database (Denmark)

    Sørensen, Mette A.; Petersen, Lars; Bundgaard, Louise

    2014-01-01

    As in other fibroproliferative disorders, hypoxia has been suggested to play a key role in the pathogenesis of exuberant granulation tissue (EGT). The purpose of this study was to investigate metabolism and blood flow locally in full-thickness wounds healing with (limb wounds) and without (body...... a significant difference between body and limb wounds. In conclusion, the metabolic disturbances may suggest an inadequate oxygen supply during the wound healing process in equine limb wounds healing with EGT. This may be related to the inherently decreased perfusion in the wound bed of limb wounds....... wounds) formation of EGT. Microdialysis was used to recover endogenous metabolites from the wounds, and laser Doppler flowmetry was used to measure blood flow. Measurements were performed before wounding and 1-28 days after wounding. Blood flow was consistently lower in limb wounds than in body wounds...

  10. Altered neural connectivity during response inhibition in adolescents with attention-deficit/hyperactivity disorder and their unaffected siblings

    Directory of Open Access Journals (Sweden)

    Daan van Rooij

    2015-01-01

    Discussion: Subjects with ADHD fail to integrate activation within the response inhibition network and to inhibit connectivity with task-irrelevant regions. Unaffected siblings show similar alterations only during failed stop trials, as well as unique suppression of motor areas, suggesting compensatory strategies. These findings support the role of altered functional connectivity in understanding the neurobiology and familial transmission of ADHD.

  11. Neural mechanism of activity spread in the cat motor cortex and its relation to the intrinsic connectivity

    DEFF Research Database (Denmark)

    Capaday, Charles; van Vreeswijk, Carl; Ethier, Christian

    2011-01-01

    NON TECHNICAL SUMMARY{NBSP}: The motor cortex (MCx) is an important brain region that initiates and controls voluntary movements. Neurons in MCx are anatomically connected by recurrent (feedback) networks. This connectivity pattern allows neurons to communicate reciprocally with each other potent...

  12. The neural basis of trait self-esteem revealed by the amplitude of low-frequency fluctuations and resting state functional connectivity.

    Science.gov (United States)

    Pan, Weigang; Liu, Congcong; Yang, Qian; Gu, Yan; Yin, Shouhang; Chen, Antao

    2016-03-01

    Self-esteem is an affective, self-evaluation of oneself and has a significant effect on mental and behavioral health. Although research has focused on the neural substrates of self-esteem, little is known about the spontaneous brain activity that is associated with trait self-esteem (TSE) during the resting state. In this study, we used the resting-state functional magnetic resonance imaging (fMRI) signal of the amplitude of low-frequency fluctuations (ALFFs) and resting state functional connectivity (RSFC) to identify TSE-related regions and networks. We found that a higher level of TSE was associated with higher ALFFs in the left ventral medial prefrontal cortex (vmPFC) and lower ALFFs in the left cuneus/lingual gyrus and right lingual gyrus. RSFC analyses revealed that the strengths of functional connectivity between the left vmPFC and bilateral hippocampus were positively correlated with TSE; however, the connections between the left vmPFC and right inferior frontal gyrus and posterior superior temporal sulcus were negatively associated with TSE. Furthermore, the strengths of functional connectivity between the left cuneus/lingual gyrus and right dorsolateral prefrontal cortex and anterior cingulate cortex were positively related to TSE. These findings indicate that TSE is linked to core regions in the default mode network and social cognition network, which is involved in self-referential processing, autobiographical memory and social cognition.

  13. Human neural stem cell transplantation rescues cognitive defects in APP/PS1 model of Alzheimer's disease by enhancing neuronal connectivity and metabolic activity

    Directory of Open Access Journals (Sweden)

    Xueyuan Li

    2016-11-01

    Full Text Available Alzheimer’s disease (AD, the most frequent type of dementia, is featured by Aβ pathology, neural degeneration and cognitive decline. To date, there is no cure for this disease. Neural stem cell (NSC transplantation provides new promise for treating AD. Many studies report that intra-hippocampal transplantation of murine NSCs improved cognition in rodents with AD by alleviating neurodegeneration via neuronal complement or replacement. However, few reports examined the potential of human NSC transplantation for AD. In this study, we implanted human brain-derived NSCs (hNSCs into bilateral hippocampus of an APP/PS1 transgenic mouse model of AD to test the effects of hNSC transplantation on Alzheimer’s behavior and neuropathology. Six weeks later, transplanted hNSCs engrafted into the brains of AD mice, migrated dispersedly in broad brain regions, and some of them differentiated into neural cell types of central nervous system. The hNSC transplantation restored the recognition, learning and memory deficits but not anxiety tasks in AD mice. Although Aβ plaques were not significantly reduced, the neuronal, synaptic and nerve fiber density was significantly increased in the frontal cortex and hippocampus of hNSC-treated AD mice, suggesting of improved neuronal connectivity in AD brains after hNSC transplantation. Ultrastructural analysis confirmed that synapses and nerve fibers maintained relatively well-structured shapes in these mice. Furthermore, in-vivo magnetic resonance spectroscopy showed that hNSC-treated mice had notably increased levels of NAA and Glu in the frontal cortex and hippocampus, suggesting that neuronal metabolic activity was improved in AD brains after hNSC transplantation. These results suggest that transplanted hNSCs rescued Alzheimer’s cognition by enhancing neuronal connectivity and metabolic activity through a compensation mechanism in APP/PS1 mice. This study provides preclinical evidence that hNSC transplantation

  14. A reinforcement learning trained fuzzy neural network controller for maintaining wireless communication connections in multi-robot systems

    Science.gov (United States)

    Zhong, Xu; Zhou, Yu

    2014-05-01

    This paper presents a decentralized multi-robot motion control strategy to facilitate a multi-robot system, comprised of collaborative mobile robots coordinated through wireless communications, to form and maintain desired wireless communication coverage in a realistic environment with unstable wireless signaling condition. A fuzzy neural network controller is proposed for each robot to maintain the wireless link quality with its neighbors. The controller is trained through reinforcement learning to establish the relationship between the wireless link quality and robot motion decision, via consecutive interactions between the controller and environment. The tuned fuzzy neural network controller is applied to a multi-robot deployment process to form and maintain desired wireless communication coverage. The effectiveness of the proposed control scheme is verified through simulations under different wireless signal propagation conditions.

  15. Augmented Nonlinear Controller for Maximum Power-Point Tracking with Artificial Neural Network in Grid-Connected Photovoltaic Systems

    OpenAIRE

    2016-01-01

    Photovoltaic (PV) systems have non-linear characteristics that generate maximum power at one particular operating point. Environmental factors such as irradiance and temperature variations greatly affect the maximum power point (MPP). Diverse offline and online techniques have been introduced for tracking the MPP. Here, to track the MPP, an augmented-state feedback linearized (AFL) non-linear controller combined with an artificial neural network (ANN) is proposed. This approach linearizes the...

  16. Functional connectivity of neural motor networks is disrupted in children with developmental coordination disorder and attention-deficit/hyperactivity disorder.

    Science.gov (United States)

    McLeod, Kevin R; Langevin, Lisa Marie; Goodyear, Bradley G; Dewey, Deborah

    2014-01-01

    Developmental coordination disorder (DCD) and attention deficit/hyperactivity disorder (ADHD) are prevalent childhood disorders that frequently co-occur. Evidence from neuroimaging research suggests that children with these disorders exhibit disruptions in motor circuitry, which could account for the high rate of co-occurrence. The primary objective of this study was to investigate the functional connections of the motor network in children with DCD and/or ADHD compared to typically developing controls, with the aim of identifying common neurophysiological substrates. Resting-state fMRI was performed on seven children with DCD, 21 with ADHD, 18 with DCD + ADHD and 23 controls. Resting-state connectivity of the primary motor cortex was compared between each group and controls, using age as a co-factor. Relative to controls, children with DCD and/or ADHD exhibited similar reductions in functional connectivity between the primary motor cortex and the bilateral inferior frontal gyri, right supramarginal gyrus, angular gyri, insular cortices, amygdala, putamen, and pallidum. In addition, children with DCD and/or ADHD exhibited different age-related patterns of connectivity, compared to controls. These findings suggest that children with DCD and/or ADHD exhibit disruptions in motor circuitry, which may contribute to problems with motor functioning and attention. Our results support the existence of common neurophysiological substrates underlying both motor and attention problems.

  17. Functional connectivity of neural motor networks is disrupted in children with developmental coordination disorder and attention-deficit/hyperactivity disorder

    Directory of Open Access Journals (Sweden)

    Kevin R. McLeod

    2014-01-01

    Full Text Available Developmental coordination disorder (DCD and attention deficit/hyperactivity disorder (ADHD are prevalent childhood disorders that frequently co-occur. Evidence from neuroimaging research suggests that children with these disorders exhibit disruptions in motor circuitry, which could account for the high rate of co-occurrence. The primary objective of this study was to investigate the functional connections of the motor network in children with DCD and/or ADHD compared to typically developing controls, with the aim of identifying common neurophysiological substrates. Resting-state fMRI was performed on seven children with DCD, 21 with ADHD, 18 with DCD + ADHD and 23 controls. Resting-state connectivity of the primary motor cortex was compared between each group and controls, using age as a co-factor. Relative to controls, children with DCD and/or ADHD exhibited similar reductions in functional connectivity between the primary motor cortex and the bilateral inferior frontal gyri, right supramarginal gyrus, angular gyri, insular cortices, amygdala, putamen, and pallidum. In addition, children with DCD and/or ADHD exhibited different age-related patterns of connectivity, compared to controls. These findings suggest that children with DCD and/or ADHD exhibit disruptions in motor circuitry, which may contribute to problems with motor functioning and attention. Our results support the existence of common neurophysiological substrates underlying both motor and attention problems.

  18. Reduced neural connectivity but increased task-related activity during working memory in de novo Parkinson patients

    NARCIS (Netherlands)

    Trujillo, James P; Gerrits, Niels J H M; Veltman, Dick J; Berendse, Henk W; van der Werf, Ysbrand D; van den Heuvel, Odile A

    2015-01-01

    OBJECTIVE: Patients with Parkinson's disease (PD) often suffer from impairments in executive functions, such as working memory deficits. It is widely held that dopamine depletion in the striatum contributes to these impairments through decreased activity and connectivity between task-related brain n

  19. The Circadian Clock Gene Period1 Connects the Molecular Clock to Neural Activity in the Suprachiasmatic Nucleus.

    Science.gov (United States)

    Kudo, Takashi; Block, Gene D; Colwell, Christopher S

    2015-01-01

    The neural activity patterns of suprachiasmatic nucleus (SCN) neurons are dynamically regulated throughout the circadian cycle with highest levels of spontaneous action potentials during the day. These rhythms in electrical activity are critical for the function of the circadian timing system and yet the mechanisms by which the molecular clockwork drives changes in the membrane are not well understood. In this study, we sought to examine how the clock gene Period1 (Per1) regulates the electrical activity in the mouse SCN by transiently and selectively decreasing levels of PER1 through use of an antisense oligodeoxynucleotide. We found that this treatment effectively reduced SCN neural activity. Direct current injection to restore the normal membrane potential partially, but not completely, returned firing rate to normal levels. The antisense treatment also reduced baseline [Ca(2+)]i levels as measured by Fura2 imaging technique. Whole cell patch clamp recording techniques were used to examine which specific potassium currents were altered by the treatment. These recordings revealed that the large conductance [Ca(2+)]i-activated potassium currents were reduced in antisense-treated neurons and that blocking this current mimicked the effects of the anti-sense on SCN firing rate. These results indicate that the circadian clock gene Per1 alters firing rate in SCN neurons and raise the possibility that the large conductance [Ca(2+)]i-activated channel is one of the targets.

  20. Determination of the Most Important Soil Parameters Affecting the Availability of Phosphorus in Sistan Plain, Using Connection Weight Method in Neural Networks

    Directory of Open Access Journals (Sweden)

    H. Mir

    2016-09-01

    neurons in the hidden layer were calculated based on the trial and error method and finally the best structure was selected according to the highest R2 and the lowest RMSE value. Moreover, quantifying the importance of variables in the neural network was done through employing connection weight approach. In this method, the connection weights of input-hidden and hidden-output neurons were used to indicate the significance of variables. Results and Discussion: The values of the coefficient of variation for the soil properties were in the range of 5.66 for pH (the lowest and 69.90 for available phosphorus (the highest. The high variation of the available phosphorus could be due to the different amounts of phosphorus fertilizers consumption and their diverse rate of conversion to less soluble forms. The validation results of regression and neural network methods showed that the latter technique was more accurate compared with the multivariate linear regression method, in the estimation of available phosphorus, as multi-layer perceptron neural network with 4-6-1 layout predicts nearly 90% of available phosphorus variability using soil properties (percentage of clay, organic matter, calcium carbonate and the amount of pH; however, the obtained regression equation could explain only 43% of phosphorus variances. The reasons for this could be: 1 considering nonlinear relations between the variables in the artificial neural network method, and 2 less sensitivity of this method to the existence of error in input data, comparing with the regression method. The values of R2 and RMSE were 0.43 and 11.23, respectively for training the multivariate linear regression method and 0.91 and 4.28, respectively for training the artificial neural network method. From the investigated soil properties in the current study, the percentage of organic matter and clay were entered in the regression model, and the values of standardized regression coefficient (beta showed that the first variable is

  1. Toward on-chip functional neuronal networks: computational study on the effect of synaptic connectivity on neural activity.

    Science.gov (United States)

    Foroushani, Armin Najarpour; Ghafar-Zadeh, Ebrahim

    2014-01-01

    This paper presents a new unified computational-experimental approach to study the role of the synaptic activity on the activity of neurons in the small neuronal networks (NNs). In a neuronal tissue/organ, this question is investigated with higher complexities by recording action potentials from population of neurons in order to find the relationship between connectivity and the recorded activities. In this approach, we study the dynamics of very small cortical neuronal networks, which can be experimentally synthesized on chip with constrained connectivity. Multi-compartmental Hodgkin-Huxley model is used in NEURON software to reproduce cells by extracting the experimental data from the synthesized NNs. We thereafter demonstrate how the type of synaptic activity affects the network response to specific spike train using the simulation results.

  2. Ketamine modulates subgenual cingulate connectivity with the memory-related neural circuit—a mechanism of relevance to resistant depression?

    Directory of Open Access Journals (Sweden)

    Jing J. Wong

    2016-02-01

    Full Text Available Background. Ketamine has been reported to have efficacy as an antidepressant in several studies of treatment-resistant depression. In this study, we investigate whether an acute administration of ketamine leads to reductions in the functional connectivity of subgenual anterior cingulate cortex (sgACC with other brain regions. Methods. Thirteen right-handed healthy male subjects underwent a 15 min resting state fMRI with an infusion of intravenous ketamine (target blood level = 150 ng/ml starting at 5 min. We used a seed region centred on the sgACC and assessed functional connectivity before and during ketamine administration. Results. Before ketamine administration, positive coupling with the sgACC seed region was observed in a large cluster encompassing the anterior cingulate and negative coupling was observed with the anterior cerebellum. Following ketamine administration, sgACC activity became negatively correlated with the brainstem, hippocampus, parahippocampal gyrus, retrosplenial cortex, and thalamus. Discussion. Ketamine reduced functional connectivity of the sgACC with brain regions implicated in emotion, memory and mind wandering. It is possible the therapeutic effects of ketamine may be mediated via this mechanism, although further work is required to test this hypothesis.

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

    Science.gov (United States)

    Vingerhoets, Guy; Clauwaert, Amanda

    2015-09-01

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

  4. Neurophysiological assessment of neural network plasticity and connectivity: Progress towards early functional biomarkers for disease interception therapies in Alzheimer's disease.

    Science.gov (United States)

    Walsh, C; Drinkenburg, W H I M; Ahnaou, A

    2017-02-01

    Despite a great deal of research into Alzheimer's disease (AD) over the last 20 years, an effective treatment to halt or slow its progression has yet to be developed. With many aspects of the disease progression still to be elucidated, focus has shifted from reducing levels of amyloid β (Aβ) in the brains of AD patients towards tau, another pathology, which initiates much earlier in deeper brainstem networks and is thought to propagate via cell-to-cell processes prior to the onset of amyloid pathology and cognitive impairments. In-vitro, ex-vivo molecular biology/biochemistry read-outs, and various transgenic animal models have been developed, yet clinical failures have highlighted a clear disconnect and inadequate use of such animal models in translational research across species. AD pathology is now estimated to begin at least 10-20 years before clinical symptoms, and imaging and cerebrospinal fluid biomarkers are leading the way in assessing the disease progression at a stage where neuronal damage has already occurred. Here, we emphasize the relevance of assessing early disruptions in network connectivity and plasticity that occur before neuropathological damage and progressive memory dysfunction, which can have high translational value for discovery of pre-symptomatic AD biomarkers and early mechanism-based disease interception therapeutics.

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

    Science.gov (United States)

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

    2013-11-20

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

  6. Self-Tuning Fully-Connected PID Neural Network System for Distributed Temperature Sensing and Control of Instrument with Multi-Modules.

    Science.gov (United States)

    Zhang, Zhen; Ma, Cheng; Zhu, Rong

    2016-10-14

    High integration of multi-functional instruments raises a critical issue in temperature control that is challenging due to its spatial-temporal complexity. This paper presents a multi-input multi-output (MIMO) self-tuning temperature sensing and control system for efficiently modulating the temperature environment within a multi-module instrument. The smart system ensures that the internal temperature of the instrument converges to a target without the need of a system model, thus making the control robust. The system consists of a fully-connected proportional-integral-derivative (PID) neural network (FCPIDNN) and an on-line self-tuning module. The experimental results show that the presented system can effectively control the internal temperature under various mission scenarios, in particular, it is able to self-reconfigure upon actuator failure. The system provides a new scheme for a complex and time-variant MIMO control system which can be widely applied for the distributed measurement and control of the environment in instruments, integration electronics, and house constructions.

  7. Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method

    Directory of Open Access Journals (Sweden)

    Xinyu Guo

    2017-08-01

    Full Text Available The whole-brain functional connectivity (FC pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes. Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150. Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t-test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross

  8. Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method.

    Science.gov (United States)

    Guo, Xinyu; Dominick, Kelli C; Minai, Ali A; Li, Hailong; Erickson, Craig A; Lu, Long J

    2017-01-01

    The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t-test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre

  9. Modular, Hierarchical Learning By Artificial Neural Networks

    Science.gov (United States)

    Baldi, Pierre F.; Toomarian, Nikzad

    1996-01-01

    Modular and hierarchical approach to supervised learning by artificial neural networks leads to neural networks more structured than neural networks in which all neurons fully interconnected. These networks utilize general feedforward flow of information and sparse recurrent connections to achieve dynamical effects. The modular organization, sparsity of modular units and connections, and fact that learning is much more circumscribed are all attractive features for designing neural-network hardware. Learning streamlined by imitating some aspects of biological neural networks.

  10. Consciousness and neural plasticity

    DEFF Research Database (Denmark)

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

  11. Generalized Adaptive Artificial Neural Networks

    Science.gov (United States)

    Tawel, Raoul

    1993-01-01

    Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.

  12. Exuberant neuronal convergence onto reduced taste bud targets with preservation of neural specificity in mice overexpressing neurotrophin in the tongue epithelium.

    Science.gov (United States)

    Zaidi, Faisal N; Krimm, Robin F; Whitehead, Mark C

    2007-12-12

    A mouse fungiform taste bud is innervated by only four to five geniculate ganglion neurons; their peripheral fibers do not branch to other buds. We examined whether the degree or specificity of this exclusive innervation pattern is influenced by brain-derived neurotrophic factor (BDNF), a prominent lingual neurotrophin implicated in taste receptoneural development. Labeled ganglion cells were counted after injecting single buds with different color markers in BDNF-lingual-overexpressing (OE) mice. To evaluate the end-organs, taste buds and a class of putative taste receptor cells were counted from progeny of BDNF-OE mice crossbred with green fluorescent protein (GFP) (gustducin) transgenic mice. Fungiform bud numbers in BDNF-OE mice are 35%, yet geniculate neuron numbers are 195%, of wild-type mice. Neurons labeled by single-bud injections in BDNF-OE animals were increased fourfold versus controls. Injecting three buds, each with different color markers, resulted in predominantly single-labeled ganglion cells, a discrete innervation pattern similar to controls. Thus, hyper-innervation of BDNF-OE buds involves many neurons innervating single buds, not increased fiber branching. Therefore, both wild-type and BDNF-OE mice exhibit, in fungiform buds, the same, "discrete" receptoneural pattern, this despite dramatic neurotrophin overexpression-related decreases in bud numbers and increases in innervation density. Hyperinnervation did not affect GFP positive cell numbers; proportions of GFP cells in BDNF-OE buds were the same as in wild-type mice. Total numbers of ganglion cells innervating buds in transgenic mice are similar to controls; the density of taste input to the brain appears maintained despite dramatically reduced receptor organs and increased ganglion cells.

  13. 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.

  14. Handbook of Brain Connectivity

    CERN Document Server

    Jirsa, Viktor K

    2007-01-01

    Our contemporary understanding of brain function is deeply rooted in the ideas of the nonlinear dynamics of distributed networks. Cognition and motor coordination seem to arise from the interactions of local neuronal networks, which themselves are connected in large scales across the entire brain. The spatial architectures between various scales inevitably influence the dynamics of the brain and thereby its function. But how can we integrate brain connectivity amongst these structural and functional domains? Our Handbook provides an account of the current knowledge on the measurement, analysis and theory of the anatomical and functional connectivity of the brain. All contributors are leading experts in various fields concerning structural and functional brain connectivity. In the first part of the Handbook, the chapters focus on an introduction and discussion of the principles underlying connected neural systems. The second part introduces the currently available non-invasive technologies for measuring struct...

  15. Exuberant sprouting of sensory and sympathetic nerve fibers in nonhealed bone fractures and the generation and maintenance of chronic skeletal pain.

    Science.gov (United States)

    Chartier, Stephane R; Thompson, Michelle L; Longo, Geraldine; Fealk, Michelle N; Majuta, Lisa A; Mantyh, Patrick W

    2014-11-01

    Skeletal injury is a leading cause of chronic pain and long-term disability worldwide. While most acute skeletal pain can be effectively managed with nonsteroidal anti-inflammatory drugs and opiates, chronic skeletal pain is more difficult to control using these same therapy regimens. One possibility as to why chronic skeletal pain is more difficult to manage over time is that there may be nerve sprouting in nonhealed areas of the skeleton that normally receive little (mineralized bone) to no (articular cartilage) innervation. If such ectopic sprouting did occur, it could result in normally nonnoxious loading of the skeleton being perceived as noxious and/or the generation of a neuropathic pain state. To explore this possibility, a mouse model of skeletal pain was generated by inducing a closed fracture of the femur. Examined animals had comminuted fractures and did not fully heal even at 90+days post fracture. In all mice with nonhealed fractures, exuberant sensory and sympathetic nerve sprouting, an increase in the density of nerve fibers, and the formation of neuroma-like structures near the fracture site were observed. Additionally, all of these animals exhibited significant pain behaviors upon palpation of the nonhealed fracture site. In contrast, sprouting of sensory and sympathetic nerve fibers or significant palpation-induced pain behaviors was never observed in naïve animals. Understanding what drives this ectopic nerve sprouting and the role it plays in skeletal pain may allow a better understanding and treatment of this currently difficult-to-control pain state. Copyright © 2014 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

  16. Disruptions in neural connectivity associated with reduced susceptibility to a depth inversion illusion in youth at ultra high risk for psychosis

    Directory of Open Access Journals (Sweden)

    Tina Gupta

    2016-01-01

    Full Text Available Patients with psychosis exhibit a reduced susceptibility to depth inversion illusions (DII in which a physically concave surface is perceived as convex (e.g., the hollow mask illusion. Here, we examined the extent to which lessened susceptibility to DII characterized youth at ultra high risk (UHR for psychosis. In this study, 44 UHR participants and 29 healthy controls judged the apparent convexity of face-like human masks, two of which were concave and the other convex. One of the concave masks was painted with realistic texture to enhance the illusion; the other was shown without such texture. Networks involved with top-down and bottom-up processing were evaluated with resting state functional connectivity magnetic resonance imaging (fcMRI. We examined regions associated with the fronto-parietal network and the visual system and their relations with susceptibility to DII. Consistent with prior studies, the UHR group was less susceptible to DII (i.e., they were characterized by more veridical perception of the stimuli than the healthy control group. Veridical responses were related to weaker connectivity within the fronto-parietal network, and this relationship was stronger in the UHR group, suggesting possible abnormalities of top-down modulation of sensory signals. This could serve as a vulnerability marker and a further clue to the pathogenesis of psychosis.

  17. Application of Artificial Neural Network (ANN in Estimation of Body Mass Index (BMI Based on the Connection Between Environmental Factors and Physical Activity

    Directory of Open Access Journals (Sweden)

    Seyed Hosein Hoseini

    2012-08-01

    Full Text Available One of the main concerns of people in modern societies is increasing the Body Mass Index (BMI level.BMI, in fact, can be considered as an indicator of overall health condition. Genetic aspects aside, the BMIlevel is affected by different factors, such as socio-economic, environmental, and physical activity level.This study investigated the effect of different factors on the BMI level of a sample population of 470 adultsof three residential neighbourhoods in Shiraz, Iran. The Pearson correlation test, independent sample Ttest and One Way ANOVA were used to extract the variables which significantly influenced the BMI. Thestatistical analysis showed that despite the apparent association of BMI with physical activity level, it isinfluenced by several factors such as age, residence record, number of children, distance to bus or taxistop, indoor or sport exercise. Then, an Artificial Neural Network (ANN was applied to predict the level ofpersonal BMI. The results of this analysis showed that the generalized estimating ANN model wassatisfactory in estimating the BMI based on the introduced pattern

  18. Application of Artificial Neural Network (ANN in Estimation of Body Mass Index (BMI Based on the Connection Between Environmental Factors and Physical Activity

    Directory of Open Access Journals (Sweden)

    Seyed Hosein Hoseini

    2012-07-01

    Full Text Available One of the main concerns of people in modern societies is increasing the Body Mass Index (BMI level. BMI, in fact, can be considered as an indicator of overall health condition. Genetic aspects aside, the BMI level is affected by different factors, such as socio-economic, environmental, and physical activity level. This study investigated the effect of different factors on the BMI level of a sample population of 470 adults of three residential neighbourhoods in Shiraz, Iran. The Pearson correlation test, independent sample Ttest and One Way ANOVA were used to extract the variables which significantly influenced the BMI. The statistical analysis showed that despite the apparent association of BMI with physical activity level, it is influenced by several factors such as age, residence record, number of children, distance to bus or taxi stop, indoor or sport exercise. Then, an Artificial Neural Network (ANN was applied to predict the level of personal BMI. The results of this analysis showed that the generalized estimating ANN model was satisfactory in estimating the BMI based on the introduced pattern.

  19. The Skin–Brain Connection Hypothesis, Bringing Together CCL27-Mediated T-Cell Activation in the Skin and Neural Cell Damage in the Adult Brain

    Science.gov (United States)

    Blatt, Nataliya L.; Khaiboullin, Timur I.; Lombardi, Vincent C.; Rizvanov, Albert A.; Khaiboullina, Svetlana F.

    2017-01-01

    Recent discovery of an association of low serum melatonin levels with relapse in multiple sclerosis (MS) opens a new horizon in understanding the pathogenesis of this disease. Skin is the main organ for sensing seasonal changes in duration of sunlight exposure. Level of melatonin production is dependent on light exposure. The molecular mechanisms connecting peripheral (skin) sensing of the light exposure and developing brain inflammation (MS) have not been investigated. We hypothesize that there is a connection between the reaction of skin to seasonal changes in sunlight exposure and the risk of MS and that seasonal changes in light exposure cause peripheral (skin) inflammation, the production of cytokines, and the subsequent inflammation of the brain. In skin of genetically predisposed individuals, cytokines attract memory cutaneous lymphocyte-associated antigen (CLA+) T lymphocytes, which then maintain local inflammation. Once inflammation is resolved, CLA+ lymphocytes return to the circulation, some of which eventually migrate to the brain. Once in the brain these lymphocytes may initiate an inflammatory response. Our observation of increased CC chemokine ligand 27 (CCL27) in MS sera supports the involvement of skin in the pathogenesis of MS. Further, the importance of our data is that CCL27 is a chemokine released by activated keratinocytes, which is upregulated in inflamed skin. We propose that high serum levels of CCL27 in MS are the result of skin inflammation due to exposure to seasonal changes in the sunlight. Future studies will determine whether CCL27 serum level correlates with seasonal changes in sunlight exposure, MS exacerbation, and skin inflammation. PMID:28138328

  20. miR-124, -128, and -137 Orchestrate Neural Differentiation by Acting on Overlapping Gene Sets Containing a Highly Connected Transcription Factor Network.

    Science.gov (United States)

    Santos, Márcia C T; Tegge, Allison N; Correa, Bruna R; Mahesula, Swetha; Kohnke, Luana Q; Qiao, Mei; Ferreira, Marco A R; Kokovay, Erzsebet; Penalva, Luiz O F

    2016-01-01

    The ventricular-subventricular zone harbors neural stem cells (NSCs) that can differentiate into neurons, astrocytes, and oligodendrocytes. This process requires loss of stem cell properties and gain of characteristics associated with differentiated cells. miRNAs function as important drivers of this transition; miR-124, -128, and -137 are among the most relevant ones and have been shown to share commonalities and act as proneurogenic regulators. We conducted biological and genomic analyses to dissect their target repertoire during neurogenesis and tested the hypothesis that they act cooperatively to promote differentiation. To map their target genes, we transfected NSCs with antagomiRs and analyzed differences in their mRNA profile throughout differentiation with respect to controls. This strategy led to the identification of 910 targets for miR-124, 216 for miR-128, and 652 for miR-137. The target sets show extensive overlap. Inspection by gene ontology and network analysis indicated that transcription factors are a major component of these miRNAs target sets. Moreover, several of these transcription factors form a highly interconnected network. Sp1 was determined to be the main node of this network and was further investigated. Our data suggest that miR-124, -128, and -137 act synergistically to regulate Sp1 expression. Sp1 levels are dramatically reduced as cells differentiate and silencing of its expression reduced neuronal production and affected NSC viability and proliferation. In summary, our results show that miRNAs can act cooperatively and synergistically to regulate complex biological processes like neurogenesis and that transcription factors are heavily targeted to branch out their regulatory effect. © 2015 AlphaMed Press.

