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Sample records for neural language organization

  1. Delayed development of neural language organization in very preterm born children.

    Science.gov (United States)

    Mürner-Lavanchy, Ines; Steinlin, Maja; Kiefer, Claus; Weisstanner, Christian; Ritter, Barbara Catherine; Perrig, Walter; Everts, Regula

    2014-01-01

    This study investigates neural language organization in very preterm born children compared to control children and examines the relationship between language organization, age, and language performance. Fifty-six preterms and 38 controls (7-12 y) completed a functional magnetic resonance imaging language task. Lateralization and signal change were computed for language-relevant brain regions. Younger preterms showed a bilateral language network whereas older preterms revealed left-sided language organization. No age-related differences in language organization were observed in controls. Results indicate that preterms maintain atypical bilateral language organization longer than term born controls. This might reflect a delay of neural language organization due to very premature birth.

  2. Learning to read words in a new language shapes the neural organization of the prior languages.

    Science.gov (United States)

    Mei, Leilei; Xue, Gui; Lu, Zhong-Lin; Chen, Chuansheng; Zhang, Mingxia; He, Qinghua; Wei, Miao; Dong, Qi

    2014-12-01

    Learning a new language entails interactions with one׳s prior language(s). Much research has shown how native language affects the cognitive and neural mechanisms of a new language, but little is known about whether and how learning a new language shapes the neural mechanisms of prior language(s). In two experiments in the current study, we used an artificial language training paradigm in combination with an fMRI to examine (1) the effects of different linguistic components (phonology and semantics) of a new language on the neural process of prior languages (i.e., native and second languages), and (2) whether such effects were modulated by the proficiency level in the new language. Results of Experiment 1 showed that when the training in a new language involved semantics (as opposed to only visual forms and phonology), neural activity during word reading in the native language (Chinese) was reduced in several reading-related regions, including the left pars opercularis, pars triangularis, bilateral inferior temporal gyrus, fusiform gyrus, and inferior occipital gyrus. Results of Experiment 2 replicated the results of Experiment 1 and further found that semantic training also affected neural activity during word reading in the subjects׳ second language (English). Furthermore, we found that the effects of the new language were modulated by the subjects׳ proficiency level in the new language. These results provide critical imaging evidence for the influence of learning to read words in a new language on word reading in native and second languages. Copyright © 2014 Elsevier Ltd. All rights reserved.

  3. How sensory-motor systems impact the neural organization for language: direct contrasts between spoken and signed language

    Science.gov (United States)

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

    2014-01-01

    To investigate the impact of sensory-motor systems on the neural organization for language, we conducted an H215O-PET study of sign and spoken word production (picture-naming) and an fMRI study of sign and audio-visual spoken language comprehension (detection of a semantically anomalous sentence) with hearing bilinguals who are native users of American Sign Language (ASL) and English. Directly contrasting speech and sign production revealed greater activation in bilateral parietal cortex for signing, while speaking resulted in greater activation in bilateral superior temporal cortex (STC) and right frontal cortex, likely reflecting auditory feedback control. Surprisingly, the language production contrast revealed a relative increase in activation in bilateral occipital cortex for speaking. We speculate that greater activation in visual cortex for speaking may actually reflect cortical attenuation when signing, which functions to distinguish self-produced from externally generated visual input. Directly contrasting speech and sign comprehension revealed greater activation in bilateral STC for speech and greater activation in bilateral occipital-temporal cortex for sign. Sign comprehension, like sign production, engaged bilateral parietal cortex to a greater extent than spoken language. We hypothesize that posterior parietal activation in part reflects processing related to spatial classifier constructions in ASL and that anterior parietal activation may reflect covert imitation that functions as a predictive model during sign comprehension. The conjunction analysis for comprehension revealed that both speech and sign bilaterally engaged the inferior frontal gyrus (with more extensive activation on the left) and the superior temporal sulcus, suggesting an invariant bilateral perisylvian language system. We conclude that surface level differences between sign and spoken languages should not be dismissed and are critical for understanding the neurobiology of language

  4. Neural organization of linguistic short-term memory is sensory modality-dependent: evidence from signed and spoken language.

    Science.gov (United States)

    Pa, Judy; Wilson, Stephen M; Pickell, Herbert; Bellugi, Ursula; Hickok, Gregory

    2008-12-01

    Despite decades of research, there is still disagreement regarding the nature of the information that is maintained in linguistic short-term memory (STM). Some authors argue for abstract phonological codes, whereas others argue for more general sensory traces. We assess these possibilities by investigating linguistic STM in two distinct sensory-motor modalities, spoken and signed language. Hearing bilingual participants (native in English and American Sign Language) performed equivalent STM tasks in both languages during functional magnetic resonance imaging. Distinct, sensory-specific activations were seen during the maintenance phase of the task for spoken versus signed language. These regions have been previously shown to respond to nonlinguistic sensory stimulation, suggesting that linguistic STM tasks recruit sensory-specific networks. However, maintenance-phase activations common to the two languages were also observed, implying some form of common process. We conclude that linguistic STM involves sensory-dependent neural networks, but suggest that sensory-independent neural networks may also exist.

  5. Neural Language Processing in Adolescent First-Language Learners: Longitudinal Case Studies in American Sign Language.

    Science.gov (United States)

    Ferjan Ramirez, Naja; Leonard, Matthew K; Davenport, Tristan S; Torres, Christina; Halgren, Eric; Mayberry, Rachel I

    2016-03-01

    One key question in neurolinguistics is the extent to which the neural processing system for language requires linguistic experience during early life to develop fully. We conducted a longitudinal anatomically constrained magnetoencephalography (aMEG) analysis of lexico-semantic processing in 2 deaf adolescents who had no sustained language input until 14 years of age, when they became fully immersed in American Sign Language. After 2 to 3 years of language, the adolescents' neural responses to signed words were highly atypical, localizing mainly to right dorsal frontoparietal regions and often responding more strongly to semantically primed words (Ferjan Ramirez N, Leonard MK, Torres C, Hatrak M, Halgren E, Mayberry RI. 2014. Neural language processing in adolescent first-language learners. Cereb Cortex. 24 (10): 2772-2783). Here, we show that after an additional 15 months of language experience, the adolescents' neural responses remained atypical in terms of polarity. While their responses to less familiar signed words still showed atypical localization patterns, the localization of responses to highly familiar signed words became more concentrated in the left perisylvian language network. Our findings suggest that the timing of language experience affects the organization of neural language processing; however, even in adolescence, language representation in the human brain continues to evolve with experience. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  6. Thought beyond language: neural dissociation of algebra and natural language.

    Science.gov (United States)

    Monti, Martin M; Parsons, Lawrence M; Osherson, Daniel N

    2012-08-01

    A central question in cognitive science is whether natural language provides combinatorial operations that are essential to diverse domains of thought. In the study reported here, we addressed this issue by examining the role of linguistic mechanisms in forging the hierarchical structures of algebra. In a 3-T functional MRI experiment, we showed that processing of the syntax-like operations of algebra does not rely on the neural mechanisms of natural language. Our findings indicate that processing the syntax of language elicits the known substrate of linguistic competence, whereas algebraic operations recruit bilateral parietal brain regions previously implicated in the representation of magnitude. This double dissociation argues against the view that language provides the structure of thought across all cognitive domains.

  7. An MEG Investigation of Neural Biomarkers and Language in Nonverbal Children with Autism Spectrum Disorders

    Science.gov (United States)

    2014-09-01

    1.Lord C, Risi S, Pickles A. Trajectory of language development in autistic spectrum disorders . In: Rice M, Warren S, eds. Developmental Language...Nonverbal Children with Autism Spectrum Disorders PRINCIPAL INVESTIGATOR: Kristina McFadden CONTRACTING ORGANIZATION: University of...SUBTITLE 5a. CONTRACT NUMBER An MEG Investigation of Neural Biomarkers and Language in Nonverbal Children with Autism Spectrum Disorders 5b

  8. Sign Language Recognition using Neural Networks

    Directory of Open Access Journals (Sweden)

    Sabaheta Djogic

    2014-11-01

    Full Text Available – Sign language plays a great role as communication media for people with hearing difficulties.In developed countries, systems are made for overcoming a problem in communication with deaf people. This encouraged us to develop a system for the Bosnian sign language since there is a need for such system. The work is done with the use of digital image processing methods providing a system that teaches a multilayer neural network using a back propagation algorithm. Images are processed by feature extraction methods, and by masking method the data set has been created. Training is done using cross validation method for better performance thus; an accuracy of 84% is achieved.

  9. Do neural nets learn statistical laws behind natural language?

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    Shuntaro Takahashi

    Full Text Available The performance of deep learning in natural language processing has been spectacular, but the reasons for this success remain unclear because of the inherent complexity of deep learning. This paper provides empirical evidence of its effectiveness and of a limitation of neural networks for language engineering. Precisely, we demonstrate that a neural language model based on long short-term memory (LSTM effectively reproduces Zipf's law and Heaps' law, two representative statistical properties underlying natural language. We discuss the quality of reproducibility and the emergence of Zipf's law and Heaps' law as training progresses. We also point out that the neural language model has a limitation in reproducing long-range correlation, another statistical property of natural language. This understanding could provide a direction for improving the architectures of neural networks.

  10. Speaking in Multiple Languages: Neural Correlates of Language Proficiency in Multilingual Word Production

    Science.gov (United States)

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

    2010-01-01

    The human brain has the fascinating ability to represent and to process several languages. Although the first and further languages activate partially different brain networks, the linguistic factors underlying these differences in language processing have to be further specified. We investigated the neural correlates of language proficiency in a…

  11. Neural bases of congenital amusia in tonal language speakers.

    Science.gov (United States)

    Zhang, Caicai; Peng, Gang; Shao, Jing; Wang, William S-Y

    2017-03-01

    Congenital amusia is a lifelong neurodevelopmental disorder of fine-grained pitch processing. In this fMRI study, we examined the neural bases of congenial amusia in speakers of a tonal language - Cantonese. Previous studies on non-tonal language speakers suggest that the neural deficits of congenital amusia lie in the music-selective neural circuitry in the right inferior frontal gyrus (IFG). However, it is unclear whether this finding can generalize to congenital amusics in tonal languages. Tonal language experience has been reported to shape the neural processing of pitch, which raises the question of how tonal language experience affects the neural bases of congenital amusia. To investigate this question, we examined the neural circuitries sub-serving the processing of relative pitch interval in pitch-matched Cantonese level tone and musical stimuli in 11 Cantonese-speaking amusics and 11 musically intact controls. Cantonese-speaking amusics exhibited abnormal brain activities in a widely distributed neural network during the processing of lexical tone and musical stimuli. Whereas the controls exhibited significant activation in the right superior temporal gyrus (STG) in the lexical tone condition and in the cerebellum regardless of the lexical tone and music conditions, no activation was found in the amusics in those regions, which likely reflects a dysfunctional neural mechanism of relative pitch processing in the amusics. Furthermore, the amusics showed abnormally strong activation of the right middle frontal gyrus and precuneus when the pitch stimuli were repeated, which presumably reflect deficits of attending to repeated pitch stimuli or encoding them into working memory. No significant group difference was found in the right IFG in either the whole-brain analysis or region-of-interest analysis. These findings imply that the neural deficits in tonal language speakers might differ from those in non-tonal language speakers, and overlap partly with the

  12. Neurally and mathematically motivated architecture for language and thought.

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    Perlovsky, L I; Ilin, R

    2010-01-01

    Neural structures of interaction between thinking and language are unknown. This paper suggests a possible architecture motivated by neural and mathematical considerations. A mathematical requirement of computability imposes significant constraints on possible architectures consistent with brain neural structure and with a wealth of psychological knowledge. How language interacts with cognition. Do we think with words, or is thinking independent from language with words being just labels for decisions? Why is language learned by the age of 5 or 7, but acquisition of knowledge represented by learning to use this language knowledge takes a lifetime? This paper discusses hierarchical aspects of language and thought and argues that high level abstract thinking is impossible without language. We discuss a mathematical technique that can model the joint language-thought architecture, while overcoming previously encountered difficulties of computability. This architecture explains a contradiction between human ability for rational thoughtful decisions and irrationality of human thinking revealed by Tversky and Kahneman; a crucial role in this contradiction might be played by language. The proposed model resolves long-standing issues: how the brain learns correct words-object associations; why animals do not talk and think like people. We propose the role played by language emotionality in its interaction with thought. We relate the mathematical model to Humboldt's "firmness" of languages; and discuss possible influence of language grammar on its emotionality. Psychological and brain imaging experiments related to the proposed model are discussed. Future theoretical and experimental research is outlined.

  13. Bayesian Recurrent Neural Network for Language Modeling.

    Science.gov (United States)

    Chien, Jen-Tzung; Ku, Yuan-Chu

    2016-02-01

    A language model (LM) is calculated as the probability of a word sequence that provides the solution to word prediction for a variety of information systems. A recurrent neural network (RNN) is powerful to learn the large-span dynamics of a word sequence in the continuous space. However, the training of the RNN-LM is an ill-posed problem because of too many parameters from a large dictionary size and a high-dimensional hidden layer. This paper presents a Bayesian approach to regularize the RNN-LM and apply it for continuous speech recognition. We aim to penalize the too complicated RNN-LM by compensating for the uncertainty of the estimated model parameters, which is represented by a Gaussian prior. The objective function in a Bayesian classification network is formed as the regularized cross-entropy error function. The regularized model is constructed not only by calculating the regularized parameters according to the maximum a posteriori criterion but also by estimating the Gaussian hyperparameter by maximizing the marginal likelihood. A rapid approximation to a Hessian matrix is developed to implement the Bayesian RNN-LM (BRNN-LM) by selecting a small set of salient outer-products. The proposed BRNN-LM achieves a sparser model than the RNN-LM. Experiments on different corpora show the robustness of system performance by applying the rapid BRNN-LM under different conditions.

  14. Recognition of sign language gestures using neural networks

    Directory of Open Access Journals (Sweden)

    Simon Vamplew

    2007-04-01

    Full Text Available This paper describes the structure and performance of the SLARTI sign language recognition system developed at the University of Tasmania. SLARTI uses a modular architecture consisting of multiple feature-recognition neural networks and a nearest-neighbour classifier to recognise Australian sign language (Auslan hand gestures.

  15. Recognition of sign language gestures using neural networks

    OpenAIRE

    Simon Vamplew

    2007-01-01

    This paper describes the structure and performance of the SLARTI sign language recognition system developed at the University of Tasmania. SLARTI uses a modular architecture consisting of multiple feature-recognition neural networks and a nearest-neighbour classifier to recognise Australian sign language (Auslan) hand gestures.

  16. Language and Cognition Interaction Neural Mechanisms

    OpenAIRE

    Perlovsky, Leonid

    2011-01-01

    How language and cognition interact in thinking? Is language just used for communication of completed thoughts, or is it fundamental for thinking? Existing approaches have not led to a computational theory. We develop a hypothesis that language and cognition are two separate but closely interacting mechanisms. Language accumulates cultural wisdom; cognition develops mental representations modeling surrounding world and adapts cultural knowledge to concrete circumstances of life. Language is a...

  17. Neural systems supporting linguistic structure, linguistic experience, and symbolic communication in sign language and gesture.

    Science.gov (United States)

    Newman, Aaron J; Supalla, Ted; Fernandez, Nina; Newport, Elissa L; Bavelier, Daphne

    2015-09-15

    Sign languages used by deaf communities around the world possess the same structural and organizational properties as spoken languages: In particular, they are richly expressive and also tightly grammatically constrained. They therefore offer the opportunity to investigate the extent to which the neural organization for language is modality independent, as well as to identify ways in which modality influences this organization. The fact that sign languages share the visual-manual modality with a nonlinguistic symbolic communicative system-gesture-further allows us to investigate where the boundaries lie between language and symbolic communication more generally. In the present study, we had three goals: to investigate the neural processing of linguistic structure in American Sign Language (using verbs of motion classifier constructions, which may lie at the boundary between language and gesture); to determine whether we could dissociate the brain systems involved in deriving meaning from symbolic communication (including both language and gesture) from those specifically engaged by linguistically structured content (sign language); and to assess whether sign language experience influences the neural systems used for understanding nonlinguistic gesture. The results demonstrated that even sign language constructions that appear on the surface to be similar to gesture are processed within the left-lateralized frontal-temporal network used for spoken languages-supporting claims that these constructions are linguistically structured. Moreover, although nonsigners engage regions involved in human action perception to process communicative, symbolic gestures, signers instead engage parts of the language-processing network-demonstrating an influence of experience on the perception of nonlinguistic stimuli.

  18. Language and Cognition Interaction Neural Mechanisms

    Directory of Open Access Journals (Sweden)

    Leonid Perlovsky

    2011-01-01

    Full Text Available How language and cognition interact in thinking? Is language just used for communication of completed thoughts, or is it fundamental for thinking? Existing approaches have not led to a computational theory. We develop a hypothesis that language and cognition are two separate but closely interacting mechanisms. Language accumulates cultural wisdom; cognition develops mental representations modeling surrounding world and adapts cultural knowledge to concrete circumstances of life. Language is acquired from surrounding language “ready-made” and therefore can be acquired early in life. This early acquisition of language in childhood encompasses the entire hierarchy from sounds to words, to phrases, and to highest concepts existing in culture. Cognition is developed from experience. Yet cognition cannot be acquired from experience alone; language is a necessary intermediary, a “teacher.” A mathematical model is developed; it overcomes previous difficulties and leads to a computational theory. This model is consistent with Arbib's “language prewired brain” built on top of mirror neuron system. It models recent neuroimaging data about cognition, remaining unnoticed by other theories. A number of properties of language and cognition are explained, which previously seemed mysterious, including influence of language grammar on cultural evolution, which may explain specifics of English and Arabic cultures.

  19. Language and cognition interaction neural mechanisms.

    Science.gov (United States)

    Perlovsky, Leonid

    2011-01-01

    How language and cognition interact in thinking? Is language just used for communication of completed thoughts, or is it fundamental for thinking? Existing approaches have not led to a computational theory. We develop a hypothesis that language and cognition are two separate but closely interacting mechanisms. Language accumulates cultural wisdom; cognition develops mental representations modeling surrounding world and adapts cultural knowledge to concrete circumstances of life. Language is acquired from surrounding language "ready-made" and therefore can be acquired early in life. This early acquisition of language in childhood encompasses the entire hierarchy from sounds to words, to phrases, and to highest concepts existing in culture. Cognition is developed from experience. Yet cognition cannot be acquired from experience alone; language is a necessary intermediary, a "teacher." A mathematical model is developed; it overcomes previous difficulties and leads to a computational theory. This model is consistent with Arbib's "language prewired brain" built on top of mirror neuron system. It models recent neuroimaging data about cognition, remaining unnoticed by other theories. A number of properties of language and cognition are explained, which previously seemed mysterious, including influence of language grammar on cultural evolution, which may explain specifics of English and Arabic cultures.

  20. Language and Cognition Interaction Neural Mechanisms

    Science.gov (United States)

    Perlovsky, Leonid

    2011-01-01

    How language and cognition interact in thinking? Is language just used for communication of completed thoughts, or is it fundamental for thinking? Existing approaches have not led to a computational theory. We develop a hypothesis that language and cognition are two separate but closely interacting mechanisms. Language accumulates cultural wisdom; cognition develops mental representations modeling surrounding world and adapts cultural knowledge to concrete circumstances of life. Language is acquired from surrounding language “ready-made” and therefore can be acquired early in life. This early acquisition of language in childhood encompasses the entire hierarchy from sounds to words, to phrases, and to highest concepts existing in culture. Cognition is developed from experience. Yet cognition cannot be acquired from experience alone; language is a necessary intermediary, a “teacher.” A mathematical model is developed; it overcomes previous difficulties and leads to a computational theory. This model is consistent with Arbib's “language prewired brain” built on top of mirror neuron system. It models recent neuroimaging data about cognition, remaining unnoticed by other theories. A number of properties of language and cognition are explained, which previously seemed mysterious, including influence of language grammar on cultural evolution, which may explain specifics of English and Arabic cultures. PMID:21876687

  1. Comparable mechanisms for action and language: Neural systems behind intentions, goals and means

    NARCIS (Netherlands)

    Schie, H.T. van; Toni, I.; Bekkering, H.

    2006-01-01

    In this position paper we explore correspondence between neural systems for language and action starting from recent electrophysiological findings on the roles of posterior and frontal areas in goal-directed grasping actions. The paper compares the perceptual and motor organization for action and

  2. Learning language with the wrong neural scaffolding: The cost of neural commitment to sounds.

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    Amy Sue Finn

    2013-11-01

    Full Text Available Does tuning to one’s native language explain the sensitive period for language learning? We explore the idea that tuning to (or becoming more selective for the properties of one’s native-language could result in being less open (or plastic for tuning to the properties of a new language. To explore how this might lead to the sensitive period for grammar learning, we ask if tuning to an earlier-learned aspect of language (sound structure has an impact on the neural representation of a later-learned aspect (grammar. English-speaking adults learned one of two miniature artificial languages over 4 days in the lab. Compared to English, both languages had novel grammar, but only one was comprised of novel sounds. After learning a language, participants were scanned while judging the grammaticality of sentences. Judgments were performed for the newly learned language and English. Learners of the similar-sounds language recruited regions that overlapped more with English. Learners of the distinct-sounds language, however, recruited the Superior Temporal Gyrus (STG to a greater extent, which was coactive with the Inferior Frontal Gyrus (IFG. Across learners, recruitment of IFG (but not STG predicted both learning success in tests conducted prior to the scan and grammatical judgment ability during the scan. Data suggest that adults’ difficulty learning language, especially grammar, could be due, at least in part, to the neural commitments they have made to the lower level linguistic components of their native language.

  3. Learning language with the wrong neural scaffolding: the cost of neural commitment to sounds

    Science.gov (United States)

    Finn, Amy S.; Hudson Kam, Carla L.; Ettlinger, Marc; Vytlacil, Jason; D'Esposito, Mark

    2013-01-01

    Does tuning to one's native language explain the “sensitive period” for language learning? We explore the idea that tuning to (or becoming more selective for) the properties of one's native-language could result in being less open (or plastic) for tuning to the properties of a new language. To explore how this might lead to the sensitive period for grammar learning, we ask if tuning to an earlier-learned aspect of language (sound structure) has an impact on the neural representation of a later-learned aspect (grammar). English-speaking adults learned one of two miniature artificial languages (MALs) over 4 days in the lab. Compared to English, both languages had novel grammar, but only one was comprised of novel sounds. After learning a language, participants were scanned while judging the grammaticality of sentences. Judgments were performed for the newly learned language and English. Learners of the similar-sounds language recruited regions that overlapped more with English. Learners of the distinct-sounds language, however, recruited the Superior Temporal Gyrus (STG) to a greater extent, which was coactive with the Inferior Frontal Gyrus (IFG). Across learners, recruitment of IFG (but not STG) predicted both learning success in tests conducted prior to the scan and grammatical judgment ability during the scan. Data suggest that adults' difficulty learning language, especially grammar, could be due, at least in part, to the neural commitments they have made to the lower level linguistic components of their native language. PMID:24273497

  4. Self-organizing map models of language acquisition

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    Li, Ping; Zhao, Xiaowei

    2013-01-01

    Connectionist models have had a profound impact on theories of language. While most early models were inspired by the classic parallel distributed processing architecture, recent models of language have explored various other types of models, including self-organizing models for language acquisition. In this paper, we aim at providing a review of the latter type of models, and highlight a number of simulation experiments that we have conducted based on these models. We show that self-organizing connectionist models can provide significant insights into long-standing debates in both monolingual and bilingual language development. We suggest future directions in which these models can be extended, to better connect with behavioral and neural data, and to make clear predictions in testing relevant psycholinguistic theories. PMID:24312061

  5. Recurrent Artificial Neural Networks and Finite State Natural Language Processing.

    Science.gov (United States)

    Moisl, Hermann

    It is argued that pessimistic assessments of the adequacy of artificial neural networks (ANNs) for natural language processing (NLP) on the grounds that they have a finite state architecture are unjustified, and that their adequacy in this regard is an empirical issue. First, arguments that counter standard objections to finite state NLP on the…

  6. Neural Correlates of High Performance in Foreign Language Vocabulary Learning

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    Macedonia, Manuela; Muller, Karsten; Friederici, Angela D.

    2010-01-01

    Learning vocabulary in a foreign language is a laborious task which people perform with varying levels of success. Here, we investigated the neural underpinning of high performance on this task. In a within-subjects paradigm, participants learned 92 vocabulary items under two multimodal conditions: one condition paired novel words with iconic…

  7. Language and Cognition Interaction Neural Mechanisms

    Science.gov (United States)

    2011-06-01

    resolution of processes in the brain, combined with magnetoencephalography (MEG), measurements of the magnetic field next to head , to provide a high...humans,” Anatomy and Embryology , vol. 210, no. 5-6, pp. 419– 421, 2005. [88] G. Rizzolatti and M. A. Arbib, “Language within our grasp,” Trends in

  8. Neural Cognition and Affective Computing on Cyber Language.

    Science.gov (United States)

    Huang, Shuang; Zhou, Xuan; Xue, Ke; Wan, Xiqiong; Yang, Zhenyi; Xu, Duo; Ivanović, Mirjana; Yu, Xueer

    2015-01-01

    Characterized by its customary symbol system and simple and vivid expression patterns, cyber language acts as not only a tool for convenient communication but also a carrier of abundant emotions and causes high attention in public opinion analysis, internet marketing, service feedback monitoring, and social emergency management. Based on our multidisciplinary research, this paper presents a classification of the emotional symbols in cyber language, analyzes the cognitive characteristics of different symbols, and puts forward a mechanism model to show the dominant neural activities in that process. Through the comparative study of Chinese, English, and Spanish, which are used by the largest population in the world, this paper discusses the expressive patterns of emotions in international cyber languages and proposes an intelligent method for affective computing on cyber language in a unified PAD (Pleasure-Arousal-Dominance) emotional space.

  9. Neural Cognition and Affective Computing on Cyber Language

    Directory of Open Access Journals (Sweden)

    Shuang Huang

    2015-01-01

    Full Text Available Characterized by its customary symbol system and simple and vivid expression patterns, cyber language acts as not only a tool for convenient communication but also a carrier of abundant emotions and causes high attention in public opinion analysis, internet marketing, service feedback monitoring, and social emergency management. Based on our multidisciplinary research, this paper presents a classification of the emotional symbols in cyber language, analyzes the cognitive characteristics of different symbols, and puts forward a mechanism model to show the dominant neural activities in that process. Through the comparative study of Chinese, English, and Spanish, which are used by the largest population in the world, this paper discusses the expressive patterns of emotions in international cyber languages and proposes an intelligent method for affective computing on cyber language in a unified PAD (Pleasure-Arousal-Dominance emotional space.

  10. Strengthening Foreign Language Professional Organizations.

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    Dunham, Lowell

    1971-01-01

    The leitmotif of this address, inspired by lines found in William B. Yeats'"The Second Coming", underscores the need for a greater display of solidarity of language teachers through increased participation in professional associations. The work of the American Council on the Teaching of Foreign Languages (ACTFL) is discussed and noted to be vital…

  11. Self-organized critical neural networks

    International Nuclear Information System (INIS)

    Bornholdt, Stefan; Roehl, Torsten

    2003-01-01

    A mechanism for self-organization of the degree of connectivity in model neural networks is studied. Network connectivity is regulated locally on the basis of an order parameter of the global dynamics, which is estimated from an observable at the single synapse level. This principle is studied in a two-dimensional neural network with randomly wired asymmetric weights. In this class of networks, network connectivity is closely related to a phase transition between ordered and disordered dynamics. A slow topology change is imposed on the network through a local rewiring rule motivated by activity-dependent synaptic development: Neighbor neurons whose activity is correlated, on average develop a new connection while uncorrelated neighbors tend to disconnect. As a result, robust self-organization of the network towards the order disorder transition occurs. Convergence is independent of initial conditions, robust against thermal noise, and does not require fine tuning of parameters

  12. 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. Copyright © 2013 Wiley Periodicals, Inc.

  13. The shared neural basis of music and language.

    Science.gov (United States)

    Yu, Mengxia; Xu, Miao; Li, Xueting; Chen, Zhencai; Song, Yiying; Liu, Jia

    2017-08-15

    Human musical ability is proposed to play a key phylogenetical role in the evolution of language, and the similarity of hierarchical structure in music and language has led to considerable speculation about their shared mechanisms. While behavioral and electrophysioglocial studies have revealed associations between music and linguistic abilities, results from functional magnetic resonance imaging (fMRI) studies on their relations are contradictory, possibly because these studies usually treat music or language as single entities without breaking down to their components. Here, we examined the relations between different components of music (i.e., melodic and rhythmic analysis) and language (i.e., semantic and phonological processing) using both behavioral tests and resting-state fMRI. Behaviorally, we found that individuals with music training experiences were better at semantic processing, but not at phonological processing, than those without training. Further correlation analyses showed that semantic processing of language was related to melodic, but not rhythmic, analysis of music. Neurally, we found that performances in both semantic processing and melodic analysis were correlated with spontaneous brain activities in the bilateral precentral gyrus (PCG) and superior temporal plane at the regional level, and with the resting-state functional connectivity of the left PCG with the left supramarginal gyrus and left superior temporal gyrus at the network level. Together, our study revealed the shared spontaneous neural basis of music and language based on the behavioral link between melodic analysis and semantic processing, which possibly relied on a common mechanism of automatic auditory-motor integration. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

  14. Habituation in non-neural organisms: evidence from slime moulds

    OpenAIRE

    Boisseau, Romain P.; Vogel, David; Dussutour, Audrey

    2016-01-01

    Learning, defined as a change in behaviour evoked by experience, has hitherto been investigated almost exclusively in multicellular neural organisms. Evidence for learning in non-neural multicellular organisms is scant, and only a few unequivocal reports of learning have been described in single-celled organisms. Here we demonstrate habituation, an unmistakable form of learning, in the non-neural organism Physarum polycephalum. In our experiment, using chemotaxis as the behavioural output and...

  15. Natural language acquisition in large scale neural semantic networks

    Science.gov (United States)

    Ealey, Douglas

    This thesis puts forward the view that a purely signal- based approach to natural language processing is both plausible and desirable. By questioning the veracity of symbolic representations of meaning, it argues for a unified, non-symbolic model of knowledge representation that is both biologically plausible and, potentially, highly efficient. Processes to generate a grounded, neural form of this model-dubbed the semantic filter-are discussed. The combined effects of local neural organisation, coincident with perceptual maturation, are used to hypothesise its nature. This theoretical model is then validated in light of a number of fundamental neurological constraints and milestones. The mechanisms of semantic and episodic development that the model predicts are then used to explain linguistic properties, such as propositions and verbs, syntax and scripting. To mimic the growth of locally densely connected structures upon an unbounded neural substrate, a system is developed that can grow arbitrarily large, data- dependant structures composed of individual self- organising neural networks. The maturational nature of the data used results in a structure in which the perception of concepts is refined by the networks, but demarcated by subsequent structure. As a consequence, the overall structure shows significant memory and computational benefits, as predicted by the cognitive and neural models. Furthermore, the localised nature of the neural architecture also avoids the increasing error sensitivity and redundancy of traditional systems as the training domain grows. The semantic and episodic filters have been demonstrated to perform as well, or better, than more specialist networks, whilst using significantly larger vocabularies, more complex sentence forms and more natural corpora.

  16. Social Interaction Affects Neural Outcomes of Sign Language Learning As a Foreign Language in Adults.

    Science.gov (United States)

    Yusa, Noriaki; Kim, Jungho; Koizumi, Masatoshi; Sugiura, Motoaki; Kawashima, Ryuta

    2017-01-01

    Children naturally acquire a language in social contexts where they interact with their caregivers. Indeed, research shows that social interaction facilitates lexical and phonological development at the early stages of child language acquisition. It is not clear, however, whether the relationship between social interaction and learning applies to adult second language acquisition of syntactic rules. Does learning second language syntactic rules through social interactions with a native speaker or without such interactions impact behavior and the brain? The current study aims to answer this question. Adult Japanese participants learned a new foreign language, Japanese sign language (JSL), either through a native deaf signer or via DVDs. Neural correlates of acquiring new linguistic knowledge were investigated using functional magnetic resonance imaging (fMRI). The participants in each group were indistinguishable in terms of their behavioral data after the instruction. The fMRI data, however, revealed significant differences in the neural activities between two groups. Significant activations in the left inferior frontal gyrus (IFG) were found for the participants who learned JSL through interactions with the native signer. In contrast, no cortical activation change in the left IFG was found for the group who experienced the same visual input for the same duration via the DVD presentation. Given that the left IFG is involved in the syntactic processing of language, spoken or signed, learning through social interactions resulted in an fMRI signature typical of native speakers: activation of the left IFG. Thus, broadly speaking, availability of communicative interaction is necessary for second language acquisition and this results in observed changes in the brain.

  17. Neural Basis of Action Understanding: Evidence from Sign Language Aphasia.

    Science.gov (United States)

    Rogalsky, Corianne; Raphel, Kristin; Tomkovicz, Vivian; O'Grady, Lucinda; Damasio, Hanna; Bellugi, Ursula; Hickok, Gregory

    2013-01-01

    The neural basis of action understanding is a hotly debated issue. The mirror neuron account holds that motor simulation in fronto-parietal circuits is critical to action understanding including speech comprehension, while others emphasize the ventral stream in the temporal lobe. Evidence from speech strongly supports the ventral stream account, but on the other hand, evidence from manual gesture comprehension (e.g., in limb apraxia) has led to contradictory findings. Here we present a lesion analysis of sign language comprehension. Sign language is an excellent model for studying mirror system function in that it bridges the gap between the visual-manual system in which mirror neurons are best characterized and language systems which have represented a theoretical target of mirror neuron research. Twenty-one life long deaf signers with focal cortical lesions performed two tasks: one involving the comprehension of individual signs and the other involving comprehension of signed sentences (commands). Participants' lesions, as indicated on MRI or CT scans, were mapped onto a template brain to explore the relationship between lesion location and sign comprehension measures. Single sign comprehension was not significantly affected by left hemisphere damage. Sentence sign comprehension impairments were associated with left temporal-parietal damage. We found that damage to mirror system related regions in the left frontal lobe were not associated with deficits on either of these comprehension tasks. We conclude that the mirror system is not critically involved in action understanding.

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

    Science.gov (United States)

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

    2009-03-01

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

  19. Neurological impressions on the organization of language networks in the human brain.

    Science.gov (United States)

    Oliveira, Fabricio Ferreira de; Marin, Sheilla de Medeiros Correia; Bertolucci, Paulo Henrique Ferreira

    2017-01-01

    More than 95% of right-handed individuals, as well as almost 80% of left-handed individuals, have left hemisphere dominance for language. The perisylvian networks of the dominant hemisphere tend to be the most important language systems in human brains, usually connected by bidirectional fibres originated from the superior longitudinal fascicle/arcuate fascicle system and potentially modifiable by learning. Neuroplasticity mechanisms take place to preserve neural functions after brain injuries. Language is dependent on a hierarchical interlinkage of serial and parallel processing areas in distinct brain regions considered to be elementary processing units. Whereas aphasic syndromes typically result from injuries to the dominant hemisphere, the extent of the distribution of language functions seems to be variable for each individual. Review of the literature Results: Several theories try to explain the organization of language networks in the human brain from a point of view that involves either modular or distributed processing or sometimes both. The most important evidence for each approach is discussed under the light of modern theories of organization of neural networks. Understanding the connectivity patterns of language networks may provide deeper insights into language functions, supporting evidence-based rehabilitation strategies that focus on the enhancement of language organization for patients with aphasic syndromes.

  20. Phonological memory in sign language relies on the visuomotor neural system outside the left hemisphere language network.

    Science.gov (United States)

    Kanazawa, Yuji; Nakamura, Kimihiro; Ishii, Toru; Aso, Toshihiko; Yamazaki, Hiroshi; Omori, Koichi

    2017-01-01

    Sign language is an essential medium for everyday social interaction for deaf people and plays a critical role in verbal learning. In particular, language development in those people should heavily rely on the verbal short-term memory (STM) via sign language. Most previous studies compared neural activations during signed language processing in deaf signers and those during spoken language processing in hearing speakers. For sign language users, it thus remains unclear how visuospatial inputs are converted into the verbal STM operating in the left-hemisphere language network. Using functional magnetic resonance imaging, the present study investigated neural activation while bilinguals of spoken and signed language were engaged in a sequence memory span task. On each trial, participants viewed a nonsense syllable sequence presented either as written letters or as fingerspelling (4-7 syllables in length) and then held the syllable sequence for 12 s. Behavioral analysis revealed that participants relied on phonological memory while holding verbal information regardless of the type of input modality. At the neural level, this maintenance stage broadly activated the left-hemisphere language network, including the inferior frontal gyrus, supplementary motor area, superior temporal gyrus and inferior parietal lobule, for both letter and fingerspelling conditions. Interestingly, while most participants reported that they relied on phonological memory during maintenance, direct comparisons between letters and fingers revealed strikingly different patterns of neural activation during the same period. Namely, the effortful maintenance of fingerspelling inputs relative to letter inputs activated the left superior parietal lobule and dorsal premotor area, i.e., brain regions known to play a role in visuomotor analysis of hand/arm movements. These findings suggest that the dorsal visuomotor neural system subserves verbal learning via sign language by relaying gestural inputs to

  1. Neural constructivism or self-organization?

    NARCIS (Netherlands)

    van der Maas, H.L.J.; Molenaar, P.C.M.

    2000-01-01

    Comments on the article by S. R. Quartz et al (see record 1998-00749-001) which discussed the constructivist perspective of interaction between cognition and neural processes during development and consequences for theories of learning. Three arguments are given to show that neural constructivism

  2. Neural systems of second language reading are shaped by native language.

    Science.gov (United States)

    Tan, Li Hai; Spinks, John A; Feng, Ching-Mei; Siok, Wai Ting; Perfetti, Charles A; Xiong, Jinhu; Fox, Peter T; Gao, Jia-Hong

    2003-03-01

    Reading in a second language (L2) is a complex task that entails an interaction between L2 and the native language (L1). To study the underlying mechanisms, we used functional magnetic resonance imaging (fMRI) to visualize Chinese-English bilinguals' brain activity in phonological processing of logographic Chinese and alphabetic English, two written languages with a sharp contrast in phonology and orthography. In Experiment 1, we found that phonological processing of Chinese characters recruits a neural system involving left middle frontal and posterior parietal gyri, cortical regions that are known to contribute to spatial information representation, spatial working memory, and coordination of cognitive resources as a central executive system. We assume that the peak activation of this system is relevant to the unique feature of Chinese that a logographic character has a square configuration that maps onto a monosyllabic unit of speech. Equally important, when our bilingual subjects performed a phonological task on English words, this neural system was most active, whereas brain areas mediating English monolinguals' fine-grained phonemic analysis, as demonstrated by Experiment 2, were only weakly activated. This suggests that our bilingual subjects were applying their L1 system to L2 reading and that the lack of letter-to-sound conversion rules in Chinese led Chinese readers to being less capable of processing English by recourse to an analytic reading system on which English monolinguals rely. Our brain imaging findings lend strongest support to the idea that language experience tunes the cortex. Copyright 2003 Wiley-Liss, Inc.

  3. Neural correlates of British sign language comprehension: spatial processing demands of topographic language.

    Science.gov (United States)

    MacSweeney, Mairéad; Woll, Bencie; Campbell, Ruth; Calvert, Gemma A; McGuire, Philip K; David, Anthony S; Simmons, Andrew; Brammer, Michael J

    2002-10-01

    In all signed languages used by deaf people, signs are executed in "sign space" in front of the body. Some signed sentences use this space to map detailed "real-world" spatial relationships directly. Such sentences can be considered to exploit sign space "topographically." Using functional magnetic resonance imaging, we explored the extent to which increasing the topographic processing demands of signed sentences was reflected in the differential recruitment of brain regions in deaf and hearing native signers of the British Sign Language. When BSL signers performed a sentence anomaly judgement task, the occipito-temporal junction was activated bilaterally to a greater extent for topographic than nontopographic processing. The differential role of movement in the processing of the two sentence types may account for this finding. In addition, enhanced activation was observed in the left inferior and superior parietal lobules during processing of topographic BSL sentences. We argue that the left parietal lobe is specifically involved in processing the precise configuration and location of hands in space to represent objects, agents, and actions. Importantly, no differences in these regions were observed when hearing people heard and saw English translations of these sentences. Despite the high degree of similarity in the neural systems underlying signed and spoken languages, exploring the linguistic features which are unique to each of these broadens our understanding of the systems involved in language comprehension.

  4. A review of organic and inorganic biomaterials for neural interfaces.

    Science.gov (United States)

    Fattahi, Pouria; Yang, Guang; Kim, Gloria; Abidian, Mohammad Reza

    2014-03-26

    Recent advances in nanotechnology have generated wide interest in applying nanomaterials for neural prostheses. An ideal neural interface should create seamless integration into the nervous system and performs reliably for long periods of time. As a result, many nanoscale materials not originally developed for neural interfaces become attractive candidates to detect neural signals and stimulate neurons. In this comprehensive review, an overview of state-of-the-art microelectrode technologies provided fi rst, with focus on the material properties of these microdevices. The advancements in electro active nanomaterials are then reviewed, including conducting polymers, carbon nanotubes, graphene, silicon nanowires, and hybrid organic-inorganic nanomaterials, for neural recording, stimulation, and growth. Finally, technical and scientific challenges are discussed regarding biocompatibility, mechanical mismatch, and electrical properties faced by these nanomaterials for the development of long-lasting functional neural interfaces.

  5. Time Series Neural Network Model for Part-of-Speech Tagging Indonesian Language

    Science.gov (United States)

    Tanadi, Theo

    2018-03-01

    Part-of-speech tagging (POS tagging) is an important part in natural language processing. Many methods have been used to do this task, including neural network. This paper models a neural network that attempts to do POS tagging. A time series neural network is modelled to solve the problems that a basic neural network faces when attempting to do POS tagging. In order to enable the neural network to have text data input, the text data will get clustered first using Brown Clustering, resulting a binary dictionary that the neural network can use. To further the accuracy of the neural network, other features such as the POS tag, suffix, and affix of previous words would also be fed to the neural network.

  6. Neural Differences in Bilingual Children's Arithmetic Processing Depending on Language of Instruction

    NARCIS (Netherlands)

    Mondt, K.; Struys, E.; Noort, M.W.M.L. van den; Balériaux, D.; Metens, T.; Paquier, P.; Craen, P. van de; Bosch, M.P.C.; Denolin, V.

    2011-01-01

    Many children in bilingual regions follow lessons in a language at school (school-language) that they hardly ever speak at home or in other informal settings. What are the neural effects of this phenomenon? This functional magnetic resonance imaging (fMRI) study investigates the effects of using

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

    Science.gov (United States)

    Chrol-Cannon, Joseph; Jin, Yaochu

    2014-11-01

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

  8. Cross-Linguistic Differences in the Neural Representation of Human Language: Evidence from Users of Signed Languages

    Science.gov (United States)

    Corina, David P.; Lawyer, Laurel A.; Cates, Deborah

    2013-01-01

    Studies of deaf individuals who are users of signed languages have provided profound insight into the neural representation of human language. Case studies of deaf signers who have incurred left- and right-hemisphere damage have shown that left-hemisphere resources are a necessary component of sign language processing. These data suggest that, despite frank differences in the input and output modality of language, core left perisylvian regions universally serve linguistic function. Neuroimaging studies of deaf signers have generally provided support for this claim. However, more fine-tuned studies of linguistic processing in deaf signers are beginning to show evidence of important differences in the representation of signed and spoken languages. In this paper, we provide a critical review of this literature and present compelling evidence for language-specific cortical representations in deaf signers. These data lend support to the claim that the neural representation of language may show substantive cross-linguistic differences. We discuss the theoretical implications of these findings with respect to an emerging understanding of the neurobiology of language. PMID:23293624

  9. Are lexical tones musical? Native language's influence on neural response to pitch in different domains.

    Science.gov (United States)

    Chen, Ao; Peter, Varghese; Wijnen, Frank; Schnack, Hugo; Burnham, Denis

    2018-04-21

    Language experience shapes musical and speech pitch processing. We investigated whether speaking a lexical tone language natively modulates neural processing of pitch in language and music as well as their correlation. We tested tone language (Mandarin Chinese), and non-tone language (Dutch) listeners in a passive oddball paradigm measuring mismatch negativity (MMN) for (i) Chinese lexical tones and (ii) three-note musical melodies with similar pitch contours. For lexical tones, Chinese listeners showed a later MMN peak than the non-tone language listeners, whereas for MMN amplitude there were no significant differences between groups. Dutch participants also showed a late discriminative negativity (LDN). In the music condition two MMNs, corresponding to the two notes that differed between the standard and the deviant were found for both groups, and an LDN were found for both the Dutch and the Chinese listeners. The music MMNs were significantly right lateralized. Importantly, significant correlations were found between the lexical tone and the music MMNs for the Dutch but not the Chinese participants. The results suggest that speaking a tone language natively does not necessarily enhance neural responses to pitch either in language or in music, but that it does change the nature of neural pitch processing: non-tone language speakers appear to perceive lexical tones as musical, whereas for tone language speakers, lexical tones and music may activate different neural networks. Neural resources seem to be assigned differently for the lexical tones and for musical melodies, presumably depending on the presence or absence of long-term phonological memory traces. Copyright © 2018 Elsevier Inc. All rights reserved.

  10. Habituation in non-neural organisms: evidence from slime moulds.

    Science.gov (United States)

    Boisseau, Romain P; Vogel, David; Dussutour, Audrey

    2016-04-27

    Learning, defined as a change in behaviour evoked by experience, has hitherto been investigated almost exclusively in multicellular neural organisms. Evidence for learning in non-neural multicellular organisms is scant, and only a few unequivocal reports of learning have been described in single-celled organisms. Here we demonstrate habituation, an unmistakable form of learning, in the non-neural organism Physarum polycephalum In our experiment, using chemotaxis as the behavioural output and quinine or caffeine as the stimulus, we showed that P. polycephalum learnt to ignore quinine or caffeine when the stimuli were repeated, but responded again when the stimulus was withheld for a certain time. Our results meet the principle criteria that have been used to demonstrate habituation: responsiveness decline and spontaneous recovery. To distinguish habituation from sensory adaptation or motor fatigue, we also show stimulus specificity. Our results point to the diversity of organisms lacking neurons, which likely display a hitherto unrecognized capacity for learning, and suggest that slime moulds may be an ideal model system in which to investigate fundamental mechanisms underlying learning processes. Besides, documenting learning in non-neural organisms such as slime moulds is centrally important to a comprehensive, phylogenetic understanding of when and where in the tree of life the earliest manifestations of learning evolved. © 2016 The Author(s).

  11. Age of language acquisition and cortical language organization in multilingual patients undergoing awake brain mapping.

    Science.gov (United States)

    Fernández-Coello, Alejandro; Havas, Viktória; Juncadella, Montserrat; Sierpowska, Joanna; Rodríguez-Fornells, Antoni; Gabarrós, Andreu

    2017-06-01

    OBJECTIVE Most knowledge regarding the anatomical organization of multilingualism is based on aphasiology and functional imaging studies. However, the results have still to be validated by the gold standard approach, namely electrical stimulation mapping (ESM) during awake neurosurgical procedures. In this ESM study the authors describe language representation in a highly specific group of 13 multilingual individuals, focusing on how age of acquisition may influence the cortical organization of language. METHODS Thirteen patients who had a high degree of proficiency in multiple languages and were harboring lesions within the dominant, left hemisphere underwent ESM while being operated on under awake conditions. Demographic and language data were recorded in relation to age of language acquisition (for native languages and early- and late-acquired languages), neuropsychological pre- and postoperative language testing, the number and location of language sites, and overlapping distribution in terms of language acquisition time. Lesion growth patterns and histopathological characteristics, location, and size were also recorded. The distribution of language sites was analyzed with respect to age of acquisition and overlap. RESULTS The functional language-related sites were distributed in the frontal (55%), temporal (29%), and parietal lobes (16%). The total number of native language sites was 47. Early-acquired languages (including native languages) were represented in 97 sites (55 overlapped) and late-acquired languages in 70 sites (45 overlapped). The overlapping distribution was 20% for early-early, 71% for early-late, and 9% for late-late. The average lesion size (maximum diameter) was 3.3 cm. There were 5 fast-growing and 7 slow-growing lesions. CONCLUSIONS Cortical language distribution in multilingual patients is not homogeneous, and it is influenced by age of acquisition. Early-acquired languages have a greater cortical representation than languages acquired

  12. A Study of Recurrent and Convolutional Neural Networks in the Native Language Identification Task

    KAUST Repository

    Werfelmann, Robert

    2018-05-24

    Native Language Identification (NLI) is the task of predicting the native language of an author from their text written in a second language. The idea is to find writing habits that transfer from an author’s native language to their second language. Many approaches to this task have been studied, from simple word frequency analysis, to analyzing grammatical and spelling mistakes to find patterns and traits that are common between different authors of the same native language. This can be a very complex task, depending on the native language and the proficiency of the author’s second language. The most common approach that has seen very good results is based on the usage of n-gram features of words and characters. In this thesis, we attempt to extract lexical, grammatical, and semantic features from the sentences of non-native English essays using neural networks. The training and testing data was obtained from a large corpus of publicly available essays written by authors of several countries around the world. The neural network models consisted of Long Short-Term Memory and Convolutional networks using the sentences of each document as the input. Additional statistical features were generated from the text to complement the predictions of the neural networks, which were then used as feature inputs to a Support Vector Machine, making the final prediction. Results show that Long Short-Term Memory neural network can improve performance over a naive bag of words approach, but with a much smaller feature set. With more fine-tuning of neural network hyperparameters, these results will likely improve significantly.

  13. Neural signatures of second language learning and control.

    Science.gov (United States)

    Bartolotti, James; Bradley, Kailyn; Hernandez, Arturo E; Marian, Viorica

    2017-04-01

    Experience with multiple languages has unique effects on cortical structure and information processing. Differences in gray matter density and patterns of cortical activation are observed in lifelong bilinguals compared to monolinguals as a result of their experience managing interference across languages. Monolinguals who acquire a second language later in life begin to encounter the same type of linguistic interference as bilinguals, but with a different pre-existing language architecture. The current study used functional magnetic resonance imaging to explore the beginning stages of second language acquisition and cross-linguistic interference in monolingual adults. We found that after English monolinguals learned novel Spanish vocabulary, English and Spanish auditory words led to distinct patterns of cortical activation, with greater recruitment of posterior parietal regions in response to English words and of left hippocampus in response to Spanish words. In addition, cross-linguistic interference from English influenced processing of newly-learned Spanish words, decreasing hippocampus activity. Results suggest that monolinguals may rely on different memory systems to process a newly-learned second language, and that the second language system is sensitive to native language interference. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Sign Language Recognition System using Neural Network for Digital Hardware Implementation

    International Nuclear Information System (INIS)

    Vargas, Lorena P; Barba, Leiner; Torres, C O; Mattos, L

    2011-01-01

    This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.

  15. Self-organized criticality in neural networks

    Science.gov (United States)

    Makarenkov, Vladimir I.; Kirillov, A. B.

    1991-08-01

    Possible mechanisms of creating different types of persistent states for informational processing are regarded. It is presented two origins of criticalities - self-organized and phase transition. A comparative analyses of their behavior is given. It is demonstrated that despite a likeness there are important differences. These differences can play a significant role to explain the physical issue of such highest functions of the brain as a short-term memory and attention. 1.

  16. Review of Data Preprocessing Methods for Sign Language Recognition Systems based on Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Zorins Aleksejs

    2016-12-01

    Full Text Available The article presents an introductory analysis of relevant research topic for Latvian deaf society, which is the development of the Latvian Sign Language Recognition System. More specifically the data preprocessing methods are discussed in the paper and several approaches are shown with a focus on systems based on artificial neural networks, which are one of the most successful solutions for sign language recognition task.

  17. Self-Organizing Map Models of Language Acquisition

    Directory of Open Access Journals (Sweden)

    Ping eLi

    2013-11-01

    Full Text Available Connectionist models have had a profound impact on theories of language. While most early models were inspired by the classic PDP architecture, recent models of language have explored various other types of models, including self-organizing models for language acquisition. In this paper we aim at providing a review of the latter type of models, and highlight a number of simulation experiments that we have conducted based on these models. We show that self-organizing connectionist models can provide significant insights into long-standing debates in both monolingual and bilingual language development.

  18. A Cognitive Neural Architecture Able to Learn and Communicate through Natural Language.

    Directory of Open Access Journals (Sweden)

    Bruno Golosio

    Full Text Available Communicative interactions involve a kind of procedural knowledge that is used by the human brain for processing verbal and nonverbal inputs and for language production. Although considerable work has been done on modeling human language abilities, it has been difficult to bring them together to a comprehensive tabula rasa system compatible with current knowledge of how verbal information is processed in the brain. This work presents a cognitive system, entirely based on a large-scale neural architecture, which was developed to shed light on the procedural knowledge involved in language elaboration. The main component of this system is the central executive, which is a supervising system that coordinates the other components of the working memory. In our model, the central executive is a neural network that takes as input the neural activation states of the short-term memory and yields as output mental actions, which control the flow of information among the working memory components through neural gating mechanisms. The proposed system is capable of learning to communicate through natural language starting from tabula rasa, without any a priori knowledge of the structure of phrases, meaning of words, role of the different classes of words, only by interacting with a human through a text-based interface, using an open-ended incremental learning process. It is able to learn nouns, verbs, adjectives, pronouns and other word classes, and to use them in expressive language. The model was validated on a corpus of 1587 input sentences, based on literature on early language assessment, at the level of about 4-years old child, and produced 521 output sentences, expressing a broad range of language processing functionalities.

  19. Exploiting Hidden Layer Responses of Deep Neural Networks for Language Recognition

    Science.gov (United States)

    2016-09-08

    Target Languages Arabic (ara) Egyptian , Iraqi, Levantine, Maghrebi,Modern Standard Chinese (chi) Cantonese, Mandarin, Min, Wu English (eng) British...Frame-by-frame DNN classification x1 x2 x3 xT-­1xT Figure 1: Frame-by-frame DNN Language Identification Figure 1 shows the architecture of the DNN...compare direct DNN system with proposed DNN I-vector system, we trained a single neural network to classify all 20 languages. The architecture of this

  20. Study of neural cells on organic semiconductor ultra thin films

    Energy Technology Data Exchange (ETDEWEB)

    Bystrenova, Eva; Tonazzini, Ilaria; Stoliar, Pablo; Greco, Pierpaolo; Lazar, Adina; Dutta, Soumya; Dionigi, Chiara; Cacace, Marcello; Biscarini, Fabio [ISMN-CNR, Bologna (Italy); Jelitai, Marta; Madarasz, Emilia [IEM- HAS, Budapest (Hungary); Huth, Martin; Nickel, Bert [LMU, Munich (Germany); Martini, Claudia [Dept. PNPB, Univ. of Pisa (Italy)

    2008-07-01

    Many technological advances are currently being developed for nano-fabrication, offering the ability to create and control patterns of soft materials. We report the deposition of cells on organic semiconductor ultra-thin films. This is a first step towards the development of active bio/non bio systems for electrical transduction. Thin films of pentacene, whose thickness was systematically varied, were grown by high vacuum sublimation. We report adhesion, growth, and differentiation of human astroglial cells and mouse neural stem cells on an organic semiconductor. Viability of astroglial cells in time was measured as a function of the roughness and the characteristic morphology of ultra thin organic film, as well as the features of the patterned molecules. Optical fluorescence microscope coupled to atomic force microscope was used to monitor the presence, density and shape of deposited cells. Neural stem cells remain viable, differentiate by retinoic acid and form dense neuronal networks. We have shown the possibility to integrate living neural cells on organic semiconductor thin films.