  1. High serotonin levels during brain development alter the structural input-output connectivity of neural networks in the rat somatosensory layer IV

    Directory of Open Access Journals (Sweden)

    Stéphanie eMiceli

    2013-06-01

    Full Text Available Homeostatic regulation of serotonin (5-HT concentration is critical for normal topographical organization and development of thalamocortical (TC afferent circuits. Down-regulation of the serotonin transporter (SERT and the consequent impaired reuptake of 5-HT at the synapse, results in a reduced terminal branching of developing TC afferents within the primary somatosensory cortex (S1. Despite the presence of multiple genetic models, the effect of high extracellular 5-HT levels on the structure and function of developing intracortical neural networks is far from being understood. Here, using juvenile SERT knockout (SERT-/- rats we investigated, in vitro, the effect of increased 5-HT levels on the structural organization of (i the thalamocortical projections of the ventroposteromedial thalamic nucleus towards S1, (ii the general barrel-field pattern and (iii the electrophysiological and morphological properties of the excitatory cell population in layer IV of S1 (spiny stellate and pyramidal cells. Our results confirmed previous findings that high levels of 5-HT during development lead to a reduction of the topographical precision of TCA projections towards the barrel cortex. Also, the barrel pattern was altered but not abolished in SERT-/- rats. In layer IV, both excitatory spiny stellate and pyramidal cells showed a significantly reduced intracolumnar organization of their axonal projections. In addition, the layer IV spiny stellate cells gave rise to a prominent projection towards the infragranular layer Vb. Our findings point to a structural and functional reorganization, of TCAs, as well as early stage intracortical microcircuitry, following the disruption of 5-HT reuptake during critical developmental periods. The increased projection pattern of the layer IV neurons suggests that the intracortical network changes are not limited to the main entry layer IV but may also affect the subsequent stages of the canonical circuits of the barrel

  2. The equilibrium of neural firing: A mathematical theory

    Energy Technology Data Exchange (ETDEWEB)

    Lan, Sizhong, E-mail: lsz@fuyunresearch.org [Fuyun Research, Beijing, 100055 (China)

    2014-12-15

    Inspired by statistical thermodynamics, we presume that neuron system has equilibrium condition with respect to neural firing. We show that, even with dynamically changeable neural connections, it is inevitable for neural firing to evolve to equilibrium. To study the dynamics between neural firing and neural connections, we propose an extended communication system where noisy channel has the tendency towards fixed point, implying that neural connections are always attracted into fixed points such that equilibrium can be reached. The extended communication system and its mathematics could be useful back in thermodynamics.

  3. Meta-Learning Evolutionary Artificial Neural Networks

    OpenAIRE

    Abraham, Ajith

    2004-01-01

    In this paper, we present MLEANN (Meta-Learning Evolutionary Artificial Neural Network), an automatic computational framework for the adaptive optimization of artificial neural networks wherein the neural network architecture, activation function, connection weights; learning algorithm and its parameters are adapted according to the problem. We explored the performance of MLEANN and conventionally designed artificial neural networks for function approximation problems. To evaluate the compara...

  4. Connected Traveler

    Energy Technology Data Exchange (ETDEWEB)

    Schroeder, Alex

    2015-11-01

    The Connected Traveler project is a multi-disciplinary undertaking that seeks to validate potential for transformative transportation system energy savings by incentivizing efficient traveler behavior. This poster outlines various aspects of the Connected Traveler project, including market opportunity, understanding traveler behavior and decision-making, automation and connectivity, and a projected timeline for Connected Traveler's key milestones.

  5. Synthetic neuronal datasets for benchmarking directed functional connectivity metrics

    National Research Council Canada - National Science Library

    Rodrigues, João; Andrade, Alexandre

    2015-01-01

    Background. Datasets consisting of synthetic neural data generated with quantifiable and controlled parameters are a valuable asset in the process of testing and validating directed functional connectivity metrics...

  6. Connecting Grammaticalisation

    DEFF Research Database (Denmark)

    Nørgård-Sørensen, Jens; Heltoft, Lars; Schøsler, Lene

    morphological, topological and constructional paradigms often connect to form complex paradigms. The book introduces the concept of connecting grammaticalisation to describe the formation, restructuring and dismantling of such complex paradigms. Drawing primarily on data from Germanic, Romance and Slavic...

  7. Neural connections between antrum and duodenum

    DEFF Research Database (Denmark)

    Kraglund, K; Schrøder, H D; Stødkilde-Jørgensen, H

    1983-01-01

    Postprandial coordination of antroduodenal motility partly takes place via intrinsic mural pathways. The nature and origin of these nerve fibers have not yet been clarified. In this investigation using fluorochromic substances injected into the antrum and duodenum it was demonstrated that common...

  8. Neural network regulation driven by autonomous neural firings

    Science.gov (United States)

    Cho, Myoung Won

    2016-07-01

    Biological neurons naturally fire spontaneously due to the existence of a noisy current. Such autonomous firings may provide a driving force for network formation because synaptic connections can be modified due to neural firings. Here, we study the effect of autonomous firings on network formation. For the temporally asymmetric Hebbian learning, bidirectional connections lose their balance easily and become unidirectional ones. Defining the difference between reciprocal connections as new variables, we could express the learning dynamics as if Ising model spins interact with each other in magnetism. We present a theoretical method to estimate the interaction between the new variables in a neural system. We apply the method to some network systems and find some tendencies of autonomous neural network regulation.

  9. Compressing Convolutional Neural Networks

    OpenAIRE

    Chen, Wenlin; Wilson, James T.; Tyree, Stephen; Weinberger, Kilian Q.; Chen, Yixin

    2015-01-01

    Convolutional neural networks (CNN) are increasingly used in many areas of computer vision. They are particularly attractive because of their ability to "absorb" great quantities of labeled data through millions of parameters. However, as model sizes increase, so do the storage and memory requirements of the classifiers. We present a novel network architecture, Frequency-Sensitive Hashed Nets (FreshNets), which exploits inherent redundancy in both convolutional layers and fully-connected laye...

  10. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

    Science.gov (United States)

    Kim, Junghoe; Calhoun, Vince D; Shim, Eunsoo; Lee, Jong-Hwan

    2016-01-01

    Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was

  11. The implications of brain connectivity in the neuropsychology of autism

    OpenAIRE

    2014-01-01

    Autism is a neurodevelopmental disorder that has been associated with atypical brain functioning. Functional connectivity MRI (fcMRI) studies examining neural networks in autism have seen an exponential rise over the last decade. Such investigations have led to characterization of autism as a distributed neural systems disorder. Studies have found widespread cortical underconnectivity, local overconnectivity, and mixed results suggesting disrupted brain connectivity as a potential neural sign...

  12. Adaptive Neurons For Artificial Neural Networks

    Science.gov (United States)

    Tawel, Raoul

    1990-01-01

    Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.

  13. Nonequilibrium landscape theory of neural networks.

    Science.gov (United States)

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

    2013-11-05

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

  14. Nonequilibrium landscape theory of neural networks

    Science.gov (United States)

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

    2013-01-01

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

  15. About Connections

    Directory of Open Access Journals (Sweden)

    Kathleen S Rockland

    2015-05-01

    Full Text Available Despite the attention attracted by connectomics, one can lose sight of the very real questions concerning What are connections? In the neuroimaging community, structural connectivity is ground truth and underlying constraint on functional or effective connectivity. It is referenced to underlying anatomy; but, as increasingly remarked, there is a large gap between the wealth of human brain mapping and the relatively scant data on actual anatomical connectivity. Moreover, connections have typically been discussed as pairwise, point x projecting to point y (or: to points y and z, or more recently, in graph theoretical terms, as nodes or regions and the interconnecting edges. This is a convenient shorthand, but tends not to capture the richness and nuance of basic anatomical properties as identified in the classic tradition of tracer studies. The present short review accordingly revisits connectional weights, heterogeneity, reciprocity, topography, and hierarchical organization, drawing on concrete examples. The emphasis is on presynaptic long-distance connections, motivated by the intention to probe current assumptions and promote discussions about further progress and synthesis.

  16. Gendered Connections

    DEFF Research Database (Denmark)

    Jensen, Steffen Bo

    2009-01-01

    This article explores the gendered nature of urban politics in Cape Town by focusing on a group of female, township politicians. Employing the Deleuzian concept of `wild connectivity', it argues that these politically entrepreneurial women were able to negotiate a highly volatile urban landscape...... space also drew on quite traditional notions of female respectability. Furthermore, the article argues, the form of wild connectivity to an extent was a function of the political transition, which destabilized formal structures of gendered authority. It remains a question whether this form...... of connectivity might endure, as Capetonian politics assumes a post-apartheid structure....

  17. Metastable dynamics in heterogeneous neural fields

    Directory of Open Access Journals (Sweden)

    Cordula eSchwappach

    2015-06-01

    Full Text Available We present numerical simulations of metastable states in heterogeneous neural fields that are connected along heteroclinic orbits. Such trajectories are possible representations of transient neural activity as observed, for example, in the electroencephalogram. Based on previous theoretical findings on learning algorithms for neural fields, we directly construct synaptic weight kernels from Lotka-Volterra neural population dynamics without supervised training approaches. We deliver a MATLAB neural field toolbox validated by two examples of one- and two-dimensional neural fields. We demonstrate trial-to-trial variability and distributed representations in our simulations which might therefore be regarded as a proof-of-concept for more advanced neural field models of metastable dynamics in neurophysiological data.

  18. Metastable dynamics in heterogeneous neural fields

    Science.gov (United States)

    Schwappach, Cordula; Hutt, Axel; beim Graben, Peter

    2015-01-01

    We present numerical simulations of metastable states in heterogeneous neural fields that are connected along heteroclinic orbits. Such trajectories are possible representations of transient neural activity as observed, for example, in the electroencephalogram. Based on previous theoretical findings on learning algorithms for neural fields, we directly construct synaptic weight kernels from Lotka-Volterra neural population dynamics without supervised training approaches. We deliver a MATLAB neural field toolbox validated by two examples of one- and two-dimensional neural fields. We demonstrate trial-to-trial variability and distributed representations in our simulations which might therefore be regarded as a proof-of-concept for more advanced neural field models of metastable dynamics in neurophysiological data. PMID:26175671

  19. HR Connect

    Data.gov (United States)

    US Agency for International Development — HR Connect is the USAID HR personnel system which allows HR professionals to process HR actions related to employee's personal and position information. This system...

  20. Neural Circuits on a Chip

    Directory of Open Access Journals (Sweden)

    Md. Fayad Hasan

    2016-09-01

    Full Text Available Neural circuits are responsible for the brain’s ability to process and store information. Reductionist approaches to understanding the brain include isolation of individual neurons for detailed characterization. When maintained in vitro for several days or weeks, dissociated neurons self-assemble into randomly connected networks that produce synchronized activity and are capable of learning. This review focuses on efforts to control neuronal connectivity in vitro and construct living neural circuits of increasing complexity and precision. Microfabrication-based methods have been developed to guide network self-assembly, accomplishing control over in vitro circuit size and connectivity. The ability to control neural connectivity and synchronized activity led to the implementation of logic functions using living neurons. Techniques to construct and control three-dimensional circuits have also been established. Advances in multiple electrode arrays as well as genetically encoded, optical activity sensors and transducers enabled highly specific interfaces to circuits composed of thousands of neurons. Further advances in on-chip neural circuits may lead to better understanding of the brain.

  1. 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.

  2. Synaptic inputs compete during rapid formation of the calyx of Held: a new model system for neural development.

    Science.gov (United States)

    Holcomb, Paul S; Hoffpauir, Brian K; Hoyson, Mitchell C; Jackson, Dakota R; Deerinck, Thomas J; Marrs, Glenn S; Dehoff, Marlin; Wu, Jonathan; Ellisman, Mark H; Spirou, George A

    2013-08-07

    Hallmark features of neural circuit development include early exuberant innervation followed by competition and pruning to mature innervation topography. Several neural systems, including the neuromuscular junction and climbing fiber innervation of Purkinje cells, are models to study neural development in part because they establish a recognizable endpoint of monoinnervation of their targets and because the presynaptic terminals are large and easily monitored. We demonstrate here that calyx of Held (CH) innervation of its target, which forms a key element of auditory brainstem binaural circuitry, exhibits all of these characteristics. To investigate CH development, we made the first application of serial block-face scanning electron microscopy to neural development with fine temporal resolution and thereby accomplished the first time series for 3D ultrastructural analysis of neural circuit formation. This approach revealed a growth spurt of added apposed surface area (ASA)>200 μm2/d centered on a single age at postnatal day 3 in mice and an initial rapid phase of growth and competition that resolved to monoinnervation in two-thirds of cells within 3 d. This rapid growth occurred in parallel with an increase in action potential threshold, which may mediate selection of the strongest input as the winning competitor. ASAs of competing inputs were segregated on the cell body surface. These data suggest mechanisms to select "winning" inputs by regional reinforcement of postsynaptic membrane to mediate size and strength of competing synaptic inputs.

  3. Connected Traveler

    Energy Technology Data Exchange (ETDEWEB)

    2016-06-01

    The Connected Traveler framework seeks to boost the energy efficiency of personal travel and the overall transportation system by maximizing the accuracy of predicted traveler behavior in response to real-time feedback and incentives. It is anticipated that this approach will establish a feedback loop that 'learns' traveler preferences and customizes incentives to meet or exceed energy efficiency targets by empowering individual travelers with information needed to make energy-efficient choices and reducing the complexity required to validate transportation system energy savings. This handout provides an overview of NREL's Connected Traveler project, including graphics, milestones, and contact information.

  4. Order-theoretical connectivity

    Directory of Open Access Journals (Sweden)

    T. A. Richmond

    1990-01-01

    Full Text Available Order-theoretically connected posets are introduced and applied to create the notion of T-connectivity in ordered topological spaces. As special cases T-connectivity contains classical connectivity, order-connectivity, and link-connectivity.

  5. Getting Connected

    Science.gov (United States)

    Larkin, Patrick

    2011-01-01

    That the world outside schools is changing faster than ever is old news. Unfortunately, that the world "inside" schools is changing at a glacial pace is even older news. As school leaders, principals have an important choice to make as they move into the second decade of the 21st century. School leaders have a moral obligation to connect and…

  6. Connecting dots

    DEFF Research Database (Denmark)

    Murakami, Kyoko; Jacobs, Rachel L.

    2017-01-01

    of connecting the dots of recalled moments of individual family members lives and is geared towards building a family’s shared future for posterity. Lastly, we consider a wider implication of family reminiscence in terms of human development. http://www.infoagepub.com/products/Memory-Practices-and-Learning...

  7. Learning Connections

    Science.gov (United States)

    Royer, Regina D.; Richards, Patricia O.

    2005-01-01

    In this edition of Learning Connections, the authors show how technology can enhance study of weather patterns, reading comprehension, real-world training, critical thinking, health education, and art criticism. The following sections are included: (1) Social Studies; (2) Language Arts; (3) Computer Science and ICT; (4) Art; and (5) Health.…

  8. Selective Manipulation of Neural Circuits.

    Science.gov (United States)

    Park, Hong Geun; Carmel, Jason B

    2016-04-01

    Unraveling the complex network of neural circuits that form the nervous system demands tools that can manipulate specific circuits. The recent evolution of genetic tools to target neural circuits allows an unprecedented precision in elucidating their function. Here we describe two general approaches for achieving circuit specificity. The first uses the genetic identity of a cell, such as a transcription factor unique to a circuit, to drive expression of a molecule that can manipulate cell function. The second uses the spatial connectivity of a circuit to achieve specificity: one genetic element is introduced at the origin of a circuit and the other at its termination. When the two genetic elements combine within a neuron, they can alter its function. These two general approaches can be combined to allow manipulation of neurons with a specific genetic identity by introducing a regulatory gene into the origin or termination of the circuit. We consider the advantages and disadvantages of both these general approaches with regard to specificity and efficacy of the manipulations. We also review the genetic techniques that allow gain- and loss-of-function within specific neural circuits. These approaches introduce light-sensitive channels (optogenetic) or drug sensitive channels (chemogenetic) into neurons that form specific circuits. We compare these tools with others developed for circuit-specific manipulation and describe the advantages of each. Finally, we discuss how these tools might be applied for identification of the neural circuits that mediate behavior and for repair of neural connections.

  9. A new perspective on behavioral inconsistency and neural noise in aging: Compensatory speeding of neural communication

    Directory of Open Access Journals (Sweden)

    S. Lee Hong

    2012-09-01

    Full Text Available This paper seeks to present a new perspective on the aging brain. Here, we make connections between two key phenomena of brain aging: 1 increased neural noise or random background activity; and 2 slowing of brain activity. Our perspective proposes the possibility that the slowing of neural processing due to decreasing nerve conduction velocities leads to a compensatory speeding of neuron firing rates. These increased firing rates lead to a broader distribution of power in the frequency spectrum of neural oscillations, which we propose, can just as easily be interpreted as neural noise. Compensatory speeding of neural activity, as we present, is constrained by the: A availability of metabolic energy sources; and B competition for frequency bandwidth needed for neural communication. We propose that these constraints lead to the eventual inability to compensate for age-related declines in neural function that are manifested clinically as deficits in cognition, affect, and motor behavior.

  10. Cognitive control network connectivity in adolescent women with and without a parental history of depression

    Directory of Open Access Journals (Sweden)

    Peter C. Clasen

    2014-01-01

    Conclusions: Depressed parents may transmit depression vulnerability to their adolescent daughters via alterations in functional connectivity within neural circuits that underlie cognitive control of emotional information.

  11. 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...

  12. Visual Grouping by Neural Oscillators

    CERN Document Server

    Yu, Guoshen

    2008-01-01

    Distributed synchronization is known to occur at several scales in the brain, and has been suggested as playing a key functional role in perceptual grouping. State-of-the-art visual grouping algorithms, however, seem to give comparatively little attention to neural synchronization analogies. Based on the framework of concurrent synchronization of dynamic systems, simple networks of neural oscillators coupled with diffusive connections are proposed to solve visual grouping problems. Multi-layer algorithms and feedback mechanisms are also studied. The same algorithm is shown to achieve promising results on several classical visual grouping problems, including point clustering, contour integration and image segmentation.

  13. Noise-Assisted Instantaneous Coherence Analysis of Brain Connectivity

    Directory of Open Access Journals (Sweden)

    Meng Hu

    2012-01-01

    visual cortex of macaque monkey while performing a generalized flash suppression task are then used to demonstrate the usefulness of our NAIC method to provide highresolution time-frequency coherence measure for connectivity analysis of neural data.

  14. Computer science: Nanoscale connections for brain-like circuits

    Science.gov (United States)

    Legenstein, Robert

    2015-05-01

    Tiny circuit elements called memristors have been used as connections in an artificial neural network - enabling the system to learn to recognize letters of the alphabet from imperfect images. See Letter p.61

  15. Evolution of Plastic Learning in Spiking Networks via Memristive Connections

    OpenAIRE

    Howard, Gerard; Gale, Ella; Bull, Larry; Costello, Ben de Lacy; Adamatzky, Andy

    2012-01-01

    This article presents a spiking neuroevolutionary system which implements memristors as plastic connections, i.e. whose weights can vary during a trial. The evolutionary design process exploits parameter self-adaptation and variable topologies, allowing the number of neurons, connection weights, and inter-neural connectivity pattern to emerge. By comparing two phenomenological real-world memristor implementations with networks comprised of (i) linear resistors (ii) constant-valued connections...

  16. The Developmental Neurobiology of Brain Connectivity in Schizophrenia

    NARCIS (Netherlands)

    T.J.H. White (Tonya)

    2010-01-01

    textabstractMuch work over the past two decades supports the concept that schizophrenia involves a disruption in the orchestration of the multiple neural networks that participate in higher cognitive functions. The connectivity between neural networks arising during normal development is

  17. Effects of topical application of silver sulfadiazine cream, triple antimicrobial ointment, or hyperosmolar nanoemulsion on wound healing, bacterial load, and exuberant granulation tissue formation in bandaged full-thickness equine skin wounds.

    Science.gov (United States)

    Harmon, Caroline C Gillespie; Hawkins, Jan F; Li, Jianming; Connell, Sean; Miller, Margaret; Saenger, Megan; Freeman, Lynetta J

    2017-05-01

    OBJECTIVE To determine the effects of 3 topically applied treatments (1% silver sulfadiazine cream [SSC], triple antimicrobial ointment [TAO], and hyperosmolar nanoemulsion [HNE]) on microbial counts, exuberant granulation tissue (EGT) development, and reepithelialization of contaminated wounds at the distal aspect of the limbs of horses. ANIMALS 8 healthy adult horses. PROCEDURES A 2.5 × 2.5-cm, full-thickness, cutaneous wound was created at the dorsal aspect of each metacarpus and metatarsus (1 wound/limb/horse), covered with nonadhesive dressing, and bandaged. Wounds were inoculated with bacteria and fungi the next day. Each wound on a given horse was randomly assigned to 1 of 4 treatment groups (SSC, TAO, HNE, or no topical treatment [control]). Bandage changes, culture of wound samples, treatments, photography for wound measurements, and biopsy were performed at predetermined time points. Time (days) until wound closure, number of EGT excisions, microbial counts, and scores for selected histologic characteristics were compared among groups. RESULTS Median time to wound closure for all groups was 42 days. Time to wound closure and histologic characteristics of wound healing did not differ among groups. Least squares mean microbial counts were significantly higher for HNE-treated wounds on days 9 and 21, compared with SSC-treated and TAO-treated wounds, but not controls. Proportions of SSC-treated (7/8) or HNE-treated (5/8) wounds needing EGT excision were significantly greater than that of TAO-treated (1/8) wounds. The proportion of SSC-treated wounds with EGT excision was greater than that of controls (3/8). CONCLUSIONS AND CLINICAL RELEVANCE None of the treatments resulted in more rapid wound closure, compared with that for untreated control wounds under the study conditions. When treatment is warranted, TAO may help to limit EGT formation.

  18. Learning Algorithms of Multilayer Neural Networks

    OpenAIRE

    Fujiki, Sumiyoshi; FUJIKI, Nahomi, M.

    1996-01-01

    A positive reinforcement type learning algorithm is formulated for a stochastic feed-forward multilayer neural network, with far interlayer synaptic connections, and we obtain a learning rule similar to that of the Boltzmann machine on the same multilayer structure. By applying a mean field approximation to the stochastic feed-forward neural network, the generalized error back-propagation learning rule is derived for a deterministic analog feed-forward multilayer network with the far interlay...

  19. CDMA and TDMA based neural nets.

    Science.gov (United States)

    Herrero, J C

    2001-06-01

    CDMA and TDMA telecommunication techniques were established long time ago, but they have acquired a renewed presence due to the rapidly increasing mobile phones demand. In this paper, we are going to see they are suitable for neural nets, if we leave the concept "connection" between processing units and we adopt the concept "messages" exchanged between them. This may open the door to neural nets with a higher number of processing units and flexible configuration.

  20. 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.

  1. Neural recording and modulation technologies

    Science.gov (United States)

    Chen, Ritchie; Canales, Andres; Anikeeva, Polina

    2017-01-01

    In the mammalian nervous system, billions of neurons connected by quadrillions of synapses exchange electrical, chemical and mechanical signals. Disruptions to this network manifest as neurological or psychiatric conditions. Despite decades of neuroscience research, our ability to treat or even to understand these conditions is limited by the capability of tools to probe the signalling complexity of the nervous system. Although orders of magnitude smaller and computationally faster than neurons, conventional substrate-bound electronics do not recapitulate the chemical and mechanical properties of neural tissue. This mismatch results in a foreign-body response and the encapsulation of devices by glial scars, suggesting that the design of an interface between the nervous system and a synthetic sensor requires additional materials innovation. Advances in genetic tools for manipulating neural activity have fuelled the demand for devices that are capable of simultaneously recording and controlling individual neurons at unprecedented scales. Recently, flexible organic electronics and bio- and nanomaterials have been developed for multifunctional and minimally invasive probes for long-term interaction with the nervous system. In this Review, we discuss the design lessons from the quarter-century-old field of neural engineering, highlight recent materials-driven progress in neural probes and look at emergent directions inspired by the principles of neural transduction.

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

    OpenAIRE

    Yardley, Nathan; García-Castro, Martín I.

    2012-01-01

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

  3. Cortical connective field estimates from resting state fMRI activity

    NARCIS (Netherlands)

    Gravel, Nicolas; Harvey, Ben; Nordhjem, Barbara; Haak, Koen V.; Dumoulin, Serge O.; Renken, Remco; Curcic-Blake, Branisalava; Cornelissen, Frans W.

    2014-01-01

    One way to study connectivity in visual cortical areas is by examining spontaneous neural activity. In the absence of visual input, such activity remains shaped by the underlying neural architecture and, presumably, may still reflect visuotopic organization. Here, we applied population connective fi

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

    Science.gov (United States)

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

    1987-01-01

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

  5. Neural Network Applications

    NARCIS (Netherlands)

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

    1995-01-01

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

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

    CERN Document Server

    Fujita, Sh; Fujita, Sh; Nishimura, H

    1994-01-01

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

  7. On the Elementary Neural Forms of Micro-Interactional Rituals:

    DEFF Research Database (Denmark)

    Heinskou, Marie Bruvik; Liebst, Lasse Suonperä

    2016-01-01

    prosocial behavior. The ritual ingre-dients of mutual attention and shared mood may, moreover, be specified as part of a social engagementsystem, neurally regulating attention and emotional arousal via a face–heart connection. The article suggeststhat this social engagement system provides part of the neural...

  8. The brain-stomach connection.

    Science.gov (United States)

    Folgueira, C; Seoane, L M; Casanueva, F F

    2014-01-01

    The stomach-brain connection has been revealed to be one of the most promising targets in treating obesity. The stomach plays a key role in the homeostatic mechanism implicating stomach-brain communication regulated under neural and hormonal control. The present review explores specific topics related to gut-brain interactions focus on the stomach-brain connection through the different known systems implied in energy balance control as ghrelin, and nesfatin. Moreover, novel mechanisms for energy balance regulation involving gastric-brain communication are described including the role of the gastric intracellular mTOR/S6K1 pathway mediating the interaction among ghrelin, nesfatin and endocannabinoid gastric systems to modulate metabolism. © 2014 S. Karger AG, Basel.

  9. Salience-Affected Neural Networks

    CERN Document Server

    Remmelzwaal, Leendert A; Ellis, George F R

    2010-01-01

    We present a simple neural network model which combines a locally-connected feedforward structure, as is traditionally used to model inter-neuron connectivity, with a layer of undifferentiated connections which model the diffuse projections from the human limbic system to the cortex. This new layer makes it possible to model global effects such as salience, at the same time as the local network processes task-specific or local information. This simple combination network displays interactions between salience and regular processing which correspond to known effects in the developing brain, such as enhanced learning as a result of heightened affect. The cortex biases neuronal responses to affect both learning and memory, through the use of diffuse projections from the limbic system to the cortex. Standard ANNs do not model this non-local flow of information represented by the ascending systems, which are a significant feature of the structure of the brain, and although they do allow associational learning with...

  10. Connectivity of communication networks

    CERN Document Server

    Mao, Guoqiang

    2017-01-01

    This book introduces a number of recent developments on connectivity of communication networks, ranging from connectivity of large static networks and connectivity of highly dynamic networks to connectivity of small to medium sized networks. This book also introduces some applications of connectivity studies in network optimization, in network localization, and in estimating distances between nodes. The book starts with an overview of the fundamental concepts, models, tools, and methodologies used for connectivity studies. The rest of the chapters are divided into four parts: connectivity of large static networks, connectivity of highly dynamic networks, connectivity of small to medium sized networks, and applications of connectivity studies.

  11. Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

    DEFF Research Database (Denmark)

    Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin

    2015-01-01

    mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online...... correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking...

  12. Exuberant optimism vs the precautionary principle

    Directory of Open Access Journals (Sweden)

    John Cairns Jr

    2001-11-01

    Full Text Available ABSTRACT: Management of Earth's resources will not attain sustainability unless tough questions are asked and the merits and disadvantages of conflicting paradigms are rigorously examined. Two major conflicting paradigms are: (1 economic growth will solve all problems, including environmental ones --- the free market has negated the dire environmental forecasts and relegated them to the status of myths; and (2 human society is dependent upon the planet's life support --- system it assumes that the present rate of biotic impoverishment (e.g., species extinction, loss of habitat will so alter the biosphere that it will be less habitable for humans. Dominant, global practices are based on the first assumption, which, if invalid, will have dire consequences for human society. For example, anthropogenic greenhouse gases causing a modest rise of global temperatures could produce 20 million environmental refugees from Bangladesh alone as a consequence of a sea level rise that would inundate 17% of the country's habitable land. Implementing the second paradigm would require major, mostly unpalatable, changes in human behavior. Since, at present, humans occupy only 1 planet, the precautionary principle suggests acting more cautiously with regard to economic growth until its effects upon the planet's ecological life support system are better understood.

  13. Exuberant innovation: The Human Genome Project

    CERN Document Server

    Gisler, Monika; Woodard, Ryan

    2010-01-01

    We present a detailed synthesis of the development of the Human Genome Project (HGP) from 1986 to 2003 in order to test the "social bubble" hypothesis that strong social interactions between enthusiastic supporters of the HGP weaved a network of reinforcing feedbacks that led to a widespread endorsement and extraordinary commitment by those involved in the project, beyond what would be rationalized by a standard cost-benefit analysis in the presence of extraordinary uncertainties and risks. The vigorous competition and race between the initially public project and several private initiatives is argued to support the social bubble hypothesis. We also present quantitative analyses of the concomitant financial bubble concentrated on the biotech sector. Confirmation of this hypothesis is offered by the present consensus that it will take decades to exploit the fruits of the HGP, via a slow and arduous process aiming at disentangling the extraordinary complexity of the human complex body. The HGP has ushered other...

  14. Modulation of frontal effective connectivity during speech.

    Science.gov (United States)

    Holland, Rachel; Leff, Alex P; Penny, William D; Rothwell, John C; Crinion, Jenny

    2016-10-15

    Noninvasive neurostimulation methods such as transcranial direct current stimulation (tDCS) can elicit long-lasting, polarity-dependent changes in neocortical excitability. In a previous concurrent tDCS-fMRI study of overt picture naming, we reported significant behavioural and regionally specific neural facilitation effects in left inferior frontal cortex (IFC) with anodal tDCS applied to left frontal cortex (Holland et al., 2011). Although distributed connectivity effects of anodal tDCS have been modelled at rest, the mechanism by which 'on-line' tDCS may modulate neuronal connectivity during a task-state remains unclear. Here, we used Dynamic Causal Modelling (DCM) to determine: (i) how neural connectivity within the frontal speech network is modulated during anodal tDCS; and, (ii) how individual variability in behavioural response to anodal tDCS relates to changes in effective connectivity strength. Results showed that compared to sham, anodal tDCS elicited stronger feedback from inferior frontal sulcus (IFS) to ventral premotor (VPM) accompanied by weaker self-connections within VPM, consistent with processes of neuronal adaptation. During anodal tDCS individual variability in the feedforward connection strength from IFS to VPM positively correlated with the degree of facilitation in naming behaviour. These results provide an essential step towards understanding the mechanism of 'online' tDCS paired with a cognitive task. They also identify left IFS as a 'top-down' hub and driver for speech change.

  15. Connection Strings Property on ADO Connection Object

    Institute of Scientific and Technical Information of China (English)

    Girigi Deogratias; Wu Min; Cao Weihua

    2002-01-01

    The connection string property on ADO connection object contains the information used to establish a connection to the data source. The syntax, the keyword of that information must be in specific format. Depending on the type of data you are connecting to, you need either specify an OLEDB provider or use on ODBC driver. The biggest problem, the industries face is the proliferation of data access interfaces, and the complexity of creating,maintaining and programming against them, and the network problem when communicating over the Intranet or the Internet. This paper first provides an in-depth look of the standard arguments supported by ADO connection string; then gives the easier way for understanding the meaning, the utility and the syntax of the connection strings property on ADO connection object, and finally proposes solution to work around the problems due to the connection strings errors.

  16. Automating parallel implementation of neural learning algorithms.

    Science.gov (United States)

    Rana, O F

    2000-06-01

    Neural learning algorithms generally involve a number of identical processing units, which are fully or partially connected, and involve an update function, such as a ramp, a sigmoid or a Gaussian function for instance. Some variations also exist, where units can be heterogeneous, or where an alternative update technique is employed, such as a pulse stream generator. Associated with connections are numerical values that must be adjusted using a learning rule, and and dictated by parameters that are learning rule specific, such as momentum, a learning rate, a temperature, amongst others. Usually, neural learning algorithms involve local updates, and a global interaction between units is often discouraged, except in instances where units are fully connected, or involve synchronous updates. In all of these instances, concurrency within a neural algorithm cannot be fully exploited without a suitable implementation strategy. A design scheme is described for translating a neural learning algorithm from inception to implementation on a parallel machine using PVM or MPI libraries, or onto programmable logic such as FPGAs. A designer must first describe the algorithm using a specialised Neural Language, from which a Petri net (PN) model is constructed automatically for verification, and building a performance model. The PN model can be used to study issues such as synchronisation points, resource sharing and concurrency within a learning rule. Specialised constructs are provided to enable a designer to express various aspects of a learning rule, such as the number and connectivity of neural nodes, the interconnection strategies, and information flows required by the learning algorithm. A scheduling and mapping strategy is then used to translate this PN model onto a multiprocessor template. We demonstrate our technique using a Kohonen and backpropagation learning rules, implemented on a loosely coupled workstation cluster, and a dedicated parallel machine, with PVM libraries.

  17. Additive Feed Forward Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1999-01-01

    This paper demonstrates a method to control a non-linear, multivariable, noisy process using trained neural networks. The basis for the method is a trained neural network controller acting as the inverse process model. A training method for obtaining such an inverse process model is applied....... A suitable 'shaped' (low-pass filtered) reference is used to overcome problems with excessive control action when using a controller acting as the inverse process model. The control concept is Additive Feed Forward Control, where the trained neural network controller, acting as the inverse process model......, is placed in a supplementary pure feed-forward path to an existing feedback controller. This concept benefits from the fact, that an existing, traditional designed, feedback controller can be retained without any modifications, and after training the connection of the neural network feed-forward controller...

  18. Additive Feed Forward Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1999-01-01

    This paper demonstrates a method to control a non-linear, multivariable, noisy process using trained neural networks. The basis for the method is a trained neural network controller acting as the inverse process model. A training method for obtaining such an inverse process model is applied....... A suitable 'shaped' (low-pass filtered) reference is used to overcome problems with excessive control action when using a controller acting as the inverse process model. The control concept is Additive Feed Forward Control, where the trained neural network controller, acting as the inverse process model......, is placed in a supplementary pure feed-forward path to an existing feedback controller. This concept benefits from the fact, that an existing, traditional designed, feedback controller can be retained without any modifications, and after training the connection of the neural network feed-forward controller...

  19. Minimum cost connection networks

    DEFF Research Database (Denmark)

    Hougaard, Jens Leth; Tvede, Mich

    In the present paper we consider the allocation of cost in connection networks. Agents have connection demands in form of pairs of locations they want to be connected. Connections between locations are costly to build. The problem is to allocate costs of networks satisfying all connection demands...

  20. Attribute-space connectivity and connected filters

    NARCIS (Netherlands)

    Wilkinson, Michael H.F.

    2007-01-01

    In this paper connected operators from mathematical morphology are extended to a wider class of operators, which are based on connectivities in higher dimensional spaces, similar to scale spaces, which will be called attribute-spaces. Though some properties of connected filters are lost, granulometr

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

    Directory of Open Access Journals (Sweden)

    Sally A. Moody

    2002-01-01

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

  2. Phase Synchronization in Small World Chaotic Neural Networks

    Institute of Scientific and Technical Information of China (English)

    WANG Qing-Yun; LU Qi-Shao

    2005-01-01

    @@ To understand collective motion of realneural networks very well, we investigate collective phase synchronization of small world chaotic Hindmarsh-Rose (HR) neural networks. By numerical simulations, we conclude that small world chaotic HR neural networks can achieve collective phase synchronization. Furthermore, it is shown that phase synchronization of small world chaotic HR neural networks is dependent on the coupling strength,the connection topology (which is determined by the probability p), as well as the coupling number. These phenomena are important to guide us to understand the synchronization of real neural networks.

  3. Neural network models: Insights and prescriptions from practical applications

    Energy Technology Data Exchange (ETDEWEB)

    Samad, T. [Honeywell Technology Center, Minneapolis, MN (United States)

    1995-12-31

    Neural networks are no longer just a research topic; numerous applications are now testament to their practical utility. In the course of developing these applications, researchers and practitioners have been faced with a variety of issues. This paper briefly discusses several of these, noting in particular the rich connections between neural networks and other, more conventional technologies. A more comprehensive version of this paper is under preparation that will include illustrations on real examples. Neural networks are being applied in several different ways. Our focus here is on neural networks as modeling technology. However, much of the discussion is also relevant to other types of applications such as classification, control, and optimization.

  4. ON THE STABILITY OF THE CELLULAR NEURAL NETWORKS WITH TIME LAGS

    OpenAIRE

    Vladimir RASVAN; Daniela DANCIU

    2004-01-01

    Cellular neural networks (CNNs) are recurrent artificial neural networks. Due to their cyclic connections and to the neurons’ nonlinear activation functions, recurrent neural networks are nonlinear dynamic systems, which display stable and unstable fixed points, limit cycles and chaotic behavior. Since the field of neural networks is still a young one, improving the stability conditions for such systems is an obvious and quasipermanent task. This paper focuses on CNNs affected by time delays....

  5. A multilayer recurrent neural network for solving continuous-time algebraic Riccati equations.

    Science.gov (United States)

    Wang, Jun; Wu, Guang

    1998-07-01

    A multilayer recurrent neural network is proposed for solving continuous-time algebraic matrix Riccati equations in real time. The proposed recurrent neural network consists of four bidirectionally connected layers. Each layer consists of an array of neurons. The proposed recurrent neural network is shown to be capable of solving algebraic Riccati equations and synthesizing linear-quadratic control systems in real time. Analytical results on stability of the recurrent neural network and solvability of algebraic Riccati equations by use of the recurrent neural network are discussed. The operating characteristics of the recurrent neural network are also demonstrated through three illustrative examples.

  6. Undifferentiated Connective Tissue Disease

    Science.gov (United States)

    ... Home Conditions Undifferentiated Connective Tissue Disease (UCTD) Undifferentiated Connective Tissue Disease (UCTD) Make an Appointment Find a Doctor ... L. Goldstein, MD, MMSc (February 01, 2016) Undifferentiated connective tissue disease (UCTD) is a systemic autoimmune disease. This ...

  7. Connective Tissue Disorders

    Science.gov (United States)

    Connective tissue is the material inside your body that supports many of its parts. It is the "cellular ... their work. Cartilage and fat are examples of connective tissue. There are over 200 disorders that impact connective ...

  8. Minimum cost connection networks

    DEFF Research Database (Denmark)

    Hougaard, Jens Leth; Tvede, Mich

    . We use three axioms to characterize allocation rules that truthfully implement cost minimizing networks satisfying all connection demands in a game where: (1) a central planner announces an allocation rule and a cost estimation rule; (2) every agent reports her own connection demand as well as all...... connection costs; and, (3) the central planner selects a cost minimizing network satisfying reported connection demands based on estimated connection costs and allocates true connection costs of the selected network....

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

    Science.gov (United States)

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

    2013-09-01

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

  10. Systolic implementation of neural networks

    Energy Technology Data Exchange (ETDEWEB)

    De Groot, A.J.; Parker, S.R.

    1989-01-01

    The backpropagation algorithm for error gradient calculations in multilayer, feed-forward neural networks is derived in matrix form involving inner and outer products. It is demonstrated that these calculations can be carried out efficiently using systolic processing techniques, particularly using the SPRINT, a 64-element systolic processor developed at Lawrence Livermore National Laboratory. This machine contains one million synapses, and forward-propagates 12 million connections per second, using 100 watts of power. When executing the algorithm, each SPRINT processor performs useful work 97% of the time. The theory and applications are confirmed by some nontrivial examples involving seismic signal recognition. 4 refs., 7 figs.

  11. Semaphorin signaling in vertebrate neural circuit assembly

    Directory of Open Access Journals (Sweden)

    Yutaka eYoshida

    2012-06-01

    Full Text Available Neural circuit formation requires the coordination of many complex developmental processes. First, neurons project axons over long distances to find their final targets and then establish appropriate connectivity essential for the formation of neuronal circuitry. Growth cones, the leading edges of axons, navigate by interacting with a variety of attractive and repulsive axon guidance cues along their trajectories and at final target regions. In addition to guidance of axons, neuronal polarization, neuronal migration and dendrite development must be precisely regulated during development to establish proper neural circuitry. Semaphorins consist of a large protein family, which includes secreted and cell surface proteins, and they play important roles in many steps of neural circuit formation. The major semaphorin receptors are plexins and neuropilins, however other receptors and co-receptors also mediate signaling by semaphorins. Upon semaphorin binding to their receptors, downstream signaling molecules transduce this event within cells to mediate further events, including alteration of microtubule and actin cytoskeletal dynamics. Here, I review recent studies on semaphorin signaling in vertebrate neural circuit assembly, with the goal of highlighting how this diverse family of cues and receptors imparts exquisite specificity to neural complex connectivity.

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

    Directory of Open Access Journals (Sweden)

    Zhe Chen

    2013-01-01

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

  13. A Neural Dynamic Model Generates Descriptions of Object-Oriented Actions.

    Science.gov (United States)

    Richter, Mathis; Lins, Jonas; Schöner, Gregor

    2017-01-01

    Describing actions entails that relations between objects are discovered. A pervasively neural account of this process requires that fundamental problems are solved: the neural pointer problem, the binding problem, and the problem of generating discrete processing steps from time-continuous neural processes. We present a prototypical solution to these problems in a neural dynamic model that comprises dynamic neural fields holding representations close to sensorimotor surfaces as well as dynamic neural nodes holding discrete, language-like representations. Making the connection between these two types of representations enables the model to describe actions as well as to perceptually ground movement phrases-all based on real visual input. We demonstrate how the dynamic neural processes autonomously generate the processing steps required to describe or ground object-oriented actions. By solving the fundamental problems of neural pointing, binding, and emergent discrete processing, the model may be a first but critical step toward a systematic neural processing account of higher cognition.

  14. Modular neural tile architecture for compact embedded hardware spiking neural network

    NARCIS (Netherlands)

    Pande, Sandeep; Morgan, Fearghal; Cawley, Seamus; Bruintjes, Tom; Smit, Gerard; McGinley, Brian; Carrillo, Snaider; Harkin, Jim; McDaid, Liam

    2013-01-01

    Biologically-inspired packet switched network on chip (NoC) based hardware spiking neural network (SNN) architectures have been proposed as an embedded computing platform for classification, estimation and control applications. Storage of large synaptic connectivity (SNN topology) information in SNN

  15. Neural simulations on multi-core architectures

    Directory of Open Access Journals (Sweden)

    Hubert Eichner

    2009-07-01

    Full Text Available Neuroscience is witnessing increasing knowledge about the anatomy and electrophysiological properties of neurons and their connectivity, leading to an ever increasing computational complexity of neural simulations. At the same time, a rather radical change in personal computer technology emerges with the establishment of multi-cores: high-density, explicitly parallel processor architectures for both high performance as well as standard desktop computers. This work introduces strategies for the parallelization of biophysically realistic neural simulations based on the compartmental modeling technique and results of such an implementation, with a strong focus on multi-core architectures and automation, i. e. user-transparent load balancing.

  16. 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.

  17. 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 ...

  18. Artificial Neural Networks

    OpenAIRE

    Chung-Ming Kuan

    2006-01-01

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

  19. The Laplacian spectrum of neural networks.

    Science.gov (United States)

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

    2014-01-13

    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.

  20. 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

  1. Effect of cocaine dependence on brain connections: Clinical implications

    Science.gov (United States)

    Ma, Liangsuo; Steinberg, Joel L.; Moeller, F. Gerard; Johns, Sade E.; Narayana, Ponnada A.

    2015-01-01

    Cocaine dependence (CD) is associated with several cognitive deficits. Accumulating evidence, based on human and animal studies, has led to models for interpreting the neural basis of cognitive functions as interactions between functionally related brain regions. In this review, we focus on the magnetic resonance imaging (MRI) studies using brain connectivity techniques as related to CD. The majority of these brain connectivity studies indicated that cocaine use is associated with altered brain connectivity between different structures, including cortical-striatal regions and default mode network. In cocaine users, some of the altered brain connectivity measures are associated with behavioral performance, history of drug use, and treatment outcome. The implications of these brain connectivity findings to the treatment of CD and the pros and cons of the major brain connectivity techniques are discussed. Finally potential future directions in cocaine use disorder research using brain connectivity techniques are briefly described. PMID:26512421

  2. Constructive neural network learning

    OpenAIRE

    Lin, Shaobo; Zeng, Jinshan; Zhang, Xiaoqin

    2016-01-01

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

  3. READING A NEURAL CODE

    NARCIS (Netherlands)

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

    1991-01-01

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

  4. Generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2013-03-01

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

  5. Recovery of directed intracortical connectivity from fMRI data

    Science.gov (United States)

    Gilson, Matthieu; Ritter, Petra; Deco, Gustavo

    2016-06-01

    The brain exhibits complex spatio-temporal patterns of activity. In particular, its baseline activity at rest has a specific structure: imaging techniques (e.g., fMRI, EEG and MEG) show that cortical areas experience correlated fluctuations, which is referred to as functional connectivity (FC). The present study relies on our recently developed model in which intracortical white-matter connections shape noise-driven fluctuations to reproduce FC observed in experimental data (here fMRI BOLD signal). Here noise has a functional role and represents the variability of neural activity. The model also incorporates anatomical information obtained using diffusion tensor imaging (DTI), which estimates the density of white-matter fibers (structural connectivity, SC). After optimization to match empirical FC, the model provides an estimation of the efficacies of these fibers, which we call effective connectivity (EC). EC differs from SC, as EC not only accounts for the density of neural fibers, but also the concentration of synapses formed at their end, the type of neurotransmitters associated and the excitability of target neural populations. In summary, the model combines anatomical SC and activity FC to evaluate what drives the neural dynamics, embodied in EC. EC can then be analyzed using graph theory to understand how it generates FC and to seek for functional communities among cortical areas (parcellation of 68 areas). We find that intracortical connections are not symmetric, which affects the dynamic range of cortical activity (i.e., variety of states it can exhibit).

  6. 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.

  7. Using computational models to relate structural and functional brain connectivity.

    Science.gov (United States)

    Hlinka, Jaroslav; Coombes, Stephen

    2012-07-01

    Modern imaging methods allow a non-invasive assessment of both structural and functional brain connectivity. This has lead to the identification of disease-related alterations affecting functional connectivity. The mechanism of how such alterations in functional connectivity arise in a structured network of interacting neural populations is as yet poorly understood. Here we use a modeling approach to explore the way in which this can arise and to highlight the important role that local population dynamics can have in shaping emergent spatial functional connectivity patterns. The local dynamics for a neural population is taken to be of the Wilson-Cowan type, whilst the structural connectivity patterns used, describing long-range anatomical connections, cover both realistic scenarios (from the CoComac database) and idealized ones that allow for more detailed theoretical study. We have calculated graph-theoretic measures of functional network topology from numerical simulations of model networks. The effect of the form of local dynamics on the observed network state is quantified by examining the correlation between structural and functional connectivity. We document a profound and systematic dependence of the simulated functional connectivity patterns on the parameters controlling the dynamics. Importantly, we show that a weakly coupled oscillator theory explaining these correlations and their variation across parameter space can be developed. This theoretical development provides a novel way to characterize the mechanisms for the breakdown of functional connectivity in diseases through changes in local dynamics.

  8. Connectivity in Autism: A review of MRI connectivity studies

    Science.gov (United States)

    Rane, Pallavi; Cochran, David; Hodge, Steven M.; Haselgrove, Christian; Kennedy, David; Frazier, Jean A.

    2016-01-01

    Autism Spectrum Disorder (ASD) affects 1 in 50 children between the ages of 6–17 years as per a 2012 CDC survey of parents. The etiology of ASD is not precisely known. ASD is an umbrella term, which includes low (IQ70) individuals. A better understanding of the disorder, and how it manifests in an individual subject can lead to more effective intervention plans to fulfill the individual’s treatment needs. Magnetic resonance imaging (MRI) is a non-invasive investigational tool that can help study the ways in which the brain develops and/or deviates from the typical developmental trajectory. MRI offers insights into the structure, function, and metabolism of the brain. In this article, we review published studies on brain connectivity changes in ASD using either resting state functional MRI or diffusion tensor imaging. The general findings of decreases in white matter integrity and long-range neural coherence are prevalent in ASD literature. However, there is somewhat less of a consensus in the detailed localization of these findings. There are even fewer studies linking these connectivity alterations with the behavioral phenotype of the disorder. Nevertheless, with the help of data sharing and large-scale analytic efforts, the field is advancing towards several convergent themes. These include reduced functional coherence of long-range intra-hemispheric cortico-cortical default mode circuitry, impaired inter-hemispheric regulation, and an associated, perhaps compensatory, increase in local and short-range cortico-subcortical coherence. PMID:26146755

  9. OCT detection of neural activity in American cockroach nervous system

    Science.gov (United States)

    Gorczyńska, Iwona; Wyszkowska, Joanna; Bukowska, Danuta; Ruminski, Daniel; Karnowski, Karol; Stankiewicz, Maria; Wojtkowski, Maciej

    2013-03-01

    We show results of a project which focuses on detection of activity in neural tissue with Optical Coherence Tomography (OCT) methods. Experiments were performed in neural cords dissected from the American cockroach (Periplaneta americana L.). Functional OCT imaging was performed with ultrahigh resolution spectral / Fourier domain OCT system (axial resolution 2.5 μm). Electrical stimulation (voltage pulses) was applied to the sensory cercal nerve of the neural cord. Optical detection of functional activation of the sample was performed in the connective between the terminal abdominal ganglion and the fifth abdominal ganglion. Functional OCT data were collected over time with the OCT beam illuminating selected single point in the connectives (i.e. OCT M-scans were acquired). Phase changes of the OCT signal were analyzed to visualize occurrence of activation in the neural cord. Electrophysiology recordings (microelectrode method) were also performed as a reference method to demonstrate electrical response of the sample to stimulation.

  10. Abnormal synchrony and effective connectivity in patients with schizophrenia and auditory hallucinations

    Directory of Open Access Journals (Sweden)

    Maria de la Iglesia-Vaya

    2014-01-01

    These data indicate that an anomalous process of neural connectivity exists when patients with AH process emotional auditory stimuli. Additionally, a central role is suggested for the cerebellum in processing emotional stimuli in patients with persistent AH.

  11. The Mind-Body Connection - How to Fight Stress and Ward Off Illness

    Science.gov (United States)

    ... to Protect Yourself By Celia Vimont Esther M. Sternberg, M.D. Photo courtesy of NIH/NIHM Today ... of the mind-body connection," says Esther M. Sternberg, M.D., director of the Integrative Neural Immune ...

  12. Auditory Hallucinations in Schizophrenia Are Associated with Reduced Functional Connectivity of the Temporo-Parietal Area

    NARCIS (Netherlands)

    Vercammen, Ans; Knegtering, Henderikus; den Boer, Johann A.; Liemburg, Edith J.; Aleman, Andre

    2010-01-01

    Background: Schizophrenia has been conceptualized as a disorder of integration of neural activity across distributed networks. However, the relationship between specific symptom dimensions and patterns of functional connectivity remains unclear. The current study aimed to investigate the relationshi

  13. Occlusion-related lateral connections stabilize kinetic depth stimuli through perceptual coupling

    NARCIS (Netherlands)

    Klink, P. Christiaan; Noest, Andre J.; van den Berg, Albert V.; van Wezel, Richard Jack Anton

    2009-01-01

    Local sensory information is often ambiguous forcing the brain to integrate spatiotemporally separated information for stable conscious perception. Lateral connections between clusters of similarly tuned neurons in the visual cortex are a potential neural substrate for the coupling of spatially

  14. Moral transgressions corrupt neural representations of value.

    Science.gov (United States)

    Crockett, Molly J; Siegel, Jenifer Z; Kurth-Nelson, Zeb; Dayan, Peter; Dolan, Raymond J

    2017-06-01

    Moral systems universally prohibit harming others for personal gain. However, we know little about how such principles guide moral behavior. Using a task that assesses the financial cost participants ascribe to harming others versus themselves, we probed the relationship between moral behavior and neural representations of profit and pain. Most participants displayed moral preferences, placing a higher cost on harming others than themselves. Moral preferences correlated with neural responses to profit, where participants with stronger moral preferences had lower dorsal striatal responses to profit gained from harming others. Lateral prefrontal cortex encoded profit gained from harming others, but not self, and tracked the blameworthiness of harmful choices. Moral decisions also modulated functional connectivity between lateral prefrontal cortex and the profit-sensitive region of dorsal striatum. The findings suggest moral behavior in our task is linked to a neural devaluation of reward realized by a prefrontal modulation of striatal value representations.

  15. Improving neural network performance on SIMD architectures

    Science.gov (United States)

    Limonova, Elena; Ilin, Dmitry; Nikolaev, Dmitry

    2015-12-01

    Neural network calculations for the image recognition problems can be very time consuming. In this paper we propose three methods of increasing neural network performance on SIMD architectures. The usage of SIMD extensions is a way to speed up neural network processing available for a number of modern CPUs. In our experiments, we use ARM NEON as SIMD architecture example. The first method deals with half float data type for matrix computations. The second method describes fixed-point data type for the same purpose. The third method considers vectorized activation functions implementation. For each method we set up a series of experiments for convolutional and fully connected networks designed for image recognition task.

  16. Neural induction and factors that stabilize a neural fate

    OpenAIRE

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

    2009-01-01

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

  17. Neural Differentiation of Incorrectly Predicted Memories.

    Science.gov (United States)

    Kim, Ghootae; Norman, Kenneth A; Turk-Browne, Nicholas B

    2017-02-22

    When an item is predicted in a particular context but the prediction is violated, memory for that item is weakened (Kim et al., 2014). Here, we explore what happens when such previously mispredicted items are later reencountered. According to prior neural network simulations, this sequence of events-misprediction and subsequent restudy-should lead to differentiation of the item's neural representation from the previous context (on which the misprediction was based). Specifically, misprediction weakens connections in the representation to features shared with the previous context and restudy allows new features to be incorporated into the representation that are not shared with the previous context. This cycle of misprediction and restudy should have the net effect of moving the item's neural representation away from the neural representation of the previous context. We tested this hypothesis using human fMRI by tracking changes in item-specific BOLD activity patterns in the hippocampus, a key structure for representing memories and generating predictions. In left CA2/3/DG, we found greater neural differentiation for items that were repeatedly mispredicted and restudied compared with items from a control condition that was identical except without misprediction. We also measured prediction strength in a trial-by-trial fashion and found that greater misprediction for an item led to more differentiation, further supporting our hypothesis. Therefore, the consequences of prediction error go beyond memory weakening. If the mispredicted item is restudied, the brain adaptively differentiates its memory representation to improve the accuracy of subsequent predictions and to shield it from further weakening.SIGNIFICANCE STATEMENT Competition between overlapping memories leads to weakening of nontarget memories over time, making it easier to access target memories. However, a nontarget memory in one context might become a target memory in another context. How do such memories

  18. Light microscopy mapping of connections in the intact brain.

    Science.gov (United States)

    Kim, Sung-Yon; Chung, Kwanghun; Deisseroth, Karl

    2013-12-01

    Mapping of neural connectivity across the mammalian brain is a daunting and exciting prospect. Current approaches can be divided into three classes: macroscale, focusing on coarse inter-regional connectivity; mesoscale, involving a finer focus on neurons and projections; and microscale, reconstructing full details of all synaptic contacts. It remains to be determined how to bridge the datasets or insights from the different levels of study. Here we review recent light-microscopy-based approaches that may help in integration across scales.

  19. Sparse neural networks with large learning diversity

    CERN Document Server

    Gripon, Vincent

    2011-01-01

    Coded recurrent neural networks with three levels of sparsity are introduced. The first level is related to the size of messages, much smaller than the number of available neurons. The second one is provided by a particular coding rule, acting as a local constraint in the neural activity. The third one is a characteristic of the low final connection density of the network after the learning phase. Though the proposed network is very simple since it is based on binary neurons and binary connections, it is able to learn a large number of messages and recall them, even in presence of strong erasures. The performance of the network is assessed as a classifier and as an associative memory.

  20. The neural circuits for arithmetic principles.

    Science.gov (United States)

    Liu, Jie; Zhang, Han; Chen, Chuansheng; Chen, Hui; Cui, Jiaxin; Zhou, Xinlin

    2017-02-15

    Arithmetic principles are the regularities underlying arithmetic computation. Little is known about how the brain supports the processing of arithmetic principles. The current fMRI study examined neural activation and functional connectivity during the processing of verbalized arithmetic principles, as compared to numerical computation and general language processing. As expected, arithmetic principles elicited stronger activation in bilateral horizontal intraparietal sulcus and right supramarginal gyrus than did language processing, and stronger activation in left middle temporal lobe and left orbital part of inferior frontal gyrus than did computation. In contrast, computation elicited greater activation in bilateral horizontal intraparietal sulcus (extending to posterior superior parietal lobule) than did either arithmetic principles or language processing. Functional connectivity analysis with the psychophysiological interaction approach (PPI) showed that left temporal-parietal (MTG-HIPS) connectivity was stronger during the processing of arithmetic principle and language than during computation, whereas parietal-occipital connectivities were stronger during computation than during the processing of arithmetic principles and language. Additionally, the left fronto-parietal (orbital IFG-HIPS) connectivity was stronger during the processing of arithmetic principles than during computation. The results suggest that verbalized arithmetic principles engage a neural network that overlaps but is distinct from the networks for computation and language processing.

  1. Optimization of Evolutionary Neural Networks Using Hybrid Learning Algorithms

    OpenAIRE

    Abraham, Ajith

    2004-01-01

    Evolutionary artificial neural networks (EANNs) refer to a special class of artificial neural networks (ANNs) in which evolution is another fundamental form of adaptation in addition to learning. Evolutionary algorithms are used to adapt the connection weights, network architecture and learning algorithms according to the problem environment. Even though evolutionary algorithms are well known as efficient global search algorithms, very often they miss the best local solutions in the complex s...

  2. Death and rebirth of neural activity in sparse inhibitory networks

    OpenAIRE

    Angulo-Garcia, David; Luccioli, Stefano; Olmi, Simona; Torcini, Alessandro

    2016-01-01

    In this paper, we clarify the mechanisms underlying a general phenomenon present in pulse-coupled heterogeneous inhibitory networks: inhibition can induce not only suppression of the neural activity, as expected, but it can also promote neural reactivation. In particular, for globally coupled systems, the number of firing neurons monotonically reduces upon increasing the strength of inhibition (neurons' death). However, the random pruning of the connections is able to reverse the action of in...

  3. Mobility Prediction in Wireless Ad Hoc Networks using Neural Networks

    CERN Document Server

    Kaaniche, Heni

    2010-01-01

    Mobility prediction allows estimating the stability of paths in a mobile wireless Ad Hoc networks. Identifying stable paths helps to improve routing by reducing the overhead and the number of connection interruptions. In this paper, we introduce a neural network based method for mobility prediction in Ad Hoc networks. This method consists of a multi-layer and recurrent neural network using back propagation through time algorithm for training.

  4. Gray Code ADC Based on an Analog Neural Circuit

    Directory of Open Access Journals (Sweden)

    L. Michaeli

    1995-04-01

    Full Text Available In this paper a new neural ADC design is presented, which is based on the idea to replace all functional components needed in the ADC block scheme by a simple connection of neurons. Transformation of ADC functional scheme into an analog neural structure and its computer simulation is one of the main results of this paper. Furthermore, a discrete component prototype of the proposed A/D converter is discussed and experimental results are also given.