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

    Science.gov (United States)

    Gallese, Vittorio

    2008-01-01

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

  2. Multi-Lingual Deep Neural Networks for Language Recognition

    Science.gov (United States)

    2016-08-08

    system architecture 2. I-VECTOR SYSTEM Most state-of-the- art language recognition systems are based on the i-vector framework [8] depicted in Figure 1...may be possible to achieve more gains on the Arabic and Chinese cluster by adding ad- ditional ASR corpora such as Callhome Egyptian Arabic or HKUST

  3. Neural correlates of pragmatic language comprehension in autism spectrum disorders

    NARCIS (Netherlands)

    Tesink, C.M.J.Y.; Buitelaar, J.K.; Petersson, K.M.; Gaag, R.J. van der; Kan, C.C.; Tendolkar, I.; Hagoort, P.

    2009-01-01

    Difficulties with pragmatic aspects of communication are universal across individuals with autism spectrum disorders (ASDs). Here we focused on an aspect of pragmatic language comprehension that is relevant to social interaction in daily life: the integration of speaker characteristics inferred from

  4. Neural correlates of pragmatic language comprehension in autism spectrum disorders.

    NARCIS (Netherlands)

    Tesink, C.M.J.Y.; Buitelaar, J.K.; Petersson, K.M.; Gaag, R.J. van der; Kan, C.C.; Tendolkar, I.; Hagoort, P.

    2009-01-01

    Difficulties with pragmatic aspects of communication are universal across individuals with autism spectrum disorders (ASDs). Here we focused on an aspect of pragmatic language comprehension that is relevant to social interaction in daily life: the integration of speaker characteristics inferred from

  5. Neural correlates of audiovisual speech processing in a second language.

    Science.gov (United States)

    Barrós-Loscertales, Alfonso; Ventura-Campos, Noelia; Visser, Maya; Alsius, Agnès; Pallier, Christophe; Avila Rivera, César; Soto-Faraco, Salvador

    2013-09-01

    Neuroimaging studies of audiovisual speech processing have exclusively addressed listeners' native language (L1). Yet, several behavioural studies now show that AV processing plays an important role in non-native (L2) speech perception. The current fMRI study measured brain activity during auditory, visual, audiovisual congruent and audiovisual incongruent utterances in L1 and L2. BOLD responses to congruent AV speech in the pSTS were stronger than in either unimodal condition in both L1 and L2. Yet no differences in AV processing were expressed according to the language background in this area. Instead, the regions in the bilateral occipital lobe had a stronger congruency effect on the BOLD response (congruent higher than incongruent) in L2 as compared to L1. According to these results, language background differences are predominantly expressed in these unimodal regions, whereas the pSTS is similarly involved in AV integration regardless of language dominance. Copyright © 2013 Elsevier Inc. All rights reserved.

  6. Sex Differences in Neural Processing of Language among Children

    Science.gov (United States)

    Burman, Douglas D.; Bitan, Tali; Booth, James R.

    2008-01-01

    Why females generally perform better on language tasks than males is unknown. Sex differences were here identified in children (ages 9-15) across two linguistic tasks for words presented in two modalities. Bilateral activation in the inferior frontal and superior temporal gyri and activation in the left fusiform gyrus of girls was greater than in…

  7. From Imitation to Prediction, Data Compression vs Recurrent Neural Networks for Natural Language Processing

    Directory of Open Access Journals (Sweden)

    Juan Andres Laura

    2018-03-01

    Full Text Available In recent studies Recurrent Neural Networks were used for generative processes and their surprising performance can be explained by their ability to create good predictions. In addition, Data Compression is also based on prediction. What the problem comes down to is whether a data compressor could be used to perform as well as recurrent neural networks in the natural language processing tasks of sentiment analysis and automatic text generation. If this is possible, then the problem comes down to determining if a compression algorithm is even more intelligent than a neural network in such tasks. In our journey, a fundamental difference between a Data Compression Algorithm and Recurrent Neural Networks has been discovered.

  8. Artificial neural network study on organ-targeting peptides

    Science.gov (United States)

    Jung, Eunkyoung; Kim, Junhyoung; Choi, Seung-Hoon; Kim, Minkyoung; Rhee, Hokyoung; Shin, Jae-Min; Choi, Kihang; Kang, Sang-Kee; Lee, Nam Kyung; Choi, Yun-Jaie; Jung, Dong Hyun

    2010-01-01

    We report a new approach to studying organ targeting of peptides on the basis of peptide sequence information. The positive control data sets consist of organ-targeting peptide sequences identified by the peroral phage-display technique for four organs, and the negative control data are prepared from random sequences. The capacity of our models to make appropriate predictions is validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC) curve (the ROC score). VHSE descriptor produces statistically significant training models and the models with simple neural network architectures show slightly greater predictive power than those with complex ones. The training and test set statistics indicate that our models could discriminate between organ-targeting and random sequences. We anticipate that our models will be applicable to the selection of organ-targeting peptides for generating peptide drugs or peptidomimetics.

  9. Language Identification in Short Utterances Using Long Short-Term Memory (LSTM) Recurrent Neural Networks.

    Science.gov (United States)

    Zazo, Ruben; Lozano-Diez, Alicia; Gonzalez-Dominguez, Javier; Toledano, Doroteo T; Gonzalez-Rodriguez, Joaquin

    2016-01-01

    Long Short Term Memory (LSTM) Recurrent Neural Networks (RNNs) have recently outperformed other state-of-the-art approaches, such as i-vector and Deep Neural Networks (DNNs), in automatic Language Identification (LID), particularly when dealing with very short utterances (∼3s). In this contribution we present an open-source, end-to-end, LSTM RNN system running on limited computational resources (a single GPU) that outperforms a reference i-vector system on a subset of the NIST Language Recognition Evaluation (8 target languages, 3s task) by up to a 26%. This result is in line with previously published research using proprietary LSTM implementations and huge computational resources, which made these former results hardly reproducible. Further, we extend those previous experiments modeling unseen languages (out of set, OOS, modeling), which is crucial in real applications. Results show that a LSTM RNN with OOS modeling is able to detect these languages and generalizes robustly to unseen OOS languages. Finally, we also analyze the effect of even more limited test data (from 2.25s to 0.1s) proving that with as little as 0.5s an accuracy of over 50% can be achieved.

  10. The Common Language Question Before International Organizations

    Science.gov (United States)

    Lapenna, Ivo

    1971-01-01

    Third report on a petition submitted to the United Nations by the Universal Esperanto Association (UEA) in 1950 to promote Esperanto as the universal language. The petition was forwarded for action to Unesco which in 1954 resolved to support any efforts in this direction undertaken within a member state. Available from Humanities Press, Inc.,…

  11. SORN: a self-organizing recurrent neural network

    Directory of Open Access Journals (Sweden)

    Andreea Lazar

    2009-10-01

    Full Text Available Understanding the dynamics of recurrent neural networks is crucial for explaining how the brain processes information. In the neocortex, a range of different plasticity mechanisms are shaping recurrent networks into effective information processing circuits that learn appropriate representations for time-varying sensory stimuli. However, it has been difficult to mimic these abilities in artificial neural network models. Here we introduce SORN, a self-organizing recurrent network. It combines three distinct forms of local plasticity to learn spatio-temporal patterns in its input while maintaining its dynamics in a healthy regime suitable for learning. The SORN learns to encode information in the form of trajectories through its high-dimensional state space reminiscent of recent biological findings on cortical coding. All three forms of plasticity are shown to be essential for the network's success.

  12. Linguistic diversity and English language use in multicultural organizations

    DEFF Research Database (Denmark)

    Lauring, Jakob; Selmer, Jan

    2013-01-01

    Two great human resource management challenges face organizations in many parts of the world. The workforce is aging leaving fewer young people to take over. At the same time, globalization leads to a pressure for internationalization with great consequences for internal collaboration in many...... organizations. Accordingly, the link between employee age and language use is of increasing importance. In this study, we report on the findings of a survey using responses from 489 members of Danish multicultural organizations. We studied the effect of linguistic diversity on English language communication...... as well as the moderating effect of respondents’ age.Wefound linguistic diversity to have positive associations with the two English language communication variables. We also found age to moderate the relationship between linguistic diversity and perceived use of English language by management. Since...

  13. Three-dimensional grammar in the brain: Dissociating the neural correlates of natural sign language and manually coded spoken language.

    Science.gov (United States)

    Jednoróg, Katarzyna; Bola, Łukasz; Mostowski, Piotr; Szwed, Marcin; Boguszewski, Paweł M; Marchewka, Artur; Rutkowski, Paweł

    2015-05-01

    In several countries natural sign languages were considered inadequate for education. Instead, new sign-supported systems were created, based on the belief that spoken/written language is grammatically superior. One such system called SJM (system językowo-migowy) preserves the grammatical and lexical structure of spoken Polish and since 1960s has been extensively employed in schools and on TV. Nevertheless, the Deaf community avoids using SJM for everyday communication, its preferred language being PJM (polski język migowy), a natural sign language, structurally and grammatically independent of spoken Polish and featuring classifier constructions (CCs). Here, for the first time, we compare, with fMRI method, the neural bases of natural vs. devised communication systems. Deaf signers were presented with three types of signed sentences (SJM and PJM with/without CCs). Consistent with previous findings, PJM with CCs compared to either SJM or PJM without CCs recruited the parietal lobes. The reverse comparison revealed activation in the anterior temporal lobes, suggesting increased semantic combinatory processes in lexical sign comprehension. Finally, PJM compared with SJM engaged left posterior superior temporal gyrus and anterior temporal lobe, areas crucial for sentence-level speech comprehension. We suggest that activity in these two areas reflects greater processing efficiency for naturally evolved sign language. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. Cognitive processes and neural basis of language switching: proposal of a new model.

    Science.gov (United States)

    Moritz-Gasser, Sylvie; Duffau, Hugues

    2009-12-09

    Although studies on bilingualism are abundant, cognitive processes and neural foundations of language switching received less attention. The aim of our study is to provide new insights to this still open question: do dedicated region(s) for language switching exist or is this function underlain by a distributed circuit of interconnected brain areas, part of a more general cognitive system? On the basis of recent behavioral, neuroimaging, and brain stimulation studies, we propose an original 'hodological' model of language switching. This process might be subserved by a large-scale cortico-subcortical network, with an executive system (prefrontal cortex, anterior cingulum, caudate nucleus) controlling a more dedicated language subcircuit, which involves postero-temporal areas, supramarginal and angular gyri, Broca's area, and the superior longitudinal fasciculus.

  15. Commonalities and differences in the neural representations of English, Portuguese, and Mandarin sentences: When knowledge of the brain-language mappings for two languages is better than one.

    Science.gov (United States)

    Yang, Ying; Wang, Jing; Bailer, Cyntia; Cherkassky, Vladimir; Just, Marcel Adam

    2017-12-01

    This study extended cross-language semantic decoding (based on a concept's fMRI signature) to the decoding of sentences across three different languages (English, Portuguese and Mandarin). A classifier was trained on either the mapping between words and activation patterns in one language or the mappings in two languages (using an equivalent amount of training data), and then tested on its ability to decode the semantic content of a third language. The model trained on two languages was reliably more accurate than a classifier trained on one language for all three pairs of languages. This two-language advantage was selective to abstract concept domains such as social interactions and mental activity. Representational Similarity Analyses (RSA) of the inter-sentence neural similarities resulted in similar clustering of sentences in all the three languages, indicating a shared neural concept space among languages. These findings identify semantic domains that are common across these three languages versus those that are more language or culture-specific. Copyright © 2017 Elsevier Inc. All rights reserved.

  16. fMRI of Simultaneous Interpretation Reveals the Neural Basis of Extreme Language Control.

    Science.gov (United States)

    Hervais-Adelman, Alexis; Moser-Mercer, Barbara; Michel, Christoph M; Golestani, Narly

    2015-12-01

    We used functional magnetic resonance imaging (fMRI) to examine the neural basis of extreme multilingual language control in a group of 50 multilingual participants. Comparing brain responses arising during simultaneous interpretation (SI) with those arising during simultaneous repetition revealed activation of regions known to be involved in speech perception and production, alongside a network incorporating the caudate nucleus that is known to be implicated in domain-general cognitive control. The similarity between the networks underlying bilingual language control and general executive control supports the notion that the frequently reported bilingual advantage on executive tasks stems from the day-to-day demands of language control in the multilingual brain. We examined neural correlates of the management of simultaneity by correlating brain activity during interpretation with the duration of simultaneous speaking and hearing. This analysis showed significant modulation of the putamen by the duration of simultaneity. Our findings suggest that, during SI, the caudate nucleus is implicated in the overarching selection and control of the lexico-semantic system, while the putamen is implicated in ongoing control of language output. These findings provide the first clear dissociation of specific dorsal striatum structures in polyglot language control, roles that are consistent with previously described involvement of these regions in nonlinguistic executive control. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  17. Prediction of pelvic organ prolapse using an artificial neural network.

    Science.gov (United States)

    Robinson, Christopher J; Swift, Steven; Johnson, Donna D; Almeida, Jonas S

    2008-08-01

    The objective of this investigation was to test the ability of a feedforward artificial neural network (ANN) to differentiate patients who have pelvic organ prolapse (POP) from those who retain good pelvic organ support. Following institutional review board approval, patients with POP (n = 87) and controls with good pelvic organ support (n = 368) were identified from the urogynecology research database. Historical and clinical information was extracted from the database. Data analysis included the training of a feedforward ANN, variable selection, and external validation of the model with an independent data set. Twenty variables were used. The median-performing ANN model used a median of 3 (quartile 1:3 to quartile 3:5) variables and achieved an area under the receiver operator curve of 0.90 (external, independent validation set). Ninety percent sensitivity and 83% specificity were obtained in the external validation by ANN classification. Feedforward ANN modeling is applicable to the identification and prediction of POP.

  18. Musical aptitude and second language pronunciation skills in school-aged children: neural and behavioral evidence.

    Science.gov (United States)

    Milovanov, Riia; Huotilainen, Minna; Välimäki, Vesa; Esquef, Paulo A A; Tervaniemi, Mari

    2008-02-15

    The main focus of this study was to examine the relationship between musical aptitude and second language pronunciation skills. We investigated whether children with superior performance in foreign language production represent musical sound features more readily in the preattentive level of neural processing compared with children with less-advanced production skills. Sound processing accuracy was examined in elementary school children by means of event-related potential (ERP) recordings and behavioral measures. Children with good linguistic skills had better musical skills as measured by the Seashore musicality test than children with less accurate linguistic skills. The ERP data accompany the results of the behavioral tests: children with good linguistic skills showed more pronounced sound-change evoked activation with the music stimuli than children with less accurate linguistic skills. Taken together, the results imply that musical and linguistic skills could partly be based on shared neural mechanisms.

  19. International language management and diversity climate in multicultural organizations

    DEFF Research Database (Denmark)

    Lauring, Jakob; Selmer, Jan

    2012-01-01

    Increasing globalization has made the use and management of language a vital element of engaging in international business activities. Despite this fact, empirical surveys with many respondents examining language management are extremely rare. Another equally important issue related to internatio......Increasing globalization has made the use and management of language a vital element of engaging in international business activities. Despite this fact, empirical surveys with many respondents examining language management are extremely rare. Another equally important issue related...... to internationalization is how to develop and support an environment that is tolerant of the diversity which exists in multicultural organizations. Based on questionnaire responses from 489 members of academic multicultural departments, we examined the relation between the management of a common language and a positive...... diversity climate. Results showed that consistency in English management communication had strong positive relationships with all of the four investigated diversity climate variables; openness to linguistic, visible, value, and informational diversity. English communication consistency had a positive...

  20. On Training Bi-directional Neural Network Language Model with Noise Contrastive Estimation

    OpenAIRE

    He, Tianxing; Zhang, Yu; Droppo, Jasha; Yu, Kai

    2016-01-01

    We propose to train bi-directional neural network language model(NNLM) with noise contrastive estimation(NCE). Experiments are conducted on a rescore task on the PTB data set. It is shown that NCE-trained bi-directional NNLM outperformed the one trained by conventional maximum likelihood training. But still(regretfully), it did not out-perform the baseline uni-directional NNLM.

  1. Interactive natural language acquisition in a multi-modal recurrent neural architecture

    Science.gov (United States)

    Heinrich, Stefan; Wermter, Stefan

    2018-01-01

    For the complex human brain that enables us to communicate in natural language, we gathered good understandings of principles underlying language acquisition and processing, knowledge about sociocultural conditions, and insights into activity patterns in the brain. However, we were not yet able to understand the behavioural and mechanistic characteristics for natural language and how mechanisms in the brain allow to acquire and process language. In bridging the insights from behavioural psychology and neuroscience, the goal of this paper is to contribute a computational understanding of appropriate characteristics that favour language acquisition. Accordingly, we provide concepts and refinements in cognitive modelling regarding principles and mechanisms in the brain and propose a neurocognitively plausible model for embodied language acquisition from real-world interaction of a humanoid robot with its environment. In particular, the architecture consists of a continuous time recurrent neural network, where parts have different leakage characteristics and thus operate on multiple timescales for every modality and the association of the higher level nodes of all modalities into cell assemblies. The model is capable of learning language production grounded in both, temporal dynamic somatosensation and vision, and features hierarchical concept abstraction, concept decomposition, multi-modal integration, and self-organisation of latent representations.

  2. Efficient Embedded Decoding of Neural Network Language Models in a Machine Translation System.

    Science.gov (United States)

    Zamora-Martinez, Francisco; Castro-Bleda, Maria Jose

    2018-02-22

    Neural Network Language Models (NNLMs) are a successful approach to Natural Language Processing tasks, such as Machine Translation. We introduce in this work a Statistical Machine Translation (SMT) system which fully integrates NNLMs in the decoding stage, breaking the traditional approach based on [Formula: see text]-best list rescoring. The neural net models (both language models (LMs) and translation models) are fully coupled in the decoding stage, allowing to more strongly influence the translation quality. Computational issues were solved by using a novel idea based on memorization and smoothing of the softmax constants to avoid their computation, which introduces a trade-off between LM quality and computational cost. These ideas were studied in a machine translation task with different combinations of neural networks used both as translation models and as target LMs, comparing phrase-based and [Formula: see text]-gram-based systems, showing that the integrated approach seems more promising for [Formula: see text]-gram-based systems, even with nonfull-quality NNLMs.

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

    Science.gov (United States)

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

    2013-08-21

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

  4. Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm

    Directory of Open Access Journals (Sweden)

    Haizhou Wu

    2016-01-01

    Full Text Available Symbiotic organisms search (SOS is a new robust and powerful metaheuristic algorithm, which stimulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. In the supervised learning area, it is a challenging task to present a satisfactory and efficient training algorithm for feedforward neural networks (FNNs. In this paper, SOS is employed as a new method for training FNNs. To investigate the performance of the aforementioned method, eight different datasets selected from the UCI machine learning repository are employed for experiment and the results are compared among seven metaheuristic algorithms. The results show that SOS performs better than other algorithms for training FNNs in terms of converging speed. It is also proven that an FNN trained by the method of SOS has better accuracy than most algorithms compared.

  5. Neural correlates of language variability in preschool-aged boys with autism spectrum disorder.

    Science.gov (United States)

    Naigles, Letitia R; Johnson, Ryan; Mastergeorge, Ann; Ozonoff, Sally; Rogers, Sally J; Amaral, David G; Nordahl, Christine Wu

    2017-06-01

    Children with autism vary widely in their language abilities, yet the neural correlates of this language variability remain unclear, especially early in development. Diffusion tensor imaging (DTI) was used to examine diffusivity measures along the length of 18 major fiber tracts in 104 preschool-aged boys with autism spectrum disorder (ASD). The boys were assigned to subgroups according to their level of language development (Low: no/low language, Middle: small vocabulary, High: large vocabulary and grammar), based on their raw scores on the expressive language (EL) and receptive language (RL) sections of the Mullen Scales of Early Learning (MSEL). Results indicate that the subgroups differed in fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) along the inferior longitudinal fasciculus (ILF) in both hemispheres. Moreover, FA correlated significantly with Mullen EL and RL raw scores, but not ADOS severity score, along the left and right ILF. Subgroups also differed in MD (but not FA) along the left superior longitudinal fasiculus and left corticospinal tract, but these differences were not correlated with language scores. These findings suggest that white matter microstructure in the left and right ILF varies in relation to lexical development in young males with ASD. The findings also support the use of raw scores on language-relevant standardized tests for assessing early language-brain relationships. Autism Res 2017. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. Autism Res 2017, 10: 1107-1119. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

  6. Static sign language recognition using 1D descriptors and neural networks

    Science.gov (United States)

    Solís, José F.; Toxqui, Carina; Padilla, Alfonso; Santiago, César

    2012-10-01

    A frame work for static sign language recognition using descriptors which represents 2D images in 1D data and artificial neural networks is presented in this work. The 1D descriptors were computed by two methods, first one consists in a correlation rotational operator.1 and second is based on contour analysis of hand shape. One of the main problems in sign language recognition is segmentation; most of papers report a special color in gloves or background for hand shape analysis. In order to avoid the use of gloves or special clothing, a thermal imaging camera was used to capture images. Static signs were picked up from 1 to 9 digits of American Sign Language, a multilayer perceptron reached 100% recognition with cross-validation.

  7. Neural convergence for language comprehension and grammatical class production in highly proficient bilinguals is independent of age of acquisition.

    Science.gov (United States)

    Consonni, Monica; Cafiero, Riccardo; Marin, Dario; Tettamanti, Marco; Iadanza, Antonella; Fabbro, Franco; Perani, Daniela

    2013-05-01

    In bilinguals, native (L1) and second (L2) languages are processed by the same neural resources that can be modulated by age of second language acquisition (AOA), proficiency level, and daily language exposure and usage. AOA seems to particularly affect grammar processing, where a complete neural convergence has been shown only in bilinguals with parallel language acquisition from birth. Despite the fact that proficiency-related neuroanatomical differences have been well documented in language comprehension (LC) and production, few reports have addressed the influence of language exposure. A still unanswered question pertains to the role of AOA, when proficiency is comparably high across languages, with respect to its modulator effects both on LC and production. Here, we evaluated with fMRI during sentence comprehension and verb and noun production tasks, two groups of highly proficient bilinguals only differing in AOA. One group learned Italian and Friulian in parallel from birth, whereas the second group learned Italian between 3 and 6 years. All participants were highly exposed to both languages, but more to Italian than Friulian. The results indicate a complete overlap of neural activations for the comprehension of both languages, not only in bilinguals from birth, but also in late bilinguals. A slightly extra activation in the left thalamus for the less-exposed language confirms that exposure may affect language processing. Noteworthy, we report for the first time that, when proficiency and exposure are kept high, noun and verb production recruit the same neural networks for L1 and L2, independently of AOA. These results support the neural convergence hypothesis. Copyright © 2012 Elsevier Ltd. All rights reserved.

  8. From phonemes to images : levels of representation in a recurrent neural model of visually-grounded language learning

    NARCIS (Netherlands)

    Gelderloos, L.J.; Chrupala, Grzegorz

    2016-01-01

    We present a model of visually-grounded language learning based on stacked gated recurrent neural networks which learns to predict visual features given an image description in the form of a sequence of phonemes. The learning task resembles that faced by human language learners who need to discover

  9. Brief Report: Anomalous Neural Deactivations and Functional Connectivity during Receptive Language in Autism Spectrum Disorder--A Functional MRI Study

    Science.gov (United States)

    Karten, Ariel; Hirsch, Joy

    2015-01-01

    Neural mechanisms that underlie language disability in autism spectrum disorder (ASD) have been associated with reduced excitatory processes observed as positive blood oxygen level dependent (BOLD) responses. However, negative BOLD responses (NBR) associated with language and inhibitory processes have been less studied in ASD. In this study,…

  10. ORGANIC ELECTRODE COATINGS FOR NEXT-GENERATION NEURAL INTERFACES

    Directory of Open Access Journals (Sweden)

    Ulises A Aregueta-Robles

    2014-05-01

    Full Text Available Traditional neuronal interfaces utilize metallic electrodes which in recent years have reached a plateau in terms of the ability to provide safe stimulation at high resolution or rather with high densities of microelectrodes with improved spatial selectivity. To achieve higher resolution it has become clear that reducing the size of electrodes is required to enable higher electrode counts from the implant device. The limitations of interfacing electrodes including low charge injection limits, mechanical mismatch and foreign body response can be addressed through the use of organic electrode coatings which typically provide a softer, more roughened surface to enable both improved charge transfer and lower mechanical mismatch with neural tissue. Coating electrodes with conductive polymers or carbon nanotubes offers a substantial increase in charge transfer area compared to conventional platinum electrodes. These organic conductors provide safe electrical stimulation of tissue while avoiding undesirable chemical reactions and cell damage. However, the mechanical properties of conductive polymers are not ideal, as they are quite brittle. Hydrogel polymers present a versatile coating option for electrodes as they can be chemically modified to provide a soft and conductive scaffold. However, the in vivo chronic inflammatory response of these conductive hydrogels remains unknown. A more recent approach proposes tissue engineering the electrode interface through the use of encapsulated neurons within hydrogel coatings. This approach may provide a method for activating tissue at the cellular scale, however several technological challenges must be addressed to demonstrate feasibility of this innovative idea. The review focuses on the various organic coatings which have been investigated to improve neural interface electrodes.

  11. On the nature and evolution of the neural bases of human language

    Science.gov (United States)

    Lieberman, Philip

    2002-01-01

    The traditional theory equating the brain bases of language with Broca's and Wernicke's neocortical areas is wrong. Neural circuits linking activity in anatomically segregated populations of neurons in subcortical structures and the neocortex throughout the human brain regulate complex behaviors such as walking, talking, and comprehending the meaning of sentences. When we hear or read a word, neural structures involved in the perception or real-world associations of the word are activated as well as posterior cortical regions adjacent to Wernicke's area. Many areas of the neocortex and subcortical structures support the cortical-striatal-cortical circuits that confer complex syntactic ability, speech production, and a large vocabulary. However, many of these structures also form part of the neural circuits regulating other aspects of behavior. For example, the basal ganglia, which regulate motor control, are also crucial elements in the circuits that confer human linguistic ability and abstract reasoning. The cerebellum, traditionally associated with motor control, is active in motor learning. The basal ganglia are also key elements in reward-based learning. Data from studies of Broca's aphasia, Parkinson's disease, hypoxia, focal brain damage, and a genetically transmitted brain anomaly (the putative "language gene," family KE), and from comparative studies of the brains and behavior of other species, demonstrate that the basal ganglia sequence the discrete elements that constitute a complete motor act, syntactic process, or thought process. Imaging studies of intact human subjects and electrophysiologic and tracer studies of the brains and behavior of other species confirm these findings. As Dobzansky put it, "Nothing in biology makes sense except in the light of evolution" (cited in Mayr, 1982). That applies with as much force to the human brain and the neural bases of language as it does to the human foot or jaw. The converse follows: the mark of evolution on

  12. Tracking down abstract linguistic meaning: neural correlates of spatial frame of reference ambiguities in language.

    Directory of Open Access Journals (Sweden)

    Gabriele Janzen

    Full Text Available This functional magnetic resonance imaging (fMRI study investigates a crucial parameter in spatial description, namely variants in the frame of reference chosen. Two frames of reference are available in European languages for the description of small-scale assemblages, namely the intrinsic (or object-oriented frame and the relative (or egocentric frame. We showed participants a sentence such as "the ball is in front of the man", ambiguous between the two frames, and then a picture of a scene with a ball and a man--participants had to respond by indicating whether the picture did or did not match the sentence. There were two blocks, in which we induced each frame of reference by feedback. Thus for the crucial test items, participants saw exactly the same sentence and the same picture but now from one perspective, now the other. Using this method, we were able to precisely pinpoint the pattern of neural activation associated with each linguistic interpretation of the ambiguity, while holding the perceptual stimuli constant. Increased brain activity in bilateral parahippocampal gyrus was associated with the intrinsic frame of reference whereas increased activity in the right superior frontal gyrus and in the parietal lobe was observed for the relative frame of reference. The study is among the few to show a distinctive pattern of neural activation for an abstract yet specific semantic parameter in language. It shows with special clarity the nature of the neural substrate supporting each frame of spatial reference.

  13. Beyond the language given: the neural correlates of inferring speaker meaning.

    Science.gov (United States)

    Bašnáková, Jana; Weber, Kirsten; Petersson, Karl Magnus; van Berkum, Jos; Hagoort, Peter

    2014-10-01

    Even though language allows us to say exactly what we mean, we often use language to say things indirectly, in a way that depends on the specific communicative context. For example, we can use an apparently straightforward sentence like "It is hard to give a good presentation" to convey deeper meanings, like "Your talk was a mess!" One of the big puzzles in language science is how listeners work out what speakers really mean, which is a skill absolutely central to communication. However, most neuroimaging studies of language comprehension have focused on the arguably much simpler, context-independent process of understanding direct utterances. To examine the neural systems involved in getting at contextually constrained indirect meaning, we used functional magnetic resonance imaging as people listened to indirect replies in spoken dialog. Relative to direct control utterances, indirect replies engaged dorsomedial prefrontal cortex, right temporo-parietal junction and insula, as well as bilateral inferior frontal gyrus and right medial temporal gyrus. This suggests that listeners take the speaker's perspective on both cognitive (theory of mind) and affective (empathy-like) levels. In line with classic pragmatic theories, our results also indicate that currently popular "simulationist" accounts of language comprehension fail to explain how listeners understand the speaker's intended message. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  14. Finger language recognition based on ensemble artificial neural network learning using armband EMG sensors.

    Science.gov (United States)

    Kim, Seongjung; Kim, Jongman; Ahn, Soonjae; Kim, Youngho

    2018-04-18

    Deaf people use sign or finger languages for communication, but these methods of communication are very specialized. For this reason, the deaf can suffer from social inequalities and financial losses due to their communication restrictions. In this study, we developed a finger language recognition algorithm based on an ensemble artificial neural network (E-ANN) using an armband system with 8-channel electromyography (EMG) sensors. The developed algorithm was composed of signal acquisition, filtering, segmentation, feature extraction and an E-ANN based classifier that was evaluated with the Korean finger language (14 consonants, 17 vowels and 7 numbers) in 17 subjects. E-ANN was categorized according to the number of classifiers (1 to 10) and size of training data (50 to 1500). The accuracy of the E-ANN-based classifier was obtained by 5-fold cross validation and compared with an artificial neural network (ANN)-based classifier. As the number of classifiers (1 to 8) and size of training data (50 to 300) increased, the average accuracy of the E-ANN-based classifier increased and the standard deviation decreased. The optimal E-ANN was composed with eight classifiers and 300 size of training data, and the accuracy of the E-ANN was significantly higher than that of the general ANN.

  15. ALPHABET SIGN LANGUAGE RECOGNITION USING LEAP MOTION TECHNOLOGY AND RULE BASED BACKPROPAGATION-GENETIC ALGORITHM NEURAL NETWORK (RBBPGANN

    Directory of Open Access Journals (Sweden)

    Wijayanti Nurul Khotimah

    2017-01-01

    Full Text Available Sign Language recognition was used to help people with normal hearing communicate effectively with the deaf and hearing-impaired. Based on survey that conducted by Multi-Center Study in Southeast Asia, Indonesia was on the top four position in number of patients with hearing disability (4.6%. Therefore, the existence of Sign Language recognition is important. Some research has been conducted on this field. Many neural network types had been used for recognizing many kinds of sign languages. However, their performance are need to be improved. This work focuses on the ASL (Alphabet Sign Language in SIBI (Sign System of Indonesian Language which uses one hand and 26 gestures. Here, thirty four features were extracted by using Leap Motion. Further, a new method, Rule Based-Backpropagation Genetic Al-gorithm Neural Network (RB-BPGANN, was used to recognize these Sign Languages. This method is combination of Rule and Back Propagation Neural Network (BPGANN. Based on experiment this pro-posed application can recognize Sign Language up to 93.8% accuracy. It was very good to recognize large multiclass instance and can be solution of overfitting problem in Neural Network algorithm.

  16. Innovative and Organized Approaches to Foreign Language Teaching

    Directory of Open Access Journals (Sweden)

    Max Florian Hertsch

    2013-03-01

    Full Text Available Innovation and organization in language education are more than just a teacher and students gathered in the same classroom at the same time, using the same materials and current motivation. The importance of innovations is highlighted by the European Label for innovative projects in language teaching and learning. For Turkey and its European Union membership ambitions, education is a prior section whose standard can be raised by innovations in foreign language education. Heyworth created a formula for innovations [C=(abc>x] which declares changes and its costs. The formula expresses that change for innovation equals several factors which must be more effective than the costs. In this article, Heyworth’s formula is transferred towards the language education system in Turkey. It will theoretically show advantages and changes and a way how Turkish organizations could change to provide more sustainable language education. Furthermore, the article will explain the already existing approaches and show their advantages and disadvantages. As a conclusion, a theoretical approach for innovations will be given and discussed.

  17. Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human--Robot Interaction

    Directory of Open Access Journals (Sweden)

    Tatsuro Yamada

    2016-07-01

    Full Text Available To work cooperatively with humans by using language, robots must not only acquire a mapping between language and their behavior but also autonomously utilize the mapping in appropriate contexts of interactive tasks online. To this end, we propose a novel learning method linking language to robot behavior by means of a recurrent neural network. In this method, the network learns from correct examples of the imposed task that are given not as explicitly separated sets of language and behavior but as sequential data constructed from the actual temporal flow of the task. By doing this, the internal dynamics of the network models both language--behavior relationships and the temporal patterns of interaction. Here, ``internal dynamics'' refers to the time development of the system defined on the fixed-dimensional space of the internal states of the context layer. Thus, in the execution phase, by constantly representing where in the interaction context it is as its current state, the network autonomously switches between recognition and generation phases without any explicit signs and utilizes the acquired mapping in appropriate contexts. To evaluate our method, we conducted an experiment in which a robot generates appropriate behavior responding to a human's linguistic instruction. After learning, the network actually formed the attractor structure representing both language--behavior relationships and the task's temporal pattern in its internal dynamics. In the dynamics, language--behavior mapping was achieved by the branching structure. Repetition of human's instruction and robot's behavioral response was represented as the cyclic structure, and besides, waiting to a subsequent instruction was represented as the fixed-point attractor. Thanks to this structure, the robot was able to interact online with a human concerning the given task by autonomously switching phases.

  18. Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human-Robot Interaction.

    Science.gov (United States)

    Yamada, Tatsuro; Murata, Shingo; Arie, Hiroaki; Ogata, Tetsuya

    2016-01-01

    To work cooperatively with humans by using language, robots must not only acquire a mapping between language and their behavior but also autonomously utilize the mapping in appropriate contexts of interactive tasks online. To this end, we propose a novel learning method linking language to robot behavior by means of a recurrent neural network. In this method, the network learns from correct examples of the imposed task that are given not as explicitly separated sets of language and behavior but as sequential data constructed from the actual temporal flow of the task. By doing this, the internal dynamics of the network models both language-behavior relationships and the temporal patterns of interaction. Here, "internal dynamics" refers to the time development of the system defined on the fixed-dimensional space of the internal states of the context layer. Thus, in the execution phase, by constantly representing where in the interaction context it is as its current state, the network autonomously switches between recognition and generation phases without any explicit signs and utilizes the acquired mapping in appropriate contexts. To evaluate our method, we conducted an experiment in which a robot generates appropriate behavior responding to a human's linguistic instruction. After learning, the network actually formed the attractor structure representing both language-behavior relationships and the task's temporal pattern in its internal dynamics. In the dynamics, language-behavior mapping was achieved by the branching structure. Repetition of human's instruction and robot's behavioral response was represented as the cyclic structure, and besides, waiting to a subsequent instruction was represented as the fixed-point attractor. Thanks to this structure, the robot was able to interact online with a human concerning the given task by autonomously switching phases.

  19. Mexican sign language recognition using normalized moments and artificial neural networks

    Science.gov (United States)

    Solís-V., J.-Francisco; Toxqui-Quitl, Carina; Martínez-Martínez, David; H.-G., Margarita

    2014-09-01

    This work presents a framework designed for the Mexican Sign Language (MSL) recognition. A data set was recorded with 24 static signs from the MSL using 5 different versions, this MSL dataset was captured using a digital camera in incoherent light conditions. Digital Image Processing was used to segment hand gestures, a uniform background was selected to avoid using gloved hands or some special markers. Feature extraction was performed by calculating normalized geometric moments of gray scaled signs, then an Artificial Neural Network performs the recognition using a 10-fold cross validation tested in weka, the best result achieved 95.83% of recognition rate.

  20. Neural networks underlying language and social cognition during self-other processing in Autism spectrum disorders.

    Science.gov (United States)

    Kana, Rajesh K; Sartin, Emma B; Stevens, Carl; Deshpande, Hrishikesh D; Klein, Christopher; Klinger, Mark R; Klinger, Laura Grofer

    2017-07-28

    The social communication impairments defining autism spectrum disorders (ASD) may be built upon core deficits in perspective-taking, language processing, and self-other representation. Self-referential processing entails the ability to incorporate self-awareness, self-judgment, and self-memory in information processing. Very few studies have examined the neural bases of integrating self-other representation and semantic processing in individuals with ASD. The main objective of this functional MRI study is to examine the role of language and social brain networks in self-other processing in young adults with ASD. Nineteen high-functioning male adults with ASD and 19 age-sex-and-IQ-matched typically developing (TD) control participants made "yes" or "no" judgments of whether an adjective, presented visually, described them (self) or their favorite teacher (other). Both ASD and TD participants showed significantly increased activity in the medial prefrontal cortex (MPFC) during self and other processing relative to letter search. Analyses of group differences revealed significantly reduced activity in left inferior frontal gyrus (LIFG), and left inferior parietal lobule (LIPL) in ASD participants, relative to TD controls. ASD participants also showed significantly weaker functional connectivity of the anterior cingulate cortex (ACC) with several brain areas while processing self-related words. The LIFG and IPL are important regions functionally at the intersection of language and social roles; reduced recruitment of these regions in ASD participants may suggest poor level of semantic and social processing. In addition, poor connectivity of the ACC may suggest the difficulty in meeting the linguistic and social demands of this task in ASD. Overall, this study provides new evidence of the altered recruitment of the neural networks underlying language and social cognition in ASD. Published by Elsevier Ltd.

  1. Fully convolutional neural networks improve abdominal organ segmentation

    Science.gov (United States)

    Bobo, Meg F.; Bao, Shunxing; Huo, Yuankai; Yao, Yuang; Virostko, Jack; Plassard, Andrew J.; Lyu, Ilwoo; Assad, Albert; Abramson, Richard G.; Hilmes, Melissa A.; Landman, Bennett A.

    2018-03-01

    Abdominal image segmentation is a challenging, yet important clinical problem. Variations in body size, position, and relative organ positions greatly complicate the segmentation process. Historically, multi-atlas methods have achieved leading results across imaging modalities and anatomical targets. However, deep learning is rapidly overtaking classical approaches for image segmentation. Recently, Zhou et al. showed that fully convolutional networks produce excellent results in abdominal organ segmentation of computed tomography (CT) scans. Yet, deep learning approaches have not been applied to whole abdomen magnetic resonance imaging (MRI) segmentation. Herein, we evaluate the applicability of an existing fully convolutional neural network (FCNN) designed for CT imaging to segment abdominal organs on T2 weighted (T2w) MRI's with two examples. In the primary example, we compare a classical multi-atlas approach with FCNN on forty-five T2w MRI's acquired from splenomegaly patients with five organs labeled (liver, spleen, left kidney, right kidney, and stomach). Thirty-six images were used for training while nine were used for testing. The FCNN resulted in a Dice similarity coefficient (DSC) of 0.930 in spleens, 0.730 in left kidneys, 0.780 in right kidneys, 0.913 in livers, and 0.556 in stomachs. The performance measures for livers, spleens, right kidneys, and stomachs were significantly better than multi-atlas (p < 0.05, Wilcoxon rank-sum test). In a secondary example, we compare the multi-atlas approach with FCNN on 138 distinct T2w MRI's with manually labeled pancreases (one label). On the pancreas dataset, the FCNN resulted in a median DSC of 0.691 in pancreases versus 0.287 for multi-atlas. The results are highly promising given relatively limited training data and without specific training of the FCNN model and illustrate the potential of deep learning approaches to transcend imaging modalities. 1

  2. A neural model of figure-ground organization.

    Science.gov (United States)

    Craft, Edward; Schütze, Hartmut; Niebur, Ernst; von der Heydt, Rüdiger

    2007-06-01

    Psychophysical studies suggest that figure-ground organization is a largely autonomous process that guides--and thus precedes--allocation of attention and object recognition. The discovery of border-ownership representation in single neurons of early visual cortex has confirmed this view. Recent theoretical studies have demonstrated that border-ownership assignment can be modeled as a process of self-organization by lateral interactions within V2 cortex. However, the mechanism proposed relies on propagation of signals through horizontal fibers, which would result in increasing delays of the border-ownership signal with increasing size of the visual stimulus, in contradiction with experimental findings. It also remains unclear how the resulting border-ownership representation would interact with attention mechanisms to guide further processing. Here we present a model of border-ownership coding based on dedicated neural circuits for contour grouping that produce border-ownership assignment and also provide handles for mechanisms of selective attention. The results are consistent with neurophysiological and psychophysical findings. The model makes predictions about the hypothetical grouping circuits and the role of feedback between cortical areas.

  3. Neural correlates of foreign-language learning in childhood: a 3-year longitudinal ERP study.

    Science.gov (United States)

    Ojima, Shiro; Nakamura, Naoko; Matsuba-Kurita, Hiroko; Hoshino, Takahiro; Hagiwara, Hiroko

    2011-01-01

    A foreign language (a language not spoken in one's community) is difficult to master completely. Early introduction of foreign-language (FL) education during childhood is becoming a standard in many countries. However, the neural process of child FL learning still remains largely unknown. We longitudinally followed 322 school-age children with diverse FL proficiency for three consecutive years, and acquired children's ERP responses to FL words that were semantically congruous or incongruous with the preceding picture context. As FL proficiency increased, various ERP components previously reported in mother-tongue (L1) acquisition (such as a broad negativity, an N400, and a late positive component) appeared sequentially, critically in an identical order to L1 acquisition. This finding was supported not only by cross-sectional analyses of children at different proficiency levels but also by longitudinal analyses of the same children over time. Our data are consistent with the hypothesis that FL learning in childhood reproduces identical developmental stages in an identical order to L1 acquisition, suggesting that the nature of the child's brain itself may determine the normal course of FL learning. Future research should test the generalizability of the results in other aspects of language such as syntax.

  4. Unique Neural Characteristics of Atypical Lateralization of Language in Healthy Individuals

    Directory of Open Access Journals (Sweden)

    Szymon P. Biduła

    2017-09-01

    Full Text Available Using functional magnetic resonance imaging (fMRI in 63 healthy participants, including left-handed and ambidextrous individuals, we tested how atypical lateralization of language—i. e., bilateral or right hemispheric language representation—differs from the typical left-hemisphere dominance. Although regardless of their handedness, all 11 participants from the atypical group engaged classical language centers, i.e., Broca's and Wernicke's areas, the right-hemisphere components of the default mode network (DMN, including the angular gyrus and middle temporal gyrus, were also critically involved during the verbal fluency task. Importantly, activity in these regions could not be explained in terms of mirroring the typical language pattern because left-hemisphere dominant individuals did not exhibit similar significant signal modulations. Moreover, when spatial extent of language-related activity across whole brain was considered, the bilateral language organization entailed more diffuse functional processing. Finally, we detected significant differences between the typical and atypical group in the resting-state connectivity at the global and local level. These findings suggest that the atypical lateralization of language has unique features, and is not a simple mirror image of the typical left hemispheric language representation.

  5. Internal mechanisms underlying anticipatory language processing: Evidence from event-related-potentials and neural oscillations.

    Science.gov (United States)

    Li, Xiaoqing; Zhang, Yuping; Xia, Jinyan; Swaab, Tamara Y

    2017-07-28

    Although numerous studies have demonstrated that the language processing system can predict upcoming content during comprehension, there is still no clear picture of the anticipatory stage of predictive processing. This electroencephalograph study examined the cognitive and neural oscillatory mechanisms underlying anticipatory processing during language comprehension, and the consequences of this prediction for bottom-up processing of predicted/unpredicted content. Participants read Mandarin Chinese sentences that were either strongly or weakly constraining and that contained critical nouns that were congruent or incongruent with the sentence contexts. We examined the effects of semantic predictability on anticipatory processing prior to the onset of the critical nouns and on integration of the critical nouns. The results revealed that, at the integration stage, the strong-constraint condition (compared to the weak-constraint condition) elicited a reduced N400 and reduced theta activity (4-7Hz) for the congruent nouns, but induced beta (13-18Hz) and theta (4-7Hz) power decreases for the incongruent nouns, indicating benefits of confirmed predictions and potential costs of disconfirmed predictions. More importantly, at the anticipatory stage, the strongly constraining context elicited an enhanced sustained anterior negativity and beta power decrease (19-25Hz), which indicates that strong prediction places a higher processing load on the anticipatory stage of processing. The differences (in the ease of processing and the underlying neural oscillatory activities) between anticipatory and integration stages of lexical processing were discussed with regard to predictive processing models. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Pattern classification and recognition of invertebrate functional groups using self-organizing neural networks.

    Science.gov (United States)

    Zhang, WenJun

    2007-07-01

    Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance

  7. Identification-based chaos control via backstepping design using self-organizing fuzzy neural networks

    International Nuclear Information System (INIS)

    Peng Yafu; Hsu, C.-F.

    2009-01-01

    This paper proposes an identification-based adaptive backstepping control (IABC) for the chaotic systems. The IABC system is comprised of a neural backstepping controller and a robust compensation controller. The neural backstepping controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller, and the robust compensation controller is designed to dispel the effect of minimum approximation error introduced by the SOFNN identifier. The SOFNN identifier is used to online estimate the chaotic dynamic function with structure and parameter learning phases of fuzzy neural network. The structure learning phase consists of the growing and pruning of fuzzy rules; thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the neural structure of fuzzy neural network. The parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. Finally, simulation results verify that the proposed IABC can achieve favorable tracking performance.

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

    OpenAIRE

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

    2017-01-01

    Massive Multiple Online Role-Playing Games (MMORPGs) have increased in popularity among children, juveniles, and adults since MMORPGs’ appearance in this digital age. MMORPGs can be applied to enhancing language learning, which is drawing researchers’ attention from different fields and many studies have validated MMORPGs’ positive effect on language learning. However, there are few studies on the underlying behavioral or neural mechanism of such effect. This paper reviews the educational app...

  9. Neural networks involved in learning lexical-semantic and syntactic information in a second language.

    Science.gov (United States)

    Mueller, Jutta L; Rueschemeyer, Shirley-Ann; Ono, Kentaro; Sugiura, Motoaki; Sadato, Norihiro; Nakamura, Akinori

    2014-01-01

    The present study used functional magnetic resonance imaging (fMRI) to investigate the neural correlates of language acquisition in a realistic learning environment. Japanese native speakers were trained in a miniature version of German prior to fMRI scanning. During scanning they listened to (1) familiar sentences, (2) sentences including a novel sentence structure, and (3) sentences containing a novel word while visual context provided referential information. Learning-related decreases of brain activation over time were found in a mainly left-hemispheric network comprising classical frontal and temporal language areas as well as parietal and subcortical regions and were largely overlapping for novel words and the novel sentence structure in initial stages of learning. Differences occurred at later stages of learning during which content-specific activation patterns in prefrontal, parietal and temporal cortices emerged. The results are taken as evidence for a domain-general network supporting the initial stages of language learning which dynamically adapts as learners become proficient.

  10. Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks.

    Science.gov (United States)

    Zhang, Xingyu; Kim, Joyce; Patzer, Rachel E; Pitts, Stephen R; Patzer, Aaron; Schrager, Justin D

    2017-10-26

    To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements. Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a cross-sectional probability sample of United States EDs from 2012 and 2013 survey years, we developed several predictive models with the outcome being admission to the hospital or transfer vs. discharge home. We included patient characteristics immediately available after the patient has presented to the ED and undergone a triage process. We used this information to construct logistic regression (LR) and multilayer neural network models (MLNN) which included natural language processing (NLP) and principal component analysis from the patient's reason for visit. Ten-fold cross validation was used to test the predictive capacity of each model and receiver operating curves (AUC) were then calculated for each model. Of the 47,200 ED visits from 642 hospitals, 6,335 (13.42%) resulted in hospital admission (or transfer). A total of 48 principal components were extracted by NLP from the reason for visit fields, which explained 75% of the overall variance for hospitalization. In the model including only structured variables, the AUC was 0.824 (95% CI 0.818-0.830) for logistic regression and 0.823 (95% CI 0.817-0.829) for MLNN. Models including only free-text information generated AUC of 0.742 (95% CI 0.731- 0.753) for logistic regression and 0.753 (95% CI 0.742-0.764) for MLNN. When both structured variables and free text variables were included, the AUC reached 0.846 (95% CI 0.839-0.853) for logistic regression and 0.844 (95% CI 0.836-0.852) for MLNN. The predictive accuracy of hospital admission or transfer for patients who presented to ED triage overall was good, and was improved with the inclusion of free text data from a patient

  11. Organic and Non-Organic Language Disorders after Awake Brain Surgery

    Directory of Open Access Journals (Sweden)

    Elke De Witte

    2014-04-01

    Full Text Available INTRODUCTION: Awake surgery with Direct Electrical Stimulation (DES is considered the ‘gold standard’ to resect brain tumours in the language dominant hemisphere (De Witte & Mariën, 2013. Although transient language impairments are common in the immediate postoperative phase, permanent postoperative language deficits seem to be rare (Duffau, 2007. Milian et al. (2014 stated that most patients tolerate the awake procedure well and would undergo a similar procedure again. However, postoperative psychological symptoms including recurrent distressing dreams and persistent avoidance of stimuli have been recorded following awake surgery (Goebel, Nabavi, Schubert, & Mehdorn, 2010; Milian et al., 2014. To the best of our knowledge, psychogenic language disturbances have never been described after awake surgery. In general, only a handful of non-organic, psychogenic language disorders have been reported in the literature (De Letter et al., 2012. We report three patients with left brain tumours (see table 1 who presented linguistic symptoms after awake surgery that were incompatible with the lesion location, suggesting a psychogenic origin. METHODS: Neurocognitive (language, memory, executive functions investigations were carried out before, during and after awake surgery (6 weeks, 6 months postsurgery on the basis of standardised tests. Pre- and postoperative (fMRI images, DTI results and intraoperative DES findings were analysed. A selection of tasks was used to map language intraoperatively (De Witte et al., 2013. In the postoperative phase spontaneous speech and behavioural phenomena to errors were video-recorded. RESULTS: Preoperative language tests did not reveal any speech or language problems. Intraoperatively, eloquent sites were mapped and preserved enabling good language skills at the end of the awake procedure. However, assessments in the first weeks postsurgery disclosed language and behavioural symptoms that support the hypothesis of a

  12. δ-Protocadherins: Organizers of neural circuit assembly.

    Science.gov (United States)

    Light, Sarah E W; Jontes, James D

    2017-09-01

    The δ-protocadherins comprise a small family of homophilic cell adhesion molecules within the larger cadherin superfamily. They are essential for neural development as mutations in these molecules give rise to human neurodevelopmental disorders, such as schizophrenia and epilepsy, and result in behavioral defects in animal models. Despite their importance to neural development, a detailed understanding of their mechanisms and the ways in which their loss leads to changes in neural function is lacking. However, recent results have begun to reveal roles for the δ-protocadherins in both regulation of neurogenesis and lineage-dependent circuit assembly, as well as in contact-dependent motility and selective axon fasciculation. These evolutionarily conserved mechanisms could have a profound impact on the robust assembly of the vertebrate nervous system. Future work should be focused on unraveling the molecular mechanisms of the δ-protocadherins and understanding how this family functions broadly to regulate neural development. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    Pulvermüller, Friedemann

    2018-01-01

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

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

    National Research Council Canada - National Science Library

    Grossberg, Stephen

    1999-01-01

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

  15. Effects of Some Neurobiological Factors in a Self-organized Critical Model Based on Neural Networks

    International Nuclear Information System (INIS)

    Zhou Liming; Zhang Yingyue; Chen Tianlun

    2005-01-01

    Based on an integrate-and-fire mechanism, we investigate the effect of changing the efficacy of the synapse, the transmitting time-delayed, and the relative refractoryperiod on the self-organized criticality in our neural network model.

  16. Neural oscillatory mechanisms during novel grammar learning underlying language analytical abilities.

    Science.gov (United States)

    Kepinska, Olga; Pereda, Ernesto; Caspers, Johanneke; Schiller, Niels O

    2017-12-01

    The goal of the present study was to investigate the initial phases of novel grammar learning on a neural level, concentrating on mechanisms responsible for individual variability between learners. Two groups of participants, one with high and one with average language analytical abilities, performed an Artificial Grammar Learning (AGL) task consisting of learning and test phases. During the task, EEG signals from 32 cap-mounted electrodes were recorded and epochs corresponding to the learning phases were analysed. We investigated spectral power modulations over time, and functional connectivity patterns by means of a bivariate, frequency-specific index of phase synchronization termed Phase Locking Value (PLV). Behavioural data showed learning effects in both groups, with a steeper learning curve and higher ultimate attainment for the highly skilled learners. Moreover, we established that cortical connectivity patterns and profiles of spectral power modulations over time differentiated L2 learners with various levels of language analytical abilities. Over the course of the task, the learning process seemed to be driven by whole-brain functional connectivity between neuronal assemblies achieved by means of communication in the beta band frequency. On a shorter time-scale, increasing proficiency on the AGL task appeared to be supported by stronger local synchronisation within the right hemisphere regions. Finally, we observed that the highly skilled learners might have exerted less mental effort, or reduced attention for the task at hand once the learning was achieved, as evidenced by the higher alpha band power. Copyright © 2017 Elsevier Inc. All rights reserved.