  5. Multi-column Deep Neural Networks for Image Classification

    OpenAIRE

    Cireşan, Dan; Meier, Ueli; Schmidhuber, Juergen

    2012-01-01

    Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional winner-take-all neurons yield large network depth, resulting in roughly as many sparsely connected neural layers as found in mammals between retina and visual cortex. Only winner neurons are trained. ...

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

    Science.gov (United States)

    Takiyama, Ken

    2017-02-01

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

  7. Developmental Changes in Brain Network Hub Connectivity in Late Adolescence.

    Science.gov (United States)

    Baker, Simon T E; Lubman, Dan I; Yücel, Murat; Allen, Nicholas B; Whittle, Sarah; Fulcher, Ben D; Zalesky, Andrew; Fornito, Alex

    2015-06-17

    The human brain undergoes substantial development throughout adolescence and into early adulthood. This maturational process is thought to include the refinement of connectivity between putative connectivity hub regions of the brain, which collectively form a dense core that enhances the functional integration of anatomically distributed, and functionally specialized, neural systems. Here, we used longitudinal diffusion magnetic resonance imaging to characterize changes in connectivity between 80 cortical and subcortical anatomical regions over a 2 year period in 31 adolescents between the ages of 15 and 19 years. Connectome-wide analysis indicated that only a small subset of connections showed evidence of statistically significant developmental change over the study period, with 8% and 6% of connections demonstrating decreased and increased structural connectivity, respectively. Nonetheless, these connections linked 93% and 90% of the 80 regions, respectively, pointing to a selective, yet anatomically distributed pattern of developmental changes that involves most of the brain. Hub regions showed a distinct tendency to be highly connected to each other, indicating robust "rich-club" organization. Moreover, connectivity between hubs was disproportionately influenced by development, such that connectivity between subcortical hubs decreased over time, whereas frontal-subcortical and frontal-parietal hub-hub connectivity increased over time. These findings suggest that late adolescence is characterized by selective, yet significant remodeling of hub-hub connectivity, with the topological organization of hubs shifting emphasis from subcortical hubs in favor of an increasingly prominent role for frontal hub regions.

  8. Learning in Neural Networks: VLSI Implementation Strategies

    Science.gov (United States)

    Duong, Tuan Anh

    1995-01-01

    Fully-parallel hardware neural network implementations may be applied to high-speed recognition, classification, and mapping tasks in areas such as vision, or can be used as low-cost self-contained units for tasks such as error detection in mechanical systems (e.g. autos). Learning is required not only to satisfy application requirements, but also to overcome hardware-imposed limitations such as reduced dynamic range of connections.

  9. Temporal association in asymmetric neural networks

    Science.gov (United States)

    Sompolinsky, H.; Kanter, I.

    1986-12-01

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

  10. Research Team for Neural Development and Plasticity

    Institute of Scientific and Technical Information of China (English)

    2012-01-01

    The brain consists of billions of nerve cells, called neurons, which make specific connections (called synapses) among them to form many neural circuits to perform various brain functions, including processing, storage, and retrieval of information. Each neuron is a polarized cell. It sends out many highly arborized dendrites on one end for receiving input signals and a single long axon oll the other end for delivery of output signals to distant target neurons.

  11. Flexible neural interfaces with integrated stiffening shank

    Science.gov (United States)

    Tooker, Angela C.; Felix, Sarah H.; Pannu, Satinderpall S.; Shah, Kedar G.; Sheth, Heeral; Tolosa, Vanessa

    2016-07-26

    A neural interface includes a first dielectric material having at least one first opening for a first electrical conducting material, a first electrical conducting material in the first opening, and at least one first interconnection trace electrical conducting material connected to the first electrical conducting material. A stiffening shank material is located adjacent the first dielectric material, the first electrical conducting material, and the first interconnection trace electrical conducting material.

  12. Neural Computing in High Energy Physics

    Institute of Scientific and Technical Information of China (English)

    O.D.Joukov; N.D.Rishe

    2001-01-01

    Artifical neural networks (ANN) are now widely used successfully as tools for hith energy physics.The paper covers two aspects.First,mapping ANNs onto the proposed ring and linear systolic array provides an efficient implementation of VLSI-based architectures since in this case all connections among processing elements are local and regular,Second.it is discussed algorthmic organizing of such structures on the base of modular algebra whose use can provide an essential increase of system throughput.

  13. Chaotic diagonal recurrent neural network

    Institute of Scientific and Technical Information of China (English)

    Wang Xing-Yuan; Zhang Yi

    2012-01-01

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

  14. 用新型连接性指数与神经网络预测取代苯酚和取代苯甲酸生物降解性%Prediction of Biodegradability of Substituted Phenols and Benzoic Acids with Novel Molecular Connectivity Index and Artificial Neural Network

    Institute of Scientific and Technical Information of China (English)

    冯长君; 堵锡华; 沐来龙

    2009-01-01

    molecules. The two structural parameters are used as the input neurons of the artificial neural network, and a 2:5:1 network architecture is employed. A satisfying QSBR model can be constructed with the back-propagation algorithm, with the correlation coefficient R~2 and the standard errors being 0.967 and 3.688, respectively, showing that the relationship between BOD and the two structural parameters has a good nonlinear correlation. The results show that the new molecular connectivity index have good rationality and efficiency for the biochemical oxygen demand of organic compounds. It can be expected that the ~mK_t~v will be used widely in quantitative structure-property/activity relationship research.

  15. EDITORIAL: Focus on the neural interface Focus on the neural interface

    Science.gov (United States)

    Durand, Dominique M.

    2009-10-01

    The possibility of an effective connection between neural tissue and computers has inspired scientists and engineers to develop new ways of controlling and obtaining information from the nervous system. These applications range from `brain hacking' to neural control of artificial limbs with brain signals. Notwithstanding the significant advances in neural prosthetics in the last few decades and the success of some stimulation devices such as cochlear prosthesis, neurotechnology remains below its potential for restoring neural function in patients with nervous system disorders. One of the reasons for this limited impact can be found at the neural interface and close attention to the integration between electrodes and tissue should improve the possibility of successful outcomes. The neural interfaces research community consists of investigators working in areas such as deep brain stimulation, functional neuromuscular/electrical stimulation, auditory prostheses, cortical prostheses, neuromodulation, microelectrode array technology, brain-computer/machine interfaces. Following the success of previous neuroprostheses and neural interfaces workshops, funding (from NIH) was obtained to establish a biennial conference in the area of neural interfaces. The first Neural Interfaces Conference took place in Cleveland, OH in 2008 and several topics from this conference have been selected for publication in this special section of the Journal of Neural Engineering. Three `perspectives' review the areas of neural regeneration (Corredor and Goldberg), cochlear implants (O'Leary et al) and neural prostheses (Anderson). Seven articles focus on various aspects of neural interfacing. One of the most popular of these areas is the field of brain-computer interfaces. Fraser et al, report on a method to generate robust control with simple signal processing algorithms of signals obtained with electrodes implanted in the brain. One problem with implanted electrode arrays, however, is that

  16. An exclusively mesodermal origin of fin mesenchyme demonstrates that zebrafish trunk neural crest does not generate ectomesenchyme

    OpenAIRE

    Lee, Raymond Teck Ho; Knapik, Ela W.; Thiery, Jean Paul; Carney, Thomas J.

    2013-01-01

    The neural crest is a multipotent stem cell population that arises from the dorsal aspect of the neural tube and generates both non-ectomesenchymal (melanocytes, peripheral neurons and glia) and ectomesenchymal (skeletogenic, odontogenic, cartilaginous and connective tissue) derivatives. In amniotes, only cranial neural crest generates both classes, with trunk neural crest restricted to non-ectomesenchyme. By contrast, it has been suggested that anamniotes might generate derivatives of both c...

  17. Investigation of efficient features for image recognition by neural networks.

    Science.gov (United States)

    Goltsev, Alexander; Gritsenko, Vladimir

    2012-04-01

    In the paper, effective and simple features for image recognition (named LiRA-features) are investigated in the task of handwritten digit recognition. Two neural network classifiers are considered-a modified 3-layer perceptron LiRA and a modular assembly neural network. A method of feature selection is proposed that analyses connection weights formed in the preliminary learning process of a neural network classifier. In the experiments using the MNIST database of handwritten digits, the feature selection procedure allows reduction of feature number (from 60 000 to 7000) preserving comparable recognition capability while accelerating computations. Experimental comparison between the LiRA perceptron and the modular assembly neural network is accomplished, which shows that recognition capability of the modular assembly neural network is somewhat better.

  18. Google matrix analysis of C.elegans neural network

    CERN Document Server

    Kandiah, Vivek

    2013-01-01

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

  19. Google matrix analysis of C.elegans neural network

    Energy Technology Data Exchange (ETDEWEB)

    Kandiah, V., E-mail: kandiah@irsamc.ups-tlse.fr; Shepelyansky, D.L., E-mail: dima@irsamc.ups-tlse.fr

    2014-05-01

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

  20. Asymptotically hyperbolic connections

    CERN Document Server

    Fine, Joel; Krasnov, Kirill; Scarinci, Carlos

    2015-01-01

    General Relativity in 4 dimensions can be equivalently described as a dynamical theory of SO(3)-connections rather than metrics. We introduce the notion of asymptotically hyperbolic connections, and work out an analog of the Fefferman-Graham expansion in the language of connections. As in the metric setup, one can solve the arising "evolution" equations order by order in the expansion in powers of the radial coordinate. The solution in the connection setting is arguably simpler, and very straightforward algebraic manipulations allow one to see how the obstruction appears at third order in the expansion. Another interesting feature of the connection formulation is that the "counter terms" required in the computation of the renormalised volume all combine into the Chern-Simons functional of the restriction of the connection to the boundary. As the Chern-Simons invariant is only defined modulo large gauge transformations, the requirement that the path integral over asymptotically hyperbolic connections is well-d...

  1. The implications of brain connectivity in the neuropsychology of autism.

    Science.gov (United States)

    Maximo, Jose O; Cadena, Elyse J; Kana, Rajesh K

    2014-03-01

    Autism is a neurodevelopmental disorder that has been associated with atypical brain functioning. Functional connectivity MRI (fcMRI) studies examining neural networks in autism have seen an exponential rise over the last decade. Such investigations have led to the characterization of autism as a distributed neural systems disorder. Studies have found widespread cortical underconnectivity, local overconnectivity, and mixed results suggesting disrupted brain connectivity as a potential neural signature of autism. In this review, we summarize the findings of previous fcMRI studies in autism with a detailed examination of their methodology, in order to better understand its potential and to delineate the pitfalls. We also address how a multimodal neuroimaging approach (incorporating different measures of brain connectivity) may help characterize the complex neurobiology of autism at a global level. Finally, we also address the potential of neuroimaging-based markers in assisting neuropsychological assessment of autism. The quest for a neural marker for autism is still ongoing, yet new findings suggest that aberrant brain connectivity may be a promising candidate.

  2. Dynamic effective connectivity of inter-areal brain circuits.

    Directory of Open Access Journals (Sweden)

    Demian Battaglia

    Full Text Available Anatomic connections between brain areas affect information flow between neuronal circuits and the synchronization of neuronal activity. However, such structural connectivity does not coincide with effective connectivity (or, more precisely, causal connectivity, related to the elusive question "Which areas cause the present activity of which others?". Effective connectivity is directed and depends flexibly on contexts and tasks. Here we show that dynamic effective connectivity can emerge from transitions in the collective organization of coherent neural activity. Integrating simulation and semi-analytic approaches, we study mesoscale network motifs of interacting cortical areas, modeled as large random networks of spiking neurons or as simple rate units. Through a causal analysis of time-series of model neural activity, we show that different dynamical states generated by a same structural connectivity motif correspond to distinct effective connectivity motifs. Such effective motifs can display a dominant directionality, due to spontaneous symmetry breaking and effective entrainment between local brain rhythms, although all connections in the considered structural motifs are reciprocal. We show then that transitions between effective connectivity configurations (like, for instance, reversal in the direction of inter-areal interactions can be triggered reliably by brief perturbation inputs, properly timed with respect to an ongoing local oscillation, without the need for plastic synaptic changes. Finally, we analyze how the information encoded in spiking patterns of a local neuronal population is propagated across a fixed structural connectivity motif, demonstrating that changes in the active effective connectivity regulate both the efficiency and the directionality of information transfer. Previous studies stressed the role played by coherent oscillations in establishing efficient communication between distant areas. Going beyond these early

  3. Dynamic Effective Connectivity of Inter-Areal Brain Circuits

    Science.gov (United States)

    Battaglia, Demian; Witt, Annette; Wolf, Fred; Geisel, Theo

    2012-01-01

    Anatomic connections between brain areas affect information flow between neuronal circuits and the synchronization of neuronal activity. However, such structural connectivity does not coincide with effective connectivity (or, more precisely, causal connectivity), related to the elusive question “Which areas cause the present activity of which others?”. Effective connectivity is directed and depends flexibly on contexts and tasks. Here we show that dynamic effective connectivity can emerge from transitions in the collective organization of coherent neural activity. Integrating simulation and semi-analytic approaches, we study mesoscale network motifs of interacting cortical areas, modeled as large random networks of spiking neurons or as simple rate units. Through a causal analysis of time-series of model neural activity, we show that different dynamical states generated by a same structural connectivity motif correspond to distinct effective connectivity motifs. Such effective motifs can display a dominant directionality, due to spontaneous symmetry breaking and effective entrainment between local brain rhythms, although all connections in the considered structural motifs are reciprocal. We show then that transitions between effective connectivity configurations (like, for instance, reversal in the direction of inter-areal interactions) can be triggered reliably by brief perturbation inputs, properly timed with respect to an ongoing local oscillation, without the need for plastic synaptic changes. Finally, we analyze how the information encoded in spiking patterns of a local neuronal population is propagated across a fixed structural connectivity motif, demonstrating that changes in the active effective connectivity regulate both the efficiency and the directionality of information transfer. Previous studies stressed the role played by coherent oscillations in establishing efficient communication between distant areas. Going beyond these early proposals, we

  4. 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...

  5. 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...

  6. Power spectrum scale invariance as a neural marker of cocaine misuse and altered cognitive control

    Directory of Open Access Journals (Sweden)

    Jaime S. Ide

    2016-01-01

    Conclusions: These findings suggest disrupted connectivity dynamics in the fronto-parietal areas in association with post-signal behavioral adjustment in cocaine addicts. These new findings support PSSI as a neural marker of impaired cognitive control in cocaine addiction.

  7. Design for CNN Templates with Performance of Global Connectivity Detection

    Institute of Scientific and Technical Information of China (English)

    LIU Jin-Zhu; MIN Le-Quan

    2004-01-01

    The cellular neural/nonlinear network (CNN) is a powerful tool for image and video signal processing,robotic and biological visions. This paper discusses a general method for designing template of the global connectivity detection (GCD) CNN, which provides parameter inequalities for determining parameter intervals for implementing the corresponding functions. The GCD CNN has stronger ability and faster rate for determining global connectivity in binary patterns than the GCD CNN proposed by Zarandy. An example for detecting the connectivity in complex patterns is given.

  8. Eccentric connectivity index

    CERN Document Server

    Ilić, Aleksandar

    2011-01-01

    The eccentric connectivity index $\\xi^c$ is a novel distance--based molecular structure descriptor that was recently used for mathematical modeling of biological activities of diverse nature. It is defined as $\\xi^c (G) = \\sum_{v \\in V (G)} deg (v) \\cdot \\epsilon (v)$\\,, where $deg (v)$ and $\\epsilon (v)$ denote the vertex degree and eccentricity of $v$\\,, respectively. We survey some mathematical properties of this index and furthermore support the use of eccentric connectivity index as topological structure descriptor. We present the extremal trees and unicyclic graphs with maximum and minimum eccentric connectivity index subject to the certain graph constraints. Sharp lower and asymptotic upper bound for all graphs are given and various connections with other important graph invariants are established. In addition, we present explicit formulae for the values of eccentric connectivity index for several families of composite graphs and designed a linear algorithm for calculating the eccentric connectivity in...

  9. On eccentric connectivity index

    CERN Document Server

    Zhou, Bo

    2010-01-01

    The eccentric connectivity index, proposed by Sharma, Goswami and Madan, has been employed successfully for the development of numerous mathematical models for the prediction of biological activities of diverse nature. We now report mathematical properties of the eccentric connectivity index. We establish various lower and upper bounds for the eccentric connectivity index in terms of other graph invariants including the number of vertices, the number of edges, the degree distance and the first Zagreb index. We determine the n-vertex trees of diameter with the minimum eccentric connectivity index, and the n-vertex trees of pendent vertices, with the maximum eccentric connectivity index. We also determine the n-vertex trees with respectively the minimum, second-minimum and third-minimum, and the maximum, second-maximum and third-maximum eccentric connectivity indices for

  10. Sensory segmentation with coupled neural oscillators.

    Science.gov (United States)

    von der Malsburg, C; Buhmann, J

    1992-01-01

    We present a model of sensory segmentation that is based on the generation and processing of temporal tags in the form of oscillations, as suggested by the Dynamic Link Architecture. The model forms the basis for a natural solution to the sensory segmentation problem. It can deal with multiple segments, can integrate different cues and has the potential for processing hierarchical structures. Temporally tagged segments can easily be utilized in neural systems and form a natural basis for object recognition and learning. The model consists of a "cortical" circuit, an array of units that act as local feature detectors. Units are formulated as neural oscillators. Knowledge relevant to segmentation is encoded by connections. In accord with simple Gestalt laws, our concrete model has intracolumnar connections, between all units with overlapping receptive fields, and intercolumnar connections, between units responding to the same quality in different positions. An inhibitory connection system prevents total correlation and controls the grain of the segmentation. In simulations with synthetic input data we show the performance of the circuit, which produces signal correlation within segments and anticorrelation between segments.

  11. Institutions for Asian Connectivity

    OpenAIRE

    Bhattacharyay, Biswa

    2010-01-01

    To make Asia more economically sustainable and resilient against external shocks, regional economies need to be rebalanced toward regional demand- and trade-driven growth through increased regional connectivity. The effectiveness of connectivity depends on the quality of hard and soft infrastructure. Of particular importance in terms of soft infrastructure which makes hard infrastructure work are the facilitating institutions that support connectivity through appropriate policies, reforms, sy...

  12. Handbook of networking & connectivity

    CERN Document Server

    McClain, Gary R

    1994-01-01

    Handbook of Networking & Connectivity focuses on connectivity standards in use, including hardware and software options. The book serves as a guide for solving specific problems that arise in designing and maintaining organizational networks.The selection first tackles open systems interconnection, guide to digital communications, and implementing TCP/IP in an SNA environment. Discussions focus on elimination of the SNA backbone, routing SNA over internets, connectionless versus connection-oriented networks, internet concepts, application program interfaces, basic principles of layering, proto

  13. Neural Networks: Implementations and Applications

    NARCIS (Netherlands)

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

    1996-01-01

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

  14. Neural Networks: Implementations and Applications

    NARCIS (Netherlands)

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

    1996-01-01

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

  15. What Are Neural Tube Defects?

    Science.gov (United States)

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

  16. Ontology Mapping Neural Network: An Approach to Learning and Inferring Correspondences among Ontologies

    Science.gov (United States)

    Peng, Yefei

    2010-01-01

    An ontology mapping neural network (OMNN) is proposed in order to learn and infer correspondences among ontologies. It extends the Identical Elements Neural Network (IENN)'s ability to represent and map complex relationships. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the…

  17. Ontology Mapping Neural Network: An Approach to Learning and Inferring Correspondences among Ontologies

    Science.gov (United States)

    Peng, Yefei

    2010-01-01

    An ontology mapping neural network (OMNN) is proposed in order to learn and infer correspondences among ontologies. It extends the Identical Elements Neural Network (IENN)'s ability to represent and map complex relationships. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the…

  18. Inverse Degree and Connectivity

    Institute of Scientific and Technical Information of China (English)

    MA Xiao-ling; TIAN Ying-zhi

    2013-01-01

    Let G be a connected graph with vertex set V(G),order n =丨V(G)丨,minimum degree δ(G) and connectivity κ(G).The graph G is called maximally connected if κ(G) =δ(G).Define the inverse degree of G with no isolated vertices as R(G) =Σv∈V(G)1/d(v),where d(v) denotes the degree of the vertex v.We show that G is maximally connected if R(G) < 1 + 2/δ + n-2δ+1/(n-1)(n-3).

  19. Minimum cost connection networks

    DEFF Research Database (Denmark)

    Hougaard, Jens Leth; Tvede, Mich

    2015-01-01

    demands. We use a few axioms to characterize allocation rules that truthfully implement cost minimizing networks satisfying all connection demands in a game where: (1) a central planner announces an allocation rule and a cost estimation rule; (2) every agent reports her own connection demand as well...... as all connection costs; (3) the central planner selects a cost minimizing network satisfying reported connection demands based on the estimated costs; and, (4) the planner allocates the true costs of the selected network. It turns out that an allocation rule satisfies the axioms if and only if relative...

  20. Asymptotically hyperbolic connections

    Science.gov (United States)

    Fine, Joel; Herfray, Yannick; Krasnov, Kirill; Scarinci, Carlos

    2016-09-01

    General relativity in four-dimensions can be equivalently described as a dynamical theory of {SO}(3)˜ {SU}(2)-connections rather than metrics. We introduce the notion of asymptotically hyperbolic connections, and work out an analogue of the Fefferman-Graham expansion in the language of connections. As in the metric setup, one can solve the arising ‘evolution’ equations order by order in the expansion in powers of the radial coordinate. The solution in the connection setting is arguably simpler, and very straightforward algebraic manipulations allow one to see how the unconstrained by Einstein equations ‘stress-energy tensor’ appears at third order in the expansion. Another interesting feature of the connection formulation is that the ‘counter terms’ required in the computation of the renormalised volume all combine into the Chern-Simons functional of the restriction of the connection to the boundary. As the Chern-Simons invariant is only defined modulo large gauge transformations, the requirement that the path integral over asymptotically hyperbolic connections is well-defined requires the cosmological constant to be quantised. Finally, in the connection setting one can deform the 4D Einstein condition in an interesting way, and we show that asymptotically hyperbolic connection expansion is universal and valid for any of the deformed theories.

  1. The Connected Traveler

    Energy Technology Data Exchange (ETDEWEB)

    Young, Stanley

    2017-04-24

    The Connected Traveler project is a multi-disciplinary undertaking that seeks to validate potential for transformative transportation system energy savings by incentivizing energy efficient travel behavior.

  2. 78 FR 55684 - ConnectED Workshop

    Science.gov (United States)

    2013-09-11

    ... content into the curriculum; and as classroom management software tools move everything from homework... consider promising strategies for achieving the President's goal of connecting virtually all K-12 students... policies and consider the most promising strategies for equipping K-12 schools for digital learning....

  3. Optically excited synapse for neural networks.

    Science.gov (United States)

    Boyd, G D

    1987-07-15

    What can optics with its promise of parallelism do for neural networks which require matrix multipliers? An all optical approach requires optical logic devices which are still in their infancy. An alternative is to retain electronic logic while optically addressing the synapse matrix. This paper considers several versions of an optically addressed neural network compatible with VLSI that could be fabricated with the synapse connection unspecified. This optical matrix multiplier circuit is compared to an all electronic matrix multiplier. For the optical version a synapse consisting of back-to-back photodiodes is found to have a suitable i-v characteristic for optical matrix multiplication (a linear region) plus a clipping or nonlinear region as required for neural networks. Four photodiodes per synapse are required. The strength of the synapse connection is controlled by the optical power and is thus an adjustable parameter. The synapse network can be programmed in various ways such as a shadow mask of metal, imaged mask (static), or light valve or an acoustooptic scanned laser beam or array of beams (dynamic). A milliwatt from LEDs or lasers is adequate power. The neuron has a linear transfer function and is either a summing amplifier, in which case the synapse signal is current, or an integrator, in which case the synapse signal is charge, the choice of which depends on the programming mode. Optical addressing and settling times of microseconds are anticipated. Electronic neural networks using single-value resistor synapses or single-bit programmable synapses have been demonstrated in the high-gain region of discrete single-value feedback. As an alternative to these networks and the above proposed optical synapses, an electronic analog-voltage vector matrix multiplier is considered using MOSFETS as the variable conductance in CMOS VLSI. It is concluded that a shadow mask addressed (static) optical neural network is promising.

  4. Hopfield Neural Network Approach to Clustering in Mobile Radio Networks

    Institute of Scientific and Technical Information of China (English)

    JiangYan; LiChengshu

    1995-01-01

    In this paper ,the Hopfield neural network(NN) algorithm is developed for selecting gateways in cluster linkage.The linked cluster(LC) architecture is assumed to achieve distributed network control in multihop radio networks throrgh the local controllers,called clusterheads and the nodes connecting these clusterheads are defined to be gateways.In Hopfield NN models ,the most critical issue being the determination of connection weights,we use the approach of Lagrange multipliers(LM) for its dynamic nature.

  5. Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks

    Science.gov (United States)

    Pyle, Ryan; Rosenbaum, Robert

    2017-01-01

    Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

  6. Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks

    Directory of Open Access Journals (Sweden)

    Zhisheng Zhang

    2016-01-01

    Full Text Available Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of K-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.

  7. Kunstige neurale net

    DEFF Research Database (Denmark)

    Hørning, Annette

    1994-01-01

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

  8. 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…

  9. 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...

  10. 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…

  11. Upregulation of cortico-cerebellar functional connectivity after motor learning.

    Science.gov (United States)

    Mehrkanoon, Saeid; Boonstra, Tjeerd W; Breakspear, Michael; Hinder, Mark; Summers, Jeffery J

    2016-03-01

    Interactions between the cerebellum and primary motor cortex are crucial for the acquisition of new motor skills. Recent neuroimaging studies indicate that learning motor skills is associated with subsequent modulation of resting-state functional connectivity in the cerebellar and cerebral cortices. The neuronal processes underlying the motor-learning-induced plasticity are not well understood. Here, we investigate changes in functional connectivity in source-reconstructed electroencephalography (EEG) following the performance of a single session of a dynamic force task in twenty young adults. Source activity was reconstructed in 112 regions of interest (ROIs) and the functional connectivity between all ROIs was estimated using the imaginary part of coherence. Significant changes in resting-state connectivity were assessed using partial least squares (PLS). We found that subjects adapted their motor performance during the training session and showed improved accuracy but with slower movement times. A number of connections were significantly upregulated after motor training, principally involving connections within the cerebellum and between the cerebellum and motor cortex. Increased connectivity was confined to specific frequency ranges in the mu- and beta-bands. Post hoc analysis of the phase spectra of these cerebellar and cortico-cerebellar connections revealed an increased phase lag between motor cortical and cerebellar activity following motor practice. These findings show a reorganization of intrinsic cortico-cerebellar connectivity related to motor adaptation and demonstrate the potential of EEG connectivity analysis in source space to reveal the neuronal processes that underpin neural plasticity.

  12. Is neural Darwinism Darwinism?

    Science.gov (United States)

    van Belle, T

    1997-01-01

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

  13. An exclusively mesodermal origin of fin mesenchyme demonstrates that zebrafish trunk neural crest does not generate ectomesenchyme.

    Science.gov (United States)

    Lee, Raymond Teck Ho; Knapik, Ela W; Thiery, Jean Paul; Carney, Thomas J

    2013-07-01

    The neural crest is a multipotent stem cell population that arises from the dorsal aspect of the neural tube and generates both non-ectomesenchymal (melanocytes, peripheral neurons and glia) and ectomesenchymal (skeletogenic, odontogenic, cartilaginous and connective tissue) derivatives. In amniotes, only cranial neural crest generates both classes, with trunk neural crest restricted to non-ectomesenchyme. By contrast, it has been suggested that anamniotes might generate derivatives of both classes at all axial levels, with trunk neural crest generating fin osteoblasts, scale mineral-forming cells and connective tissue cells; however, this has not been fully tested. The cause and evolutionary significance of this cranial/trunk dichotomy, and its absence in anamniotes, are debated. Recent experiments have disputed the contribution of fish trunk neural crest to fin osteoblasts and scale mineral-forming cells. This prompted us to test the contribution of anamniote trunk neural crest to fin connective tissue cells. Using genetics-based lineage tracing in zebrafish, we find that these fin mesenchyme cells derive entirely from the mesoderm and that neural crest makes no contribution. Furthermore, contrary to previous suggestions, larval fin mesenchyme cells do not generate the skeletogenic cells of the adult fin, but persist to form fibroblasts associated with adult fin rays. Our data demonstrate that zebrafish trunk neural crest does not generate ectomesenchymal derivatives and challenge long-held ideas about trunk neural crest fate. These findings have important implications for the ontogeny and evolution of the neural crest.

  14. Connecting Arithmetic to Algebra

    Science.gov (United States)

    Darley, Joy W.; Leapard, Barbara B.

    2010-01-01

    Algebraic thinking is a top priority in mathematics classrooms today. Because elementary school teachers lay the groundwork to develop students' capacity to think algebraically, it is crucial for teachers to have a conceptual understanding of the connections between arithmetic and algebra and be confident in communicating these connections. Many…

  15. Making Connections with Estimation.

    Science.gov (United States)

    Lobato, Joanne E.

    1993-01-01

    Describes four methods to structure estimation activities that enable students to make connections between their understanding of numbers and extensions of those concepts to estimating. Presents activities that connect estimation with other curricular areas, other mathematical topics, and real-world applications. (MDH)

  16. Tokens of Connection

    Science.gov (United States)

    Crowley, Theresa

    2016-01-01

    When teachers make the effort to build a solid relationship with each student, built on trust, they often engender a life-long connection, one that's life-changing for the student. But how can teachers grow such long-lasting relationships with all students, especially disenfranchised learners and those who make it hard to connect? Crowley, a…

  17. 用于本构模型的新的神经网络%NEW NEURAL NETWORK FOR CONSTITU- TIVE MODELING

    Institute of Scientific and Technical Information of China (English)

    赵启林; 王思敬; 金广谦

    2003-01-01

    In this paper,a new neural network is developed to connect FE analysis with the feed-forward neural network. With this new neural network,the constitutive model of material may be determined from the information of nodal's force and displacement. In this methodology,the stage which takes long time to obtain stress and strain by FE analysis is prevented.