  17. The impact of iconic gestures on foreign language word learning and its neural substrate.

    Science.gov (United States)

    Macedonia, Manuela; Müller, Karsten; Friederici, Angela D

    2011-06-01

    Vocabulary acquisition represents a major challenge in foreign language learning. Research has demonstrated that gestures accompanying speech have an impact on memory for verbal information in the speakers' mother tongue and, as recently shown, also in foreign language learning. However, the neural basis of this effect remains unclear. In a within-subjects design, we compared learning of novel words coupled with iconic and meaningless gestures. Iconic gestures helped learners to significantly better retain the verbal material over time. After the training, participants' brain activity was registered by means of fMRI while performing a word recognition task. Brain activations to words learned with iconic and with meaningless gestures were contrasted. We found activity in the premotor cortices for words encoded with iconic gestures. In contrast, words encoded with meaningless gestures elicited a network associated with cognitive control. These findings suggest that memory performance for newly learned words is not driven by the motor component as such, but by the motor image that matches an underlying representation of the word's semantics. Copyright © 2010 Wiley-Liss, Inc.

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

    Science.gov (United States)

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

    2017-01-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

  20. Quantitative analysis of volatile organic compounds using ion mobility spectra and cascade correlation neural networks

    Science.gov (United States)

    Harrington, Peter DEB.; Zheng, Peng

    1995-01-01

    Ion Mobility Spectrometry (IMS) is a powerful technique for trace organic analysis in the gas phase. Quantitative measurements are difficult, because IMS has a limited linear range. Factors that may affect the instrument response are pressure, temperature, and humidity. Nonlinear calibration methods, such as neural networks, may be ideally suited for IMS. Neural networks have the capability of modeling complex systems. Many neural networks suffer from long training times and overfitting. Cascade correlation neural networks train at very fast rates. They also build their own topology, that is a number of layers and number of units in each layer. By controlling the decay parameter in training neural networks, reproducible and general models may be obtained.

  1. Germ layers, the neural crest and emergent organization in development and evolution.

    Science.gov (United States)

    Hall, Brian K

    2018-04-10

    Discovered in chick embryos by Wilhelm His in 1868 and named the neural crest by Arthur Milnes Marshall in 1879, the neural crest cells that arise from the neural folds have since been shown to differentiate into almost two dozen vertebrate cell types and to have played major roles in the evolution of such vertebrate features as bone, jaws, teeth, visceral (pharyngeal) arches, and sense organs. I discuss the discovery that ectodermal neural crest gave rise to mesenchyme and the controversy generated by that finding; the germ layer theory maintained that only mesoderm could give rise to mesenchyme. A second topic of discussion is germ layers (including the neural crest) as emergent levels of organization in animal development and evolution that facilitated major developmental and evolutionary change. The third topic is gene networks, gene co-option, and the evolution of gene-signaling pathways as key to developmental and evolutionary transitions associated with the origin and evolution of the neural crest and neural crest cells. © 2018 Wiley Periodicals, Inc.

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

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2014-06-01

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

  3. Neural representations of emotion are organized around abstract event features.

    Science.gov (United States)

    Skerry, Amy E; Saxe, Rebecca

    2015-08-03

    Research on emotion attribution has tended to focus on the perception of overt expressions of at most five or six basic emotions. However, our ability to identify others' emotional states is not limited to perception of these canonical expressions. Instead, we make fine-grained inferences about what others feel based on the situations they encounter, relying on knowledge of the eliciting conditions for different emotions. In the present research, we provide convergent behavioral and neural evidence concerning the representations underlying these concepts. First, we find that patterns of activity in mentalizing regions contain information about subtle emotional distinctions conveyed through verbal descriptions of eliciting situations. Second, we identify a space of abstract situation features that well captures the emotion discriminations subjects make behaviorally and show that this feature space outperforms competing models in capturing the similarity space of neural patterns in these regions. Together, the data suggest that our knowledge of others' emotions is abstract and high dimensional, that brain regions selective for mental state reasoning support relatively subtle distinctions between emotion concepts, and that the neural representations in these regions are not reducible to more primitive affective dimensions such as valence and arousal. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. Distribution of language-related Cntnap2 protein in neural circuits critical for vocal learning.

    Science.gov (United States)

    Condro, Michael C; White, Stephanie A

    2014-01-01

    Variants of the contactin associated protein-like 2 (Cntnap2) gene are risk factors for language-related disorders including autism spectrum disorder, specific language impairment, and stuttering. Songbirds are useful models for study of human speech disorders due to their shared capacity for vocal learning, which relies on similar cortico-basal ganglia circuitry and genetic factors. Here we investigate Cntnap2 protein expression in the brain of the zebra finch, a songbird species in which males, but not females, learn their courtship songs. We hypothesize that Cntnap2 has overlapping functions in vocal learning species, and expect to find protein expression in song-related areas of the zebra finch brain. We further expect that the distribution of this membrane-bound protein may not completely mirror its mRNA distribution due to the distinct subcellular localization of the two molecular species. We find that Cntnap2 protein is enriched in several song control regions relative to surrounding tissues, particularly within the adult male, but not female, robust nucleus of the arcopallium (RA), a cortical song control region analogous to human layer 5 primary motor cortex. The onset of this sexually dimorphic expression coincides with the onset of sensorimotor learning in developing males. Enrichment in male RA appears due to expression in projection neurons within the nucleus, as well as to additional expression in nerve terminals of cortical projections to RA from the lateral magnocellular nucleus of the nidopallium. Cntnap2 protein expression in zebra finch brain supports the hypothesis that this molecule affects neural connectivity critical for vocal learning across taxonomic classes. Copyright © 2013 Wiley Periodicals, Inc.

  5. An Initial Investigation of the Neural Correlates of Word Processing in Preschoolers With Specific Language Impairment.

    Science.gov (United States)

    Haebig, Eileen; Leonard, Laurence; Usler, Evan; Deevy, Patricia; Weber, Christine

    2018-03-15

    Previous behavioral studies have found deficits in lexical-semantic abilities in children with specific language impairment (SLI), including reduced depth and breadth of word knowledge. This study explored the neural correlates of early emerging familiar word processing in preschoolers with SLI and typical development. Fifteen preschoolers with typical development and 15 preschoolers with SLI were presented with pictures followed after a brief delay by an auditory label that did or did not match. Event-related brain potentials were time locked to the onset of the auditory labels. Children provided verbal judgments of whether the label matched the picture. There were no group differences in the accuracy of identifying when pictures and labels matched or mismatched. Event-related brain potential data revealed that mismatch trials elicited a robust N400 in both groups, with no group differences in mean amplitude or peak latency. However, the typically developing group demonstrated a more robust late positive component, elicited by mismatch trials. These initial findings indicate that lexical-semantic access of early acquired words, indexed by the N400, does not differ between preschoolers with SLI and typical development when highly familiar words are presented in isolation. However, the typically developing group demonstrated a more mature profile of postlexical reanalysis and integration, indexed by an emerging late positive component. The findings lay the necessary groundwork for better understanding processing of newly learned words in children with SLI.

  6. The neural dynamics of competition resolution for language production in the prefrontal cortex.

    Science.gov (United States)

    Bourguignon, Nicolas J; Ohashi, Hiroki; Nguyen, Don; Gracco, Vincent L

    2018-03-01

    Previous research suggests a pivotal role of the prefrontal cortex (PFC) in word selection during tasks of confrontation naming (CN) and verb generation (VG), both of which feature varying degrees of competition between candidate responses. However, discrepancies in prefrontal activity have also been reported between the two tasks, in particular more widespread and intense activation in VG extending into (left) ventrolateral PFC, the functional significance of which remains unclear. We propose that these variations reflect differences in competition resolution processes tied to distinct underlying lexico-semantic operations: Although CN involves selecting lexical entries out of limited sets of alternatives, VG requires exploration of possible semantic relations not readily evident from the object itself, requiring prefrontal areas previously shown to be recruited in top-down retrieval of information from lexico-semantic memory. We tested this hypothesis through combined independent component analysis of functional imaging data and information-theoretic measurements of variations in selection competition associated with participants' performance in overt CN and VG tasks. Selection competition during CN engaged the anterior insula and surrounding opercular tissue, while competition during VG recruited additional activity of left ventrolateral PFC. These patterns remained after controlling for participants' speech onset latencies indicative of possible task differences in mental effort. These findings have implications for understanding the neural-computational dynamics of cognitive control in language production and how it relates to the functional architecture of adaptive behavior. © 2017 Wiley Periodicals, Inc.

  7. American Sign Language Alphabet Recognition Using a Neuromorphic Sensor and an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Miguel Rivera-Acosta

    2017-09-01

    Full Text Available This paper reports the design and analysis of an American Sign Language (ASL alphabet translation system implemented in hardware using a Field-Programmable Gate Array. The system process consists of three stages, the first being the communication with the neuromorphic camera (also called Dynamic Vision Sensor, DVS sensor using the Universal Serial Bus protocol. The feature extraction of the events generated by the DVS is the second part of the process, consisting of a presentation of the digital image processing algorithms developed in software, which aim to reduce redundant information and prepare the data for the third stage. The last stage of the system process is the classification of the ASL alphabet, achieved with a single artificial neural network implemented in digital hardware for higher speed. The overall result is the development of a classification system using the ASL signs contour, fully implemented in a reconfigurable device. The experimental results consist of a comparative analysis of the recognition rate among the alphabet signs using the neuromorphic camera in order to prove the proper operation of the digital image processing algorithms. In the experiments performed with 720 samples of 24 signs, a recognition accuracy of 79.58% was obtained.

  8. Cochlear Implantation (CI for prelingual deafness: the relevance of studies of brain organization and the role of first language acquisition in considering outcome success.

    Directory of Open Access Journals (Sweden)

    Ruth eCampbell

    2014-10-01

    Full Text Available Cochlear implantation (CI for profound congenital hearing impairment, while often successful in restoring hearing to the deaf child, does not always result in effective speech processing. Exposure to non-auditory signals during the pre-implantation period is widely held to be responsible for such failures. Here, we question the inference that such exposure irreparably distorts the function of auditory cortex, negatively impacting the efficacy of cochlear implantation. Animal studies suggest that in congenital early deafness there is a disconnection between (disordered activation in primary auditory cortex (A1 and activation in secondary auditory cortex (A2. In humans, one factor contributing to this functional decoupling is assumed to be abnormal activation of A1 by visual projections – including exposure to sign language. In this paper we show that that this abnormal activation of A1 does not routinely occur, while A2 functions effectively supramodally and multimodally to deliver spoken language irrespective of hearing status. What, then, is responsible for poor outcomes for some individuals with CI and for apparent abnormalities in cortical organization in these people? Since infancy is a critical period for the acquisition of language, deaf children born to hearing parents are at risk of developing inefficient neural structures to support skilled language processing. A sign language, acquired by a deaf child as a first language in a signing environment, is cortically organized like a heard spoken language in terms of specialization of the dominant perisylvian system. However, very few deaf children are exposed to sign language in early infancy. Moreover, no studies to date have examined sign language proficiency in relation to cortical organization in individuals with CI. Given the paucity of such relevant findings, we suggest that the best guarantee of good language outcome after CI is the establishment of a secure first language pre

  9. Functional organization of the language network in three- and six-year-old children.

    Science.gov (United States)

    Vissiennon, Kodjo; Friederici, Angela D; Brauer, Jens; Wu, Chiao-Yi

    2017-04-01

    The organization of the language network undergoes continuous changes during development as children learn to understand sentences. In the present study, functional magnetic resonance imaging and behavioral measures were utilized to investigate functional activation and functional connectivity (FC) in three-year-old (3yo) and six-year-old (6yo) children during sentence comprehension. Transitive German sentences varying the word order (subject-initial and object-initial) with case marking were presented auditorily. We selected children who were capable of processing the subject-initial sentences above chance level accuracy from each age group to ensure that we were tapping real comprehension. Both age groups showed a main effect of word order in the left posterior superior temporal gyrus (pSTG), with greater activation for object-initial compared to subject-initial sentences. However, age differences were observed in the FC between left pSTG and the left inferior frontal gyrus (IFG). The 6yo group showed stronger FC between the left pSTG and Brodmann area (BA) 44 of the left IFG compared to the 3yo group. For the 3yo group, in turn, the FC between left pSTG and left BA 45 was stronger than with left BA 44. Our study demonstrates that while task-related activation was comparable, the small behavioral differences between age groups were reflected in the underlying functional organization revealing the ongoing development of the neural language network. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  10. The neural organization of perception in chess experts.

    Science.gov (United States)

    Krawczyk, Daniel C; Boggan, Amy L; McClelland, M Michelle; Bartlett, James C

    2011-07-20

    The human visual system responds to expertise, and it has been suggested that regions that process faces also process other objects of expertise including chess boards by experts. We tested whether chess and face processing overlap in brain activity using fMRI. Chess experts and novices exhibited face selective areas, but these regions showed no selectivity to chess configurations relative to other stimuli. We next compared neural responses to chess and to scrambled chess displays to isolate areas relevant to expertise. Areas within the posterior cingulate, orbitofrontal cortex, and right temporal cortex were active in this comparison in experts over novices. We also compared chess and face responses within the posterior cingulate and found this area responsive to chess only in experts. These findings indicate that the configurations in chess are not strongly processed by face-selective regions that are selective for faces in individuals who have expertise in both domains. Further, the area most consistently involved in chess did not show overlap with faces. Overall, these results suggest that expert visual processing may be similar at the level of recognition, but need not show the same neural correlates. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  11. Morphological self-organizing feature map neural network with applications to automatic target recognition

    Science.gov (United States)

    Zhang, Shijun; Jing, Zhongliang; Li, Jianxun

    2005-01-01

    The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

  12. Does Handedness Affect the Cerebral Organization of Speech and Language in Individuals with Aphasia?

    Directory of Open Access Journals (Sweden)

    Juliana Baldo

    2014-04-01

    Although some earlier studies suggested distinct cerebral organizations for right- versus non-right-handed individuals, the neural correlates of fluency and comprehension were greatly overlapping between these groups in our sample of left hemisphere patients with aphasia.

  13. Lexical Organization in Second Language Acquisition: Does the Critical Period Matter?

    Science.gov (United States)

    Cardimona, Kimberly; Smith, Pamela; Roberts, Lauren Sones

    2016-01-01

    This study examined lexical organization in English language learners (ELLs) who acquired their second language (L2) either during or after the period of maximal sensitivity related to the critical period hypothesis. Twenty-three native-Spanish-speaking ELLs completed psycholinguistic tasks to examine age effects in bilingual lexical organization.…

  14. Organization of Anti-Phase Synchronization Pattern in Neural Networks: What are the Key Factors?

    Science.gov (United States)

    Li, Dong; Zhou, Changsong

    2011-01-01

    Anti-phase oscillation has been widely observed in cortical neural network. Elucidating the mechanism underlying the organization of anti-phase pattern is of significance for better understanding more complicated pattern formations in brain networks. In dynamical systems theory, the organization of anti-phase oscillation pattern has usually been considered to relate to time delay in coupling. This is consistent to conduction delays in real neural networks in the brain due to finite propagation velocity of action potentials. However, other structural factors in cortical neural network, such as modular organization (connection density) and the coupling types (excitatory or inhibitory), could also play an important role. In this work, we investigate the anti-phase oscillation pattern organized on a two-module network of either neuronal cell model or neural mass model, and analyze the impact of the conduction delay times, the connection densities, and coupling types. Our results show that delay times and coupling types can play key roles in this organization. The connection densities may have an influence on the stability if an anti-phase pattern exists due to the other factors. Furthermore, we show that anti-phase synchronization of slow oscillations can be achieved with small delay times if there is interaction between slow and fast oscillations. These results are significant for further understanding more realistic spatiotemporal dynamics of cortico-cortical communications. PMID:22232576

  15. The neural correlates of highly iconic structures and topographic discourse in French Sign Language as observed in six hearing native signers.

    Science.gov (United States)

    Courtin, C; Hervé, P-Y; Petit, L; Zago, L; Vigneau, M; Beaucousin, V; Jobard, G; Mazoyer, B; Mellet, E; Tzourio-Mazoyer, N

    2010-09-01

    "Highly iconic" structures in Sign Language enable a narrator to act, switch characters, describe objects, or report actions in four-dimensions. This group of linguistic structures has no real spoken-language equivalent. Topographical descriptions are also achieved in a sign-language specific manner via the use of signing-space and spatial-classifier signs. We used functional magnetic resonance imaging (fMRI) to compare the neural correlates of topographic discourse and highly iconic structures in French Sign Language (LSF) in six hearing native signers, children of deaf adults (CODAs), and six LSF-naïve monolinguals. LSF materials consisted of videos of a lecture excerpt signed without spatially organized discourse or highly iconic structures (Lect LSF), a tale signed using highly iconic structures (Tale LSF), and a topographical description using a diagrammatic format and spatial-classifier signs (Topo LSF). We also presented texts in spoken French (Lect French, Tale French, Topo French) to all participants. With both languages, the Topo texts activated several different regions that are involved in mental navigation and spatial working memory. No specific correlate of LSF spatial discourse was evidenced. The same regions were more activated during Tale LSF than Lect LSF in CODAs, but not in monolinguals, in line with the presence of signing-space structure in both conditions. Motion processing areas and parts of the fusiform gyrus and precuneus were more active during Tale LSF in CODAs; no such effect was observed with French or in LSF-naïve monolinguals. These effects may be associated with perspective-taking and acting during personal transfers. 2010 Elsevier Inc. All rights reserved.

  16. Prediction of ozone tropospheric degradation rate constant of organic compounds by using artificial neural networks

    International Nuclear Information System (INIS)

    Fatemi, M.H.

    2006-01-01

    Ozone tropospheric degradation of organic compound is very important in environmental chemistry. The lifetime of organic chemicals in the atmosphere can be calculated from the knowledge of the rate constant of their reaction with free radicals such as OH and NO 3 or O 3 . In the present work, the rate constant for the tropospheric degradation of 137 organic compounds by reaction with ozone, the least widely and successfully modeled degradation process, are predicted by quantitative structure activity relationships modeling based on a variety of theoretical descriptors, which screened and selected by genetic algorithm variable subset selection procedure. These descriptors which can be used as inputs for generated artificial neural networks are; HOMO-LUMO gap, number of double bonds, number of single bonds, maximum net charge on C atom, minimum (>0.1) bond order of C atom and Minimum e-e repulsion of H atom. After generation, optimization and training of artificial neural network, network was used for the prediction of log KO 3 for the validation set. The root mean square error for the neural network calculated log KO 3 for training, prediction and validation set are 0.357, 0.460 and 0.481, respectively, which are smaller than those obtained by multiple linear regressions model (1.217, 0.870 and 0.968, respectively). Results obtained reveal the reliability and good predictivity of neural network model for the prediction of ozone tropospheric degradations rate constant of organic compounds

  17. RM-SORN: a reward-modulated self-organizing recurrent neural network.

    Science.gov (United States)

    Aswolinskiy, Witali; Pipa, Gordon

    2015-01-01

    Neural plasticity plays an important role in learning and memory. Reward-modulation of plasticity offers an explanation for the ability of the brain to adapt its neural activity to achieve a rewarded goal. Here, we define a neural network model that learns through the interaction of Intrinsic Plasticity (IP) and reward-modulated Spike-Timing-Dependent Plasticity (STDP). IP enables the network to explore possible output sequences and STDP, modulated by reward, reinforces the creation of the rewarded output sequences. The model is tested on tasks for prediction, recall, non-linear computation, pattern recognition, and sequence generation. It achieves performance comparable to networks trained with supervised learning, while using simple, biologically motivated plasticity rules, and rewarding strategies. The results confirm the importance of investigating the interaction of several plasticity rules in the context of reward-modulated learning and whether reward-modulated self-organization can explain the amazing capabilities of the brain.

  18. Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

    Science.gov (United States)

    Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

    2015-04-01

    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 might be to

  19. 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

  20. Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills

    Science.gov (United States)

    Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

    2015-01-01

    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 might be to

  1. A Study of Recurrent and Convolutional Neural Networks in the Native Language Identification Task

    KAUST Repository

    Werfelmann, Robert

    2018-01-01

    around the world. The neural network models consisted of Long Short-Term Memory and Convolutional networks using the sentences of each document as the input. Additional statistical features were generated from the text to complement the predictions

  2. Fgf8-related secondary organizers exert different polarizing planar instructions along the mouse anterior neural tube.

    Science.gov (United States)

    Crespo-Enriquez, Ivan; Partanen, Juha; Martinez, Salvador; Echevarria, Diego

    2012-01-01

    Early brain patterning depends on proper arrangement of positional information. This information is given by gradients of secreted signaling molecules (morphogens) detected by individual cells within the responding tissue, leading to specific fate decisions. Here we report that the morphogen FGF8 exerts initially a differential signal activity along the E9.5 mouse neural tube. We demonstrate that this polarizing activity codes by RAS-regulated ERK1/2 signaling and depends on the topographical location of the secondary organizers: the isthmic organizer (IsO) and the anterior neural ridge (anr) but not on zona limitans intrathalamica (zli). Our results suggest that Sprouty2, a negative modulator of RAS/ERK pathway, is important for regulating Fgf8 morphogenetic signal activity by controlling Fgf8-induced signaling pathways and positional information during early brain development.

  3. Neurolinguistics Aspects of Second Language Acquisition

    Directory of Open Access Journals (Sweden)

    Laleh Fakhraee Faruji

    2011-12-01

    Full Text Available   Fundamental breakthroughs in the neurosciences, combined with technical innovations for measuring brain activity, are shedding new light on the neural basis of second language (L2
    processing, and on its relationship to native language processing (L1 (Perani & Abutalebi, 2005.  Over the past two decades, a large body of neuroimaging studies has been devoted to the study of the neural organization of language (De´monet, Thierry, & Cardebat, 2005; Indefrey & Levelt, 2004; Price, 2000 as cited in Abutalebi, 2008. The value that functional neuroimaging adds to language research is to improve the perspective on the distributed anatomy of language. Thus, it can be used with considerable precision to identify the neural networks underlying the different domains of language processing. In this paper some main issues related to neurolinguistics and second language acquisition with a focus on bilingualism will be discussed.

  4. The N400 effect during speaker-switch – Towards a conversational approach of measuring neural correlates of language

    Directory of Open Access Journals (Sweden)

    Tatiana Goregliad Fjaellingsdal

    2016-11-01

    Full Text Available Language occurs naturally in conversations. However, the study of the neural underpinnings of language has mainly taken place in single individuals using controlled language material. The interactive elements of a conversation (e.g., turn-taking are often not part of neurolinguistic setups. The prime reason is the difficulty to combine open unrestricted conversations with the requirements of neuroimaging. It is necessary to find a trade-off between the naturalness of a conversation and the restrictions imposed by neuroscientific methods to allow for ecologically more valid studies.Here we make an attempt to study the effects of a conversational element, namely turn-taking, on linguistic neural correlates, specifically the N400 effect. We focus on the physiological aspect of turn-taking, the speaker-switch, and its effect on the detectability of the N400 effect. The N400 event-related potential reflects expectation violations in a semantic context; the N400 effect describes the difference of the N400 amplitude between semantically expected and unexpected items.Sentences with semantically congruent and incongruent final words were presented in two turn-taking modes: (1 reading aloud first part of the sentence and listening to speaker-switch for the final word, and (2 listening to first part of the sentence and speaker-switch for the final word.A significant N400 effect was found for both turn-taking modes, which was not influenced by the mode itself. However, the mode significantly affected the P200, which was increased for the reading aloud mode compared to the listening mode.Our results show that an N400 effect can be detected during a speaker-switch. Speech articulation (reading aloud before the analyzed sentence fragment did also not impede the N400 effect detection for the final word. The speaker-switch, however, seems to influence earlier components of the electroencephalogram, related to processing of salient stimuli. We conclude that the N

  5. Artificial neural networks applied to quantitative elemental analysis of organic material using PIXE

    International Nuclear Information System (INIS)

    Correa, R.; Chesta, M.A.; Morales, J.R.; Dinator, M.I.; Requena, I.; Vila, I.

    2006-01-01

    An artificial neural network (ANN) has been trained with real-sample PIXE (particle X-ray induced emission) spectra of organic substances. Following the training stage ANN was applied to a subset of similar samples thus obtaining the elemental concentrations in muscle, liver and gills of Cyprinus carpio. Concentrations obtained with the ANN method are in full agreement with results from one standard analytical procedure, showing the high potentiality of ANN in PIXE quantitative analyses

  6. Artificial neural networks applied to quantitative elemental analysis of organic material using PIXE

    Energy Technology Data Exchange (ETDEWEB)

    Correa, R. [Universidad Tecnologica Metropolitana, Departamento de Fisica, Av. Jose Pedro Alessandri 1242, Nunoa, Santiago (Chile)]. E-mail: rcorrea@utem.cl; Chesta, M.A. [Universidad Nacional de Cordoba, Facultad de Matematica, Astronomia y Fisica, Medina Allende s/n Ciudad Universitaria, 5000 Cordoba (Argentina)]. E-mail: chesta@famaf.unc.edu.ar; Morales, J.R. [Universidad de Chile, Facultad de Ciencias, Departamento de Fisica, Las Palmeras 3425, Nunoa, Santiago (Chile)]. E-mail: rmorales@uchile.cl; Dinator, M.I. [Universidad de Chile, Facultad de Ciencias, Departamento de Fisica, Las Palmeras 3425, Nunoa, Santiago (Chile)]. E-mail: mdinator@uchile.cl; Requena, I. [Universidad de Granada, Departamento de Ciencias de la Computacion e Inteligencia Artificial, Daniel Saucedo Aranda s/n, 18071 Granada (Spain)]. E-mail: requena@decsai.ugr.es; Vila, I. [Universidad de Chile, Facultad de Ciencias, Departamento de Ecologia, Las Palmeras 3425, Nunoa, Santiago (Chile)]. E-mail: limnolog@uchile.cl

    2006-08-15

    An artificial neural network (ANN) has been trained with real-sample PIXE (particle X-ray induced emission) spectra of organic substances. Following the training stage ANN was applied to a subset of similar samples thus obtaining the elemental concentrations in muscle, liver and gills of Cyprinus carpio. Concentrations obtained with the ANN method are in full agreement with results from one standard analytical procedure, showing the high potentiality of ANN in PIXE quantitative analyses.

  7. Drums for pump organs | Setiloane | Marang: Journal of Language ...

    African Journals Online (AJOL)

    Marang: Journal of Language and Literature. Journal Home · ABOUT THIS JOURNAL · Advanced Search · Current Issue · Archives · Journal Home > Vol 2, No 1 (1978) >. Log in or Register to get access to full text downloads. Username, Password, Remember me, or Register. DOWNLOAD FULL TEXT Open Access ...

  8. The Language of Organizations: The Case of the Navy,

    Science.gov (United States)

    1980-01-01

    rating has a common nickname that only Navy folk know: A Radioman ,RM) is -.. ,...-. The Language of Organizatins 131 known as sparks. a Signalman (SM) is...er. ar aviator *.,s tailed a "’outnie. though this term is (iow wore (minmoriv tised to reter to thise ho favor Admiral Zutnwalt’s style ot leadership

  9. Detecting insider threats to organizations through language change

    NARCIS (Netherlands)

    Taylor, Paul J; Dando, C.; Omerod, T.; Ball, L.; Jenkins, M.; Sandham, A.; Menacere, T

    2013-01-01

    The act of conducting an insider attack carries with it cognitive and social challenges that may affect an offender’s day-to-day work behavior. We test this hypothesis by examining the language used in e-mails that were sent as part of a 6-hr workplace simulation. The simulation involved

  10. The Role of "Outside" Organizations in Foreign Language Teacher Education.

    Science.gov (United States)

    Love, William D., Ed.

    A short review of the various "revolutions" in methodology which foreign language instruction has undergone since its inception as an area of study leads into an appraisal of the role of government and private foundations in the education of teachers. The four-point program of the Washington Fourth Draft plan for teacher education and…

  11. Beyond the language given: The neural correlates of inferring speaker meaning

    NARCIS (Netherlands)

    Bašnáková, J.; Weber, K.; Petersson, K.M.; van Berkum, J.J.A.; Hagoort, P.

    2014-01-01

    Even though language allows us to say exactly what we mean, we often use language to say things indirectly, in a way that depends on the specific communicative context. For example, we can use an apparently straightforward sentence like “It is hard to give a good presentation” to convey deeper

  12. Beyond the Language Given: The Neural Correlates of Inferring Speaker Meaning

    NARCIS (Netherlands)

    Basnakova, J.; Weber, K.M.; Petersson, K.M.; Berkum, J.J.A. van; Hagoort, P.

    2014-01-01

    Even though language allows us to say exactly what we mean, we often use language to say things indirectly, in a way that depends on the specific communicative context. For example, we can use an apparently straightforward sentence like "It is hard to give a good presentation" to convey deeper

  13. Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network.

    Science.gov (United States)

    Han, Hong-Gui; Zhang, Lu; Hou, Ying; Qiao, Jun-Fei

    2016-02-01

    A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.

  14. Effects of different language and tDCS interventions in PPA and their neural correlates

    Directory of Open Access Journals (Sweden)

    Kyrana Tsapkini

    2015-05-01

    Results: First, we replicated our previous results obtained with fewer participants: all improved in both tDCS and sham conditions on trained items. Generalization of treatment on untrained items was significant only in tDCS condition. Therapy gains lasted longer in tDCS condition as well. Second, preliminary analyses of rs-fMRI show changes of functional connectivity between written language areas in the tDCS and sham conditions. Conclusions: tDCS represents an increasingly valuable treatment option in language rehabilitation even in neurodegeneration. Late intervention is as beneficial as early intervention but improvement seems more dramatic in early cases. Different possibilities are discussed: tDCS may indeed change the course of the disease, i.e., it may slow down the rate of decline or, language improvement due to tDCS (or delay in language deterioration due to the course of the disease may hold the spread of decline in other cognitive functions, thus, early interventions appear more beneficial. The correlation between functional connectivity and language production outcomes is expected to shed light on how tDCS works in the brains of people with a neurodegenerative disease. Implications of functional connectivity changes between language areas involved in the targeted language function will inform further interventions.

  15. Criticality meets learning: Criticality signatures in a self-organizing recurrent neural network.

    Science.gov (United States)

    Del Papa, Bruno; Priesemann, Viola; Triesch, Jochen

    2017-01-01

    Many experiments have suggested that the brain operates close to a critical state, based on signatures of criticality such as power-law distributed neuronal avalanches. In neural network models, criticality is a dynamical state that maximizes information processing capacities, e.g. sensitivity to input, dynamical range and storage capacity, which makes it a favorable candidate state for brain function. Although models that self-organize towards a critical state have been proposed, the relation between criticality signatures and learning is still unclear. Here, we investigate signatures of criticality in a self-organizing recurrent neural network (SORN). Investigating criticality in the SORN is of particular interest because it has not been developed to show criticality. Instead, the SORN has been shown to exhibit spatio-temporal pattern learning through a combination of neural plasticity mechanisms and it reproduces a number of biological findings on neural variability and the statistics and fluctuations of synaptic efficacies. We show that, after a transient, the SORN spontaneously self-organizes into a dynamical state that shows criticality signatures comparable to those found in experiments. The plasticity mechanisms are necessary to attain that dynamical state, but not to maintain it. Furthermore, onset of external input transiently changes the slope of the avalanche distributions - matching recent experimental findings. Interestingly, the membrane noise level necessary for the occurrence of the criticality signatures reduces the model's performance in simple learning tasks. Overall, our work shows that the biologically inspired plasticity and homeostasis mechanisms responsible for the SORN's spatio-temporal learning abilities can give rise to criticality signatures in its activity when driven by random input, but these break down under the structured input of short repeating sequences.

  16. Language

    DEFF Research Database (Denmark)

    Sanden, Guro Refsum

    2016-01-01

    Purpose: – The purpose of this paper is to analyse the consequences of globalisation in the area of corporate communication, and investigate how language may be managed as a strategic resource. Design/methodology/approach: – A review of previous studies on the effects of globalisation on corporate...... communication and the implications of language management initiatives in international business. Findings: – Efficient language management can turn language into a strategic resource. Language needs analyses, i.e. linguistic auditing/language check-ups, can be used to determine the language situation...... of a company. Language policies and/or strategies can be used to regulate a company’s internal modes of communication. Language management tools can be deployed to address existing and expected language needs. Continuous feedback from the front line ensures strategic learning and reduces the risk of suboptimal...

  17. Application of self-organizing competition artificial neural network to logging data explanation of sandstone-hosted uranium deposits

    International Nuclear Information System (INIS)

    Xu Jianguo; Xu Xianli; Wang Weiguo

    2008-01-01

    The article describes the model construction of self-organizing competition artificial neural network, its principle and automatic recognition process of borehole lithology in detail, and then proves the efficiency of the neural network model for automatically recognizing the borehole lithology with some cases. The self-organizing competition artificial neural network has the ability of self- organization, self-adjustment and high permitting errors. Compared with the BP algorithm, it takes less calculation quantity and more rapidly converges. Furthermore, it can automatically confirm the category without the known sample information. Trial results based on contrasting the identification results of the borehole lithology with geological documentations, indicate that self-organizing artificial neural network can be well applied to automatically performing the category of borehole lithology, during the logging data explanation of sandstone-hosted uranium deposits. (authors)

  18. Homotopic organization of essential language sites in right and bilateral cerebral hemispheric dominance.

    Science.gov (United States)

    Chang, Edward F; Wang, Doris D; Perry, David W; Barbaro, Nicholas M; Berger, Mitchel S

    2011-04-01

    Language dominance in the right hemisphere is rare. Therefore, the organization of essential language sites in the dominant right hemisphere is unclear, especially compared with cases involving the more prevalent left dominant hemisphere. The authors reviewed the medical records of 15 patients who underwent awake craniotomy for tumor or epilepsy surgery and speech mapping of right hemisphere perisylvian language areas at the University of California, San Francisco. All patients were determined to have either complete right-sided or bilateral language dominance by preoperative Wada testing. All patients but one were left-handed. Of more than 331 total stimulation sites, 27 total sites were identified as essential for language function (14 sites for speech arrest/anarthria; 12 for anomia; and 1 for alexia). While significant interindividual variability was observed, the general pattern of language organization was similar to classic descriptions of frontal language production and posterior temporal language integration for the left hemisphere. Speech arrest sites were clustered in the ventral precentral gyrus and pars opercularis. Anomia sites were more widely distributed, but were focused in the posterior superior and middle temporal gyri as well as the inferior parietal gyrus. One alexia site was found over the superior temporal gyrus. Face sensory and motor cortical sites were also identified along the ventral sensorimotor strip. The prevalence and specificity of essential language sites were greater in unilateral right hemisphere-dominant patients, compared with those with bilateral dominance by Wada testing. The authors' results suggest that the organization of language in right hemisphere dominance mirrors that of left hemisphere dominance. Awake speech mapping is a safe and reliable surgical adjunct in these rare clinical cases and should be done in the setting of right hemisphere dominance to avoid preventable postoperative aphasia.

  19. Lifelong learning of human actions with deep neural network self-organization.

    Science.gov (United States)

    Parisi, German I; Tani, Jun; Weber, Cornelius; Wermter, Stefan

    2017-12-01

    Lifelong learning is fundamental in autonomous robotics for the acquisition and fine-tuning of knowledge through experience. However, conventional deep neural models for action recognition from videos do not account for lifelong learning but rather learn a batch of training data with a predefined number of action classes and samples. Thus, there is the need to develop learning systems with the ability to incrementally process available perceptual cues and to adapt their responses over time. We propose a self-organizing neural architecture for incrementally learning to classify human actions from video sequences. The architecture comprises growing self-organizing networks equipped with recurrent neurons for processing time-varying patterns. We use a set of hierarchically arranged recurrent networks for the unsupervised learning of action representations with increasingly large spatiotemporal receptive fields. Lifelong learning is achieved in terms of prediction-driven neural dynamics in which the growth and the adaptation of the recurrent networks are driven by their capability to reconstruct temporally ordered input sequences. Experimental results on a classification task using two action benchmark datasets show that our model is competitive with state-of-the-art methods for batch learning also when a significant number of sample labels are missing or corrupted during training sessions. Additional experiments show the ability of our model to adapt to non-stationary input avoiding catastrophic interference. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  20. Speaking Two Languages Enhances an Auditory but Not a Visual Neural Marker of Cognitive Inhibition

    Directory of Open Access Journals (Sweden)

    Mercedes Fernandez

    2014-09-01

    Full Text Available The purpose of the present study was to replicate and extend our original findings of enhanced neural inhibitory control in bilinguals. We compared English monolinguals to Spanish/English bilinguals on a non-linguistic, auditory Go/NoGo task while recording event-related brain potentials. New to this study was the visual Go/NoGo task, which we included to investigate whether enhanced neural inhibition in bilinguals extends from the auditory to the visual modality. Results confirmed our original findings and revealed greater inhibition in bilinguals compared to monolinguals. As predicted, compared to monolinguals, bilinguals showed increased N2 amplitude during the auditory NoGo trials, which required inhibitory control, but no differences during the Go trials, which required a behavioral response and no inhibition. Interestingly, during the visual Go/NoGo task, event related brain potentials did not distinguish the two groups, and behavioral responses were similar between the groups regardless of task modality. Thus, only auditory trials that required inhibitory control revealed between-group differences indicative of greater neural inhibition in bilinguals. These results show that experience-dependent neural changes associated with bilingualism are specific to the auditory modality and that the N2 event-related brain potential is a sensitive marker of this plasticity.

  1. Image Fusion Based on the Self-Organizing Feature Map Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZHANG Zhaoli; SUN Shenghe

    2001-01-01

    This paper presents a new image datafusion scheme based on the self-organizing featuremap (SOFM) neural networks.The scheme consists ofthree steps:(1) pre-processing of the images,whereweighted median filtering removes part of the noisecomponents corrupting the image,(2) pixel clusteringfor each image using two-dimensional self-organizingfeature map neural networks,and (3) fusion of the im-ages obtained in Step (2) utilizing fuzzy logic,whichsuppresses the residual noise components and thusfurther improves the image quality.It proves thatsuch a three-step combination offers an impressive ef-fectiveness and performance improvement,which isconfirmed by simulations involving three image sen-sors (each of which has a different noise structure).

  2. Laser fluorimetry of mixtures of polyatomic organic compounds using artificial neural networks

    International Nuclear Information System (INIS)

    Dolenko, S A; Gerdova, I V; Dolenko, T A; Fadeev, V V

    2001-01-01

    New possibilities of laser fluorimetry offered by the use of algorithms for solving inverse problems based on artificial neural networks are demonstrated. A two-component mixture of polyatomic organic compounds is analysed by three methods of laser fluorimetry: a direct analysis of the fluorescence band, the kinetic fluorimetry (when durations of the laser pulse and the detector gate pulse are comparable with the fluorescence lifetimes or exceed them), and the saturation fluorimetry. The numerical experiments showed that the use of artificial neural networks in these methods provides a high practical stability of the solution of inverse problems and ensures a high sensitivity and a high accuracy of determining the contribution of components to fluorescence and of measuring molecular photophysical parameters, which can be used for the identification of components. (laser applications and other topics in quantum electronics)

  3. Implementation of self-organizing neural networks for visuo-motor control of an industrial robot.

    Science.gov (United States)

    Walter, J A; Schulten, K I

    1993-01-01

    The implementation of two neural network algorithms for visuo-motor control of an industrial robot (Puma 562) is reported. The first algorithm uses a vector quantization technique, the ;neural-gas' network, together with an error correction scheme based on a Widrow-Hoff-type learning rule. The second algorithm employs an extended self-organizing feature map algorithm. Based on visual information provided by two cameras, the robot learns to position its end effector without an external teacher. Within only 3000 training steps, the robot-camera system is capable of reducing the positioning error of the robot's end effector to approximately 0.1% of the linear dimension of the work space. By employing adaptive feedback the robot succeeds in compensating not only slow calibration drifts, but also sudden changes in its geometry. Hardware aspects of the robot-camera system are discussed.

  4. Sustained activity in hierarchical modular neural networks: self-organized criticality and oscillations

    Directory of Open Access Journals (Sweden)

    Sheng-Jun Wang

    2011-06-01

    Full Text Available Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. They are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality. We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. It was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We find that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and self-organized criticality, which are not present in the respective random networks. The underlying mechanism is that each dense module cannot sustain activity on its own, but displays self-organized criticality in the presence of weak perturbations. The hierarchical modular networks provide the coupling among subsystems with self-organized criticality. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivityof critical state and predictability and timing of oscillations for efficient

  5. Use of Graphic Organizers in a Language Teachers' Professional Development

    Science.gov (United States)

    Chien, Chin-Wen

    2012-01-01

    Starting from 2009 academic year, the instructional coaches in a school district in a northwest American city began to provide Workshop II (pseudonym) to elementary school English teachers. This study aims to discuss the use of graphic organizers in English teachers' professional development. Different types of graphic organizers such as…

  6. A common neural substrate for language production and verbal working memory.

    Science.gov (United States)

    Acheson, Daniel J; Hamidi, Massihullah; Binder, Jeffrey R; Postle, Bradley R

    2011-06-01

    Verbal working memory (VWM), the ability to maintain and manipulate representations of speech sounds over short periods, is held by some influential models to be independent from the systems responsible for language production and comprehension [e.g., Baddeley, A. D. Working memory, thought, and action. New York, NY: Oxford University Press, 2007]. We explore the alternative hypothesis that maintenance in VWM is subserved by temporary activation of the language production system [Acheson, D. J., & MacDonald, M. C. Verbal working memory and language production: Common approaches to the serial ordering of verbal information. Psychological Bulletin, 135, 50-68, 2009b]. Specifically, we hypothesized that for stimuli lacking a semantic representation (e.g., nonwords such as mun), maintenance in VWM can be achieved by cycling information back and forth between the stages of phonological encoding and articulatory planning. First, fMRI was used to identify regions associated with two different stages of language production planning: the posterior superior temporal gyrus (pSTG) for phonological encoding (critical for VWM of nonwords) and the middle temporal gyrus (MTG) for lexical-semantic retrieval (not critical for VWM of nonwords). Next, in the same subjects, these regions were targeted with repetitive transcranial magnetic stimulation (rTMS) during language production and VWM task performance. Results showed that rTMS to the pSTG, but not the MTG, increased error rates on paced reading (a language production task) and on delayed serial recall of nonwords (a test of VWM). Performance on a lexical-semantic retrieval task (picture naming), in contrast, was significantly sensitive to rTMS of the MTG. Because rTMS was guided by language production-related activity, these results provide the first causal evidence that maintenance in VWM directly depends on the long-term representations and processes used in speech production.

  7. Nonlinear dynamics analysis of a self-organizing recurrent neural network: chaos waning.

    Science.gov (United States)

    Eser, Jürgen; Zheng, Pengsheng; Triesch, Jochen

    2014-01-01

    Self-organization is thought to play an important role in structuring nervous systems. It frequently arises as a consequence of plasticity mechanisms in neural networks: connectivity determines network dynamics which in turn feed back on network structure through various forms of plasticity. Recently, self-organizing recurrent neural network models (SORNs) have been shown to learn non-trivial structure in their inputs and to reproduce the experimentally observed statistics and fluctuations of synaptic connection strengths in cortex and hippocampus. However, the dynamics in these networks and how they change with network evolution are still poorly understood. Here we investigate the degree of chaos in SORNs by studying how the networks' self-organization changes their response to small perturbations. We study the effect of perturbations to the excitatory-to-excitatory weight matrix on connection strengths and on unit activities. We find that the network dynamics, characterized by an estimate of the maximum Lyapunov exponent, becomes less chaotic during its self-organization, developing into a regime where only few perturbations become amplified. We also find that due to the mixing of discrete and (quasi-)continuous variables in SORNs, small perturbations to the synaptic weights may become amplified only after a substantial delay, a phenomenon we propose to call deferred chaos.

  8. Stages of didactic games organizing in junior pupils’ foreign language teaching

    Directory of Open Access Journals (Sweden)

    Tamara Marchii-Dmytrash

    2017-02-01

    Full Text Available The feasibility of didactic games using as a method of educational activity organizingand learning of a foreign language in primary school in the context of realization theregularity and consistency principle are justified in the article. It is concretized the stages ofdidactic games organizing in foreign language teaching (preparatory, executive, analyticalcorrectiveand the tasks of each of them, and the impact of didactic games on the forming ofjunior pupils’ foreign language knowledge are defined.Key words: didactic game, method, educational activity, primary school, foreignlanguage, junior pupils, stages.

  9. Low-level neural auditory discrimination dysfunctions in specific language impairment—A review on mismatch negativity findings

    Directory of Open Access Journals (Sweden)

    Teija Kujala

    2017-12-01

    Full Text Available In specific language impairment (SLI, there is a delay in the child’s oral language skills when compared with nonverbal cognitive abilities. The problems typically relate to phonological and morphological processing and word learning. This article reviews studies which have used mismatch negativity (MMN in investigating low-level neural auditory dysfunctions in this disorder. With MMN, it is possible to tap the accuracy of neural sound discrimination and sensory memory functions. These studies have found smaller response amplitudes and longer latencies for speech and non-speech sound changes in children with SLI than in typically developing children, suggesting impaired and slow auditory discrimination in SLI. Furthermore, they suggest shortened sensory memory duration and vulnerability of the sensory memory to masking effects. Importantly, some studies reported associations between MMN parameters and language test measures. In addition, it was found that language intervention can influence the abnormal MMN in children with SLI, enhancing its amplitude. These results suggest that the MMN can shed light on the neural basis of various auditory and memory impairments in SLI, which are likely to influence speech perception. Keywords: Specific language impairment, Auditory processing, Mismatch negativity (MMN

  10. Organization of anti-phase synchronization pattern in neural networks: what are the key factors?

    Directory of Open Access Journals (Sweden)

    Dong eLi

    2011-12-01

    Full Text Available Anti-phase oscillation has been widely observed in cortical neuralnetwork. Elucidating the mechanism underlying the organization ofanti-phase pattern is of significance for better understanding morecomplicated pattern formations in brain networks. In dynamicalsystems theory, the organization of anti-phase oscillation patternhas usually been considered to relate to time-delay in coupling.This is consistent to conduction delays in real neural networks inthe brain due to finite propagation velocity of action potentials.However, other structural factors in cortical neural network, suchas modular organization (connection density and the coupling types(excitatory or inhibitory, could also play an important role. Inthis work, we investigate the anti-phase oscillation patternorganized on a two-module network of either neuronal cell model orneural mass model, and analyze the impact of the conduction delaytimes, the connection densities, and coupling types. Our resultsshow that delay times and coupling types can play key roles in thisorganization. The connection densities may have an influence on thestability if an anti-phase pattern exists due to the other factors.Furthermore, we show that anti-phase synchronization of slowoscillations can be achieved with small delay times if there isinteraction between slow and fast oscillations. These results aresignificant for further understanding more realistic spatiotemporaldynamics of cortico-cortical communications.

  11. Neural Decoding of Bistable Sounds Reveals an Effect of Intention on Perceptual Organization.

    Science.gov (United States)

    Billig, Alexander J; Davis, Matthew H; Carlyon, Robert P

    2018-03-14

    Auditory signals arrive at the ear as a mixture that the brain must decompose into distinct sources based to a large extent on acoustic properties of the sounds. An important question concerns whether listeners have voluntary control over how many sources they perceive. This has been studied using pure high (H) and low (L) tones presented in the repeating pattern HLH-HLH-, which can form a bistable percept heard either as an integrated whole (HLH-) or as segregated into high (H-H-) and low (-L-) sequences. Although instructing listeners to try to integrate or segregate sounds affects reports of what they hear, this could reflect a response bias rather than a perceptual effect. We had human listeners (15 males, 12 females) continuously report their perception of such sequences and recorded neural activity using MEG. During neutral listening, a classifier trained on patterns of neural activity distinguished between periods of integrated and segregated perception. In other conditions, participants tried to influence their perception by allocating attention either to the whole sequence or to a subset of the sounds. They reported hearing the desired percept for a greater proportion of time than when listening neutrally. Critically, neural activity supported these reports; stimulus-locked brain responses in auditory cortex were more likely to resemble the signature of segregation when participants tried to hear segregation than when attempting to perceive integration. These results indicate that listeners can influence how many sound sources they perceive, as reflected in neural responses that track both the input and its perceptual organization. SIGNIFICANCE STATEMENT Can we consciously influence our perception of the external world? We address this question using sound sequences that can be heard either as coming from a single source or as two distinct auditory streams. Listeners reported spontaneous changes in their perception between these two interpretations while

  12. A Common Neural Substrate for Language Production and Verbal Working Memory

    Science.gov (United States)

    Acheson, Daniel J.; Hamidi, Massihullah; Binder, Jeffrey R.; Postle, Bradley R.

    2011-01-01

    Verbal working memory (VWM), the ability to maintain and manipulate representations of speech sounds over short periods, is held by some influential models to be independent from the systems responsible for language production and comprehension [e.g., Baddeley, A. D. "Working memory, thought, and action." New York, NY: Oxford University Press,…

  13. Sustained Activity in Hierarchical Modular Neural Networks: Self-Organized Criticality and Oscillations

    Science.gov (United States)

    Wang, Sheng-Jun; Hilgetag, Claus C.; Zhou, Changsong

    2010-01-01

    Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. In particular, they are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality (SOC). We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. Previously, it was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We found that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and SOC, which are not present in the respective random networks. The mechanism underlying the sustained activity is that each dense module cannot sustain activity on its own, but displays SOC in the presence of weak perturbations. Therefore, the hierarchical modular networks provide the coupling among subsystems with SOC. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivity of critical states and the predictability and timing of oscillations for efficient information

  14. Lexical processing and organization in bilingual first language acquisition: Guiding future research.

    Science.gov (United States)

    DeAnda, Stephanie; Poulin-Dubois, Diane; Zesiger, Pascal; Friend, Margaret

    2016-06-01

    A rich body of work in adult bilinguals documents an interconnected lexical network across languages, such that early word retrieval is language independent. This literature has yielded a number of influential models of bilingual semantic memory. However, extant models provide limited predictions about the emergence of lexical organization in bilingual first language acquisition (BFLA). Empirical evidence from monolingual infants suggests that lexical networks emerge early in development as children integrate phonological and semantic information. These findings tell us little about the interaction between 2 languages in early bilingual memory. To date, an understanding of when and how languages interact in early bilingual development is lacking. In this literature review, we present research documenting lexical-semantic development across monolingual and bilingual infants. This is followed by a discussion of current models of bilingual language representation and organization and their ability to account for the available empirical evidence. Together, these theoretical and empirical accounts inform and highlight unexplored areas of research and guide future work on early bilingual memory. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  15. Development of objective flow regime identification method using self-organizing neural network

    International Nuclear Information System (INIS)

    Lee, Jae Young; Kim, Nam Seok; Kwak, Nam Yee

    2004-01-01

    Two-phase flow shows various flow patterns according to the amount of the void and its relative velocity to the liquid flow. This variation directly affect the interfacial transfer which is the key factor for the design or analysis of the phase change systems. Especially the safety analysis of the nuclear power plant has been performed based on the numerical code furnished with the proper constitutive relations depending highly upon the flow regimes. Heavy efforts have been focused to identify the flow regime and at this moment we stand on relative very stable engineering background compare to the other research field. However, the issues related to objectiveness and transient flow regime are still open to study. Lee et al. and Ishii developed the method for the objective and instantaneous flow regime identification based on the neural network and new index of probability distribution of the flow regime which allows just one second observation for the flow regime identification. In the present paper, we developed the self-organized neural network for more objective approach to this problem. Kohonen's Self-Organizing Map (SOM) has been used for clustering, visualization, and abstraction. The SOM is trained through unsupervised competitive learning using a 'winner takes it all' policy. Therefore, its unsupervised training character delete the possible interference of the regime developer to the neural network training. After developing the computer code, we evaluate the performance of the code with the vertically upward two-phase flow in the pipes of 25.4 and 50.4 cmm I.D. Also, the sensitivity of the number of the clusters to the flow regime identification was made

  16. Efficient organ localization using multi-label convolutional neural networks in thorax-abdomen CT scans

    Science.gov (United States)

    Efrain Humpire-Mamani, Gabriel; Arindra Adiyoso Setio, Arnaud; van Ginneken, Bram; Jacobs, Colin

    2018-04-01

    Automatic localization of organs and other structures in medical images is an important preprocessing step that can improve and speed up other algorithms such as organ segmentation, lesion detection, and registration. This work presents an efficient method for simultaneous localization of multiple structures in 3D thorax-abdomen CT scans. Our approach predicts the location of multiple structures using a single multi-label convolutional neural network for each orthogonal view. Each network takes extra slices around the current slice as input to provide extra context. A sigmoid layer is used to perform multi-label classification. The output of the three networks is subsequently combined to compute a 3D bounding box for each structure. We used our approach to locate 11 structures of interest. The neural network was trained and evaluated on a large set of 1884 thorax-abdomen CT scans from patients undergoing oncological workup. Reference bounding boxes were annotated by human observers. The performance of our method was evaluated by computing the wall distance to the reference bounding boxes. The bounding boxes annotated by the first human observer were used as the reference standard for the test set. Using the best configuration, we obtained an average wall distance of 3.20~+/-~7.33 mm in the test set. The second human observer achieved 1.23~+/-~3.39 mm. For all structures, the results were better than those reported in previously published studies. In conclusion, we proposed an efficient method for the accurate localization of multiple organs. Our method uses multiple slices as input to provide more context around the slice under analysis, and we have shown that this improves performance. This method can easily be adapted to handle more organs.

  17. How age of bilingual exposure can change the neural systems for language in the developing brain: a functional near infrared spectroscopy investigation of syntactic processing in monolingual and bilingual children.

    Science.gov (United States)

    Jasinska, K K; Petitto, L A

    2013-10-01

    Is the developing bilingual brain fundamentally similar to the monolingual brain (e.g., neural resources supporting language and cognition)? Or, does early-life bilingual language experience change the brain? If so, how does age of first bilingual exposure impact neural activation for language? We compared how typically-developing bilingual and monolingual children (ages 7-10) and adults recruit brain areas during sentence processing using functional Near Infrared Spectroscopy (fNIRS) brain imaging. Bilingual participants included early-exposed (bilingual exposure from birth) and later-exposed individuals (bilingual exposure between ages 4-6). Both bilingual children and adults showed greater neural activation in left-hemisphere classic language areas, and additionally, right-hemisphere homologues (Right Superior Temporal Gyrus, Right Inferior Frontal Gyrus). However, important differences were observed between early-exposed and later-exposed bilinguals in their earliest-exposed language. Early bilingual exposure imparts fundamental changes to classic language areas instead of alterations to brain regions governing higher cognitive executive functions. However, age of first bilingual exposure does matter. Later-exposed bilinguals showed greater recruitment of the prefrontal cortex relative to early-exposed bilinguals and monolinguals. The findings provide fascinating insight into the neural resources that facilitate bilingual language use and are discussed in terms of how early-life language experiences can modify the neural systems underlying human language processing. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

  18. Using Graphic Organizers as a Tool for the Development of Scientific Language

    Science.gov (United States)

    Mercuri, Sandra P.