  18. Neural development features: Spatio-temporal development of the Caenorhabditis elegans neuronal network

    CERN Document Server

    Varier, Sreedevi; 10.1371/journal.pcbi.1001044

    2011-01-01

    The nematode Caenorhabditis elegans, with information on neural connectivity, three-dimensional position and cell linage provides a unique system for understanding the development of neural networks. Although C. elegans has been widely studied in the past, we present the first statistical study from a developmental perspective, with findings that raise interesting suggestions on the establishment of long-distance connections and network hubs. Here, we analyze the neuro-development for temporal and spatial features, using birth times of neurons and their three-dimensional positions. Comparisons of growth in C. elegans with random spatial network growth highlight two findings relevant to neural network development. First, most neurons which are linked by long-distance connections are born around the same time and early on, suggesting the possibility of early contact or interaction between connected neurons during development. Second, early-born neurons are more highly connected (tendency to form hubs) than late...

  19. Models of Innate Neural Attractors and Their Applications for Neural Information Processing.

    Science.gov (United States)

    Solovyeva, Ksenia P; Karandashev, Iakov M; Zhavoronkov, Alex; Dunin-Barkowski, Witali L

    2015-01-01

    In this work we reveal and explore a new class of attractor neural networks, based on inborn connections provided by model molecular markers, the molecular marker based attractor neural networks (MMBANN). Each set of markers has a metric, which is used to make connections between neurons containing the markers. We have explored conditions for the existence of attractor states, critical relations between their parameters and the spectrum of single neuron models, which can implement the MMBANN. Besides, we describe functional models (perceptron and SOM), which obtain significant advantages over the traditional implementation of these models, while using MMBANN. In particular, a perceptron, based on MMBANN, gets specificity gain in orders of error probabilities values, MMBANN SOM obtains real neurophysiological meaning, the number of possible grandma cells increases 1000-fold with MMBANN. MMBANN have sets of attractor states, which can serve as finite grids for representation of variables in computations. These grids may show dimensions of d = 0, 1, 2,…. We work with static and dynamic attractor neural networks of the dimensions d = 0 and 1. We also argue that the number of dimensions which can be represented by attractors of activities of neural networks with the number of elements N = 10(4) does not exceed 8.

  20. Generalized connectivity of graphs

    CERN Document Server

    Li, Xueliang

    2016-01-01

    Noteworthy results, proof techniques, open problems and conjectures in generalized (edge-) connectivity are discussed in this book. Both theoretical and practical analyses for generalized (edge-) connectivity of graphs are provided. Topics covered in this book include: generalized (edge-) connectivity of graph classes, algorithms, computational complexity, sharp bounds, Nordhaus-Gaddum-type results, maximum generalized local connectivity, extremal problems, random graphs, multigraphs, relations with the Steiner tree packing problem and generalizations of connectivity. This book enables graduate students to understand and master a segment of graph theory and combinatorial optimization. Researchers in graph theory, combinatorics, combinatorial optimization, probability, computer science, discrete algorithms, complexity analysis, network design, and the information transferring models will find this book useful in their studies.

  1. Transition to chaos in random networks with cell-type-specific connectivity

    Science.gov (United States)

    Aljadeff, Johnatan; Stern, Merav; Sharpee, Tatyana

    2015-01-01

    In neural circuits, statistical connectivity rules strongly depend on cell-type identity. We study dynamics of neural networks with cell-type specific connectivity by extending the dynamic mean field method, and find that these networks exhibit a phase transition between silent and chaotic activity. By analyzing the locus of this transition, we derive a new result in random matrix theory: the spectral radius of a random connectivity matrix with block-structured variances. We apply our results to show how a small group of hyper-excitable neurons within the network can significantly increase the network’s computational capacity by bringing it into the chaotic regime. PMID:25768781

  2. On the design of dynamic associative neural memories.

    Science.gov (United States)

    Savran, M E; Morgul, O

    1994-01-01

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

  3. On the Elementary Neural Forms of Micro-Interactional Rituals

    DEFF Research Database (Denmark)

    Heinskou, Marie Bruvik; Liebst, Lasse Suonperä

    2016-01-01

    prosocial behavior. The ritual ingredients of mutual attention and shared mood may, moreover, be specified as part of a social engagement system, neurally regulating attention and emotional arousal via a face–heart connection. The article suggests that this social engagement system provides part...

  4. Probing the basins of attraction of a recurrent neural network

    NARCIS (Netherlands)

    M. Heerema; W.A. van Leeuwen

    2000-01-01

    Analytical expressions for the weights $w_{ij}(b)$ of the connections of a recurrent neural network are found by taking explicitly into account basins of attraction, the size of which is characterized by a basin parameter $b$. It is shown that a network with $b \

  5. A recurrent neural network with ever changing synapses

    NARCIS (Netherlands)

    M. Heerema; W.A. van Leeuwen

    2000-01-01

    A recurrent neural network with noisy input is studied analytically, on the basis of a Discrete Time Master Equation. The latter is derived from a biologically realizable learning rule for the weights of the connections. In a numerical study it is found that the fixed points of the dynamics of the n

  6. IR wireless cluster synapses of HYDRA very large neural networks

    Science.gov (United States)

    Jannson, Tomasz; Forrester, Thomas

    2008-04-01

    RF/IR wireless (virtual) synapses are critical components of HYDRA (Hyper-Distributed Robotic Autonomy) neural networks, already discussed in two earlier papers. The HYDRA network has the potential to be very large, up to 10 11-neurons and 10 18-synapses, based on already established technologies (cellular RF telephony and IR-wireless LANs). It is organized into almost fully connected IR-wireless clusters. The HYDRA neurons and synapses are very flexible, simple, and low-cost. They can be modified into a broad variety of biologically-inspired brain-like computing capabilities. In this third paper, we focus on neural hardware in general, and on IR-wireless synapses in particular. Such synapses, based on LED/LD-connections, dominate the HYDRA neural cluster.

  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. Quantifying bicycle network connectivity.

    Science.gov (United States)

    Lowry, Michael; Loh, Tracy Hadden

    2017-02-01

    The intent of this study was to compare bicycle network connectivity for different types of bicyclists and different neighborhoods. Connectivity was defined as the ability to reach important destinations, such as grocery stores, banks, and elementary schools, via pathways or roads with low vehicle volumes and low speed limits. The analysis was conducted for 28 neighborhoods in Seattle, Washington under existing conditions and for a proposed bicycle master plan, which when complete will provide over 700 new bicycle facilities, including protected bike lanes, neighborhood greenways, and multi-use trails. The results showed different levels of connectivity across neighborhoods and for different types of bicyclists. Certain projects were shown to improve connectivity differently for confident and non-confident bicyclists. The analysis showed a positive correlation between connectivity and observed utilitarian bicycle trips. To improve connectivity for the majority of bicyclists, planners and policy-makers should provide bicycle facilities that allow immediate, low-stress access to the street network, such as neighborhood greenways. The analysis also suggests that policies and programs that build confidence for bicycling could greatly increase connectivity.

  9. Training Deep Spiking Neural Networks Using Backpropagation.

    Science.gov (United States)

    Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

    2016-01-01

    Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

  10. Training Deep Spiking Neural Networks using Backpropagation

    Directory of Open Access Journals (Sweden)

    Jun Haeng Lee

    2016-11-01

    Full Text Available Deep spiking neural networks (SNNs hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

  11. 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.

  12. AUV fuzzy neural BDI

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

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

  13. Singularities of invariant connections

    Energy Technology Data Exchange (ETDEWEB)

    Amores, A.M. (Universidad Complutense, Madrid (Spain)); Gutierrez, M. (Universidad Politecnica, Madrid (Spain))

    1992-12-01

    A reductive homogeneous space M = P/G is considered, endowed with an invariant connection, i.e., such that all left translations of M induced by members of P preserve it. The authors study the set of singularities of such connections giving sufficient conditions for it to be empty, or, in other cases, familities of b-incomplete curves converging to singularities. A full description of the b-completion of a connection with M = R[sup m] (or a quotient of it) is given with information on its topology. 5 refs.

  14. Covariant Magnetic Connection Hypersurfaces

    CERN Document Server

    Pegoraro, F

    2016-01-01

    In the single fluid, nonrelativistic, ideal-Magnetohydrodynamic (MHD) plasma description magnetic field lines play a fundamental role by defining dynamically preserved "magnetic connections" between plasma elements. Here we show how the concept of magnetic connection needs to be generalized in the case of a relativistic MHD description where we require covariance under arbitrary Lorentz transformations. This is performed by defining 2-D {\\it magnetic connection hypersurfaces} in the 4-D Minkowski space. This generalization accounts for the loss of simultaneity between spatially separated events in different frames and is expected to provide a powerful insight into the 4-D geometry of electromagnetic fields when ${\\bf E} \\cdot {\\bf B} = 0$.

  15. Event-driven neural integration and synchronicity in analog VLSI.

    Science.gov (United States)

    Yu, Theodore; Park, Jongkil; Joshi, Siddharth; Maier, Christoph; Cauwenberghs, Gert

    2012-01-01

    Synchrony and temporal coding in the central nervous system, as the source of local field potentials and complex neural dynamics, arises from precise timing relationships between spike action population events across neuronal assemblies. Recently it has been shown that coincidence detection based on spike event timing also presents a robust neural code invariant to additive incoherent noise from desynchronized and unrelated inputs. We present spike-based coincidence detection using integrate-and-fire neural membrane dynamics along with pooled conductance-based synaptic dynamics in a hierarchical address-event architecture. Within this architecture, we encode each synaptic event with parameters that govern synaptic connectivity, synaptic strength, and axonal delay with additional global configurable parameters that govern neural and synaptic temporal dynamics. Spike-based coincidence detection is observed and analyzed in measurements on a log-domain analog VLSI implementation of the integrate-and-fire neuron and conductance-based synapse dynamics.

  16. Wind power prediction based on genetic neural network

    Science.gov (United States)

    Zhang, Suhan

    2017-04-01

    The scale of grid connected wind farms keeps increasing. To ensure the stability of power system operation, make a reasonable scheduling scheme and improve the competitiveness of wind farm in the electricity generation market, it's important to accurately forecast the short-term wind power. To reduce the influence of the nonlinear relationship between the disturbance factor and the wind power, the improved prediction model based on genetic algorithm and neural network method is established. To overcome the shortcomings of long training time of BP neural network and easy to fall into local minimum and improve the accuracy of the neural network, genetic algorithm is adopted to optimize the parameters and topology of neural network. The historical data is used as input to predict short-term wind power. The effectiveness and feasibility of the method is verified by the actual data of a certain wind farm as an example.

  17. Neural network chips for trigger purposes in high energy physics

    Energy Technology Data Exchange (ETDEWEB)

    Gemmeke, H.; Eppler, W.; Fischer, T. [Research Center Karlsruhe (Germany)] [and others

    1996-12-31

    Two novel neural chips SAND (Simple Applicable Neural Device) and SIOP (Serial Input - Operating Parallel) are described. Both are highly usable for hardware triggers in particle physics. The chips are optimized for a high input data rate at a very low cost basis. The performance of a single SAND chip is 200 MOPS due to four parallel 16 bit multipliers and 40 bit adders working in one clock cycle. The chip is able to implement feedforward neural networks, Kohonen feature maps and radial basis function networks. Four chips will be implemented on a PCI-board for simulation and on a VUE board for trigger and on- and off-line analysis. For small sized feedforward neural networks the bit-serial neuro-chip SIOP may lead to even smaller latencies because each synaptic connection is implemented by its own bit serial multiplier and adder.

  18. Impulsive Neural Networks Algorithm Based on the Artificial Genome Model

    Directory of Open Access Journals (Sweden)

    Yuan Gao

    2014-05-01

    Full Text Available To describe gene regulatory networks, this article takes the framework of the artificial genome model and proposes impulsive neural networks algorithm based on the artificial genome model. Firstly, the gene expression and the cell division tree are applied to generate spiking neurons with specific attributes, neural network structure, connection weights and specific learning rules of each neuron. Next, the gene segment duplications and divergence model are applied to design the evolutionary algorithm of impulsive neural networks at the level of the artificial genome. The dynamic changes of developmental gene regulatory networks are controlled during the whole evolutionary process. Finally, the behavior of collecting food for autonomous intelligent agent is simulated, which is driven by nerves. Experimental results demonstrate that the algorithm in this article has the evolutionary ability on large-scale impulsive neural networks

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

    Science.gov (United States)

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

    2016-08-01

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

  20. Linearizing the Characteristics of Gas Sensors using Neural Network

    Directory of Open Access Journals (Sweden)

    Gowri shankari B

    2015-03-01

    Full Text Available The paper describes implementing arbitrary connected neural network with more powerful network architecture to be embedded in inexpensive microcontroller. Our objective is to extend linear region of operation of nonlinear sensors. In order to implement more powerful neural network architectures on microcontrollers, the special Neuron by Neuron computing routine was developed in assembly language to allow fastest and shortest code. Embedded neural network requires hyperbolic tangent with great precision was used as a neuron activation function. Implementing neural network in microcontroller makes superior to other systems in faster response, smaller errors, and smoother surfaces. But its efficient implementation on microcontroller with simplified arithmetic was another challenge. This process was then demonstrated on gas sensor problem as they were mainly used accurately in measuring gas leakage in industry.

  1. Functional connectivity of emotional processing in depression.

    LENUS (Irish Health Repository)

    Carballedo, Angela

    2012-02-01

    OBJECTIVES: The aim of the study is to map a neural network of emotion processing and to identify differences in major depression compared to healthy controls. It is hypothesized that intentional perception of emotional faces activates connections between amygdala (Demir et al.), orbitofrontal cortex (OFC), anterior cingulate cortex (ACC) and prefrontal cortex (PFC) and that frontal-amygdala connections are altered in major depressive disorder (MDD). METHODS: Fifteen medication-free patients with MDD and fifteen healthy controls were enrolled. All subjects were assessed using the same face-matching functional Magnetic Resonance Imaging (fMRI) task, known to involve those areas. Brain activations were obtained using Statistical Parametric Mapping version 5 (SPM5) for data analysis and MARSBAR for extracting of fMRI time series. Then data was analyzed using structural equation modeling (SEM). RESULTS: A valid model was established for the left and the right hemispheres showing a circuit involving ACC, OFC, PFC and AMY. The left hemisphere shows significant lower connectivity strengths in patients than controls, for the pathway that goes from AMY to the OF11, and a trend of higher connectivity in patients for the path that goes from the PF9 to the OF11. In the right hemisphere, patients show lower connectivity coefficients in the paths from the AMY to OF11, from the AMY to ACC, and from the ACC to PF9. By the contrary, controls show lower connectivity strengths for the path that goes from ACC to AMY. CONCLUSIONS: Functional disconnection between limbic and frontal brain regions could be demonstrated using structural equation modeling. The interpretation of these findings could be that there is an emotional processing bias with disconnection bilaterally between amygdala to orbitofrontal cortices and in addition a right disconnection between amygdala and ACC as well as between ACC and prefrontal cortex possibly in line with a more prominent role for the right hemisphere

  2. Pattern Classification using Simplified Neural Networks

    CERN Document Server

    Kamruzzaman, S M

    2010-01-01

    In recent years, many neural network models have been proposed for pattern classification, function approximation and regression problems. This paper presents an approach for classifying patterns from simplified NNs. Although the predictive accuracy of ANNs is often higher than that of other methods or human experts, it is often said that ANNs are practically "black boxes", due to the complexity of the networks. In this paper, we have an attempted to open up these black boxes by reducing the complexity of the network. The factor makes this possible is the pruning algorithm. By eliminating redundant weights, redundant input and hidden units are identified and removed from the network. Using the pruning algorithm, we have been able to prune networks such that only a few input units, hidden units and connections left yield a simplified network. Experimental results on several benchmarks problems in neural networks show the effectiveness of the proposed approach with good generalization ability.

  3. New Computer Simulations of Macular Neural Functioning

    Science.gov (United States)

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

    1994-01-01

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

  4. Connective Tissue Naevus

    Directory of Open Access Journals (Sweden)

    Bhat Ramesh M

    1999-01-01

    Full Text Available A young adult female patient of connective tissue naevus presented with papules and indurated plaques on both les and left arm. Histopathology showed increased amount of collagen in the dermis. Osteopoikilosis was absent.

  5. Strengthening connections: functional connectivity and brain plasticity

    OpenAIRE

    2014-01-01

    The ascendancy of functional neuroimaging has facilitated the addition of network-based approaches to the neuropsychologist’s toolbox for evaluating the sequelae of brain insult. In particular, intrinsic functional connectivity (iFC) mapping of resting state fMRI (R-fMRI) data constitutes an ideal approach to measuring macro-scale networks in the human brain. Beyond the value of iFC mapping for charting how the functional topography of the brain is altered by insult and injury, iFC analyses c...

  6. Dynamic recurrent Elman neural network based on immune clonal selection algorithm

    Science.gov (United States)

    Wang, Limin; Han, Xuming; Li, Ming; Sun, Haibo; Li, Qingzhao

    2012-04-01

    Owing to the immune clonal selection algorithm introduced into dynamic threshold strategy has better advantage on optimizing multi-parameters, therefore a novel approach that the immune clonal selection algorithm introduced into dynamic threshold strategy, is used to optimize the dynamic recursion Elman neural network is proposed in the paper. The concrete structure of the recursion neural network, the connect weight and the initial values of the contact units etc. are done by evolving training and learning automatically. Thus it could realize to construct and design for dynamic recursion Elman neural networks. It could provide a new effective approach for immune clonal selection algorithm optimizing dynamic recursion neural networks.

  7. Connective Tissue Disorder

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    2008349 A clinical analysis of 32 patients with diffuse alveolar hemorrhage in diffuse connective tissue diseases. CHEN Guangxing(陈光星), et al. Dept Rheumatol, PUMC & CAMS Beijing 100730. Chin J Intern Med 2008;47(5):362-365.Objective To provide clues to diagnosis and treatment for diffuse alveolar hemorrhage(DAH)in patients with diffuse connective tissue diseases(CTD).Method To analyze restropectively the data of clinical features,

  8. Reliability of power connections

    Institute of Scientific and Technical Information of China (English)

    BRAUNOVIC Milenko

    2007-01-01

    Despite the use of various preventive maintenance measures, there are still a number of problem areas that can adversely affect system reliability. Also, economical constraints have pushed the designs of power connections closer to the limits allowed by the existing standards. The major parameters influencing the reliability and life of Al-Al and Al-Cu connections are identified. The effectiveness of various palliative measures is determined and the misconceptions about their effectiveness are dealt in detail.

  9. NEACP Onboard Connectivity Study

    Science.gov (United States)

    1990-03-30

    Methodology Framework .............................. 6-3 6.2.2 Sources of ME Cost Savings with NOCH ............... 6-5 6.2.3 Additional Benefits of 1OCU...processing system (MPS) installation connects all record and data communications equipment to a common MIL -STD-1553B bus and automates many of the manual...Local Area Network Concepts A NOCH developed around a generic bus would provide connectivity throughout the aircraft, thereby reducing or eliminating

  10. Connections between Frontier Markets

    Directory of Open Access Journals (Sweden)

    Eliza-Olivia Lungu

    2013-06-01

    Full Text Available The global financial system presents a high degree of connectivity and the network theory provides the natural framework for visualizing the structure of it connections. I analyse the financial links established between the frontier markets and how these links evolve over a 10 years period (2001 - 2011. I identify patterns in the network looking both at the node specific statistics (degree, strength and clustering coefficient and at the aggregated network statistics (network density and network asymmetry index.

  11. Three-dimensional thinning by neural networks

    Science.gov (United States)

    Shen, Jun; Shen, Wei

    1995-10-01

    3D thinning is widely used in 3D object representation in computer vision and in trajectory planning in robotics to find the topological structure of the free space. In the present paper, we propose a 3D image thinning method by neural networks. Each voxel in the 3D image corresponds to a set of neurons, called 3D Thinron, in the network. Taking the 3D Thinron as the elementary unit, the global structure of the network is a 3D array in which each Thinron is connected with the 26 neighbors in the neighborhood 3 X 3 X 3. As to the Thinron itself, the set of neurons are organized in multiple layers. In the first layer, we have neurons for boundary analysis, connectivity analysis and connectivity verification, taking as input the voxels in the 3 X 3 X 3 neighborhood and the intermediate outputs of neighboring Thinrons. In the second layer, we have the neurons for synthetical analysis to give the intermediate output of Thinron. In the third layer, we have the decision neurons whose state determines the final output. All neurons in the Thinron are the adaline neurons of Widrow, except the connectivity analysis and verification neurons which are nonlinear neurons. With the 3D Thinron neural network, the state transition of the network will take place automatically, and the network converges to the final steady state, which gives the result medial surface of 3D objects, preserving the connectivity in the initial image. The method presented is simulated and tested for 3D images, experimental results are reported.

  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 branching and, in doing so, simulates observed scaling laws as pervasive to neural and behavioral activity. These scaling laws are related to neural and cognitive functions, in that critical branching is shown to yield spiking activity with maximal memory and encoding capacities when analyzed using reservoir computing techniques. The model is also shown to account for findings of pervasive 1/f scaling in speech and cued response behaviors that are difficult to explain by isolable causes. Issues and questions raised by the model and its results are discussed from the perspectives of physics, neuroscience, computer and information sciences, and psychological and cognitive sciences.

  13. 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...

  14. Neural Oscillators Programming Simplified

    Directory of Open Access Journals (Sweden)

    Patrick McDowell

    2012-01-01

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

  15. Stress-Induced Activation of the HPA Axis Predicts Connectivity between Subgenual Cingulate and Salience Network during Rest in Adolescents

    Science.gov (United States)

    Thomason, Moriah E.; Hamilton, J. Paul; Gotlib, Ian H.

    2011-01-01

    Background: Responses to stress vary greatly in young adolescents, and little is known about neural correlates of the stress response in youth. The purpose of this study was to examine whether variability in cortisol responsivity following a social stress test in young adolescents is associated with altered neural functional connectivity (FC) of…

  16. Neural networks and graph theory

    Institute of Scientific and Technical Information of China (English)

    许进; 保铮

    2002-01-01

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

  17. [Connective tissue and inflammation].

    Science.gov (United States)

    Jakab, Lajos

    2014-03-23

    The author summarizes the structure of the connective tissues, the increasing motion of the constituents, which determine the role in establishing the structure and function of that. The structure and function of the connective tissue are related to each other in the resting as well as inflammatory states. It is emphasized that cellular events in the connective tissue are part of the defence of the organism, the localisation of the damage and, if possible, the maintenance of restitutio ad integrum. The organism responds to damage with inflammation, the non specific immune response, as well as specific, adaptive immunity. These processes are located in the connective tissue. Sterile and pathogenic inflammation are relatively similar processes, but inevitable differences are present, too. Sialic acids and glycoproteins containing sialic acids have important roles, and the role of Siglecs is also highlighted. Also, similarities and differences in damages caused by pathogens and sterile agents are briefly summarized. In addition, the roles of adhesion molecules linked to each other, and the whole event of inflammatory processes are presented. When considering practical consequences it is stressed that the structure (building up) of the organism and the defending function of inflammation both have fundamental importance. Inflammation has a crucial role in maintaining the integrity and the unimpaired somato-psychological state of the organism. Thus, inflammation serves as a tool of organism identical with the natural immune response, inseparably connected with the specific, adaptive immune response. The main events of the inflammatory processes take place in the connective tissue.

  18. Building a Neural Computer

    OpenAIRE

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

    1998-01-01

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

  19. 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.

  20. Imaging the Neural Symphony.

    Science.gov (United States)

    Svoboda, Karel

    2016-01-01

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

  1. Building a Neural Computer

    OpenAIRE

    1998-01-01

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

  2. Neural cryptography with feedback.

    Science.gov (United States)

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

    2004-04-01

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

  3. Pathological personality traits modulate neural interactions.

    Science.gov (United States)

    James, Lisa M; Engdahl, Brian E; Leuthold, Arthur C; Krueger, Robert F; Georgopoulos, Apostolos P

    2015-12-01

    The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), includes an empirically supported dimensional model of personality pathology that is assessed via the Personality Inventory for the DSM-5 (PID-5). Here we used magnetoencephalography (MEG; 248 sensors) to evaluate resting-state neural network properties associated with the five primary DSM-5 maladaptive personality domains (negative affect, detachment, antagonism, disinhibition, and psychoticism) in 150 healthy veterans ("control" group) and 179 veterans with various psychiatric disorders ("psychopathology" group). Since a fundamental network property is the strength of functional connectivity among network elements, we used the absolute value of the pairwise correlation coefficient (aCC) between prewhitened MEG sensor time series as a measure of neural functional connectivity and assessed its relations to the quantitative PID-5 scores in a linear regression model, where the log-transformed aCC was the dependent variable and individual PID scores, age, and gender were the independent variables. The partial regression coefficient (pRC) for a specific PID-5 score in that model provided information concerning the direction (positive, negative) and size (absolute value) of the PID effect on the strength of neural correlations. We found that, overall, PID domains had a negative effect (i.e., negative pRC; decorrelation) on aCC in the control group, but a positive one (i.e., positive pRC; hyper-correlation) in the psychopathology group. This dissociation of PID effects on aCC was especially pronounced for disinhibition, psychoticism, and negative affect. These results document for the first time a fundamental difference in neural-PID relations between control and psychopathology groups.

  4. Global attractivity in delayed Cohen-Grossberg neural network models

    Energy Technology Data Exchange (ETDEWEB)

    Li, C.-H. [Department of Mathematics, National Central University, Jhongli City 32001, Taiwan (China)], E-mail: 93241006@cc.ncu.edu.tw; Yang, S.-Y. [Department of Mathematics, National Central University, Jhongli City 32001, Taiwan (China)], E-mail: syyang@math.ncu.edu.tw

    2009-02-28

    In this paper, we investigate the global attractivity of Cohen-Grossberg neural network models with connection time delays for both discrete and distributed cases via the Lyapunov functional method. Without assuming the monotonicity and differentiability of activation functions and the symmetry of connection matrix, we establish three new sufficient conditions for the global exponential stability of a unique equilibrium for the delayed Cohen-Grossberg neural network no matter whether the connection time delay is of discrete type or distributed type. In particular, all the three new criteria are independent of time delays and do not include one another. To demonstrate the differences and features of the new stability criteria, several examples are discussed to compare the present results with the existing ones.

  5. Global exponential stability of Hopfield-type neural network and its applications

    Institute of Scientific and Technical Information of China (English)

    梁学斌; 吴立德

    1995-01-01

    If the matrix measure of connection weight of Hopfield-type continuous feedback neural network is less than the reciprocal of maximal product of resistance and gain constants, then the network system is globally and exponentially stable. The above reciprocal is a sharp upper bound of matrix measure of connection weight which guarantees that the above conclusion holds. The above result answers partially the open problem proposed by Vidyasagar recently, i. e whether neural network with "nearly" symmetric connection weight can exhibit limit cycles. The relation between the network time constant and the global exponential convergence rate is pointed out, and application to optimization computation of our results is also given.

  6. Heritable Disorders of Connective Tissue

    Science.gov (United States)

    ... Connective Tissue Find a Clinical Trial Journal Articles Connective Tissue August 2016 Questions and Answers about Heritable Disorders of Connective Tissue This publication contains general information about heritable (genetic) ...

  7. Algebraic connectivity and graph robustness.

    Energy Technology Data Exchange (ETDEWEB)

    Feddema, John Todd; Byrne, Raymond Harry; Abdallah, Chaouki T. (University of New Mexico)

    2009-07-01

    Recent papers have used Fiedler's definition of algebraic connectivity to show that network robustness, as measured by node-connectivity and edge-connectivity, can be increased by increasing the algebraic connectivity of the network. By the definition of algebraic connectivity, the second smallest eigenvalue of the graph Laplacian is a lower bound on the node-connectivity. In this paper we show that for circular random lattice graphs and mesh graphs algebraic connectivity is a conservative lower bound, and that increases in algebraic connectivity actually correspond to a decrease in node-connectivity. This means that the networks are actually less robust with respect to node-connectivity as the algebraic connectivity increases. However, an increase in algebraic connectivity seems to correlate well with a decrease in the characteristic path length of these networks - which would result in quicker communication through the network. Applications of these results are then discussed for perimeter security.

  8. Linear connections on matrix geometries

    CERN Document Server

    Madore, J; Mourad, J; Madore, John; Masson, Thierry; Mourad, Jihad

    1994-01-01

    A general definition of a linear connection in noncommutative geometry has been recently proposed. Two examples are given of linear connections in noncommutative geometries which are based on matrix algebras. They both possess a unique metric connection.

  9. Regenerative Electrode Interfaces for Neural Prostheses.

    Science.gov (United States)

    Thompson, Cort H; Zoratti, Marissa J; Langhals, Nicholas B; Purcell, Erin K

    2016-04-01

    Neural prostheses are electrode arrays implanted in the nervous system that record or stimulate electrical activity in neurons. Rapid growth in the use of neural prostheses in research and clinical applications has occurred in recent years, but instability and poor patency in the tissue-electrode interface undermines the longevity and performance of these devices. The application of tissue engineering strategies to the device interface is a promising approach to improve connectivity and communication between implanted electrodes and local neurons, and several research groups have developed new and innovative modifications to neural prostheses with the goal of seamless device-tissue integration. These approaches can be broadly categorized based on the strategy used to maintain and regenerate neurons at the device interface: (1) redesign of the prosthesis architecture to include finer-scale geometries and/or provide topographical cues to guide regenerating neural outgrowth, (2) incorporation of material coatings and bioactive molecules on the prosthesis to improve neuronal growth, viability, and adhesion, and (3) inclusion of cellular grafts to replenish the local neuron population or provide a target site for reinnervation (biohybrid devices). In addition to stabilizing the contact between neurons and electrodes, the potential to selectively interface specific subpopulations of neurons with individual electrode sites is a key advantage of regenerative interfaces. In this study, we review the development of regenerative interfaces for applications in both the peripheral and central nervous system. Current and future development of regenerative interfaces has the potential to improve the stability and selectivity of neural prostheses, improving the patency and resolution of information transfer between neurons and implanted electrodes.

  10. Construction of a Piezoresistive Neural Sensor Array

    Science.gov (United States)

    Carlson, W. B.; Schulze, W. A.; Pilgrim, P. M.

    1996-01-01

    The construction of a piezoresistive - piezoelectric sensor (or actuator) array is proposed using 'neural' connectivity for signal recognition and possible actuation functions. A closer integration of the sensor and decision functions is necessary in order to achieve intrinsic identification within the sensor. A neural sensor is the next logical step in development of truly 'intelligent' arrays. This proposal will integrate 1-3 polymer piezoresistors and MLC electroceramic devices for applications involving acoustic identification. The 'intelligent' piezoresistor -piezoelectric system incorporates printed resistors, composite resistors, and a feedback for the resetting of resistances. A model of a design is proposed in order to simulate electromechanical resistor interactions. The goal of optimizing a sensor geometry for improving device reliability, training, & signal identification capabilities is the goal of this work. At present, studies predict performance of a 'smart' device with a significant control of 'effective' compliance over a narrow pressure range due to a piezoresistor percolation threshold. An interesting possibility may be to use an array of control elements to shift the threshold function in order to change the level of resistance in a neural sensor array for identification, or, actuation applications. The proposed design employs elements of: (1) conductor loaded polymers for a 'fast' RC time constant response; and (2) multilayer ceramics for actuation or sensing and shifting of resistance in the polymer. Other material possibilities also exist using magnetoresistive layered systems for shifting the resistance. It is proposed to use a neural net configuration to test and to help study the possible changes required in the materials design of these devices. Numerical design models utilize electromechanical elements, in conjunction with structural elements in order to simulate piezoresistively controlled actuators and changes in resistance of sensors

  11. Resting-state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons.

    Science.gov (United States)

    Ma, Ying; Shaik, Mohammed A; Kozberg, Mariel G; Kim, Sharon H; Portes, Jacob P; Timerman, Dmitriy; Hillman, Elizabeth M C

    2016-12-27

    Brain hemodynamics serve as a proxy for neural activity in a range of noninvasive neuroimaging techniques including functional magnetic resonance imaging (fMRI). In resting-state fMRI, hemodynamic fluctuations have been found to exhibit patterns of bilateral synchrony, with correlated regions inferred to have functional connectivity. However, the relationship between resting-state hemodynamics and underlying neural activity has not been well established, making the neural underpinnings of functional connectivity networks unclear. In this study, neural activity and hemodynamics were recorded simultaneously over the bilateral cortex of awake and anesthetized Thy1-GCaMP mice using wide-field optical mapping. Neural activity was visualized via selective expression of the calcium-sensitive fluorophore GCaMP in layer 2/3 and 5 excitatory neurons. Characteristic patterns of resting-state hemodynamics were accompanied by more rapidly changing bilateral patterns of resting-state neural activity. Spatiotemporal hemodynamics could be modeled by convolving this neural activity with hemodynamic response functions derived through both deconvolution and gamma-variate fitting. Simultaneous imaging and electrophysiology confirmed that Thy1-GCaMP signals are well-predicted by multiunit activity. Neurovascular coupling between resting-state neural activity and hemodynamics was robust and fast in awake animals, whereas coupling in urethane-anesthetized animals was slower, and in some cases included lower-frequency (neural activity. The patterns of bilaterally-symmetric spontaneous neural activity revealed by wide-field Thy1-GCaMP imaging may depict the neural foundation of functional connectivity networks detected in resting-state fMRI.

  12. Connectable solar air collectors

    Energy Technology Data Exchange (ETDEWEB)

    Oestergaard Jensen, S.; Bosanac, M.

    2002-02-01

    The project has proved that it is possible to manufacture solar air collector panels, which in an easy way can be connected into large collector arrays with integrated ducting without loss of efficiency. The developed connectable solar air collectors are based on the use of matrix absorbers in the form of perforated metal sheets. Three interconnected solar air collectors of the above type - each with an transparent area of approx. 3 m{sup 2} - was tested and compared with parallel tests on two single solar air collectors also with a transparent area of approx. 3 m{sup 2} One of the single solar air collectors has an identical absorber as the connectable solar air collectors while the absorber of the other single solar air collector was a fibre cloth. The efficiency of the three solar air collectors proved to be almost identical in the investigated range of mass flow rates and temperature differences. The solar air collectors further proved to be very efficient - as efficient as the second most efficient solar air collectors tested in the IEA task 19 project Solar Air Systems. Some problems remain although to be solved: the pressure drop across especially the connectable solar air collectors is too high - mainly across the inlets of the solar air collectors. It should, however, be possible to considerably reduce the pressure losses with a more aerodynamic design of the inlet and outlet of the solar air collectors; The connectable solar air collectors are easy connectable but the air tightness of the connections in the present form is not good enough. As leakage leads to lower efficiencies focus should be put on making the connections more air tight without loosing the easiness in connecting the solar air collectors. As a spin off of the project a simple and easy way to determine the efficiency of solar, air collectors for pre-heating of fresh air has been validated. The simple method of determining the efficiency has with success been compared with an advance method

  13. Contextual behavior and neural circuits

    Directory of Open Access Journals (Sweden)

    Inah eLee

    2013-05-01

    Full Text Available Animals including humans engage in goal-directed behavior flexibly in response to items and their background, which is called contextual behavior in this review. Although the concept of context has long been studied, there are differences among researchers in defining and experimenting with the concept. The current review aims to provide a categorical framework within which not only the neural mechanisms of contextual information processing but also the contextual behavior can be studied in more concrete ways. For this purpose, we categorize contextual behavior into three subcategories as follows by considering the types of interactions among context, item, and response: contextual response selection, contextual item selection, and contextual item-response selection. Contextual response selection refers to the animal emitting different types of responses to the same item depending on the context in the background. Contextual item selection occurs when there are multiple items that need to be chosen in a contextual manner. Finally, when multiple items and multiple contexts are involved, contextual item-response selection takes place whereby the animal either choose an item or inhibit such a response depending on item-context paired association. The literature suggests that the rhinal cortical regions and the hippocampal formation play key roles in mnemonically categorizing and recognizing contextual representations and the associated items. In addition, it appears that the fronto-striatal cortical loops in connection with the contextual information-processing areas critically control the flexible deployment of adaptive action sets and motor responses for maximizing goals. We suggest that contextual information processing should be investigated in experimental settings where contextual stimuli and resulting behaviors are clearly defined and measurable, considering the dynamic top-down and bottom-up interactions among the neural systems for

  14. Contextual behavior and neural circuits

    Science.gov (United States)

    Lee, Inah; Lee, Choong-Hee

    2013-01-01

    Animals including humans engage in goal-directed behavior flexibly in response to items and their background, which is called contextual behavior in this review. Although the concept of context has long been studied, there are differences among researchers in defining and experimenting with the concept. The current review aims to provide a categorical framework within which not only the neural mechanisms of contextual information processing but also the contextual behavior can be studied in more concrete ways. For this purpose, we categorize contextual behavior into three subcategories as follows by considering the types of interactions among context, item, and response: contextual response selection, contextual item selection, and contextual item–response selection. Contextual response selection refers to the animal emitting different types of responses to the same item depending on the context in the background. Contextual item selection occurs when there are multiple items that need to be chosen in a contextual manner. Finally, when multiple items and multiple contexts are involved, contextual item–response selection takes place whereby the animal either chooses an item or inhibits such a response depending on item–context paired association. The literature suggests that the rhinal cortical regions and the hippocampal formation play key roles in mnemonically categorizing and recognizing contextual representations and the associated items. In addition, it appears that the fronto-striatal cortical loops in connection with the contextual information-processing areas critically control the flexible deployment of adaptive action sets and motor responses for maximizing goals. We suggest that contextual information processing should be investigated in experimental settings where contextual stimuli and resulting behaviors are clearly defined and measurable, considering the dynamic top-down and bottom-up interactions among the neural systems for contextual behavior

  15. Changes in corticostriatal connectivity during reinforcement learning in humans.

    Science.gov (United States)

    Horga, Guillermo; Maia, Tiago V; Marsh, Rachel; Hao, Xuejun; Xu, Dongrong; Duan, Yunsuo; Tau, Gregory Z; Graniello, Barbara; Wang, Zhishun; Kangarlu, Alayar; Martinez, Diana; Packard, Mark G; Peterson, Bradley S

    2015-02-01

    Many computational models assume that reinforcement learning relies on changes in synaptic efficacy between cortical regions representing stimuli and striatal regions involved in response selection, but this assumption has thus far lacked empirical support in humans. We recorded hemodynamic signals with fMRI while participants navigated a virtual maze to find hidden rewards. We fitted a reinforcement-learning algorithm to participants' choice behavior and evaluated the neural activity and the changes in functional connectivity related to trial-by-trial learning variables. Activity in the posterior putamen during choice periods increased progressively during learning. Furthermore, the functional connections between the sensorimotor cortex and the posterior putamen strengthened progressively as participants learned the task. These changes in corticostriatal connectivity differentiated participants who learned the task from those who did not. These findings provide a direct link between changes in corticostriatal connectivity and learning, thereby supporting a central assumption common to several computational models of reinforcement learning.

  16. Altered functional connectivity of prefrontal cortex in chronic heroin abusers

    Institute of Scientific and Technical Information of China (English)

    Yinbao Qi; Xianming Fu; Ruobing Qian; Chaoshi Niu; Xiangpin Wei

    2011-01-01

    In this study, we investigated alterations in the resting-state functional connectivity of the pre-frontal cortex in chronic heroin abusers using functional magnetic resonance imaging. We found that, compared with normal controls, in heroin abusers the left prefrontal cortex showed decreased functional connectivity with the left hippocampus, right anterior cingulate, left middle frontal gyrus, right middle frontal gyrus and right precuneus. However, the right prefrontal cortex showed decreased functional connectivity with the left orbital frontal cortex and the left middle frontal gyrus in chronic heroin abusers. These alterations of resting-state functional connectivity in the prefrontal cortices of heroin abusers suggest that their frontal executive neural network may be impaired, and that this may contribute to their continued heroin abuse and relapse after withdrawal.

  17. Lateral-Medial Dissociation in Orbitofrontal Cortex-Hypothalamus Connectivity.

    Science.gov (United States)

    Hirose, Satoshi; Osada, Takahiro; Ogawa, Akitoshi; Tanaka, Masaki; Wada, Hiroyuki; Yoshizawa, Yasunori; Imai, Yoshio; Machida, Toru; Akahane, Masaaki; Shirouzu, Ichiro; Konishi, Seiki

    2016-01-01

    The orbitofrontal cortex (OFC) is involved in cognitive functions, and is also closely related to autonomic functions. The OFC is densely connected with the hypothalamus, a heterogeneous structure controlling autonomic functions that can be divided into two major parts: the lateral and the medial. Resting-state functional connectivity has allowed us to parcellate the cerebral cortex into putative functional areas based on the changes in the spatial pattern of connectivity in the cerebral cortex when a seed point is moved from one voxel to another. In the present high spatial-resolution fMRI study, we investigate the connectivity-based organization of the OFC with reference to the hypothalamus. The OFC was parcellated using resting-state functional connectivity in an individual subject approach, and then the functional connectivity was examined between the parcellated areas in the OFC and the lateral/medial hypothalamus. We found a functional double dissociation in the OFC: the lateral OFC (the lateral orbital gyrus) was more likely connected with the lateral hypothalamus, whereas the medial OFC (the medial orbital and rectal gyri) was more likely connected with the medial hypothalamus. These results demonstrate the fundamental heterogeneity of the OFC, and suggest a potential neural basis of the OFC-hypothalamic functional interaction.

  18. Lateral–Medial Dissociation in Orbitofrontal Cortex–Hypothalamus Connectivity

    Science.gov (United States)

    Hirose, Satoshi; Osada, Takahiro; Ogawa, Akitoshi; Tanaka, Masaki; Wada, Hiroyuki; Yoshizawa, Yasunori; Imai, Yoshio; Machida, Toru; Akahane, Masaaki; Shirouzu, Ichiro; Konishi, Seiki

    2016-01-01

    The orbitofrontal cortex (OFC) is involved in cognitive functions, and is also closely related to autonomic functions. The OFC is densely connected with the hypothalamus, a heterogeneous structure controlling autonomic functions that can be divided into two major parts: the lateral and the medial. Resting-state functional connectivity has allowed us to parcellate the cerebral cortex into putative functional areas based on the changes in the spatial pattern of connectivity in the cerebral cortex when a seed point is moved from one voxel to another. In the present high spatial-resolution fMRI study, we investigate the connectivity-based organization of the OFC with reference to the hypothalamus. The OFC was parcellated using resting-state functional connectivity in an individual subject approach, and then the functional connectivity was examined between the parcellated areas in the OFC and the lateral/medial hypothalamus. We found a functional double dissociation in the OFC: the lateral OFC (the lateral orbital gyrus) was more likely connected with the lateral hypothalamus, whereas the medial OFC (the medial orbital and rectal gyri) was more likely connected with the medial hypothalamus. These results demonstrate the fundamental heterogeneity of the OFC, and suggest a potential neural basis of the OFC–hypothalamic functional interaction. PMID:27303281

  19. Topology and dynamics of attractor neural networks: The role of loopiness

    Science.gov (United States)

    Zhang, Pan; Chen, Yong

    2008-07-01

    We derive an exact representation of the topological effect on the dynamics of sequence processing neural networks within signal-to-noise analysis. A new network structure parameter, loopiness coefficient, is introduced to quantitatively study the loop effect on network dynamics. A large loopiness coefficient means a high probability of finding loops in the networks. We develop recursive equations for the overlap parameters of neural networks in terms of their loopiness. It was found that a large loopiness increases the correlation among the network states at different times and eventually reduces the performance of neural networks. The theory is applied to several network topological structures, including fully-connected, densely-connected random, densely-connected regular and densely-connected small-world, where encouraging results are obtained.

  20. Enhanced interhemispheric functional connectivity compensates for anatomical connection damages in subcortical stroke.

    Science.gov (United States)

    Liu, Jingchun; Qin, Wen; Zhang, Jing; Zhang, Xuejun; Yu, Chunshui

    2015-04-01

    Motor recovery after stroke has been shown to be correlated with both the fractional anisotropy (FA) of the affected corticospinal tract (CST) and the interhemispheric resting-state functional connectivity (rsFC) of the primary motor cortex (M1). However, the role of the restoration or enhancement of the M1-M1 rsFC in motor recovery remains largely unknown. We aimed to clarify this issue by investigating the correlations between the M1-M1 rsFC and the integrity of the M1-M1 anatomic connection and the affected CST in chronic subcortical stroke patients with good motor outcomes. Twenty patients and 16 healthy controls underwent multimodal magnetic resonance imaging examinations. Diffusion tensor imaging was used to reconstruct the M1-M1 anatomic connection and bilateral CSTs. White matter integrity of these tracts was assessed using FA. Resting-state functional magnetic resonance imaging was used to calculate M1-M1 rsFC. Group differences in these measures were compared. Correlations between M1-M1 rsFC and FA of the M1-M1 anatomic connection and the affected CST were analyzed in patients with stroke. Compared with healthy controls, patients with stroke exhibited significantly reduced FA in the affected CST and the M1-M1 anatomic connection and a significantly increased M1-M1 rsFC. The FA values of the affected CST were positively correlated with the M1-M1 anatomic connection, and these FA values were negatively correlated with the M1-M1 rsFC in these patients. Our findings suggest that the M1-M1 anatomic connection impairment is secondary to CST damage, and the M1-M1 rsFC enhancement may reflect compensatory or reactive neural plasticity in stroke patients with CST impairment. © 2015 American Heart Association, Inc.

  1. Network connections that evolve to circumvent the inverse optics problem.

    Science.gov (United States)

    Ng, Cherlyn; Sundararajan, Janani; Hogan, Michael; Purves, Dale

    2013-01-01

    A fundamental problem in vision science is how useful perceptions and behaviors arise in the absence of information about the physical sources of retinal stimuli (the inverse optics problem). Psychophysical studies show that human observers contend with this problem by using the frequency of occurrence of stimulus patterns in cumulative experience to generate percepts. To begin to understand the neural mechanisms underlying this strategy, we examined the connectivity of simple neural networks evolved to respond according to the cumulative rank of stimulus luminance values. Evolved similarities with the connectivity of early level visual neurons suggests that biological visual circuitry uses the same mechanisms as a means of creating useful perceptions and behaviors without information about the real world.

  2. Network connections that evolve to circumvent the inverse optics problem.

    Directory of Open Access Journals (Sweden)

    Cherlyn Ng

    Full Text Available A fundamental problem in vision science is how useful perceptions and behaviors arise in the absence of information about the physical sources of retinal stimuli (the inverse optics problem. Psychophysical studies show that human observers contend with this problem by using the frequency of occurrence of stimulus patterns in cumulative experience to generate percepts. To begin to understand the neural mechanisms underlying this strategy, we examined the connectivity of simple neural networks evolved to respond according to the cumulative rank of stimulus luminance values. Evolved similarities with the connectivity of early level visual neurons suggests that biological visual circuitry uses the same mechanisms as a means of creating useful perceptions and behaviors without information about the real world.

  3. Muscle networks: Connectivity analysis of EMG activity during postural control

    Science.gov (United States)

    Boonstra, Tjeerd W.; Danna-Dos-Santos, Alessander; Xie, Hong-Bo; Roerdink, Melvyn; Stins, John F.; Breakspear, Michael

    2015-12-01

    Understanding the mechanisms that reduce the many degrees of freedom in the musculoskeletal system remains an outstanding challenge. Muscle synergies reduce the dimensionality and hence simplify the control problem. How this is achieved is not yet known. Here we use network theory to assess the coordination between multiple muscles and to elucidate the neural implementation of muscle synergies. We performed connectivity analysis of surface EMG from ten leg muscles to extract the muscle networks while human participants were standing upright in four different conditions. We observed widespread connectivity between muscles at multiple distinct frequency bands. The network topology differed significantly between frequencies and between conditions. These findings demonstrate how muscle networks can be used to investigate the neural circuitry of motor coordination. The presence of disparate muscle networks across frequencies suggests that the neuromuscular system is organized into a multiplex network allowing for parallel and hierarchical control structures.

  4. Neural coding in graphs of bidirectional associative memories.

    Science.gov (United States)

    Bouchain, A David; Palm, Günther

    2012-01-24

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

  5. Shared neural mechanisms underlying social warmth and physical warmth.

    Science.gov (United States)

    Inagaki, Tristen K; Eisenberger, Naomi I

    2013-11-01

    Many of people's closest bonds grow out of socially warm exchanges and the warm feelings associated with being socially connected. Indeed, the neurobiological mechanisms underlying thermoregulation may be shared by those that regulate social warmth, the experience of feeling connected to other people. To test this possibility, we placed participants in a functional MRI scanner and asked them to (a) read socially warm and neutral messages from friends and family and (b) hold warm and neutral-temperature objects (a warm pack and a ball, respectively). Findings showed an overlap between physical and social warmth: Participants felt warmer after reading the positive (compared with neutral) messages and more connected after holding the warm pack (compared with the ball). In addition, neural activity during social warmth overlapped with neural activity during physical warmth in the ventral striatum and middle insula, but neural activity did not overlap during another pleasant task (soft touch). Together, these results suggest that a common neural mechanism underlies physical and social warmth.

  6. The CONNECT project

    DEFF Research Database (Denmark)

    Assaf, Yaniv; Alexander, Daniel C; Jones, Derek K

    2013-01-01

    diameter and axonal density). This unique insight into both tissue microstructure and connectivity has enormous potential value in understanding the structure and organization of the brain as well as providing unique insights to abnormalities that underpin disease states. The CONNECT (Consortium......In recent years, diffusion MRI has become an extremely important tool for studying the morphology of living brain tissue, as it provides unique insights into both its macrostructure and microstructure. Recent applications of diffusion MRI aimed to characterize the structural connectome using...... tractography to infer connectivity between brain regions. In parallel to the development of tractography, additional diffusion MRI based frameworks (CHARMED, AxCaliber, ActiveAx) were developed enabling the extraction of a multitude of micro-structural parameters (axon diameter distribution, mean axonal...

  7. Skeletal muscle connective tissue

    DEFF Research Database (Denmark)

    Brüggemann, Dagmar Adeline

      The connective tissue content of skeletal muscle is believed to be the major factor responsible for defining the eating quality of different meat cuts, although attempts to correlate quantifications based on traditional histological methods have not as yet been able to prove this relation....... Collagen, being the major protein in connective tissue, has been extensively investigated with regard to its relation to meat tenderness, but the results have been rather conflicting. Meat from older animals is tougher than that from younger animals, and changes in the properties of the collagen due...... that collagen plays a significant role in determining the tenderness of meat. What are we missing? Therefore, fundamental aspects of connective tissue research have been the centre of attention throughout this thesis. A holistic view has been applied, glancing at this complex tissue which has many facets...

  8. Connectivity and superconductivity

    CERN Document Server

    Rubinstein, Jacob

    2000-01-01

    The motto of connectivity and superconductivity is that the solutions of the Ginzburg--Landau equations are qualitatively influenced by the topology of the boundaries, as in multiply-connected samples. Special attention is paid to the "zero set", the set of the positions (also known as "quantum vortices") where the order parameter vanishes. The effects considered here usually become important in the regime where the coherence length is of the order of the dimensions of the sample. It takes the intuition of physicists and the awareness of mathematicians to find these new effects. In connectivity and superconductivity, theoretical and experimental physicists are brought together with pure and applied mathematicians to review these surprising results. This volume is intended to serve as a reference book for graduate students and researchers in physics or mathematics interested in superconductivity, or in the Schrödinger equation as a limiting case of the Ginzburg--Landau equations.

  9. Linked Sex Differences in Cognition and Functional Connectivity in Youth.

    Science.gov (United States)

    Satterthwaite, Theodore D; Wolf, Daniel H; Roalf, David R; Ruparel, Kosha; Erus, Guray; Vandekar, Simon; Gennatas, Efstathios D; Elliott, Mark A; Smith, Alex; Hakonarson, Hakon; Verma, Ragini; Davatzikos, Christos; Gur, Raquel E; Gur, Ruben C

    2015-09-01

    Sex differences in human cognition are marked, but little is known regarding their neural origins. Here, in a sample of 674 human participants ages 9-22, we demonstrate that sex differences in cognitive profiles are related to multivariate patterns of resting-state functional connectivity MRI (rsfc-MRI). Males outperformed females on motor and spatial cognitive tasks; females were faster in tasks of emotion identification and nonverbal reasoning. Sex differences were also prominent in the rsfc-MRI data at multiple scales of analysis, with males displaying more between-module connectivity, while females demonstrated more within-module connectivity. Multivariate pattern analysis using support vector machines classified subject sex on the basis of their cognitive profile with 63% accuracy (P cognitive profile was "male" or "female" was significantly related to the masculinity or femininity of their pattern of brain connectivity (P = 2.3 × 10(-7)). This relationship was present even when considering males and female separately. Taken together, these results demonstrate for the first time that sex differences in patterns of cognition are in part represented on a neural level through divergent patterns of brain connectivity. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  10. Neural Elements for Predictive Coding

    Directory of Open Access Journals (Sweden)

    Stewart SHIPP

    2016-11-01

    Full Text Available Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backwards in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many ‘illusory’ instances of perception where what is seen (heard, etc is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forwards and backwards pathways should be completely separate, given their functional distinction; this aspect of circuitry – that neurons with extrinsically bifurcating axons do not project in both directions – has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy formulation of predictive coding is combined with the classic ‘canonical microcircuit’ and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a updates in the microcircuitry of primate visual cortex, and (b rapid technical advances made

  11. Neural networks in seismic discrimination

    Energy Technology Data Exchange (ETDEWEB)

    Dowla, F.U.

    1995-01-01

    Neural networks are powerful and elegant computational tools that can be used in the analysis of geophysical signals. At Lawrence Livermore National Laboratory, we have developed neural networks to solve problems in seismic discrimination, event classification, and seismic and hydrodynamic yield estimation. Other researchers have used neural networks for seismic phase identification. We are currently developing neural networks to estimate depths of seismic events using regional seismograms. In this paper different types of network architecture and representation techniques are discussed. We address the important problem of designing neural networks with good generalization capabilities. Examples of neural networks for treaty verification applications are also described.

  12. Aspects of randomness in neural graph structures

    CERN Document Server

    Rudolph-Lilith, Michelle

    2013-01-01

    In the past two decades, significant advances have been made in understanding the structural and functional properties of biological networks, via graph-theoretic analysis. In general, most graph-theoretic studies are conducted in the presence of serious uncertainties, such as major undersampling of the experimental data. In the specific case of neural systems, however, a few moderately robust experimental reconstructions do exist, and these have long served as fundamental prototypes for studying connectivity patterns in the nervous system. In this paper, we provide a comparative analysis of these "historical" graphs, both in (unmodified) directed and (often symmetrized) undirected forms, and focus on simple structural characterizations of their connectivity. We find that in most measures the networks studied are captured by simple random graph models; in a few key measures, however, we observe a marked departure from the random graph prediction. Our results suggest that the mechanism of graph formation in th...

  13. A new approach to artificial neural networks.

    Science.gov (United States)

    Baptista Filho, B D; Cabral, E L; Soares, A J

    1998-01-01

    A novel approach to artificial neural networks is presented. The philosophy of this approach is based on two aspects: the design of task-specific networks, and a new neuron model with multiple synapses. The synapses' connective strengths are modified through selective and cumulative processes conducted by axo-axonic connections from a feedforward circuit. This new concept was applied to the position control of a planar two-link manipulator exhibiting excellent results on learning capability and generalization when compared with a conventional feedforward network. In the present paper, the example shows only a network developed from a neuronal reflexive circuit with some useful artifices, nevertheless without the intention of covering all possibilities devised.

  14. Best connected rectangular arrangements

    Directory of Open Access Journals (Sweden)

    Krishnendra Shekhawat

    2016-03-01

    Full Text Available It can be found quite often in the literature that many well-known architects have employed either the golden rectangle or the Fibonacci rectangle in their works. On contrary, it is rare to find any specific reason for using them so often. Recently, Shekhawat (2015 proved that the golden rectangle and the Fibonacci rectangle are one of the best connected rectangular arrangements and this may be one of the reasons for their high presence in architectural designs. In this work we present an algorithm that generates n-4 best connected rectangular arrangements so that the proposed solutions can be further used by architects for their designs.

  15. Existence and uniform stability analysis of fractional-order complex-valued neural networks with time delays.

    Science.gov (United States)

    Rakkiyappan, R; Cao, Jinde; Velmurugan, G

    2015-01-01

    This paper deals with the problem of existence and uniform stability analysis of fractional-order complex-valued neural networks with constant time delays. Complex-valued recurrent neural networks is an extension of real-valued recurrent neural networks that includes complex-valued states, connection weights, or activation functions. This paper explains sufficient condition for the existence and uniform stability analysis of such networks. Three numerical simulations are delineated to substantiate the effectiveness of the theoretical results.

  16. Complex networks: new trends for the analysis of brain connectivity

    CERN Document Server

    Chavez, Mario; Latora, Vito; Martinerie, Jacques

    2010-01-01

    Today, the human brain can be studied as a whole. Electroencephalography, magnetoencephalography, or functional magnetic resonance imaging techniques provide functional connectivity patterns between different brain areas, and during different pathological and cognitive neuro-dynamical states. In this Tutorial we review novel complex networks approaches to unveil how brain networks can efficiently manage local processing and global integration for the transfer of information, while being at the same time capable of adapting to satisfy changing neural demands.

  17. Implementing Signature Neural Networks with Spiking Neurons.

    Science.gov (United States)

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

    of inhibitory connections. These parameters also modulate the memory capabilities of the network. The dynamical modes observed in the different informational dimensions in a given moment are independent and they only depend on the parameters shaping the information processing in this dimension. In view of these results, we argue that plasticity mechanisms inside individual cells and multicoding strategies can provide additional computational properties to spiking neural networks, which could enhance their capacity and performance in a wide variety of real-world tasks.

  18. Implementing Signature Neural Networks with Spiking Neurons

    Science.gov (United States)

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

    absence of inhibitory connections. These parameters also modulate the memory capabilities of the network. The dynamical modes observed in the different informational dimensions in a given moment are independent and they only depend on the parameters shaping the information processing in this dimension. In view of these results, we argue that plasticity mechanisms inside individual cells and multicoding strategies can provide additional computational properties to spiking neural networks, which could enhance their capacity and performance in a wide variety of real-world tasks. PMID:28066221

  19. Cultured Neural Networks: Optimization of Patterned Network Adhesiveness and Characterization of their Neural Activity

    Directory of Open Access Journals (Sweden)

    W. L. C. Rutten

    2006-01-01

    Full Text Available One type of future, improved neural interface is the “cultured probe”. It is a hybrid type of neural information transducer or prosthesis, for stimulation and/or recording of neural activity. It would consist of a microelectrode array (MEA on a planar substrate, each electrode being covered and surrounded by a local circularly confined network (“island” of cultured neurons. The main purpose of the local networks is that they act as biofriendly intermediates for collateral sprouts from the in vivo system, thus allowing for an effective and selective neuron–electrode interface. As a secondary purpose, one may envisage future information processing applications of these intermediary networks. In this paper, first, progress is shown on how substrates can be chemically modified to confine developing networks, cultured from dissociated rat cortex cells, to “islands” surrounding an electrode site. Additional coating of neurophobic, polyimide-coated substrate by triblock-copolymer coating enhances neurophilic-neurophobic adhesion contrast. Secondly, results are given on neuronal activity in patterned, unconnected and connected, circular “island” networks. For connected islands, the larger the island diameter (50, 100 or 150 μm, the more spontaneous activity is seen. Also, activity may show a very high degree of synchronization between two islands. For unconnected islands, activity may start at 22 days in vitro (DIV, which is two weeks later than in unpatterned networks.