    2010-01-01

    This observational study examines the effectiveness of graphic organizers two elementary teachers in California, United States use to teach the content and the academic language of science. The study was done during the 2006-2007 school year. The data was collected through field-notes and the audio recording of instructional activities, and they…

  19. Covalent growth factor tethering to direct neural stem cell differentiation and self-organization.

    Science.gov (United States)

    Ham, Trevor R; Farrag, Mahmoud; Leipzig, Nic D

    2017-04-15

    Tethered growth factors offer exciting new possibilities for guiding stem cell behavior. However, many of the current methods present substantial drawbacks which can limit their application and confound results. In this work, we developed a new method for the site-specific covalent immobilization of azide-tagged growth factors and investigated its utility in a model system for guiding neural stem cell (NSC) behavior. An engineered interferon-γ (IFN-γ) fusion protein was tagged with an N-terminal azide group, and immobilized to two different dibenzocyclooctyne-functionalized biomimetic polysaccharides (chitosan and hyaluronan). We successfully immobilized azide-tagged IFN-γ under a wide variety of reaction conditions, both in solution and to bulk hydrogels. To understand the interplay between surface chemistry and protein immobilization, we cultured primary rat NSCs on both materials and showed pronounced biological effects. Expectedly, immobilized IFN-γ increased neuronal differentiation on both materials. Expression of other lineage markers varied depending on the material, suggesting that the interplay of surface chemistry and protein immobilization plays a large role in nuanced cell behavior. We also investigated the bioactivity of immobilized IFN-γ in a 3D environment in vivo and found that it sparked the robust formation of neural tube-like structures from encapsulated NSCs. These findings support a wide range of potential uses for this approach and provide further evidence that adult NSCs are capable of self-organization when exposed to the proper microenvironment. For stem cells to be used effectively in regenerative medicine applications, they must be provided with the appropriate cues and microenvironment so that they integrate with existing tissue. This study explores a new method for guiding stem cell behavior: covalent growth factor tethering. We found that adding an N-terminal azide-tag to interferon-γ enabled stable and robust Cu-free 'click

  20. Transformation-invariant visual representations in self-organizing spiking neural networks.

    Science.gov (United States)

    Evans, Benjamin D; Stringer, Simon M

    2012-01-01

    The ventral visual pathway achieves object and face recognition by building transformation-invariant representations from elementary visual features. In previous computer simulation studies with rate-coded neural networks, the development of transformation-invariant representations has been demonstrated using either of two biologically plausible learning mechanisms, Trace learning and Continuous Transformation (CT) learning. However, it has not previously been investigated how transformation-invariant representations may be learned in a more biologically accurate spiking neural network. A key issue is how the synaptic connection strengths in such a spiking network might self-organize through Spike-Time Dependent Plasticity (STDP) where the change in synaptic strength is dependent on the relative times of the spikes emitted by the presynaptic and postsynaptic neurons rather than simply correlated activity driving changes in synaptic efficacy. Here we present simulations with conductance-based integrate-and-fire (IF) neurons using a STDP learning rule to address these gaps in our understanding. It is demonstrated that with the appropriate selection of model parameters and training regime, the spiking network model can utilize either Trace-like or CT-like learning mechanisms to achieve transform-invariant representations.

  1. Transform-invariant visual representations in self-organizing spiking neural networks

    Directory of Open Access Journals (Sweden)

    Benjamin eEvans

    2012-07-01

    Full Text Available The ventral visual pathway achieves object and face recognition by building transform-invariant representations from elementary visual features. In previous computer simulation studies with rate-coded neural networks, the development of transform invariant representations has been demonstrated using either of two biologically plausible learning mechanisms, Trace learning and Continuous Transformation (CT learning. However, it has not previously been investigated how transform invariant representations may be learned in a more biologically accurate spiking neural network. A key issue is how the synaptic connection strengths in such a spiking network might self-organize through Spike-Time Dependent Plasticity (STDP where the change in synaptic strength is dependent on the relative times of the spikes emitted by the pre- and postsynaptic neurons rather than simply correlated activity driving changes in synaptic efficacy. Here we present simulations with conductance-based integrate-and-fire (IF neurons using a STDP learning rule to address these gaps in our understanding. It is demonstrated that with the appropriate selection of model pa- rameters and training regime, the spiking network model can utilize either Trace-like or CT-like learning mechanisms to achieve transform-invariant representations.

  2. Self: an adaptive pressure arising from self-organization, chaotic dynamics, and neural Darwinism.

    Science.gov (United States)

    Bruzzo, Angela Alessia; Vimal, Ram Lakhan Pandey

    2007-12-01

    In this article, we establish a model to delineate the emergence of "self" in the brain making recourse to the theory of chaos. Self is considered as the subjective experience of a subject. As essential ingredients of subjective experiences, our model includes wakefulness, re-entry, attention, memory, and proto-experiences. The stability as stated by chaos theory can potentially describe the non-linear function of "self" as sensitive to initial conditions and can characterize it as underlying order from apparently random signals. Self-similarity is discussed as a latent menace of a pathological confusion between "self" and "others". Our test hypothesis is that (1) consciousness might have emerged and evolved from a primordial potential or proto-experience in matter, such as the physical attractions and repulsions experienced by electrons, and (2) "self" arises from chaotic dynamics, self-organization and selective mechanisms during ontogenesis, while emerging post-ontogenically as an adaptive pressure driven by both volume and synaptic-neural transmission and influencing the functional connectivity of neural nets (structure).

  3. A Self-Organizing Incremental Neural Network based on local distribution learning.

    Science.gov (United States)

    Xing, Youlu; Shi, Xiaofeng; Shen, Furao; Zhou, Ke; Zhao, Jinxi

    2016-12-01

    In this paper, we propose an unsupervised incremental learning neural network based on local distribution learning, which is called Local Distribution Self-Organizing Incremental Neural Network (LD-SOINN). The LD-SOINN combines the advantages of incremental learning and matrix learning. It can automatically discover suitable nodes to fit the learning data in an incremental way without a priori knowledge such as the structure of the network. The nodes of the network store rich local information regarding the learning data. The adaptive vigilance parameter guarantees that LD-SOINN is able to add new nodes for new knowledge automatically and the number of nodes will not grow unlimitedly. While the learning process continues, nodes that are close to each other and have similar principal components are merged to obtain a concise local representation, which we call a relaxation data representation. A denoising process based on density is designed to reduce the influence of noise. Experiments show that the LD-SOINN performs well on both artificial and real-word data. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. Learning second language vocabulary: neural dissociation of situation-based learning and text-based learning.

    Science.gov (United States)

    Jeong, Hyeonjeong; Sugiura, Motoaki; Sassa, Yuko; Wakusawa, Keisuke; Horie, Kaoru; Sato, Shigeru; Kawashima, Ryuta

    2010-04-01

    Second language (L2) acquisition necessitates learning and retrieving new words in different modes. In this study, we attempted to investigate the cortical representation of an L2 vocabulary acquired in different learning modes and in cross-modal transfer between learning and retrieval. Healthy participants learned new L2 words either by written translations (text-based learning) or in real-life situations (situation-based learning). Brain activity was then measured during subsequent retrieval of these words. The right supramarginal gyrus and left middle frontal gyrus were involved in situation-based learning and text-based learning, respectively, whereas the left inferior frontal gyrus was activated when learners used L2 knowledge in a mode different from the learning mode. Our findings indicate that the brain regions that mediate L2 memory differ according to how L2 words are learned and used. Copyright 2009 Elsevier Inc. All rights reserved.

  5. Extending Deacon’s Notion of Teleodynamics to Culture, Language, Organization, Science, Economics and Technology (CLOSET

    Directory of Open Access Journals (Sweden)

    Robert K. Logan

    2015-10-01

    Full Text Available Terrence Deacon’s (2012 notion developed in his book Incomplete Nature (IN that living organisms are teleodynamic systems that are self-maintaining, self-correcting and self-reproducing is extended to human social systems. The hypothesis is developed that culture, language, organization, science, economics and technology (CLOSET can be construed as living organisms that evolve, maintain and reproduce themselves and are self-correcting, and hence are teleodynamic systems. The elements of CLOSET are to a certain degree autonomous, even though they are obligate symbionts dependent on their human hosts for the energy that sustains them.

  6. A NEW RECOGNITION TECHNIQUE NAMED SOMP BASED ON PALMPRINT USING NEURAL NETWORK BASED SELF ORGANIZING MAPS

    Directory of Open Access Journals (Sweden)

    A. S. Raja

    2012-08-01

    Full Text Available The word biometrics refers to the use of physiological or biological characteristics of human to recognize and verify the identity of an individual. Palmprint has become a new class of human biometrics for passive identification with uniqueness and stability. This is considered to be reliable due to the lack of expressions and the lesser effect of aging. In this manuscript a new Palmprint based biometric system based on neural networks self organizing maps (SOM is presented. The method is named as SOMP. The paper shows that the proposed SOMP method improves the performance and robustness of recognition. The proposed method is applied to a variety of datasets and the results are shown.

  7. Artificial neural network with self-organizing mapping for reactor stability monitoring

    International Nuclear Information System (INIS)

    Okumura, Motofumi; Tsuji, Masashi; Shimazu, Yoichiro; Narabayashi, Tadashi

    2008-01-01

    In BWR stability monitoring damping ratio has been used as a stability index. A method for estimating the damping ratio by applying Principal Component Analysis (PCA) to neutron detector signals measured with local power range monitors (LPRMs) had been developed; In this method, measured fluctuating signal is decomposed into some independent components and the signal component directly related to stability is extracted among them to determine the damping ratio. For online monitoring, it is necessary to select stability related signal component efficiently. The self-organizing map (SOM) is one of the artificial neural networks and has the characteristics such that online learning is possible without supervised learning within a relatively short time. In the present study, the SOM was applied to extract the relevant signal component more quickly and more accurately, and the availability was confirmed through the feasibility study. (author)

  8. Neural network system and methods for analysis of organic materials and structures using spectral data

    Science.gov (United States)

    Meyer, Bernd J.; Sellers, Jeffrey P.; Thomsen, Jan U.

    1993-01-01

    Apparatus and processes for recognizing and identifying materials. Characteristic spectra are obtained for the materials via spectroscopy techniques including nuclear magnetic resonance spectroscopy, infrared absorption analysis, x-ray analysis, mass spectroscopy and gas chromatography. Desired portions of the spectra may be selected and then placed in proper form and format for presentation to a number of input layer neurons in an offline neural network. The network is first trained according to a predetermined training process; it may then be employed to identify particular materials. Such apparatus and processes are particularly useful for recognizing and identifying organic compounds such as complex carbohydrates, whose spectra conventionally require a high level of training and many hours of hard work to identify, and are frequently indistinguishable from one another by human interpretation.

  9. Lifetime prediction for organic coating under alternating hydrostatic pressure by artificial neural network

    Science.gov (United States)

    Tian, Wenliang; Meng, Fandi; Liu, Li; Li, Ying; Wang, Fuhui

    2017-01-01

    A concept for prediction of organic coatings, based on the alternating hydrostatic pressure (AHP) accelerated tests, has been presented. An AHP accelerated test with different pressure values has been employed to evaluate coating degradation. And a back-propagation artificial neural network (BP-ANN) has been established to predict the service property and the service lifetime of coatings. The pressure value (P), immersion time (t) and service property (impedance modulus |Z|) are utilized as the parameters of the network. The average accuracies of the predicted service property and immersion time by the established network are 98.6% and 84.8%, respectively. The combination of accelerated test and prediction method by BP-ANN is promising to evaluate and predict coating property used in deep sea. PMID:28094340

  10. Effect of socioeconomic status disparity on child language and neural outcome: how early is early?

    Science.gov (United States)

    Hurt, Hallam; Betancourt, Laura M

    2016-01-01

    It is not news that poverty adversely affects child outcome. The literature is replete with reports of deleterious effects on developmental outcome, cognitive function, and school performance in children and youth. Causative factors include poor nutrition, exposure to toxins, inadequate parenting, lack of cognitive stimulation, unstable social support, genetics, and toxic environments. Less is known regarding how early in life adverse effects may be detected. This review proposes to elucidate "how early is early" through discussion of seminal articles related to the effect of socioeconomic status on language outcome and a discussion of the emerging literature on effects of socioeconomic status disparity on brain structure in very young children. Given the young ages at which such outcomes are detected, the critical need for early targeted interventions for our youngest is underscored. Further, the fiscal reasonableness of initiating quality interventions supports these initiatives. As early life adversity produces lasting and deleterious effects on developmental outcome and brain structure, increased focus on programs and policies directed to reducing the impact of socioeconomic disparities is essential.

  11. Comparison of fMRI on the cortical organization using two language tasks in normal subjects

    International Nuclear Information System (INIS)

    Jiang Zhen; Zhang Caiyuan; Cai Wu; Shen Junkang; Gong Zhigang

    2008-01-01

    Objective: To comparatively study the cortical organization using two different language tasks by BOLD-fMRI in normal subjects: Methods: BOLD-fMRI scan was performed in 8 healthy volunteers with right handiness during executing the two language tasks: picture-naming and word-generation. The AFNI software was used to analyze the functional data and to generate the statistical parametric maps for comparatively studying the activation areas of each task. Results: Both activation patterns for two language tasks shared a common brain network dispersed in frontal, parietal, and occipital lobe. The activation areas of occipital lobe for picture-naming was more obvious than those for word-generation. By contraries, the areas related to language processing for word-generation was more active than picture-imaging. Compared with picture naming, the activation patterns for word-generation was mainly left-lateralized. Conclusion: Both of two tasks can activate the brain network which dedicate to language processing, but each of them has its own characteristics according to the processing patterns. (authors)

  12. Differential receptive field organizations give rise to nearly identical neural correlations across three parallel sensory maps in weakly electric fish.

    Science.gov (United States)

    Hofmann, Volker; Chacron, Maurice J

    2017-09-01

    Understanding how neural populations encode sensory information thereby leading to perception and behavior (i.e., the neural code) remains an important problem in neuroscience. When investigating the neural code, one must take into account the fact that neural activities are not independent but are actually correlated with one another. Such correlations are seen ubiquitously and have a strong impact on neural coding. Here we investigated how differences in the antagonistic center-surround receptive field (RF) organization across three parallel sensory maps influence correlations between the activities of electrosensory pyramidal neurons. Using a model based on known anatomical differences in receptive field center size and overlap, we initially predicted large differences in correlated activity across the maps. However, in vivo electrophysiological recordings showed that, contrary to modeling predictions, electrosensory pyramidal neurons across all three segments displayed nearly identical correlations. To explain this surprising result, we incorporated the effects of RF surround in our model. By systematically varying both the RF surround gain and size relative to that of the RF center, we found that multiple RF structures gave rise to similar levels of correlation. In particular, incorporating known physiological differences in RF structure between the three maps in our model gave rise to similar levels of correlation. Our results show that RF center overlap alone does not determine correlations which has important implications for understanding how RF structure influences correlated neural activity.

  13. Atypical cortical language organization in epilepsy patients: evidence for divergent hemispheric dominance for receptive and expressive language function.

    Science.gov (United States)

    Eliashiv, Dawn S; Kurelowech, Lacey; Quint, Patti; Chung, Jeffrey M; Otis, Shirley M; Gage, Nicole M

    2014-06-01

    The central goal of presurgical language mapping is to identify brain regions that subserve cortical language function to minimize postsurgical language deficits. Presurgical language mapping in patients with epilepsy presents a key challenge because of the atypical pattern of hemispheric language dominance found in this population, with higher incidences of bilateral and right-biased language dominance than typical. In this prospective study, we combine magnetoencephalography with a panel of tasks designed to separately assess receptive and expressive function to provide a sensitive measure of language function in 15 candidates for resective surgery. We report the following: 4 of 15 patients (27%) showed left hemisphere dominance across all tasks, 4 of 15 patients (27%) showed right hemisphere dominance across all tasks, and 7 of 15 (46%) showed discordant language dominance, with right-dominant receptive and left-dominant expressive language. All patients with discordant language dominance showed this right-receptive and left-expressive pattern. Results provide further evidence supporting the importance of using a panel of tasks to assess separable aspects of language function. The clinical relevance of the findings is discussed, especially about current clinical operative measures for assessing language dominance, which use single hemisphere procedure (intracarotid amobarbital procedure and awake intraoperative stimulation) for determining language laterality.

  14. Prediction of the rejection of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ammi, Yamina; Khaouane, Latifa; Hanini, Salah [University of Medea, Medea (Algeria)

    2015-11-15

    This work investigates the use of neural networks in modeling the rejection processes of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes. Three feed-forward neural network (NN) models, characterized by a similar structure (eleven neurons for NN1 and NN2 and twelve neurons for NN3 in the input layer, one hidden layer and one neuron in the output layer), are constructed with the aim of predicting the rejection of organic compounds (neutral and ionic). A set of 956 data points for NN1 and 701 data points for NN2 and NN3 were used to test the neural networks. 80%, 10%, and 10% of the total data were used, respectively, for the training, the validation, and the test of the three models. For the most promising neural network models, the predicted rejection values of the test dataset were compared to measured rejections values; good correlations were found (R= 0.9128 for NN1, R=0.9419 for NN2, and R=0.9527 for NN3). The root mean squared errors for the total dataset were 11.2430% for NN1, 9.0742% for NN2, and 8.2047% for NN3. Furthermore, the comparison between the predicted results and QSAR models shows that the neural network models gave far better.

  15. Prediction of the rejection of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes using neural networks

    International Nuclear Information System (INIS)

    Ammi, Yamina; Khaouane, Latifa; Hanini, Salah

    2015-01-01

    This work investigates the use of neural networks in modeling the rejection processes of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes. Three feed-forward neural network (NN) models, characterized by a similar structure (eleven neurons for NN1 and NN2 and twelve neurons for NN3 in the input layer, one hidden layer and one neuron in the output layer), are constructed with the aim of predicting the rejection of organic compounds (neutral and ionic). A set of 956 data points for NN1 and 701 data points for NN2 and NN3 were used to test the neural networks. 80%, 10%, and 10% of the total data were used, respectively, for the training, the validation, and the test of the three models. For the most promising neural network models, the predicted rejection values of the test dataset were compared to measured rejections values; good correlations were found (R= 0.9128 for NN1, R=0.9419 for NN2, and R=0.9527 for NN3). The root mean squared errors for the total dataset were 11.2430% for NN1, 9.0742% for NN2, and 8.2047% for NN3. Furthermore, the comparison between the predicted results and QSAR models shows that the neural network models gave far better.

  16. Dynamic spatial organization of the occipito-temporal word form area for second language processing.

    Science.gov (United States)

    Gao, Yue; Sun, Yafeng; Lu, Chunming; Ding, Guosheng; Guo, Taomei; Malins, Jeffrey G; Booth, James R; Peng, Danling; Liu, Li

    2017-08-01

    Despite the left occipito-temporal region having shown consistent activation in visual word form processing across numerous studies in different languages, the mechanisms by which word forms of second languages are processed in this region remain unclear. To examine this more closely, 16 Chinese-English and 14 English-Chinese late bilinguals were recruited to perform lexical decision tasks to visually presented words in both their native and second languages (L1 and L2) during functional magnetic resonance imaging scanning. Here we demonstrate that visual word form processing for L1 versus L2 engaged different spatial areas of the left occipito-temporal region. Namely, the spatial organization of the visual word form processing in the left occipito-temporal region is more medial and posterior for L2 than L1 processing in Chinese-English bilinguals, whereas activation is more lateral and anterior for L2 in English-Chinese bilinguals. In addition, for Chinese-English bilinguals, more lateral recruitment of the occipito-temporal region was correlated with higher L2 proficiency, suggesting higher L2 proficiency is associated with greater involvement of L1-preferred mechanisms. For English-Chinese bilinguals, higher L2 proficiency was correlated with more lateral and anterior activation of the occipito-temporal region, suggesting higher L2 proficiency is associated with greater involvement of L2-preferred mechanisms. Taken together, our results indicate that L1 and L2 recruit spatially different areas of the occipito-temporal region in visual word processing when the two scripts belong to different writing systems, and that the spatial organization of this region for L2 visual word processing is dynamically modulated by L2 proficiency. Specifically, proficiency in L2 in Chinese-English is associated with assimilation to the native language mechanisms, whereas L2 in English-Chinese is associated with accommodation to second language mechanisms. Copyright © 2017

  17. Organic chemistry as a language and the implications of chemical linguistics for structural and retrosynthetic analyses.

    Science.gov (United States)

    Cadeddu, Andrea; Wylie, Elizabeth K; Jurczak, Janusz; Wampler-Doty, Matthew; Grzybowski, Bartosz A

    2014-07-28

    Methods of computational linguistics are used to demonstrate that a natural language such as English and organic chemistry have the same structure in terms of the frequency of, respectively, text fragments and molecular fragments. This quantitative correspondence suggests that it is possible to extend the methods of computational corpus linguistics to the analysis of organic molecules. It is shown that within organic molecules bonds that have highest information content are the ones that 1) define repeat/symmetry subunits and 2) in asymmetric molecules, define the loci of potential retrosynthetic disconnections. Linguistics-based analysis appears well-suited to the analysis of complex structural and reactivity patterns within organic molecules. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. Use of Artificial Neural Network Models to Predict Indicator Organism Concentrations in an Urban Watershed

    Science.gov (United States)

    Mas, D. M.; Ahlfeld, D. P.

    2004-05-01

    Forecasting stream water quality is important for numerous aspects of resource protection and management. Fecal coliform and enteroccocus are primary indicator organisms used to assess potential pathogen contamination. Consequently, modeling the occurrence and concentration of fecal coliform and enterococcus is an important tool in watershed management. In addition, analyzing the relationship between model input and predicted indicator organisms is useful for elucidating possible sources of contamination and mechanisms of transport. While many process-based, statistical, and empirical models exist for water quality prediction, artificial neural network (ANN) models are increasingly being used for forecasting of water resources variables because ANNs are often capable of modeling complex systems for which behavioral rules are either unknown or difficult to simulate. The performance of ANNs compared to more established modeling approaches such as multiple linear regression (MLR) remains an importance research question. Data collected the U.S. Geological Survey in the lower Charles River in Massachusetts, USA in 1999-2000 was examined to determine correlation between various water quality constituents and indicator organisms and to explore the relationship between rainfall characteristics and indicator organism concentrations. Using the results of the statistical analysis to guide the selection of explanatory variables, MLR was performed to develop predictive equations for wet weather and dry weather conditions. The results show that the best-performing predictor variables are generally consistent for both indicator organisms considered. In addition, the regression equations show increasing indicator organism concentrations as a function of suspended sediment concentrations and length of time since last precipitation event, suggesting accumulation and wash off as a key mechanism of pathogen transport under wet weather conditions. This research also presents the

  19. A Correlational Study of Graphic Organizers and Science Achievement of English Language Learners

    Science.gov (United States)

    Clarke, William Gordon

    English language learners (ELLs) demonstrate lower academic performance and have lower graduation and higher dropout rates than their non-ELL peers. The primary purpose of this correlational quantitative study was to investigate the relationship between the use of graphic organizer-infused science instruction and science learning of high school ELLs. Another objective was to determine if the method of instruction, socioeconomic status (SES), gender, and English language proficiency (ELP) were predictors of academic achievement of high school ELLs. Data were gathered from a New York City (NYC) high school fall 2012-2013 archival records of 145 ninth-grade ELLs who had received biology instruction in freestanding English as a second language (ESL) classes, followed by a test of their learning of the material. Fifty-four (37.2%) of these records were of students who had learned science by the conventional textbook method, and 91 (62.8%) by using graphic organizers. Data analysis employed the Statistical Package for the Social Sciences (SPSS) software for multiple regression analysis, which found graphic organizer use to be a significant predictor of New York State Regents Living Environment (NYSRLE) test scores (p < .01). One significant regression model was returned whereby, when combined, the four predictor variables (method of instruction, SES, gender, and ELP) explained 36% of the variance of the NYSRLE score. Implications of the study findings noted graphic organizer use as advantageous for ELL science achievement. Recommendations made for practice were for (a) the adoption of graphic organizer infused-instruction, (b) establishment of a protocol for the implementation of graphic organizer-infused instruction, and (c) increased length of graphic organizer instructional time. Recommendations made for future research were (a) a replication quantitative correlational study in two or more high schools, (b) a quantitative quasi-experimental quantitative study to

  20. The small GTPase RhoA is required to maintain spinal cord neuroepithelium organization and the neural stem cell pool

    DEFF Research Database (Denmark)

    Herzog, Dominik; Loetscher, Pirmin; van Hengel, Jolanda

    2011-01-01

    ablation. We show that, in the spinal cord neuroepithelium, RhoA is essential to localize N-cadherin and ß-catenin to AJs and maintain apical-basal polarity of neural progenitor cells. Ablation of RhoA caused the loss of AJs and severe abnormalities in the organization of cells within the neuroepithelium......Dia1), does not localize to apical AJs in which it likely stabilizes intracellular adhesion by promoting local actin polymerization and microtubule organization. Furthermore, expressing a dominant-negative form of mDia1 in neural stem/progenitor cells results in a similar phenotype compared...... with that of the RhoA conditional knock-out, namely the loss of AJs and apical polarity. Together, our data show that RhoA signaling is necessary for AJ regulation and for the maintenance of mammalian neuroepithelium organization preventing precocious cell-cycle exit and differentiation....

  1. An Expedient Study on Back-Propagation (BPN) Neural Networks for Modeling Automated Evaluation of the Answers and Progress of Deaf Students' That Possess Basic Knowledge of the English Language and Computer Skills

    Science.gov (United States)

    Vrettaros, John; Vouros, George; Drigas, Athanasios S.

    This article studies the expediency of using neural networks technology and the development of back-propagation networks (BPN) models for modeling automated evaluation of the answers and progress of deaf students' that possess basic knowledge of the English language and computer skills, within a virtual e-learning environment. The performance of the developed neural models is evaluated with the correlation factor between the neural networks' response values and the real value data as well as the percentage measurement of the error between the neural networks' estimate values and the real value data during its training process and afterwards with unknown data that weren't used in the training process.

  2. Model of the Dynamic Construction Process of Texts and Scaling Laws of Words Organization in Language Systems.

    Science.gov (United States)

    Li, Shan; Lin, Ruokuang; Bian, Chunhua; Ma, Qianli D Y; Ivanov, Plamen Ch

    2016-01-01

    Scaling laws characterize diverse complex systems in a broad range of fields, including physics, biology, finance, and social science. The human language is another example of a complex system of words organization. Studies on written texts have shown that scaling laws characterize the occurrence frequency of words, words rank, and the growth of distinct words with increasing text length. However, these studies have mainly concentrated on the western linguistic systems, and the laws that govern the lexical organization, structure and dynamics of the Chinese language remain not well understood. Here we study a database of Chinese and English language books. We report that three distinct scaling laws characterize words organization in the Chinese language. We find that these scaling laws have different exponents and crossover behaviors compared to English texts, indicating different words organization and dynamics of words in the process of text growth. We propose a stochastic feedback model of words organization and text growth, which successfully accounts for the empirically observed scaling laws with their corresponding scaling exponents and characteristic crossover regimes. Further, by varying key model parameters, we reproduce differences in the organization and scaling laws of words between the Chinese and English language. We also identify functional relationships between model parameters and the empirically observed scaling exponents, thus providing new insights into the words organization and growth dynamics in the Chinese and English language.

  3. Model of the Dynamic Construction Process of Texts and Scaling Laws of Words Organization in Language Systems.

    Directory of Open Access Journals (Sweden)

    Shan Li

    Full Text Available Scaling laws characterize diverse complex systems in a broad range of fields, including physics, biology, finance, and social science. The human language is another example of a complex system of words organization. Studies on written texts have shown that scaling laws characterize the occurrence frequency of words, words rank, and the growth of distinct words with increasing text length. However, these studies have mainly concentrated on the western linguistic systems, and the laws that govern the lexical organization, structure and dynamics of the Chinese language remain not well understood. Here we study a database of Chinese and English language books. We report that three distinct scaling laws characterize words organization in the Chinese language. We find that these scaling laws have different exponents and crossover behaviors compared to English texts, indicating different words organization and dynamics of words in the process of text growth. We propose a stochastic feedback model of words organization and text growth, which successfully accounts for the empirically observed scaling laws with their corresponding scaling exponents and characteristic crossover regimes. Further, by varying key model parameters, we reproduce differences in the organization and scaling laws of words between the Chinese and English language. We also identify functional relationships between model parameters and the empirically observed scaling exponents, thus providing new insights into the words organization and growth dynamics in the Chinese and English language.

  4. Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems.

    Science.gov (United States)

    Wang, Sheng-Jun; Ouyang, Guang; Guang, Jing; Zhang, Mingsha; Wong, K Y Michael; Zhou, Changsong

    2016-01-08

    Self-organized critical states (SOCs) and stochastic oscillations (SOs) are simultaneously observed in neural systems, which appears to be theoretically contradictory since SOCs are characterized by scale-free avalanche sizes but oscillations indicate typical scales. Here, we show that SOs can emerge in SOCs of small size systems due to temporal correlation between large avalanches at the finite-size cutoff, resulting from the accumulation-release process in SOCs. In contrast, the critical branching process without accumulation-release dynamics cannot exhibit oscillations. The reconciliation of SOCs and SOs is demonstrated both in the sandpile model and robustly in biologically plausible neuronal networks. The oscillations can be suppressed if external inputs eliminate the prominent slow accumulation process, providing a potential explanation of the widely studied Berger effect or event-related desynchronization in neural response. The features of neural oscillations and suppression are confirmed during task processing in monkey eye-movement experiments. Our results suggest that finite-size, columnar neural circuits may play an important role in generating neural oscillations around the critical states, potentially enabling functional advantages of both SOCs and oscillations for sensitive response to transient stimuli.

  5. Robots with language

    Directory of Open Access Journals (Sweden)

    Domenico Parisi

    2010-11-01

    Full Text Available Trying to understand human language by constructing robots that have language necessarily implies an embodied view of language, where the meaning of linguistic expressions is derived from the physical interactions of the organism with the environment. The paper describes a neural model of language according to which the robot’s behaviour is controlled by a neural network composed of two sub-networks, one dedicated to the non-linguistic interactions of the robot with the environment and the other one to processing linguistic input and producing linguistic output. We present the results of a number of simulations using the model and we suggest how the model can be used to account for various language-related phenomena such as disambiguation, the metaphorical use of words, the pervasive idiomaticity of multi-word expressions, and mental life as talking to oneself.. The model implies a view of the meaning of words and multi-word expressions as a temporal process that takes place in the entire brain and has no clearly defined boundaries. The model can also be extended to emotional words if we assume that an embodied view of language includes not only the interactions of the robot’s brain with the external environment but also the interactions of the brain with what is inside the body.

  6. Usage of self-organizing neural networks in evaluation of consumer behaviour

    Directory of Open Access Journals (Sweden)

    Jana Weinlichová

    2010-01-01

    Full Text Available This article deals with evaluation of consumer data by Artificial Intelligence methods. In methodical part there are described learning algorithms for Kohonen maps on the principle of supervised learning, unsupervised learning and semi-supervised learning. The principles of supervised learning and unsupervised learning are compared. On base of binding conditions of these principles there is pointed out an advantage of semi-supervised learning. Three algorithms are described for the semi-supervised learning: label propagation, self-training and co-training. Especially usage of co-training in Kohonen map learning seems to be promising point of other research. In concrete application of Kohonen neural network on consumer’s expense the unsupervised learning method has been chosen – the self-organization. So the features of data are evaluated by clustering method called Kohonen maps. These input data represents consumer expenses of households in countries of European union and are characterised by 12-dimension vector according to commodity classification. The data are evaluated in several years, so we can see their distribution, similarity or dissimilarity and also their evolution. In the article we discus other usage of this method for this type of data and also comparison of our results with results reached by hierarchical cluster analysis.

  7. Artificial neural network with self-organizing mapping for reactor stability monitoring

    International Nuclear Information System (INIS)

    Okumura, Motofumi; Tsuji, Masashi; Shimazu, Yoichiro

    2009-01-01

    In boiling water reactor (BWR) stability monitoring, damping ratio has been used as a stability index. A method for estimating the damping ratio by applying Principal Component Analysis (PCA) to neutron detector signals measured with local power range monitors (LPRMs) had been developed; in this method, measured fluctuating signal is decomposed into some independent components and the signal components directly related to stability are extracted among them to determine the damping ratio. For online monitoring, it is necessary to select stability related signal components efficiently. The self-organizing map (SOM) is one of the artificial neural networks (ANNs) and has the characteristics such that online learning is possible without supervised learning within a relatively short time. In the present study, the SOM was applied to extract the relevant signal components more quickly and more accurately, and the availability was confirmed through the feasibility study. For realizing online stability monitoring only with ANNs, another type of ANN that performs online processing of PCA was combined with SOM. And stability monitoring performance was investigated. (author)

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

    Directory of Open Access Journals (Sweden)

    Hilary A. Marusak

    2015-01-01

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

  9. Cerebral organization of oral and signed language responses: case study evidence from amytal and cortical stimulation studies.

    Science.gov (United States)

    Mateer, C A; Rapport, R L; Kettrick, C

    1984-01-01

    A normally hearing left-handed patient familiar with American Sign Language (ASL) was assessed under sodium amytal conditions and with left cortical stimulation in both oral speech and signed English. Lateralization was mixed but complementary in each language mode: the right hemisphere perfusion severely disrupted motoric aspects of both types of language expression, the left hemisphere perfusion specifically disrupted features of grammatical and semantic usage in each mode of expression. Both semantic and syntactic aspects of oral and signed responses were altered during left posterior temporal-parietal stimulation. Findings are discussed in terms of the neurological organization of ASL and linguistic organization in cases of early left hemisphere damage.

  10. Testing the Language of German Cerebral Palsy Patients with Right Hemispheric Language Organization after Early Left Hemispheric Damage

    Science.gov (United States)

    Schwilling, Eleonore; Krageloh-Mann, Ingeborg; Konietzko, Andreas; Winkler, Susanne; Lidzba, Karen

    2012-01-01

    Language functions are generally represented in the left cerebral hemisphere. After early (prenatally acquired or perinatally acquired) left hemispheric brain damage language functions may be salvaged by reorganization into the right hemisphere. This is different from brain lesions acquired in adulthood which normally lead to aphasia. Right…

  11. PSYCHOLOGICAL AND PEDAGOGICAL FACTORS OF STUDENT SELF-STUDY ORGANIZATION ON ACQUIRING FOREIGN LANGUAGE COMMUNICATIVE COMPETENCE

    Directory of Open Access Journals (Sweden)

    Iryna Zadorozhna

    2016-12-01

    Full Text Available Psychological and pedagogical prerequisites of student self-study organization on acquiring foreign language communicative competence have been defined and characterized. It has been proved that self-study effectiveness depends on self-regulation and motivation. The latter is amplified by creating a situation of development, modelling personally meaningful learning context aimed at creating a real product; collaborative learning, incorporating modern technologies, using problematic tasks, regular feedback, professionally-oriented learning. On the basis of scientific literature analysis it has been concluded that self-regulation of future foreign language teachers has the following structure: defining objectives, modelling meaningful conditions, action programming, results evaluation, program correction. Ways of developing self-control, self-evaluation and self-correction have been analyzed in the article. Pedagogical preconditions of effective self-study are the following: student knowledge of efficient methods and procedures of foreign language learning; selection of procedures and strategies adequate to the defined goals; an appropriate level of student information culture; ability to manage time and control results; timely correction on the basis of current control and self-control.

  12. Evolution, brain, and the nature of language.

    Science.gov (United States)

    Berwick, Robert C; Friederici, Angela D; Chomsky, Noam; Bolhuis, Johan J

    2013-02-01

    Language serves as a cornerstone for human cognition, yet much about its evolution remains puzzling. Recent research on this question parallels Darwin's attempt to explain both the unity of all species and their diversity. What has emerged from this research is that the unified nature of human language arises from a shared, species-specific computational ability. This ability has identifiable correlates in the brain and has remained fixed since the origin of language approximately 100 thousand years ago. Although songbirds share with humans a vocal imitation learning ability, with a similar underlying neural organization, language is uniquely human. Copyright © 2012 Elsevier Ltd. All rights reserved.

  13. Language and Recursion

    Science.gov (United States)

    Lowenthal, Francis

    2010-11-01

    This paper examines whether the recursive structure imbedded in some exercises used in the Non Verbal Communication Device (NVCD) approach is actually the factor that enables this approach to favor language acquisition and reacquisition in the case of children with cerebral lesions. For that a definition of the principle of recursion as it is used by logicians is presented. The two opposing approaches to the problem of language development are explained. For many authors such as Chomsky [1] the faculty of language is innate. This is known as the Standard Theory; the other researchers in this field, e.g. Bates and Elman [2], claim that language is entirely constructed by the young child: they thus speak of Language Acquisition. It is also shown that in both cases, a version of the principle of recursion is relevant for human language. The NVCD approach is defined and the results obtained in the domain of language while using this approach are presented: young subjects using this approach acquire a richer language structure or re-acquire such a structure in the case of cerebral lesions. Finally it is shown that exercises used in this framework imply the manipulation of recursive structures leading to regular grammars. It is thus hypothesized that language development could be favored using recursive structures with the young child. It could also be the case that the NVCD like exercises used with children lead to the elaboration of a regular language, as defined by Chomsky [3], which could be sufficient for language development but would not require full recursion. This double claim could reconcile Chomsky's approach with psychological observations made by adherents of the Language Acquisition approach, if it is confirmed by researches combining the use of NVCDs, psychometric methods and the use of Neural Networks. This paper thus suggests that a research group oriented towards this problematic should be organized.

  14. EDUCATIONAL PROGRAMMES OF BRITISH ORGANIZATIONS IN AZERBAIJAN AS AN ELEMENT OF THE UNITED KINGDOM FOREIGN LANGUAGE POLICY

    Directory of Open Access Journals (Sweden)

    G. Y. Niyazova

    2014-10-01

    Full Text Available The article considers the United Kingdom language policy on the territory of Azerbaijan after the Soviet Union collapse, which is a bright example of the world political map redrawing. Taking into account the fact that the language is an important tool of the extending one’s influence over the country, we can say with certainty that the success of the specific state laying a claim to play the leading role in on the global political arena strongly depends on its ability to promote its language abroad, to enhance its status and to create such conditions where in the foreign country its language conquers the status close to the status of the native language. In this regard, the United Kingdom activity can serve as an example of a successful foreign language state policy.The authors analyze the activity of such organizations as the British Council, the BBC and BP on spreading the English language. The aforesaid British organizations are the master plate of the efficient language state policy tools, as long as they not only actively develop the global picture of the world, but also promote the interests of the United Kingdom on the territory of the former Soviet Union.Azerbaijan encourages the United Kingdom intention to spread the English language on its territory and does its best in assisting in the implementation of the proposed initiatives, realizing that the spread of the English language being the language of a global communication in Azerbaijan would contribute to the raising of Azerbaijan status on the world arena.

  15. Self-Organizing Neural Integration of Pose-Motion Features for Human Action Recognition

    Directory of Open Access Journals (Sweden)

    German Ignacio Parisi

    2015-06-01

    Full Text Available The visual recognition of complex, articulated human movements is fundamental for a wide range of artificial systems oriented towards human-robot communication, action classification, and action-driven perception. These challenging tasks may generally involve the processing of a huge amount of visual information and learning-based mechanisms for generalizing a set of training actions and classifying new samples. To operate in natural environments, a crucial property is the efficient and robust recognition of actions, also under noisy conditions caused by, for instance, systematic sensor errors and temporarily occluded persons. Studies of the mammalian visual system and its outperforming ability to process biological motion information suggest separate neural pathways for the distinct processing of pose and motion features at multiple levels and the subsequent integration of these visual cues for action perception. We present a neurobiologically-motivated approach to achieve noise-tolerant action recognition in real time. Our model consists of self-organizing Growing When Required (GWR networks that obtain progressively generalized representations of sensory inputs and learn inherent spatiotemporal dependencies. During the training, the GWR networks dynamically change their topological structure to better match the input space. We first extract pose and motion features from video sequences and then cluster actions in terms of prototypical pose-motion trajectories. Multi-cue trajectories from matching action frames are subsequently combined to provide action dynamics in the joint feature space. Reported experiments show that our approach outperforms previous results on a dataset of full-body actions captured with a depth sensor, and ranks among the best 21 results for a public benchmark of domestic daily actions.

  16. Cortical Motor Organization, Mirror Neurons, and Embodied Language: An Evolutionary Perspective

    Directory of Open Access Journals (Sweden)

    Leonardo Fogassi

    2012-11-01

    Full Text Available The recent conceptual achievement that the cortical motor system plays a crucial role not only in motor control but also in higher cognitive functions has given a new perspective also on the involvement of motor cortex in language perception and production. In particular, there is evidence that the matching mechanism based on mirror neurons can be involved in both pho-nological recognition and retrieval of meaning, especially for action word categories, thus suggesting a contribution of an action–perception mechanism to the automatic comprehension of semantics. Furthermore, a compari-son of the anatomo-functional properties of the frontal motor cortex among different primates and their communicative modalities indicates that the combination of the voluntary control of the gestural communication systems and of the vocal apparatus has been the critical factor in the transition from a gestural-based communication into a predominantly speech-based system. Finally, considering that the monkey and human premotor-parietal motor system, plus the prefrontal cortex, are involved in the sequential motor organization of actions and in the hierarchical combination of motor elements, we propose that elements of such motor organization have been exploited in other domains, including some aspects of the syntactic structure of language.

  17. Development of efficiency module of organization of Arctic sea cargo transportation with application of neural network technologies

    Science.gov (United States)

    Sobolevskaya, E. Yu; Glushkov, S. V.; Levchenko, N. G.; Orlov, A. P.

    2018-05-01

    The analysis of software intended for organizing and managing the processes of sea cargo transportation has been carried out. The shortcomings of information resources are presented, for the organization of work in the Arctic and Subarctic regions of the Far East: the lack of decision support systems, the lack of factor analysis to calculate the time and cost of delivery. The architecture of the module for calculating the effectiveness of the organization of sea cargo transportation has been developed. The simulation process has been considered, which is based on the neural network. The main classification factors with their weighting coefficients have been identified. The architecture of the neural network has been developed to calculate the efficiency of the organization of sea cargo transportation in Arctic conditions. The architecture of the intellectual system of organization of sea cargo transportation has been developed, taking into account the difficult navigation conditions in the Arctic. Its implementation will allow one to provide the management of the shipping company with predictive analytics; to support decision-making; to calculate the most efficient delivery route; to provide on demand online transportation forecast, to minimize the shipping cost, delays in transit, and risks to cargo safety.

  18. A Neural Assembly-Based View on Word Production: The Bilingual Test Case

    Science.gov (United States)

    Strijkers, Kristof

    2016-01-01

    I will propose a tentative framework of how words in two languages could be organized in the cerebral cortex based on neural assembly theory, according to which neurons that fire synchronously are bound into large-scale distributed functional units (assemblies), which represent a mental event as a whole ("gestalt"). For language this…

  19. ORGANIZATIONAL AND METHODICAL BASES OF MULTICULTURAL SELF-ORGANIZATION OF STUDENTS IN THE ADDITIONAL FOREIGN LANGUAGE EDUCATION

    Directory of Open Access Journals (Sweden)

    V N Kartashova

    2016-12-01

    Full Text Available The article deals with the organizational and methodical bases of multicultural self-organization of the linguistic personality of the student in terms of additional foreign language education. According to the authors’ opinion, the methodological basis is a dialogue of cultures as a philosophy of mutual understanding, mutual relations in today’s global environment. Changing the socio-cultural context of foreign language education paradigm allows you to select multicultural (teaching a foreign language on the principle of “native culture - the culture of the foreign language speaking country - the culture of the world” and active (the development and functioning of a person in the normal course of his activities, where the starting point is the introduction of man to the world of culture and his self-development approach as a priority. The contents of additional education is oriented towards the disclosure and a possible solution of specific problems in the educational practice, and based on the principles of humanism and diversification.The article analyzes the individual programs of additional education and professional training in the field of foreign languages, developed by universities in recent decades. The authors present the experience of the scientific and methodological development and implementation at the department of foreign languages and teaching methods of Bunin Yelets State University of the additional foreign language education system that provides the multicultural self-organization of the student’s linguistic personality. The description of the program of professional retraining “The multicultural self-organization expert in the field of business, management and tourism (English”, designed for the students of non-language training areas, is presented, too. The positive dynamics of mastering foreign languages, the development of global attitude of the linguistic personality to the culture and his/her cultural

  20. Prediction of air-to-blood partition coefficients of volatile organic compounds using genetic algorithm and artificial neural network

    International Nuclear Information System (INIS)

    Konoz, Elahe; Golmohammadi, Hassan

    2008-01-01

    An artificial neural network (ANN) was constructed and trained for the prediction of air-to-blood partition coefficients of volatile organic compounds. The inputs of this neural network are theoretically derived descriptors that were chosen by genetic algorithm (GA) and multiple linear regression (MLR) features selection techniques. These descriptors are: R maximal autocorrelation of lag 1 weighted by atomic Sanderson electronegativities (R1E+), electron density on the most negative atom in molecule (EDNA), maximum partial charge for C atom (MXPCC), surface weighted charge partial surface area (WNSA1), fractional charge partial surface area (FNSA2) and atomic charge weighted partial positive surface area (PPSA3). The standard errors of training, test and validation sets for the ANN model are 0.095, 0.148 and 0.120, respectively. Result obtained showed that nonlinear model can simulate the relationship between structural descriptors and the partition coefficients of the molecules in data set accurately

  1. Family veto in organ donation in Canada: framing within English-language newspaper articles.

    Science.gov (United States)

    Anthony, Samantha J; Toews, Maeghan; Caulfield, Timothy; Wright, Linda

    2017-10-17

    Because organ transplantation relies on public support for donation, an analysis of public discourse around organ donation is essential. We investigated the portrayal of family veto - when a family overrides the deceased person's prior legally executed wishes to donate - in Canadian news media. Using the Canadian Newsstream database, we identified articles published in English-language newspapers addressing family veto between 2000 and 2016. Guided by the theoretical perspectives of framing of media effects, we conducted a systematic content analysis of the articles to examine how the Canadian media framed family veto. An initial in-depth analysis of the data set in which themes and patterns were captured and recorded identified coding categories, including primary framing of family veto, prevalence, reasons, ethical or legal concerns and overall tone of the article. Two coders analyzed the data set to ensure intercoder reliability. A total of 133 relevant articles were identified. Family veto was framed predominantly as something that should not be allowed (81 articles [60.9%]) and as a reality that is little understood outside the transplantation community (45 [33.8%]). One-quarter of the articles (32 [24.1%]) highlighted ethical principles of autonomy and justice associated with family veto. Family veto was represented as a stumbling block in the present organ donation system, with most publications (107 [80.4%]) calling for change. There were differing interpretations of organ donation legislation, with 82 articles (61.6%) erroneously stating or suggesting that existing legislation permits family veto. Family veto in organ donation was portrayed predominantly negatively. Many publications reflected a misunderstanding of the law concerning this issue. Although the framing of family veto highlighted important ethical and legal concerns as well as practice and policy considerations, research is needed to enhance the understanding of family veto in organ donation

  2. Applications of self-organizing neural networks in virtual screening and diversity selection.

    Science.gov (United States)

    Selzer, Paul; Ertl, Peter

    2006-01-01

    Artificial neural networks provide a powerful technique for the analysis and modeling of nonlinear relationships between molecular structures and pharmacological activity. Many network types, including Kohonen and counterpropagation, also provide an intuitive method for the visual assessment of correspondence between the input and output data. This work shows how a combination of neural networks and radial distribution function molecular descriptors can be applied in various areas of industrial pharmaceutical research. These applications include the prediction of biological activity, the selection of screening candidates (cherry picking), and the extraction of representative subsets from large compound collections such as combinatorial libraries. The methods described have also been implemented as an easy-to-use Web tool, allowing chemists to perform interactive neural network experiments on the Novartis intranet.

  3. Documentary languages and knowledge organization systems in the context of the semantic web

    Directory of Open Access Journals (Sweden)

    Marilda Lopes Ginez de Lara

    Full Text Available The aim of this study was to discuss the need for formal documentary languages as a condition for it to function in the Semantic Web. Based on a bibliographic review, Linked Open Data is presented as an initial condition for the operationalization of the Semantic Web, similar to the movement of Linked Open Vocabularies that aimed to promote interoperability among vocabularies. We highlight the Simple Knowledge Organization System format by analyzing its main characteristics and presenting the new standard ISO 25964-1/2:2011/2012 -Thesauri and interoperability with other vocabularies, that revises previous recommendations, adding requirements for the interoperability and mapping of vocabularies. We discuss conceptual problems in the formalization of vocabularies and the need to invest critically in its operationalization, suggesting alternatives to harness the mapping of vocabularies.

  4. Neural ECM in laminar organization and connectivity development in healthy and diseased human brain

    NARCIS (Netherlands)

    Jovanov Milošević, Nataša; Judaš, Miloš; Aronica, Eleonora; Kostovic, Ivica

    2014-01-01

    The neural extracellular matrix (ECM) provides a supportive framework for differentiating cells and their processes and regulates morphogenetic events by spatially and temporally relevant localization of signaling molecules and by direct signaling via receptor and/or coreceptor-mediated action. The

  5. Self-organizing neural networks for automatic detection and classification of contrast-enhancing lesions in dynamic MR-mammography

    International Nuclear Information System (INIS)

    Vomweg, T.W.; Teifke, A.; Kauczor, H.U.; Achenbach, T.; Rieker, O.; Schreiber, W.G.; Heitmann, K.R.; Beier, T.; Thelen, M.

    2005-01-01

    Purpose: Investigation and statistical evaluation of 'Self-Organizing Maps', a special type of neural networks in the field of artificial intelligence, classifying contrast enhancing lesions in dynamic MR-mammography. Material and Methods: 176 investigations with proven histology after core biopsy or operation were randomly divided into two groups. Several Self-Organizing Maps were trained by investigations of the first group to detect and classify contrast enhancing lesions in dynamic MR-mammography. Each single pixel's signal/time curve of all patients within the second group was analyzed by the Self-Organizing Maps. The likelihood of malignancy was visualized by color overlays on the MR-images. At last assessment of contrast-enhancing lesions by each different network was rated visually and evaluated statistically. Results: A well balanced neural network achieved a sensitivity of 90.5% and a specificity of 72.2% in predicting malignancy of 88 enhancing lesions. Detailed analysis of false-positive results revealed that every second fibroadenoma showed a 'typical malignant' signal/time curve without any chance to differentiate between fibroadenomas and malignant tissue regarding contrast enhancement alone; but this special group of lesions was represented by a well-defined area of the Self-Organizing Map. Discussion: Self-Organizing Maps are capable of classifying a dynamic signal/time curve as 'typical benign' or 'typical malignant'. Therefore, they can be used as second opinion. In view of the now known localization of fibroadenomas enhancing like malignant tumors at the Self-Organizing Map, these lesions could be passed to further analysis by additional post-processing elements (e.g., based on T2-weighted series or morphology analysis) in the future. (orig.)

  6. Prediction of activity coefficients at infinite dilution for organic solutes in ionic liquids by artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Nami, Faezeh [Department of Chemistry, Shahid Beheshti University, G.C., Evin-Tehran 1983963113 (Iran, Islamic Republic of); Deyhimi, Farzad, E-mail: f-deyhimi@sbu.ac.i [Department of Chemistry, Shahid Beheshti University, G.C., Evin-Tehran 1983963113 (Iran, Islamic Republic of)

    2011-01-15

    To our knowledge, this work illustrates for the first time the ability of artificial neural network (ANN) to predict activity coefficients at infinite dilution for organic solutes in ionic liquids (ILs). Activity coefficient at infinite dilution ({gamma}{sup {infinity}}) is a useful parameter which can be used for the selection of effective solvent in the separation processes. Using a multi-layer feed-forward network with Levenberg-Marquardt optimization algorithm, the resulting ANN model generated activity coefficient at infinite dilution data over a temperature range of 298 to 363 K. The unavailable input data concerning softness (S) of organic compounds (solutes) and dipole moment ({mu}) of ionic liquids were calculated using GAMESS suites of quantum chemistry programs. The resulting ANN model and its validation are based on the investigation of up to 24 structurally different organic compounds (alkanes, alkenes, alkynes, cycloalkanes, aromatics, and alcohols) in 16 common imidazolium-based ionic liquids, at different temperatures within the range of 298 to 363 K (i.e. a total number of 914 {gamma}{sub Solute}{sup {infinity}}for each IL data point). The results show a satisfactory agreement between the predicted ANN and experimental data, where, the root mean square error (RMSE) and the determination coefficient (R{sup 2}) of the designed neural network were found to be 0.103, 0.996 for training data and 0.128, 0.994 for testing data, respectively.

  7. Self-Organizing Maps Neural Networks Applied to the Classification of Ethanol Samples According to the Region of Commercialization

    Directory of Open Access Journals (Sweden)

    Aline Regina Walkoff

    2017-10-01

    Full Text Available Physical-chemical analysis data were collected, from 998 ethanol samples of automotive ethanol commercialized in the northern, midwestern and eastern regions of the state of Paraná. The data presented self-organizing maps (SOM neural networks, which classified them according to those regions. The self-organizing maps best configuration had a 45 x 45 topology and 5000 training epochs, with a final learning rate of 6.7x10-4, a final neighborhood relationship of 3x10-2 and a mean quantization error of 2x10-2. This neural network provided a topological map depicting three separated groups, each one corresponding to samples of a same region of commercialization. Four maps of weights, one for each parameter, were presented. The network established the pH was the most important variable for classification and electrical conductivity the least one. The self-organizing maps application allowed the segmentation of alcohol samples, therefore identifying them according to the region of commercialization. DOI: http://dx.doi.org/10.17807/orbital.v9i4.982

  8. Prediction of activity coefficients at infinite dilution for organic solutes in ionic liquids by artificial neural network

    International Nuclear Information System (INIS)

    Nami, Faezeh; Deyhimi, Farzad

    2011-01-01

    To our knowledge, this work illustrates for the first time the ability of artificial neural network (ANN) to predict activity coefficients at infinite dilution for organic solutes in ionic liquids (ILs). Activity coefficient at infinite dilution (γ ∞ ) is a useful parameter which can be used for the selection of effective solvent in the separation processes. Using a multi-layer feed-forward network with Levenberg-Marquardt optimization algorithm, the resulting ANN model generated activity coefficient at infinite dilution data over a temperature range of 298 to 363 K. The unavailable input data concerning softness (S) of organic compounds (solutes) and dipole moment (μ) of ionic liquids were calculated using GAMESS suites of quantum chemistry programs. The resulting ANN model and its validation are based on the investigation of up to 24 structurally different organic compounds (alkanes, alkenes, alkynes, cycloalkanes, aromatics, and alcohols) in 16 common imidazolium-based ionic liquids, at different temperatures within the range of 298 to 363 K (i.e. a total number of 914 γ Solute ∞ for each IL data point). The results show a satisfactory agreement between the predicted ANN and experimental data, where, the root mean square error (RMSE) and the determination coefficient (R 2 ) of the designed neural network were found to be 0.103, 0.996 for training data and 0.128, 0.994 for testing data, respectively.