  20. Rule Extraction:Using Neural Networks or for Neural Networks?

    Institute of Scientific and Technical Information of China (English)

    Zhi-Hua Zhou

    2004-01-01

    In the research of rule extraction from neural networks, fidelity describes how well the rules mimic the behavior of a neural network while accuracy describes how well the rules can be generalized. This paper identifies the fidelity-accuracy dilemma. It argues to distinguish rule extraction using neural networks and rule extraction for neural networks according to their different goals, where fidelity and accuracy should be excluded from the rule quality evaluation framework, respectively.

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

    CERN Document Server

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

    2008-01-01

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

  2. Neural correlates of emotional responses to music: an EEG study.

    Science.gov (United States)

    Daly, Ian; Malik, Asad; Hwang, Faustina; Roesch, Etienne; Weaver, James; Kirke, Alexis; Williams, Duncan; Miranda, Eduardo; Nasuto, Slawomir J

    2014-06-24

    This paper presents an EEG study into the neural correlates of music-induced emotions. We presented participants with a large dataset containing musical pieces in different styles, and asked them to report on their induced emotional responses. We found neural correlates of music-induced emotion in a number of frequencies over the pre-frontal cortex. Additionally, we found a set of patterns of functional connectivity, defined by inter-channel coherence measures, to be significantly different between groups of music-induced emotional responses.

  3. Fuzzy Multiresolution Neural Networks

    Science.gov (United States)

    Ying, Li; Qigang, Shang; Na, Lei

    A fuzzy multi-resolution neural network (FMRANN) based on particle swarm algorithm is proposed to approximate arbitrary nonlinear function. The active function of the FMRANN consists of not only the wavelet functions, but also the scaling functions, whose translation parameters and dilation parameters are adjustable. A set of fuzzy rules are involved in the FMRANN. Each rule either corresponding to a subset consists of scaling functions, or corresponding to a sub-wavelet neural network consists of wavelets with same dilation parameters. Incorporating the time-frequency localization and multi-resolution properties of wavelets with the ability of self-learning of fuzzy neural network, the approximation ability of FMRANN can be remarkable improved. A particle swarm algorithm is adopted to learn the translation and dilation parameters of the wavelets and adjusting the shape of membership functions. Simulation examples are presented to validate the effectiveness of FMRANN.

  4. Natural connections given by general linear and classical connections

    OpenAIRE

    Janyška, Josef

    2004-01-01

    We assume a vector bundle $p: E\\to M$ with a general linear connection $K$ and a classical linear connection $\\Lam$ on $M$. We prove that all classical linear connections on the total space $E$ naturally given by $(\\Lam, K)$ form a 15-parameter family. Further we prove that all connections on $J^1 E$ naturally given by $(\\Lam, K)$ form a 14-parameter family. Both families of connections are described geometrically.

  5. The connected brain

    NARCIS (Netherlands)

    van den Heuvel, M.P.

    2009-01-01

    The connected brain Martijn van den Heuvel, 2009 Our brain is a network. It is a network of different brain regions that are all functionally and structurally linked to each other. In the past decades, neuroimaging studies have provided a lot of information about the specific functions of each separ

  6. Preschool Connected Speech Inventory.

    Science.gov (United States)

    DiJohnson, Albert; And Others

    This speech inventory developed for a study of aurally handicapped preschool children (see TM 001 129) provides information on intonation patterns in connected speech. The inventory consists of a list of phrases and simple sentences accompanied by pictorial clues. The test is individually administered by a teacher-examiner who presents the spoken…

  7. 18.CONNECTIVE TISSUE DISORDER

    Institute of Scientific and Technical Information of China (English)

    1993-01-01

    930734 Measurement of serum soluble interleukin—2 receptor in connective tissue diseases.CAI Houronget al.Dept Intern Med,Affili Gulou Hosp,Med School,Nanjing Univ,Nanjing,210008,ShanghaiJ Immunol 1993;13(4):216—218December 1993 Vol 10 No 4

  8. Clip, connect, clone

    DEFF Research Database (Denmark)

    Fujima, Jun; Lunzer, Aran; Hornbæk, Kasper

    2010-01-01

    using three mechanisms: clipping of input and result elements from existing applications to form cells on a spreadsheet; connecting these cells using formulas, thus enabling result transfer between applications; and cloning cells so that multiple requests can be handled side by side. We demonstrate...

  9. Revisiting city connectivity

    NARCIS (Netherlands)

    Mans, U.

    2014-01-01

    This article introduces a new perspective on city connectivity in order to analyze non-hub cities and their position in the world economy. The author revisits the different approaches discussed in the Global Commodity Chains (GCC), Global Production Networks (GPN) and World City Network (WCN) discou

  10. Wireless Connectivity and Capacity

    CERN Document Server

    Halldorsson, Magnus M

    2011-01-01

    Given $n$ wireless transceivers located in a plane, a fundamental problem in wireless communications is to construct a strongly connected digraph on them such that the constituent links can be scheduled in fewest possible time slots, assuming the SINR model of interference. In this paper, we provide an algorithm that connects an arbitrary point set in $O(\\log n)$ slots, improving on the previous best bound of $O(\\log^2 n)$ due to Moscibroda. This is complemented with a super-constant lower bound on our approach to connectivity. An important feature is that the algorithms allow for bi-directional (half-duplex) communication. One implication of this result is an improved bound of $\\Omega(1/\\log n)$ on the worst-case capacity of wireless networks, matching the best bound known for the extensively studied average-case. We explore the utility of oblivious power assignments, and show that essentially all such assignments result in a worst case bound of $\\Omega(n)$ slots for connectivity. This rules out a recent cla...

  11. Connecting with Your Audience.

    Science.gov (United States)

    Mamchur, Carolyn

    1989-01-01

    A workshop model on presentation skills for teachers in the classroom is presented. The goals and techniques would apply to many teaching situations in the college classroom, as well as lectures and symposium presentations. Making a personal connection, focusing on audience, and empowering the audience are discussed. (MLW)

  12. Connecting Competing Memories

    NARCIS (Netherlands)

    Laarse, van der R.; Saloul, I.A.M.

    Research Expert Meeting: Connecting Competing Memories of War in Contemporary Europe5 March 2014NIAS hosts, 6 - 7 March, the expert meeting of the Consortium for 'The Cultural Heritage of War in Contemporary Europe'. The aim is to draft main themes and discuss financial and research structures regar

  13. Technology and Internet Connections.

    Science.gov (United States)

    Allen, Denise; Lindroth, Linda

    1996-01-01

    Suggests that teachers can use computer software and Internet connections to enhance curriculum and capitalize student's natural interest in sports and sports figures. Provides a list of activities that students can do in relation to the Olympic games and gives information on how technology can assist in such activities. Appropriate Internet…

  14. The Anansi Connection.

    Science.gov (United States)

    Carger, Chris Liska

    1998-01-01

    Describes a teacher educator's efforts to connect children's literature, sponsored by a partnership between Northern Illinois University and Chicago Public Schools. In one project, student teachers used award-winning picture books to inspire African-American eighth graders to create pastels on black paper. In another, regional folk tales inspired…

  15. Preschool Connected Speech Inventory.

    Science.gov (United States)

    DiJohnson, Albert; And Others

    This speech inventory developed for a study of aurally handicapped preschool children (see TM 001 129) provides information on intonation patterns in connected speech. The inventory consists of a list of phrases and simple sentences accompanied by pictorial clues. The test is individually administered by a teacher-examiner who presents the spoken…

  16. Strengthening connections: functional connectivity and brain plasticity.

    Science.gov (United States)

    Kelly, Clare; Castellanos, F Xavier

    2014-03-01

    The ascendancy of functional neuroimaging has facilitated the addition of network-based approaches to the neuropsychologist's toolbox for evaluating the sequelae of brain insult. In particular, intrinsic functional connectivity (iFC) mapping of resting state fMRI (R-fMRI) data constitutes an ideal approach to measuring macro-scale networks in the human brain. Beyond the value of iFC mapping for charting how the functional topography of the brain is altered by insult and injury, iFC analyses can provide insights into experience-dependent plasticity at the macro level of large-scale functional networks. Such insights are foundational to the design of training and remediation interventions that will best facilitate recovery of function. In this review, we consider what is currently known about the origin and function of iFC in the brain, and how this knowledge is informative in neuropsychological settings. We then summarize studies that have examined experience-driven plasticity of iFC in healthy control participants, and frame these findings in terms of a schema that may aid in the interpretation of results and the generation of hypotheses for rehabilitative studies. Finally, we outline some caveats to the R-fMRI approach, as well as some current developments that are likely to bolster the utility of the iFC paradigm for neuropsychology.

  17. 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.

  18. 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.

  19. 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....

  20. On contravariant product conjugate connections

    Directory of Open Access Journals (Sweden)

    A. M. Blaga

    2012-02-01

    Full Text Available Invariance properties for the covariant and contravariant connections on a Riemannian manifold with respect to an almost product structure are stated. Restricting to a distribution of the contravariant connections is also discussed. The particular case of the conjugate connection is investigated and properties of the extended structural and virtual tensors for the contravariant connections are given.

  1. Hardware implementation of a neural vision system based on a neural network using integrated and fire neurons

    Science.gov (United States)

    González, M.; Lamela, H.; Jiménez, M.; Gimeno, J.; Ruiz-Llata, M.

    2007-04-01

    In this paper we present the scheme for a control circuit used in an image processing system which is to be implemented in a neural network which has a high level of connectivity and reconfiguration of neurons for integration and trigger based on the Address-Event Representation. This scheme will be employed as a pre-processing stage for a vision system which employs as its core processing an Optical Broadcast Neural Network (OBNN). [Optical Engineering letters 42 (9), 2488(2003)]. The proposed vision system allows the possibility to introduce patterns from any acquisition system of images, for posterior processing.

  2. On-board neural processor design for intelligent multisensor microspacecraft

    Science.gov (United States)

    Fang, Wai-Chi; Sheu, Bing J.; Wall, James

    1996-03-01

    A compact VLSI neural processor based on the Optimization Cellular Neural Network (OCNN) has been under development to provide a wide range of support for an intelligent remote sensing microspacecraft which requires both high bandwidth communication and high- performance computing for on-board data analysis, thematic data reduction, synergy of multiple types of sensors, and other advanced smart-sensor functions. The OCNN is developed with emphasis on its capability to find global optimal solutions by using a hardware annealing method. The hardware annealing function is embedded in the network. It is a parallel version of fast mean-field annealing in analog networks, and is highly efficient in finding globally optimal solutions for cellular neural networks. The OCNN is designed to perform programmable functions for fine-grained processing with annealing control to enhance the output quality. The OCNN architecture is a programmable multi-dimensional array of neurons which are locally connected with their local neurons. Major design features of the OCNN neural processor includes massively parallel neural processing, hardware annealing capability, winner-take-all mechanism, digitally programmable synaptic weights, and multisensor parallel interface. A compact current-mode VLSI design feasibility of the OCNN neural processor is demonstrated by a prototype 5 X 5-neuroprocessor array chip in a 2-micrometers CMOS technology. The OCNN operation theory, architecture, design and implementation, prototype chip, and system applications have been investigated in detail and presented in this paper.

  3. Altered Synchronizations among Neural Networks in Geriatric Depression.

    Science.gov (United States)

    Wang, Lihong; Chou, Ying-Hui; Potter, Guy G; Steffens, David C

    2015-01-01

    Although major depression has been considered as a manifestation of discoordinated activity between affective and cognitive neural networks, only a few studies have examined the relationships among neural networks directly. Because of the known disconnection theory, geriatric depression could be a useful model in studying the interactions among different networks. In the present study, using independent component analysis to identify intrinsically connected neural networks, we investigated the alterations in synchronizations among neural networks in geriatric depression to better understand the underlying neural mechanisms. Resting-state fMRI data was collected from thirty-two patients with geriatric depression and thirty-two age-matched never-depressed controls. We compared the resting-state activities between the two groups in the default-mode, central executive, attention, salience, and affective networks as well as correlations among these networks. The depression group showed stronger activity than the controls in an affective network, specifically within the orbitofrontal region. However, unlike the never-depressed controls, geriatric depression group lacked synchronized/antisynchronized activity between the affective network and the other networks. Those depressed patients with lower executive function has greater synchronization between the salience network with the executive and affective networks. Our results demonstrate the effectiveness of the between-network analyses in examining neural models for geriatric depression.

  4. High level cognitive information processing in neural networks

    Science.gov (United States)

    Barnden, John A.; Fields, Christopher A.

    1992-01-01

    Two related research efforts were addressed: (1) high-level connectionist cognitive modeling; and (2) local neural circuit modeling. The goals of the first effort were to develop connectionist models of high-level cognitive processes such as problem solving or natural language understanding, and to understand the computational requirements of such models. The goals of the second effort were to develop biologically-realistic model of local neural circuits, and to understand the computational behavior of such models. In keeping with the nature of NASA's Innovative Research Program, all the work conducted under the grant was highly innovative. For instance, the following ideas, all summarized, are contributions to the study of connectionist/neural networks: (1) the temporal-winner-take-all, relative-position encoding, and pattern-similarity association techniques; (2) the importation of logical combinators into connection; (3) the use of analogy-based reasoning as a bridge across the gap between the traditional symbolic paradigm and the connectionist paradigm; and (4) the application of connectionism to the domain of belief representation/reasoning. The work on local neural circuit modeling also departs significantly from the work of related researchers. In particular, its concentration on low-level neural phenomena that could support high-level cognitive processing is unusual within the area of biological local circuit modeling, and also serves to expand the horizons of the artificial neural net field.

  5. Functional model of biological neural networks.

    Science.gov (United States)

    Lo, James Ting-Ho

    2010-12-01

    A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

  6. Reading Neural Encodings using Phase Space Methods

    CERN Document Server

    Abarbanel, Henry D I; Abarbanel, Henry D I; Tumer, Evren C.

    2003-01-01

    Environmental signals sensed by nervous systems are often represented in spike trains carried from sensory neurons to higher neural functions where decisions and functional actions occur. Information about the environmental stimulus is contained (encoded) in the train of spikes. We show how to "read" the encoding using state space methods of nonlinear dynamics. We create a mapping from spike signals which are output from the neural processing system back to an estimate of the analog input signal. This mapping is realized locally in a reconstructed state space embodying both the dynamics of the source of the sensory signal and the dynamics of the neural circuit doing the processing. We explore this idea using a Hodgkin-Huxley conductance based neuron model and input from a low dimensional dynamical system, the Lorenz system. We show that one may accurately learn the dynamical input/output connection and estimate with high precision the details of the input signals from spike timing output alone. This form of "...

  7. Neuronify: An Educational Simulator for Neural Circuits

    Science.gov (United States)

    Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne

    2017-01-01

    Abstract Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker). We aim to provide a low entry point to simulation-based neuroscience by allowing students with no programming experience to create and simulate neural networks. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt and runs on smart phones (Android, iOS) and tablet computers as well personal computers (Windows, Mac, Linux). PMID:28321440

  8. Neuronify: An Educational Simulator for Neural Circuits.

    Science.gov (United States)

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

    2017-01-01

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

  9. Phase Diagram of Spiking Neural Networks

    Directory of Open Access Journals (Sweden)

    Hamed eSeyed-Allaei

    2015-03-01

    Full Text Available In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probablilty of 2%, 20% of neurons are inhibitory and 80% are excitatory. These common values are based on experiments, observations. but here, I take a different perspective, inspired by evolution. I simulate many networks, each with a different set of parameters, and then I try to figure out what makes the common values desirable by nature. Networks which are configured according to the common values, have the best dynamic range in response to an impulse and their dynamic range is more robust in respect to synaptic weights. In fact, evolution has favored networks of best dynamic range. I present a phase diagram that shows the dynamic ranges of different networks of different parameteres. This phase diagram gives an insight into the space of parameters -- excitatory to inhibitory ratio, sparseness of connections and synaptic weights. It may serve as a guideline to decide about the values of parameters in a simulation of spiking neural network.

  10. An improved molecular connectivity index

    Institute of Scientific and Technical Information of China (English)

    李新华; 俞庆森; 朱龙观

    2000-01-01

    Through modification of the delta values of the molecular connectivity indexes, and connecting the quantum chemistry with topology method effectively, the molecular connectivity indexes are converted into quantum-topology indexes. The modified indexes not only keep all information obtained from the original molecular connectivity method but also have their own virtue in application, and at the same time make up some disadvantages of the quantum and molecular connectivity methods.

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

    Science.gov (United States)

    Masud, Mohammad Shahed; Borisyuk, Roman

    2011-03-15

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

  12. Optogenetics in Silicon: A Neural Processor for Predicting Optically Active Neural Networks.

    Science.gov (United States)

    Luo, Junwen; Nikolic, Konstantin; Evans, Benjamin D; Dong, Na; Sun, Xiaohan; Andras, Peter; Yakovlev, Alex; Degenaar, Patrick

    2016-08-17

    We present a reconfigurable neural processor for real-time simulation and prediction of opto-neural behaviour. We combined a detailed Hodgkin-Huxley CA3 neuron integrated with a four-state Channelrhodopsin-2 (ChR2) model into reconfigurable silicon hardware. Our architecture consists of a Field Programmable Gated Array (FPGA) with a custom-built computing data-path, a separate data management system and a memory approach based router. Advancements over previous work include the incorporation of short and long-term calcium and light-dependent ion channels in reconfigurable hardware. Also, the developed processor is computationally efficient, requiring only 0.03 ms processing time per sub-frame for a single neuron and 9.7 ms for a fully connected network of 500 neurons with a given FPGA frequency of 56.7 MHz. It can therefore be utilized for exploration of closed loop processing and tuning of biologically realistic optogenetic circuitry.

  13. Optogenetics in Silicon: A Neural Processor for Predicting Optically Active Neural Networks.

    Science.gov (United States)

    Junwen Luo; Nikolic, Konstantin; Evans, Benjamin D; Na Dong; Xiaohan Sun; Andras, Peter; Yakovlev, Alex; Degenaar, Patrick

    2017-02-01

    We present a reconfigurable neural processor for real-time simulation and prediction of opto-neural behaviour. We combined a detailed Hodgkin-Huxley CA3 neuron integrated with a four-state Channelrhodopsin-2 (ChR2) model into reconfigurable silicon hardware. Our architecture consists of a Field Programmable Gated Array (FPGA) with a custom-built computing data-path, a separate data management system and a memory approach based router. Advancements over previous work include the incorporation of short and long-term calcium and light-dependent ion channels in reconfigurable hardware. Also, the developed processor is computationally efficient, requiring only 0.03 ms processing time per sub-frame for a single neuron and 9.7 ms for a fully connected network of 500 neurons with a given FPGA frequency of 56.7 MHz. It can therefore be utilized for exploration of closed loop processing and tuning of biologically realistic optogenetic circuitry.

  14. Neural coordination can be enhanced by occasional interruption of normal firing patterns: a self-optimizing spiking neural network model.

    Science.gov (United States)

    Woodward, Alexander; Froese, Tom; Ikegami, Takashi

    2015-02-01

    The state space of a conventional Hopfield network typically exhibits many different attractors of which only a small subset satisfies constraints between neurons in a globally optimal fashion. It has recently been demonstrated that combining Hebbian learning with occasional alterations of normal neural states avoids this problem by means of self-organized enlargement of the best basins of attraction. However, so far it is not clear to what extent this process of self-optimization is also operative in real brains. Here we demonstrate that it can be transferred to more biologically plausible neural networks by implementing a self-optimizing spiking neural network model. In addition, by using this spiking neural network to emulate a Hopfield network with Hebbian learning, we attempt to make a connection between rate-based and temporal coding based neural systems. Although further work is required to make this model more realistic, it already suggests that the efficacy of the self-optimizing process is independent from the simplifying assumptions of a conventional Hopfield network. We also discuss natural and cultural processes that could be responsible for occasional alteration of neural firing patterns in actual brains. Copyright © 2014 Elsevier Ltd. All rights reserved.

  15. 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....

  16. Interval probabilistic neural network.

    Science.gov (United States)

    Kowalski, Piotr A; Kulczycki, Piotr

    2017-01-01

    Automated classification systems have allowed for the rapid development of exploratory data analysis. Such systems increase the independence of human intervention in obtaining the analysis results, especially when inaccurate information is under consideration. The aim of this paper is to present a novel approach, a neural networking, for use in classifying interval information. As presented, neural methodology is a generalization of probabilistic neural network for interval data processing. The simple structure of this neural classification algorithm makes it applicable for research purposes. The procedure is based on the Bayes approach, ensuring minimal potential losses with regard to that which comes about through classification errors. In this article, the topological structure of the network and the learning process are described in detail. Of note, the correctness of the procedure proposed here has been verified by way of numerical tests. These tests include examples of both synthetic data, as well as benchmark instances. The results of numerical verification, carried out for different shapes of data sets, as well as a comparative analysis with other methods of similar conditioning, have validated both the concept presented here and its positive features.

  17. TUTORIAL: Neural blackboard architectures: the realization of compositionality and systematicity in neural networks

    Science.gov (United States)

    de Kamps, Marc; van der Velde, Frank

    2006-03-01

    In this paper, we will first introduce the notions of systematicity and combinatorial productivity and we will argue that these notions are essential for human cognition and probably for every agent that needs to be able to deal with novel, unexpected situations in a complex environment. Agents that use compositional representations are faced with the so-called binding problem and the question of how to create neural network architectures that can deal with it is essential for understanding higher level cognition. Moreover, an architecture that can solve this problem is likely to scale better with problem size than other neural network architectures. Then, we will discuss object-based attention. The influence of spatial attention is well known, but there is solid evidence for object-based attention as well. We will discuss experiments that demonstrate object-based attention and will discuss a model that can explain the data of these experiments very well. The model strongly suggests that this mode of attention provides a neural basis for parallel search. Next, we will show a model for binding in visual cortex. This model is based on a so-called neural blackboard architecture, where higher cortical areas act as processors, specialized for specific features of a visual stimulus, and lower visual areas act as a blackboard for communication between these processors. This implies that lower visual areas are involved in more than bottom-up visual processing, something which already was apparent from the large number of recurrent connections from higher to lower visual areas. This model identifies a specific role for these feedback connections. Finally, we will discuss the experimental evidence that exists for this architecture. .

  18. Neural blackboard architectures: the realization of compositionality and systematicity in neural networks.

    Science.gov (United States)

    de Kamps, Marc; van der Velde, Frank

    2006-03-01

    In this paper, we will first introduce the notions of systematicity and combinatorial productivity and we will argue that these notions are essential for human cognition and probably for every agent that needs to be able to deal with novel, unexpected situations in a complex environment. Agents that use compositional representations are faced with the so-called binding problem and the question of how to create neural network architectures that can deal with it is essential for understanding higher level cognition. Moreover, an architecture that can solve this problem is likely to scale better with problem size than other neural network architectures. Then, we will discuss object-based attention. The influence of spatial attention is well known, but there is solid evidence for object-based attention as well. We will discuss experiments that demonstrate object-based attention and will discuss a model that can explain the data of these experiments very well. The model strongly suggests that this mode of attention provides a neural basis for parallel search. Next, we will show a model for binding in visual cortex. This model is based on a so-called neural blackboard architecture, where higher cortical areas act as processors, specialized for specific features of a visual stimulus, and lower visual areas act as a blackboard for communication between these processors. This implies that lower visual areas are involved in more than bottom-up visual processing, something which already was apparent from the large number of recurrent connections from higher to lower visual areas. This model identifies a specific role for these feedback connections. Finally, we will discuss the experimental evidence that exists for this architecture.

  19. Connecting Science with Society

    DEFF Research Database (Denmark)

    awareness of the important questions of our society reflected in scientific research and of the answers produced by these research activities. The CRIS2010 conference, entitled “Bringing Science to Society”, therefore seeks to highlight the role of Current Research Information Systems for communicating......CRIS2010, the 10th conference in the bi-annual series organized by euroCRIS, focuses on the connecting role of Current Research Information Systems (CRIS). Aalborg, Denmark where CRIS2010 is held, is located near the intersection of the Northern Sea and Kattegat, a place were not only the waters...... of two seas are exchanged, but also goods and culture. In a similar way, Current Research Information Systems are at the intersection between (publicly funded) research and society. They do not only connect actors, activities and results within the research domain but also play a crucial role in raising...

  20. Connecting to Everyday Practices

    DEFF Research Database (Denmark)

    Iversen, Ole Sejer; Smith, Rachel Charlotte

    2012-01-01

    construction and reproduction of cultural heritage creating novel connections between self and others and between past, present and future. We present experiences from a current research project, the Digital Natives exhibition, in which social media was designed as an integral part of the exhibition to connect...... issues of digital heritage with audiences’ everyday practices in a museum. We point to the fact the use of social media in museums not only challenge us to rethink the design of technology for museum experiences. Social media also challenge us to rethink conceptions of museums and cultural heritage......We suggest that social media can contribute to reconnecting audiences’ everyday practices to issues of cultural heritage in museum institutions. Social media can support the creation of dialogical spaces in the museum, both playful and reflective, that allow audiences to engage in the ongoing...

  1. Weldless Flange Connections

    OpenAIRE

    Andersson, Mattias; Jonsson, Henrik; Löfqvist, Stefan; Maigne, Remi; Bravo, Unai

    2004-01-01

    This development project is a bachelor’s degree thesis work that will conclude the education program ”Development Technology” at Blekinge Institute of Technology. The development project has been done in cooperation with Faurecia Exhaust Systems AB in Torsås that constructs and manufactures manifolds, catalytic converters, mufflers and whole exhaust systems. The task with this project was to find a new solution concept for the connection of pipes into flanges in manifolds. The concept that Fa...

  2. Connecting textual segments

    DEFF Research Database (Denmark)

    Brügger, Niels

    2017-01-01

    In “Connecting textual segments: A brief history of the web hyperlink” Niels Brügger investigates the history of one of the most fundamental features of the web: the hyperlink. Based on the argument that the web hyperlink is best understood if it is seen as another step in a much longer and broader......-alone computers and in local and global digital networks....

  3. Connecting with Citizens

    DEFF Research Database (Denmark)

    Jørgensen, Poul Erik Flyvholm; Isaksson, Maria

    2017-01-01

    /2007. If Norway, like Denmark, significantly reduces its number of municipalities, the majority of municipalities will undergo significant change and experience loss of identity. Each new municipality will need to create meaningful new identities attractive to publics fearful of alienation inside a community...... they have no relationship to. The study examines how municipalities reach out to connect with their publics, and whether they employ emotional and engaging discourse. Our data consists of 20 Norwegian and 20 Danish municipal websites....

  4. A study of reactor monitoring method with neural network

    Energy Technology Data Exchange (ETDEWEB)

    Nabeshima, Kunihiko [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

    2001-03-01

    The purpose of this study is to investigate the methodology of Nuclear Power Plant (NPP) monitoring with neural networks, which create the plant models by the learning of the past normal operation patterns. The concept of this method is to detect the symptom of small anomalies by monitoring the deviations between the process signals measured from an actual plant and corresponding output signals from the neural network model, which might not be equal if the abnormal operational patterns are presented to the input of the neural network. Auto-associative network, which has same output as inputs, can detect an kind of anomaly condition by using normal operation data only. The monitoring tests of the feedforward neural network with adaptive learning were performed using the PWR plant simulator by which many kinds of anomaly conditions can be easily simulated. The adaptively trained feedforward network could follow the actual plant dynamics and the changes of plant condition, and then find most of the anomalies much earlier than the conventional alarm system during steady state and transient operations. Then the off-line and on-line test results during one year operation at the actual NPP (PWR) showed that the neural network could detect several small anomalies which the operators or the conventional alarm system didn't noticed. Furthermore, the sensitivity analysis suggests that the plant models by neural networks are appropriate. Finally, the simulation results show that the recurrent neural network with feedback connections could successfully model the slow behavior of the reactor dynamics without adaptive learning. Therefore, the recurrent neural network with adaptive learning will be the best choice for the actual reactor monitoring system. (author)

  5. White matter and cognition: making the connection.

    Science.gov (United States)

    Filley, Christopher M; Fields, R Douglas

    2016-11-01

    Whereas the cerebral cortex has long been regarded by neuroscientists as the major locus of cognitive function, the white matter of the brain is increasingly recognized as equally critical for cognition. White matter comprises half of the brain, has expanded more than gray matter in evolution, and forms an indispensable component of distributed neural networks that subserve neurobehavioral operations. White matter tracts mediate the essential connectivity by which human behavior is organized, working in concert with gray matter to enable the extraordinary repertoire of human cognitive capacities. In this review, we present evidence from behavioral neurology that white matter lesions regularly disturb cognition, consider the role of white matter in the physiology of distributed neural networks, develop the hypothesis that white matter dysfunction is relevant to neurodegenerative disorders, including Alzheimer's disease and the newly described entity chronic traumatic encephalopathy, and discuss emerging concepts regarding the prevention and treatment of cognitive dysfunction associated with white matter disorders. Investigation of the role of white matter in cognition has yielded many valuable insights and promises to expand understanding of normal brain structure and function, improve the treatment of many neurobehavioral disorders, and disclose new opportunities for research on many challenging problems facing medicine and society.

  6. Connective tissue ulcers.

    Science.gov (United States)

    Dabiri, Ganary; Falanga, Vincent

    2013-11-01

    Connective tissue disorders (CTD), which are often also termed collagen vascular diseases, include a number of related inflammatory conditions. Some of these diseases include rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis (scleroderma), localized scleroderma (morphea variants localized to the skin), Sjogren's syndrome, dermatomyositis, polymyositis, and mixed connective tissue disease. In addition to the systemic manifestations of these diseases, there are a number of cutaneous features that make these conditions recognizable on physical exam. Lower extremity ulcers and digital ulcers are an infrequent but disabling complication of long-standing connective tissue disease. The exact frequency with which these ulcers occur is not known, and the cause of the ulcerations is often multifactorial. Moreover, a challenging component of CTD ulcerations is that there are still no established guidelines for their diagnosis and treatment. The morbidity associated with these ulcerations and their underlying conditions is very substantial. Indeed, these less common but intractable ulcers represent a major medical and economic problem for patients, physicians and nurses, and even well organized multidisciplinary wound healing centers.

  7. Energy storage connection system

    Science.gov (United States)

    Benedict, Eric L.; Borland, Nicholas P.; Dale, Magdelena; Freeman, Belvin; Kite, Kim A.; Petter, Jeffrey K.; Taylor, Brendan F.