  9. Hierarchical modular structure enhances the robustness of self-organized criticality in neural networks

    International Nuclear Information System (INIS)

    Wang Shengjun; Zhou Changsong

    2012-01-01

    One of the most prominent architecture properties of neural networks in the brain is the hierarchical modular structure. How does the structure property constrain or improve brain function? It is thought that operating near criticality can be beneficial for brain function. Here, we find that networks with modular structure can extend the parameter region of coupling strength over which critical states are reached compared to non-modular networks. Moreover, we find that one aspect of network function—dynamical range—is highest for the same parameter region. Thus, hierarchical modularity enhances robustness of criticality as well as function. However, too much modularity constrains function by preventing the neural networks from reaching critical states, because the modular structure limits the spreading of avalanches. Our results suggest that the brain may take advantage of the hierarchical modular structure to attain criticality and enhanced function. (paper)

  10. Generalized regression neural network (GRNN)-based approach for colored dissolved organic matter (CDOM) retrieval: case study of Connecticut River at Middle Haddam Station, USA.

    Science.gov (United States)

    Heddam, Salim

    2014-11-01

    The prediction of colored dissolved organic matter (CDOM) using artificial neural network approaches has received little attention in the past few decades. In this study, colored dissolved organic matter (CDOM) was modeled using generalized regression neural network (GRNN) and multiple linear regression (MLR) models as a function of Water temperature (TE), pH, specific conductance (SC), and turbidity (TU). Evaluation of the prediction accuracy of the models is based on the root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (CC), and Willmott's index of agreement (d). The results indicated that GRNN can be applied successfully for prediction of colored dissolved organic matter (CDOM).

  11. A new approach to self-organizing fuzzy polynomial neural networks guided by genetic optimization

    International Nuclear Information System (INIS)

    Oh, Sung-Kwun; Pedrycz, Witold

    2005-01-01

    In this study, we introduce a new topology of Fuzzy Polynomial Neural Networks (FPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology. The underlying methodology involves mechanisms of genetic optimization, especially genetic algorithms (GAs). Let us recall that the design of the 'conventional' FPNNs uses an extended Group Method of Data Handling (GMDH) and exploits a fixed fuzzy inference type located at each FPN of the FPNN as well as considers a fixed number of input nodes at FPNs (or nodes) located in each layer. The proposed FPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. The structural optimization is realized via GAs whereas in the case of the parametric optimization we proceed with a standard least square method based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. The performance of the proposed gFPNN is quantified through experimentation that exploits standard data already being used in fuzzy modeling. The results reveal superiority of the proposed networks over the existing fuzzy and neural models

  12. Language-dependent changes in pitch-relevant neural activity in the auditory cortex reflect differential weighting of temporal attributes of pitch contours

    Science.gov (United States)

    Krishnan, Ananthanarayan; Gandour, Jackson T.; Xu, Yi; Suresh, Chandan H.

    2016-01-01

    There remains a gap in our knowledge base about neural representation of pitch attributes that occur between onset and offset of dynamic, curvilinear pitch contours. The aim is to evaluate how language experience shapes processing of pitch contours as reflected in the amplitude of cortical pitch-specific response components. Responses were elicited from three nonspeech, bidirectional (falling-rising) pitch contours representative of Mandarin Tone 2 varying in location of the turning point with fixed onset and offset. At the frontocentral Fz electrode site, Na–Pb and Pb–Nb amplitude of the Chinese group was larger than the English group for pitch contours exhibiting later location of the turning point relative to the one with the earliest location. Chinese listeners’ amplitude was also greater than that of English in response to those same pitch contours with later turning points. At lateral temporal sites (T7/T8), Na–Pb amplitude was larger in Chinese listeners relative to English over the right temporal site. In addition, Pb–Nb amplitude of the Chinese group showed a rightward asymmetry. The pitch contour with its turning point located about halfway of total duration evoked a rightward asymmetry regardless of group. These findings suggest that neural mechanisms processing pitch in the right auditory cortex reflect experience-dependent modulation of sensitivity to weighted integration of changes in acceleration rates of rising and falling sections and the location of the turning point. PMID:28713201

  13. Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets.

    Science.gov (United States)

    Hu, Peijun; Wu, Fa; Peng, Jialin; Bao, Yuanyuan; Chen, Feng; Kong, Dexing

    2017-03-01

    Multi-organ segmentation from CT images is an essential step for computer-aided diagnosis and surgery planning. However, manual delineation of the organs by radiologists is tedious, time-consuming and poorly reproducible. Therefore, we propose a fully automatic method for the segmentation of multiple organs from three-dimensional abdominal CT images. The proposed method employs deep fully convolutional neural networks (CNNs) for organ detection and segmentation, which is further refined by a time-implicit multi-phase evolution method. Firstly, a 3D CNN is trained to automatically localize and delineate the organs of interest with a probability prediction map. The learned probability map provides both subject-specific spatial priors and initialization for subsequent fine segmentation. Then, for the refinement of the multi-organ segmentation, image intensity models, probability priors as well as a disjoint region constraint are incorporated into an unified energy functional. Finally, a novel time-implicit multi-phase level-set algorithm is utilized to efficiently optimize the proposed energy functional model. Our method has been evaluated on 140 abdominal CT scans for the segmentation of four organs (liver, spleen and both kidneys). With respect to the ground truth, average Dice overlap ratios for the liver, spleen and both kidneys are 96.0, 94.2 and 95.4%, respectively, and average symmetric surface distance is less than 1.3 mm for all the segmented organs. The computation time for a CT volume is 125 s in average. The achieved accuracy compares well to state-of-the-art methods with much higher efficiency. A fully automatic method for multi-organ segmentation from abdominal CT images was developed and evaluated. The results demonstrated its potential in clinical usage with high effectiveness, robustness and efficiency.

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

    Directory of Open Access Journals (Sweden)

    Asaranti Kar

    2015-01-01

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

  15. PREDICTING SOIL SORPTION COEFFICIENTS OF ORGANIC CHEMICALS USING A NEURAL NETWORK MODEL

    Science.gov (United States)

    The soil/sediment adsorption partition coefficient normalized to organic carbon (Koc) is extensively used to assess the fate of organic chemicals in hazardous waste sites. Several attempts have been made to estimate the value of Koc from chemical structure ...

  16. Dynamic concision for three-dimensional reconstruction of human organ built with virtual reality modelling language (VRML)*

    OpenAIRE

    Yu, Zheng-yang; Zheng, Shu-sen; Chen, Lei-ting; He, Xiao-qian; Wang, Jian-jun

    2005-01-01

    This research studies the process of 3D reconstruction and dynamic concision based on 2D medical digital images using virtual reality modelling language (VRML) and JavaScript language, with a focus on how to realize the dynamic concision of 3D medical model with script node and sensor node in VRML. The 3D reconstruction and concision of body internal organs can be built with such high quality that they are better than those obtained from the traditional methods. With the function of dynamic c...

  17. Organization of the sleep-related neural systems in the brain of the minke whale (Balaenoptera acutorostrata).

    Science.gov (United States)

    Dell, Leigh-Anne; Karlsson, Karl Ae; Patzke, Nina; Spocter, Muhammad A; Siegel, Jerome M; Manger, Paul R

    2016-07-01

    The current study analyzed the nuclear organization of the neural systems related to the control and regulation of sleep and wake in the basal forebrain, diencephalon, midbrain, and pons of the minke whale, a mysticete cetacean. While odontocete cetaceans sleep in an unusual manner, with unihemispheric slow wave sleep (USWS) and suppressed REM sleep, it is unclear whether the mysticete whales show a similar sleep pattern. Previously, we detailed a range of features in the odontocete brain that appear to be related to odontocete-type sleep, and here present our analysis of these features in the minke whale brain. All neural elements involved in sleep regulation and control found in bihemispheric sleeping mammals and the harbor porpoise were present in the minke whale, with no specific nuclei being absent, and no novel nuclei being present. This qualitative similarity relates to the cholinergic, noradrenergic, serotonergic and orexinergic systems, and the GABAergic elements of these nuclei. Quantitative analysis revealed that the numbers of pontine cholinergic (274,242) and noradrenergic (203,686) neurons, and hypothalamic orexinergic neurons (277,604), are markedly higher than other large-brained bihemispheric sleeping mammals. Small telencephalic commissures (anterior, corpus callosum, and hippocampal), an enlarged posterior commissure, supernumerary pontine cholinergic and noradrenergic cells, and an enlarged peripheral division of the dorsal raphe nuclear complex of the minke whale, all indicate that the suite of neural characteristics thought to be involved in the control of USWS and the suppression of REM in the odontocete cetaceans are present in the minke whale. J. Comp. Neurol. 524:2018-2035, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

  18. Non parametric, self organizing, scalable modeling of spatiotemporal inputs: the sign language paradigm.

    Science.gov (United States)

    Caridakis, G; Karpouzis, K; Drosopoulos, A; Kollias, S

    2012-12-01

    Modeling and recognizing spatiotemporal, as opposed to static input, is a challenging task since it incorporates input dynamics as part of the problem. The vast majority of existing methods tackle the problem as an extension of the static counterpart, using dynamics, such as input derivatives, at feature level and adopting artificial intelligence and machine learning techniques originally designed for solving problems that do not specifically address the temporal aspect. The proposed approach deals with temporal and spatial aspects of the spatiotemporal domain in a discriminative as well as coupling manner. Self Organizing Maps (SOM) model the spatial aspect of the problem and Markov models its temporal counterpart. Incorporation of adjacency, both in training and classification, enhances the overall architecture with robustness and adaptability. The proposed scheme is validated both theoretically, through an error propagation study, and experimentally, on the recognition of individual signs, performed by different, native Greek Sign Language users. Results illustrate the architecture's superiority when compared to Hidden Markov Model techniques and variations both in terms of classification performance and computational cost. Copyright © 2012 Elsevier Ltd. All rights reserved.

  19. Ordination of self-organizing feature map neural networks and its application to the study of plant communities

    Institute of Scientific and Technical Information of China (English)

    Jintun ZHANG; Dongping MENG; Yuexiang XI

    2009-01-01

    A self-organizing feature map (SOFM) neural network is a powerful tool in analyzing and solving complex, non-linear problems. According to its features, a SOFM is entirely compatible with ordination studies of plant communities. In our present work, mathematical principles, and ordination techniques and procedures are introduced. A SOFM ordination was applied to the study of plant communities in the middle of the Taihang mountains. The ordination was carried out by using the NNTool box in MATLAB. The results of 68 quadrats of plant communities were distributed in SOFM space. The ordination axes showed the ecological gradients clearly and provided the relationships between communities with ecological meaning. The results are consistent with the reality of vegetation in the study area. This suggests that SOFM ordination is an effective technique in plant ecology. During ordination procedures, it is easy to carry out clustering of communities and so it is beneficial for combining classification and ordination in vegetation studies.

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

    Science.gov (United States)

    Gallese, Vittorio; Cuccio, Valentina

    2018-03-01

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

  1. Unique Organization of the Nuclear Envelope in the Post-natal Quiescent Neural Stem Cells

    Directory of Open Access Journals (Sweden)

    Arantxa Cebrián-Silla

    2017-07-01

    Full Text Available Neural stem cells (B1 astrocytes; NSCs in the adult ventricular-subventricular-zone (V-SVZ originate in the embryo. Surprisingly, recent work has shown that B1 cells remain largely quiescent. They are reactivated postnatally to function as primary progenitors for neurons destined for the olfactory bulb and some corpus callosum oligodendrocytes. The cellular and molecular properties of quiescent B1 cells remain unknown. Here we found that a subpopulation of B1 cells has a unique nuclear envelope invagination specialization similar to envelope-limited chromatin sheets (ELCS, reported in certain lymphocytes and some cancer cells. Using molecular markers, [3H]thymidine birth-dating, and Ara-C, we found that B1 cells with ELCS correspond to quiescent NSCs. ELCS begin forming in embryonic radial glia cells and represent a specific nuclear compartment containing particular epigenetic modifications and telomeres. These results reveal a unique nuclear compartment in quiescent NSCs, which is useful for identifying these primary progenitors and study their gene regulation.

  2. Interfaces, syntactic movement, and neural activation: A new perspective on the implementation of language in the brain

    DEFF Research Database (Denmark)

    Christensen, Ken Ramshøj

    2008-01-01

    Studies of language deficits as well as neuroimaging studies indicate that syntactic processing of displaced constituents is implemented in the brain as a distributed cortical network of modules. The data from the present fMRI study on two types of syntactic movement in Danish offers further...... support for such a distributed syntactic network. These results, together with the results from a number of other fMRI studies in the literature, form the basis for the Domain Hypothesis according to which differential activation in the subcomponents of the cortical network reflects computation...... of different syntactic domains—the interface levels between syntax, semantics, and pragmatics. The activation patters result from the interaction between movement and target domain, not (non-) canonicity or working memory per se. Specifically, movement to the CP-domain activates areas including Broca's area...

  3. Neurally adjusted ventilatory assist decreases ventilator-induced lung injury and non-pulmonary organ dysfunction in rabbits with acute lung injury

    NARCIS (Netherlands)

    Brander, Lukas; Sinderby, Christer; Lecomte, François; Leong-Poi, Howard; Bell, David; Beck, Jennifer; Tsoporis, James N.; Vaschetto, Rosanna; Schultz, Marcus J.; Parker, Thomas G.; Villar, Jesús; Zhang, Haibo; Slutsky, Arthur S.

    2009-01-01

    OBJECTIVE: To determine if neurally adjusted ventilatory assist (NAVA) that delivers pressure in proportion to diaphragm electrical activity is as protective to acutely injured lungs (ALI) and non-pulmonary organs as volume controlled (VC), low tidal volume (Vt), high positive end-expiratory

  4. Age of acquisition effects on the functional organization of language in the adult brain.

    Science.gov (United States)

    Mayberry, Rachel I; Chen, Jen-Kai; Witcher, Pamela; Klein, Denise

    2011-10-01

    Using functional magnetic resonance imaging (fMRI), we neuroimaged deaf adults as they performed two linguistic tasks with sentences in American Sign Language, grammatical judgment and phonemic-hand judgment. Participants' age-onset of sign language acquisition ranged from birth to 14 years; length of sign language experience was substantial and did not vary in relation to age of acquisition. For both tasks, a more left lateralized pattern of activation was observed, with activity for grammatical judgment being more anterior than that observed for phonemic-hand judgment, which was more posterior by comparison. Age of acquisition was linearly and negatively related to activation levels in anterior language regions and positively related to activation levels in posterior visual regions for both tasks. Copyright © 2011 Elsevier Inc. All rights reserved.

  5. Why don't men understand women? Altered neural networks for reading the language of male and female eyes.

    Directory of Open Access Journals (Sweden)

    Boris Schiffer

    Full Text Available Men are traditionally thought to have more problems in understanding women compared to understanding other men, though evidence supporting this assumption remains sparse. Recently, it has been shown, however, that meńs problems in recognizing women's emotions could be linked to difficulties in extracting the relevant information from the eye region, which remain one of the richest sources of social information for the attribution of mental states to others. To determine possible differences in the neural correlates underlying emotion recognition from female, as compared to male eyes, a modified version of the Reading the Mind in the Eyes Test in combination with functional magnetic resonance imaging (fMRI was applied to a sample of 22 participants. We found that men actually had twice as many problems in recognizing emotions from female as compared to male eyes, and that these problems were particularly associated with a lack of activation in limbic regions of the brain (including the hippocampus and the rostral anterior cingulate cortex. Moreover, men revealed heightened activation of the right amygdala to male stimuli regardless of condition (sex vs. emotion recognition. Thus, our findings highlight the function of the amygdala in the affective component of theory of mind (ToM and in empathy, and provide further evidence that men are substantially less able to infer mental states expressed by women, which may be accompanied by sex-specific differences in amygdala activity.

  6. Why don't men understand women? Altered neural networks for reading the language of male and female eyes.

    Science.gov (United States)

    Schiffer, Boris; Pawliczek, Christina; Müller, Bernhard W; Gizewski, Elke R; Walter, Henrik

    2013-01-01

    Men are traditionally thought to have more problems in understanding women compared to understanding other men, though evidence supporting this assumption remains sparse. Recently, it has been shown, however, that meńs problems in recognizing women's emotions could be linked to difficulties in extracting the relevant information from the eye region, which remain one of the richest sources of social information for the attribution of mental states to others. To determine possible differences in the neural correlates underlying emotion recognition from female, as compared to male eyes, a modified version of the Reading the Mind in the Eyes Test in combination with functional magnetic resonance imaging (fMRI) was applied to a sample of 22 participants. We found that men actually had twice as many problems in recognizing emotions from female as compared to male eyes, and that these problems were particularly associated with a lack of activation in limbic regions of the brain (including the hippocampus and the rostral anterior cingulate cortex). Moreover, men revealed heightened activation of the right amygdala to male stimuli regardless of condition (sex vs. emotion recognition). Thus, our findings highlight the function of the amygdala in the affective component of theory of mind (ToM) and in empathy, and provide further evidence that men are substantially less able to infer mental states expressed by women, which may be accompanied by sex-specific differences in amygdala activity.

  7. Triadic (ecological, neural, cognitive) niche construction: a scenario of human brain evolution extrapolating tool use and language from the control of reaching actions.

    Science.gov (United States)

    Iriki, Atsushi; Taoka, Miki

    2012-01-12

    Hominin evolution has involved a continuous process of addition of new kinds of cognitive capacity, including those relating to manufacture and use of tools and to the establishment of linguistic faculties. The dramatic expansion of the brain that accompanied additions of new functional areas would have supported such continuous evolution. Extended brain functions would have driven rapid and drastic changes in the hominin ecological niche, which in turn demanded further brain resources to adapt to it. In this way, humans have constructed a novel niche in each of the ecological, cognitive and neural domains, whose interactions accelerated their individual evolution through a process of triadic niche construction. Human higher cognitive activity can therefore be viewed holistically as one component in a terrestrial ecosystem. The brain's functional characteristics seem to play a key role in this triadic interaction. We advance a speculative argument about the origins of its neurobiological mechanisms, as an extension (with wider scope) of the evolutionary principles of adaptive function in the animal nervous system. The brain mechanisms that subserve tool use may bridge the gap between gesture and language--the site of such integration seems to be the parietal and extending opercular cortices.

  8. AN EMBRYONIC CHICK PANCREAS ORGAN CULTURE MODEL: CHARACTERIZATION AND NEURAL CONTROL OF EXOCRINE RELEASE

    Science.gov (United States)

    An embryonic chick (Gallus domesticus) whole-organ pancreas culture system was developed for use as an in vitro model to study cholinergic regulation of exocrine pancreatic function. The culture system was examined for characteristic exocrine function and viability by measuring e...

  9. Organization of the sleep-related neural systems in the brain of the harbour porpoise (Phocoena phocoena).

    Science.gov (United States)

    Dell, Leigh-Anne; Patzke, Nina; Spocter, Muhammad A; Siegel, Jerome M; Manger, Paul R

    2016-07-01

    The present study provides the first systematic immunohistochemical neuroanatomical investigation of the systems involved in the control and regulation of sleep in an odontocete cetacean, the harbor porpoise (Phocoena phocoena). The odontocete cetaceans show an unusual form of mammalian sleep, with unihemispheric slow waves, suppressed REM sleep, and continuous bodily movement. All the neural elements involved in sleep regulation and control found in bihemispheric sleeping mammals were present in the harbor porpoise, with no specific nuclei being absent, and no novel nuclei being present. This qualitative similarity of nuclear organization relates to the cholinergic, noradrenergic, serotonergic, and orexinergic systems and is extended to the γ-aminobutyric acid (GABA)ergic elements involved with these nuclei. Quantitative analysis of the cholinergic and noradrenergic nuclei of the pontine region revealed that in comparison with other mammals, the numbers of pontine cholinergic (126,776) and noradrenergic (122,878) neurons are markedly higher than in other large-brained bihemispheric sleeping mammals. The diminutive telencephalic commissures (anterior commissure, corpus callosum, and hippocampal commissure) along with an enlarged posterior commissure and supernumerary pontine cholinergic and noradrenergic neurons indicate that the control of unihemispheric slow-wave sleep is likely to be a function of interpontine competition, facilitated through the posterior commissure, in response to unilateral telencephalic input related to the drive for sleep. In addition, an expanded peripheral division of the dorsal raphe nuclear complex appears likely to play a role in the suppression of REM sleep in odontocete cetaceans. Thus, the current study provides several clues to the understanding of the neural control of the unusual sleep phenomenology present in odontocete cetaceans. J. Comp. Neurol. 524:1999-2017, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals

  10. Cerebral and cerebellar language organization in a right-handed subject with a left temporal porencephalic cyst : An fMRI study

    NARCIS (Netherlands)

    De Coninck, Mattias; Van Hecke, Wim; Crols, Roe; van Dun, Kim; Van Dam, Debby; De Deyn, Peter P.; Brysbaert, Marc; Marien, Peter

    To test the hypothesis of crossed cerebro-cerebellar language dominance (Marien, Engelborghs, Fabbro, & De Deyn, 2001) in atypical populations, the pattern of cerebral and cerebellar language organization in a right-handed woman with a large porencephalic cyst in the left temporal lobe with no

  11. Self-organized neural network for the quality control of 12-lead ECG signals

    International Nuclear Information System (INIS)

    Chen, Yun; Yang, Hui

    2012-01-01

    Telemedicine is very important for the timely delivery of health care to cardiovascular patients, especially those who live in the rural areas of developing countries. However, there are a number of uncertainty factors inherent to the mobile-phone-based recording of electrocardiogram (ECG) signals such as personnel with minimal training and other extraneous noises. PhysioNet organized a challenge in 2011 to develop efficient algorithms that can assess the ECG signal quality in telemedicine settings. This paper presents our efforts in this challenge to integrate multiscale recurrence analysis with a self-organizing map for controlling the ECG signal quality. As opposed to directly evaluating the 12-lead ECG, we utilize an information-preserving transform, i.e. Dower transform, to derive the 3-lead vectorcardiogram (VCG) from the 12-lead ECG in the first place. Secondly, we delineate the nonlinear and nonstationary characteristics underlying the 3-lead VCG signals into multiple time-frequency scales. Furthermore, a self-organizing map is trained, in both supervised and unsupervised ways, to identify the correlations between signal quality and multiscale recurrence features. The efficacy and robustness of this approach are validated using real-world ECG recordings available from PhysioNet. The average performance was demonstrated to be 95.25% for the training dataset and 90.0% for the independent test dataset with unknown labels. (paper)

  12. Organic cation transporter-mediated ergothioneine uptake in mouse neural progenitor cells suppresses proliferation and promotes differentiation into neurons.

    Directory of Open Access Journals (Sweden)

    Takahiro Ishimoto

    Full Text Available The aim of the present study is to clarify the functional expression and physiological role in neural progenitor cells (NPCs of carnitine/organic cation transporter OCTN1/SLC22A4, which accepts the naturally occurring food-derived antioxidant ergothioneine (ERGO as a substrate in vivo. Real-time PCR analysis revealed that mRNA expression of OCTN1 was much higher than that of other organic cation transporters in mouse cultured cortical NPCs. Immunocytochemical analysis showed colocalization of OCTN1 with the NPC marker nestin in cultured NPCs and mouse embryonic carcinoma P19 cells differentiated into neural progenitor-like cells (P19-NPCs. These cells exhibited time-dependent [(3H]ERGO uptake. These results demonstrate that OCTN1 is functionally expressed in murine NPCs. Cultured NPCs and P19-NPCs formed neurospheres from clusters of proliferating cells in a culture time-dependent manner. Exposure of cultured NPCs to ERGO or other antioxidants (edaravone and ascorbic acid led to a significant decrease in the area of neurospheres with concomitant elimination of intracellular reactive oxygen species. Transfection of P19-NPCs with small interfering RNA for OCTN1 markedly promoted formation of neurospheres with a concomitant decrease of [(3H]ERGO uptake. On the other hand, exposure of cultured NPCs to ERGO markedly increased the number of cells immunoreactive for the neuronal marker βIII-tubulin, but decreased the number immunoreactive for the astroglial marker glial fibrillary acidic protein (GFAP, with concomitant up-regulation of neuronal differentiation activator gene Math1. Interestingly, edaravone and ascorbic acid did not affect such differentiation of NPCs, in contrast to the case of proliferation. Knockdown of OCTN1 increased the number of cells immunoreactive for GFAP, but decreased the number immunoreactive for βIII-tubulin, with concomitant down-regulation of Math1 in P19-NPCs. Thus, OCTN1-mediated uptake of ERGO in NPCs inhibits

  13. Dynamic concision for three-dimensional reconstruction of human organ built with virtual reality modelling language (VRML)*

    Science.gov (United States)

    Yu, Zheng-yang; Zheng, Shu-sen; Chen, Lei-ting; He, Xiao-qian; Wang, Jian-jun

    2005-01-01

    This research studies the process of 3D reconstruction and dynamic concision based on 2D medical digital images using virtual reality modelling language (VRML) and JavaScript language, with a focus on how to realize the dynamic concision of 3D medical model with script node and sensor node in VRML. The 3D reconstruction and concision of body internal organs can be built with such high quality that they are better than those obtained from the traditional methods. With the function of dynamic concision, the VRML browser can offer better windows for man-computer interaction in real-time environment than ever before. 3D reconstruction and dynamic concision with VRML can be used to meet the requirement for the medical observation of 3D reconstruction and have a promising prospect in the fields of medical imaging. PMID:15973760

  14. Distinct steps of neural induction revealed by Asterix, Obelix and TrkC, genes induced by different signals from the organizer.

    Directory of Open Access Journals (Sweden)

    Sonia Pinho

    2011-04-01

    Full Text Available The amniote organizer (Hensen's node can induce a complete nervous system when grafted into a peripheral region of a host embryo. Although BMP inhibition has been implicated in neural induction, non-neural cells cannot respond to BMP antagonists unless previously exposed to a node graft for at least 5 hours before BMP inhibitors. To define signals and responses during the first 5 hours of node signals, a differential screen was conducted. Here we describe three early response genes: two of them, Asterix and Obelix, encode previously undescribed proteins of unknown function but Obelix appears to be a nuclear RNA-binding protein. The third is TrkC, a neurotrophin receptor. All three genes are induced by a node graft within 4-5 hours but they differ in the extent to which they are inducible by FGF: FGF is both necessary and sufficient to induce Asterix, sufficient but not necessary to induce Obelix and neither sufficient nor necessary for induction of TrkC. These genes are also not induced by retinoic acid, Noggin, Chordin, Dkk1, Cerberus, HGF/SF, Somatostatin or ionomycin-mediated Calcium entry. Comparison of the expression and regulation of these genes with other early neural markers reveals three distinct "epochs", or temporal waves, of gene expression accompanying neural induction by a grafted organizer, which are mirrored by specific stages of normal neural plate development. The results are consistent with neural induction being a cascade of responses elicited by different signals, culminating in the formation of a patterned nervous system.

  15. New levels of language processing complexity and organization revealed by Granger causation

    Directory of Open Access Journals (Sweden)

    David W Gow

    2012-11-01

    Full Text Available Granger causation analysis of high spatiotemporal resolution reconstructions of brain activation offers a new window on the dynamic interactions between brain areas that support language processing. Premised on the observation that causes both precede and uniquely predict their effects, this approach provides an intuitive, model-free means of identifying directed causal interactions in the brain. It requires the analysis of all nonredundant potentially interacting signals, and has shown that even early processes such as speech perception involve interactions of many areas in a strikingly large network that extends well beyond traditional left hemisphere perisylvian cortex that play out over hundreds of milliseconds. In this paper we describe this technique and review several general findings that reframe the way we think about language processing and brain function in general. These include the extent and complexity of language processing networks, the central role of interactive processing dynamics, the role of processing hubs where the input from many distinct brain regions are integrated, and the degree to which task requirements and stimulus properties influence processing dynamics and inform our understanding of language-specific localized processes.

  16. New levels of language processing complexity and organization revealed by granger causation.

    Science.gov (United States)

    Gow, David W; Caplan, David N

    2012-01-01

    Granger causation analysis of high spatiotemporal resolution reconstructions of brain activation offers a new window on the dynamic interactions between brain areas that support language processing. Premised on the observation that causes both precede and uniquely predict their effects, this approach provides an intuitive, model-free means of identifying directed causal interactions in the brain. It requires the analysis of all non-redundant potentially interacting signals, and has shown that even "early" processes such as speech perception involve interactions of many areas in a strikingly large network that extends well beyond traditional left hemisphere perisylvian cortex that play out over hundreds of milliseconds. In this paper we describe this technique and review several general findings that reframe the way we think about language processing and brain function in general. These include the extent and complexity of language processing networks, the central role of interactive processing dynamics, the role of processing hubs where the input from many distinct brain regions are integrated, and the degree to which task requirements and stimulus properties influence processing dynamics and inform our understanding of "language-specific" localized processes.

  17. Applying self-organizing map and modified radial based neural network for clustering and routing optimal path in wireless network

    Science.gov (United States)

    Hoomod, Haider K.; Kareem Jebur, Tuka

    2018-05-01

    Mobile ad hoc networks (MANETs) play a critical role in today’s wireless ad hoc network research and consist of active nodes that can be in motion freely. Because it consider very important problem in this network, we suggested proposed method based on modified radial basis function networks RBFN and Self-Organizing Map SOM. These networks can be improved by the use of clusters because of huge congestion in the whole network. In such a system, the performance of MANET is improved by splitting the whole network into various clusters using SOM. The performance of clustering is improved by the cluster head selection and number of clusters. Modified Radial Based Neural Network is very simple, adaptable and efficient method to increase the life time of nodes, packet delivery ratio and the throughput of the network will increase and connection become more useful because the optimal path has the best parameters from other paths including the best bitrate and best life link with minimum delays. Proposed routing algorithm depends on the group of factors and parameters to select the path between two points in the wireless network. The SOM clustering average time (1-10 msec for stall nodes) and (8-75 msec for mobile nodes). While the routing time range (92-510 msec).The proposed system is faster than the Dijkstra by 150-300%, and faster from the RBFNN (without modify) by 145-180%.

  18. Classification and source determination of medium petroleum distillates by chemometric and artificial neural networks: a self organizing feature approach.

    Science.gov (United States)

    Mat-Desa, Wan N S; Ismail, Dzulkiflee; NicDaeid, Niamh

    2011-10-15

    Three different medium petroleum distillate (MPD) products (white spirit, paint brush cleaner, and lamp oil) were purchased from commercial stores in Glasgow, Scotland. Samples of 10, 25, 50, 75, 90, and 95% evaporated product were prepared, resulting in 56 samples in total which were analyzed using gas chromatography-mass spectrometry. Data sets from the chromatographic patterns were examined and preprocessed for unsupervised multivariate analyses using principal component analysis (PCA), hierarchical cluster analysis (HCA), and a self organizing feature map (SOFM) artificial neural network. It was revealed that data sets comprised of higher boiling point hydrocarbon compounds provided a good means for the classification of the samples and successfully linked highly weathered samples back to their unevaporated counterpart in every case. The classification abilities of SOFM were further tested and validated for their predictive abilities where one set of weather data in each case was withdrawn from the sample set and used as a test set of the retrained network. This revealed SOFM to be an outstanding mechanism for sample discrimination and linkage over the more conventional PCA and HCA methods often suggested for such data analysis. SOFM also has the advantage of providing additional information through the evaluation of component planes facilitating the investigation of underlying variables that account for the classification. © 2011 American Chemical Society

  19. Le Rapport langue-culture dans les organisations internationales: Pour Une Sociologie des organisations internationales (The Relationship between Language and Culture in International Organizations: Toward a Sociology of International Organizations).

    Science.gov (United States)

    Jastrab de Saint Robert, de Marie-Josee

    1988-01-01

    Understanding the work of international organizations requires an understanding of the relationship between language and culture, a relationship evident in the activities of the international organizations. This relationship is partly responsible for the negative image of such organizations. Research in the sociology of international organizations…

  20. Associative memory for online learning in noisy environments using self-organizing incremental neural network.

    Science.gov (United States)

    Sudo, Akihito; Sato, Akihiro; Hasegawa, Osamu

    2009-06-01

    Associative memory operating in a real environment must perform well in online incremental learning and be robust to noisy data because noisy associative patterns are presented sequentially in a real environment. We propose a novel associative memory that satisfies these requirements. Using the proposed method, new associative pairs that are presented sequentially can be learned accurately without forgetting previously learned patterns. The memory size of the proposed method increases adaptively with learning patterns. Therefore, it suffers neither redundancy nor insufficiency of memory size, even in an environment in which the maximum number of associative pairs to be presented is unknown before learning. Noisy inputs in real environments are classifiable into two types: noise-added original patterns and faultily presented random patterns. The proposed method deals with two types of noise. To our knowledge, no conventional associative memory addresses noise of both types. The proposed associative memory performs as a bidirectional one-to-many or many-to-one associative memory and deals not only with bipolar data, but also with real-valued data. Results demonstrate that the proposed method's features are important for application to an intelligent robot operating in a real environment. The originality of our work consists of two points: employing a growing self-organizing network for an associative memory, and discussing what features are necessary for an associative memory for an intelligent robot and proposing an associative memory that satisfies those requirements.

  1. New Levels of Language Processing Complexity and Organization Revealed by Granger Causation

    OpenAIRE

    Gow, David W.; Caplan, David N.

    2012-01-01

    Granger causation analysis of high spatiotemporal resolution reconstructions of brain activation offers a new window on the dynamic interactions between brain areas that support language processing. Premised on the observation that causes both precede and uniquely predict their effects, this approach provides an intuitive, model-free means of identifying directed causal interactions in the brain. It requires the analysis of all non-redundant potentially interacting signals, and has shown that...

  2. Appraisal Psychology, Neurobiology, and Language.

    Science.gov (United States)

    Schumann, John H.

    2001-01-01

    Proposes that the confluence of stimulus appraisal and social cognition that is effected by the neural system in the brain has important implications for language and learning theories. Describes the anatomy and functions of this neural system and discusses how it may operate in motivation for second language acquisition and how in conjunction…

  3. Neural overlap in processing music and speech.

    Science.gov (United States)

    Peretz, Isabelle; Vuvan, Dominique; Lagrois, Marie-Élaine; Armony, Jorge L

    2015-03-19

    Neural overlap in processing music and speech, as measured by the co-activation of brain regions in neuroimaging studies, may suggest that parts of the neural circuitries established for language may have been recycled during evolution for musicality, or vice versa that musicality served as a springboard for language emergence. Such a perspective has important implications for several topics of general interest besides evolutionary origins. For instance, neural overlap is an important premise for the possibility of music training to influence language acquisition and literacy. However, neural overlap in processing music and speech does not entail sharing neural circuitries. Neural separability between music and speech may occur in overlapping brain regions. In this paper, we review the evidence and outline the issues faced in interpreting such neural data, and argue that converging evidence from several methodologies is needed before neural overlap is taken as evidence of sharing. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

  4. Neural overlap in processing music and speech

    Science.gov (United States)

    Peretz, Isabelle; Vuvan, Dominique; Lagrois, Marie-Élaine; Armony, Jorge L.

    2015-01-01

    Neural overlap in processing music and speech, as measured by the co-activation of brain regions in neuroimaging studies, may suggest that parts of the neural circuitries established for language may have been recycled during evolution for musicality, or vice versa that musicality served as a springboard for language emergence. Such a perspective has important implications for several topics of general interest besides evolutionary origins. For instance, neural overlap is an important premise for the possibility of music training to influence language acquisition and literacy. However, neural overlap in processing music and speech does not entail sharing neural circuitries. Neural separability between music and speech may occur in overlapping brain regions. In this paper, we review the evidence and outline the issues faced in interpreting such neural data, and argue that converging evidence from several methodologies is needed before neural overlap is taken as evidence of sharing. PMID:25646513

  5. Organic Computing

    CERN Document Server

    Würtz, Rolf P

    2008-01-01

    Organic Computing is a research field emerging around the conviction that problems of organization in complex systems in computer science, telecommunications, neurobiology, molecular biology, ethology, and possibly even sociology can be tackled scientifically in a unified way. From the computer science point of view, the apparent ease in which living systems solve computationally difficult problems makes it inevitable to adopt strategies observed in nature for creating information processing machinery. In this book, the major ideas behind Organic Computing are delineated, together with a sparse sample of computational projects undertaken in this new field. Biological metaphors include evolution, neural networks, gene-regulatory networks, networks of brain modules, hormone system, insect swarms, and ant colonies. Applications are as diverse as system design, optimization, artificial growth, task allocation, clustering, routing, face recognition, and sign language understanding.

  6. Time, Language and Action - A Unified Long-Term Memory Model for Sensory-Motor Chains and Word Schemata

    OpenAIRE

    Chersi, Fabian; Ferro, Marcello; Pezzulo, Giovanni; Pirrelli, Vito

    2011-01-01

    Action and language are known to be organized as closely-related brain subsystems. An Italian CNR project implemented a computational neural model where the ability to form chains of goal-directed actions and chains of linguistic units relies on a unified memory architecture obeying the same organizing principles.

  7. The Neural Correlates of Highly Iconic Structures and Topographic Discourse in French Sign Language as Observed in Six Hearing Native Signers

    Science.gov (United States)

    Courtin, C.; Herve, P. -Y.; Petit, L.; Zago, L.; Vigneau, M.; Beaucousin, V.; Jobard, G.; Mazoyer, B.; Mellet, E.; Tzourio-Mazoyer, N.

    2010-01-01

    "Highly iconic" structures in Sign Language enable a narrator to act, switch characters, describe objects, or report actions in four-dimensions. This group of linguistic structures has no real spoken-language equivalent. Topographical descriptions are also achieved in a sign-language specific manner via the use of signing-space and…

  8. 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.

  9. ANT Advanced Neural Tool

    International Nuclear Information System (INIS)

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

    1996-01-01

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

  10. Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media.

    Science.gov (United States)

    Bail, Christopher Andrew

    2016-10-18

    Social media sites are rapidly becoming one of the most important forums for public deliberation about advocacy issues. However, social scientists have not explained why some advocacy organizations produce social media messages that inspire far-ranging conversation among social media users, whereas the vast majority of them receive little or no attention. I argue that advocacy organizations are more likely to inspire comments from new social media audiences if they create "cultural bridges," or produce messages that combine conversational themes within an advocacy field that are seldom discussed together. I use natural language processing, network analysis, and a social media application to analyze how cultural bridges shaped public discourse about autism spectrum disorders on Facebook over the course of 1.5 years, controlling for various characteristics of advocacy organizations, their social media audiences, and the broader social context in which they interact. I show that organizations that create substantial cultural bridges provoke 2.52 times more comments about their messages from new social media users than those that do not, controlling for these factors. This study thus offers a theory of cultural messaging and public deliberation and computational techniques for text analysis and application-based survey research.

  11. Language Philosophy in the context of knowledge organization in the interactive virtual platform

    Directory of Open Access Journals (Sweden)

    Luciana De Souza Gracioso

    2012-12-01

    Full Text Available Over the past years we have pursued epistemological paths that enabled us to reflect on the meaning of language as information, especially in the interactive virtual environments. The main objective of this investigation did not specifically aim at the identification or development of methodological tools, but rather the configuration of a theoretical discourse framework about the pragmatic epistemological possibilities of study and research in the Science of Information within the context of information actions in virtual technology. Thus, we present our thoughts and conjectures about the prerogatives and the obstacles encountered in that theoretical path, concluding with some communicative implications that are inherent to the meaning of information from its use, which in turn, configure the informational activities on the Internet with regard to the existing interactive platforms, better known as Web 2.0, or Pragmatic Web.

  12. Classroom Organization by Prior Performance Interactions as Predictors of Literacy and Language Achievement

    Science.gov (United States)

    Pilcher, Heather

    2016-01-01

    Teachers' interactions with children represent an important source of influence in children's learning and development. Classroom organization, or the way the teacher manages the physical and behavioral aspects of the classroom environment, is one way that teachers can provide needed support to students who might otherwise struggle to be…

  13. Students' Interpretations of Mechanistic Language in Organic Chemistry before Learning Reactions

    Science.gov (United States)

    Galloway, Kelli R.; Stoyanovich, Carlee; Flynn, Alison B.

    2017-01-01

    Research on mechanistic thinking in organic chemistry has shown that students attribute little meaning to the electron-pushing (i.e., curved arrow) formalism. At the University of Ottawa, a new curriculum has been developed in which students are taught the electron-pushing formalism prior to instruction on specific reactions--this formalism is…

  14. Limitations to Plasticity of Language Network Reorganization in Localization Related Epilepsy

    Science.gov (United States)

    Mbwana, J.; Berl, M. M.; Ritzl, E. K.; Rosenberger, L.; Mayo, J.; Weinstein, S.; Conry, J. A.; Pearl, P. L.; Shamim, S.; Moore, E. N.; Sato, S.; Vezina, L. G.; Theodore, W. H.; Gaillard, W. D.

    2009-01-01

    Neural networks for processing language often are reorganized in patients with epilepsy. However, the extent and location of within and between hemisphere re-organization are not established. We studied 45 patients, all with a left hemisphere seizure focus (mean age 22.8, seizure onset 13.3), and 19 normal controls (mean age 24.8) with an fMRI…

  15. USAGE OF SOCIAL SERVICES IN THE PROCESS OF ORGANIZATION OF COMMUNICATION FOR FOREIGN LANGUAGE TEACHERS

    Directory of Open Access Journals (Sweden)

    Myloslava M. Chernii

    2013-10-01

    Full Text Available Today especially urgent problem is communication and the development of communication skills of future teachers as well as communicative culture is the main structural component of his professionalism. Schools and classes, both conventional and virtual, must have teachers, armed with technology resources and skills, and able to effectively teach the subject using information and communication technologies. It all comes down to the fact that the modern teacher has to be aware of the latest technologies that can help him to organize trainings and communication. Therefore, a special role is given to the training of future teachers and vector of application of social services in the organization of communication in the learning process.

  16. Automated Microscopy: Macro Language Controlling a Confocal Microscope and its External Illumination: Adaptation for Photosynthetic Organisms.

    Science.gov (United States)

    Steinbach, Gábor; Kaňa, Radek

    2016-04-01

    Photosynthesis research employs several biophysical methods, including the detection of fluorescence. Even though fluorescence is a key method to detect photosynthetic efficiency, it has not been applied/adapted to single-cell confocal microscopy measurements to examine photosynthetic microorganisms. Experiments with photosynthetic cells may require automation to perform a large number of measurements with different parameters, especially concerning light conditions. However, commercial microscopes support custom protocols (through Time Controller offered by Olympus or Experiment Designer offered by Zeiss) that are often unable to provide special set-ups and connection to external devices (e.g., for irradiation). Our new system combining an Arduino microcontroller with the Cell⊕Finder software was developed for controlling Olympus FV1000 and FV1200 confocal microscopes and the attached hardware modules. Our software/hardware solution offers (1) a text file-based macro language to control the imaging functions of the microscope; (2) programmable control of several external hardware devices (light sources, thermal controllers, actuators) during imaging via the Arduino microcontroller; (3) the Cell⊕Finder software with ergonomic user environment, a fast selection method for the biologically important cells and precise positioning feature that reduces unwanted bleaching of the cells by the scanning laser. Cell⊕Finder can be downloaded from http://www.alga.cz/cellfinder. The system was applied to study changes in fluorescence intensity in Synechocystis sp. PCC6803 cells under long-term illumination. Thus, we were able to describe the kinetics of phycobilisome decoupling. Microscopy data showed that phycobilisome decoupling appears slowly after long-term (>1 h) exposure to high light.

  17. Real-Time Processing of ASL Signs: Delayed First Language Acquisition Affects Organization of the Mental Lexicon

    Science.gov (United States)

    Lieberman, Amy M.; Borovsky, Arielle; Hatrak, Marla; Mayberry, Rachel I.

    2015-01-01

    Sign language comprehension requires visual attention to the linguistic signal and visual attention to referents in the surrounding world, whereas these processes are divided between the auditory and visual modalities for spoken language comprehension. Additionally, the age-onset of first language acquisition and the quality and quantity of…

  18. Lexical-Semantic Organization in Bilingually Developing Deaf Children with ASL-Dominant Language Exposure: Evidence from a Repeated Meaning Association Task

    Science.gov (United States)

    Mann, Wolfgang; Sheng, Li; Morgan, Gary

    2016-01-01

    This study compared the lexical-semantic organization skills of bilingually developing deaf children in American Sign Language (ASL) and English with those of a monolingual hearing group. A repeated meaning-association paradigm was used to assess retrieval of semantic relations in deaf 6-10-year-olds exposed to ASL from birth by their deaf…

  19. French-language version of the World Health Organization quality of life spirituality, religiousness and personal beliefs instrument

    Science.gov (United States)

    2012-01-01

    Background A valid assessment of spirituality and religiousness is necessary for clinical and research purposes. We developed and assessed the validity of a French-language version of the World Health Organization Quality of Life Spirituality, Religiousness and Personal Beliefs Instrument (WHOQOL-SRPB). Methods The SRPB was translated into French according to the methods recommended by the WHOQOL group. An Internet survey was conducted in 561 people in 2010, with follow-up 2 weeks later (n = 231, 41%), to assess reliability, factor structure, social desirability bias and construct validity of this scale. Tests were performed based on item-response theory. Results A modal score of 1 (all answers=”not at all”) was observed for Faith (in 34% of participants), Connectedness (27%), and Spiritual Strength (14%). All scales had test-retest reliability coefficients ≥0.7. Cronbach’s alpha coefficients were high for all subscales (0.74 to 0.98) and very high (>0.9) for three subscales (Connectedness, Spiritual Strength and Faith). Scores of Faith, Connectedness, Spiritual Strength and Meaning of Life were higher for respondents with religious practice than for those who had no religious practice. No association was found between SRPB and age or sex. The Awe subscale had a low information function for all levels of the Awe latent trait and may benefit from inclusion of an additional item. Conclusions The French language version of the SRPB retained many properties of the original version. However, the SRPB could be improved by trimming redundant items. The strength of SRPB relies on its multinational development and validation, allowing for cross-cultural comparisons. PMID:22515747

  20. French-language version of the World Health Organization quality of life spirituality, religiousness and personal beliefs instrument

    Directory of Open Access Journals (Sweden)

    Mandhouj Olfa

    2012-04-01

    Full Text Available Abstract Background A valid assessment of spirituality and religiousness is necessary for clinical and research purposes. We developed and assessed the validity of a French-language version of the World Health Organization Quality of Life Spirituality, Religiousness and Personal Beliefs Instrument (WHOQOL-SRPB. Methods The SRPB was translated into French according to the methods recommended by the WHOQOL group. An Internet survey was conducted in 561 people in 2010, with follow-up 2 weeks later (n = 231, 41%, to assess reliability, factor structure, social desirability bias and construct validity of this scale. Tests were performed based on item-response theory. Results A modal score of 1 (all answers=”not at all” was observed for Faith (in 34% of participants, Connectedness (27%, and Spiritual Strength (14%. All scales had test-retest reliability coefficients ≥0.7. Cronbach’s alpha coefficients were high for all subscales (0.74 to 0.98 and very high (>0.9 for three subscales (Connectedness, Spiritual Strength and Faith. Scores of Faith, Connectedness, Spiritual Strength and Meaning of Life were higher for respondents with religious practice than for those who had no religious practice. No association was found between SRPB and age or sex. The Awe subscale had a low information function for all levels of the Awe latent trait and may benefit from inclusion of an additional item. Conclusions The French language version of the SRPB retained many properties of the original version. However, the SRPB could be improved by trimming redundant items. The strength of SRPB relies on its multinational development and validation, allowing for cross-cultural comparisons.

  1. Prediction of octanol-water partition coefficients of organic compounds by multiple linear regression, partial least squares, and artificial neural network.

    Science.gov (United States)

    Golmohammadi, Hassan

    2009-11-30

    A quantitative structure-property relationship (QSPR) study was performed to develop models those relate the structure of 141 organic compounds to their octanol-water partition coefficients (log P(o/w)). A genetic algorithm was applied as a variable selection tool. Modeling of log P(o/w) of these compounds as a function of theoretically derived descriptors was established by multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN). The best selected descriptors that appear in the models are: atomic charge weighted partial positively charged surface area (PPSA-3), fractional atomic charge weighted partial positive surface area (FPSA-3), minimum atomic partial charge (Qmin), molecular volume (MV), total dipole moment of molecule (mu), maximum antibonding contribution of a molecule orbital in the molecule (MAC), and maximum free valency of a C atom in the molecule (MFV). The result obtained showed the ability of developed artificial neural network to prediction of partition coefficients of organic compounds. Also, the results revealed the superiority of ANN over the MLR and PLS models. Copyright 2009 Wiley Periodicals, Inc.

  2. Neural electrical activity and neural network growth.

    Science.gov (United States)

    Gafarov, F M

    2018-05-01

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

  3. Music and language: relations and disconnections.

    Science.gov (United States)

    Kraus, Nina; Slater, Jessica

    2015-01-01

    Music and language provide an important context in which to understand the human auditory system. While they perform distinct and complementary communicative functions, music and language are both rooted in the human desire to connect with others. Since sensory function is ultimately shaped by what is biologically important to the organism, the human urge to communicate has been a powerful driving force in both the evolution of auditory function and the ways in which it can be changed by experience within an individual lifetime. This chapter emphasizes the highly interactive nature of the auditory system as well as the depth of its integration with other sensory and cognitive systems. From the origins of music and language to the effects of auditory expertise on the neural encoding of sound, we consider key themes in auditory processing, learning, and plasticity. We emphasize the unique role of the auditory system as the temporal processing "expert" in the brain, and explore relationships between communication and cognition. We demonstrate how experience with music and language can have a significant impact on underlying neural function, and that auditory expertise strengthens some of the very same aspects of sound encoding that are deficient in impaired populations. © 2015 Elsevier B.V. All rights reserved.

  4. Viewing brain processes as Critical State Transitions across levels of organization: Neural events in Cognition and Consciousness, and general principles.

    Science.gov (United States)

    Werner, Gerhard

    2009-04-01

    In this theoretical and speculative essay, I propose that insights into certain aspects of neural system functions can be gained from viewing brain function in terms of the branch of Statistical Mechanics currently referred to as "Modern Critical Theory" [Stanley, H.E., 1987. Introduction to Phase Transitions and Critical Phenomena. Oxford University Press; Marro, J., Dickman, R., 1999. Nonequilibrium Phase Transitions in Lattice Models. Cambridge University Press, Cambridge, UK]. The application of this framework is here explored in two stages: in the first place, its principles are applied to state transitions in global brain dynamics, with benchmarks of Cognitive Neuroscience providing the relevant empirical reference points. The second stage generalizes to suggest in more detail how the same principles could also apply to the relation between other levels of the structural-functional hierarchy of the nervous system and between neural assemblies. In this view, state transitions resulting from the processing at one level are the input to the next, in the image of a 'bucket brigade', with the content of each bucket being passed on along the chain, after having undergone a state transition. The unique features of a process of this kind will be discussed and illustrated.

  5. Recognition of abstract objects via neural oscillators: interaction among topological organization, associative memory and gamma band synchronization.

    Science.gov (United States)

    Ursino, Mauro; Magosso, Elisa; Cuppini, Cristiano

    2009-02-01

    Synchronization of neural activity in the gamma band is assumed to play a significant role not only in perceptual processing, but also in higher cognitive functions. Here, we propose a neural network of Wilson-Cowan oscillators to simulate recognition of abstract objects, each represented as a collection of four features. Features are ordered in topological maps of oscillators connected via excitatory lateral synapses, to implement a similarity principle. Experience on previous objects is stored in long-range synapses connecting the different topological maps, and trained via timing dependent Hebbian learning (previous knowledge principle). Finally, a downstream decision network detects the presence of a reliable object representation, when all features are oscillating in synchrony. Simulations performed giving various simultaneous objects to the network (from 1 to 4), with some missing and/or modified properties suggest that the network can reconstruct objects, and segment them from the other simultaneously present objects, even in case of deteriorated information, noise, and moderate correlation among the inputs (one common feature). The balance between sensitivity and specificity depends on the strength of the Hebbian learning. Achieving a correct reconstruction in all cases, however, requires ad hoc selection of the oscillation frequency. The model represents an attempt to investigate the interactions among topological maps, autoassociative memory, and gamma-band synchronization, for recognition of abstract objects.

  6. Neural Correlates of Task-Irrelevant First and Second Language Emotion Words — Evidence from the Face-Word Stroop Task

    Directory of Open Access Journals (Sweden)

    Lin Fan

    2016-11-01

    Full Text Available Emotionally valenced words have thus far not been empirically examined in a bilingual population with the emotional face-word Stroop paradigm. Chinese-English bilinguals were asked to identify the facial expressions of emotion with their first (L1 or second (L2 language task-irrelevant emotion words superimposed on the face pictures. We attempted to examine how the emotional content of words modulates behavioral performance and cerebral functioning in the bilinguals’ two languages. The results indicated that there were significant congruency effects for both L1 and L2 emotion words, and that identifiable differences in the magnitude of Stroop effect between the two languages were also observed, suggesting L1 is more capable of activating the emotional response to word stimuli. For event-related potentials (ERPs data, an N350-550 effect was observed only in L1 task with greater negativity for incongruent than congruent trials. The size of N350-550 effect differed across languages, whereas no identifiable language distinction was observed in the effect of conflict slow potential (conflict SP. Finally, more pronounced negative amplitude at 230-330 ms was observed in L1 than in L2, but only for incongruent trials. This negativity, likened to an orthographic decoding N250, may reflect the extent of attention to emotion word processing at word-form level, while N350-550 reflects a complicated set of processes in the conflict processing. Overall, the face-word congruency effect has reflected identifiable language distinction at 230-330 and 350-550 ms, which provides supporting evidence for the theoretical proposals assuming attenuated emotionality of L2 processing.

  7. RLL-1: A Representation Language Language

    Science.gov (United States)

    1980-10-01

    adaptable organisms over those which contain, built-in optimized features. Compare the extinct dinosaur , unable to adapt to new situations, with two of...natural language understandirq for KRL [Bobrow & Winograd] and OWL [Szolovits, et alD. For this reason , his language is ofL.:n inadequate for any...for no particular reason , switch the organ into its Oboe state. That is, the sequence which triggers a change to the organ is a Punction of the organ’s

  8. Artificial neural network based gynaecological image-guided adaptive brachytherapy treatment planning correction of intra-fractional organs at risk dose variation

    Directory of Open Access Journals (Sweden)

    Ramin Jaberi

    2017-12-01

    Full Text Available Purpose : Intra-fractional organs at risk (OARs deformations can lead to dose variation during image-guided adaptive brachytherapy (IGABT. The aim of this study was to modify the final accepted brachytherapy treatment plan to dosimetrically compensate for these intra-fractional organs-applicators position variations and, at the same time, fulfilling the dosimetric criteria. Material and methods : Thirty patients with locally advanced cervical cancer, after external beam radiotherapy (EBRT of 45-50 Gy over five to six weeks with concomitant weekly chemotherapy, and qualified for intracavitary high-dose-rate (HDR brachytherapy with tandem-ovoid applicators were selected for this study. Second computed tomography scan was done for each patient after finishing brachytherapy treatment with applicators in situ. Artificial neural networks (ANNs based models were used to predict intra-fractional OARs dose-volume histogram parameters variations and propose a new final plan. Results : A model was developed to estimate the intra-fractional organs dose variations during gynaecological intracavitary brachytherapy. Also, ANNs were used to modify the final brachytherapy treatment plan to compensate dosimetrically for changes in ‘organs-applicators’, while maintaining target dose at the original level. Conclusions : There are semi-automatic and fast responding models that can be used in the routine clinical workflow to reduce individually IGABT uncertainties. These models can be more validated by more patients’ plans to be able to serve as a clinical tool.

  9. Artificial neural network based gynaecological image-guided adaptive brachytherapy treatment planning correction of intra-fractional organs at risk dose variation.

    Science.gov (United States)

    Jaberi, Ramin; Siavashpour, Zahra; Aghamiri, Mahmoud Reza; Kirisits, Christian; Ghaderi, Reza

    2017-12-01

    Intra-fractional organs at risk (OARs) deformations can lead to dose variation during image-guided adaptive brachytherapy (IGABT). The aim of this study was to modify the final accepted brachytherapy treatment plan to dosimetrically compensate for these intra-fractional organs-applicators position variations and, at the same time, fulfilling the dosimetric criteria. Thirty patients with locally advanced cervical cancer, after external beam radiotherapy (EBRT) of 45-50 Gy over five to six weeks with concomitant weekly chemotherapy, and qualified for intracavitary high-dose-rate (HDR) brachytherapy with tandem-ovoid applicators were selected for this study. Second computed tomography scan was done for each patient after finishing brachytherapy treatment with applicators in situ. Artificial neural networks (ANNs) based models were used to predict intra-fractional OARs dose-volume histogram parameters variations and propose a new final plan. A model was developed to estimate the intra-fractional organs dose variations during gynaecological intracavitary brachytherapy. Also, ANNs were used to modify the final brachytherapy treatment plan to compensate dosimetrically for changes in 'organs-applicators', while maintaining target dose at the original level. There are semi-automatic and fast responding models that can be used in the routine clinical workflow to reduce individually IGABT uncertainties. These models can be more validated by more patients' plans to be able to serve as a clinical tool.

  10. Cracking the neural code, treating paralysis and the future of bioelectronic medicine.

    Science.gov (United States)

    Bouton, C

    2017-07-01

    The human nervous system is a vast network carrying not only sensory and movement information, but also information to and from our organs, intimately linking it to our overall health. Scientists and engineers have been working for decades to tap into this network and 'crack the neural code' by decoding neural signals and learning how to 'speak' the language of the nervous system. Progress has been made in developing neural decoding methods to decipher brain activity and bioelectronic technologies to treat rheumatoid arthritis, paralysis, epilepsy and for diagnosing brain-related diseases such as Parkinson's and Alzheimer's disease. In a recent first-in-human study involving paralysis, a paralysed male study participant regained movement in his hand, years after his injury, through the use of a bioelectronic neural bypass. This work combined neural decoding and neurostimulation methods to translate and re-route signals around damaged neural pathways within the central nervous system. By extending these methods to decipher neural messages in the peripheral nervous system, status information from our bodily functions and specific organs could be gained. This, one day, could allow real-time diagnostics to be performed to give us a deeper insight into a patient's condition, or potentially even predict disease or allow early diagnosis. The future of bioelectronic medicine is extremely bright and is wide open as new diagnostic and treatment options are developed for patients around the world. © 2017 The Association for the Publication of the Journal of Internal Medicine.

  11. Development of a Real-Time Thermal Performance Diagnostic Monitoring system Using Self-Organizing Neural Network for Kori-2 Nuclear Power Unit

    International Nuclear Information System (INIS)

    Kang, Hyun Gook; Seong, Poong Hyun

    1996-01-01

    In this work, a PC-based thermal performance monitoring system is developed for the nuclear power plants. the system performs real-time thermal performance monitoring and diagnosis during plant operation. Specifically, a prototype for the Kori-2 nuclear power unit is developed and examined is very difficult because the system structure is highly complex and the components are very much inter-related. In this study, some major diagnostic performance parameters are selected in order to represent the thermal cycle effectively and to reduce the computing time. The Fuzzy ARTMAP, a self-organizing neural network, is used to recognize the characteristic pattern change of the performance parameters in abnormal situation. By examination, the algorithm is shown to be ale to detect abnormality and to identify the fault component or the change of system operation condition successfully. For the convenience of operators, a graphical user interface is also constructed in this work. 5 figs., 3 tabs., 11 refs. (Author)

  12. Profile of the biodiesel B100 commercialized in the region of Londrina: application of artificial neural networks of the type self organizing maps

    Directory of Open Access Journals (Sweden)

    Vilson Machado de Campos Filho

    2015-10-01

    Full Text Available The 97 samples were grouped according to the year of analysis. For each year, letters from A to D were attributed, between 2010 and 2013; A (33 B (25 C (24 and D (15. The parameters of compliance previously analyzed are those established by the National Agency of Petroleum, Natural Gas and Biofuels (ANP, through resolution ANP 07/2008. The parameters analyzed were density, flash point, peroxide and acid value. The observed values were presented to Artificial Neural Network (ANN Self Organizing MAP (SOM in order to classify, by physical-chemical properties, each sample from year of production. The ANN was trained on different days and randomly divided samples into two groups, training and test set. It was found that SOM network differentiated samples by the year and the compliance parameters, allowing to identify that the density and the flash point were the most significant compliance parameters, so good for the distinction and classification of these samples.

  13. Language and Culture

    Science.gov (United States)

    Kramsch, Claire

    2014-01-01

    This paper surveys the research methods and approaches used in the multidisciplinary field of applied language studies or language education over the last fourty years. Drawing on insights gained in psycho- and sociolinguistics, educational linguistics and linguistic anthropology with regard to language and culture, it is organized around five…

  14. Language-invariant verb processing regions in Spanish-English bilinguals.

    Science.gov (United States)

    Willms, Joanna L; Shapiro, Kevin A; Peelen, Marius V; Pajtas, Petra E; Costa, Albert; Moo, Lauren R; Caramazza, Alfonso

    2011-07-01

    Nouns and verbs are fundamental grammatical building blocks of all languages. Studies of brain-damaged patients and healthy individuals have demonstrated that verb processing can be dissociated from noun processing at a neuroanatomical level. In cases where bilingual patients have a noun or verb deficit, the deficit has been observed in both languages. This suggests that the noun-verb distinction may be based on neural components that are common across languages. Here we investigated the cortical organization of grammatical categories in healthy, early Spanish-English bilinguals using functional magnetic resonance imaging (fMRI) in a morphophonological alternation task. Four regions showed greater activity for verbs than for nouns in both languages: left posterior middle temporal gyrus (LMTG), left middle frontal gyrus (LMFG), pre-supplementary motor area (pre-SMA), and right middle occipital gyrus (RMOG); no regions showed greater activation for nouns. Multi-voxel pattern analysis within verb-specific regions showed indistinguishable activity patterns for English and Spanish, indicating language-invariant bilingual processing. In LMTG and LMFG, patterns were more similar within than across grammatical category, both within and across languages, indicating language-invariant grammatical class information. These results suggest that the neural substrates underlying verb-specific processing are largely independent of language in bilinguals, both at the macroscopic neuroanatomical level and at the level of voxel activity patterns. Copyright © 2011 Elsevier Inc. All rights reserved.

  15. Music and Language Syntax Interact in Broca's Area: An fMRI Study.

    Directory of Open Access Journals (Sweden)

    Richard Kunert

    Full Text Available Instrumental music and language are both syntactic systems, employing complex, hierarchically-structured sequences built using implicit structural norms. This organization allows listeners to understand the role of individual words or tones in the context of an unfolding sentence or melody. Previous studies suggest that the brain mechanisms of syntactic processing may be partly shared between music and language. However, functional neuroimaging evidence for anatomical overlap of brain activity involved in linguistic and musical syntactic processing has been lacking. In the present study we used functional magnetic resonance imaging (fMRI in conjunction with an interference paradigm based on sung sentences. We show that the processing demands of musical syntax (harmony and language syntax interact in Broca's area in the left inferior frontal gyrus (without leading to music and language main effects. A language main effect in Broca's area only emerged in the complex music harmony condition, suggesting that (with our stimuli and tasks a language effect only becomes visible under conditions of increased demands on shared neural resources. In contrast to previous studies, our design allows us to rule out that the observed neural interaction is due to: (1 general attention mechanisms, as a psychoacoustic auditory anomaly behaved unlike the harmonic manipulation, (2 error processing, as the language and the music stimuli contained no structural errors. The current results thus suggest that two different cognitive domains-music and language-might draw on the same high level syntactic integration resources in Broca's area.

  16. Organização neural de diferentes tipos de medo e suas implicações na ansiedade Neural organization of different types of fear: implications for the understanding of anxiety

    Directory of Open Access Journals (Sweden)

    Marcus Lira Brandão

    2003-12-01

    espécie de interface comutando os estímulos para os substratos neurais apropriados para elaboração das respostas defensivas condicionadas ou incondicionadas.The dangerous stimuli may be potentially dangerous, distal or proximal and the recognition by the animals of each one of these conditions is determinant for the nature of the fear responses. In the present article a parallel with this particular process is drawn taking into account that different fear responses are generated by light, tones and contexts used as conditioned stimuli and by unconditioned stimulation of the dorsal periaqueductal gray (dPAG. In this review we summarize the efforts that have been made to characterize the neural circuits recruited in the organization of defensive reactions to the conditioned and unconditioned aversive stimulations, particularly evidence linking the brain's defense response systems to the concept of fear-stress-anxiety. The dPAG constitute the main neural substrates for the integration of aversive states in response to proximal aversive stimuli. In fact, panic-like behaviors often result when this structure is electrically or chemically stimulated. On the other hand, successful preparatory processes of danger-orientation and preparedness to flee seem to be linked to anxiety. The pre-frontal and cingulate cortex, median raphe nucleus, septum and hippocampus seem to be implicated in the elaboration and organization of these responses. As a working hypothesis, it is advanced that increasing the intensity and proximity of the danger may lead to an emotional shift. When the animals are submitted to this gradual increase in aversiveness there is a switch from the neural circuits responsible for the production of the orientated and organized motor patterns of appropriate defensive response to a conditioned stimulus towards the incomplete and uncoordinated defense responses related to panic attacks. The circuits in the amygdala and the medial hypothalamus responsible for the

  17. Program Helps Simulate Neural Networks

    Science.gov (United States)

    Villarreal, James; Mcintire, Gary

    1993-01-01

    Neural Network Environment on Transputer System (NNETS) computer program provides users high degree of flexibility in creating and manipulating wide variety of neural-network topologies at processing speeds not found in conventional computing environments. Supports back-propagation and back-propagation-related algorithms. Back-propagation algorithm used is implementation of Rumelhart's generalized delta rule. NNETS developed on INMOS Transputer(R). Predefines back-propagation network, Jordan network, and reinforcement network to assist users in learning and defining own networks. Also enables users to configure other neural-network paradigms from NNETS basic architecture. Small portion of software written in OCCAM(R) language.

  18. Neural cell adhesion molecule, NCAM, regulates thalamocortical axon pathfinding and the organization of the cortical somatosensory representation in mouse

    Science.gov (United States)

    Enriquez-Barreto, Lilian; Palazzetti, Cecilia; Brennaman, Leann H.; Maness, Patricia F.; Fairén, Alfonso

    2012-01-01

    To study the potential role of neural cell adhesion molecule (NCAM) in the development of thalamocortical (TC) axon topography, wild type, and NCAM null mutant mice were analyzed for NCAM expression, projection, and targeting of TC afferents within the somatosensory area of the neocortex. Here we report that NCAM and its α-2,8-linked polysialic acid (PSA) are expressed in developing TC axons during projection to the neocortex. Pathfinding of TC axons in wild type and null mutant mice was mapped using anterograde DiI labeling. At embryonic day E16.5, null mutant mice displayed misguided TC axons in the dorsal telencephalon, but not in the ventral telencephalon, an intermediate target that initially sorts TC axons toward correct neocortical areas. During the early postnatal period, rostrolateral TC axons within the internal capsule along the ventral telencephalon adopted distorted trajectories in the ventral telencephalon and failed to reach the neocortex in NCAM null mutant animals. NCAM null mutants showed abnormal segregation of layer IV barrels in a restricted portion of the somatosensory cortex. As shown by Nissl and cytochrome oxidase staining, barrels of the anterolateral barrel subfield (ALBSF) and the most distal barrels of the posteromedial barrel subfield (PMBSF) did not segregate properly in null mutant mice. These results indicate a novel role for NCAM in axonal pathfinding and topographic sorting of TC axons, which may be important for the function of specific territories of sensory representation in the somatosensory cortex. PMID:22723769

  19. Neural coding in graphs of bidirectional associative memories.

    Science.gov (United States)

    Bouchain, A David; Palm, Günther

    2012-01-24

    In the last years we have developed large neural network models for the realization of complex cognitive tasks in a neural network architecture that resembles the network of the cerebral cortex. We have used networks of several cortical modules that contain two populations of neurons (one excitatory, one inhibitory). The excitatory populations in these so-called "cortical networks" are organized as a graph of Bidirectional Associative Memories (BAMs), where edges of the graph correspond to BAMs connecting two neural modules and nodes of the graph correspond to excitatory populations with associative feedback connections (and inhibitory interneurons). The neural code in each of these modules consists essentially of the firing pattern of the excitatory population, where mainly it is the subset of active neurons that codes the contents to be represented. The overall activity can be used to distinguish different properties of the patterns that are represented which we need to distinguish and control when performing complex tasks like language understanding with these cortical networks. The most important pattern properties or situations are: exactly fitting or matching input, incomplete information or partially matching pattern, superposition of several patterns, conflicting information, and new information that is to be learned. We show simple simulations of these situations in one area or module and discuss how to distinguish these situations based on the overall internal activation of the module. This article is part of a Special Issue entitled "Neural Coding". Copyright © 2011 Elsevier B.V. All rights reserved.

  20. Artificial neural network modelling for organic and total nitrogen removal of aerobic granulation under steady-state condition.

    Science.gov (United States)

    Gong, H; Pishgar, R; Tay, J H

    2018-04-27

    Aerobic granulation is a recent technology with high level of complexity and sensitivity to environmental and operational conditions. Artificial neural networks (ANNs), computational tools capable of describing complex non-linear systems, are the best fit to simulate aerobic granular bioreactors. In this study, two feedforward backpropagation ANN models were developed to predict chemical oxygen demand (Model I) and total nitrogen removal efficiencies (Model II) of aerobic granulation technology under steady-state condition. Fundamentals of ANN models and the steps to create them were briefly reviewed. The models were respectively fed with 205 and 136 data points collected from laboratory-, pilot-, and full-scale studies on aerobic granulation technology reported in the literature. Initially, 60%, 20%, and 20%, and 80%, 10%, and 10% of the points in the corresponding datasets were randomly chosen and used for training, testing, and validation of Model I, and Model II, respectively. Overall coefficient of determination (R 2 ) value and mean squared error (MSE) of the two models were initially 0.49 and 15.5, and 0.37 and 408, respectively. To improve the model performance, two data division methods were used. While one method is generic and potentially applicable to other fields, the other can only be applied to modelling the performance of aerobic granular reactors. R 2 value and MSE were improved to 0.90 and 2.54, and 0.81 and 121.56, respectively, after applying the new data division methods. The results demonstrated that ANN-based models were capable simulation approach to predict a complicated process like aerobic granulation.

  1. Neural organization of first optic neuropils in the littoral crab Hemigrapsus oregonensis and the semiterrestrial species Chasmagnathus granulatus.

    Science.gov (United States)

    Sztarker, Julieta; Strausfeld, Nicholas; Andrew, David; Tomsic, Daniel

    2009-03-10

    Crustaceans are among the most extensively distributed arthropods, occupying many ecologies and manifesting a great variety of compound eye optics; but in comparison with insects, relatively little is known about the organization and neuronal morphologies of their underlying optic neuropils. Most studies, which have been limited to descriptions of the first neuropil--the lamina--suggest that different species have approximately comparable cell types. However, such studies have been limited with regard to the types of neurons they identify and most omit their topographic relationships. It is also uncertain whether similarities, such as they are, are independent of visual ecologies. The present account describes and compares the morphologies and dispositions of monopolar and other efferent neurons as well as the organization of tangential and smaller centrifugal neurons in two grapsoid crabs, one from the South Atlantic, the other from the North Pacific. Because these species occupy significantly disparate ecologies we ask whether this might be reflected in differences of cell arrangements within the most peripheral levels of the visual system. The present study identifies such differences with respect to the organization of centrifugal neurons to the lamina. We also identify in both species neurons in the lamina that have hitherto not been identified in crustaceans and we draw specific comparisons between the layered organization of the grapsoid lamina and layered laminas of insects.

  2. Spectrophotometric determination of iron species using a combination of artificial neural networks and dispersive liquid–liquid microextraction based on solidification of floating organic drop

    International Nuclear Information System (INIS)

    Moghadam, Masoud Rohani; Shabani, Ali Mohammad Haji; Dadfarnia, Shayessteh

    2011-01-01

    Highlights: ► Combination of DLLME-SFO/fiber optic-linear array detection/chemometric methods. ► Simultaneous determination of complexes with overlapping spectra. ► A novel DLLME-SFO method is proposed for extraction of iron species. ► The extracted iron species are simultaneous determined using PC-ANNs. ► The enhancement factor of 162 and 125 are achieved for Fe 3+ and Fe 2+ , respectively. - Abstract: A dispersive liquid–liquid microextraction based on solidification of floating organic drop (DLLME-SFO) and artificial neural networks method was developed for the simultaneous separation/preconcentration and speciation of iron in water samples. In this method, an appropriate mixture of ethanol (as the disperser solvent) and 1-undecanol (as the extracting solvent) containing appropriate amount of 2-thenoyltrifluoroacetone (TTA) (as the complexing agent) was injected rapidly into the water sample containing iron (II) and iron (III) species. At this step, the iron species interacted with the TTA and extracted into the 1-undecanol. After the phase separation, the absorbance of the extracted irons was measured in the wavelength region of 450–600 nm. The artificial neural networks were then applied for simultaneous determination of individual iron species. Under optimum conditions, the calibration graphs were linear in the range of 95–1070 μg L −1 and 31–350 μg L −1 with detection limits of 25 and 8 μg L −1 for iron (II) and iron (III), respectively. The relative standard deviations (R.S.D., n = 6) were lower than 4.2%. The enhancement factor of 162 and 125 were obtained for Fe 3+ and Fe 2+ ions, respectively. The procedure was applied to power plant drum water and several potable water samples; and accuracy was assessed through the recovery experiments and independent analysis by graphite furnace atomic absorption spectrometry.

  3. Artificial neural networks environmental forecasting in comparison with multiple linear regression technique: From heavy metals to organic micropollutants screening in agricultural soils

    Science.gov (United States)

    Bonelli, Maria Grazia; Ferrini, Mauro; Manni, Andrea

    2016-12-01

    The assessment of metals and organic micropollutants contamination in agricultural soils is a difficult challenge due to the extensive area used to collect and analyze a very large number of samples. With Dioxins and dioxin-like PCBs measurement methods and subsequent the treatment of data, the European Community advises the develop low-cost and fast methods allowing routing analysis of a great number of samples, providing rapid measurement of these compounds in the environment, feeds and food. The aim of the present work has been to find a method suitable to describe the relations occurring between organic and inorganic contaminants and use the value of the latter in order to forecast the former. In practice, the use of a metal portable soil analyzer coupled with an efficient statistical procedure enables the required objective to be achieved. Compared to Multiple Linear Regression, the Artificial Neural Networks technique has shown to be an excellent forecasting method, though there is no linear correlation between the variables to be analyzed.

  4. Language Contact.

    Science.gov (United States)

    Nelde, Peter Hans

    1995-01-01

    Examines the phenomenon of language contact and recent trends in linguistic contact research, which focuses on language use, language users, and language spheres. Also discusses the role of linguistic and cultural conflicts in language contact situations. (13 references) (MDM)

  5. Effect of Vomeronasal Organ Removal From Male Mice on Their Preference for and Neural Fos Responses to Female Urinary Odors

    OpenAIRE

    Pankevich, Diana E.; Cherry, James A.; Baum, Michael J.

    2006-01-01

    Four experiments were conducted to determine whether vomeronasal organ (VNO) inputs in male mice mediate the rewarding properties of estrous female urinary odors. Sexually naive male mice with either an intact (VNOi) or lesioned (VNOx) VNO preferred to investigate female urine over water in Y-maze tests. Subsequently, VNOi males ran significantly more quickly and remained in nasal contact longer with estrous female urine than with male urine, whereas VNOx males investigated these odors equall...

  6. Collaborative Recurrent Neural Networks forDynamic Recommender Systems

    Science.gov (United States)

    2016-11-22

    JMLR: Workshop and Conference Proceedings 63:366–381, 2016 ACML 2016 Collaborative Recurrent Neural Networks for Dynamic Recommender Systems Young...an unprece- dented scale. Although such activity logs are abundantly available, most approaches to recommender systems are based on the rating...Recurrent Neural Network, Recommender System , Neural Language Model, Collaborative Filtering 1. Introduction As ever larger parts of the population

  7. Neural networks

    International Nuclear Information System (INIS)

    Denby, Bruce; Lindsey, Clark; Lyons, Louis

    1992-01-01

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

  8. A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system

    Science.gov (United States)

    Kaplan, Bernhard A.; Lansner, Anders

    2014-01-01

    Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin–Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian–Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian–Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures. PMID

  9. Neural Architectures for Control

    Science.gov (United States)

    Peterson, James K.

    1991-01-01

    The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs.

  10. Prediction of total organic carbon content in shale reservoir based on a new integrated hybrid neural network and conventional well logging curves

    Science.gov (United States)

    Zhu, Linqi; Zhang, Chong; Zhang, Chaomo; Wei, Yang; Zhou, Xueqing; Cheng, Yuan; Huang, Yuyang; Zhang, Le

    2018-06-01

    There is increasing interest in shale gas reservoirs due to their abundant reserves. As a key evaluation criterion, the total organic carbon content (TOC) of the reservoirs can reflect its hydrocarbon generation potential. The existing TOC calculation model is not very accurate and there is still the possibility for improvement. In this paper, an integrated hybrid neural network (IHNN) model is proposed for predicting the TOC. This is based on the fact that the TOC information on the low TOC reservoir, where the TOC is easy to evaluate, comes from a prediction problem, which is the inherent problem of the existing algorithm. By comparing the prediction models established in 132 rock samples in the shale gas reservoir within the Jiaoshiba area, it can be seen that the accuracy of the proposed IHNN model is much higher than that of the other prediction models. The mean square error of the samples, which were not joined to the established models, was reduced from 0.586 to 0.442. The results show that TOC prediction is easier after logging prediction has been improved. Furthermore, this paper puts forward the next research direction of the prediction model. The IHNN algorithm can help evaluate the TOC of a shale gas reservoir.

  11. Neural correlates of continuous causal word generation.

    Science.gov (United States)

    Wende, Kim C; Straube, Benjamin; Stratmann, Mirjam; Sommer, Jens; Kircher, Tilo; Nagels, Arne

    2012-09-01

    Causality provides a natural structure for organizing our experience and language. Causal reasoning during speech production is a distinct aspect of verbal communication, whose related brain processes are yet unknown. The aim of the current study was to investigate the neural mechanisms underlying the continuous generation of cause-and-effect coherences during overt word production. During fMRI data acquisition participants performed three verbal fluency tasks on identical cue words: A novel causal verbal fluency task (CVF), requiring the production of multiple reasons to a given cue word (e.g. reasons for heat are fire, sun etc.), a semantic (free association, FA, e.g. associations with heat are sweat, shower etc.) and a phonological control task (phonological verbal fluency, PVF, e.g. rhymes with heat are meat, wheat etc.). We found that, in contrast to PVF, both CVF and FA activated a left lateralized network encompassing inferior frontal, inferior parietal and angular regions, with further bilateral activation in middle and inferior as well as superior temporal gyri and the cerebellum. For CVF contrasted against FA, we found greater bold responses only in the left middle frontal cortex. Large overlaps in the neural activations during free association and causal verbal fluency indicate that the access to causal relationships between verbal concepts is at least partly based on the semantic neural network. The selective activation in the left middle frontal cortex for causal verbal fluency suggests that distinct neural processes related to cause-and-effect-relations are associated with the recruitment of middle frontal brain areas. Copyright © 2012 Elsevier Inc. All rights reserved.

  12. Neural overlap of L1 and L2 semantic representations in speech : A decoding approach

    NARCIS (Netherlands)

    Van De Putte, Eowyn; De Baene, W.; Brass, Marcel; Duyck, Wouter

    2017-01-01

    Although research has now converged towards a consensus that both languages of a bilingual are represented in at least partly shared systems for language comprehension, it remains unclear whether both languages are represented in the same neural populations for production. We investigated the neural

  13. The neural response properties and cortical organization of a rapidly adapting muscle sensory group response that overlaps with the frequencies that elicit the kinesthetic illusion.

    Science.gov (United States)

    Marasco, Paul D; Bourbeau, Dennis J; Shell, Courtney E; Granja-Vazquez, Rafael; Ina, Jason G

    2017-01-01

    Kinesthesia is the sense of limb movement. It is fundamental to efficient motor control, yet its neurophysiological components remain poorly understood. The contributions of primary muscle spindles and cutaneous afferents to the kinesthetic sense have been well studied; however, potential contributions from muscle sensory group responses that are different than the muscle spindles have not been ruled out. Electrophysiological recordings in peripheral nerves and brains of male Sprague Dawley rats with a degloved forelimb preparation provide evidence of a rapidly adapting muscle sensory group response that overlaps with vibratory inputs known to generate illusionary perceptions of limb movement in humans (kinesthetic illusion). This group was characteristically distinct from type Ia muscle spindle fibers, the receptor historically attributed to limb movement sensation, suggesting that type Ia muscle spindle fibers may not be the sole carrier of kinesthetic information. The sensory-neural structure of muscles is complex and there are a number of possible sources for this response group; with Golgi tendon organs being the most likely candidate. The rapidly adapting muscle sensory group response projected to proprioceptive brain regions, the rodent homolog of cortical area 3a and the second somatosensory area (S2), with similar adaption and frequency response profiles between the brain and peripheral nerves. Their representational organization was muscle-specific (myocentric) and magnified for proximal and multi-articulate limb joints. Projection to proprioceptive brain areas, myocentric representational magnification of muscles prone to movement error, overlap with illusionary vibrational input, and resonant frequencies of volitional motor unit contraction suggest that this group response may be involved with limb movement processing.

  14. The neural response properties and cortical organization of a rapidly adapting muscle sensory group response that overlaps with the frequencies that elicit the kinesthetic illusion.

    Directory of Open Access Journals (Sweden)

    Paul D Marasco

    Full Text Available Kinesthesia is the sense of limb movement. It is fundamental to efficient motor control, yet its neurophysiological components remain poorly understood. The contributions of primary muscle spindles and cutaneous afferents to the kinesthetic sense have been well studied; however, potential contributions from muscle sensory group responses that are different than the muscle spindles have not been ruled out. Electrophysiological recordings in peripheral nerves and brains of male Sprague Dawley rats with a degloved forelimb preparation provide evidence of a rapidly adapting muscle sensory group response that overlaps with vibratory inputs known to generate illusionary perceptions of limb movement in humans (kinesthetic illusion. This group was characteristically distinct from type Ia muscle spindle fibers, the receptor historically attributed to limb movement sensation, suggesting that type Ia muscle spindle fibers may not be the sole carrier of kinesthetic information. The sensory-neural structure of muscles is complex and there are a number of possible sources for this response group; with Golgi tendon organs being the most likely candidate. The rapidly adapting muscle sensory group response projected to proprioceptive brain regions, the rodent homolog of cortical area 3a and the second somatosensory area (S2, with similar adaption and frequency response profiles between the brain and peripheral nerves. Their representational organization was muscle-specific (myocentric and magnified for proximal and multi-articulate limb joints. Projection to proprioceptive brain areas, myocentric representational magnification of muscles prone to movement error, overlap with illusionary vibrational input, and resonant frequencies of volitional motor unit contraction suggest that this group response may be involved with limb movement processing.

  15. Workshop on Language Student Attrition

    National Research Council Canada - National Science Library

    Whelan, Bree

    2001-01-01

    Seventy individuals from Government agencies (military and civilian), academia, and contractor organizations attended all or parts of a Workshop on student Attrition held at the Defense Language Institute Foreign Language Center (DLIFLC...

  16. A more randomly organized grey matter network is associated with deteriorating language and global cognition in individuals with subjective cognitive decline.

    Science.gov (United States)

    Verfaillie, Sander C J; Slot, Rosalinde E R; Dicks, Ellen; Prins, Niels D; Overbeek, Jozefien M; Teunissen, Charlotte E; Scheltens, Philip; Barkhof, Frederik; van der Flier, Wiesje M; Tijms, Betty M

    2018-03-30

    Grey matter network disruptions in Alzheimer's disease (AD) are associated with worse cognitive impairment cross-sectionally. Our aim was to investigate whether indications of a more random network organization are associated with longitudinal decline in specific cognitive functions in individuals with subjective cognitive decline (SCD). We included 231 individuals with SCD who had annually repeated neuropsychological assessment (3 ± 1 years; n = 646 neuropsychological investigations) available from the Amsterdam Dementia Cohort (54% male, age: 63 ± 9, MMSE: 28 ± 2). Single-subject grey matter networks were extracted from baseline 3D-T1 MRI scans and we computed basic network (size, degree, connectivity density) and higher-order (path length, clustering, betweenness centrality, normalized path length [lambda] and normalized clustering [gamma]) parameters at whole brain and/or regional levels. We tested associations of network parameters with baseline and annual cognition (memory, attention, executive functioning, language composite scores, and global cognition [all domains with MMSE]) using linear mixed models, adjusted for age, sex, education, scanner and total gray matter volume. Lower network size was associated with steeper decline in language (β ± SE = 0.12 ± 0.05, p organized grey matter network was associated with a steeper decline of cognitive functioning, possibly indicating the start of cognitive impairment. © 2018 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

  17. Language Revitalization.

    Science.gov (United States)

    Hinton, Leanne

    2003-01-01

    Surveys developments in language revitalization and language death. Focusing on indigenous languages, discusses the role and nature of appropriate linguistic documentation, possibilities for bilingual education, and methods of promoting oral fluency and intergenerational transmission in affected languages. (Author/VWL)

  18. The functional neuroanatomy of language

    Science.gov (United States)

    Hickok, Gregory

    2009-09-01

    There has been substantial progress over the last several years in understanding aspects of the functional neuroanatomy of language. Some of these advances are summarized in this review. It will be argued that recognizing speech sounds is carried out in the superior temporal lobe bilaterally, that the superior temporal sulcus bilaterally is involved in phonological-level aspects of this process, that the frontal/motor system is not central to speech recognition although it may modulate auditory perception of speech, that conceptual access mechanisms are likely located in the lateral posterior temporal lobe (middle and inferior temporal gyri), that speech production involves sensory-related systems in the posterior superior temporal lobe in the left hemisphere, that the interface between perceptual and motor systems is supported by a sensory-motor circuit for vocal tract actions (not dedicated to speech) that is very similar to sensory-motor circuits found in primate parietal lobe, and that verbal short-term memory can be understood as an emergent property of this sensory-motor circuit. These observations are considered within the context of a dual stream model of speech processing in which one pathway supports speech comprehension and the other supports sensory-motor integration. Additional topics of discussion include the functional organization of the planum temporale for spatial hearing and speech-related sensory-motor processes, the anatomical and functional basis of a form of acquired language disorder, conduction aphasia, the neural basis of vocabulary development, and sentence-level/grammatical processing.

  19. Organization of the sleep-related neural systems in the brain of the river hippopotamus (Hippopotamus amphibius): A most unusual cetartiodactyl species.

    Science.gov (United States)

    Dell, Leigh-Anne; Patzke, Nina; Spocter, Muhammad A; Bertelsen, Mads F; Siegel, Jerome M; Manger, Paul R

    2016-07-01

    This study provides the first systematic analysis of the nuclear organization of the neural systems related to sleep and wake in the basal forebrain, diencephalon, midbrain, and pons of the river hippopotamus, one of the closest extant terrestrial relatives of the cetaceans. All nuclei involved in sleep regulation and control found in other mammals, including cetaceans, were present in the river hippopotamus, with no specific nuclei being absent, but novel features of the cholinergic system, including novel nuclei, were present. This qualitative similarity relates to the cholinergic, noradrenergic, serotonergic, and orexinergic systems and is extended to the γ-aminobutyric acid (GABA)ergic elements of these nuclei. Quantitative analysis reveals that the numbers of pontine cholinergic (259,578) and noradrenergic (127,752) neurons, and hypothalamic orexinergic neurons (68,398) are markedly higher than in other large-brained mammals. These features, along with novel cholinergic nuclei in the intralaminar nuclei of the dorsal thalamus and the ventral tegmental area of the midbrain, as well as a major expansion of the hypothalamic cholinergic nuclei and a large laterodorsal tegmental nucleus of the pons that has both parvocellular and magnocellular cholinergic neurons, indicates an unusual sleep phenomenology for the hippopotamus. Our observations indicate that the hippopotamus is likely to be a bihemispheric sleeper that expresses REM sleep. The novel features of the cholinergic system suggest the presence of an undescribed sleep state in the hippopotamus, as well as the possibility that this animal could, more rapidly than other mammals, switch cortical electroencephalographic activity from one state to another. J. Comp. Neurol. 524:2036-2058, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  20. Programming Languages RESONAN

    Indian Academy of Sciences (India)

    Introduction. Programming languages for computers are developed with the ... detailed algorithm to solve a problem is the starting point and it is expressed as ... All modern programming .... which precisely specify the 'words' of the language, and how they may .... network within an organization using protocols and providing.

  1. Technologies for Language Assessment.

    Science.gov (United States)

    Burstein, Jill; And Others

    1996-01-01

    Reviews current and developing technology uses that are relevant to language assessment and discusses examples of recent linguistic applications from the laboratory at the Educational Testing Service. The processes of language test development are described and the functions they serve from the perspective of a large testing organization are…

  2. Neural Networks

    International Nuclear Information System (INIS)

    Smith, Patrick I.

    2003-01-01

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

  3. EDITORIAL: Why we need a new journal in neural engineering

    Science.gov (United States)

    Durand, Dominique M.

    2004-03-01

    The field of neural engineering crystallizes for many engineers and scientists an area of research at the interface between neuroscience and engineering. For the last 15 years or so, the discipline of neural engineering (neuroengineering) has slowly appeared at conferences as a theme or track. The first conference devoted entirely to this area was the 1st International IEEE EMBS Conference on Neural Engineering which took place in Capri, Italy in 2003. Understanding how the brain works is considered the ultimate frontier and challenge in science. The complexity of the brain is so great that understanding even the most basic functions will require that we fully exploit all the tools currently at our disposal in science and engineering and simultaneously develop new methods of analysis. While neuroscientists and engineers from varied fields such as brain anatomy, neural development and electrophysiology have made great strides in the analysis of this complex organ, there remains a great deal yet to be uncovered. The potential for applications and remedies deriving from scientific discoveries and breakthroughs is extremely high. As a result of the growing availability of micromachining technology, research into neurotechnology has grown relatively rapidly in recent years and appears to be approaching a critical mass. For example, by understanding how neuronal circuits process and store information, we could design computers with capabilities beyond current limits. By understanding how neurons develop and grow, we could develop new technologies for spinal cord repair or central nervous system repair following neurological disorders. Moreover, discoveries related to higher-level cognitive function and consciousness could have a profound influence on how humans make sense of their surroundings and interact with each other. The ability to successfully interface the brain with external electronics would have enormous implications for our society and facilitate a

  4. Evolvable synthetic neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2009-01-01

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

  5. Reliability and Validity of the English-, Chinese- and Malay-Language Versions of the World Health Organization Quality of Life (WHOQOL-BREF) Questionnaire in Singapore.

    Science.gov (United States)

    Cheung, Yin Bun; Yeo, Khung Keong; Chong, Kok Joon; Khoo, Eric Yh; Wee, Hwee Lin

    2017-12-01

    The World Health Organization Quality of Life (WHOQOL-BREF) questionnaire is a 26-item questionnaire that evaluates 4 domains of quality of life (QoL), namely Physical, Psychological, Social Relationships and Environment. This study aimed to evaluate the validity and reliability of the WHOQOL-BREF among Singapore residents aged 21 and above. We recruited participants from the general population by using multistage cluster sampling and participants from 2 hospitals by using convenience sampling. Participants completed either English, Chinese or Malay versions of the WHOQOL-BREF and the EuroQoL 5 Dimension 5 Levels (EQ-5D-5L) questionnaires. Confirmatory factor analysis, known-group validity, internal consistency (Cronbach's alpha) and test-retest reliability using the intraclass correlation coefficient (ICC) were performed. Data from 1316 participants were analysed (Chinese: 46.9%, Malay: 41.0% and Indian: 11.7%; 57.5% mean, mean standard deviation [SD, range] age: 51.9 [15.68, 24 to 90] years); 154 participants took part in the retest in various languages (English: 60, Chinese: 49 and Malay: 45). Tucker-Lewis Index (TLI) was 0.919, 0.913 and 0.909 for the English, Chinese and Malay versions, respectively. Cronbach's alpha exceeded 0.7 and ICC exceeded 0.4 for all domains in all language versions. The WHOQOL-BREF is valid and reliable for assessing QoL in Singapore. Model fit is reasonable with room for improvement.

  6. Proceedings of the Organization of 1990 Meeting of International Neural Network Society Jointed with IEEE Held in Washington, DC on January 15 - 19, 1990. Volume 1. Theory Track Neural and cognitive Sciences Track

    Science.gov (United States)

    1990-11-30

    M. Romero £ UNIVERSIDAD AUTONOMA DEL ESTADO DE MEXICO UNIVERSIDAD AUTONOMA METROPOLITANA--IZTAPALAPA Laboratorio de Sistemas Complejos Ap. Postal 70...linear program. 1. Introduction. In a companion paper [2], we study two general neural network models for Linear Programming. Here, we introduce a... Sistemas Complejos Ap. Postal 70-499. C.P. 04510 Mexico, D.F. MEXICO Statistical Mechanics offers an interssting theoretical framework for the study of

  7. Organics.

    Science.gov (United States)

    Chian, Edward S. K.; DeWalle, Foppe B.

    1978-01-01

    Presents water analysis literature for 1978. This review is concerned with organics, and it covers: (1) detergents and surfactants; (2) aliphatic and aromatic hydrocarbons; (3) pesticides and chlorinated hydrocarbons; and (4) naturally occurring organics. A list of 208 references is also presented. (HM)

  8. Organizers.

    Science.gov (United States)

    Callison, Daniel

    2000-01-01

    Focuses on "organizers," tools or techniques that provide identification and classification along with possible relationships or connections among ideas, concepts, and issues. Discusses David Ausubel's research and ideas concerning advance organizers; the implications of Ausubel's theory to curriculum and teaching; "webbing," a…

  9. HAL/S language specification

    Science.gov (United States)

    Newbold, P. M.

    1974-01-01

    A programming language for the flight software of the NASA space shuttle program was developed and identified as HAL/S. The language is intended to satisfy virtually all of the flight software requirements of the space shuttle. The language incorporates a wide range of features, including applications-oriented data types and organizations, real time control mechanisms, and constructs for systems programming tasks.

  10. Plain language and organisational challenges

    DEFF Research Database (Denmark)

    Pedersen, Karsten

    2014-01-01

    Changing the language in an organization is a major organizational change. In this article, I discuss some of the organizational challenges for one specific language change implementation, taking the stance that language change must be treated as any other organizational change for it to have an ...

  11. Cerebellar language mapping and cerebral language dominance in pediatric epilepsy surgery patients

    Directory of Open Access Journals (Sweden)

    Jennifer N. Gelinas, MD, PhD

    2014-01-01

    Conclusions: Cerebellar language activation occurs in homologous regions of Crus I/II contralateral to cerebral language activation in patients with both right and left cerebral language dominance. Cerebellar language laterality could contribute to comprehensive pre-operative evaluation of language lateralization in pediatric epilepsy surgery patients. Our data suggest that patients with atypical cerebellar language activation are at risk for having atypical cerebral language organization.

  12. Visual cortex entrains to sign language.

    Science.gov (United States)

    Brookshire, Geoffrey; Lu, Jenny; Nusbaum, Howard C; Goldin-Meadow, Susan; Casasanto, Daniel

    2017-06-13

    Despite immense variability across languages, people can learn to understand any human language, spoken or signed. What neural mechanisms allow people to comprehend language across sensory modalities? When people listen to speech, electrophysiological oscillations in auditory cortex entrain to slow ([Formula: see text]8 Hz) fluctuations in the acoustic envelope. Entrainment to the speech envelope may reflect mechanisms specialized for auditory perception. Alternatively, flexible entrainment may be a general-purpose cortical mechanism that optimizes sensitivity to rhythmic information regardless of modality. Here, we test these proposals by examining cortical coherence to visual information in sign language. First, we develop a metric to quantify visual change over time. We find quasiperiodic fluctuations in sign language, characterized by lower frequencies than fluctuations in speech. Next, we test for entrainment of neural oscillations to visual change in sign language, using electroencephalography (EEG) in fluent speakers of American Sign Language (ASL) as they watch videos in ASL. We find significant cortical entrainment to visual oscillations in sign language sign is strongest over occipital and parietal cortex, in contrast to speech, where coherence is strongest over the auditory cortex. Nonsigners also show coherence to sign language, but entrainment at frontal sites is reduced relative to fluent signers. These results demonstrate that flexible cortical entrainment to language does not depend on neural processes that are specific to auditory speech perception. Low-frequency oscillatory entrainment may reflect a general cortical mechanism that maximizes sensitivity to informational peaks in time-varying signals.

  13. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks.

    Science.gov (United States)

    Men, Kuo; Dai, Jianrong; Li, Yexiong

    2017-12-01

    Delineation of the clinical target volume (CTV) and organs at risk (OARs) is very important for radiotherapy but is time-consuming and prone to inter-observer variation. Here, we proposed a novel deep dilated convolutional neural network (DDCNN)-based method for fast and consistent auto-segmentation of these structures. Our DDCNN method was an end-to-end architecture enabling fast training and testing. Specifically, it employed a novel multiple-scale convolutional architecture to extract multiple-scale context features in the early layers, which contain the original information on fine texture and boundaries and which are very useful for accurate auto-segmentation. In addition, it enlarged the receptive fields of dilated convolutions at the end of networks to capture complementary context features. Then, it replaced the fully connected layers with fully convolutional layers to achieve pixel-wise segmentation. We used data from 278 patients with rectal cancer for evaluation. The CTV and OARs were delineated and validated by senior radiation oncologists in the planning computed tomography (CT) images. A total of 218 patients chosen randomly were used for training, and the remaining 60 for validation. The Dice similarity coefficient (DSC) was used to measure segmentation accuracy. Performance was evaluated on segmentation of the CTV and OARs. In addition, the performance of DDCNN was compared with that of U-Net. The proposed DDCNN method outperformed the U-Net for all segmentations, and the average DSC value of DDCNN was 3.8% higher than that of U-Net. Mean DSC values of DDCNN were 87.7% for the CTV, 93.4% for the bladder, 92.1% for the left femoral head, 92.3% for the right femoral head, 65.3% for the intestine, and 61.8% for the colon. The test time was 45 s per patient for segmentation of all the CTV, bladder, left and right femoral heads, colon, and intestine. We also assessed our approaches and results with those in the literature: our system showed superior

  14. Estimating temporal and spatial variation of ocean surface pCO2 in the North Pacific using a self-organizing map neural network technique

    Directory of Open Access Journals (Sweden)

    S. Nakaoka

    2013-09-01

    Full Text Available This study uses a neural network technique to produce maps of the partial pressure of oceanic carbon dioxide (pCO2sea in the North Pacific on a 0.25° latitude × 0.25° longitude grid from 2002 to 2008. The pCO2sea distribution was computed using a self-organizing map (SOM originally utilized to map the pCO2sea in the North Atlantic. Four proxy parameters – sea surface temperature (SST, mixed layer depth, chlorophyll a concentration, and sea surface salinity (SSS – are used during the training phase to enable the network to resolve the nonlinear relationships between the pCO2sea distribution and biogeochemistry of the basin. The observed pCO2sea data were obtained from an extensive dataset generated by the volunteer observation ship program operated by the National Institute for Environmental Studies (NIES. The reconstructed pCO2sea values agreed well with the pCO2sea measurements, with the root-mean-square error ranging from 17.6 μatm (for the NIES dataset used in the SOM to 20.2 μatm (for independent dataset. We confirmed that the pCO2sea estimates could be improved by including SSS as one of the training parameters and by taking into account secular increases of pCO2sea that have tracked increases in atmospheric CO2. Estimated pCO2sea values accurately reproduced pCO2sea data at several time series locations in the North Pacific. The distributions of pCO2sea revealed by 7 yr averaged monthly pCO2sea maps were similar to Lamont-Doherty Earth Observatory pCO2sea climatology, allowing, however, for a more detailed analysis of biogeochemical conditions. The distributions of pCO2sea anomalies over the North Pacific during the winter clearly showed regional contrasts between El Niño and La Niña years related to changes of SST and vertical mixing.

  15. Language Travel or Language Tourism: Have Educational Trips Changed So Much?

    Science.gov (United States)

    Laborda, Jesus Garcia

    2007-01-01

    This article points out the changes in organization, students and language learning that language trips, as contrasted with educational trips (of which language trips are a subgroup) have gone through in the last years. The article emphasizes the need to differentiate between language trips and language tourism based on issues of additional…

  16. Tracking Typological Traits of Uralic Languages in Distributed Language Representations

    DEFF Research Database (Denmark)

    Bjerva, Johannes; Augenstein, Isabelle

    2018-01-01

    Although linguistic typology has a long history, computational approaches have only recently gained popularity. The use of distributed representations in computational linguistics has also become increasingly popular. A recent development is to learn distributed representations of language...... for model transfer between Uralic languages in deep neural networks. We then investigate which typological features are encoded in these representations by attempting to predict features in the World Atlas of Language Structures, at various stages of fine-tuning of the representations. We focus on Uralic...

  17. Onset age of L2 acquisition influences language network in early and late Cantonese-Mandarin bilinguals.

    Science.gov (United States)

    Liu, Xiaojin; Tu, Liu; Wang, Junjing; Jiang, Bo; Gao, Wei; Pan, Ximin; Li, Meng; Zhong, Miao; Zhu, Zhenzhen; Niu, Meiqi; Li, Yanyan; Zhao, Ling; Chen, Xiaoxi; Liu, Chang; Lu, Zhi; Huang, Ruiwang

    2017-11-01

    Early second language (L2) experience influences the neural organization of L2 in neuro-plastic terms. Previous studies tried to reveal these plastic effects of age of second language acquisition (AoA-L2) and proficiency-level in L2 (PL-L2) on the neural basis of language processing in bilinguals. Although different activation patterns have been observed during language processing in early and late bilinguals by task-fMRI, few studies reported the effect of AoA-L2 and high PL-L2 on language network at resting state. In this study, we acquired resting-state fMRI (R-fMRI) data from 10 Cantonese (L1)-Mandarin (L2) early bilinguals (acquired L2: 3years old) and 11 late bilinguals (acquired L2: 6years old), and analyzed their topological properties of language networks after controlling the language daily exposure and usage as well as PL in L1 and L2. We found that early bilinguals had significantly a higher clustering coefficient, global and local efficiency, but significantly lower characteristic path length compared to late bilinguals. Modular analysis indicated that compared to late bilinguals, early bilinguals showed significantly stronger intra-modular functional connectivity in the semantic and phonetic modules, stronger inter-modular functional connectivity between the semantic and phonetic modules as well as between the phonetic and syntactic modules. Differences in global and local parameters may reflect different patterns of neuro-plasticity respectively for early and late bilinguals. These results suggested that different L2 experience influences topological properties of language network, even if late bilinguals achieve high PL-L2. Our findings may provide a new perspective of neural mechanisms related to early and late bilinguals. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. Organizations

    DEFF Research Database (Denmark)

    Hatch, Mary Jo

    and considers many more. Mary Jo Hatch introduces the concept of organizations by presenting definitions and ideas drawn from the a variety of subject areas including the physical sciences, economics, sociology, psychology, anthropology, literature, and the visual and performing arts. Drawing on examples from......Most of us recognize that organizations are everywhere. You meet them on every street corner in the form of families and shops, study in them, work for them, buy from them, pay taxes to them. But have you given much thought to where they came from, what they are today, and what they might become...... prehistory and everyday life, from the animal kingdom as well as from business, government, and other formal organizations, Hatch provides a lively and thought provoking introduction to the process of organization....

  19. Nanoengineered Polystyrene Surfaces with Nanopore Array Pattern Alters Cytoskeleton Organization and Enhances Induction of Neural Differentiation of Human Adipose-Derived Stem Cells.

    Science.gov (United States)

    Jung, Ae Ryang; Kim, Richard Y; Kim, Hyung Woo; Shrestha, Kshitiz Raj; Jeon, Seung Hwan; Cha, Kyoung Je; Park, Yong Hyun; Kim, Dong Sung; Lee, Ji Youl

    2015-07-01

    Human adipose-derived stem cells (hADSCs) can differentiate into various cell types depending on chemical and topographical cues. One topographical cue recently noted to be successful in inducing differentiation is the nanoengineered polystyrene surface containing nanopore array-patterned substrate (NP substrate), which is designed to mimic the nanoscale topographical features of the extracellular matrix. In this study, efficacies of NP and flat substrates in inducing neural differentiation of hADSCs were examined by comparing their substrate-cell adhesion rates, filopodia growth, nuclei elongation, and expression of neural-specific markers. The polystyrene nano Petri dishes containing NP substrates were fabricated by a nano injection molding process using a nickel electroformed nano-mold insert (Diameter: 200 nm. Depth of pore: 500 nm. Center-to-center distance: 500 nm). Cytoskeleton and filopodia structures were observed by scanning electron microscopy and F-actin staining, while cell adhesion was tested by vinculin staining after 24 and 48 h of seeding. Expression of neural specific markers was examined by real-time quantitative polymerase chain reaction and immunocytochemistry. Results showed that NP substrates lead to greater substrate-cell adhesion, filopodia growth, nuclei elongation, and expression of neural specific markers compared to flat substrates. These results not only show the advantages of NP substrates, but they also suggest that further study into cell-substrate interactions may yield great benefits for biomaterial engineering.

  20. Proceedings of the Organization of 1990 Meeting of International Neural Network Society Jointed with IEEE Held in Washington, DC on January 15 - 19, 1990. Volume 2. Applications Track.

    Science.gov (United States)

    1990-11-30

    the face recognitio task using a conven- tional nonparametric classification technique. In Section IV, we present an analysis of our implementa- tion...Markov Models ...................................................................... 11-409 W. D. Mao and S. Y. Kung Princeton University Human Face ...1 Introduction Thbs article describes an ongoing project which is concerned with the development of neural inter- faces to the human nervous system

  1. Detecting resting-state functional connectivity in the language system using functional near-infrared spectroscopy

    Science.gov (United States)

    Zhang, Yu-Jin; Lu, Chun-Ming; Biswal, Bharat B.; Zang, Yu-Feng; Peng, Dan-Lin; Zhu, Chao-Zhe

    2010-07-01

    Functional connectivity has become one of the important approaches to understanding the functional organization of the human brain. Recently, functional near-infrared spectroscopy (fNIRS) was demonstrated as a feasible method to study resting-state functional connectivity (RSFC) in the sensory and motor systems. However, whether such fNIRS-based RSFC can be revealed in high-level and complex functional systems remains unknown. In the present study, the feasibility of such an approach is tested on the language system, of which the neural substrates have been well documented in the literature. After determination of a seed channel by a language localizer task, the correlation strength between the low frequency fluctuations of the fNIRS signal at the seed channel and those at all other channels is used to evaluate the language system RSFC. Our results show a significant RSFC between the left inferior frontal cortex and superior temporal cortex, components both associated with dominant language regions. Moreover, the RSFC map demonstrates left lateralization of the language system. In conclusion, the present study successfully utilized fNIRS-based RSFC to study a complex and high-level neural system, and provides further evidence for the validity of the fNIRS-based RSFC approach.

  2. Approaches for Language Identification in Mismatched Environments

    Science.gov (United States)

    2016-09-08

    domain adaptation, unsupervised learning , deep neural networks, bottleneck features 1. Introduction and task Spoken language identification (LID) is...the process of identifying the language in a spoken speech utterance. In recent years, great improvements in LID system performance have been seen...be the case in practice. Lastly, we conduct an out-of-set experiment where VoA data from 9 other languages (Amharic, Creole, Croatian, English

  3. Social in, social out: How the brain responds to social language with more social language.

    Science.gov (United States)

    O'Donnell, Matthew Brook; Falk, Emily B; Lieberman, Matthew D

    Social connection is a fundamental human need. As such, people's brains are sensitized to social cues, such as those carried by language, and to promoting social communication. The neural mechanisms of certain key building blocks in this process, such as receptivity to and reproduction of social language, however, are not known. We combined quantitative linguistic analysis and neuroimaging to connect neural activity in brain regions used to simulate the mental states of others with exposure to, and re-transmission of, social language. Our results link findings on successful idea transmission from communication science, sociolinguistics and cognitive neuroscience to prospectively predict the degree of social language that participants utilize when re-transmitting ideas as a function of 1) initial language inputs and 2) neural activity during idea exposure.