    2012-07-03

    A power system for connecting a variable voltage power source, such as a power controller, with a plurality of energy storage devices, at least two of which have a different initial voltage than the output voltage of the variable voltage power source. The power system includes a controller that increases the output voltage of the variable voltage power source. When such output voltage is substantially equal to the initial voltage of a first one of the energy storage devices, the controller sends a signal that causes a switch to connect the variable voltage power source with the first one of the energy storage devices. The controller then causes the output voltage of the variable voltage power source to continue increasing. When the output voltage is substantially equal to the initial voltage of a second one of the energy storage devices, the controller sends a signal that causes a switch to connect the variable voltage power source with the second one of the energy storage devices.

  8. Implementing Signature Neural Networks with Spiking Neurons

    Directory of Open Access Journals (Sweden)

    José Luis Carrillo-Medina

    2016-12-01

    Full Text Available Spiking Neural Networks constitute the most promising approach to develop realistic ArtificialNeural Networks (ANNs. Unlike traditional firing rate-based paradigms, information coding inspiking models is based on the precise timing of individual spikes. Spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition. In recent years, majorbreakthroughs in neuroscience research have discovered new relevant computational principles indifferent living neural systems. Could ANNs benefit from some of these recent findings providingnovel elements of inspiration? This is an intriguing question and the development of spiking ANNsincluding novel bio-inspired information coding and processing strategies is gaining attention. Fromthis perspective, in this work, we adapt the core concepts of the recently proposed SignatureNeural Network paradigm – i.e., neural signatures to identify each unit in the network, localinformation contextualization during the processing and multicoding strategies for informationpropagation regarding the origin and the content of the data – to be employed in a spiking neuralnetwork. To the best of our knowledge, none of these mechanisms have been used yet in thecontext of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicabilityin such networks. Computer simulations show that a simple network model like the discussed hereexhibits complex self-organizing properties. The combination of multiple simultaneous encodingschemes allows the network to generate coexisting spatio-temporal patterns of activity encodinginformation in different spatio-temporal spaces. As a function of the network and/or intra-unitparameters shaping the corresponding encoding modality, different forms of competition amongthe evoked patterns can emerge even in the absence of inhibitory connections. These parametersalso

  9. Correspondence between stimulus encoding- and maintenance-related neural processes underlies successful working memory.

    Science.gov (United States)

    Cohen, Jessica R; Sreenivasan, Kartik K; D'Esposito, Mark

    2014-03-01

    The ability to actively maintain information in working memory (WM) is vital for goal-directed behavior, but the mechanisms underlying this process remain elusive. We hypothesized that successful WM relies upon a correspondence between the neural processes associated with stimulus encoding and the neural processes associated with maintenance. Using functional magnetic resonance imaging, we identified regional activity and inter-regional connectivity during stimulus encoding and the maintenance of those stimuli when they were no longer present. We compared correspondence in these neural processes across encoding and maintenance epochs with WM performance. Critically, greater correspondence between encoding and maintenance in 1) regional activity in the lateral prefrontal cortex (PFC) and 2) connectivity between lateral PFC and extrastriate cortex was associated with increased performance. These findings suggest that the conservation of neural processes across encoding and maintenance supports the integrity of representations in WM.

  10. A multichannel neural signal detecting module: Its design and test in animal experiments

    Institute of Scientific and Technical Information of China (English)

    Wang Yufeng; Wang Zhigong; Lü Xiaoying; Gu Xiaosong; Li Wenyuan; Wang Huiling; Jiang Zhenlin; Lü Guangming; K. P. Koch

    2007-01-01

    A four-channel neural signal detecting module with an implantable 12-contact cuff electrode was designed for real-time neural signal recording on peripheral and central nerves. The mathematic coupling model between nerve and electronic system was analyzed. Electrode connection configurations were considered. The detecting circuit included an input coupling network, a pre-amplifier, and some filtering and notching stages. Shield guarding and the right-leg-driven circuit were developed for further elimination of common mode interference. By electrode switches, the module could cooperate with a nerve functional electrical stimulation circuit, building a neural channel bridge-connection system. It was tested by recording experiments on rat's sciatic and spine nerves. The signals in spontaneous and evoked conditions have been captured successfully. In addition, an implantable neural signal detecting CMOS IC has been introduced.

  11. Optoelectronic implementation of multilayer perceptron and Hopfield neural networks

    Science.gov (United States)

    Domanski, Andrzej W.; Olszewski, Mikolaj K.; Wolinski, Tomasz R.

    2004-11-01

    In this paper we present an optoelectronic implementation of two networks based on multilayer perceptron and the Hopfield neural network. We propose two different methods to solve a problem of lack of negative optical signals that are necessary for connections between layers of perceptron as well as within the Hopfield network structure. The first method applied for construction of multilayer perceptron was based on division of signals into two channels and next to use both of them independently as positive and negative signals. The second one, applied for implementation of the Hopfield model, was based on adding of constant value for elements of matrix weight. Both methods of compensation of lack negative optical signals were tested experimentally as optoelectronic models of multilayer perceptron and Hopfield neural network. Special configurations of optical fiber cables and liquid crystal multicell plates were used. In conclusion, possible applications of the optoelectronic neural networks are briefly discussed.

  12. Feasibility study for future implantable neural-silicon interface devices.

    Science.gov (United States)

    Al-Armaghany, Allann; Yu, Bo; Mak, Terrence; Tong, Kin-Fai; Sun, Yihe

    2011-01-01

    The emerging neural-silicon interface devices bridge nerve systems with artificial systems and play a key role in neuro-prostheses and neuro-rehabilitation applications. Integrating neural signal collection, processing and transmission on a single device will make clinical applications more practical and feasible. This paper focuses on the wireless antenna part and real-time neural signal analysis part of implantable brain-machine interface (BMI) devices. We propose to use millimeter-wave for wireless connections between different areas of a brain. Various antenna, including microstrip patch, monopole antenna and substrate integrated waveguide antenna are considered for the intra-cortical proximity communication. A Hebbian eigenfilter based method is proposed for multi-channel neuronal spike sorting. Folding and parallel design techniques are employed to explore various structures and make a trade-off between area and power consumption. Field programmable logic arrays (FPGAs) are used to evaluate various structures.

  13. Multi-column Deep Neural Networks for Image Classification

    CERN Document Server

    Cireşan, Dan; Schmidhuber, Juergen

    2012-01-01

    Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional winner-take-all neurons yield large network depth, resulting in roughly as many sparsely connected neural layers as found in mammals between retina and visual cortex. Only winner neurons are trained. Several deep neural columns become experts on inputs preprocessed in different ways; their predictions are averaged. Graphics cards allow for fast training. On the very competitive MNIST handwriting benchmark, our method is the first to achieve near-human performance. On a traffic sign recognition benchmark it outperforms humans by a factor of two. We also improve the state-of-the-art on a plethora of common image classification benchmarks.

  14. Robust adaptive synchronization of chaotic neural networks by slide technique

    Institute of Scientific and Technical Information of China (English)

    Lou Xu-Yang; Cui Bao-Tong

    2008-01-01

    In this paper,we focus on the robust adaptive synchronization between two coupled chaotic neural networks with all the parameters unknown and time-varying delay.In order to increase the robustness of the two coupled neural networks,the key idea is that a sliding-mode-type controller is employed.Moreover,without the estimate values of the network unknown parameters taken as an updating object,a new updating object is introduced in the constructing of controller.Using the proposed controller,without any requirements for the boundedness,monotonicity and differentiability of activation functions,and symmetry of connections,the two coupled chaotic neural networks can achieve global robust synchronization no matter what their initial states are.Finally,the numerical simulation validates the effectiveness and feasibility of the proposed technique.

  15. Neural Behavior Chain Learning of Mobile Robot Actions

    Directory of Open Access Journals (Sweden)

    Lejla Banjanovic-Mehmedovic

    2012-01-01

    Full Text Available This paper presents a visual/motor behavior learning approach, based on neural networks. We propose Behavior Chain Model (BCM in order to create a way of behavior learning. Our behavior-based system evolution task is a mobile robot detecting a target and driving/acting towards it. First, the mapping relations between the image feature domain of the object and the robot action domain are derived. Second, a multilayer neural network for offline learning of the mapping relations is used. This learning structure through neural network training process represents a connection between the visual perceptions and motor sequence of actions in order to grip a target. Last, using behavior learning through a noticed action chain, we can predict mobile robot behavior for a variety of similar tasks in similar environment. Prediction results suggest that the methodology is adequate and could be recognized as an idea for designing different mobile robot behaviour assistance.

  16. Ideomotor feedback control in a recurrent neural network.

    Science.gov (United States)

    Galtier, Mathieu

    2015-06-01

    The architecture of a neural network controlling an unknown environment is presented. It is based on a randomly connected recurrent neural network from which both perception and action are simultaneously read and fed back. There are two concurrent learning rules implementing a sort of ideomotor control: (i) perception is learned along the principle that the network should predict reliably its incoming stimuli; (ii) action is learned along the principle that the prediction of the network should match a target time series. The coherent behavior of the neural network in its environment is a consequence of the interaction between the two principles. Numerical simulations show a promising performance of the approach, which can be turned into a local and better "biologically plausible" algorithm.

  17. Musical expertise affects neural bases of letter recognition.

    Science.gov (United States)

    Proverbio, Alice Mado; Manfredi, Mirella; Zani, Alberto; Adorni, Roberta

    2013-02-01

    It is known that early music learning (playing of an instrument) modifies functional brain structure (both white and gray matter) and connectivity, especially callosal transfer, motor control/coordination and auditory processing. We compared visual processing of notes and words in 15 professional musicians and 15 controls by recording their synchronized bioelectrical activity (ERPs) in response to words and notes. We found that musical training in childhood (from age ~8 years) modifies neural mechanisms of word reading, whatever the genetic predisposition, which was unknown. While letter processing was strongly left-lateralized in controls, the fusiform (BA37) and inferior occipital gyri (BA18) were activated in both hemispheres in musicians for both word and music processing. The evidence that the neural mechanism of letter processing differed in musicians and controls (being absolutely bilateral in musicians) suggests that musical expertise modifies the neural mechanisms of letter reading.

  18. Gene expression in the rodent brain is associated with its regional connectivity.

    Science.gov (United States)

    Wolf, Lior; Goldberg, Chen; Manor, Nathan; Sharan, Roded; Ruppin, Eytan

    2011-05-01

    The putative link between gene expression of brain regions and their neural connectivity patterns is a fundamental question in neuroscience. Here this question is addressed in the first large scale study of a prototypical mammalian rodent brain, using a combination of rat brain regional connectivity data with gene expression of the mouse brain. Remarkably, even though this study uses data from two different rodent species (due to the data limitations), we still find that the connectivity of the majority of brain regions is highly predictable from their gene expression levels-the outgoing (incoming) connectivity is successfully predicted for 73% (56%) of brain regions, with an overall fairly marked accuracy level of 0.79 (0.83). Many genes are found to play a part in predicting both the incoming and outgoing connectivity (241 out of the 500 top selected genes, p-valueregional connectivity in the rodent is significantly correlated with the annotation profile of genes previously found to determine neural connectivity in C. elegans (Pearson correlation of 0.24, p<1e-6 for the outgoing connections and 0.27, p<1e-5 for the incoming). Overall, the association between connectivity and gene expression in a specific extant rodent species' brain is likely to be even stronger than found here, given the limitations of current data.

  19. Persistent activity in neural networks with dynamic synapses.

    Directory of Open Access Journals (Sweden)

    Omri Barak

    2007-02-01

    Full Text Available Persistent activity states (attractors, observed in several neocortical areas after the removal of a sensory stimulus, are believed to be the neuronal basis of working memory. One of the possible mechanisms that can underlie persistent activity is recurrent excitation mediated by intracortical synaptic connections. A recent experimental study revealed that connections between pyramidal cells in prefrontal cortex exhibit various degrees of synaptic depression and facilitation. Here we analyze the effect of synaptic dynamics on the emergence and persistence of attractor states in interconnected neural networks. We show that different combinations of synaptic depression and facilitation result in qualitatively different network dynamics with respect to the emergence of the attractor states. This analysis raises the possibility that the framework of attractor neural networks can be extended to represent time-dependent stimuli.

  20. Evolving Spiking Neural Networks for Control of Artificial Creatures

    Directory of Open Access Journals (Sweden)

    Arash Ahmadi

    2013-10-01

    Full Text Available To understand and analysis behavior of complicated and intelligent organisms, scientists apply bio-inspired concepts including evolution and learning to mathematical models and analyses. Researchers utilize these perceptions in different applications, searching for improved methods andapproaches for modern computational systems. This paper presents a genetic algorithm based evolution framework in which Spiking Neural Network (SNN of artificial creatures are evolved for higher chance of survival in a virtual environment. The artificial creatures are composed ofrandomly connected Izhikevich spiking reservoir neural networks using population activity rate coding. Inspired by biological neurons, the neuronal connections are considered with different axonal conduction delays. Simulations results prove that the evolutionary algorithm has thecapability to find or synthesis artificial creatures which can survive in the environment successfully.

  1. Dimensionality reduction in conic section function neural network

    Indian Academy of Sciences (India)

    Tulay Yildirim; Lale Ozyilmaz

    2002-12-01

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

  2. Weak functional connectivity in the human fetal brain prior to preterm birth

    Science.gov (United States)

    Thomason, Moriah E.; Scheinost, Dustin; Manning, Janessa H.; Grove, Lauren E.; Hect, Jasmine; Marshall, Narcis; Hernandez-Andrade, Edgar; Berman, Susan; Pappas, Athina; Yeo, Lami; Hassan, Sonia S.; Constable, R. Todd; Ment, Laura R.; Romero, Roberto

    2017-01-01

    It has been suggested that neurological problems more frequent in those born preterm are expressed prior to birth, but owing to technical limitations, this has been difficult to test in humans. We applied novel fetal resting-state functional MRI to measure brain function in 32 human fetuses in utero and found that systems-level neural functional connectivity was diminished in fetuses that would subsequently be born preterm. Neural connectivity was reduced in a left-hemisphere pre-language region, and the degree to which connectivity of this left language region extended to right-hemisphere homologs was positively associated with the time elapsed between fMRI assessment and delivery. These results provide the first evidence that altered functional connectivity in the preterm brain is identifiable before birth. They suggest that neurodevelopmental disorders associated with preterm birth may result from neurological insults that begin in utero. PMID:28067865

  3. Weak functional connectivity in the human fetal brain prior to preterm birth.

    Science.gov (United States)

    Thomason, Moriah E; Scheinost, Dustin; Manning, Janessa H; Grove, Lauren E; Hect, Jasmine; Marshall, Narcis; Hernandez-Andrade, Edgar; Berman, Susan; Pappas, Athina; Yeo, Lami; Hassan, Sonia S; Constable, R Todd; Ment, Laura R; Romero, Roberto

    2017-01-09

    It has been suggested that neurological problems more frequent in those born preterm are expressed prior to birth, but owing to technical limitations, this has been difficult to test in humans. We applied novel fetal resting-state functional MRI to measure brain function in 32 human fetuses in utero and found that systems-level neural functional connectivity was diminished in fetuses that would subsequently be born preterm. Neural connectivity was reduced in a left-hemisphere pre-language region, and the degree to which connectivity of this left language region extended to right-hemisphere homologs was positively associated with the time elapsed between fMRI assessment and delivery. These results provide the first evidence that altered functional connectivity in the preterm brain is identifiable before birth. They suggest that neurodevelopmental disorders associated with preterm birth may result from neurological insults that begin in utero.

  4. Two distinct neural mechanisms underlying indirect reciprocity.

    Science.gov (United States)

    Watanabe, Takamitsu; Takezawa, Masanori; Nakawake, Yo; Kunimatsu, Akira; Yamasue, Hidenori; Nakamura, Mitsuhiro; Miyashita, Yasushi; Masuda, Naoki

    2014-03-18

    Cooperation is a hallmark of human society. Humans often cooperate with strangers even if they will not meet each other again. This so-called indirect reciprocity enables large-scale cooperation among nonkin and can occur based on a reputation mechanism or as a succession of pay-it-forward behavior. Here, we provide the functional and anatomical neural evidence for two distinct mechanisms governing the two types of indirect reciprocity. Cooperation occurring as reputation-based reciprocity specifically recruited the precuneus, a region associated with self-centered cognition. During such cooperative behavior, the precuneus was functionally connected with the caudate, a region linking rewards to behavior. Furthermore, the precuneus of a cooperative subject had a strong resting-state functional connectivity (rsFC) with the caudate and a large gray matter volume. In contrast, pay-it-forward reciprocity recruited the anterior insula (AI), a brain region associated with affective empathy. The AI was functionally connected with the caudate during cooperation occurring as pay-it-forward reciprocity, and its gray matter volume and rsFC with the caudate predicted the tendency of such cooperation. The revealed difference is consistent with the existing results of evolutionary game theory: although reputation-based indirect reciprocity robustly evolves as a self-interested behavior in theory, pay-it-forward indirect reciprocity does not on its own. The present study provides neural mechanisms underlying indirect reciprocity and suggests that pay-it-forward reciprocity may not occur as myopic profit maximization but elicit emotional rewards.

  5. Quiet connections: Reduced fronto-temporal connectivity in nondemented Parkinson's Disease during working memory encoding.

    Science.gov (United States)

    Wiesman, Alex I; Heinrichs-Graham, Elizabeth; McDermott, Timothy J; Santamaria, Pamela M; Gendelman, Howard E; Wilson, Tony W

    2016-09-01

    Parkinson's disease (PD) is a common neurodegenerative disorder characterized primarily by motor symptoms such as bradykinesia, muscle rigidity, and resting tremor. It is now broadly accepted that these motor symptoms frequently co-occur with cognitive impairments, with deficits in working memory and attention being among the most common cognitive sequelae associated with PD. While these cognitive impairments are now recognized, the underlying neural dynamics and precise regions involved remain largely unknown. To this end, we examined the oscillatory dynamics and interregional functional connectivity that serve working memory processing in a group of unmedicated adults with PD and a matched group without PD. Each participant completed a high-load, Sternberg-type working memory task during magnetoencephalography (MEG), and we focused on the encoding and maintenance phases. All data were transformed into the time-frequency domain and significant oscillatory activity was imaged using a beamforming approach. Phase-coherence (connectivity) was also computed among the brain subregions exhibiting the strongest responses. Our most important findings were that unmedicated patients with PD had significantly diminished working memory performance (i.e., accuracy), and reduced functional connectivity between left inferior frontal cortices and left supramarginal-superior temporal cortices compared to participants without PD during the encoding phase of working memory processing. We conclude that patients with PD have reduced neural interactions between left prefrontal executive circuits and temporary verbal storage centers in the left supramarginal/superior temporal cortices during the stimulus encoding phase, which may underlie their diminished working memory function. Hum Brain Mapp 37:3224-3235, 2016. © 2016 Wiley Periodicals, Inc.

  6. Effects of Methylphenidate on Resting-State Functional Connectivity of the Mesocorticolimbic Dopamine Pathways in Cocaine Addiction

    Energy Technology Data Exchange (ETDEWEB)

    Konova, Anna B.; Moeller, Scott J.; Tomasi, Dardo; Volkow, Nora D.; Goldstein, Rita Z.

    2013-08-01

    Cocaine addiction is associated with altered resting-state functional connectivity among regions of the mesocorticolimbic dopamine pathways. Methylphenidate hydrochloride, an indirect dopamine agonist, normalizes task-related regional brain activity and associated behavior in cocaine users; however, the neural systems–level effects of methylphenidate in this population have not yet been described. To use resting-state functional magnetic resonance imaging to examine changes in mesocorticolimbic connectivity with methylphenidate and how connectivity of affected pathways relates to severity of cocaine addiction.

  7. Neural dynamics based on the recognition of neural fingerprints

    Directory of Open Access Journals (Sweden)

    José Luis eCarrillo-Medina

    2015-03-01

    Full Text Available Experimental evidence has revealed the existence of characteristic spiking features in different neural signals, e.g. individual neural signatures identifying the emitter or functional signatures characterizing specific tasks. These neural fingerprints may play a critical role in neural information processing, since they allow receptors to discriminate or contextualize incoming stimuli. This could be a powerful strategy for neural systems that greatly enhances the encoding and processing capacity of these networks. Nevertheless, the study of information processing based on the identification of specific neural fingerprints has attracted little attention. In this work, we study (i the emerging collective dynamics of a network of neurons that communicate with each other by exchange of neural fingerprints and (ii the influence of the network topology on the self-organizing properties within the network. Complex collective dynamics emerge in the network in the presence of stimuli. Predefined inputs, i.e. specific neural fingerprints, are detected and encoded into coexisting patterns of activity that propagate throughout the network with different spatial organization. The patterns evoked by a stimulus can survive after the stimulation is over, which provides memory mechanisms to the network. The results presented in this paper suggest that neural information processing based on neural fingerprints can be a plausible, flexible and powerful strategy.

  8. Q-valued neural network as a system of fast identification and pattern recognition

    OpenAIRE

    Alieva, D. I.; Kryzhanovsky, B. V.; V.M. Kryzhanovsky; Fonarev, A. B.

    2004-01-01

    An effective neural network algorithm of the perceptron type is proposed. The algorithm allows us to identify strongly distorted input vector reliably. It is shown that its reliability and processing speed are orders of magnitude higher than that of full connected neural networks. The processing speed of our algorithm exceeds the one of the stack fast-access retrieval algorithm that is modified for working when there are noises in the input channel.

  9. Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

    Directory of Open Access Journals (Sweden)

    Kai Olav Ellefsen

    2015-04-01

    Full Text Available A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand. To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1 that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2 that one benefit of the modularity ubiquitous in the brains of natural animals

  10. Diminished neural adaptation during implicit learning in autism.

    Science.gov (United States)

    Schipul, Sarah E; Just, Marcel Adam

    2016-01-15

    Neuroimaging studies have shown evidence of disrupted neural adaptation during learning in individuals with autism spectrum disorder (ASD) in several types of tasks, potentially stemming from frontal-posterior cortical underconnectivity (Schipul et al., 2012). The aim of the current study was to examine neural adaptations in an implicit learning task that entails participation of frontal and posterior regions. Sixteen high-functioning adults with ASD and sixteen neurotypical control participants were trained on and performed an implicit dot pattern prototype learning task in a functional magnetic resonance imaging (fMRI) session. During the preliminary exposure to the type of implicit prototype learning task later to be used in the scanner, the ASD participants took longer than the neurotypical group to learn the task, demonstrating altered implicit learning in ASD. After equating task structure learning, the two groups' brain activation differed during their learning of a new prototype in the subsequent scanning session. The main findings indicated that neural adaptations in a distributed task network were reduced in the ASD group, relative to the neurotypical group, and were related to ASD symptom severity. Functional connectivity was reduced and did not change as much during learning for the ASD group, and was related to ASD symptom severity. These findings suggest that individuals with ASD show altered neural adaptations during learning, as seen in both activation and functional connectivity measures. This finding suggests why many real-world implicit learning situations may pose special challenges for ASD.

  11. Neural activity when people solve verbal problems with insight.

    Directory of Open Access Journals (Sweden)

    Mark Jung-Beeman

    2004-04-01

    Full Text Available People sometimes solve problems with a unique process called insight, accompanied by an "Aha!" experience. It has long been unclear whether different cognitive and neural processes lead to insight versus noninsight solutions, or if solutions differ only in subsequent subjective feeling. Recent behavioral studies indicate distinct patterns of performance and suggest differential hemispheric involvement for insight and noninsight solutions. Subjects solved verbal problems, and after each correct solution indicated whether they solved with or without insight. We observed two objective neural correlates of insight. Functional magnetic resonance imaging (Experiment 1 revealed increased activity in the right hemisphere anterior superior temporal gyrus for insight relative to noninsight solutions. The same region was active during initial solving efforts. Scalp electroencephalogram recordings (Experiment 2 revealed a sudden burst of high-frequency (gamma-band neural activity in the same area beginning 0.3 s prior to insight solutions. This right anterior temporal area is associated with making connections across distantly related information during comprehension. Although all problem solving relies on a largely shared cortical network, the sudden flash of insight occurs when solvers engage distinct neural and cognitive processes that allow them to see connections that previously eluded them.

  12. Neural Network Modeling of UH-60A Pilot Vibration

    Science.gov (United States)

    Kottapalli, Sesi

    2003-01-01

    Full-scale flight-test pilot floor vibration is modeled using neural networks and full-scale wind tunnel test data for low speed level flight conditions. Neural network connections between the wind tunnel test data and the tlxee flight test pilot vibration components (vertical, lateral, and longitudinal) are studied. Two full-scale UH-60A Black Hawk databases are used. The first database is the NASMArmy UH-60A Airloads Program flight test database. The second database is the UH-60A rotor-only wind tunnel database that was acquired in the NASA Ames SO- by 120- Foot Wind Tunnel with the Large Rotor Test Apparatus (LRTA). Using neural networks, the flight-test pilot vibration is modeled using the wind tunnel rotating system hub accelerations, and separately, using the hub loads. The results show that the wind tunnel rotating system hub accelerations and the operating parameters can represent the flight test pilot vibration. The six components of the wind tunnel N/rev balance-system hub loads and the operating parameters can also represent the flight test pilot vibration. The present neural network connections can significandy increase the value of wind tunnel testing.

  13. Chemical implementation and thermodynamics of collective neural networks.

    Science.gov (United States)

    Hjelmfelt, A; Ross, J

    1992-01-01

    The chemical implementation of a neuron and connections among neurons described in prior work is used to construct collective neural networks. With stated approximations, these chemical networks are reduced to networks of the Hopfield type. Chemical networks approaching a stationary or equilibrium state provide a Liapunov function with the same extremal properties as Hopfield's energy function. Numerical comparisons of chemical and Hopfield networks with small numbers (2-16) of neurons show agreement on the results of given computations. PMID:1729709

  14. A scale-free neural network for modelling neurogenesis

    Science.gov (United States)

    Perotti, Juan I.; Tamarit, Francisco A.; Cannas, Sergio A.

    2006-11-01

    In this work we introduce a neural network model for associative memory based on a diluted Hopfield model, which grows through a neurogenesis algorithm that guarantees that the final network is a small-world and scale-free one. We also analyze the storage capacity of the network and prove that its performance is larger than that measured in a randomly dilute network with the same connectivity.

  15. Using Artificial Neural Networks to Predict Stock Prices

    OpenAIRE

    Kozdraj, Tomasz

    2009-01-01

    Artificial neural networks constitute one of the most developed conception of artificial intelligence. They are based on pragmatic mathematical theories adopted to tasks resolution. A wide range of their applications also includes financial investments issues. The reason for NN's popularity is mainly connected with their ability to solve complex or not well recognized computational tasks, efficiency in finding solutions as well as the possibility of learning based on patterns or without them....

  16. DESIGN AND ANALOG VLSI IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK

    OpenAIRE

    2011-01-01

    Nature has evolved highly advanced systems capable of performing complex computations, adoption and learning using analog computations. Furthermore nature has evolved techniques to deal with imprecise analog computations by using redundancy and massive connectivity. In this paper we are making use of Artificial Neural Network to demonstrate the way in which the biological system processes in analog domain. We are using 180nm CMOS VLSI technology for implementing circuits which ...

  17. Learning and forgetting on asymmetric, diluted neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Derrida, B.; Nadal, J.P.

    1987-12-01

    It is possible to construct diluted asymmetric models of neural networks for which the dynamics can be calculated exactly. The authors test several learning schemes, in particular, models for which the values of the synapses remain bounded and depend on the history. Our analytical results on the relative efficiencies of the various learning schemes are qualitatively similar to the corresponding ones obtained numerically on fully connected symmetric networks.

  18. Neural mechanism of optimal limb coordination in crustacean swimming.

    Science.gov (United States)

    Zhang, Calvin; Guy, Robert D; Mulloney, Brian; Zhang, Qinghai; Lewis, Timothy J

    2014-09-23

    A fundamental challenge in neuroscience is to understand how biologically salient motor behaviors emerge from properties of the underlying neural circuits. Crayfish, krill, prawns, lobsters, and other long-tailed crustaceans swim by rhythmically moving limbs called swimmerets. Over the entire biological range of animal size and paddling frequency, movements of adjacent swimmerets maintain an approximate quarter-period phase difference with the more posterior limbs leading the cycle. We use a computational fluid dynamics model to show that this frequency-invariant stroke pattern is the most effective and mechanically efficient paddling rhythm across the full range of biologically relevant Reynolds numbers in crustacean swimming. We then show that the organization of the neural circuit underlying swimmeret coordination provides a robust mechanism for generating this stroke pattern. Specifically, the wave-like limb coordination emerges robustly from a combination of the half-center structure of the local central pattern generating circuits (CPGs) that drive the movements of each limb, the asymmetric network topology of the connections between local CPGs, and the phase response properties of the local CPGs, which we measure experimentally. Thus, the crustacean swimmeret system serves as a concrete example in which the architecture of a neural circuit leads to optimal behavior in a robust manner. Furthermore, we consider all possible connection topologies between local CPGs and show that the natural connectivity pattern generates the biomechanically optimal stroke pattern most robustly. Given the high metabolic cost of crustacean swimming, our results suggest that natural selection has pushed the swimmeret neural circuit toward a connection topology that produces optimal behavior.

  19. A model for the neural control of pineal periodicity

    Science.gov (United States)

    de Oliveira Cruz, Frederico Alan; Soares, Marilia Amavel Gomes; Cortez, Celia Martins

    2016-12-01

    The aim of this work was verify if a computational model associating the synchronization dynamics of coupling oscillators to a set of synaptic transmission equations would be able to simulate the control of pineal by a complex neural pathway that connects the retina to this gland. Results from the simulations showed that the frequency and temporal firing patterns were in the range of values found in literature.

  20. Process for forming synapses in neural networks and resistor therefor

    Science.gov (United States)

    Fu, C.Y.

    1996-07-23

    Customizable neural network in which one or more resistors form each synapse is disclosed. All the resistors in the synaptic array are identical, thus simplifying the processing issues. Highly doped, amorphous silicon is used as the resistor material, to create extremely high resistances occupying very small spaces. Connected in series with each resistor in the array is at least one severable conductor whose uppermost layer has a lower reflectivity of laser energy than typical metal conductors at a desired laser wavelength. 5 figs.