  4. Self-organizing neural networks for automatic detection and classification of contrast-enhancing lesions in dynamic MR-mammography; Selbstorganisierende neuronale Netze zur automatischen Detektion und Klassifikation von Kontrast(mittel)-verstaerkten Laesionen in der dynamischen MR-Mammographie

    Energy Technology Data Exchange (ETDEWEB)

    Vomweg, T.W.; Teifke, A.; Kauczor, H.U.; Achenbach, T.; Rieker, O.; Schreiber, W.G.; Heitmann, K.R.; Beier, T.; Thelen, M. [Klinik und Poliklinik fuer Radiologie, Klinikum der Univ. Mainz (Germany)

    2005-05-01

    Purpose: Investigation and statistical evaluation of 'Self-Organizing Maps', a special type of neural networks in the field of artificial intelligence, classifying contrast enhancing lesions in dynamic MR-mammography. Material and Methods: 176 investigations with proven histology after core biopsy or operation were randomly divided into two groups. Several Self-Organizing Maps were trained by investigations of the first group to detect and classify contrast enhancing lesions in dynamic MR-mammography. Each single pixel's signal/time curve of all patients within the second group was analyzed by the Self-Organizing Maps. The likelihood of malignancy was visualized by color overlays on the MR-images. At last assessment of contrast-enhancing lesions by each different network was rated visually and evaluated statistically. Results: A well balanced neural network achieved a sensitivity of 90.5% and a specificity of 72.2% in predicting malignancy of 88 enhancing lesions. Detailed analysis of false-positive results revealed that every second fibroadenoma showed a 'typical malignant' signal/time curve without any chance to differentiate between fibroadenomas and malignant tissue regarding contrast enhancement alone; but this special group of lesions was represented by a well-defined area of the Self-Organizing Map. Discussion: Self-Organizing Maps are capable of classifying a dynamic signal/time curve as 'typical benign' or 'typical malignant'. Therefore, they can be used as second opinion. In view of the now known localization of fibroadenomas enhancing like malignant tumors at the Self-Organizing Map, these lesions could be passed to further analysis by additional post-processing elements (e.g., based on T2-weighted series or morphology analysis) in the future. (orig.)

  5. International Conference on Artificial Neural Networks (ICANN)

    CERN Document Server

    Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics

    2015-01-01

    The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...

  6. Principles of neural information processing

    CERN Document Server

    Seelen, Werner v

    2016-01-01

    In this fundamental book the authors devise a framework that describes the working of the brain as a whole. It presents a comprehensive introduction to the principles of Neural Information Processing as well as recent and authoritative research. The books´ guiding principles are the main purpose of neural activity, namely, to organize behavior to ensure survival, as well as the understanding of the evolutionary genesis of the brain. Among the developed principles and strategies belong self-organization of neural systems, flexibility, the active interpretation of the world by means of construction and prediction as well as their embedding into the world, all of which form the framework of the presented description. Since, in brains, their partial self-organization, the lifelong adaptation and their use of various methods of processing incoming information are all interconnected, the authors have chosen not only neurobiology and evolution theory as a basis for the elaboration of such a framework, but also syst...

  7. Functional MRI language mapping in pre-surgical epilepsy patients ...

    African Journals Online (AJOL)

    Background. Functional magnetic resonance imaging (fMRI) is commonly applied to study the neural substrates of language in clinical research and for neurosurgical planning. fMRI language mapping is used to assess language lateralisation, or determine hemispheric dominance, and to localise regions of the brain ...

  8. Dynamical Languages

    Science.gov (United States)

    Xie, Huimin

    The following sections are included: * Definition of Dynamical Languages * Distinct Excluded Blocks * Definition and Properties * L and L″ in Chomsky Hierarchy * A Natural Equivalence Relation * Symbolic Flows * Symbolic Flows and Dynamical Languages * Subshifts of Finite Type * Sofic Systems * Graphs and Dynamical Languages * Graphs and Shannon-Graphs * Transitive Languages * Topological Entropy

  9. Optimal serum and red blood cell folate concentrations in women of reproductive age for prevention of neural tube defects: World Health Organization guidelines.

    Science.gov (United States)

    Cordero, Amy M; Crider, Krista S; Rogers, Lisa M; Cannon, Michael J; Berry, R J

    2015-04-24

    Neural tube defects (NTDs) such as spina bifida, anencephaly, and encephalocele are serious birth defects of the brain and spine that occur during the first month of pregnancy when the neural tube fails to close completely. Randomized controlled trials and observational studies have shown that adequate daily consumption of folic acid before and during early pregnancy considerably reduces the risk for NTDs. The U.S. Public Health Service recommends that women capable of becoming pregnant consume 400 µg of folic acid daily for NTD prevention. Furthermore, fortification of staple foods (e.g., wheat flour) with folic acid has decreased folate-sensitive NTD prevalence in multiple settings and is a highly cost-effective intervention.

  10. 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.

  11. Patterns of cortical oscillations organize neural activity into whole-brain functional networks evident in the fMRI BOLD signal

    Directory of Open Access Journals (Sweden)

    Jennifer C Whitman

    2013-03-01

    Full Text Available Recent findings from electrophysiology and multimodal neuroimaging have elucidated the relationship between patterns of cortical oscillations evident in EEG / MEG and the functional brain networks evident in the BOLD signal. Much of the existing literature emphasized how high-frequency cortical oscillations are thought to coordinate neural activity locally, while low-frequency oscillations play a role in coordinating activity between more distant brain regions. However, the assignment of different frequencies to different spatial scales is an oversimplification. A more informative approach is to explore the arrangements by which these low- and high-frequency oscillations work in concert, coordinating neural activity into whole-brain functional networks. When relating such networks to the BOLD signal, we must consider how the patterns of cortical oscillations change at the same speed as cognitive states, which often last less than a second. Consequently, the slower BOLD signal may often reflect the summed neural activity of several transient network configurations. This temporal mismatch can be circumvented if we use spatial maps to assess correspondence between oscillatory networks and BOLD networks.

  12. Distributed Language and Dialogism

    DEFF Research Database (Denmark)

    Steffensen, Sune Vork

    2015-01-01

    addresses Linell’s critique of Distributed Language as rooted in biosemiotics and in theories of organism-environment systems. It is argued that Linell’s sense-based approach entails an individualist view of how conspecific Others acquire their status as prominent parts of the sense-maker’s environment......This article takes a starting point in Per Linell’s (2013) review article on the book Distributed Language (Cowley, 2011a) and other contributions to the field of ‘Distributed Language’, including Cowley et al. (2010) and Hodges et al. (2012). The Distributed Language approach is a naturalistic...... and anti-representational approach to language that builds on recent developments in the cognitive sciences. With a starting point in Linell’s discussion of the approach, the article aims to clarify four aspects of a distributed view of language vis-à-vis the tradition of Dialogism, as presented by Linell...

  13. Second language processing : when are first and second languages processed similarly?

    NARCIS (Netherlands)

    Sabourin, Laura; Stowe, Laurie A.

    In this article we investigate the effects of first language (L1) on second language (L2) neural processing for two grammatical constructions (verbal domain dependency and grammatical gender), focusing on the event-related potential P600 effect, which has been found in both L1 and L2 processing.

  14. Neural Control of the Immune System

    Science.gov (United States)

    Sundman, Eva; Olofsson, Peder S.

    2014-01-01

    Neural reflexes support homeostasis by modulating the function of organ systems. Recent advances in neuroscience and immunology have revealed that neural reflexes also regulate the immune system. Activation of the vagus nerve modulates leukocyte cytokine production and alleviates experimental shock and autoimmune disease, and recent data have…

  15. The Advantages of Human Milk Recognize the Spatiotemporal Locations of Toxins and Intelligently Bypass Them by Forming a Hummingbird-Like Hovering Neural Network Circuitry Based on an Organic Biomimetic Choline Acetyltransferase Memristor/Memcapacitor Prosthesis

    Directory of Open Access Journals (Sweden)

    E. T. CHEN

    2016-08-01

    Full Text Available We have demonstrated a unique approach to study human milk’s advantage in promoting and protecting infant early brain cognitive development by recognizing toxins and intelligently bypassing the toxin by forming high frequency oscillation (HFO in the brain circuitry when compared with organic cow milk samples based on an organic memristor/memcapacitor biomimetic Choline Acetyltransferase (CHAT neural network circuitry prosthesis along with a 3D Energy-sensory dynamic mapping method under antibody- free, radiolabeling-free, and reagent-less conditions. We also demonstrated cow milk is unfit for infant cognitive development, and it is actually harmful in terms of mutating infant brain synapse circuitry conformation, current flow direction, and energy output that lead to multiple Pathological High Frequency Oscillation (pHFO formations, and further, it led to sudden infant death syndrome (SIDS based on our prediction.

  16. Modelling language

    CERN Document Server

    Cardey, Sylviane

    2013-01-01

    In response to the need for reliable results from natural language processing, this book presents an original way of decomposing a language(s) in a microscopic manner by means of intra/inter‑language norms and divergences, going progressively from languages as systems to the linguistic, mathematical and computational models, which being based on a constructive approach are inherently traceable. Languages are described with their elements aggregating or repelling each other to form viable interrelated micro‑systems. The abstract model, which contrary to the current state of the art works in int

  17. Neural networks mediating sentence reading in the deaf

    Directory of Open Access Journals (Sweden)

    Elizabeth Ann Hirshorn

    2014-06-01

    Full Text Available The present work addresses the neural bases of sentence reading in deaf populations. To better understand the relative role of deafness and English knowledge in shaping the neural networks that mediate sentence reading, three populations with different degrees of English knowledge and depth of hearing loss were included – deaf signers, oral deaf and hearing individuals. The three groups were matched for reading comprehension and scanned while reading sentences. A similar neural network of left perisylvian areas was observed, supporting the view of a shared network of areas for reading despite differences in hearing and English knowledge. However, differences were observed, in particular in the auditory cortex, with deaf signers and oral deaf showing greatest bilateral superior temporal gyrus (STG recruitment as compared to hearing individuals. Importantly, within deaf individuals, the same STG area in the left hemisphere showed greater recruitment as hearing loss increased. To further understand the functional role of such auditory cortex re-organization after deafness, connectivity analyses were performed from the STG regions identified above. Connectivity from the left STG toward areas typically associated with semantic processing (BA45 and thalami was greater in deaf signers and in oral deaf as compared to hearing. In contrast, connectivity from left STG toward areas identified with speech-based processing was greater in hearing and in oral deaf as compared to deaf signers. These results support the growing literature indicating recruitment of auditory areas after congenital deafness for visually-mediated language functions, and establish that both auditory deprivation and language experience shape its functional reorganization. Implications for differential reliance on semantic vs. phonological pathways during reading in the three groups is discussed.

  18. Prenatal Nicotine Exposure Disrupts Infant Neural Markers of Orienting.

    Science.gov (United States)

    King, Erin; Campbell, Alana; Belger, Aysenil; Grewen, Karen

    2017-08-17

    Prenatal nicotine exposure (PNE) from maternal cigarette-smoking is linked to developmental deficits, including impaired auditory processing, language, generalized intelligence, attention and sleep. Fetal brain undergoes massive growth, organization and connectivity during gestation, making it particularly vulnerable to neurotoxic insult. Nicotine binds to nicotinic acetylcholine receptors, which are extensively involved in growth, connectivity and function of developing neural circuitry and neurotransmitter systems. Thus, PNE may have long-term impact on neurobehavioral development. The purpose of this study was to compare the auditory K-complex, an event-related potential reflective of auditory gating, sleep preservation and memory consolidation during sleep, in infants with and without PNE and to relate these neural correlates to neurobehavioral development. We compared brain responses to an auditory paired-click paradigm in 3 to 5-month-old infants during Stage 2 sleep, when the K-complex is best observed. We measured component amplitude and delta activity during the K-complex. PNE may impair auditory sensory gating, which may contribute to disrupted sleep and to reduced auditory discrimination and learning, attention re-orienting and/or arousal during wakefulness reported in other studies. Links between PNE and reduced K-complex amplitude and delta power may represent altered cholinergic and GABAergic synaptic programming, and possibly reflect early neural bases for PNE-linked disruptions in sleep quality and auditory processing. These may pose significant disadvantage for language acquisition, attention, and social interaction necessary for academic and social success. © The Author 2017. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  19. Mapping Language Problems in the Brain

    Science.gov (United States)

    ... issue Health Capsule Mapping Language Problems in the Brain En español Send us your comments We often ... more about how language is organized in the brain, an NIH-funded research team studied people with ...

  20. Endangered Languages.

    Science.gov (United States)

    Hale, Ken; And Others

    1992-01-01

    Endangered languages, or languages on the verge of becoming extinct, are discussed in relation to the larger process of loss of cultural and intellectual diversity. This article summarizes essays presented at the 1991 Linguistic Society of America symposium, "Endangered Languages and Their Preservation." (11 references) (LB)

  1. Measuring language lateralisation with different language tasks: a systematic review

    Directory of Open Access Journals (Sweden)

    Abigail R. Bradshaw

    2017-10-01

    Full Text Available Language lateralisation refers to the phenomenon in which one hemisphere (typically the left shows greater involvement in language functions than the other. Measurement of laterality is of interest both to researchers investigating the neural organisation of the language system and to clinicians needing to establish an individual’s hemispheric dominance for language prior to surgery, as in patients with intractable epilepsy. Recently, there has been increasing awareness of the possibility that different language processes may develop hemispheric lateralisation independently, and to varying degrees. However, it is not always clear whether differences in laterality across language tasks with fMRI are reflective of meaningful variation in hemispheric lateralisation, or simply of trivial methodological differences between paradigms. This systematic review aims to assess different language tasks in terms of the strength, reliability and robustness of the laterality measurements they yield with fMRI, to look at variability that is both dependent and independent of aspects of study design, such as the baseline task, region of interest, and modality of the stimuli. Recommendations are made that can be used to guide task design; however, this review predominantly highlights that the current high level of methodological variability in language paradigms prevents conclusions as to how different language functions may lateralise independently. We conclude with suggestions for future research using tasks that engage distinct aspects of language functioning, whilst being closely matched on non-linguistic aspects of task design (e.g., stimuli, task timings etc; such research could produce more reliable and conclusive insights into language lateralisation. This systematic review was registered as a protocol on Open Science Framework: https://osf.io/5vmpt/.

  2. Insights into neural crest development from studies of avian embryos

    OpenAIRE

    Gandhi, Shashank; Bronner, Marianne E.

    2018-01-01

    The neural crest is a multipotent and highly migratory cell type that contributes to many of the defining features of vertebrates, including the skeleton of the head and most of the peripheral nervous system. 150 years after the discovery of the neural crest, avian embryos remain one of the most important model organisms for studying neural crest development. In this review, we describe aspects of neural crest induction, migration and axial level differences, highlighting what is known about ...

  3. Study of functional brain imaging for bilingual language cognition

    International Nuclear Information System (INIS)

    Sun Da

    2008-01-01

    Bilingual and multilingual brain studies of language recognition is an interdisciplinary subject which needs to identify different levels involved in the neural representation of languages, such as neuroanatomical, neurofunctional, biochemical, psychological and linguistic levels. Furthermore, specific factor's such as age, manner of acquisition and environmental factors seem to affect the neural representation. Functional brain imaging, such as PET, SPECT and functional MRI can explore the neurolinguistics representation of bilingualism in the brain in subjects, and elucidate the neuronal mechanisms of bilingual language processing. Functional imaging methods show differences in the pattern of cerebral activation associated with a second language compared with the subject's native language. It shows that verbal memory processing in two unrelated languages is mediated by a common neural system with some distinct cortical areas. The different patterns of activation differ according to the language used. It also could be ascribed either to age of acquisition or to proficiency level. And attained proficiency is more important than age of acquisition as a determinant of the cortical representation of the second language. The study used PET and SPECT shows that sign and spoken language seem to be localized in the same brain areas, and elicit similar regional cerebral blood flow patterns. But for sign language perception, the functional anatomy overlaps that of language processing contain both auditory and visual components. And the sign language is dependent on spatial information too. (authors)

  4. Many languages, one classroom teaching dual and English language learners

    CERN Document Server

    Nemeth, Karen

    2009-01-01

    Even the most experienced teacher can feel a bit unsure about meeting the unique needs of children from different language backgrounds. Many Languages, One Classroom applies the latest information about best practices to all aspects of a preschool program. Organized by interest areas and times of the day, you'll find everything you need to open the doors of literacy and learning for English language learners during dramatic play, outdoor play, reading, science, blocks, and circle time.

  5. Learning and adaptation: neural and behavioural mechanisms behind behaviour change

    Science.gov (United States)

    Lowe, Robert; Sandamirskaya, Yulia

    2018-01-01

    This special issue presents perspectives on learning and adaptation as they apply to a number of cognitive phenomena including pupil dilation in humans and attention in robots, natural language acquisition and production in embodied agents (robots), human-robot game play and social interaction, neural-dynamic modelling of active perception and neural-dynamic modelling of infant development in the Piagetian A-not-B task. The aim of the special issue, through its contributions, is to highlight some of the critical neural-dynamic and behavioural aspects of learning as it grounds adaptive responses in robotic- and neural-dynamic systems.

  6. Programming languages for synthetic biology.

    Science.gov (United States)

    Umesh, P; Naveen, F; Rao, Chanchala Uma Maheswara; Nair, Achuthsankar S

    2010-12-01

    In the backdrop of accelerated efforts for creating synthetic organisms, the nature and scope of an ideal programming language for scripting synthetic organism in-silico has been receiving increasing attention. A few programming languages for synthetic biology capable of defining, constructing, networking, editing and delivering genome scale models of cellular processes have been recently attempted. All these represent important points in a spectrum of possibilities. This paper introduces Kera, a state of the art programming language for synthetic biology which is arguably ahead of similar languages or tools such as GEC, Antimony and GenoCAD. Kera is a full-fledged object oriented programming language which is tempered by biopart rule library named Samhita which captures the knowledge regarding the interaction of genome components and catalytic molecules. Prominent feature of the language are demonstrated through a toy example and the road map for the future development of Kera is also presented.

  7. Native-language N400 and P600 predict dissociable language-learning abilities in adults.

    Science.gov (United States)

    Qi, Zhenghan; Beach, Sara D; Finn, Amy S; Minas, Jennifer; Goetz, Calvin; Chan, Brian; Gabrieli, John D E

    2017-04-01

    Language learning aptitude during adulthood varies markedly across individuals. An individual's native-language ability has been associated with success in learning a new language as an adult. However, little is known about how native-language processing affects learning success and what neural markers of native-language processing, if any, are related to success in learning. We therefore related variation in electrophysiology during native-language processing to success in learning a novel artificial language. Event-related potentials (ERPs) were recorded while native English speakers judged the acceptability of English sentences prior to learning an artificial language. There was a trend towards a double dissociation between native-language ERPs and their relationships to novel syntax and vocabulary learning. Individuals who exhibited a greater N400 effect when processing English semantics showed better future learning of the artificial language overall. The N400 effect was related to syntax learning via its specific relationship to vocabulary learning. In contrast, the P600 effect size when processing English syntax predicted future syntax learning but not vocabulary learning. These findings show that distinct neural signatures of native-language processing relate to dissociable abilities for learning novel semantic and syntactic information. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Native-language N400 and P600 predict dissociable language-learning abilities in adults

    Science.gov (United States)

    Qi, Zhenghan; Beach, Sara D.; Finn, Amy S.; Minas, Jennifer; Goetz, Calvin; Chan, Brian; Gabrieli, John D.E.

    2018-01-01

    Language learning aptitude during adulthood varies markedly across individuals. An individual’s native-language ability has been associated with success in learning a new language as an adult. However, little is known about how native-language processing affects learning success and what neural markers of native-language processing, if any, are related to success in learning. We therefore related variation in electrophysiology during native-language processing to success in learning a novel artificial language. Event-related potentials (ERPs) were recorded while native English speakers judged the acceptability of English sentences prior to learning an artificial language. There was a trend towards a double dissociation between native-language ERPs and their relationships to novel syntax and vocabulary learning. Individuals who exhibited a greater N400 effect when processing English semantics showed better future learning of the artificial language overall. The N400 effect was related to syntax learning via its specific relationship to vocabulary learning. In contrast, the P600 effect size when processing English syntax predicted future syntax learning but not vocabulary learning. These findings show that distinct neural signatures of native-language processing relate to dissociable abilities for learning novel semantic and syntactic information. PMID:27737775

  9. Language Acquisition and Language Revitalization

    Science.gov (United States)

    O'Grady, William; Hattori, Ryoko

    2016-01-01

    Intergenerational transmission, the ultimate goal of language revitalization efforts, can only be achieved by (re)establishing the conditions under which an imperiled language can be acquired by the community's children. This paper presents a tutorial survey of several key points relating to language acquisition and maintenance in children,…

  10. The evolution of language and thought.

    Science.gov (United States)

    Lieberman, Philip

    2016-06-20

    Language primarily evolved as a vocal medium that transmits the attributes of human culture and the necessities of daily communication. Human language has a long, complex evolutionary history. Language also serves as an instrument of thought since it has become evident that in the course of this process neural circuits that initially evolved to regulate motor control, motor responses to external events, and ultimately talking were recycled to serve tasks such as working memory, cognitive flexibility linguistic tasks such as comprehending distinctions in meaning conveyed by syntax. This precludes the human brain possessing an organ devoted exclusively to language, such as the Faculty of Language proposed by Chomsky (1972, 2012). In essence like Fodor's (1983) modular model, a restatement of archaic phrenological theories (Spurzheim, 1815). The subcortical basal ganglia can be traced back to early anurans. Although our knowledge of the neural circuits of the human brain is at a very early stage and incomplete, the findings of independent studies over the past 40 years, discussed here, have identified circuits linking the basal ganglia with various areas of prefrontal cortex, posterior cortical regions and other subcortical structures. These circuits are active in linguistic tasks such as lexical access, comprehending distinctions in meaning conferred by syntax and the range of higher cognitive tasks involving executive control and play a critical role in conferring cognitive flexibility. The cingulate cortex which appeared in Therapsids, transitional mammal-like reptiles who lived in age of the dinosaurs, most likely enhanced mother-infant interaction, contributing to success in the Darwinian (1859) "Struggle for Existence" - the survival of progeny. They continue to fill that role in present-day mammals as well as being involved in controlling laryngeal phonation during speech and directing attention (Newman & MacLean, 1983; Cummings, 1993". The cerebellum and

  11. ERP correlates of German Sign Language processing in deaf native signers.

    Science.gov (United States)

    Hänel-Faulhaber, Barbara; Skotara, Nils; Kügow, Monique; Salden, Uta; Bottari, Davide; Röder, Brigitte

    2014-05-10

    The present study investigated the neural correlates of sign language processing of Deaf people who had learned German Sign Language (Deutsche Gebärdensprache, DGS) from their Deaf parents as their first language. Correct and incorrect signed sentences were presented sign by sign on a computer screen. At the end of each sentence the participants had to judge whether or not the sentence was an appropriate DGS sentence. Two types of violations were introduced: (1) semantically incorrect sentences containing a selectional restriction violation (implausible object); (2) morphosyntactically incorrect sentences containing a verb that was incorrectly inflected (i.e., incorrect direction of movement). Event-related brain potentials (ERPs) were recorded from 74 scalp electrodes. Semantic violations (implausible signs) elicited an N400 effect followed by a positivity. Sentences with a morphosyntactic violation (verb agreement violation) elicited a negativity followed by a broad centro-parietal positivity. ERP correlates of semantic and morphosyntactic aspects of DGS clearly differed from each other and showed a number of similarities with those observed in other signed and oral languages. These data suggest a similar functional organization of signed and oral languages despite the visual-spacial modality of sign language.

  12. Twitter and the Welsh Language

    Science.gov (United States)

    Jones, Rhys James; Cunliffe, Daniel; Honeycutt, Zoe R.

    2013-01-01

    The emergence of new domains, such as the Internet, can prove challenging for minority languages. Welsh is a minority, regional language and is considered "vulnerable" by the United Nations Educational, Scientific and Cultural Organization (UNESCO). The Welsh-speaking community appears to have responded positively to the Internet and the…

  13. Language, Perception, and the Schematic Representation of Spatial Relations

    Science.gov (United States)

    Amorapanth, Prin; Kranjec, Alexander; Bromberger, Bianca; Lehet, Matthew; Widick, Page; Woods, Adam J.; Kimberg, Daniel Y.; Chatterjee, Anjan

    2012-01-01

    Schemas are abstract nonverbal representations that parsimoniously depict spatial relations. Despite their ubiquitous use in maps and diagrams, little is known about their neural instantiation. We sought to determine the extent to which schematic representations are neurally distinguished from language on the one hand, and from rich perceptual…

  14. Specialized languages

    DEFF Research Database (Denmark)

    Mousten, Birthe; Laursen, Anne Lise

    2016-01-01

    Across different fields of research, one feature is often overlooked: the use of language for specialized purposes (LSP) as a cross-discipline. Mastering cross-disciplinarity is the precondition for communicating detailed results within any field. Researchers in specialized languages work cross...... science fields communicate their findings. With this article, we want to create awareness of the work in this special area of language studies and of the inherent cross-disciplinarity that makes LSP special compared to common-core language. An acknowledgement of the importance of this field both in terms...... of more empirical studies and in terms of a greater application of the results would give language specialists in trade and industry a solid and updated basis for communication and language use....

  15. Exploring the Neural Substrates of Phonological Recovery for Symposium: Neural Correlates of Recovery and Rehabilitation

    Directory of Open Access Journals (Sweden)

    Pelagie M Beeson

    2015-10-01

    All participants improved written language abilities in response to treatment, but one subgroup was limited in their ability to regain phonological skills. Both anterior and posterior components of the perisylvian phonological network were damaged in that group. These findings are consistent with fMRI activation when healthy adults write nonwords, and provide insight regarding neural support necessary for phonological rehabilitation.

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

    Science.gov (United States)

    Keene, Alex C; Waddell, Scott

    2007-05-01

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

  17. Cross-language and second language speech perception

    DEFF Research Database (Denmark)

    Bohn, Ocke-Schwen

    2017-01-01

    in cross-language and second language speech perception research: The mapping issue (the perceptual relationship of sounds of the native and the nonnative language in the mind of the native listener and the L2 learner), the perceptual and learning difficulty/ease issue (how this relationship may or may...... not cause perceptual and learning difficulty), and the plasticity issue (whether and how experience with the nonnative language affects the perceptual organization of speech sounds in the mind of L2 learners). One important general conclusion from this research is that perceptual learning is possible at all...

  18. Análise crítica dos sistemas neurais envolvidos nas respostas de medo inato Critical analysis of the neural systems organizing innate fear responses

    Directory of Open Access Journals (Sweden)

    Newton Sabino Canteras

    2003-12-01

    Full Text Available O nosso entendimento das bases neurofisiológicas da reação emocional do medo baseia-se em grande parte nos estudos que envolvem respostas condicionadas a estímulos fisicamente aversivos, como, por exemplo, o choque elétrico nas patas. Enquanto este paradigma parece ser útil para avaliarmos os sistemas neurais envolvidos na resposta do, assim chamado, medo condicionado (que tipicamente tem se limitado à observação da resposta de congelamento, este paradigma parece ter sérias limitações para investigarmos as bases neurais das respostas de medo em circunstancias naturais. Trabalhos recentes utilizando técnicas de lesões neurais bem como de mapeamento funcional em animais expostos a predadores naturais, ou somente ao odor destes predadores, revelam uma série de estruturas neurais como responsáveis pelas respostas de medo inato, bastante distintas daquelas previamente implicadas nas respostas de condicionamento aversivo. Como revisto no presente trabalho, entre estas estruturas temos distritos diferenciados da zona medial do hipotálamo; setores específicos da amídala e do sistema septo-hipocampal, envolvidos, respectivamente no processamento de pistas relacionadas à presença do predador e na análise contextual do ambiente; e setores da matéria cinzenta periaquedutal, já classicamente envolvidos na expressão de respostas de defesa. Estas informações podem ser potencialmente importantes para a análise e terapêutica de psicopatologias relacionadas aos distúrbios da reação emocional de medo.Unconditioned emotional responses elicited by exposure to a predator have served as the prototypical exemplar for analyses of the behavioral biology of fear-related emotionality. However, the primary research model for the study of fear has involved shock-based cue and context conditioning. While these shock-based models have provided a good understanding of neural systems regulating specific conditioned fear-related behaviors

  19. Evolution of Tonal Organization in Music Optimizes Neural Mechanisms in Symbolic Encoding of Perceptual Reality. Part-2: Ancient to 17th century

    OpenAIRE

    Aleksey eNikolsky

    2016-01-01

    This paper reveals the way in which musical pitch works as a peculiar form of cognition that reflects upon the organization of the surrounding world as perceived by majority of music users within a socio-cultural formation.Part-1 of this paper described the origin of tonal organization from verbal speech, its progress from indefinite to definite pitch, and the emergence of two main harmonic orders: heptatonic and pentatonic, each characterized by its own method of handling tension at both dom...

  20. Evolution of Tonal Organization in Music Optimizes Neural Mechanisms in Symbolic Encoding of Perceptual Reality. Part-2: Ancient to Seventeenth Century

    OpenAIRE

    Nikolsky, Aleksey

    2016-01-01

    This paper reveals the way in which musical pitch works as a peculiar form of cognition that reflects upon the organization of the surrounding world as perceived by majority of music users within a socio-cultural formation. Part-1 of this paper described the origin of tonal organization from verbal speech, its progress from indefinite to definite pitch, and the emergence of two main harmonic orders: heptatonic and pentatonic, each characterized by its own method of handling tension at both do...

  1. Fuzzy Languages

    Science.gov (United States)

    Rahonis, George

    The theory of fuzzy recognizable languages over bounded distributive lattices is presented as a paradigm of recognizable formal power series. Due to the idempotency properties of bounded distributive lattices, the equality of fuzzy recognizable languages is decidable, the determinization of multi-valued automata is effective, and a pumping lemma exists. Fuzzy recognizable languages over finite and infinite words are expressively equivalent to sentences of the multi-valued monadic second-order logic. Fuzzy recognizability over bounded ℓ-monoids and residuated lattices is briefly reported. The chapter concludes with two applications of fuzzy recognizable languages to real world problems in medicine.

  2. Language Policy

    DEFF Research Database (Denmark)

    Lauridsen, Karen M.

    2008-01-01

    Like any other text, instructive texts function within a given cultural and situational setting and may only be available in one language. However, the end users may not be familiar with that language and therefore unable to read and understand the instructions. This article therefore argues...... that instructive texts should always be available in a language that is understood by the end users, and that a corporate communication policy which includes a language policy should ensure that this is in fact the case for all instructive texts....

  3. Graph theoretical analysis of functional network for comprehension of sign language.

    Science.gov (United States)

    Liu, Lanfang; Yan, Xin; Liu, Jin; Xia, Mingrui; Lu, Chunming; Emmorey, Karen; Chu, Mingyuan; Ding, Guosheng

    2017-09-15

    Signed languages are natural human languages using the visual-motor modality. Previous neuroimaging studies based on univariate activation analysis show that a widely overlapped cortical network is recruited regardless whether the sign language is comprehended (for signers) or not (for non-signers). Here we move beyond previous studies by examining whether the functional connectivity profiles and the underlying organizational structure of the overlapped neural network may differ between signers and non-signers when watching sign language. Using graph theoretical analysis (GTA) and fMRI, we compared the large-scale functional network organization in hearing signers with non-signers during the observation of sentences in Chinese Sign Language. We found that signed sentences elicited highly similar cortical activations in the two groups of participants, with slightly larger responses within the left frontal and left temporal gyrus in signers than in non-signers. Crucially, further GTA revealed substantial group differences in the topologies of this activation network. Globally, the network engaged by signers showed higher local efficiency (t (24) =2.379, p=0.026), small-worldness (t (24) =2.604, p=0.016) and modularity (t (24) =3.513, p=0.002), and exhibited different modular structures, compared to the network engaged by non-signers. Locally, the left ventral pars opercularis served as a network hub in the signer group but not in the non-signer group. These findings suggest that, despite overlap in cortical activation, the neural substrates underlying sign language comprehension are distinguishable at the network level from those for the processing of gestural action. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. 22nd Italian Workshop on Neural Nets

    CERN Document Server

    Bassis, Simone; Esposito, Anna; Morabito, Francesco

    2013-01-01

    This volume collects a selection of contributions which has been presented at the 22nd Italian Workshop on Neural Networks, the yearly meeting of the Italian Society for Neural Networks (SIREN). The conference was held in Italy, Vietri sul Mare (Salerno), during May 17-19, 2012. The annual meeting of SIREN is sponsored by International Neural Network Society (INNS), European Neural Network Society (ENNS) and IEEE Computational Intelligence Society (CIS). The book – as well as the workshop-  is organized in three main components, two special sessions and a group of regular sessions featuring different aspects and point of views of artificial neural networks and natural intelligence, also including applications of present compelling interest.

  5. Dynamic decomposition of spatiotemporal neural signals.

    Directory of Open Access Journals (Sweden)

    Luca Ambrogioni

    2017-05-01

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

  6. Transcranial magnetic stimulation: language function.

    Science.gov (United States)

    Epstein, C M

    1998-07-01

    Studies of language using transcranial magnetic stimulation (TMS) have focused both on identification of language areas and on elucidation of function. TMS may result in either inhibition or facilitation of language processes and may operate directly at a presumptive site of language cortex or indirectly through intracortical networks. TMS has been used to create reversible "temporary lesions," similar to those produced by Wada tests and direct cortical electrical stimulation, in cerebral cortical areas subserving language function. Rapid-rate TMS over the left inferior frontal region blocks speech output in most subjects. However, the results are not those predicted from classic models of language organization. Speech arrest is obtained most easily over facial motor cortex, and true aphasia is rare, whereas right hemisphere or bilateral lateralization is unexpectedly prominent. A clinical role for these techniques is not yet fully established. Interfering with language comprehension and verbal memory is currently more difficult than blocking speech output, but numerous TMS studies have demonstrated facilitation of language-related tasks, including oral word association, story recall, digit span, and picture naming. Conversely, speech output also facilitates motor responses to TMS in the dominant hemisphere. Such new and often-unexpected findings may provide important insights into the organization of language.

  7. Evolution of Tonal Organization in Music Optimizes Neural Mechanisms in Symbolic Encoding of Perceptual Reality. Part-2: Ancient to Seventeenth Century.

    Science.gov (United States)

    Nikolsky, Aleksey

    2016-01-01

    This paper reveals the way in which musical pitch works as a peculiar form of cognition that reflects upon the organization of the surrounding world as perceived by majority of music users within a socio-cultural formation. Part-1 of this paper described the origin of tonal organization from verbal speech, its progress from indefinite to definite pitch, and the emergence of two main harmonic orders: heptatonic and pentatonic, each characterized by its own method of handling tension at both domains, of tonal and social organization. Part-2, here, completes the line of historic development from Antiquity to seventeenth century. Vast archeological data is used to identify the perception of music structures that tells apart the temple/palace music of urban civilizations and the folk music of village cultures. The "mega-pitch-set" (MPS) organization is found to constitute the principal contribution of a math-based music theory to a new diatonic order. All ramifications for psychology of music are discussed in detail. "Non-octave hypermode" is identified as a peculiar homogenous type of MPS, typical for plainchant. The origin of chromaticism is thoroughly examined as an earmark of "art-music" that opposes earlier forms of folk music. The role of aesthetic emotions in formation of chromatic alteration is defined. The development of chromatic system is traced throughout history, highlighting its modern implementation in "hemiolic modes." The connection between tonal organization in music and spatial organization in pictorial art is established in the Baroque culture, and then tracked back to prehistoric times. Both are shown to present a form of abstraction of environmental topographic schemes, and music is proposed as the primary medium for its cultivation through the concept of pitch. The comparison of stages of tonal organization and typologies of musical texture is used to define the overall course of tonal evolution. Tonal organization of pitch reflects the culture of

  8. Evolution of Tonal Organization in Music Optimizes Neural Mechanisms in Symbolic Encoding of Perceptual Reality. Part-2: Ancient to 17th century

    Directory of Open Access Journals (Sweden)

    Aleksey eNikolsky

    2016-03-01

    Full Text Available This paper reveals the way in which musical pitch works as a peculiar form of cognition that reflects upon the organization of the surrounding world as perceived by majority of music users within a socio-cultural formation.Part-1 of this paper described the origin of tonal organization from verbal speech, its progress from indefinite to definite pitch, and the emergence of two main harmonic orders: heptatonic and pentatonic, each characterized by its own method of handling tension at both domains, of tonal and social organization. Part-2, here, completes the line of historic development from Antiquity to 17th century. Vast archeological data is used to identify the perception of music structures that tells apart the temple/palace music of urban civilizations and the folk music of village cultures. The mega-pitch-set (MPS organization is found to constitute the principal contribution of a math-based music theory to a new diatonic order. All ramifications for psychology of music are discussed in detail. Non-octave hypermode is identified as a peculiar homogenous type of MPS, typical for plainchant.The origin of chromaticism is thoroughly examined as an earmark of art-music that opposes earlier forms of folk music. The role of aesthetic emotions in formation of chromatic alteration is defined. The development of chromatic system is traced throughout history, highlighting its modern implementation in hemiolic modes.The connection between tonal organization in music and spatial organization in pictorial art is established in the Baroque culture, and then tracked back to prehistoric times. Both are shown to present a form of abstraction of environmental topographic schemes, and music is proposed as the primary medium for its cultivation through the concept of pitch. The comparison of stages of tonal organization and typologies of musical texture is used to define the overall course of tonal evolution. Tonal organization of pitch reflects the culture

  9. Modular representation of layered neural networks.

    Science.gov (United States)

    Watanabe, Chihiro; Hiramatsu, Kaoru; Kashino, Kunio

    2018-01-01

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

  10. Language differences in the brain network for reading in naturalistic story reading and lexical decision.

    Directory of Open Access Journals (Sweden)

    Xiaojuan Wang

    Full Text Available Differences in how writing systems represent language raise important questions about whether there could be a universal functional architecture for reading across languages. In order to study potential language differences in the neural networks that support reading skill, we collected fMRI data from readers of alphabetic (English and morpho-syllabic (Chinese writing systems during two reading tasks. In one, participants read short stories under conditions that approximate natural reading, and in the other, participants decided whether individual stimuli were real words or not. Prior work comparing these two writing systems has overwhelmingly used meta-linguistic tasks, generally supporting the conclusion that the reading system is organized differently for skilled readers of Chinese and English. We observed that language differences in the reading network were greatly dependent on task. In lexical decision, a pattern consistent with prior research was observed in which the Middle Frontal Gyrus (MFG and right Fusiform Gyrus (rFFG were more active for Chinese than for English, whereas the posterior temporal sulcus was more active for English than for Chinese. We found a very different pattern of language effects in a naturalistic reading paradigm, during which significant differences were only observed in visual regions not typically considered specific to the reading network, and the middle temporal gyrus, which is thought to be important for direct mapping of orthography to semantics. Indeed, in areas that are often discussed as supporting distinct cognitive or linguistic functions between the two languages, we observed interaction. Specifically, language differences were most pronounced in MFG and rFFG during the lexical decision task, whereas no language differences were observed in these areas during silent reading of text for comprehension.

  11. Language differences in the brain network for reading in naturalistic story reading and lexical decision.

    Science.gov (United States)

    Wang, Xiaojuan; Yang, Jianfeng; Yang, Jie; Mencl, W Einar; Shu, Hua; Zevin, Jason David

    2015-01-01

    Differences in how writing systems represent language raise important questions about whether there could be a universal functional architecture for reading across languages. In order to study potential language differences in the neural networks that support reading skill, we collected fMRI data from readers of alphabetic (English) and morpho-syllabic (Chinese) writing systems during two reading tasks. In one, participants read short stories under conditions that approximate natural reading, and in the other, participants decided whether individual stimuli were real words or not. Prior work comparing these two writing systems has overwhelmingly used meta-linguistic tasks, generally supporting the conclusion that the reading system is organized differently for skilled readers of Chinese and English. We observed that language differences in the reading network were greatly dependent on task. In lexical decision, a pattern consistent with prior research was observed in which the Middle Frontal Gyrus (MFG) and right Fusiform Gyrus (rFFG) were more active for Chinese than for English, whereas the posterior temporal sulcus was more active for English than for Chinese. We found a very different pattern of language effects in a naturalistic reading paradigm, during which significant differences were only observed in visual regions not typically considered specific to the reading network, and the middle temporal gyrus, which is thought to be important for direct mapping of orthography to semantics. Indeed, in areas that are often discussed as supporting distinct cognitive or linguistic functions between the two languages, we observed interaction. Specifically, language differences were most pronounced in MFG and rFFG during the lexical decision task, whereas no language differences were observed in these areas during silent reading of text for comprehension.

  12. Neural network recognition of mammographic lesions

    International Nuclear Information System (INIS)

    Oldham, W.J.B.; Downes, P.T.; Hunter, V.

    1987-01-01

    A method for recognition of mammographic lesions through the use of neural networks is presented. Neural networks have exhibited the ability to learn the shape andinternal structure of patterns. Digitized mammograms containing circumscribed and stelate lesions were used to train a feedfoward synchronous neural network that self-organizes to stable attractor states. Encoding of data for submission to the network was accomplished by performing a fractal analysis of the digitized image. This results in scale invariant representation of the lesions. Results are discussed

  13. Probabilistic language models in cognitive neuroscience: Promises and pitfalls.

    Science.gov (United States)

    Armeni, Kristijan; Willems, Roel M; Frank, Stefan L

    2017-12-01

    Cognitive neuroscientists of language comprehension study how neural computations relate to cognitive computations during comprehension. On the cognitive part of the equation, it is important that the computations and processing complexity are explicitly defined. Probabilistic language models can be used to give a computationally explicit account of language complexity during comprehension. Whereas such models have so far predominantly been evaluated against behavioral data, only recently have the models been used to explain neurobiological signals. Measures obtained from these models emphasize the probabilistic, information-processing view of language understanding and provide a set of tools that can be used for testing neural hypotheses about language comprehension. Here, we provide a cursory review of the theoretical foundations and example neuroimaging studies employing probabilistic language models. We highlight the advantages and potential pitfalls of this approach and indicate avenues for future research. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Neural implementation of musical expertise and cognitive transfers: Could they be promising in the framework of normal cognitive aging?

    Directory of Open Access Journals (Sweden)

    Baptiste eFAUVEL

    2013-10-01

    Full Text Available Brain plasticity allows the central nervous system of a given organism to cope with environmental demands. Therefore, the quality of mental processes relies partly on the interaction between the brain’s physiological maturation and individual daily experiences.In this review, we focus on the neural implementation of musical expertise at both an anatomical and a functional level. We then discuss how this neural implementation can explain transfers from musical learning to a broad range of nonmusical cognitive functions, including language, especially during child development. Finally, given that brain plasticity is still present in aging, we gather arguments to propose that musical practice could be a good environmental enrichment to promote cerebral and cognitive reserves, thereby reducing the deleterious effect of aging on cognitive functions.

  15. Implicit memory in music and language.

    Science.gov (United States)

    Ettlinger, Marc; Margulis, Elizabeth H; Wong, Patrick C M

    2011-01-01

    Research on music and language in recent decades has focused on their overlapping neurophysiological, perceptual, and cognitive underpinnings, ranging from the mechanism for encoding basic auditory cues to the mechanism for detecting violations in phrase structure. These overlaps have most often been identified in musicians with musical knowledge that was acquired explicitly, through formal training. In this paper, we review independent bodies of work in music and language that suggest an important role for implicitly acquired knowledge, implicit memory, and their associated neural structures in the acquisition of linguistic or musical grammar. These findings motivate potential new work that examines music and language comparatively in the context of the implicit memory system.

  16. Neural Darwinism and consciousness.

    Science.gov (United States)

    Seth, Anil K; Baars, Bernard J

    2005-03-01

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

  17. High school music classes enhance the neural processing of speech

    OpenAIRE

    Tierney, Adam; Krizman, Jennifer; Skoe, Erika; Johnston, Kathleen; Kraus, Nina

    2013-01-01

    Should music be a priority in public education? One argument for teaching music in school is that private music instruction relates to enhanced language abilities and neural function. However, the directionality of this relationship is unclear and it is unknown whether school-based music training can produce these enhancements. Here we show that two years of group music classes in high school enhance the subcortical encoding of speech. To tease apart the relationships between music and neural...

  18. Stone tools, language and the brain in human evolution

    Science.gov (United States)

    Stout, Dietrich; Chaminade, Thierry

    2012-01-01

    Long-standing speculations and more recent hypotheses propose a variety of possible evolutionary connections between language, gesture and tool use. These arguments have received important new support from neuroscientific research on praxis, observational action understanding and vocal language demonstrating substantial functional/anatomical overlap between these behaviours. However, valid reasons for scepticism remain as well as substantial differences in detail between alternative evolutionary hypotheses. Here, we review the current status of alternative ‘gestural’ and ‘technological’ hypotheses of language origins, drawing on current evidence of the neural bases of speech and tool use generally, and on recent studies of the neural correlates of Palaeolithic technology specifically. PMID:22106428

  19. Neural-Network Object-Recognition Program

    Science.gov (United States)

    Spirkovska, L.; Reid, M. B.

    1993-01-01

    HONTIOR computer program implements third-order neural network exhibiting invariance under translation, change of scale, and in-plane rotation. Invariance incorporated directly into architecture of network. Only one view of each object needed to train network for two-dimensional-translation-invariant recognition of object. Also used for three-dimensional-transformation-invariant recognition by training network on only set of out-of-plane rotated views. Written in C language.

  20. Artificial neural network modeling and cluster analysis for organic facies and burial history estimation using well log data: A case study of the South Pars Gas Field, Persian Gulf, Iran

    Science.gov (United States)

    Alizadeh, Bahram; Najjari, Saeid; Kadkhodaie-Ilkhchi, Ali

    2012-08-01

    Intelligent and statistical techniques were used to extract the hidden organic facies from well log responses in the Giant South Pars Gas Field, Persian Gulf, Iran. Kazhdomi Formation of Mid-Cretaceous and Kangan-Dalan Formations of Permo-Triassic Data were used for this purpose. Initially GR, SGR, CGR, THOR, POTA, NPHI and DT logs were applied to model the relationship between wireline logs and Total Organic Carbon (TOC) content using Artificial Neural Networks (ANN). The correlation coefficient (R2) between the measured and ANN predicted TOC equals to 89%. The performance of the model is measured by the Mean Squared Error function, which does not exceed 0.0073. Using Cluster Analysis technique and creating a binary hierarchical cluster tree the constructed TOC column of each formation was clustered into 5 organic facies according to their geochemical similarity. Later a second model with the accuracy of 84% was created by ANN to determine the specified clusters (facies) directly from well logs for quick cluster recognition in other wells of the studied field. Each created facies was correlated to its appropriate burial history curve. Hence each and every facies of a formation could be scrutinized separately and directly from its well logs, demonstrating the time and depth of oil or gas generation. Therefore potential production zone of Kazhdomi probable source rock and Kangan- Dalan reservoir formation could be identified while well logging operations (especially in LWD cases) were in progress. This could reduce uncertainty and save plenty of time and cost for oil industries and aid in the successful implementation of exploration and exploitation plans.

  1. 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.

  2. 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 ...

  3. Building Languages

    Science.gov (United States)

    ... Glossary Contact Information Information For… Media Policy Makers Building Languages Recommend on Facebook Tweet Share Compartir Communicating ... any speech and only very loud sounds. Close × “Building Blocks” “Building Blocks” refers to the different skills ...

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

    Directory of Open Access Journals (Sweden)

    Stefan Fürtinger

    2014-11-01

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

  5. Neural tissue-spheres

    DEFF Research Database (Denmark)

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

    2007-01-01

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

  6. APPLICATION OF A FULL-COHERENT ARTIFICIAL NEURAL NETWORK FOR FORECASTING OF THE MODES OF STORAGE OF DOMESTIC LOW-OLIVE RAW MATERIALS IN CONTROLLED ENVIRONMENTS

    Directory of Open Access Journals (Sweden)

    N. S. Rodionova

    2015-01-01

    Full Text Available Summary. Researches on increase in an expiration date of the wheat germs (WG with use of compositions of organic acids are conducted. With a research objective of influence of concentration of mixes of organic acids on change of indicators of quality at storage of the SALARY in various modes investigated quality indicators in the range of concentration of 1-7% to the mass of a product. As control the raw SALARIES served. Skilled products stored in refrigerator conditions (temperature 4-6 ºС, relative humidity of air of 75-80% and a warehouse (temperature 20-22 ºС, relative humidity of air of 70-80%. The software product on the basis of the program of training and the analysis of training of an artificial full-coherent neural network (INS in the Python 2.7 language with program libraries of mathematical processing of scientific data of "scipy" is developed. As input parameters of a neural network were considered: humidity of wheaten germs (х1, %, relative humidity of air (х2, %, ambient temperature (х3, ºС and concentration of mix of organic acids (х4, %. By means of the software, some neural networks were designed and trained. For modeling the network with two layers was used. Applying the developed and trained neural network it is possible constructed dependence у(х1, х2, х3, х4. For visualization in three-dimensional space limited amount of arguments of function by two. Results of work of neural networks y (x1, x4 with the recorded entrance parameters (x2 = 60, %, x3=20, ºC and a neural network y (x2, x3 with the recorded input parameters are presented (x1 = 15%, x4 = 5%. The received mathematical model which on the set set of certain parameters of storage, allows to receive concrete value of output parameter and to plan the storage modes in controlled environments.

  7. What are the goals of language teaching?

    Directory of Open Access Journals (Sweden)

    Vivian Cook

    2013-01-01

    Full Text Available For many centuries people who speak more than one language, that is to say second language (L2 users, have been admired. In the 16th century an advisor to Elizabeth I of England said: ‘For even as a hawk flieth not high with one wing, even so a man reacheth not to excellency with one tongue.’ Roger Ascham, The Scholemaster, 1570 In the 21st century the education minister for Elizabeth II proclaimed: ‘It is literally the case that learning languages makes you smarter. The neural networks in the brain strengthen as a result of language learning.’ Michael Gove, UK Education Secretary, 2011 Yet, despite these public statements, bilingualism is more often seen as a problem to be solved than an asset to be developed. Second language (L2 users indeed have problems, whether social, psychological or economic – like everyone else. But few of these stem from their bilingualism itself.

  8. Self-organizing feature map (neural networks) as a tool to select the best indicator of road traffic pollution (soil, leaves or bark of Robinia pseudoacacia L.).

    Science.gov (United States)

    Samecka-Cymerman, A; Stankiewicz, A; Kolon, K; Kempers, A J

    2009-07-01

    Concentrations of the elements Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn were measured in the leaves and bark of Robinia pseudoacacia and the soil in which it grew, in the town of Oleśnica (SW Poland) and at a control site. We selected this town because emission from motor vehicles is practically the only source of air pollution, and it seemed interesting to evaluate its influence on soil and plants. The self-organizing feature map (SOFM) yielded distinct groups of soils and R. pseudoacacia leaves and bark, depending on traffic intensity. Only the map classifying bark samples identified an additional group of highly polluted sites along the main highway from Wrocław to Warszawa. The bark of R. pseudoacacia seems to be a better bioindicator of long-term cumulative traffic pollution in the investigated area, while leaves are good indicators of short-term seasonal accumulation trends.

  9. How Language Is Embodied in Bilinguals and Children with Specific Language Impairment

    Science.gov (United States)

    Adams, Ashley M.

    2016-01-01

    This manuscript explores the role of embodied views of language comprehension and production in bilingualism and specific language impairment. Reconceptualizing popular models of bilingual language processing, the embodied theory is first extended to this area. Issues such as semantic grounding in a second language and potential differences between early and late acquisition of a second language are discussed. Predictions are made about how this theory informs novel ways of thinking about teaching a second language. Secondly, the comorbidity of speech, language, and motor impairments and how embodiment theory informs the discussion of the etiology of these impairments is examined. A hypothesis is presented suggesting that what is often referred to as specific language impairment may not be so specific due to widespread subclinical motor deficits in this population. Predictions are made about how weaknesses and instabilities in speech motor control, even at a subclinical level, may disrupt the neural network that connects acoustic input, articulatory motor plans, and semantics. Finally, I make predictions about how this information informs clinical practice for professionals such as speech language pathologists and occupational and physical therapists. These new hypotheses are placed within the larger framework of the body of work pertaining to semantic grounding, action-based language acquisition, and action-perception links that underlie language learning and conceptual grounding. PMID:27582716

  10. The neural basis of speech sound discrimination from infancy to adulthood

    OpenAIRE

    Partanen, Eino

    2013-01-01

    Rapid processing of speech is facilitated by neural representations of native language phonemes. However, some disorders and developmental conditions, such as developmental dyslexia, can hamper the development of these neural memory traces, leading to language delays and poor academic achievement. While the early identification of such deficits is paramount so that interventions can be started as early as possible, there is currently no systematically used ecologically valid paradigm for the ...

  11. Neural Tuning to Low-Level Features of Speech throughout the Perisylvian Cortex.

    Science.gov (United States)

    Berezutskaya, Julia; Freudenburg, Zachary V; Güçlü, Umut; van Gerven, Marcel A J; Ramsey, Nick F

    2017-08-16

    Despite a large body of research, we continue to lack a detailed account of how auditory processing of continuous speech unfolds in the human brain. Previous research showed the propagation of low-level acoustic features of speech from posterior superior temporal gyrus toward anterior superior temporal gyrus in the human brain (Hullett et al., 2016). In this study, we investigate what happens to these neural representations past the superior temporal gyrus and how they engage higher-level language processing areas such as inferior frontal gyrus. We used low-level sound features to model neural responses to speech outside of the primary auditory cortex. Two complementary imaging techniques were used with human participants (both males and females): electrocorticography (ECoG) and fMRI. Both imaging techniques showed tuning of the perisylvian cortex to low-level speech features. With ECoG, we found evidence of propagation of the temporal features of speech sounds along the ventral pathway of language processing in the brain toward inferior frontal gyrus. Increasingly coarse temporal features of speech spreading from posterior superior temporal cortex toward inferior frontal gyrus were associated with linguistic features such as voice onset time, duration of the formant transitions, and phoneme, syllable, and word boundaries. The present findings provide the groundwork for a comprehensive bottom-up account of speech comprehension in the human brain. SIGNIFICANCE STATEMENT We know that, during natural speech comprehension, a broad network of perisylvian cortical regions is involved in sound and language processing. Here, we investigated the tuning to low-level sound features within these regions using neural responses to a short feature film. We also looked at whether the tuning organization along these brain regions showed any parallel to the hierarchy of language structures in continuous speech. Our results show that low-level speech features propagate throughout the

  12. Neural overlap of L1 and L2 semantic representations across visual and auditory modalities : A decoding approach

    NARCIS (Netherlands)

    Van De Putte, Eowyn; De Baene, W.; Price, Cathy J; Duyck, Wouter

    2018-01-01

    This study investigated whether brain activity in Dutch-French bilinguals during semantic access to concepts from one language could be used to predict neural activation during access to the same concepts from another language, in different language modalities/tasks. This was tested using

  13. A universal multilingual weightless neural network tagger via quantitative linguistics.

    Science.gov (United States)

    Carneiro, Hugo C C; Pedreira, Carlos E; França, Felipe M G; Lima, Priscila M V

    2017-07-01

    In the last decade, given the availability of corpora in several distinct languages, research on multilingual part-of-speech tagging started to grow. Amongst the novelties there is mWANN-Tagger (multilingual weightless artificial neural network tagger), a weightless neural part-of-speech tagger capable of being used for mostly-suffix-oriented languages. The tagger was subjected to corpora in eight languages of quite distinct natures and had a remarkable accuracy with very low sample deviation in every one of them, indicating the robustness of weightless neural systems for part-of-speech tagging tasks. However, mWANN-Tagger needed to be tuned for every new corpus, since each one required a different parameter configuration. For mWANN-Tagger to be truly multilingual, it should be usable for any new language with no need of parameter tuning. This article proposes a study that aims to find a relation between the lexical diversity of a language and the parameter configuration that would produce the best performing mWANN-Tagger instance. Preliminary analyses suggested that a single parameter configuration may be applied to the eight aforementioned languages. The mWANN-Tagger instance produced by this configuration was as accurate as the language-dependent ones obtained through tuning. Afterwards, the weightless neural tagger was further subjected to new corpora in languages that range from very isolating to polysynthetic ones. The best performing instances of mWANN-Tagger are again the ones produced by the universal parameter configuration. Hence, mWANN-Tagger can be applied to new corpora with no need of parameter tuning, making it a universal multilingual part-of-speech tagger. Further experiments with Universal Dependencies treebanks reveal that mWANN-Tagger may be extended and that it has potential to outperform most state-of-the-art part-of-speech taggers if better word representations are provided. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Language Networks as Complex Systems

    Science.gov (United States)

    Lee, Max Kueiming; Ou, Sheue-Jen

    2008-01-01

    Starting in the late eighties, with a growing discontent with analytical methods in science and the growing power of computers, researchers began to study complex systems such as living organisms, evolution of genes, biological systems, brain neural networks, epidemics, ecology, economy, social networks, etc. In the early nineties, the research…

  15. Self-organizing feature map (neural networks) as a tool to select the best indicator of road traffic pollution (soil, leaves or bark of Robinia pseudoacacia L.)

    Energy Technology Data Exchange (ETDEWEB)

    Samecka-Cymerman, A., E-mail: sameckaa@biol.uni.wroc.p [Department of Ecology, Biogeochemistry and Environmental Protection, Wroclaw University, ul. Kanonia 6/8, 50-328 Wroclaw (Poland); Stankiewicz, A.; Kolon, K. [Department of Ecology, Biogeochemistry and Environmental Protection, Wroclaw University, ul. Kanonia 6/8, 50-328 Wroclaw (Poland); Kempers, A.J. [Department of Environmental Sciences, Radboud University of Nijmegen, Toernooiveld, 6525 ED Nijmegen (Netherlands)

    2009-07-15

    Concentrations of the elements Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn were measured in the leaves and bark of Robinia pseudoacacia and the soil in which it grew, in the town of Olesnica (SW Poland) and at a control site. We selected this town because emission from motor vehicles is practically the only source of air pollution, and it seemed interesting to evaluate its influence on soil and plants. The self-organizing feature map (SOFM) yielded distinct groups of soils and R. pseudoacacia leaves and bark, depending on traffic intensity. Only the map classifying bark samples identified an additional group of highly polluted sites along the main highway from Wroclaw to Warszawa. The bark of R. pseudoacacia seems to be a better bioindicator of long-term cumulative traffic pollution in the investigated area, while leaves are good indicators of short-term seasonal accumulation trends. - Once trained, SOFM could be used in the future to recognize types of pollution.

  16. Neural Control of Rising and Falling Tones in Mandarin Speakers Who Stutter

    Science.gov (United States)

    Howell, Peter; Jiang, Jing; Peng, Danling; Lu, Chunming

    2012-01-01

    Neural control of rising and falling tones in Mandarin people who stutter (PWS) was examined by comparing with that which occurs in fluent speakers [Howell, Jiang, Peng, and Lu (2012). Neural control of fundamental frequency rise and fall in Mandarin tones. "Brain and Language, 121"(1), 35-46]. Nine PWS and nine controls were scanned. Functional…

  17. Application of a Shallow Neural Network to Short-Term Stock Trading

    OpenAIRE

    Madahar, Abhinav; Ma, Yuze; Patel, Kunal

    2017-01-01

    Machine learning is increasingly prevalent in stock market trading. Though neural networks have seen success in computer vision and natural language processing, they have not been as useful in stock market trading. To demonstrate the applicability of a neural network in stock trading, we made a single-layer neural network that recommends buying or selling shares of a stock by comparing the highest high of 10 consecutive days with that of the next 10 days, a process repeated for the stock's ye...

  18. Neural networks and applications tutorial

    Science.gov (United States)

    Guyon, I.

    1991-09-01

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

  19. Monitoring Different Phonological Parameters of Sign Language Engages the Same Cortical Language Network but Distinctive Perceptual Ones.

    Science.gov (United States)

    Cardin, Velia; Orfanidou, Eleni; Kästner, Lena; Rönnberg, Jerker; Woll, Bencie; Capek, Cheryl M; Rudner, Mary

    2016-01-01

    The study of signed languages allows the dissociation of sensorimotor and cognitive neural components of the language signal. Here we investigated the neurocognitive processes underlying the monitoring of two phonological parameters of sign languages: handshape and location. Our goal was to determine if brain regions processing sensorimotor characteristics of different phonological parameters of sign languages were also involved in phonological processing, with their activity being modulated by the linguistic content of manual actions. We conducted an fMRI experiment using manual actions varying in phonological structure and semantics: (1) signs of a familiar sign language (British Sign Language), (2) signs of an unfamiliar sign language (Swedish Sign Language), and (3) invented nonsigns that violate the phonological rules of British Sign Language and Swedish Sign Language or consist of nonoccurring combinations of phonological parameters. Three groups of participants were tested: deaf native signers, deaf nonsigners, and hearing nonsigners. Results show that the linguistic processing of different phonological parameters of sign language is independent of the sensorimotor characteristics of the language signal. Handshape and location were processed by different perceptual and task-related brain networks but recruited the same language areas. The semantic content of the stimuli did not influence this process, but phonological structure did, with nonsigns being associated with longer RTs and stronger activations in an action observation network in all participants and in the supramarginal gyrus exclusively in deaf signers. These results suggest higher processing demands for stimuli that contravene the phonological rules of a signed language, independently of previous knowledge of signed languages. We suggest that the phonological characteristics of a language may arise as a consequence of more efficient neural processing for its perception and production.

  20. Complementary Languages

    DEFF Research Database (Denmark)

    Preisler, Bent

    2009-01-01

    society is everywhere unproblematic. A case in point is Higher Education. I will also argue that the recently proposed solution to ‘domain loss' - Danish and English used ‘in parallel', ‘parallel languages' - because it is unrealistic as well as undesirable as a consistent principle - should be replaced......The Danish language debate is dominated by two key concepts: ‘domain loss' and its opposite, ‘parallel languages' (parallelsproglighed). The under­stood reference is to the relationship between Danish and English - i.e. the spread of English at the expense of Danish vs. the coexistence of Danish...... and English within relevant ‘domains' of Danish society. In this article I am going to argue that the concept of ‘domain loss' is not theoretically tenable - its usual depiction ranging from the vague to the nonsensical - which is not to say that the relationship between English and Danish within Danish...

  1. Chaotic diagonal recurrent neural network

    International Nuclear Information System (INIS)

    Wang Xing-Yuan; Zhang Yi

    2012-01-01

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

  2. Gestures Enhance Foreign Language Learning

    Directory of Open Access Journals (Sweden)

    Manuela Macedonia

    2012-11-01

    Full Text Available Language and gesture are highly interdependent systems that reciprocally influence each other. For example, performing a gesture when learning a word or a phrase enhances its retrieval compared to pure verbal learning. Although the enhancing effects of co-speech gestures on memory are known to be robust, the underlying neural mechanisms are still unclear. Here, we summarize the results of behavioral and neuroscientific studies. They indicate that the neural representation of words consists of complex multimodal networks connecting perception and motor acts that occur during learning. In this context, gestures can reinforce the sensorimotor representation of a word or a phrase, making it resistant to decay. Also, gestures can favor embodiment of abstract words by creating it from scratch. Thus, we propose the use of gesture as a facilitating educational tool that integrates body and mind.

  3. Biomarker case-detection and prediction with potential for functional psychosis screening: development and validation of a model related to biochemistry, sensory neural timing and end organ performance.

    Directory of Open Access Journals (Sweden)

    Stephanie eFryar-Williams

    2016-04-01

    Full Text Available The Mental Health Biomarker Project aimed to discover case-predictive biomarkers for functional psychosis. In a retrospective, cross-sectional study, candidate marker results from 67, highly-characterized symptomatic participants were compared with results from 67 gender and age matched controls. Urine samples were analysed for catecholamines, their metabolites and hydroxylpyrolline-2-one, an oxidative stress marker. Blood samples were analyzed for vitamin and trace element cofactors of enzymes in the catecholamine synthesis and metabolism pathways. Cognitive, auditory and visual processing measures were assessed using a simple 45 minute, office-based procedure. Receiver Operating Curve (ROC and Odds Ratio analysis discovered biomarkers for deficits in folate, vitamin D and B6 and elevations in free copper to zinc ratio, catecholamines and the oxidative stress marker. Deficits were discovered in peripheral visual and auditory end-organ function, intra-cerebral auditory and visual processing speed and dichotic-listening performance. 15 ROC biomarker variables were divided into 5 functional domains. Through a repeated ROC process, individual ROC variables, followed by domains and finally the overall 15 set model, were dichotomously scored and tallied for abnormal results upon which it was found that ≥ 3 out of 5 abnormal domains achieved an AUC of 0.952 with a sensitivity of 84 per cent and a specificity of 90 percent. Six additional middle ear biomarkers in a 21 biomarker set increased sensitivity to 94% percent. Fivefold cross-validation yielded a mean sensitivity of 85% for the 15 biomarker set. Non-parametric regression analysis confirmed that ≥ 3 out of 5 abnormally scored domains predicted > 50% risk of case-ness whilst 4 abnormally-scored domains predicted 88% risk of case-ness and 100% diagnostic certainty was reached when all 5 domains were abnormally scored. These findings require validation in prospective cohorts and other mental

  4. ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network.

    Science.gov (United States)

    Cao, Renzhi; Freitas, Colton; Chan, Leong; Sun, Miao; Jiang, Haiqing; Chen, Zhangxin

    2017-10-17

    With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological experimental techniques. Protein function prediction has been a long standing challenge to fill the gap between the huge amount of protein sequences and the known function. In this paper, we propose a novel method to convert the protein function problem into a language translation problem by the new proposed protein sequence language "ProLan" to the protein function language "GOLan", and build a neural machine translation model based on recurrent neural networks to translate "ProLan" language to "GOLan" language. We blindly tested our method by attending the latest third Critical Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the performance of our methods on selected proteins whose function was released after CAFA competition. The good performance on the training and testing datasets demonstrates that our new proposed method is a promising direction for protein function prediction. In summary, we first time propose a method which converts the protein function prediction problem to a language translation problem and applies a neural machine translation model for protein function prediction.

  5. Emotion in languaging: Language and emotion as affective, adaptive and flexible behavior in social interaction

    Directory of Open Access Journals (Sweden)

    Thomas Wiben Jensen

    2014-07-01

    Full Text Available This article argues for a view on languaging as inherently affective. Informed by recent ecological tendencies within cognitive science and distributed language studies a distinction between first order languaging (language as whole-body sense making and second order language (language as system like constraints is put forward. Contrary to common assumptions within linguistics and communication studies separating language-as-a-system from language use (resulting in separations between language vs. body-language and verbal vs. non-verbal communication etc. the first/second order distinction sees language as emanating from behavior making it possible to view emotion and affect as integral parts languaging behavior. Likewise, emotion and affect are studied, not as inner mental states, but as processes of organism-environment interactions. Based on video recordings of interaction between 1 children with special needs, and 2 couple in therapy and the therapist patterns of reciprocal influences between interactants are examined. Through analyzes of affective stance and patterns of inter-affectivity it is exemplified how language and emotion should not be seen as separate phenomena combined in language use, but rather as completely intertwined phenomena in languaging behavior constrained by second order patterns.

  6. Infrared neural stimulation (INS) inhibits electrically evoked neural responses in the deaf white cat

    Science.gov (United States)

    Richter, Claus-Peter; Rajguru, Suhrud M.; Robinson, Alan; Young, Hunter K.

    2014-03-01

    Infrared neural stimulation (INS) has been used in the past to evoke neural activity from hearing and partially deaf animals. All the responses were excitatory. In Aplysia californica, Duke and coworkers demonstrated that INS also inhibits neural responses [1], which similar observations were made in the vestibular system [2, 3]. In deaf white cats that have cochleae with largely reduced spiral ganglion neuron counts and a significant degeneration of the organ of Corti, no cochlear compound action potentials could be observed during INS alone. However, the combined electrical and optical stimulation demonstrated inhibitory responses during irradiation with infrared light.

  7. 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.

  8. Altered functional connectivity of the language network in ASD: Role of classical language areas and cerebellum☆

    Science.gov (United States)

    Verly, Marjolein; Verhoeven, Judith; Zink, Inge; Mantini, Dante; Peeters, Ronald; Deprez, Sabine; Emsell, Louise; Boets, Bart; Noens, Ilse; Steyaert, Jean; Lagae, Lieven; De Cock, Paul; Rommel, Nathalie; Sunaert, Stefan

    2014-01-01

    The development of language, social interaction and communicative skills is remarkably different in the child with autism spectrum disorder (ASD). Atypical brain connectivity has frequently been reported in this patient population. However, the neural correlates underlying their disrupted language development and functioning are still poorly understood. Using resting state fMRI, we investigated the functional connectivity properties of the language network in a group of ASD patients with clear comorbid language impairment (ASD-LI; N = 19) and compared them to the language related connectivity properties of 23 age-matched typically developing children. A verb generation task was used to determine language components commonly active in both groups. Eight joint language components were identified and subsequently used as seeds in a resting state analysis. Interestingly, both the interregional and the seed-based whole brain connectivity analysis showed preserved connectivity between the classical intrahemispheric language centers, Wernicke's and Broca's areas. In contrast however, a marked loss of functional connectivity was found between the right cerebellar region and the supratentorial regulatory language areas. Also, the connectivity between the interhemispheric Broca regions and modulatory control dorsolateral prefrontal region was found to be decreased. This disruption of normal modulatory control and automation function by the cerebellum may underlie the abnormal language function in children with ASD-LI. PMID:24567909

  9. Altered functional connectivity of the language network in ASD: Role of classical language areas and cerebellum

    Directory of Open Access Journals (Sweden)

    Marjolein Verly

    2014-01-01

    Full Text Available The development of language, social interaction and communicative skills is remarkably different in the child with autism spectrum disorder (ASD. Atypical brain connectivity has frequently been reported in this patient population. However, the neural correlates underlying their disrupted language development and functioning are still poorly understood. Using resting state fMRI, we investigated the functional connectivity properties of the language network in a group of ASD patients with clear comorbid language impairment (ASD-LI; N = 19 and compared them to the language related connectivity properties of 23 age-matched typically developing children. A verb generation task was used to determine language components commonly active in both groups. Eight joint language components were identified and subsequently used as seeds in a resting state analysis. Interestingly, both the interregional and the seed-based whole brain connectivity analysis showed preserved connectivity between the classical intrahemispheric language centers, Wernicke's and Broca's areas. In contrast however, a marked loss of functional connectivity was found between the right cerebellar region and the supratentorial regulatory language areas. Also, the connectivity between the interhemispheric Broca regions and modulatory control dorsolateral prefrontal region was found to be decreased. This disruption of normal modulatory control and automation function by the cerebellum may underlie the abnormal language function in children with ASD-LI.

  10. Simplexity, languages and human languaging

    DEFF Research Database (Denmark)

    Cowley, Stephen; Gahrn-Andersen, Rasmus

    2018-01-01

    Building on a distributed perspective, the Special Issue develops Alain Berthoz's concept of simplexity. By so doing, neurophysiology is used to reach beyond observable and, specifically, 1st-order languaging. While simplexity clarifies how language uses perception/action, a community's ‘lexicon......’ (a linguistic 2nd order) also shapes human powers. People use global constraints to make and construe wordings and bring a social/individual duality to human living. Within a field of perception-action-language, the phenomenology of ‘words’ and ‘things’ drives people to sustain their own experience....... Simplex tricks used in building bodies co-function with action that grants humans access to en-natured culture where, together, they build human knowing....

  11. Interview with Erzsébet Barát, Organizer of the Annual Conference, Language, Ideology, Media: Gender/Sexuality Relations in Hungary

    Directory of Open Access Journals (Sweden)

    Erzsébet László

    2011-01-01

    Full Text Available Language, Ideology, Media: Gender/Sexuality Relations in Hungary is an annual interdisciplinary conference first launched in September 2005 that has grown into “the only regular research forum for feminist scholarship concerned with Hungarian cultural practices of gender and sexuality” (http://primus.arts.u-szeged.hu/ieas/gender/index.html. Since the first contextualizing/grounding event in 2005, whose theme was ‘A nő helye a magyar nyelvhasználatban’ (Woman’s Place in Hungarian Language Use, the conference has touched upon such important issues as stereotypes of “woman” and “femininity” (2006, feminine/masculine identity and experience (2007, the relation of “woman” and body/sensuality (2008, the spaces of sexuality (2009 institutionalizations of gender relations with a specific focus on the intersection of gender and nation(alism in Hungary (2010. (See the Archive section of the webpage for detailed information. The theme of the upcoming 2011 conference will concern issues of gender relations and feminism in post-socialist Hungary. To date the conference is the only academic forum in Hungary that provides an opportunity to explore contemporary issues of the relations of Hungarian language and power, cultural representations and ideology, and Hungarian women and feminist thought from an interdisciplinary perspective attracting scholars from Hungarian as well as non-Hungarian universities. Speakers of the conference include well-established feminist scholars with international visibility, such as Louise O. Vasvári (New York University, Stony Brook University, Andrea Virginas, Sapientia, Transylvanian Hungarian University, Cluj, Bolemant Lilla Comenius University, Bratislava, Mária Joó ELTE, Budapest, Judit Friedrich, ELTE Budapest, Nóra Sélley, University of Debrecen, or Erzsébet Barát (University of Szeged, Central European University, Budapest, and Anna Kérchy (University of Szeged. The

  12. Local language

    NARCIS (Netherlands)

    Monique Turkenburg

    2002-01-01

    Original title: Taal lokaal. Children of immigrants living in the Netherlands have for years had the opportunity to receive lessons in their mother tongue at primary school. Since 1998 this has been referred to as minority language teaching (OALT in Dutch), and has been the responsibility

  13. Body Language.

    Science.gov (United States)

    Pollard, David E.

    1993-01-01

    Discusses how the use of body language in Chinese fiction strikes most Westerners as unusual, if not strange. Considers that, although this may be the result of differences in gestures or different conventions in fiction, it is a problem for translators, who handle the differences by various strategies, e.g., omission or expansion. (NKA)

  14. Language Pathology.

    Science.gov (United States)

    Fletcher, Paul

    1989-01-01

    Discusses the role of linguistics in the investigation of language disorders, focusing on the application of phonetics, descriptive grammatic frameworks, grammatical theory, and concepts from semantics and pragmatics to a variety of disorders and their remediation. Some trends and examples from the field of clinical linguistics are discussed. (GLR)

  15. Numerical exploration of the influence of neural noise on the ...

    Indian Academy of Sciences (India)

    PRAKASH KUMAR

    from the lack of detailed neural biophysical elements in the present simulation. ... of perception, the lack of knowledge of all these details is equally impressive. .... I would like to thank Carmen Gómez Sos for her English language editorial ...

  16. Neural network based system for script identification in Indian ...

    Indian Academy of Sciences (India)

    2016-08-26

    Aug 26, 2016 ... The paper describes a neural network-based script identification system which can be used in the machine reading of documents written in English, Hindi and Kannada language scripts. Script identification is a basic requirement in automation of document processing, in multi-script, multi-lingual ...

  17. Representation of linguistic form and function in recurrent neural networks

    NARCIS (Netherlands)

    Kadar, Akos; Chrupala, Grzegorz; Alishahi, Afra

    2017-01-01

    We present novel methods for analyzing the activation patterns of recurrent neural networks from a linguistic point of view and explore the types of linguistic structure they learn. As a case study, we use a standard standalone language model, and a multi-task gated recurrent network architecture

  18. Phonematic translation of Polish texts by the neural network

    International Nuclear Information System (INIS)

    Bielecki, A.; Podolak, I.T.; Wosiek, J.; Majkut, E.

    1996-01-01

    Using the back propagation algorithm, we have trained the feed forward neural network to pronounce Polish language, more precisely to translate Polish text into its phonematic counterpart. Depending on the input coding and network architecture, 88%-95% translation efficiency was achieved. (author)

  19. A Constructive Neural-Network Approach to Modeling Psychological Development

    Science.gov (United States)

    Shultz, Thomas R.

    2012-01-01

    This article reviews a particular computational modeling approach to the study of psychological development--that of constructive neural networks. This approach is applied to a variety of developmental domains and issues, including Piagetian tasks, shift learning, language acquisition, number comparison, habituation of visual attention, concept…

  20. Diagnosis of aphasia using neural and fuzzy techniques

    DEFF Research Database (Denmark)

    Jantzen, Jan; Axer, H.; Keyserlingk, D. Graf von

    2000-01-01

    The language disability Aphasia has several sub-diagnoses such as Amnestic, Broca, Global, and Wernicke. Data concerning 265 patients is available in the form of test scores and diagnoses, made by physicians according to the Aachen Aphasia Test. A neural network model has been built, which...

  1. Diagnosis Of Aphasia Using Neural And Fuzzy Techniques

    DEFF Research Database (Denmark)

    Jantzen, Jan; Axer, Hubertus; Keyserlingk, Diedrich Graf von

    2002-01-01

    The language disability aphasia has several sub-diagnoses such as Amnestic, Broca, Global, and Wernicke. Data concerning 265 patients is available in the form of test scores and diagnoses, made by physicians according to the Aachen Aphasia Test. A neural network model has been built, which...

  2. Pragmatic Bootstrapping: A Neural Network Model of Vocabulary Acquisition

    Science.gov (United States)

    Caza, Gregory A.; Knott, Alistair

    2012-01-01

    The social-pragmatic theory of language acquisition proposes that children only become efficient at learning the meanings of words once they acquire the ability to understand the intentions of other agents, in particular the intention to communicate (Akhtar & Tomasello, 2000). In this paper we present a neural network model of word learning which…

  3. Neural mechanisms of song memory formation in juvenile zebra finches

    NARCIS (Netherlands)

    Moorman, S.

    2015-01-01

    There are many parallels between the acquisition of spoken language in human infants and song learning in songbirds, at the behavioural, neural, genetic and cognitive levels. Both human infants and juvenile songbirds are able to imitate sounds from adults of the same species (often their parents),

  4. Language distance and tree reconstruction

    International Nuclear Information System (INIS)

    Petroni, Filippo; Serva, Maurizio

    2008-01-01

    Languages evolve over time according to a process in which reproduction, mutation and extinction are all possible. This is very similar to haploid evolution for asexual organisms and for the mitochondrial DNA of complex ones. Exploiting this similarity, it is possible, in principle, to verify hypotheses concerning the relationship among languages and to reconstruct their family tree. The key point is the definition of the distances among pairs of languages in analogy with the genetic distances among pairs of organisms. Distances can be evaluated by comparing grammar and/or vocabulary, but while it is difficult, if not impossible, to quantify grammar distance, it is possible to measure a distance from vocabulary differences. The method used by glottochronology computes distances from the percentage of shared 'cognates', which are words with a common historical origin. The weak point of this method is that subjective judgment plays a significant role. Here we define the distance of two languages by considering a renormalized edit distance among words with the same meaning and averaging over the two hundred words contained in a Swadesh list. In our approach the vocabulary of a language is the analogue of DNA for organisms. The advantage is that we avoid subjectivity and, furthermore, reproducibility of results is guaranteed. We apply our method to the Indo-European and the Austronesian groups, considering, in both cases, fifty different languages. The two trees obtained are, in many respects, similar to those found by glottochronologists, with some important differences as regards the positions of a few languages. In order to support these different results we separately analyze the structure of the distances of these languages with respect to all the others

  5. Language distance and tree reconstruction

    Science.gov (United States)

    Petroni, Filippo; Serva, Maurizio

    2008-08-01

    Languages evolve over time according to a process in which reproduction, mutation and extinction are all possible. This is very similar to haploid evolution for asexual organisms and for the mitochondrial DNA of complex ones. Exploiting this similarity, it is possible, in principle, to verify hypotheses concerning the relationship among languages and to reconstruct their family tree. The key point is the definition of the distances among pairs of languages in analogy with the genetic distances among pairs of organisms. Distances can be evaluated by comparing grammar and/or vocabulary, but while it is difficult, if not impossible, to quantify grammar distance, it is possible to measure a distance from vocabulary differences. The method used by glottochronology computes distances from the percentage of shared 'cognates', which are words with a common historical origin. The weak point of this method is that subjective judgment plays a significant role. Here we define the distance of two languages by considering a renormalized edit distance among words with the same meaning and averaging over the two hundred words contained in a Swadesh list. In our approach the vocabulary of a language is the analogue of DNA for organisms. The advantage is that we avoid subjectivity and, furthermore, reproducibility of results is guaranteed. We apply our method to the Indo-European and the Austronesian groups, considering, in both cases, fifty different languages. The two trees obtained are, in many respects, similar to those found by glottochronologists, with some important differences as regards the positions of a few languages. In order to support these different results we separately analyze the structure of the distances of these languages with respect to all the others.

  6. 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...

  7. 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...

  8. Language Guidelines

    CERN Document Server

    Goy, Pascale; CERN. Geneva. HR Department

    2017-01-01

    L&D, Integration level to ensure a successful integration in the Organization and/or the local area, is recognized as centrally defined training. • Performance of a function or a role in the Organization

  9. Neural substrates of sublexical processing for spelling.

    Science.gov (United States)

    DeMarco, Andrew T; Wilson, Stephen M; Rising, Kindle; Rapcsak, Steven Z; Beeson, Pélagie M

    2017-01-01

    We used fMRI to examine the neural substrates of sublexical phoneme-grapheme conversion during spelling in a group of healthy young adults. Participants performed a writing-to-dictation task involving irregular words (e.g., choir), plausible nonwords (e.g., kroid), and a control task of drawing familiar geometric shapes (e.g., squares). Written production of both irregular words and nonwords engaged a left-hemisphere perisylvian network associated with reading/spelling and phonological processing skills. Effects of lexicality, manifested by increased activation during nonword relative to irregular word spelling, were noted in anterior perisylvian regions (posterior inferior frontal gyrus/operculum/precentral gyrus/insula), and in left ventral occipito-temporal cortex. In addition to enhanced neural responses within domain-specific components of the language network, the increased cognitive demands associated with spelling nonwords engaged domain-general frontoparietal cortical networks involved in selective attention and executive control. These results elucidate the neural substrates of sublexical processing during written language production and complement lesion-deficit correlation studies of phonological agraphia. Copyright © 2016 Elsevier Inc. All rights reserved.

  10. Early Language Learning and the Social Brain.

    Science.gov (United States)

    Kuhl, Patricia K

    2014-01-01

    Explaining how every typically developing child acquires language is one of the grand challenges of cognitive neuroscience. Historically, language learning provoked classic debates about the contributions of innately specialized as opposed to general learning mechanisms. Now, new data are being brought to bear from studies that employ magnetoencephalograph (MEG), electroencephalograph (EEG), magnetic resonance imaging (MRI), and diffusion tensor imaging (DTI) studies on young children. These studies examine the patterns of association between brain and behavioral measures. The resulting data offer both expected results and surprises that are altering theory. As we uncover what it means to be human through the lens of young children, and their ability to speak, what we learn will not only inform theories of human development, but also lead to the discovery of neural biomarkers, early in life, that indicate risk for language impairment and allow early intervention for children with developmental disabilities involving language. Copyright © 2014 Cold Spring Harbor Laboratory Press; all rights reserved.

  11. The Recruitment Theory of Language Origins

    Science.gov (United States)

    Steels, Luc

    Tremendous progress has been made recently on the fascinating question of the origins and evolution of language (see e.g. (55), (7), (9), (31)). There is no widely accepted complete theory yet, but several proposals are on the table and observations and experiments are proceeding. This chapter focuses on the recruitment theory of language origins which we have been exploring for almost ten years now. This theory argues that language users recruit and try out different strategies for solving the task of communication and retain those that maximise communicative success and cognitive economy. Each strategy requires specific cognitive neural mechanisms, which in themselves serve a wide range of purposes and therefore may have evolved or could be learned independently of language.

  12. Spatial Language Learning

    Science.gov (United States)

    Fu, Zhengling

    2016-01-01

    Spatial language constitutes part of the basic fabric of language. Although languages may have the same number of terms to cover a set of spatial relations, they do not always do so in the same way. Spatial languages differ across languages quite radically, thus providing a real semantic challenge for second language learners. The essay first…

  13. Language and the Law.

    Science.gov (United States)

    Gibbons, John

    1999-01-01

    Discusses the language of law and its general interest to the field of applied linguistics. Specific focus is on legal language, the problems and remedies of legal communication (e.g., language and disadvantage before the law, improving legal communication) the legislation of language (e.g., language rights, language crimes), and forensic…

  14. Neural Networks: Implementations and Applications

    OpenAIRE

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

    1996-01-01

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

  15. The language phenomenon human communication from milliseconds to millennia

    CERN Document Server

    Smith, K

    2013-01-01

    This volume contains a contemporary, integrated description of the processes of language. These range from fast scales (fractions of a second) to slow ones (over a million years). The contributors, all experts in their fields, address language in the brain, production of sentences and dialogues, language learning, transmission and evolutionary processes that happen over centuries or millenia, the relation between language and genes, the origins of language, self-organization, and language competition and death. The book as a whole will help to show how processes at different scales affect each other, thus presenting language as a dynamic, complex and profoundly human phenomenon.

  16. Central neural pathways for thermoregulation

    Science.gov (United States)

    Morrison, Shaun F.; Nakamura, Kazuhiro

    2010-01-01

    Central neural circuits orchestrate a homeostatic repertoire to maintain body temperature during environmental temperature challenges and to alter body temperature during the inflammatory response. This review summarizes the functional organization of the neural pathways through which cutaneous thermal receptors alter thermoregulatory effectors: the cutaneous circulation for heat loss, the brown adipose tissue, skeletal muscle and heart for thermogenesis and species-dependent mechanisms (sweating, panting and saliva spreading) for evaporative heat loss. These effectors are regulated by parallel but distinct, effector-specific neural pathways that share a common peripheral thermal sensory input. The thermal afferent circuits include cutaneous thermal receptors, spinal dorsal horn neurons and lateral parabrachial nucleus neurons projecting to the preoptic area to influence warm-sensitive, inhibitory output neurons which control thermogenesis-promoting neurons in the dorsomedial hypothalamus that project to premotor neurons in the rostral ventromedial medulla, including the raphe pallidus, that descend to provide the excitation necessary to drive thermogenic thermal effectors. A distinct population of warm-sensitive preoptic neurons controls heat loss through an inhibitory input to raphe pallidus neurons controlling cutaneous vasoconstriction. PMID:21196160

  17. Natural Language Processing with Small Feed-Forward Networks

    OpenAIRE

    Botha, Jan A.; Pitler, Emily; Ma, Ji; Bakalov, Anton; Salcianu, Alex; Weiss, David; McDonald, Ryan; Petrov, Slav

    2017-01-01

    We show that small and shallow feed-forward neural networks can achieve near state-of-the-art results on a range of unstructured and structured language processing tasks while being considerably cheaper in memory and computational requirements than deep recurrent models. Motivated by resource-constrained environments like mobile phones, we showcase simple techniques for obtaining such small neural network models, and investigate different tradeoffs when deciding how to allocate a small memory...

  18. Epigenetic learning in non-neural organisms

    Indian Academy of Sciences (India)

    Prakash

    2008-09-19

    Sep 19, 2008 ... neurobiology and psychology directly implies latency and learning. However ... The notion of cell memory is important in studies of cell biology and .... Paramecium following induction of new phenotypes by various physical ...

  19. NEURAL ORGANIZATION OF SENSORY INFORMATIONS FOR TASTE,

    Science.gov (United States)

    TASTE , ELECTROPHYSIOLOGY), (*NERVES, *TONGUE), NERVE CELLS, NERVE IMPULSES, PHYSIOLOGY, NERVOUS SYSTEM, STIMULATION(PHYSIOLOGY), NERVE FIBERS, RATS...HAMSTERS, STIMULATION(PHYSIOLOGY), PERCEPTION, COOLING, BEHAVIOR, PSYCHOPHYSIOLOGY, TEMPERATURE, THRESHOLDS(PHYSIOLOGY), CHEMORECEPTORS , STATISTICAL ANALYSIS, JAPAN

  20. Portrayals of canine obesity in English-language newspapers and in leading veterinary journals, 2000-2009: implications for animal welfare organizations and veterinarians as public educators.

    Science.gov (United States)

    Degeling, Chris; Rock, Melanie; Toews, Lorraine; Teows, Lorraine

    2011-01-01

    In industrialized societies, more than 1 in 3 dogs and people currently qualify as overweight or obese. Experts in public health expect both these figures to rise. Although clinical treatment remains important, so are public perceptions and social norms. This article presents a thematic analysis of English-language mass media coverage on canine obesity from 2000 through 2009 and compares these results with a thematic analysis of articles on canine obesity in leading veterinary journals during the same time period. Drawing on Giddens's theory of structuration, this study identified articles that emphasized individual agency, environmental structure, or both as contributors to canine obesity. Comparisons with weight-related health problems in human populations were virtually absent from the veterinary sample. Although such comparisons were almost always present in the media sample, quotations from veterinarians and other spokespeople for the welfare of nonhuman animals emphasized the agency of individual caregivers (owners) over structural influences. Now that weight gain and obesity have been established as a pressing animal welfare problem, these results suggest a need for research and for interventions, such as media advocacy, that emphasize intersections between animal-owner agency, socioenvironmental determinants, and connections between animal welfare and human health.

  1. Bilingualism, dementia, cognitive and neural reserve.

    Science.gov (United States)

    Perani, Daniela; Abutalebi, Jubin

    2015-12-01

    We discuss the role of bilingualism as a source of cognitive reserve and we propose the putative neural mechanisms through which lifelong bilingualism leads to a neural reserve that delays the onset of dementia. Recent findings highlight that the use of more than one language affects the human brain in terms of anatomo-structural changes. It is noteworthy that recent evidence from different places and cultures throughout the world points to a significant delay of dementia onset in bilingual/multilingual individuals. This delay has been reported not only for Alzheimer's dementia and its prodromal mild cognitive impairment phase, but also for other dementias such as vascular and fronto-temporal dementia, and was found to be independent of literacy, education and immigrant status. Lifelong bilingualism represents a powerful cognitive reserve delaying the onset of dementia by approximately 4 years. As to the causal mechanism, because speaking more than one language heavily relies upon executive control and attention, brain systems handling these functions are more developed in bilinguals resulting in increases of gray and white matter densities that may help protect from dementia onset. These neurocognitive benefits are even more prominent when second language proficiency and exposure are kept high throughout life.

  2. VAL language: description and analysis

    International Nuclear Information System (INIS)

    McGraw, J.R.

    1982-01-01

    VAL is a high-level, function-based language designed for use on data flow computers. A data flow computer has many small processors organized to cooperate in the executive of a single computation. A computation is represented by its data flow graph; each operator in a graph is scheduled for execution on one of the processors after all of its operands' values are known. VAL promotes the indentification of concurrency in algorithms and simplifies the mapping into data graphs. This paper presents a detailed introduction to VAL and analyzes its usefulness for programming in a highly concurrent environment. VAL provides implicit concurrency (operations that can execute simultaneously are evident without the need for any explicit language notation). The language uses function- and expression-based features that prohibit all side effects, which simplifies translation to graphs. The salient language features are described and illustrated through examples taken from a complete VAL program for adaptive quadrature. Analysis of the language shows that VAL meets the critical needs for a data flow environment. The language encourages programmers to think in terms of general concurrency, enhances readability (due to the absence of side effects), and possesses a structure amenable to verification techniques. However, VAL is still evolving. The language definition needs refining, and more support tools for programmer use need to be developed. Also, some new kinds of optimization problems should be addressed

  3. 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…

  4. 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...

  5. Language learning interventions | Kilfoil | Journal for Language ...

    African Journals Online (AJOL)

    The results for that intervention show that the hypothesis was correct and students need more time and structure if they are to improve their language competence sufficiently. Keywords: language learning interventions, English for specific purposes, language competence, fossilization. Journal for Language Teaching Vol.

  6. Structured Memory for Neural Turing Machines

    OpenAIRE

    Zhang, Wei; Yu, Yang; Zhou, Bowen

    2015-01-01

    Neural Turing Machines (NTM) contain memory component that simulates "working memory" in the brain to store and retrieve information to ease simple algorithms learning. So far, only linearly organized memory is proposed, and during experiments, we observed that the model does not always converge, and overfits easily when handling certain tasks. We think memory component is key to some faulty behaviors of NTM, and better organization of memory component could help fight those problems. In this...

  7. FOXP2 and the neuroanatomy of speech and language.

    Science.gov (United States)

    Vargha-Khadem, Faraneh; Gadian, David G; Copp, Andrew; Mishkin, Mortimer

    2005-02-01

    That speech and language are innate capacities of the human brain has long been widely accepted, but only recently has an entry point into the genetic basis of these remarkable faculties been found. The discovery of a mutation in FOXP2 in a family with a speech and language disorder has enabled neuroscientists to trace the neural expression of this gene during embryological development, track the effects of this gene mutation on brain structure and function, and so begin to decipher that part of our neural inheritance that culminates in articulate speech.

  8. 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.

  9. Functional language and data flow architectures

    Science.gov (United States)

    Ercegovac, M. D.; Patel, D. R.; Lang, T.

    1983-01-01

    This is a tutorial article about language and architecture approaches for highly concurrent computer systems based on the functional style of programming. The discussion concentrates on the basic aspects of functional languages, and sequencing models such as data-flow, demand-driven and reduction which are essential at the machine organization level. Several examples of highly concurrent machines are described.

  10. A step beyond local observations with a dialog aware bidirectional GRU network for Spoken Language Understanding

    OpenAIRE

    Vukotic , Vedran; Raymond , Christian; Gravier , Guillaume

    2016-01-01

    International audience; Architectures of Recurrent Neural Networks (RNN) recently become a very popular choice for Spoken Language Understanding (SLU) problems; however, they represent a big family of different architectures that can furthermore be combined to form more complex neural networks. In this work, we compare different recurrent networks, such as simple Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Gated Memory Units (GRU) and their bidirectional versions,...

  11. Character Recognition Using Genetically Trained Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

    1998-10-01

    Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the

  12. Language sampling

    DEFF Research Database (Denmark)

    Rijkhoff, Jan; Bakker, Dik

    1998-01-01

    This article has two aims: [1] to present a revised version of the sampling method that was originally proposed in 1993 by Rijkhoff, Bakker, Hengeveld and Kahrel, and [2] to discuss a number of other approaches to language sampling in the light of our own method. We will also demonstrate how our...... sampling method is used with different genetic classifications (Voegelin & Voegelin 1977, Ruhlen 1987, Grimes ed. 1997) and argue that —on the whole— our sampling technique compares favourably with other methods, especially in the case of exploratory research....

  13. High school music classes enhance the neural processing of speech.

    Science.gov (United States)

    Tierney, Adam; Krizman, Jennifer; Skoe, Erika; Johnston, Kathleen; Kraus, Nina

    2013-01-01

    Should music be a priority in public education? One argument for teaching music in school is that private music instruction relates to enhanced language abilities and neural function. However, the directionality of this relationship is unclear and it is unknown whether school-based music training can produce these enhancements. Here we show that 2 years of group music classes in high school enhance the neural encoding of speech. To tease apart the relationships between music and neural function, we tested high school students participating in either music or fitness-based training. These groups were matched at the onset of training on neural timing, reading ability, and IQ. Auditory brainstem responses were collected to a synthesized speech sound presented in background noise. After 2 years of training, the neural responses of the music training group were earlier than at pre-training, while the neural timing of students in the fitness training group was unchanged. These results represent the strongest evidence to date that in-school music education can cause enhanced speech encoding. The neural benefits of musical training are, therefore, not limited to expensive private instruction early in childhood but can be elicited by cost-effective group instruction during adolescence.

  14. 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.

  15. Dynamics of neural cryptography

    International Nuclear Information System (INIS)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-01-01

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

  16. 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.

  17. Language and Dementia: Neuropsychological Aspects

    OpenAIRE

    Kempler, Daniel; Goral, Mira

    2008-01-01

    This article reviews recent evidence for the relationship between extralinguistic cognitive and language abilities in dementia. A survey of data from investigations of three dementia syndromes (Alzheimer's disease, semantic dementia and progressive nonfluent aphasia) reveals that, more often than not, deterioration of conceptual organization appears associated with lexical impairments, whereas impairments in executive function are associated with sentence- and discourse-level deficits. These ...

  18. Precursors to language: Social cognition and pragmatic inference in primates.

    Science.gov (United States)

    Seyfarth, Robert M; Cheney, Dorothy L

    2017-02-01

    Despite their differences, human language and the vocal communication of nonhuman primates share many features. Both constitute forms of coordinated activity, rely on many shared neural mechanisms, and involve discrete, combinatorial cognition that includes rich pragmatic inference. These common features suggest that during evolution the ancestors of all modern primates faced similar social problems and responded with similar systems of communication and cognition. When language later evolved from this common foundation, many of its distinctive features were already present.

  19. Language training

    CERN Multimedia

    2015-01-01

    If one of your New Year’s resolutions is to learn a language, there is no excuse any more.    You can attend one of our English or French courses and you can practise the language with a tandem partner!   General & Professional French courses The next General & Professional French course will start on 26 January. These collective courses aim to bring participants who have at least level A1 to higher levels (up to C2). Each level consists of a combination of face-to-face sessions (40 hours) with personal work (20 hours) following a specially designed programme. A final progress test takes place at the end of the term. Please note that it is mandatory to take the placement test. Please sign up here. French courses for beginners The aim of this course is to give some basic skills to beginners in order to communicate in simple everyday situations in both social and professional life. These courses can start at any time during the year, as soon as a group of beg...

  20. Language Training

    CERN Multimedia

    HR Department

    Permanence A "permanence" for language Training has been set up. If anyone has a question or requires information on any aspect of English or French training please come to our office 5 4-016 at the following times. Lucette Fournier - French courses Monday 13.30 - 15.30 Tuesday\t10.30 - 12.30 Tessa Osborne - English courses Wednesday\t12.00 - 14.00 Thursday\t11.00 - 13.00   New courses Specific English and French courses - Exam preparation/ We are now offering specific courses in English and French leading to a recognised external examination (e.g. Cambridge, DELF, DALF). If you are interested in following one of these courses and have at least an upper intermediate level of English or French, please enrol through the following link:  English courses French courses Or contact: Tessa Osborne 72957 (English courses) Lucette Fournier 73483 (French courses) Language Training Nathalie Dumeaux Tel. 78144 nathalie.dumeaux@cern.ch

  1. Language Training

    CERN Multimedia

    HR Department

    2009-01-01

    PermanenceA "permanence" for language Training has been set up. If anyone has a question or requires information on any aspect of English or French training please come to our office 5 4-016 at the following times. Lucette Fournier French courses Monday 13.30 - 15.30 Tuesday\t10.30 - 12.30 Tessa Osborne English courses Wednesday\t12.00 - 14.00 Thursday\t11.00 - 13.00 New courses Specific English and French courses - Exam preparation/ We are now offering specific courses in English and French leading to a recognised external examination (e.g. Cambridge, DELF and BULATS). If you are interested in following one of these courses and have at least an upper intermediate level of English or French, please enrol through the following link: http://English courses http://French courses Or contact: Tessa Osborne 72957 (English courses) Lucette Fournier 73483 (French courses) Language Training Nathalie Dumeaux Tel. 78144 mailto:nathalie.dumeaux@cern.ch

  2. LANGUAGE TRAINING

    CERN Multimedia

    2004-01-01

    If you wish to participate in one of the following courses, please discuss with your supervisor and apply electronically directly from the course description pages that can be found on the Web at: http://www.cern.ch/Training/ or fill in an "application for training" form available from your Divisional Secretariat or from your DTO (Divisional Training Officer). Applications will be accepted in the order of their receipt. LANGUAGE TRAINING Françoise Benz tel. 73127 language.training@cern.ch FRENCH TRAINING General and Professional French Courses The next session will take place from 26 January to 02 April 2004. These courses are open to all persons working on the Cern site, and to their spouses. For registration and further information on the courses, please consult our Web pages: http://cern.ch/Training or contact Mrs. Benz: Tel. 73127. Writing Professional Documents in French The next session will take place from 26 January to 02 April 2004. This course is designed for people wi...

  3. LANGUAGE TRAINING

    CERN Multimedia

    2004-01-01

    If you wish to participate in one of the following courses, please discuss with your supervisor and apply electronically directly from the course description pages that can be found on the Web at: http://www.cern.ch/Training/ or fill in an "application for training" form available from your Divisional Secretariat or from your DTO (Divisional Training Officer). Applications will be accepted in the order of their receipt. LANGUAGE TRAINING Françoise Benz tel. 73127 language.training@cern.ch FRENCH TRAINING General and Professional French Courses The next session will take place from 26 January to 02 April 2004. These courses are open to all persons working on the Cern site, and to their spouses. For registration and further information on the courses, please consult our Web pages: http://cern.ch/Training or contact Mrs. Benz : Tel. 73127. Writing Professional Documents in French The next session will take place from 26 January to 02 April 2004. This course is designed for peop...

  4. Foreign Language Teachers' Language Proficiency and Their Language Teaching Practice

    Science.gov (United States)

    Richards, Heather; Conway, Clare; Roskvist, Annelies; Harvey, Sharon

    2013-01-01

    Teachers' subject knowledge is recognized as an essential component of effective teaching. In the foreign language context, teachers' subject knowledge includes language proficiency. In New Zealand high schools, foreign languages (e.g. Chinese, French, German, Japanese and Spanish) have recently been offered to learners earlier in their schooling,…

  5. Technology in Language Use, Language Teaching, and Language Learning

    Science.gov (United States)

    Chun, Dorothy; Smith, Bryan; Kern, Richard

    2016-01-01

    This article offers a capacious view of technology to suggest broad principles relating technology and language use, language teaching, and language learning. The first part of the article considers some of the ways that technological media influence contexts and forms of expression and communication. In the second part, a set of heuristic…

  6. Pax7 lineage contributions to the mammalian neural crest.

    Directory of Open Access Journals (Sweden)

    Barbara Murdoch

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

  7. BIBLIOGRAPHY ON LANGUAGE DEVELOPMENT.

    Science.gov (United States)

    Harvard Univ., Cambridge, MA. Graduate School of Education.

    THIS BIBLIOGRAPHY LISTS MATERIAL ON VARIOUS ASPECTS OF LANGUAGE DEVELOPMENT. APPROXIMATELY 65 UNANNOTATED REFERENCES ARE PROVIDED TO DOCUMENTS DATING FROM 1958 TO 1966. JOURNALS, BOOKS, AND REPORT MATERIALS ARE LISTED. SUBJECT AREAS INCLUDED ARE THE NATURE OF LANGUAGE, LINGUISTICS, LANGUAGE LEARNING, LANGUAGE SKILLS, LANGUAGE PATTERNS, AND…

  8. Inference in `poor` languages

    Energy Technology Data Exchange (ETDEWEB)

    Petrov, S.

    1996-10-01

    Languages with a solvable implication problem but without complete and consistent systems of inference rules (`poor` languages) are considered. The problem of existence of finite complete and consistent inference rule system for a ``poor`` language is stated independently of the language or rules syntax. Several properties of the problem arc proved. An application of results to the language of join dependencies is given.

  9. Let There Be Languages!

    Science.gov (United States)

    Gunnarsson, Petur

    1992-01-01

    Examines the resilience of small languages in the face of larger ones. Highlights include the concept of one dominant language, such as Esperanto; the threat of television to small visual-language societies; the power of visual media; man's relationship to language; and the resilience of language. (LRW)

  10. Language as Pure Potential

    Science.gov (United States)

    Park, Joseph Sung-Yul

    2016-01-01

    Language occupies a crucial position in neoliberalism, due to the reimagination of language as commodified skill. This paper studies the role of language ideology in this transformation by identifying a particular ideology that facilitates this process, namely the ideology which views language as pure potential. Neoliberalism treats language as a…

  11. Linguistics in Language Education

    Science.gov (United States)

    Kumar, Rajesh; Yunus, Reva

    2014-01-01

    This article looks at the contribution of insights from theoretical linguistics to an understanding of language acquisition and the nature of language in terms of their potential benefit to language education. We examine the ideas of innateness and universal language faculty, as well as multilingualism and the language-society relationship. Modern…

  12. Language Teachers' Target Language Project: Language for Specific Purposes of Language Teaching

    Science.gov (United States)

    Korenev, Alexey; Westbrook, Carolyn; Merry, Yvonne; Ershova, Tatiana

    2016-01-01

    The Language Teachers' Target Language project (LTTL) aims to describe language teachers' target language use domain (Bachman & Palmer 2010) and to develop a language test for future teachers of English. The team comprises four researchers from Moscow State University (MSU) and Southampton Solent University.

  13. Foreign Language Attrition.

    Science.gov (United States)

    de Bot, Kees; Weltens, Bert

    1995-01-01

    Reviews recent research on language maintenance and language loss, focusing on the loss of a second language in a first language environment, the linguistic aspects of loss, and relearning a "lost" language. An annotated bibliography discusses nine important works in the field. (43 references) (MDM)

  14. Thought and Language in Cognitive Science

    Directory of Open Access Journals (Sweden)

    Destéfano, Mariela

    2012-01-01

    Full Text Available In cognitive science, the discussion about the relations between language and thought is very heterogeneous. It involves developments on linguistics, philosophy, psychology, etc. Carruthers and Boucher (1998 identify different criteria that would organize the diversity of positions about language and thought assumed in linguistics, philosophy and psychology. One of them is the constitution thesis (CT, which establishes that language is constitutively involved in thought. In this paper I would like to show some problems of CT in order to understand the relation between language and thought in cognitive science.

  15. Language variety, language hierarchy, and language choice in the international university

    DEFF Research Database (Denmark)

    Haberland, Hartmut; Mortensen, Janus

    2012-01-01

    Introduction to thematic issue on Language variety, language hierarchy, and language choice in the international university......Introduction to thematic issue on Language variety, language hierarchy, and language choice in the international university...

  16. Getting the word out: Neural correlates of enthusiastic message propagation

    Directory of Open Access Journals (Sweden)

    Emily eFalk

    2012-11-01

    Full Text Available What happens in the mind of a person who first hears a potentially exciting idea? We examined the neural precursors of spreading ideas with enthusiasm, and dissect enthusiasm into component processes that can be identified through automated linguistic analysis, gestalt human ratings of combined linguistic and non-verbal cues, and points of convergence/divergence between the two. We combined tools from natural language processing with data gathered using fMRI, to link the neurocognitive mechanisms that are set in motion during initial exposure to ideas and subsequent behaviors of these message communicators outside of the scanner. Participants’ neural activity was recorded as they reviewed ideas for potential television show pilots. Participants’ language from video-taped interviews collected post-scan was transcribed and given to an automated linguistic sentiment analysis classifier, which returned ratings for evaluative language (evaluative vs. descriptive and valence (positive vs. negative. Separately, human coders rated the enthusiasm with which participants transmitted each idea. More positive sentiment ratings by the automated classifier were associated with activation in neural regions including medial prefrontal cortex; MPFC, precuneus/posterior cingulate cortex; PC/PCC, and medial temporal lobe; MTL. More evaluative, positive, descriptions were associated exclusively with neural activity in temporal parietal junction (TPJ. Finally, human ratings indicative of more enthusiastic sentiment were associated with activation across these regions (MPFC, PC/PCC, DMPFC, TPJ, MTL as well as in ventral striatum, inferior parietal lobule and premotor cortex. Taken together, these data demonstrate novel links between neural activity during initial idea encoding and the enthusiasm with which the ideas are subsequently delivered. These data also demonstrate the novel use of machine learning tools to link natural language data to neuroimaging data.

  17. Computational Investigations of Multiword Chunks in Language Learning.

    Science.gov (United States)

    McCauley, Stewart M; Christiansen, Morten H

    2017-07-01

    Second-language learners rarely arrive at native proficiency in a number of linguistic domains, including morphological and syntactic processing. Previous approaches to understanding the different outcomes of first- versus second-language learning have focused on cognitive and neural factors. In contrast, we explore the possibility that children and adults may rely on different linguistic units throughout the course of language learning, with specific focus on the granularity of those units. Following recent psycholinguistic evidence for the role of multiword chunks in online language processing, we explore the hypothesis that children rely more heavily on multiword units in language learning than do adults learning a second language. To this end, we take an initial step toward using large-scale, corpus-based computational modeling as a tool for exploring the granularity of speakers' linguistic units. Employing a computational model of language learning, the Chunk-Based Learner, we compare the usefulness of chunk-based knowledge in accounting for the speech of second-language learners versus children and adults speaking their first language. Our findings suggest that while multiword units are likely to play a role in second-language learning, adults may learn less useful chunks, rely on them to a lesser extent, and arrive at them through different means than children learning a first language. Copyright © 2017 Cognitive Science Society, Inc.

  18. Statistical physics of language dynamics

    Science.gov (United States)

    Loreto, Vittorio; Baronchelli, Andrea; Mukherjee, Animesh; Puglisi, Andrea; Tria, Francesca

    2011-04-01

    Language dynamics is a rapidly growing field that focuses on all processes related to the emergence, evolution, change and extinction of languages. Recently, the study of self-organization and evolution of language and meaning has led to the idea that a community of language users can be seen as a complex dynamical system, which collectively solves the problem of developing a shared communication framework through the back-and-forth signaling between individuals. We shall review some of the progress made in the past few years and highlight potential future directions of research in this area. In particular, the emergence of a common lexicon and of a shared set of linguistic categories will be discussed, as examples corresponding to the early stages of a language. The extent to which synthetic modeling is nowadays contributing to the ongoing debate in cognitive science will be pointed out. In addition, the burst of growth of the web is providing new experimental frameworks. It makes available a huge amount of resources, both as novel tools and data to be analyzed, allowing quantitative and large-scale analysis of the processes underlying the emergence of a collective information and language dynamics.

  19. Classroom Management in Foreign Language Education: An Exploratory Review

    Science.gov (United States)

    Macías, Diego Fernando

    2018-01-01

    This review examines studies in the area of classroom management in foreign language education. It is organized into three large areas: The first area focuses on the distinctive characteristics of foreign language instruction that are more likely to impact classroom management in foreign language classes. The second area provides a description of…

  20. 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...